Methane

Aerobically produced methane (CH₄) from plants is influenced by several environmental factors, but wind velocity has yet to be investigated for its potential role in plant-derived CH₄ emissions. We tested three wind velocities (0, 6, and 12 km h⁻¹) on a wind-susceptible, Raphanus sativus (radish), and a wind-tolerant, Brassica oleracea var. sabellica (kale) plant species to investigate the effects of wind on plant-derived CH₄, and to compare how varying tolerances to wind affect CH₄ emissions. We found that wind exposure resulted in a decrease in leaf surface area, root and total dry mass, and an increase in leaf water potential for radish plants, while kale plants were affected minimally by wind. Radish plants emitted more CH₄ than kale plants, although the effect of wind velocity on CH₄ emissions and several of the measured traits was insignificant. Our study revealed that short-term exposure to lower wind velocities is generally insufficient to induce significant changes in plant growth and functioning. However, we showed that radish plants were more stressed by exposure to wind compared to kale plants, as indicated by lower plant growth and higher CH₄ emissions. Full article

The compositional heterogeneity of food waste greatly influences its bioconversion in microbial electrolysis cell (MEC)-assisted anaerobic digestion (AD), but the underlying mechanism remains unclear. Therefore, this study assessed two typical food wastes, i.e., starch-rich rice and cellulose-rich vegetables, on methane production, microbial constituents, and digestate dewaterability in single-chamber MECs. The results demonstrated that, while the rice-fed MEC (258.56 mL/g VS) achieved a higher methane yield compared to the vegetable-fed MEC (161.79 mL/g VS), the latter achieved higher methane purity. Temporal profiles of volatile fatty acids (VFAs) revealed rapid acidification and consumption in rice-fed systems, whereas vegetable-fed MEC exhibited delayed degradation. Additionally, the substrate type greatly influenced digestate dewaterability, since digestate from the vegetable-fed MEC exhibited lower specific resistance to filtration (3.25 × 10¹² m/kg vs. 12.46 × 10¹² m/kg) and capillary suction time (8.16 s·L/g vs. 19.14 s·L/g) compared to that from the rice-fed MEC. This improvement was likely attributed to high polysaccharides in extracellular polymeric substances (EPS) and cellulose’s structural properties, which promoted the formation of a porous, less compressible sludge cake that facilitated sludge dewaterability. Microbial community analysis revealed a substrate-driven specialization, as the rice-fed MECs enriched exoelectrogens (e.g., Geobacter, Trichococcus) and hydrogenotrophic methanogens (i.e., Methanobacterium), while the vegetables enriched Bacteroides and Methanosarcina. Collectively, these results suggest substrate composition profoundly influences methane yield, metabolic pathways, microbial ecology, and digestate properties in MEC-assisted AD. This work provides key insights into the role of feedstock characteristics in shaping MEC-assisted AD systems. Full article

Neutral red (NR) is a phenazine dye that has been implicated in electron transfer processes in methanogenic archaea. NR has been previously observed to enhance methane production but its effects on Methanosarcina barkeri are unknown. This study aimed to investigate the effects of NR on M. barkeri DSM-804. M. barkeri cultures were grown in the presence of 10 and 250 µM NR for four weeks, and proteomic analysis was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The results showed that methane production was significantly reduced in the presence of NR, at lower concentrations of both 10 and 250 µM NR treatments, compared to the control. Proteomic analysis revealed the downregulation of proteins related to substrate metabolism and methanogenesis, such as the heterodisulfide reductase subunits D (HDRD_METBF) and E (HDRE_METBF), suggesting that NR hindered essential metabolic processes. Proteomic analysis also revealed that M. barkeri lacked methanophenazine in its membrane, which is a component essential for electron transport via neutral red (NR) that supports enhanced growth and methane production. Further research is needed to explore the role of methanophenazine and understand the mechanisms underlying NR’s effects of NR on methanogenesis in M. barkeri. Full article

Agriculture is a significant contributor to greenhouse gas (GHG) emissions, with enteric methane (Ent_CH4) from cattle production being a major source. In Zambia, cattle play a critical role in rural livelihoods and food security, yet the contribution of cattle production systems to national GHG emissions remains poorly quantified. This study used the Intergovernmental Panel on Climate Change (IPCC) Tier 2 method to estimate Ent_CH4 from Zambia’s cattle population from 1994 to 2022. The Tier 2 method provides a more accurate estimate than the Tier 1 method by incorporating country-specific data on cattle population demographics, husbandry, and feeding practices. The results show significant variations in Ent_CH4 over time, driven by changes in cattle population dynamics and production practices. This study underscored the importance of transitioning from the generalized Tier 1 to the Tier 2 method to capture the unique characteristics of Zambia’s cattle production systems. The present findings provide critical insights for developing targeted mitigation strategies that will support Zambia’s ongoing efforts to address climate change while promoting sustainable livestock production. Full article

The cost of “carbon net zero by year 2050” for the UK will be high, and this target date can only be achieved if the project is undertaken in a progressive and timely manner; otherwise, costs will escalate. The base power source behind the UK approach to “net zero” is nuclear fission electricity power stations, and the ones currently on order are running significantly late. Renewables will provide some supply together with interconnectors, but only approx. twenty percent of the planned wind turbines are in place. The electricity distribution grid must change to satisfy the UK’s planned “electricity-based” future. Energy use for transport is also a significant fraction of total UK energy consumption and we include predictions for their associated emissions. These must be reduced in a progressive and timely fashion. Intermittent support for unreliable renewables is necessary and methods employing both liquid as well as gaseous fuels are suggested. Means to use and upgrade the existing infrastructure are considered, and a few of the basic building blocks of the future are examined regarding their installation without significant interruption to the basic UK economy. ANR/AMR and SMR are included as potential renewables support as well as base load generators, and the approx. quantity of CO₂e emissions avoided is estimated. Even though methane is a powerful greenhouse gas, the main support for renewables will be UK natural gas (methane content ~95%), with Avtur/diesel as a recommended reserve. It is suggested that methane has a significant short- to medium-term future as a transition fuel. Full article

Rice cultivation accounts for roughly 10% of worldwide anthropogenic greenhouse gas emissions, making it a significant source of methane (CH₄) Despite modest observational constraints, estimates of worldwide CH₄ emissions from rice agriculture range from 18–115 Tg CH₄ yr⁻¹. CH₄ is a potent greenhouse gas, and its oxidation produces tropospheric ozone (O₃), which is harmful to public health and crop production when combined with nitrogen oxides (NOx) and sunlight. Elevated O₃ levels reduce air quality, crop productivity, and human respiratory health. This study presents an AI-driven framework that combines ensemble learning, hyperparameter optimisation (HPs), and SHAP-based explainability to enhance CH₄ emission predictions from rice paddies in India, Bangladesh, and Vietnam. The model consists of two stages: (1) a classification stage to distinguish between zero and non-zero CH₄ emissions, and (2) a regression stage to estimate emission magnitudes for non-zero situations. The framework also incorporates O₃ and asthma incidence data to assess the downstream impacts of CH₄-driven ozone formation on air quality and health outcomes. Understanding the factors that drive optimal model performance and the relative importance of features affecting model outputs is still an ongoing field of research. To address these issues, we present an integrated approach that utilises recent improvements in model optimisation and employs SHapley Additive ExPlanations (SHAP) to find the most relevant variables affecting methane (CH₄) emission forecasts. In addition, we developed a web-based artificial intelligence platform to help policymakers and stakeholders with climate strategy and sustainable agriculture by visualising methane fluxes from 2018 to 2020, ensuring practical applicability. Our findings show that ensemble learning considerably improves the accuracy of CH₄ emission prediction, minimises uncertainty, and shows the wider benefits of methane reduction for climate stability, air quality, and public health. Full article

While methane emissions from cattle contribute to greenhouse gases, supplementing with red seaweed Asparagopsis taxiformis (AT) demonstrates an up to 90% methane reduction in controlled feeding studies. However, methods for delivery of AT in grazing systems remain unexplored. This study evaluated AT with mineral supplementation to 112 weaned steers grazing on annual rangeland over 157 days. Cattle were randomly assigned to access mineral with freeze-dried AT (targeting 150 mg bromoform/head/day) or mineral without AT. Methane emissions were measured using laser methane detection (LMD) and body weight, mineral consumption, and blood selenium levels were monitored. Average daily mineral consumption was lower than targeted, resulting in suboptimal bromoform intake (89.2 mg/head/day). No significant differences were observed between treatments for mineral consumption, weight gain, or blood selenium levels. Cattle with access to mineral with AT had lower peak emissions than control cattle when measured at day 25, but no differences in peak emissions were measured at day 115 or day 157. The lack of methane reduction was attributed to insufficient bromoform dosing, potential compound degradationduring field storage, and limitations of laser methane detection. Achieving consistent dosing and accurate methane assessment in extensive grazing systems requires improved delivery mechanisms, compound stabilization, and measurement techniques. Full article

In micro gas turbines, electrical power from the high-speed generator is delivered to the grid through a converter that influences overall efficiency and energy quality. This subsystem is often overlooked in efforts to improve turbine performance, which have traditionally focused on combustors and turbomachinery. This study investigates how replacing conventional silicon switching devices in the converter with silicon carbide technology can directly reduce greenhouse gas emissions from micro gas turbines. Although silicon carbide is widely used in electric vehicles and distributed energy systems, its emission reduction impact has not been assessed in micro gas turbines. A MATLAB-based model of a 100 kW Ansaldo Energia micro gas turbine was used to compare the performance of silicon and silicon carbide converters across the 20–100 kW operating range. Silicon carbide reduced total converter losses from 4.316 kW to 3.426 kW at full load, a decrease of 0.889 kW. This improvement lowered carbon dioxide emissions by 5.7 g/kWh and increased net electrical efficiency from 30.03% to 30.29%. Each turbine can therefore avoid about 1.53 tonnes of carbon dioxide annually, or 11.61 tonnes over a 50,000 h service life, without altering turbine design, combustor geometry, or fuel composition. This work establishes the first quantitative link between wide-bandgap semiconductor performance and direct greenhouse gas mitigation in micro gas turbines, demonstrating that upgrading converter technology from silicon to silicon carbide offers a deployable pathway to reduce emissions from micro gas turbines and, by extension, lower the carbon intensity of distributed generation systems. Full article

In this study, three different reactor concepts for the oxidative coupling of methane (OCM) reaction are examined at the miniplant scale. Their performance and response to variations in key process parameters, such as temperature and gas hourly space velocity (GHSV), are evaluated over a wide range. In addition to the conventional Packed Bed Reactor (PBR), Packed Bed Membrane Reactor (PBMR), and Chemical Looping Reactor (CLR) approaches were tested. The PBMR was realized with a porous ceramic $\alpha$-Alumina membrane as air/O₂ distributor. The CLR was operated in a poly-cyclic operation. Similarities of the different reactor concepts as well as layout-immanent differences with regard to changes in reaction conditions could be identified and advantages and disadvantages of the processes highlighted. The results show that C₂ selectivity can be improved by both PBMR and CLR in comparison to conventional PBR, possibly reducing cost-intensive downstream units. While a PBMR can slightly improve selectivity (23%) while keeping the same conversion compared to a PBR, the use of a CLR allows for achieving exceptionally high selectivities of up to 90%. In order to address the low conversion, CLR tests were carried out with an additional O₂ carrier material, which led to a significant improvement in terms of C₂ yield. In addition to an evaluation and comparison of the different reactor concepts, the findings at the miniplant scale provide estimates of their potential use and scalability. Full article

We perform a molecular dynamics simulation of a bulk eight-component hydrocarbon mixture that roughly represents a composition of hydrocarbon fluid in a volatile oil reservoir. For that goal, we have developed a method for building molecular models of hydrocarbon mixtures which can include various branched molecules. We have used self-periodical simulation boxes with different aspect ratios. Our main focus here is the phase behavior of a multicomponent mixture in the presence of gas–liquid interfaces of different shapes: spherical, cylindrical, and slab-like gas bubbles. We have developed a method for calculating properties of coexisting phases in molecular simulations of multicomponent systems. In particular, it allows us to analyze the local composition of the mixture and to calculate the molar densities of components in liquid and gas phases, and inside the interface layer between them. For the values of model parameters that we have used so far, the mixture is homogeneous at a high pressure and undergoes liquid–gas phase separation upon decreasing the pressure. We have kept the same temperature $T=375.15 \mathrm{K}$, the same composition and the same number of molecules in all systems and used several combinations of the simulation box size and shape to control the overall density, and therefore also the pressure, as well as the presence or absence of a liquid–gas interface and its shape. The gas bubble that appears in the system is mainly composed of methane. There is also a small number of ethane and butane molecules, a tiny number of hexane molecules, and no molecules of heavier components at all. In the liquid phase, all components are present. We also show that inside the gas–liquid interface layer, which is actually quite broad, the molar density of methane is also higher than that of other components and even reaches a maximum value in the middle of the interface. Ethane behaves similarly: its molar density also reaches a maximum inside the interface. The molar density of heavier components grows monotonically from the inner part of the interface towards its outer part and shows a very small (almost not visible) maximum at the outer side of the bubble. Full article

Methane, a greenhouse gas which has a global warming potential 80 times greater than carbon dioxide on a 20-year time scale, greatly contributes to global warming. Removing 1 Gt of atmospheric methane by 2050 would limit global temperature increase from reaching 1.5 °C. Currently, biotrickling filter systems for removing atmospheric methane via methanotrophs exist, but not for very low methane concentrations (<1 v%). Recent work at the University of Washington to isolate and improve a microbial strain which thrives at 500 ppmv CH₄ has removed one obstacle in making this technology feasible. In this study, techno-economic and environmental life cycle assessment analyses conducted on this process have assessed its economic feasibility, greenhouse gas reduction potential, and possible areas of improvement. Study results show that at 500 ppmv CH₄, this process could remove atmospheric methane at a cost of USD 3992–5224/tCH₄. The best-performing case also produces annual net reductions in warming potential by 276–311 tCO₂e/120 m³ process unit deployed. Many opportunities exist to improve the outcomes of the baseline analysis even further, especially related to reducing the transport distance of media and harvested biomass. Full article

In 2024, the global average temperature reached 1.55 °C above the pre-industrial level for the first time. However, we could still keep the long-term global average temperature below 2 °C if all possible measures are taken to mitigate greenhouse gases. It is widely accepted that methane (CH₄) mitigation can slow global warming in the near term. Among all approaches toward this goal, the utilization of aerobic methanotrophs, which are natural catalysts for the conversion of CH₄, emerges as a promising solution. Previously, we identified a candidate for CH₄ mitigation, Methylotuvimicrobium buryatense 5GB1C, which exhibits a greater growth rate and CH₄ consumption rate than other known methanotrophs at 500 ppm CH₄. In this study, we address aspects of the practical applications of this methanotroph for CH₄ mitigation. We first examined temperature and medium conditions to optimize M. buryatense 5GB1C growth at 500 ppm CH₄. The results show that M. buryatense 5GB1C has a broad optimal temperature range for growth at 500 ppm, from 15 °C to 30 °C, and that its growth rate is consistently improved by 20–30% in 10-fold-diluted medium. Next, to demonstrate the feasibility of CH₄ removal at low concentrations by this methanotroph, we applied it in a laboratory-scale packed-bed column reactor for the treatment of 500 ppm CH₄ and tested different packing materials. The column reactor experiments revealed a maximum elimination capacity of 2.1 g CH₄ m⁻³ h⁻¹ with 2 mm cellulose beads as the packing material. These results demonstrate that with further technological innovation, this methanotroph has the potential for real-world methane mitigation. Full article

Methane emissions (CH₄-em) from dairy cows are a major environmental concern, contributing to greenhouse gases and energy loss in dairy cows. This study implemented advanced data analysis techniques to understand how different diet ingredients and production traits in dairy production systems can affect methane emissions. We analyzed a comprehensive meta dataset compiled from 225 peer-reviewed studies including 303 observations across multiple traits, using Bayesian networks and various machine learning models to explore the relationships between MEs, diet chemical ingredients, and production traits in dairy cattle. Eight models were applied, including linear models (OLS, LASSO, ridge, elastic net) and non-linear models (PLSR, spline regression, support vector machine, Gaussian process), to assess predictive performance. CH₄-em showed correlations ranged from −0.43 (with diet starch; STR) to 0.50 (with neutral detergent fiber; NDF) for diet-related factors, and 0.18 (with body weight; BW) to 0.29 (with milk yield; MY) for production traits. Also, Bayesian network analysis indicated that CH₄-em was a downstream variable for diet-related factors and an upstream variable for production traits. Additionally, the likelihood ratio test identified NDF as significant variable among the diet-related factors, while MY and milk fat (FAT) were crucial for production traits. non-linear models, particularly spline regression (SPL) and Gaussian process (GP), outperformed linear models in predicting CH₄-em. For production traits, support vector machine (SVM) and GP models showed superior predictive capabilities. Model performance was evaluated using R² and mean squared error (MSE) metrics. We found that while larger cows emitted more methane overall, they were generally more efficient, as methane intensity decreased with increasing MY regardless of body size. These findings offer valuable insights for developing sustainable methane mitigation strategies in dairy cattle production. Full article

Methane emissions are responsible for approximately $0.5$$°\mathrm{C}$, or about 30%, of total greenhouse-gas-induced warming. For many countries, methane represents an even larger share of their overall warming footprint. Assessing the warming contributions of individual methane-emitting countries to global warming is not straightforward due to methane’s short atmospheric lifetime and the non-linear (convex) relationship between radiative forcing and the atmospheric concentration of this gas. This study addresses this challenge using a simple climate model in combination with a warming allocation approach derived from cooperative game theory. Applying this method, the warming contributions of several high-methane-emitting countries and regional groupings are quantified relative to the early industrial period. The analysis reveals that the commonly used marginal attribution method underestimates methane-induced warming by approximately 20%. This discrepancy is due to the substantial rise in the atmospheric concentration of methane since early industrial times. Full article

This study assessed the effects of NiFe-based metal catalysts on CO₂ conversion to methane (CH₄) and carboxylic acids in microbial electrosynthesis (MES) cells. A NiFeBi alloy, when electrodeposited on a conductive bioring cathode, significantly decreased CH₄ production from 0.55 to 0.12 L (Lc d)⁻¹ while enhancing acetate production to 1.0 g (Lc d)⁻¹, indicating suppressed methanogenic activity and improved acetogenic activity. On the other hand, NiFeMn and NiFeSn catalysts showed varied effects, with NiFeSn increasing both CH₄ and acetate production and suggesting potential in promoting both chain elongation and CO₂ uptake. When these alloys were electrodeposited on a 3D-printed conductive polylactide (cPLA) lattice, the production of longer-chain carboxylic acids like butyrate and caproate increased significantly, indicating enhanced biocompatibility and nutrient delivery. The NiFeSn-coated cPLA lattice increased caproate production, which was further enhanced using an acetogenic enrichment. However, the overall throughput remained low at 0.1 g (Lc d)⁻¹. Cyclic voltammetric analysis demonstrated improved electrochemical responses with catalyst coatings, indicating better electron transfer. These findings underscore the importance of catalyst selection and cathode design in optimizing MES systems for efficient CO₂ conversion to value-added products, contributing to environmental sustainability and industrial applications. Full article

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

Wetlands are the most important natural sources of methane. Studies on the distribution and diversity of methanotrophs, especially in tropical wetlands, are limited. The studies on wetland methanotrophs help bridge the gap in the literature for understanding the community structure of methanotrophs in tropical wetlands. Our present study documents the methanotroph diversity from various wetland habitats across Western India. Samples from various sites, such as freshwater ponds, lake sediments, mangroves, etc., located in Western India, were collected and enriched for methanotroph isolation. An established protocol for the isolation of methanotrophs from Indian rice fields, involving serial dilution and long-term incubations, was slightly modified and used. Obtaining entirely pure cultures of methanotrophs is a labor-intensive and technically challenging process. Hence, for primary level characterization, ‘methanotroph monocultures’, which have a single methanotroph culture with minimal contamination, were established. Twenty monocultures and eight pure cultures of methanotrophs were obtained in this study. The pmoA gene has been used for the phylogenetic characterization of methanotrophs for the last 25 years. Monocultures were from seven genera: the Methylomonas, Methylocystis, Methylosinus, Methylocaldum, Methylocucumis, Methylomagnum, and Methylolobus genera. Eight pure cultures were obtained, which were strains of Methylomonas koyamae, Methylosinus sporium, and Methylolobus aquaticus. A maximum number of cultures belonged to the Type I genus Methylomonas and to the Type II genus Methylocystis. Thus, the cultivation-based community studies of methanotrophs from wetland habitats in India expanded the current knowledge about the methanotroph diversity in such regions. Additionally, the cultivation approach helped us obtain new methanotrophs from this previously unexplored habitat, which can be used for further biotechnological and environmental applications. The isolated monocultures can either be used as MMCs (mixed methanotroph consortia) for environmental applications or further purified and used as pure cultures. Full article

This work examined the effects of secondary annular flow area on flame stability in an experimental diffusion flame burner. The burner was composed of a horizontally mounted, rectangular chamber that utilized a retractable spark plug for ignition and an inverse coaxial injector. The primary and secondary gaseous reactants were oxygen and methane, respectively. Three injectors were assessed to have a fixed primary flow area and secondary flow impingement angle of 30 degrees with the primary flow and distinct secondary annular flow areas. Resultant flames and flame standoff distances were recorded via optical windows aligned parallel to the burner axis. Flame stability regime maps were generated based on the reactant equivalence ratio, the methane Reynolds number, and the injector secondary annular flow area. Results showed that among the injectors, the greater the secondary annular flow area with an impingement angle, the better the likelihood of generating a stable, anchored, fuel-rich diffusion flame for hydrogen production over the largest range of Reynolds numbers. As the secondary flow area decreased, stable diffusion flames transitioned from existing at highly turbulent flows to experiencing near-blowoff or no ignition under the same conditions. Secondary annular flow area significantly influences the location and range of stable, anchored methane/oxygen diffusion flames. Full article

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