Local air quality monitoring strategies with heterogeneous networks of low-cost and high quality mobile sensors

01 January 2012 → 30 September 2016
Regional and community funding: various
Research disciplines
  • Natural sciences
    • Applied mathematics in specific fields
    • Artificial intelligence
    • Computer architecture and networks
    • Distributed computing
    • Information sciences
    • Information systems
    • Programming languages
    • Scientific computing
    • Theoretical computer science
    • Visual computing
    • Other information and computing sciences
  • Social sciences
    • Cognitive science and intelligent systems
  • Engineering and technology
    • Sustainable and environmental engineering
mobile monitoring Black Carbon machine learning urban air quality low-cost sensors spatial interpolation
Project description

This PhD research focuses on the development and application of an integrated methodology for air quality monitoring at a high temporal and spatial resolution. The monitoring will be performed with both high quality mobile instruments and low-cost devices, and be complemented with machine learning techniques to deal with the low-cost sensors and spatial interpolation techniques.