Objective
FLAIR aims at developing an airborne, compact and cost-effective air quality sampling sensor for sensitive and selective detection of molecular fingerprints in the 2-5 μm and 8-12 μm infrared atmospheric windows. The sensor is based on an innovative supercontinuum laser that provides ultra-bright emission across the entire spectrum of interest. Such a light source in combination with a novel type of multipass cell in conjunction with specifically developed uncooled detector arrays will ensure highly sensitive detection. Broadband single-shot 2D high resolution absorption spectra capture will allow highly selective molecular detection in complex gas mixtures in the ppbv levels in real time. This high performance sensor constitutes a breakthrough in the field of trace gas spectroscopy. Moreover, in a hybrid approach, the main spectroscopic sensor will be complemented by a fine particle detector in order to obtain a complete picture of the air quality.
Mounted on an adapted and optimized UAV (drone), the sensor will enable pervasive sensing on large scales outside urban environments where air quality monitoring remains challenging, e.g. along gas pipelines or around chemical plants. Also, FLAIR can guide emergency measures in case of chemical fires or leaks, wildfires or volcanic eruptions or even serve for oil and gas exploration or explosives related molecules detection, by far more cost-effectively than for missions on manned research aircraft. As such FLAIR provides a novel and ubiquitous tool addressing air quality related safety issues.
The sensor prototype will be tested at TRL 4 in the lab and at TRL 5 on-board a UAV in the context of a well-defined and controlled validation test setting. The project will be carried out by 3 SMEs, 1 industrial partner and 4 RTDs, covering the full value chain (development, implementation and application) of such a sensor for air quality monitoring. Business cases for commercialization routes in a global market will be provided
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsoptical sensors
- engineering and technologymechanical engineeringvehicle engineeringaerospace engineeringaircraft
- engineering and technologyenvironmental engineeringenergy and fuelsfossil energynatural gas
- engineering and technologyenvironmental engineeringair pollution engineering
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringroboticsautonomous robotsdrones
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RIA - Research and Innovation actionCoordinator
6525 XZ Nijmegen
Netherlands