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Ammonia (NH 3 ) and greenhouse gas (GHG) emissions are substantial contributors to C and N loss in composting. Lignite can increase N retention by absorbing N H 4 + and NH 3 . However, the effects of co-composting on NH 3 and GHG emissions in view of closing nutrient cycle are still poorly investigated. In the study, poultry litter was composted without (CK) or with lignite (T1) or dewatered lignite (T2), and their respective composts N H 4 + Com_CK, Com_T1, and Com_T2) were tested in a soil incubation to assess NH 3 and GHG emission during composting and following soil utilization. The cumulative NH 3 flux in T1 and T2 were reduced by 39.3% and 50.2%, while N 2 O emissions were increased by 7.5 and 15.6 times, relative to CK. The total GHG emission in T2 was reduced by 16.8% compared to CK. Lignite addition significantly increased nitrification and denitrification as evidenced by the increased abundances of amoA , amoB , nirK , and nirS . The increased reduction on NH 3 emission by dewatered lignite could be attributed to reduced pH and enhanced cation exchangeable capacity than lignite. The increased N 2 O was related to enhanced nitrification and denitrification. In the soil incubation experiment, compost addition reduced NH 3 emission by 72%∼83% while increased emissions of CO 2 and N 2 O by 306%∼740% and 208%∼454%, compared with urea. Com_T2 strongly reduced NH 3 and GHG emissions after soil amendment compared to Com_CK. Overall, dewatered lignite, as an effective additive, exhibits great potential to simultaneously mitigate NH 3 and GHG secondary pollution during composting and subsequent utilization of manure composts.

Methane is one of the major greenhouse gases (GHGs) and agriculture is recognized as its primary emitter. Methane accounting is a prerequisite for developing effective agriculture mitigation strategies. In this review, methane accounting methods and research status for various agricultural emission source including rice fields, animal enteric fermentation and livestock and poultry manure management were overview, and the influencing factors of each emission source were analyzed and discussed. At the same time, it analyzes the different research efforts involving agricultural methane accounting and makes recommendations based on the actual situation. Finally, mitigation strategies based on accounting results and actual situation are proposed. This review aims to provide basic data and reference for agriculture-oriented countries and regions to actively participate in climate action and carry out effective methane 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 © 2023. Published by Elsevier B.V.)

Methane (CH 4 ) emissions from manure management on livestock farms are a key source of greenhouse gas emissions in some regions and for some production systems, and the opportunities for mitigation may be significant if emissions can be adequately documented. We investigated a method for estimating CH 4 emissions from liquid manure (slurry) that is based on anaerobic incubation of slurry collected from commercial farms. Methane production rates were used to derive a parameter of the Arrhenius temperature response function, lnA', representing the CH 4 production potential of the slurry at the time of sampling. Results were used for parameterization of an empirical model to estimate annual emissions with daily time steps, where CH 4 emissions from individual sources (barns, outside storage tanks) can be calculated separately. A monitoring program was conducted in four countries, i.e., Denmark, Sweden, Germany and the Netherlands, during a 12-month period where slurry was sampled to represent barn and outside storage on finishing pig and dairy farms. Across the four countries, lnA' was higher in pig slurry compared to cattle slurry (p < 0.01), and higher in slurry from barns compared to outside storage (p < 0.01). In a separate evaluation of the incubation method, in-vitro CH 4 production rates were comparable with in-situ emissions. The results indicate that lnA' in barns increases with slurry age, probably due to growth or adaptation of the methanogenic microbial community. Using lnA' values determined experimentally, empirical models with daily time steps were constructed for finishing pig and dairy farms and used for scenario analyses. Annual emissions from pig slurry were predicted to be 2.5 times higher than those from cattle slurry. Changing the frequency of slurry export from the barn on the model pig farm from 40 to 7 d intervals reduced total annual CH 4 emissions by 46 %; this effect would be much less on cattle farms with natural ventilation. In a scenario with cattle slurry, the empirical model was compared with the current IPCC methodology. The seasonal dynamics were less pronounced, and annual CH 4 emissions were lower than with the current methodology, which calls for further investigations. Country-specific models for individual animal categories and point sources could be a tool for assessing CH 4 emissions and mitigation potentials at farm level., 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 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Septic tanks in low- and middle-income countries are often not emptied for a long time, potentially resulting in poor pollutant removal efficiency and increased greenhouse gas emissions, including methane (CH 4 ). We examined the impact of long emptying intervals (4.0-23 years) on the biochemical oxygen demand (BOD) removal efficiency of 15 blackwater septic tanks and the CH 4 emission rates of 23 blackwater septic tanks in Hanoi. The average BOD removal efficiency was 37% (-2-65%), and the average CH 4 emission rate was 10.9 (2.2-26.8) g/(cap·d). The emptying intervals were strongly negatively correlated with BOD removal efficiency ( R = -0.676, p = 0.006) and positively correlated with CH 4 emission rates ( R = 0.614, p = 0.001). CH 4 emission rates were positively correlated with sludge depth ( R = 0.596, p = 0.002), but against expectation, negatively correlated with BOD removal efficiency ( R = -0.219, p = 0.451). These results suggest that shortening the emptying interval improves the BOD removal efficiency and reduces the CH 4 emission rate. Moreover, the CH 4 emission estimation of the Intergovernmental Panel on Climate Change, which is a positive conversion of BOD removal, might be inaccurate for septic tanks with long emptying intervals. Our findings suggest that emptying intervals, sludge depth, and per-capita emission factors reflecting long emptying intervals are potential parameters for accurately estimating CH 4 emissions from septic tanks.

Livestock production systems with ruminants play a relevant role in the emission of the greenhouse gas CH 4 , which is known to significantly contribute to global warming. Consequently, it is a major societal concern to develop strategies in mitigating such emissions. In addition to breeding toward low-emitting cows, management strategies could also help in reducing greenhouse gas emissions from dairy farms. However, information is required for appropriate decision making. To the best of our knowledge, this is the first study that considers different, already available equations to estimate CH 4 emissions of small-scale dairy farms in the mountain region, which largely differ from large dairy farms in the lowlands concerning management and production. For this study, 2 different production systems, both typical for small-scale dairy farming in mountain regions, were simultaneously run over 3 yr at an experimental farm as follows: (1) a high-input production system, characterized by intensive feeding with high amounts of external concentrates and maize silage, year-round housing, and high yielding Simmental cattle breed, and (2) a low-input production system, characterized by prevailing hay and pasture feeding and silage ban, thus covering most of the energy requirements by forage harvested on-farm and the use of the local Tyrolean Grey cattle breed. Results reveal that feeding management has a significant effect on the amount of CH 4 emissions. The low-input production system produced less CH 4 per cow and per day compared with the high-input production system. However, if calculated per kilogram of milk, the high-input scenario produced proportionally less CH 4 than the low-input one. Findings of this study highlight the potential to assess in a fast and cost-effective way the CH 4 emission in different dairy production systems. This information contributes to the debate about the future of sustainable milk production in mountain regions, where the production of feed resources is climatically constrained, and could be useful for breeding purposes toward lower CH 4 -emissions., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

The objective of this study was to use a random-effects model of meta-analysis to merge various heritability estimates of different gas emission traits (methane yield [METY], methane production [METP], carbon dioxide production [CO 2 ], the sum of carbon dioxide and methane production [METP + CO 2 ], METP METP + CO 2 ratio, and oxygen consumption [O 2 ]) and their genetic association with growth and partial efficiency traits in sheep. A total of 53 genetic correlations and 47 heritability estimates from 13 scientific articles were used in the meta-analysis. The included papers were published between 2010 and 2022. To measure heterogeneity, Chi-square (Q) test was performed, and the I 2 statistic was determined. The average heritability estimates for the studied traits were low to moderate and ranged from 0.137 (for METY) to 0.250 (for METP + CO 2 ). The heterogeneity test of heritability estimates indicated that heritability estimates for METY, O 2 consumption, and METP METP + CO 2 had low Q values and non-significant heterogeneity (p > 0.10). However, the average heritability estimates for other traits experienced significant heterogeneities (p < 0.10). The genetic correlation estimate between METP with O 2 was -0.597 (p < 0.05), but its genetic correlations with other gas traits ranged from 0.593 (with METP + CO 2 ) to 0.653 (CO 2 ; p < 0.05). Also, mean estimates of genetic correlation between METP with live weight (LW), feed intake (FI), and residual feed intake (RFI) were 0.719, 0.598, and 0.408, respectively. The genetic correlations of CO 2 with performance traits varied from 0.641 (with RFI) to 0.833 (with FI; p < 0.05). This meta-analysis showed gas emission traits in sheep are under low-to-moderate genetic control. The average genetic parameter estimates obtained in this study could be considered in the genetic selection programmes for sheep, especially when there is no access to accurate phenotypic records or genetic parameter estimates for gas emission traits., (© 2022 John Wiley & Sons Ltd.)

Despite their predominance worldwide, few studies have been conducted to look at the impact of sheep production systems relying on transhumance practices in arid and continental conditions, on farm-level greenhouse gas (GHG) emissions. Using Turkey as an example, this paper examines on farm-level GHG emissions calculated for two contrasting sheep production systems under arid and continental climate conditions. Production and management data were obtained through face-to-face interviews carried out on 10 transhumance and 15 semi-intensive meat sheep farms in Turkey. A total of seven GHG emission estimates were then calculated for each farm with the Agricultural Resource Efficiency Calculator (AgRECalc©) tool; i) total Carbon Dioxide (CO2) from energy use (kg CO2e), ii) total Carbon Dioxide equivalent (CO2e) from methane (kg CO2e), iii) total CO 2 e from nitrous oxide (kg CO 2 e), iv) whole farm and enterprise CO 2 e emissions (kg CO 2 e), v) net emission from land use (kg CO 2 e), vi) whole farm CO 2 e emissions per kg of farm output (kg CO 2 e/kg output), vii) product CO 2 e emissions (meat): kg CO 2 e / kg live weight, and viii) farm output (kg of sheep). Multivariate analyses (using R software) were carried out to compare both farm types and their respective carbon emissions. The total farm output per ewe was lower in the transhumance farms (7.4 kg/ewe) than in the semi-intensive farms (7.7 kg/ewe). The kg CO 2 e per kg of output was also lower for the transhumance farms (46.2 kg CO 2 e) than for the semi-intensive ones (56.5 kg CO 2 e). This trend was similar for the amount of CO 2 e per kg of live weight produced (20.8 kg and 25.4 kg for the transhumance and the semi-intensive farms, respectively). Despite overall net emissions from land use being greater on average for the transhumance farms, once measured per hectare, they were found to be lower than those for the semi-intensive farms. This study provides a reference point for different sheep production systems' GHG emission impact in continental rangelands in Turkey., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Vietnam has been one of the nations strongly affected by climate change; hence, finding and promoting solutions in order to adapt and proactively respond to climate change have played an extremely significant role. A strategy of developing a low-carbon economy with eco-friendly production and consumption models, limiting the use of fossil fuels, and rising clean as well as renewable energy have been built with the aim to reduce total greenhouse gas (GHG) emissions by roughly 8% by the year 2030 compared to the business-as-usual (BAU) scenario under the Paris Agreement 2015, in particular, focusing on producing a plan to drop considerably methane (CH 4 ) emission and recover energy from landfill sites as the waste sector has been the third-highest GHG contributor in Vietnam. This study concentrates on forecasting and evaluating CH 4 , CO, H 2 S, and CH 3 SH emissions for a case study in the Mekong Delta region of Vietnam: in specific, two dumping sites of Thanh Hoa and Kien Tuong according to four different scenarios so that the landfill gas (LFG) emissions are controlled under the local authority orientation of installing a gas collection system and treating captured gases by an LFG flaring approach from 2021 to 2030. Quantification of the generated LFG is carried out using a mathematical modeling method having validation with field measured data, which is a WebGIS software named EnLandFill with integrated mathematical models, environmental information, and databases allowing analysis of the main influence of two L 0 and k factors on LFG emission levels. On the one hand, based on the development planning of Long An province from 2021 to 2030, the municipal solid waste (MSW) volume is estimated at around 3.5 million tonnes and the potential waste generation index is predicted between 0.7 and 1.2 kg person -1 day -1 . A large amount of CH 4 emissions are considered to be a remarkable contributor to GHG emissions. It can be seen that there are about 32.1-253.6 million m 3 CH 4 (or equivalent to 512-589 thousand tCO 2 -eq) and 1.0-5.1 million m 3 CH 4 (or equivalent to 16-81 thousand tCO2-eq) created at Thanh Hoa and Kien Tuong dumping sites, respectively, under the established scenarios in the 2021-2030 period. On the other hand, the proposal of scenario 4 is found to be the optimal solution for GHG emission control and decline from this study area in the next years. Furthermore, the obtained study outcomes are considered as a basis for planning and managing the issues of MSW in Long An province until 2050 towards a circular economy., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This research explores possible options to reduce greenhouse gas (GHG) emissions in the Australian dairy industry by (1) including an environmental component in the national breeding program and (2) estimating the economic and environmental impacts of implementation of the subsequent indexes. A total of 12 possible selection indexes were considered. These indexes were developed to predict changes in gross per-animal methane production (using 3 scenarios depending on availability and efficacy of a direct methane trait breeding value prediction) with 4 different carbon prices, integrating them into an augmentation of the current conventional national selection index. Although some economic response is lost with inclusion of the GHG subindexes in the Balanced Performance Index, options do exist where this loss is marginal and, even in scenarios where all selection pressure is based on the environmental weighting, economic progress is still made in all cases. When including environmental traits within an index, if a relatively low percentage of economic gain or index progression is sacrificed, then approximately 40 to 50% of the maximum possible reductions in emissions may be achieved. This concurrent selection of estimated breeding values that have a correlated favorable response in emissions in addition to direct selection on a residual methane trait allows a high level of methane reduction to be achieved with a realized cost to farmers that is far lower than the economic value placed on carbon. By implementing a GHG subindex in the national breeding program, we can achieve up to a 7.9% decrease in residual methane and 9 times the reduction in gross emissions in 10 yr, compared with the current breeding program, with little to no cost to farmers. By 2050, selection based on one of the more moderate index scenarios at a carbon price of AUD$250/t (AUD$1 = US$0.71), or opportunity cost to farmers of AUD$87.22, will reduce gross emissions by 8.23% and emissions intensity by 21.25%, therefore offering a mitigation strategy that will be effective at reducing emissions with little compromise to profit., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Agricultural soil acts as a source and sink of important greenhouse gases (GHGs) like methane (CH 4 ), nitrous oxide (N 2 O), and carbon dioxide (CO 2 ). Rice paddies have been a major concern to scientific community, because they produce the threatening and long-lasting GHGs mainly CH 4 and N 2 O. Around 30% and 11% of global agricultural CH 4 and N 2 O, respectively, emitted from rice fields. Thus, it is urgent to concurrently quantify the fluxes of CH 4 and N 2 O to improve understanding of both the gases from rice fields and to develop mitigation strategies for upcoming climate change reduction. An effort is being made in this review to discuss exclusively the emission of CH 4 and N 2 O under normal and controlled conditions in different locations of India and also addresses the current synthesis of available data on how field and crop management activities influence CH 4 and N 2 O emissions in rice fields. Making changes to conventional crop management regimes could have a significant impact on reducing GHG emissions from rice field. Environmental and agricultural factors related to soil could be easily altered by management practices. So, knowing the mechanism of CH 4 and N 2 O production and release in the rice field and factors controlling the emissions is fundamental to develop well-organized strategies to reduce emissions from rice cultivated soil. This will help the regulatory bodies or policy makers to formulate adequate policies for agricultural farmers to refine the GHG emissions as well as minimize the global climate change.

Tropical peatlands are vital ecosystems that play an important role in global carbon storage and cycles. Current estimates of greenhouse gases from these peatlands are uncertain as emissions vary with environmental conditions. This study provides the first comprehensive analysis of managed and natural tropical peatland GHG fluxes: heterotrophic (i.e. soil respiration without roots), total CO 2 respiration rates, CH 4 and N 2 O fluxes. The study documents studies that measure GHG fluxes from the soil (n = 372) from various land uses, groundwater levels and environmental conditions. We found that total soil respiration was larger in managed peat ecosystems (median = 52.3 Mg CO 2  ha -1  year -1 ) than in natural forest (median = 35.9 Mg CO 2  ha -1  year -1 ). Groundwater level had a stronger effect on soil CO 2 emission than land use. Every 100 mm drop of groundwater level caused an increase of 5.1 and 3.7 Mg CO 2  ha -1  year -1 for plantation and cropping land use, respectively. Where groundwater is deep (≥0.5 m), heterotrophic respiration constituted 84% of the total emissions. N 2 O emissions were significantly larger at deeper groundwater levels, where every drop in 100 mm of groundwater level resulted in an exponential emission increase (exp(0.7) kg N ha -1  year -1 ). Deeper groundwater levels induced high N 2 O emissions, which constitute about 15% of total GHG emissions. CH 4 emissions were large where groundwater is shallow; however, they were substantially smaller than other GHG emissions. When compared to temperate and boreal peatland soils, tropical peatlands had, on average, double the CO 2 emissions. Surprisingly, the CO 2 emission rates in tropical peatlands were in the same magnitude as tropical mineral soils. This comprehensive analysis provides a great understanding of the GHG dynamics within tropical peat soils that can be used as a guide for policymakers to create suitable programmes to manage the sustainability of peatlands effectively., (© 2020 John Wiley & Sons Ltd.)

Optimum fertilization is an efficient method to maintain rice yield and reduce N-losses. It is essential though to evaluate methane emissions from paddy fields, to further understand its impact on greenhouse gas budget. Therefore, a field experiment was conducted to investigate the effect of long-term optimum fertilization on CH 4 emissions and rice yield. We collected data in the 7th and 8th year from a field experiment initiated in 2010. Four optimum fertilization strategies, reduced N-fertilizer and zero-P treatment (RNP, 200 kg N/ha), sulfur-coated urea combined with uncoated urea treatment (SCU, 200 kg N/ha), organic fertilizer combined chemical fertilizer treatment (OCN, 200 kg N/ha), organic fertilizer treatment (OF, 200 kg N/ha); and two controls, the farmers' N management (FN, 270 kg N/ha) and zero-N treatment (N0), were employed. The results showed the rice yields achieved for the optimum fertilization treatments (RNP, SCU, OCN, and OF) were similar with those for the FN. No significant differences in CH 4 emissions among all treatments. Cumulative seasonal CH 4 emissions were negatively correlated with grain yield (P < 0.05). In the RNP and SCU treatments, soil available K, mcrA gene and available P were the key variables affecting CH 4 emissions; soil available K, available P and SOC contents were the key emissions factors for OCN and OF treatments. The SCU achieved the highest rice yield and lowest CH 4 emission intensity among optimum fertilization treatments. These results suggest that long-term application of sulfur-coated urea combined with uncoated urea can maintain rice yield and reduce methane emissions from rice paddies., 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 © 2020 Elsevier B.V. All rights reserved.)

Aquatic system is the major source of atmospheric methane. This study explored the influences of ferrihydrite, which is widely existed in natural aquatic system, on methane emission. Results showed that the presence of ferrihydrite led to 26.4% more methane emission. By tracking the transformation of organic compounds, it is revealed that the enhanced methane emission was attributed to greater hydrolysis and degradation of refractory compounds. More specifically, the remaining humic-like substances (HS) in ferrihydrite group (46.4 mg/L-C) were only half of that in control group (80.1 g/L-C) after 30-day incubation. The X-ray photoelectron spectroscopy spectrum confirmed the more active oxidation of organics occurred in ferrihydrite group. It was also found that ferrihydrite aided in sustaining microbial activity at stationary and starvation phases. Further study on microbial communities found that ferrihydrite promoted the enrichment of both functional and electroactive genera. This study provides insights into the greenhouse gas emission in natural environment., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Carbon sources are a critical requirement for the proliferation of algae and the occurrence of harmful algal blooms (HABs), but are often turned into methane (CH 4 ) after the collapse of severe HABs. Here, we attempt to remove HABs, reduce algal-derived CH 4 emissions, and repair the broken carbon biogeochemical cycle in aquatic systems using an integrated ecological approach including flocculation, capping, and submerged macrophyte induction, preliminary at a microcosm scale. This strategy sustainably reached 98% algal removal after 65 days of incubation and resulted in an aerobic microenvironment (ORP = +12 mv) at the sediment-water interface. The approach contributed to an approximate 60% decline in CH 4 released from the aquatic environment into the atmosphere jointly through assimilation of mineralized organic carbon by submerged macrophytes, production of carbon dioxide (CO 2 ) under aerobic conditions, and aerobic CH 4 oxidation. Some of the CO 2 produced in the aquatic phase contributed to inorganic carbon and formed the submerged macrophytes biomass. A combination of flocculation, capping, and submerged macrophyte incubation were significant contributors to altering the carbon budget and sealing nearly 99% of the carbon in the simulated ecosystem (the majority in sediment, followed by submerged macrophytes), providing a sustainable way to reuse algal-derived carbon and reduce CH 4 emissions., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Lowland tropical rice-rice system has a unique micrometrological characteristic that affects both energy component and net ecosystem energy. Periodic and seasonal variations of methane (CH 4 ), carbon dioxide (CO 2 ), and energy exchange from irrigated lowland rice-rice ecosystem were studied using open-path eddy covariance (EC) system during the dry (DS) and wet (WS) seasons in 2015. Concurrently, the manual chamber method was employed in nitrous oxide (N 2 O) measurement efflux. Cumulative net ecosystem carbon exchange (NEE) was observed highest (- 232.55 g C m -2 ) during the WS and lowest (- 14.81 g C m -2 ) during wet fallow (WF). Similarly, the cumulative net ecosystem methane exchange (NEME) was found highest (13,456.5 mg CH 4 m -2 ) during the WS and lowest (2014.3 mg CH 4 m -2 ) during the WF. Surface energy fluxes, i.e., sensible (Hs) and latent heat (LE) fluxes, showed a similar trend. With the advancement of time, the ratio of ecosystem respiration (Re) and gross primary production (GPP) increased. The cumulative global warming potential (GWP) for the two cropping seasons including two fallows was 13,224.1 kg CO 2 equivalent ha -1 . The GWP and NEME showed a similar trend as soil enzymes and labile carbon pools in both seasons (except GWP at the harvesting stage in the wet season). The mean NEE exhibited a more negative value with decrease in labile pools from panicle initiation to harvesting stage in the WS. Soil labile C and soil enzymes can be used as an indicator of NEE, NEME, and GWP in lowland rice ecology. Graphical abstract Schematic presentation of GHG emission and energy exchange in lowland rice.

Methane (CH 4 ) emissions from tropical wetlands contribute 60%-80% of global natural wetland CH 4 emissions. Decreased wetland CH 4 emissions can act as a negative feedback mechanism for future climate warming and vice versa. The impact of the El Niño-Southern Oscillation (ENSO) on CH 4 emissions from wetlands remains poorly quantified at both regional and global scales, and El Niño events are expected to become more severe based on climate models' projections. We use a process-based model of global wetland CH 4 emissions to investigate the impacts of the ENSO on CH 4 emissions in tropical wetlands for the period from 1950 to 2012. The results show that CH 4 emissions from tropical wetlands respond strongly to repeated ENSO events, with negative anomalies occurring during El Niño periods and with positive anomalies occurring during La Niña periods. An approximately 8-month time lag was detected between tropical wetland CH 4 emissions and ENSO events, which was caused by the combined time lag effects of ENSO events on precipitation and temperature over tropical wetlands. The ENSO can explain 49% of interannual variations for tropical wetland CH 4 emissions. Furthermore, relative to neutral years, changes in temperature have much stronger effects on tropical wetland CH 4 emissions than the changes in precipitation during ENSO periods. The occurrence of several El Niño events contributed to a lower decadal mean growth rate in atmospheric CH 4 concentrations throughout the 1980s and 1990s and to stable atmospheric CH 4 concentrations from 1999 to 2006, resulting in negative feedback to global warming., (© 2017 John Wiley & Sons Ltd.)

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

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

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

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

• A high cooling capacity Stirling cryocooler is developed to liquefy boil-off gas. • The relative Carnot efficiency of the cryocooler reaches 33.28% at 77 K. • The cryocooler is installed in the skid-mounted BOG liquefaction system. • The system produces 27.3 L/h LNG and shows a great application potential. Natural gas is a cost-effective energy supply for the development of low-carbon economy and environmental protection worldwide. Despite the environmentally friendly properties of natural gas, methane is a potent greenhouse gas. Therefore, efficiently recovering the boil-off methane gas (BOG) generated from liquefied natural gas (LNG) devices is becoming a pressing problem involving both the economy and the environment. The Stirling cryocooler is a promising technology in dealing with BOG. This technology has superior characteristics, including large cooling capacity, high efficiency, compact configuration, and flexible operating characteristics. In the study, a high cooling capacity Stirling cryocooler was developed. The influences of the regenerator on the cryocooler were studied. The refrigeration performance and the operating characteristics were systematically analyzed. A cooling power of 1050 W at 77 K with a relative Carnot efficiency of 33.28 % was achieved. Accordingly, the cryocooler outperforms the reported Stirling-type cryocoolers in terms of overall performance. A BOG liquefaction system based on the cryocooler was installed in an LNG refueling station. On-site test results demonstrated that the system can produce 27.3 L/h LNG with a power consumption of 14.5 kW. The present research lays a good foundation for future optimizations and applications of the cryocooler in liquefying BOG. [ABSTRACT FROM AUTHOR]

This study investigates methane emissions in the mangrove forests of Xuan Thuy National Park in Vietnam, examining seasonal variations and the influence of environmental factors. Data from the field measurements present methane flux rates ranging from 0.01 to 10.42 mg m-2 day-1, are substantially lower than the default estimations recommended by IPCC guidelines. The study highlights discrepancies between actual field measurements and suggested default values, emphasizing the necessity for site-specific monitoring to avoid overestimating greenhouse gas emissions, particularly in mangrove areas. The analysis reveals strong correlations between methane flux and environmental parameters. Factors such as water pH, turbidity, temperature, and nitrogen content significantly influence methane emissions. The study emphasizes the interconnectedness of various greenhouse gas emissions within mangrove ecosystems and underlines the importance of accurate, location-specific data in environmental assessments and policy-making. [ABSTRACT FROM AUTHOR]

Methane (CH4) is globally the second most critical greenhouse gas after carbon dioxide, contributing to 16%–25% of the observed atmospheric warming. Wetlands are the primary natural source of methane emissions globally. However, wetland methane emission estimates from biogeochemistry models contain considerable uncertainty. One of the main sources of this uncertainty arises from the numerous uncertain model parameters within various physical, biological, and chemical processes that influence methane production, oxidation, and transport. Sensitivity Analysis (SA) can help identify critical parameters for methane emission and achieve reduced biases and uncertainties in future projections. This study performs SA for 19 selected parameters responsible for critical biogeochemical processes in the methane module of the Energy Exascale Earth System Model (E3SM) land model (ELM). The impact of these parameters on various CH4 fluxes is examined at 14 FLUXNET‐ CH4 sites with diverse vegetation types. Given the extensive number of model simulations needed for global variance‐based SA, we employ a machine learning (ML) algorithm to emulate the complex behavior of ELM methane biogeochemistry. We found that parameters linked to CH4 production and diffusion generally present the highest sensitivities despite apparent seasonal variation. Comparing simulated emissions from perturbed parameter sets against FLUXNET‐CH4 observations revealed that better performances can be achieved at each site compared to the default parameter values. This presents a scope for further improving simulated emissions using parameter calibration with advanced optimization techniques. Plain Language Summary: Methane is a critical greenhouse gas, and wetlands are the largest natural source of it. Accurately predicting methane emissions from wetlands is key to tackling climate change. But these predictions, made through computer models, are seldom spot‐on. Why? Because there are many factors in the models that lead to uncertain predictions. A major source of this uncertainty arises from the empirical model parameters. Just as tuning a radio dial ensures clear reception, models need properly adjusted parameters for accurate predictions. A sensitivity analysis was performed to determine which parameters are most crucial for accurate predictions. Instead of running the complex numerical model every time, machine learning was employed to create a faster and simpler version. Using this approach, five parameters were pinpointed as particularly sensitive, significantly impacting the predictions. The comparison of model‐predicted methane emissions with real‐world measurements showed that the model performed well in some cases but needed tweaking in others. Refining these sensitive parameters with more real‐world observations could make better predictions in the future. Key Points: Identified five key sensitive parameters for methane emissions using the Sobol sensitivity analysis methodParameters linked to production and diffusion present the highest sensitivities despite apparent seasonal variationFourteen out of nineteen model parameters exert negligible influence on methane emissions [ABSTRACT FROM AUTHOR]

In the framework of the eutrophic Ob fen study, methane fluxes were measured by the chamber method, and water was sampled for the analysis of general chemical composition, content of biophilic elements (C, N, and P) and δ13C of the dissolved inorganic carbon (δ13C-DIC). Samples were taken from open and forested areas within the fen, with the latter having long received discharged domestic wastewaters. The methane emissions positively correlate with the concentrations of nitrogen compounds and dissolved organic carbon (DOC). In both areas, higher methane emissions were observed from waterlogged microdepressions than from dry elevated microlandscape features. The largest emission was observed from the microdepressions in the forested area near the wastewater discharge point, where the extreme values of CH4 fluxes were almost 30 times higher than the background ones. However, the methane fluxes decrease to the background level at 160 m from the wastewater discharge point, together with the concentrations of almost all nitrogen and carbon compounds dissolved in the fen water. This led us to conclude that wastewater pollution significantly affects the intensity of methane fluxes by increasing ebullitive methane emission near the pollution source. The isotopic composition of the dissolved inorganic carbon (DIC) was heavier in the forested area (δ13C-DIC = –9.64 to –9.21‰) than in the open one (–12.83 to –11.24‰). In the open area of the fen, DIC isotopic composition became heavier away from the dry upland, correlating with the increase in the methane fluxes. The obtained data indicate that methane-generating processes are more active in the forested area of the fen than in the open one and become more active in the open area with increasing distance from the dry upland. This highlights the potentialities of using δ13C-DIC as a fingerprint of wastewater pollution, at least in the Ob fen area. [ABSTRACT FROM AUTHOR]

The ongoing yearly rise in worldwide methane (CH4) emissions is mostly due to human activities. Nevertheless, since over half of these emissions are scattered and have a concentration of less than 3% (v/v), traditional physical–chemical methods are not very effective in reducing them. In this context, biotechnologies like biofiltration using methane-consuming bacteria, also known as methanotrophs, offer a cost-efficient and practical approach to addressing diffuse CH4 emissions. The present review describes recent findings in biofiltration processes as one of the earliest biotechnologies for treating polluted air. Specifically, impacts of biotic (such as cooperation between methanotrophs and non-methanotrophic bacteria and fungi) and abiotic factors (such as temperature, salinity, and moisture) that influence CH4 biofiltration were compiled. Understanding the processes of methanogenesis and methanotrophy holds significant importance in the development of innovative agricultural practices and industrial procedures that contribute to a more favourable equilibrium of greenhouse gases. The integration of advanced genetic analyses can enable holistic approaches for unravelling the potential of biological systems for methane mitigation. This study pioneers a holistic approach to unravelling the biopotential of methanotrophs, offering unprecedented avenues for biotechnological applications.

Currently, carbon-based substances are widely used in agriculture and industry, leading to obvious accumulation in ecosystems. Natural ecosystems, especially water-logged areas, are important sources of greenhouse gases. However, it is not clear whether the dimensions of carbon-based substances can affect their emission. Herein, the effects of three kinds of carbon-based substances (including carbon nanotubes, graphene, and graphite powder) with dimensions from one to three on the CO2 and CH4 production potential were tested from a saltmarsh wetland soil. Carbon nanotubes inhibited CH4 production, and graphene showed slight inhibition. In contrast, graphite powder could promote methanogenesis, and the maximum CH4 concentration was 53.02 mmol g−1 L−1. With a decrease, the maximum CH4 concentration with carbon nanotubes was only 38.09 mmol g−1 L−1. Interestingly, the effect of carbon-based substances on CO2 production was opposite to that on CH4. Carbon nanotubes showed the greatest promotion effect on CO2 production. In addition, by comparing the effects of carbon-based substances on global warming equivalent, graphite powder showed a promotion effect compared with the control, while carbon nanotubes decreased it. This study provides basic data for understanding the effects of carbon-based substances with diverse dimensions on greenhouse gas dynamics in saltmarsh wetland soil. [ABSTRACT FROM AUTHOR]

Simple Summary: Consumer perception related to health and environmental issues associated with ruminant products, such as greenhouse gas emissions, has led to a paradigm shift aimed at mitigating the potential harmful effects of ruminant production. This study consolidated the current body of research on dietary rumen manipulating strategies with an aim to quantify their impact on rumen fermentation, enteric methane (CH4) emission and productivity by creating a global database on in vivo evaluation studies. A meta-analytical approach was used to achieve the study's aim and the result showed that nitrate, saponin, oils, biochar and 3-nitroxypropanol (3-NOP) were effective dietary rumen modulating strategies to mitigate enteric CH4 emission. Of these effective strategies, oils and 3-NOP provided a co-benefit in terms of improving productivity in ruminant livestock. Concentrate feeding equally improved production without any significant effect on enteric methane emissions. A meta-analysis was conducted with an aim to quantify the beneficial effects of nine different dietary rumen modulating strategies which includes: the use of plant-based bioactive compounds (saponin, tannins, oils, and ether extract), feed additives (nitrate, biochar, seaweed, and 3-nitroxy propanol), and diet manipulation (concentrate feeding) on rumen fermentation, enteric methane (CH4) production (g/day), CH4 yield (g/kg dry matter intake) and CH4 emission intensity (g/kg meat or milk), and production performance parameters (the average daily gain, milk yield and milk quality) of ruminant livestock. The dataset was constructed by compiling global data from 110 refereed publications on in vivo studies conducted in ruminants from 2005 to 2023 and anlayzed using a meta-analytical approach.. Of these dietary rumen manipulation strategies, saponin and biochar reduced CH4 production on average by 21%. Equally, CH4 yield was reduced by 15% on average in response to nitrate, oils, and 3-nitroxy propanol (3-NOP). In dairy ruminants, nitrate, oils, and 3-NOP reduced the intensity of CH4 emission (CH4 in g/kg milk) on average by 28.7%. Tannins and 3-NOP increased on average ruminal propionate and butyrate while reducing the acetate:propionate (A:P) ratio by 12%, 13.5% and 13%, respectively. Oils increased propionate by 2% while reducing butyrate and the A:P ratio by 2.9% and 3.8%, respectively. Use of 3-NOP increased the production of milk fat (g/kg DMI) by 15% whereas oils improved the yield of milk fat and protein (kg/d) by 16% and 20%, respectively. On the other hand, concentrate feeding improved dry matter intake and milk yield (g/kg DMI) by 23.4% and 19%, respectively. However, feed efficiency was not affected by any of the dietary rumen modulating strategies. Generally, the use of nitrate, saponin, oils, biochar and 3-NOP were effective as CH4 mitigating strategies, and specifically oils and 3-NOP provided a co-benefit of improving production parameters in ruminant livestock. Equally concentrate feeding improved production parameters in ruminant livestock without any significant effect on enteric methane emission. Therefore, it is advisable to refine further these strategies through life cycle assessment or modelling approaches to accurately capture their influence on farm-scale production, profitability and net greenhouse gas emissions. The adoption of the most viable, region-specific strategies should be based on factors such as the availability and cost of the strategy in the region, the specific goals to be achieved, and the cost–benefit ratio associated with implementing these strategies in ruminant livestock production systems. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) imagery, notably Sentinel-1A's C-band, VV, and VH polarized SAR, has emerged as a crucial tool for mapping rice fields, especially in regions where cloud cover hinders optical imagery. Employing multi-temporal characteristics, SAR data were regularly collected and parameterized using MAPscape-Rice software, which integrates a fully automated processing chain to convert the data into terrain-geocoded σ° values. This facilitated the generation of rice area maps through a rule-based classifier approach, with classification accuracies ranging from 88.5 to 91.5 and 87.5 percent in 2017, 2018, and 2022, respectively. To estimate methane emissions, IPCC (37.13 kg/ha/season, 42.10 kg/ha/season, 43.19 kg/ha/season) and LST (36.05 kg/ha/season, 41.44 kg/ha/season, 38.07 kg/ha/season) factors were utilized in 2017, 2018 and 2022. Total methane emissions were recorded as 19.813 Gg, 20.661 Gg, and 25.72 Gg using IPCC and 19.155 Gg, 20.373 Gg, and 22.76 Gg using LST factors in 2017, 2018 and 2022. Overall accuracy in methane emission estimation, assessed against field observations, ranged from (IPCC) 85.71, 91.32, and 80.25 percent to (LST) 83.69, 91.43, and 84.69 percent for the years 2017, 2018 and 2022, respectively, confirming the efficacy of remote sensing in greenhouse gas monitoring and its potential for evaluating the impact of large-scale water management strategies on methane emissions and carbon credit-based ecosystem services at regional or national levels. [ABSTRACT FROM AUTHOR]

The ongoing yearly rise in worldwide methane (CH4) emissions is mostly due to human activities. Nevertheless, since over half of these emissions are scattered and have a concentration of less than 3% (v/v), traditional physical–chemical methods are not very effective in reducing them. In this context, biotechnologies like biofiltration using methane-consuming bacteria, also known as methanotrophs, offer a cost-efficient and practical approach to addressing diffuse CH4 emissions. The present review describes recent findings in biofiltration processes as one of the earliest biotechnologies for treating polluted air. Specifically, impacts of biotic (such as cooperation between methanotrophs and non-methanotrophic bacteria and fungi) and abiotic factors (such as temperature, salinity, and moisture) that influence CH4 biofiltration were compiled. Understanding the processes of methanogenesis and methanotrophy holds significant importance in the development of innovative agricultural practices and industrial procedures that contribute to a more favourable equilibrium of greenhouse gases. The integration of advanced genetic analyses can enable holistic approaches for unravelling the potential of biological systems for methane mitigation. This study pioneers a holistic approach to unravelling the biopotential of methanotrophs, offering unprecedented avenues for biotechnological applications. [ABSTRACT FROM AUTHOR]

Freshwater lakes represent a potential source of methane (CH4) emission into the atmosphere. However, the CH4 emission contribution to the total emission in the littoral zones of lakes, especially emergent macrophytes (e.g., lotus), is poorly known. Lotus has been cultivated in almost all provinces in China; it is not only an aquatic plant, but also a kind of vegetable. In this study, two sampling zones (lotus plant and open water) were established in the lake of the middle reaches of the Yangtze River. The CH4 emission was measured using a floating opaque chamber and gas chromatography between April and December in the years 2021 and 2022. The results indicated that the flux of CH4 emissions ranged from 0.10 to 59.75 mg m−2 h−1, with an average value of 5.61 mg m−2 h−1, in the open water, while ranging from 0.19 to 57.32 mg m−2 h−1, with an average value of 17.14 mg m−2 h−1, in the lotus plant zone. The maximal CH4 emissions occurred in July and August for the open water, which was highly related to the air and water temperature, whereas it happened in September for the lotus plant zone, possibly due to the high vegetation biomass, indirectly enhancing the high soil organic carbon content, plant-mediated CH4 emission, as well as the lower dissolved oxygen concentration, thus strengthening the production and emissions of CH4. Considering the carbon emissions (both CH4 and CO2) and plant productivity, although greater CH4 emission occurred in the lotus plant zone, it could still represent a potential carbon sink (213 g m−2 yr−1) compared to the open water. [ABSTRACT FROM AUTHOR]

Reducing CO2 emissions is one of the highest priorities in animal production. Regarding methane reduction, feed additives are of growing importance. As shown in a meta-analysis, the use of the essential oil (EO) blend Agolin Ruminant affects methane production per day (− 8.8%), milk yield (+ 4.1%), and feed efficiency (+ 4.4%). Building on these results, the present study investigated the effect of varying individual parameters on the carbon footprint of milk. The environmental and operational management system REPRO was applied to calculate the CO2 emissions. Calculation of CO2 emissions include enteric and storage-related CH4, storage-, and pasture-related N2O as well as direct and indirect energy expenditures. Three feed rations were created, differing in their basic feed components such as grass silage, corn silage, and pasture. Each feed ration was differentiated into three variants: variant 1 CON (no additive), variant 2 EO, and variant 3 (15% reduction of enteric methane compared to CON). Due to the reducing effect of EO on enteric methane production, a reduction potential of up to 6% could be calculated for all rations. Considering other variable parameters, such as the positive effects on ECM yield and feed efficiency, a GHG reduction potential of up to 10% can be achieved for the silage rations and almost 9% for the pasture ration. Modeling showed that indirect methane reduction strategies are important contributors to environmental impacts. Reduction of enteric methane emissions is fundamental, as they account for the largest share of GHG emissions from dairy production. [ABSTRACT FROM AUTHOR]

Because two carbon-based greenhouse gases, CH4 and CO2, can share a common soil carbon source, reduced emissions of one gas may enhance emissions of the other. The current study aimed to compare CH4 and CO2 emissions and identify some related mechanisms caused by biochar addition to paddy soils. Two soils of relatively high organic carbon (OC = 3.05%) and low OC (OC = 0.54%) were taken from two paddy fields, mixed with biochar at 0%, 2%, and 4%, submerged, and incubated in closed plastic jars for 53 days. On days 1, 4, 8, 13, 19, 26, 34, 43, and 53, gas from the jar's headspace was measured for CH4 and CO2, and standing water in individual jars was measured for pH and EC. The results revealed that biochar reduced CH4 emissions by 13% and 74% while increasing CO2 emissions by 36% and 86% in high-OC and low-OC soil, respectively. Biochar had greater impacts on pH, EC, and greenhouse gas emissions in low-OC soil than in high-OC soil. The inverse relationship between CH4 emissions and pH and EC can be explained by biotic mechanisms, which suppressed microbial activities, lowing CH4 emissions. CO2 emissions were proportionally or non-significantly correlated with EC and pH depending on tested soils, which may be explained by abiotic processes. In summary, biochar addition can suppress CH4 emissions while increasing CO2 emissions via biotic and abiotic mechanisms, respectively, dependent on soil pH and EC changes, and biochar effects were stronger in low-OC soil than in high-OC soil. [ABSTRACT FROM AUTHOR]

While ASEAN economies are enjoying economic growth, their environmental condition is worsening. One effective solution is to shift toward green investment in order to increase renewable energy production. This study examines the role of environmental degradation and green investment on renewable energy production in the context of ASEAN economies. The study analyzed critical factors of environmental degradation such as carbon, GHG, NO2 emissions, GDP, and finally green investment in the sample of ASEAN markets. Secondary data was used and extracted from WDI indicators from 2001 to 2020. To achieve the study objective, method of moments quantile regression (MMQR) model has been applied to reveal the association among variables. The empirical evidences divulged that environmental degradation could increase renewable energy production. Results implies that with the threatening impact of harmful emissions, economies shift their interest toward renewable energy production so that the cycle could be reversed. Also, the findings revealed the positive impact of green investment on renewable energy production that assures that clean energy investments are crucial for economies; thus, these shreds of evidences aid in the development of renewable energy policies which could be helpful to mitigate environmental challenges. [ABSTRACT FROM AUTHOR]

[Display omitted] • Photosynthetic O 2 -supported algal-bacterial AGS was achieved with gas circulation. • Gas circulation promoted biological utilization of GHGs including CO 2 , N 2 O and CH 4. • Enriched algae in biomass lowered CO 2 emissions but elevated N 2 O emissions. • The increased presence of algae in granules may deteriorate granular structure. Photosynthetic O 2 -supported algal-bacterial aerobic granular sludge (AGS) presents great potential in carbon emission mitigation and operation cost saving in wastewater treatment processes. This study used gas circulation to enclose the photosynthetic O 2 and the emitted CO 2 for the development of photosynthetic O 2 -supported algal-bacterial AGS in a pseudo-closed sequencing batch reactors (SBR), targeting greenhouse gas emission mitigation (including CO 2 , N 2 O and CH 4). Results showed that photosynthetic O 2 -supported AGS with abundant Cyanobacteria were achieved in this pseudo-closed SBR under strong light conditions. Although gas circulation enclosed acidic CO 2 , excessive Chlorophyll- a (Chl- a) in biomass still led to the increase of pH, followed by the occurrence of CO 2 limitation and the loss of functional strains and extracellular polymeric substances, eventually collapsing granular structure. Interestingly, gas circulation can reduce GHG emissions due to prolonged contact time between gases and microorganisms. Chl- a contents showed an inversely proportional relationship with CO 2 emission factors (0.04–0.61 kg-CO 2 /kg-COD), but over-high algae in biomass may induce more N 2 O emissions (0.45 % to 5.3 %) by affecting nitrification and denitrification processes. Compared to CO 2 and N 2 O, the emission of CH 4 was negligible. It was estimated that zero non-CO 2 GHG emissions could be achieved when stable granules containing Chl- a content of > 5.8 mg/g-MLVSS. This study proposed that achieving low-carbon photosynthetic O 2 -supported algal-bacterial AGS requires minimizing the adverse impact of existing algae on denitrification and nitrification. [ABSTRACT FROM AUTHOR]

Returning straw directly to the field soil is an important strategy that is widely advocated for the disposal of cereal straw. Straw carbonization to form biochar is an innovative approach for treating straw, and its incorporation into the soil is beneficial for improving soil quality. To assess the long-term impacts of these two technologies on greenhouse gas (GHG) emissions and crop yields, a decade-long field study, comparing three treatments, namely direct incorporation of straw from the crop into the field (SCF), incorporation of biochar from straw into the field (BCF), both combined with chemical fertilizer, and the incorporation of chemical fertilizer alone into the field (CF), was conducted on a rice-wheat rotation system in eastern China. Results showed that SCF and BCF achieved the same rice yield as CF treatment alone, while BCF caused a slight decrease in wheat yield. However, there was no significant difference in wheat yield between SCF and BCF, with no significant 'season × treatment' interactions observed. Over the ten-year observation period, rice yields were found to be greatly influenced by air temperature and precipitation, whereas wheat yields were more dependent on precipitation. In terms of GHG emissions, SCF increased annual methane (CH 4) emissions by 144 % but reduced annual nitrous oxide (N 2 O) emissions by 40 %, resulting in an 80 % increase in annual global warming potential (GWP) compared with CF. On the other hand, BCF reduced CH 4 and N 2 O emissions by 58 % and 25 %, respectively, relative to SCF, in the rice season. Despite slightly higher N 2 O emissions in the wheat season, BCF reduced the annual GWP by 51 %, relative to SCF. Additionally, the relative CH 4 emissions of SCF/CF and BCF/CF decreased over time, with BCF showing lower CH 4 emissions than CF after five years. Overall, the findings suggest that straw carbonization into biochar before soil incorporation could be a promising approach to mitigating GHG emissions and maintaining crop yields compared with incorporating straw directly into the soil in the rice-based rice-wheat rotation systems. • SCF and BCF achieved the same rice yield as CF treatment alone. • There was no significant difference in wheat yield between SCF and BCF. • SCF increased annual GWP by 80 % compared with CF. • BCF decreased annual GWP by 12 % relative to CF and by 51 % compared with SCF. • SCF/CF and BCF/CF ratio in CH 4 emissions decreased over the ten years. [ABSTRACT FROM AUTHOR]

Freshwater lakes represent a potential source of methane (CH4) emission into the atmosphere. However, the CH4 emission contribution to the total emission in the littoral zones of lakes, especially emergent macrophytes (e.g., lotus), is poorly known. Lotus has been cultivated in almost all provinces in China; it is not only an aquatic plant, but also a kind of vegetable. In this study, two sampling zones (lotus plant and open water) were established in the lake of the middle reaches of the Yangtze River. The CH4 emission was measured using a floating opaque chamber and gas chromatography between April and December in the years 2021 and 2022. The results indicated that the flux of CH4 emissions ranged from 0.10 to 59.75 mg m−2 h−1, with an average value of 5.61 mg m−2 h−1, in the open water, while ranging from 0.19 to 57.32 mg m−2 h−1, with an average value of 17.14 mg m−2 h−1, in the lotus plant zone. The maximal CH4 emissions occurred in July and August for the open water, which was highly related to the air and water temperature, whereas it happened in September for the lotus plant zone, possibly due to the high vegetation biomass, indirectly enhancing the high soil organic carbon content, plant-mediated CH4 emission, as well as the lower dissolved oxygen concentration, thus strengthening the production and emissions of CH4. Considering the carbon emissions (both CH4 and CO2) and plant productivity, although greater CH4 emission occurred in the lotus plant zone, it could still represent a potential carbon sink (213 g m−2 yr−1) compared to the open water.

Methane (CH4) is an important greenhouse gas. There is increasing attention to CH4 abatement strategies because of its contribution to short-term warming and strong benefits of decreasing CH4 emissions. China greenhouse gas inventory methods are used to predict CH4 emissions from the energy industry and to assess the potentials of CH4 abatement policies and techniques by 2050. The NDC scenario results show using oil and gas as transitional clean energy sources instead of coal will increase CH4 emissions from oil and gas industries at least 70%, but CH4 emissions from the coal industry will decrease 45%, meaning total CH4 emissions from the energy industry will continually decrease at least 30% in 2030 compared with 2020. Energy-related CH4 emissions might peak around 2025, ahead of CO2 emission peaking. CH4 emissions will then decrease slightly and decrease markedly after 2030. Emissions in 2050 are expected to be 32% lower than emissions in 2020. In an extreme scenario, emissions may be 90% lower in 2050 than in 2020. It is suggested that the verification system for the energy industry's CH4 emission accounting at the national level be improved and CH4 control targets in line with national emission targets and the "14th Five-Year Plan" development stage be formulated. [ABSTRACT FROM AUTHOR]

Lakes represent an important source of atmospheric methane (CH4); however, there are few studies on which lake-dwelling invasive aquatic plants generate CH4. Therefore, in this study, CH4 emissions were measured using a floating chamber and gas chromatography in a subtropical lake in China. We considered four community zones of invasive plants (Eichhornia crassipes), emergent vegetation (Zizania latifolia), floating-plant (Trapa natans) and open-water zones. The results indicate that the flux of CH4 emissions varied between −5.38 and 102.68 mg m−2 h−1. The higher emission values were attributed to lake eutrophication. Moreover, the flux of CH4 emissions in the invasive plant zone was 140–220% higher than that in the open-water and the floating-plant zones. However, there was no significant difference in CH4 emissions between the invasive plant and the emergent vegetation zones. This may be due to a higher production of plants, as well as the rapid reproductive rate of the invasive plants. Finally, CH4 emissions were positively associated with the air and water temperature; however, the emissions were also negatively associated with water depth. Our results suggest that invasive plants enhance freshwater CH4 emissions, thus contributing to global warming. [ABSTRACT FROM AUTHOR]

This paper evaluates the performance of footprint models through a line source (LS) system releasing methane (CH4) measured by an eddy covariance (EC) system in a rice‐wheat rotation agro‐ecosystem. Two footprint models namely Kormann and Meixner (KM) and Flux Footprint Prediction (FFP), are used. The "line source width (LW)" was introduced, which could highly impact the model estimation, leading to an error of ∼500% for the FFP and ∼200% for the KM. The surface roughness length (z0) also affects the FFP estimation, which is much weaker than the effect of LW with a 15% error. An overestimation of 217.8% is observed when the LS is close to the EC system, which reduces to 12.2% when the source stays distant. The analysis of different experimental configurations illustrated that the estimated emission calculated based on footprint model improves with increasing emission height, averaging period, and emission rate. The most accurate estimations are achieved by the configurations of canopy level (CL; 5.5% overestimation), 15 min averaging period (1% underestimation), and moderate emission (ME; 1.9% underestimation), respectively. For KM, the CL‐ME‐30 min setup leads to the best performance, while SL (soil level)‐ME‐15 min is the best configuration for the FFP estimation. A multivariate regression model is proposed to provide preliminary guidance for the footprint model application. The designed LS system is valuable for validating the performance of flux measurements in the specific ecosystems (e.g., grassland, sand land, and forest) as well as other greenhouse gases (e.g., N2O and NH3). Plain Language Summary: A line‐source system, which can automatically simulate methane emission in the paddy fields, was designed to evaluate the performance of eddy covariance footprint models. Different emission heights, emission rates, and source distances were conducted. The result suggested that the outputs of the footprint model improved with increasing emission height and emission rate. The most accurate model estimation was found at the canopy height and the moderate emission. Finally, the range of estimation errors was calculated quantitatively, and a multivariate regression model was proposed to evaluate footprint model performance. The designed line‐source system is also available for validation in other ecosystems, such as grassland and sand land. Key Points: Footprint model estimation improves with increasing source emission height, averaging period, and emission rateSource width and emission distance can significantly affect the model performanceThe most accurate estimations are achieved at the canopy level, 15 min averaging period, and moderate emission [ABSTRACT FROM AUTHOR]

Industrial activities are one of the most important emission sources of greenhouse gases at a global level. The process of production, transportation, electricity consumption, and industrial wastewater are the four major components in producing greenhouse gases. Industrial wastewater management (collection, treatment, and disposal) results in direct emission of greenhouse gases (including carbon dioxide, methane, and nitrous oxide). Also, energy consumption in the wastewater treatment process causes indirect carbon dioxide emissions. The present study aimed to estimate the contribution of industrial wastewater treatment plants in Iran from this emission, in addition to identifying sources of greenhouse gas emissions in the industrial wastewater treatment plant and estimating greenhouse gas emissions from the industrial wastewater sector in Iran. In this research, the emission calculations were conducted by using the methodology of Intergovernmental Panel on Climate Change (IPCC) guidelines for calculating greenhouse gases emission. Based on the estimations performed in this study, 1,305.98 kt of CH4 were emitted directly from wastewater in 2017 in the entire industrial wastewater sector. Further, the results indicated that industrial wastewater treatment plants in Iran’s industrial parks generate 46.53 kt of CH4 directly and 259.5 kt of CO2 indirectly. According to the studies, the food industry, especially the industries involved in processing agricultural products (with 48.74 % of total methane emissions) has the highest greenhouse gas emissions in the country, followed by the paper production industry (with 27.46 % of total methane emissions) in the second place. One of the best strategies for reducing greenhouse gas emissions in industrial wastewater treatment plants is energy production from methane produced in large treatment plants and implementing necessary amendments in production processes to decrease wastewater production.

Lakes represent an important source of atmospheric methane (CH4); however, there are few studies on which lake-dwelling invasive aquatic plants generate CH4. Therefore, in this study, CH4 emissions were measured using a floating chamber and gas chromatography in a subtropical lake in China. We considered four community zones of invasive plants (Eichhornia crassipes), emergent vegetation (Zizania latifolia), floating-plant (Trapa natans) and open-water zones. The results indicate that the flux of CH4 emissions varied between −5.38 and 102.68 mg m−2 h−1. The higher emission values were attributed to lake eutrophication. Moreover, the flux of CH4 emissions in the invasive plant zone was 140–220% higher than that in the open-water and the floating-plant zones. However, there was no significant difference in CH4 emissions between the invasive plant and the emergent vegetation zones. This may be due to a higher production of plants, as well as the rapid reproductive rate of the invasive plants. Finally, CH4 emissions were positively associated with the air and water temperature; however, the emissions were also negatively associated with water depth. Our results suggest that invasive plants enhance freshwater CH4 emissions, thus contributing to global warming.

Water hyacinth is able to sequester large amounts of carbon dioxide (CO2) in wetlands. At the same time, the high production of organic matter combined with the plant's capacity to limit the diffusion of oxygen from the atmosphere into the water creates favorable conditions for the production of methane (CH4). The combination of these mechanisms challenges the prediction of water hyacinth's net effects on greenhouse gas (GHG) emissions. To unravel the impact of water hyacinth on GHG fluxes, we performed an extensive fieldwork study encompassing 22 sites dominated by water hyacinth in the Pantanal and Amazon during two different seasons. The highest CH4 emissions from water hyacinth beds occurred in shallow systems where sediment rooting enabled plant-mediated CH4 transport (307 ± 407 mg CH4 m−2 day−1 in waters shallower than 1 m, as opposed to 6.1 ± 10.6 mg CH4 m−2 day−1 in deeper waters). When CO2 uptake rates are added to the GHG budget (in terms of global warming potential), the water bodies were usually a GHG sink (− 5.2 ± 10 gCO2 eq m−2 day−1). The strength of the sink is highest in deeper systems where even a low water hyacinth coverage may already offset open water emissions. This dual effect of strong CO2 uptake—and at least temporal carbon storage in biomass—in combination with a high CO2–to-biomass-to-CH4 (and possibly back to CO2) conversion highlights the necessity to include vegetation characteristics in relation to depth when estimating GHG fluxes for tropical wetlands. [ABSTRACT FROM AUTHOR]

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This study estimated the effect of essential fatty acid (FA) supplementation on fermentation indices, greenhouse gases, microbes, and FA profiles in the rumen. The treatments used pure FAs consisting of C18:2n-6 FA (LA), C18:3n-3 FA (LNA), or a mixture of these FAs at 1:1 ratio (Combo). In vitro rumen incubation was performed in 50 mL glass serum bottles containing 2 mg of pure FAs, 15 mL of rumen buffer (rumen fluid+anaerobe culture medium = 1:2), and 150 mg of synthetic diet (411 g cellulose, 411 g starch, and 178 g casein/kg dry matter) at 39°C for 8 h with five replications and three blanks. In rumen fermentation indices, LA exhibited highest (P < 0.05) ammonia-N and total gas volume after 8 h of incubation. Furthermore, LA presented lower (P < 0.05) pH with higher (P < 0.05) total volatile fatty acid (P = 0.034) than Combo, while LNA was not different compared with those in the other treatments. Additionally, Combo produced highest (P < 0.05) CO2 with lowest (P < 0.05) CH4. In the early hours of incubation, LA improved (P < 0.005) Fibrobacter succinogenes and Ruminococcus flavefaciens , while LNA improved (P < 0.005) Ruminococcus albus. After 8 h of incubation, LNA had lower (P < 0.05) methanogenic archaea than LA and Combo but had higher (P < 0.05) rumen ciliates than LA. R. albus was higher (P < 0.05) in LA than in LNA and Combo. It was observed that the rate of biohydrogenation of n-6 and n-3 FAs was comparatively lowest (P < 0.05) in Combo, characterized by higher C18:2n-6 and/or C18:3n-3 FA and polyunsaturated FA (PUFA) concentrations with lower (P < 0.05) concentrations of C18:0 and saturated FA and the ratio of saturated FAs to PUFAs. Therefore, this study concluded that dietary C18:2n-6 could improve populations of fibrolytic bacteria and rumen fermentation indices, but dietary mixture of pure C18:2n-6 and C18:3n-3 is recommended because it is effective in reducing enteric methane emissions and resisting biohydrogenation in the rumen with less effect on rumen microbes. [ABSTRACT FROM AUTHOR]

The continuous growth in population, urbanization, and industrial development has been increasing the generation of solid waste (SW) in the Kingdom of Saudi Arabia. Consequently, the associated greenhouse gas (GHG) emission is also following an increasing trend. The collection and use of greenhouse gases emitted from solid waste management practices are still limited. A causality analysis examined the driving factors of the emissions from solid waste management. The methane (CH4) emissions from municipal solid waste (MSW) increased with an increase in gross domestic product (GDP) per capita and urban population, and an increase in foreign direct investment (FDI) inflows and literacy rate was likely to reduce CH4 emissions from municipal solid waste and vice versa. The CH4 emission generated from industrial solid wastes was found to be positively related to GDP per capita, urban population, and FDI inflows. However, a decrease in the unemployment rate was likely to increase CH4 emissions from industrial solid wastes. The future greenhouse gas emissions were projected under different possible socio-economic conditions. The scenario analysis based on different variations of population and GDP growth revealed that methane emission from total waste would increase at an average annual rate of 5.13% between 2020 and 2050, and is projected to reach about 4000 Gg by the end of the year 2050. Although the Kingdom has been taking some initiatives towards climate change mitigation, it has significant opportunities to adopt some of the best practices in solid waste management including reduction, recycling, composting and waste-to-energy, and carbon capture and utilization. This study also put emphasis on developing appropriate policy approaches for climate change mitigation based on the circular economy which is gaining momentum in the Kingdom. [ABSTRACT FROM AUTHOR]

Anaerobic decomposition of organic compounds in landfills is responsible for generation of greenhouse gases. The present study aimed to determine the total gas and methane emission from a landfill located in Hamedan (west of Iran) from 2011 to 2030. LandGEM 3.02 model was used to estimate the gas emission with the volumetric methane percent of 60%, production potential of 107, and methane generation rate of 0.2. Spatial distribution of annual methane and total landfill gas emission rate in the study area at three decades were provided through ArcGIS software. The results showed that organic and food wastes had the maximum amounts in the solid waste stream (over 75%). The results showed that 4.371×108 m3 methane would be produced after 20 years, mostly (4.053×106m3) in the first year. In addition, methane production capacity in Hamedan landfill site was 107 m3/Mg. According to the results, the maximum and minimum gas generation rates are in summer (the hottest season) and winter (the coldest season) respectively. The results of the LandGEM model represented that the total gas and methane generation rates will be significant in the first 10 years. The potential of rapidly degradable organic compounds for gas emission will be higher than that of slowly degradable organic compounds. The results obtained in the present study can be beneficially used in planning for energy production and other applications in landfill sites.

Carbon dioxide emission and greenhouse gas emissions are considered core issue in the world that influence agricultural production and also cause climate change. The present study seeks to investigate the linkage of methane emissions, nitrous oxide emissions, carbon dioxide emission, and greenhouse gas emissions with agricultural gross domestic product in China. The long-term association was checked by using an autoregressive distributed lag (ARDL) bounds testing approach, fully modified least squares method, and canonical cointegrating regression analysis. The results from long-run analysis exposed that carbon dioxide emission and greenhouse gas emissions have positive coefficients that demonstrate the long-run linkage with the agricultural gross domestic product having p values of 0.5709 and 0.3751, respectively. Similarly, results also revealed that agricultural methane emissions and agricultural nitrous oxide emissions have a negative association with the agricultural gross domestic product having p values of 0.1737 and 0.0559. China is a huge emitter of CO2 emission and greenhouse gas emissions. Possible conservative policies are required to form the Chinese government to tackle this challenge to decrease CO2 emission in order to increase agricultural production. [ABSTRACT FROM AUTHOR]

Objectives: The study was undertaken to investigate the greenhouse gas (GHG) emission from livestock in Bangladesh. Materials and Methods: The GHG emission inventory of livestock in Bangladesh was estimated according to the tier 1 approach of the Intergovernmental Panel on Climate Change (IPCC) using livestock population data from 2005 to 2018. It was also extrapolated for the next three decades, according to the growth of the livestock population. Results: According to the calculation, the GHG emission from livestock was 66,586 Gg/year CO2 equivalent (CO2e) in 2018. This emission may rise to 69,869, 80,618, 94,638, and 113,098 Gg/year CO2e in 2020, 2030, 2040, and 2050, respectively. The share of enteric methane, manure methane, direct nitrous oxide emission, and indirect nitrous oxide emission in the total GHG emissions represented 44.0%, 3.6%, 51.5%, and 0.9%, respectively, in 2018. It may arise at a rate of 1.54%-1.74% annually until 2050. Conclusion: The GHG inventory may guide professionals to formulate and undertake the effective mitigation measures of GHG emissions from livestock in Bangladesh. However, this inventory can be amended following the tier 2 approach recommended by the IPCC if necessary data are available at the national level. [ABSTRACT FROM AUTHOR]

Abstract In the context of reservoirs, sediment trapping, and aquatic greenhouse gas (GHG) production, knowledge about the distribution of hot and low spots is essential for improved measurement strategies. It is also a key to a precise assessment of the GHG emissions of each reservoir. Large numbers of reservoirs are used mainly for hydroelectric power generation and, hence, affected by strong changes in water level. Drawdown events may lead to significant changes in spatial sediment and organic carbon distribution and, consequently, strongly alter the GHG emission patterns of the water body. We combined hydroacoustic sediment classification, sediment magnitude detection, and ebullition flux assessment with in-situ pore water investigations and sediment coring to detect ebullition distribution patterns after strong reservoir drawdown. The research was conducted in the Capivari Reservoir in the southeast of Brazil, which was affected by up to 15 m of drawdown within the last 10 years. Results show severe changes in sediment accumulation and composition. The focusing of sediment divides the reservoir in extreme hot and low spots. Methane pore water concentrations are highly correlated with acoustic backscatter values (r2 = 0.97) as well as with the organic carbon content (r2 = 0.55) and allow for a precise detection of the newly created emission patterns. Highly productive sediment could be acoustically distinguished from non-productive areas. Only 23.6% of the reservoir surface produced 64% of the detected bubbles. An organic carbon content in the sediment of 2.4% was found to be a prerequisite for the formation of GHG emission hot spots. These findings may help to complement the still insufficient knowledge of methane ebullition fluxes from reservoirs with changing water levels. Graphical abstract Unlabelled Image Highlights • Emission hot spots within reservoirs will significantly shift after drawdown events. • Historic water level fluctuations are important for emission extrapolations. • No simplified relations between depth and methane emissions • Future climate- and management-related level fluctuation will lead to different emission patterns. [ABSTRACT FROM AUTHOR]