Efficient air pollution mapping in extensive regions with fully autonomous unmanned aerial vehicles: A numerical...

It is noteworthy that comprehensive exploration of atmospheric measurements in the horizontal plane using aerial platforms, necessitating high autonomy, has not been extensively covered in the existing literature. This research presents a systematic numerical approach to effective air pollution mapping achieved through the integration of horizontal and vertical air pollution measurements conducted using a fully autonomous unmanned aerial vehicle (UAV) platform. The developed robust navigation model enables the UAV to efficiently scan the extensive measurement area, which is subdivided into smaller sub-areas using the polygonal decomposition technique, resulting in a comprehensive map of the entire region. Furthermore, technical analysis determines the optimal flight speed, leading to air pollution measurements in up to 30 % more areas and ensuring more consistent results. The simulation results illustrate the effective mapping of the entire area by aggregating air pollution measurements from sub-areas, with seamless transitions emphasizing the accuracy and consistency of the employed air pollution mapping technique. This systematic method offers numerous advantages, including rapid air pollution source identification and swift response capabilities. Moreover, this approach holds potential for various applications, such as forest fire monitoring and natural resources assessment, by equipping UAVs with additional equipment like cameras alongside atmospheric sensors.