the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantitative imaging of carbon dioxide plumes using a ground-based shortwave infrared spectral camera
Abstract. We present the first results of a ground-based imaging experiment using a shortwave infrared spectral camera to quantify carbon dioxide (CO2) emissions from a coal-fired power plant in Mannheim, Germany. The power plant emits more than 4.9 MtCO2/year and is a validation opportunity for the emission estimation technique. The camera is a hyperspectral imaging spectrometer that covers the spectral range from 900 nm to 2500 nm with a spectral resolution of 7 nm. We identify CO2 enhancements from hourly averaged images using an iterative matched filter retrieval using the 2000 nm absorption band of CO2. We present 11 plume images from five days in 2021 and 2022 covering a variety of ambient conditions. We design a forward model based on a three-dimensional, bent-over Gaussian plume rise simulation and compare our observed emission plumes with the forward model. The model depends on the parameters ambient wind velocity, wind direction, plume dispersion, and emission rate. We retrieve the emission rate by minimizing the least-squares difference between the measured and the simulated images. We find an overall reasonable agreement between the retrieved and expected emissions for power plant emission rates between 223 tCO2/h and 587 tCO2/h. The retrieved emissions average to 89 % of the expected emissions and have a mean relative uncertainty of 25 %. The technique works at wind speeds down to 1.4 m/s and can follow diurnal emission dynamics. We also include observations with unfavorable ambient conditions, such as background heterogeneity and slant observation angles. These conditions are shown to produce considerable biases in the retrieved emission rates, yet they can be filtered out reliably in most cases. Thus, this emission estimation technique is a promising tool for independently verifying reported emissions from large point sources and provides complementary information to existing monitoring techniques.
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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
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Supplement
(4010 KB) - BibTeX
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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-2023-1857', Anonymous Referee #2, 19 Oct 2023
The authors present a very novel ground-based observational study to estimate power plant CO2 emissions. Passive plume-mapping hyperspectral instruments have been deployed from many suborbital and orbital platforms, but utilizing a ground-based spectrometer for this plume-mapping use-case is quite new and exciting. The manuscript itself is very clear and all steps from observation to emission quantification are clearly described. The manuscript should proceed to publication, though I have a few minor comments, which I detail below.
1. Is the dimension of your covariance matrix sufficient to constrain (i.e., not underestimate) CO2 concentration in the CMF algorithm? Reading from the text, it appears that a the dimension of your data cube is 286 x 384 x 288 - with 286 being the number of frames. How many active bands do you use in your retrieval? If it's 6-7nm spectral sampling between 1900-2100, that would roughly 30 bands, so a 30x30 dimension covariance matrix. Are 286 elements sufficient? Previous studies have found that too few pixels (aerial hyperspectral imagers) in the along-track direction can result in concentration enhancements that are biased low.
2. How much of the uncertainty comes from your fit mask? An attractiveness of your approach is that you only need a statistically representative sample of pixels within the plume to make an assumption about emission rates. Why consider the mask from the simulation? Looking through the plume masks in the main manuscript and the SI, seems like many of the masks incorporate areas of null enhancement within the observation. Is this biasing any of your results? Is there much of a difference if you use an observation only plume mask?3. Figure S22 - you speak in the manuscript of the inability to get a good emission estimate on 2022-05-13 and point to problems with the width scaling factor. Curious however if you think another quantification approach could be well suited for this problem - many plume-mapping aerial/satellite algorithms use the integrated mass enhancement method, which is mostly concerned with getting the mass of the plume correct and less the transport, rise, etc. Could this be an option for this problem, or not given the nature of the observation?
Citation: https://doi.org/10.5194/egusphere-2023-1857-RC1 - AC2: 'Reply on RC1 of Reviewer 2', Marvin Knapp, 15 Nov 2023
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RC2: 'Comment on egusphere-2023-1857', Anonymous Referee #1, 04 Nov 2023
GENERAL COMMENTS
Estimating the emission amounts is more challenging than detecting the enhancement due to large uncertainty in wind speed and direction. Retrieving plume heights, wind height and direction is difficult from a snapshot of satellite data. The measurement technique and analytical method described here are unique. The authors are measuring the plume structure with an imaging spectrometer and a wind lidar and then comparing with a model. The paper demonstrates the difficulty and importance of field measurements. 5-day data set with various weather condition are very valuable for scientific community. Minor revisions will help readers’ understanding. I recommend publication after revisions.
SPECIFIC COMMENTS
(1) Page 2, Line 33, analogue techniques
An additional explanation on “analogue techniques” is helpful.
(2) Page 4, Figure 1
Descriptions such as the height of the chimney, distance between three chimneys, and distance between spectrometers and the chimneys in the figure or the figure caption will helpful.
(3) Page 8, Line 185, “sparsity constraint on enhancement”
A brief description will help readers without referring the paper.
(4) Page 25, Line 507 “Favorable observation condition”
Disadvantage of this observation is a weak scattered light as a light source. Scattering depends on the geometry of the sun, target, and observer. Is the back-scattered geometry such as “the sun is behind the observer” favorable?
(5) Supplement P2, Figure 2 the right panel
Do colors in the right panel mean something?
TECHNICAL CORRECTIONS
No specific comments.
Citation: https://doi.org/10.5194/egusphere-2023-1857-RC2 - AC1: 'Reply on RC2 of Reviewer 1', Marvin Knapp, 15 Nov 2023
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-1857', Anonymous Referee #2, 19 Oct 2023
The authors present a very novel ground-based observational study to estimate power plant CO2 emissions. Passive plume-mapping hyperspectral instruments have been deployed from many suborbital and orbital platforms, but utilizing a ground-based spectrometer for this plume-mapping use-case is quite new and exciting. The manuscript itself is very clear and all steps from observation to emission quantification are clearly described. The manuscript should proceed to publication, though I have a few minor comments, which I detail below.
1. Is the dimension of your covariance matrix sufficient to constrain (i.e., not underestimate) CO2 concentration in the CMF algorithm? Reading from the text, it appears that a the dimension of your data cube is 286 x 384 x 288 - with 286 being the number of frames. How many active bands do you use in your retrieval? If it's 6-7nm spectral sampling between 1900-2100, that would roughly 30 bands, so a 30x30 dimension covariance matrix. Are 286 elements sufficient? Previous studies have found that too few pixels (aerial hyperspectral imagers) in the along-track direction can result in concentration enhancements that are biased low.
2. How much of the uncertainty comes from your fit mask? An attractiveness of your approach is that you only need a statistically representative sample of pixels within the plume to make an assumption about emission rates. Why consider the mask from the simulation? Looking through the plume masks in the main manuscript and the SI, seems like many of the masks incorporate areas of null enhancement within the observation. Is this biasing any of your results? Is there much of a difference if you use an observation only plume mask?3. Figure S22 - you speak in the manuscript of the inability to get a good emission estimate on 2022-05-13 and point to problems with the width scaling factor. Curious however if you think another quantification approach could be well suited for this problem - many plume-mapping aerial/satellite algorithms use the integrated mass enhancement method, which is mostly concerned with getting the mass of the plume correct and less the transport, rise, etc. Could this be an option for this problem, or not given the nature of the observation?
Citation: https://doi.org/10.5194/egusphere-2023-1857-RC1 - AC2: 'Reply on RC1 of Reviewer 2', Marvin Knapp, 15 Nov 2023
-
RC2: 'Comment on egusphere-2023-1857', Anonymous Referee #1, 04 Nov 2023
GENERAL COMMENTS
Estimating the emission amounts is more challenging than detecting the enhancement due to large uncertainty in wind speed and direction. Retrieving plume heights, wind height and direction is difficult from a snapshot of satellite data. The measurement technique and analytical method described here are unique. The authors are measuring the plume structure with an imaging spectrometer and a wind lidar and then comparing with a model. The paper demonstrates the difficulty and importance of field measurements. 5-day data set with various weather condition are very valuable for scientific community. Minor revisions will help readers’ understanding. I recommend publication after revisions.
SPECIFIC COMMENTS
(1) Page 2, Line 33, analogue techniques
An additional explanation on “analogue techniques” is helpful.
(2) Page 4, Figure 1
Descriptions such as the height of the chimney, distance between three chimneys, and distance between spectrometers and the chimneys in the figure or the figure caption will helpful.
(3) Page 8, Line 185, “sparsity constraint on enhancement”
A brief description will help readers without referring the paper.
(4) Page 25, Line 507 “Favorable observation condition”
Disadvantage of this observation is a weak scattered light as a light source. Scattering depends on the geometry of the sun, target, and observer. Is the back-scattered geometry such as “the sun is behind the observer” favorable?
(5) Supplement P2, Figure 2 the right panel
Do colors in the right panel mean something?
TECHNICAL CORRECTIONS
No specific comments.
Citation: https://doi.org/10.5194/egusphere-2023-1857-RC2 - AC1: 'Reply on RC2 of Reviewer 1', Marvin Knapp, 15 Nov 2023
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Marvin Knapp
Ralph Kleinschek
Sanam N. Vardag
Felix Külheim
Helge Haveresch
Moritz Sindram
Tim Siegel
Bruno Burger
Andre Butz
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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(4010 KB) - BibTeX
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