Estimates of Uncertainty in Urban Air Quality Model Predictions

Vardoulakis, Sotiris; Fisher, Bernard; Gonzalez-Flesca, Norbert; Pericleous, K.

doi:10.1007/0-306-47813-7_49

Cited by 1 publication

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“…Medians together with maximum and minimum concentrations were calculated for each ensemble set of 27 model realisations corresponding to a specific time and location within the two streets in Paris. The extreme concentrations were thought to give a rough estimate of model uncertainty in the predictions (Vardoulakis at al., 2001). As it can be seen in Fig.…”

Section: Concentration Ranges Using Three Modelsmentioning

confidence: 99%

Model sensitivity and uncertainty analysis using roadside air quality measurements

Vardoulakis

Fisher

Gonzalez-Flesca

et al. 2002

Atmospheric Environment

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International audienceMost of the air quality modelling work has been so far oriented towards deterministic simulations of ambient pollutant concentrations. This traditional approach, which is based on the use of one selected model and one data set of discrete input values, does not reflect the uncertainties due to errors in model formulation and input data. Given the complexities of urban environments and the inherent limitations of mathematical modelling, it is unlikely that a single model based on routinely available meteorological and emission data will give satisfactory short-term predictions. In this study, different methods involving the use of more than one dispersion model, in association with different emission simulation methodologies and meteorological data sets, were explored for predicting best CO and benzene estimates, and related confidence bounds. The different approaches were tested using experimental data obtained during intensive monitoring campaigns in busy street canyons in Paris, France. Three relative simple dispersion models (STREET, OSPM and AEOLIUS) that are likely to be used for regulatory purposes were selected for this application. A sensitivity analysis was conducted in order to identify internal model parameters that might significantly affect results. Finally, a probabilistic methodology for assessing urban air quality was proposed

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Section: Concentration Ranges Using Three Modelsmentioning

confidence: 99%