Superfund Research Program
Exposure and Risk Assessment of Complex Mixtures
Project Leader: Robin L. Autenrieth
Grant Number: P42ES004917
Funding Period: 2000-2008
Project Summary (2005-2008)
Research conducted by the Texas A&M SBRP has focused on the development of sampling, analytical and bioassay procedures to assess the hazard of a Superfund site. The goal of Project 6 is to utilize analytical measurements, bioassays and field studies to identify the major sources of uncertainty in risk assessment; and, to integrate modeling and exposure studies in wildlife and humans to develop improved methods for risk assessment of complex mixtures. The accuracy of environmental risk assessment is highly dependent on the adequacy of the available data and the assumptions necessary to estimate risk. Among the parameters having the greatest uncertainty, and that most affect the validity of risk estimated for the identified contaminants, are environmental concentration, exposure pathway, and chemical toxicity.
Project researchers are taking four approaches to generate data specifically to improve the risk assessment process for these three essential parameters. Contaminant impacts on the environment and biomarkers of exposure are being measured in areas affected by a synthetic rubber plant (primarily polycyclic aromatic hydrocarbons). Toxicity data are being generated for the identified contaminants in laboratory bioassays (Projects 1-3) and compared with biomarkers of effects in exposed wildlife and human populations. Chemical fate in the environment is being determined through biodegradation studies and estimates of physical/chemical characteristics for compounds. Models that use chemical structure to predict compound behavior are being trained with data generated from the first three approaches. This information is then be incorporated into a risk assessment model. The strength of this project lies in a well integrated series of experiments that span laboratory to field conditions. Collaboration with the other projects facilitates developing a relationship between estimates of exposure and predictions of adverse health effects in the study populations. This multidisciplinary approach improves substantially the risk assessment process.