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Article

Development of Drone-Mounted Multiple Sensing System with Advanced Mobility for In Situ Atmospheric Measurement: A Case Study Focusing on PM2.5 Local Distribution

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Faculty of Software and Information Science, Iwate Prefectural University, Takizawa 020-0693, Japan
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Faculty of Systems Science and Technology, Akita Prefectural University, Yurihonjo 015-0055, Japan
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Faculty of Bioresource Sciences, Akita Prefectural University, Akita 010-0195, Japan
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College of Agriculture, Food and Environment Sciences, Rakuno Gakuen University, Ebetsu 069-0851, Japan
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Graduate School of International Resource Sciences, Akita University, Akita 010-8502, Japan
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Institute of Engineering Innovation, Graduate School of Engineering, The University of Tokyo, Tokyo 113-8656, Japan
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Author to whom correspondence should be addressed.
Academic Editor: Giuseppe Di Stefano
Sensors 2021, 21(14), 4881; https://doi.org/10.3390/s21144881
Received: 18 May 2021 / Revised: 8 July 2021 / Accepted: 14 July 2021 / Published: 17 July 2021
(This article belongs to the Special Issue Drone Sensing and Imaging for Environment Monitoring)
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 PM2.5 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 PM2.5 trends obtained using long short-term memory (LSTM) networks trained using the respective datasets. View Full-Text
Keywords: PM2.5; drone; in situ atmospheric measurement; LoRa; long short-term memory PM2.5; drone; in situ atmospheric measurement; LoRa; long short-term memory
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MDPI and ACS Style

Madokoro, H.; Kiguchi, O.; Nagayoshi, T.; Chiba, T.; Inoue, M.; Chiyonobu, S.; Nix, S.; Woo, H.; Sato, K. Development of Drone-Mounted Multiple Sensing System with Advanced Mobility for In Situ Atmospheric Measurement: A Case Study Focusing on PM2.5 Local Distribution. Sensors 2021, 21, 4881. https://doi.org/10.3390/s21144881

AMA Style

Madokoro H, Kiguchi O, Nagayoshi T, Chiba T, Inoue M, Chiyonobu S, Nix S, Woo H, Sato K. Development of Drone-Mounted Multiple Sensing System with Advanced Mobility for In Situ Atmospheric Measurement: A Case Study Focusing on PM2.5 Local Distribution. Sensors. 2021; 21(14):4881. https://doi.org/10.3390/s21144881

Chicago/Turabian Style

Madokoro, Hirokazu, Osamu Kiguchi, Takeshi Nagayoshi, Takashi Chiba, Makoto Inoue, Shun Chiyonobu, Stephanie Nix, Hanwool Woo, and Kazuhito Sato. 2021. "Development of Drone-Mounted Multiple Sensing System with Advanced Mobility for In Situ Atmospheric Measurement: A Case Study Focusing on PM2.5 Local Distribution" Sensors 21, no. 14: 4881. https://doi.org/10.3390/s21144881

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