THE BENEFITS OF PROBABILISTIC EXPOSURE ASSESSMENT - 3 CASE-STUDIES INVOLVING CONTAMINATED AIR, WATER, AND SOIL

Probabilistic risk assessments are enjoying increasing popularity as a tool to characterize the health hazards associated with exposure to c hemicals in the environment. Because probabilistic analyses provide mu ch more information to the risk manager than standard ''point'' risk e stimates, this approach has generally been heralded as one which could significantly improve the conduct of health risk assessments. The pri mary obstacles to replacing point estimates with probabilistic techniq ues include a general lack of familiarity with the approach and a lack of regulatory policy and guidance. This paper discusses some of the a dvantages and disadvantages of the point estimate vs. probabilistic ap proach. Three case studies are presented which contrast and compare th e results of each. The first addresses the risks associated with house hold exposure to volatile chemicals in tapwater. The second evaluates airborne dioxin emissions which can enter the food-chain. The third il lustrates how to derive health-based cleanup levels for dioxin in soil . It is shown that, based on the results of Monte Carlo analyses of pr obability density functions (PDFs), the point estimate approach requir ed by most regulatory agencies will nearly always overpredict the risk for the 95th percentile person by a factor of up to 5. When the asses sment requires consideration of 10 or more exposure variables, the poi nt estimate approach will often predict risks representative of the 99 .9th percentile person rather than the 50th or 95th percentile person. This paper recommends a number of data distributions for various expo sure variables that we believe are now sufficiently well understood to be used with confidence in most exposure assessments. A list of expos ure variables that may require additional research before adequate dat a distributions can be developed are also discussed.