Dermal uptake directly from air under transient conditions: advances in modeling and comparisons with experimental results for human subjects

To better understand the dermal exposure pathway, we enhance an existing mechanistic model of transdermal uptake by including skin surface lipids (SSL) and consider the impact of clothing. Addition of SSL increases the overall resistance to uptake of SVOCs from air but also allows for rapid transfer of SVOCs to sinks like clothing or clean air. We test the model by simulating di-ethyl phthalate (DEP) and di-n-butyl phthalate (DnBP) exposures of six bare-skinned (Weschler et al. 2015, Environ. Health Perspect., 123, 928) and one clothed participant (Morrison et al. 2016, J. Expo. Sci. Environ. Epidemiol., 26, 113). The model predicts total uptake values that are consistent with the measured values. For bare-skinned participants, the model predicts a normalized mass uptake of DEP of 3.1 (μg/m² )/(μg/m³ ), whereas the experimental results range from 1.0 to 4.3 (μg/m² )/(μg/m³ ); uptake of DnBP is somewhat overpredicted: 4.6 (μg/m² )/(μg/m³ ) vs. the experimental range of 0.5-3.2 (μg/m² )/(μg/m³ ). For the clothed participant, the model predicts higher than observed uptake for both species. Uncertainty in model inputs, including convective mass transfer coefficients, partition coefficients, and diffusion coefficients, could account for overpredictions. Simulations that include transfer of skin oil to clothing improve model predictions. A dynamic model that includes SSL is more sensitive to changes that impact external mass transfer such as putting on and removing clothes and bathing.

Machine learning models accurately predict ozone exposure during wildfire events

Epidemiologists use prediction models to downscale (i.e., interpolate) air pollution exposure where monitoring data is insufficient. This study compares machine learning prediction models for ground-level ozone during wildfires, evaluating the predictive accuracy of ten algorithms on the daily 8-hour maximum average ozone during a 2008 wildfire event in northern California. Models were evaluated using a leave-one-location-out cross-validation (LOLO CV) procedure to account for the spatial and temporal dependence of the data and produce more realistic estimates of prediction error. LOLO CV avoids both the well-known overly optimistic bias of k-fold cross-validation on dependent data and the conservative bias of evaluating prediction error over a coarser spatial resolution via leave-k-locations-out CV. Gradient boosting was the most accurate of the ten machine learning algorithms with the lowest LOLO CV estimated root mean square error (0.228) and the highest LOLO CV Rˆ² (0.677). Random forest was the second best performing algorithm with an LOLO CV Rˆ² of 0.661. The LOLO CV estimates of predictive accuracy were less optimistic than 10-fold CV estimates for all ten models. The difference in estimated accuracy between the 10-fold CV and LOLO CV was greater for more flexible models like gradient boosting and random forest. The order of estimated model accuracy depended on the choice of evaluation metric, indicating that 10-fold CV and LOLO CV may select different models or sets of covariates as optimal, which calls into question the reliability of 10-fold CV for model (or variable) selection. These prediction models are designed for interpolating ozone exposure, and are not suited to inferring the effect of wildfires on ozone or extrapolating to predict ozone in other spatial or temporal domains. This is demonstrated by the inability of the best performing models to accurately predict ozone during 2007 southern California wildfires.

Spatiotemporal exposure modeling of ambient erythemal ultraviolet radiation

BACKGROUND

Ultraviolet B (UV-B) radiation plays a multifaceted role in human health, inducing DNA damage and representing the primary source of vitamin D for most humans; however, current U.S. UV exposure models are limited in spatial, temporal, and/or spectral resolution. Area-to-point (ATP) residual kriging is a geostatistical method that can be used to create a spatiotemporal exposure model by downscaling from an area- to point-level spatial resolution using fine-scale ancillary data.

METHODS

A stratified ATP residual kriging approach was used to predict average July noon-time erythemal UV (UV_Ery) (mW/m²) biennially from 1998 to 2012 by downscaling National Aeronautics and Space Administration (NASA) Total Ozone Mapping Spectrometer (TOMS) and Ozone Monitoring Instrument (OMI) gridded remote sensing images to a 1 km spatial resolution. Ancillary data were incorporated in random intercept linear mixed-effects regression models. Modeling was performed separately within nine U.S. regions to satisfy stationarity and account for locally varying associations between UV_Ery and predictors. Cross-validation was used to compare ATP residual kriging models and NASA grids to UV-B Monitoring and Research Program (UVMRP) measurements (gold standard).

RESULTS

Predictors included in the final regional models included surface albedo, aerosol optical depth (AOD), cloud cover, dew point, elevation, latitude, ozone, surface incoming shortwave flux, sulfur dioxide (SO₂), year, and interactions between year and surface albedo, AOD, cloud cover, dew point, elevation, latitude, and SO₂. ATP residual kriging models more accurately estimated UV_Ery at UVMRP monitoring stations on average compared to NASA grids across the contiguous U.S. (average mean absolute error [MAE] for ATP, NASA: 15.8, 20.3; average root mean square error [RMSE]: 21.3, 25.5). ATP residual kriging was associated with positive percent relative improvements in MAE (0.6-31.5%) and RMSE (3.6-29.4%) across all regions compared to NASA grids.

CONCLUSIONS

ATP residual kriging incorporating fine-scale spatial predictors can provide more accurate, high-resolution UV_Ery estimates compared to using NASA grids and can be used in epidemiologic studies examining the health effects of ambient UV.

iSTREEM(®) : An approach for broad-scale in-stream exposure assessment of "down-the-drain" chemicals

The "in-stream exposure model" iSTREEM(®) , a Web-based model made freely available to the public by the American Cleaning Institute, provides a means to estimate concentrations of "down-the-drain" chemicals in effluent, receiving waters, and drinking water intakes across national and regional scales under mean annual and low-flow conditions. We provide an overview of the evolution and utility of the iSTREEM model as a screening-level risk assessment tool relevant for down-the-drain products. The spatial nature of the model, integrating point locations of facilities along a hydrologic network, provides a powerful framework to assess environmental exposure and risk in a spatial context. A case study compared national distributions of modeled concentrations of the fragrance 1,3,4,6,7,8-Hexahydro-4,6,6,7,8,8,-hexamethylcyclopenta-γ-2-benzopyran (HHCB) and the insect repellent N,N-Diethyl-m-toluamide (DEET) to available monitoring data at comparable flow conditions. The iSTREEM low-flow model results yielded a conservative distribution of values, whereas the mean-flow model results more closely resembled the concentration distribution of monitoring data. We demonstrate how model results can be used to construct a conservative estimation of the distribution of chemical concentrations for effluents and streams leading to the derivation of a predicted environmental concentration (PEC) using the high end of the concentration distribution (e.g., 90th percentile). Data requirements, assumptions, and applications of iSTREEM are discussed in the context of other down-the-drain modeling approaches to enhance understanding of comparative advantages and uncertainties for prospective users interested in exposure modeling for ecological risk assessment. Integr Environ Assess Manag 2016;12:782-792. © 2016 SETAC.

Human Exposure Assessment for Air Pollution

Assessment of human exposure to air pollution is a fundamental part of the more general process of health risk assessment. The measurement methods for exposure assessment now include personal exposure monitoring, indoor-outdoor sampling, mobile monitoring, and exposure assessment modeling (such as proximity models, interpolation model, air dispersion models, and land-use regression (LUR) models). Among these methods, personal exposure measurement is considered to be the most accurate method of pollutant exposure assessment until now, since it can better quantify observed differences and better reflect exposure among smaller groups of people at ground level. And since the great differences of geographical environment, source distribution, pollution characteristics, economic conditions, and living habits, there is a wide range of differences between indoor, outdoor, and individual air pollution exposure in different regions of China. In general, the indoor particles in most Chinese families comprise infiltrated outdoor particles, particles generated indoors, and a few secondary organic aerosol particles, and in most cases, outdoor particle pollution concentrations are a major contributor to indoor concentrations in China. Furthermore, since the time, energy, and expense are limited, it is difficult to measure the concentration of pollutants for each individual. In recent years, obtaining the concentration of air pollutants by using a variety of exposure assessment models is becoming a main method which could solve the problem of the increasing number of individuals in epidemiology studies.

Assessment of outdoor radiofrequency electromagnetic field exposure through hotspot localization using kriging-based sequential sampling

In this study, a novel methodology is proposed to create heat maps that accurately pinpoint the outdoor locations with elevated exposure to radiofrequency electromagnetic fields (RF-EMF) in an extensive urban region (or, hotspots), and that would allow local authorities and epidemiologists to efficiently assess the locations and spectral composition of these hotspots, while at the same time developing a global picture of the exposure in the area. Moreover, no prior knowledge about the presence of radiofrequency radiation sources (e.g., base station parameters) is required. After building a surrogate model from the available data using kriging, the proposed method makes use of an iterative sampling strategy that selects new measurement locations at spots which are deemed to contain the most valuable information-inside hotspots or in search of them-based on the prediction uncertainty of the model. The method was tested and validated in an urban subarea of Ghent, Belgium with a size of approximately 1 km2. In total, 600 input and 50 validation measurements were performed using a broadband probe. Five hotspots were discovered and assessed, with maximum total electric-field strengths ranging from 1.3 to 3.1 V/m, satisfying the reference levels issued by the International Commission on Non-Ionizing Radiation Protection for exposure of the general public to RF-EMF. Spectrum analyzer measurements in these hotspots revealed five radiofrequency signals with a relevant contribution to the exposure. The radiofrequency radiation emitted by 900 MHz Global System for Mobile Communications (GSM) base stations was always dominant, with contributions ranging from 45% to 100%. Finally, validation of the subsequent surrogate models shows high prediction accuracy, with the final model featuring an average relative error of less than 2dB (factor 1.26 in electric-field strength), a correlation coefficient of 0.7, and a specificity of 0.96.

Development of a standard documentation protocol for communicating exposure models

An important step in building a computational model is its documentation; a comprehensive and structured documentation can improve the model applicability and transparency in science/research and for regulatory purposes. This is particularly crucial and challenging for environmental and/or human exposure models that aim to establish quantitative relationships between personal exposure levels and their determinants. Exposure models simulate the transport and fate of a contaminant from the source to the receptor and may involve a large set of entities (e.g. all the media the contaminants may pass though). Such complex models are difficult to be described in a comprehensive, unambiguous and accessible way. Bad communication of assumptions, theory, structure and/or parameterization can lead to lack of confidence by the user and it may be source of errors. The goal of this paper is to propose a standard documentation protocol (SDP) for exposure models, i.e. a generic format and a standard structure by which all exposure models could be documented. For this purpose, a CEN (European Committee for Standardisation) workshop was set up with objective to agree on minimum requirements for the amount and type of information to be provided on exposure models documentation along with guidelines for the structure and presentation of the information. The resulting CEN workshop agreement (CWA) was expected to facilitate a more rigorous formulation of exposure models description and the understanding by users. This paper intends to describe the process followed for defining the SDP, the standardisation approach, as well as the main components of the SDP resulting from a wide consultation of interested stakeholders. The main outcome is a CEN CWA which establishes terms and definitions for exposure models and their elements, specifies minimum requirements for the amount and type of information to be documented, and proposes a structure for communicating the documentation to different users.

Integrating climate model projections into environmental risk assessment: A probabilistic modeling approach

The Society of Environmental Toxicology and Chemistry (SETAC) convened a Pellston workshop in 2022 to examine how information on climate change could be better incorporated into the ecological risk assessment (ERA) process for chemicals as well as other environmental stressors. A major impetus for this workshop is that climate change can affect components of ecological risks in multiple direct and indirect ways, including the use patterns and environmental exposure pathways of chemical stressors such as pesticides, the toxicity of chemicals in receiving environments, and the vulnerability of species of concern related to habitat quality and use. This article explores a modeling approach for integrating climate model projections into the assessment of near- and long-term ecological risks, developed in collaboration with climate scientists. State-of-the-art global climate modeling and downscaling techniques may enable climate projections at scales appropriate for the study area. It is, however, also important to realize the limitations of individual global climate models and make use of climate model ensembles represented by statistical properties. Here, we present a probabilistic modeling approach aiming to combine projected climatic variables as well as the associated uncertainties from climate model ensembles in conjunction with ERA pathways. We draw upon three examples of ERA that utilized Bayesian networks for this purpose and that also represent methodological advancements for better prediction of future risks to ecosystems. We envision that the modeling approach developed from this international collaboration will contribute to better assessment and management of risks from chemical stressors in a changing climate. Integr Environ Assess Manag 2024;20:367-383. © 2023 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Fate and transport of furrow-applied granular tefluthrin and seed-coated clothianidin insecticides: Comparison of field-scale observations and model estimates

The transport of agricultural insecticides to water bodies may create risk of exposure to non-target organisms. Similarly, widespread use of furrow-applied and seed-coated insecticides may increase risk of exposure, yet accessible exposure models are not easily adapted for furrow application, and only a few examples of model validation of furrow-applied insecticides exist using actual field data. The goal of the current project was to apply an exposure model, the Pesticide in Water Calculator (PWC), to estimate the concentrations of two in-furrow insecticides applied to maize: the granular pyrethroid, tefluthrin, and the seed-coated neonicotinoid, clothianidin. The concentrations of tefluthrin and clothianidin in surface runoff water, sampled from a field in central Illinois (USA), were compared to the PWC modeled pesticide concentrations in surface runoff. The tefluthrin concentrations were used to optimize the application method in the PWC, and the addition of particulate matter and guttation droplets improved the models prediction of clothianidin concentrations. Next, the tefluthrin and clothianidin concentrations were calculated for a standard farm pond using both the optimized application method and the application methods provided in PWC. Estimated concentrations in a standard farm pond varied by a factor of 100 for tefluthrin and 50 for clothianidin depending on the application method used. The addition of guttation droplets and particulate matter to the model increased the annual clothianidin concentration in a standard farm pond by a factor of 1.5, which suggested that these transport routes should also be considered when assessing neonicotinoid exposure.

Computational estimates of daily aggregate exposure to PFOA/PFOS from 2011 to 2017 using a basic intake model

BACKGROUND

Human exposure to per- and polyfluoroalkyl substances has been modeled to estimate serum concentrations. Given that the production and use of these compounds have decreased in recent years, especially PFOA and PFOS, and that additional concentration data have become available from the US and other industrialized countries over the past decade, aggregate median intakes of these two compounds were estimated using more recent data.

METHODS

Summary statistics from secondary sources were collected, averaged, and mapped for indoor and outdoor air, water, dust, and soil for PFOA and PFOS to estimate exposures for adults and children. European dietary intake estimates were used to estimate daily intake from food.

RESULTS

In accordance with decreased concentrations in media, daily intake estimates among adults, i.e., 40 ng/day PFOA and 40 ng/day PFOS, are substantially lower than those reported previously, as are children's estimates of 14 ng/day PFOA and 17 ng/day PFOS. Using a first-order pharmacokinetic model, these results compare favorably to the National Health and Nutrition Examination Survey serum concentration measurements.

CONCLUSION

Concomitant blood concentrations support this enhanced estimation approach that captures the decline of PFOA/PFOS serum concentration over a decade.

Personal inhalable paper dust exposure and potential determinants among paper industry workers in Ethiopia

PURPOSE

Excessive paper dust during paper production may harm the workers' respiratory health. We wanted to assess the inhalable paper dust levels and its determinants among paper industry workers.

METHODS

A study was conducted in Ethiopia to assess the level of personal inhalable paper dust exposure among four paper mills. A total of 150 samples were collected using the IOM sampler attached to Side Kick Casella pumps at a flow rate of 2 L/min. The samples were analyzed in Nemko Norlab, Norway. Linear mixed-effect models were applied to identify determinants of inhalable paper dust.

RESULTS

The geometric mean of personal inhalable paper dust was 3.3 mg/m3 with 80% of the measurements exceeding the Swedish occupational exposure limit (OEL) of 2 mg/m3. The linear mixed-effects model showed that the level of dust was 28% higher when using high-speed than when using low-speed rewinding machines, while paper mills with an average of more than four machines per job group had 22% higher exposure than paper mills with a lower number of machines. Furthermore, working in packing and preparation was associated with higher dust exposure than in other areas.

CONCLUSIONS

The dust exposure levels were above the Swedish OEL for 80% of the samples. This indicates that preventive measures should be established in the industry. The exposure model identified high-speed rewinding machines, a high number of machines, and work in preparation and packing as associated with high levels of paper dust exposure.

Exposure to oxybenzone from sunscreens: daily transdermal uptake estimation

BACKGROUND

Fugacity, the driving force for transdermal uptake of chemicals, can be difficult to predict based only on the composition of complex, non-ideal mixtures such as personal care products.

OBJECTIVE

Compare the predicted transdermal uptake of benzophenone-3 (BP-3) from sunscreen lotions, based on direct measurements of BP-3 fugacity in those products, to results of human subject experiments.

METHODS

We measured fugacity relative to pure BP-3, for commercial sunscreens and laboratory mixtures, using a previously developed/solid-phase microextraction (SPME) method. The measured fugacity was combined with a transdermal uptake model to simulate urinary excretion rates of BP-3 resulting from sunscreen use. The model simulations were based on the reported conditions of four previously published human subject studies, accounting for area applied, time applied, showering and other factors.

RESULTS

The fugacities of commercial lotions containing 3-6% w/w BP-3 were ~20% of the supercooled liquid vapor pressure. Simulated dermal uptake, based on these fugacities, are within a factor of 3 of the mean results reported from two human-subject studies. However, the model significantly underpredicts total excreted mass from two other human-subject studies. This discrepancy may be due to limitations in model inputs, such as fugacity of BP-3 in lotions used in those studies.

SIGNIFICANCE

The results suggest that combining measured fugacity with such a model may provide order-of-magnitude accurate predictions of transdermal uptake of BP-3 from daily application of sunscreen products.

Development of a model (SWNano) to assess the fate and transport of TiO2 engineered nanoparticles in sewer networks

A new model, SWNano (Sewer-Water Nano), has been developed in the present study that quantitatively simulates the spatio-temporal changes in the concentrations of TiO₂ ENPs of dispersed and aggregated forms in the sewage water and sediment of a sewer network. As a brief example of SWNano applications, a small section of the entire sewer network of Seoul, Korea, was chosen to study where the sewage water was experimentally characterized. The predictions of SWNano present important findings that i) heteroaggregation is the most significant process following the advective transport among the fate and transport processes in the sewer pipes, ii) the heteroaggregation of TiO₂ ENPs with SPMs in the sewage water can substantially (a few % to more than 50%) reduce the freely dispersed TiO₂ ENPs depending on the magnitude of attachment efficiency, and iii) accurate determination of attachment efficiency is of critical importance in predicting the quantity of individual forms of ENPs exiting the sewer system. The predictions strongly suggest that the fate and transport of TiO₂ ENPs in the sewer networks be taken into account to improve the assessment of exposure to TiO₂ ENPs in the aquatic ecosystems, which warrants further development and use of models like SWNano.

Pesticide exposure in greenspaces: Comparing field measurement of dermal contamination with values predicted by registration models

Since 2014, the Agricultural Operator Exposure Model (AOEM) has been the harmonised European model used for estimating non-dietary operator exposure to pesticide. It is based on studies conducted by the pesticide companies and it features 13 different crops including non-agricultural areas such as amenity grasslands. The objective of this study was to compare the dermal exposure measured during a field study conducted in a non-agricultural area with the corresponding values estimated by the model AOEM. The non-controlled field study was conducted in France in 2011 and included 24 private and public gardeners who apply glyphosate with knapsack sprayers. Dermal exposure was measured using the whole-body method and cotton gloves. Each measured value had an estimated value given by AOEM and we tested their correlation using linear regression. The model overestimated body exposure for all observations and there was no correlation between values. However, it underestimated hand exposure by 42 times and it systematically underestimated the exposure when the operators were wearing gloves, especially during the application. The model failed at being conservative regarding hand exposure and highly overestimated the protection afforded by the gloves. At a time of glyphosate renewed approval in Europe, non-controlled field studies conducted by academics are needed to improve AOEM model, especially in the non-agricultural sector. Indeed, among the 34 studies included in the model, none were conducted on a non-agricultural area and only four assessed the exposure when using a knapsack sprayer. Moreover, knapsack sprayers being the main equipment used worldwide in both agricultural and non-agricultural settings, it is also crucial to integrate new data specific to this equipment in the model. Operator exposure should be estimated with accuracy in the registration process of pesticides to ensure proper safety as well as in epidemiological studies to improve exposure assessment.

Is the European regulatory model for predicting worker exposure conservative? Comparison of predicted and measured under usual working conditions exposures in fruit growing

INTRODUCTION

Pesticide exposure increases the risk of chronic disease among farmers. Understanding exposure is necessary for epidemiological and regulatory purposes. In Europe, worker exposure is assessed during the registration process using the OPEX model, which is based on a limited number of studies, often unpublished and carried out by pesticide companies. We assessed the conservativeness of OPEX for workers performing post-application tasks (re-entry, harvesting).

METHODS

In 2016-2017, dermal exposure to captan/THPI and dithianon was measured in French fruit farm workers during 65 re-entry (net folding and deployment, thinning, tying) and 58 harvesting days, using patches and cotton gloves. We used linear regression to compare measured and corresponding OPEX-calculated exposure using 1) default parameters; 2) field parameters (actual task duration, measured dislodgeable foliar residues) for 20 observations.

RESULTS

Workers were exposed several days after the last application, which is not considered in the pesticide registration process. We found that the model underestimated exposure calculated with field parameters in all observations for dithianon and 60% for captan, linked to an underestimation of OPEX transfer coefficients (ratio of 0.40 for captan and 0.26 for dithianon between default and measured transfer coefficients).

DISCUSSION

When observation occurred several days after application, OPEX tended to underestimate exposure. An industry study conducted under controlled working conditions found divergent results. It seems important to include field studies conducted under usual working conditions in the registration process to ensure a truly conservative approach and to consider cumulative exposure, since post-application tasks account for around 600 working hours a year.

Cellular and metabolic impacts of repeated sub-acute exposures to biomass-burning extracts in vitro

The increasing exposure to biomass-burning emissions underscores the need to understand their toxicological impacts on human health. In this study, we developed a laboratory model to evaluate the effects of single and repeated sub-acute exposures to water-soluble wood tar (WT) extracts, a product of biomass burning, on human lung, liver, and immune cells. Using representative cell lines for different tissues, we examined the cytotoxic effects under conditions mimicking sub-acute environmental exposure levels relevant to humans. Our findings indicate that repeated sub-acute exposures to water-soluble WT extracts significantly enhance the inflammatory response, evidenced by increased IL6, IL8, and TNFa cytokine levels, compared to a single exposure. Additionally, oxidative stress responses were more pronounced with increased lipid peroxidation and HMOX1, GCLC and CYP1A1 gene expression following repeated exposures. Metabolomics analyses of polar and lipid metabolites revealed changes related to energy production and consumption that emerge even after a single exposure at sub-acute levels and vary across different cell types representing the different tissues. Impaired cellular respiration, measured by oxygen consumption rate, corroborates the observed changes. These results provide important insights into the cellular mechanisms driving the response to biomass-burning exposure and highlight the potential health risks associated with sub-acute exposure to environmental pollutants.