the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Nov 2024
Estimation of diurnal emissions of CO2 from thermal power plants using spaceborne IPDA lidar
Abstract. Coal-fired power plants are a major source of global carbon emissions, and accurately accounting for these significant emission sources is crucial in addressing global warming. Many previous studies have used Gaussian plume models to estimate power plant emissions, but there is a gap in observation capabilities for high-latitude regions and nighttime emissions. However, large emitting power plants exist in high-latitude areas. The DQ-1 satellite is equipped with the world’s first active remote sensing lidar for detecting CO2 column concentrations, which, compared to passive remote sensing satellites, enables observations in these regions. This paper applies a two-dimensional Gaussian plume model to the XCO2 results from the DQ-1 satellite and analyses the instantaneous CO2 emissions of 10 power plants globally. Among these, 15 cases of data are from nighttime observations, and 3 cases are from power plants located above 60° N latitude. The estimation results show good consistency when compared with emission inventories such as Climate TRACE and Carbon Brief, with a correlation coefficient R = 0.97. The correlation coefficient between the model fits and satellite observations ranges from 0.49 to 0.88, and the overall relative random error in the estimates is 15.11 %. This paper also analyses the diurnal and seasonal variations in CO2 emissions from the power plants, finding that emission variations align with changes in electricity consumption in the surrounding regions. This method is effective for monitoring the diurnal variations of strong emission sources like power plants.
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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-2024-3152', Anonymous Referee #1, 26 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3152/egusphere-2024-3152-RC1-supplement.pdf
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AC1: 'Reply on RC1', Xuanye Zhang, 12 Mar 2025
Dear Reviewer,
We greatly appreciate your valuable time for reviewing our research paper and providing suggestions.
We have revised the manuscript according to your comments point-to-point and the response is presented below as the supplement.
Many thanks and best regards.
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AC1: 'Reply on RC1', Xuanye Zhang, 12 Mar 2025
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RC2: 'Comment on egusphere-2024-3152', Anonymous Referee #2, 13 Feb 2025
The authors present a nice analysis of the nighttime and daytime power plant emissions at the GRES power plant in Russia using the novel DQ-1 lidar data. Overall the concept of the study is well-formed and will eventually be worthy of publication after some clarifications of the methodologies and revisions of the presentation as detailed below.A major concern for this reviewer is the lack of access to the DQ-1 data on which the study is based.
Specific Comments:
Line 62: "OCO-2 is widely used..." for what purpose?
Line 65: Even beyond quantifying emissions, Nassar et al (2017,2021) quantify uncertainties - these should be mentioned.
Line 71: OCO-2 and OCO-3 have resolution 1.5km x 2.25km, GOSAT has resolution of 10km x 10km. They also sample differently, so this statement is a bit too glib of a comparison.
Line 78: I would replace "accuracy" with "uncertainty". Is the 1ppm number for a single 330m footprint? That isn't clear from how this is worded. Previous experience from the ASCENDS flight campaigns required at least a few kilometers of along track averaging to get the random errors below 1 ppm.
Section 2.1.1: I believe this section needs a bit more detail on the retrieval of XCO2 from the lidar. This is not a technology that many readers will be familiar with and so deserves a bit more detail regarding differential optical depths and weighting function correction. Are you correcting for water vapor (which is also released by power plants), etc? Even though the retrievals are not the focus of this paper, the data quality is a big concern and that is not addressed here.
Section 2.1.2: It's not clear how the wind data is being created here. Are you using the full 3D winds and interpolating them to 240 + 250m? You mention the use of "ground-level" winds, but I'm not sure if you mean the surface or the planetary boundary layer. Perhaps a figure would be helpful to show how you use the model fields to create the steady state wind value for the GP model. Models are also notoriously bad at nighttime PBL depths. Did you evaluate these wind fields against any atmospheric data, especially at night?
Equation 3: is there a mismatch between this and Equation 1? How are the mean and standard deviation and the parameter a specified?
Line 158-161: Can you provide a reference for the interpolation? Is there an equation you're working with? For this process, are you simultaneously optimizing the CO2 emissions and the stability parameter at the same time?
Line 161-162: What sort of smoothing? What are the length scales?
Line 172-173: This is a bit unclear - are you using the inferred emission rates from the two models' wind fields to compute the errors? Similarly for the other error terms? What is the 1-sigma parameter uncertainty in your stability parameterization and how is it calculated?
Section 3.1: This analysis is very interesting, but a bit hard to follow. I suggest you separate this section into subsections by dates of overpass and provide a table with the different inferred emission rates for all of the 19 days and the variations in posterior errors. Perhaps this is the information contained in Figure 7? Can you adjust the figure caption to specify the meaning of the shading?
Figure 2: please cite the source of these images.
Line 222: "The slightly higher result..." - the lower result in the inventory is well within 1-sigma of the DQ-1 informed estimate. It could just be due to random error from wind speeds, etc
Line 225: "...lower operational efficiency...resulting in CO2 emissions exceeding the inventory" - wouldn't this situation produce less CO2 and more CO emissions?
Line 237: It's a small nit, but earlier you said you excluded tracks that were > 30km from the source.
Line 279: Unauthorized emissions are mentioned a few times in the paper, but it's not clear what the regulations are and who is enforcing them. Is there a regulation in Russia about CO2 emissions at night vs. in the daytime?
Line 293-298: Is the uncertainty on atmospheric stability well-defined, since you are fitting for it in your retrieval?
Section 3.2: This is a key section, but there are no comparisons to previous findings. How do your uncertainties compare to work by Nassar and others?
Section 3.3: This is not really a "validation" of emissions, but rather just a comparison with the inventories. Some of your conclusions here are speculative and need to be augmented with the appropriate caveats. Right now they read as unsupported statements without citations. Are there any stack monitors at these power plants?
Figure 8: I think your axis labels have the wrong units - do you mean kg/h?
Citation: https://doi.org/10.5194/egusphere-2024-3152-RC2 -
AC2: 'Reply on RC2', Xuanye Zhang, 12 Mar 2025
Dear Reviewer,
We greatly appreciate your valuable time for reviewing our research paper and providing suggestions.
We have revised the manuscript according to your comments point-to-point and the response is presented below as the supplement.
Many thanks and best regards.
-
AC2: 'Reply on RC2', Xuanye Zhang, 12 Mar 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3152', Anonymous Referee #1, 26 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3152/egusphere-2024-3152-RC1-supplement.pdf
-
AC1: 'Reply on RC1', Xuanye Zhang, 12 Mar 2025
Dear Reviewer,
We greatly appreciate your valuable time for reviewing our research paper and providing suggestions.
We have revised the manuscript according to your comments point-to-point and the response is presented below as the supplement.
Many thanks and best regards.
-
AC1: 'Reply on RC1', Xuanye Zhang, 12 Mar 2025
-
RC2: 'Comment on egusphere-2024-3152', Anonymous Referee #2, 13 Feb 2025
The authors present a nice analysis of the nighttime and daytime power plant emissions at the GRES power plant in Russia using the novel DQ-1 lidar data. Overall the concept of the study is well-formed and will eventually be worthy of publication after some clarifications of the methodologies and revisions of the presentation as detailed below.A major concern for this reviewer is the lack of access to the DQ-1 data on which the study is based.
Specific Comments:
Line 62: "OCO-2 is widely used..." for what purpose?
Line 65: Even beyond quantifying emissions, Nassar et al (2017,2021) quantify uncertainties - these should be mentioned.
Line 71: OCO-2 and OCO-3 have resolution 1.5km x 2.25km, GOSAT has resolution of 10km x 10km. They also sample differently, so this statement is a bit too glib of a comparison.
Line 78: I would replace "accuracy" with "uncertainty". Is the 1ppm number for a single 330m footprint? That isn't clear from how this is worded. Previous experience from the ASCENDS flight campaigns required at least a few kilometers of along track averaging to get the random errors below 1 ppm.
Section 2.1.1: I believe this section needs a bit more detail on the retrieval of XCO2 from the lidar. This is not a technology that many readers will be familiar with and so deserves a bit more detail regarding differential optical depths and weighting function correction. Are you correcting for water vapor (which is also released by power plants), etc? Even though the retrievals are not the focus of this paper, the data quality is a big concern and that is not addressed here.
Section 2.1.2: It's not clear how the wind data is being created here. Are you using the full 3D winds and interpolating them to 240 + 250m? You mention the use of "ground-level" winds, but I'm not sure if you mean the surface or the planetary boundary layer. Perhaps a figure would be helpful to show how you use the model fields to create the steady state wind value for the GP model. Models are also notoriously bad at nighttime PBL depths. Did you evaluate these wind fields against any atmospheric data, especially at night?
Equation 3: is there a mismatch between this and Equation 1? How are the mean and standard deviation and the parameter a specified?
Line 158-161: Can you provide a reference for the interpolation? Is there an equation you're working with? For this process, are you simultaneously optimizing the CO2 emissions and the stability parameter at the same time?
Line 161-162: What sort of smoothing? What are the length scales?
Line 172-173: This is a bit unclear - are you using the inferred emission rates from the two models' wind fields to compute the errors? Similarly for the other error terms? What is the 1-sigma parameter uncertainty in your stability parameterization and how is it calculated?
Section 3.1: This analysis is very interesting, but a bit hard to follow. I suggest you separate this section into subsections by dates of overpass and provide a table with the different inferred emission rates for all of the 19 days and the variations in posterior errors. Perhaps this is the information contained in Figure 7? Can you adjust the figure caption to specify the meaning of the shading?
Figure 2: please cite the source of these images.
Line 222: "The slightly higher result..." - the lower result in the inventory is well within 1-sigma of the DQ-1 informed estimate. It could just be due to random error from wind speeds, etc
Line 225: "...lower operational efficiency...resulting in CO2 emissions exceeding the inventory" - wouldn't this situation produce less CO2 and more CO emissions?
Line 237: It's a small nit, but earlier you said you excluded tracks that were > 30km from the source.
Line 279: Unauthorized emissions are mentioned a few times in the paper, but it's not clear what the regulations are and who is enforcing them. Is there a regulation in Russia about CO2 emissions at night vs. in the daytime?
Line 293-298: Is the uncertainty on atmospheric stability well-defined, since you are fitting for it in your retrieval?
Section 3.2: This is a key section, but there are no comparisons to previous findings. How do your uncertainties compare to work by Nassar and others?
Section 3.3: This is not really a "validation" of emissions, but rather just a comparison with the inventories. Some of your conclusions here are speculative and need to be augmented with the appropriate caveats. Right now they read as unsupported statements without citations. Are there any stack monitors at these power plants?
Figure 8: I think your axis labels have the wrong units - do you mean kg/h?
Citation: https://doi.org/10.5194/egusphere-2024-3152-RC2 -
AC2: 'Reply on RC2', Xuanye Zhang, 12 Mar 2025
Dear Reviewer,
We greatly appreciate your valuable time for reviewing our research paper and providing suggestions.
We have revised the manuscript according to your comments point-to-point and the response is presented below as the supplement.
Many thanks and best regards.
-
AC2: 'Reply on RC2', Xuanye Zhang, 12 Mar 2025
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Journal article(s) based on this preprint
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Xuanye Zhang
Hailong Yang
Lingbing Bu
Zengchang Fan
Binglong Chen
Lu Zhang
Sihan Liu
Zhongting Wang
Jiqiao Liu
Weibiao Chen
Xuhui Lee
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1660 KB) - Metadata XML