Searching for the fine particulate matter (PM 2.5) pollutant emission source using a drone

In this research, we propose a method with three search algorithms, namely Greedy, Dynamic, and Hybrid, to exploit the ability of a drone to efficiently and accurately locate air pollution (i.e., particulate matter) emission sources. Currently, most of the existing stationed environment sensor systems can provide continuous monitoring information of air quality such as PM 2.5 (Particulate Metter 2.5) or CO₂ (carbon dioxide) but are unable to pinpoint the location of the emission source. The proposed method utilizes the PM2.5 concentration information provided by the existing sensing system to initialize the search plan by limiting a search area. Based on the initial sensing information, a drone with an onboard air quality sensor will adjust its searching direction and distance to an intermediate point. After an intermediate location point is reached, the drone will pause and sense the PM2.5 concentration at the current location. Next, the drone continues to adjust the search path with its searching direction and distance based on the sensed PM 2.5 concentration level until the emission source is located. Utilizing the information provided by both an existing sensing system and an onboard sensor, the drone is able to make correct decisions when searching for pollution sources. In the experiments, three proposed algorithms and two common search methods are compared under different settings. Experiment results showed our proposed method is able to achieve a location estimation error below 2 m within 20 min.