Enhanced transport-related air pollution prediction through a novel metamodel approach

This research proposes a novel approach to improve the ability to forecast low frequency extreme events of transport-related pollution in urban areas using a limited input data set. The approach is based on the idea of a self-managing model, able to adapt to unexpected changes in pollution level. In more detail, for a given combination of variables, it selects the most suitable prediction model within a set of alternative air quality models, estimated for a wider range of locations and conditions. In this study, the new approach is tested for the prediction of nitrogen dioxide concentration in the United Kingdom (UK), specifically in an air quality monitoring site of the Greater Manchester Area, by comparing it with a context-specific statistical model (ARIMAX). The analysis results show that the two methods are similar in terms of global covariance and difference between observed and simulated concentrations, however the performance of the new approach in the prediction of extreme air pollution events is up to 27% better than the standard statistical approach and up to 113% better than the artificial neural network method.