Identification of long-range aerosol transport patterns to Toronto via classification of back trajectories by cluster analysis and neural network techniques

In this work, back trajectories of air masses arriving in Toronto were classified into distinct transport patterns by cluster analysis and, for the first time, by a neural network (Adaptive Resonance Theory—ART-2a). Different similarity criteria were used by the two classification techniques, the former relying on the Euclidean distances between trajectories, the latter on the Euclidean angles between trajectories. Nevertheless, both techniques provided similar conclusions as to the location of PM2.5 emission sources and the level of pollution associated with a given air transport pattern. Both techniques illustrated the cleaner nature of northerly and northwesterly transport patterns in comparison to southerly and southwesterly ones, as well as the effect of near stagnant air masses. In addition, ART-2a resolved a much larger percentage of trajectories than cluster analysis into groups with clearly identifiable transport patterns and compared favourably with cluster analysis with respect to the precision of the classification.