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

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.