Natural Gas Fugitive Leak Detection Using an Unmanned Aerial Vehicle: Measurement System Description and Mass Balance Approach

Progress in monitoring methane emissions from landfills using drones: an overview of the last ten years

Solid waste landfills are responsible for much of the anthropogenic methane emitted from the waste sector. The quantification of fugitive CH4 emissions from a landfill is to date characterised by high uncertainty and several methodologies have been devised to estimate emission fluxes. Unmanned Aerial Vehicles (UAVs, also known as drones) are revolutionising the way CH4 emission monitoring is conceived and offer new opportunities for quantifying emission fluxes from a landfill, mainly due to recent advances in sensor miniaturisation that make these instruments lighter and more suitable to be equipped on a drone. The paper analyses publications from the period 2014-2024 that illustrate UAV-based methods that can be used for this purpose, identifying experiences in the field and the current state of research. The review has highlighted a current research status characterised by a strong experimental focus, with few tests carried out in landfills under real emission conditions (33 % of the reviewed papers). Since 2018, there has been a growing interest in open-path sensors, tested in some controlled-release experiments according to different configurations which have given promising results, but experiences are limited and there are no experiments conducted directly in landfills. In general, the UAV-based methods identified by this systematic review are characterised by unclear uncertainties. Drones are a viable alternative to traditional monitoring methods at landfills and allow data to be acquired with a spatial and temporal resolution that can hardly be achieved by other low-cost methods. However, further studies and field trials are needed to better understand methodological aspects: especially the uncertainty of each step in the quantification process need to be properly analysed and quantified more precisely.

A Non-Source Optical Fiber Sensor for Multi-Point Methane Detection

Fast, accurate, real-time measurement of gas concentration is an important task for preventing coal mining disasters. In order to develop an accurate monitoring method for methane gas concentration at different locations in a mine environment, a non-source optical fiber sensor for multi-point methane detection has been developed in this paper. A 16-channel fiber splitter and a multi-channel time-sharing acquisition module are employed within the sensor, enabling simultaneous detection of methane gas at 16 points by a host. Furthermore, the methane sensors are connected to the monitoring host via an all-optical method, achieving non-source and long-range detection of methane. To assess the performance of the methane gas sensor, experiments were conducted to evaluate its detection range, response time, and stability. The results indicated that the average detection error was approximately 1.84% across the full range, and the response time did not exceed 10 s. The minimum detection limit of the sensor, as determined by the 1σ criteria, was obtained as 58.42 ppm. Additionally, the concentrations of methane gas measured at varying distances (1 km, 2 km, 5 km) were found to be essentially consistent over an extended period. These results suggest that the development of this non-source optical fiber sensor holds significant potential for providing a method for mine environment, multi-point online methane gas detection.

Gas concentration mapping and source localization for environmental monitoring through unmanned aerial systems using model-free reinforcement learning agents

There are three primary objectives of this work; first: to establish a gas concentration map; second: to estimate the point of emission of the gas; and third: to generate a path from any location to the point of emission for UAVs or UGVs. A mountable array of MOX sensors was developed so that the angles and distances among the sensors, alongside sensors data, were utilized to identify the influx of gas plumes. Gas dispersion experiments under indoor conditions were conducted to train machine learning algorithms to collect data at numerous locations and angles. Taguchi's orthogonal arrays for experiment design were used to identify the gas dispersion locations. For the second objective, the data collected after pre-processing was used to train an off-policy, model-free reinforcement learning agent with a Q-learning policy. After finishing the training from the training data set, Q-learning produces a table called the Q-table. The Q-table contains state-action pairs that generate an autonomous path from any point to the source from the testing dataset. The entire process is carried out in an obstacle-free environment, and the whole scheme is designed to be conducted in three modes: search, track, and localize. The hyperparameter combinations of the RL agent were evaluated through trial-and-error technique and it was found that ε = 0.9, γ = 0.9 and α = 0.9 was the fastest path generating combination that took 1258.88 seconds for training and 6.2 milliseconds for path generation. Out of 31 unseen scenarios, the trained RL agent generated successful paths for all the 31 scenarios, however, the UAV was able to reach successfully on the gas source in 23 scenarios, producing a success rate of 74.19%. The results paved the way for using reinforcement learning techniques to be used as autonomous path generation of unmanned systems alongside the need to explore and improve the accuracy of the reported results as future works.

State of the Art and Future Perspectives of Atmospheric Chemical Sensing Using Unmanned Aerial Vehicles: A Bibliometric Analysis

In recent years, unmanned aerial vehicles (UAVs) have been increasingly used to monitor and assess air quality. The interest in the application of UAVs in monitoring air pollutants and greenhouse gases is evidenced by the recent emergence of sensors with the most diverse specifications designed for UAVs or even UAVs designed with integrated sensors. The objective of this study was to conduct a comprehensive review based on bibliometrics to identify dynamics and possible trends in scientific production on UAV-based sensors to monitor air quality. A bibliometric analysis was carried out in the VOSViewer software (version 1.6.17) from the Scopus and Web of Science reference databases in the period between 2012 and 2022. The main countries, journals, scientific organizations, researchers and co-citation networks with greater relevance for the study area were highlighted. The literature, in general, has grown rapidly and has attracted enormous attention in the last 5 years, as indicated by the increase in articles after 2017. It was possible to notice the rapid development of sensors, resulting in smaller and lighter devices, with greater sensitivity and capacity for remote work. Overall, this analysis summarizes the evolution of UAV-based sensors and their applications, providing valuable information to researchers and developers of UAV-based sensors to monitor air pollutants.

Air pollution forecasting based on wireless communications: review

The development of contemporary artificial intelligence (AI) methods such as artificial neural networks (ANNs) has given researchers around the world new opportunities to address climate change and air quality issues. The small size, low cost, and low power consumption of sensors can facilitate obtaining the values of polluting gases in the atmosphere. However, several problems with using air pollution technique relate to various effects such as sensing accuracy, sensor drifts, and sluggish reactions to changes in pollution levels. Recently, machine learning has made it feasible to build a more intelligent, context-aware system that can anticipate events and monitor present conditions. This paper focuses on the use of environment sensors for detecting air pollution based on several types of wireless protocols, including Wi-Fi, Bluetooth, ZigBee, LoRa, Global Positioning System (GPS), and 4G/5G. Furthermore, it classifies previous published articles on the topic according to the wireless protocol and compared in terms of several performance metrics such as the adopted air pollution sensors, hardware platform, adopted algorithm, power consumption or power savings, and sensing accuracy. In addition, this work highlights the challenges and limitations facing drones during their mission for detecting air pollution. As a result, we suggest to build and implement at base station an intelligent system based on backpropagation (BP) neural networks, which provides flexibility to track and predict the true values of polluting gases in the atmosphere to overcome the above problems. Finally, this work addresses the advantages of using drones in the air pollution field.

Pipeline Leakage Detection Based on Secondary Phase Transform Cross-Correlation

Leaks from pipes and valves are a reputational issue in industry. Maintenance of pipeline integrity is becoming a growing challenge due to the serious socioeconomic consequences. This paper presents a secondary phase transform (PHAT) cross-correlation method to improve the performance of the acoustic methods based on cross-correlation for pipeline leakage detection. Acoustic emission signals generated by pipe leakage are first captured by the sensors at different locations, and are subsequently analyzed using the cross-correlation curve to determine whether leakage is occurring. When leakage occurs, time delay estimation (TDE) is further carried out by peak search in the cross-correlation curve between the two sensor signals. In the analysis, the proposed method calculates the secondary cross-correlation function before the PHAT operation. A sinc interpolation method is then introduced for automatic searching the peak value of the cross-correlation curve. Numerical simulations and experimental results confirm the improved performance of the proposed method for noise suppression and accurate TDE compared to the basic cross-correlation method, which may be beneficial in engineering applications.

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.

Methods for quantifying methane emissions using unmanned aerial vehicles: a review

Methane is an important greenhouse gas, emissions of which have vital consequences for global climate change. Understanding and quantifying the sources (and sinks) of atmospheric methane is integral for climate change mitigation and emission reduction strategies, such as those outlined in the 2015 UN Paris Agreement on Climate Change. There are ongoing international efforts to constrain the global methane budget, using a wide variety of measurement platforms across a range of spatial and temporal scales. The advancements in unmanned aerial vehicle (UAV) technology over the past decade have opened up a new avenue for methane emission quantification. UAVs can be uniquely equipped to monitor natural and anthropogenic emissions at local scales, displaying clear advantages in versatility and manoeuvrability relative to other platforms. Their use is not without challenge, however: further miniaturization of high-performance methane instrumentation is needed to fully use the benefits UAVs afford. Developments in the models used to simulate atmospheric transport and dispersion across small, local scales are also crucial to improved flux accuracy and precision. This paper aims to provide an overview of currently available UAV-based technologies and sampling methodologies which can be used to quantify methane emission fluxes at local scales. This article is part of a discussion meeting issue 'Rising methane: is warming feeding warming? (part 1)'.

Advanced Leak Detection and Quantification of Methane Emissions Using sUAS

Detecting and quantifying methane emissions is gaining an increasingly vital role in mitigating emissions for the oil and gas industry through early detection and repair and will aide our understanding of how emissions in natural ecosystems are playing a role in the global carbon cycle and its impact on the climate. Traditional methods of measuring and quantifying emissions utilize chamber methods, bagging individual equipment, or require the release of a tracer gas. Advanced leak detection techniques have been developed over the past few years, utilizing technologies, such as optical gas imaging, mobile surveyors equipped with sensitive cavity ring down spectroscopy (CRDS), and manned aircraft and satellite approaches. More recently, sUAS-based approaches have been developed to provide, in some ways, cheaper alternatives that also offer sensing advantages to traditional methods, including not being constrained to roadways and being able to access class G airspace (0–400 ft) where manned aviation cannot travel. This work looks at reviewing methods of quantifying methane emissions that can be, or are, carried out using small unmanned aircraft systems (sUAS) as well as traditional methods to provide a clear comparison for future practitioners. This includes the current limitations, capabilities, assumptions, and survey details. The suggested technique for LDAQ depends on the desired accuracy and is a function of the survey time and survey distance. Based on the complexity and precision, the most promising sUAS methods are the near-field Gaussian plume inversion (NGI) and the vertical flux plane (VFP), which have comparable accuracy to those found in conventional state-of-the-art methods.

Development of a low-cost UAV-based system for CH4 monitoring over oil fields

The recent booming of oil and gas production in the U.S. sparks a growing concern about greenhouse gas emissions from the petroleum industry. This study aimed to develop a low-cost UAV-based system for CH₄ monitoring over oil fields. The system consisted of an airborne sensor node, a ground station, and a laptop. The sensor node was comprised of low-cost gas sensors, a microcontroller, a LoRa wireless transceiver, a GPS module, and an SD card reader. The ground station included a microcontroller and a LoRa transceiver. Both the sensor node and the ground station were programmed using Arduino. A graphic user interface was created using LabVIEW for data logging and visualization on the laptop. A preliminary test showed that the system was capable of measuring CH₄ concentrations, along with temperature, humidity, and GPS location; while sending the real-time data to the ground station up to ∼1 km away without any loss. The system was further tested at two oil production sites in North Dakota. The results revealed a heterogeneous distribution of CH₄ over the sites due to flares and road traffic, demonstrating a potential application of the system for air quality survey and gas leakage detection. The advantage of this system lies in its light weight (∼540 g), low cost (∼$300), expandability, ease of deployment and operation. Data quality collected by the system depended on the gas sensors chosen. With advancement in sensor technologies, it is expected that the quality of monitoring data can be considerably improved in the near future.

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

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-to-reach 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.

A Simulation Framework for the Integration of Artificial Olfaction into Multi-Sensor Mobile Robots

The simulation of how a gas disperses in a environment is a necessary asset for the development of olfaction-based autonomous agents. A variety of simulators already exist for this purpose, but none of them allows for a sufficiently convenient integration with other types of sensing (such as vision), which hinders the development of advanced, multi-sensor olfactory robotics applications. In this work, we present a framework for the simulation of gas dispersal and sensing alongside vision by integrating GADEN, a state-of-the-art Gas Dispersion Simulator, with the Unity 3D, a video game development engine that is used in many different areas of research and helps with the creation of visually realistic, complex environments. We discuss the motivation for the development of this tool, describe its characteristics, and present some potential use cases that are based on cutting-edge research in the field of olfactory robotics.

Palm-Sized Laser Spectrometer with High Robustness and Sensitivity for Trace Gas Detection Using a Novel Double-Layer Toroidal Cell

A palm-sized laser spectrometer has been developed for detecting trace gases based on tunable diode laser absorption spectroscopy in combination with a novel double-layer toroidal cell. With the benefit of a homemade electronic system and compact optical design, the physical dimensions of the sensor are minimized to 24 × 15× 16 cm³. A toroidal absorption cell, with 84 reflections in 2 layers for an effective optical path length of 8.35 m, was used to enhance the absorption signals of gaseous species. A homemade electronic system was designed for implementing a distributed feedback (DFB) diode laser controller, an analog lock-in amplifier, data acquisition, and communication. Calibration-free scanned wavelength modulation spectroscopy was employed to determine the concentration of the gas and reduce the random fluctuations from electronical noise and mechanical vibration. The measurement of CH₄ in ambient air was demonstrated using a DFB laser at 1.653 μm. The rise time and fall time for renewing the gas mixture are approximately 16 and 14 s, respectively. Vibration and temperature tests have been carried out for verifying the performance of the spectrometer, and standard deviations of 0.38 ppm and 0.11 ppm for 20 ppm CH₄ at different vibration frequencies and temperatures, respectively, have been determined. According to the Allan deviation analysis, the minimum detection limit for CH₄ can reach 22 ppb at an integration time of 57.8 s. The continuous measurement of atmospheric CH₄ for 2 days validated the feasibility and robustness of our laser spectrometer, providing a promising laser spectral sensor for deploying in unmanned aerial vehicles or mobile robots.

Detection of Natural Gas Leakages Using a Laser-Based Methane Sensor and UAV

The safety of the gas transmission infrastructure is one of the main concerns for infrastructure operating companies. Common gas pipelines’ tightness control is tedious and time-consuming. The development of new methods is highly desirable. This paper focuses on the applications of air-borne methods for inspections of the natural gas pipelines. The main goal of this study is to test an unmanned aerial vehicle (UAV), equipped with a remote sensing methane detector, for natural gas leak detection from the pipeline network. Many studies of the use of the UAV with laser detectors have been presented in the literature. These studies include experiments mainly on the artificial methane sources simulating gas leaks. This study concerns the experiments on a real leakage of natural gas from a pipeline. The vehicle at first monitored the artificial source of methane to determine conditions for further experiments. Then the experiments on the selected section of the natural gas pipelines were conducted. The measurement data, along with spatial coordinates, were collected and analyzed using machine learning methods. The analysis enabled the identification of groups of spatially correlated regions which have increased methane concentrations. Investigations on the flight altitude influence on the accuracy of measurements were also carried out. A range of between 4 m and 15 m was depicted as optimal for data collection in the natural gas pipeline inspections. However, the results from the field experiments showed that areas with increased methane concentrations are significantly more difficult to identify, though they are still noticeable. The experiments also indicate that the lower altitudes of the UAV flights should be chosen. The results showed that UAV monitoring can be used as a tool for the preliminary selection of potentially untight gas pipeline sections.

Environmental chemical sensing using small drones: A review

Recent advances in miniaturization of chemical instrumentation and in low-cost small drones are catalyzing exponential growth in the use of such platforms for environmental chemical sensing applications. The versatility of chemically sensitive drones is reflected by their rapid adoption in scientific, industrial, and regulatory domains, such as in atmospheric research studies, industrial emission monitoring, and in enforcement of environmental regulations. As a result of this interdisciplinarity, progress to date has been reported across a broad spread of scientific and non-scientific databases, including scientific journals, press releases, company websites, and field reports. The aim of this paper is to assemble all of these pieces of information into a comprehensive, structured and updated review of the field of chemical sensing using small drones. We exhaustively review current and emerging applications of this technology, as well as sensing platforms and algorithms developed by research groups and companies for tasks such as gas concentration mapping, source localization, and flux estimation. We conclude with a discussion of the most pressing technological and regulatory limitations in current practice, and how these could be addressed by future research.

Sniffing out fugitive methane emissions: autonomous remote gas inspection with a mobile robot

Air pollution causes millions of premature deaths every year, and fugitive emissions of, e.g., methane are major causes of global warming. Correspondingly, air pollution monitoring systems are urgently needed. Mobile, autonomous monitoring can provide adaptive and higher spatial resolution compared with traditional monitoring stations and allows fast deployment and operation in adverse environments. We present a mobile robot solution for autonomous gas detection and gas distribution mapping using remote gas sensing. Our “Autonomous Remote Methane Explorer” ([Formula: see text]) is equipped with an actuated spectroscopy-based remote gas sensor, which collects integral gas measurements along up to 30 m long optical beams. State-of-the-art 3D mapping and robot localization allow the precise location of the optical beams to be determined, which then facilitates gas tomography (tomographic reconstruction of local gas distributions from sets of integral gas measurements). To autonomously obtain informative sampling strategies for gas tomography, we reduce the search space for gas inspection missions by defining a sweep of the remote gas sensor over a selectable field of view as a sensing configuration. We describe two different ways to find sequences of sensing configurations that optimize the criteria for gas detection and gas distribution mapping while minimizing the number of measurements and distance traveled. We evaluated an [Formula: see text] prototype deployed in a large, challenging indoor environment with eight gas sources. In comparison with human experts teleoperating the platform from a distant building, the autonomous strategy produced better gas maps with a lower number of sensing configurations and a slightly longer route.

Standoff Chemical Detection Using Laser Absorption Spectroscopy: A Review

Remote chemical detection in the atmosphere or some specific space has always been of great interest in many applications for environmental protection and safety. Laser absorption spectroscopy (LAS) is a highly desirable technology, benefiting from high measurement sensitivity, improved spectral selectivity or resolution, fast response and capability of good spatial resolution, multi-species and standoff detection with a non-cooperative target. Numerous LAS-based standoff detection techniques have seen rapid development recently and are reviewed herein, including differential absorption LiDAR, tunable laser absorption spectroscopy, laser photoacoustic spectroscopy, dual comb spectroscopy, laser heterodyne radiometry and active coherent laser absorption spectroscopy. An update of the current status of these various methods is presented, covering their principles, system compositions, features, developments and applications for standoff chemical detection over the last decade. In addition, a performance comparison together with the challenges and opportunities analysis is presented that describes the broad LAS-based techniques within the framework of remote sensing research and their directions of development for meeting potential practical use.

Gathering Pipeline Methane Emissions in Utica Shale Using an Unmanned Aerial Vehicle and Ground-Based Mobile Sampling

The United States Environmental Protection Agency Greenhouse Gas Inventory only recently updated the emission factors of natural gas gathering pipelines in April 2019 from the previous estimates based on a 1990s study of distribution pipelines. Additional measurements are needed from different basins for more accurate assessments of methane emissions from natural gas midstream industries and hence the overall climate implications of natural gas as the interim major energy source for the next decade. We conducted an unmanned aerial vehicle (UAV) survey and a ground-based vehicle sampling campaign targeting gathering pipeline systems in the Utica Shale from March to April in 2019. Out of 73 km of pipeline systems surveyed, we found no leaks on pipelines and two leaks on an accessory block valve with leak rates of 3.8 ± 0.4 and 7.6 ± 0.8 mg/s. The low leak frequency phenomenon was also observed in the only existing gathering pipeline study in Fayetteville Shale. The UAV sampling system facilitated ease of access, broadened the availability of pipelines for leak detection, and was estimated to detect methane leaks down to 0.07 g/s using Gaussian dispersion modeling. For future UAV surveys adopting similar instrument setup and dispersion models, we recommend arranging controlled release experiments first to understand the system’s detection limit and choosing sampling days with steady and low wind speeds (2 m/s).

Comparing estimates of fugitive landfill methane emissions using inverse plume modeling obtained with Surface Emission Monitoring (SEM), Drone Emission Monitoring (DEM), and Downwind Plume Emission Monitoring (DWPEM)

As part of the global effort to quantify and manage anthropogenic greenhouse gas emissions, there is considerable interest in quantifying methane emissions in municipal solid waste landfills. A variety of analytical and experimental methods are currently in use for this task. In this paper, an optimization-based estimation method is employed to assess fugitive landfill methane emissions. The method combines inverse plume modeling with ambient air methane concentration measurements. Three different measurement approaches are tested and compared. The method is combined with surface emission monitoring (SEM), above ground drone emission monitoring (DEM), and downwind plume emission monitoring (DWPEM). The methodology is first trialed and validated using synthetic datasets in a hand-generated case study. A field study is also presented where SEM, DEM and DWPEM are tested and compared. Methane flux during two-days measurement campaign was estimated to be between 228 and 350 g/s depending on the type of measurements used. Compared to SEM, using unmanned aerial systems (UAS) allows for a rapid and comprehensive coverage of the site. However, as showed through this work, advancement of DEM-based methane sampling is governed by the advances that could be made in UAS-compatible measurement instrumentations. Downwind plume emission monitoring led to a smaller estimated flux compared with SEM and DEM without information about positions of major leak points in the landfill. Even though, the method is simple and rapid for landfill methane screening. Finally, the optimization-based methodology originally developed for SEM, shows promising results when it is combined with the drone-based collected data and downwind concentration measurements. The studied cases also discovered the limitations of the studied sampling strategies which is exploited to identify improvement strategies and recommendations for a more efficient assessment of fugitive landfill methane emissions.Implications: Fugitive landfill methane emission estimation is tackled in the present study. An optimization-based method combined with inverse plume modeling is employed to treat data from surface emission monitoring, drone-based emission monitoring and downwind plume emission monitoring. The study helped revealing the advantages and the limitations of the studied sampling strategies. Recommendations for an efficient assessment of landfill methane emissions are formulated. The method trialed in this study for fugitive landfill methane emission could also be appropriate for rapid screening of analogous greenhouse gas emission hotspots.

Cavity Ring-Down Methane Sensor for Small Unmanned Aerial Systems

We present the development, integration, and testing of an open-path cavity ring-down spectroscopy (CRDS) methane sensor for deployment on small unmanned aerial systems (sUAS). The open-path configuration used here (without pump or flow-cell) enables a low mass (4 kg) and low power (12 W) instrument that can be readily integrated to sUAS, defined here as having all-up mass of <25 kg. The instrument uses a compact telecom style laser at 1651 nm (near-infrared) and a linear 2-mirror high-finesse cavity. We show test results of flying the sensor on a DJI Matrice 600 hexacopter sUAS. The high sensitivity of the CRDS method allows sensitive methane detection with a precision of ~10-30 ppb demonstrated for actual flight conditions. A controlled release setup, where known mass flows are delivered, was used to simulate point-source methane emissions. Examples of methane plume detection from flight tests suggest that isolated plumes from sources with a mass flow as low as ~0.005 g/s can be detected. The sUAS sensor should have utility for emissions monitoring and quantification from natural gas infrastructure. To the best of our knowledge, it is also the first CRDS sensor directly deployed onboard an sUAS.

Characterizing anthropogenic methane sources in the Houston and Barnett Shale areas of Texas using the isotopic signature δ13C in CH4

Methane (CH₄) is an important greenhouse gas with its mixing ratio increasing in the global atmosphere. Identifying fingerprints of CH₄ emissions is critical to understanding potential impacts of various anthropogenic sources in the Greater Houston area (GHA) and extensive natural gas operations in the Barnett Shale area (BSA) of Texas. Stable carbon isotope ratios of CH₄ (δ¹³C_CH4) has been proposed to be a useful technique for differentiating individual CH₄ sources. Measurements of CH₄ mixing ratios and δ¹³C_CH4 were sampled using a mobile laboratory equipped with cavity ring-down spectrometers (CRDS). Areal CH₄ distributions and the background δ¹³C_CH4 signature were obtained from filtered ambient signals; -47.0‰ (GHA) and - 48.5‰ (BSA) were calculated. The fingerprint of thirty-three anthropogenic sources in the two study areas were sampled with forty-four δ¹³C analyses conducted. Repeated measurements indicated the natural variation of δ¹³C_CH4 signatures of individual CH₄ sources. An unexpected massive CH₄ fugitive leak was detected near the San Jacinto River Fleet site in Houston exhibiting an δ¹³C_CH4 value around -42‰. Our results and findings demonstrate the utility of δ¹³C_CH4for facilitating emission inventories and atmospheric modeling.

Suitability of a Non-Dispersive Infrared Methane Sensor Package for Flux Quantification Using an Unmanned Aerial Vehicle

Point-source methane emission flux quantification is required to help constrain the global methane budget. Facility-scale fluxes can be derived using in situ methane mole fraction sampling, near-to-source, which may be acquired from an unmanned aerial vehicle (UAV) platform. We test a new non-dispersive infrared methane sensor by mounting it onto a small UAV, which flew downwind of a controlled methane release. Nine UAV flight surveys were conducted on a downwind vertical sampling plane, perpendicular to mean wind direction. The sensor was first packaged in an enclosure prior to sampling which contained a pump and a recording computer, with a total mass of 1.0 kg. The packaged sensor was then characterised to derive a gain factor of 0.92 ± 0.07, independent of water mole fraction, and an Allan deviation precision (at 1 Hz) of ±1.16 ppm. This poor instrumental precision and possible short-term drifts made it non-trivial to define a background mole fraction during UAV surveys, which may be important where any measured signal is small compared to sources of instrumental uncertainty and drift. This rendered the sensor incapable of deriving a meaningful flux from UAV sampling for emissions of the order of 1 g s^-1. Nevertheless, the sensor may indeed be useful when sampling mole fraction enhancements of the order of at least 10 ppm (an order of magnitude above the 1 Hz Allan deviation), either from stationary ground-based sampling (in baseline studies) or from mobile sampling downwind of sources with greater source flux than those observed in this study. While many methods utilising low-cost sensors to determine methane flux are being developed, this study highlights the importance of adequately characterising and testing all new sensors before they are used in scientific research.

A Near-Field Gaussian Plume Inversion Flux Quantification Method, Applied to Unmanned Aerial Vehicle Sampling

The accurate quantification of methane emissions from point sources is required to better quantify emissions for sector-specific reporting and inventory validation. An unmanned aerial vehicle (UAV) serves as a platform to sample plumes near to source. This paper describes a near-field Gaussian plume inversion (NGI) flux technique, adapted for downwind sampling of turbulent plumes, by fitting a plume model to measured flux density in three spatial dimensions. The method was refined and tested using sample data acquired from eight UAV flights, which measured a controlled release of methane gas. Sampling was conducted to a maximum height of 31 m (i.e. above the maximum height of the emission plumes). The method applies a flux inversion to plumes sampled near point sources. To test the method, a series of random walk sampling simulations were used to derive an NGI upper uncertainty bound by quantifying systematic flux bias due to a limited spatial sampling extent typical for short-duration small UAV flights (less than 30 min). The development of the NGI method enables its future use to quantify methane emissions for point sources, facilitating future assessments of emissions from specific source-types and source areas. This allows for atmospheric measurement-based fluxes to be derived using downwind UAV sampling for relatively rapid flux analysis, without the need for access to difficult-to-reach areas.