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User subjectivity in Monte Carlo modeling of pesticide exposure
Author(s) -
Beulke Sabine,
Brown Colin D.,
Dubus Igor G.,
Galicia Hector,
Jarvis Nicholas,
Schaefer Dieter,
Trevisan Marco
Publication year - 2006
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1897/05-332r.1
Subject(s) - monte carlo method , percentile , statistics , sampling (signal processing) , probabilistic logic , confidence interval , probability distribution , computer science , uncertainty analysis , environmental science , mathematics , filter (signal processing) , computer vision
Monte Carlo techniques are increasingly used in pesticide exposure modeling to evaluate the uncertainty in predictions arising from uncertainty in input parameters and to estimate the confidence that should be assigned to the modeling results. The approach typically involves running a deterministic model repeatedly for a large number of input values sampled from statistical distributions. In the present study, six modelers made choices regarding the type and parameterization of distributions assigned to degradation and sorption data for an example pesticide, the correlation between the parameters, the tool and method used for sampling, and the number of samples generated. A leaching assessment was carried out using a single model and scenario and all data for sorption and degradation generated by the six modelers. The distributions of sampled parameters differed between the modelers, and the agreement with the measured data was variable. Large differences were found between the upper percentiles of simulated concentrations in leachate. The probability of exceeding 0.1 μg/L ranged from 0 to 35.7%. The present study demonstrated that subjective choices made in Monte Carlo modeling introduce variability into probabilistic modeling and that the results need to be interpreted with care.