the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Dec 2024
Evidence of successful methane mitigation in one of Europe's most important oil production region
Abstract. Reducing methane emissions from the oil and gas production infrastructure is a cost-effective way for limiting global warming. In 2019, a measurement campaign in southern Romania found emission rates from the oil and gas sector substantially higher than the nationally reported emissions with a few high-emitting sources (“super-emitters”) contributing disproportionately to total emissions. In 2021, our follow-up airborne remote sensing campaign, covering over 80 % of production sites, revealed a marked decrease in super-emitters. The observed change in the number of emitters is consistent with an emission reduction by 20–60 % from 2019 to 2021. This reduction is likely due to improvements in production infrastructure following the first campaign in 2019. This is further supported by additional site visits, which showed that many of the leaks identified in 2019 had indeed been mitigated. However, our top-down quantification remains higher than the bottom-up emission reports. Our study highlights the importance of measurement-based emission monitoring of climate change mitigation measures, and illustrates the value of a multi-scale assessment integrating ground-based observations with large-scale airborne mapping to capture both the primary mode of emission sources and the rare, but significant, super-emitters.
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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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Supplement
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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.
- Preprint
(9091 KB) - Metadata XML
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Supplement
(4734 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3494', Anonymous Referee #1, 03 Jan 2025
The manuscript is an interesting showcase of the impact of a measurement campaign. It is important to highlight the combination of so many techniques and measurement methodologies. The authors have been capable to include the point source measurements, distributions of emissions and global emissions budgets. They also compared to inventories and explored different hypothesis for emission reductions. Although this is the most remarkable part of the manuscript, it is also the most challenging part since many areas need to be effectively covered and reviewed. Thus, in some cases, the manuscript needs some clarifications/review. The major/minor points that are suggested for improvement are the following:
- Page 2 line 29. Clarify here (e.g. with a footnote) what you define as a Some might set the threshold at 10kg/h, other at 100kg/h. The latter typically for satellite studies.
- Page 2 line 42.Do you refer to Fig1 entirely? in that case, it should be Fig1 rather than Fig1a.
- Page 2 line 44. Do you mean it has same spectral sampling/resolution? you can clarify writing down "5nm spectral sampling and resolution"
- Page 4 line 84. Please specify the dataset/web where you got the values of CO2 and CH4
- Equation (7) it can be assumed that the equation is used to generate Lo and Leps; consequently retrieving s(lambda). Please, clarify in the text how you effectively use the equation.
- Page 4 line 93 The authors are correct since more bands implies a more demanding spectrum to match but can also lead to an underestimation of the methane signal. Can you further discuss this point and how it affects the AVIRIS measurements? you have a good discussion here https://doi.org/10.5194/amt-17-1333-2024
- Page 6 line 120 please specify that this enhancement must be transformed from concentrations ppm *m to g/cm2 or similar units.
- Page 6 line 122 From Kuhlmann 2024 the effective wind speed is taken from the provided wind speeds at the source location. It seems you directly take the wind speed U10 as Ueffand consider the height as an uncertainty in the budget. Do you take a spatial or temporal interpolation? just the single pixel?
- Page 6 line 122 From Kuhlmann 2024 a decay time can be provided to compute the decay time correction term. Why is not applied? are there significant differences?
- Figure 3 is a very interesting exercise. It shows important errors for parameters such as SZA, VZA or AOD. These three parameters are directly linked to the previous questions on the model (equation 7). However, these are systematic known errors and theoretically should be directly compensated. Since this is not the case, you can estimate the specific error per plume and add them linearly (not quadratically) in the expanded uncertainty budget (see JCGM 100:2008 GUM 1995 with minor corrections). Please, specify your methodology to include them in the uncertainty budget.
- Page 8 line 180. Whereas the separation into systematic and random components is positive, it might be more adequate to distinguish between spatially correlated and uncorrelated components. Thus, we can connect the impact of CH4 uncertainty in the final flux rates.
- Page 8 line 185. the plume length includes an uncertainty itself as correctly explained here but this uncertainty needs to be propagated to Q. Please specify this and how the correlation between IME and L might partially compensate.
- Page 8 line 186.Once the pixels are aggregated into an integrated mass, we would expect that the systematic component dominates whereas the random one is highly reduced due to spatial error correlation. Please, clarify why you consider the random error component of CH4 map.
- Page 9 line 197. the ERA5 ensemble only provides a small range of sensitivities. https://confluence.ecmwf.int/display/CKB/ERA5%3A+uncertainty+estimationunfortunately, most systematic (and dominant) effects are not taken into account. It is very positive that height dependency is considered but important effects such as the U10 spatiotemporal representativeness and ERA5 modelling errors are not included. Please review this uncertainty contribution and clarify the final figures.
- Page 9 line 223. It should be better clarified which assumptions are used to combine the uncertainty components in the MonteCarlo simulation. Specifically, the combination of the different AVIRIS uncertainty estimates.
- Page 12 line 286. you measured the same spot three times and, it is assumed, in relative short time. Did you find consistency? it would be very positive to disclose this information for a more robust validation and understanding of the emission source potential changes.
- Figure 6d this image shows an outlier following the wind direction. Is the continuation of the detected plume? Is there any explanation for it?
- Page 16 line 365 Are there any difference in wind speeds between 2019 and 2021? although unlikely explaining all these differences, it could be helpful to support the discussion.
Citation: https://doi.org/10.5194/egusphere-2024-3494-RC1 -
AC1: 'Reply on RC1', Gerrit Kuhlmann, 10 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3494/egusphere-2024-3494-AC1-supplement.pdf
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RC2: 'Comment on egusphere-2024-3494', Anonymous Referee #2, 27 Jan 2025
The manuscript presents airborne remote sensing observations and quantification of methane plumes in Romania over two days in 2021. The findings are compared with previous ground-based and drone measurements from 2019, and a total emission estimate for the region is calculated. The study discusses the advantages and limitations of each measurement technique, concluding that methane emissions in the region have decreased between the two studies. While comparing the results is challenging due to the variability in the oil and gas production landscape and differences in the design of the studies, the authors have made a commendable effort to address these complexities.
We have the following comments that need clarification/correction:
Line 32: How do you define super-emitters? Are all AVIRES-NG detections declared super-emitters here?
Line 122: The two profiles here are theoretical and not used in this study. Please clarify here.
Line 123: change “a” to “at”.
Line 124: The local wind speed uncertainty might be very different from an uncertainty at 10 km resolution. Please comment.
Line 185: Where does this assumption stem from?
Line 186: “plumes”
Line 197: Where do the 15% for the height-dependency uncertainty stem from?
Line 200: “would be”
Line 200: What is the contribution of wind speed uncertainty to the total uncertainty?
Line 249: remove “that”
Line 252: Please explain why you chose these scenarios and what they infer for the change in emissions.
Line 264: The table states and EF of 1.2 for scenario 4.
Line 293: That is quite a high contribution from "unassigned" sources. Are these findings considered when relating to 2019 findings?
Figure 5: Please show the values of the individual emissions estimates that contribute to the mean emissions.
Line 364: “a robust estimate”
Line 410: Replace “surveys with instruments” with “surveys using instruments”
Line 413: Change “entirely” to “entirety”
Citation: https://doi.org/10.5194/egusphere-2024-3494-RC2 -
AC2: 'Reply on RC2', Gerrit Kuhlmann, 10 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3494/egusphere-2024-3494-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Gerrit Kuhlmann, 10 Feb 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3494', Anonymous Referee #1, 03 Jan 2025
The manuscript is an interesting showcase of the impact of a measurement campaign. It is important to highlight the combination of so many techniques and measurement methodologies. The authors have been capable to include the point source measurements, distributions of emissions and global emissions budgets. They also compared to inventories and explored different hypothesis for emission reductions. Although this is the most remarkable part of the manuscript, it is also the most challenging part since many areas need to be effectively covered and reviewed. Thus, in some cases, the manuscript needs some clarifications/review. The major/minor points that are suggested for improvement are the following:
- Page 2 line 29. Clarify here (e.g. with a footnote) what you define as a Some might set the threshold at 10kg/h, other at 100kg/h. The latter typically for satellite studies.
- Page 2 line 42.Do you refer to Fig1 entirely? in that case, it should be Fig1 rather than Fig1a.
- Page 2 line 44. Do you mean it has same spectral sampling/resolution? you can clarify writing down "5nm spectral sampling and resolution"
- Page 4 line 84. Please specify the dataset/web where you got the values of CO2 and CH4
- Equation (7) it can be assumed that the equation is used to generate Lo and Leps; consequently retrieving s(lambda). Please, clarify in the text how you effectively use the equation.
- Page 4 line 93 The authors are correct since more bands implies a more demanding spectrum to match but can also lead to an underestimation of the methane signal. Can you further discuss this point and how it affects the AVIRIS measurements? you have a good discussion here https://doi.org/10.5194/amt-17-1333-2024
- Page 6 line 120 please specify that this enhancement must be transformed from concentrations ppm *m to g/cm2 or similar units.
- Page 6 line 122 From Kuhlmann 2024 the effective wind speed is taken from the provided wind speeds at the source location. It seems you directly take the wind speed U10 as Ueffand consider the height as an uncertainty in the budget. Do you take a spatial or temporal interpolation? just the single pixel?
- Page 6 line 122 From Kuhlmann 2024 a decay time can be provided to compute the decay time correction term. Why is not applied? are there significant differences?
- Figure 3 is a very interesting exercise. It shows important errors for parameters such as SZA, VZA or AOD. These three parameters are directly linked to the previous questions on the model (equation 7). However, these are systematic known errors and theoretically should be directly compensated. Since this is not the case, you can estimate the specific error per plume and add them linearly (not quadratically) in the expanded uncertainty budget (see JCGM 100:2008 GUM 1995 with minor corrections). Please, specify your methodology to include them in the uncertainty budget.
- Page 8 line 180. Whereas the separation into systematic and random components is positive, it might be more adequate to distinguish between spatially correlated and uncorrelated components. Thus, we can connect the impact of CH4 uncertainty in the final flux rates.
- Page 8 line 185. the plume length includes an uncertainty itself as correctly explained here but this uncertainty needs to be propagated to Q. Please specify this and how the correlation between IME and L might partially compensate.
- Page 8 line 186.Once the pixels are aggregated into an integrated mass, we would expect that the systematic component dominates whereas the random one is highly reduced due to spatial error correlation. Please, clarify why you consider the random error component of CH4 map.
- Page 9 line 197. the ERA5 ensemble only provides a small range of sensitivities. https://confluence.ecmwf.int/display/CKB/ERA5%3A+uncertainty+estimationunfortunately, most systematic (and dominant) effects are not taken into account. It is very positive that height dependency is considered but important effects such as the U10 spatiotemporal representativeness and ERA5 modelling errors are not included. Please review this uncertainty contribution and clarify the final figures.
- Page 9 line 223. It should be better clarified which assumptions are used to combine the uncertainty components in the MonteCarlo simulation. Specifically, the combination of the different AVIRIS uncertainty estimates.
- Page 12 line 286. you measured the same spot three times and, it is assumed, in relative short time. Did you find consistency? it would be very positive to disclose this information for a more robust validation and understanding of the emission source potential changes.
- Figure 6d this image shows an outlier following the wind direction. Is the continuation of the detected plume? Is there any explanation for it?
- Page 16 line 365 Are there any difference in wind speeds between 2019 and 2021? although unlikely explaining all these differences, it could be helpful to support the discussion.
Citation: https://doi.org/10.5194/egusphere-2024-3494-RC1 -
AC1: 'Reply on RC1', Gerrit Kuhlmann, 10 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3494/egusphere-2024-3494-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2024-3494', Anonymous Referee #2, 27 Jan 2025
The manuscript presents airborne remote sensing observations and quantification of methane plumes in Romania over two days in 2021. The findings are compared with previous ground-based and drone measurements from 2019, and a total emission estimate for the region is calculated. The study discusses the advantages and limitations of each measurement technique, concluding that methane emissions in the region have decreased between the two studies. While comparing the results is challenging due to the variability in the oil and gas production landscape and differences in the design of the studies, the authors have made a commendable effort to address these complexities.
We have the following comments that need clarification/correction:
Line 32: How do you define super-emitters? Are all AVIRES-NG detections declared super-emitters here?
Line 122: The two profiles here are theoretical and not used in this study. Please clarify here.
Line 123: change “a” to “at”.
Line 124: The local wind speed uncertainty might be very different from an uncertainty at 10 km resolution. Please comment.
Line 185: Where does this assumption stem from?
Line 186: “plumes”
Line 197: Where do the 15% for the height-dependency uncertainty stem from?
Line 200: “would be”
Line 200: What is the contribution of wind speed uncertainty to the total uncertainty?
Line 249: remove “that”
Line 252: Please explain why you chose these scenarios and what they infer for the change in emissions.
Line 264: The table states and EF of 1.2 for scenario 4.
Line 293: That is quite a high contribution from "unassigned" sources. Are these findings considered when relating to 2019 findings?
Figure 5: Please show the values of the individual emissions estimates that contribute to the mean emissions.
Line 364: “a robust estimate”
Line 410: Replace “surveys with instruments” with “surveys using instruments”
Line 413: Change “entirely” to “entirety”
Citation: https://doi.org/10.5194/egusphere-2024-3494-RC2 -
AC2: 'Reply on RC2', Gerrit Kuhlmann, 10 Feb 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3494/egusphere-2024-3494-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Gerrit Kuhlmann, 10 Feb 2025
Peer review completion
Journal article(s) based on this preprint
Data sets
Dataset of the AVIRIS-NG methane campaign in Romania in 2021 G. Kuhlmann et al. https://doi.org/10.5281/zenodo.14054126
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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.
- Preprint
(9091 KB) - Metadata XML
-
Supplement
(4734 KB) - BibTeX
- EndNote
- Final revised paper