Publications

Electronic noses have improved in terms of reliability in the last two decades. Their design has allowed for model training and validation for predicting odour emissions and classifying odour sources. In the last few years, interest has turned to designing e-noses capable of flying in drones. Fast 3D mapping of areas where odour might become a problem, such as Wastewater Treatment Plants, Compost Plants, and Refineries, has been the main target of some recent studies. Here, a fully functional design of a drone-embedded, E-Nosebased measurement platform is presented. The design of its ENose and its fast-sampling frequency will allow for fast tracking of plumes and fast 3D odour mapping. In this contribution, a description of the system is provided in addition to preliminary measurements in the laboratory characterizing the fast response of the E-Nose to an odour stimulus.

JTD Keywords: Autonomous, Drone, E-nose, Iot, Machine learnin, Odour

Instrumental Odour Monitoring Systems are often based on gas sensor arrays, or eventually on single sensor solutions for major odorants. In this work, we investigate the use of a portable Ion Mobility Spectrometer (IMS) as an Instrumental Odour Monitoring System (IOMS) for monitoring odorous emissions from wastewater treatment plants (WWTPs). This preliminary study was carried out on two plants in Pinedo (Valencia), i.e., P1 and P2. Three field campaigns (JanuaryJune-July) captured seasonal and wastewater variations, employing chemometric analysis and Principal Component Analysis (PCA) to distinguish Water line and Sludge line emissions. Random Forest (RF) and Partial Least Squares Discriminant Analysis (PLS-DA) were used to develop an odour classification model. External validation achieved an 87% accuracy for P1 July and results for P2 January-June. These results prove the IMS potential to be used for enhanced odour emission classification, possibly in combination with other monitoring techniques.

JTD Keywords: Discrimination, Ioms, Odour classification, Pc, Pls-da, Source

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.Copyright © 2022. Published by Elsevier B.V.

JTD Keywords: calibration, chemical sensors, drone, dynamic olfactometry, electronic nose, odourquantification, olfaction, volatile organic-compounds, wwtp, Calibration, Chemical sensors, Drone, Dynamic olfactometry, Electronic nose, Environmental monitoring, Odour quantification, Olfaction, Variable selection methods, Wwtp

Wastewater treatment plants (WWTPs) are sources of greenhouse gases, hazardous air pollutants and offensive odors. These emissions can have negative repercussions in and around the plant, degrading the quality of life of surrounding neighborhoods, damaging the environment, and reducing employee’s overall job satisfaction. Current monitoring methodologies based on fixed gas detectors and sporadic olfactometric measurements (human panels) do not allow for an accurate spatial representation of such emissions. In this paper we use a small drone equipped with an array of electrochemical and metal oxide (MOX) sensors for mapping odorous gases in a mid-sized WWTP. An innovative sampling system based on two (10 m long) flexible tubes hanging from the drone allowed near-source sampling from a safe distance with negligible influence from the downwash of the drone’s propellers. The proposed platform is very convenient for monitoring hard-toreach emission sources, such as the plant’s deodorization chimney, which turned out to be responsible for the strongest odor emissions. The geo-localized measurements visualized in the form of a two-dimensional (2D) gas concentration map revealed the main emission hotspots where abatement solutions were needed. A principal component analysis (PCA) of the multivariate sensor signals suggests that the proposed system can also be used to trace which emission source is responsible for a certain measurement.

JTD Keywords: air pollution, environmental monitoring, gas sensors, industrial emissions, mapping, odour, uav, Air pollution, Drone, Environmental monitoring, Gas sensors, Industrial emissions, Mapping, Odour, Sensors, Uav

Neural coding of chemical information is still under strong debate. It is clear that, in vertebrates, neural representation in the olfactory bulb is a key for understanding a putative odour code. To explore this code, in this work we have studied a public dataset of radio images of 2-Deoxyglucose uptake (2-DG) in the olfactory bulb of rats in response to diverse odorants using univariate pixel selection algorithms: rank-products and Mann-Whitney U (MWU) test. Initial results indicate that some chemical properties of odorants preferentially activate certain areas of the rat olfactory bulb. While non-parametric test (MWU) has difficulties to detect these regions, rank-product provides a higher power of detection.

JTD Keywords: 2-Deoxyglucose uptake, Chemotopy, Feature selection, Odour coding, Olfaction, Olfactory bulb