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An UAV Mounted Intelligent Monitoring System for Impromptu Air Quality Assessments

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Sensors (CNS 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 539))

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Abstract

Air quality is a source of increasing concern in several cities, due to the adverse health effects of significant pollution levels. For this reason, the need to assess the concentration of pollutants at high temporal and spatial resolution is perceived as very urgent. As such, there is a growing interest in building pervasive networks integrating different sensing technologies to achieve this capability. In this view, low cost smart sensors data could be fused together with fixed but more accurate multisensor devices and certified analyzers data to build hi-res maps (<100 m) of pollutant concentrations. Low cost UAVs are versatile platforms capable to host playloads integrating multi sensor technologies for short term, mobile monitoring tasks. The recently proposed tethered UAV platforms can be deemed as interesting solutions to build and rapidly deploy impromptu networks of air quality analyzers for mid-term environmental monitoring actions. In this work, we propose the integration of the ENEA MONICA multisensor platform as a measurement payload for the TopView SAV-ES UAV. The prototype has been flow on board of the SAV-ES platform for test flight targeting the measurement of a plume generated with a brushwood controlled fire. The functional proof of concept flight have confirmed the possibility to use MONICA as a measurement payload for the targeted platform.

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References

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Correspondence to S. De Vito .

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Carrozzo, M. et al. (2019). An UAV Mounted Intelligent Monitoring System for Impromptu Air Quality Assessments. In: Andò, B., et al. Sensors. CNS 2018. Lecture Notes in Electrical Engineering, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-030-04324-7_60

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  • DOI: https://doi.org/10.1007/978-3-030-04324-7_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04323-0

  • Online ISBN: 978-3-030-04324-7

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