Probabilistic exposure assessment to food chemicals based on extreme value theory. Application to heavy metals from fish and sea products

Abstract

This paper presents new statistical methods in the field of exposure assessment. We focus on the estimation of the probability for the exposure to exceed a fixed safe level such as the provisional tolerable weekly intake (PTWI), when both consumption data and contamination data are independently available. Various calculations of exposure are proposed and compared. For many contaminants, PTWI belongs to the exposure tail distribution, which suggests the use of extreme value theory (EVT) to evaluate the risk. Our approach consists in modelling the exposure tail by a Pareto type distribution characterized by a Pareto index which may be seen as a measure of the risk of exceeding the PTWI. Using propositions by EVT specialists, we correct the bias of the usual Hill estimator to accurately estimate this risk index. We compare the results with an empirical plug-in method and show that the Pareto adjustment is relevant and efficient when exposure is low compared to the PTWI while the plug-in method should be used when exposure is higher. To illustrate our approach, we present some exposure assessment for heavy metals (lead, cadmium, mercury) via sea product consumption.