Search Results (100)

Anaerobic digestion (AD) is an effective technology for sustainable waste management, renewable energy production and resource recovery within a circular economy. This study offers a systematic bibliometric review of global advances in AD research and assesses their relevance to Ghana. Using the PRISMA framework, the literature from 2011 to 2025 was sourced from the Scopus database and analysed through bibliometric and thematic methods. The review emphasises four key factors affecting AD performance: municipal solid waste as feedstock, pretreatment technologies, biochemical methane potential (BMP) assessment and process optimisation. Studies were included if they addressed any of these themes. Non-English publications, inaccessible full texts and papers lacking bibliographic metadata were excluded. After screening 3424 records, 61 studies were included in the systematic review. After metadata screening, 3374 of the 3424 retrieved records were retained for bibliometric analysis. Results show that municipal solid waste, food waste, agricultural residues and sewage sludge are promising sources for biogas generation. Pretreatment techniques, including thermal, chemical, mechanical and biological, significantly enhance substrate biodegradability and methane production. BMP assessment is a reliable way to gauge feedstock suitability and energy recovery potential. Optimising parameters like pH, temperature, organic loading, hydraulic retention time and co-digestion ratios improves process stability and biogas yield. The review highlights the increasing use of modelling and optimisation to boost digester performance and facilitate scale-up. In Ghana, abundant organic waste offers significant opportunities for biogas development. Employing advanced feedstock characterisation, pretreatment, BMP evaluation and optimisation can improve AD efficiency, support renewable energy, reduce waste disposal issues and promote Ghana’s shift toward a sustainable circular bioeconomy. Full article

Methane (CH₄) is the second most significant greenhouse gas after carbon dioxide (CO₂). Due to its high short-term global warming potential and the feasibility of emission abatement, monitoring methane point-source emissions has become a critical strategy for mitigating climate change. To address existing technical bottlenecks associated with spatial coverage limitations and surface-induced signal interference in satellite-based monitoring, this study proposes an integrated methane monitoring framework termed Multi-Band Multi-Constraint (MBMC) using Sentinel-2 MSI imagery. The MBMC framework combines a Low-Reflectance Artifact Detection (LRAD) algorithm, a Multi-Band Multi-Pass (MBMP) differential absorption retrieval model, and Integrated Mass Enhancement (IME)-based emission quantification. The LRAD module effectively suppresses artifacts caused by low-reflectance surfaces and heterogeneous backgrounds, thereby improving the signal-to-noise ratio (SNR) and retrieval accuracy of methane column enhancements. In addition, a semi-automatic plume segmentation workflow integrating morphological operations with a spatial database is developed to improve methane plume extraction and source localization. The framework was validated using data from single-blind controlled methane release experiments conducted in Arizona, USA. Results show that the proposed method achieved a mean absolute percentage error (MAPE) of 21.6% for emission rates ranging from 0.5 to 3.0 t/h, demonstrating promising performance for Sentinel-2-based screening and quantification of methane point sources, particularly for emissions above approximately 0.5 t/h under favorable observation conditions. The framework was further applied to Sentinel-2 observations over a natural gas field in Northwest China, where multiple methane point sources associated with gas gathering stations were successfully identified and quantified. The proposed framework provides a practical approach for high-resolution and high-frequency satellite monitoring of methane point sources and supports the refinement of methane emission inventories and mitigation strategies in the oil and gas sector. Full article

Methane ($C{H}_4$) is the second most important anthropogenic greenhouse gas, accounting for approximately 30% of current global warming. Since 2007, atmospheric methane concentrations have been increasing at an accelerating rate, reaching a record 1945.85 ppb in November 2025. The emergence of high-resolution satellite constellations has transformed our ability to detect, quantify, and attribute methane emissions from space. This review provides a comprehensive analysis of the current state of high-resolution global methane mapping, examining: (1) the evolution of satellite missions from coarse-resolution sounders like TROPOMI (5.5 × 7 km) to very high-resolution imagers including WorldView-3 (3.7 m), GHGSat (50 m), and the recently launched Tanager-1 (30 m); (2) advances in retrieval algorithms, including the transition from physics-based matched filter methods to deep learning approaches such as U-Net architectures achieving F1-scores of 78.4% on Sentinel-2 imagery; (3) integration of satellite observations with atmospheric inverse models for flux estimation; (4) the impact of satellite-derived data on policy frameworks including the Global Methane Pledge and EPA’s Super-Emitter Program; and (5) remaining challenges including cloud contamination, detection limit trade-offs, and the need for sustained validation networks. We synthesize findings from over 200 peer-reviewed studies and analyze 42 years of NOAA global methane observations to demonstrate how the convergence of improved spatial resolution, machine learning, and international coordination is enabling unprecedented transparency in global methane monitoring. The review concludes with recommendations for future satellite missions and data assimilation strategies needed to meet the Global Methane Pledge target of 30% emission reductions by 2030. Full article

Global climate change poses escalating ecological challenges, with agriculture contributing approximately 30% of anthropogenic greenhouse gas emissions, primarily from nitrous oxide (N₂O) and methane (CH₄). The farmland carbon-nitrogen cycle represents a key nexus for coordinating pollution control and carbon mitigation. This study applies bibliometric methods, including co-occurrence analysis, clustering, and burst detection, to 1286 publications retrieved from the Web of Science Core Collection (1990–2025) and CiteSpace 6.2.R4. Results indicate that China (444 papers, centrality 0.42), the United States (211 papers), and Germany (151 papers) are leading contributors, with major institutions forming a multi-centered international collaboration network. Keyword analysis identified 11 core clusters (modularity Q = 0.82, silhouette S = 0.91), with nitrous oxide emerging as the central theme (frequency 670). The field has evolved through three stages: fundamental emission mechanism studies (1990–2005), agricultural management practices (2006–2015), and integrated mitigation strategies with microbial mechanism exploration (2016–2025). Current frontiers emphasize microbial-mediated carbon-nitrogen cycling and yield-scaled emission assessments bridging theory and practice. Future research should prioritize cross-scale coupling analysis, multi-objective management frameworks, smart agricultural technologies, and policy integration. This study provides a systematic bibliometric mapping of the evolution of synergistic carbon-nitrogen research in agricultural systems, offering a quantitative overview of development trends and research gaps. Full article

Metallurgical coal operations are a significant but poorly constrained source of methane (CH₄) in Canada. We present a multi-instrument analysis of 63 methane plume detections at Fording River Operations, British Columbia (January 2022–March 2026), using the Airborne Visible/Infrared Imaging Spectrometer—Next Generation (AVIRIS-NG; n = 39), the Earth Surface Mineral Dust Source Investigation (EMIT; n = 4) and Tanager-1 (n = 20). Of these, 41 plumes (65%) were quantified, with retrieved emission rates of 34–3622 kg CH₄ h⁻¹; 54% exceeded the 500 kg h⁻¹ super-emitter threshold. Because 73% of detections fall in September and no detections are available for 2023, results characterize the late-summer overpass window and should not be extrapolated seasonally without further coverage. The central finding is a systematic, asymmetric wind-speed disagreement between two numerical weather prediction (NWP) products that maps onto the Integrated Mass Enhancement (IME) quantification outcome. A univariate logistic regression identifies HRRR wind speed as a significant predictor of quantification success (OR = 0.29 per m s⁻¹, 95% CI [0.15, 0.56], p < 0.001; AUC = 0.80; 5-fold cross-validated AUC = 0.79 ± 0.20, fold range 0.45–1.00). Cross-validation against ERA5-Land shows that HRRR exceeds ERA5 by a mean of +0.86 m s⁻¹ (+43%) for unquantified events but shows near-zero disagreement for quantified events (–0.09 m s⁻¹, –6%). A sensitivity analysis restricted to HRRR-forced retrievals (EMIT + Tanager-1, n = 24) confirms the finding is not an artefact of mixed wind data sources (OR = 0.28, AUC = 0.83, p = 0.018). Full article

The performance of a Mayenite-supported nickel-based catalyst were investigated by using an in-house-designed, assembled and set-up chemical pilot plant, which was developed to provide experimental insights relevant to industrial scale up. In particular, the proposed heterogeneous catalytic system was structured in mm-sized spheres and tested in a large-scale experiment, in a fixed-bed reactor for the CO₂ methanation process, and the results were compared with the output achieved with a Ni/alumina catalyst produced by an analogous route as the benchmark. The obtained findings highlighted the effective potential of the Mayenite structure supporting metallic active sites in promoting CO₂ reduction under the selected operating conditions (450 °C, 4 bar), along with long-term stability and high CH₄ selectivity. Moreover, the available experimental equipment was optimized to achieve accurate estimations of amounts of reaction by-product, as confirmed by the optimal agreement with the mass balance retrieved from the measured gaseous outlet composition. Such an achievement, notable for a large-scale chemical plant, plays a capital role in terms of industrial applications due to the critical impact of residual carbon and water in establishing the viability of innovative catalyst systems for the CO₂ recycling process. Full article

Biogas production through anaerobic digestion is increasingly recognised as a strategic renewable energy pathway capable of addressing South Africa’s energy insecurity, organic waste management challenges, and climate mitigation goals. However, the water-intensive nature of anaerobic digestion raises critical sustainability concerns in water-scarce regions. This systematic review critically examines the water footprint of biogas-based bioenergy systems, with a specific focus on South Africa’s water-stressed context, to understand how water availability, feedstock selection, digester configuration, and governance frameworks influence system viability and scalability. This study adopts a systematic literature review (SLR) approach guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology; peer-reviewed literature published between 2010 and 2025 was retrieved from Scopus and Web of Science and synthesised through descriptive analysis and qualitative meta-synthesis. The review integrates blue, green, and greywater footprint concepts to assess water use across diverse biogas pathways, including livestock manure, agricultural residues, food waste, wastewater sludge, and aquatic biomass. Findings indicate that wet digestion systems, dominant in South Africa, are highly sensitive to freshwater availability, particularly where slurry dilution relies on blue water. In contrast, wastewater-integrated, semi-wet, and co-digestion systems substantially reduce freshwater demand while enhancing methane yields and process stability. The reuse of greywater, industrial effluents, and digestate emerges as a key strategy for lowering water footprints and strengthening circular water–energy linkages. Despite strong technical potential, the adoption of water-efficient anaerobic digestion systems remains constrained by fragmented governance, infrastructure deficits, and limited empirical data on dry and low-water digestion technologies. The review concludes that embedding water footprint considerations into bioenergy planning, policy, and system design is essential for the sustainable expansion of biogas in South Africa. Integrated water–energy–waste governance, coupled with targeted technological innovation, is critical to ensuring that biogas development enhances both energy security and water sustainability in water-scarce regions. Full article

Spaceborne hyperspectral imaging spectrometers enable refined retrieval and quantification of methane point-source emissions. However, the conventional matched filter (MF) systematically underestimates methane enhancements under high-concentration conditions and remains sensitive to spectral inconsistencies across varying observation scenarios. To address these limitations, we improve MF-based retrieval from two aspects: the observation model and the unit absorption spectrum (UAS) representation. First, a Levenberg–Marquardt matched filter (LMMF) is developed by extending the MF framework to a nonlinear retrieval formulation while retaining its data-driven and background-statistics-based characteristics. Specifically, the exponential absorption term is preserved, and methane enhancement is iteratively solved in the nonlinear domain, enabling a more physically consistent retrieval without requiring precise external prior knowledge. Building upon this framework, a spectrally corrected LMMF (SC-LMMF) is further proposed by introducing a lookup-table-based dynamic UAS correction to account for variations in observation geometry, surface elevation, and atmospheric state. Comprehensive validation using idealized and noise-perturbed simulations, end-to-end simulations, and controlled-release experiments demonstrates that the LMMF mitigates high-concentration underestimation relative to the MF. The SC-LMMF further reduces cross-scene systematic biases, shifting retrievals toward a near 1:1 relationship. In controlled-release experiments, the SC-LMMF increased the coefficient of determination (R²) by approximately 50% while reducing the root mean square error (RMSE) and mean absolute error (MAE) by approximately 70% relative to the MF. Overall, the proposed framework enhances the robustness and quantitative consistency of methane point-source retrievals across multisource hyperspectral satellite observations. Full article

Satellite observations of methane are frequently compromised by extensive data gaps caused by cloud cover and aerosol contamination, limiting their utility for continuous regional monitoring. To reconstruct these spatiotemporal discontinuities, this study developed the Stacked Ensemble Reconstruction Framework for Methane (SERF-XCH₄). By integrating Sentinel-5P TROPOMI retrievals with 25 multi-source environmental covariates, we generated a spatiotemporally continuous, high-resolution (0.1°) monthly dataset (SERF-XCH₄-IM) for Inner Mongolia spanning 2019 to 2023. Comprehensive validation demonstrates that the framework achieves exceptional predictive fidelity with a Coefficient of Determination ($R^2$) of 0.93 and a Root Mean Square Error (RMSE) of 7.89 ppb, significantly surpassing the performance of individual base learners and traditional interpolation methods. Furthermore, spatial block cross-validation confirmed robust generalization capabilities ($R^2=0.90$) in data-void regions. To unravel the “black box” of the model, SHapley Additive exPlanations (SHAP) analysis was employed, revealing that temporal factors (contributing 63.9%), air temperature, and elevation are the dominant drivers governing XCH₄ variability. Spatiotemporal analysis further identified the Hulunbuir region as a significant growth “hotspot” with an annual increase rate exceeding 18.5 ppb/yr, a trend primarily driven by intensified emissions during the autumn and winter seasons. Consequently, this framework establishes a high-precision, interpretable paradigm for regional methane monitoring and geo-information reconstruction. Full article

Arctic–boreal wetlands and lakes are among the most significant and most uncertain natural sources of atmospheric methane. Rapid Arctic amplification, permafrost thaw, hydrological change, and increasing ecosystem productivity are expected to intensify methane emissions from high-latitude landscapes. Yet, significant uncertainties persist in quantifying their magnitude, seasonality, and spatial distribution. This review synthesizes the current state of the art in monitoring methane emissions from Arctic–boreal wetlands and lakes through complementary bottom-up and top-down approaches. We examine Earth observation (EO) capabilities, including optical, thermal infrared (TIR), and synthetic aperture radar (SAR) missions, as well as new emerging satellite platforms. We also assess in situ measurement networks, wetland and lake inventories, empirical and process-based models, and atmospheric inversion frameworks. Key gaps remain in representing small waterbodies, shoreline heterogeneity, winter emissions, inventory harmonization, and integration between atmospheric retrievals and surface-based flux models. Moreover, advances in multi-sensor data fusion, explainable artificial intelligence (XAI), physics-informed inversion methods, and geospatial foundation models offer strong potential to reduce these uncertainties. A coordinated integration of satellite observations, field measurements, and transparent modeling frameworks is essential to improve Arctic–boreal methane budgets and strengthen projections of climate feedback in a rapidly warming region. Full article

In this study, continuous near-real-time measurements of greenhouse gases (GHGs), particularly carbon dioxide (CO₂) and methane (CH₄), and aerosol optical depth (AOD) were conducted at the Socheongcho Ocean Research Station (SORS) from January 2021 to April 2022. Specifically, AOD products retrieved from the Geo-KOMPSAT-2B sensors—Geostationary Environment Monitoring Spectrometer and Geostationary Ocean Color Imager II—were compared and validated against ground-based Aerosol Robotic Network (AERONET) observations. Both satellite products exhibited overall good agreement with AERONET AOD data and showed low bias. The GHG measurements based on cavity ring-down spectroscopy indicated that CO₂ reached its highest seasonal mean in the spring of 2022, while CH₄ attained its maximum during the wet summer of 2022. Temperature, relative humidity, and evaporation were closely associated with AOD variability during the dry summer period, while elevated temperatures may have contributed to enhanced photochemical activity and modulation of CH₄ concentrations. In the cold season, concurrent increases in GHGs and combustion-related pollutants (PM_2.5, CO, and black carbon) were observed, suggesting reduced oxidation capacity under stable atmospheric conditions. Overall, these findings underscore the potential value of integrating satellite and in situ observations to better characterize GHG–aerosol interactions and support emission mitigation strategies in the Northeast Asian marine environment. Full article

In recent years, natural bioactive compounds have been increasingly investigated as functional feed additives to enhance livestock production. The present study aims to provide an update on the potential use of these compounds to enhance animal health and the quality of animal products, while critically assessing their principal limitations and future practical applicability. The review is based on peer-reviewed articles published between 2020 and 2025 and retrieved from the Scopus database, ensuring the inclusion of recent and high-impact scientific contributions. Phytogenic feed additives, including polyphenols, terpenoids, and alkaloids, exert beneficial effects on animal health by modulating oxidative stress and inflammatory pathways. Improvements in milk and meat quality are mainly associated with enhanced antioxidant capacity and lipid stability, rather than with the direct transfer of phytochemicals into animal-derived products. In ruminants, selected bioactive compounds may also contribute to methane mitigation through modulation of rumen fermentation and microbial ecology. However, their efficacy remains highly context-dependent and requires precise characterization of composition, dosage, and species-specific application. Future research should therefore prioritize deeper elucidation of metabolic mechanisms, systemic physiological responses, and productive outcomes to better define the conditions under which these compounds exert consistent and biologically meaningful effects. Full article

Methane is the second most important contributor to global warming, and monitoring super-emitters from space is critical for climate mitigation. Despite the advancements in hyperspectral remote sensing, comparing methane observations across diverse imaging spectrometers remains a challenging task. Different retrieval algorithms, plume segmentation techniques and uncertainty treatments make it very hard to perform fair comparisons between different products. To overcome these difficulties, this study presents HyGAS (Hyperspectral Gas Analysis Suite), a unified, open-source framework for sensor-agnostic methane retrieval and flux estimation. Starting from the established clutter-matched-filter (CMF) formalism and a physical calibration in concentration–path-length units (ppm·m), we propagate both instrument noise and surface-driven background variability consistently from methane enhancement to Integrated Mass Enhancement (IME) and flux. The framework further includes a spectrally matched background-selection strategy, scale-aware segmentation with fixed physical criteria across resolutions, and emission-rate estimation via an IME–$U_{eff}$ approach informed by Large Eddy Simulation (LES). We demonstrate the framework on near-simultaneous observations of landfills and gas infrastructure in Argentina, Turkmenistan, and Pakistan, spanning Level-1 radiance workflows (PRISMA, EnMAP, Tanager-1) and Level-2 methane products (EMIT, GHGSat). The standardised chain enables systematic inter-comparison of methane enhancement products and reduces methodological bias, supporting robust multi-mission assessment and future global monitoring. Full article

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