Biomass and natural gas co-firing – evaluation of GHG emissions

Methane emissions along biomethane and biogas supply chains are underestimated

In recent years， the rapid socio-economic development and the improvement of people's diets have driven the conversion of paddy soil to upland crop cultivation， leading to changes in soil water content， carbon and nitrogen availability， and the intensity of greenhouse gas emission. Therefore， it is crucial to study the effects of changes in soil water content and carbon and nitrogen availability on greenhouse gas CH4 and CO2 emissions and identify the key controlling factors upon rice paddy conversion into upland field， especially during the initial stage of conversion. Soil samples used in the present study were collected from a long-term rice paddy field and an adjacent upland field previously converted from rice paddy. The paddy soil was set into submerged （water to soil ratio of 2∶1） and from submerged to a slowly draining treatment （water to soil ratio of 2∶1 slowly decreased to 70% field water capacity and then remained stable） and compared with the upland soil （soil water content remained at 70% field water capacity）. Under each water gradient， the soil was supplied with labile C and N to change substrate availability： ① control （no substrate addition）， ② C addition （glucose）， ③ N addition （NH4Cl）， and ④ C and N additions （glucose+NH4Cl）. CH4 and CO2 emissions and soil biochemical properties were measured regularly during the incubation period so as to investigate the effects of soil water content， carbon and nitrogen availability， and their interaction on CH4 and CO2 emissions in paddy soil. The changes in contents of soil microbial biomass carbon （ΔMBC）， dissolved organic carbon （ΔDOC）， and soil mineral N （ΔMineral-N， containing ΔNH4+-N and ΔNO3--N） over the incubation period were calculated by subtracting the initial values from the final values at the end of the incubation period. The results showed that as compared to the submerged condition， the drainage of submerged paddy soil significantly reduced CH4 emission by 95% on average and increased CO2 emission by 46% on average. The cumulative emissions of CH4 and CO2 were significantly higher in drained paddy soil （1.36 mg·kg-1 and 584.13 mg·kg-1 for CH4 and CO2， respectively） relative to those in upland soil （0.01 mg·kg-1 and 407.70 mg·kg-1）. CH4 emissions from the submerged paddy soil significantly increased by 40% after carbon addition and decreased by 63% after nitrogen addition. The simultaneous additions of carbon and nitrogen had little effect on the CH4 emissions from submerged paddy soil. CH4 emissions from the drained paddy soil increased significantly by 48% after carbon addition， but there was no significant difference among other substrate addition treatments. In upland soil， the additions of carbon and nitrogen had no significant effect on CH4 emissions but significantly increased CO2 emissions by 45%-109%. The additions of carbon and nitrogen had little effect on CO2 emissions in submerged paddy soil. The concurrent addition of carbon and nitrogen significantly increased CO2 emissions by 36% in drained paddy soil. The interactions between soil water change and N addition had no significant effect on CH4 emissions， while the interactions between soil water change and C and CN additions significantly affected CH4 emissions. No significant interactions between soil water change and C and N availability were observed for CO2 emissions. The conversion of submerged paddy to upland soil decreased soil pH， DOC， MBC， and NH4+-N contents but increased NO3--N content. The additions of carbon and nitrogen significantly affected soil biochemical properties. The results of correlation analysis showed that CH4 emissions were significantly positively correlated with soil pH， ΔMBC， and ΔNH4+-N and negatively correlated with ΔNO3--N among treatments. Conversely， CO2 emissions were significantly positively correlated with ΔNO3--N but negatively correlated with pH， ΔDOC， ΔMBC， and ΔNH4+-N. The changes of soil chemical and biological properties induced by soil water change and carbon and nitrogen availability were the main factors influencing CH4 and CO2 emissions from paddy soil. In summary， changes in soil water content and carbon and nitrogen availability affect CH4 and CO2 emissions by altering soil biochemical properties. Drainage of paddy soil is an effective measure to reduce CH4 emissions， but the risk of increased CO2 emissions during the short-term period upon drainage should be considered. Therefore， when developing strategies for rice paddy management， it is crucial to consider the combined effects of water and C and N management so as to achieve effective greenhouse gas mitigation and green and sustainable agricultural production.

Residential gas supply, gas losses and CO2 emissions in China

Greenhouse gas emission from direct seeding paddy field under different rice tillage systems in central China

Although lakes are important sources of methane (CH4) and carbon dioxide (CO2) to the atmosphere contributing to global warming, their CH4 and CO2 emissions are rarely assessed. In particular, increasing inputs of terrestrial dissolved organic carbon (DOC) may affect gas dynamics and alter seasonal changes in gas production. Here, we analysed variations in CH4 and CO2 dynamics in sub‐basins of an acidic bog lake, which was artificially divided into four quarters three decades ago, leading to divergence in water chemistry and biology. In the divided lake, only the south‐west basin (SW) received DOC inputs from an adjacent peat bog, while the north‐east basin (NE) was hydrologically disconnected. A year‐long determination of CH4 and CO2 production and emission patterns in the two contrasting basins exposed the indirect mechanisms by which DOC supply exercised control on greenhouse gas dynamics in this shallow lake. In both basins, dissolved CH4 was negatively correlated with dissolved oxygen (O2) through the water column, suggesting that aerobic methanotrophy is an important regulator of CH4 emissions in this lake. In contrast, the amount of CO2 stored in oxic and anoxic layers was not significantly different between the basins, suggesting that O2 is not the most important driver of dissolved CO2. Estimated total CH4 and CO2 emissions were 2.1 and 1.7 times lower in the NE basin than in the SW basin, with major CH4 and CO2 emissions occurring during the fall turnover. The differences in CH4 and CO2 emissions suggest that the hydro‐physical properties, namely seasonal temperature, the duration of stratification and O2 availability, are the main drivers of CH4 and CO2 emissions to the atmosphere from small shallow lakes under the influence of DOC inputs under global warming pressure.

Planting hedgerows on farm field edges can help mitigate greenhouse gas (GHG) emissions from agricultural landscapes by sequestering carbon (C) in woody biomass and in soil. Sequestration rates however, must be assessed in terms of their overall global warming potential (GWP) which must also consider GHG emissions. The objectives of this study were to (1) compare carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions from two types of hedgerows and adjacent annual agricultural production fields, and 2) better understand how climate, soil properties and plant species configurations affect hedgerow GHG emissions. At eight study sites in the lower Fraser River delta of British Columbia, we measured emissions from soil in both planted (P-Hedgerow) and remnant hedgerows (R-Hedgerow), as well as in adjacent annual crop production fields over 1 year using a closed-static chamber method. CO2 emissions were 59 % higher in P-Hedgerow than R-Hedgerow, yet there were no significant differences of relative emissions of CH4 and N2O. The environmental variables that explained the variation in emissions differed for the three GHGs. CO2 emissions were significantly correlated with soil temperature. CH4 and N2O and emissions were marginally significantly correlated with soil organic carbon (SOC) and soil water-filled pore space (WFPS), respectively. Emissions were not significantly correlated with hedgerow plant species diversity. While hedgerows sequester carbon in their woody biomass, we demonstrated that it is critical to measure hedgerow emissions to accurately ascertain their overall GHG mitigation potential. Our results show that there are no CO2e emission differences between the management options that plant new diverse hedgerows or conserve existing hedgerows.

Abstract. Uncertainties in the magnitude and seasonality of various gas emission modes, particularly among different lake types, limit our ability to estimate methane (CH4) and carbon dioxide (CO2) emissions from northern lakes. Here we assessed the relationship between CH4 and CO2 emission modes in 40 lakes along a latitudinal transect in Alaska to lakes' physicochemical properties and geographic characteristics, including permafrost soil type surrounding lakes. Emission modes included direct ebullition, diffusion, storage flux, and a newly identified ice-bubble storage (IBS) flux. We found that all lakes were net sources of atmospheric CH4 and CO2, but the climate warming impact of lake CH4 emissions was 2 times higher than that of CO2. Ebullition and diffusion were the dominant modes of CH4 and CO2 emissions, respectively. IBS, ~10% of total annual CH4 emissions, is the release to the atmosphere of seasonally ice-trapped bubbles when lake ice confining bubbles begins to melt in spring. IBS, which has not been explicitly accounted for in regional studies, increased the estimate of springtime emissions from our study lakes by 320%. Geographically, CH4 emissions from stratified, mixotrophic interior Alaska thermokarst (thaw) lakes formed in icy, organic-rich yedoma permafrost soils were 6-fold higher than from non-yedoma lakes throughout the rest of Alaska. The relationship between CO2 emissions and geographic parameters was weak, suggesting high variability among sources and sinks that regulate CO2 emissions (e.g., catchment waters, pH equilibrium). Total CH4 emission was correlated with concentrations of soluble reactive phosphorus and total nitrogen in lake water, Secchi depth, and lake area, with yedoma lakes having higher nutrient concentrations, shallower Secchi depth, and smaller lake areas. Our findings suggest that permafrost type plays important roles in determining CH4 emissions from lakes by both supplying organic matter to methanogenesis directly from thawing permafrost and by enhancing nutrient availability to primary production, which can also fuel decomposition and methanogenesis.

A review on the main affecting factors of greenhouse gases emission in constructed wetlands

Peat extraction substantially alters the carbon dynamics, peat structure, and hydrology of peatland sites. In Canada, companies install drainage ditches every ~30 m, dividing the sites into fields of peat bounded by ditches, and remove the surface vegetation and upper acrotelm. Peat is then vacuum harvested, processed, and sold for horticulture use. Despite this disturbance covering only a small percentage of Canadian peatlands, the shift from being a net sink to a net source of carbon during the 15-35 years of extraction makes them an important system to study.We conducted research at eight actively extracted peatland study sites in Quebec (Eastern Canada) and Alberta (Western Canada), ranging from 3–28 years post the start of extraction. Our objectives were to i) assess spatial distribution of CO2 and CH4 emissions; 2) assess seasonal and interannual variability of these emissions; and 3) understand their environmental drivers. To do this, we employed measurement techniques at the plot and ecosystem scale. Plot scale chamber-based measurements of CO2 and CH4 were conducted weekly to biweekly from May to September at eight sites from 2018 to 2022, with each site being measured in at least one study year. The drainage ditches were hotspots of carbon emissions with around double and at least seven times the CO2 and CH4 emissions respectively, of the fields. Time since the start of extraction was a useful metric to estimate current CO2 emissions when sites were within one bog complex. More research will be required to extrapolate emissions to other locations however, as peat substrate quality differences between locations also contributed to variation in carbon loss.Ecosystem scale measurements of daytime March to October CO2 and CH4 emissions were conducted at a subset of the study sites for two to three years using the eddy covariance technique. We observed comparable March and April CO2 emissions to those in July, highlighting the importance of thaw dynamics on the yearly carbon budget. Interannually, CO2 emissions were lowest during a dry summer, suggesting a moisture limitation for decomposition at the surface under severe drainage. We found weak dependence of CO2 emissions on soil temperature, though it was strongest when the water table was within the top 40 cm of the peat.This research will aid in validating Canada’s emission factor values for peat extraction, which are currently based on a few measurements in Quebec at post extracted, unrestored peatlands. Using several different assumptions for wintertime emissions, we estimated annual CO2 budget of 256 – 385 g C m-2 yr-1, which agrees with Canada’s current Tier 2 emission factor value of 310 g C m-2 yr-1. Methane emissions accounted for < 1 g C m-2 yr-1. This research will also support process-based models looking at the effect of site management, and the changing climate, on carbon emissions from these sites.

Municipal solid waste (MSW) landfills are among the major sources of greenhouse gas (GHG) emissions affecting global warming and the Earth’s climate. In Bulgaria, 53 regional non-hazardous waste landfills (RNHWL) are in operation, which necessitates conducting studies to determine the environmental risk from the emitted GHGs. This study attempted to assess the CH4 and CO2 emissions from three gas wells of a cell (in active and closed phases, each of 2.5 years duration) in an RNHWL, Harmanli (41°54′24.29″ N; 25°53′45.17″ E), based on monthly in situ measurements by portable equipment, using the Interrupted Time Series (ITS) ARMA model. The obtained results showed a significant variation of the CH4 and CO2 concentrations (2.06–15.1% v/v) and of the CH4 and CO2 emission rates (172.81–1762.76 kg/y) by gas wells (GWs), months and years, indicating the dynamics of the biodegradation of the deposited waste in the areas of the three GWs. Throughout most of the monitoring period (2018–2022), the CH4 concentrations were higher than the CO2 concentrations (% v/v), while CO2 emissions were lower than CH4 emissions (kg/y), a fact that could be explained by the differences in the mass of the two gases. The emissions rates of both gases from GW2 dominated over those from GW1 and GW3, giving a reason to determine the zone of GW2 as a hotspot of Cell-1. On the whole, CH4 and CO2 emission rates were higher in the winter (December–February) and partly in the spring (March–May) compared to summer–autumn (June–November). However, the CH4 and CO2 concentrations and emissions decreased drastically after the Cell-1 closure. The CH4/CO2 ratio (0.68–2.01) by months and gas wells demonstrated a great sensitivity, making it a suitable indicator for the assessment of organic waste biodegradation level in the landfills. The ITS ARMA model confirmed the negative and significant effect of the cell closure on CH4 and CO2 emissions; the correlations found between predicted and observed values were strong and positive (0.739–0.896).

Enhanced life cycle modelling of a micro gas turbine fuelled with various fuels for sustainable electricity production

Oil, coal, and gas account for approximately 80% of global primary energy, but only a portion of total airborne CO2eq (approx. 35% at GWP20 to 65% at GWP100), even though they account for 95% of total measured CO2 emissions. The benefits of these energy sources, as well as their related costs, are not all incorporated in current energy policy discussions. Global greenhouse gas policies must include documented changes in measured airborne CO2eq to avoid spending large amounts of public funds on ineffective or sub-optimal policies. The authors examined airborne CO2, which is less than half of emitted CO2, as well as reported CH4 emissions and the global warming potential of CH4 as published by the IPCC for coal and natural gas. The surprising conclusion is that surfaced-mined coal appears 'better for the climate' than the average natural gas, and all coal appears beneficial over LNG. Therefore, current CO2-only reduction policies and CO2 taxes are leading to unintended consequences and the switch from coal to natural gas, especially LNG, will not have the desired impact of reducing predicted future global warming; in fact, quite the contrary. A large portion of anthropogenic global warming is attributed by the IPCC and IEA to CH4, but it must be noted that CH4 emissions from natural sources and from agriculture account for approximately 40% and 25% of annual global CH4 emissions respectively. Energy accounts for about 20% of documented CH4 emissions. CO2 contributes only approximately 35% of annual airborne anthropogenic GHG emissions after accounting for CH4, over a 20-year horizon. At a 100-year horizon, the contribution of CO2 increases to approximately 60%. Energy policies that do not consider all GHG emissions along the entire value chain will lead to undesired economic and environmental distortions. All carbon taxation and CO2 pricing schemes are incorrect and need to be revised. At IPCC's GWP20 an approximately 2%1 higher loss of CH4 across the value chain prior to combustion of natural gas versus coal will lead to 'climate parity' of coal with natural gas. According to public data, natural gas value chains have high CH4 and undocumented CO2 losses. On a global average, using only IEA-documented CH4 data, natural gas emits approximately 15% more CO2eq than surface-mined coal, over a 20-year horizon. This difference will increase as the use of shale gas and LNG expands. Investors should support all energy systems in a manner that avoids an energy crisis, including intermittent renewable energy systems where they make sense. If CO2 emissions need to be reduced, one of the most effective ways would be to install ultra-supercritical power plants with CCUS technology. However, the undisputed benefits of increased CO2 concentrations in the atmosphere because of its promotion of photosynthesis and plant growth effects (fertilization) need to be considered in energy policy decisions as well. The authors suggest that future research and development should concentrate on reducing net emissions from fossil fuel power plants and providing cost-effective and reliable new conventional power generation capacity, utilizing clean coal and clean natural gas technology.

ABSTRACTDrained cultivated peatlands are recognized as substantial global carbon emission sources, prompting the exploration of water level elevation as a mitigation strategy. However, the efficacy of raised water table level (WTL) in Arctic/subarctic regions, characterized by continuous summer daylight, low temperatures and short growing seasons, remains poorly understood. This study presents a two‐year field experiment conducted at a northernmost cultivated peatland site in Norway. We used sub‐daily CO2, CH4, and N2O fluxes measured by automatic chambers to assess the impact of WTL, fertilization, and biomass harvesting on greenhouse gas (GHG) budgets and carbon balance. Well‐drained plots acted as GHG sources as substantial as those in temperate regions. Maintaining a WTL between −0.5 and −0.25 m effectively reduces CO2 emissions, without significant CH4 and N2O emissions, and can even result in a net GHG sink. Elevated temperatures, however, were found to increase CO2 emissions, potentially attenuating the benefits of water level elevation. Notably, high WTL resulted in a greater suppression of maximum photosynthetic CO2 uptake compared to respiration, and, yet caused lower net CO2 emissions due to a low light compensation point that lengthens the net CO2 uptake periods. Furthermore, the long summer photoperiod in the Arctic also enhanced net CO2 uptake and, thus, the efficacy of CO2 mitigation. Fertilization primarily enhanced biomass production without substantially affecting CO2 or CH4 emissions. Conversely, biomass harvesting led to a significant carbon depletion, even at a high WTL, indicating a risk of land degradation. These results suggest that while elevated WTL can effectively mitigate GHG emissions from cultivated peatlands, careful management of WTL, fertilization, and harvesting is crucial to balance GHG reduction with sustained agricultural productivity and long‐term carbon storage. The observed compatibility of GHG reduction and sustained grass productivity highlights the potential for future paludiculture implementation in the Arctic.

Climatic temperature controls the geographical patterns of coastal marshes greenhouse gases emissions over China

Climate change is one of the most pressing global challenges that could potentially threaten ecosystems, human populations, and weather patterns over time. Impacts including rising sea levels and soil salinization are caused by climate change, primarily driven by human activities such as fossil fuel combustion for energy production. The resulting greenhouse gas (GHG) emissions, particularly carbon dioxide (CO2) emissions, amplify the greenhouse effect and accelerate global warming, underscoring the urgent need for effective mitigation strategies. This study investigates the performance and outcomes of various machine learning regression models for predicting CO2 emissions. A comprehensive overview of performance metrics, including R2, mean absolute error, mean squared error, and root-mean-squared error, and cross-validation scores for decision tree, random forest, multiple linear regression, k-nearest neighbors, gradient boosting, and support vector regression models was conducted. The biggest source of CO2 emissions was coal (46.11%), followed by natural gas (25.49%) and electricity (26.70%). Random forest and gradient boosting both performed well, but multiple linear regression had the highest prediction accuracy among machine learning models (R2 = 0.98 training, 0.99 testing). Support vector regression (SVR) and k-nearest neighbors (KNN) demonstrated lower accuracies, whereas decision tree displayed overfitting. The decision tree, random forest, multiple linear regression, and gradient boosting models were found to be extremely sensitive to coal, natural gas, and petroleum (transportation sector) based on sensitivity analysis. Random forest and gradient boosting demonstrated the most sensitivity to coal usage, whereas KNN and SVR maintained excellent R2 scores (0.94–0.98) but were less susceptible to changes in the variables. This analysis provides insights into the agreement and discrepancies between predicted and actual CO2 emissions, highlighting the models’ effectiveness and potential limitations.