Sources of uncertainty in pesticide fate modelling

• Published in: The Science of the total environment Vol. 317; no. 1; pp. 53 - 72
• Main Authors: Dubus, Igor G, Brown, Colin D, Beulke, Sabine
• Format: Journal Article
• Language: English
• Published: Shannon Elsevier B.V 30.12.2003; Elsevier Science
• Subjects: Animals; Applied sciences; Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Environmental Exposure; Exact sciences and technology; Global environmental pollution; Humans; Leaching; Models, Theoretical; Monte carlo; Monte Carlo Method; Pesticide; Pesticides - analysis; Pesticides - poisoning; Pollution; Pollution, environment geology; Probabilistic; Reproducibility of Results; Risk Assessment; Sensitivity; Uncertainty; Uncertainty; Sensitivity; Pesticide; Probabilistic; Monte carlo; Model; Leaching; Probabilistic approach; Pollutant behavior; Pesticides; Modeling; Pollution; Environment
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/S0048-9697(03)00362-0

There is worldwide interest in the application of probabilistic approaches to pesticide fate models to account for uncertainty in exposure assessments. The first steps in conducting a probabilistic analysis of any system are: (i) to identify where the uncertainties come from; and (ii) to pinpoint those uncertainties that are likely to affect most of the predictions made. This article aims at addressing those two points within the context of exposure assessment for pesticides through a review of the different sources of uncertainty in pesticide fate modelling. The extensive listing of sources of uncertainty clearly demonstrates that pesticide fate modelling is laced with uncertainty. More importantly, the review suggests that the probabilistic approaches, which are typically being deployed to account for uncertainty in the pesticide fate modelling, such as Monte Carlo modelling, ignore a number of key sources of uncertainty, which are likely to have a significant effect on the prediction of environmental concentrations for pesticides (e.g. model error, modeller subjectivity). Future research should concentrate on quantifying the impact these uncertainties have on exposure assessments and on developing procedures that enable their integration within probabilistic assessments.