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Articles | Volume XLIV-4/W2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W2-2020, 37–43, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W2-2020-37-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIV-4/W2-2020, 37–43, 2020
https://doi.org/10.5194/isprs-archives-XLIV-4-W2-2020-37-2020

  15 Sep 2020

15 Sep 2020

AIR QUALITY MONITORING AND DATA MANAGEMENT IN GERMANY – STATUS QUO AND SUGGESTIONS FOR IMPROVEMENT

L. Petry1, H. Herold1, G. Meinel1, T. Meiers3, I. Müller2, E. Kalusche2, T. Erbertseder2, H. Taubenböck2, E. Zaunseder3, V. Srinivasan3, A. Osman3, B. Weber4, S. Jäger5, C. Mayer6, and C. Gengenbach7 L. Petry et al.
  • 1Leibniz Institute of Ecological Urban and Regional Development (IOER), Dresden, Germany
  • 2German Remote Sensing Data Center, German Aerospace Center (DLR), Weßling-Oberpfaffenhofen, Germany
  • 3Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute (HHI), 10587 Berlin, Germany
  • 4Hof University of Applied Sciences (HSH), 95028 Hof, Germany
  • 5Geomer GmbH, Heidelberg, Germany
  • 6Meggsimum, Mutterstadt, Germany
  • 7Software AG, Darmstadt, Germany

Keywords: Monitoring, Air Quality, Smart Data, Co-Design

Abstract. This paper proposes a novel approach to facilitate air quality aware decision making and to support planning actors to take effective measures for improving the air quality in cities and regions. Despite many improvements over the past decades, air pollutants such as particulate matter (PM), nitrogen dioxide (NO2) and ground-level ozone (O3) pose still one of the major risks to human health and the environment. Based on both a general analysis of the air quality situation and regulations in the EU and Germany as well as an in-depth analysis of local management practices requirements for better decision making are identified. The requirements are used to outline a system architecture following a co-design approach, i.e., besides scientific and industry partners, local experts and administrative actors are actively involved in the system development. Additionally, the outlined system incorporates two novel methodological strands: (1) it employs a deep neural network (DNN) based data analytics approach and (2) makes use of a new generation of satellite data, namely Sentinel-5 Precursor (Sentinel-5P). Hence, the system allows for providing areal and high-resolution (e.g., street-level) real-time and forecast (up to 48 hours) data to inform decision makers for taking appropriate short-term measures, and secondly, to simulate air quality under different planning options and long-term actions such as modified traffic flows and various urban layouts.