| Popis: |
Complex simulation models are available to predict the level of exposure to bystanders and residents after a crop spraying event. In this paper we consider a particle-tracking spray drift model whose input parameters define particular scenarios of interest. Model outputs based on fixed values for these inputs ignore natural random variation and therefore give no indication of realistic variation in exposures, nor do they quantify the probability of rare extreme exposures. We describe a probabilistic modelling framework that allows the effect of variability in the input parameters to be quantified. An efficient statistical method for approximating the spray drift model is used, by creating a statistical emulator. An additional statistical model is then used to link airborne spray outputs to bystander exposures based on measured data. Uncertainty and variability are quantified in this model component. Validation of our approach is considered in two stages: first the accuracy of the emulator is assessed, as a surrogate for the true spray model. Secondly, the overall probabilistic outputs are compared with corresponding field measurements. Results are presented for a selection of typical exposure risk scenarios for bystanders and residents, illustrating the potential to generate a richer source of information for decision-makers. Sensitivity analysis results suggest strategies to reduce risk, such as minimising boom height. |
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