Journal of Exposure Science & Environmental Epidemiology

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Online ISSN: 1559-064X
Print ISSN: 1559-0631
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Maps of pollution intensity for each modeled constituent of the mixture
For comparability, the pollutants have been normalized, and eight quantiles were determined for each pollutant. Quantile 4 represents the mean exposure in the cohort. Each dot represents an approximate home address for subjects living in and around North Carolina although there are subjects living across the United States. Please see Table 2 for ranges of the original exposure values. Maps developed using leaflet package in R.
Directed acyclic graph showing hypothesized relationships between air pollution, our included covariates, and our outcome of autoimmune skin diseases
The green circle with arrow-head represent our primary mixture exposure; the green circles represent the air pollution exposures; grey circles represent air pollution sources or mitigation factors; the blue circle with an I is our outcome of interest, and the plain blue circles are biological covariates. Pink represents confounders and shows that the minimally sufficient set of covariates is the subject’s age.
Characteristics of Cohort.
  • Melissa E. LoweMelissa E. Lowe
  • Farida S. AkhtariFarida S. Akhtari
  • Taylor A. PotterTaylor A. Potter
  • [...]
  • Kyle P. MessierKyle P. Messier
Background Autoimmune (AI) diseases appear to be a product of genetic predisposition and environmental triggers. Disruption of the skin barrier causes exacerbation of psoriasis/eczema. Oxidative stress is a mechanistic pathway for pathogenesis of the disease and is also a primary mechanism for the detrimental effects of air pollution. Methods We evaluated the association between autoimmune skin diseases (psoriasis or eczema) and air pollutant mixtures in 9060 subjects from the Personalized Environment and Genes Study (PEGS) cohort. Pollutant exposure data on six criteria air pollutants are publicly available from the Center for Air, Climate, and Energy Solutions and the Atmospheric Composition Analysis Group. For increased spatial resolution, we included spatially cumulative exposure to volatile organic compounds from sites in the United States Environmental Protection Agency Toxic Release Inventory and the density of major roads within a 5 km radius of a participant’s address from the United States Geological Survey. We applied logistic regression with quantile g-computation, adjusting for age, sex, diagnosis with an autoimmune disease in family or self, and smoking history to evaluate the relationship between self-reported diagnosis of an AI skin condition and air pollution mixtures. Results Only one air pollution variable, sulfate, was significant individually (OR = 1.06, p = 3.99E−2); however, the conditional odds ratio for the combined mixture components of PM 2.5 (black carbon, sulfate, sea salt, and soil), CO, SO 2 , benzene, toluene, and ethylbenzene is 1.10 ( p -value = 5.4E−3). Significance While the etiology of autoimmune skin disorders is not clear, this study provides evidence that air pollutants are associated with an increased prevalence of these disorders. The results provide further evidence of potential health impacts of air pollution exposures on life-altering diseases. Significance and impact statement The impact of air pollution on non-pulmonary and cardiovascular diseases is understudied and under-reported. We find that air pollution significantly increased the odds of psoriasis or eczema in our cohort and the magnitude is comparable to the risk associated with smoking exposure. Autoimmune diseases like psoriasis and eczema are likely impacted by air pollution, particularly complex mixtures and our study underscores the importance of quantifying air pollution-associated risks in autoimmune disease.
 
Spatial distribution of oil and gas development in Texas, 1985–2019
Map displays the deciles of oil and gas development density across the state of Texas for sites that were spudded between 1985 and 2019.
Associations between nearest active spud to a maternal residence and odds of congenital anomalies
Modelcoefficients are relative to the maternal addresses whose nearest active drilling site is 9–10km from their residences. All models contain include birth year (indicator), county of maternal residence at delivery (categorical), infant sex (male, female), gestational age (continuous), birth weight (continuous), maternalage (continuous), maternal race and ethnicity (white non-Hispanic, black non-Hispanic, Hispanic, other),maternal education (less than high school, high school, some college, bachelors, more than bachelors),smoking (yes, no, missing), prenatal care initiated (yes, no, missing), distance to nearest highways inmeters (continuous), census tract unemployment rate (continuous), census tract median household income (continuous), and census tract percent white population (continuous).
  • Mary D. WillisMary D. Willis
  • Susan E. CarozzaSusan E. Carozza
  • Perry HystadPerry Hystad
Background Oil and gas extraction-related activities produce air and water pollution that contains known and suspected teratogens. To date, health impacts of in utero exposure to these activities is largely unknown. Objective We investigated associations between in utero exposure to oil and gas extraction activity in Texas, one of the highest producers of oil and gas, and congenital anomalies. Methods We created a population-based birth cohort between 1999 and 2009 with full maternal address at delivery and linked to the statewide congenital anomaly surveillance system (n = 2,234,138 births, 86,315 cases). We examined extraction-related exposures using tertiles of inverse distance-squared weighting within 5 km for drilling site count, gas production, oil production, and produced water. In adjusted logistic regression models, we calculated odds of any congenital anomaly and 10 specific organ sites using two comparison groups: 1) 5 km of future drilling sites that are not yet operating (a priori main models), and 2) 5–10 km of an active well. Results Using the temporal comparison group, we find increased odds of any congenital anomaly in the highest tertile exposure group for site count (OR: 1.25; 95% CI: 1.21, 1.30), oil production (OR: 1.08; 95% CI: 1.04, 1.12), gas production (1.20; 95% CI: 1.17, 1.23), and produced water (OR: 1.17; 95% CI: 1.14, 1.20). However, associations did not follow a consistent exposure-response pattern across tertiles. Associations are highly attenuated, but still increased, with the spatial comparison group in the highest tertile exposure group. Cardiac and circulatory defects are strongly and consistently associated with all exposure metrics. Significance Increased odds of congenital anomalies, particularly cardiac and circulatory defects, were associated with exposures related to oil and gas extraction in this large population-based study. Future research is needed to confirm findings, examine specific exposure pathways, and identify potential avenues to reduce exposures among local populations. Impact About 5% of the U.S. population (~17.6 million people) resides within 1.6 km of an active oil or gas extraction site, yet the influence of this industry on population health is not fully understood. In this analysis, we examined associations between oil and gas extraction-related exposures and congenital anomalies by organ site using birth certificate and congenital anomaly surveillance data in Texas (1999–2009). Increased odds of congenital anomalies, particularly cardiac and circulatory defects, were associated with exposures related to oil and gas extraction in this large population-based study. Future research is needed to confirm these findings.
 
Overall cross-sectional associations between the level of asthma control and indoor exposures, indoor concentrations, and indoor-to-outdoor (I/O) ratios of air pollutants
Logistic regression models were adjusted for age, race/ethnicity of head of household, and clustering effects within homes. Adjusted odds ratio (aOR) and 95% confidence interval (95% CI) are presented for the interquartile range (IQR) of indoor exposures, indoor concentrations, and I/O ratios. Symbols denote: *p < 0.05; **p < 0.01; ***p < 0.001.
Incidence rate ratios (IRRs) of emergency department (ED) visits associated with health outcomes and indoor-to-outdoor (I/O) ratios of air pollutants
Poisson regression models were adjusted for age, race/ethnicity of head of household, and clustering effects within homes. Incidence rate ratio (IRR) and 95% confidence interval (95% CI) are presented for a 1-point increase in health scores and a unit of 0.1 increase in I/O ratios. Symbols denote: * p < 0.05; **p < 0.01; ***p < 0.001.
Background Residential environments are known to contribute to asthma. Objective To examine the joint impacts of exposures to residential indoor and outdoor air pollutants and housing risk factors on adult asthma-related health outcomes. Methods We analyzed >1-year of data from 53 participants from 41 homes in the pre-intervention period of the Breathe Easy Project prior to ventilation and filtration retrofits. Health outcomes included surveys of asthma control, health-related quality of life, stress, and healthcare utilizations. Environmental assessments included quarterly measurements of indoor and outdoor pollutants (e.g., HCHO, CO, CO2, NO2, O3, and PM), home walk-throughs, and surveys of environmental risk factors. Indoor pollutant concentrations were also matched with surveys of time spent at home to estimate indoor pollutant exposures. Results Cross-sectional analyses using mixed-effects models indicated that lower annual average asthma control test (ACT) scores were associated (p < 0.05) with higher indoor NO2 (concentration/exposure: β = −2.42/−1.57), indoor temperature (β = −1.03 to −0.94), and mold/dampness (β = −3.09 to −2.41). In longitudinal analysis, lower ACT scores were also associated (p < 0.05) with higher indoor NO2 concentrations (β = −0.29), PM1 (concentration/exposure: β = −0.12/−0.24), PM2.5 (concentration/exposure: β = −0.12/−0.26), and PM10 (concentration/exposure: β = 10.14/-0.28). Emergency department visits were associated with poorer asthma control [incidence rate ratio (IRR) = 0.84; p < 0.001], physical health (IRR = 0.95; p < 0.05), mental health (IRR = 0.95; p < 0.05), higher I/O NO2 ratios (IRR = 1.30; p < 0.05), and higher indoor temperatures (IRR = 1.41; p < 0.05). Significance Findings suggest that residential risk factors, including indoor air pollution (especially NO2 and particulate matter), higher indoor temperature, and mold/dampness, may contribute to poorer asthma control. Impact This study highlights the importance of residential indoor air quality and environmental risk factors for asthma control, health-related quality of life, and emergency department visits for asthma. Two timescales of mixed models suggest that exposure to indoor NO2 and particulate matter, higher indoor temperature, and mold/dampness was associated with poorer asthma control. Additionally, emergency department visits were associated with poorer asthma control and health-related quality of life, as well as higher I/O NO2 ratios and indoor temperatures. These findings deepen our understanding of the interrelationships between housing, air quality, and health, and have important implications for programs and policy.
 
Background Thousands of chemicals are observed in freshwater, typically at trace levels. Measurements are collected for different purposes, so sample characteristics vary. Due to inconsistent data availability for exposure and hazard, it is complex to prioritize which chemicals may pose risks. Objective We evaluated the influence of data curation and statistical practices aggregating surface water measurements of organic chemicals into exposure distributions intended for prioritizing based on nation-scale potential risk. Methods The Water Quality Portal includes millions of observations describing over 1700 chemicals in 93% of hydrologic subbasins across the United States. After filtering to maintain quality and applicability while including all possible samples, we compared concentrations across sample types. We evaluated statistical methods to estimate per-chemical distributions for chosen samples. Overlaps between resulting exposure ranges and distributions representing no-effect concentrations for multiple freshwater species were used to rank estimated chemical risks for further assessment. Results When we apply explicit data quality and statistical assumptions, we find that there are 186 organic chemicals for which we can make screening-level estimates of surface water chemical concentration. Of the original 1700 observed chemicals, this number decreased primarily due to a predominance of censored values (that is, observations indicating concentrations too low to be measured). We further identify 423 chemicals where all measurements were censored but, through consideration of detection limits, risk might still be prioritized based on the detection limits themselves. In the final set of 1.5 million samples, the median environmental concentration of one chemical (acetic acid) exceeded the 5th percentile of no-effect concentrations for the most delicate freshwater species (the highest priority risk condition identified here), and a further 29 chemicals were identified for possible further evaluation based on a small margin between occurrence and toxicity values. Significance This method shows the broad range of chemical concentrations seen for organic chemicals across the country and identifies methods of determining their central tendency, allowing for researchers to characterize higher-than-normal or lower-than-normal surface water conditions as well as providing an overall indication of the presence of organic chemicals in the United States. The highest chemical concentrations did not always indicate the highest-risk conditions. Even when accounting for the high level of uncertainty in these data due to differences in data collection and reporting across the set, some chemicals may still be categorized as higher environmental risk than others using this method, providing value to chemical safety decision makers and researchers by suggesting avenues for more focused investigation.
 
The rapid characterization of risk to humans and ecosystems from exogenous chemicals requires information on both hazard and exposure. The U.S. Environmental Protection Agency's ToxCast program and the interagency Tox21 initiative have screened thousands of chemicals in various high-throughput (HT) assay systems for in vitro bioactivity. EPA's ExpoCast program is developing complementary HT methods for characterizing the human and ecological exposures necessary to interpret HT hazard data in a real-world risk context. These new approach methodologies (NAMs) for exposure include computational and analytical tools for characterizing multiple components of the complex pathways chemicals take from their source to human and ecological receptors. Here, we analyze the landscape of exposure NAMs developed in ExpoCast in the context of various chemical lists of scientific and regulatory interest, including the ToxCast and Tox21 libraries and the Toxic Substances Control Act (TSCA) inventory. We examine the landscape of traditional and exposure NAM data covering chemical use, emission, environmental fate, toxicokinetics, and ultimately external and internal exposure. We consider new chemical descriptors, machine learning models that draw inferences from existing data, high-throughput exposure models, statistical frameworks that integrate multiple model predictions, and non-targeted analytical screening methods that generate new HT monitoring information. We demonstrate that exposure NAMs drastically improve the coverage of the chemical landscape compared to traditional approaches and recommend a set of research activities to further expand the development of HT exposure data for application to risk characterization. Continuing to develop exposure NAMs to fill priority data gaps identified here will improve the availability and defensibility of risk-based metrics for use in chemical prioritization and screening. IMPACT: This analysis describes the current state of exposure assessment-based new approach methodologies across varied chemical landscapes and provides recommendations for filling key data gaps.
 
Digital model of the Experimental setup indicating
A climate chamber, airflow distribution, as well as sampling location for each unique trial (modeled in Rhinoceros software), (B) experimental procedure and the number of breath mints consumed by the participant for each trial.
The concentration of breath mint in the headspace of a 250 mL glass container
A Concentration of breath tracer compounds (menthol, menthone, and monoterpenes) in the headspace of a 250 mL glass chamber as a function the time when a breath mint is placed inside, (B) Concentration of the three target compounds when the participant exhaled their breath once into the 250 mL chamber while consuming the breath mint.
Comparison of 0.762 m (2.5 ft), 1.524 m (5 ft), 2.28 m (7.5 ft) trials normalized by volume-averaged concentration (VAC) whereby values below 1.0 indicate concentrations proportionally lower than the VAC at that time point, and values higher than 1.0 indicate concentrations proportionally higher than the VAC at that time point. Note that VAC changes over time.
Background Several studies suggest that far-field transmission (>6 ft) explains a significant number of COVID-19 superspreading outbreaks. Objective Therefore, quantifying the ratio of near- and far-field exposure to emissions from a source is key to better understanding human-to-human airborne infectious disease transmission and associated risks. Methods In this study, we used an environmentally-controlled chamber to measure volatile organic compounds (VOCs) released from a healthy participant who consumed breath mints, which contained unique tracer compounds. Tracer measurements were made at 0.76 m (2.5 ft), 1.52 m (5 ft), 2.28 m (7.5 ft) from the participant, as well as in the exhaust plenum of the chamber. Results We observed that 0.76 m (2.5 ft) trials had ~36–44% higher concentrations than other distances during the first 20 minutes of experiments, highlighting the importance of the near-field exposure relative to the far-field before virus-laden respiratory aerosol plumes are continuously mixed into the far-field. However, for the conditions studied, the concentrations of human-sourced tracers after 20 minutes and approaching the end of the 60-minute trials at 0.76 m, 1.52 m, and 2.28 m were only ~18%, ~11%, and ~7.5% higher than volume-averaged concentrations, respectively. Significance This study suggests that for rooms with similar airflow parameters disease transmission risk is dominated by near-field exposures for shorter event durations (e.g., initial 20–25-minutes of event) whereas far-field exposures are critical throughout the entire event and are increasingly more important for longer event durations. Impact statement We offer a novel methodology for studying the fate and transport of airborne bioaerosols in indoor spaces using VOCs as unique proxies for bioaerosols. We provide evidence that real-time measurement of VOCs can be applied in settings with human subjects to estimate the concentration of bioaerosol at different distances from the emitter. We also improve upon the conventional assumption that a well-mixed room exhibits instantaneous and perfect mixing by addressing spatial distances and mixing over time. We quantitatively assessed the exposure levels to breath tracers at alternate distances and provided more insights into the changes on “near-field to far-field” ratios over time. This method can be used in future to estimate the benefits of alternate environmental conditions and occupant behaviors.
 
Background A child’s ability to succeed in social interactions and in a school setting are important for their development and growth. Exposure to environmental pollutants has been associated with poorer school performance and fewer social interaction in children. Fly ash, a waste product generated when burning coal for energy, is comprised of small glass spheres with neurotoxic heavy metal(loid)s found to be risk factors for learning and social problems in school. Objective The purpose of this novel study was to assess the association of fly ash in children’s homes with school and social competency. Methods We recruited children aged 6–14 years old from communities located within 10 miles of two coal-burning power plants. In homes of the participants, fly ash was collected on polycarbonate filters using personal modular impactors. We measured school competency and social competency using the validated Child Behavioral Checklist. Using Tobit and linear regression we investigated the relationship of indoor fly ash with school and social competency. Results Forty-three percent of children in the study had fly ash in their homes. In covariate-adjusted Tobit models, children with fly ash in their homes scored on average 2.63 (95% CI: −4.98, −0.28) points lower on the school competency scale than peers without ash in their homes. We did not observe that fly ash in homes was related with lower social competency. Significance Results from this study suggest that children with fly ash in their homes had poorer performance in the school setting, compared to peers without fly ash in their homes. In the US, coal-fired power plants are being closed, however health concerns about pollution from coal ash storage facilities remains. Findings from this study can provide impetus for creating of public health policy and to highlight the need future research on children’s exposure to fly ash. Impact Children’s growth and development are impacted by their social interactions and ability to perform in school settings. Environmental pollutants may impact these essential elements of development. Millions of children are exposed to fly ash which is a waste product generated from burning coal. Fly ash, an environmental health threat throughout the world, is comprised of small glass spheres with trace concentrations of neurotoxic metal(loid)s. Findings from this research show that children with fly ash in their homes are significantly more likely to have poorer school performance than children without fly ash in their homes.
 
Background Sanitary sewage overflows (SSOs) release raw sewage, which may contaminate the drinking water supply. Boil water advisories (BWAs) are issued during low or negative pressure events, alerting customers to potential contamination in the drinking water distribution system. Objective We evaluated the associations between SSOs and BWAs and diagnoses of gastrointestinal (GI) illness in Columbia, South Carolina, and neighboring communities, 2013–2017. Methods A symmetric bi-directional case-crossover study design was used to assess the role of SSOs and BWAs on Emergency Room and Urgent Care visits with a primary diagnosis of GI illness. Cases were considered exposed if an SSO or BWA occurred 0–4 days, 5–9 days, or 10–14 days prior to the diagnosis, within the same residential zip code. Effect modification was explored via stratification on participant-level factors (e.g., sex, race, age) and season (January-March versus April-December). Results There were 830 SSOs, 423 BWAs, and 25,969 cases of GI illness. Highest numbers of SSOs, BWAs and GI cases were observed in a zip code where >80% of residents identified as Black or African-American. SSOs were associated with a 13% increase in the odds of a diagnosis for GI illness during the 0–4 day hazard period, compared to control periods (Odds Ratio: 1.13, 95% Confidence Interval: 1.09, 1.18), while no associations were observed during the other hazard periods. BWAs were not associated with increased or decreased odds of GI illness during all three hazard periods. However, in stratified analyses BWAs issued between January-March were associated with higher odds of GI illness, compared to advisories issued between April-December, in all three hazard periods. Significance SSOs (all months) and BWAs (January-March) were associated with increased odds of a diagnosis of GI illness. Future research should examine sewage contamination of the drinking water distribution system, and mechanisms of sewage intrusion from SSOs. Impact Sewage contains pathogens, which cause gastrointestinal (GI) illness. In Columbia, South Carolina, USA, between 2013–2017, there were 830 sanitary sewage overflows (SSOs). There were also 423 boil water advisories, which were issued during negative pressure events. Using case-crossover design, SSOs (all months) and boil water advisories (January-March) were associated with increased odds of Emergency Room and Urgent Care diagnoses of GI illness, potentially due to contamination of the drinking water distribution system. Lastly, we identified a community where >80% of residents identified as Black or African-American, which experienced a disproportionate burden of sewage exposure, compared to the rest of Columbia.
 
Flow chart for the study selection process
*Filter samples that yielded an OP activity ≤0 were excluded.
Causal diagram summarizing the hypothesized associations of oxidative potential (OP) of PM2.5 to foetal growth and other factors possibly acting as confounders in an epidemiologic study of air pollution effects on foetal growth restrictions
Arrows show potential effects of a specific factor over another not mediated by another factor present in the diagram. A dotted arrow represents not established relationship. An arrow from a factor A intersecting an arrow from B to C indicates that A may modify the effect of B on C. Note: ETSa (Environmental Tobacco Smoke), BMIb (Body Mass Index), and SESc (socioeconomic status (e.g. maternal education)). Image adapted from Slama et al. (2008).
Map of Europe (left) and the cohort study area (right) with average personal PM2.5 mass concentration (µg/m³) corresponding to the volunteers’ home address
©OpenStreetMap contributors 2021. Distributed under a Creative Commons BY-SA License.
Overview of measured personal exposures
Time-series of individual measurements (left), seasonal dispersion (center), and distribution by pregnancy period (right) of personal exposures (PM2.5, OPvDTT\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$OP_v^{DTT}$$\end{document}, and OPvAA\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$OP_v^{AA}$$\end{document}; 679 PM2.5 measurements in 367 women from the SEPAGES cohort). Note: Each month for the seasonal dispersion is denoted by numbers starting from 1 (January) to 12 (December).
Background: Prenatal exposure to fine particulate matter (PM2.5) assessed through its mass concentration has been associated with foetal growth restriction in studies based on outdoor levels. Oxidative potential of PM2.5 (OP) is an emerging metric a priori relevant to mechanisms of action of PM on health, with very limited evidence to indicate its role on birth outcomes. Objectives: We investigated the association of OP with birth outcomes and compared it with that of PM2.5 mass concentration. Methods: 405 pregnant women from SEPAGES cohort (Grenoble area) carried PM2.5 personal dosimeters for one or two one-week periods. OP was measured using dithiothreitol (DTT) and ascorbic acid (AA) assays from the collected filters. Associations of each exposure metric with offspring weight, height, and head circumference at birth were estimated adjusting for potential confounders. Results: The correlation between PM2.5 mass concentration and [Formula: see text] was 0.7. An interquartile range increase in .. was associated with reduced weight (adjusted change, -64 g, -166 to -11, p = 0.02) and height (-4 mm, -6 to -1, p = 0.01) at birth. PM2.5 mass concentration showed similar associations with weight (-53 g, -99 to -8, p = 0.02) and height (-2 mm, -5 to 0, p = 0.05). In birth height models mutually adjusted for the two exposure metrics, the association with [Formula: see text] was less attenuated than that with mass concentration, while for weight both effect sizes attenuated similarly. There was no clear evidence of associations with head circumference for any metric, nor for [Formula: see text] with any growth parameter. Impact: PM2.5 pregnancy exposure assessed from personal dosimeters was associated with altered foetal growth. Personal OP exposure was associated with foetal growth restrictions, specifically decreased weight and height at birth, possibly to a larger extent than PM2.5 mass concentration alone. These results support OP assessed from DTT as being a health-relevant metric. Larger scale cohort studies are recommended to support our findings.
 
Example of the Mechanical Turk platform where perception data was collected
Here participants are shown two images and asked “which street has a higher quality of nature?”. Participants used the slider bar to select what image they thought had higher nature quality and how strongly they felt about this choice.
Architecture of the deep learning model used to predict continuous perception scores for GSV images.
Morans’ I values for adjusted TrueSkill scores and bias, for the entire dataset and stratified by urban size category (Seattle images, urban area (UA) images, and urban clusters (UC) images).
Background Perceptions of the built environment, such as nature quality, beauty, relaxation, and safety, may be key factors linking the built environment to human health. However, few studies have examined these types of perceptions due to the difficulty in quantifying them objectively in large populations. Objective To measure and predict perceptions of the built environment from street-view images using crowd-sourced methods and deep learning models for application in epidemiologic studies. Methods We used the Amazon Mechanical-Turk crowdsourcing platform where participants compared two street-view images and quantified perceptions of nature quality, beauty, relaxation, and safety. We optimized street-view image sampling methods to improve the quality and resulting perception data specific to participants enrolled in the Washington State Twin Registry (WSTR) health study. We used a transfer learning approach to train deep learning models by leveraging existing image perception data from the PlacePulse 2.0 dataset, which includes 1.1 million image comparisons, and refining based on new WSTR perception data. Resulting models were applied to WSTR addresses to estimate exposures and evaluate associations with traditional built environment measures. Results We collected over 36,000 image comparisons and calculated perception measures for each image. Our final deep learning models explained 77.6% of nature quality, 68.1% of beauty, 72.0% of relaxation, and 64.7% of safety in pairwise image comparisons. Applying transfer learning with the new perception labels specific to the WSTR yielded an average improvement of 3.8% for model performance. Perception measures were weakly to moderately correlated with traditional built environment exposures for WSTR participant addresses; for example, nature quality and NDVI (r = 0.55), neighborhood area deprivation (r = −0.16), and walkability (r = −0.20), respectively. Significance We were able to measure and model perceptions of the built environment optimized for a specific health study. Future applications will examine associations between these exposure measures and mental health in the WSTR. Impact statement Built environments influence health through complex pathways. Perceptions of nature quality, beauty, relaxation and safety may be particularly import for understanding these linkages, but few studies to-date have examined these perceptions objectively for large populations. For quantitative research, an exposure measure must be reproducible, accurate, and precise––here we work to develop such measures for perceptions of the urban environment. We created crowd-sourced and image-based deep learning methods that were able to measure and model these perceptions. Future applications will apply these models to examine associations with mental health in the Washington State Twin Registry.
 
Map of SEARCH Network Monitors and FEM Monitoring Sites.
Time Series for the first week of February, 2019 (highest reference concentration week on record) comparing linear regression (red) and NGBoost (blue) predictions to the MDE reference measurements (gray) where the NGBoost tracks peaks and valleys more effectively than the linear model.
Mean (left) and 95th Percentile (right) PM2.5 exposures by Community Statistical Association (CSA) for June 5, 2019 (a) and August 1, 2019 (b) with major roads and highways denoted.
Probability of daily mean PM2.5 exceeding 12 mg/m³ by CSA on June 5, 2019 with major highways and roads.
Proportion of days with a predicted daily mean PM2.5 exceeding 10 mg/m³ by Census Tract from January 2019 – November 2019 with major highways and roads.
Background Low-cost sensor networks for monitoring air pollution are an effective tool for expanding spatial resolution beyond the capabilities of existing state and federal reference monitoring stations. However, low-cost sensor data commonly exhibit non-linear biases with respect to environmental conditions that cannot be captured by linear models, therefore requiring extensive lab calibration. Further, these calibration models traditionally produce point estimates or uniform variance predictions which limits their downstream in exposure assessment. Objective Build direct field-calibration models using probabilistic gradient boosted decision trees (GBDT) that eliminate the need for resource-intensive lab calibration and that can be used to conduct probabilistic exposure assessments on the neighborhood level. Methods Using data from Plantower A003 particulate matter (PM) sensors deployed in Baltimore, MD from November 2018 through November 2019, a fully probabilistic NGBoost GBDT was trained on raw data from sensors co-located with a federal reference monitoring station and compared against linear regression trained on lab calibrated sensor data. The NGBoost predictions were then used in a Monte Carlo interpolation process to generate high spatial resolution probabilistic exposure gradients across Baltimore. Results We demonstrate that direct field-calibration of the raw PM2.5 sensor data using a probabilistic GBDT has improved point and distribution accuracies compared to the linear model, particularly at reference measurements exceeding 25 μg/m³, and also on monitors not included in the training set. Significance We provide a framework for utilizing the GBDT to conduct probabilistic spatial assessments of human exposure with inverse distance weighting that predicts the probability of a given location exceeding an exposure threshold and provides percentiles of exposure. These probabilistic spatial exposure assessments can be scaled by time and space with minimal modifications. Here, we used the probabilistic exposure assessment methodology to create high quality spatial-temporal PM2.5 maps on the neighborhood-scale in Baltimore, MD. Impact statement We demonstrate how the use of open-source probabilistic machine learning models for in-place sensor calibration outperforms traditional linear models and does not require an initial laboratory calibration step. Further, these probabilistic models can create uniquely probabilistic spatial exposure assessments following a Monte Carlo interpolation process. Graphical abstract
 
Estimates of exposure are critical to prioritize and assess chemicals based on risk posed to public health and the environment. The U.S. Environmental Protection Agency (EPA) is responsible for regulating thousands of chemicals in commerce and the environment for which exposure data are limited. Since 2009 the EPA's ExpoCast ("Exposure Forecasting") project has sought to develop the data, tools, and evaluation approaches required to generate rapid and scientifically defensible exposure predictions for the full universe of existing and proposed commercial chemicals. This review article aims to summarize issues in exposure science that have been addressed through initiatives affiliated with ExpoCast. ExpoCast research has generally focused on chemical exposure as a statistical systems problem intended to inform thousands of chemicals. The project exists as a companion to EPA's ToxCast ("Toxicity Forecasting") project which has used in vitro high-throughput screening technologies to characterize potential hazard posed by thousands of chemicals for which there are limited toxicity data. Rapid prediction of chemical exposures and in vitro-in vivo extrapolation (IVIVE) of ToxCast data allow for prioritization based upon risk of adverse outcomes due to environmental chemical exposure. ExpoCast has developed (1) integrated modeling approaches to reliably predict exposure and IVIVE dose, (2) highly efficient screening tools for chemical prioritization, (3) efficient and affordable tools for generating new exposure and dose data, and (4) easily accessible exposure databases. The development of new exposure models and databases along with the application of technologies like non-targeted analysis and machine learning have transformed exposure science for data-poor chemicals. By developing high-throughput tools for chemical exposure analytics and translating those tools into public health decisions ExpoCast research has served as a crucible for identifying and addressing exposure science knowledge gaps.
 
Example of monitor placement in the Shared office 1 (4 participants) and exposure measurement (CO2, PM) in the breathing zone of the reference participant
Each monitor location is marked with an ID number which is described in Table 1. Notes: E1 = Exhaust 1, E2 = Exhaust 2.
Input and output variables in composing MLR models
Selection criteria were applied while separating the collected data into sitting and standing activities. Notes: Exhaust 2 was not included as input in MLR analysis. Tskin stands for skin temperature, HR for heart rate, and ACC for resultant acceleration (ACC=ACC_x2+ACC_y2+ACC_z2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{{{{{{\mathrm{ACC}}}}}}}} = \sqrt {ACC\_x^2 + ACC\_y^2 + ACC\_z^2}$$\end{document}). Description of sitting and standing activities is shown in Figure S2.
The CO2, PM2.5 and PM10 concentration at different stationary monitors across all activities and experiments.
Pearson correlations of CO2, PM2.5, and PM10 measurements during sitting, standing, and combined participant activities.
Background Modern health concerns related to air pollutant exposure in buildings have been exacerbated owing to several factors. Methods for assessing inhalation exposures indoors have been restricted to stationary air pollution measurements, typically assuming steady-state conditions. Objective We aimed to examine the feasibility of several proxy methods for estimating inhalation exposure to CO2, PM2.5, and PM10 in simulated office environments. Methods In a controlled climate chamber mimicking four different office setups, human participants performed a set of scripted sitting and standing office activities. Three proxy sensing techniques were examined: stationary indoor air quality (IAQ) monitoring, individual monitoring of physiological status by wearable wristband, human presence detection by Passive Infrared (PIR) sensors. A ground-truth of occupancy was obtained from video recordings of network cameras. The results were compared with the concurrent IAQ measurements in the breathing zone of a reference participant by means of multiple linear regression (MLR) analysis with a combination of different input parameters. Results Segregating data onto sitting and standing activities could lead to improved accuracy of exposure estimation model for CO2 and PM by 9–60% during sitting activities, relative to combined activities. Stationary PM2.5 and PM10 monitors positioned at the ceiling-mounted ventilation exhaust in vicinity of the seated reference participant accurately estimated inhalation exposure (adjusted R² = 0.91 and R² = 0.87). Measurement at the front edge of the desk near abdomen showed a moderate accuracy (adjusted R² = 0.58) in estimating exposure to CO2. Combining different sensing techniques improved the CO2 exposure detection by twofold, whereas the improvement for PM exposure detection was small (~10%). Significance This study contributes to broadening the knowledge of proxy methods for personal exposure estimation under dynamic occupancy profiles. The study recommendations on optimal monitor combination and placement could help stakeholders better understand spatial air pollutant gradients indoors which can ultimately improve control of IAQ.
 
Conceptual overview of the approaches used in this study to derive iTTCs
External dose NOELs were converted using a general toxicokinetic model to generate the cumulative distribution of internal NOELs, and then lower 5th percentile internal NOEL value was divided by a 100-fold adjustment factor to calculate the iTTC.
Tiered approach and workflow for parameterizing toxicokinetics models with whole-body total (terminal) elimination rate constant (kT) and whole-body biotransformation rate constant (kB) data to calculate internal doses from the reported NOELs in the Munro TTC database [11, 39] as derived from external oral doses
Blood concentrations (CB or Cblood) are then calculated from whole body concentrations (CWB) as shown in Eq. (3). Absorption efficiency (AE) is used in the parameterization of the default and alternative #1 models while oral bioavailability (F) is used in the alternative model #2 calculations.
External and internal doses corresponding to NOELs in the Munro TTC database [11, 39]
The dashed diagonal line represents the 1:1 line.
Cumulative distribution of modeled blood concentrations corresponding to NOELs reported in the Munro TTC database [11, 39] using the default and alternative assumptions (Alt #1, Alt #2, see “Methods” section)
The figure on the right provides a better view of the lower 20% of the data in the cumulative distribution.
Cumulative distribution of modeled whole-body concentrations corresponding to NOELs reported in the Munro TTC database [11, 39] using the default and alternative assumptions (Alt #1, Alt #2, see “Methods” section)
The figure on the right provides a better view of the lower 20% of the data in the cumulative distribution.
Background Threshold of Toxicological Concern (TTC) approaches are used for chemical safety assessment and risk-based priority setting for data poor chemicals. TTCs are derived from in vivo No Observed Effect Level (NOEL) datasets involving an external administered dose from a single exposure route, e.g., oral intake rate. Thus, a route-specific TTC can only be compared to a route-specific exposure estimate and such TTCs cannot be used for other exposure scenarios such as aggregate exposures. Objective Develop and apply a method for deriving internal TTCs (iTTCs) that can be used in chemical assessments for multiple route-specific exposures (e.g., oral, inhalation or dermal) or aggregate exposures. Methods Chemical-specific toxicokinetics (TK) data and models are applied to calculate internal concentrations (whole-body and blood) from the reported administered oral dose NOELs used to derive the Munro TTCs. The new iTTCs are calculated from the 5th percentile of cumulative distributions of internal NOELs and the commonly applied uncertainty factor of 100 to extrapolate animal testing data for applications in human health assessment. Results The new iTTCs for whole-body and blood are 0.5 nmol/kg and 0.1 nmol/L, respectively. Because the iTTCs are expressed on a molar basis they are readily converted to chemical mass iTTCs using the molar mass of the chemical of interest. For example, the median molar mass in the dataset is 220 g/mol corresponding to an iTTC of 22 ng/L-blood (22 pg/mL-blood). The iTTCs are considered broadly applicable for many organic chemicals except those that are genotoxic or acetylcholinesterase inhibitors. The new iTTCs can be compared with measured or estimated whole-body or blood exposure concentrations for chemical safety screening and priority-setting. Significance Existing Threshold of Toxicological Concern (TTC) approaches are limited in their applications for route-specific exposure scenarios only and are not suitable for chemical risk and safety assessments under conditions of aggregate exposure. New internal Threshold of Toxicological Concern (iTTC) values are developed to address data gaps in chemical safety estimation for multi-route and aggregate exposures.
 
Background Toxicokinetic (TK) data needed for chemical risk assessment are not available for most chemicals. To support a greater number of chemicals, the U.S. Environmental Protection Agency (EPA) created the open-source R package “httk” (High Throughput ToxicoKinetics). The “httk” package provides functions and data tables for simulation and statistical analysis of chemical TK, including a population variability simulator that uses biometrics data from the National Health and Nutrition Examination Survey (NHANES). Objective Here we modernize the “HTTK-Pop” population variability simulator based on the currently available data and literature. We provide explanations of the algorithms used by “httk” for variability simulation and uncertainty propagation. Methods We updated and revised the population variability simulator in the “httk” package with the most recent NHANES biometrics (up to the 2017–18 NHANES cohort). Model equations describing glomerular filtration rate (GFR) were revised to more accurately represent physiology and population variability. The model output from the updated “httk” package was compared with the current version. Results The revised population variability simulator in the “httk” package now provides refined, more relevant, and better justified estimations. Significance Fulfilling the U.S. EPA’s mission to provide open-source data and models for evaluations and applications by the broader scientific community, and continuously improving the accuracy of the “httk” package based on the currently available data and literature.
 
Number of chemicals of concern in 31 products analyzed
Each number in the vertical axis represents a unique product, for a total of 31 products. Products 1-10 were from the Black/African American community, Products 11-20 were from the Latina community, and Products 21-30 were from the Vietnamese community. Product 31 was not from any of the partner communities but was marketed as “natural” and “safe” and for use by anyone. Product #16 and #24 did not have ingredient labels.
Background Personal care products (PCPs) may contain chemicals associated with adverse health effects. Prior studies found differences in product use by race/ethnicity and suggest some women are disproportionately exposed to chemicals of concern (CoCs). Objective We quantified chemicals linked to cancer, reproductive or developmental harm, or endocrine disruption in PCPs used by women of color. Methods We documented PCPs in stores frequented by Black, Latina, and Vietnamese women in their communities in California and CoCs on ingredient labels of 546 unique hair, skin, makeup, nail, deodorant/perfume, and intimate care products. Community partners chose 31 products for a combined targeted and suspect screen (National Institute of Standards and Technology mass spectral library search) two-dimensional gas chromatography time-of-flight mass spectrometry (GCxGC-TOFMS) analysis to detect chemicals not on ingredient labels. Results We found that 65% of labels included CoCs, and 74% of labels had undisclosed ingredients listed as “fragrance.” The most prevalent chemicals were parabens, cyclosiloxanes, and formaldehyde releasers. GCxGC-TOFMS found additional CoCs, including fragrances, solvents, preservatives, ultraviolet filters, and contaminants. Significance These findings contribute to awareness of potentially hazardous chemicals in PCPs, can help estimate disparities in chemical exposure, and complement research on health inequities due to chemical exposures from various contributors. Impact statement This study is one of the first detailed assessments of chemicals of concern found in various types of PCPs used by several racial/ethnic groups. We found that over half of the 546 products selected by community partners as marketed to and/or used by them contained ingredients linked to cancer, reproductive or developmental harm, or endocrine disruption. Laboratory analysis identified additional chemicals in a subset of products, including unlabeled fragrance chemicals and contaminants. Elucidating exposures to chemicals in PCPs is important for risk assessment and health inequity research.
 
Percentage (black circle and dashed line, %) of Chinese adults in the top 11 travel patterns and their travel time (median ± interquartile range (IQR), min/d)
Error bars represent IQR.
Distribution of travel number and travel time for the top 11 travel patterns in China, left and the upper tree structure is for clustering
a Distribution of travel number (The data is standardized by row, larger legend values represent more travel number). b Distribution of travel time (The data is standardized by row, larger legend values represent longer travel times).
Comparison of regional population and GDP data for 2011 and 2019
a regional population (unit: million). b regional, GDP (unit: billion).
Background Traffic-related air pollutants lead to increased risks of many diseases. Understanding travel patterns and influencing factors are important for mitigating traffic exposures. However, there is a lack of national large-scale research. Objective This study aimed to evaluate the daily travel patterns of Chinese adults and provide basic data for traffic exposure and health risk research. Methods We conducted the first nation-wide survey of travel patterns of adults (aged 18 and above) in China during 2011–2012. We conducted a cross-sectional study based on a nationally representative sample of 91, 121 adults from 31 provinces in China. We characterized typical travel patterns by cluster analysis and identified the associated factors of each pattern using multiple logistic regression and generalized linear regression models. Results We found 115 typical daily travel patterns of Chinese adults and the top 11 accounted for 94% of the population. The interaction of age, urban and rural areas, income levels, gender, educational levels, city population and temperature affect people’s choice of travel patterns. The average travel time of Chinese adults is 45 ± 40 min/day, with the longest travel time by the combination of walking and car (70 min/day). Gender has the largest effect on travel time (B = −8.94, 95% CI: −8.95, −8.93), followed by city GDP (B = −4.23, 95% CI: −4.23, −4.22), urban and rural areas (B = −3.62, 95% CI: −3.63, −3.61), age (B = −2.21, 95% CI: −2.21, −2.2), educational levels (B = −1.53, 95% CI: −1.53, −1.52), city area (B = −1.4, 95% CI: −1.4, −1.39) and temperature (B = 1.21, 95% CI: 1.2, 1.21). Significance This study was the first nation-wide study on traffic activity patterns in China, which provides basic data for traffic exposure and health risk research and provides the basis for the state to formulate transportation-related policies.
 
Example individual time series showing personal PM2.5 exposure and estimated wearing compliance during a 2-day monitoring period for a participant in the control arm in July 2015
Partially-transparent points in the top panels show individual one-minute estimates of PM2.5 concentrations and the black line shows an 11 min smoothed average (5 min before and after the given minute), truncated at 500 µg/m³. Some points and exposure peaks are not shown as a result. Top (PM2.5) panel y-axis is on a log scale. Bottom panel shows the raw data at a one-minute resolution from the personal air pollution exposure monitor (RTI microPEM) accelerometer in three axes. Pink vertical lines in the top panel are when our algorithm detects device wearing at a 1 min resolution.
Summarizing detected device wearing and the distribution of personal PM2.5 exposures across hours of the day
Top panel shows the 10th, 25th, 50th, 75th, and 90th percentiles of estimated personal PM2.5 exposure for each hour of the day, estimated using all available data points from valid monitoring sessions falling during a given hour of the day (around 240,000 1 min observations for each hour). Bottom panel shows the percent of all observations in that same hour of the day where the device is estimated to be worn. For example, across all one-minute 236,052 data points observed between 3:00 am and 3:59 am only 3745 had wearing detected or ~1%.
Detected personal air pollution exposure device wearing declined throughout the study period
Partially-transparent points show the percent of waking hours (5:00 am–8:59 pm, ends included) where wearing is detected for each valid PM2.5 monitoring session over the full data collection period from late 2013 to early 2016, colored according to the study arm. A trend line is plotted to show average 48 h wearing compliance over time. Middle panel is a cumulative distribution function showing the distribution of individual observations by study arm. Right panel shows box-and-whisker plots displaying the 25th, 50th, and 75th percentiles in the box and whiskers extend to 1.96 times in the interquartile range of the data.
Model-based prediction of the association between daytime device wearing and mean personal PM2.5 exposure
Scatter plots show the percent of daytime observations where average detected wearing per day over the 2-day monitoring period is on the x-axis and mean 48 h personal maternal PM2.5 exposure is along the y-axis. Points are color coded by study arm as indicated in the bottom panel. Lines show the predicted values from GEEs for each study arm at every percent daytime device wearing. The y-axis on the top panel is truncated in the top panel and some points are missing at very low and high air pollution concentrations. Bottom panel shows the distribution of detected device wearing during daytime hours by study arm in normalized density plots, annotated with vertical lines at the 25th, 50th, and 75th percentiles. Predicted concentrations for CO are shown in Fig. S9.
Background Personal monitoring can estimate individuals’ exposures to environmental pollutants; however, accuracy depends on consistent monitor wearing, which is under evaluated. Objective To study the association between device wearing and personal air pollution exposure. Methods Using personal device accelerometry data collected in the context of a randomized cooking intervention in Ghana with three study arms (control, improved biomass, and liquified petroleum gas (LPG) arms; N = 1414), we account for device wearing to infer parameters of PM2.5 and CO exposure. Results Device wearing was positively associated with exposure in the control and improved biomass arms, but weakly in the LPG arm. Inferred community-level air pollution was similar across study arms (~45 μg/m³). The estimated direct contribution of individuals’ cooking to PM2.5 exposure was 64 μg/m³ for the control arm, 74 μg/m³ for improved biomass, and 6 μg/m³ for LPG. Arm-specific average PM2.5 exposure at near-maximum wearing was significantly lower in the LPG arm as compared to the improved biomass and control arms. Analysis of personal CO exposure mirrored PM2.5 results. Conclusions Personal monitor wearing was positively associated with average air pollution exposure, emphasizing the importance of high device wearing during monitoring periods and directly assessing device wearing for each deployment. Significance We demonstrate that personal monitor wearing data can be used to refine exposure estimates and infer unobserved parameters related to the timing and source of environmental exposures. Impact statements In a cookstove trial among pregnant women, time-resolved personal air pollution device wearing data were used to refine exposure estimates and infer unobserved exposure parameters, including community-level air pollution, the direct contribution of cooking to personal exposure, and the effect of clean cooking interventions on personal exposure. For example, in the control arm, while average 48 h personal PM2.5 exposure was 77 μg/m³, average predicted exposure at near-maximum daytime device wearing was 108 μg/m³ and 48 μg/m³ at zero daytime device wearing. Wearing-corrected average 48 h personal PM2.5 exposures were 50% lower in the LPG arm than the control and improved biomass and inferred direct cooking contributions to personal PM2.5 from LPG were 90% lower than the other arms. Our recommendation is that studies assessing personal exposures should examine the direct association between device wearing and estimated mean personal exposure.
 
Participant’s residential locations are primarily within the greater Boston metropolitan area
Map of PRISM cohort participants included in the study (n = 195).
The NSSI ranges from −0.88 to 3.78
Higher NSSI scores indicate more positive feelings of neighborhood sentiment and safety. Massachusetts neighborhood sentiment and safety index spatial rendering.
Zoomed in look at the greater Boston metropolitan area
Neighborhood sentiment and safety index spatial rendering for the greater Boston Metropolitan Area.
Share Care community well being dimension index and the Neighborhood Sentiment and Safety Index rank by county.
Background The communities we live in are central to our health. Neighborhood disadvantage is associated with worse physical and mental health and even early mortality, while resident sense of safety and positive neighborhood sentiment has been repeatedly linked to better physical and mental health outcomes. Therefore, understanding where negative neighborhood sentiment and safety are salient concerns can help inform public health interventions and as a result, improve health outcomes. To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys. Objective The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories. Methods The NSSI was created using Principal Component Analysis. Mineigen and minimum loading values were 1 and 0.3, respectively. Throughout the step-wise PCA process, variables were excluded if their loading value was below 0.3 or if variables loaded into multiple components. Results The novel index was validated against standardized survey items from a longitudinal cohort study in the Northeastern United States characterizing experiences of (1) Neighborhood Characteristics with a Pearson correlation of −0.34 (p < 0.001) and, (2) Neighborhood Behavior Impact with a Pearson correlation of −0.33 (p < 0.001). It also accurately predicted the Share Care Community Well Being Index (Spearman correlation = 0.46) and the neighborhood deprivation index (NDI) (Spearman correlation = −0.75). Significance Our NSSI can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies. Impact statement To date, fear of crime and neighborhood sentiment data or indices have largely been based on the administration of time consuming and costly standardized surveys. The current study aims to develop a Neighborhood Sentiment and Safety Index (NSSI) at the census tract level, building on publicly available data repositories, including the US Census and ACS surveys, Data Axle, and ESRI repositories. The NSSI was validated against four separate measures and can serve as a predictor of neighborhood experience where data is either unavailable or too resource consuming to practically implement in planned studies.
 
of activity data types (macro- meso- and micro) and examples related to soil exposure estimation for agricultural context
This figure summarizes the types of activity-pattern data within the agricultural context. The top row defines farming as a macro-activity. The middle row defines examples of six tasks or meso-activities that emerged from in-depth interviews (IDIs). The bottom row contains examples of micro-activities described directly by growers in IDIs.
Environmental, Activity, Timing – Receptor (EAT-R) Framework describing factors impacting soil exposure in the agricultural context
This figure summarizes the environmental, activity, timing and receptor factors that may impact soil exposure in the agricultural context. The bold text indicates the four broad categories of factors identified via interviews with growers. The plain text identifies the ten sub-factors that impact soil exposures. The arrows indicate the direction of potential influences of factors on each other.
Background Agricultural workers’ exposure to soil contaminants is not well characterized. Activity pattern data are a useful exposure assessment tool to estimate extent of soil contact, though existing data do not sufficiently capture the range and magnitude of soil contact in the agricultural context. Objective We introduce meso-activity, or specific tasks, to improve traditional activity pattern methodology. We propose a conceptual framework to organize the factors that may modify soil exposure and impact soil contact estimates within each meso-activity in agriculture. We build upon models from the US EPA to demonstrate an application of this framework to dose estimation. Methods We conducted in-depth interviews with sixteen fruit and vegetable growers in Maryland to characterize factors that influence soil exposure in agriculture. For illustrative purposes, we demonstrate the application of the framework to translate our qualitative data into quantitative estimates of soil contact using US EPA models for ingestion and dermal exposure. Results Growers discussed six tasks, or meso-activities, involving interaction with soil and described ten factors that may impact the frequency, duration and intensity of soil contact. We organized these factors into four categories (i.e., Environmental, Activity, Timing and Receptor; EAT-R) and developed a framework to improve agricultural exposure estimation and guide future research. Using information from the interviews, we estimated average daily doses for several agricultural exposure scenarios. We demonstrated how the integration of EAT-R qualitative factors into quantitative tools for exposure assessment produce more rigorous estimates of exposure that better capture the true variability in agricultural work. Significance Our study demonstrates how a meso-activity-centered framework can be used to refine estimates of exposure for agricultural workers. This framework will support the improvement of indirect exposure assessment tools (e.g., surveys and questionnaires) and inform more comprehensive and appropriate direct observation approaches to derive quantitative estimations of soil exposure. Impact statement We propose a novel classification of activity pattern data that links macro and micro-activities through the quantification and characterization of meso-activities and demonstrate how the application of our qualitative framework improves soil exposure estimation for agricultural workers. These methodological advances may inform a more rigorous approach to the evaluation of pesticide and other chemical and biological exposures incurred by persons engaged in the cultivation of agricultural commodities in soil.
 
Background Despite their large numbers and widespread use, very little is known about the extent to which per- and polyfluoroalkyl substances (PFAS) can cross the placenta and expose the developing fetus. Objective The aim of our study is to develop a computational approach that can be used to evaluate the of extend to which small molecules, and in particular PFAS, can cross to cross the placenta and partition to cord blood. Methods We collected experimental values of the concentration ratio between cord and maternal blood (RCM) for 260 chemical compounds and calculated their physicochemical descriptors using the cheminformatics package Mordred. We used the compiled database to, train and test an artificial neural network (ANN). And then applied the best performing model to predict RCM for a large dataset of PFAS chemicals (n = 7982). We, finally, examined the calculated physicochemical descriptors of the chemicals to identify which properties correlated significantly with RCM. Results We determined that 7855 compounds were within the applicability domain and 127 compounds are outside the applicability domain of our model. Our predictions of RCM for PFAS suggested that 3623 compounds had a log RCM > 0 indicating preferable partitioning to cord blood. Some examples of these compounds were bisphenol AF, 2,2-bis(4-aminophenyl)hexafluoropropane, and nonafluoro-tert-butyl 3-methylbutyrate. Significance These observations have important public health implications as many PFAS have been shown to interfere with fetal development. In addition, as these compounds are highly persistent and many of them can readily cross the placenta, they are expected to remain in the population for a long time as they are being passed from parent to offspring. Impact Understanding the behavior of chemicals in the human body during pregnancy is critical in preventing harmful exposures during critical periods of development. Many chemicals can cross the placenta and expose the fetus, however, the mechanism by which this transport occurs is not well understood. In our study, we developed a machine learning model that describes the transplacental transfer of chemicals as a function of their physicochemical properties. The model was then used to make predictions for a set of 7982 per- and polyfluorinated alkyl substances that are listed on EPA’s CompTox Chemicals Dashboard. The model can be applied to make predictions for other chemical categories of interest, such as plasticizers and pesticides. Accurate predictions of RCM can help scientists and regulators to prioritize chemicals that have the potential to cause harm by exposing the fetus.
 
The USDA’s Pesticide Data Program (PDP) celebrated its 30th anniversary in 2021 and is one of the world’s largest monitoring programs for pesticide residues. The PDP database contains over 42 million data points for a pesticide paired to a commodity that have resulted from the analysis of nearly 310,000 food samples of 126 different commodities. Over the decades of the program, sampling methods and infrastructure, major milestones, developments, and accomplishments have unfolded. Comparisons of data for four commodities that were in the program early on illustrate that over time pesticide residues on foods change, particularly when new pesticides are registered, and updated data, such as those provided by PDP, are key for exposure and risk assessment.
 
Frequency of hand sanitizer use by adults before and during the pandemic
The frequency of use as self-reported by adult respondents (%) before the pandemic presented in the dark bar; during the pandemic shown in the light bar, with 95% confidence interval range shown as a line.
Frequency of hand sanitizer use in children ≤ 3 years of age during the pandemic
Frequency of hand sanitizer use in children ≤ 3 years of age as reported by adult caretakers at home (left) or at school (right), with 95% confidence interval range shown as a line.
Amount of pump form of hand sanitizer used by children in home and school settings as reported by adult respondents
Amount of pump form of hand sanitizer used per application by children (as reported by adult caretakers), by increasing age group from left to right, with 95% confidence interval range shown as line.
Background The use of hand sanitizers has been one of the key public health measures recommended to reduce the transmission of SARS-CoV-2 during the pandemic. As such, its daily use among the general population has reportedly increased dramatically since the onset of the COVID-19 pandemic. Objective To better understand the impact of this recommendation, hand sanitizer use, including the frequency and amount handled, was examined among adults in a non-occupational setting and children in both the home and school/childcare settings. Methods An online survey of Canadians (conducted from September to October 2021) was employed to estimate use frequency, amount, and pattern of hand sanitizer use. Results Responses were received from 655 adults in the general population and 298 teachers of children up to the age of 18 years. The frequency of hand sanitizer use during the pandemic was found to be as high as 25 times per day in children and over 9 times per day in adults. Notable differences were found when comparing the frequency of hand sanitizer use by children in the home to children in a school or childcare setting. Significance This is the first study, known to the authors, examining hand sanitizer use among children during the pandemic, including use in a childcare or school setting. This study illustrates the importance of examining the change in consumer behaviors during a pandemic and the need to look beyond the home when attempting to understand product use patterns in children. Impact statement This research explores uses of hand sanitizer, before and during pandemic conditions, in the general population of Canada with a particular focus on use among children. The results can be used to estimate exposure to chemicals in hand sanitizer from non-occupational use in Canada and among similar populations and signal the importance of examining changing consumer behaviors and use of consumer products in school settings, especially among children.
 
Background: The absence of air pollution monitoring networks makes it difficult to assess historical fine particulate matter (PM2.5) exposures for countries in the areas, such as Kuwait, which are severe impacted by desert dust and anthropogenic pollution. Objective: We constructed an ensemble machine learning model to predict daily PM2.5 concentrations for regions lack of PM2.5 observations. Methods: The model was constructed based on daily PM2.5, visibility, and other meteorological data collected at two sites in Kuwait. Then, our model was applied to predict the daily level of PM2.5 concentrations for eight airports located in Kuwait and Iraq from 2013 to 2020. Results: As compared to traditional statistic models, the proposed machine learning methods improved the accuracy in using visibility to predict daily PM2.5 concentrations with a cross-validation R2 of 0.68. The predicted level of daily PM2.5 concentrations were consistent with previous measurements. The predicted average yearly PM2.5 concentration for the eight stations is 50.65 µg/m3. For all stations, the monthly average PM2.5 concentrations reached their maximum in July and their minimum in November. Significance: These findings make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in regions with few particulate matter monitoring stations. Impact statement: The scarcity of air pollution ground monitoring networks makes it difficult to assess historical fine particulate matter exposures for countries in arid areas such as Kuwait. Visibility is closely related to atmospheric particulate matter concentrations and historical airport visibility records are commonly available in most countries. Our model make it possible to retrospectively estimate daily PM2.5 exposures using the large-scale databases of historical visibility in arid regions with few particulate matter ground monitoring stations. The product of such models can be critical for environmental risk assessments and population health studies.
 
Distribution of leukocyte telomere length among 18–23 months old children in Bhaktapur, Nepal by biomass fuel use in the household
The group from households using biomass fuel is indicated by solid lines, the group from houshoulds using other cooking fuels is indicated by dotted lines.
Association between biomass cooking fuel and leukocyte telomere length in pre-defined subgroups
Regression coefficients (mean difference) and 95% confidence intervals (95%CI) are from linear regression models, adjusted for the WAMI index, age, sex of the child, maternal age and indoor smoking.
Association between biomass cooking fuel and pre-defined percentiles distribution of leukocyte telomere length
Regression coefficients (ES) and 95% confidence intervals are from quantile regression models adjusted for WAMI, age, and sex of the child, maternal age, and indoor smoking.
Background Biomass fuels are still in use for cooking by many households in resource poor countries such as Nepal and is a major source of household air pollution (HAP). Chronic exposure to HAP has been shown to be associated with shorter telomere length in adults. Objectives To measure the association between exposure related to household biomass fuel in infancy and leukocyte telomere length (LTL) at 18–23 months of age among 497 children from Bhaktapur, Nepal. Methods In a prospective cohort study design, we have collected information on household cooking fuel use and several clinical, anthropometric, demographic, and socioeconomic variables. We estimated the association between biomass fuel use and the relative LTL in multiple linear regression models. Results Most of the families (78%) reported liquified petroleum gas (LPG) as the primary cooking fuel, and 18.7% used biomass. The mean relative (SD) LTL was 1.03 (0.19). Children living in households using biomass fuel had on average 0.09 (95% CI: 0.05 to 0.13) units shorter LTL than children in households with no biomass fuel use. The observed association was unaltered after adjusting for relevant confounders. The association between LTL and biomass use was strongest among children from households with ≤2 rooms and without separate kitchen. Significance Exposure to biomass fuel use in early life might have consequences for longevity, and risk of chronic illnesses reflected in shortening of the telomeres. Our findings support the ongoing effort to reduce exposure to biomass fuel in low-resource settings. Impact statements Biomass for cooking is a leading source of household air pollution in low and middle-income countries, contributing to many chronic diseases and premature deaths. Chronic exposure to biomass fuel through oxidative stress and inflammation has been associated with a shortening of the telomeres, a “biological marker” of longevity. This prospective cohort study describes the association between household biomass fuel use and leukocyte telomere length among 497 toddlers. Leukocyte telomere length was significantly shorter among children living in households with biomass fuel than in children from homes where mainly LPG was used for cooking. Clinical Trial registration Clinicaltrials.gov: NCT02272842, registered October 21, 2014, Universal Trial Number: U1111-1161-5187 (September 8, 2014)
 
Background The Integrated Exposure Uptake Biokinetic Model for Lead in Children (IEUBK model) was developed by the U.S. Environmental Protection Agency to support assessments of health risks to children from exposures to lead (Pb). Objective This study evaluated performance of IEUBK model (v2.0) as it would be typically applied at Superfund sites to predict blood Pb levels (BLLs) in populations of children. Methods The model was evaluated by comparing model predictions of BLLs to 1144 observed BLLs in a population of children at the Bunker Hill Superfund Site for which there were paired estimates of environmental Pb concentrations. Results Predicted population geometric mean (GM) BLLs (GM: 3.4 µg/dL, 95% CI: 3.3, 3.5) were within 0.3 µg/dL of observed (GM: 3.6 µg/dL, 95% CI: 3.5, 3.8). The model predicted the observed age trend in GM BLLs and explained ~90% of the variance in the observed age-stratified GM BLLs. The mean predicted probability of exceeding 5 µg/dL (P5) was 27% (95% CI: 24, 29) and observed P5 was 32% (95% CI: 29, 35), a difference of 5%. Differences between geographic area stratified mean P5 (predicted minus observed) ranged from −11 to 14% (mean difference: 2.3%). Significance Although the more general applicability of these findings to other populations remains to be determined in future studies, our results support applications of the IEUBK model (v2.0) for informing risk-based decisions regarding remediation of soils and mitigation of exposures at Superfund sites where the majority of the exposure unit GM BLLs are expected to be ≤5 µg/dL and where it is desired to limit the predicted probability of exceeding 5 µg/dL to <5%.
 
Background Phthalate exposure in pregnancy is typically estimated using maternal urinary phthalate metabolite levels. Our aim was to evaluate the association of urinary and placental tissue phthalates, and to explore the role of maternal and pregnancy characteristics that may bias estimates. Methods Fifty pregnancies were selected from the CANDLE Study, recruited from 2006 to 2011 in Tennessee. Linear models were used to estimate associations of urinary phthalates (2nd, 3rd trimesters) and placental tissue phthalates (birth). Potential confounders and modifiers were evaluated in categories: temporality (time between urine and placenta sample), fetal sex, demographics, social advantage, reproductive history, medication use, nutrition and adiposity. Molar and quantile normalized phthalates were calculated to facilitate comparison of placental and urinary levels. Results Metabolites detectable in >80% of both urine and placental samples were MEP, MnBP, MBzP, MECPP, MEOHP, MEHHP, and MEHP. MEP was most abundant in urine (geometric mean [GM] 7.00 ×10² nmol/l) and in placental tissue (GM 2.56 ×10⁴ nmol/l). MEHP was the least abundant in urine (GM 5.32 ×10¹ nmol/l) and second most abundant in placental tissue (2.04 ×10⁴ nmol/l). In aggregate, MEHP differed the most between urine and placenta (2.21 log units), and MEHHP differed the least (0.07 log units). MECPP was positively associated between urine and placenta (regression coefficient: 0.31 95% CI 0.09, 0.53). Other urine-placenta metabolite associations were modified by measures of social advantage, reproductive history, medication use, and adiposity. Conclusion Phthalates were ubiquitous in 50 full-term placental samples, as has already been shown in maternal urine. MEP and MEHP were the most abundant. Measurement and comparison of urinary and placental phthalates can advance knowledge on phthalate toxicity in pregnancy and provide insight into the validity and accuracy of relying on maternal urinary concentrations to estimate placental exposures. Impact statement This is the first report of correlations/associations of urinary and placental tissue phthalates in human pregnancy. Epidemiologists have relied exclusively on maternal urinary phthalate metabolite concentrations to assess exposures in pregnant women and risk to their fetuses. Even though it has not yet been confirmed empirically, it is widely assumed that urinary concentrations are strongly and positively correlated with placental and fetal levels. Our data suggest that may not be the case, and these associations may vary by phthalate metabolite and associations may be modified by measures of social advantage, reproductive history, medication use, and adiposity.
 
Background Both influenza and SARS-CoV-2 viruses show a strong seasonal spreading in temperate regions. Several studies indicated that changes in indoor humidity could be one of the key factors explaining this. Objective The purpose of this study is to quantify the association between relevant epidemiological metrics and humidity in both influenza and SARS-CoV-2 epidemic periods. Methods The atmospheric dew point temperature serves as a proxy for indoor relative humidity. This study considered the weekly mortality rate in the Netherlands between 1995 and 2019 to determine the correlation between the dew point and the spread of influenza. During influenza epidemic periods in the Netherlands, governmental restrictions were absent; therefore, there is no need to control this confounder. During the SARS-CoV-2 pandemic, governmental restrictions strongly varied over time. To control this effect, periods with a relatively constant governmental intervention level were selected to analyze the reproduction rate. We also examine SARS-CoV-2 deaths in the nursing home setting, where health policy and social factors were less variable. Viral transmissibility was measured by computing the ratio between the estimated daily number of infectious persons in the Netherlands and the lagged mortality figures in the nursing homes. Results For both influenza and SARS-CoV-2, a significant correlation was found between the dew point temperature and the aforementioned epidemiological metrics. The findings are consistent with the anticipated mechanisms related to droplet evaporation, stability of virus in the indoor environment, and impairment of the natural defenses of the respiratory tract in dry air. Significance This information is helpful to understand the seasonal pattern of respiratory viruses and motivate further study to what extent it is possible to alter the seasonal pattern by actively intervening in the adverse role of low humidity during fall and winter in temperate regions. Impact A solid understanding and quantification of the role of humidity on the transmission of respiratory viruses is imperative for epidemiological modeling and the installation of non-pharmaceutical interventions. The results of this study indicate that improving the indoor humidity by humidifiers could be a promising technology for reducing the spread of both influenza and SARS-CoV-2 during winter and fall in the temperate zone. The identification of this potential should be seen as a strong motivation to invest in further prospective testing of this non-pharmaceutical intervention.
 
Background Metals may influence reproductive health, but few studies have investigated correlates of metal body burden among reproductive-aged women outside of pregnancy. Furthermore, while there is evidence of racial disparities in exposure to metals among U.S. women, there is limited research about correlates of metal body burden among Black women. Objective To identify correlates of whole blood metal concentrations among reproductive-aged Black women. Methods We analyzed cross-sectional data from a cohort of 1664 Black women aged 23–35 years in Detroit, Michigan, 2010–2012. We collected blood samples and questionnaire data. We measured concentrations of 17 metals in whole blood using inductively-coupled plasma-mass spectrometer-triple quadrupole and total mercury using Direct Mercury Analyzer-80. We used multivariable linear regression models to identify sociodemographic, environmental, reproductive, and dietary correlates of individual metal concentrations. Results In adjusted models, age was positively associated with multiple metals, including arsenic, cadmium, and mercury. Education and income were inversely associated with cadmium and lead. Current smoking was strongly, positively associated with cadmium and lead. Alcohol intake in the past year was positively associated with arsenic, barium, copper, lead, mercury, vanadium, and zinc. Having pumped gasoline in the past 24 h was positively associated with cadmium, chromium, and molybdenum. Having lived in an urban area for the majority of residence in Michigan was positively associated with arsenic, lead, and nickel. Higher water intake in the past year was positively associated with several metals, including lead. Fish intake in the past year was positively associated with arsenic, cesium, and mercury. We also observed associations with body mass index, season, and other environmental, reproductive, and dietary factors. Significance We identified potential sources of exposure to metals among reproductive-aged Black women. Our findings improve understanding of exposures to metals among non-pregnant reproductive-aged women, and can inform policies in support of reducing disparities in exposures. Impact statement There are racial disparities in exposures to metals. We analyzed correlates of blood metal concentrations among reproductive-aged Black women in the Detroit, Michigan metropolitan area. We identified sociodemographic, anthropometric, lifestyle, environmental, reproductive, and dietary correlates of metal body burden. Age was positively associated with several metals. Education and income were inversely associated with cadmium and lead, indicating socioeconomic disparities. We identified potential exposure sources of metals among reproductive-aged Black women, including smoking, environmental tobacco smoke, pumping gasoline, living in an urban area, and intake of alcohol, water, fish, and rice.
 
Map showing spatial location of all polluted sites (HWS) in NC or within 5 miles of NC
The map includes all sites that were present on a public EPA registry of industrial facilities, a public EPA registry of Superfund sites, or a public NCDEQ registry of landfills, during at least one year from 2002–2015.
Background Preterm birth (PTB) and term low birth weight (LBW) have been associated with pollution and other environmental exposures, but the relationship between these adverse outcomes and specific characteristics of polluted sites is not well studied. Objectives We conducted a retrospective cohort study to examine relationships between residential proximity to polluted sites in North Carolina (NC) and PTB and LBW. We further stratified exposure to polluted sites by route of contaminant emissions and specific contaminants released at each site. Methods We created an integrated exposure geodatabase of polluted sites in NC from 2002 to 2015 including all landfills, Superfund sites, and industrial sites. Using birth certificates, we assembled a cohort of 1,494,651 singleton births in NC from 2003 to 2015. We geocoded the gestational parent residential address on the birth certificate, and defined exposure to polluted sites as residence within one mile of a site. We used log-binomial regression models to estimate adjusted risk ratios (aRR) and 95% confidence intervals (CI). Binomial models were used to estimate adjusted risk differences (aRD) per 10,000 births and 95% CIs for associations between exposure to polluted sites and PTB or LBW. Results We observed weak associations between residential proximity to polluted sites and PTB [aRR(95% CI): 1.07(1.06,1.09); aRD(95% CI): 61(48,74)] and LBW [aRR(95% CI): 1.09(1.06,1.12); aRD(95% CI): 24(17,31)]. Secondary analyses showed increased risk of both PTB and LBW among births exposed to sites characterized by water emissions, air emissions, and land impoundment. In analyses of specific contaminants, increased risk of PTB was associated with proximity to sites containing arsenic, benzene, cadmium, lead, mercury, and polycyclic aromatic hydrocarbons. LBW was associated with exposure to arsenic, benzene, cadmium, lead, and mercury. Significance This study provides evidence for potential reproductive health effects of polluted sites, and underscores the importance of accounting for heterogeneity between polluted sites when considering these exposures. Impact statement We documented an overall increased risk of both PTB and LBW in births with gestational exposure to polluted sites using a harmonized geodatabase of three site types, and further examined exposures stratified by site characteristics (route of emission, specific contaminants present). We observed increased risk of both PTB and LBW among births exposed to sites with water emissions or air emissions, across site types. Increased risk of PTB was associated with gestational proximity to sites containing arsenic, benzene, cadmium, lead, mercury, and polycyclic aromatic hydrocarbons; increased risk of LBW was associated with exposure to arsenic, benzene, cadmium, lead, and mercury.
 
Map of study area in Central Mexico
The study area used for our PM2.5 models in the Mexico City Metropolitan Area (MCMA).
Maps of the averaged annual daily mean and daily max PM2.5 concentrations for 2019 in the Mexico City Metropolitan Area
Solid and dotted lines indicate the Mexico City Metropolitan Area and Mexican states boundaries, respectively. Black dots indicate ground monitors.
Heatmaps of mean temperature and mean PM2.5, counting all grid cells and days equally
Darker areas indicate more grid cells, more days, or both. Temperature and PM2.5 predictions are already rounded to the nearest tenth, so no further grouping is needed for a heatmap. For legibility, the temperature scale only shows the middle 95% of the data for each season, and the PM2.5 scale only goes up to the 98th percentile for all seasons. Blue lines show the quartiles of PM2.5 conditional on temperature.
Population estimated annual average exposures
The figure shows an empirical cumulative distribution curve for each year from 2004 to 2019, generated from our daily mean model and using the 2010 census population density. Specific quantiles are labeled for the year 2019, where only 10% of the population in the study region had an annual average exposure below 20.6 μg/m³.
Background Machine-learning algorithms are becoming popular techniques to predict ambient air PM 2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM 2.5 concentrations (mean PM 2.5 ) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM 2.5 ). Objective Our goal was to develop a machine-learning model to predict mean PM 2.5 and max PM 2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019. Methods We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM 2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM 2.5 and heat, compliance with local air-quality standards, and the relationship of PM 2.5 exposure with social marginalization. Results Our models for mean and max PM 2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 μg/m ³ , respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 μg/m ³ . In 2010, everybody in the study region was exposed to unhealthy levels of PM 2.5 . Hotter days had greater PM 2.5 concentrations. Finally, we found similar exposure to PM 2.5 across levels of social marginalization. Significance Machine learning algorithms can be used to predict highly spatiotemporally resolved PM 2.5 concentrations even in regions with sparse monitoring. Impact Our PM 2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.
 
Best fit lines of cross-validated short-term predictions for 30 campaigns vs the gold standard predictions for NOx
Thin transparent lines are individual campaigns, colored by design version; thicker lines are the overall version trend.
Site-specific NOx prediction errors for short-term designs (N = 30 campaigns) as compared to the gold standard predictions (long-term Balanced Design Version 1)
Showing a stratified random sample of 12 sites, stratified by whether true concentrations were in the low (Conc < 0.25), middle (0.25 ≤ Conc ≤ 0.75) or high (Conc > 0.75) concentration quantile and arranged within each stratum with lower concentration sites closer to the bottom.
Model performances
Performances (MSE-based R², regression-based R², and RMSE) are determined by each campaign’s cross-validated predictions relative to: a the true averages (long-term Balanced Version 1), and b Its respective campaign averages. Boxplots are for short-term approaches (30 campaigns), while squares are for long-term approaches (1 campaign).
Background Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. Objective We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces. Methods We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design’s land use regression prediction model. Results The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. Significance A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts. Impact statement Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.
 
Background Epidemiologic investigations increasingly employ remote sensing data to estimate residential proximity to agriculture as a means of approximating individual-level pesticide exposure. Few studies have examined the accuracy of these methods and the implications for exposure misclassification. Objectives Compare metrics of residential proximity to agricultural land between a groundtruth approach and commonly-used satellite-based estimates. Methods We inspected 349 fields and identified crops in current production within a 0.5 km radius of 40 residences in Idaho. We calculated the distance from each home to the nearest agricultural field and the total acreage of agricultural fields within a 0.5 km buffer. We compared these groundtruth estimates to satellite-derived estimates from three widely used datasets: CropScape, the National Land Cover Database (NLCD), and Landsat imagery (using Normalized Difference Vegetation Index thresholds). Results We found poor to moderate agreement between the classification of individuals living within 0.5 km of an agricultural field between the groundtruth method and the comparison datasets (53.1–77.6%). All satellite-derived estimates overestimated the acreage of agricultural land within 0.5 km of each home (average = 82.8–148.9%). Using two satellite-derived datasets in conjunction resulted in substantial improvements; specifically, combining CropScape or NLCD with Landsat imagery had the highest percent agreement with the groundtruth data (92.8–93.8% agreement). Significance Residential proximity to agriculture is frequently used as a proxy for pesticide exposure in epidemiologic investigations, and remote sensing-derived datasets are often the only practical means of identifying cultivated land. We found that estimates of agricultural proximity obtained from commonly-used satellite-based datasets are likely to result in exposure misclassification. We propose a novel approach that capitalizes on the complementary strengths of different sources of satellite imagery, and suggest the combined use of one dataset with high temporal resolution (e.g., Landsat imagery) in conjunction with a second dataset that delineates agricultural land use (e.g., CropScape or NLCD).
 
Study Timelineǂ and Design
£ No study activities were conducted on these days. ǂCollection times are approximate, urine sample collected within 30 min from given time. &Demographic/health questionnaires administered and physiological measurements recorded. *Urine is first morning void. **UNIFORM type (NewU or CurrU) Underline and Bold- urine samples collected when wearing Study uniform.
Median creatinine-adjusted permethrin urinary biomarker concentrations in µg/g of creatinine (with 25th and 75th percentiles)
A 3-PBA (µg/g of creatinine) across study period B ∑DCCA (µg/g of creatinine) across study period. CurrU NewU 3-PBA = 3-phenoxybenzoic acid. trans-DCCA = trans-3-(2,2-dichlorovinyl)-2,2-dimethyl-cyclopropane-1-carboxylic acid. cis-DCCA = cis-3-(2,2-dichlorovinyl)-2,2-dimethyl-cyclopropane-1-carboxylic acid ∑DCCA = sum of trans-DCCA + cis-DCCA.
Background Evidence suggests that wearing permethrin-treated military uniforms is not associated with current adverse health conditions. However, exposure through this route results in permethrin biomarker concentrations considerably higher than those in the U.S. population. The U.S. Army is exploring different methods of uniform treatment that reduce exposure while maintaining effective protection from insect vector-borne diseases. Objective To compare permethrin exposure when wearing two types of permethrin-treated military uniforms. Methods Eight male soldiers participated in a 32-day crossover design study to compare permethrin exposure when wearing the current Army uniform (CurrU) and a uniform with a new applied fabric treatment (NewU). Each soldier wore the uniforms for designated 8 h/day time periods over 3 consecutive days separated by a ‘wash-out’ week of no exposure. Permethrin exposure was assessed from the urinary concentrations of 3-phenoxybenzoic acid (3-PBA) and of the sum of cis- and trans-3-(2,2-dichlorovinyl)-2,2-dimethylcyclopropane-1-carboxylic acid (∑DCCA). Estimated dose was determined based on ∑DCCA concentrations. Results Permethrin exposure biomarkers were 21% (3-PBA, p = 0.025) and 35% (∑DCCA, p < 0.001) lower when wearing the NewU compared to the CurrU; the dose was 33% lower (p = 0.05). Significance Findings suggest the new treatment reduces human permethrin exposure biomarkers resulting from wearing-treated military uniforms.
 
Map of sociodemographic indicators and greenspace metrics in New Haven, CT
Note. EVI: Average EVI values of all days in the years 2008–2013.
Map of sociodemographic indicators and greenspace metrics in Baltimore, MD
Note. EVI: Average EVI values of all days in the years 2008–2013.
Map of sociodemographic indicators and greenspace metrics in Durham, NC. Note
EVI: Average EVI values of all days in the years 2008–2013.
Comparison of exposure to greenspace among quantile groups of percent of people with low income, people of color, lower education, linguistic isolation, and individuals less than age 5 or over age 64 years (n = 2285)
(A) Percent greenspace; (B) accessibility to park entrance in the study block groups. Note. Red asterisk indicates the average within each group. Green and blue points indicate value of greenspace metrics in each block group.
Descriptive statistics of sociodemographic variables, greenspace metrics, and mortality rate.
Background Study results are inconclusive regarding how access to greenspace differs by sociodemographic status potentially due to lack of consideration of varying dimensions of greenspace. Objective We investigated how provision of greenspace by sociodemographic status varies by greenspace metrics reflecting coverage and accessibility of greenspace. Methods We used vegetation levels measured by Enhanced Vegetation Index (EVI), percent of greenspace, percent tree cover, percent tree cover along walkable roads, and percent of people living ≤500 m of a park entrance (park accessibility). We considered data for 2008–2013 in Census block groups in 3 US regions: New Haven, Connecticut; Baltimore, Maryland; and Durham, North Carolina. We examined geographical distribution of greenspace metrics and their associations with indicators of income, education, linguistic isolation, race/ethnicity, and age. We used logistic regression to examine associations between these greenspace metrics and age-standardized mortality controlling for sociodemographic indicators. Results Which region had the highest greenspace depended on the greenspace metric used. An interquartile range (33.6%) increase in low-income persons was associated with a 6.2% (95% CI: 3.1, 9.3) increase in park accessibility, whereas it was associated with 0.03 (95% CI: −0.035, −0.025) to 7.3% (95% CI: −8.7, −5.9) decreases in other greenspace metrics. A 15.5% increase in the lower-education population was associated with a 2.1% increase (95% CI: −0.3%, 4.6%) in park accessibility but decreases with other greenspace metrics (0.02 to 5.0%). These results were consistent across the 3 study areas. The odds of mortality rate more than the 75th percentile rate were inversely associated with all greenspace metrics except for annual average EVI (OR 1.27, 95% CI: 0.43, 3.79) and park accessibility (OR 1.40, 95% CI: 0.52, 3.75). Significance Environmental justice concerns regarding greenspace differ by the form of natural resources, and pathways of health benefits can differ by form of greenspace and socioeconomic status within communities. Impact statement Comparisons of exposure to greenspace between different greenspace metrics should be incorporated in decision-making within local contexts.
 
Sample collection bottle instructions.
Household Pb (A) and As (B) results overlaid on a map of the United States. Maps were constructed using the R packages “ggplot”, “sf”, and “ggspatial”. Cu results are similarly presented in Figure S2.
Background Exposure to lead (Pb), arsenic (As) and copper (Cu) may cause significant health issues including harmful neurological effects, cancer or organ damage. Determination of human exposure-relevant concentrations of these metal(loids) in drinking water, therefore, is critical. Objective We sought to characterize exposure-relevant Pb, As, and Cu concentrations in drinking water collected from homes participating in the American Healthy Homes Survey II, a national survey that monitors the prevalence of Pb and related hazards in United States homes. Methods Drinking water samples were collected from a national survey of 678 U.S. homes where children may live using an exposure-based composite sampling protocol. Relationships between metal(loid) concentration, water source and house age were evaluated. Results 18 of 678 (2.6%) of samples analyzed exceeded 5 µg Pb L⁻¹ (Mean = 1.0 µg L⁻¹). 1.5% of samples exceeded 10 µg As L⁻¹ (Mean = 1.7 µg L⁻¹) and 1,300 µg Cu L⁻¹ (Mean = 125 µg L⁻¹). Private well samples were more likely to exceed metal(loid) concentration thresholds than public water samples. Pb concentrations were correlated with Cu and Zn, indicative of brass as a common Pb source is samples analyzed. Significance Results represent the largest national-scale effort to date to inform exposure risks to Pb, As, and Cu in drinking water in U.S. homes using an exposure-based composite sampling approach. Impact Statement To date, there are no national-level estimates of Pb, As and Cu in US drinking water collected from household taps using an exposure-based sampling protocol. Therefore, assessing public health impacts from metal(loids) in drinking water remains challenging. Results presented in this study represent the largest effort to date to test for exposure-relevant concentrations of Pb, As and Cu in US household drinking water, providing a critical step toward improved understanding of metal(loid) exposure risk.
 
Background Knowing which environmental chemicals contribute to metabolites observed in humans is necessary for meaningful estimates of exposure and risk from biomonitoring data. Objective Employ a modeling approach that combines biomonitoring data with chemical metabolism information to produce chemical exposure intake rate estimates with well-quantified uncertainty. Methods Bayesian methodology was used to infer ranges of exposure for parent chemicals of biomarkers measured in urine samples from the U.S population by the National Health and Nutrition Examination Survey (NHANES). Metabolites were probabilistically linked to parent chemicals using the NHANES reports and text mining of PubMed abstracts. Results Chemical exposures were estimated for various population groups and translated to risk-based prioritization using toxicokinetic (TK) modeling and experimental data. Exposure estimates were investigated more closely for children aged 3 to 5 years, a population group that debuted with the 2015–2016 NHANES cohort. Significance The methods described here have been compiled into an R package, bayesmarker, and made publicly available on GitHub. These inferred exposures, when coupled with predicted toxic doses via high throughput TK, can help aid in the identification of public health priority chemicals via risk-based bioactivity-to-exposure ratios.
 
The effect of extraction time
a the extraction effiency of THMs; b the extraction efficiency of HAAs.
The effect of extraction temperature on the extraction efficiency of HAAs.
Background Chlorine-based disinfectants are often used to sanitize fruit and vegetables to produce a product called ready-to-eat (RTE) vegetables. During the disinfection process, disinfection byproducts (DBPs), such as trihalomethanes (THMs) and haloacetic acids (HAAs), might be formed via chlorination. Objective To determine the amounts of DBPs that occur in RTE vegetables in Taiwan, an analytical method which can detect THMs and HAAs simultaneously was developed for this study. Methods For HAAs, dimethyl sulfate (DMS) was first added into the sample as derivatization reagent and tetrabutylammonium hydrogen sulfate (TBA-H2SO4) was used as the ion-pairing agent to improve the derivatization process. Afterwards, the solid-phase microextraction (SPME) procedure coupled with gas chromatography with tandem mass spectrometers (GC/MS/MS) was performed to measure the HAAs derivatives and THMs in the sample. Results A total of 92 single RTE ingredients were analyzed in this study. Among various THMs and HAAs, the results showed that dibromochloromethane (21%) and dichloroacetic acid (12%) had the highest detection rates, respectively. Compared with fruits, vegetables were more easily to contain DBPs. For adults in Taiwan, the maximum daily exposure of THMs and HAAs estimated via the consumption of RTE vegetables were 28.53 and 77.83 μg, respectively. Significance The findings from this study suggest that the exposure of DBPs from RTE vegetables is an important food safety issue in Taiwan.
 
Total customers experiencing power outages
We provide an hourly stacked area plot between February 14–19, 2021 with a corresponding map. The 15 counties that contributed the most person-hours without power are shown in color. Counties in plot are ordered by maximum number of customers without power with the counties that had the highest number without power on the bottom. In the corresponding map, the ERCOT boundary is overlaid (yellow).
County-level distribution and duration of power outages
We depict the range in consecutive hours that a 20% of customers, or b 10,000 customers in a county were without power between February 10–24, 2021. Darker blues indicate longer outage durations. We overlaid the ERCOT boundary (yellow).
Background Precipitated by an unusual winter storm, the 2021 Texas Power Crisis lasted February 10 to 27 leaving millions of customers without power. Such large-scale outages can have severe health consequences, especially among vulnerable subpopulations such as those reliant on electricity to power medical equipment, but limited studies have evaluated sociodemographic disparities associated with outages. Objective To characterize the 2021 Texas Power Crisis in relation to distribution, duration, preparedness, and issues of environmental justice. Methods We used hourly Texas-wide county-level power outage data to estimate geographic clustering and association between outage exposure (distribution and duration) and six measures of racial, social, political, and/or medical vulnerability: Black and Hispanic populations, the Centers for Disease Control and Prevention (CDC) Social Vulnerability Index (SVI), Medicare electricity-dependent durable medical equipment (DME) usage, nursing homes, and hospitals. To examine individual-level experience and preparedness, we used a preexisting and non-representative internet survey. Results At the peak of the Texas Power Crisis, nearly 1/3 of customers statewide (N = 4,011,776 households/businesses) lost power. We identified multiple counties that faced a dual burden of racial/social/medical vulnerability and power outage exposure, after accounting for multiple comparisons. County-level spatial analyses indicated that counties where more Hispanic residents resided tended to endure more severe outages (OR = 1.16, 95% CI: 1.02, 1.40). We did not observe socioeconomic or medical disparities. With individual-level survey data among 1038 respondents, we found that Black respondents were more likely to report outages lasting 24+ hours and that younger individuals and those with lower educational attainment were less likely to be prepared for outages. Significance Power outages can be deadly, and medically vulnerable, socioeconomically vulnerable, and marginalized groups may be disproportionately impacted or less prepared. Climate and energy policy must equitably address power outages, future grid improvements, and disaster preparedness and management.
 
Exposure assessment of inorganic arsenic is challenging due to the existence of multiple species, complexity of arsenic metabolism, and variety of exposure sources. Exposure assessment of arsenic during pregnancy is further complicated by the physiological changes that occur to support fetal growth. Given the well-established toxicity of inorganic arsenic at high concentrations, continued research into the potential health effects of low-level exposure on maternal and fetal health is necessary. Our objectives were to review the value of and challenges inherent in measuring inorganic arsenic species in pregnancy and highlight related research priorities. We discussed how the physiological changes of pregnancy influence arsenic metabolism and necessitate the need for pregnancy-specific data. We reviewed the biomonitoring challenges according to common and novel biological matrices and discussed how each matrix differs according to half-life, bioavailability, availability of laboratory methods, and interpretation within pregnancy. Exposure assessment in both established and novel matrices that accounts for the physiological changes of pregnancy and complexity of speciation is a research priority. Standardization of laboratory method for novel matrices will help address these data gaps. Research is particularly lacking in contemporary populations of pregnant women without naturally elevated arsenic drinking water concentrations (i.e. <10 µg/l).
 
Background: Emerging evidence suggests that per- and polyfluoroalkyl substances (PFAS) are endocrine disruptors and may contribute to the etiology of diabetes. Objectives: This study aimed to systematically review the epidemiological evidence on the associations of PFAS with mortality and morbidity of diabetes and to quantitatively evaluate the summary effect estimates of the existing literature. Methods: We searched three electronic databases for epidemiological studies concerning PFAS and diabetes published before April 1, 2022. Summary odds ratio (OR), hazard ratio (HR), or β and their 95% confidence intervals (CIs) were respectively calculated to evaluate the association between PFAS and diabetes using random-effects model by the exposure type, and dose-response meta-analyses were also performed when possible. We also assessed the risk of bias of the studies included and the confidence in the body of evidence. Results: An initial literature search identified 1969 studies, of which 22 studies were eventually included. The meta-analyses indicated that the observed statistically significant PFAS-T2DM associations were consistent in cohort studies, while the associations were almost non-significant in case-control and cross-sectional studies. Dose-response meta-analysis showed a "parabolic-shaped" association between perfluorooctanoate acid (PFOA) exposure and T2DM risk. Available evidence was rated with "low" risk of bias, and the level of evidence for PFAS and incident T2DM was considered "moderate". Conclusions: Our findings suggest that PFAS exposure may increase the risk of incident T2DM, and that PFOA may exert non-monotonic dose-response effect on T2DM risk. Considering the widespread exposure, persistence, and potential for adverse health effects of PFAS, further cohort studies with improvements in expanding the sample size, adjusting the covariates, and considering different types of PFAS exposure at various doses, are needed to elucidate the putative causal associations and potential mode of action of different PFAS on diabetes. Impact statement: A growing body of evidence suggests that per- and polyfluoroalkyl substances (PFAS) are endocrine disruptors and may contribute to the development of diabetes. However, epidemiological evidence on the associations of PFAS and diabetes is inconsistent. We performed this comprehensive systematic review and meta-analysis to quantitatively synthesize the evidence. The findings of this study suggest that exposure to PFAS may increase diabetes risk among the general population. Reduced exposure to these "forever and everywhere chemicals" may be an important preventative approach to reducing the risk of diabetes across the population.
 
PRISMA flow diagram for identification of studies for final inclusion in the review. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
Background: Dried blood spot (DBS) sampling is a simple, cost-effective, and minimally invasive alternative to venipuncture for measuring exposure biomarkers in public health and epidemiological research. DBS sampling provides advantages in field-based studies conducted in low-resource settings and in studies involving infants and children. In addition, DBS samples are routinely collected from newborns after birth (i.e., newborn dried blood spots, NDBS), with many states in the United States permitting access to archived NDBS samples for research purposes. Objectives: We review the state of the science for analyzing exposure biomarkers in DBS samples, both archived and newly collected, and provide guidance on sample collection, storage, and blood volume requirements associated with individual DBS assays. We discuss recent progress regarding analytical methods, analytical sensitivity, and specificity, sample volume requirements, contamination considerations, estimating extracted blood volumes, assessing stability and analyte recovery, and hematocrit effects. Methods: A systematic search of PubMed (MEDLINE), Embase (Elsevier), and CINAHL (EBSCO) was conducted in March 2022. DBS method development and application studies were divided into three main chemical classes: environmental tobacco smoke, trace elements (including lead, mercury, cadmium, and arsenic), and industrial chemicals (including endocrine-disrupting chemicals and persistent organic pollutants). DBS method development and validation studies were scored on key quality-control and performance parameters by two members of the review team. Results: Our search identified 47 published reports related to measuring environmental exposure biomarkers in human DBS samples. A total of 28 reports (37 total studies) were on methods development and validation and 19 reports were primarily the application of previously developed DBS assays. High-performing DBS methods have been developed, validated, and applied for detecting environmental exposures to tobacco smoke, trace elements, and several important endocrine-disrupting chemicals and persistent organic pollutants. Additional work is needed for measuring cadmium, arsenic, inorganic mercury, and bisphenol A in DBS and NDBS samples. Significance: We present an inventory and critical review of available assays for measuring environmental exposure biomarkers in DBS and NDBS samples to help facilitate this sampling medium as an emerging tool for public health (e.g., screening programs, temporal biomonitoring) and environmental epidemiology (e.g., field-based studies).
 
Roadmap for ISES Europe Actions from 2018-2030 in response to the strategic objectives (SO) for exposure models. The roadmap is described from bottom to top in green bars: strategic process, blue bars: action points for ISES Europe (different WG will cooperate for the action points).
Relationships between stakeholders with a role for exposure models. Dark green box and arrows: proposed coordination role for ISES Europe, dark blue boxes: strategical stakeholders (funding), blue boxes: operational stakeholders, light green boxes: tasks along the life cycle of models, blue arrows: funding, green arrows: scientific contributions (the thickness of arrows shows the strength of the relationship).
Strategic objectives SO (grey) for exposure modelling (based on identified needs) and associated actions and recommendations.
Exposure models are essential in almost all relevant contexts for exposure science. To address the numerous challenges and gaps that exist, exposure modelling is one of the priority areas of the European Exposure Science Strategy developed by the European Chapter of the International Society of Exposure Science (ISES Europe). A strategy was developed for the priority area of exposure modelling in Europe with four strategic objectives. These objectives are (1) improvement of models and tools, (2) development of new methodologies and support for understudied fields, (3) improvement of model use and (4) regulatory needs for modelling. In a bottom-up approach, exposure modellers from different European countries and institutions who are active in the fields of occupational, population and environmental exposure science pooled their expertise under the umbrella of the ISES Europe Working Group on exposure models. This working group assessed the state-of-the-art of exposure modelling in Europe by developing an inventory of exposure models used in Europe and reviewing the existing literature on pitfalls for exposure modelling, in order to identify crucial modelling-related strategy elements. Decisive actions were defined for ISES Europe stakeholders, including collecting available models and accompanying information in a living document curated and published by ISES Europe, as well as a long-term goal of developing a best-practices handbook. Alongside these actions, recommendations were developed and addressed to stakeholders outside of ISES Europe. Four strategic objectives were identified with an associated action plan and roadmap for the implementation of the European Exposure Science Strategy for exposure modelling. This strategic plan will foster a common understanding of modelling-related methodology, terminology and future research in Europe, and have a broader impact on strategic considerations globally.
 
MTA bus fleet composition in 2009 and 2014
The proportion of buses classified as “clean” increased between 2009 and 2014, whether considering the narrow or broad clean bus criteria.
Proportional changes in the distance traveled by clean buses were not evenly spatially distributed across the city
Cell-specific changes in the proportion of VMT by clean-fuel buses (broad definition). Positive values represent a proportional increase in clean bus proportion over the time period. Cells with no bus traffic in either year correspondingly had no change in bus type and are greyed out on this map.
Decreases in air pollutant concentrations are of similar magnitude in grid cells regardless of their clean fleet change category.
Background Motor vehicles, including public transit buses, are a major source of air pollution in New York City (NYC) and worldwide. To address this problem, governments and transit agencies have implemented policies to introduce cleaner vehicles into transit fleets. Beginning in 2000, the Metropolitan Transit Agency began deploying compressed natural gas, hybrid electric, and low-sulfur diesel buses to reduce urban air pollution. Objective We hypothesized that bus fleet changes incorporating cleaner vehicles would have detectable effects on air pollution concentrations between 2009 and 2014, as measured by the New York City Community Air Survey (NYCCAS). Methods Depot- and route-specific information allowed identification of areas with larger or smaller changes in the proportion of distance traveled by clean buses. Data were assembled for 9670 300 m × 300 m grid cell areas with annual concentration estimates for nitrogen oxide (NO), nitrogen dioxide (NO 2 ), and black carbon (BC) from NYCCAS. Spatial error models adjusted for truck route presence and total traffic volume. Results While concentrations of all three pollutants declined between 2009 and 2014 even in the 39.7% of cells without bus service, the decline in concentrations of NO and NO 2 was greater in areas with more bus service and with higher proportional shifts toward clean buses. Conversely, the decline in BC concentration was slower in areas with more bus service and higher proportional clean bus shifts. Significance These results provide evidence that the NYC clean bus program impacted concentrations of air pollution, particularly in reductions of NO 2 . Further work can investigate the potential impact of these changes on health outcomes in NYC residents. Impact Statement Urban air pollution from diesel-burning buses is an important health exposure. The New York Metropolitan Transit Agency has worked to deploy cleaner buses into their fleet, but the impact of this policy has not been evaluated. Successful reductions in air pollution are critical for public health.
 
Design of the fresh air passive samplers for personal exposure assessment
A Components of the sampler are shown, including the PDMS sorbent bar used to collected airborne contaminants, the housing chamber, and wearable clip attachment. B Wearable passive samplers were worn in four positions by study participants (head, chest, wrist, and foot).
of chemical exposures detected by each wearable form across all study participants
Differences across sampler placements were found chemical classes that are bolded (p < 0.05). See Table S6 for measured levels by compound class. When comparing exposure measurements by sampler placement, the mean of all detected compounds differed between (1) wrist and foot, (2) wrist and head, and (3) chest and foot samplers (p < 0.001; Fig. S4); 33 compounds were found to drive the contrasting exposure levels for these comparisons (FDR < 0.05; Table S5).
Correlations across airborne contaminants detected by the four fresh air sampler worn by participants on the head, chest, wrist, and foot
A Correlations evaluated within each sampler location. B Correlation assessed between each of the four sampler positions. Displayed correlations were limited to those that were significant (p < 0.05) Spearman’s rho > 0.8; no negative correlations were found (Spearman’s rho < −0.8).
Comparison of personal exposure to airborne contaminants detected between season and housing location
A Volcano plot of detected exposues averaged across all wearable samplers comparing winter and summer sampling periods. B Volcano plot of detected exposues averaged across all wearable samplers comparing participants residing in on- and off-campus housing. Log2 fold change was set to be less than −1, and greater than 1.
Background 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. Objective To compare personal exposure to airborne contaminants detected using wearable passive air samplers placed at different positions on the body. Methods 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. Results 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 Rho > 0.8, 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 Rho > 0.8, p < 0.05). Significance The placement of wearable passive air samplers influences the exposures captured and should be considered in future exposure and epidemiological studies. Impact statement 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.
 
Top-cited authors
Michael Jerrett
  • University of California, Los Angeles
Talar Sahsuvaroglu
Bernardo Beckerman
  • University of California, Berkeley
Jason Boyd Morrison
  • University of Manitoba
Dimitris Potoglou
  • Cardiff University