Student information

MSc thesis subject: Smart Emission: fitness-for-purpose and geostatistical analysis of sensor data

“Smart Emission” is a research project executed by a consortium of Dutch knowledge institutes, government, (ICT- and sensor) companies together with citizens in the city of Nijmegen. The goal of the project is to monitor, visualize and communicate a real-time, fine-grained “environmental footprint” of the city. For that reason, an innovative set of low-cost outdoor sensors and a related Open Geo Data infrastructure were developed. Simultaneously, a participatory process is organized to collaborate with citizens and consortium professionals with the shared purpose of “collective sense-making”. The future vision is to combine bottom-up and top-down communication and governance for the purpose of increasing urban health. The project consortium aims to innovate and learn about low-cost sensors, shared citizen science in an urban setting, and Open Data applications through standardizing the data models and dataflow (according to Inspire and OGC standards).

A particular aspect in the second phase of the “Smart Emission” project is to evaluate the quality of PM2.5 (fine particulate matter) sensor data in combination with model output to assess fitness-for-purpose. Optimal sensor placement in space and time will be assessed using the concept of expected value of information by WUR, in cooperation with the project team. The first step concerns developing and implementing a geostatistical approach in which sensor data are optimally combined with model output (e.g. from a Land Use Regression model) to produce spatially and temporally explicit PM2.5 levels. The second step consists of an experiment with citizens moving through a PM2.5 field and experiencing the relevance of sensor measurements in space and time by means of an app. This thesis topic will contribute to those efforts.

Objectives (choose from)

  • Deterministic modelling of PM2.5 diffusion
  • Combine model output and measurement data using geostatistical approach
  • Assess best locations for sensor measurements Demonstrate procedures in a (web) tool
  • To be discussed ...


  • Carton, L.J. & Ache, P.M. (2017). Citizen-sensor-networks to confront government decision-makers: Two lessons from the Netherlands. Journal of Environmental Management, 196, 234-251.
  • de Bruin, S., Ballari, D., Bregt, A.K. (2010) Where and when should sensors move? Sampling using the expected value of information. Sensors (Switzerland), 12 (12), pp. 16274-16290.
  • Heuvelink, G.B.M., Jiang, Z., de Bruin, S., Twenhöfel, C.J.W. (2010) Optimization of mobile radioactivity monitoring networks. International Journal of Geographical Information Science, 24 (3), pp. 365-382.
  • Volten, H., Devilee, J., Apituley, A., Carton, L.J., Grothe, M., Keller, C., Kresin, F., Land-Zandstra, A., Noordijk, E., Putten, E. van, Rietjes, J., Snik, F., Tielemans, E., Vonk, J., Voogt, M. & Wesseling, J. (2016). Citizen science with small sensor networks. Collaboration between a Dutch EPA (RIVM) and local initiatives. In A. Bonn, S. Hecker, M. Haklay, L. Robinson, J. Vogel & K. Vohland (Eds.), ECSA book 2016. Citizen Science, Innovation in Open Science, Society and Policy. Berlin: European Citizen Science Association.
  • Smartemission


  • Strong analytical skills (including statistical analysis)
  • Scripting skills

Theme(s): Sensing & measuring, Modelling & visualisation