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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

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, lightwei...

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Detalles Bibliográficos
Autores principales: Madokoro, Hirokazu, Kiguchi, Osamu, Nagayoshi, Takeshi, Chiba, Takashi, Inoue, Makoto, Chiyonobu, Shun, Nix, Stephanie, Woo, Hanwool, Sato, Kazuhito
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309946/
https://www.ncbi.nlm.nih.gov/pubmed/34300619
http://dx.doi.org/10.3390/s21144881
Descripción
Sumario: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 [Formula: see text] 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 [Formula: see text] trends obtained using long short-term memory (LSTM) networks trained using the respective datasets.