Validation Studies of Monte Carlo Modeling of Children's Pesticide and Arsenic Exposures Due to Residential Soil ContaminationEPA Grant Number: U914992
Title: Validation Studies of Monte Carlo Modeling of Children's Pesticide and Arsenic Exposures Due to Residential Soil Contamination
Investigators: Wawrukiewicz, Ann
Institution: University of Washington
EPA Project Officer: Carleton, James N
Project Period: January 1, 1996 through January 1, 1998
Project Amount: $102,000
RFA: STAR Graduate Fellowships (1996) RFA Text | Recipients Lists
Research Category: Fellowship - Toxicology , Academic Fellowships , Health Effects
Use of stochastic rather than deterministic estimates of exposure parameters offers the promise of more realistic dose predictions. Separating population variability from uncertainty because of a lack of information allows generation of confidence limits on dose distributions and analysis of predictive capability. Few studies, however, have compared model-generated estimates to biomonitoring data, because of the scarcity of appropriate data sets. The objective of this research project was to compare predicted doses to measured body burdens for two cases: (1) measurements of organophosphate pesticides in soil and dust in and around houses of farm workers in eastern Washington and measurements of the workers' children's urinary pesticide metabolites; and (2) a copper smelter in Tacoma, Washington, near which arsenic levels in soil and dust were monitored. Urinary arsenic was measured in children living within 0.5 miles of the smelter in two studies 2 years apart.
A nested Monte Carlo simulation incorporating variability and uncertainty was conducted for each case, while modeling soil and dust exposure pathways. Dose distributions were calculated with confidence intervals, and predicted values were compared to biomonitoring data. The arsenic results indicated that plausible parameters, utilizing best available knowledge, can result in consistency between model prediction and observations.
The current arsenic model utilized several nontraditional input distributions; however, these may not be seen in a typical regulatory exposure assessment. Therefore, these positive results should be used with caution. In the pesticide case, actual doses were underpredicted by two orders of magnitude, suggesting an additional, unknown exposure pathway; as a result, little could be concluded about the validity of the pesticide soil and dust pathway parameters. The arsenic case lends support to the promise of Monte Carlo analysis as an exposure assessment tool, but additional cases in which soil and dust are the dominant pathways are needed.