New approaches to uncertainty analysis for use in aggregate and cumulative risk assessment of pesticides.

Risk assessments for human exposures to plant protection products (PPPs) have traditionally focussed on single routes of exposure and single compounds. Extensions to estimate aggregate (multi-source) and cumulative (multi-compound) exposure from PPPs present many new challenges and additional uncertainties that should be addressed as part of risk analysis and decision-making. A general approach is outlined for identifying and classifying the relevant uncertainties and variabilities. The implementation of uncertainty analysis within the MCRA software, developed as part of the EU-funded ACROPOLIS project to address some of these uncertainties, is demonstrated. An example is presented for dietary and non-dietary exposures to the triazole class of compounds. This demonstrates the chaining of models, linking variability and uncertainty generated from an external model for bystander exposure with variability and uncertainty in MCRA dietary exposure assessments. A new method is also presented for combining pesticide usage survey information with limited residue monitoring data, to address non-detect uncertainty. The results show that incorporating usage information reduces uncertainty in parameters of the residue distribution but that in this case quantifying uncertainty is not a priority, at least for UK grown crops. A general discussion of alternative approaches to treat uncertainty, either quantitatively or qualitatively, is included.

Investigation into the experimental protocols required to determine maximum residue limits (MRLs) in honey.

There is current debate within the EU, and internationally, on how withdrawal periods and maximum residue limits (MRLs) may be set for honey production. Whilst comprehensive EU guidelines exist for calculating the withdrawal times of veterinary medicines in most food-producing species, the analytical variables to be studied for bees/honey are not well defined. The objective of this study was therefore to investigate and understand the factors, for example sampling variability, that is important in the development of a harmonized protocol that can be used to generate the robust scientific data necessary to assist risk assessors in proposing MRLs for honey. Ten bee colonies were treated in the spring with a model compound (ciprofloxacin). One hive was used to study intra-hive variation in residue concentrations and the other nine were used in an inter-hive study over a 41-week sampling period. All samples were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The highest mean concentration from nine hives used in the inter-hive study was 4627 µg/kg eight days (D8) after treatment. The concentration of ciprofloxacin declined to an average concentration of 1756 µg/kg at D30 and 733 µg/kg at D283 (over-winter sample). A generalized additive model was used to fit a smooth curve for trend estimation. For some individual hives the concentration of ciprofloxacin increased slightly at the later sampling time-points. Consequently it was not possible to interpolate, with confidence, a finite withdrawal period for ciprofloxacin at theoretical MRLs between 25 and 500 µg/kg. The observed variation in concentration of ciprofloxacin between hives indicates that the validity of the EU guideline for bees/honey, which requires five samples from five hives to calculate a withdrawal period, may require revision.

A hierarchical Bayesian model for extreme pesticide residues.

The number of residue measurements in an individual field trial, carried out to provide data for a pesticide registration for a particular crop, is generally too small to estimate upper tails of the residue distribution for that crop with any certainty. We present a new method, using extreme value theory, which pools information from various field trials, with different crop and pesticide combinations, to provide a common model for the upper tails of residue distributions generally. The method can be used to improve the estimation of high quantiles of a particular residue distribution. It provides a flexible alternative to the direct fitting of a distribution to each individual dataset, and does not require strong distributional assumptions. By using a hierarchical Bayesian model, our method also accounts for parameter uncertainty. The method is applied to a range of supervised trials containing residues on individual items (e.g. on individual apples), and the results illustrate the variation in tail properties amongst all commodities and pesticides. The outputs could be used to select conservative high percentile residue levels as part of a deterministic risk assessment, taking account of the variability between crops and pesticides and also the uncertainty due to relatively small datasets.