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Methane (CH4) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH4 fluxes is examined at 14 FLUXNET- CH4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance-based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET-CH4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques.

This study continued the in vitro screening of locally available ruminant feedstuffs for optimum nutrient composition and low methane (CH4) production in Ethiopia. The best bet feeds from the in vitro study, hereafter called the test feeds, include dried leaves of Acacia nilotica , Ziziphus spina-christi , and brewery spent grains (BSG). The study involves four treatments: Control, Acacia, BSG, and Ziziphus; each treatment provided an equivalent crude protein and estimated enteric CH4 emissions using Modeling and a Laser CH4 detector (LMD). The experiment was designed as a randomized complete block, using initial weight as the blocking factor for 21 yearling castrated Menz sheep. The study spanned 90 days, and digestibility trials were carried out following a month of the feeding trial. The control group exhibited a significantly (p < 0.001) lower dry matter intake (DMI) compared to the test feed group, which had a higher intake, particularly in the Ziziphus group. However, the Ziziphus group demonstrated significantly (p < 0.01) lower CP digestibility than the other groups. The test diet also led to a significantly (p < 0.001) higher weight gain. Notably, the Ziziphus group demonstrated superior performance in weight change (BWC), final body weight (FBW), and average daily gain (ADG). Similar results were observed for CH4 production (g/day), CH4 yield (g/kg DMI), and CH4 intensity (g CH4/kg ADG) using both CH4 measuring methods. The CH4 emission intensity was significantly (p < 0.04) lower in the test feed groups than in the control group. The control group emitted 808.7 and 825.3 g of CH4, while the Ziziphus group emitted 220 and 265.3 g of CH4 per kg of ADG using the Modeling and LMD methods, respectively. This study indicates that LMD could yield biologically plausible data for sheep. Although the small sample size in the Ziziphus group was a limitation of this study, leaf meals from Ziziphus spina-christi and Acacia nilotica , which are rich in condensed tannins (CTs), have resulted in considerable weight gain and enhanced feed efficiency, thereby making these leaf meals a viable and sustainable feed option for ruminants in Ethiopia. [ABSTRACT FROM AUTHOR]

Background: Methane emission from enteric rumen fermentation is a main source of greenhouse gas (GHG) emission and a major concern for global warming. Results: In this study, we isolated methanotroph-methylotroph consortium NC52PC from the rumen after a series of sub-culture and repetitive streaking on an agar plate and polycarbonate membrane filter. The NC52PC comprises methanotroph species (Methylocystis sp.) and methylotroph species (Methylobacterium sp.), forming a consortium capable of growing solely on methane as a carbon source. Their morphology, growth, and genome sequence were characterized. We assessed its effectiveness in mitigating methane emissions through both in vitro and in vivo experiments. During the in vitro trial, the introduction of NC52PC (at a concentration of 5.1 × 107 CFUs/ml) demonstrated a reduction in methane production exceeding 40% and 50% after 12 and 24 h, respectively. Also, NC52PC did not significantly alter other aspects of the in vitro rumen fermentation parameters such as pH, total gas production, and digestibility. Further investigation involved testing NC52PC as a dietary supplement in 12 young Hanwoo steers over three 30-day test periods. The steers received a diet comprising 70.8% concentrate and 29.2% bluegrass on a dry matter basis, with variations including 3 × 107 CFUs/ml of NC52PC (LOW) and 3 × 108 CFUs/ml (HIGH) of NC52PC, and without NC52PC as a control (CON). Steers administered with HIGH and LOW concentrations of NC52PC exhibited reduced enteric methane emission (g/day) by 14.4% and 12.0%, respectively. Conclusion: Feeding methanotroph-methylotroph consortium NC52PC significantly reduced methane emissions in Korean beef cattle without any adverse effects on animal health. These findings suggest that this probiotic could serve as a promising feed additive to effectively mitigate methane emissions from ruminants. However, further research is needed to evaluate the long-term effects of NC52PC on animal health, and on meat and milk quality. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this study was to investigate how in vitro gas production (GP) and ruminal fermentation characteristics were affected by increasing concentrations of green algae plant (C. reinhardtii) extracts in combination with nanoparticles MgO and MgS. Methods: A solution containing 0.1 M MgCl2 was prepared in 300 mL for the green production of MgCl nanoparticles. The mixture was refluxed for two hours at 85°C using a reflux condenser after 10 mL of pomegranate plant extract was added. The green algal plant (C. reinhardtii), which has many non-toxic antioxidants, was used as a carbon source to produce carbon quantum dots (CQD). Chemical analysis was conducted in accordance with AOAC (2005) recommendations. Rumen fluid from recently slaughtered calves is used to produce in vitro gas immediately following slaughter. Analysis of variance (ANOVA) was performed on the obtained data from the in vitro study in a completely randomized design using the mixed model of SAS (version 9.4; Inc., Cary NC, USA). Results and Discussion: The variance analysis results and the average values of the chemical compositions were significantly influenced by the extracts (all p < 0.0001). In this line, the values of net gas, pH, OMD, ME, NEl, and ME were found to be the highest for Algae + 50 MgO and the lowest for Algae + 50 MgS, respectively (all p < 0.0001). These promising results imply that extracts from C. Reinhardtii may be able to mitigate the adverse consequences of rumen fermentation. To precisely ascertain the impact particular Rhodophyta on greenhouse gas emissions, additional investigation is needed. [ABSTRACT FROM AUTHOR]

Abstract Background Methane emission from enteric rumen fermentation is a main source of greenhouse gas (GHG) emission and a major concern for global warming. Results In this study, we isolated methanotroph-methylotroph consortium NC52PC from the rumen after a series of sub-culture and repetitive streaking on an agar plate and polycarbonate membrane filter. The NC52PC comprises methanotroph species (Methylocystis sp.) and methylotroph species (Methylobacterium sp.), forming a consortium capable of growing solely on methane as a carbon source. Their morphology, growth, and genome sequence were characterized. We assessed its effectiveness in mitigating methane emissions through both in vitro and in vivo experiments. During the in vitro trial, the introduction of NC52PC (at a concentration of 5.1 × 107 CFUs/ml) demonstrated a reduction in methane production exceeding 40% and 50% after 12 and 24 h, respectively. Also, NC52PC did not significantly alter other aspects of the in vitro rumen fermentation parameters such as pH, total gas production, and digestibility. Further investigation involved testing NC52PC as a dietary supplement in 12 young Hanwoo steers over three 30-day test periods. The steers received a diet comprising 70.8% concentrate and 29.2% bluegrass on a dry matter basis, with variations including 3 × 107 CFUs/ml of NC52PC (LOW) and 3 × 108 CFUs/ml (HIGH) of NC52PC, and without NC52PC as a control (CON). Steers administered with HIGH and LOW concentrations of NC52PC exhibited reduced enteric methane emission (g/day) by 14.4% and 12.0%, respectively. Conclusion Feeding methanotroph-methylotroph consortium NC52PC significantly reduced methane emissions in Korean beef cattle without any adverse effects on animal health. These findings suggest that this probiotic could serve as a promising feed additive to effectively mitigate methane emissions from ruminants. However, further research is needed to evaluate the long-term effects of NC52PC on animal health, and on meat and milk quality.

This study aimed to evaluate the effects of encapsulation and combining probiotics with different nitrate forms on methane emission and the in vitro fermentation process of ruminants. Sodium nitrate (NaNO3) and nitric acid (HNO3) were used as nitrate forms, while lactic acid bacteria Lactiplantibacillus plantarum TSD-10 was used as a probiotic source. Twelve different treatments with four replicates were allocated in the factorial block design (2 × 2 × 3). During each replicate, the test was conducted individually in a different week so that each block could be considered separately. Data analysis followed the analysis of variance (ANOVA) and then continued with the Duncan multiple range test. After encapsulation, significant increases (p < 0.05) in gas production, gas kinetics, total volatile fatty acids (TVFAs), and production of propionic acid were observed. In addition, encapsulation significantly decreased (p < 0.05) the pH, ammonia concentration (NH3), nutrient digestibility, and the ratio of acetic to propionic acid (p < 0.05). The addition of combined encapsulated probiotics and encapsulated nitrate significantly increased (p < 0.05) gas production, maximum gas production, TVFAs, and the molar portion of propionic acid, and significantly decreased (p < 0.05) enteric methane emission, acetic acid, ammonia concentration, pH, and nutrient digestibility. The addition of sodium nitrate significantly increased (p < 0.05) the concentration of TVFAs and acetic acid, while nitric acid significantly increased (p < 0.05) the gas production rate. However, there was no significant effect due to combining unencapsulated probiotics with unencapsulated nitrate forms on the rumen fermentation process. There was a significant interaction (p < 0.05) between encapsulation probiotics and nitrate on ammonia concentration. In conclusion, combining encapsulated probiotics with encapsulated nitrate is an alternative method for enhancing the fermentation process and mitigating enteric methane emission in ruminants.

ABSTRACT: Inhibition of methyl-coenzyme M reductase can suppress the activity of ruminal methanogens, thereby reducing enteric methane emissions of ruminants. However, developing specific and environmentally friendly inhibitors is a challenging endeavor. To identify a natural and effective methane inhibitor that specifically targets methyl-coenzyme M reductase, molecular docking technology was employed to screen a library of phytogenic compounds. A total of 52 candidate compounds were obtained through molecular docking technique. Rosmarinic acid (RA) was one of the compounds that could traverse a narrow channel and bind to the active sites of methyl-coenzyme M reductase, with a calculated binding free energy of −9.355 kcal/mol. Furthermore, the effects of RA supplementation on methane production, rumen fermentation, and the microorganism community in dairy cows were investigated through in vitro rumen fermentation simulations according to a random design. Supplementation of RA resulted in a 15% decrease in methane production compared with the control. In addition, RA increased the molar proportion of acetate and propionate, whereas the sum of acetate and butyrate divided by propionate was decreased. At the bacterial level, the relative abundance of Rikenellaceae RC9 gut group, Christensenellaceae R7 group, Candidatus Saccharimonas, Desulfovibrio, and Lachnospiraceae FE2018 group decreased with RA supplementation. Conversely, the addition of RA significantly increased the relative abundance of DNF00809 (a genus from Eggerthellaceae), Denitrobacterium, an unclassified genus from Eggerthellaceae, an unclassified genus from Bacteroidales, and an unclassified genus from Atopobiaceae. At the archaeal level, the relative abundance of Methanobrevibacter decreased, whereas that of Methanosphaera increased with RA supplementation. These findings suggested that RA has the potential to be used as a novel natural additive for inhibiting ruminal methane production.

Simple Summary: Enteric methane (CH4) production in cattle accounts for a significant portion of global greenhouse gas emissions. Measurement of enteric methane emission is complex, expensive, and large-scale measurement is impractical. Therefore, in the absence of measurements, modeling can be used to predict CH4 production and help investigate mitigation options. In this study, we used dietary nutrient composition (g/kg), nutrients (kg/day), energy (MJ/day), and energy and organic matter (OM) digestibility (g/kg) as predictors of CH4 production to develop linear and nonlinear statistical models for predicting enteric methane emission from beef and dairy cattle and to evaluate the few available models. The objective of this study was to develop linear and nonlinear statistical models for predicting enteric methane emissions from beef and dairy cattle (EME, MJ/day). Ration nutrient composition (g/kg), nutrient (kg/day), energy (MJ/day), and energy and organic matter (OM) digestibility (g/kg) were used as predictors of CH4 production. Three databases of beef cattle, dairy cattle, and their combinations were developed using 34 published experiments to model EME predictions. Linear and nonlinear regression models were developed using a mixed-model approach to predict CH4 production (MJ/day) of individual animals based on feed composition. For the beef cattle database, Equation methane (MJ/d) = 1.6063 (±0.757) + 0.4256 (±0.0745) × DMI + 1.2213 (±0.1715) × NDFI + −0.475 (±0.446) × ADFI had the smallest RMSPE (21.99%), with 83.51% of this coming from random error and a regression bias was 16.49%. For the dairy cattle database, the RMSPE was minimized (15.99%) for methane (MJ/d) = 0.3989 (±1.1073) + 0.8685 (±0.1585) × DMI + 0.6675 (±0.4264) × NDFI, of which 85.11% was from random error and the regression deviation was 14.89%. When the beef and dairy cattle databases were combined, the RMSPE was minimized (24.4%) for methane(MJ/d) = −0.3496 (±0.723) + 0.5941 (±0.0851) × DMI + 1.388 (±0.2203) × NDFI + −0.027 (±0.4223) × ADFI, of which 85.62% was from the random error and the regression bias was 14.38%. Among the nonlinear equations for the three databases, the DMI-based exponential model outperformed the other nonlinear models, but the predictability and goodness of fit of the equations did not improve compared to the linear model. The existing equations overestimate CH4 production with low accuracy and precision. Therefore, the equations developed in this study improve the preparation of methane inventories and thus improve the estimation of methane production in beef and dairy cattle. [ABSTRACT FROM AUTHOR]

Wastewater treatment (WWT) contributes 2–9% of global greenhouse gas (GHG) emissions. The noticeable uncertainty in emissions estimation is due in large part to the lack of measurement data. Several methods have recently been developed for monitoring fugitive GHG emissions from WWT. However, limited by the short duration of the monitoring, only "snapshot" data can be obtained, necessitating extrapolation of the limited data for estimating annual emissions. Extrapolation introduces substantial errors, as it fails to account for the spatial and temporal variations of fugitive emissions. This research evaluated the feasibility of studying the long-term CH4 emissions from WWT by analyzing high spatial resolution Sentinel-2 data. Satellite images of a WWT plant in Calgary, Canada, taken between 2019 and 2023, were processed to retrieve CH4 column concentration distributions. Digital image processing techniques were developed and used for extracting the time- and space-varying features of CH4 emissions, which revealed daily, monthly, seasonal, and annual variations. Emission hotspots were also identified and corroborated with ground-based measurements. Despite limitations due to atmospheric scattering, cloud cover, and sensor resolution, which affect precise ground-level concentration assessments, the findings reveal the dynamic nature of fugitive GHG emissions from WWT, indicating the need for continuous monitoring. The results also show the potential of utilizing satellite images for cost-effectively evaluating fugitive CH4 emissions. [ABSTRACT FROM AUTHOR]

The study of the effect of the mechanism of urea addition to sewage sludge and sawdust-composting substrates on methane production is still limited. In the present study, the systematic investigation of the effect of urea addition (0.18, 0.9 and 1.8 kg) on methane production is discussed through the dynamics of physical properties, enzymes, and the microbial community during composting. The results showed that high urea addition (1.8 kg) suppressed methane production, with a lower rate and a shorter duration of warming in the thermophilic phase, but significantly enhanced cellulase activity, urease, and peroxidase, and promoted the degradation of organic carbon, as well as the loss of nitrogen. A high addition of urea stimulated the growth and reproduction of Sinibacillus, Pseudogracilibacillus, Sporosarcina, and Oceanobacillus. The random forest model indicated that the top six independent determinants of CH4 emissions were Methanobacterium, temperature, organic matter (OM), Methanospirillum, and NH4+-N. Furthermore, structural equation modeling displayed that NH4+-N, O2, and pH were the main physicochemical properties affecting CH4 emissions. Methanobacterium, Methanosarcina, and Methanosphaera were the main archaea, and Bacillaceae were the main bacteria affecting CH4 emissions. This study provides new insights and a theoretical basis for optimizing urea addition strategies during composting. [ABSTRACT FROM AUTHOR]

This study aimed to improve the nutritional value of Sargassum horneri (SH) and evaluate its effect on rumen fermentation characteristics and greenhouse gas emissions. The first trial estimated the effects of blanching temperatures (21, 60, 70, and 80 °C) and durations (1, 2, and 3 min) on the chemical and mineral content of SH. The second trial evaluated the effects of SH supplementation levels (0, 1, 3, and 5%) and blanching temperatures (21 and 70 °C) on in vitro rumen fermentation characteristics and greenhouse gas emissions. As the blanching temperature and time increased, the dry matter and crude ash content decreased (p < 0.05), and the ether extract, neutral detergent fiber, and acid detergent fiber content increased (p < 0.05). There were significant reductions in Na, K, and As (p < 0.01) when the blanching temperatures increased. As the SH supplementation levels increased, in vitro digestibility of both dry and organic matter decreased (p < 0.001) and the methane production reduced (p < 0.001). Therefore, this study shows that blanching SH at 70 °C for 3 min optimized its nutritional value and improved its potential as a feed source that has methane mitigation characteristics at 3 to 5% supplementation levels. [ABSTRACT FROM AUTHOR]

Simple Summary: Agricultural greenhouse gas emissions account for 14.5% of global anthropogenic emissions, with beef and dairy cattle contributing 35% and 30% of global livestock emissions, respectively. This study focuses on dairy cattle, exploring the relationships between rumen microbiota and methane emission. Using a laser methane detector (LMD), methane emissions from 968 lactating cows were measured, and 107 cows were selected for high and low emission groups. The results showed that the abundance of Bacteroidales and Prevotellaceae in the rumen of high methane-emitting cows was significantly higher than that in the low methane-emitting cows. Additionally, it was found that bacterial functions related to biosynthesis and carbohydrate metabolism were more active in the high methane-emitting cows. These findings provide new insights for developing strategies to reduce methane emissions, supporting the sustainable development of the dairy industry. Agricultural activities are a significant contributor to global greenhouse gas emissions, accounting for 14.5% of total anthropogenic emissions. Specifically, greenhouse gas emissions from beef cattle and dairy cattle constitute 35% and 30% of total global livestock emissions, respectively. This study focuses on dairy cattle, exploring the complex relationships between rumen microbiota and methane emission. The methane emissions of 968 lactating Holstein cows were measured using a laser methane detector (LMD, Shanghai Hesai Technology Co., Ltd., Shanghai, China). Among the measured cows, 107 individuals were further selected into high (HME) and low methane-emitting (LME) groups, including 50 cows in the HME group and 57 in the LME group. This study analyzed differences in rumen microbiota and microbial functions between cows with varying levels of methane emissions. The results showed significant differences in the Simpson and Pielou indices of rumen bacterial communities between the HME and LME groups. Beta diversity analysis revealed significant differences in microbial community structure between the two groups. It was found that the abundance of Bacteroidales and Prevotellaceae in the rumen of cows in the HME group cows was significantly higher than that of cows in the LME group (LDA > 3, p < 0.05). Additionally, bacterial functions related to biosynthesis and carbohydrate metabolism were more active in the HME group. This study revealed distinct differences in the rumen bacterial communities between HME and LME cow in Chinese Holstein cattle, and identified specific bacteria and their functional differences in the HME group. The microbial characteristics and metabolic pathways provide new insights for developing strategies to reduce methane emissions, supporting the sustainable development of the dairy industry. [ABSTRACT FROM AUTHOR]

Ventilation Air Methane (VAM) significantly contributes to global warming. Capturing and mitigating these emissions can help combat climate change. One effective method is the thermal decomposition of methane, but it requires careful control to prevent explosions from the high temperatures involved. This research investigates the influence of methane concentration and coal dust particle properties on the minimum ignition energy (MIE) required for fugitive methane thermal decomposition and flame propagation properties. This knowledge is crucial for the mining industry to effectively prevent and mitigate accidental fires and explosions in VAM abatement plants. Coal dust samples from three different sources were selected for this study. Experiments were conducted using a modified Hartmann glass tube and a Thermal Gravimetric Analyser (TGA). The chemical properties of coal dust were determined through ultimate and proximate analysis. The particle size distribution was determined using a Mastersizer 3000 apparatus (manufactured by Malvern Panalytical, Malvern, UK). The results showed that the MIE is significantly affected by coal dust particle size, with smaller particles (<74 µm) requiring less energy to ignite compared to coarser particles. Additionally, blending methane with coal dust further reduces the MIE. Introducing methane concentrations of 1% and 2.5% into the combustion space reduced the MIE by 25% and 74%, respectively, for the <74 µm coal dust size fraction. It was observed that coal dust concentration can either raise or lower the MIE. Larger coal dust concentrations, acting as a heat sink, reduce the likelihood of ignition and increase the MIE. This effect was noted at a methane concentration of 2.5% and coal dust levels above 3000 g/m3. In contrast, small amounts of coal dust had little impact on MIE variation. Moreover, the presence of methane during combustion increased the upward flame travel distance and propagation velocity. The flame's vertical travel distance increased from 124 mm to 300 mm for a coal dust concentration of 300 g·m−3 blended with 1% and 2.5% methane, respectively. [ABSTRACT FROM AUTHOR]

This study explores the relationship between greenhouse gases (GHGs) and particulate emissions and quality of life. The aim is to understand how emissions affect quality of life globally—across countries, regions, and the global population. Statistical methods were used to examine the impact of various emissions' indicators on different aspects of quality of life. The study highlights the urgent need for climate change action and encourages policymakers to take strategic steps. Climate change adversely affects numerous aspects of daily life, leading to significant consequences that must be addressed through policy changes and global governance recommendations. Key findings include that higher CO2 and methane emissions and air pollution negatively impact quality of life. CO2 emissions are positively associated with electricity while air pollution is positively associated with GDP and negatively with unemployment. Air pollution has an adverse effect on all three aspects of the children's welfare dimension of quality of life. These results provide timely and convincing insights for policy- and decision-making aimed at mitigating the impact of emissions on quality of life. [ABSTRACT FROM AUTHOR]

Methane production in ruminants is primarily due to the conversion of metabolic hydrogen (H2), produced during anaerobic microbial fermentation, into methane by ruminal methanogens. While this process plays a crucial role in efficiently disposes of H2, it also contributes to environmental pollution and eliminating methane production in the rumen has proven to be challenging. This study investigates the use of probiotics, specifically propionate-producing bacteria, to redirect accumulated H2 in a methane-mitigated environment. For this objective, we supplemented experimental groups with Lactiplantibacillus plantarum and Megasphaera elsdenii for the reinforced acrylate pathway (RA) and Selenomonas ruminantium and Acidipropionibacterium thoenii for the reinforced succinate pathway (RS), as well as a consortium of all four strains (CB), with the total microbial concentration at 1.0 × 1010 cells/mL. To create a methane-mitigated environment, 2-bromoethanesulfonate (BES) was added to all experimental groups at a dose of 15 mg/0.5 g of feed. BES reduced methane production by 85% in vitro, and the addition of propionate-producing bacteria with BES further decreased methane emission by up to 94% compared with the control (CON) group. Although BES did not affect the alpha diversity of the ruminal bacteriome, it reduced total volatile fatty acid production and altered beta diversity of ruminal bacteriota, indicating microbial metabolic adaptations to H2 accumulation. Despite using different bacterial strains targeting divergent metabolic pathways (RA and RS), a decrease in the dominance of the [Eubacterium] ruminantium group suggesting that both approaches may have a similar modulatory effect. An increase in the relative abundance of Succiniclasticum in the CB group suggests that propionate metabolism is enhanced by the addition of a propionate-producing bacterial consortium. These findings recommend using a consortium of propionate-producing bacteria to manage H2 accumulation by altering the rumen bacteriome, thus mitigating the negative effects of methane reduction strategies. [ABSTRACT FROM AUTHOR]

The agronomic benefits of pesticides combined with amino acid application to increase rice production have been recognized, but they are still not well-known for greenhouse gas (GHG) emissions and mitigation in irrigated paddy fields. Thus, this study was conducted to investigate the combined effects of pesticide and amino acid application on rice yield and methane (CH4) emissions in a Korean rice paddy. A field experiment was conducted with five levels: none (no pesticide application, T1), different conventional practices (combined application of insecticides and fungicide, T2 and T3), and GroMore® programs (combined application of insecticides, fungicides, and amino acids, T4 and T5). Rice grain yield and yield components were obtained using agronomic measurements. To determine the greenhouse gas intensity (GHGI) of each treatment, CH4 emissions were measured throughout the rice growing period. Results showed that the chemical applications in combination with amino acids in T4 obtained a higher grain yield and number of panicles per plant compared to T1, T2, and T3, while T4 and T5 showed no difference on filled spikelets except for T2. T3 and T5 showed lower respective cumulative CH4 emissions by 30% and 32% during the entire rice growing season, compared to no chemical application (T1). Meanwhile, N2O emissions were negligible in all treatments because the paddy field was flooded most of the growing season. The results of the impact of GroMore® programs on relatively higher grain yield and lower GHG emissions are presented. In conclusion, the application of pesticides combined with amino acids obtained lower GHGI values. [ABSTRACT FROM AUTHOR]

Background and Objective: The initial step in managing methane emissions is quantification. This study aims to comprehensively investigate the methods for estimating and measuring methane gas emissions in municipal solid waste landfills. Materials and Methods: This systematic review includes studies published in English and Farsi between January 2005 and May 2023. English-language articles were included from PubMed, Web of Science, and Scopus databases, while Persian-language articles were included from SID, Majiran and Google Scholar. Results: After evaluating the studies, 90 studies providing information on methods for measuring and estimating methane gas emissions in urban landfills were selected. The results showed that emission estimates based on widely used models like LandGEM and IPCC, despite being lower in cost and providing faster results, are often associated with relatively high uncertainty. Therefore, quantitative and qualitative methods of direct measurement are preferred for accurately determining methane emissions from landfills. The most commonly used methods for measuring greenhouse gases, especially methane from landfills, include direct reading equipment and closed flux determination chambers. Conclusion: Estimating greenhouse gas emissions from primary sources allows responsible authorities to understand the current status of methane emissions and to formulate reduction strategies. The findings of estimated methane emissions from landfills can differ significantly from the actual measurements in some situations. Therefore, while these estimation methods are useful, fast and cost-effective tools, their inherent uncertainties should be considered when using them. [ABSTRACT FROM AUTHOR]

Atmospheric methane emissions from the energy sector, particularly coal mines, are a component of total global methane emissions. Its presence causes climate changes that contribute to global warming. Methane is a potent greenhouse gas (GHG) with a global warming potential (GWP) approximately 30 times greater than that of carbon dioxide over a 100-year period. The estimation of methane emissions from both underground hard coal mines and open-pit lignite mines is performed based on guidelines that follow the methods recommended in the core publications of the International Panel on Climate Change (IPCC). The methane emission rate determination method developed by scientific institutions is allowed. This article focuses on the analysis and formulation of guidelines for determining the potential methane emissions from open-pit lignite mines, which are determined based on the emission factor and coal production. Coalbed methane content was tested using two methods to determine the methane emission factor. Results of sorption tests were also presented. The results obtained can be used for the development of new solutions or the improvement of current solutions for the determination of the methane emission rate in open-pit lignite mines. [ABSTRACT FROM AUTHOR]

The increase in global temperatures over the past few decades due to greenhouse gas emissions has raised concerns and necessitated further research in climate change mitigation and adaptation. Methane is a prominent greenhouse gas that significantly contributes to climate change, with a substantial amount generated through fermentation processes occurring in the rumen of ruminant animals. The potential of plant secondary metabolites, especially those derived from tannin-rich plants, warrants investigation to modify rumen fermentation and mitigate methane emissions in livestock diets. The objective of this study was to assess the impact of extracts obtained from green and black tea waste on rumen fermentation dynamics and gas (methane) production, utilizing in vitro methods. For this purpose, rumen fluid was collected from two fistulated sheep and subjected to three treatments: (1) a basal diet (control), (2) a basal diet + green tea waste extract (5% of dry matter), (3) a basal diet + black tea waste extract (5% of dry matter). The study assessed the effects of incorporating extracts from green and black tea waste on various parameters, including digestibility, protozoa population, ammonia nitrogen levels, volatile fatty acids, and methane gas production following a 24-h incubation period. Statistical analysis of the data was conducted using SAS software within a completely randomized design framework. The findings indicated that the addition of green and black tea waste extracts significantly decreased methane gas production (p < 0.05), protozoa count (p < 0.05), and ammonia nitrogen concentrations in rumen fluid (p < 0.05) when compared to the control group. The addition of green and black tea waste extracts has significantly altered the concentration of VFAs in rumen fluid (p < 0.05). Specifically, the addition of green tea waste extract has led to a highly significant reduction in acetic acid, (p < 0.01) and the addition of both extracts has resulted in a significant increase in propionic acid (p < 0.05). Consequently, the results suggest that the inclusion of green and black tea waste extracts in livestock diets may effectively mitigate methane emissions in the rumen, thereby reducing feed costs and reducing environmental pollution. [ABSTRACT FROM AUTHOR]

Abstract Wetlands are a significant natural source of methane (CH4), and their emissions are highly sensitive to climatic variability. This study investigated the impact of El Niño‐Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events on wetland CH4 emission anomalies based on a model simulation data set from 1980 to 2022. Results showed El Niño reduced CH4 emission anomalies, whereas La Niña increased them. Both ENSO and IOD phases greatly altered climatic variables, which in turn affected CH4 emission anomalies. The global mean sensitivities of CH4 emission anomalies to ENSO and IOD were −0.17 and −0.08 mg CH4 m−2 month−1, respectively, which exhibited pronounced temporal and spatial variability, mainly driven by soil moisture and temperature. This study underscored the pronounced temporal and spatial variability of CH4 emission anomalies under ENSO and IOD phases, which would improve the accuracy of predictive CH4 models and inform strategies for mitigating climate change.

This study continued the in vitro screening of locally available ruminant feedstuffs for optimum nutrient composition and low methane (CH4) production in Ethiopia. The best bet feeds from the in vitro study, hereafter called the test feeds, include dried leaves of Acacia nilotica, Ziziphus spina-christi, and brewery spent grains (BSG). The study involves four treatments: Control, Acacia, BSG, and Ziziphus; each treatment provided an equivalent crude protein and estimated enteric CH4 emissions using Modeling and a Laser CH4 detector (LMD). The experiment was designed as a randomized complete block, using initial weight as the blocking factor for 21 yearling castrated Menz sheep. The study spanned 90 days, and digestibility trials were carried out following a month of the feeding trial. The control group exhibited a significantly (p

IntroductionThe purpose of this study was to investigate how in vitro gas production (GP) and ruminal fermentation characteristics were affected by increasing concentrations of green algae plant (C. reinhardtii) extracts in combination with nanoparticles MgO and MgS.MethodsA solution containing 0.1 M MgCl2 was prepared in 300 mL for the green production of MgCl nanoparticles. The mixture was refluxed for two hours at 85°C using a reflux condenser after 10 mL of pomegranate plant extract was added. The green algal plant (C. reinhardtii), which has many non-toxic antioxidants, was used as a carbon source to produce carbon quantum dots (CQD). Chemical analysis was conducted in accordance with AOAC (2005) recommendations. Rumen fluid from recently slaughtered calves is used to produce in vitro gas immediately following slaughter. Analysis of variance (ANOVA) was performed on the obtained data from the in vitro study in a completely randomized design using the mixed model of SAS (version 9.4; Inc., Cary NC, USA).Results and DiscussionThe variance analysis results and the average values of the chemical compositions were significantly influenced by the extracts (all p < 0.0001). In this line, the values of net gas, pH, OMD, ME, NEl, and ME were found to be the highest for Algae + 50 MgO and the lowest for Algae + 50 MgS, respectively (all p < 0.0001). These promising results imply that extracts from C. Reinhardtii may be able to mitigate the adverse consequences of rumen fermentation. To precisely ascertain the impact particular Rhodophyta on greenhouse gas emissions, additional investigation is needed.

Abstract The objective of this work was to evaluate the growth performance, enteric methane production, carcass traits, and fatty acid profile of the Longissimus thoracis muscle of young Nellore and Angus x Nellore beef bulls fed with a high-lipid diet and finished in feedlot. Fifty young bulls were evaluated, being divided into two groups: Nellore (n=25) and Angus × Nellore (n=25), randomly assigned to four pens in a completely randomized design. The feedlot phase covered 105 days of feed. The diet (80% concentrate) was formulated to meet or exceed beef cattle requirements to reach an average daily gain of 1.6 kg per day. The crossbred animals showed a higher dry matter intake, feed efficiency, initial body weight, final body weight, average daily gain, average daily carcass gain, hot carcass weight, and percentage of rib. However, dressing percentage was similar for both groups. The Nellore animals produced more enteric methane, expressed in g kg-1 average daily gain), but, also, higher concentrations of polyunsaturated fatty acids and conjugated linoleic acid. The two genetic groups show satisfactory growth rates, but the crossbred animals present a better growth performance and produce less methane per unit of beef. The animal’s genetic composition modifies its fatty acid profile.

Dietary fat content can reduce the methane production of dairy cows; however, the relevance fatty acid (FA) composition has towards this inhibitory effect is debatable. Furthermore, in-depth studies elucidating the effects of unsaturated fatty acids (UFA) on rumen function and the mechanism of reducing methane (CH4) production are lacking. This study exposed 10 Holstein cows with the same parity, similar milk yield to two total mixed rations: low unsaturated FA (LUFA) and high unsaturated FA (HUFA) with similar fat content. The LUFA group mainly added fat powder (C16:0 > 90%), and the HUFA group mainly replaced fat powder with extruded flaxseed. The experiment lasted 26 d, the last 5 d of which, gas exchange in respiratory chambers was conducted to measure gas emissions. We found that an increase in the UFA in diet did not affect milk production (P > 0.05) and could align the profile of milk FAs more closely with modern human nutritional requirements. Furthermore, we found that increasing the UFA content in the diet lead to a decrease in the abundance of Methanobrevibacter in the rumen (|linear discriminant analysis [LDA] score| > 2 and P 2 and P

Abstract The layout of methane sensors in the working face cannot meet the needs for monitoring methane concentrations within confined spaces, and it is challenging to determine the precise locations for manual inspections. Therefore, the working face is firstly divided into different areas and grids. Then combined with the characteristics of methane emissions and the measured data on site, the boundary conditions of simulation experiments are set up and the research is carried out on the diffusion law of methane in the confined space of the working face under different conditions. The experimental results show that methane emission intensity from coal walls affects its distribution. As emission intensity rises, methane nearer the coal wall decreases, while methane further away increases. Among coal mining points, point 2 shows the widest methane diffusion range. Rising wind speeds decrease methane diffusion from the coal wall, increasing vertical diffusion distance. Methane from the coal wall shifts to the air inlet, while methane from the mining point diffuses increasingly to the downwind side. The location of the maximum methane concentration generated from falling coal and its transportation process is only related to the location of the coal mining point. The key areas for methane monitoring in confined spaces of the working face should be the overlapping locations of the vertical‐3 and vertical‐4 areas and the horizontal‐1 and horizontal‐3 areas. The key areas for manual inspection should be the overlapping locations of the vertical‐2 and vertical‐3 areas and the horizontal‐1 area.

ABSTRACT: Reduction of methane emission may become necessary for sustainable milk production. Several studies indicate a relationship between rumination time and the level of methane emission. The objectives of the current study were to estimate environmental factors affecting daily rumination time in high-yielding dairy cattle, genetic parameters for rumination time across parities, and environmental and genetic correlations between rumination time and economic traits, and to predict the consequence of inclusion of this trait in the Israeli breeding index. The data included more than 30 million daily records from 77,697 Israeli Holstein cows for rumination time and milk production. A lactation measure of daily rumination time per cow was computed as the mean of the residuals from a linear model analysis with rumination time as the dependent variable. The independent variables were parity and the square root, linear, quadradic and inverse of DIM by parity. Because of the shape of the lactation curve for rumination time, separate linear model analyses were performed for records up to 40 DIM and records with >40 DIM. The phenotypic correlation between first- and second-parity lactations for rumination time was almost 0.8, and close to 0.7 for milk. The heritability of lactation rumination time was close to 0.44 for parities 1 to 3. Heritability for milk production decreased from 0.5 in first parity to 0.3 in third parity. For both traits, genetic correlations among parities were all >0.9. Thus, for routine genetic analysis of rumination time, records in the different parities can be considered the same trait. The genetic correlation between rumination time and milk on first parity was 0.25 and increased slightly with increase in parity. Genetic correlations between rumination time, based on the first 40 DIM, were economically unfavorable with retained placenta but economically favorable with metritis, ketosis, and displaced abomasum. Genetic correlations between rumination time and the 9 traits included in the Israeli breeding index (milk, fat, and protein production; SCS; female fertility; herd-life; milk production persistency; calving ease; and calf mortality) were all economically favorable, except for the correlation of 0.17 with SCS. With the current index, daily rumination time with a current mean of 536 min and SD of 90 min is expected to increase by 11 min/d after 10 yr of selection. Inclusion of this trait with a positive index weight equivalent to 10% of the index should increase rumination time by 19 min. All changes in expected gains due to inclusion of rumination time in the index were economically positive, except for fat and SCS. Inclusion of rumination time in the index should result in 1 kg less gain in fat, a miniscule gain of 0.03 for SCS; and gains of 1.5 kg protein, 0.3% female fertility, and 5 d herd-life. Even though the case for a genetic correlation between rumination time and methane emission is still weak, inclusion of this trait in the commercial index may be justified, considering that equipment is now commercially available for routine recording at reasonable cost.

The layout of methane sensors in the working face cannot meet the needs for monitoring methane concentrations within confined spaces, and it is challenging to determine the precise locations for manual inspections. Therefore, the working face is firstly divided into different areas and grids. Then combined with the characteristics of methane emissions and the measured data on site, the boundary conditions of simulation experiments are set up and the research is carried out on the diffusion law of methane in the confined space of the working face under different conditions. The experimental results show that methane emission intensity from coal walls affects its distribution. As emission intensity rises, methane nearer the coal wall decreases, while methane further away increases. Among coal mining points, point 2 shows the widest methane diffusion range. Rising wind speeds decrease methane diffusion from the coal wall, increasing vertical diffusion distance. Methane from the coal wall shifts to the air inlet, while methane from the mining point diffuses increasingly to the downwind side. The location of the maximum methane concentration generated from falling coal and its transportation process is only related to the location of the coal mining point. The key areas for methane monitoring in confined spaces of the working face should be the overlapping locations of the vertical‐3 and vertical‐4 areas and the horizontal‐1 and horizontal‐3 areas. The key areas for manual inspection should be the overlapping locations of the vertical‐2 and vertical‐3 areas and the horizontal‐1 area. [ABSTRACT FROM AUTHOR]

Methane, with a global warming potential roughly 86 times greater than carbon dioxide over a 20‐year timeframe, plays a crucial role in global warming. Remote sensing retrieval is a pivotal methodology for identifying methane emission sources, with accuracy influenced largely by surface and atmospheric properties, including aerosols. In this study, we propose an Aerosol‐Calibrated Matched Filter (ACMF) algorithm to improve the traditional Matched Filter (MF) method. Our new approach incorporates an aerosol scattering correction factor to reduce the aerosol‐induced bias on methane retrievals. Validating our algorithm through simulated spectra, we demonstrate that considering the aerosol scattering effect significantly reduces retrieval errors compared to MF methods by an average of approximately 90%. We apply our newly developed algorithm to hyperspectral data obtained from the Airborne Visible/Infrared Imaging Spectrometer—Next Generation in the Los Angeles Basin and focus on 11 plumes identified through case studies. Our results reveal that ACMF estimates of emission rates and inversion uncertainties exhibit an average reduction of approximately 4% compared to corresponding MF results, with deviation increasing with aerosol optical depth (AOD). Plain Language Summary: Emissions from facilities like oil and gas plants, coal mines, and waste management sites are a major contributor to atmospheric methane, which is a greenhouse gas that significantly impacts global warming. We can remotely measure these emissions using hyperspectral instruments. However, atmospheric particulates (aerosols) can skew these measurements by affecting how sunlight travels through the atmosphere. In our study, we have developed a new, computationally efficient approach to adjust for aerosol effects when analyzing data from these instruments. Tests with simulated data show that our method reduces errors caused by aerosols by about 90% compared with existing schemes. Investigations over 11 different methane emission plumes in the Los Angeles area indicate that traditional methods overestimate methane releases, especially when aerosols are present. Key Points: The Aerosol‐Calibrated Matched Filter (ACMF) is proposed for correction of aerosol‐induced bias in methane point source emission retrievalsThe ACMF method decreased the bias in methane concentration retrieval, demonstrating a clear improvement over the Matched Filter (MF) methodThe ACMF method, implemented in 11 cases over the LA Basin, yielded lower flux rate estimates and uncertainties compared to the MF method [ABSTRACT FROM AUTHOR]

The purpose of this research was to investigate the impact of dietary supplementation of Caragana korshinskii tannin (CKT) on rumen fermentation, methane emission, methanogen community and metabolome in rumen of sheep. A total of 15 crossbred sheep of the Dumont breed with similar body conditions, were divided into three groups (n=5), which were fed with CKT addition at 0, 2 and 4%/kg DM. The study spanned a total of 74 days, with a 14-day period dedicated to adaptation and a subsequent 60-day period for conducting treatments. The results indicated that the levels of ammonia nitrogen (NH3-N) and acetate were reduced (p < 0.05) in rumen sheep fed with 2 and 4% CKT; The crude protein (CP) digestibility of sheep in 2 and 4% CKT groups was decreased(p < 0.05); while the neutral detergent fiber (NDF) digestibility was increased (p < 0.05) in 4% CKT group. Furthermore, the supplementation of CKT resulted in a decrease (p < 0.05) in daily CH4 emissions from sheep by reducing the richness and diversity of ruminal methanogens community, meanwhile decreasing (p < 0.05) concentrations of tyramine that contribute to methane synthesis and increasing (p < 0.05) concentrations of N-methy-L-glutamic acid that do not contribute to CH4 synthesis. However, CH4 production of DMI, OMI, NDFI and metabolic weight did not differ significantly across the various treatments. To sum up, the addition of 4% CKT appeared to be a viable approach for reducing CH4 emissions from sheep without no negative effects. These findings suggest that CKT hold promise in mitigating methane emissions of ruminant. Further investigation is required to evaluate it effectiveness in practical feeding strategies for livestock. [ABSTRACT FROM AUTHOR]

The world's population is proliferating, and the growing population needs more food resources. Meat is one of the essential dietary components for humans, but the available meat resources are insufficient to fulfill consumers' demands. In this scenario, the meat alternative could be a sustainable resource for fulfilling the meat demand. Cultured meat is one of the best meat alternatives and also has the potential to fulfill the meat demand of the growing population. Cultured meat is produced by cultivating animal stem cells and allowing them to undergo a growth and division process that closely mimics the natural cellular development inside living organisms. The primary reasons behind cultured meat production are to minimize the environmental effect by reducing methane emissions, to minimize the meat cost so it will be economical for the consumer, and to provide antibiotic-free meat. This review explains cultured meat production and new technologies being introduced to create opportunities to improve the overall production of cultured meat. It also explores the cultured meat sector's market opportunities and challenges to the cultured meat industry.

To calculate methane and nitrous oxide emissions in livestock farming requires the characteristics of produced manure and its treatment systems per a single farm and the whole region. The study aim is to specify the percentage of cattle manure processing technologies in different natural and climatic conditions of the Russian Federation and to revise the emission factor of greenhouse gases. The survey of cattle complexes established the type and amount of manure produced with a breakdown by moisture content and storage volumes. The study calculated the mass of manure processed according to the applied processing technologies. The study acquired the data on the types of manure collection and storage systems and revealed their ratio by federal districts, in more detail – in the federal subjects with bigger stock of dairy cattle. The obtained ratios allowed adjusting the coefficient required to calculate methane and nitrous oxide emissions. The coefficient reflected the share of emitted nitrogen treated or processed by a certain technology. The study calculated the emissions for cows and cattle (without cows) with the use of the adjusted coefficient and the methods of the Intergovernmental Panel on Climate Change for the Northwestern Federal District, which featured the greatest difference between the indicators from the National Inventory and the data obtained in the study. Direct nitrous oxide and methane emission in this District in terms of CO2-eq. according to the Inventory data (2021) was 180.7 thousand t/year; when calculated by the basic distribution of technologies (2021) – 388.7 thousand t/ year; when calculated by the forecast distribution of technologies (2025) – 375.8 thousand t/year. The study results demonstrated that the current tendency to upgrading the manure processing technologies towards the most environmentally friendly and compliant with the principles of best available techniques resulted in reduction in greenhouse gas emissions.

Methane, the second most prevalent greenhouse gas globally, is characterized by its high global warming potential and short atmospheric lifetime. It is primarily emitted from sectors such as coal mining, oil and gas production, agriculture, livestock rearing, and waste management. Active, prudent, and orderly control of methane emissions offers a multifaceted benefit: it mitigates global warming, enhances the economic value through the resource utilization of energy, provides environmental advantages by control of pollutants, and improves safety by reducing production accidents. Methane is also the principal greenhouse gas emitted in coal mine operations. Currently, monitoring and researching the distribution of methane emissions and their concentration within the three-dimensional space of coal mines have become a key focus in China’s methane emission control efforts, which is of significant importance for coal mining enterprises to align with national strategies. A study conducted on a high-gas mine in Shanxi and a low-gas mine in Shaanxi for methane emission monitoring and accounting utilized fixed monitoring, manual monitoring, drone monitoring, and post-mining activity monitoring to investigate the distribution of methane in high and low gas mines in China and to analyze their methane emissions. The results indicate that: ① The methane emission volumes for a certain high-gas mine in Shanxi and a low-gas mine in Shaanxi published by the U.S.-based Climate TRACE website for the year 2022 show a significant discrepancy from the results calculated using the ground measurement method, with the published annual methane emission volume for a certain low-gas mine being 10.92 times that of the ground measurement calculation. The top-down monitoring and accounting methods have been found to be insufficiently accurate and unable to provide reliable data support for carbon trading. ② In the monitoring and accounting of post-mining activities, the use of default values recommended by the IPCC results in methane emissions that are 3 to 5 times higher than those calculated using the ground measurement method, and there is a significant variation in the emissions calculated based on coal samples of different sizes and exposure durations. There is an urgent need for a unified and precise testing standard to ensure fair and accurate data for future carbon market transactions.

Abstract Monitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable approaches for monitoring methane emissions at various scales, from point-source high-resolution monitoring to regional and global estimates. The TROPOMI satellite instrument provides daily XCH4 data globally, offering the opportunity to monitor methane at a moderate spatial resolution with an acceptable level of sensitivity. To infer emissions from TROPOMI data, we used the prior emission estimates from global and national inventories and the GEOS-Chem chemical transport model to simulate atmospheric methane along with actual observations of TROPOMI. In this study, methane emissions from Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City have been estimated using the analytical solution of Bayesian inversion using the cloud-based Integrated Methane Inversion (IMI) framework. Using the result from ensemble inversions, and city boundaries, the average total emissions were as follows: Toronto 230.52 Gg a−1, Montreal 111.54 Gg a−1, New York 144.38 Gg a−1, Los Angeles 207.03 Gg a−1, Houston 650.16 Gg a−1, and Mexico City 280.81 Gg a−1. The resulting gridded scale factors ranged from 0.22 to 6.2, implying methane prior emission underestimations in most of these cities. As such, this study underscores the key role of remote sensing in accurately assessing urban methane emissions, informing essential climate mitigation efforts.

Wetlands are the single largest natural source of atmospheric methane (CH _4 ), contributing approximately 30% of total surface CH _4 emissions, and they have been identified as the largest source of uncertainty in the global CH _4 budget based on the most recent Global Carbon Project CH _4 report. High uncertainties in the bottom–up estimates of wetland CH _4 emissions pose significant challenges for accurately understanding their spatiotemporal variations, and for the scientific community to monitor wetland CH _4 emissions from space. In fact, there are large disagreements between bottom–up estimates versus top–down estimates inferred from inversion of atmospheric CH _4 concentrations. To address these critical gaps, we review recent development, validation, and applications of bottom–up estimates of global wetland CH _4 emissions, as well as how they are used in top–down inversions. These bottom–up estimates, using (1) empirical biogeochemical modeling (e.g. WetCHARTs: 125–208 TgCH _4 yr ^−1 ); (2) process-based biogeochemical modeling (e.g. WETCHIMP: 190 ± 39 TgCH _4 yr ^−1 ); and (3) data-driven machine learning approach (e.g. UpCH4: 146 ± 43 TgCH _4 yr ^−1 ). Bottom–up estimates are subject to significant uncertainties (∼80 Tg CH _4 yr ^−1 ), and the ranges of different estimates do not overlap, further amplifying the overall uncertainty when combining multiple data products. These substantial uncertainties highlight gaps in our understanding of wetland CH _4 biogeochemistry and wetland inundation dynamics. Major tropical and arctic wetland complexes are regional hotspots of CH _4 emissions. However, the scarcity of satellite data over the tropics and northern high latitudes offer limited information for top–down inversions to improve bottom–up estimates. Recent advances in surface measurements of CH _4 fluxes (e.g. FLUXNET-CH _4 ) across a wide range of ecosystems including bogs, fens, marshes, and forest swamps provide an unprecedented opportunity to improve existing bottom–up estimates of wetland CH _4 estimates. We suggest that continuous long-term surface measurements at representative wetlands, high fidelity wetland mapping, combined with an appropriate modeling framework, will be needed to significantly improve global estimates of wetland CH _4 emissions. There is also a pressing unmet need for fine-resolution and high-precision satellite CH _4 observations directed at wetlands.

Due to natural and anthropogenic disturbances, natural gas hydrates with morphologies of nodules and chunks dissociate and release massive free gas, creating large cavities within fine-grained marine sediments. However, it is still a challenge to quantify the impact of gas cavities on mechanical properties of cavitied fine-grained marine sediments as there is a lack of efforts focusing on the inner structure visualization. In this study, an oedometer test and X-ray computed tomography scans are jointly conducted on marine clayey silt with gas cavities, and the confined compressibility as well as the inner structure change under an undrained condition are explored, followed by development of a theoretical model depicting the void ratio change. The results show that vertical loading induces a void ratio reduction, and the reduced void ratio can fully recover after being unloaded. Although being fully recovered, unrecovered changes of the inner structure still remain after being unloaded. Examples include closed cracks in the lower matrix, new occurring cracks in the upper matrix, and the fragmented gas cavity. In addition, the void ratio linearly increases with the increasing inverse of normalized pore gas pressure, while the coefficient of the effective stress linearly decreases with the increasing inverse of normalized vertical loading stress. The proposed theoretical model captures the essential physics behind undrained confined deformation of fine-grained marine sediments with gas cavities when subjected to loading and unloading. [ABSTRACT FROM AUTHOR]

Monitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable approaches for monitoring methane emissions at various scales, from point-source high-resolution monitoring to regional and global estimates. The TROPOMI satellite instrument provides daily XCH4 data globally, offering the opportunity to monitor methane at a moderate spatial resolution with an acceptable level of sensitivity. To infer emissions from TROPOMI data, we used the prior emission estimates from global and national inventories and the GEOS-Chem chemical transport model to simulate atmospheric methane along with actual observations of TROPOMI. In this study, methane emissions from Toronto, Montreal, New York, Los Angeles, Houston, and Mexico City have been estimated using the analytical solution of Bayesian inversion using the cloud-based Integrated Methane Inversion (IMI) framework. Using the result from ensemble inversions, and city boundaries, the average total emissions were as follows: Toronto 230.52 Gg a−1, Montreal 111.54 Gg a−1, New York 144.38 Gg a−1, Los Angeles 207.03 Gg a−1, Houston 650.16 Gg a−1, and Mexico City 280.81 Gg a−1. The resulting gridded scale factors ranged from 0.22 to 6.2, implying methane prior emission underestimations in most of these cities. As such, this study underscores the key role of remote sensing in accurately assessing urban methane emissions, informing essential climate mitigation efforts. [ABSTRACT FROM AUTHOR]

Reducing methane emissions in the oil and gas industry is a top priority for the current international community in addressing climate change. Methane emissions from the energy sector exhibit strong temporal variability and ground monitoring networks can provide time-continuous measurements of methane concentrations, enabling the rapid detection of sudden methane leaks in the oil and gas industry. Therefore, identifying specific locations within oil fields to establish a cost-effective and reliable methane monitoring ground network is an urgent and significant task. In response to this challenge, this study proposes a technical workflow that, utilizing emission inventories, atmospheric transport models, and intelligent computing techniques, automatically determines the optimal locations for monitoring stations based on the input quantity of monitoring sites. This methodology can automatically and quantitatively assess the observational effectiveness of the monitoring network. The effectiveness of the proposed technical workflow is demonstrated using the Shengli Oilfield, the second-largest oil and gas extraction base in China, as a case study. We found that the Genetic Algorithm can help find the optimum locations effectively. Besides, the overall observation effectiveness grew from 1.7 to 5.6 when the number of site increased from 1 to 9. However, the growth decreased with the increasing site number. Such a technology can assist the oil and gas industry in better monitoring methane emissions resulting from oil and gas extraction. [ABSTRACT FROM AUTHOR]

China is likely the world's largest anthropogenic source of methane emissions, with coal mine methane (CMM) being the predominant contributor. Here, we deploy 2 years of satellite observations to survey facility‐level CMM emitters in Shanxi, the most prolific coal mining province in China. A total of 138 detected episodic events at 82 facilities are estimated to emit 1.20 (+0.24/−0.20, 95% CI) million tons of methane per year (Mt CH4/yr) during 2021–2023, roughly equivalent to 4.2 times the integrated flux from the Permian plumes and four times of the integrated flux from the Four Corners plumes, two of the world's largest hotspots for oil and gas methane emissions. This work reveals the heavy‐tailed distribution characteristic of CMM emission sources for the first time, with 20% of emitters contributing approximately 50% of total emissions. Comparison with the Global Energy Monitor (GEM) inventory reveals that the GEM estimate is about 4.1 times our estimate. Plain Language Summary: This study examines methane emissions from coal mines in Shanxi, China, identified as a significant source of global methane, a potent greenhouse gas. Using satellite data over 2 years, we found 138 detected episodic events at 82 facilities in Shanxi emitting an estimated 1.2 million tons of methane annually, far exceeding emissions from known global hotspots in the oil and gas sectors. Our analysis shows that a small percentage of these facilities contribute to the majority of emissions. The results, which challenge previous estimates from environmental monitoring organizations, underscore the need for direct, observational methods to accurately assess and address methane emissions. This work aims to guide efforts in mitigating climate change by identifying key areas for reducing methane release. Key Points: Satellite data identify 82 major methane emitters in Shanxi, China, with high annual emissions of up to 1.2 MtTop 20% of coal mines contribute to half of the region's total methane emissionsFindings highlight the importance of direct measurements for accurate emission estimates [ABSTRACT FROM AUTHOR]

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