Pesticide risk assessment for food products involves combining information from consumption and concentration data sets to estimate a distribution for the pesticide intake in a human population. Using this distribution one can obtain probabilities of individuals exceeding the safe levels of consumption.

In this paper we present a probabilistic, Bayesian approach to modelling the intake of the pesticide Iprodione though multiple food products. Modelling data on food consumption and pesticide concentration poses a variety of problems, such as the large proportions of consumptions and concentrations that are recorded as zero, and correlation between the intakes of different foods. We consider food consumption data from the Netherlands National Food Consumption Survey and concentration data collected by the Netherlands Ministry of Agriculture. We develop a multivariate latent-Gaussian model for the consumption data which allows for correlated intakes between products. For the concentration data we propose a univariate latent-t model. We then combine predicted intakes and concentrations from these models to obtain a distribution for individual Iprodione exposure.

The latent-variable models allow for both skewness and large numbers of zeros in the consumption and concentration data. The use of a probabilistic approach is intended to yield more robust estimates of high percentiles of the exposure distribution than an empirical approach. Bayesian inference is used to facilitate the treatment of data with a complex structure.