Sensitive and fast greenhouse gas mapping from a drone
|Coordinator||Linköpings universitet - Tema Miljöförändring|
|Funding from Vinnova||SEK 1 287 095|
|Project duration||May 2018 - June 2019|
|Call||Drones of the future - Drones for citizens and community|
Purpose and goal
We have developed a very sensitive method for mapping of the greenhouse gas methane from a drone, where concentrations can be measured with an accuracy of 1 ppb (parts-per-billion). This, comined with in-situ wind speed and direction measurements (using an anemometer at the top of a carbon fibre rod mounted on the drone, far from its propellers). This way a sensitivity is reached that previously was only obtainable in lab conditions using instruments weighing tens of kilos. Emissions can be mapped over large areas and in complex industrial environments that are otherwise inaccessable.
Expected results and effects
Test-measurements of controlled methane releases have shown that the high sensitivity is reached in real conditions and that the method is robust with negligible interference from water wapor. Methane emissions are calculated using mass balance by flying back and forth covering an area oriented perpendicular to the wind direction. Many parameters are logged, the most important being methane concentration, wind speed and direction. Data is also sent to a laptop on the ground i real time to allow decisions before mapping starts and for the possibility to detect unknown emissions.
Planned approach and implementation
We started the project by surveying and testing different methane sensors regarding sensitivity, response time, and disturbance from other parameters such as water vapor and air temperature. The selected sensor weighs 1.4 kg and was integrated, with other sensors, a data logger, and a transmitter on a DJI Matrice 210 (allowing payloads over 2 kg). We developed a method for carrying out measuremens and flux calculations. An important factor is that wind speeds are measured on the drone instead of at the ground level. We have optimized the system and the real-time visualization.