Mawrence Raphael, Munniks Sandra, Valente João
Laboratory of Geo-Information Sciences and Remote Sensing at Wageningen University & Research (WUR), Droevendaalsesteeg 3, 6708 PB Wageningen, The Netherlands.
Wageningen Food Safety Research, Akkermaalsbos 2, 6708 WB Wageningen, The Netherlands.
Sensors (Basel). 2020 Dec 20;20(24):7332. doi: 10.3390/s20247332.
For years, urban air quality networks have been set up by private organizations and governments to monitor toxic gases like NO. However, these networks can be very expensive to maintain, so their distribution is usually widely spaced, leaving gaps in the spatial resolution of the resulting air quality data. Recently, electrochemical sensors and their integration with unmanned aerial vehicles (UAVs) have attempted to fill these gaps through various experiments, none of which have considered the influence of a UAV when calibrating the sensors. Accordingly, this research attempts to improve the reliability of NO measurements detected from electrochemical sensors while on board an UAV by introducing rotor speed as part of the calibration model. This is done using a DJI Matrice 100 quadcopter and Alphasense sensors, which are calibrated using regression calculations in different environments. This produces a predictive r-squared up to 0.97. The sensors are then calibrated with rotor speed as an additional variable while on board the UAV and flown in a series of flights to evaluate the performance of the model, which produces a predictive r-squared up to 0.80. This methodological approach can be used to obtain more reliable NO measurements in future outdoor experiments that include electrochemical sensor integration with UAV's.
多年来，私人组织和政府都已建立城市空气质量监测网络来监测诸如一氧化氮等有毒气体。然而，维护这些网络的成本可能非常高昂，所以其分布通常较为稀疏，导致所得空气质量数据的空间分辨率存在空白。最近，电化学传感器及其与无人机（UAV）的集成试图通过各种实验来填补这些空白，但在对传感器进行校准时，均未考虑无人机的影响。因此，本研究试图通过在校准模型中引入旋翼速度来提高无人机搭载的电化学传感器检测一氧化氮的可靠性。这是通过使用大疆经纬M100四轴飞行器和阿尔法Sense传感器来完成的，这些传感器在不同环境下通过回归计算进行校准，得到的预测决定系数高达0.97。然后在无人机上以旋翼速度作为额外变量对传感器进行校准，并进行一系列飞行以评估模型的性能，得到的预测决定系数高达0.80。这种方法可用于在未来涉及电化学传感器与无人机集成的户外实验中获得更可靠的一氧化氮测量结果。
