麻豆AV

A human population鈥檚 exposure to contaminants is typically assessed through the analysis of representative samples, typically blood, in so-called biomonitoring programs. Such programs generate cross-sectional data when different members of a population are sampled at the same point in time, or longitudinal data when a cohort of individuals is sampled multiple times at different ages. Human biomonitoring of contaminant levels typically reveals a huge range in exposures within a population and therefore it is of considerable interest to quantify and assign sources of variability in the observed human contaminant burdens. Whereas this is typically done using statistical analyses, we rely instead on a mechanistic, non-steady state, environmental fate and human food-chain bioaccu颅mulation model to predict exposure of humans to persistent organic pollutants. This is done by first calculating longitudinal exposures for different human cohorts, which are then 鈥渟ampled鈥� to generate cross-sectional data. I will illustrate how we can use such a mechanistic modeling approach to shed light on the role of age, diet and body mass index on contaminant exposure. Particularly intriguing is the possibility to decipher with the help of the model to what extent long-term temporal trends in these parameters (i.e. an ageing population, changing dietary patterns, and the obesity epidemic) may confound long term trends in a population鈥檚 contamination status obtained from cross-sectional sampling.