CH4 Measurements Research Articles

Design and simulation of a full-order robust adaptive observer for monitoring of the biogas process

Abstract In this article, state variable re-construction in continuous biodigester was investigated. We proposed a Robust Adaptive Observer to estimate biomass and substrate concentrations based on CO2 and CH4 measurement. The observer in question is a robust and adaptable model-based methodology, designed to accommodate uncertainties in model parameters, process variability, and in-line noisy measurements. The numerical results demonstrated superior performance of the Robust Adaptive Observer over traditional robust, reduced-order observers. Finally, observers were validated through a comparative analysis using Integral Absolute Error, Time-Weighted Absolute Error, and Integral Squared Error metrics. In this context, this study presents a novel alternative for advancing cutting-edge detection technologies and their integration into the monitoring of bioprocesses, aligning with the paradigms of Industry 5.0.

Data‐driven framework for predicting the sorption capacity of carbon dioxide and methane in tight reservoirs

AbstractAs energy demand continues to rise and conventional fuel sources dwindle, there is growing emphasis on previously overlooked reservoirs, such as tight reservoirs. Shale and coal formations have emerged as highly attractive options due to their substantial contributions to global gas reserves. Enhanced shale gas recovery (ESGR) and enhanced coalbed methane recovery (ECBM) based on gas injection are advanced techniques used to increase the extraction of gas from shale and coal formations. One of the key challenges associated with these formations and their enhanced recovery methods is accurately predicting the sorption process and its profile. This is crucial because it affects how methane (CH4) and carbon dioxide (CO2) are stored and released from the rock, and it significantly impacts the evaluation of gas content and the potential productivity of these formations. Due to the high cost of experimental procedures and the moderate accuracy of existing predictive approaches, this study proposes various cheap and consistent data‐driven schemes for predicting the sorption of CH4 and CO2 in shale and coal formations. In this regard, three intelligent models, including generalized regression neural network (GRNN), radial basis function neural network (RBFNN), and categorical boosting (CatBoost), were taught and tested using more than 3800 real measurements of CH4 and CO2 sorption in shale and coal formations. To find automatically their appropriate control parameters and improve their prediction ability, RBFNN and CatBoost were evolved using grey wolf optimization (GWO). The obtained results exhibited the encouraging prediction capabilities of the suggested models. In addition, it was found that CatBoost‐GWO is the most accurate scheme with total root mean square (RMSE) and determination coefficient (R2) of 0.1229 and 0.9993 for CO2 sorption, and 0.0681 and 0.9970 for CH4 sorption, respectively. Additionally, this approach demonstrated its physical validity by respecting the real sorption tendencies with respect to operational parameters. Furthermore, the CatBoost‐GWO model outperforms recently published machine learning approaches. Lastly, the findings of this study offer a significant contribution by demonstrating that the suggested model can greatly improve the ease of estimating CO2 and CH4 sorption in tight formations, thereby facilitating the simulation of other parameters related to this process. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.

Higher temperatures exacerbate effects of antibiotics on methanogenesis in freshwater sediment

Methane (CH4) emissions from natural systems are rising in a concerning manner with an incomplete understanding of its drivers. Recently, chemical stressors such as antibiotics have been suggested as a thus far overlooked factor increasing methanogenesis in freshwaters. Since usage and toxicological impact of antibiotics could increase in a warming climate, we assessed the temperature-dependence of antibiotic effects on methanogenesis. In this light, we conducted anaerobic incubations with freshwater sediment at 10, 15, and 20 °C in presence of a mixture of five antibiotics at field-relevant concentrations. Weekly measurements of CH4 showed a strong temperature dependence of antibiotic effects by changing effect sizes, directions and dynamics. While antibiotics reduced CH4 production at 10 °C, methanogenesis was elevated at 15 °C with the most pronounced increase occurring at 20 °C. Furthermore, antibiotics changed the prokaryotic assemblage at all temperatures and effect patterns of CH4 producing Methanomicrobia strongly followed the patterns observed for methanogenesis. While analyses of compound-specific stable isotopes and the metatranscriptome suggest the acetoclastic pathway as most relevant, linking prokaryotic structure to function remains one of the most significant research challenges. Nevertheless, the evidence provided by this study suggests a positive relationship between temperature and the stimulating effects of antibiotics on CH4 production.

Quantifying Global Wetland Methane Emissions With In Situ Methane Flux Data and Machine Learning Approaches.

Wetland methane (CH4) emissions have a significant impact on the global climate system. However, the current estimation of wetland CH4 emissions at the global scale still has large uncertainties. Here we developed six distinct bottom-up machine learning (ML) models using in situ CH4 fluxes from both chamber measurements and the Fluxnet-CH4 network. To reduce uncertainties, we adopted a multi-model ensemble (MME) approach to estimate CH4 emissions. Precipitation, air temperature, soil properties, wetland types, and climate types are considered in developing the models. The MME is then extrapolated to the global scale to estimate CH4 emissions from 1979 to 2099. We found that the annual wetland CH4 emissions are 146.6±12.2TgCH4yr-1 (1Tg=1012g) from 1979 to 2022. Future emissions will reach 165.8±11.6, 185.6±15.0, and 193.6±17.2TgCH4yr-1 in the last two decades of the 21st century under SSP126, SSP370, and SSP585 scenarios, respectively. Northern Europe and near-equatorial areas are the current emission hotspots. To further constrain the quantification uncertainty, research priorities should be directed to comprehensive CH4 measurements and better characterization of spatial dynamics of wetland areas. Our data-driven ML-based global wetland CH4 emission products for both the contemporary and the 21st century shall facilitate future global CH4 cycle studies.

Expanding seawater carbon dioxide and methane measuring capabilities with a Seaglider

Abstract. Warming, ocean acidification, and deoxygenation are increasingly putting pressure on marine ecosystems. At the same time, thawing permafrost and decomposing hydrates in Arctic shelf seas may release large amounts of methane (CH4) into the water column, which could accelerate local ocean acidification and contribute to climate change. The key parameters to observing and understanding these complex processes and feedback mechanisms are vastly undersampled throughout the oceans. We developed carbon dioxide (CO2) and CH4 gliders, including standard operational procedures, with the goal that CO2 and CH4 measurements will become more common for glider operations. The Seagliders with integrated Contros HydroC CO2 or CH4 sensors also include conductivity, temperature, depth, oxygen, chlorophyll a, backscatter, and fluorescent dissolved organic matter sensors. Communication via satellite allows for near-real-time data transmission, sensor adjustments, and adaptive sampling. Several sea trials with the CO2 Seaglider in the Gulf of Alaska and data evaluation with discrete water and underway samples suggest nearly “weather-quality” CO2 data as defined by the Global Ocean Acidification Network. A winter mission in Resurrection Bay, Alaska, provided the first insights into the water column inorganic carbon dynamics during this otherwise undersampled season. The CH4 Seaglider passed its flight trials in Resurrection Bay but needs to be tested during a field mission in an area with CH4 concentrations beyond background noise. Both sensing systems are available to the science community through the industry partners (Advanced Offshore Operations and -4H-JENA engineering GmbH) of this project.

A miniaturized multi-mechanism resonance-enhanced fiber optic photoacoustic multi-gas sensor

A multi-mechanism resonance-enhanced fiber-optic photoacoustic multi-gas sensor (MR-FOPMS) is reported, in which the acoustic resonance of the T-type resonator is employed for C2H2 gas sensing and the mechanical resonance of the silicon cantilever is employed for the detection of CH4 gas sensing. The silicon cantilever fiber-optic microphone and T-type photoacoustic resonator are designed using theoretical and finite element methods. The optimized cavity volume of the entire sensor is only 9.3 cm3 and the optimized silicon cantilever microphone has an ultra-high sensitivity of 62540.2 nm/Pa at the resonance. The ability of the sensor to detect multiple gases is demonstrated by simultaneous measurement of C2H2 and CH4 using two DFB lasers at 1532.8 nm and 1650.96 nm as excitation sources. The lowest detection limits of the sensor are determined to be 158 and 382 ppb for C2H2 and CH4, respectively, corresponding to normalized noise equivalent absorption coefficients of 2.82 × 10-9, 1.43 × 10-9 cm−1 W Hz−1/2, respectively.

Characterizing multifaceted environmental risks of oil and gas well leakage through soil and well methane and hydrogen sulfide emissions

Oil and gas wells (OGWs) can lead to soil and well emissions of methane (CH4), a potent greenhouse gas, and hydrogen sulfide (H2S), a highly toxic gas, both of which reduce air quality and can cause explosions when emitted into confined spaces. Developments have been occurring over OGWs, posing health and safety risks. However, to our knowledge, previous studies have not conjunctively analyzed well and soil emissions while considering development on or near OGWs. In this paper, we characterize 343 CH4 and H2S emission rate measurements from 67 non-producing (abandoned) and 35 producing (active) OGWs, including 205 measurements from soils surrounding 81 OGWs in Ontario and Quebec. We also provide the first emission rate estimates from an abandoned water and OGW-linked explosion and map OGWs in urban and built-up areas in Ontario and Quebec. We estimate the explosion-linked emissions to be 3,000 g CH4/hour and 7 g H2S/hour. Moreover, we find that 7,264 and 161 OGWs in Ontario and Quebec, respectively, are in urban and built-up areas, with 94% of these wells being abandoned. For the 102 wells we measured, of which 9.7% had H2S detections, we find OGW emission rate ranges of −16 to 47,000 mg CH4/hour and 0.001 to 3,300 mg H2S/hour. Although soil CH4 emissions at a 1-m distance from the wells are most correlated with well emissions, the highest soil emission rate was observed at a 3-m distance, indicating the potential for OGW-related emissions into buildings to occur away from the well. Overall, our multi-faceted measurement dataset provides a basis for conjunctive analysis of the broad range of environmental risks of OGWs to climate, indoor and outdoor air quality, and explosions.

Calibration-free heterodyne phase-sensitive dispersion spectroscopy: quantitative gas sensing and recovery of absorption spectra.

Heterodyne phase-sensitive dispersion spectroscopy (HPSDS) is a quantitative non-intrusive gas sensing technique based on the determination of the refractive index of the target gas in the vicinity of an absorption transition. Since the phase instead of the intensity of the probing laser light is targeted, the technique boasts the advantage of being normalization-free. It is thus largely immune to laser intensity fluctuations due to either system instability or ambient interferences. Previous HPSDS-based sensors typically require calibration using standard mixtures to establish a look-up table between the measured phase signal and gas concentrations, which is both cumbersome and problematic when there are significant compositional variations between the calibration standards and the target gas. In this work, we present a robust and generic technique that addresses this issue with a successful realization of fully calibration-free measurements. Spectral-fitting to the entire dispersion spectra with free variables related to transition linecenter, broadening width, and integrated absorbance were used to eliminate the effects of unknown spectral broadening coefficients. What we believe to be a novel analytical model was proposed to unify both direct injection-current dithering-based HPSDS that includes simultaneous frequency/intensity modulation, and the external electro-optic modulator (EOM) modulation-based HPSDS with a non-ideal linear response of EOM. The proposed technique was first validated via numerical experiment to determine the gas concentration and the recovery of the absorption profiles. Actual experiments were subsequently performed for the measurement of CH4 near 1.65 µm, N2O near 4.46 µm, and NO near 5.26 µm, collectively demonstrating the capability of the technique for both near- and mid-infrared lasers with diverse modulation characteristics. Further demonstrations were performed to measure NH3 concentrations at elevated temperatures through the fitting of the multiple dispersion spectra near 9.06 µm. The robust iterative spectral-fitting strategy and the measurement accuracies confirm the robustness of the proposed calibration-free (CF) HPSDS technique for quantitative gas sensing.

Foliar methane and nitrous oxide fluxes in tropical tree species

Methane (CH₄) and nitrous oxide (N₂O) are critical biogenic greenhouse gases (GHGs) with global warming potentials substantially greater than that of carbon dioxide (CO₂). The exchange of these gases in tropical forests, particularly via foliar processes, remains poorly understood. We quantified foliar CH₄ and N₂O fluxes among tropical tree species and examined their potential association with the leaf economics spectrum (LES) traits. Sampling within Lawachara National Park, Bangladesh, we used in-situ measurements of foliar CH₄ and N₂O fluxes employing off-axis integrated cavity output spectroscopy (CH₄, CO₂ and H₂O) and optical feedback–cavity enhanced absorption spectroscopy (N₂O) analyzers. Leaves were measured under dark, low, and high (0, 100, and 1000 μmol·m−2·s−1) light conditions. Surveyed tree species exhibited both net foliar uptake and efflux of CH₄, with a mean flux not different from zero, suggesting negligible net foliar emissions at the stand level. Plant families showed differences in CH₄, but not N₂O fluxes. Consistent efflux was observed for N₂O, with a mean of 0.562 ± 0.060 pmol·m−2·s−1. Pioneer species exhibited a higher mean N₂O flux (0.81 ± 0.17 pmol·m−2·s−1) compared to late-successional species (0.37 ± 0.05 pmol·m−2·s−1). Pioneer species also showed a trend toward a higher mean CH₄ flux (0.24 ± 0.21 nmol·m−2·s−1) compared to mid-successional (−0.01 ± 0.26 nmol·m−2·s−1) and late-successional species (−0.05 ± 0.28 nmol·m−2·s−1). Moreover, among all leaf traits within the leaf economic spectrum, a significant positive relationship was observed between leaf N₂O flux and total leaf nitrogen. Our results suggest that pioneer tree species significantly contribute to net CH₄ and N₂O emissions, potentially counteracting the carbon sequestration benefits in regenerating tropical forests. These findings indicate that accurate GHG budgeting should include direct measurements of foliar CH₄ and N₂O fluxes. Moreover, the results suggest that forest conservation and management strategies that prioritize late successional species will better mitigate GHG emissions.

A high‐frequency greenhouse gas flux analysis tool: Insights from automated non‐steady‐state transparent soil chambers

AbstractNon‐steady‐state chambers are widely employed for quantifying soil emissions of CO2, CH4, and N2O. Automated non‐steady‐state (a‐NSS) soil chambers, when coupled with online gas analysers, offer the ability to capture high‐frequency measurements of greenhouse gas (GHG) fluxes. While these sampling systems provide valuable insights into GHG emissions, they present post‐measurement challenges, including the management of extensive datasets, intricate flux calculations, and considerations for temporal upscaling. In this study, a computationally efficient algorithm was developed to compute instantaneous fluxes and estimate diel flux patterns using continuous, high‐resolution data obtained from an a‐NSS sampling system. Applied to a 38‐day dataset, the algorithm captured concurrent field measurements of CO2, CH4, and N2O fluxes. The automated sampling system enables the acquisition of high‐frequency data, allowing the detection of episodic gas flux events. By using shape‐constrained additive models, a median percentage deviation (bias) of −1.031 and −4.340% was achieved for CO2 and N2O fluxes, respectively. Simpson's rule allowed for efficient upscale from instantaneous to diel flux values. As a result, the proposed algorithm can rapidly and simultaneously calculate CO2, CH4, and N2O fluxes, providing both instantaneous and diel values directly from raw, high‐temporal‐resolution data. These advancements significantly contribute to the field of GHG flux measurement, enhancing both the efficiency and accuracy of calculations for a‐NSS soil chambers and deepening our understanding of GHG emissions and their temporal dynamics.

High rates of hydrogen sulfide emissions measured from marginal oil wells near Austin and San Antonio, Texas

Marginal oil and gas wells, or wells that produce less than 15 barrels of oil equivalent per day, represent 80% of actively producing wells in the United States, although they produce less than 10% of energy supply. Marginal wells are a disproportionate source of methane (CH4) relative to their production, and they emit harmful air pollutants, such as benzene and other hydrocarbons found in oil and natural gas. We made direct measurements of CH4 and hydrogen sulfide (H2S) emissions from 46 wellheads in the Luling Field, Caldwell County, Texas, just east of the Austin/San Antonio Metroplex. We found that these wells are venting natural gas and are a large source of hydrogen sulfide (H2S), a poisonous air pollutant. Hydrogen sulfide emission rates ranged from 0 to 5 ± 0.5 g H2S hr−1 with an average emission rate of 1.6 ± 0.1 g H2S hr−1. We also found ambient concentrations of H2S at dangerous levels (>100 ppm) near many of the wells. Methane emission rates were in line with previous studies of marginal wells, ranging from 0.0 to 2770 ± 390 g CH4 hr−1, with a skewed distribution and average emission rate of 710 ± 100 g CH4 hr−1. Oil production records from Texas were incomplete: some wells had oil production data from the year of sampling, but many had no production data for several years or decades, although they were actively pumping while we were on site. Interviews with local residents indicate that the closing of the county gas processing plant and subsequent loss of gathering lines may be the cause of gas venting and CH4 and H2S emissions from production sites. This deserves further scrutiny, as marginal wells in this region are a major source of H2S, a health hazard to people living and working nearby.

Temporal characteristics and vertical profiles of atmospheric CH4 at the northern foot of the Qinling Mountains in China

A two-year (March 2021 to February 2023) continuous in-situ atmospheric CH4 measurements and periodic vertical CH4 measurements (<2000 m) in July and November 2021 were conducted at the northern foot of the Qinling Mountains in Xi'an, China, aiming to study the temporal and vertical variations in atmospheric CH4 and the influence of air mass transport. The two-year average CH4 concentration at this site was 2119.6 ± 108.8 ppb. Seasonal CH4 concentrations were the highest in winter (2177.6 ± 121.5 ppb) and lower in summer (2079.1 ± 77.6 ppb) and spring (2073.9 ± 68.4 ppb). Diurnal CH4 peaked at 10:00–11:00. Atmospheric CH4 there was mainly from local source emissions from Xi'an and short distance transport from the southern Qinling Mountains through the valleys. On the July sampling days, vertical CH4 concentrations increased in the morning (08:00) and at mid-night (23:30), while they decreased in the early afternoon (13:00) and late afternoon (18:00) from near the surface (20 m) to 200 m, increased at 200–500 m, and then decreased at 500–1000 m. During the heating period in November, vertical CH4 concentrations increased by 20.0–86.8 ppb at 20–200 m due to horizontal transport and poor atmospheric dispersion conditions, and then decreased up to 2000 m, with the fastest decrease rate (−44.4 ± 51.4 ∼ −17.6 ± 19.3 ppb/100 m) at 500–1000 m. Vertical CH4 concentrations increased at all heights especially below 500 m in serve haze, mainly attributed to horizontal transport from the northwestern and southeastern polluted regions. Vertical observations further confirmed the important influence of transport on CH4 levels at this site.

Real-time monitoring of CH4 and N2O emissions from livestock using mid-infrared external cavity quantum cascade laser absorption spectroscopy

The largest share of greenhouse gas (GHG) emissions related to livestock originates from methane (CH4) and nitrous oxide (N2O) which have a far higher influence on global warming, it is therefore necessary to accurately monitor CH4 and N2O emissions to provide theoretical and practical basis for further estimating and regulating GHG emissions from livestock and improving livestock production performance. For the purpose of sensing CH4 and N2O emissions during livestock living process in real time, an optical sensor based on continuous-wave (CW) external cavity quantum cascade laser (EC-QCL) operating at room temperature was developed. CH4 and N2O absorption lines, located around 8 μm, of the ν4 and ν1 fundamental vibrational bands, respectively, were chosen for direct absorption spectroscopy, which allows for sensitive, selective and simultaneous measurement of CH4 and N2O concentrations. Use of a Herriot multi-pass cell with an effective path-length of 100 m, 1σ (SNR = 1) limits of detection of 26.8 ppbv, 20.3 ppbv and 0.01 % for CH4, N2O and H2O vapor were achieved, respectively. Field measurement of CH4 and N2O emissions from horses has been carried out in a stable over two weeks at the Vernaelde farm in Couderkerque Branche city, France. Concentrations of CH4 and N2O up to 10 times and 1.5 times higher than their levels in the local ambient air (∼ 2.12 ppmv and ∼ 427 ppbv) were observed, respectively.

Highly sensitive CH4, C2H2 and CO simultaneous measurement LITES sensor based on multi-pass cell with overlapped spots pattern and QTFs with low resonant frequency.

This paper presents a simultaneous measurement light-induced thermoelectric spectroscopy (LITES) sensor with high sensitivity for detecting methane (CH4), carbon monoxide (CO) and acetylene (C2H2). It employs a multi-pass cell (MPC) with an overlapped spots pattern and low resonant frequency circle-head quartz tuning forks (QTFs) for the first time. The fiber-coupled MPC with an optical length (OPL) of 40 m was combined with a thin-film filter (TFF) to improve the laser absorption and enable light spots multiplexing on mirrors. Three self-designed circle-head QTFs with low resonant frequencies of less than 10 kHz and a quality factor of ∼ 11500 were adopted to improve the detection ability. The LITES sensor detected multi-gas signals based on their unique absorption spectrum, enabling real-simultaneous measurement of CH4, CO, and C2H2. After optimization, the minimum detection limits (MDLs) of 0.5 ppm, 126.9 ppm and 0.4 ppm for these gases were obtained, respectively. With integration times of 300 s for CH4 and 200 s each for CO and C2H2, the MDLs could be further reduced to 0.09 ppm, 57.1 ppm, and 0.07 ppm, correspondingly. The paper concludes with a discussion of potential strategies for further improving the performance of such LITES sensors.

Miniaturization study on helmholtz-based photoacoustic cell for PAS-based trace gas detection

In this study, the acoustic behavior and gas flow noise in a Helmholtz photoacoustic cell were simulated and optimized to study the influence of the cell dimensions and buffer structure. Numerical analysis using finite element analysis and experimental validation were performed in this study. A spindle-type photoacoustic cell with a resonator volume of 0.5 mL and buffer volume of 0.4 mL was selected for CH4 detection. Besides its strong photoacoustic enhancement, the spindle-type buffer structure has the best ability to eliminate flow noise compared to the other buffer structures. A linearity of 0.9992 was achieved over a wide concentration range of 0–10000 ppm in the CH4 measurement experiment. The 1σ equivalent detection limit for a response time of 10 s was measured to be 0.49 ppm. Allan deviation analysis indicated that a minimum detection limit of 17.9 ppb can be achieved by extending the integration time to 4760 s.

The effect of feeding and visiting behavior on methane and hydrogen emissions of dairy cattle measured with the GreenFeed system under different dietary conditions

The objectives were to investigate the effect of feeding and visiting behavior of dairy cattle on CH4 and H2 production measured with voluntary visits to the GreenFeed system (GF) and to determine whether these effects depended on basal diet (BD) and 3-nitrooxypropanol (3-NOP) supplementation. The experiment involved 64 lactating dairy cattle (146 ± 45 d in milk at the start of trial; mean ± SD) in 2 overlapping crossover trials, each consisting of 2 measurement periods. Cows within block were randomly allocated to 1 of 3 types of BD: a grass silage-based diet consisting of 30% concentrates and 70% grass silage (DM basis), a grass silage- and corn silage-mixed diet consisting of 30% concentrates, 42% grass silage, and 28% corn silage (DM basis), or a corn silage-based diet consisting of 30% concentrates, 14% grass silage, and 56% corn silage (DM basis). Each type of BD was subsequently supplemented with 0 and 60 mg 3-NOP/kg DM in one crossover, or 0 and 80 mg 3-NOP/kg DM in the other crossover. Diets were provided in feed bins which automatically recorded feed intake and feeding behavior, with additional concentrate fed in the GF. All visits to the GF that resulted in a spot measurement of both CH4 and H2 emission were analyzed in relation to feeding behavior (e.g., meal size and time interval to preceding meal) as well as GF visiting behavior (e.g., duration of visit). Feeding and GF visiting behavior was related to CH4 and H2 production measured with the GF, in particular the meal size before a GF measurement and the time interval between a GF measurement and the preceding meal. Relationships between gas production and both feeding and GF visiting behavior were affected both by type of BD and 3-NOP supplementation. With an increase of the time interval between a GF measurement and the preceding meal, CH4 production decreased with 0 mg 3-NOP/kg DM but increased with 60 and 80 mg 3-NOP/kg DM, whereas type of BD did not affect these relationships. In contrast, CH4 production increased with 0 mg 3-NOP/kg DM but decreased with 60 and 80 mg 3-NOP/kg DM upon an increase in the size of the meal preceding a GF measurement. With an increase of the time interval between a GF measurement and the preceding meal, or with a decrease of the size of the meal preceding a GF measurement, H2 production decreased for all treatments, although the effect was generally somewhat stronger for 60 and 80 mg 3-NOP/kg DM than for 0 mg 3-NOP/kg DM. Hence, the timing of GF measurements next to feeding and GF visiting behavior are essential when assessing the effect of dietary treatment on the production of CH4 and H2 in a setting where a spot sampling device such as a GF is used and where the measurements depend on voluntary visits from the cows.

Combining short-term breath measurements to develop methane prediction equations from cow milk mid-infrared spectra

Predicting methane (CH4) emission from milk mid-infrared (MIR) spectra provides large amounts of data which is necessary for genomic selection. Recent prediction equations were developed using the GreenFeed system, which required averaging multiple CH4 measurements to obtain an accurate estimate, resulting in large data loss when animals unfrequently visit the GreenFeed. This study aimed to determine if calibrating equations on CH4 emissions corrected for diurnal variations or modeled throughout lactation would improve the accuracy of the predictions by reducing data loss compared with standard averaging methods used with GreenFeed data. The calibration dataset included 1 822 spectra from 235 cows (Holstein, Montbéliarde, and Abondance), and the validation dataset included 104 spectra from 46 (Holstein and Montbéliarde). The predictive ability of the equations calibrated on MIR spectra only was low to moderate (R2v = 0.22–0.36, RMSE = 57–70 g/d). Equations using CH4 averages that had been pre−corrected for diurnal variations tended to perform better, especially with respect to the error of prediction. Furthermore, pre−correcting CH4 values allowed to use all the data available without requiring a minimum number of spot measures at the GreenFeed device for calculating averages. This study provides advice for developing new prediction equations, in addition to a new set of equations based on a large and diverse population.

Disaggregating the Carbon Exchange of Degrading Permafrost Peatlands Using Bayesian Deep Learning

AbstractExtensive regions in the permafrost zone are projected to become climatically unsuitable to sustain permafrost peatlands over the next century, suggesting transformations in these landscapes that can leave large amounts of permafrost carbon vulnerable to post‐thaw decomposition. We present 3 years of eddy covariance measurements of CH4 and CO2 fluxes from the degrading permafrost peatland Iškoras in Northern Norway, which we disaggregate into separate fluxes of palsa, pond, and fen areas using information provided by the dynamic flux footprint in a novel ensemble‐based Bayesian deep neural network framework. The 3‐year mean CO2‐equivalent flux is estimated to be 106 gCO2 m−2 yr−1 for palsas, 1,780 gCO2 m−2 yr−1 for ponds, and −31 gCO2 m−2 yr−1 for fens, indicating that possible palsa degradation to thermokarst ponds would strengthen the local greenhouse gas forcing by a factor of about 17, while transformation into fens would slightly reduce the current local greenhouse gas forcing.

Formation of Abnormal Gas-Geochemical Fields and Dissolved Gases Transport at the Shallow Northeastern Shelf of Sakhalin Island in Warm Season: Expedition Data and Remote Sensing

Our paper deals with gas-geochemical measurements of CH4 and CO2, as well as the first measurements of dissolved H2 and He in the waters of the eastern shelf of Sakhalin Island, obtained during cruise 68 on the R/V Akademik Oparin (OP68) on 12–18 August 2023. The shallow eastern shelf has high concentrations of dissolved methane and helium in the water. The combined anomalies of methane and helium indicate the presence of an ascending deep fluid. The sources of methane in the studied area are the underlying oil- and gas-bearing rocks extending to the coast of the island. The deep faults of the region and the minor discontinuities that accompany them along the eastern coast of Sakhalin Island create a fluid-permeable geological environment both on the shallow shelf and on the coastal part of the island. East Sakhalin current and counter-current influence gases that migrate from lithospheric sources; these currents form a special hydrological regime that ensures high solubility of the gases released and their transfer under the lower boundary of the seasonal pycnocline to the east, where they are involved in the general circulation of the Sea of Okhotsk.

Atmospheric constraints on changing Arctic CH4 emissions

Rapid warming in the Arctic has the potential to release vast reservoirs of carbon into the atmosphere as methane (CH4) resulting in a strong positive climate feedback. This raises the concern that, after a period of near-zero growth in atmospheric CH4 burden from 1999 to 2006, the increase since then may be in part related to increased Arctic emissions. Measurements of CH4 in background air samples provide useful, direct information to determine if Arctic CH4 emissions are increasing. One sensitive first-order indicator for large emission change is the Interpolar Difference, that is the difference in surface atmospheric annual means between polar northern and southern zones (53°–90°), which has varied interannually, but did not increase from 1992 to 2019. The Interpolar Difference has increased moderately during 2020–2022 when the global CH4 burden increased significantly, but not yet to its peak values in the late-1980s. For quantitative assessment of changing Arctic CH4 emissions, the atmospheric measurements must be combined with an atmospheric tracer transport model. Based on multiple studies including some using CH4 isotopes, it is clear that most of the increase in global atmospheric CH4 burden is driven by increased emissions from microbial sources in the tropics, and that Arctic emissions have not increased significantly since the beginning of our measurement record in 1983 through 2022.