the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 May 2024
A Low-cost UAV Coordinated Carbon observation Network (LUCCN): an analysis of environment impact on ground base measurement node
Abstract. Most anthropogenic carbon dioxide (CO2) emissions originate from urban areas. To improve understandings of urban and regional emissions, we design and construct a low-cost UAV coordinated carbon observation network (LUCCN) which uses mid-accuracy (±1 ppm) CO2 sensors. In this paper, we introduce our multi-variable non-linear regression method for calibrating the non-dispersive infrared (NDIR) CO2 sensors for LUCCN’s ground stations. We tested our calibration method with concentration data collected at the Xinglong Atmospheric Background Observatory. With comparison against data simultaneously collected by a high-accuracy cavity ring-down spectrometer, we found the maximum standard deviation of LUCCN’s sensors to be 0.782 ppm in a controlled laboratory environment with a 1-second window size and 0.53 ppm in an outdoor environment with a 1-hour running average window size. As validation of LUCCN’s ground measurements, we identify and present consistent trends between local CO2 concentration variations and aerosol pollution events captured by the space-based moderate resolution imaging spectrometer (MODIS).
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Status: closed (peer review stopped)
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RC1: 'Comment on amt-2024-49', Anonymous Referee #1, 31 May 2024
General Comments:
The impetus behind this study is clear and the goals outlined are worth pursuing. Establishing dense networks for taking CO2 measurements is highly relevant to improving our understanding of sources of CO2 and verifying emission inventories. Moreover, a framework for ensuring the accuracy of lower cost sensors is valuable in rendering these networks accessible to a greater number of researchers. The techniques outlined in this paper for calibrating the low-cost sensors are robust and seem to yield good results when compared to higher precision instruments. However, this study, in its current condition, is not fit for publication for the reasons I outline below.
Specific comments:
1. The title of this paper references the term UAV, an acronym which is never defined or expanded on throughout the text. As I understand it, it stands for unmanned aerial vehicle. Therefore, the concept of ground-based, UAV network is self-contradictory. This is a rather significant oversight for a concept which is central to the study design.
2. The case studies used the validate the sensors' performance leave much to be desired. The mere fact that CO2 concentrations are seen to rise concurrently with satellite detection of aerosol pollution events is not a convincing proof of their true performance capabilities. If we want to utilize this network to ask more nuanced questions regarding sources of CO2, this qualitative approach to validation does not present strong evidence of the network's usefulness.
3. Even if we had established the network's true potential in terms of accurately sensing variability in atmospheric CO2, I do not believe that this study represent a novel advancement for the field. The background of this paper lists examples of how these same CO2 sensors have already been proven to work in other domains. I do not see new insights gained from this analysis.
Technical Corrections:
Notwithstanding the issues I present above, the language used in this paper is unfortunately lacking in many regards. There are simply too many typographical errors to be listed here. Inaccuracies in grammar, sentence structure, and word choice render this work largely unintelligible. If this paper were to be resubmitted, it would first require very substantial copy-editing.
Citation: https://doi.org/10.5194/amt-2024-49-RC1 -
RC2: 'Reply on RC1', Anonymous Referee #1, 01 Jun 2024
It occurs to me now that the authors might have wanted to use "UAV" to refer to the MODIS satellite instrument. If so, this is a misnomer. Additionally, featuring "UAV coordinated" in the title of the study might also be a stretch given the qualitative nature of the manner in which the satellite data was used to corroborate ground-based measurements.
Citation: https://doi.org/10.5194/amt-2024-49-RC2 -
RC3: 'Reply on RC2', Anonymous Referee #1, 01 Jun 2024
Upon further investigation, I found another paper by the corresponding author referencing this network (DOI: 10.1007/s00376-023-3107-5). This latter paper leads me to believe that the authors did in fact intend to use "UAV" to refer to drone-based measurements. However, they omitted to describe any component of this analysis in the current paper being reviewed. My confusion here is a testament to the difficulty of comprehending this text.
Citation: https://doi.org/10.5194/amt-2024-49-RC3
-
RC3: 'Reply on RC2', Anonymous Referee #1, 01 Jun 2024
-
RC2: 'Reply on RC1', Anonymous Referee #1, 01 Jun 2024
-
RC4: 'Comment on amt-2024-49', Anonymous Referee #2, 15 Aug 2024
This paper explores the performance of the low-cost carbon dioxide (CO2) sensors calibrated in lab and in field. Laboratory assessment of low-cost CO2 gas sensors show the response of the sensors to the environmental variables which vary by sensors. In field assessment performed in long term show the robust performance. It is not entirely clear to me whether the results are sufficiently exciting and compelling for publication. No new insights are apparent from the listed previous studies. I will point them out in general comments. Additionally, the manuscript requires improved clarity and organization, along with thorough English proofreading, before publication.
General Comments:
1. The only new insight I can find if I look hard is the application of the sensors to the unmanned aerial vehicle (UAV). However, no description and connection to the UAV system is provided. The overall concept of the LUCCN system needs to be described in detail including the purpose of the ground-based measurements.
2. Calibration strategy and assessment has to be designed to suit the UAV system which seems not or not described well. How long period of the data did you use for the calibration of the ground-based sensors? Are the sensors getting applied to the UAV getting calibrated in the field calibration system? How long period does the sensor has to be in field for calibration to get a robust performance?
3. The case studies do not provide any contribution to validating the sensor's performance. The observation of CO2 concentrations increase alongside satellite-detected aerosol pollution events alone does not provide sufficient evidence, especially without any detailed information of does pollution events. What exactly is happening chemically during those periods? What is the source? Is there any evidence that CO2 should also increase alongside those events?
Minor Comments:
- Line 67: Performance of Picarro is better than 0.1 ppm
- Section 4: Please use words of exact scientific explanation, not just 'polluted weather', 'clean weather', and etc… Also, the word 'Aerosol' is only mentioned in the abstract and nowhere in the main text.
Citation: https://doi.org/10.5194/amt-2024-49-RC4
Status: closed (peer review stopped)
-
RC1: 'Comment on amt-2024-49', Anonymous Referee #1, 31 May 2024
General Comments:
The impetus behind this study is clear and the goals outlined are worth pursuing. Establishing dense networks for taking CO2 measurements is highly relevant to improving our understanding of sources of CO2 and verifying emission inventories. Moreover, a framework for ensuring the accuracy of lower cost sensors is valuable in rendering these networks accessible to a greater number of researchers. The techniques outlined in this paper for calibrating the low-cost sensors are robust and seem to yield good results when compared to higher precision instruments. However, this study, in its current condition, is not fit for publication for the reasons I outline below.
Specific comments:
1. The title of this paper references the term UAV, an acronym which is never defined or expanded on throughout the text. As I understand it, it stands for unmanned aerial vehicle. Therefore, the concept of ground-based, UAV network is self-contradictory. This is a rather significant oversight for a concept which is central to the study design.
2. The case studies used the validate the sensors' performance leave much to be desired. The mere fact that CO2 concentrations are seen to rise concurrently with satellite detection of aerosol pollution events is not a convincing proof of their true performance capabilities. If we want to utilize this network to ask more nuanced questions regarding sources of CO2, this qualitative approach to validation does not present strong evidence of the network's usefulness.
3. Even if we had established the network's true potential in terms of accurately sensing variability in atmospheric CO2, I do not believe that this study represent a novel advancement for the field. The background of this paper lists examples of how these same CO2 sensors have already been proven to work in other domains. I do not see new insights gained from this analysis.
Technical Corrections:
Notwithstanding the issues I present above, the language used in this paper is unfortunately lacking in many regards. There are simply too many typographical errors to be listed here. Inaccuracies in grammar, sentence structure, and word choice render this work largely unintelligible. If this paper were to be resubmitted, it would first require very substantial copy-editing.
Citation: https://doi.org/10.5194/amt-2024-49-RC1 -
RC2: 'Reply on RC1', Anonymous Referee #1, 01 Jun 2024
It occurs to me now that the authors might have wanted to use "UAV" to refer to the MODIS satellite instrument. If so, this is a misnomer. Additionally, featuring "UAV coordinated" in the title of the study might also be a stretch given the qualitative nature of the manner in which the satellite data was used to corroborate ground-based measurements.
Citation: https://doi.org/10.5194/amt-2024-49-RC2 -
RC3: 'Reply on RC2', Anonymous Referee #1, 01 Jun 2024
Upon further investigation, I found another paper by the corresponding author referencing this network (DOI: 10.1007/s00376-023-3107-5). This latter paper leads me to believe that the authors did in fact intend to use "UAV" to refer to drone-based measurements. However, they omitted to describe any component of this analysis in the current paper being reviewed. My confusion here is a testament to the difficulty of comprehending this text.
Citation: https://doi.org/10.5194/amt-2024-49-RC3
-
RC3: 'Reply on RC2', Anonymous Referee #1, 01 Jun 2024
-
RC2: 'Reply on RC1', Anonymous Referee #1, 01 Jun 2024
-
RC4: 'Comment on amt-2024-49', Anonymous Referee #2, 15 Aug 2024
This paper explores the performance of the low-cost carbon dioxide (CO2) sensors calibrated in lab and in field. Laboratory assessment of low-cost CO2 gas sensors show the response of the sensors to the environmental variables which vary by sensors. In field assessment performed in long term show the robust performance. It is not entirely clear to me whether the results are sufficiently exciting and compelling for publication. No new insights are apparent from the listed previous studies. I will point them out in general comments. Additionally, the manuscript requires improved clarity and organization, along with thorough English proofreading, before publication.
General Comments:
1. The only new insight I can find if I look hard is the application of the sensors to the unmanned aerial vehicle (UAV). However, no description and connection to the UAV system is provided. The overall concept of the LUCCN system needs to be described in detail including the purpose of the ground-based measurements.
2. Calibration strategy and assessment has to be designed to suit the UAV system which seems not or not described well. How long period of the data did you use for the calibration of the ground-based sensors? Are the sensors getting applied to the UAV getting calibrated in the field calibration system? How long period does the sensor has to be in field for calibration to get a robust performance?
3. The case studies do not provide any contribution to validating the sensor's performance. The observation of CO2 concentrations increase alongside satellite-detected aerosol pollution events alone does not provide sufficient evidence, especially without any detailed information of does pollution events. What exactly is happening chemically during those periods? What is the source? Is there any evidence that CO2 should also increase alongside those events?
Minor Comments:
- Line 67: Performance of Picarro is better than 0.1 ppm
- Section 4: Please use words of exact scientific explanation, not just 'polluted weather', 'clean weather', and etc… Also, the word 'Aerosol' is only mentioned in the abstract and nowhere in the main text.
Citation: https://doi.org/10.5194/amt-2024-49-RC4
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