@article{https://doi.org/10.2903/j.efsa.2019.e170905,
title = {Joint venture on the further development of chemical exposure assessment by use of probabilistic modelling},
author = {Christina Vlachou and Daniela Hofstädter},
url = {https://efsa.onlinelibrary.wiley.com/doi/abs/10.2903/j.efsa.2019.e170905},
doi = {https://doi.org/10.2903/j.efsa.2019.e170905},
year = {2019},
date = {2019-01-01},
journal = {EFSA Journal},
volume = {17},
number = {S2},
pages = {e170905},
abstract = {Abstract Exposure assessment is a fundamental component of the risk assessment process and has a significant contribution to the overall uncertainty of the risk estimates. The aim of the present project, implemented within the framework of the EU-FORA Fellowship, was to develop a structured approach for probabilistic modelling of the dietary exposure to chemical contaminants, which shall be used as a refined alternative to the more conservative deterministic approach or as part of a Tier 2 assessment. The fellow received training and worked in close cooperation with the project team on three case studies of contaminants in food (cadmium, acrylamide and deoxynivalenol). The modelling of the dietary intake was based on relevant EFSA Guidance and employed the Monte Carlo simulation methodology with the use of a standard software tool (Monte Carlo Risk Assessment (MCRA) platform) and/or a tailor-made risk model in the programming language R. The strengths and the limitations of every approach were explored and discussed. The conclusion from the critical comparison of the outputs was that the former can be a tool for the generation of fast preliminary estimates of the usual dietary exposure, whereas the latter may be used by the risk assessors as a more sophisticated, ‘state-of-the-art’ strategy, which will lead to more realistic estimates of the exposure. The outcomes of the project are being currently incorporated in a Guidance Document on probabilistic exposure assessment, which will highly contribute to more informed risk management decisions and to more effective risk communication.},
keywords = {dietary exposure, probabilistic modelling, risk assessment},
pubstate = {published},
tppubtype = {article}
}
