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ABSTRACT: Methane is a potent greenhouse gas produced during the ruminal fermentation and is associated with a loss of feed energy. Therefore, efforts to reduce methane emissions have been ongoing in the last decades. Methane production is highly influenced by factors such as the ruminal microbiome and host genetics. Previous studies have proposed to use the ruminal microbiome to reduce long-term methane emissions, as ruminal microbiome composition is a moderately heritable trait and genetic improvement accumulates over time. Lactation stage is another important factor that might influence methane production, but potential associations with the ruminal microbiome have not been evaluated previously. This study sought to examine the changes in ruminal microbiome over the lactation period of primiparous Holstein cows differing in methane intensity (MI) and estimate the heritability of the abundance of relevant microorganisms. Ruminal content samples from 349 primiparous Holstein cows with 14 to 378 DIM were collected from May 2018 to June 2019. Methane intensity of each cow was calculated as methane concentration/milk yield. Up to 64 taxonomic features (TF) from 20 phyla had a significant differential abundance between cows with low and high MI early in lactation, 16 TF during mid lactation, and none late in lactation. Taxonomical features within the Firmicutes, Proteobacteria, Melainabacteria, Cyanobacteria, Bacteroidetes, and Actinobacteria phyla were associated with low MI, whereas eukaryotic TF and those within the Euryarchaeota, Verrucomicrobia, Kiritimatiellaeota, and Lentisphaerae phyla were associated with high MI. Out of the 60 TF that were found to be differentially abundant between early and late lactation in cows with low MI, 56 TF were also significant when cows with low and high MI were compared in the first third of the lactation. In general, microbes associated with low MI were more abundant early in lactation (e.g., Acidaminococcus, Aeromonas, and Weimeria genera) and showed low to moderate heritabilities (0.03 to 0.33). These results suggest some potential to modulate the rumen microbiome composition through selective breeding for lower MI. Differences in the ruminal microbiome of cows with extreme MI levels likely result from variations in the ruminal physiology of these cows and were more noticeable early in lactation, probably due to important interactions between the host phenotype and environmental factors associated with that period. Our results suggest that the ruminal microbiome evaluated early in lactation may be more precise for MI difference, and hence, this should be considered to optimize sampling periods to establish a reference population in genomic selection scenarios.

The emergence of waterlogged Oryza species ∼15Mya (million years ago) supplied an anoxic warm bed for methane-producing microorganisms, and methane emissions have hence accompanied the entire evolutionary history of the genus Oryza. However, to date no study has addressed how methane emission has been altered during Oryza evolution. In this paper we used a diverse collection of wild and cultivated Oryza species to study the relation between Oryza evolution and methane emissions. Phylogenetic analyses and methane detection identified a co-evolutionary pattern between Oryza and methane emissions, mediated by the diversity of the rhizospheric ecosystems arising from different oxygen levels. Fumarate was identified as an oxygen substitute used to retain the electron transport/energy production in the anoxic rice root, and the contribution of fumarate reductase to Oryza evolution and methane emissions has also been assessed. We confirmed the between-species patterns using genetic dissection of the traits in a cross between a low and high methane-emitting species. Our findings provide novel insights on the evolutionary processes of rice paddy methane emissions: the evolution of wild rice produces different Oryza species with divergent rhizospheric ecosystem attributing to the different oxygen levels and fumarate reductase activities, methane emissions are comprehensively assessed by the rhizospheric environment of diversity Oryza species and result in a co-evolution pattern.

Methane (CH4), an often-overlooked greenhouse gas (GHG), has a significant impact on the environment. Although it receives less attention than carbon dioxide (CO2), it is the second most important GHG in terms of its ability to trap heat in the atmosphere. Few studies have analyzed the determinants of CH4 emissions, especially those from the energy sector. Therefore, this study provides relevant information on the impact of GDP, primary and renewable energy consumption, human development index and trade openness on methane emissions in OECD countries. Using advanced cointegration approaches, we find that GDP and primary energy consumption increase CH4 emissions, while renewable energy consumption and human development mitigate their growth. However, the impact of these variables varied over time. No significant effect of trade openness on methane emissions was found. We recommend specific policies for OECD countries to reduce methane emissions, especially for the most polluting countries. Governments should promote renewable energy sources (solar, wind, hydro) to reduce reliance on fossil fuels, thereby minimizing methane leakage during extraction and transport. In addition, investing in human development can promote sustainable behaviors and further reduce emissions, addressing both environmental and social concerns.

ABSTRACT: Enteric methane (CH4) emissions of 3 genetic groups (GG) of dairy cows were recorded across the grazing season (early March to late October). The 3 GG were (1) high economic breeding index (EBI) Holstein-Friesian (HF) representative of the top 1% of dairy cows in Ireland at the time of the study (elite), (2) national average (NA) EBI, which were representative of the average HF dairy cow in Ireland, and (3) purebred Jersey (JE) cows. Enteric CH4 was recorded using GreenFeed technology. Seasonal variation in CH4 was observed, with the lowest daily CH4 emissions and CH4 expressed per unit of dry matter intake occurring in spring (253 g/d and 15.56 g/kg, respectively), intermediate in summer (303 g/d and 18.26 g/kg, respectively), and greatest in autumn (324 g/d and 19.80 g/kg, respectively). Seasonal variation was also observed in the proportion of gross energy intake converted to CH4 (Ym); in the spring the Ym was lowest at 0.046, increasing to 0.053 and 0.058 in the summer and autumn, respectively. There was no difference in daily CH4 between the elite and NA, whereas JE had lower CH4 emissions compared with the elite. When expressed per unit of milk solids (fat + protein yield; MS), the elite and JE produced 6.8% and 9.7% less CH4 per kilogram of MS, respectively, compared with NA. There was no difference between the GG for CH4 per unit of DMI or the Ym. This research emphasizes the variation in CH4 emissions across the grazing season and among cows of differing genetic merit for CH4 emission intensities but not for CH4 per unit of DMI or the Ym.

The environmental impact of dairy production can be reduced in several ways, including increasing feed efficiency and reducing methane (CH4) emissions. There is no consensus on their relationship. This study aimed at estimating the correlations between residual feed intake (RFI) and CH4 emissions expressed in g/d methane production (MeP), g/kg of fat- and protein-corrected milk methane intensity (MeI), or g/kg of DM intake methane yield (MeY) throughout lactation. We collected CH4 data using GreenFeed devices from 107 Holstein cows, as well as production and intake phenotypes. RFI was predicted from DM intake, fat- and protein-corrected milk, BW, and body condition score. Five-trait random regression models were used to estimate the individual variance components of the CH4 and production traits, which were used to calculate the correlations between RFI and CH4 traits throughout lactation. We found positive correlations of RFI with MeP and MeI ranging from 0.05 to 0.47 throughout the lactation. Correlations between RFI and MeY are low and vary from positive to negative, ranging from −0.18 to 0.17. Both MeP and MeI are favorably correlated with RFI, as is MeY during the first half of lactation. These correlations are mostly favorable for genetic selection, but the confirmation of these results is needed with genetic correlations over a larger dataset.

ABSTRACT: To reduce methane (CH4) emissions of dairy cows by animal breeding, CH4 measurements have to be recorded on thousands of individual cows. Currently, several techniques are used to phenotype cows for CH4, differing in costs and applicability. However, there is uncertainty about the agreement between techniques. To judge the similarity and repeatability between measurements of different recording techniques, the repeatability, heritability, and genetic correlation are useful metrics. Therefore, our objective was to estimate (1) the repeatability and heritability for CH4 and carbon dioxide production recorded by GreenFeed (GF) and for CH4 and carbon dioxide concentration measured by cost-effective but less accurate sniffers, and (2) the genetic correlation between CH4 recorded with these 2 different on farm and high throughput techniques. Data were available from repeated measurements of CH4 production (grams/day) by GF units and of CH4 concentration (ppm) by sniffers, recorded on commercial dairy farms in the Netherlands. The final data comprised 24,284 GF daily means from 822 cows, 170,826 sniffer daily means from 1,800 cows, and 1,786 daily means from 75 cows by both GF and sniffer (in the same period). Additionally, CH4 records were averaged per week. For daily and weekly mean GF CH4 the heritabilities were 0.19 ± 0.02 and 0.33 ± 0.04, and for daily and weekly mean sniffer CH4 the heritabilities were similar and were 0.18 ± 0.01 and 0.32 ± 0.02, respectively. Phenotypic correlations between GF CH4 production and sniffer CH4 concentration were moderate (0.39 ± 0.03 for daily means and 0.37 ± 0.05 for weekly means). However, genetic correlations were high; 0.71 ± 0.13 for daily means and 0.76 ± 0.15 for weekly means. The high genetic correlation indicates that selection on low CH4 concentrations (ppm) recorded by the cost-effective sniffer method, will result in reduced CH4 production (grams/day) as recorded with GF.

Cattle production in Colombia has an important social and economic role but causes considerable environmental impacts, such as deforestation and greenhouse gas emissions by ruminants, particularly methane. Thus, technological innovations aimed at reducing these impacts must focus on both economic and environmental sustainability. Silvo-pastoral systems (SPS) offer productivity increases while generating environmental benefits and ecosystem services and are therefore at the center of debate around sustainable production alternatives. The objective of this article is to evaluate the economic-environmental performance of two proposed SPS for a cattle fattening system for the Colombian context: (i) Urochloa brizantha cv. Toledo and (ii) Urochloa hybrid cv. Cayman, both in association with Leucaena leucocephala trees for browsing and shade provision. They are compared with the respective base scenarios of only using the grasses in monocultures. The study consists of a financial analysis, which estimates potential profitability increases in beef production in the SPS, and an environmental evaluation, which estimates the monetary values of microclimatic regulation and reduction of methane emissions. The value of methane emission reductions is then integrated into a combined economic-environmental evaluation. Results show that both SPS improve the profitability indicators of the production system and reduce the probability of economic loss. Likewise, the reduction of methane emissions in the SPS is estimated at US$6.12 per cattle, and the economic value of microclimatic regulation at US$2,026 per hectare.

The quantification of gas emissions from waste storage and treatment ponds is an important problem. The objective of this study was to better understand the use of micrometeorological techniques for this purpose. Methane emissions were estimated from a large tailings pond (surface area >11 km2) at an oil sands mine site using datasets collected by different groups over a nine-month period. Emissions were calculated with eddy-covariance (EC) and inverse dispersion modelling (IDM) techniques. Three different IDM calculations were made using methane concentrations measured with either fixed-point sensors (IDM-LGR), a long-path laser (IDM-GL), or an unmanned aerial vehicle (IDM-UAV). Emissions were also estimated from a flux-chamber (FC) survey. Although the temporal overlap between the different datasets was limited, the results indicate substantial differences in emission-rate estimates. During a summer interval the EC, IDM-LGR, and IDM-GL estimates were 19%, 41%, and 56% of the FC-estimated rate, respectively. The overall ordering was EC ≈ IDM-UAV

ABSTRACT: Animal breeding techniques offer potential to reduce enteric emissions of ruminants to lower the environmental impact of dairy farming. The aim of this study was to estimate the heritability and repeatability of methane (CH4) concentrations, using the largest data set from long-term repeatedly recorded CH4 on cows to date, and to evaluate (1) the accuracy of breeding values for different CH4 traits, including using visits or weekly means, and (2) recording strategies (with varying numbers of records and recorded daughters per sire). The data comprised of long-term recording of CH4 and carbon dioxide (CO2), from 1,746 Holstein Friesian cows, on 14 commercial dairy farms throughout the Netherlands. Emissions were recorded in 10- to 35-s intervals, between 64 and 436 d, depending on farms. From each robot visit, CH4 and CO2 concentrations were summarized into various traits, averaged per visit and per week: mean, median, mean log, and mean CH4/CO2 ratio. Genetic parameters were estimated with animal repeatability models, using a restricted maximum likelihood procedure, and a relationship matrix based on genotypes and pedigree. The heritability was equal for mean and median CH4 per visit (0.13) but lower for logCH4 and CH4/CO2 (0.07 and 0.01, respectively). Phenotypic and genetic correlations were high (≥0.78) between the CH4 traits, apart from the genetic correlations with the CH4/CO2 trait, which were negative. To achieve a minimum reliability of 50% for the estimated breeding value of a bull, 25 records on mean CH4, measured on 10 different daughters, were sufficient. Although the heritability and repeatability were higher for weekly (0.32 and 0.68, respectively) than for visit mean CH4 (0.13 and 0.30, respectively), the reliabilities of estimated breeding values from visit or weekly means were equal; thus, we found no advantage in averaging records to weekly means for genetic evaluations.

A common practice within the current regulatory framework for gas distribution uses an approach where operators report their network emissions by applying an average emission factor for all leaks, sometimes sorted by pipe material and type of assets. Such an approach has two drawbacks: first, it does not account for the specificities of the gas systems and the maintenance processes of the operators; and second, it does not enable the prioritization of large leaks that is key for an effective emissions abatement program. This article describes a method using a mobile leak detection and quantification system to assess methane emissions from a gas distribution network and to reduce them by accelerating the detection and repair of larger leaks. The approach allows for data-driven system-wide emissions quantification that is specific to the network and not subject to operator’s leak detection practices that may affect their traditional emission factor-based reporting. Furthermore, we show that for a sensor with a sufficiently low detection limit, the calculated emissions are independent of the precision of the measurement if the uncertainties are correctly addressed. Such a result is important because it assures that methane emissions estimates are not biased and can be used to assess the performance of abatement programs. Finally, we illustrate how the approach can be practically implemented through a program where the largest leaks are rapidly identified and repaired to abate methane emissions while minimizing costs.

In April of 2018, an optical gas imaging (OGI) and full flow sampler (FFS) emissions measurement study of pneumatic controllers (PCs) was conducted at 15 oil and natural gas production sites in West Virginia. The objective of the study was to identify and characterize PC systems with excessive emissions caused by maintenance issues or nonoptimized process conditions. A total of 391 PC systems were found on the sites and all were classified by the operator as snap-acting (on/off) intermittent venting PCs (IPCs) that should exhibit little gas release while the PC is closed between actuation events. The population was comprised of two groups, 259 infrequently actuating, lower emitting (LE) IPCs and 132 gas processing unit (GPU) liquid level IPCs and associated dump valve actuators that vent more frequently and have larger emission volumes. Using a PC-specific OGI inspection protocol with an assumed whole gas OGI detection threshold of 2.0 scfh, only 2 out of 259 LE-IPCs exhibited OGI detectable emissions indicating good inspection and maintenance practices for this category. Due to combined (ganged) GPU exhaust vents, the OGI inspection of the GPU liquid level IPCs was comparatively less informative and determination of single component IPC emissions by the FFS was more difficult. The time resolved FFS measurements of GPU IPCs defined three categories of operation: one that indicated proper function and two associated with higher emissions that may result from an IPC maintenance or process issues. The overall GPU IPC emission distribution was heavy tailed, with a median value of 12.8 scfh, similar to the 13.5 scfh whole gas IPC emission factor (EF). Total emissions were dominated by non-optimal temporal profile high-emitter IPC cases with the top 20% of IPC systems accounting for between 51.3% and 70.7% of GPU liquid level IPC emissions by volume. The uncertainty in the estimate was due to the ganged nature of the GPU exhaust vents. The highest GPU IPC emission came from a single malfunctioning unit with a measured whole gas value of 157 scfh. Up to six IPCs exceeded 100 scfh. An analysis of FFS emission measurements compared to liquids production per IPC unit employed indicated that production sites operating at a high level of liquids production test the limits of the site engineering, likely resulting in higher IPC emissions. Overall, this study found that the LE-IPCs with OGI-verified low closed bleed rates may emit well below the IPC EF while GPU liquid level IPC systems are likely well represented by the current IPC EF. IPCs that are experiencing a maintenance or process issue or that are operating at sites with a very high product throughput per IPC employed can emit at rates exceeding ten times IPC EF.

Methane emissions from natural gas production are of increasing importance as they threaten efforts to mitigate climate change. Current inventory estimates carry high uncertainties due to difficulties in measuring emission sources across large regions. Satellite measurements of atmospheric methane could provide new understanding of emissions. This paper provides insight into the effectiveness of using satellite data to inform and improve methane inventories for natural gas activities. TROPOMI data are used to quantify methane emissions from natural gas within the Montney basin region of Canada and results are compared with existing inventories. Emissions estimated using TROPOMI data were 2.6 ± 2.2 kt/day which is 7.4 ± 6.4 times the inventory estimates. Pixels (7 by 7 km) that contained gas facilities had on average 11 ppb more methane than the background. 7.4% of pixels containing gas sites displayed consistently high methane levels that were not reflected in the inventory. The satellite data were not sufficiently granular to correlate with inventories on a facility scale. This illustrates the spatial limitations of using satellite data to corroborate bottom-up inventories.

Researchers are searching for ways to better quantify methane emissions from natural gas infrastructure. Current indirect quantification techniques (IQTs) allow for more frequent or continuous measurements with fewer personnel resources than direct methods but lack accuracy and repeatability. Two IQTs are Other Test Method (OTM) 33A and Eddy Covariance (EC). We examined a novel approach to improve the accuracy of single sensor IQT whereby the results from both OTM and EC were combined with two machine learning (ML) models, a random forest (RF) and a neural network (NN). Then, models were enhanced with feature reduction and hyper-parameter tuning and compared to traditional quantification methods. The NN and RF improved upon the default OTM by an average of 44% and 78%, respectively. When compared to traditional OTM estimates with low Data Quality Indicators (DQIs), RF and NN models reduced 1σ errors from ±66% to ±13% and ±34%, respectively. Models also reduced the standard deviation of estimates with 93% and 85% of estimates falling within ±50% of the known release rate. This approach can be deployed with single sensor systems at well sites to improve confidence in reported emissions, reducing the number of anomalous overestimates that would trigger unnecessary site evaluations. Additional improvements could be realized by expanding training datasets with more methane release rates. Further, deployment of such models in a variety of situations could enhance their ability help close the gap between bottom-up inventory and top-down studies by enabling continuous monitoring of temporal emissions that could identify with improved confidence, atypically higher emissions. Accurate remote single sensor systems are key in developing an improved understanding of methane emissions to enable industry to identify and reduce methane emissions.

A measurement campaign characterized methane and other emissions from 15 natural gas production sites. Sites were surveyed using optical gas imaging (OGI) cameras to identify fugitive and vented emissions, with the methane mass emission rate quantified using a full flow sampler. We present storage tank emissions in context of all site emissions, followed by a detailed account of the former. In total, 224 well pad emission sources at 15 sites were quantified yielding a total emission rate of 57.5 ± 2.89 kg/hr for all sites. Site specific emissions ranged from 0.4 to 10.5 kg/hr with arithmetic and geometric means of 3.8 and 2.2 kg/hr, respectively. The two largest categories of emissions by mass were pneumatic devices (35 kg/hr or ∼61% of total) and tanks (14.3 kg/hr or ∼25% of total). Produced water and condensate tanks at all sites employed emissions control devices. Nevertheless, tanks may still lose gas via component leaks as observed in this study. The total number of tanks at all sites was 153. One site experienced a major malfunction and direct tank measurements were not conducted due to safety concerns and may have represented a super-emitter as found in other studies. The remaining sites had 143 tanks, which accounted for 42 emissions sources. Leaks on controlled tanks were associated with ERVs, PRVs, and thief hatches. Since measurements represented snapshots-in-time and could only be compared with modeled tank emission data, it was difficult to assess real capture efficiencies accurately. Our estimates suggest that capture efficiency ranged from 63 to 92% for controlled tanks.

Mangrove forests represent important sources of methane, partly thwarting their ecosystem function as an efficient atmospheric carbon dioxide sink. Many studies have focused on the spatial and temporal variability of methane emissions from mangrove ecosystems, yet little is known about the microbial and physical controls on the release of biogenic methane from tidally influenced mangrove sediments. Here, we show that aerobic methane oxidation is a key microbial process that effectively reduces methane emissions from mangrove sediments. We further demonstrate clear links between the tidal cycle and fluctuations in methane fluxes, with contrasting methane emission rates under different tidal amplitudes. Our data suggest that both the microbial methane oxidation activity and pressure-induced advective transport modulated methane fluxes in the mangrove sediments. Methane oxidation activity is limited by the availability of oxygen in the surface sediments, which in turn is controlled by tidal dynamics, further highlighting the interactive physico-biogeochemical controls on biological methane fluxes. Although we found some molecular evidence for anaerobic methanotrophs in the deeper sediments, anaerobic methane oxidation seems to play only a minor role in the mangrove sediments, with potential rates being two orders of magnitude lower than those of aerobic methane oxidation. Our findings confirmed the importance of surface sediments as biological barrier for methane. Specifically, when sediments were exposed to the air, methane consumption increased by ∼227%, and the methane flux was reduced by ∼62%, compared to inundated conditions. Our data demonstrate how tides can orchestrate the daily rhythm of methane consumption and production within mangrove sediments, thus explaining the temporal variability of methane emissions in the tidally influenced coastal mangrove systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

In carbon-intensive industries like oil and gas production, ESG (Environmental, Social, Governance) reports provide investors with information about carbon intensity performance and there is link between environmental and financial performance. In Canada, ESG reporting is not obligatory, and multiple frameworks are used making performance comparisons across producers challenging. Additionally, methane emissions from the oil and gas industry are underestimated in Canada which also suggests systemic under-reporting of emissions in this key area of carbon intensity. Here we investigate 2019 methane emission intensity gaps for 70 of Canada's largest oil and gas producers, between 1) provincial regulatory submissions, 2) assumed contributions to the federal inventory estimates, 3) a mobile ground laboratory measurement-based inventory, 4) and producer self-published ESG. We de-aggregated existing methane inventory estimates and measurements for Canada's largest producing province, Alberta, and found a greater than 1000-fold variation in methane intensities within the cohort. We also found surprisingly broad agreement in assumed methane emission intensities between (2) and (3), suggesting they capture the same signals. Regulatory submissions captured only a small amount of the total methane inventory and we also observed a low bias in regulatory reporting. In conclusion, we believe that changes to required and voluntary reporting, and associated systems management frameworks are needed to differentiate oil and gas producers and preserve market access based on carbon intensity.

Flooding of paddy fields enhances methane (CH 4 ) emissions and arsenic (As) mobilisation, which are crucial issues for agricultural greenhouse gas emissions and food safety. Birnessite (δ-MnO 2 ) is a common natural oxidant and scavenger for heavy metals. In this study, birnessite was applied to As-contaminated paddy soil. The capacity for simultaneously alleviating CH 4 emissions and As mobility was explored. Soil microcosm incubation results indicated that birnessite addition simultaneously reduced CH 4 emissions by 47 %-54 % and As release by 38 %-85 %. The addition of birnessite decreased the dissolved organic carbon (DOC) contents and altered its chemical properties. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) results showed that birnessite reduced the labile fractions of proteins, carbohydrates, lignins, tannins, and unsaturated hydrocarbons, however, increased the abundance of condensed aromatic structures, suggesting the polymerisation of dissolved organic matter (DOM) by birnessite. The degradation of labile fractions and the polymerisation of DOM resulted in an inventory of recalcitrant DOM, which is difficult for microbes to metabolise, thus inhibiting methanogenesis. In contrast, birnessite addition increased CH 4 oxidation, as the particulate methane monooxygenase (pmoA) gene abundance increased by 30 %. The enhanced polymerisation of DOM by birnessite also increased As complexation with organics, leading to the transfer of As to the organic bound phase. In addition, the decrease in ferrous ion [Fe(II)] concentrations with birnessite indicated that the reductive dissolution of Fe oxides was suppressed, which limited the release of arsenite [As(III)] under reducing conditions. Furthermore, birnessite decreased As methylation and shaped the soil microbial community structure by enriching the metal-reducing bacterium Bacillus. Overall, our results provide a promising method to suppress greenhouse gas emissions and the risk of As contamination in paddy soils, although further studies are needed to verify its efficacy and effectiveness under field conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

A large quantity of CH4 is emitted to the atmosphere via ventilation shafts of underground coal mines. According to the European Pollutant Release and Transfer Register (E-PRTR), hard coal mines in the Upper Silesia Coal Basin (USCB) are a strong contributor (447 kt CH4 in 2017) to the annual European CH4 emissions. However, atmospheric emissions of CH4 from coal mines are poorly characterized, as they are dispersed over large areas. As part of the Carbon Dioxide and CH4 Mission (CoMet) pre-campaign, a study of the USCB's regional CH4 emissions took place in August 2017. We flew a recently developed active AirCore system aboard an unmanned aerial vehicle (UAV) to obtain CH4 mole fractions downwind of a single coal mining ventilation shaft. Besides CH4, we also measured CO2, CO, atmospheric temperature, pressure, and relative humidity. Wind-speed and wind-direction measurements were made using a lightweight balloon-borne radiosonde. Fifteen UAV flights were performed flying perpendicular to the wind direction at several altitude levels, to effectively build a ‘curtain’ of CH4 mole fractions in a two-dimensional plane at a distance between 150 and 350 m downwind of a single ventilation shaft. Furthermore, we have developed an inverse Gaussian approach for quantifying CH4 emissions from a point source using the UAV-based observations, and have applied it as well as the mass balance approach to both simulated data and actual flight data to quantify CH4 emissions. The simulated data experiments revealed the importance of having multiple transects at different altitudes, appropriate vertical spacing between the individual transects, and proper distance between the center height of the plume and the center flight transect. They also showed that the inverse Gaussian approach performed better than the mass balance approach. Our estimate of the CH4 emission rates from the sampled shaft ranges from 0.5 to 14.5 kt/year using a mass balance approach, and between 1.1 and 9.0 kt/year using an inverse Gaussian method. The average difference between the mass balance and the inverse Gaussian approach was 2.3 kt/year. Based on the observed correlation between CO2 and CH4 (R-squared > 0.69), the CO2 emissions from the shaft were estimated to be between 0.3 and 9.8 kt/year. This study demonstrates that the UAV-based active AirCore system provides an effective way of quantifying coal mining shaft emissions of CH4 and CO2.

Rice is a staple food for more than half of humanity, and 90 % of rice is grown and consumed in Asia. However, paddy rice cultivation creates an ideal environment for the production and release of methane (CH 4 ). How to estimate regional CH 4 emissions accurately and how to mitigate them efficiently have been of key concern. Here, with a machine learning method, we investigate the spatiotemporal changes, the major controlling factors and mitigation potentials of paddy rice CH 4 emissions across Monsoon Asia at a resolution of 0.1° (∼10 km). Spatially CH 4 emissions are highly heterogeneous, with the Indo-Gangetic Plain, Deltas of the Mekong, and Yangtze River Basin as the hotspots. Nationwide, China, India, Bangladesh and Vietnam are the major emitters. Straw applied on season is a critical controlling factor for CH 4 emission in rice fields. The single-season rice contributes to over 80 % of the total emissions. CH 4 emissions from Monsoon Asia have notably declined since 2007. Three mitigation strategies, including water management techniques, off-season straw return, and straw to biochar, may reduce CH 4 emissions by 27.66 %, 23.78 %, and 21.79 %, respectively, with the most effective strategy being rice cultivation type-specific and environment-specific. Our findings gain new insights into CH 4 emissions and mitigations across Monsoon Asia, providing evidence to adopt precise mitigation strategies based on rice cultivation types and local environment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

An accurate value for metabolizable energy (ME) requirement for maintenance (MEm) is essential to enable sheep husbandry practice to reach its potential. The objectives of the study were to use calorimetry chamber data of dry ewes (Hu × thin-tail Han F1 crossbred) to develop updated MEm, examine effects of substituting concentrate feed with lucerne hay on energy partitioning, and explore the relationships between energy utilization and fasting heat production (FHP). Data were collected from three experiments. In Exps. 1, 2a and 2b, lucerne hay was used to replace concentrates in three levels (0:40%, 15:25% and 30:10%), with diets containing 60% maize stover (Exp. 1), fresh rye forage (Exp. 2a) or dry rye forage (Exp. 2b). Within each experiment, diets were isoenergetic (digestible energy, DE) and isonitrogenous. Exp. 3 aimed at evaluating effects of three BW levels on nutrient utilization of dry ewes offered diets containing 60% maize stover, 15% lucerne hay and 25% concentrates. Energy metabolism data were measured using the respiration calorimeter chamber technique in all three experiments, followed by the measurement of FHP in Exps. 1, 2b and 3. The MEm derived from the linear regression between energy balance (EB) and ME intake was 0.440 MJ/kg BW0.75. The average FHP was 0.326 MJ/kg BW0.75. The fasting metabolism, net energy requirement for maintenance (NEm) and MEm were estimated to be 0.336, 0.359 and 0.511 MJ/kg BW0.75, respectively, through adjustment of FHP using fasting urinary energy output, activity allowance and efficiency of ME use for maintenance. The FHP was negatively correlated to EB/metabolic BW, ME/gross energy (GE), ME/DE, EB/GE intake and EB/ME intake, while positively correlated to HP/GE intake, HP/ME intake and CH4-E/GE intake. Compared to zero lucerne hay diet, the 15% lucerne hay intake decreased HP (MJ/d), and had no negative effects on EB (MJ/d) or energy utilization efficiencies. The results indicate that nutrient requirement standards currently used across the world are likely to underestimate MEm for dry ewes, and the selection of low FHP ewes for breeding has the potential to improve sheep production efficiency.

Optimizing milk production efficiency implies diets allowing low methane (CH4) emissions and high dairy performance. We hypothesize that nature of energy (starch v. lipids) and lipid supplement types (monounsaturated fatty acid (MUFA) v. polyunsaturated fatty acid (PUFA) mitigate CH4 emissions and can induce low milk fat content via different pathways. The main objective of this experiment was to study the effects of starch-rich or lipid-supplemented diets that induce milk fat depression (MFD) on rumen biohydrogenation (RBH) of unsaturated fatty acids (FA) and enteric CH4 emissions in dairy cows. Four multiparous lactating Holstein cows (days in milk=61±11 days) were used in a 4×4 Latin square design with four periods of 28 days. Four dietary treatments, three of which are likely to induce MFD, were based (dry matter basis) on 56% maize silage, 4% hay and 40% concentrates rich in: (1) saturated fatty acid (SFA) from Ca salts of palm oil (PALM); (2) starch from maize grain and wheat (MFD-Starch); (3) MUFA (cis-9 C18:1) from extruded rapeseeds (MFD-RS); and (4) PUFA (C18:2n-6) from extruded sunflower seeds (MFD-SF). Intake and milk production were measured daily. Milk composition and FA profile, CH4 emissions and total-tract digestibility were measured simultaneously when animals were in open-circuit respiration chambers. Fermentation parameters were analysed from rumen fluid samples taken before feeding. Dry matter intake, milk production, fat and protein contents, and CH4 emissions were similar among the four diets. We observed a higher milk SFA concentration with PALM and MFD-Starch, and lower milk MUFA and trans-10 C18:1 concentrations in comparison to MFD-RS and MFD-SF diets, while trans-11 C18:1 remained unchanged among diets. Milk total trans FA concentration was greater for MFD-SF than for PALM and MFD-Starch, with the value for MFD-RS being intermediate. Milk C18:3n-3 content was higher for MFD-RS than MFD-SF. The MFD seems more severe with MFD-SF and MFD-RS than PALM and MFD-Starch diets, because of a decrease in milk SFA concentration and a stronger shift from trans-11 C18:1 to trans-10 C18:1 in milk. The MFD-SF diet increased milk trans FA (+60%), trans-10 C18:1 (+31%), trans-10,cis-12 CLA (+27%) and PUFA (+36%) concentrations more than MFD-RS, which explains the numerically lowest milk fat yield and indicates that RBH pathways of PUFA differ between these two diets. Maize silage-based diets rich in starch or different unsaturated FA induced MFD with changes in milk FA profiles, but did not modify CH4 emissions.

The environmental impact of dairy production can be reduced in several ways, including increasing feed efficiency and reducing methane (CH 4 ) emissions. There is no consensus on their relationship. This study aimed at estimating the correlations between residual feed intake (RFI) and CH 4 emissions expressed in g/d methane production (MeP), g/kg of fat- and protein-corrected milk methane intensity (MeI), or g/kg of DM intake methane yield (MeY) throughout lactation. We collected CH 4 data using GreenFeed devices from 107 Holstein cows, as well as production and intake phenotypes. RFI was predicted from DM intake, fat- and protein-corrected milk, BW, and body condition score. Five-trait random regression models were used to estimate the individual variance components of the CH 4 and production traits, which were used to calculate the correlations between RFI and CH 4 traits throughout lactation. We found positive correlations of RFI with MeP and MeI ranging from 0.05 to 0.47 throughout the lactation. Correlations between RFI and MeY are low and vary from positive to negative, ranging from -0.18 to 0.17. Both MeP and MeI are favorably correlated with RFI, as is MeY during the first half of lactation. These correlations are mostly favorable for genetic selection, but the confirmation of these results is needed with genetic correlations over a larger dataset., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Shale gas, with its abundance and lower carbon footprint compared to other fossil fuels, is an important bridge fuel in the ongoing energy transition. However, a notable concern in shale gas exploration is fugitive methane emissions during the extraction, development, and transport of natural gas. While most existing works evaluate methane emissions released by well fracking, completion and operation, the greenhouse footprint of unproductive shale gas wells (often abandoned or orphaned) has received little scrutiny. A large fraction of these emissions from abandoned shale gas wells are due to the diffusive transport of methane trapped in nanoporous shale matrix, which is poorly understood. Here, we develop a theoretical kinetic approach to predict methane diffusive flux from heterogeneous shale matrix. Our theoretical model is based on a layer sequence formulation and accurately considers multiple flow mechanisms, including viscous flow, gas slippage, and Knudsen diffusion and their mutual interactions. The model is validated against the observed methane diffusion data obtained from high-pressure and high-temperature experimental measurements on Marcellus shale. We find that methane diffusive flux increases as reservoir pressure decreases. We estimate methane emission due to diffusive transport up to 20 × 10 3 m 3 per well per day, which is comparable to emissions from flowback fluid. For the first time, unrecovered natural gas in the shale matrix is demonstrated to be the main source of methane emissions from abandoned shale gas wells. Given the long-lasting nature of diffusive transport to shale gas seepage, it is suggested that regulatory requirements should be implemented to provide long-term monitoring of methane emissions from abandoned shale gas wells., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Methane is a potent greenhouse gas that accounts for one-quarter of the world's radiative forcing. Methane emissions from the natural gas sector are prevalent throughout the natural gas (NG) chain. Studies have shown that methane emissions from post-meter uses of natural gas are vastly understated. A surge in the number of natural gas users, for example, would amplify the climate impact of methane emissions during the installation of natural gas meters. Thus, quantifying methane emissions during the installation of natural gas meters is critical in light of severe global climate change and urgent reduction targets. In this study, we used a mass balance approach to calculate methane emissions during the separate installation of 1444 residential natural gas meters and 51 commercial natural gas meters. Our results revealed the methane emission had a fat tail distribution. Specifically, the estimated mean methane emissions for household users were 0.008 (0.001-0.022) kg per household and 0.192 (0.013-0.816) kg per commercial user. Extrapolating these statistics to the whole of China, total emissions from 2007 to 2021 were 3.80 million metric tons (MMt) CH 4 , with an annual average of 0.25 MMt. Notably, in terms of economic development and population size, the provinces with the highest methane emissions were concentrated in the southeast. Our findings close a gap in measuring CH 4 emissions in China across the natural gas chain and provide data to support the reduction targets set and the development of reduction technologies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Because of an increasing demand for animal-source foods, an increasing desire to reduce poverty and an increasing need to reduce the environmental impact of livestock production, tropical farming systems with livestock must increase their productivity. An important share of the global human and livestock populations are found within smallholder mixed-crop–livestock systems, which should, therefore, contribute significantly towards this increase in livestock production. The present paper argues that increased livestock production in smallholder mixed-crop–livestock systems faces many constraints at the level of the farm and the value chain. The present paper aims to describe and explain the impact of increased production from the farm and farmers’ perspective, in order to understand the constraints for increased livestock production. A framework is presented that links farming systems to livestock value chains. It is concluded that farming systems that pass from subsistence to commercial livestock production will: (1) shift from rural to urban markets; (2) become part of a different value chain (with lower prices, higher demands for product quality and increased competition from peri-urban producers and imports); and (3) have to face changes in within-farm mechanisms and crop–livestock relationships. A model study showed that feed limitation, which is common in tropical farming systems with livestock, implies that maximum herd output is achieved with small herd sizes, leaving low-quality feeds unutilised. Maximal herd output is not achieved at maximal individual animal output. Having more animals than required for optimal production – which is often the case as a larger herd size supports non-production functions of livestock, such as manure production, draught, traction and capital storage – goes at the expense of animal-source food output. Improving low-quality feeds by treatment allows keeping more animals while maintaining the same level of production. Ruminant methane emission per kg of milk produced is mainly determined by the level of milk production per cow. Part of the methane emissions, however, should be attributed to the non-production functions of ruminants. It was concluded that understanding the farm and farmers’ perceptions of increased production helps with the understanding of productivity increase constraints and adds information to that reported in the literature at the level of technology, markets and institutions.

Mobile monitoring platforms (MMP) are popular in air quality studies. One application of MMP is in estimating pollutant emissions from area sources. The MMP is used to measure concentrations of the relevant species at several locations around the area source, while the associated meteorological information is measured at the same time. Emissions from the area source are inferred by fitting the measured concentrations to estimates from dispersion models. These models require meteorological inputs, such as the kinematic heat flux and the surface friction velocity, that are best computed with measurements of time resolved velocity and temperature made with 3-D sonic anemometers. Because the setting up and dismantling of a 3-D sonic anemometer is not compatible with the necessary mobility of the MMP, it is useful to use alternative instrumentation and methods that provide accurate estimates of these inputs. In this study, we demonstrate such a method based on measurements of horizontal wind speed and temperature fluctuations at a single height. The method was evaluated by comparing methane emissions from a dairy manure lagoon inferred from a dispersion model that uses modeled meteorological inputs to those inferred from measurements with 3-D sonic anemometers. The emission estimates from the modeled meteorological inputs were close to those based on measurements made with 3-D sonic anemometers. We then demonstrate how this approach can be adapted for mobile platform applications by showing that winds measured using a 2-D sonic anemometer and temperature fluctuations measured with a bead thermistor, which can all be carried or mounted on a MMP, yields results that are close to those from a 3-D sonic anemometer., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Akula Venkatram reports financial support was provided by UC National Laboratory Fees Research Program and California Department of Transport (Caltrans). Francesca Hopkins reports financial support was provided by UC National Laboratory Fees Research Program., (Copyright © 2023 Elsevier B.V. All rights reserved.)

In April of 2018, an optical gas imaging (OGI) and full flow sampler (FFS) emissions measurement study of pneumatic controllers (PCs) was conducted at 15 oil and natural gas production sites in West Virginia. The objective of the study was to identify and characterize PC systems with excessive emissions caused by maintenance issues or nonoptimized process conditions. A total of 391 PC systems were found on the sites and all were classified by the operator as snap-acting (on/off) intermittent venting PCs (IPCs) that should exhibit little gas release while the PC is closed between actuation events. The population was comprised of two groups, 259 infrequently actuating, lower emitting (LE) IPCs and 132 gas processing unit (GPU) liquid level IPCs and associated dump valve actuators that vent more frequently and have larger emission volumes. Using a PC-specific OGI inspection protocol with an assumed whole gas OGI detection threshold of 2.0 scfh, only 2 out of 259 LE-IPCs exhibited OGI detectable emissions indicating good inspection and maintenance practices for this category. Due to combined (ganged) GPU exhaust vents, the OGI inspection of the GPU liquid level IPCs was comparatively less informative and determination of single component IPC emissions by the FFS was more difficult. The time resolved FFS measurements of GPU IPCs defined three categories of operation: one that indicated proper function and two associated with higher emissions that may result from an IPC maintenance or process issues. The overall GPU IPC emission distribution was heavy tailed, with a median value of 12.8 scfh, similar to the 13.5 scfh whole gas IPC emission factor (EF). Total emissions were dominated by non-optimal temporal profile high-emitter IPC cases with the top 20% of IPC systems accounting for between 51.3% and 70.7% of GPU liquid level IPC emissions by volume. The uncertainty in the estimate was due to the ganged nature of the GPU exhaust vents. The highest GPU IPC emission came from a single malfunctioning unit with a measured whole gas value of 157 scfh. Up to six IPCs exceeded 100 scfh. An analysis of FFS emission measurements compared to liquids production per IPC unit employed indicated that production sites operating at a high level of liquids production test the limits of the site engineering, likely resulting in higher IPC emissions. Overall, this study found that the LE-IPCs with OGI-verified low closed bleed rates may emit well below the IPC EF while GPU liquid level IPC systems are likely well represented by the current IPC EF. IPCs that are experiencing a maintenance or process issue or that are operating at sites with a very high product throughput per IPC employed can emit at rates exceeding ten times IPC EF.

A measurement campaign characterized methane and other emissions from 15 natural gas production sites. Sites were surveyed using optical gas imaging (OGI) cameras to identify fugitive and vented emissions, with the methane mass emission rate quantified using a full flow sampler. We present storage tank emissions in context of all site emissions, followed by a detailed account of the former. In total, 224 well pad emission sources at 15 sites were quantified yielding a total emission rate of 57.5 ± 2.89 kg/hr for all sites. Site specific emissions ranged from 0.4 to 10.5 kg/hr with arithmetic and geometric means of 3.8 and 2.2 kg/hr, respectively. The two largest categories of emissions by mass were pneumatic devices (35 kg/hr or ~61% of total) and tanks (14.3 kg/hr or ~25% of total). Produced water and condensate tanks at all sites employed emissions control devices. Nevertheless, tanks may still lose gas via component leaks as observed in this study. The total number of tanks at all sites was 153. One site experienced a major malfunction and direct tank measurements were not conducted due to safety concerns and may have represented a super-emitter as found in other studies. The remaining sites had 143 tanks, which accounted for 42 emissions sources. Leaks on controlled tanks were associated with ERVs, PRVs, and thief hatches. Since measurements represented snapshots-in-time and could only be compared with modeled tank emission data, it was difficult to assess real capture efficiencies accurately. Our estimates suggest that capture efficiency ranged from 63 to 92% for controlled tanks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Current methods for estimating sanitation emissions underestimate the significance of methane emissions from non-sewered sanitation systems (NSSS), which are prevalent in many countries. NSSS play a vital role in the safe management of fecal sludge, accounting for approximately half of all existing sanitation provisions. We analyzed the distribution of global NSSS and used IPCC accounting methods to estimate the total methane emissions profiles from these systems. Then, we examined the literature to establish the level of uncertainty associated with this accounting estimate. The global methane emissions from NSSS in 2020 was estimated to as 377 (22-1003) Mt CO 2 e/year or 4.7% (0.3%-12.5%) of global anthropogenic methane emissions, which are comparable to the greenhouse gas (GHG) emissions from wastewater treatment plants. NSSS is the major option for open defecation and is expected to increase by 55 Mt CO 2 e/year after complete open defecation free. It is time to acknowledge the GHG emissions from the NSSS as a non-negligible source., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

In comparing observation based methane emission estimates for oil and gas well sites to routine emissions reported in inventories, the time scale of the measurement should match the time scale over which the inventoried emissions are estimated. Since many measurements are of relatively short duration (seconds to hours), a tool is needed to estimate emissions over these time scales rather than the annual totals reported in most emission inventories. This work presents a tool for estimating routine emissions from oil and gas well sites at multiple time scales; emissions at well sites vary over time due to changes in oil and gas production rates, operating practices and operational modes at the sites. Distributions of routine emissions (expected and inventoried) from well sites are generally skewed, and the nature and degree to which the distributions are skewed depends on the time scales over which emissions are aggregated. Abnormal emissions can create additional skew in these distributions. At very short time scales (emissions aggregated over 1 min) case study distributions presented in this work are both skewed and bimodal, with the modes depending on whether liquid storage tanks are flashing at the time of the measurement and whether abnormal emissions are occurring. At longer time scales (emissions aggregated over 1 day) distributions of routine emissions simulated in this work can have multiple modes if short duration, high emission rate events, such as liquid unloadings or large abnormal emissions, occur at the site. Multiple applications of the methane emission estimation tool (MEET), developed in this work, are presented. These results emphasize the importance of developing detailed emission inventories, which incorporate operational data, when comparing measurements to routine emissions. The model described in this work supports such comparisons and is freely available., Competing Interests: Declaration of competing interest The authors declare the following competing financial interest(s): This work was supported by the Collaboratory to Advance Methane Science (CAMS), a group of companies operating facilities in the natural gas supply chain., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Recent attention to methane emissions from oil and gas infrastructure has increased interest in comparing measurements with inventory emission estimates. While measurement methods typically estimate emissions over a few periods that are seconds to hours in length, current inventory methods typically produce long-term average emission estimates. This temporal mis-alignment complicates comparisons and leads to underestimates in the uncertainty of measurement methods. This study describes a new temporally and spatially resolved inventory emission model (MEET), and demonstrates the model by application to compressor station emissions - the key facility type in midstream natural gas operations The study looks at three common facility measurement methods: tracer flux methods for measuring station emissions, the use of ethane-methane ratios for source attribution of basin-scale estimates, and the behavior of continuous monitoring for leak detection at stations. Simulation results indicate that measurement methods likely underestimate uncertainties in emission estimates by failing to account for the variability in normal facility emissions and variations in ethane/methane ratios. A tracer-based measurement campaign could estimate emissions outside the 95% confidence interval of annual emissions 30% of the time, while ethane/methane ratios could be mis-estimated by as much as 50%. Use of MEET also highlights the need to improve data reporting from measurement campaigns to better capture the temporal and spatial variation in observed emissions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Daniel Zimmerle reports financial support was provided by Collaboratory to Advance Methane Science. David T Allen reports a relationship with Exxon Mobil Upstream Research Company that includes: funding grants. David T Allen reports a relationship with National Institute for Clean and low Carbon Energy (NICE) that includes: funding grants. Daniel Zimmerle reports a relationship with Chevron Inc. that includes: funding grants. Daniel Zimmerle reports a relationship with BP Plc that includes: funding grants. Daniel Zimmerle reports a relationship with The Environmental Partnership (API) that includes: funding grants. Daniel Zimmerle reports a relationship with Renewable Natural Gas Coalition that includes: funding grants. Daniel Zimmerle reports a relationship with Environmental Defense Fund that includes: funding grants. David T Allen reports a relationship with Eastern Research Group that includes: funding grants. Felipe Cardoza-Saldana reports a relationship with Exxon Mobil Upstream Research Company that includes: employment. Daniel Zimmerle reports a relationship with Colorado Air Quality Enterprise Board, as Colorado State Agency that includes: board membership. Over the past five years, multiple authors have also worked on methane emission measurement projects that have been supported by multiple natural gas producers, developers of leak detection solutions, and the Environmental Defense Fund. Multiple co-authors have been active the funded research projects declared by the Corresponding Author (Zimmerle) and co-Author (Allen)., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

A significant amount of CH 4 is emitting from livestock manure (LM) storage tank, which is being counted according to the guidelines provided by the Intergovernmental Panel on Climate Change (IPCC). Among various parameters affecting CH 4 conversion factor (MCF) of LM, temperature is known as the most influential factor. As a degree of temperature, atmospheric temperature (T a ), not the manure temperature (T m ), is used for determining the MCF. Currently, the closed-type tank is more common than open-type tank, which would cause the substantial difference between T a and T m , probably due to the automatic temperature rise (ATR). Here, we repeatedly observed the ATR by storing pig slurry (PS) in a pilot-scale tank (30 m 3 , surface/volume ratio of 1.9), and its consequent impact on the increased CH 4 emissions by comparing with the results from a lab-scale tank (1 L, surface/volume ratio of 72.2) controlled at 30 °C. As storage began, the T m increased gradually from 16 to 23 °C to above 30 °C even in winter (-5 °C < T a < 15 °C). During 30 d of storage, the CH 4 emissions of 1.3-2.5 kg CH 4 /ton PS (MCF 26-29%) was observed in the lab-scale tank, while the emissions was increased to 2.6-4.2 kg CH 4 /ton PS (MCF 40-50%) in the pilot-scale tank (Two-Tail test, |t t |<|t c |). For the first time, a detailed heat energy balance considering the waste heat from organic degradation, the heat requirement for warm up, and the heat loss by convection, was conducted, proving that the waste heat generated during storage was enough to reach above 30 °C. Cooling-down of LM at 20 °C was found to be effective for reducing CH 4 emissions by 90%, which sufficiently offset the greenhouse gas emissions in power consumption for cooling. Our findings strongly suggest that more CH 4 is emitting from LM storage tank than expected, and therefore, the IPCC needs to develop guidelines more accurately in determining MCF., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

There is growing awareness and concern on methane (CH 4 ) emissions from China's oil and natural gas (ONG) systems owing to the carbon neutral target. This paper aims to present a comprehensive review on the bottom-up inventories of the CH 4 emissions from the perspective of the ONG systems in China. The trend and magnitude of total emissions in the last four decades were revealed and limitations of current estimations were explored. Previous studies showed that the average CH 4 emissions from China's ONG systems have almost tripled from 1980 (760 Gg) to 2015 (2180 Gg) with a trend of steady increase. However, the estimated values varied by an order-of-magnitude with the largest discrepancy of 2700 Gg. This discrepancy was unlikely caused mainly by the incompleteness of estimation, since dominant emission sources were all covered by representative studies. Moreover, the differences of activity-level data were within ±10%, which ruled out the possibility that it was the main contributor to the large discrepancies. The emissions estimate has huge variation in large part because of differences in assumed emission factors (EFs) that vary by an order of magnitude. The difficulty was to determine which of the EFs were accurate due to measurement-based data availability. Thus, the large discrepancies stem from the scarcity of publicly available data, which enlarged the impact from various methods adopted by previous studies. For better understanding of CH 4 emissions from the ONG systems in China, the measurements of facility-level emissions and statistics on the ONG infrastructure are required urgently. Due to the high cost and experience-oriented measurement work, international cooperation and communications are critical prerequisites for future CH 4 emission estimates and effective mitigation strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Cold heavy oil production with or without sand (CHOPS, or CHOP) are prevalent methods of oil extraction in western Canada. CHOP(S) sites account for over 40% of all reported vented methane (CH 4 ) from oil production in Alberta, and high rates of CH 4 emissions have been confirmed in independent measurement studies. In this study, we used truck-based surveys coupled with qualitative optical gas imaging (OGI) to quantify and characterize methane emission rates and sources at nearly 1350 and 940 well sites in two major CHOP(S) developments respectively in 2016 and 2018. The studies were conducted in Lloydminster, Alberta, where produced gases are sweet (i.e., <0.5% sulfur) and non-olfactory, and Peace River, Alberta, where regulations were implemented in 2017 to manage sour (i.e., >0.5% sulfur) odorous emissions (hydrogen sulfide, BTEX, etc.). Based on results from all surveys, in Peace River, 43% of measured sites were emitting CH 4 , compared to 37% in Lloydminster. The measured CH 4 emission rates in Peace River were, however, significantly lower than in Lloydminster for both years, and had fallen from 2016 to 2018. In 2018, emissions in Lloydminster were fairly unchanged relative to previous measurements taken in 2016. OGI showed that tanks in Peace River continue to emit CH 4 despite regulatory interventions and a reported venting rate of zero. The continued emissions were thus classified as "unintended venting", which can be a consequence of the non-routine malfunction (e.g., inappropriate operator action or poor equipment design/sizing) of vapor recovery equipment. Mitigation strategies implemented in Peace River targeting olfactory compounds were beneficial in reducing and keeping CH 4 emissions lower, since these gases are co-emitted, and could even be co-regulated provincially. Reciprocal to that, we might expect future air quality improvements as a consequence of the new provincial requirements to reduce CH 4 emissions under amended Directives 060 and 017., Competing Interests: Declaration of competing interest The authors declare no competing financial interests. The research presented in this manuscript was exclusively under the contracts to the authors' academic affiliations., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The intensifying globalization contributes to the anthropogenic methane (CH 4 ) emissions outsourcing, a strong greenhouse gas and harmful air pollutant, through the increasingly complex global trade network. However, the CH 4 flow patterns embodied in global traded goods and services have not been interpreted from the perspective of a complex network. In this paper, we integrate global CH 4 emission inventory from the EDGAR (the Emission Database for Global Atmospheric Research) databases, global multi-regional input-output model from the GTAP database, and complex network analysis to reveal the structural characteristics of the global CH 4 flow network (GCFN). In the GCFN, more than one quarter of the global anthropogenic CH 4 emissions in 2014 are associated with international trade. The top 20 economies contribute to about 70% of the total embodied CH 4 emission flows. The GCFNs mainly consist of tripartite patterns centered on China, the USA and Russia. Some emerging countries, such as Thailand and Brazil, also exhibit dominated positions in different kinds of GCFNs. Moreover, the core-periphery structure of the GCFN confirms the existence of a few hub economies associated with a large amount of CH 4 emissions. The results emphasize the multinational cooperation on global CH 4 emission mitigation, and well-focused mitigation policies should be implemented on some key economies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Northern Italy represents the most important rice-growing district in Europe. In this area, rice is the main annual crop and the main revenues source for farmers. However, Italian climatic condition led to a traditional cultivation characterized by continuous flooding, causing emissions of methane into the atmosphere due to the organic matter fermentation in anaerobic conditions, and, consequently, a high environmental impact. The water conditions of paddy fields also affect heavy metals uptake by rice plants. In this context, this study focuses on the evaluation of environmental impact and of heavy metal content in paddy rice, and it may represent an important step in mitigating the environmental impact of rice production. In detail, this study quantifies the environmental benefits related to the adoption of an alternative water management characterized by an additional aeration period during stem elongation. To this purpose, field trials were carried out and the Life Cycle Assessment (LCA) approach was applied with a cradle-to-farm gate perspective. The potential environmental impact of the production of two rice varieties (Carnaroli and Caravaggio) was analysed in terms of 12 different impact categories and dehulled rice grain were analysed for arsenic and cadmium content. Alternative flooding decreases CH 4 emissions in all cases evaluated (from 15% to 52%), resulting in a reduction in the climate change impact of rice cultivation (from 12% to 32%). Furthermore, the alternative water management does not influence grain yield and it reduces all the other environmental impact categories in 2 out of 4 cases. Regarding the heavy metals contents, the arsenic content in the grain decreases in all alternative scenarios, whereas the cadmium content increases, while remaining well below the legal limits., Competing Interests: Declaration of competing interest In order to ensure that all authors of papers submitted to Science of the Total Environment follow the journal's ethical guidelines, the lead or corresponding author should sign this form on behalf of all of the paper's authors, and return it to the Editor handling their paper. This paper has not been submitted to another journal for simultaneous consideration. All authors are aware of, and accept responsibility for, the manuscript. All authors have made a significant intellectual contribution to the research and have permission (if required) to submit the manuscript for possible publication. The authors have not plagiarized any parts of this manuscript; this includes the verbatim copying of text, the uncited use of another's work, or the use of information gained during a confidential manuscript or proposal review process. None of the data, tables, figures, or text in this paper have been published elsewhere (in any language), with the exception of abstracts of papers presented at conferences and figures or tables for which permission to use them has been obtained from the copyright holder. Any authors who have a financial interest in the results of the paper have disclosed this interest to the Editors in the manuscript submittal letter. These financial interests may include, but are not limited to, paid consultancies, patent interests, stock holdings, professional affiliations, advisory positions, board memberships, and other financial holdings. Interests that might detract from an author's objectivity in the presentation of study results should be disclosed. The Editors will determine whether the material disclosed to them should be published as part of the paper. The authors have accurately listed their affiliations. This list must include academic, significant corporate and industrial, and non-governmental organization (NGO) affiliations. As the Editors deem appropriate, items in this list will be included in the author affiliations printed in the paper. The authors have accurately listed the funding sources for the work presented in the paper in the acknowledgements section. All financial contributions to the work being reported, including contributions “in kind”, must be reported. All funding sources will be listed in the published paper. The authors declare that they have read the current Instructions to Authors and understand Science of the Total Environment full Conflict of Interest Policy., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The observed atmospheric methane (CH 4 ) concentration in China has grown rapidly in recent years, showing marked spatial-temporal variation. However, existing inventories, most of which are yearly, provincial, and incomplete, have failed to reflect the spatial variation and seasonal trends of CH 4 emissions. This study aims to develop a high-resolution (0.05° × 0.05°) monthly inventory of CH 4 emissions across China in 2015 from eight major natural and anthropogenic sources. The inventory evaluation of CH 4 emissions was based on the gridded activity data and high spatial-temporal resolution emission factors, which were estimated by their relationship with environmental factors in most source sectors. The results showed that the annual CH 4 emissions across China were 61.65 Tg, of which 85% was associated with anthropogenic emissions. Energy activities, livestock, and paddy fields were the largest contributors, accounting for 31% (19.06 Tg), 24% (15.01 Tg) and 19% (11.45 Tg) of the total emissions respectively, followed by vegetation (7%, 4.52 Tg), wetlands (7%, 4.20 Tg), wastewater (6%, 3.43 Tg), municipal solid waste, (4%, 2.59 Tg) and biomass burning (2%, 1.40 Tg). However, these proportions varied by month; paddy fields, vegetation, and wetlands emitted the most CH 4 in July and August with approximately 29%, 14%, and 8% of total emissions, respectively, and least in January and December with 0%, 2%, and 2%, respectively, leading to a CH 4 emissions peak in summer and a valley in winter. Moreover, the major contributing provinces of CH 4 emissions in China were Inner Mongolia, Shanxi, Sichuan, Guizhou, and Hunan, accounting for 33% of China's total emissions. The dominant emission sources were energy activities in Mongolia, Shanxi, and Guizhou; livestock in Sichuan; and paddy fields in Hunan. This improved inventory of CH 4 emissions can help understanding the spatial-temporal variation of CH 4 concentration in the atmosphere and formulating regional-seasonal-specific emission reduction policies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

With the expansion of economic globalization and the growth of international trade, the pulling effect of household consumption to global anthropogenic CH 4 emissions related to production activities is becoming increasingly evident. This paper adopts a new perspective from the household-consumption side to investigate the CH 4 emissions of major economies in 2014 and compares it with the scenario under the final-demand-based perspective by combing the world input-output database and the latest emission data from the UNFCCC and EDGAR v5.0 database. Budgets of CH 4 emissions for 43 economies are established and trade connections & balances among major economies are explored. Results show that consumption-driven economies are allocated more CH 4 emissions in the household-consumption-based accounting (HCBA) framework compared to the final-demand-based accounting (FDBA) framework. The total trade-related transfer of CH 4 emissions is shown to sum up to 19% and 27% of the global total under the HCBA and FDBA frameworks, respectively. The household-consumption-based CH 4 emissions of China, India, Indonesia and Mexico are much lower than their final-demand-based CH 4 emissions, while the converse is true for the United States, Russia, Japan, the United Kingdom and Germany. The new accounting framework provides a new view to understand trade-related CH 4 emissions of major economies and to identify the role of household consumption in global supply chains, offering important implications for greenhouse gas emission mitigation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

An accurate value for metabolizable energy (ME) requirement for maintenance (ME m ) is essential to enable sheep husbandry practice to reach its potential. The objectives of the study were to use calorimetry chamber data of dry ewes (Hu × thin-tail Han F1 crossbred) to develop updated ME m , examine effects of substituting concentrate feed with lucerne hay on energy partitioning, and explore the relationships between energy utilization and fasting heat production (FHP). Data were collected from three experiments. In Exps. 1, 2a and 2b, lucerne hay was used to replace concentrates in three levels (0:40%, 15:25% and 30:10%), with diets containing 60% maize stover (Exp. 1), fresh rye forage (Exp. 2a) or dry rye forage (Exp. 2b). Within each experiment, diets were isoenergetic (digestible energy, DE) and isonitrogenous. Exp. 3 aimed at evaluating effects of three BW levels on nutrient utilization of dry ewes offered diets containing 60% maize stover, 15% lucerne hay and 25% concentrates. Energy metabolism data were measured using the respiration calorimeter chamber technique in all three experiments, followed by the measurement of FHP in Exps. 1, 2b and 3. The ME m derived from the linear regression between energy balance (EB) and ME intake was 0.440 MJ/kg BW 0.75 . The average FHP was 0.326 MJ/kg BW 0.75 . The fasting metabolism, net energy requirement for maintenance (NE m ) and ME m were estimated to be 0.336, 0.359 and 0.511 MJ/kg BW 0.75 , respectively, through adjustment of FHP using fasting urinary energy output, activity allowance and efficiency of ME use for maintenance. The FHP was negatively correlated to EB/metabolic BW, ME/gross energy (GE), ME/DE, EB/GE intake and EB/ME intake, while positively correlated to HP/GE intake, HP/ME intake and CH 4 -E/GE intake. Compared to zero lucerne hay diet, the 15% lucerne hay intake decreased HP (MJ/d), and had no negative effects on EB (MJ/d) or energy utilization efficiencies. The results indicate that nutrient requirement standards currently used across the world are likely to underestimate ME m for dry ewes, and the selection of low FHP ewes for breeding has the potential to improve sheep production efficiency., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)