Optimising citizen-driven air quality monitoring networks for cities

Shivam Gupta, Edzer Pebesma, Auriol Degbelo, Ana Cristina Costa

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
34 Downloads (Pure)


Air quality has had a significant impact on public health, the environment and eventually on the economy of countries for decades. Effectively mitigating air pollution in urban areas necessitates accurate air quality exposure information. Recent advancements in sensor technology and the increasing popularity of volunteered geographic information (VGI) open up new possibilities for air quality exposure assessment in cities. However, citizens and their sensors are put in areas deemed to be subjectively of interest (e.g., where citizens live, school of their kids or working spaces), and this leads to missed opportunities when it comes to optimal air quality exposure assessment. In addition, while the current literature on VGI has extensively discussed data quality and citizen engagement issues, few works, if any, offer techniques to fine-tune VGI contributions for an optimal air quality exposure assessment. This article presents and tests an approach to minimise land use regression prediction errors on citizen-contributed data. The approach was evaluated using a dataset (N = 116 sensors) from the city of Stuttgart, Germany. The comparison between the existing network design and the combination of locations selected by the optimisation method has shown a drop in spatial mean prediction error by 52%. The ideas presented in this article are useful for the systematic deployment of VGI air quality sensors, and can aid in the creation of higher resolution, more realistic maps for air quality monitoring in cities.

Original languageEnglish
Article number468
JournalISPRS International Journal of Geo-Information
Issue number12
Publication statusPublished - 30 Nov 2018


  • Air quality monitoring
  • Citizen engagement
  • Crowdsourcing
  • Land use regression
  • Sensor location optimisation
  • Spatial simulated annealing
  • Volunteered geographic information


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