Uncertainty In Exposure Estimates Research Articles

Wearable Passive Samplers for Assessing Environmental Exposure to Organic Chemicals: Current Approaches and Future Directions.

We are continuously exposed to dynamic mixtures of airborne contaminants that vary by location. Understanding the compositional diversity of these complex mixtures and the levels to which we are each exposed requires comprehensive exposure assessment. This comprehensive analysis is often lacking in population-based studies due to logistic and analytical challenges associated with traditional measurement approaches involving active air sampling and chemical-by-chemical analysis. The objective of this review is to provide an overview of wearable passive samplers as alternative tools to active samplers in environmental health research. The review highlights the advances and challenges in using wearable passive samplers for assessing personal exposure to organic chemicals and further presents a framework to enable quantitative measurements of exposure and expanded use of this monitoring approach to the population scale. Overall, wearable passive samplers are promising tools for assessing personal exposure to environmental contaminants, evident by the increased adoption and use of silicone-based devices in recent years. When combined with high throughput chemical analysis, these exposure assessment tools present opportunities for advancing our ability to assess personal exposures to complex mixtures. Most designs of wearable passive samplers used for assessing exposure to semi-volatile organic chemicals are currently uncalibrated, thus, are mostly used for qualitative research. The challenge with using wearable samplers for quantitative exposure assessment mostly lies with the inherent complexity in calibrating these wearable devices. Questions remain regarding how they perform under various conditions and the uncertainty of exposure estimates. As popularity of these samplers grows, it is critical to understand the uptake kinetics of chemicals and the different environmental and meteorological conditions that can introduce variability. Wearable passive samplers enable evaluation of exposure to hundreds of chemicals. The review presents the state-of-the-art of technology for assessing personal exposure to environmental chemicals. As more studies calibrate wearable samplers, these tools present promise for quantitatively assessing exposure at both the individual and population levels.

Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities.

Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.

Mapping blood lead levels in French children due to environmental contamination using a modeling approach

The decrease in levels of lead in air and drinking water over the last 40 years has resulted in an overall decrease in blood lead levels (BLLs). However, there is no known safe level of lead regarding developmental effects in children. This paper maps predicted BLLs of children in France, resulting from a simulated chronic exposure in two steps, with the aim of identifying areas with environmentally overexposed populations. Probabilistic estimates of BLLs based on environmental contamination were obtained and compared to biomonitoring data. First, the contribution of various environmental exposure pathways was estimated using a multimedia exposure model: spatialized data on soil, drinking water and air contamination, together with data on food contamination and ingestion, was joined using geostatistical approaches. In a second step, a Physiologically Based Toxicokinetic (PBTK) model provided estimates of BLLs. Probabilistic estimates of BLLs were obtained by simulating uncertainty and variability of exposure levels, physiological characteristics and lead-specific parameters in the PBTK model.The median and 95th percentile of predicted BLLs in children aged 1 to 11 were compared to recent biomonitoring data obtained in France in young children (SATURNINF study): median predictions were overestimated in infants and in agreement with median observed BLLs in children aged 3 to 6. Upper bounds of predicted BLLs were protective due to uncertainties in exposure estimates. The main source of exposure appeared to be drinking water in children over 2 years old, and vegetal food and milk in children under 2 years old. Although elevated drinking water lead levels were not related to large geographical areas, the relatively fine resolution map also pinpointed geographical areas of concern due to elevated soil lead levels.

Assessing Human Exposure to Chemicals in Materials, Products and Articles: The International Risk Management Landscape for Phthalates.

Risk-based chemical safety assessments are increasingly being conducted to support chemical management decisions and informed substitution to protect public health. Rapid evaluation and prioritization of large numbers of chemicals used in materials, products, and other indoor articles has become a major focus of chemical risk management strategies. Internationally, although a shared understanding of the value of rapid risk-based evaluations appears to be emerging, implementation strategies and associated management decisions vary from one agency and jurisdiction to another. This paper highlights the international chemical risk management landscape focusing on phthalates as an example, and reviews how phthalate exposure assessments have been performed, resulting at times in different decisions based on the application of scientific information within different policy contexts. In general, the need for efficient and effective risk-based assessment approaches is driving increased needs for high-quality exposure data and validated, mechanistic exposure models. Further development of mechanistic models and related parameters will reduce uncertainties in exposure estimates and support scientific risk-based evaluations of chemical/product combinations for a variety of decisions.

Impact of Exposure Uncertainty on the Association between Perfluorooctanoate and Preeclampsia in the C8 Health Project Population

BackgroundUncertainty in exposure estimates from models can result in exposure measurement error and can potentially affect the validity of epidemiological studies. We recently used a suite of environmental models and an integrated exposure and pharmacokinetic model to estimate individual perfluorooctanoate (PFOA) serum concentrations and assess the association with preeclampsia from 1990 through 2006 for the C8 Health Project participants.ObjectivesThe aims of the current study are to evaluate impact of uncertainty in estimated PFOA drinking-water concentrations on estimated serum concentrations and their reported epidemiological association with preeclampsia.MethodsFor each individual public water district, we used Monte Carlo simulations to vary the year-by-year PFOA drinking-water concentration by randomly sampling from lognormal distributions for random error in the yearly public water district PFOA concentrations, systematic error specific to each water district, and global systematic error in the release assessment (using the estimated concentrations from the original fate and transport model as medians and a range of 2-, 5-, and 10-fold uncertainty).ResultsUncertainty in PFOA water concentrations could cause major changes in estimated serum PFOA concentrations among participants. However, there is relatively little impact on the resulting epidemiological association in our simulations. The contribution of exposure uncertainty to the total uncertainty (including regression parameter variance) ranged from 5% to 31%, and bias was negligible.ConclusionsWe found that correlated exposure uncertainty can substantially change estimated PFOA serum concentrations, but results in only minor impacts on the epidemiological association between PFOA and preeclampsia.CitationAvanasi R, Shin HM, Vieira VM, Savitz DA, Bartell SM. 2016. Impact of exposure uncertainty on the association between perfluorooctanoate and preeclampsia in the C8 Health Project population. Environ Health Perspect 124:126–132; http://dx.doi.org/10.1289/ehp.1409044

Estimating Benzene Exposure at a Solvent Parts Washer

A mathematical model is described for estimating benzene exposure at a parts washer using petroleum distillates solvent containing benzene. The basic assumptions are that the benzene mass emission rate exponentially decreases over time, and that the air above the parts washer basin to which a worker is exposed is part of a well-mixed air zone termed the near field (relative to the source location). Two previously conducted simulations of the parts washer process are described. A single 1-hour time-weighted average (TWA) benzene concentration was measured during Simulation #1, and two 4-hour TWA benzene concentrations were measured during Simulation #2. The initial benzene concentrations in the solvents were known, and the exponential loss rate constants were estimated from subsequent determinations of the benzene concentrations. Values for the interzonal airflow rate were estimated based on the conceptual geometry of the near field zone and sparse information on air speed near the parts washers. Minimum values for the room supply/exhaust air rate were estimated based on the room volumes and ventilation conditions. The modeled benzene concentrations were within a multiplicative range of one-half to twofold the measured concentrations. Uncertainty in a model estimate was quantified by Monte Carlo analysis; the distributions of model estimates exhibited coefficients of variation of approximately 40%. Issues related to uncertainty in exposure estimates made by mathematical modeling are discussed.

Uncertainty in exposure estimates made by modeling versus monitoring.

To conduct an initial exposure assessment for an airborne toxicant, industrial hygienists usually prefer air monitoring to mathematical modeling, even if only one exposure value is to be measured. This article argues that mathematical modeling may provide a more accurate (less uncertain) exposure estimate than monitoring if only a few air samples are to be collected, if anticipated exposure variability is high, and if information on exposure determinants is not too uncertain. To explore this idea, a hypothetical "true" distribution of 8-hour time-weighted average airborne exposure values, C, is posited based on an NF exposure model. The C distribution is approximately lognormal. Estimation of the mean value, microC (the long-term average exposure level), is considered. Based on simple random sampling of workdays and use of the sample mean C to estimate microC, accuracy (uncertainty) in the estimate is measured by the mean square error, MSE(C). In the alternative, a modeling estimate can be made using estimates of the mean chemical emission rate microG, the mean room dilution supply air rate microQ, and the mean dilution ventilation rate in the NF of the source mu beta. By positing uniform distributions for the estimates microG, microQ, and mu beta, an equation for the modeling mean square error MSE(microC) is presented. It is shown that for a sample size of three or fewer workdays, mathematical modeling rather than air monitoring should provide a more accurate estimate of microC if the anticipated geometric standard deviation for the C distribution exceeds 2.3.

Exposure to Pesticides by Medium and Route: The 90th Percentile and Related Uncertainties

Multiple route, multiple media distributions of exposure to chlorpyrifos and diazinon and associated uncertainties are investigated using the database generated by the National Human Exposure Assessment Survey in Arizona (NHEXAS-AZ). Exposure to pesticides and associated uncertainties may be estimated using either deterministic or probabilistic methods. This paper employs probabilistic models to estimate the 90th percentile of inhalation, dietary ingestion, dermal, and nondietary ingestion exposures to chlorpyrifos and diazinon. Questions associated with the uncertainty of exposure estimates for the two pesticides are also investigated. Probabilistic models are used to formulate distributions of exposures to each of the two pesticides for several subpopulation groups. Parameter uncertainties associated with the 90th and other percentiles of population and subpopulation groups are addressed in this paper. Highly exposed subpopulations have been defined as those with route-specific 90th percentile exposure that is higher than the population 90th percentile. All subjects exposed to levels above the 90th percentile of inhalation exposure live in dwellings with carpeting covering over 50% of their floor areas. The relative uncertainty of the 90th percentile of dermal exposure is larger than corresponding uncertainties for inhalation, dietary ingestion, and nondietary exposure.

A Rationale and Framework for Establishing the Quality of Human Exposure Assessments

Exposure assessments, performed as input to an evaluation of potential human health risk, are an element of risk assessment. Risk assessment results feed governmental and corporate risk management decisions, which seek to balance the estimated potential human health risks with other factors. Risk managers presume that the risk information provided to them is scientifically valid and accurate. Government agencies have begun to apply a system known as good laboratory practices (GLP) to ensure adequate data quality on animal studies, which are often the first step in the health-effects evaluation of risk assessment. This paper explores a rationale and framework for establishing the quality of human exposure assessments and proposes a set of good exposure assessment practices (GEAP). The components of the proposed GEAP include the writing of a study protocol before conducting the study, consideration of available resources, specification of an exposure model, a study design (including sampling and analytical methods and data analysis), quality assurance, archiving, communications, and a statement of overall uncertainty in exposure estimates. The GEAP concept is offered as a starting point for developing a consensus among the community of exposure assessors regarding a minimum standard for good practices. If a consensus on GEAP can be reached and applied, exposure assessments would have improved scientific bases, interpretability, and utility.

A rationale and framework for establishing the quality of human exposure assessments.

Exposure assessments, performed as input to an evaluation of potential human health risk, are an element of risk assessment. Risk assessment results feed governmental and corporate risk management decisions, which seek to balance the estimated potential human health risks with other factors. Risk managers presume that the risk information provided to them is scientifically valid and accurate. Government agencies have begun to apply a system known as good laboratory practices (GLP) to ensure adequate data quality on animal studies, which are often the first step in the health-effects evaluation of risk assessment. This paper explores a rationale and framework for establishing the quality of human exposure assessments and proposes a set of good exposure assessment practices (GEAP). The components of the proposed GEAP include the writing of a study protocol before conducting the study, consideration of available resources, specification of an exposure model, a study design (including sampling and analytical methods and data analysis), quality assurance, archiving, communications, and a statement of overall uncertainty in exposure estimates. The GEAP concept is offered as a starting point for developing a consensus among the community of exposure assessors regarding a minimum standard for good practices. If a consensus on GEAP can be reached and applied, exposure assessments would have improved scientific bases, interpretability, and utility.