Mechanistically Modeling Human Exposure to Persistent Organic Pollutants

Abstract

Mechanistic modeling approaches simulate human exposure by describing the processes that are responsible for delivering contaminants to the human body. Starting with environmental concentrations, emissions, or production volumes, these approaches yield metrics of internal human exposure, either for individuals or for a generic representative of a population. These models are particularly useful for quantifying exposure to persistent organic pollutants (POPs), whose long residence time in the human body implies that past exposures influence current contaminant levels and thus need to be considered. Mechanistic models allow for the reconstruction of past exposures without relying on back-extrapolations with empirical surrogate parameters; consequently, they can account for lag periods between emissions and exposure as well as for shifts in the main exposure route over time. Such shifts can occur as a result of changes in chemical use and emission scenario, exposure factors, or the environment. However, because mechanistic models require an in-depth understanding of the relevant processes including the ability to numerically parameterize them, they have so far been applied to only a limited number of data-rich POPs. When used in simulations involving a large number of hypothetical property combinations, these models facilitate the comparison of the exposure potential of different substances and the identification of thresholds that separate chemicals with different dominant exposure routes. The motivations for mechanistic modeling of exposure to POPs are manifold, and include risk assessment and management, support of biomonitoring and epidemiological investigations, and the identification of chemicals and human populations with high exposure potential.