Vardoulakis, Sotiris, Fisher, Bernard E.A., Gonzalez-Flesca, Norbert and Pericleous, Koulis ORCID: https://orcid.org/0000-0002-7426-9999 (2002) Model sensitivity and uncertainty analysis using roadside air quality measurements. Atmospheric Environment, 36 (13). pp. 2121-2134. ISSN 1352-2310 (doi:10.1016/S1352-2310(02)00201-7)

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Abstract

Most 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.

Item Type:	Article
Uncontrolled Keywords:	air pollution, model sensitivity, uncertainty, street canyon, traffic emissions, meteorological data
Subjects:	G Geography. Anthropology. Recreation > GE Environmental Sciences Q Science > QA Mathematics
Pre-2014 Departments:	School of Computing & Mathematical Sciences School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis School of Computing & Mathematical Sciences > Centre for Numerical Modelling & Process Analysis > Computational Science & Engineering Group School of Computing & Mathematical Sciences > Department of Computer Systems Technology
Related URLs:	Publisher Organisation
Last Modified:	02 Mar 2019 15:50
URI:	http://gala.gre.ac.uk/id/eprint/546

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