the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Sep 2025
Towards routine shipborne measurements of columnar CO2, CH4, CO, and NO2: a case study for tracking regional-scale emission patterns
Abstract. Mobile remote sensing observations from shipborne platforms offer a unique opportunity for validating satellite observations and sampling plumes of greenhouse gases and short-lived air pollutants from the world's highly populated coastal megacities and industrial sites. Here, we demonstrate the capabilities of a shipborne setup that combines a sun-viewing EM27/SUN Fourier transform spectrometer for the shortwave-infrared spectral range with a DOAS (Differential Optical Absorption Spectroscopy) spectrometer for the visible spectral range, enabling simultaneous measurements of the column abundances of carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), and nitrogen dioxide (NO2). For several months in 2023 and 2024, the instruments were operating autonomously on a commercial vessel traveling back and forth along the coast of Japan. We show that, for CO2, CH4, and CO, precision and repeatability comply with the standards of the Collaborative Carbon Column Observing Network (COCCON). Further, for a case study in the vicinity of Nagoya, we demonstrate the scientific leverage of this mobile multi-species approach: Simultaneous measurement of CO2, CO, and NO2 enhancements is used to successfully disentangle emissions from different sources. Our study demonstrates that routine shipborne deployment is possible. The setup delivers highly precise and accurate concentration records of the target species, as required for satellite validation, and enables emission monitoring of sources due to their distinct emission ratios.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.-
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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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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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Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-4552', Anonymous Referee #1, 03 Nov 2025
- AC1: 'Reply on RC1', Vincent Enders, 16 Feb 2026
-
RC2: 'Comment on egusphere-2025-4552', Anonymous Referee #2, 29 Dec 2025
Enders et al. present an approach for distinguishing emission sources using the ratios of CO2, CH4 and CO dry-air mole fractions measured by a shipborne FTIR (EM27/Sun) instrument together with NOx derived from NO2 vertical column densities measured by a direct-sun DOAS instrument.
The instrumentation and methods are briefly explained in this manuscript and have been discussed in previous studies.
The general feasability of the approach is demonstrated through a case study based on measurements conducted in Mikawa Bay, Japan, in 2023. The derived emissions from this case study are compared with the EDGAR and REAS inventory data sets and show good spatial agreement with the
location of the emitters.
The paper is well written, the analyses are thorough, and the approach shows potential for validating satellite data sets in coastal regions.General comments:
My two main concerns relate to the case study itself and its application to satellite validation:
1. The presented case study does not conveincingly demonstrate that the indentified emitters are responsible for the observed measurements. Although you mention that self-emissions are negligible, some of the recorded enhancements (particularly the strong signals after leaving Mikawa Bay) could be influenced by the vessel's own emissions. In addition, only wind direction is shown, while wind speed is missing. What was the vessel’s cruising speed? Furthermore, why is only a single time series example presented, despite several months of available data?
To strengthen this analysis, I recommend including a map of wind trajectories for the selected case study, for instance using HYSPLIT or FLEXPART simulations.
2. While the main motivation for this work is to support the validation of satellite measurements, the manuscript does not explain how the proposed emitter estimation approach could be applied in that context. Please include a short discussion clarifying how this method could contribute to satellite
data validation.Specific comments:
L4: Please change to: "direct-sun DOAS"
L12: I recommend replacing "concentration" with "volume mixing ratio" throughout the manuscript, as only VMR (and VCD) values are presented in the figures.
L14: You mention emission monitoring but do not provide emission estimated for the case study. It should be possible to calculate emissions using the VCD data together with wind/vessel speed vectors.
L90: Which part of the light beam is used for the DOAS measurements? Please elaborate on this point.
Section 2.2: Since stable temperature is crucial for DOAS performance, what was the average detector temperature variation during the case study? How might any temperature variations have influenced your results?
L146: If ancillary in situ data are available, please specify what kind of data these are and explain why they were not included in the manuscript.
L153: It seems unlikely that no ship plumes were detected during several months of measurements. The wind and vessel velocity vectors in the first figure even suggest possible self-emissions. Please elaborate on how you ensured that signals from the vessel’s own emissions were excluded.
L162: How many days of daytime measurements are available, and how many of these contained useful data?
Table 2: Which windows were used? Why do you show all windows here?
L204: What percentage of your measurements was filtered based on the pressure comparison? What is the maximum deviation between surface and measured pressure values?
L218: Is this correction also applied to the NO2 VCDs? Typically, such effects are accounted for using air mass factors derived from radiative transfer models. Have you considered using model-based AMFs, and how would you expect the results to differ from those obtained with the geometric AMF,
including the applied correction? The geometric AMF does not account for aerosols, which were likely present. Please discuss how aerosol effects may influence your results and the implications for the AMF approach.
Table 3: Why did you include IO in your fit? Did you detect any tropospheric IO above the instrument's detection limit?
L246: Did you test alternative noon spectra, and if so, how did this affect your reference SCD?
L314: Please provide an explanation for the higher mean error (ME).
L330: It is unclear to me why an additional scaling is necessary given that a correction was already applied earlier. Could you please include an example (perhaps in the appendix) illustrating how your corrections and scaling steps affect the final results?
Figure 7: Please include wind speed information. If the wind speed remained relatively constant, state this in the figure caption. If not, update the figure to show the wind speed variations. Additionally, include the vessel’s velocity in the caption and discuss the potential contamination from the vessel’s own emissions. Could you estimate a characteristic gas signature of the vessel’s plume?
L364: Were any CH4 enhancements observed in the measurements? Please also explain the decreasing trend shown in Figure 8.
Figure 10: Adjust the color scale so that weaker emitters are visible. It would also be helpful to include a comparable figure illustrating CO2 emissions.
L396: From Table 7 it seems that you refer to oil rather than lignite because the NOx/CO2 ratio is larger for oil.
L414: meaured --> measured
L463: Please add wind trajectories to the discussion.Citation: https://doi.org/10.5194/egusphere-2025-4552-RC2 - AC2: 'Reply on RC2', Vincent Enders, 16 Feb 2026
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2025-4552', Anonymous Referee #1, 03 Nov 2025
I am grateful for the opportunity to review this manuscript, which contains thorough methodology and suggests a potential way to expand trace gas column measurements to the ocean. I believe that this study offers a novel contribution to this field with great promise for future developments that would be valuable for atmospheric remote sensing. The authors describe their efforts to test methods for shipbourne observations with a modified EM27/SUN solar viewing spectrometer and coupled DOAS instrument. Both instruments share a uniquely designed solar tracker that has been described and tested in previous literature. They demonstrate the capability of these instruments to measure total column dry-air mole fractions of CO2, CH4 and CO (XCO2, XCH4, and XCO) with the EM27, as well as partial columns of NO2 with the DOAS instrument, on board a ship that is in motion. Their methods for ensuring the performance of these instruments and the quality of the data collected are thorough and well established by previous work.
They demonstrate that their shipbourne measurements may be useful for quantifying emissions from coastal urban areas through one instance of an observed enhancement. Although this may be sufficient as a proof of concept, a more robust analysis of multiple observed enhancements, perhaps requiring a longer campaign, is warranted to get a better idea of the efficacy. It is unclear how often the conditions would be right to observe enhancements that could be reasonably attributed to specific sources or what revisit times would be required. In the discussion here, the source attributions are fairly speculative and there may be other interpretations for the results.
While it is true that these types of shipbourne measurements have the potential for use in satellite validation in coastal regions or even over the open ocean, this use of the data is not demonstrated in the paper. For the purposes of expanding these efforts for satellite validation over the open ocean, there are some other considerations for satellite validation that are not discussed, such as coincidence criteria. This should probably be noted as follow up for a future study, instead of claiming that it was proved in this manuscript.
Overall, I would suggest this manuscript for publication after some revisions and careful attention to the confidence with which some conclusions are presented.
Specific comments:
P 4-5, L90-91: I think there should be more explanation of how the beam is split between the two instruments.
P 8, L161: Could the vehicles being loaded and unloaded impact your observations when the ship is docked?
P 11, L217: Table 6 is referenced before Tables 3, 4, and 5. This can make the paper a little confusing to navigate.
P11, L222-224: If the Xgas values are calculated from the VCD, as stated in Eq. 2, why would you need to have an additional correction applied to Xgas?
P11, L232-235: It would be helpful to state the typical time period for the 100-spectra co-addition.
P12, L242-243: Would the prevalence of high-altitude clouds in this area impact the usefulness of similar campaigns in the future, since this would likely limit the amount of time you could actively observe? These clouds may also affect the accuracy and precision of the EM27/SUN measurements.
P14, L275: What time intervals are used for averaging?
Section 4.3: It seems like somewhere in this section you should report on the variance in the co-added spectra from the EM27/SUN while the ship is in motion. All of your performance metrics are based on observations taken while the instrument is stationary, but the objective of this paper is to show that the spectrometer can perform on a moving ship.
P16, L328: How can you be sure that the larger ratios are caused by differences in the retrieval algorithms? Can you elaborate on which specific differences are most likely contributing?
Figure 9: The peak enhancement ratio in panel e appears to occur between the two enhancement events. This would cast some doubt on the validity of this ratio. If this point were discounted the overall range of the CO/NOx ratios would be closer to 25-40, and this might change your assessment of the likely emission sources. In addition, the ratios after the enhancement are not very different from those during the event.
P21, L404-406: The uncertainty in the retrievals should also be considered here.
Table 7: I suggest ordering the columns of the ratios to match the order of plots in Figure 9.
P24, L419: Your conclusion that this is an enhancement from the steel factory is based on the designation of the high CO grid cell being labeled as steel manufacturing in the inventories and wind direction; however, your enhancement ratios are very different from those reported by the inventories for this category. You also pointed out that you confirmed with the inventory that they had misplaced the steel factory. All of this disagreement suggests that there may be another explanation for your results. From the ratios you cite in Table 7, the sources that match the best are cement factories (with CO/CO2) and urban gasoline or motorway gasoline emissions (with NOx/CO2 and CO/NOx). You do mention later that there is likely a contribution from transportation emissions, but you do not discuss a possible contribution from cement manufacturing. While you do not show any cement industry emissions in Figure 7, have you investigated whether there is any cement manufacturing not reported by the Climate TRACE coalition or the inventories. As this is an industrial area, I would be surprised if there were no cement factories in the region.
P24, L420-421: The CO/CO2 emission ratios reported by Schneising et al. are an order of magnitude larger than what you observed. How does this support your argument?
P24, L423-424: The Van der Maas ratios for CO/NOx are also quite a bit larger than what you observed and this is especially true if you exclude the dubious peak in CO/NOx ratios that occurs at the end of the first enhancement event.
P24, L428-429: While I think all the information provided is interesting and should be considered, it seems like you are overstating the certainty that one should have about the steel factory source attribution.
P24, L430-433: Glad to see this finally being discussed.
P25, L461: Should this say “bottom-up” instead of “top-down”?
P25, L468: I would suggest replacing “without permanent human attendance” with “with only part-time remote oversight” or something similar. It was my understanding that the instrument operations were supervised remotely. Did you encounter any problems that required intervention throughout the course of the campaign or did the instruments run automatically for the whole time without incident?
P25, L472-473: I don’t believe you proved that your setup is suitable for satellite validation because you did not actually compare to any satellite measurements.
P26, L481: You did not operate your instrument over the open ocean.
Citation: https://doi.org/10.5194/egusphere-2025-4552-RC1 - AC1: 'Reply on RC1', Vincent Enders, 16 Feb 2026
-
RC2: 'Comment on egusphere-2025-4552', Anonymous Referee #2, 29 Dec 2025
Enders et al. present an approach for distinguishing emission sources using the ratios of CO2, CH4 and CO dry-air mole fractions measured by a shipborne FTIR (EM27/Sun) instrument together with NOx derived from NO2 vertical column densities measured by a direct-sun DOAS instrument.
The instrumentation and methods are briefly explained in this manuscript and have been discussed in previous studies.
The general feasability of the approach is demonstrated through a case study based on measurements conducted in Mikawa Bay, Japan, in 2023. The derived emissions from this case study are compared with the EDGAR and REAS inventory data sets and show good spatial agreement with the
location of the emitters.
The paper is well written, the analyses are thorough, and the approach shows potential for validating satellite data sets in coastal regions.General comments:
My two main concerns relate to the case study itself and its application to satellite validation:
1. The presented case study does not conveincingly demonstrate that the indentified emitters are responsible for the observed measurements. Although you mention that self-emissions are negligible, some of the recorded enhancements (particularly the strong signals after leaving Mikawa Bay) could be influenced by the vessel's own emissions. In addition, only wind direction is shown, while wind speed is missing. What was the vessel’s cruising speed? Furthermore, why is only a single time series example presented, despite several months of available data?
To strengthen this analysis, I recommend including a map of wind trajectories for the selected case study, for instance using HYSPLIT or FLEXPART simulations.
2. While the main motivation for this work is to support the validation of satellite measurements, the manuscript does not explain how the proposed emitter estimation approach could be applied in that context. Please include a short discussion clarifying how this method could contribute to satellite
data validation.Specific comments:
L4: Please change to: "direct-sun DOAS"
L12: I recommend replacing "concentration" with "volume mixing ratio" throughout the manuscript, as only VMR (and VCD) values are presented in the figures.
L14: You mention emission monitoring but do not provide emission estimated for the case study. It should be possible to calculate emissions using the VCD data together with wind/vessel speed vectors.
L90: Which part of the light beam is used for the DOAS measurements? Please elaborate on this point.
Section 2.2: Since stable temperature is crucial for DOAS performance, what was the average detector temperature variation during the case study? How might any temperature variations have influenced your results?
L146: If ancillary in situ data are available, please specify what kind of data these are and explain why they were not included in the manuscript.
L153: It seems unlikely that no ship plumes were detected during several months of measurements. The wind and vessel velocity vectors in the first figure even suggest possible self-emissions. Please elaborate on how you ensured that signals from the vessel’s own emissions were excluded.
L162: How many days of daytime measurements are available, and how many of these contained useful data?
Table 2: Which windows were used? Why do you show all windows here?
L204: What percentage of your measurements was filtered based on the pressure comparison? What is the maximum deviation between surface and measured pressure values?
L218: Is this correction also applied to the NO2 VCDs? Typically, such effects are accounted for using air mass factors derived from radiative transfer models. Have you considered using model-based AMFs, and how would you expect the results to differ from those obtained with the geometric AMF,
including the applied correction? The geometric AMF does not account for aerosols, which were likely present. Please discuss how aerosol effects may influence your results and the implications for the AMF approach.
Table 3: Why did you include IO in your fit? Did you detect any tropospheric IO above the instrument's detection limit?
L246: Did you test alternative noon spectra, and if so, how did this affect your reference SCD?
L314: Please provide an explanation for the higher mean error (ME).
L330: It is unclear to me why an additional scaling is necessary given that a correction was already applied earlier. Could you please include an example (perhaps in the appendix) illustrating how your corrections and scaling steps affect the final results?
Figure 7: Please include wind speed information. If the wind speed remained relatively constant, state this in the figure caption. If not, update the figure to show the wind speed variations. Additionally, include the vessel’s velocity in the caption and discuss the potential contamination from the vessel’s own emissions. Could you estimate a characteristic gas signature of the vessel’s plume?
L364: Were any CH4 enhancements observed in the measurements? Please also explain the decreasing trend shown in Figure 8.
Figure 10: Adjust the color scale so that weaker emitters are visible. It would also be helpful to include a comparable figure illustrating CO2 emissions.
L396: From Table 7 it seems that you refer to oil rather than lignite because the NOx/CO2 ratio is larger for oil.
L414: meaured --> measured
L463: Please add wind trajectories to the discussion.Citation: https://doi.org/10.5194/egusphere-2025-4552-RC2 - AC2: 'Reply on RC2', Vincent Enders, 16 Feb 2026
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Vincent Enders
Astrid Müller
Matthias Max Frey
Frank Hase
Ralph Kleinschek
Marvin Knapp
Benedikt Löw
Isamu Morino
Shin-Ichiro Nakaoka
Hideki Nara
Sanam N. Vardag
Karolin Voss
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(20818 KB) - Metadata XML
I am grateful for the opportunity to review this manuscript, which contains thorough methodology and suggests a potential way to expand trace gas column measurements to the ocean. I believe that this study offers a novel contribution to this field with great promise for future developments that would be valuable for atmospheric remote sensing. The authors describe their efforts to test methods for shipbourne observations with a modified EM27/SUN solar viewing spectrometer and coupled DOAS instrument. Both instruments share a uniquely designed solar tracker that has been described and tested in previous literature. They demonstrate the capability of these instruments to measure total column dry-air mole fractions of CO2, CH4 and CO (XCO2, XCH4, and XCO) with the EM27, as well as partial columns of NO2 with the DOAS instrument, on board a ship that is in motion. Their methods for ensuring the performance of these instruments and the quality of the data collected are thorough and well established by previous work.
They demonstrate that their shipbourne measurements may be useful for quantifying emissions from coastal urban areas through one instance of an observed enhancement. Although this may be sufficient as a proof of concept, a more robust analysis of multiple observed enhancements, perhaps requiring a longer campaign, is warranted to get a better idea of the efficacy. It is unclear how often the conditions would be right to observe enhancements that could be reasonably attributed to specific sources or what revisit times would be required. In the discussion here, the source attributions are fairly speculative and there may be other interpretations for the results.
While it is true that these types of shipbourne measurements have the potential for use in satellite validation in coastal regions or even over the open ocean, this use of the data is not demonstrated in the paper. For the purposes of expanding these efforts for satellite validation over the open ocean, there are some other considerations for satellite validation that are not discussed, such as coincidence criteria. This should probably be noted as follow up for a future study, instead of claiming that it was proved in this manuscript.
Overall, I would suggest this manuscript for publication after some revisions and careful attention to the confidence with which some conclusions are presented.
Specific comments:
P 4-5, L90-91: I think there should be more explanation of how the beam is split between the two instruments.
P 8, L161: Could the vehicles being loaded and unloaded impact your observations when the ship is docked?
P 11, L217: Table 6 is referenced before Tables 3, 4, and 5. This can make the paper a little confusing to navigate.
P11, L222-224: If the Xgas values are calculated from the VCD, as stated in Eq. 2, why would you need to have an additional correction applied to Xgas?
P11, L232-235: It would be helpful to state the typical time period for the 100-spectra co-addition.
P12, L242-243: Would the prevalence of high-altitude clouds in this area impact the usefulness of similar campaigns in the future, since this would likely limit the amount of time you could actively observe? These clouds may also affect the accuracy and precision of the EM27/SUN measurements.
P14, L275: What time intervals are used for averaging?
Section 4.3: It seems like somewhere in this section you should report on the variance in the co-added spectra from the EM27/SUN while the ship is in motion. All of your performance metrics are based on observations taken while the instrument is stationary, but the objective of this paper is to show that the spectrometer can perform on a moving ship.
P16, L328: How can you be sure that the larger ratios are caused by differences in the retrieval algorithms? Can you elaborate on which specific differences are most likely contributing?
Figure 9: The peak enhancement ratio in panel e appears to occur between the two enhancement events. This would cast some doubt on the validity of this ratio. If this point were discounted the overall range of the CO/NOx ratios would be closer to 25-40, and this might change your assessment of the likely emission sources. In addition, the ratios after the enhancement are not very different from those during the event.
P21, L404-406: The uncertainty in the retrievals should also be considered here.
Table 7: I suggest ordering the columns of the ratios to match the order of plots in Figure 9.
P24, L419: Your conclusion that this is an enhancement from the steel factory is based on the designation of the high CO grid cell being labeled as steel manufacturing in the inventories and wind direction; however, your enhancement ratios are very different from those reported by the inventories for this category. You also pointed out that you confirmed with the inventory that they had misplaced the steel factory. All of this disagreement suggests that there may be another explanation for your results. From the ratios you cite in Table 7, the sources that match the best are cement factories (with CO/CO2) and urban gasoline or motorway gasoline emissions (with NOx/CO2 and CO/NOx). You do mention later that there is likely a contribution from transportation emissions, but you do not discuss a possible contribution from cement manufacturing. While you do not show any cement industry emissions in Figure 7, have you investigated whether there is any cement manufacturing not reported by the Climate TRACE coalition or the inventories. As this is an industrial area, I would be surprised if there were no cement factories in the region.
P24, L420-421: The CO/CO2 emission ratios reported by Schneising et al. are an order of magnitude larger than what you observed. How does this support your argument?
P24, L423-424: The Van der Maas ratios for CO/NOx are also quite a bit larger than what you observed and this is especially true if you exclude the dubious peak in CO/NOx ratios that occurs at the end of the first enhancement event.
P24, L428-429: While I think all the information provided is interesting and should be considered, it seems like you are overstating the certainty that one should have about the steel factory source attribution.
P24, L430-433: Glad to see this finally being discussed.
P25, L461: Should this say “bottom-up” instead of “top-down”?
P25, L468: I would suggest replacing “without permanent human attendance” with “with only part-time remote oversight” or something similar. It was my understanding that the instrument operations were supervised remotely. Did you encounter any problems that required intervention throughout the course of the campaign or did the instruments run automatically for the whole time without incident?
P25, L472-473: I don’t believe you proved that your setup is suitable for satellite validation because you did not actually compare to any satellite measurements.
P26, L481: You did not operate your instrument over the open ocean.