One of the biggest sources of anthropogenic methane emissions is coal mining. Methane entrapped in coal seams and the surrounding strata is released during coal production. According to estimates from the US EPA (2019), 11% of all methane emissions from human activities worldwide come from the coal mining sector. Many studies contend that methane emissions from fossil fuels are currently underestimated. Furthermore, due to a lack of in-situ and field measurements, there is a considerable level of uncertainty surrounding these emissions.

This study has considered a large number of active coal mines in the Shanxi region, which is one of the significant coal mining regions of the globe, and has implemented a synergistic approach of using both top-down and bottom-up approaches plus validation from nearby ground-based measurements. The paper then goes on to develop correction factors to estimate the coal mine methane (CMM) emissions and compares these emissions with the commonly used CMM from the EDGAR and GFEI v2 datasets to address the biases and uncertainties associated with CMM emissions. This work therefore provides a platform by which a spatially, and temporally quantifiable set of CMM emissions can be obtained.

The major highlight and novelty of the paper is that it has conducted a robust uncertainty analysis, which is then used to create a bound on the CMM emissions on a mine-by-mine, type-by-type, and day-by-day basis. Both spatial as well as individual sites are analyzed, revealing that CMM is underestimated in general against currently widely used emission inventory datasets. In addition, the study approach also paves the way to correct top-down approaches and improve upon emission estimation uncertainties in general.

For these reasons, the paper provides extensive information and covers enough scenarios to produce the best set of observations possible, enabling policy makers to have the data needed to work towards CMM mitigation. This will allow for a more comprehensive and well-supported range of emissions to be controlled.

I would recommend the paper for publication and consideration as an excellent paper, after a few more specific details are elaborated upon:

(1) The AD and rank sourced from the http://nyj.shanxi.gov.cn/mkscnldxgscysxxgg/ggl which is only accessible in Chinese. If there is no English data or translation available, can this be mentioned more clearly?

(2) Could the ranking of mines and their corresponding coal emission types (used for EF calculation) be elaborated in a simpler way? What is the reason behind assuming Default mines EF can be weighted from high gas mine and low gas mine alone? Does this make a significant difference from a different assumption?

(3) Para 70. Typo error “emissions”

(4) As many other parts of the world, in particular India, the USA, Indonesia, Australia, and Russia also heavily depend on coal as a fossil fuel, they also actively contribute to global CMM. Across these regions, some have coal and geography similar to those in Shanxi, while others do not. How could the work herein be applied to these other regions? What changes would need to be adapted, and what methods and analytical techniques could be retained? Could the authors clarify which input data would be required as a baseline to adapt and replicate this approach (as different nations may have different ranking systems or produce different types of coal)? What is the overall potential for applying this strategy to other parts of the world?

Review of “Individual Coal Mine Methane Emissions Constrained by Eddy-Covariance Measurements: Low Bias and Missing Sources”.

This paper is conducted to quantify the emissions of methane from coal mines in Shanxi, China, which is treated as a significant source of methane emissions due to its high coal production. The research employs a combination of bottom-up and top-down methodologies to estimate coal mine methane (CMM) emissions. Data from 636 coal mines are scrutinized, and the measurements from an eddy-covariance flux tower is utilized to constrain the CMM results. When juxtaposed with global datasets such as EDGAR and GFEI v2, the study yields several intriguing findings.

The authors have presented an innovative set of methods to combine the in-situ observations to retrieve the CH₄ emissions in Shanxi Province with only one flux tower. However, there are so many complex landscapes in Shanxi Province, such as the Taihang Mountains, and valleys alongside the rivers, will this only one CH₄ flux tower data represent the real emissions within the whole Shanxi Province? As this is an innovative method to help gain emissions, the plausibility of applying its measurements to the other coal mines in Shanxi Province should be clearly clarified.

For the comparison of global scale CH4 data (EDGAR and GFEI) with the CMM results derived in this paper, the timing of these emission inventories could be taken into account to increase the reliability of the comparison results.

When the CMM results of this study is compared with those of Edgar and GFEI, it is observed that the emissions reported in this paper are derived from point sources, whereas the emissions reported by the other two inventories are aggregated within a grid range. Consequently, a pertinent question arises as to whether the point sources considered in this study encompass all coal mine sources within the grid. If not, it is plausible that the higher emissions reported by Edgar could be attributed to this discrepancy.

Besies, some minor comments should be corrected inside the manuscript.

Minor comments:

1. The line number is shown with mistakes so that I cannot label the issues or errors with the line number correctly, the authors should correct it.
2. More details of the time periods (55 days in two years) of the data used and the filtering method should be mentioned in Section 2.
3. Line 85 in Page 11, “flux-tower” should be “flux tower” which corresponds to the title of Section 2.3.
4. Line 22 in Page 12, “smapling” should be “sampling”.
5. Some sentences should be decreased to be clear. Such as, Line 38 in Page 13, “Since the differences between the measured high frequency CH4 fluxes is very small between 2021 and 2022”, and Line 60 in Page 14, “which have CMM emissions which are not known in terms of their geospatial locaction.”
6. Some writing or spelling errors in the context and captions of figures and tables, such as “whichi s much” in Line 70 in Page 29 and “R5)” in Line 2 in Page 25. The authors should recheck and correct them.
7. The CH₄ data in both EDGAR and GFEI are used to compare with the CMM results, but few details on the two emission inventories are mentioned in the manuscript.