Evaluation of human health risks associated with pesticide dietary intake - an overview on quantitative uncertainty analysis

Petronela Cozma1, Marius Gavrilescu2, Mihaela Rosca1, Laura Carmen Apostol1,3, Raluca-Maria Hlihor1,4, Maria Gavrilescu1,5

1 Gheorghe Asachi Technical University of Iasi, Cristofor Simionescu Faculty of Chemical Engineering and Environmental Protection, Department of Environmental Engineering and Management, 73 Prof. Dr. Docent D. Mangeron Str., 700050 Iasi, Romania
2 Gheorghe Asachi Technical University of Iasi, Faculty of Automatic Control and Computer Engineering, Department of Computer Engineering, 27 Prof. Dr. Docent D. Mangeron Str., 700050 Iasi, Romania
3 Stefan cel Mare University of Suceava, Faculty of Food Engineering, 13 University Street, Suceava, Romania
4 Ion Ionescu de la Brad University of Agricultural Sciences and Veterinary Medicine of Iasi, Faculty of Horticulture, Department of Horticultural Technologies, 3 Mihail Sadoveanu Alley, 700490 Iasi, Romania
5 Academy of Romanian Scientists, 54 Splaiul Independentei, RO-050094 Bucharest, Romania


The probabilistic estimation and the risk analysis of the exposure to pesticides through the ingestion of plants and food products is an important task for ensuring informed decision making and appropriate consumer protection. Monte Carlo-based methods are powerful tools in this regard, allowing for the empirical estimation of the distribution of exposure values, as well as for carrying out a corresponding uncertainty analysis. Such findings are important for assessing the exposure risk for multiple categories of the general population, divided by age groups, body weight, food consumption etc. The general model used for determining the exposure allows for a detailed assessment and analysis of the distribution of exposure values along a determined range, and of the probabilities of occurrence for acute and chronic exposure levels, while also accounting for potential uncertainties in the input parameters. Researchers in the related fields propose various probabilistic approaches using several distribution shapes to estimate each parameter of the model. Furthermore, the related literature contains a series of guidelines for carrying out the aforementioned tasks, for various types of data with a wide assortment of distributions. Consequently, this study presents a general framework and characterization of exposure as a result of food consumption, as well as common practices for carrying out an assessment of exposure levels, with an emphasis on significant related work from the state-of-the-art in the field. The findings of the present study indicate that probabilistic approaches are powerful tools for aiding the regulatory decision-making process in the case of acute or chronic dietary exposure.


exposure; fruits and vegetables; Monte Carlo analysis; pesticide residues; probabilistic modeling

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