Petersen, Erik

Philipp, Andreas

Redelstein, Johanna

English

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Mid-term and 1st International Networking Workshop of the SmartAQnet Project (December 2018, Munich) 6 Mobile Measurements and Applications of Smart Air Quality Networks Andreas Philipp 1, Erik Petersen 1, and Johanna Redelstein 1 1 University of Augsburg, Institute for Geography, Augsburg, Germany, E-mail: a.philipp@geo.uni-augsburg.de  Measurement networks utilizing mobile and stationary low-cost sensors for particulate matter are developing as a trend in the context of citizen science but also developed in atmospheric and en-vironmental science and for administrative interests. However, devices for mobile measurements on aerial and ground-based platforms are still under development and their operational use still quite demanding. However, also the characteristics of the produced datasets concerning the vari-ability of spatial and temporal coverage as well as reliability may be quite challenging for imple-mentation of applications based on those networks. In order to learn more about these difficulties within the SmartAQnet project [1], an experimental statistical forecast model will be developed using reference class forecasts and neuronal networks for several meteorological and urban envi-ronment predictors, addressing the temporal variability. Further a spatial traffic routing application will be developed, which allows people to decide preferred routes based on pollution and may help to avoid critical situations at pollution hot spots.  References  [1] Matthias Budde, Till Riedel, Michael Beigl, Klaus Schäfer, Stefan Emeis, Josef Cyrys, Jürgen Schnelle-Kreis, Andreas Philipp, Volker Ziegler, Hans Grimm, Thomas Gratza (2017) SmartAQnet: Remote and In-Situ Sensing of Urban Air Quality, Proc. SPIE 10424, Remote Sensing of Clouds and the Atmosphere XXII, 104240C, doi:10.1117/12.2282698   

Mobile Measurements and Applications of Smart Air Quality Networks

https://publikationen.bibliothek.kit.edu/1000093246/24904985

Mobile Measurements and Applications of Smart Air Quality Networks

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