Methane budget of East Asia, 1990-2015: A bottom-up evaluation

Dynamic Methane Emissions from China's Fossil-Fuel and Food Systems: Socioeconomic Drivers and Policy Optimization Strategies

In response to the 2023 "Action Plan for Methane Emission Control" in China, which mandates precise methane (CH4) emission accounting, we developed a dynamic model to estimate CH4 emissions from fossil-fuel and food systems in China for the period 1990-2020. We also analyzed their socioeconomic drivers through the Logarithmic Mean Divisia Index (LMDI) model. Our analysis revealed an accelerated emission increase (850.4 Gg/year) during 2005-2015, compared to 570.4 Gg/year in the preceding period (1990-2005), with a downward trend (-1216.6 Gg/year) detected after 2015. The fossil-fuel system was the primary contributor to these changes, with emissions positively correlated with per capita GDP and negatively influenced by energy intensity at the production stage and wastewater discharge intensity at the disposal stage. In the food system, CH4 emission intensity and waste treatment practices were the most significant negative drivers at production and disposal stages, respectively. Urbanization also played a notable role, contributing to 19.3% and 18.1% in livestock and rice cultivation emission reductions, respectively. Despite the observed changes, coal mining, livestock, and rice remain the dominant sources of CH4 emissions. Our findings suggest that effective CH4 emission mitigation can be achieved through strategies such as reducing energy intensity, improving agricultural production efficiency, and advancing urbanization efforts.

Nitrite-dependent anaerobic methane oxidation (N-DAMO) in global aquatic environments: A review

The vast majority of processes in the carbon and nitrogen cycles are driven by microorganisms. The nitrite-dependent anaerobic oxidation of methane (N-DAMO) process links carbon and nitrogen cycles, offering a novel approach for the simultaneous reduction of methane emissions and nitrite pollution. However, there is currently no comprehensive summary of the current status of the N-DAMO process in natural aquatic environments. Therefore, our study aims to fill this knowledge gap by conducting a comprehensive review of the global research trends in N-DAMO processes in various aquatic environments (excluding artificial bioreactors). Our review mainly focused on molecular identification, global study sites, and their interactions with other elemental cycling processes. Furthermore, we performed a data integration analysis to unveil the effects of key environmental factors on the abundance of N-DAMO bacteria and the rate of N-DAMO process. By combining the findings from the literature review and data integration analysis, we proposed future research perspectives on N-DAMO processes in global aquatic environments. Our overarching goal is to advance the understanding of the N-DAMO process and its role in synergistically reducing carbon emissions and removing nitrogen. By doing so, we aim to make a significant contribution to the timely achievement of China's carbon peak and carbon neutrality targets.

Evaluation of Korean methane emission sources with satellite retrievals by spatial correlation analysis

Methane is a significant greenhouse gas (GHG), and it is imperative to understand its spatiotemporal distribution and primary sources in areas with higher methane concentrations, as such insights are essential for informing effective mitigation policies. In this study, we employed TROPOMI satellite retrievals to analyze the spatiotemporal patterns of methane distributions and identify major emission sources in South Korea over the period from August 2018 to July 2019. Additionally, we examined the spatial correlations between satellite methane retrievals and emission sources to characterize regions with higher methane levels on an annual basis.Concerning spatial distributions, concentrations exceeding 1870 ppb were predominantly observed in western non-mountainous regions, particularly in rice paddy areas. Moreover, sporadic concentrations exceeding 1880 ppb were detected in large ports and industrial zones, primarily located in coastal regions of South Korea.Our spatial correlation analysis, conducted using the SDMSelect method, identified specific emissions contributing to regions with higher methane concentrations. There were some areas with relatively strong correlations between high XCH4 and emissions from the domestic livestock industry, fossil fuel utilization (specifically, the oil and gas sector), landfills, and rice paddies. This analysis, incorporating domestic emission inventories and satellite data, provides valuable insights into the characteristics of regional methane concentrations. In addition, this analysis can assess national methane emissions inventories, where there is limited information on the spatial distributions, offering critical information for the prioritization of domestic regional policies aimed at reducing greenhouse gas emissions.

Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations on Hateruma and Yonaguni Islands, Japan

We developed a near-real-time estimation method for temporal changes in fossil fuel CO₂ (FFCO₂) emissions from China for 3 months [January, February, March (JFM)] based on atmospheric CO₂ and CH₄ observations on Hateruma Island (HAT, 24.06° N, 123.81° E) and Yonaguni Island (YON, 24.47° N, 123.01° E), Japan. These two remote islands are in the downwind region of continental East Asia during winter because of the East Asian monsoon. Previous studies have revealed that monthly averages of synoptic-scale variability ratios of atmospheric CO₂ and CH₄ (ΔCO₂/ΔCH₄) observed at HAT and YON in JFM are sensitive to changes in continental emissions. From the analysis based on an atmospheric transport model with all components of CO₂ and CH₄ fluxes, we found that the ΔCO₂/ΔCH₄ ratio was linearly related to the FFCO₂/CH₄ emission ratio in China because calculating the variability ratio canceled out the transport influences. Using the simulated linear relationship, we converted the observed ΔCO₂/ΔCH₄ ratios into FFCO₂/CH₄ emission ratios in China. The change rates of the emission ratios for 2020-2022 were calculated relative to those for the preceding 9-year period (2011-2019), during which relatively stable ΔCO₂/ΔCH₄ ratios were observed. These changes in the emission ratios can be read as FFCO₂ emission changes under the assumption of no interannual variations in CH₄ emissions and biospheric CO₂ fluxes for JFM. The resulting average changes in the FFCO₂ emissions in January, February, and March 2020 were 17 ± 8%, - 36 ± 7%, and - 12 ± 8%, respectively, (- 10 ± 9% for JFM overall) relative to 2011-2019. These results were generally consistent with previous estimates. The emission changes for January, February, and March were 18 ± 8%, - 2 ± 10%, and 29 ± 12%, respectively, in 2021 (15 ± 10% for JFM overall) and 20 ± 9%, - 3 ± 10%, and - 10 ± 9%, respectively, in 2022 (2 ± 9% for JFM overall). These results suggest that the FFCO₂ emissions from China rebounded to the normal level or set a new high record in early 2021 after a reduction during the COVID-19 lockdown. In addition, the estimated reduction in March 2022 might be attributed to the influence of a new wave of COVID-19 infections in Shanghai.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s40645-023-00542-6.

Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use

The dramatic increase of natural gas use in China, as a substitute for coal, helps to reduce CO₂ emissions and air pollution, but the climate mitigation benefit can be offset by methane leakage into the atmosphere. We estimate methane emissions from 2010 to 2018 in four regions of China using the GOSAT satellite data and in-situ observations with a high-resolution (0.1° × 0.1°) inverse model and analyze interannual changes of emissions by source sectors. We find that estimated methane emission over the north-eastern China region contributes the largest part (0.77 Tg CH₄ yr^-1) of the methane emission growth rate of China (0.87 Tg CH₄ yr^-1) and is largely attributable to the growth in natural gas use. The results provide evidence of a detectable impact on atmospheric methane observations by the increasing natural gas use in China and call for methane emission reductions throughout the gas supply chain and promotion of low emission end-use facilities.

Evaluation of comprehensive monthly-gridded methane emissions from natural and anthropogenic sources in China

The observed atmospheric methane (CH₄) concentration in China has grown rapidly in recent years, showing marked spatial-temporal variation. However, existing inventories, most of which are yearly, provincial, and incomplete, have failed to reflect the spatial variation and seasonal trends of CH₄ emissions. This study aims to develop a high-resolution (0.05° × 0.05°) monthly inventory of CH₄ emissions across China in 2015 from eight major natural and anthropogenic sources. The inventory evaluation of CH₄ emissions was based on the gridded activity data and high spatial-temporal resolution emission factors, which were estimated by their relationship with environmental factors in most source sectors. The results showed that the annual CH₄ emissions across China were 61.65 Tg, of which 85% was associated with anthropogenic emissions. Energy activities, livestock, and paddy fields were the largest contributors, accounting for 31% (19.06 Tg), 24% (15.01 Tg) and 19% (11.45 Tg) of the total emissions respectively, followed by vegetation (7%, 4.52 Tg), wetlands (7%, 4.20 Tg), wastewater (6%, 3.43 Tg), municipal solid waste, (4%, 2.59 Tg) and biomass burning (2%, 1.40 Tg). However, these proportions varied by month; paddy fields, vegetation, and wetlands emitted the most CH₄ in July and August with approximately 29%, 14%, and 8% of total emissions, respectively, and least in January and December with 0%, 2%, and 2%, respectively, leading to a CH₄ emissions peak in summer and a valley in winter. Moreover, the major contributing provinces of CH₄ emissions in China were Inner Mongolia, Shanxi, Sichuan, Guizhou, and Hunan, accounting for 33% of China's total emissions. The dominant emission sources were energy activities in Mongolia, Shanxi, and Guizhou; livestock in Sichuan; and paddy fields in Hunan. This improved inventory of CH₄ emissions can help understanding the spatial-temporal variation of CH₄ concentration in the atmosphere and formulating regional-seasonal-specific emission reduction policies.

GOSAT CH4 Vertical Profiles over the Indian Subcontinent: Effect of a Priori and Averaging Kernels for Climate Applications

We examined methane (CH4) variability over different regions of India and the surrounding oceans derived from thermal infrared (TIR) band observations (TIR CH4) by the Thermal and Near-infrared Sensor for carbon Observation—Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) for the period 2009–2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, on the basis of the averaging kernel (AK) functions and a priori assumptions, as applied to the simulated concentrations by the MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM). We stress that this is of particular importance when the satellite-derived products are analyzed using different ACTMs other than those used as retrieved a priori. A comparison of modeled and retrieved CH4 vertical profiles shows that the GOSAT/TANSO-FTS TIR instrument has sufficient sensitivity to provide critical information about the transport of CH4 from the top of the boundary layer to the upper troposphere. The mean mismatch between TIR CH4 and model is within 50 ppb, except for the altitude range above 150 hPa, where the sensitivity of TIR CH4 observations becomes very low. Convolved model profiles with TIR CH4 AK reduces the mismatch to less than the retrieval uncertainty. Distinct seasonal variations of CH4 have been observed near the atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (300 hPa) over the northern and southern regions of India, corresponding to the southwest monsoon (July–September) and post-monsoon (October–December) seasons. Analysis of the transport and emission contributions to CH4 suggests that the CH4 seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. The major contrast between monsoon, and pre- and post-monsoon profiles of CH4 over Indian regions are noticed near the boundary layer heights, which is mainly caused by seasonal change in local emission strength with a peak during summer due to increased emissions from the paddy fields and wetlands. A strong difference between seasons in the middle and upper troposphere is caused by convective transport of the emission signals from the surface and redistribution in the monsoon anticyclone of upper troposphere. TIR CH4 observations provide additional information on CH4 in the region compared to what is known from in situ data and total-column (XCH4) measurements. Based on two emission sensitivity simulations compared to TIR CH4 observations, we suggest that the emissions of CH4 from the India region were 51.2 ± 4.6 Tg year−1 during the period 2009–2014. Our results suggest that improvements in the a priori profile shape in the upper troposphere and lower stratosphere (UT/LS) region would help better interpretation of CH4 cycling in the earth’s environment.

Mapping Paddy Fields in Japan by Using a Sentinel-1 SAR Time Series Supplemented by Sentinel-2 Images on Google Earth Engine

Paddy fields play very important environmental roles in food security, water resource management, biodiversity conservation, and climate change. Therefore, reliable broad-scale paddy field maps are essential for understanding these issues related to rice and paddy fields. Here, we propose a novel paddy field mapping method that uses Sentinel-1 synthetic aperture radar (SAR) time series that are robust for cloud cover, supplemented by Sentinel-2 optical images that are more reliable than SAR data for extracting irrigated paddy fields. Paddy fields were provisionally specified by using the Sentinel-1 SAR data and a conventional decision tree method. Then, an additional mask using water and vegetation indexes based on Sentinel-2 optical images was overlaid to remove non-paddy field areas. We used the proposed method to develop a paddy field map for Japan in 2018 with a 30 m spatial resolution. The producer’s accuracy of this map (92.4%) for non-paddy reference agricultural fields was much higher than that of a map developed by the conventional method (57.0%) using only Sentinel-1 data. Our proposed method also reproduced paddy field areas at the prefecture scale better than existing paddy field maps developed by a remote sensing approach.