Monthly methane emissions in China from 2018-2024 based on source-level data.

Satellite-based emissions estimate indicates progress toward China's methane mitigation goals

China emits the most methane of any country worldwide, but there are large uncertainties in recent emissions trends, sources, and the potential impacts of policy actions. This study focuses on a period when the government initiated ambitious methane control efforts, linking sectoral policies with atmospheric evidence on sectoral, sub-national, and seasonal emissions during 2019--2024. We quantify daily methane emissions from China using a regional atmospheric inverse model with TROPOMI satellite observations. Our results reveal an average methane emissions increase rate of 0.3 Tg yr$^{-2}$ in Eastern&Central China, likely a milder trend than in the 2010s. Coal industry methane emissions intensity declined for the first time (-3.2% yr$^{-1}$) despite rising production, possibly associated with diverse policy instruments, mandates, and incentives. We further highlight two emerging challenges for future mitigation: leaks from expanding urban gas use amid the energy transition and rising agricultural emission yet with substantial uncertainty in estimates. Lastly, declining emissions intensity of coal mines points to the future role of targeted mandates and incentives in encouraging methane reduction for other sectors.

City-Scale, Source-Resolved Methane Inventories Reveal Drivers and Mitigation Pathways Across China’s Cities

City-scale, source-resolved methane (CH4) inventories are needed in China because prefecture-level cities differ in energy systems, agricultural activities, and waste management. These differences lead to divergent dominant sources and mitigation needs. Yet city-level CH4 inventories across the energy, agriculture, and waste sectors remain scarce. This scarcity limits policy design, targeting, and evaluation. Here we develop a harmonized, annually consistent, multisector, source-resolved CH4 inventory for 339 prefecture-level cities in China during 2018–2024. We further use Logarithmic Mean Divisia Index (LMDI) driver attribution and scenario analysis to examine recent emission drivers and explore possible mitigation pathways. Over this period, national anthropogenic CH4 emissions increased by 2.61%, with regional heterogeneity shaped by differences in energy dependence, livestock management, and waste treatment. LMDI decomposition identifies economic activity as the dominant positive driver. Emission-intensity effects alternated between mitigating and reinforcing impacts, while structural effects were minor and population effects varied across space and time. Scenario simulations suggest that integrated multisector strategies could reduce national CH4 from 61.69 Mt in 2024–51.26 Mt in 2030 and 22.26 Mt in 2060, corresponding to a 63.9% reduction. These reductions are primarily driven by energy-sector measures, complemented by improvements in agriculture and waste management. This data set and attribution inform city-specific mitigation planning and source targeting, support monitoring, reporting, and verification, and provide a baseline for benchmarking progress toward national methane targets.

Understanding the sources and variability of methane emissions in Madrid with Sentinel-5P TROPOMI, GHGSat, and aircraft observations

A long-term high-resolution national assessment of N2O emissions from Chinese lakes and reservoirs

Satellite-based estimation of ambient ammonia reveals meteorological influences and emission inventory uncertainties in South Korea.

Atmospheric ammonia (NH3) is an important precursor gas of secondary PM2.5; however, sparse ground-based NH3 monitoring limits the characterization of its spatiotemporal distribution and provides insufficient observational evidence for evaluating emission inventories. To address these gaps, this study developed a spatiotemporal model to estimate monthly ground-level NH3 concentrations at 15 km resolution across South Korea for 2013-2017. A two-stage framework combining a linear mixed-effects model (LMM) and a generalized additive model (GAM) was applied to refine NH3 information from Cross-track Infrared Sounder satellite observations. The LMM incorporated meteorological variables and the Clean Air Policy Support System emission inventory, while the GAM characterized residual spatial patterns not fully represented by these predictors. The model demonstrated robust performance, with cross-validation R2 = 0.73, mean absolute error = 0.15, and root mean squared error = 0.21. Estimated NH3 concentrations were highest in agricultural areas, increasing from March to June, then declined. In the LMM, all meteorological factors were significantly associated with monthly NH3 concentrations. Temperature showed the strongest association with NH3 from March to June, peaking in June (+18.9% per +1 °C), while relative humidity and wind speed had their largest effects in March (+2.1% per +1% RH and -18.7% per +1 m/s). The GAM captured month-specific LMM residual patterns and identified agricultural NH3 hotspots that may reflect emission inventory gaps. These findings improve understanding of meteorological and emission-related controls on NH3 concentrations and support refinement of emission inventories, agricultural hotspots identification, and improved future PM2.5 air pollution assessment under changing environmental conditions.

Whole-Site Quantification of Methane Emissions from 65 German Biogas Plants Reveals Systematic Underestimation in Current Emission Inventories

With around 9600 biogas plants, Germany has the largest biogas sector in Europe, where methane (CH4) losses can substantially counterbalance the climate benefits of this renewable energy source. Nevertheless, comprehensive empirical data on CH4 emissions and loss rates across different plant sizes and configurations in Germany remain limited. In this study, we quantified CH4 emissions at 65 biogas production sites using mobile measurement surveys combined with Gaussian plume dispersion modeling. Observed emission rates varied between 0.3 and 255 kg CH4 h–1, corresponding to size-dependent CH4 loss rates. Small plants averaged substantially higher losses of the produced CH4 (∼8.8%), whereas medium and large plants as well as upgrading facilities showed lower, more consistent loss rates of 2.9–4.3%. Using two different upscaling approaches, we estimate that German biogas plants emit approximately 360–370 kt CH4 yr–1, contributing ∼23% of Germany’s total CH4 emissions. These sector-wide emissions exceed bottom-up estimates, highlighting a systematic underestimation in current inventories. Our results represent one of the most comprehensive measurement-based assessments of CH4 emissions from German biogas infrastructure to date, showing that this method could improve national CH4 budgets, and develop and verify mitigation strategies. The observed emissions further underscore the need for leak reduction and mitigation measures to preserve the climate benefits of biogas plants.