Aerial Mapping of Odorous Gases in a Wastewater Treatment Plant Using a Small Drone

Quartz-enhanced photoacoustic spectroscopy (QEPAS) and Beat Frequency-QEPAS techniques for air pollutants detection: A comparison in terms of sensitivity and acquisition time

In this work, a comparison between Quartz Enhanced Photoacoustic Spectroscopy (QEPAS) and Beat Frequency-QEPAS (BF-QEPAS) techniques for environmental monitoring of pollutants is reported. A spectrophone composed of a T-shaped Quartz Tuning Fork (QTF) coupled with resonator tubes was employed as a detection module. An interband cascade laser has been used as an exciting source, allowing the targeting of two NO absorption features, located at 1900.07 cm^-1 and 1900.52 cm^-1, and a water vapor absorption feature, located at 1901.76 cm^-1. Minimum detection limits of 90 ppb and 180 ppb were achieved with QEPAS and BF-QEPAS techniques, respectively, for NO detection. The capability to detect multiple components in the same gas mixture using BF-QEPAS was also demonstrated.

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 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.

Odors Emitted from Biological Waste and Wastewater Treatment Plants: A Mini-Review

In recent decades, a new generation of waste treatment plants based on biological treatments (mainly anaerobic digestion and/or composting) has arisen all over the world. These plants have been progressively substituted for incineration facilities and landfills. Although these plants have evident benefits in terms of their environmental impact and higher recovery of material and energy, the release into atmosphere of malodorous compounds and its mitigation is one of the main challenges that these plants face. In this review, the methodology to determine odors, the main causes of having undesirable gaseous emissions, and the characterization of odors are reviewed. Finally, another important topic of odor abatement technologies is treated, especially those related to biological low-impact processes. In conclusion, odor control is the main challenge for a sustainable implementation of modern waste treatment plants.

Toward UAV-based methane emission mapping of Arctic terrestrial ecosystems

Methane is an important greenhouse gas, and emissions are expected to rise in Arctic wetland ecosystems when temperatures increase due to climate change. However, current emission estimates are associated with large uncertainties because methane shows high spatial variability. A central problem is that existing methods are often spatially restricted due to limitations in access, cost, power availability, and in need of high maintenance levels. Our study explores how a setup consisting of an unmanned aerial vehicle and a high-precision trace gas analyzer can complement well-established methods, like mobile flux chambers and eddy covariance towers, by providing independent maps of spatial variability in emissions at the landscape scale. In Zackenberg Valley, Northeast Greenland, we mapped concentration measurements from a high-precision trace gas analyzer with a reported precision of 0.6 parts per billion in a high-Arctic tundra fen ecosystem. We connected the analyzer via a long tube to a consumer-grade quadcopter, finding that the combined setup could differentiate near-surface methane concentrations of less than 5 parts per billion within a few meters under favorable weather conditions. Five of ten campaigns showed that relative methane concentration hot spots and cold spots significantly correlated with areas showing relatively high and low emissions (ranging from 1.40 to 7.4 mg m^-2 h^-1) during study campaigns in previous years. Concurrent measurements in a stationary automated chamber setup showed comparatively low methane emissions (~0.1 to 3.9 mg m^-2 h^-1) compared to previous years, indicating that a further improved UAV-analyzer setup could demonstrate clear differences in an ecosystem where methane emissions are generally higher. Calm conditions with some degree of air mixing near the surface were best suited for the mapping. Windy and wet conditions should be avoided, both for the reliability of the mapping and for safely navigating the unmanned aerial vehicle.

Motion planning of unmanned aerial vehicles in dynamic 3D space: a potential force approach

This research focuses on a collision-free real-time motion planning system for unmanned aerial vehicles (UAVs) in complex three-dimensional (3D) dynamic environments based on generalized potential force functions. The UAV must survive in such a complex heterogeneous environment while tracking a dynamic target and avoiding multiple stationary or dynamic obstacles, especially at low hover flying conditions. The system framework consists of two parts. The first part is the target tracking part employing a generalized extended attractive potential force into 3D space. In contrast, the second part is the obstacle avoidance part employing a generalized extended repulsive potential force into 3D space. These forces depend on the relative position and relative velocity between the UAV and respective obstacles. As a result, the UAV is attracted to a moving or stationary target and repulsed away from moving or static obstacles simultaneously in 3D space. Accordingly, it changes its altitude and projected planner position concurrently. A real-time implementation for the system is conducted in the SPACE laboratory to perform motion planning in 3D space. The system performance is validated in real-time experiments using three platforms: two parrot bebop drones and one turtlebot robot. The pose information of the vehicles is estimated using six Vicon cameras during real-time flights. The demonstrated results show the motion planning system’s effectiveness. Also, we propose a successful mathematical solution of the local minima problem associated with the potential field method in both stationary and dynamic environments.

RHINOS: A lightweight portable electronic nose for real-time odor quantification in wastewater treatment plants

Quantification of odor emissions in wastewater treatment plants (WWTPs) is key to minimize odor impact to surrounding communities. Odor measurements in WWTPs are usually performed via either expensive and discontinuous olfactometry hydrogen sulfide detectors or via fixed electronic noses. We propose a portable lightweight electronic nose specially designed for real-time odor monitoring in WWTPs using small drones. The so-called RHINOS e-nose allows odor measurements with high spatial resolution, and its accuracy is only slightly worse than that of dynamic olfactometry. The device has been calibrated using odor samples collected in a WWTP in Spain over a period of six months and validated in the same WWTP three weeks after calibration. The promising results obtained support the suitability of the proposed instrument to identify the odor sources having the highest emissions, which may give a useful indication to the plant managers as regards odor control and abatement.

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A portable e-nose for real time odor quantification according to EN13725 is described

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The e-nose is installed on a small drone for dense spatial measurements

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The e-nose is demonstrated and validated in a wastewater treatment plant

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Errors in odor quantification are only slightly worse than dynamic olfactometry

The Multi-Gas Sensor for Remote UAV and UGV Missions-Development and Tests

In this article, we present a versatile gas detector that can operate on an unmanned aerial vehicle (UAV) or unmanned ground vehicle (UGV). The device has six electrochemical modules, which can be selected to measure specific gases, according to the mission requirements. The gas intake is realized by a miniaturized vacuum pump, which provides immediate gas distribution to the sensors and improves a fast response. The measurement data are sent wirelessly to the operator’s computer, which continuously stores results and presents them in real time. The 2 m tubing allows measurements to be taken in places that are not directly accessible to the UGV or the UAV. While UAVs significantly enhanced the versatility of sensing applications, point gas detection is challenging due to the downwash effect and gas dilution produced by the rotors. In our work, we demonstrated the method of downwash effect reduction at aerial point gas measurements by applying a long-distance probe, which was kept between the UAV and the examined object. Moreover, we developed a safety connection protecting the UAV and sensor in case of accidental jamming of the tubing inside the examined cavity. The methods presented provide an effective gas metering strategy using UAVs.