ABSTRACT
Methane (CH4) is the second most abundant greenhouse gas. China is the largest CH4 emitter in the world, with coal mine methane (CMM) being one of the main anthropogenic contributions. Thus, there is an urgent need for comprehensive estimates and strategies for reducing CMM emissions in China. However, the development of effective strategies is currently challenged by a lack of information on temporal variations in the contributions of different CMM sources and the absence of provincial spatial analysis. Here, considering five sources and utilization, we build a comprehensive inventory of China's CMM emissions from 1980 to 2022 and quantify the contributions of individual sources to the overall CMM emissions at the national and provincial levels. Our results highlight a significant shift in the source contributions of CMM emissions, with the largest contributor, underground mining, decreasing from 89% in 1980 to 69% in 2022. Underground abandoned coal mines, which were ignored or underestimated in past inventories, have become the second source of CMM emissions since 1999. From 2011 to 2022, we identified Shanxi, Guizhou, and Shaanxi as the three largest CMM-emitting provinces, while the Emissions Database for Global Atmospheric Research (EDGAR) v8 overestimated emissions from Inner Mongolia, ranking it third. Notably, we observed a substantial decrease (exceeding 1 Mt) in CMM emissions in Sichuan, Henan, Liaoning, and Hunan between 2011 and 2022, which was not captured by EDGAR v8. To develop targeted CMM emission reduction strategies at the provincial level, we classified 31 provinces into four groups based on their CMM emission structures. In 2022, the number of provinces with CMM emissions mainly from abandoned coal mines has exceeded that of provinces with mainly underground mines, which requires attention. This study reveals the characteristics of the source of CMM emissions in China and provides emission reduction directions for four groups of provinces.
ABSTRACT
As the most abundant greenhouse gas, atmospheric carbon dioxide (CO2) is considered one of the main attributors to climate change. Atmospheric CO2 concentrations can be measured by ground-based monitoring networks, mobile monitoring campaigns, and carbon-observing satellites. However, the worldwide ground-based monitoring networks are composed of sparsely distributed sites and are inadequate to represent the spatiotemporal distributions of CO2. Satellite-based remote sensing features repeated, long-term, and large-scale measurements, so it plays a crucial role in monitoring the global distributions of atmospheric CO2. However, due to the presence of heavy clouds (or aerosols) and the limitation of satellite orbiting tracks, there exist large amounts of missing data in satellite retrievals. Various methods, including chemical transport models (CTMs), geostatistical methods, and regression-based models, have been employed to derive full-coverage spatiotemporal distributions of CO2 based on the limited CO2 measurements. This review summarizes the strengths and limitations of these methods. However, CTMs simulation results can have high uncertainty due to imperfect knowledge of the real world, and the interpolation accuracy of all geostatistical methods is limited by the large amount of data gaps in current satellite retrieved CO2 products. To overcome these limitations, regression-based methods (especially machine learning models) have the ability to predict CO2 with superior predictive performance, so this review also summarizes the framework of the machine learning approach. Leveraging the ongoing advancements of satellite instrumentation, the satellite-based CO2 products have been improving dramatically in recent decades, and this review will describe and critically assess the advantages and disadvantages of the currently used systems in detail. For future improvements, we recommend the fusion of data from multiple satellite retrievals and CTMs by using machine learning algorithms in order to obtain even longer-term, larger-scale, finer-resolution, and higher-accuracy CO2 datasets.
