the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Oct 2023
Quantifying CH4 emissions from coal mine aggregation areas in Shanxi, China using TROPOMI observations and the wind-assigned anomaly method
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 process 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 covering the Changzhi, Jincheng and Yangquan regions. The derived emission strengths are 8.4× 1026 ± 1.6 × 1025 molec. s-1 (0.706 ± 0.013 Tg yr-1), 1.4 × 1027 ± 1.9 × 1025 molec. s-1 (1.176 ± 0.016 Tg yr-1), and 4.9 × 1026± 1.8 × 1025 molec. s-1 (0.412 ± 0.015 Tg yr-1), respectively. Our results exhibit biases of -18 %, 8 %, and 14 % when compared to the bottom-up inventory. Larger discrepancies are found when comparing the estimates to the CAMS-GLOB-ANT and EDGARv7.0 inventories. This suggests that the two inventories may be overestimating the CH4 emissions in the Jincheng and Yangquan regions. Our estimates provide a comprehensive characterization of the regions within the Shanxi province, contribute to the validation of emission inventories, and help to develop climate mitigation strategies.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-2155', Anonymous Referee #2, 10 Nov 2023
Please find the full comment in the Supplement pdf file.
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AC1: 'Reply on RC1', Qiansi Tu, 27 Jan 2024
We would like to thank reviewer #2 for taking the time to review this manuscript and for providing valuable, constructive feedback and corresponding suggestions that helped us to further improve the manuscript.
In this author's comment, all the points raised by the reviewer are copied here one by one and shown in blue color, along with the corresponding reply from the authors in black.
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AC1: 'Reply on RC1', Qiansi Tu, 27 Jan 2024
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RC2: 'Comment on egusphere-2023-2155', Anonymous Referee #1, 14 Nov 2023
Tu et al. present an analysis of TROPOMI methane observations over the coal-rich Shanxi province of China. They use their wind-assigned anomaly method to quantify regional methane emissions for three clusters of Shanxi coal mines. They compare their estimates with three bottom-up emission inventories, EDGAR v7.0, CAMS-GLOB-ANT, and a measurement-based coal mine methane inventory by Qin et al. (2023). They find good agreement with the Qin et al. estimates but much lower emissions (~factor of 2-3) than reported in the EDGAR and CAMS inventories.
The paper is interesting and a good fit for ACP, but in my view substantial changes are needed before it can be published. I see two major weaknesses. First, the methods need to be explained in much more detail, not merely by pointing to previous publications. I found it very difficult to follow the discussion of results because the paper does not adequately explain the wind-assigned anomaly method and its interpretation. Second, the uncertainty analysis appears to be incomplete. The authors report uncertainties <4% (<2% in two of three cases) on their regional methane emission estimates inferred from TROPOMI. These values are unrealistically low; regional emission errors reported elsewhere in the literature are routinely in the range ~20%-30%. I am therefore left with the impression that the authors have overlooked important sources of error, for example having to do with background subtraction and wind speed.
Specific comments
- L. 18-19: The reported uncertainties (<4%) are unrealistically small. Uncertainty in regional emissions derived from TROPOMI tend to be in the 20%-30% range (or more). There must be other, larger sources of error besides what is reported.
- Shen et al. (2022) used TROPOMI to estimate methane emissions for ~20 US oil and gas basins and reported mean errors of 30% based on an elaborate uncertainty analysis (https://acp.copernicus.org/articles/22/11203/2022/). Error bars for emissions from individual countries estimated by Shen et al. (2023) are of similar magnitude (https://www.nature.com/articles/s41467-023-40671-6). TROPOMI analyses by Cusworth et al. (2022), Chen et al. (2023), and many others found similar results.
- L. 20: Which bottom-up inventory?
- L. 21-22: That may be, but it’s not entirely clear given the unrealistically small uncertainties reported for the TROPOMI emission estimates.
- L. 23: How do the estimates help to develop climate mitigation strategies?
- L. 31: Would it not be more accurate to say that China is “the leading emitter”, rather than just “one of” them?
- L. 33: China did not sign the 2021 Global Methane Pledge, so for clarity it would be best to use another word besides “pledge” here.
- L. 33-35: It would be useful to include a reference for the Glasgow Agreement. Perhaps something like this 2021 US State Department press release: https://www.state.gov/u-s-china-joint-glasgow-declaration-on-enhancing-climate-action-in-the-2020s/
- L. 68: It’s unclear what “solar radiation […] radiated from the Earth” means.
- L. 88: What wind speed is used? The speed at 10-m? 50-m? Something else?
- L. 95: Regions of China or of Shanxi?
- Figure 1: Suggest increasing font size for legibility.
- L. 104-105: What are those estimates by Qin et al. (2023) based on? A brief description of the dataset would be valuable.
- L. 105-106: I do not understand the sentence beginning “Near 30 small coal mines…”
- L. 110: Qin et al. (2023) used eddy covariance measurements to construct their facility-scale inventory. Would it be appropriate to describe their work as a measurement-based inventory of coal mine emissions?
- Subsection 3.2: Suggest moving this subsection to section 2 (data and methods), including description of the Qin et al. (2023) dataset.
- Figure 4 (left): A log y-scale would be helpful here.
- Section 3.3 and Fig. 5: Significantly more explanation is needed on how the wind-assigned anomalies are calculated and how the wind direction segmentation is performed and what these things mean. The methods section on the wind assigned anomaly method only describes the cone plume model. Section 3.3 is very difficult to follow, and readers shouldn’t need to read the authors’ previous papers to understand what is going on; the paper should be readable on its own. It’s unclear to me what the middle panel of Fig. 5 is showing. How are the estimated emissions distributed between the different coal mines within a cluster? How are the emissions calculated from the TROPOMI wind-assigned anomalies? Are all the mines scaled up/down together following the spatial distribution of the underlying inventory?
- How is the TROPOMI methane background subtracted? Background subtraction tends to be a major source of error in regional emission estimation.
- L. 231: What “calculated average” value is being reported here? It’s unclear what the ppb values refer to.
- The uncertainty analysis varying the plume model, wind product, and inventory is a good start, but not sufficient. What is the sensitivity to background subtraction scheme? What about wind speed levels (e.g., using the 10 m wind rather than 100 m)?
- How are the current error values calculated? Do they represent 1-sigma errors? Reporting the uncertainty as the range of estimates from a broader estimation ensemble might be clearer.
- L. 246-250: These sentences are contradictory. Should the first sentence only refer to the “bottom-up” inventory (which, again, does not seem to me to be a “bottom-up” inventory – rather a measurement-based inventory).
Typos
- 14: “process” → “progress” ?
- 27: “emission” → “emissions”
- 51: “emissions” → “emission estimates”
- 53: “from satellite” → “from satellites”
- 70: “unprecedented high spatial resolution…” → “unprecedented combination of high spatial resolution…”
- 6: “bottum” → “bottom”
- 243: “achived” → “achieved” ?
- 252: “boarded” → “bordered” ?
References
Chen, Z., Jacob, D. J., Gautam, R., Omara, M., Stavins, R. N., Stowe, R. C., Nesser, H., Sulprizio, M. P., Lorente, A., Varon, D. J., Lu, X., Shen, L., Qu, Z., Pendergrass, D. C., and Hancock, S.: Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action, Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, 2023.
Cusworth, D. H., Thorpe, A. K., Ayasse, A. K., Stepp, D., Heckler, J., Asner, G. P., Miller, C. E., Chapman, J. W., Eastwood, M. L., Green, R. O., Hmiel, B., Lyon, D., and Duren, R. M.: Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the U.S., Earth ArXiv, https://doi.org/10.31223/X53P88, 2022.
Shen, L., Gautam, R., Omara, M., Zavala-Araiza, D., Maasakkers, J. D., Scarpelli, T. R., Lorente, A., Lyon, D., Sheng, J., Varon, D. J., Nesser, H., Qu, Z., Lu, X., Sulprizio, M. P., Hamburg, S. P., and Jacob, D. J.: Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins, Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, 2022.
Shen, L., Jacob, D.J., Gautam, R. et al. National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations. Nat Commun 14, 4948 (2023). https://doi.org/10.1038/s41467-023-40671-6
Citation: https://doi.org/10.5194/egusphere-2023-2155-RC2 -
AC2: 'Reply on RC2', Qiansi Tu, 27 Jan 2024
We would like to thank reviewer #1 for taking the time to review this manuscript and for providing valuable, constructive feedback and corresponding suggestions that helped us to further improve the manuscript.
In this author's comment, all the points raised by the reviewer are copied here one by one and shown in blue color, along with the corresponding reply from the authors in black.
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2155', Anonymous Referee #2, 10 Nov 2023
Please find the full comment in the Supplement pdf file.
-
AC1: 'Reply on RC1', Qiansi Tu, 27 Jan 2024
We would like to thank reviewer #2 for taking the time to review this manuscript and for providing valuable, constructive feedback and corresponding suggestions that helped us to further improve the manuscript.
In this author's comment, all the points raised by the reviewer are copied here one by one and shown in blue color, along with the corresponding reply from the authors in black.
-
AC1: 'Reply on RC1', Qiansi Tu, 27 Jan 2024
-
RC2: 'Comment on egusphere-2023-2155', Anonymous Referee #1, 14 Nov 2023
Tu et al. present an analysis of TROPOMI methane observations over the coal-rich Shanxi province of China. They use their wind-assigned anomaly method to quantify regional methane emissions for three clusters of Shanxi coal mines. They compare their estimates with three bottom-up emission inventories, EDGAR v7.0, CAMS-GLOB-ANT, and a measurement-based coal mine methane inventory by Qin et al. (2023). They find good agreement with the Qin et al. estimates but much lower emissions (~factor of 2-3) than reported in the EDGAR and CAMS inventories.
The paper is interesting and a good fit for ACP, but in my view substantial changes are needed before it can be published. I see two major weaknesses. First, the methods need to be explained in much more detail, not merely by pointing to previous publications. I found it very difficult to follow the discussion of results because the paper does not adequately explain the wind-assigned anomaly method and its interpretation. Second, the uncertainty analysis appears to be incomplete. The authors report uncertainties <4% (<2% in two of three cases) on their regional methane emission estimates inferred from TROPOMI. These values are unrealistically low; regional emission errors reported elsewhere in the literature are routinely in the range ~20%-30%. I am therefore left with the impression that the authors have overlooked important sources of error, for example having to do with background subtraction and wind speed.
Specific comments
- L. 18-19: The reported uncertainties (<4%) are unrealistically small. Uncertainty in regional emissions derived from TROPOMI tend to be in the 20%-30% range (or more). There must be other, larger sources of error besides what is reported.
- Shen et al. (2022) used TROPOMI to estimate methane emissions for ~20 US oil and gas basins and reported mean errors of 30% based on an elaborate uncertainty analysis (https://acp.copernicus.org/articles/22/11203/2022/). Error bars for emissions from individual countries estimated by Shen et al. (2023) are of similar magnitude (https://www.nature.com/articles/s41467-023-40671-6). TROPOMI analyses by Cusworth et al. (2022), Chen et al. (2023), and many others found similar results.
- L. 20: Which bottom-up inventory?
- L. 21-22: That may be, but it’s not entirely clear given the unrealistically small uncertainties reported for the TROPOMI emission estimates.
- L. 23: How do the estimates help to develop climate mitigation strategies?
- L. 31: Would it not be more accurate to say that China is “the leading emitter”, rather than just “one of” them?
- L. 33: China did not sign the 2021 Global Methane Pledge, so for clarity it would be best to use another word besides “pledge” here.
- L. 33-35: It would be useful to include a reference for the Glasgow Agreement. Perhaps something like this 2021 US State Department press release: https://www.state.gov/u-s-china-joint-glasgow-declaration-on-enhancing-climate-action-in-the-2020s/
- L. 68: It’s unclear what “solar radiation […] radiated from the Earth” means.
- L. 88: What wind speed is used? The speed at 10-m? 50-m? Something else?
- L. 95: Regions of China or of Shanxi?
- Figure 1: Suggest increasing font size for legibility.
- L. 104-105: What are those estimates by Qin et al. (2023) based on? A brief description of the dataset would be valuable.
- L. 105-106: I do not understand the sentence beginning “Near 30 small coal mines…”
- L. 110: Qin et al. (2023) used eddy covariance measurements to construct their facility-scale inventory. Would it be appropriate to describe their work as a measurement-based inventory of coal mine emissions?
- Subsection 3.2: Suggest moving this subsection to section 2 (data and methods), including description of the Qin et al. (2023) dataset.
- Figure 4 (left): A log y-scale would be helpful here.
- Section 3.3 and Fig. 5: Significantly more explanation is needed on how the wind-assigned anomalies are calculated and how the wind direction segmentation is performed and what these things mean. The methods section on the wind assigned anomaly method only describes the cone plume model. Section 3.3 is very difficult to follow, and readers shouldn’t need to read the authors’ previous papers to understand what is going on; the paper should be readable on its own. It’s unclear to me what the middle panel of Fig. 5 is showing. How are the estimated emissions distributed between the different coal mines within a cluster? How are the emissions calculated from the TROPOMI wind-assigned anomalies? Are all the mines scaled up/down together following the spatial distribution of the underlying inventory?
- How is the TROPOMI methane background subtracted? Background subtraction tends to be a major source of error in regional emission estimation.
- L. 231: What “calculated average” value is being reported here? It’s unclear what the ppb values refer to.
- The uncertainty analysis varying the plume model, wind product, and inventory is a good start, but not sufficient. What is the sensitivity to background subtraction scheme? What about wind speed levels (e.g., using the 10 m wind rather than 100 m)?
- How are the current error values calculated? Do they represent 1-sigma errors? Reporting the uncertainty as the range of estimates from a broader estimation ensemble might be clearer.
- L. 246-250: These sentences are contradictory. Should the first sentence only refer to the “bottom-up” inventory (which, again, does not seem to me to be a “bottom-up” inventory – rather a measurement-based inventory).
Typos
- 14: “process” → “progress” ?
- 27: “emission” → “emissions”
- 51: “emissions” → “emission estimates”
- 53: “from satellite” → “from satellites”
- 70: “unprecedented high spatial resolution…” → “unprecedented combination of high spatial resolution…”
- 6: “bottum” → “bottom”
- 243: “achived” → “achieved” ?
- 252: “boarded” → “bordered” ?
References
Chen, Z., Jacob, D. J., Gautam, R., Omara, M., Stavins, R. N., Stowe, R. C., Nesser, H., Sulprizio, M. P., Lorente, A., Varon, D. J., Lu, X., Shen, L., Qu, Z., Pendergrass, D. C., and Hancock, S.: Satellite quantification of methane emissions and oil–gas methane intensities from individual countries in the Middle East and North Africa: implications for climate action, Atmos. Chem. Phys., 23, 5945–5967, https://doi.org/10.5194/acp-23-5945-2023, 2023.
Cusworth, D. H., Thorpe, A. K., Ayasse, A. K., Stepp, D., Heckler, J., Asner, G. P., Miller, C. E., Chapman, J. W., Eastwood, M. L., Green, R. O., Hmiel, B., Lyon, D., and Duren, R. M.: Strong methane point sources contribute a disproportionate fraction of total emissions across multiple basins in the U.S., Earth ArXiv, https://doi.org/10.31223/X53P88, 2022.
Shen, L., Gautam, R., Omara, M., Zavala-Araiza, D., Maasakkers, J. D., Scarpelli, T. R., Lorente, A., Lyon, D., Sheng, J., Varon, D. J., Nesser, H., Qu, Z., Lu, X., Sulprizio, M. P., Hamburg, S. P., and Jacob, D. J.: Satellite quantification of oil and natural gas methane emissions in the US and Canada including contributions from individual basins, Atmos. Chem. Phys., 22, 11203–11215, https://doi.org/10.5194/acp-22-11203-2022, 2022.
Shen, L., Jacob, D.J., Gautam, R. et al. National quantifications of methane emissions from fuel exploitation using high resolution inversions of satellite observations. Nat Commun 14, 4948 (2023). https://doi.org/10.1038/s41467-023-40671-6
Citation: https://doi.org/10.5194/egusphere-2023-2155-RC2 -
AC2: 'Reply on RC2', Qiansi Tu, 27 Jan 2024
We would like to thank reviewer #1 for taking the time to review this manuscript and for providing valuable, constructive feedback and corresponding suggestions that helped us to further improve the manuscript.
In this author's comment, all the points raised by the reviewer are copied here one by one and shown in blue color, along with the corresponding reply from the authors in black.
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Qiansi Tu
Frank Hase
Jason Blake Cohen
Farahnaz Khosrawi
Xinrui Zou
Matthias Schneider
Fan Lu
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2085 KB) - Metadata XML