Development of Drone-Mounted Multiple Sensing System with Advanced Mobility for In Situ Atmospheric Measurement: A Case Study Focusing on PM_2.5 Local Distribution

• Main Authors: Hirokazu Madokoro, Osamu Kiguchi, Takeshi Nagayoshi, Takashi Chiba, Makoto Inoue, Shun Chiyonobu, Stephanie Nix, Hanwool Woo, Kazuhito Sato
• Format: Article
• Language: English
• Published: MDPI AG 2021-07-01
• Series: Sensors
• Subjects: PM_2.5; drone; in situ atmospheric measurement; LoRa; long short-term memory
• Online Access: https://www.mdpi.com/1424-8220/21/14/4881

This study was conducted using a drone with advanced mobility to develop a unified sensor and communication system as a new platform for in situ atmospheric measurements. As a major cause of air pollution, particulate matter (PM) has been attracting attention globally. We developed a small, lightweight, simple, and cost-effective multi-sensor system for multiple measurements of atmospheric phenomena and related environmental information. For in situ local area measurements, we used a long-range wireless communication module with real-time monitoring and visualizing software applications. Moreover, we developed four prototype brackets with optimal assignment of sensors, devices, and a camera for mounting on a drone as a unified system platform. Results of calibration experiments, when compared to data from two upper-grade PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></semantics></math></inline-formula> sensors, demonstrated that our sensor system followed the overall tendencies and changes. We obtained original datasets after conducting flight measurement experiments at three sites with differing surrounding environments. The experimentally obtained prediction results matched regional PM<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></semantics></math></inline-formula> trends obtained using long short-term memory (LSTM) networks trained using the respective datasets.