@phdthesis{8085027,
  abstract     = {{Exposure to air pollution has a severe impact on human health. Especially in urban areas, where most of the European population lives and which are typically hot-spots of air pollution, a lot of people are exposed to air pollution. However, the urban environment shows a high variability in air pollutant concentrations and available data are often lacking to accurately estimate the actual concentration levels citizens are exposed to.
The emergence of lower-cost and portable sensors makes it possible to perform mobile measurements and to collect additional data at locations where stationary measurements are lacking. Further, this also makes it possible to engage citizens in participatory monitoring techniques. However, several issues on spatial and temporal representativeness can interfere with the real-life applicability of mobile monitoring. This thesis presents the possibilities and challenges of the use of mobile data to map the urban air quality.
Based on an extensive targeted campaign, it is shown that mobile monitoring is a suitable approach to map the urban air quality at a high spatial resolution when using a carefully developed methodology. However, a large number of repeated measurements are still required to obtain representative results. A possible way to gather a large number of measurements is to make use of people’s common daily routines to move measurement devices around, which is defined as opportunistic measurements. An example case study with the collaboration of the city wardens of Antwerp is presented in this thesis. Mobile monitoring typically does not yet result in city-wide pollution maps. Based on the data, regression models can be built to predict the concentration levels at other locations. The results highlighted the potential to construct near-real-time pollution maps that can be used for providing personalized information about air quality to citizens.}},
  author       = {{Van den Bossche, Joris}},
  isbn         = {{9789059899193}},
  keywords     = {{black carbon,urban air quality,spatial variation,land use regression,mobile monitoring}},
  language     = {{eng}},
  pages        = {{XVIII, 267}},
  publisher    = {{Ghent University. Faculty of Bioscience Engineering}},
  school       = {{Ghent University}},
  title        = {{Towards high spatial resolution air quality mapping : a methodology to assess street-level exposure based on mobile monitoring}},
  year         = {{2016}},
}