Experiences fom the application of a parameter estimation and identifiability analysis methodology to the operational street pollution model (OSPM)
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Crown Copyright 2014 Dstl. Uncertainty and sensitivity analysis can potentially increase the transparency in the modelling process and guide research in the relationship between model and data. The uncertainty and sensitivity of the Operational Street Pollution Model (OSPM), being an example of a semi-parameterised air quality model, have not been studied before, and it is therefore the aim to explore the potential advantages of this type of analyses on atmospheric models. An iterative parameter estimation and identifiability analysis methodology along with two different data splitting methodologies were chosen for the present study. The results show that this type of methodology can be informative applied to an atmospheric model, in that the methodology successfully balances the model-measurement errors among the different streets and the different species. Moreover, the results indicate where future research effort in model improvement should be directed, with respect to parameterisations and model parameter uncertainty.