Get to know Soarability’s products
Experience in person how to use Sniffer4D and Sniffer4D Mapper to obtain hyper-local air pollution information.
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Active Air Intake System Stabilizes the Airflow Inside Sniffer4D Under Different Speeds
Reduce the influence of airflow outside Sniffer4D. Allow internally mounted sensors to quickly and effectively contact the outside air to shorten the response time.
Mounted on the Top of the Drones to Reduce Turbulence from Drone Propellers
Taking flow field characteristics around drones into account, gas detectors’ intake should be installed on the top of the drones, and equipped with an active air intake system.
Local air pollutant infomation
Usually at city or district level with no altitude information and low spatial resolution. This only reveals pollutant distributions for very large areas (e.g. national level).
Hyper-local air pollutant information
High spatial resolution in both horizontal and vertical directions. This reveals variations in air pollutants street by street or even building by building.
Industry leading data quality (long-term data error <±10%) in co-location comparison with scientific grade monitoring station.
High data correlation (R²) 0.81-0.95 in co-location comparison with scientific grade monitoring station.
Efficiently and accurately locate suspected pollution sources in industrial areas, construction sites and ports.
Quickly evaluate the concentrations of pollutants and the spatial extent of contaminants in harzardous accidents.
Pipeline & Tank Inspection
Find suspected leakages in tanks and pipelines by mapping CH4, H2S and VOCs distributions.
Evaluate the environment more efficiently and reduce cost.
Help research teams easily obtain rich air quality & pollutant information in 3D.
Reveal potential anomalies in chemical plants by periodically monitoring air pollutant distributions
Make smarter management decisions using hyper-local air quality information. For example force HPEV to EV mode in heavily polluted urban areas.
Provide precision marketing to groups exposed to different air quality levels.
Use hyper-local air quality information to support decision making in daily life such as whether to open the window, skin care, travel plan etc.