A Study of a Miniature TDLAS System Onboard Two Unmanned Aircraft to Independently Quantify Methane Emissions from Oil and Gas Production Assets and Other Industrial Emitters

Neural Network Based Aliasing Spectral Decoupling Algorithm for Precise Mid-Infrared Multicomponent Gases Sensing

Owing to the overlapping and cross-interference of absorption lines in multicomponent gases, the simultaneous measurement of such gases via laser absorption spectroscopy frequently necessitates the use of supplementary pressure sensors to distinguish the spectral lines. Alternatively, it requires multiple lasers combined with time-division multiplexing to independently scan the absorption peaks of each gas, thereby preventing interference from other gases. This inevitably escalates both the cost of the system and the complexity of the gas pathway. In response to these challenges, a mid-infrared sensor employing a neural network-based decoupling algorithm for aliasing spectral is developed, enabling the simultaneous detection of methane(CH4), water vapor(H2O), and ethane(C2H6). The sensor system underwent evaluation in a controlled laboratory environment. Allan deviation analysis revealed that the minimum detection limits for CH4,H2O, and C2H6 were 6.04, 118.44, and 1 ppb, respectively, with an averaging time of 3 s. The performance of the proposed sensor demonstrates that the aliasing spectral decoupling algorithm based on neural network combined with wavelength-modulated spectroscopy technology has the advantages of high sensitivity, low cost and low complexity, showing its potential for simultaneous detection of multicomponent trace gases in various fields.

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.

Informing Methane Emissions Inventories Using Facility Aerial Measurements at Midstream Natural Gas Facilities

Increased interest in greenhouse gas (GHG) emissions, including recent legislative action and voluntary programs, has increased attention on quantifying and ultimately reducing methane emissions from the natural gas supply chain. While inventories used for public or corporate GHG policies have traditionally utilized bottom-up (BU) methods to estimate emissions, the validity of such inventories has been questioned. Therefore, there is attention on utilizing full-facility measurements using airborne, satellite, or drone (top-down (TD)) techniques to inform, improve, or validate inventories. This study utilized full-facility estimates from two independent TD methods at 15 midstream natural gas facilities in the U.S.A., which were compared with a contemporaneous daily inventory assembled by the facility operator, employing comprehensive inventory methods. Estimates from the two TD methods statistically agreed in 2 of 28 paired measurements. Operator inventories, which included extensions to capture sources beyond regular inventory requirements and integration of local measurements, estimated significantly lower emissions than the TD estimates for 40 of 43 paired comparisons. Significant disagreement was observed at most facilities, both between the two TD methods and between the TD estimates and operator inventory. These findings have two implications. First, improving inventory estimates will require additional on-site or ground-based diagnostic screening and measurement of all sources. Second, the TD full-facility measurement methods need to undergo further testing, characterization, and potential improvement specifically tailored for complex midstream facilities.

Toward Multiscale Measurement-Informed Methane Inventories: Reconciling Bottom-Up Site-Level Inventories with Top-Down Measurements Using Continuous Monitoring Systems

Government policies and corporate strategies aimed at reducing methane emissions from the oil and gas sector increasingly rely on measurement-informed, site-level emission inventories, as conventional bottom-up inventories poorly capture temporal variability and the heavy-tailed nature of methane emissions. This work is based on an 11-month methane measurement campaign at oil and gas production sites. We find that operator-level top-down methane measurements are lower during the end-of-project phase than during the baseline phase. However, gaps persist between end-of-project top-down measurements and bottom-up site-level inventories, which we reconcile with high-frequency data from continuous monitoring systems (CMS). Specifically, we use CMS to (i) validate specific snapshot measurements and determine how they relate to the temporal emission profile of a given site and (ii) create a measurement-informed, site-level inventory that can be validated with top-down measurements to update conventional bottom-up inventories. This work presents a real-world demonstration of how to reconcile CMS rate estimates and top-down snapshot measurements jointly with bottom-up inventories at the site level. More broadly, it demonstrates the importance of multiscale measurements when creating measurement-informed, site-level emission inventories, which is a critical aspect of recent regulatory requirements in the Inflation Reduction Act, voluntary methane initiatives such as the Oil and Gas Methane Partnership 2.0, and corporate strategies.

Highly sensitive and reliable optical fiber TDLAS gas detection system for methane in situ monitoring in near space

A highly sensitive and reliable tunable diode laser absorption spectroscopy gas detection system with a temperature-pressure compensation algorithm is demonstrated for detecting C H 4 concentrations in near space. Near space generally refers to the airspace 20-100 km away from the ground, where temperature and pressure changes are complex. Since the gas absorption spectrum is easily affected by temperature and pressure, a temperature-pressure compensation algorithm is proposed and used in the C H 4 sensor to improve the detection accuracy of the sensor. First, we measured the basic characteristics of the sensor in the laboratory, such as linearity and long-term stability. Experimental results showed that the linear correlation coefficient R-square can reach 0.999, and the concentration fluctuation of C H 4 is less than 0.17 ppm within 3.5 h. Then the sensor was applied to a research activity in Qinghai Province, China, in September, and the results show that the sensor can effectively monitor the C H 4 concentration in near space.

CH4/C2H6 dual gas sensing system using a single mid-infrared laser

Methane (CH₄) and ethane (C₂H₆) dual gas sensor with low system complexity and strong stability is proposed. The correction method based on absorbance spectrum is applied, and the cross-interference of C₂H₆ to CH₄ is eliminated. In the single gas concentration measurement, linear fitting is performed between the absorbance and concentration of CH₄ and C₂H₆, and the correlation coefficients of R² = 0.99959 and R² = 0.99994 are obtained respectively, which proves that the accuracy of the dual gas sensor is robust. In the dual gas concentration measurement, we carry out continuous measurement of five mixed gases and a long-term measurement of a mixture of gases, which verifies that our sensor has the fast response speed and strong stability. The minimum detectable column densities of 0.62 ppm·m for CH₄ and 0.1 ppm·m for C₂H₆ are achieved, respectively. The CH₄/C₂H₆ dual gas sensor assisted by the correction method has high sensitivity and strong robustness to cross-interference, and has great potential for application in various scenarios.

Reconciling Multiple Methane Detection and Quantification Systems at Oil and Gas Tank Battery Sites

Emission rates were estimated for >100 oil and gas production sites with significant liquid-handling equipment (tank battery sites) in the Permian Basin of west Texas. Emission estimates based on equipment counts and emission factors, but not accounting for large uninventoried emission events, led to ensemble average emission rates of 1.8-3.6 kg/h per site. None of the site-specific emission estimates for individual sites, based on equipment counts, exceeded 10 kg/h. On-site drone-based emission measurements led to similar emission estimates for inventoried sources. Multiple aircraft measurement platforms were deployed and reported emissions exceeding 10 kg/h at 14-27% of the sites, and these high-emission rate sites accounted for 80-90% of total emissions for the ensemble of sites. The aircraft measurement systems were deployed asynchronously but within a 5 day period. At least half of the sites with emission rates above 10 kg/h detected by aircraft had emissions that did not persist at a level above 10 kg/h for repeat measurements, suggesting typical high-emission rate durations of a few days or less for many events. The two aircraft systems differed in their estimates of total emissions from the ensembles of sites sampled by more than a factor of 2; however, the normalized distributions of emissions for sites with emission rates of >10 kg/h were comparable for the two aircraft-based methods. The differences between the two aircraft-based platforms are attributed to a combination of factors; however, both aircraft-based emission measurement systems attribute a large fraction of emissions to sites with an emission rate of >10 kg/h.

Estimating Regional Methane Emission Factors from Energy and Agricultural Sector Sources Using a Portable Measurement System: Case Study of the Denver-Julesburg Basin

Methane (CH₄), a powerful greenhouse gas (GHG), has been identified as a key target for emission reduction in the Paris agreement, but it is not currently clear where efforts should be focused to make the greatest impact. Currently, activity data and standard emission factors (EF) are used to generate GHG emission inventories. Many of the EFs are globally uniform and do not account for regional variability in industrial or agricultural practices and/or regulation. Regional EFs can be derived from top-down emissions measurements and used to make bespoke regional GHG emission inventories that account for geopolitical and social variability. However, most large-scale top-down approaches campaigns require significant investment. To address this, lower-cost driving surveys (DS) have been identified as a viable alternative to more established methods. DSs can take top-down measurements of many emission sources in a relatively short period of time, albeit with a higher uncertainty. To investigate the use of a portable measurement system, a 2260 km DS was conducted throughout the Denver-Julesburg Basin (DJB). The DJB covers an area of 8000 km² north of Denver, CO and is densely populated with CH₄ emission sources, including oil and gas (O and G) operations, agricultural operations (AGOs), lakes and reservoirs. During the DS, 157 individual CH₄ emission sources were detected; 51%, 43% and 4% of sources were AGOs, O and G operations, and natural sources, respectively. Methane emissions from each source were quantified using downwind concentration and meteorological data and AGOs and O and G operations represented nearly all the CH₄ emissions in the DJB, accounting for 54% and 37% of the total emission, respectively. Operations with similar emission sources were grouped together and average facility emission estimates were generated. For agricultural sources, emissions from feedlot cattle, dairy cows and sheep were estimated at 5, 31 and 1 g CH₄ head^-1 h^-1, all of which agreed with published values taken from focused measurement campaigns. Similarly, for O and G average emissions for well pads, compressor stations and gas processing plants (0.5, 14 and 110 kg CH₄ facility^-1 h^-1) were in reasonable agreement with emission estimates from intensive measurement campaigns. A comparison of our basin wide O and G emissions to measurements taken a decade ago show a decrease of a factor of three, which can feasibly be explained by changes to O and G regulation over the past 10 years, while emissions from AGOs have remained constant over the same time period. Our data suggest that DSs could be a low-cost alternative to traditional measurement campaigns and used to screen many emission sources within a region to derive representative regionally specific and time-sensitive EFs. The key benefit of the DS is that many regions can be screened and emission reduction targets identified where regional EFs are noticeably larger than the regional, national or global averages.

Landfill Emissions of Methane Inferred from Unmanned Aerial Vehicle and Mobile Ground Measurements

Municipal solid waste landfills are significant sources of atmospheric methane, the second most important greenhouse gas after carbon dioxide. Large emissions of methane from landfills contribute not only to global climate change, but also to local ozone formation due to the enhancement of radical chain lengths in atmospheric reactions of volatile organic compounds and nitrogen oxides. Several advanced techniques were deployed to measure methane emissions from two landfills in the Southeast Michigan ozone nonattainment area during the Michigan–Ontario Ozone Source Experiment (MOOSE). These techniques included mobile infrared cavity ringdown spectrometry, drone-mounted meteorological sensors and tunable diode laser spectrometry, estimation of total landfill emissions of methane based on flux plane measurements, and Gaussian plume inverse modeling of distributed methane emissions in the presence of complex landfill terrain. The total methane emissions measured at the two landfills were of the order of 500 kg/h, with an uncertainty of around 50%. The results indicate that both landfill active faces and leaking gas collection systems are important sources of methane emissions.