O-156 Pesticide exposure in fruit growers: comparison of levels and determinants from the CANEPA field study and from the ‘Agricultural Operator Exposure Model’ (AOEM) predictions

Introduction Pesticide exposures increase the risk of chronic diseases in farmers. Knowledge of exposure levels are needed for epidemiological studies and for regulation. In pesticide registration process, operator’s exposure is predicted by the AOEM, set up in 2014 by the European Food Safety Authority, based on thirty studies conducted by the pesticide industry. To date, we are not aware of any field study comparing predicted data with those measured under real working conditions. Objective We aimed to compare operators’ exposures during treatment days in apple-growing under non-controlled conditions of work and values predicted by AOEM. Methods Thirty apple growers from the French CANEPA study, were observed applying two fungicides (captan/dithianon) in 2016–2017. Dermal exposure was measured by body patches and cotton gloves. Detailed parameters about the farm, operator, application day, spraying equipment and personal protective equipment (PPE) used were recorded. For each observation, the corresponding exposure was calculated by the AOEM, using these parameters. The relationship between measured and calculated exposures was studied by linear regression. Results Significant linear correlation was observed between calculated and measured daily exposures. Overall, the model overestimated daily exposure and exposure during application. However exposure was underestimated at mixing/loading in many observations, especially when the operator wore long working clothes or gloves. Conclusion The AOEM model did not appear conservative in the sense that it did not overestimate exposures in all circumstances. More specifically: 1) the overestimation at spraying appeared a consequence of the overestimation of daily treated area, 2) the protection provided by PPE appeared overestimated, 3) mixing/loading exposure, a phase in which operators are exposed to concentrated products, appeared underestimated. These discrepancies could be due to optimal working conditions (larger farms, newer equipment) under which industries’ studies are conducted that are not representative of operators’ actual working conditions in fruit growing.