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PROBABILISTIC MODELING FOR ADVANCED HUMAN EXPOSURE ASSESSMENT
Citation:
OZKAYNAK, H. A. PROBABILISTIC MODELING FOR ADVANCED HUMAN EXPOSURE ASSESSMENT. Presented at SRA Europe Meeting, The Hague, NETHERLANDS, June 17 - 19, 2007.
Impact/Purpose:
The overall objective of this research is to develop, apply, and evaluate a human exposure model for predicting population exposures to the components of particulate matter (PM) identified as potential toxic agents contributing to adverse health effects.
Description:
Human exposures to environmental pollutants widely vary depending on the emission patterns that result in microenvironmental pollutant concentrations, as well as behavioral factors that determine the extent of an individual's contact with these pollutants. Probabilistic human exposure models provide an analytic structure for combining these various types data generated from disparate studies in a manner that may make more complete use of the existing information related to exposures to a particular contaminant than is possible by direct study methods. Validated models can then be used to investigate the efficacy of various strategies for managing public health risks associated with exposures due to environmental contaminants of concern. However, each component of the source-concentration-exposure-dose-effects human health risk paradigm has inherent variability and uncertainty due to complexity of the underlying environmental and biological systems. Consequently, probabilistic human exposure methods are used during the course of human health risk assessments to explicitly quantify the variability and uncertainty in the prediction endpoints and to identify the key factors that contribute to these variations or uncertainties. This presentation describes the probabilistic exposure modeling methods used in air pollution and multimedia human exposure assessments, along with specific examples and case-studies demonstrating the application of these tools for characterizing the variability and uncertainty in the predicted population exposure distributions.