Bayesian uncertainty estimation methodology applied to air pollution modelling

✍ Scribed by Renata Romanowicz; Helen Higson; Ian Teasdale

• Publisher: John Wiley and Sons
• Year: 2000
• Tongue: English
• Weight: 182 KB
• Volume: 11
• Category: Article
• ISSN: 1180-4009
• DOI: 10.1002/(sici)1099-095x(200005/06)11:3<351::aid-env424>3.0.co;2-z

✦ Synopsis

The aim of the study is an uncertainty analysis of an air dispersion model[ The model used is described in NRPB!R80 "Clarke\ 0868#\ a model for short and medium range dispersion of radionuclides released into the atmosphere[ Uncertainties in the model predictions arise both from the uncertainty of the input variables and the model simpli_cations\ resulting in parameter uncertainty[ The uncertainty of the predictions is well described by the credibility intervals of the predictions "prediction limits#\ which in turn are derived from the distribution of the predictions[ The methodology for estimating this distribution consists of running multiple simulations of the model for discrete values of input parameters following some assumed random distributions[ The value of the prediction limits lies in their objectivity[ However\ they depend on the assumed input distributions and their ranges "as do the model results#[ Hence the choice of distributions is very important for the reliability of the uncertainty analysis[ In this work\ the choice of input distributions is analysed from the point of view of the reliability of the predictive uncertainty of the model[ An analysis of the in~uence of di}erent assumptions regarding model input parameters is performed[ Of the parameters investigated "i[e[ roughness length\ release height\ wind ~uctuation coe.cient and wind speed#\ the model showed the greatest sensitivity to wind speed values[ A major in~uence on the results of the stability condition speci_cation is also demonstrated[ Copyright