Semantic Data Layers in Air Quality Monitoring for Smarter Cities

Air pollution is one of the key indicators for quality of life in urban environments, and is also the subject of global health concern, given the number of mortal diseases associated to exposure to pollutants. Assessing and monitoring air quality is an important step in order to better understand the impact of pollution on the health of the population. Nevertheless, in order to scale to the city level, traditional high-quality stationary sensing stations are not enough. Limitations include lack of coverage, the cost of deployment and maintenance, as well as the resolution of the observed phenomena. The OpenSense2 project aims at providing a city-level sensing deployment that combines different levels of air quality sensing: reference stations, mobile sensing on public transportation, and participatory crowdsensing. In this paper we highlight some of the key challenges of managing the data captured by such infrastructure, taking the city of Lausanne as a driving use-case. Furthermore, we present a semantics-based approach for characterizing and exposing the air quality data, so that it can be made available to citizens and application developers in a way that it can be usable and understood effectively.