A parameter estimation and identifiability analysis methodology applied to a street canyon air pollution model. (October 2016) - Ottosen, Thor-Bjørn; Ketzel, Matthias; Skov, Henrik; Hertel, Ole; Brandt, Jørgen; Kakosimos, Konstantinos E.
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. Highlights: Valuable insights into the functionality of atmospheric dispersion models. Parameter estimation can successfully balance the model biases. Advantages of the parameter estimation and identifiability analysis methodology. The frequentist approach underestimates the parameter uncertainties. Methodology with the potential way to guide future research.