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Browsing by Subject "exposure assessment"

Now showing 1 - 2 of 2
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    Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response
    (2017-07) Groth, Caroline
    In April 2010, the Deepwater Horizon oil rig caught fire and sank, sending approximately 5 million barrels of oil into the Gulf of Mexico over the ensuing 3 months. Thousands of workers were involved in the response and cleanup efforts. Many harmful chemicals were released into the air from crude oil, including total hydrocarbons (THC), benzene, toluene, ethylbenzene, xylene, hexane (BTEXH), and volatile organic compounds (VOCs). NIEHS's GuLF STUDY investigators are estimating the exposures the workers experienced related to their response and cleanup work and evaluating associations between the exposures and detrimental health outcomes. My research focuses on developing statistical methods to quantify airborne chemical exposures in response to this event and to other settings in environmental health. Factors complicating the exposure estimation include analytical method and data collection limitations. All analytical methods used to measure chemical concentrations have a limit of detection (LOD), or a threshold below which exposure cannot be detected with the analytical method (measurements below the LOD are called censored measurements). However, even these low exposures must be assessed to provide accurate estimates of exposure. Similarly, due to the scope of this event, it was not possible to take measurements in all scenarios where workers were involved in the response. Therefore, we must develop methods that allow us to estimate exposures under these limitations. I introduce a strategy that uses chemical linear relationships to inform exposure estimates. We describe a Bayesian linear model for quantifying exposure while accounting for censoring in both a chemical predictor and a response. We further expand this model to quantify exposure in multiple EGs. Then, I describe a multivariate Bayesian linear model used to quantify exposures under various amounts of LOD censoring in the chemical response and multiple chemical predictors. We assess our model's performance against simpler models at a variety of censoring levels using WAIC. We apply our model to assess vapor exposures from measurements of volatile substances in crude oil on the Ocean Intervention III taken during the Deepwater Horizon oil spill response and cleanup. Next, I explain how we used a database of over 26 million VOC measurements to supplement information in THC and BTEXH. I discuss the methods we used to convert this large VOC database into a exposure metric that could be compared with THC exposure. Then, I describe how we used the VOC exposure metrics to estimate THC and BTEXH exposure when VOC information was available but THC/BTEXH measurements were unavailable. Finally, I expand the Bayesian linear framework to a spatial setting that allows us to estimate exposure for particular areas in the Gulf of Mexico while accounting for values below LOD in both the response and predictor of interest. We also investigate imputation strategies designed to allow us to estimate exposure to our chemical predictor (providing input to our model) so we can better estimate our chemical response. I conclude with a brief description of our current investigation of environmental exposures during the Deepwater Horizon response and cleanup efforts.
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    Evaluation of the Utility of Deterministic Models for Dermal Exposure Assessment of Solids and Investigation of the Potential Influence of Repeated Contacts and Skin Hydration on Quantitative Dermal Loading and Transfer
    (2021-02) Elliott, Jennifer
    Exposure assessment is an important tool for occupational, environmental and consumer health professionals to evaluate the level of risk associated with various scenarios. While exposure assessment methods have been well defined for inhalation exposures for many decades, dermal exposure assessment methods are often lacking in quality and accuracy. A number of deterministic models have been developed to evaluate the potential for dermal exposure and uptake or ingestion in the absence of substance-specific or scenario-specific dermal loading measurement data. These models typically use default assumptions regarding the quantity of loading on the skin surface, frequently requiring the use of data with unknown relevance. Further, these models routinely incorporate an assumption of additive transfer with each successive or routine contact with the substance of interest. Overall, however, relatively few data are available to characterize the factors or determinants most likely to influence the accurate estimation or calculation of dermal exposure potential for specific substances. The purpose of the research in this dissertation was therefore to quantitatively consider the influence of current loading-related assumptions used in deterministic dermal exposure models available to exposure assessment practitioners and to explore these influences further. This research included several specific aims. First, a study of the accuracy of exposure assessment practitioners when using available deterministic, scenario-specific dermal exposure assessment models was performed. Second, the outcomes of this analysis were used along with a comprehensive literature review to identify determinants with the greatest potential to influence dermal loading and transfer. Two specific determinants, repeated dermal contacts and skin hydration, were noted to have a statistically-significant influence on quantitative dermal loading in the literature and were reported to have potentially complex relationships with dermal loading and transfer. These two determinants were therefore identified for additional quantitative analysis with respect to their potential to influence dermal loading and transfer. To assess the influence of repeated contacts on dermal loading, a study design based on a dermal conceptual model was developed for human skin in vivo to characterize the different pathways and compartments and their contributions to dermal loading following repeated contacts with a dermal test substance (elemental metallic lead). To assess the influence of skin condition and hydration, first, different measurement methods were quantitatively compared for multiple skin sites and were evaluated for their utility in skin surface sampling studies. The quantitative influence of normal skin hydration on dermal loading was then also investigated for the same test substance. Overall, the results suggested that, contrary to the general acceptance and use of additive loading principles for many dermal exposure assessment models, the application of this assumption for repeated contact scenarios in dermal exposure modeling resulted in overestimates or substantial overestimates of dermal exposure by exposure assessment practitioners. When tested using experimental measurements for scenarios including source-to-skin, skin-to-skin, and skin-to-gloves, repeated transfer tests appeared to approach a steady state loading on the skin between five and ten contacts with the test substance, consistent with other data in the literature. The results also suggested that substantial variability exists in skin hydration characteristics between different skin sites, and that skin hydration may influence quantitative dermal loading and transfer. Although additional data are needed for other substances, the measurement data collected point to a need to reconsider the way that influences on dermal loading and transfer, and particularly the additive loading assumption, are used in current deterministic dermal exposure assessment models.