Thesis subject

MSc thesis topic: Spatio-temporal analysis of fine particulate matter (PM2.5) sensor data acquired by citizen scientists

“Smart Emission” was 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 was 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 was 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 aimed 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).

An important aspect in the second phase of the “Smart Emission” project was to evaluate the quality of PM2.5 (fine particulate matter) sensor data in combination with model output to assess fitness-for-purpose. Under different weather conditions, two experiments were conducted in which citizens living in Lent (part of the municipality of Nijmegen) walked through their neighbourhood while collecting data with low-cost PM2.5 sensors. Since at least a year, a few residents have also been collecting data using stationary, better-quality MP2.5 sensors. Apart from these sensor data, there is data from nearby RIVM reference stations. Additional research efforts are needed for calibrating the citizen sensor data and for using and interpreting these data for near real time warning purposes.


  • Learn about PM2.5 pollution and PM2.5 sensors
  • Calibrate low-cost PM2.5 sensor data using data from reference stations
  • Interpolate PM2.5 data in space and time
  • Interpret PM2.5 maps Assess citizen PM2.5 information needs
  • Potentially implement a local PM2.5 portal


  • 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.
  • Giordano, M.R., Malings, C., et al., 2021. From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors, Journal of Aerosol Science, 105833.
  • 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.


  • Experience with programming in R

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