Search Results (70)

The dataset presented in this manuscript consists of three distinct sets of data collected during a laboratory experiment aimed at quantifying the emissions of greenhouse gases (GHGs), specifically methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O). The experiment was conducted in three phases, each initiated at different times. The first phase began on 6 June 2022, using a biocover composed of 60% fine-fraction waste, 20% clay soil, and 20% stabilized compost. The second phase commenced on 26 August 2022, with two biocover variants: one composed of 50% fine-fraction waste and 50% clay soil, and the other consisting of 40% fine-fraction waste, 40% clay soil, and 20% shredded paper. The final phase started on 27 October 2022, introducing two biocovers: one containing 25% dried algae, 25% fine-fraction waste, 25% gravel (0–20 mm), and 25% ash, and the other composed of 40% fine-fraction waste, 40% dried algae, and 20% chernozem. Emission assessments were conducted three weeks after the biocover installation to allow for settling and stabilization, followed by weekly measurements two to three days before irrigation with 250 mL of water to simulate field conditions. GHG emission quantification was carried out using the Cavity Ring-Down Spectroscopy gas measurement device, Picarro G2508. This dataset offers substantial scientific value for advancing biocover technologies aimed at reducing GHG emissions in landfill environments, particularly for mitigating methane emissions. In addition to initial experimental use, the dataset offers a wide range of possibilities for reuse, including modeling landfill gas emissions, validating gas flow measurement methods, developing machine learning models, and performing meta-analyses. Its detailed structure facilitates multi-faceted environmental research and supports optimization of landfill management. Full article

Accurate methane emission estimates are essential for climate policy, yet current field methods often struggle with spatial constraints and source complexity. Ground-based mobile approaches frequently miss key plume features, introducing bias and uncertainty in emission rate estimates. This study addresses these limitations by using small unmanned aerial systems equipped with precision gas sensors to measure methane alongside co-released tracers. We tested whether arc-shaped flight paths and alternative ratio estimation methods could improve the accuracy of tracer-based emission quantification under real-world constraints. Controlled releases using ethane and nitrous oxide tracers showed that (1) arc flights provided stronger plume capture and higher correlation between methane and tracer concentrations than traditional flight paths; (2) the cumulative sum method yielded the lowest relative error (as low as 3.3%) under ideal mixing conditions; and (3) the arc flight pattern yielded the lowest relative error and uncertainty across all experimental configurations, demonstrating its robustness for quantifying methane emissions from downwind plume measurements. These findings demonstrate a practical and scalable approach to reducing uncertainty in methane quantification. The method is well-suited for challenging environments and lays the groundwork for future applications at the facility scale. Full article

The transportation sector significantly contributes to greenhouse gas (GHG) emissions and remains a key research focus on emission quantification and mitigation. Although numerous models exist for estimating vehicle-based emissions, most lack accuracy at regional scales, particularly in remote or underdeveloped areas, including backcountry national parks and mountainous regions lacking basic infrastructure. This study addresses that gap by developing and applying a terrain-adjusted, segment-based methodology to estimate GHG emissions from tourist vehicles in Altai Tavan Bogd National Park, one of Mongolia’s most remote protected areas. The proposed method uses Tier 1 IPCC emission factors but incorporates field-segmented route analysis, vehicle categorization, and terrain-based fuel adjustments to achieve a spatially disaggregated Tier 1 approach. Results show that carbon dioxide (CO₂) emissions increased from 118.7 tons in 2018 to 2239 tons in 2024. Tourist vehicle entries increased from 712 in 2018 to 13,192 in 2024, with 99.1% of entries occurring between May and October. Over the same period, cumulative methane (CH₄) and nitrous oxide (N₂O) emissions were estimated at 300.9 kg and 45.75 kg, respectively. This modular approach is especially suitable for high-altitude, infrastructure-limited regions where real-time emissions monitoring is not feasible. By integrating localized travel patterns with global frameworks such as the IPCC 2006 Guidelines, this model enables more precise and context-sensitive GHG estimates from vehicles in national parks and similar environments. Full article

Recent studies estimate that emissions from oil and gas production facilities contribute between 20 and 50% of the total methane ($C{H}_4$) emitted in the US; therefore, quantifying and reducing these emissions are crucial for achieving climate goals. Methane quantification depends on both measuring methane concentrations and converting them to emissions through a modeling framework. Currently, simple atmospheric dispersion models are primarily used to quantify emissions and concentrations, but these estimates are highly uncertain when quantifying emissions from complex aerodynamic sources, such as oil and gas facilities. This investigation used a CFD modeling approach, which can account for aerodynamic complexity but has hitherto not been used to model methane concentrations downwind of a methane release of a known rate, and compared it against in situ measurements. High-time-resolution (1 Hz) methane concentration and meteorological data were measured during experiments conducted at the METEC on 21 March and 11 July 2024. The METEC site configuration, measured wind data, and controlled emission rates were used as input for the CONVERGE CFD model to model downwind $C{H}_4$ concentration. The modeling was carried out between 20 and 70 m, from two different points of release in two separate controlled-release experiments, one from a separator and another from a wellhead. In these experiments, we found that the CFD model could predict the $C{H}_4$ concentrations downwind of the release to a good degree. The model was evaluated on multiple metrics to assess its performance in estimating methane concentrations at typical fence line distances (∼30 m). These results help us to understand external flows and the ability of CFD models to predict downwind concentrations in aerodynamically complex environments. Full article

Quantifying methane (CH₄) emissions is essential for climate change mitigation; however, current estimation methods often suffer from substantial uncertainties, particularly at the site level. This study introduces a drone-based approach for measuring CH₄ emissions using an open-path Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor mounted parallel to the ground, rather than in the traditional nadir-pointing configuration. Controlled CH₄ release experiments were conducted to evaluate the method’s accuracy, employing a modified mass-balance technique to estimate emission rates. Two wind data processing strategies were compared: a logarithmic wind profile (LW) and a constant scalar wind speed (SW). The LW approach yielded highly accurate results, with an average recovery rate of 98%, while the SW approach showed greater variability with increasing distance from the source, although it remained reliable in close proximity. The method demonstrated the ability to quantify emissions as low as 0.08 g s⁻¹ with approximately 4% error, given sufficient sampling. These findings suggest that the proposed UAV-based system is a promising, cost-effective tool for accurate CH₄ emission quantification in sectors, such as agriculture, energy, and waste management, where traditional monitoring techniques may be impractical or limited. Full article

Nitrogen (N) and organic-source fertilizers in agriculture are important to sustain crop production for feeding the growing global population. However, their use can result in significant greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O), which are important climate drivers. This review discusses the interactive effects, uncovering both additive and suppressive outcomes of emissions under various soil and climatic conditions. In addition to examining the effects of nitrogen and the nitrogen use efficiency (NUE), it is crucial to comprehend the mechanisms and contributions of organic fertilizers to GHG emissions. This understanding is vital for developing mitigation strategies that effectively reduce emissions while maintaining agricultural productivity. In this review, the current knowledge is utilized for the management of nitrogen practices, such as the optimization of fertilization rates, timing, and methods of application, in terms of the nitrogen use efficiency and the related GHG emissions. Moreover, we discuss the role of organic fertilizers, including straw, manure, and biochar, as a mitigation strategy in relation to GHG emissions through soil carbon sequestration and enhanced nutrient cycling. Important strategies such as crop rotation, tillage, irrigation, organic fertilizers, and legume crops are considered as suitable approaches for minimizing emissions. Even with the progress made in mitigating fertilizer-related emissions, research gaps remain, specifically concerning the long-term effect of organic fertilizers and the interactions between microbial communities in the soil and fertilization practices. Furthermore, the differences in application practices and environmental conditions present considerable obstacles to accurate emission quantification. This review underlines the importance of conducting more thorough research on the combined application of N and organic fertilizers in multiple cropping systems to evolve region-specific mitigation strategies. Full article

Since pre-industrial times, anthropogenic methane emissions have increased and are partly responsible for a changing global climate. Natural gas and oil extraction activities are one significant source of anthropogenic methane. While methods have been developed and refined to quantify onshore methane emissions, the ability of methods to directly quantify emissions from offshore production facilities remains largely unknown. Here, we review recent studies that have directly measured emissions from offshore production facilities and critically evaluate the suitability of these measurement strategies for emission quantification in a marine environment. The average methane emissions from production platforms measured using downwind dispersion methods were 32 kg h⁻¹ from 188 platforms; 118 kg h⁻¹ from 104 platforms using mass balance methods; 284 kg h⁻¹ from 151 platforms using aircraft remote sensing; and 19,088 kg h⁻¹ from 10 platforms using satellite remote sensing. Upon review of the methods, we suggest the unusually large emissions, or zero emissions observed could be caused by the effects of a decoupling of the marine boundary layer (MBL). Decoupling can happen when the MBL becomes too deep or when there is cloud cover and results in a stratified MBL with air layers of different depths moving at different speeds. Decoupling could cause: some aircraft remote sensing observations to be biased high (lower wind speed at the height of the plume); the mass balance measurements to be biased high (narrow plume being extrapolated too far vertically) or low (transects miss the plume); and the downwind dispersion measurements much lower than the other methods or zero (plume lofting in a decoupled section of the boundary layer). To date, there has been little research on the marine boundary layer, and guidance on when decoupling happens is not currently available. We suggest an offshore controlled release program could provide a better understanding of these results by explaining how and when stratification happens in the MBL and how this affects quantification methodologies. Full article

This entry describes the methods used to quantify methane emissions from either point or area sources using downwind methods. The methods described could be used as a practical guide to quantify emissions of any trace gas type from either a point or area emission source. Methane is a relatively strong greenhouse gas, its GWP is 25 times larger than CO₂ over a 100-year period, and an increase in methane anthropogenic emissions has been correlated to a changing global climate. Emission estimates that are calculated and used for national inventories are usually derived from bottom-up approaches, however there is now an increasing pressure for these to be validated by direct measurement. Calculating emission rates from downwind measurements has proven to be a versatile and relatively simple approach for direct measurement. Downwind measurement method descriptions are presented here as a practicable guide to quantifying point and area source emissions. Emission quantification is a two-stage process where methane concentration and meteorological data must be measured downwind of a source and then converted to emissions using an atmospheric dispersion model. Only four technology types currently measure in the range typical of downwind methane concentrations: metal oxide sensors, non-dispersive infrared sensors, tunable diode laser absorption spectrometers and optical cavity instruments. The choice of methane measurement is typically determined by the size of the emission source, location and the budget of the project. Meteorological data are essential to quantifying emissions, especially regarding wind speed and direction. In most cases, simple atmospheric dispersion approaches can be used to quantify both area and point emissions using these downwind measurements. Emissions can be generated using limited data (only methane concentration, wind speed, wind direction, and locations are necessary), but quantification uncertainty can be reduced using more input data. Site selection and location of instrument deployment are essential because quantification approaches assume a flat fetch (no aerodynamic obstructions) and constant wind fields. When modeling assumptions are violated, quantification uncertainty can range between +250% and −100% of the actual emission rate. At present there, is no happy medium between modeling complexity and computational time, and this remains the biggest challenge for downwind emission quantification. Full article

As global atmospheric methane concentrations surge at an unprecedented rate, the identification of methane super-emitters with significant mitigation potential has become imperative. In this study, we utilize remote sensing satellite data with varying spatiotemporal coverage and resolutions to detect and quantify methane emissions. We exploit the synergistic potential of Sentinel-2, EnMAP, and GF5-02-AHSI for methane plume detection. Employing a matched filtering algorithm based on EnMAP and AHSI, we detect and extract methane plumes within emission hotspots in China and the United States, and estimate the emission flux rates of individual methane point sources using the IME model. We present methane plumes from industries such as oil and gas (O&G) and coal mining, with emission rates ranging from 1 to 40 tons per h, as observed by EnMAP and GF5-02-AHSI. For selected methane emission hotspots in China and the United States, we conduct long-term monitoring and analysis using Sentinel-2. Our findings reveal that the synergy between Sentinel-2, EnMAP, and GF5-02-AHSI enables the precise identification of methane plumes, as well as the quantification and monitoring of their corresponding sources. This methodology is readily applicable to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. The high-frequency satellite-based detection of anomalous methane point sources can facilitate timely corrective actions, contributing to the reduction in global methane emissions. This study underscores the potential of spaceborne multispectral imaging instruments, combining fine pixel resolution with rapid revisit rates, to advance the global high-frequency monitoring of large methane point sources. Full article

When using liquefied natural gas (LNG) as fuel for shipping, the sulphur emissions are negligible and low NO_x and particle emissions can be reached together with lower CO₂ emissions compared to diesel-based fuels. The drawback of LNG usage is the unburned fuel, i.e., methane can be found in the exhaust. Reliable emission detection and quantification will play a key role, as methane is also becoming regulated. In this study, different methods to measure methane are studied in the engine laboratory and on board with state-of-the-art engines. Four different measurement methods are found to give similar methane results with few exceptions. Measurements performed downstream of the methane abatement catalyst show that all instruments could detect the methane conversion efficiency to be above 95%. Comparing results from onboard studies to earlier published onboard studies with similar engines indicate that the engine (46 DF) behaved rather similarly, and the measurements carried out at different occasions on board by different devices and parties gave similar results. To measure total hydrocarbons, a flame ionization detector (FID) has generally been the accepted method (e.g., in NO_x Technical Code). Based on this study, other methods as reliable as FID for methane measurement exist and these methods can also be utilized on board. Full article

Quantifying methane emissions from facilities with complex emissions profiles can present a substantial challenge. Real-world emission scenarios can involve dynamic operational background emissions and temporally overlapping asynchronous emission events with varying rates from multiple sources. Previous studies have involved simpler testing setups, often with synchronous emission sources and constant rates. This work is among the first to assess the performance of continuous monitoring systems (CMSs) under dynamic, overlapping emission scenarios with time-varying baselines. The data were collected as part of a novel single-blind controlled release study, where release sources and emission rates are not disclosed during the testing period. Several error metrics are defined and evaluated across a range of relevant averaging times, demonstrating that despite significant error variance in short-duration estimates, the low bias of the system results in substantially improved emission estimates when aggregated to longer timescales. Over the 4-week duration of this study, 700 kg of methane was released by the testing center, while the estimated quantity shows a final mass of 673 kg, an underestimation by 27 kg (4%). These results demonstrate that advanced CMSs can accurately quantify cumulative site-level emissions in complex scenarios, highlighting their potential for enhanced future emissions monitoring and regulatory applications in the oil and gas sector. Full article

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT have been successfully employed to detect and quantify methane point source leaks. In this study, a matched filter (MF) algorithm is improved using data from the EMIT instrument and applied to data from the Advanced Hyperspectral Imager (AHSI) onboard the Ziyuan-1 (ZY-1) satellite. Validation by comparison with EMIT′s L2 XCH₄ products shows the good performance of the improved MF algorithm, in spite of the lower spectral resolution of AHSI/ZY-1 in comparison with other point source imagers. The improved MF algorithm applied to AHSI/ZY-1 data was used to detect and quantify methane super-emitters in global methane hotspot regions. The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. Sixteen methane plumes were detected in global hotspot regions, originating from coal mines, oil and gas fields, and landfills, with emission rates ranging from 0.57 to 78.85 t/h. The largest plume was located at an offshore oil and gas field in the Gulf of Mexico, with instantaneous emissions nearly equal to the combined total of the other 15 plumes. The findings demonstrate that AHSI, despite its lower spectral resolution, can detect sources with emission rates as small as 571 kg/h and achieve faster retrieval speeds, showing significant potential for global methane monitoring. Additionally, this study highlights the need to focus on methane emissions from marine sources, alongside terrestrial sources, to efficiently implement reduction strategies. Full article

The quantification of methane concentrations in air is essential for the quantification of methane emissions, which in turn is necessary to determine absolute emissions and the efficacy of emission mitigation strategies. These are essential if countries are to meet climate goals. Large-scale deployment of methane analyzers across millions of emission sites is prohibitively expensive, and lower-cost instrumentation has been recently developed as an alternative. Currently, it is unclear how cheaper instrumentation will affect measurement resolution or accuracy. To test this, the Wireless Autonomous Transportable Methane Emission Reporting System (WATCH₄ERS) has been developed, comprising four commercially available sensing technologies: metal oxide (MOx,), Non-dispersion Infrared (NDIR), integrated infrared (INIR), and tunable diode laser absorption spectrometer (TDLAS). WATCHERS is the accumulated knowledge of several long-term methane measurement projects at Colorado State University’s Methane Emission Technology Evaluation Center (METEC), and this study describes the integration of these sensors into a single unit and reports initial instrument response to calibration procedures and controlled release experiments. Specifically, this paper aims to describe the development of the WATCH₄ERS unit, report initial sensor responses, and describe future research goals. Meanwhile, future work will use data gathered by multiple WATCH₄ERS units to 1. better understand the cost–benefit balance of methane sensors, and 2. identify how decreasing instrumentation costs could increase deployment coverage and therefore inform large-scale methane monitoring strategies. Both calibration and response experiments indicate the INIR has little practical use for measuring methane concentrations less than 500 ppm. The MOx sensor is shown to have a logarithmic response to methane concentration change between background and 600 ppm but it is strongly suggested that passively sampling MOx sensors cannot respond fast enough to report concentrations that change in a sub-minute time frame. The NDIR sensor reported a linear change to methane concentration between background and 600 ppm, although there was a noticeable lag in reporting changing concentration, especially at higher values, and individual peaks could be observed throughout the experiment even when the plumes were released 5 s apart. The TDLAS sensor reported all changes in concentration but remains prohibitively expensive. Our findings suggest that each sensor technology could be optimized by either operational design or deployment location to quantify methane emissions. The WATCH₄ERS units will be deployed in real-world environments to investigate the utility of each in the future. Full article

Mitigating the environmental impact of agricultural practices, particularly intensive rice farming, is critical in the face of climate change. This study focuses on mapping rice residue burn areas and their dates while estimating the greenhouse gas (GHG) emissions associated with residue burning and rice cultivation. By using Sentinel-2 satellite imagery, machine learning algorithms, and ground truth data, we analyzed changes in rice cultivation patterns before and after the Kaleshwaram intervention. The Near-Infrared Region (NIR) band was instrumental in accurately identifying residue burn areas and pinpointing burn dates, enabling timely alerts for decision-makers to act. Detailed quantifications of CO₂, CH₄, and N₂O emissions from crop residue burning, alongside methane emissions from rice cultivation, highlight the significant contribution of these practices to overall GHG emissions. Key findings reveal a significant 82.1% increase in rice cultivation area from 2018–2019 to 2022–2023, accompanied by a worrying rise in residue burning, with some regions experiencing up to a 276% increase in burn areas. This research not only reveals the dual challenges of residue burning and GHG emissions but also emphasizes the importance of integrating precise burn date monitoring with emission data. The findings provide a strong foundation for implementing sustainable crop residue management strategies and developing informed policies to mitigate the adverse environmental effects of rice farming. Full article

In the oil and gas sectors, as well as in waste landfills, the commitment to greater sustainability is leading to increased efforts in the search for methane leaks, both to avoid the emission of a major greenhouse gas and to enable greater fuel recovery. For rapid leak detection and flow estimation, drone-mounted sensors are used, which require a balanced configuration of the detection and measurement system, adequate for the specific sensor used. In the present work, the search for methane leaks is carried out using a tunable diode laser absorption spectrometer (TDLAS) mounted on a drone. Once the survey is carried out, the data obtained feed the algorithms necessary for estimating the methane flow using the mass balance approach. Various algorithms are tested in the background measurement phases and in the actual detection phase, integrated with each other in order to constitute a single balanced set-up for the estimation of the flow emitted. The research methodology adopted is that of field testing through controlled releases of methane. Three different flows are released to simulate different emission intensities: 0.054, 1.91 and 95.9 kg/h. Various data configurations are developed in order to capture the set-up that best represents the emission situation. The results show that for the correction of methane background errors, the threshold that best fits appears to be the one that combines an initial application of the 2σ threshold on the mean values with the subsequent application of the new 2σ threshold calculated on the remaining values. Among the detection algorithms, however, the use of a threshold of the 75th percentile on a series of 25 consecutive readings to ascertain the presence of methane is reported as an optimal result. For a sustainable approach to become truly practicable, it is necessary to have effective and reliable measurement systems. In this context, the integrated use of the highlighted algorithms allows for a greater identification of false positives which are therefore excluded both from the physical search for the leak and from the flow estimation calculations, arriving at a more consistent quantification, especially in the presence of low-emission flows. Full article