6 October 2017 SmartAQnet: remote and in-situ sensing of urban air quality
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Proceedings Volume 10424, Remote Sensing of Clouds and the Atmosphere XXII; 104240C (2017); doi: 10.1117/12.2282698
Event: SPIE Remote Sensing, 2017, Warsaw, Poland
Abstract
Air quality and the associated subjective and health-related quality of life are among the important topics of urban life in our time. However, it is very difficult for many cities to take measures to accommodate today’s needs concerning e.g. mobility, housing and work, because a consistent fine-granular data and information on causal chains is largely missing. This has the potential to change, as today, both large-scale basic data as well as new promising measuring approaches are becoming available. The project “SmartAQnet”, funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), is based on a pragmatic, data driven approach, which for the first time combines existing data sets with a networked mobile measurement strategy in the urban space. By connecting open data, such as weather data or development plans, remote sensing of influencing factors, and new mobile measurement approaches, such as participatory sensing with low-cost sensor technology, “scientific scouts” (autonomous, mobile smart dust measurement device that is auto-calibrated to a high-quality reference instrument within an intelligent monitoring network) and demand-oriented measurements by light-weight UAVs, a novel measuring and analysis concept is created within the model region of Augsburg, Germany. In addition to novel analytics, a prototypical technology stack is planned which, through modern analytics methods and Big Data and IoT technologies, enables application in a scalable way.
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Matthias Budde, Till Riedel, Michael Beigl, Klaus Schäfer, Stefan Emeis, Josef Cyrys, Jürgen Schnelle-Kreis, Andreas Philipp, Volker Ziegler, Hans Grimm, Thomas Gratza, "SmartAQnet: remote and in-situ sensing of urban air quality", Proc. SPIE 10424, Remote Sensing of Clouds and the Atmosphere XXII, 104240C (6 October 2017); doi: 10.1117/12.2282698; http://dx.doi.org/10.1117/12.2282698
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KEYWORDS
Sensors

Data modeling

Aerosols

Measurement devices

Climatology

Environmental sensing

Remote sensing

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