Characterization of odour emissions in a wastewater treatment plant using a drone-based chemical sensor system

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 sen-sors. 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 dy-namic olfactometry. The feasibility of the proposed system is assessed in a WWTP in Spain through several measure-ment 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 per-formance 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.

Automated summary

This study proposes a method to monitor and map odor emissions from wastewater treatment plants using a small drone equipped with a lightweight electronic nose. The drone analyzes air samples in real-time using an array of gas sensors and machine learning algorithms. The feasibility of the proposed system is demonstrated through measurement campaigns in a WWTP in Spain. The comparison of electronic nose predictions with dynamic olfactometry measurements shows a negligible bias and 95% limits of agreement within a factor of four.