- Atmospheric Air Pollution Monitoring using Flying Robots
IOE Conference Series: Materials Science and Engineering, October 2020
Product: Air Quality Recording Drone
Environmental Impact: Detects and records pollutants in the air
Planetary Boundary: Atmospheric Particle Pollution
Keywords: Flying robotic equipment; Mobile gas analyzers; Atmospheric Particle Pollution; UAV; Drone; Responsive solution.
Description: The study conducted and described in this report focuses on detecting air pollution with a given accuracy using a mobile instrument platform (MIP) with an onboard gas analyzer. In the end, the paper proposes an algorithm for determining the coordinates of a CO concentration source using the MIP. - Autonomous Monitoring, Analysis, and Countering of Air Pollution using Environmental Drones
Heliyon Journal, January 2020
Product: Pollution Measuring Drone
Environmental Impact: Measures pollution concentration in the atmosphere
Planetary Boundary: Atmospheric Particle Pollution
Keywords: Automatic air pollution monitoring, and measurement, Pollution abatement, Air quality health index, Atmospheric Particle Pollution, Aerial robotics, UAV, Drone, Preventive solution, Responsive solution
Description: This paper investigates large-scale air pollution elimination; Environmental Drones (or E-drones) can autonomously monitor the air quality of a specified location. If they detect that there are pollutants above the recommended threshold, the E-drones then implement “on-board pollution abatement solutions” for those pollutants. - Using UAV-Based Systems to Monitor Air Pollution in Areas with Poor Accessibility
Journal of Advanced Transportation, August 2017
Product: Pollution Measuring Drone
Environmental Impact: Monitors levels of atmospheric particle pollution
Planetary Boundary: Atmospheric Particle Pollution
Keywords: Automatic air pollution monitoring, and measurement, Air pollution detection, Atmospheric particle pollution, Aerial robotics, UAV, Drone; Preventive solution
Description: Proposes an algorithm to autonomously guide UAVs equipped with off-the-shelf sensors to perform air pollution monitoring tasks. The results of this research demonstrate that the algorithm drives the UAV to construct pollution maps focusing on areas where there is a higher concentration of pollutants, making the creation of these maps faster than similar strategies.