Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Bayesian uncertainty estimation methodology applied to air pollution modelling.
AU - Romanowicz, Renata
AU - Higson, Helen
AU - Teasdale, Ian
PY - 2000/5
Y1 - 2000/5
N2 - The aim of the study is an uncertainty analysis of an air dispersion model. The model used is described in NRPB-R91 (Clarke, 1979), 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 simplifications, 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 influence of different assumptions regarding model input parameters is performed. Of the parameters investigated (i.e. roughness length, release height, wind fluctuation coefficient and wind speed), the model showed the greatest sensitivity to wind speed values. A major influence on the results of the stability condition specification is also demonstrated.
AB - The aim of the study is an uncertainty analysis of an air dispersion model. The model used is described in NRPB-R91 (Clarke, 1979), 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 simplifications, 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 influence of different assumptions regarding model input parameters is performed. Of the parameters investigated (i.e. roughness length, release height, wind fluctuation coefficient and wind speed), the model showed the greatest sensitivity to wind speed values. A major influence on the results of the stability condition specification is also demonstrated.
KW - Gaussian air dispersion model
KW - sensitivity analysis
KW - Bayesian uncertainty estimation
KW - likelihood functions
KW - prior and posterior probability density functions
KW - prediction errors
KW - prediction limits
U2 - 10.1002/(SICI)1099-095X(200005/06)11:3<351::AID-ENV424>3.0.CO;2-Z
DO - 10.1002/(SICI)1099-095X(200005/06)11:3<351::AID-ENV424>3.0.CO;2-Z
M3 - Journal article
VL - 11
SP - 351
EP - 371
JO - Environmetrics
JF - Environmetrics
SN - 1099-095X
IS - 3
ER -