ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume VI-4/W2-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4/W2-2020, 119–126, 2020
https://doi.org/10.5194/isprs-annals-VI-4-W2-2020-119-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., VI-4/W2-2020, 119–126, 2020
https://doi.org/10.5194/isprs-annals-VI-4-W2-2020-119-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Sep 2020

15 Sep 2020

APPLICATION OF DISTRIBUTED URBAN SENSOR NETWORKS FOR ACTIONABLE AIR QUALITY DATA

E. Morris1, X. Liu1, A. Manwar1, D. Y. Zang1, G. Evans2, J. Brook2, B. Rousseau2, C. Clark2, and J. MacIsaac3 E. Morris et al.
  • 1A.U.G. Signals Ltd., Toronto, Canada
  • 2Southern Ontario Centre for Atmospheric Aerosol Research, Department of Chemical Engineering & Applied Chemistry, University of Toronto, Toronto, Canada
  • 3Office of the CAO, City of Oshawa, Canada

Keywords: Ambient Air Pollution, Air Quality Monitoring, Distributed Sensor Network, Smart Cities, Internet of Things

Abstract. Ambient air pollution continues to be a major human health burden around the world. Cities with existing smart data infrastructure, and those with smart city aspirations, would benefit from the integration of real-time data from an air quality sensor network. AirSENCE™ is one such sensor which monitors eight common pollutants at low cost. It has been deployed in Canada in cities of the Greater Toronto Area, e.g. the City of Oshawa, to augment the existing urban data network and study the impacts of traffic flow and land usage on air quality. Results reveal that distributed sensors are highly useful for detecting localized pollution events that would otherwise go undetected, providing policymakers with a valuable, actionable data for protecting public health. Coupling air quality sensors with other smart city data (traffic monitors in this case) was shown to provide a more comprehensive representation of how air pollutant levels are affected by human activity, which can better inform city planning decisions.