the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Sep 2023
A method for estimating localized CO2 emissions from co-located satellite XCO2 and NO2 images
Abstract. Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas. Its atmospheric concentration has increased by almost 50 % since the beginning of the industrial era, causing climate change. Fossil fuel combustion is responsible for most of the atmospheric CO2 increase, which originates to a large extent from localized sources such as power stations. Independent estimates of the emissions from these sources are key to tracking the effectiveness of implemented climate policies to mitigate climate change. We developed a procedure to quantify CO2 emissions from localized sources based on a cross-sectional mass-balance approach and applied it to infer CO2 emissions from the Bełchatów Power Station, in Poland, using atmospheric observations from the Orbiting Carbon Observatory 3 (OCO-3) in its Snapshot Area Map (SAM) mode. As a result of the challenge of identifying CO2 emission plumes from satellite data with adequate accuracy, we located and constrained the shape of emission plumes using TROPOspheric Monitoring Instrument (TROPOMI) NO2 column densities. We analysed all available OCO-3 overpasses over the Bełchatów Power Station from July 2019 to November 2022 and found a total of 9 that were suitable for the estimation of CO2 emissions using our method. The mean uncertainty of the obtained estimates was 5.8 Mt CO2 y−1 (22.0 %), mainly driven by the dispersion of the cross-sectional fluxes downwind of the source, e.g. due to turbulence. This dispersion uncertainty was characterized using a semivariogram, possible thanks to the OCO-3 imaging capability over a target region in SAM mode, which provides observations containing plume information up to several tens of kilometres downwind of the source. A bottom-up emission estimate was computed based on the hourly power plant generated power and emission factors to validate the satellite-based estimates. We found that the two independent estimates agree within their 1 sigma uncertainty in 8 out of 9 analysed overpasses and have a high Pearson's correlation coefficient of 0.92. Our results confirm the potential for monitoring large localized CO2 emission sources from space-based observations and the usefulness of NO2 estimates for plume detection. They illustrate as well the potential to improve CO2 monitoring capabilities with the planned Copernicus Anthropogenic CO2 Monitoring (CO2M) satellite constellation, which will provide simultaneously retrieved XCO2 and NO2 maps.
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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.
- Preprint
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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-2023-2085', Ray Nassar, 27 Oct 2023
“A method for estimating localized CO2 emissions from co-located satellite XCO2 and NO2 images” by B. Fuentes Andrade et al. is an interesting new study presenting a method for estimating CO2 emissions from localized sources such as power plants. XCO2 observations from OCO-3 and NO2 observations from TROPOMI are used to estimate CO2 emissions from the Bełchtów power station in Poland on 9 dates from 2019-2022. The method presented is based on the cross-sectional flux approach which is extended to estimate the emissions along with uncertainties. The results are compared with those of Nassar et al. (2022), which used a Gaussian Plume Model based approach for the same power plant on many of the same dates. This is a useful study, complementary to the Nassar et al. (2022) study, presenting some advantages and limitations to the cross-sectional flux method that has been developed. Continually improving our understanding of methods, biases, uncertainties and limitations for emission estimation in advance of CO2M is useful for the scientific community gearing up to deliver operational emission estimates from satellite data in the near future. The approach presented here is sound and rigorous. I see no major issues with this study and thus would recommend publication after some relatively minor revisions.
Specific points
Line 14: “possible thanks to” would better be rephrased as “made possible by”
Line 60 and 61: capitalization of ENVISAT and TANSO is the advised, although TANSO-FTS is the complete name of the instrument.
65: A Gaussian plume model does not account for eddies, however, it relies on the reasonable assumption that their effects are negligible for multi-kilometer spatial scales. It is recommended that the sentence is expanded to clarify this fact.
Line 116: “instantaneous hourly” would be more informative than just “hourly” to distinguish from an hourly average value.
Figure 1 caption “gross” should be “cross” or X.
Line 156: Is there any justification of the requirement of less than 5 hours? Obviously a shorter offset in time is better, but are there any studies to quantify the effect that might justify this value? Both wind speed and direction could change significantly over a period of 5 hours, as discussed later around line 190.
Line 208: This approach to account for swath bias is interesting and likely contributes to an improvement in emission estimates, however, should the swath numbering be “j = 1,2,… n”, rather than only going up to n-1? Is it n-1 since the first swath has no offset, so j = 0,1,2 … n-1, where s0 = 0?
Line 277: 1-3 hours for the characteristic time used to determine the bottom-up value is consistent with the findings of Nassar et al. (2021, https://doi.org/10.1016/j.rse.2021.112579, e.g. Figure 1 and sec 2.5), which considered the plume extent, time since emissions to derive a time-weighted or ‘dynamic’ bottom-up value. This similar analysis is worth mentioning very briefly and citing.
Section 2.3, uncertainty. Is there any uncertainty related to the observations? It was not entirely clear to me if this was indirectly included in the dispersion or sensitivity terms. The sensitivity term does account for uncertainty in the observations for background, but not necessarily the plume. Can the authors clarify?
Line 559: “lead” should be “led”
Line 595: It is not surprising that the difference between applying quality filters and ignoring them reduced when observations near the Bełchatów lignite pit were excluded. The digital elevation model for OCO-3 v10 data does not account for recent anthropogenic effects on topography such as this, so biased XCO2 data will result through erroneous surface pressures. Although no DEM will be perfectly up to date with respect to anthropogenic effects on topography, the Copernicus DEM which will be used in OCO-3 v11 data will reduce the problem and thus the difference between quality-filtering and not, will be reduced.
Citation: https://doi.org/10.5194/egusphere-2023-2085-RC1 - AC1: 'Reply on RC1', Blanca Fuentes Andrade, 30 Nov 2023
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RC2: 'Comment on egusphere-2023-2085', Christopher O'Dell, 02 Nov 2023
Reviewer comments provided in the supplemental file.
- AC2: 'Reply on RC2', Blanca Fuentes Andrade, 30 Nov 2023
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2023-2085', Ray Nassar, 27 Oct 2023
“A method for estimating localized CO2 emissions from co-located satellite XCO2 and NO2 images” by B. Fuentes Andrade et al. is an interesting new study presenting a method for estimating CO2 emissions from localized sources such as power plants. XCO2 observations from OCO-3 and NO2 observations from TROPOMI are used to estimate CO2 emissions from the Bełchtów power station in Poland on 9 dates from 2019-2022. The method presented is based on the cross-sectional flux approach which is extended to estimate the emissions along with uncertainties. The results are compared with those of Nassar et al. (2022), which used a Gaussian Plume Model based approach for the same power plant on many of the same dates. This is a useful study, complementary to the Nassar et al. (2022) study, presenting some advantages and limitations to the cross-sectional flux method that has been developed. Continually improving our understanding of methods, biases, uncertainties and limitations for emission estimation in advance of CO2M is useful for the scientific community gearing up to deliver operational emission estimates from satellite data in the near future. The approach presented here is sound and rigorous. I see no major issues with this study and thus would recommend publication after some relatively minor revisions.
Specific points
Line 14: “possible thanks to” would better be rephrased as “made possible by”
Line 60 and 61: capitalization of ENVISAT and TANSO is the advised, although TANSO-FTS is the complete name of the instrument.
65: A Gaussian plume model does not account for eddies, however, it relies on the reasonable assumption that their effects are negligible for multi-kilometer spatial scales. It is recommended that the sentence is expanded to clarify this fact.
Line 116: “instantaneous hourly” would be more informative than just “hourly” to distinguish from an hourly average value.
Figure 1 caption “gross” should be “cross” or X.
Line 156: Is there any justification of the requirement of less than 5 hours? Obviously a shorter offset in time is better, but are there any studies to quantify the effect that might justify this value? Both wind speed and direction could change significantly over a period of 5 hours, as discussed later around line 190.
Line 208: This approach to account for swath bias is interesting and likely contributes to an improvement in emission estimates, however, should the swath numbering be “j = 1,2,… n”, rather than only going up to n-1? Is it n-1 since the first swath has no offset, so j = 0,1,2 … n-1, where s0 = 0?
Line 277: 1-3 hours for the characteristic time used to determine the bottom-up value is consistent with the findings of Nassar et al. (2021, https://doi.org/10.1016/j.rse.2021.112579, e.g. Figure 1 and sec 2.5), which considered the plume extent, time since emissions to derive a time-weighted or ‘dynamic’ bottom-up value. This similar analysis is worth mentioning very briefly and citing.
Section 2.3, uncertainty. Is there any uncertainty related to the observations? It was not entirely clear to me if this was indirectly included in the dispersion or sensitivity terms. The sensitivity term does account for uncertainty in the observations for background, but not necessarily the plume. Can the authors clarify?
Line 559: “lead” should be “led”
Line 595: It is not surprising that the difference between applying quality filters and ignoring them reduced when observations near the Bełchatów lignite pit were excluded. The digital elevation model for OCO-3 v10 data does not account for recent anthropogenic effects on topography such as this, so biased XCO2 data will result through erroneous surface pressures. Although no DEM will be perfectly up to date with respect to anthropogenic effects on topography, the Copernicus DEM which will be used in OCO-3 v11 data will reduce the problem and thus the difference between quality-filtering and not, will be reduced.
Citation: https://doi.org/10.5194/egusphere-2023-2085-RC1 - AC1: 'Reply on RC1', Blanca Fuentes Andrade, 30 Nov 2023
-
RC2: 'Comment on egusphere-2023-2085', Christopher O'Dell, 02 Nov 2023
Reviewer comments provided in the supplemental file.
- AC2: 'Reply on RC2', Blanca Fuentes Andrade, 30 Nov 2023
Peer review completion
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Blanca Fuentes Andrade
Michael Buchwitz
Maximilian Reuter
Heinrich Bovensmann
Andreas Richter
Hartmut Boesch
John P. Burrows
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(24254 KB) - Metadata XML