Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028 Barcelona, Spain; Department of Electronics and Biomedical Engineering, Universitat de Barcelona, Marti i Franqués 1, 08028 Barcelona, Spain.
Depuración de Aguas del Mediterráneo (DAM), Avenida Benjamín Franklin 21, Parque Tecnológico, Paterna 46980, Spain.
Sci Total Environ. 2022 Nov 10;846:157290. doi: 10.1016/j.scitotenv.2022.157290. Epub 2022 Jul 14.
Conventionally, odours emitted by different sources present in wastewater treatment plants (WWTPs) are measured by dynamic olfactometry, where a human panel sniffs and analyzes air bags collected from the plant. Although the method is considered the gold standard, the process is costly, slow, and infrequent, which does not allow operators to quickly identify and respond to problems. To better monitor and map WWTP odour emissions, here we propose a small rotary-wing drone equipped with a lightweight (1.3-kg) electronic nose. The "sniffing drone" sucks in air via a ten-meter (33-foot) tube and delivers it to a sensor chamber where it is analyzed in real-time by an array of 21 gas sensors. From the sensor signals, machine learning (ML) algorithms predict the odour concentration that a human panel using the EN13725 methodology would report. To calibrate and validate the predictive models, the drone also carries a remotely controlled sampling device (compliant with EN13725:2022) to collect sample air in bags for post-flight dynamic olfactometry. The feasibility of the proposed system is assessed in a WWTP in Spain through several measurement campaigns covering diverse operating regimes of the plant and meteorological conditions. We demonstrate that training the ML algorithms with dynamic (transient) sensor signals measured in flight conditions leads to better performance than the traditional approach of using steady-state signals measured in the lab via controlled exposures to odour bags. The comparison of the electronic nose predictions with dynamic olfactometry measurements indicates a negligible bias between the two measurement techniques and 95 % limits of agreement within a factor of four. This apparently large disagreement, partly caused by the high uncertainty of olfactometric measurements (typically a factor of two), is more than offset by the immediacy of the predictions and the practical advantages of using a drone-based system.
传统上，污水处理厂（WWTP）中不同来源排放的气味通过动态嗅闻法进行测量，人类嗅闻小组嗅闻并分析从工厂收集的气袋。尽管该方法被认为是金标准，但该过程成本高、速度慢且不频繁，这使得操作人员无法快速识别和响应问题。为了更好地监测和绘制 WWTP 气味排放图，我们在这里提出了一种配备轻量级（1.3 公斤）电子鼻的小型旋翼无人机。“嗅探无人机”通过一根 10 米（33 英尺）的管子吸入空气，并将其输送到传感器室，在那里由 21 个气体传感器阵列实时分析。从传感器信号中，机器学习（ML）算法预测人类小组使用 EN13725 方法报告的气味浓度。为了校准和验证预测模型，无人机还携带一个远程控制采样装置（符合 EN13725：2022），以在飞行后收集气袋中的样品空气进行动态嗅闻法。通过涵盖工厂不同运行模式和气象条件的多次测量活动，在西班牙的一个 WWTP 中评估了所提出系统的可行性。我们证明，使用在飞行条件下测量的动态（瞬态）传感器信号训练 ML 算法比传统方法（使用通过受控暴露于气味袋在实验室中测量的稳态信号）性能更好。电子鼻预测与动态嗅闻法测量的比较表明，两种测量技术之间几乎没有偏差，95%的一致性界限在四倍以内。这种明显的大分歧部分是由嗅闻测量的高不确定性（通常为两倍）引起的，但通过预测的即时性和使用基于无人机系统的实际优势得到了弥补。
