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

机译:具有先进流动性的无人驾驶多种传感系统的开发用于原位大气测量 - 以PM2.5局部分布为重点研究

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摘要

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.
机译:本研究使用具有先进移动性的无人机进行,以开发统一的传感器和通信系统作为原位大气测量的新平台。作为空气污染的主要原因,颗粒物(PM)一直在全球引起关注。我们开发了一种小型,轻巧,简单,经济高效的多传感器系统,用于多部测量大气现象和相关环境信息。对于原位本地区域测量,我们使用了远程无线通信模块,具有实时监控和可视化软件应用程序。此外,我们开发了四种原型括号,具有最佳分配传感器,设备和用于将无人机安装为统一的系统平台的相机。校准实验结果与来自两个上级PM2.5传感器的数据相比,表明我们的传感器系统遵循整体趋势和变化。在具有不同周围环境的三个地点进行飞行测量实验后,我们获得了原始数据集。实验获得的预测结果匹配使用使用相应数据集训练的长短期存储器(LSTM)网络获得的区域PM2.5趋势。

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