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Techniques employed in the assessment of consumer exposure to pesticides are currently being reviewed in the UK. This is not a formal process as is happening in the USA. However, the advent of probabilistic approaches and sophisticated computer models has prompted regulators, industry and other stakeholders in the UK to recognize the need for refinements in the risk-assessment process. Sources of information and data necessary to explore such refinements are disparate. This review aims to collate the information to present a coherent picture of the current knowledge, the data available and the stakeholders involved. It can then be used as a resource with which to investigate further more specific issues. Although focussing on the UK, the European context is included and reference is made to US models and developments that should be investigated. Factors hampering progress include the lack of sufficient data on which to base quantitative analysis, especially in the residential pesticides sector, and lack of experience in using and interpreting probabilistic models. At present, such techniques are being approached with some caution in the UK and in Europe, although their utility for cumulative assessment is accepted. Communicating results to both risk managers and consumers will be a considerable challenge.

The assessment of consumer exposure to pesticides is an important part of pesticide regulation. Probabilistic modelling allows analysis of uncertainty and variability in risk assessments. The output of any assessment will be influenced by the characteristics and uncertainty of the inputs, model structure and assumptions. While the use of probabilistic models is well established in the United States, in Europe problems of low acceptance, sparse data and lack of guidelines are slowing the development. The analyses in the current paper focused on the dietary pathway and the exposure of UK toddlers. Three single food, single pesticide case studies were used to parameterize a simple probabilistic model built in Crystal Ball. Data on dietary consumption patterns were extracted from National Diet and Nutrition Surveys, and levels of pesticide active ingredients in foods were collected from Pesticide Residues Committee monitoring. The effect of uncertainty on the exposure estimate was analysed using scenarios, reflecting different assumptions related to sources of uncertainty. The most influential uncertainty issue was the distribution type used to represent input variables. Other sources that most affected model output were non-detects, unit-to-unit variability and processing. Specifying correlation between variables was found to have little effect on exposure estimates. The findings have important implications for how probabilistic modelling should be conducted, communicated and used by policy and decision makers as part of consumer risk assessment of pesticides.

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