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Characterizing Air-Quality Data Through Unsupervised Analytics Methods

  • Elena Daraio
  • Evelina Di Corso
  • Tania Cerquitelli
  • Silvia Chiusano
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 909)

Abstract

Several cities have built on-the-ground air quality monitoring stations to measure daily concentration of air pollutants, like \(\textit{PM}_{10}\) and \(\textit{NO}_{2}\). 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.

Keywords

Data mining Data exploration Pollutant data Meteorological data Sensor data 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Elena Daraio
    • 1
  • Evelina Di Corso
    • 1
  • Tania Cerquitelli
    • 1
  • Silvia Chiusano
    • 2
  1. 1.Dipartimento di Automatica e InformaticaPolitecnico di TorinoTurinItaly
  2. 2.Dipartimento Interateneo di Scienze, Progetto e Politiche del TerritorioPolitecnico di TorinoTurinItaly

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