the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Nov 2024
Observation of GHG vertical profile in the boundary layer of the Mount Qomolangma region using a multirotor UAV
Abstract. Understanding the vertical profile of greenhouse gases (GHGs) is crucial for elucidating their sources and sinks, transport pathways, and influence on Earth’s radiative balance, as well as for enhancing predictive capabilities for climate change. Remote sensing methods for measuring vertical GHG profiles often involve substantial uncertainties, while in-situ measurements are limited by high equipment costs and operational expenses, rendering them impractical for long-term continuous observation efforts. In this study, we have developed an automatic low-cost and user-friendly multi-altitude atmospheric sampling device designed for small and medium-sized unmanned aerial vehicles (UAVs), balloons, and other flight platforms. A field campaign was carried out in the Mount Qomolangma region, at an average surface altitude of 4300 m above sea level (a.s.l.). In total, we conducted 15 flights with 139 samples from the ground surface up to a height of 1215 m using the device mounted on a hexacopter UAV platform. The samples were analyzed using the Angilent gas chromatography (GC) 7890A, and the vertical profiles of four GHG species (CO2, CH4, N2O, and SF6) were archived. The new data depict the vertical distribution of GHGs in the boundary layer of the Mount Qomolangma region.
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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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RC1: 'Comment on egusphere-2024-3478', Anonymous Referee #1, 26 Dec 2024
Dear authors, congratulations on an exciting work. I believe that with minor adjustments, your article can be improved to enhance its impact. The main adjustments I believe are necessary are a contextualization of this research in the larger body of UAS-based GHG measurement literature, more details on the calibration and use of the iMET XQ2, and more details on the impact of the aircraft on sampling method (up vs. down). Below are a few recommendations (combining broad and detailed):
Introduction/Motivation/Conclusion: In your argument, you juxtapose in-situ and remote measurements, and within in-situ technologies, you highlight the limitations of towers and manned aircraft to set your reason for developing this technology. However, you fail to contrast your technology with previous UAV-based GHG sampling. I believe the readers would greatly benefit if you were able to highlight the differences between your method and prior methods, for example:
- https://amt.copernicus.org/articles/11/1833/2018/
- https://amt.copernicus.org/articles/15/5599/2022/
- https://amt.copernicus.org/articles/14/153/2021/
- https://amt.copernicus.org/articles/17/677/2024/
I say this because each method has various advantages and disadvantages, and readers interested in your method should clearly know the cases in which your method works best, as well as its limitations. For example, Kunz and Azevedo (linked above) have much lighter, cheaper, and higher spatial resolution technologies than yours. However, their technologies are limited to one or two gases, whereas yours is a multi-gas solution (which is more straightforward to compare with satellites and towers). Nonetheless, your 10 samples produce a much coarser atmospheric profile that is harder to relate to the ABL cycle.
My comment here is further corroborated by your conclusion, where you emphasize the low cost and lightweight aspects of your solution, which is relatively not entirely correct (see more comments below).
So, please add a paragraph in your introduction or method contextualizing your work within the UAV-based GHG sampling literature, highlighting the advantages and best applications of your method, as well as its limitations.
Line 69-70: You say your payload has 2.4 Kg, and it can easily be carried by any small UAV. Because the terms easily and small are relative, this can cause confusion. Within UAV work, 2.4 Kg is not customarily considered small/light. In fact, very few commercial multirotors have this payload capacity. Within the multirotors that have this capacity, most would only be able to carry it for less than 15-20 minutes. Therefore, I recommend you eliminate the adjectives "small" and "easily". Just say that the payload is vehicle-independent and can be used on any multirotor capable of handling this size and weight. If you think making the point that many other vehicles could carry it is necessary, give the reader at least five different examples of commercial vehicles with such capability (I believe you will struggle to find more than 3 that meet your requirements).
- Additionally, in UAV work, payloads are often considered in terms of SWaP (size, weight, and power). So, I recommend you also add the power consumption of your system (in Watts) because that can also limit platform selection. For example, do the pumps operate at 5 or 12V? If it is 12V, you can't use a UAV that uses a 3S battery.
- Another comment. Since you are driving the point that the measurement technology can be used in other vehicles, it implies that others can use it. So, it begs the question, is it open source? Is it available to the larger community? If it is, you should mention it because it makes the vehicle independence argument more important.
- Finally, as you experienced, UAVs capable of carrying 2.4 Kg often can't handle wind speeds larger than 15 m/s, which is a considerable limitation for folks considering adopting this technology for year-round GHG monitoring (say, one flight per day, every day), or as you put it "a new test-bed for long-term and continuous (...) monitoring." Therefore, 2.4 kg is not small or easy.
- - This tone adjustment should also be reviewed in your conclusion.
Figures (all): they are all too small and unreadable in print. I hope this is an artifact of this pre-print format. If not, be sure to increase them in the final paper.
Line 75: What is the motivation to calculate altitude from the iMET XQ2 sensors? Was it not possible to use the UAV's barometric+GPS filtered altitude?
iMET XQ2: I understand this paper focuses on GHG and not atmospheric boundary layer (ABL) measurements. However, since you chose to correlate your measurements with ABL behavior and chose to use the XQ2 as your source for altitude measurements, the following comments are critical for the scientific relevance of your article:
- The XQ2 is a notoriously bad UAV PTU solution because it does not account for solar shielding, UAV-based heat sources, sensor air flow minimums, and other measurement interference sources.
- Given your extensive use of its information, it is necessary that you provide the reader with information on how you calibrated it and integrated it into the platform. Otherwise, it will be harder to trust your data.
- For example, the PT-100 bead thermistor on the XQ2 requires at least a 5 m/s flow over it. However, its 1-second time-response limits its flight speeds for good ABL reconstruction to 2.5 m/s (this issue is often solved with independent aspiration fans). The same is true for the HYT-271 hygrometer inside the XQ2. For good references on why placement, aspiration, and operation UAV for reasonable PTU measurements, you can take a look at other papers on this matter:
- https://amt.copernicus.org/articles/11/5519/2018/
- https://www.mdpi.com/1424-8220/19/6/1470
- https://journals.ametsoc.org/view/journals/atot/35/8/jtech-d-18-0019.1.xml
- https://www.mdpi.com/1424-8220/19/9/2179
Line 80: What is "Just Go"? I suggest explaining it in words or substituting it with an actual technical term.
Line 82: Here, you explain your procedure for sampling during the descent. However, figure 2 and the conclusion allude to data collection during the ascent. Given that you are producing a relatively "low" resolution profile (with samples at approximately 100 meters), this is not a problem for considerations regarding propeller layer mixing for the gas samples. Nonetheless, it will considerably impact your PTU measurements and potentially your Z calculation. Therefore, you should make these limitations more explicit and describe your system's best use (up or down).
- For a resource on the limitations of ABL PTU measurements on ascent versus descent, I recommend this paper https://amt.copernicus.org/articles/9/2675/2016/.
- This comment should also affect the tone of your conclusion.
Line 87: "Angilent" is misspelled.
Line 116: I believe adding one more word, such as "at" or "by," to the sentence "Institute of Tibetan Plateau Research, the Chinese Academy of Sciences" might make it better for readers not familiar with the relationship between these two institutions.
Line 117: I believe adding one more word, such as "near" or "at," to the sentence "located in Tingri County, Rikaze City" might make it better for readers unfamiliar with the local geography.
Table 2: Please provide more information about how this mean is calculated in the text (a simple one-liner). It is unclear if it is the mean for the whole dataset (all samples, flights, and days). If it is, what is the relevance of this mean? It seems it only indicates that GHG variations at the sites are minor (compared with sites at lower altitudes or near urban centers). For example, near urban centers, CO2 measurements can show up to a 30 ppm gradient (surface to top of profile at 1500 m) in a flight due to a low altitude inversion in the ABL.
Line 129: I believe there is an editing mistake here. How are potential temperature and specific humidity derived from GPS? Were you not using PTU to calculate Z?
Figures 4 - 7: In the methodology, you mentioned the data was gathered during the descent, but later you mention data from the ascent. Were all the data points in Figures 4 -7 only collected during the descent?
Line 157:
- As detailed above, this is not a lightweight system.
- Either I missed it, or you never mentioned the cost of your system. So why are you concluding it is a low-cost system? Given its dependence on a US $ 11k machine (GC 7890), it is costly compared to the other systems detailed above (which cost less than US $ 300).
- Granted, these solutions have fewer gases and lesser accuracies, although they have much higher spatial resolution.
- Therefore, I would rephrase this conclusion to say it is platform-independent (making it flexible) and less expensive than other solutions for the same gases with the same measurement accuracy (if that is actually true. I am unsure).
All in all, this is fascinating work. Congratulations. I hope my comments have provided you with resources to make it even better for the community in the final publication. Best of luck.
Citation: https://doi.org/10.5194/egusphere-2024-3478-RC1 - AC1: 'Reply on RC1', Ying Zhou, 28 Jan 2025
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RC2: 'Comment on egusphere-2024-3478', Anonymous Referee #2, 28 Dec 2024
The comments of “Observation of GHG vertical profile in the boundary layer of the Mount Qomolangma region using a multirotor UAV” by Zhou et al (ID: egusphere-2024-3478).
Greenhouse gases such as CO2 and CH4 are thought to be the primary human activities contribute to the current global warming, therefore many efforts, such as ground-based and space-based measurements along as the flux modelling, have been done to figure out the amount of such contributes. Those measurements focused on the column integrated amount, not the vertical profiles. Based on sampling method and UAV, the authors provided a simple and economic method for vertical profile of four GHG species (CO2, CH4, N2O, and SF6) in remote and inaccessible Tibetan area. CO distribution of but less measurements. The work is exciting and encouraging for the research of “Carbon” source and sink, and introduces an automatic low-cost and user-friendly multi-altitude atmospheric sampling device that can be mounted on small and medium-sized unmanned aerial vehicles, balloons, and other flight platforms to collect air samples at multiple altitudes. A five-day continuous observation campaign was conducted at Mount Cho Oyu Basecamp and Mount Qomolangma Station to analyze and explore the vertical distribution characteristics of four greenhouse gases. These measurements are critical for elucidating their sources and sinks, transport pathways, and influence on Earth’s radiative balance, as well as for enhancing predictive capabilities for climate change. Overall, the article is well-structured, provides valuable insights, and language well-written. Further clarification can be made in some areas before published, and specific comments are as follows.
Main Comments:
- The innovative aspects of the study can be more explicitly emphasized. Additionally, the structure of the article should be introduced at the end of the introduction.
- What impact does the change in BLH have on the vertical distribution and concentration of greenhouse gases?
- Does the vertical distribution of greenhouse gas concentrations change due to potential long-range transport?
Detail comments:
- “Figure 5. Same as Figure 5…” confusing.
- The conclusions of the article need further in-depth discussion.
- The text in figures is relatively small and needs to be improved.
- Line 13-20: Reference support required.
- Line 65: give the full name of iMET XQ2 and its main parameters
- Line 77: Eq（1）：iMET XQ2 should also provide height information, say GPS height，please provide the comparison of the two data.
- Line 80: please explain “Just go”
- Line 83: “1300 a.g.l.“ should be “1300m above ground”??
- Line 87: it’s better to change section 2.3 “Lab analysis” to “air sample analysis”
- Line 158: CMU or MCU?
- Line 163: what’s “GPS profile”?
Citation: https://doi.org/10.5194/egusphere-2024-3478-RC2 - AC2: 'Reply on RC2', Ying Zhou, 28 Jan 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3478', Anonymous Referee #1, 26 Dec 2024
Dear authors, congratulations on an exciting work. I believe that with minor adjustments, your article can be improved to enhance its impact. The main adjustments I believe are necessary are a contextualization of this research in the larger body of UAS-based GHG measurement literature, more details on the calibration and use of the iMET XQ2, and more details on the impact of the aircraft on sampling method (up vs. down). Below are a few recommendations (combining broad and detailed):
Introduction/Motivation/Conclusion: In your argument, you juxtapose in-situ and remote measurements, and within in-situ technologies, you highlight the limitations of towers and manned aircraft to set your reason for developing this technology. However, you fail to contrast your technology with previous UAV-based GHG sampling. I believe the readers would greatly benefit if you were able to highlight the differences between your method and prior methods, for example:
- https://amt.copernicus.org/articles/11/1833/2018/
- https://amt.copernicus.org/articles/15/5599/2022/
- https://amt.copernicus.org/articles/14/153/2021/
- https://amt.copernicus.org/articles/17/677/2024/
I say this because each method has various advantages and disadvantages, and readers interested in your method should clearly know the cases in which your method works best, as well as its limitations. For example, Kunz and Azevedo (linked above) have much lighter, cheaper, and higher spatial resolution technologies than yours. However, their technologies are limited to one or two gases, whereas yours is a multi-gas solution (which is more straightforward to compare with satellites and towers). Nonetheless, your 10 samples produce a much coarser atmospheric profile that is harder to relate to the ABL cycle.
My comment here is further corroborated by your conclusion, where you emphasize the low cost and lightweight aspects of your solution, which is relatively not entirely correct (see more comments below).
So, please add a paragraph in your introduction or method contextualizing your work within the UAV-based GHG sampling literature, highlighting the advantages and best applications of your method, as well as its limitations.
Line 69-70: You say your payload has 2.4 Kg, and it can easily be carried by any small UAV. Because the terms easily and small are relative, this can cause confusion. Within UAV work, 2.4 Kg is not customarily considered small/light. In fact, very few commercial multirotors have this payload capacity. Within the multirotors that have this capacity, most would only be able to carry it for less than 15-20 minutes. Therefore, I recommend you eliminate the adjectives "small" and "easily". Just say that the payload is vehicle-independent and can be used on any multirotor capable of handling this size and weight. If you think making the point that many other vehicles could carry it is necessary, give the reader at least five different examples of commercial vehicles with such capability (I believe you will struggle to find more than 3 that meet your requirements).
- Additionally, in UAV work, payloads are often considered in terms of SWaP (size, weight, and power). So, I recommend you also add the power consumption of your system (in Watts) because that can also limit platform selection. For example, do the pumps operate at 5 or 12V? If it is 12V, you can't use a UAV that uses a 3S battery.
- Another comment. Since you are driving the point that the measurement technology can be used in other vehicles, it implies that others can use it. So, it begs the question, is it open source? Is it available to the larger community? If it is, you should mention it because it makes the vehicle independence argument more important.
- Finally, as you experienced, UAVs capable of carrying 2.4 Kg often can't handle wind speeds larger than 15 m/s, which is a considerable limitation for folks considering adopting this technology for year-round GHG monitoring (say, one flight per day, every day), or as you put it "a new test-bed for long-term and continuous (...) monitoring." Therefore, 2.4 kg is not small or easy.
- - This tone adjustment should also be reviewed in your conclusion.
Figures (all): they are all too small and unreadable in print. I hope this is an artifact of this pre-print format. If not, be sure to increase them in the final paper.
Line 75: What is the motivation to calculate altitude from the iMET XQ2 sensors? Was it not possible to use the UAV's barometric+GPS filtered altitude?
iMET XQ2: I understand this paper focuses on GHG and not atmospheric boundary layer (ABL) measurements. However, since you chose to correlate your measurements with ABL behavior and chose to use the XQ2 as your source for altitude measurements, the following comments are critical for the scientific relevance of your article:
- The XQ2 is a notoriously bad UAV PTU solution because it does not account for solar shielding, UAV-based heat sources, sensor air flow minimums, and other measurement interference sources.
- Given your extensive use of its information, it is necessary that you provide the reader with information on how you calibrated it and integrated it into the platform. Otherwise, it will be harder to trust your data.
- For example, the PT-100 bead thermistor on the XQ2 requires at least a 5 m/s flow over it. However, its 1-second time-response limits its flight speeds for good ABL reconstruction to 2.5 m/s (this issue is often solved with independent aspiration fans). The same is true for the HYT-271 hygrometer inside the XQ2. For good references on why placement, aspiration, and operation UAV for reasonable PTU measurements, you can take a look at other papers on this matter:
- https://amt.copernicus.org/articles/11/5519/2018/
- https://www.mdpi.com/1424-8220/19/6/1470
- https://journals.ametsoc.org/view/journals/atot/35/8/jtech-d-18-0019.1.xml
- https://www.mdpi.com/1424-8220/19/9/2179
Line 80: What is "Just Go"? I suggest explaining it in words or substituting it with an actual technical term.
Line 82: Here, you explain your procedure for sampling during the descent. However, figure 2 and the conclusion allude to data collection during the ascent. Given that you are producing a relatively "low" resolution profile (with samples at approximately 100 meters), this is not a problem for considerations regarding propeller layer mixing for the gas samples. Nonetheless, it will considerably impact your PTU measurements and potentially your Z calculation. Therefore, you should make these limitations more explicit and describe your system's best use (up or down).
- For a resource on the limitations of ABL PTU measurements on ascent versus descent, I recommend this paper https://amt.copernicus.org/articles/9/2675/2016/.
- This comment should also affect the tone of your conclusion.
Line 87: "Angilent" is misspelled.
Line 116: I believe adding one more word, such as "at" or "by," to the sentence "Institute of Tibetan Plateau Research, the Chinese Academy of Sciences" might make it better for readers not familiar with the relationship between these two institutions.
Line 117: I believe adding one more word, such as "near" or "at," to the sentence "located in Tingri County, Rikaze City" might make it better for readers unfamiliar with the local geography.
Table 2: Please provide more information about how this mean is calculated in the text (a simple one-liner). It is unclear if it is the mean for the whole dataset (all samples, flights, and days). If it is, what is the relevance of this mean? It seems it only indicates that GHG variations at the sites are minor (compared with sites at lower altitudes or near urban centers). For example, near urban centers, CO2 measurements can show up to a 30 ppm gradient (surface to top of profile at 1500 m) in a flight due to a low altitude inversion in the ABL.
Line 129: I believe there is an editing mistake here. How are potential temperature and specific humidity derived from GPS? Were you not using PTU to calculate Z?
Figures 4 - 7: In the methodology, you mentioned the data was gathered during the descent, but later you mention data from the ascent. Were all the data points in Figures 4 -7 only collected during the descent?
Line 157:
- As detailed above, this is not a lightweight system.
- Either I missed it, or you never mentioned the cost of your system. So why are you concluding it is a low-cost system? Given its dependence on a US $ 11k machine (GC 7890), it is costly compared to the other systems detailed above (which cost less than US $ 300).
- Granted, these solutions have fewer gases and lesser accuracies, although they have much higher spatial resolution.
- Therefore, I would rephrase this conclusion to say it is platform-independent (making it flexible) and less expensive than other solutions for the same gases with the same measurement accuracy (if that is actually true. I am unsure).
All in all, this is fascinating work. Congratulations. I hope my comments have provided you with resources to make it even better for the community in the final publication. Best of luck.
Citation: https://doi.org/10.5194/egusphere-2024-3478-RC1 - AC1: 'Reply on RC1', Ying Zhou, 28 Jan 2025
-
RC2: 'Comment on egusphere-2024-3478', Anonymous Referee #2, 28 Dec 2024
The comments of “Observation of GHG vertical profile in the boundary layer of the Mount Qomolangma region using a multirotor UAV” by Zhou et al (ID: egusphere-2024-3478).
Greenhouse gases such as CO2 and CH4 are thought to be the primary human activities contribute to the current global warming, therefore many efforts, such as ground-based and space-based measurements along as the flux modelling, have been done to figure out the amount of such contributes. Those measurements focused on the column integrated amount, not the vertical profiles. Based on sampling method and UAV, the authors provided a simple and economic method for vertical profile of four GHG species (CO2, CH4, N2O, and SF6) in remote and inaccessible Tibetan area. CO distribution of but less measurements. The work is exciting and encouraging for the research of “Carbon” source and sink, and introduces an automatic low-cost and user-friendly multi-altitude atmospheric sampling device that can be mounted on small and medium-sized unmanned aerial vehicles, balloons, and other flight platforms to collect air samples at multiple altitudes. A five-day continuous observation campaign was conducted at Mount Cho Oyu Basecamp and Mount Qomolangma Station to analyze and explore the vertical distribution characteristics of four greenhouse gases. These measurements are critical for elucidating their sources and sinks, transport pathways, and influence on Earth’s radiative balance, as well as for enhancing predictive capabilities for climate change. Overall, the article is well-structured, provides valuable insights, and language well-written. Further clarification can be made in some areas before published, and specific comments are as follows.
Main Comments:
- The innovative aspects of the study can be more explicitly emphasized. Additionally, the structure of the article should be introduced at the end of the introduction.
- What impact does the change in BLH have on the vertical distribution and concentration of greenhouse gases?
- Does the vertical distribution of greenhouse gas concentrations change due to potential long-range transport?
Detail comments:
- “Figure 5. Same as Figure 5…” confusing.
- The conclusions of the article need further in-depth discussion.
- The text in figures is relatively small and needs to be improved.
- Line 13-20: Reference support required.
- Line 65: give the full name of iMET XQ2 and its main parameters
- Line 77: Eq（1）：iMET XQ2 should also provide height information, say GPS height，please provide the comparison of the two data.
- Line 80: please explain “Just go”
- Line 83: “1300 a.g.l.“ should be “1300m above ground”??
- Line 87: it’s better to change section 2.3 “Lab analysis” to “air sample analysis”
- Line 158: CMU or MCU?
- Line 163: what’s “GPS profile”?
Citation: https://doi.org/10.5194/egusphere-2024-3478-RC2 - AC2: 'Reply on RC2', Ying Zhou, 28 Jan 2025
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Ying Zhou
Congcong Qiao
Minqiang Zhou
Yilong Wang
Xiangjun Tian
Yinghong Wang
Minzheng Duan
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(8308 KB) - Metadata XML