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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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 |
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author | Madokoro, Hirokazu Kiguchi, Osamu Nagayoshi, Takeshi Chiba, Takashi Inoue, Makoto Chiyonobu, Shun Nix, Stephanie Woo, Hanwool Sato, Kazuhito |
author_facet | Madokoro, Hirokazu Kiguchi, Osamu Nagayoshi, Takeshi Chiba, Takashi Inoue, Makoto Chiyonobu, Shun Nix, Stephanie Woo, Hanwool Sato, Kazuhito |
author_sort | Madokoro, Hirokazu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8309946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83099462021-07-25 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 Madokoro, Hirokazu Kiguchi, Osamu Nagayoshi, Takeshi Chiba, Takashi Inoue, Makoto Chiyonobu, Shun Nix, Stephanie Woo, Hanwool Sato, Kazuhito Sensors (Basel) Article 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. MDPI 2021-07-17 /pmc/articles/PMC8309946/ /pubmed/34300619 http://dx.doi.org/10.3390/s21144881 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Madokoro, Hirokazu Kiguchi, Osamu Nagayoshi, Takeshi Chiba, Takashi Inoue, Makoto Chiyonobu, Shun Nix, Stephanie Woo, Hanwool Sato, Kazuhito 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309946/ https://www.ncbi.nlm.nih.gov/pubmed/34300619 http://dx.doi.org/10.3390/s21144881 |
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