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Project
Local air quality monitoring strategies with heterogeneous networks of low-cost and high quality mobile sensors
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.
Date:1 Jan 2012 → 30 Sep 2016
Keywords:mobile monitoring, Black Carbon, machine learning, urban air quality, low-cost sensors, spatial interpolation
Disciplines:Computer architecture and networks, Applied mathematics in specific fields, Theoretical computer science, Cognitive science and intelligent systems, Other information and computing sciences, Information sciences, Distributed computing, Scientific computing, Sustainable and environmental engineering, Artificial intelligence, Programming languages, Information systems, Visual computing