Development of new tools for bringing pesticide risk assessment in the small size enterprises and in the developing world

The role of pesticides in the modern society has been strengthened by the need for higher yield in food production and the ongoing battle against vector borne diseases in public health. Nevertheless, the toxicity of these chemicals is not fully specific to target organisms, thus posing a potential health threat to humans. In this frame, risk assessment and management are fundamental. In the occupational settings, variability of meteorological conditions, use of different concentrations of highboy variable mixtures, and significant variations in the application times and modalities make this task very complicated, underlining the need for novel approaches for conducting ‘in field’ preventive activities. New developments in pesticide risk assessment should start from the fact that the amount of information collected during the process of authorisation of new active ingredients is unique, similar to that available for pharmaceutical products, and that this significant amount of information is scarcely used in the post market risk assessment activities. In this light, a possible way forward for pesticide risk assessment is represented by a better exploitation of these data, in approaches with variable levels of complexity; the simplest, is the evaluation of the adherence of the use scenario under evaluation with the one checked and approved in the authorisation process and synthesised in the good agricultural practices. Other parameters such as Acceptable Operator Exposure Level (AOEL), acute reference dose (ArD) as well data regarding skin absorption, metabolism and relevant metabolites in animals can find use in the realisation of models adequate to estimate the dose and the risk without doing analysis, as well as to calculate provisional biological exposure indices, defining the dose supposed to be excreted in a subject exposed at the AOEL level. This can be done conducing real-life field studies to usable refine and validate the risk hypotheses generated through modelling.