Joseph (Jay) Smith III

Project title: Reconciliation of Aggregate Probabilistic Exposure Model Predictions with Observed Biomarkers: A Case Study Using Data from the CTEPP Child Cohorts

Degree: MS (Thesis) | Program: Environmental Health (EH) | Project type: Thesis/Dissertation
Completed in: 2006 | Faculty advisor: John C. Kissel


Approaches to assessment of potential exposures to contaminants in the environment are evolving with advances in analytical chemistry, computing power and the methodologies available to researchers. The implementation of single-pathway exposure assessments has, for the most part, given way to complex aggregate exposure models that examine a wider scope of human interaction with the environment. Additionally, mathematical and computing advances have made probabilistic models, which allow for more accurate reflection of population variability and parameter uncertainty, widely available to scientists.

In 1993 the National Research Council issues recommendations about the importance of using comprehensive aggregate exposures when dealing with the assessment of risk to children. Their evaluation of current approaches supported the use of more complex statistical methods in calculations of exposure (NRH, 1993). The federal government also advocated the use of aggregate models in legislation, such as the Food Quality and Protection Act of 1996, but did not go into detail regarding specific modeling methods to be used. What is clear from these endorsements of more complex exposure assessment methodologies is that regulatory agencies realize that point estimates can only illuminate a very small part of what is known about population exposures, while probabilistic methods, which aim to include the variability and uncertainty of model parameters, have the potential to be far more informative.

Taken from the beginning of thesis.