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A Parameter Estimation and Identifiability Analysis Methodology Applied to a Street Canyon Air Pollution Model

Ottosen, Thor-Bjørn and Ketzel, M. and Skov, H. and Hertel, O. and Brandt, J. and Kakosimos, K.E. (2016) A Parameter Estimation and Identifiability Analysis Methodology Applied to a Street Canyon Air Pollution Model. Environmental Modelling & Software, 84. pp. 165-176. ISSN 1364-8152

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Abstract

Mathematical models are increasingly used in environmental science thus increasing the importance of uncertainty and sensitivity analyses. In the present study, an iterative parameter estimation and identifiability analysis methodology is applied to an atmospheric model – the Operational Street Pollution Model (OSPMr). To assess the predictive validity of the model, the data is split into an estimation and a prediction data set using two data splitting approaches and data preparation techniques (clustering and outlier detection) are analysed. The sensitivity analysis, being part of the identifiability analysis, showed that some model parameters were significantly more sensitive than others. The application of the determined optimal parameter values was shown to succesfully equilibrate the model biases among the individual streets and species. It was as well shown that the frequentist approach applied for the uncertainty calculations underestimated the parameter uncertainties. The model parameter uncertainty was qualitatively assessed to be significant, and reduction strategies were identified.

Item Type: Article
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Uncontrolled Keywords: uncertainty, sensitivity, OSPM, data splitting, exploratory data analysis, Matlab
Subjects: Q Science > Q Science (General)
Divisions: Academic Departments > Institute of Science and the Environment
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Copyright Info: This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Depositing User: Thor-Bjorn Ottosen
Date Deposited: 05 Jul 2016 09:14
Last Modified: 11 Nov 2016 10:33
URI: https://eprints.worc.ac.uk/id/eprint/4580

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