Search Results (87)

This study presents a novel top-down approach to quantify diffuse methane (CH₄) emissions at oil and gas well sites. It uses an unmanned aerial vehicle (UAV) equipped with a scanning–sampling tunable diode laser absorption spectroscopy (TDLAS) CH₄ measurement instrument. By integrating the top-down emission rate retrieval algorithm (TERRA) and adopting concentric circular sampling, the method aims to overcome the limitations of traditional ground-based measurements. The UAV system was deployed at 11 oil and gas sites in the Changqing Oilfield. The results show that the average CH₄ emission rate detected by the UAV is 1.425 kg/h (excluding non-detected samples), which is larger than the 1.061 kg/h obtained from ground-based onsite direct measurement. This discrepancy may be because the UAV’s scanning–sampling capability can cover a larger area, capturing scattered or hidden diffuse emission sources that might be missed by ground-based onsite direct measurement. The study demonstrates that the UAV-based approach with a scanning–sampling TDLAS CH₄ measurement instrument, integrated with the TERRA and concentric circular sampling, is effective in capturing diffuse CH₄ emissions at oil and gas well sites, providing a viable method for large-scale and efficient monitoring of such emissions. This approach could provide an effective pathway for large-scale, efficient, and cost-effective monitoring of methane emissions. Full article

Satellite remote sensing has become an increasingly important approach for detecting and quantifying methane emissions across spatial and temporal scales. While most reviews in the literature have addressed aspects of methane monitoring, they often focus primarily on satellite platforms or provide discussions on retrieval methodologies. This review offers an integrated assessment of recent developments in satellite-based methane detection, combining technical evaluations of satellite instruments with detailed analysis of retrieval techniques and sector-specific applications. The paper distinguishes between area flux mappers and point-source imagers and reviews both established and recent satellite missions, including GHGSat, MethaneSAT, and PRISMA. Retrieval methods are critically compared, covering full-physics models, CO₂ proxy approaches, optimal estimation, and emerging data-driven techniques such as machine learning. The review further examines methane emission characteristics in key sectors, i.e., oil and gas, coal mining, agriculture, and waste management, and discusses how satellite data are applied in emission estimation and mitigation contexts. The paper concludes by identifying technical and operational challenges and outlining research directions to enhance the accuracy, accessibility, and policy relevance of satellite-based methane monitoring. Full article

Methane emissions from coal mines, especially surface operations, are spatially diffuse, presenting significant challenges for accurate quantification. Satellites such as TROPOMI, GHGSat, PRISMA, GaoFen-5, and GOSAT have been extensively used for detecting methane emissions at various scales, from individual point sources to regional and global assessments. Despite various advancements, methane quantification via satellite observations remains subject to several challenges. Various quantification methods for the same observation can produce variable results. Also, meteorological conditions, terrain complexity, and surface heterogeneity introduce uncertainties in emission estimates. The selection of wind speed and direction, along with retrieval-algorithm limitations, can lead to significant discrepancies in reported emissions. Additionally, satellite-based observations capture emissions only at specific overpass times, which may introduce temporal uncertainties compared to inventories derived from continuous emission estimations. This study provides a comprehensive review of satellite-based coal mine methane (CMM) monitoring, evaluating current methodologies, their limitations, and recent technological advancements. We discussed the potential of emerging machine-learning techniques, improved atmospheric modelling, and integrated observational approaches to enhance methane emission quantification. By refining satellite-based monitoring techniques and addressing existing challenges, this research will support the development of more accurate emission inventories and effective mitigation strategies for the coal mining sector. Full article

Methane (CH₄) emissions are a major contributor to greenhouse gases and pose significant challenges to global climate mitigation efforts. The accurate determination of CH₄ concentrations via remote sensing is crucial for emission monitoring but remains impeded by surface spectral heterogeneity—notably albedo variations and land cover diversity. This study systematically assessed the sensitivity of three mainstream algorithms, namely, matched filter (MF), albedo-corrected reweighted-L1-matched filter (ACRWL1MF), and differential optical absorption spectroscopy (DOAS), to surface type, albedo, and emission rate through high-fidelity simulation experiments, and proposed a dynamic regularized adaptive matched filter (DRAMF) algorithm. The experiments simulated airborne hyperspectral imagery from the Airborne Visible/InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) with known CH₄ concentrations over diverse surfaces (including vegetation, soil, and water) and controlled variations in albedo through the large-eddy simulation (LES) mode of the Weather Research and Forecasting (WRF) model and the MODTRAN radiative transfer model. The results show the following: (1) MF and DOAS have higher true positive rates (TP > 90%) in high-reflectivity scenarios, but the problem of false positives is prominent (TN < 52%); ACRWL1MF significantly improves the true negative rate (TN = 95.9%) through albedo correction but lacks the ability to detect low concentrations of CH₄ (TP = 63.8%). (2) All algorithms perform better at high emission rates (1000 kg/h) than at low emission rates (500 kg/h), but ACRWL1MF performs more robustly in low-albedo scenarios. (3) The proposed DRAMF algorithm improves the F1 score (0.129) by about 180% compared to the MF and DOAS algorithms and improves TP value (81.4%) by about 128% compared to the ACRWL1MF algorithm through dynamic background updates and an iterative reweighting mechanism. In practical applications, the DRAMF algorithm can also effectively monitor plumes. This research indicates that algorithms should be selected considering the specific application scenario and provides a direction for technical improvements (e.g., deep learning model) for monitoring gas emission. Full article

Population growth drives increasing energy demands, agricultural production, and organic waste generation. The organic waste contributes to greenhouse gas emissions and increasing landfill burdens, highlighting the need for novel closed-loop technologies that integrate water, energy, and food resources. Within the context of the Water–energy–food Nexus (WEF), wastewater can be recycled for food production and food waste can be converted into clean energy, both contributing to environmental impact reduction and resource sustainability. A novel household-scale, closed-loop WEF system was designed, installed and operated to manage organic waste while retrieving water for irrigation, nutrients for plant growth, and biogas for energy generation. The system included a biodigester for energy production, a sand filter system to regulate nutrient levels in the effluent, and a hydroponic setup for growing food crops using the nutrient-rich effluent. These components are operated with a daily batch feeder coupled with automated sensors to monitor effluent flow from the biodigester, sand filter system, and the feeder to the hydroponic system. This novel system was operated continuously for two months using typical household waste composition. Controlled experimental tests were conducted weekly to measure the nutrient content of the effluent at four locations and to analyze the composition of biogas. Gas chromatography was used to analyze biogas composition, while test strips and In-Situ Aqua Troll Multi-Parameter Water Quality Sonde were employed for water quality measurements during the experimental study. Experimental results showed that the system consistently produced biogas with 76.7% (±5.2%) methane, while effluent analysis confirmed its potential as a nutrient source with average concentrations of phosphate (20 mg/L), nitrate (26 mg/L), and nitrite (5 mg/L). These nutrient values indicate suitability for hydroponic crop growth and reduced reliance on synthetic fertilizers. This novel system represents a significant step toward integrating waste management, energy production, and food cultivation at the source, in this case, the household. Full article

We employed a global high-resolution inverse model to estimate sectoral methane emissions, integrating observations from the GOSAT-2 satellite for the first time, along with observations from the surface observation network. A similar set of inversions using GOSAT observations was carried out to evaluate the consistency between emissions estimates derived from these two satellites and to ensure that GOSAT-2 data could seamlessly integrate with the existing data series without disrupting the continuity of flux estimates. This analysis, covering the period from 2019 to 2022, utilized prior anthropogenic emissions data mainly from EDGAR v6 and incorporated additional natural sources and sinks as outlined by global methane budget, 2020. Our analysis reveals a general agreement between total methane emissions estimates from GOSAT and GOSAT-2. However, on a sectoral basis, we found notable regional differences in the flux estimates. While GOSAT inversion estimates ~8 Tg a⁻¹ more anthropogenic emissions for China and around 4 Tg a⁻¹ more wetland emissions for Brazil and Indonesia, the posterior error distribution suggests that GOSAT-2 inversion is closer to surface observations over Asia. These discrepancies are found in regions with significant differences in XCH₄ data from the two satellites, such as East Asia and North America, tropical South America, and tropical Africa. These regional biases persist due to limited representative surface reference sites for Level 2 bias correction. The relatively lower data volume from GOSAT also introduces seasonal biases in the flux estimates when the quality filtering of Level 2 data persistently reduces usable observations during certain seasons, resulting in inadequate representation of the seasonal cycle in regions such as East Asia. Similarly, in tropical South America, where the model is relatively under-constrained by the limited surface observations, the lower data volume of GOSAT-2 suffers. While the two inversions exhibit consistent overall performance across North America and Europe, the GOSAT-2-based inversion demonstrates a better performance over East Asia. Therefore, while the two satellite datasets are broadly consistent, considering the fact that the biases in the XCH₄ data overlap with regions under-constrained by surface observations, establishing additional surface reference measurement sites is desirable to ensure consistent inversion results. Full article

The bacterial phylum Verrucomicrobiota accommodates free-living and symbiotic microorganisms, which inhabit a wide range of environments and specialize in polysaccharide degradation. Due to difficulties in cultivation, much of the currently available knowledge about these bacteria originated from cultivation-independent studies. A phylogenetic clade defined by the free-living bacterium from oilsands tailings pond, Oleiharenicola alkalitolerans, and the symbiont of the tunicate Lissoclinum sp., Candidatus Didemniditutus mandelae, is a poorly studied verrucomicrobial group. This clade includes two dozen methagenome-assembled genomes (MAGs) retrieved from aquatic and soil habitats all over the world. A new member of this clade, strain Vm1, was isolated from a methane-fed laboratory bioreactor with a Methylococcus-dominated methane-oxidizing consortium and characterized in this study. Strain Vm1 was represented by ultra-small, motile cocci with a mean diameter of 0.4 µm that grew in oxic and micro-oxic conditions at temperatures between 20 and 42 °C. Stable development of strain Vm1 in a co-culture with Methylococcus was due to the ability to utilize organic acids excreted by the methanotroph and its exopolysaccharides. The finished genome of strain Vm1 was 4.8 Mb in size and contained about 4200 predicted protein-coding sequences, including a wide repertoire of CAZyme-encoding genes. Among these CAZymes, two proteins presumably responsible for xylan and arabinan degradation, were encoded in several MAGs of Vm1-related free-living verrucomicrobia, thus offering an insight into the reasons behind wide distribution of these bacteria in the environment. Apparently, many representatives of the Oleiharenicola–Candidatus Didemniditutus clade may occur in nature in trophic associations with methanotrophic bacteria, thus participating in the cycling of methane-derived carbon. Full article

This study is a systematic critical review aimed at rigorously evaluating the potential of Hermetia illucens larvae (black soldier fly larvae, BSFL) as a sustainable protein source for animal feed through a standardized methodological framework. To address the significant challenge posed by the increasing global demand for protein feed to agricultural sustainability, we retrieved relevant studies published between October 2008 and June 2025 from three core databases—PubMed, ScienceDirect, and Web of Science—and conducted study screening and data extraction in accordance with the PRISMA guidelines. BSFL represent a viable alternative, with a high protein content of 40–60% and efficient organic waste conversion capabilities. This systematic review explores the potential of BSFL to replace traditional protein sources such as fishmeal and soybean meal in animal feed, highlighting their advantages in enhancing growth performance, improving gut health, and reducing methane emissions in ruminants. However, there are still critical research gaps, including the need for standardized safety assessments regarding heavy metal accumulation and chitin digestibility. Addressing these challenges through optimized rearing techniques and rigorous safety evaluations will be crucial for scaling up BSFL production and advancing the development of circular agriculture. Full article

This study presents TeS v.3, a thermodynamic equilibrium simulator integrated with an artificial intelligence agent (AI), ThermoAgent, to enhance the analysis of complex chemical systems. Developed in Python, the simulator employs Gibbs energy minimization for isothermal reactors and entropy maximization for adiabatic reactors. ThermoAgent leverages the LangChain framework to interpret natural language commands, autonomously execute simulations, and query a scientific knowledge base through a Retrieval-Augmented Generation (RAG) approach. The validation of TeS v.3 demonstrated high accuracy, with coefficients of determination (R² > 0.95) compared to reference simulation data and strong correlation (R² > 0.88) with experimental data from the steam methane reforming (SMR) process. The SMR analysis correctly distinguished the high conversions in isothermal reactors from the limited conversions in adiabatic reactors, due to the reaction temperature drop. ThermoAgent successfully executed simulations and provided justified analyses, combining generated data with information from reference publications. The successful integration of the simulator with the AI agent represents a significant advancement, offering a powerful tool that accurately calculates equilibrium and accelerates knowledge extraction through intuitive interaction. Full article

Satellite-based monitoring of atmospheric column-averaged dry-air mole fraction (XCH₄) is essential for quantifying methane (CH₄) emissions, yet uncharacterized spatially varying biases in XCH₄ observations can cause misattribution in flux estimates. This study assesses the potential of the upcoming TanSat-2 satellite mission to estimate China’s CH₄ emission using a series of Observing System Simulation Experiments (OSSEs) based on an Ensemble Kalman Filter (EnKF) inversion framework coupled with GEOS-Chem on a 0.5° × 0.625° grid, alongside an evaluation of current TROPOMI-based products against Total Carbon Column Observing Network (TCCON) observations. Assuming a target precision of 8 ppb, TanSat-2 could achieve an annual national emission estimate accuracy of 2.9% ± 4.2%, reducing prior uncertainty by 84%, with regional deviations below 5.0% across Northeast, Central, East, and Southwest China. In contrast, limited coverage in South China due to persistent cloud cover leads to a 26.1% discrepancy—also evident in pseudo TROPOMI OSSEs—highlighting the need for complementary ground-based monitoring strategies. Sensitivity analyses show that satellite retrieval biases strongly affect inversion robustness, reducing the accuracy in China’s total emission estimates by 5.8% for every 1 ppb increase in bias level across scenarios, particularly in Northeast, Central and East China. We recommend expanding ground-based XCH₄ observations in these regions to support the correction of satellite-derived biases and improve the reliability of satellite-constrained inversion results. 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

This paper is about the characteristics of and a method to recognize the onset of limit cycle thermoacoustic oscillations in a gas turbine-like combustor with a premixed turbulent methane/air flame. Information on the measured time series data of the pressure and the OH* chemiluminescence is acquired and postprocessed. This is performed for a combustor with variation in two parameters: fuel/air equivalence ratio and combustor length. It is of prime importance to acknowledge the nonlinear dynamic nature of these instabilities. A method is studied to interpret thermoacoustic instability phenomena and assess quantitatively the transition of the combustor from a stable to an unstable regime. In this method, three-phase portraits are created on the basis of data retrieved from the measured acoustics and flame intensity in the laboratory-scale test combustor. In the path to limit cycle oscillation, the random distribution in the three-phase portrait contracts to an attractor. The phase portraits obtained when changing operating conditions, moving from the stable to the unstable regime and back, are analyzed. Subsequently, the attractor dimension is determined for quantitative analysis. On the basis of the trajectories from the stable to unstable and back in one run, a study is performed of the hysteresis dynamics in bifurcation diagrams. Finally, the onset of the instability is demonstrated to be recognized by the 0-1 criterion for chaos. The method was developed and demonstrated on a low-power atmospheric methane combustor with the aim to apply it subsequently on a high-power pressurized diesel combustor. Full article

Accurate retrieval of column-averaged dry-air mole fraction of methane (XCH₄) in the atmosphere is important for greenhouse gas emission management. Traditional XCH₄ retrieval methods are complex, while machine learning can be used to model nonlinear relationships by analyzing large datasets, providing an efficient alternative. This study proposes an XGBoost algorithm-based retrieval method to improve the efficiency of atmospheric XCH₄ retrieval. First, the key wavelengths affecting XCH₄ retrieval were determined using a radiative transfer model. The TROPOspheric Monitoring Instrument (TROPOMI) L1B satellite data, L2 XCH₄ products, and auxiliary data were matched to construct the dataset. The dataset constructed was used to train the XGBoost model and obtain the TRO_XGB_XCH₄ model. Finally, the accuracy of the proposed model was evaluated using various parameter values and validated against XCH₄ products and Total Carbon Column Observing Network (TCCON) ground-based observations. The results showed that the proposed TRO_XGB_XCH₄ model had a tenfold cross-validation accuracy R of 0.978, a ground-based validation R of 0.749, and a temporal extension accuracy R of 0.863. Therefore, the accuracy of the TRO_XGB_XCH₄ retrieval model is comparable to that of the official TROPOMI L2 product. Full article

The effective monitoring of methane (CH₄) point sources is important for climate change research. Satellite-based observations have demonstrated significant potential for emission estimation. In this study, the methane plumes with different emission rates are modelled and pseudo-observations with diverse spatial resolution, spectral resolution, and signal-to-noise ratios (SNR) are simulated by the radiative transfer model. The iterative maximum a posteriori–differential optical absorption spectroscopy (IMAP-DOAS) algorithm is applied to retrieve the column-averaged methane dry air mole fraction (XCH₄), a three-dimensional matrix of estimated plume emission rates is then constructed. The results indicate that an optimal plume estimation requires high spatial and spectral resolution alongside an adequate SNR. While a spatial resolution degradation within 120 m has little impact on quantification, a high spatial resolution is important for detecting low-emission plumes. Additionally, a fine spectral resolution (<5 nm) is more beneficial than a higher SNR for precise plume retrieval. Scientific SNR settings can also help to accurately quantify methane plumes, but there is no need to pursue an overly extreme SNR. Finally, miniaturized spectroscopic systems, such as dispersive spectrometers or Fabry–Pérot interferometers, meet current detection needs, offering a faster and resource-efficient deployment pathway. The results can provide a reference for the development of current detection instruments for methane plumes. Full article

Methane (CH₄) emissions from the waste industry in the U.S.A. and the European Union (EU) have decreased by over 38% from 1990 to 2021. The success in CH₄ emission reduction in the U.S.A. is attributable to two main reasons. Firstly, the increase in the recycling and composting share to 32% of managed waste, thus removing decomposable material from landfills, and secondly, the implementation of methane capture and utilization programs, which have reduced the CH₄ released into the atmosphere from 1990 to 2022 by over 60%. By 2022, the EU had reduced landfilling to 23% of the total waste, with waste-to-energy and composting more than double that of their U.S. counterparts, and recycling alone attaining a share of 30%. The EU’s success has been the result of aggressive European legislation requiring biodegradable MSW going to landfills to be reduced by 2035 to 10% of that in 1995, and 65% of packaging waste to be retrieved and recycled by 2025. In terms of N₂O emissions, in the EU there was a decrease from wastewater processes from 1990 to 2021, but an overall increase due to waste-to-energy operations, whereas in the U.S.A., both wastewater treatment and solid waste incineration appear to contribute to N₂O emissions. Full article