HA7 Phthalate Esters: Fate and Effects
201 Oregon Ballroom
8:00 AM - 12:00 PM, Thursday
() Estimating the uncertainty in predicting environmental fate of phthalate esters versus PCBs.
Comber, Mike1, Clark, Kathryn2, Cowan-Ellsberry, Christina3, 1 ExxonMobil Biomedical Sciences Inc, Machelen, Belgium2 BEC Technologies Inc., Aurora, Ontario, Canada3 The Procter & Gamble Company, Cincinnati, Ohio, USA
ABSTRACT- Modeling the environmental fate and predicting environmental exposure concentrations of any chemical has associated with it a certain level of uncertainty. This uncertainty is due to the variable nature of the environment, the uncertainties associated with the fate and partitioning properties of the chemical and the level of accuracy and /or detail in the model. An understanding of environmental fate and exposure concentrations and their uncertainties can be developed using the 5-step process (Mackay et al 1996) which is comprised of obtaining partitioning and degradation data, emission data and conducting evaluative, regional and local assessment fate and exposure assessments using multi-media models. Multi-media models (MMM) are used to identify in which compartments the chemical accumulates and thus the compartment within which the potential for biological effects need to be explored. These estimates can be compared with known monitoring data and/or used in the design of monitoring programmes to understand or reduce the uncertainty. MMM have been invaluable in prioritizing chemicals and the research needed to reduce the uncertainty in the exposure estimate. This paper will show how the 5-step process can be applied using the available information with respect to phthalates and PCBs and how the uncertainty can be estimated. This will then enable a comparison to be made of the uncertainties involved in this assessment and the extent to which the properties of rapidly degrading chemicals and extremely persistent chemicals impact the uncertainty involved in the predictions.
Key words: fate, phthalates, multimedia modeling