Quantifying CH4emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method

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Abstract

China stands out as a major contributor to anthropogenic methane (CH4) emissions, with coal mine methane (CMM) playing a crucial role. To control and reduce CH4 emissions, China has made a dedicated commitment and formulated an ambitious mitigation plan. To verify the progress made, the consistent acquisition of independent CH4 emission data is required. This paper aims to implement a wind-assigned anomaly method for the precise determination of regional-scale CMM emissions within the coal-rich Shanxi province. We use the TROPOspheric Monitoring Instrument (TROPOMI) CH4 observations from May 2018 to May 2023, coupled with ERA5 wind and a bottom-up inventory dataset based on the IPCC (Intergovernmental Panel on Climate Change) Tier 2 approach covering the Changzhi, Jincheng, and Yangquan regions of the Shanxi province. The derived emission strengths are 8.4×1026molec.s-1 (0.706Tgyr-1, ±25%), 1.4×1027molec.s-1 (1.176Tgyr-1, ±20%), and 4.9×1026molec.s-1 (0.412Tgyr-1, ±21%), respectively. Our results exhibit biases of -18%, 8%, and 14%, respectively, when compared to the IPCC Tier 2 bottom-up inventory. Larger discrepancies are found when comparing the estimates to the Copernicus Atmosphere Monitoring Service global anthropogenic emissions (CAMS-GLOB-ANT) and Emissions Database for Global Atmospheric Research (EDGARv7.0) inventories (64%-176%), suggesting that the two inventories may be overestimating CH4 emissions from the studied coal mining regions. Our estimates provide a comprehensive characterization of the regions within the Shanxi province, contribute to the validation of emission inventories, and provide additional insights into CMM emission mitigation.

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APA

Tu, Q., Hase, F., Qin, K., Cohen, J. B., Khosrawi, F., Zou, X., … Lu, F. (2024). Quantifying CH4emissions from coal mine aggregation areas in Shanxi, China, using TROPOMI observations and the wind-assigned anomaly method. Atmospheric Chemistry and Physics, 24(8), 4875–4894. https://doi.org/10.5194/acp-24-4875-2024

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