A parameter estimation and identifiability analysis methodology applied to a street canyon air pollution model

Thor Bjørn Ottosen, Matthias Ketzel, Henrik Skov, Ole Hertel, Jørgen Brandt, Konstantinos Kakosimos

Research output: Contribution to journalArticle

7 Citations (Scopus)

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 (OSPM®). 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 successfully 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.

Original languageEnglish
Pages (from-to)165-176
Number of pages12
JournalEnvironmental Modelling and Software
Volume84
DOIs
Publication statusPublished - 1 Oct 2016

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Keywords

  • Data splitting
  • Exploratory data analysis
  • Matlab
  • OSPM
  • Sensitivity
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Environmental Engineering
  • Ecological Modelling

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