Discovering air quality patterns in urban environments

Monitoring air quality is currently a critical issue in smart cities. Air pollution-related data are commonly acquired through sensors deployed throughout the city area. To analyze these data collections, data analytics algorithms should be combined with reporting tools for discovering critical conditions and informing citizens and municipality actors. This paper proposes a new data mining engine to discover air quality patterns from air pollution-related data. This class of patterns includes many established patterns proposed in the data mining literature. In this study, we focused on a specific type of patterns, namely the frequent weighted itemsets, to identify combinations of pollutants that are, on average, in a critical condition. To show the usefulness of the proposed approach, the proposed engine was tested on real data acquired in a major Italian city.