新兴的无人机智能城市低成本空气质量监测技术

Piyush Yadav, Tejas Porwal, Vedanta Jha, S. Indu
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引用次数: 1

摘要

空气质量是一种局部现象,也就是说,它以一种显著的方式从一个点到另一个点发生变化,因此,从稀缺的静态空气质量传感器绘制空气质量地图实际上是微不足道的。因此,使用移动源进行空气质量监测具有巨大的潜力,因为它使我们能够使用几个移动传感器对地理上广泛的区域进行时空污染测绘,并且成本几乎可以忽略地面静态传感器。无人机技术已经成为一个非常重要的平台,可以对我们周围的各种物理现象进行传感,通过在无人机上集成低成本和轻质的空气质量传感器,可以获得细粒度的时空空气质量数据,从而更好地了解污染变化并确定其来源。在这项研究中,我们设计并制造了一种固定翼太阳能无人机,具有利用太阳能进行永久飞行并实时生成空气质量数据的潜力。固定翼无人机设计被选择为这项研究由于较少的螺旋桨洗涤传感器读数和更多的飞行时间可能比四轴飞行器。该无人机系统在低空成功进行了现场测试,并在地面上生成了空气质量数据,通过由低成本OPC R1传感器、树莓派和Pixhawk飞行控制器组成的数据融合模块收集、存储和传输数据。我们对系统产生的PM 2.5数据进行时空分析,这对于识别城市地区、工业区、智慧城市的污染热点和定位秸秆焚烧地点非常有用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emerging Low-Cost Air Quality Monitoring Techniques for Smart Cities with UAV
Air quality is a local phenomenon, that is, it changes in a significant manner from point to point, thus making air quality mapping from scarce static air quality sensors, practically insignificant. Thus, air quality monitoring using mobile sources holds enormous potential as it gives us the ability to perform spatiotemporal pollution mapping of a geographically wide region using just a few mobile sensors and at a cost that is almost negligible to ground-based static sensors. Drone technology has emerged as a very important platform to do sensing of various physical phenomena around us and by integrating low-cost and light-weight air quality sensors on a UAV, it is possible to get fine-grained spatiotemporal air quality data to have a better overview of the pollution variation and locate its source of origin. In this study, we have designed and manufactured a fixed-wing solar-powered UAV, with potential to perform perpetual flight using solar power and generate air quality data in real-time. Fixed-wing UAV design was chosen for this study due to less propeller wash on sensor readings and more flight time possible than a quadcopter. This UAV system was successfully field-tested at a low altitude and air quality data was generated on the ground, collecting, storing and transmitting data through a data fusion module consisting of low-cost OPC R1 sensor, Raspberry Pi and Pixhawk Flight controller. We do spatiotemporal analysis of the generated PM 2.5 data from the system, which could be very useful to identify pollution hotspots in urban areas, industrial region, smart cities and locate stubble burning sites.
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