Characterizing Air-Quality Data Through Unsupervised Analytics Methods

Several cities have built on-the-ground air quality monitoring stations to measure daily concentration of air pollutants, like PM10 and NO2. The identification of the causalities for air pollution will help governments' decision-making on mitigating air pollution and on prioritizing recommendations. This paper presents a two-level methodology based on unsupervised analytics methods, named PANDA, to discover interesting insights from air quality-related data. First, PANDA discovers groups of pollutants that have occurred with similar concentrations. Then, each cluster is locally characterized through three forms of human-readable knowledge to provide interesting correlations between air pollution and meteorological conditions at different abstraction level. As a case study, PANDA has been validated on real pollutant measurements collected in a major Italian city. Preliminary experimental results show that PANDA is effective in discovering cohesive and well-separated groups of similar concentrations of pollutants along with different forms of interpretable correlations among air pollution and weather data.