Individual Coal Mine Methane Emissions Constrained by Eddy-Covariance Measurements: Low Bias and Missing Sources

Abstract. China’s Shanxi Province accounts for 12 % of global coal output, and therefore is responsible for a very large fraction of the total global methane (CH₄) emissions, as well as being a large source of uncertainty due to the lack of in-situ and field measurements. This work introduces the first comprehensive attempt to compute the coal mine methane emissions (CMM) throughout Shanxi, using a mixture of bottom-up and top-down approaches. First, public and private data from 636 individual coal mines in Shanxi Province were analyzed following the IPCC Tier 2 approach, using three to five sets of observed emission factors, and rank information based on methods issued by the National Coal Mine Safety Administration and the National Energy Administration, to compile a range of bottom-up CMM on a mine-by-mine basis. An eddy-covariance tower is set up near the output flue of a well-characterized high rank coal mine in Changzhi, and used to produce an average observed CH₄ flux over two two-month long periods (Winter 2021 and Autumn 2022). The observed half-hourly CH₄ flux variability is found to be roughly stable over the entire observed time, and is subsequently used to produce a set of scaling factors (RATIO correction) to updating the preliminary bottom-up coal mine methane emissions to account for both bias and high-frequency temporal variabiliy. The resulting emissions dataset have been compared against commonly used global CMM datasets including EDGAR and GFEI v2, and yield three unique scientific conclusions. First, their total CH₄ emissions over Shanxi lie in between this work’s 50^th percentile and 70^th percentile range, meaning they are slightly high. Second, both datasets have a very large amount of emissions which occur where there are no coal mines and no CH₄ emitting industry, indicating that there are significant spatial disparities, with the overlapped portion of CMM emissions where mines exist consistently close to the 30^th percentile of this work’s emissions, meaning they underestimate CMM in general on a mine-by-mine basis. Third, some of the mines have average emissions values which are more than the 90^th percentile of the computed mine-by-mine emissions, while many are far below the 10^th percentile, showing that there is a significant issue with the sampling not capturing the observed temporal variability. It is hoped that this mine-by-mine and high frequency approximation of CMM emissions can improve both top-down observation campaigns as well as provide quantitative support and identification of mitigation opportunities.