Kettler Susanne & Marc Kennedy, Cronan McNamara, Regina Oberdörfer, CianO'Mahony, Jürgen Schnabel, Benjamin Smith, Corinne Sprong, Roland Faludi, DavidTennant

Food and Chemical Toxicology - Volume 82, August 2015, Pages 79-95

15/04/2015

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Assessing and reporting uncertainties in dietary exposure analysis: Mapping of uncertainties in a tiered approach

Authors: Kettler Susanne & Marc Kennedy, Cronan McNamara, Regina Oberdörfer, CianO’Mahony, Jürgen Schnabel, Benjamin Smith, Corinne Sprong, Roland Faludi, DavidTennant
Keywords: Dietary exposure assessments, Uncertainty analysis, Tiered approach, Deterministic models, Point estimates, Probabilistic models

Uncertainty analysis is an important component of dietary exposure assessments in order to understand correctly the strength and limits of its results. Often, standard screening procedures are applied in a first step which results in conservative estimates. If through those screening procedures a potential exceedance of health-based guidance values is indicated, within the tiered approach more refined models are applied. However, the sources and types of uncertainties in deterministic and probabilistic models can vary or differ.

A key objective of this work has been the mapping of different sources and types of uncertainties to better understand how to best use uncertainty analysis to generate more realistic comprehension of dietary exposure. In dietary exposure assessments, uncertainties can be introduced by knowledge gaps about the exposure scenario, parameter and the model itself. With this mapping, general and model-independent uncertainties have been identified and described, as well as those which can be introduced and influenced by the specific model during the tiered approach.

This analysis identifies that there are general uncertainties common to point estimates (screening or deterministic methods) and probabilistic exposure assessment methods. To provide further clarity, general sources of uncertainty affecting many dietary exposure assessments should be separated from model-specific uncertainties.

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