M11 PM Internal Exposure
Monday, 14 November 2005: 1:50 PM - 5:30 PM in 343-344

(MCK-1117-857025) Merging fate models with biomonitoring data to assess internal exposure for persistent pollutants and pesticides.

McKone, T.^{1, 2}, ¹ University of California, Berkeley, CA, United States² Lawrence Berkeley National Laboratory, Berkeley, CA, United States

ABSTRACT- The goal of this paper is to evaluate human and ecosystem biomonitoring data at different spatial scales and over different time scales as a means of assessing the fate and distribution of persistent organic chemicals and pesticides. The particular focus of this talk is on the reliability of biological samples to infer internal exposure and the source of that exposure. Our premise is the where models or samples alone are highly unreliable, merging models with samples improves our conceptual understanding and increases the power of source-to-dose assessment. We use two case studies. First we consider how the levels of dioxin-like compounds in vegetation, animal, and human tissue relate to emissions on regional and continental scales. Next we consider organophosphate pesticides with a particular emphasis on human biomonitoring data. For a series of organophosphate pesticides and for a large and geographically dispersed population cohort in California, we have assembled pesticide use, environmental sample, and biological tissue exposure data. We use a three-step process to define source-to-internal exposure relationships for this cohort. First, we use a multimedia mass balance model with local and regional pesticide usage data to obtain probabilistic predictions of the magnitude and uncertainty of internal exposures. Second, we use results of the modeling exercise to develop premises about relative contributions from different sources and pathways of exposure. Finally, we compare internal exposure data collected from this population to the magnitude and variation of the same internal exposures for the whole of the United States population. These case studies reveal the value of systematically merging mass-balance models with biological samples from humans, animals and vegetation as reliable tools for characterizing environmental concentrations and internal exposures.

Key words: internal exposure, biomonitoring, fate models, pesticides