Cumulative Exposure Assessment of Triazole Pesticides
The present document has been produced and adopted by the bodies identified above as authors. In accordance with Article 36 of Regulation (EC) No 178/2002, this task has been carried out exclusively by the authors in the context of a grant agreement between the European Food Safety Authority and the authors. The present document is published complying with the transparency principle to which the European Food Safety Authority is subject. It may not be considered as an output adopted by EFSA. EFSA reserves its rights, view and position as regards the issues addressed and the conclusions reached in the present document, without prejudice to the rights of the authors.
In the EFSA opinion on identification of new approaches to assess cumulative and synergistic risks from pesticides a tiered approach for cumulative risk assessment has been proposed.
The aim of this study is to demonstrate the feasibility of a higher tier assessment of cumulative exposure using probabilistic modelling. The input for the probabilistic software consisted of food consumption and residue concentration databases from Czech Republic, France, Italy, the Netherlands, Sweden, United Kingdom and Finland. Food as eaten as reported in each consumption database was converted back to their corresponding raw agricultural commodities.
A probabilistic Monte Carlo model was used to estimate the short-term intake. For long-term intake assessments three statistical models ISUF, BBN and IOM were used. The probabilistic models and the data were organized in the Monte Carlo Risk Assessment (MCRA) software.
Cumulative exposure assessments were performed for different countries and different age groups. Half of the scenarios aimed at calculating the actual and half for MRL setting.
Short-term and long-term cumulative dietary exposure to triazoles can be calculated with probabilistic models in the European context. A statistical model assuming that the nonanalyzed triazoles in samples are zero values might result in an underestimation of exposure.
For long-term exposure assessments not all models applied in all cases and a significant model uncertainty was observed especially in scenarios where the exposure was bimodal distributed. The IOM method can be applied in all cases but it was recognized that this method overestimates the exposure in the upper tail of the exposure distribution.
It was recommended to develop new statistical models to account for unbalanced data sets and bimodal distributions. Once the residue and consumption data are organised in the MCRA platform, which is the case for the countries included in this project, the simulation can be done easily via internet.