the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 May 2024
Measurement report: Temporal variability of vertical profiles of CO2 and CH4 over urban environment
Abstract. Understanding the boundary layer dynamics over urban areas is important to improve estimates of the emissions of greenhouse gases (GHG), and predict their atmospheric mole fractions in these areas. Here we present the results of the annual vertical profiling measurement campaign performed in Krakow (Southern Poland). The campaign consisted of 12 monthly-based diurnal measurements of CO2 and CH4 molar fraction vertical profiles supplemented by meteorological parameters focused on the investigation of the dynamics of nocturnal boundary layer vertical structure within the urban boundary layer. The profile data were collected using two platforms: (i) a tethered touristic balloon operating commercially in the city centre and (ii) a drone system, with the selection of the platform based on operational availability and meteorological conditions. CO2 and CH4 molar fractions were measured using Picarro G2311-f (Picarro Inc., Santa Clara, California, USA) cavity ring-down spectrometer, while the meteorological conditions along the profile were measured using a set of temperature, relative humidity, pressure and wind low-cost sensors dedicated for application on-board of UAV platforms. The obtained results allowed us to analyse in-depth the formation, development and disappearance of the nocturnal boundary layer. In selected profiles, a CO2 and CH4 plumes located over the inversion layer (150–250 m AGL) were detected during the nighttime and morning hours. The application of high-resolution numerical simulations using the WRF-GHG model made it possible to identify the source of CO2 plume as a power plant located ca. 10 km southwest of the balloon launch location.
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Status: closed
- RC1: 'Comment on egusphere-2024-1167', Anonymous Referee #1, 22 Jul 2024
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RC2: 'Comment on egusphere-2024-1167', Anonymous Referee #2, 24 Jul 2024
The manuscript presents a measurement report on urban vertical profiles of atmospheric CO2 and CH4 using in situ measurements collected over 11 intensive periods. The data collection platforms included UAVs and tethered balloons. The study aims to provide insights into the vertical distribution of these GHGs and explores the influence of boundary layer development and nearby emission sources, which could significantly contribute to the understanding and quantification of urban emissions.
After carefully reading the manuscript and the other reviewers' comments, I agree that this manuscript may need a major revision. For example, the selection strategy for the case studies is not clearly explained. Providing a rationale for choosing specific case studies would help in understanding their relevance and importance. The analysis of boundary layer height (BLH) development is overly simplistic and does not offer new insights into atmospheric behavior. The CO2 profiles with enhancements seem inadequately explained, even with the use of high-resolution simulations. A deeper investigation into these profiles is necessary to elucidate the underlying mechanisms.
Citation: https://doi.org/10.5194/egusphere-2024-1167-RC2 -
AC1: 'Comment on egusphere-2024-1167', Miroslaw Zimnoch, 31 Oct 2024
The authors would like to thank two anonymous reviewers for very detailed and accurate comments. We considered and respond for all remarks specified below.
We believe that those comments allowed to significantly improve the quality of the manuscript and hope that after correction it will be suitable for publication in the form of Measurement Report in ACP.
General comments
The paper is a Measurement report on urban vertical profiles of atmospheric CO2 and CH4 in situ measurements. The measurements were collected during 11 intensive period covering days, roughly every month. The platforms used alternate UAV (sampling air directly injected in a Picarro) and touristic tethered balloon. The data is commented through a general overview of the dataset, through examples of daily boundary layer development and its link to vertical profiles of trace gas species, and finally through a case study of the influence of nearby coal plant exploiting high resolution WRF simulation.
The collection of vertical profiles in repeated intensive periods is certainly valuable and such measurements could ultimately contribute to a better quantification of urban emissions.
Here, a few case studies are selected and analyzed. The strategy behind the selection of the case studies is not really explained. The results on BLH development are in essence short pedagogical illustrations of expected atmospheric behavior; they do not bring novel information.
Since the manuscript is intended as a measurement report, not a full research paper, thorough analysis of all the collected cases is not required nor needed. General description of the observations was included along with several cases chosen that are interesting from pollution transport modelling point of view.
As a part of general description of the obtained data and a background upon which pollution plumes events were observed, BL diurnal dynamics was analyzed. As this part may seem unnecessary and bookcase to a reader accustomed to the subject, valuable conclusions are reached: 1) validity of the utilized methods is proved, 2) a baseline diurnal variability of the BLH to investigate events upon is established. Furthermore, an assumption of “standard” BLH diurnal course should not be done freely. It may not be that be obvious in urban areas, and in a complex terrain even more so. In fact, observation of any bookcase features in real atmosphere is quite rare and worth showing.
The profiles with CO2 enhancements are left half unexplained, even after mobilizing a high-resolution simulation. The couple of CH4 profiles are simply introduced in a figure but no fair analysis is offered.
The shallow and descriptive analysis of the case studies falls short of demonstrating the value of vertical profiles for urban GHG emission research.
The presented manuscript is meant to be a measurement report, not a research article. The main scope is to present the dataset together with the general background of greenhouse gases measurements in urban areas and a summary of the key findings. According to the ACP Measurement report description1, this type of publication may include the results of more limited scope than in research articles.
Given the lack of novelty extracted from the data reported here, this paper could be reworked to improve the case studies and their interpretation, and to be considered for submission as a dataset description paper than as an ACP measurement report.
In our opinion, scarcity of this type of data, collected regularly over an entire year in an urban area, and on top of that, in the city which is located in a concave terrain, makes them more than desired by the modelling community. They provide a mean to verify modeled BL dynamics in complex urban areas. The unexpected added value of encountered plumes was analyzed in detail. While this is true that the extensive dataset description does not belong to a research paper, the detailed analysis of cases which is supported by atmospheric modelling, does not belong to a dataset description paper either. This is why a measurement report is the best choice in this case.
Specific comments
L7 Abstract: indicate maximum altitude reached for each platform?
Max altitude information added.
Introduction: I find the introduction theoretical and generic. It could be reinforced on developing a more solid argument for the vertical measurements reported here and lay the ground for the selected case studies (e.G. CO2 point sources in a mixture of urban emissions).
The introduction is supposed to introduce the reader, who is not necessarily familiar with the field, into the research problem. It is impossible to do without theoretical and generic sentences. The section was supplemented by a justification on the need of vertical profiles of GHGs.
L31 I suggest to replace ‘development’ by ‘validation’.
Changed
L31 : ‘and also in the calibration and validation of satellite-borne measurements’ – this is true only for profiles that span full troposphere and lower stratosphere
This aspect is included in the discussion.
L34-47 It is not immediately clear what is the point of this paragraph. Please highlight to the reader the relevance of this paragraph
The paragraph was extended.
L38 ‘The variability… ‘ - this is relevant for CO2, but other GHG (CH4) deserve a significantly different description.
The sentence “variability of GHG fluxes is linked to land use and land cover” is true for both CO2 and CH4. Human activities releasing GHGs is also true for both gases. The differences are highlighted in following sentences. The paragraph was rewritten.
L50-52: please also include vertical profiles from tall towers, and aircores in the discussion
Profiles from tall towers were already included – see Richardson et al. 2017, aircore systems were added to the discussion.
L55 in this case UAV with discrete sampling should be differentiated from aircore sampling in their ability to achieve detailed vertical resolution
corrected
L140 please provide this literature review and the other reason for this choice
(McKinney et al. 2019, doi:10.5194/amt-12-3123-2019, Hedworth et al. 2022, doi: 10.3390/drones6090253) The text has been extended “Furthermore, since the air inlet was located above the balloon gondola, during the descent measured air might be contaminated with the passengers breathing”.
L157 the paper should briefly mention how is the WMO calibration scale propagated to the working standards
A sentence was added: For maintaining the WMO scale the laboratory is participating in a periodic intercomparison exercises.
L159 for the 200 m tubing is a flush pump used or is the Picarro pump sufficient?
modified
L254+ what are the meaningful spatial resolution (if applicable) of land surface databases? Is it relevant and justified to use 200m/1s resolution for the simulation? Given the reason for modelling given at L77 this complex model configuration is maybe an oversize solution?
Modified. The resolution of emission databases is insufficient to be able to resolve point sources, especially in highly heterogeneous urban environment. Parametrization of unresolved features usually leads to increased uncertainty of the model. The complex model configuration is a part of extensive analysis that should not be in a data description paper, but it certainly does belong to a measurement report, as was argued in the answer to general comments. The modeling system was designed as a part of other task and is the subject of a separate publication. Here we used this system as designed to support the interpretation of observed CO2 enhancements observed at high altitudes.
Figure 2 could be more interesting to replace daily means in panels B by an envelope extending from daytime mean to nighttime means daily values.
The figure has been modified
Figure 3. Maybe averaged profiles per period of the day and per season could be more explicit?
The authors carefully considered the different methods of presentation of the profile data to to make them as readable as possible. The averaging option was also considered, but due to different flights altitude and inhomogeneous temporal coverage the averaging was not possible (it produces artifacts and make the data not representative). We realize that figures can be difficult to interpret at first glance, but in our opinion this method is the most appropriate.
L292 I would not describe the interpretation of this figure as ‘easily’ identified – given the density of the figure the wording sounds a little bit ironic.
The paragraph was rewritten, the sentence in question was rephrased.
L293 please define SBL determination in this context and describe the data stratification that was performed to arrive at these mean values.
The text has been modified
Section 3.2 is interesting as academic illustrations but it is unclear what is the added value of this description. The selected academic examples are not put in the perspective of new information that would be expected from such a paper.
Establishing a background BL dynamics for the dataset is important for the following case studies. On top of that, observation of “academic” weather features in real atmosphere is not that common as it may appear.
Figure 4 – too many profiles: the visual clutter and the colors make it challenging to follow the interpretation. Given the title of section 3.2.1, why show all day’s measurements? Top row in the figure do not bring information.
The colors for all the figures were adjusted according to ACP accessibility guidelines. Small daytime variations in measured parameters are still variations and not showing them would be an omission. In this case, also valuable information can be obtained: for example, the highest wind speed in the whole profile was measured at 14:56UTC (around 17:00 local time), late afternoon when a short-time increase of wind speed is often reported in the area by aviation users and weather office forecasters. This is a time when daytime turbulence starts to decline and a gradual decrease of wind speed should be observed. This feature is worth investigating further and may potentially be beneficial for the local weather forecasting (information obtained directly from the aviation forecasting office).
L326-327 : are these the only two flights where this enhancement has been observed? Or are they just selected examples? How are they selected? How representative are they?
Our intention is not a full analysis of all of the events, as this is a measurement report, and not a comprehensive research article. These two events presented in the main text are the most clearly observed plumes that have been encountered – in two out of 12 measurement campaigns. Others were not that clear, but still noticeable – for example, CO2 peaks at 19:41UTC in April (Fig. S7), or CH4 peaks at 250 m agl at 02:15 UTC in June (Fig. S9).
L345 remind what different tagged tracer were included.
See section 2.4 line 270
Figure 6 why not also show CH4?
CH4 plume was not detected in the campaign 5, see Figure S13. Campaign 7 CH4 plumes are presented in Figure 8.
Figure 7 so the likelihood of source receptor relationship is fairly well established for this case, but how useful is it? it would be interesting to show also the simulation using total CO2 emissions next to the power plant tagged tracer
The purpose of presenting the figure 7 was to confirm the origin of CO2 enhancement observed in the vertical profiles. While the manuscript is focused on the presentation of the dataset, the additional model analysis (being the subject of the separate paper) were not consider by authors here.
L349 So what may have caused this enhancement, comparable in importance to the other one associated to the power plant? How would that be processed? What other important sources are influencing the measurements in this dataset?
There are several possible CO2 emission sources located in the urban areas which may influence the observed CO2 molar fraction. Observed CO2 enhancements located far above the inversion layer clearly seen in the profiles had to be attributed to one of industrial high emission sources existing nearby. It has been confirmed by numerical simulation presented on fig.7.
L350-355: is it possible that the choice of 200m spatial resolution is not appropriate or running out of control? How sensitive is the simulation to injection height? Injection velocity and gas temperature?
The detailed investigation of model configuration and performance is out of the scope of this measurement report.
L360 could the same information be obtained with a simpler approach?
Probably yes, although the results of simplified analyses based on e.g. Gaussian plum models could also be questioned due to a number of simplifications in this method. Since the authors had the opportunity to use a high-resolution model developed in other studies, they used it to demonstrate the origin of the observed plume.
L364 what is a quasi gaussian plume here?
We agree that quasi Gaussian term is not precisely used here (it is rather referring to the approximation of the probability distribution rather than gas plum shape. The term was removed from the text.
Fig 8 why not also show CO2 here?
The CO2 was presented and discussed on previous section (Fig. 6). It was decided to separate CO2 and CH4 cases into separate sections to make the text more clear and better organized.
Section 3.3.1: there is no articulated information in this section, I suggest to remove it completely.
The information we convey here is that while for CO2 it was relatively easy to locate the plume sources, it is not that obvious for CH4 – this GHG is more complicated in this regard as there are numerous possible sources, and more sophisticated data are required to identify them in the specific events.
L375-378 I find this statement poorly supported by the current text. However I assume that deeper work on the interpretation of the dataset may ultimately support such a finding.
Observational data, especially these describing “standard” dynamics of the weather parameters, are extremely valuable for validating of model performance.
L380-381 Same as above, I find this statement poorly supported by the text.
The sentence was supplemented by highlighting a need for more sophisticated data in case of methane plumes source identification.
Conclusion: I suggest to finish the conclusion with some opening toward potential applications and suggestions rather than on an 'impossible' measurement (I agree that reporting negative findings is useful for the community but maybe elsewhere in the conclusion). Maybe further research using different anemometer would solve the problem? or ground based lidar if available?
A recommendation of using different wind direction measurement methods was added. “Impossible” was rephrased to “unreliable”. This report describes in detail the methodology of the measurements, hence any recommendations, including those coming from a negative experience, should be emphasized.
Editorial
L32 : all ‘these’ components- not ‘the’?
done
L57ff : this paragraph is challenging to read. I encourage the authors to review the logic, and to simplify with shorter sentences
Paragraph modified
L361 maybe this section should be numbered 3.4? why is the CH4 case study numbering one level lower than CO2?
done
Citation: https://doi.org/10.5194/egusphere-2024-1167-AC1
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1167', Anonymous Referee #1, 22 Jul 2024
General comments
The paper is a Measurement report on urban vertical profiles of atmospheric CO2 and CH4 in situ measurements. The measurements were collected during 11 intensive period covering days, roughly every month. The platforms used alternate UAV (sampling air directly injected in a Picarro) and touristic tethered balloon. The data is commented through a general overview of the dataset, through examples of daily boundary layer development and its link to vertical profiles of trace gas species, and finally through a case study of the influence of nearby coal plant exploiting high resolution WRF simulation.
The collection of vertical profiles in repeated intensive periods is certainly valuable and such measurements could ultimately contribute to a better quantification of urban emissions.
Here, a few case studies are selected and analyzed. The strategy behind the selection of the case studies is not really explained. The results on BLH development are in essence short pedagogical illustrations of expected atmospheric behavior; they do not bring novel information. The profiles with CO2 enhancements are left half unexplained, even after mobilizing a high-resolution simulation. The couple of CH4 profiles are simply introduced in a figure but no fair analysis is offered.
The shallow and descriptive analysis of the case studies falls short of demonstrating the value of vertical profiles for urban GHG emission research.
Given the lack of novelty extracted from the data reported here, this paper could be reworked to improve the case studies and their interpretation, and to be considered for submission as a dataset description paper than as an ACP measurement report.
Specific comments
L7 Abstract: indicate maximum altitude reached for each platform?
Introduction: I find the introduction theoretical and generic. It could be reinforced on developing a more solid argument for the vertical measurements reported here and lay the ground for the selected case studies (e.G. CO2 point sources in a mixture of urban emissions).
L31 I suggest to replace ‘development’ by ‘validation’.
L31 : ‘and also in the calibration and validation of satellite-borne measurements’ – this is true only for profiles that span full troposphere and lower stratosphere
L34-47 It is not immediately clear what is the point of this paragraph. Please highlight to the reader the relevance of this paragraph
L38 ‘The variability… ‘ - this is relevant for CO2, but other GHG (CH4) deserve a significantly different description.
L50-52: please also include vertical profiles from tall towers, and aircores in the discussion
L55 in this case UAV with discrete sampling should be differentiated from aircore sampling in their ability to achieve detailed vertical resolution
L140 please provide this literature review and the other reason for this choice
L157 the paper should briefly mention how is the WMO calibration scale propagated to the working standards
L159 for the 200 m tubing is a flush pump used or is the Picarro pump sufficient?
L254+ what are the meaningful spatial resolution (if applicable) of land surface databases? Is it relevant and justified to use 200m/1s resolution for the simulation? Given the reason for modelling given at L77 this complex model configuration is maybe an oversize solution?
Figure 2 could be more interesting to replace daily means in panels B by an envelope extending from daytime mean to nighttime means daily values.
Figure 3. Maybe averaged profiles per period of the day and per season could be more explicit?
L292 I would not describe the interpretation of this figure as ‘easily’ identified – given the density of the figure the wording sounds a little bit ironic.
L293 please define SBL determination in this context and describe the data stratification that was performed to arrive at these mean values.
Section 3.2 is interesting as academic illustrations but it is unclear what is the added value of this description. The selected academic examples are not put in the perspective of new information that would be expected from such a paper.
Figure 4 – too many profiles: the visual clutter and the colors make it challenging to follow the interpretation. Given the title of section 3.2.1, why show all day’s measurements? Top row in the figure do not bring information.
L326-327 : are these the only two flights where this enhancement has been observed? Or are they just selected examples? How are they selected? How representative are they?
L345 remind what different tagged tracer were included.
Figure 6 why not also show CH4?
Figure 7 so the likelihood of source receptor relationship is fairly well established for this case, but how useful is it? it would be interesting to show also the simulation using total CO2 emissions next to the power plant tagged tracer
L349 So what may have caused this enhancement, comparable in importance to the other one associated to the power plant? How would that be processed? What other important sources are influencing the measurements in this dataset?
L350-355: is it possible that the choice of 200m spatial resolution is not appropriate or running out of control?
How sensitive is the simulation to injection height? Injection velocity and gas temperature?
L360 could the same information be obtained with a simpler approach?
L364 what is a quasi gaussian plume here?
Fig 8 why not also show CO2 here?
Section 3.3.1: there is no articulated information in this section, I suggest to remove it completely.
L375-378 I find this statement poorly supported by the current text. However I assume that deeper work on the interpretation of the dataset may ultimately support such a finding.
L380-381 Same as above, I find this statement poorly supported by the text.
Conclusion: I suggest to finish the conclusion with some opening toward potential applications and suggestions rather than on an 'impossible' measurement (I agree that reporting negative findings is useful for the community but maybe elsewhere in the conclusion). Maybe further research using different anemometer would solve the problem? or ground based lidar if available?
Editorial
L32 : all ‘these’ components- not ‘the’?
L57ff : this paragraph is challenging to read. I encourage the authors to review the logic, and to simplify with shorter sentences
L361 maybe this section should be numbered 3.4? why is the CH4 case study numbering one level lower than CO2?
Citation: https://doi.org/10.5194/egusphere-2024-1167-RC1 -
RC2: 'Comment on egusphere-2024-1167', Anonymous Referee #2, 24 Jul 2024
The manuscript presents a measurement report on urban vertical profiles of atmospheric CO2 and CH4 using in situ measurements collected over 11 intensive periods. The data collection platforms included UAVs and tethered balloons. The study aims to provide insights into the vertical distribution of these GHGs and explores the influence of boundary layer development and nearby emission sources, which could significantly contribute to the understanding and quantification of urban emissions.
After carefully reading the manuscript and the other reviewers' comments, I agree that this manuscript may need a major revision. For example, the selection strategy for the case studies is not clearly explained. Providing a rationale for choosing specific case studies would help in understanding their relevance and importance. The analysis of boundary layer height (BLH) development is overly simplistic and does not offer new insights into atmospheric behavior. The CO2 profiles with enhancements seem inadequately explained, even with the use of high-resolution simulations. A deeper investigation into these profiles is necessary to elucidate the underlying mechanisms.
Citation: https://doi.org/10.5194/egusphere-2024-1167-RC2 -
AC1: 'Comment on egusphere-2024-1167', Miroslaw Zimnoch, 31 Oct 2024
The authors would like to thank two anonymous reviewers for very detailed and accurate comments. We considered and respond for all remarks specified below.
We believe that those comments allowed to significantly improve the quality of the manuscript and hope that after correction it will be suitable for publication in the form of Measurement Report in ACP.
General comments
The paper is a Measurement report on urban vertical profiles of atmospheric CO2 and CH4 in situ measurements. The measurements were collected during 11 intensive period covering days, roughly every month. The platforms used alternate UAV (sampling air directly injected in a Picarro) and touristic tethered balloon. The data is commented through a general overview of the dataset, through examples of daily boundary layer development and its link to vertical profiles of trace gas species, and finally through a case study of the influence of nearby coal plant exploiting high resolution WRF simulation.
The collection of vertical profiles in repeated intensive periods is certainly valuable and such measurements could ultimately contribute to a better quantification of urban emissions.
Here, a few case studies are selected and analyzed. The strategy behind the selection of the case studies is not really explained. The results on BLH development are in essence short pedagogical illustrations of expected atmospheric behavior; they do not bring novel information.
Since the manuscript is intended as a measurement report, not a full research paper, thorough analysis of all the collected cases is not required nor needed. General description of the observations was included along with several cases chosen that are interesting from pollution transport modelling point of view.
As a part of general description of the obtained data and a background upon which pollution plumes events were observed, BL diurnal dynamics was analyzed. As this part may seem unnecessary and bookcase to a reader accustomed to the subject, valuable conclusions are reached: 1) validity of the utilized methods is proved, 2) a baseline diurnal variability of the BLH to investigate events upon is established. Furthermore, an assumption of “standard” BLH diurnal course should not be done freely. It may not be that be obvious in urban areas, and in a complex terrain even more so. In fact, observation of any bookcase features in real atmosphere is quite rare and worth showing.
The profiles with CO2 enhancements are left half unexplained, even after mobilizing a high-resolution simulation. The couple of CH4 profiles are simply introduced in a figure but no fair analysis is offered.
The shallow and descriptive analysis of the case studies falls short of demonstrating the value of vertical profiles for urban GHG emission research.
The presented manuscript is meant to be a measurement report, not a research article. The main scope is to present the dataset together with the general background of greenhouse gases measurements in urban areas and a summary of the key findings. According to the ACP Measurement report description1, this type of publication may include the results of more limited scope than in research articles.
Given the lack of novelty extracted from the data reported here, this paper could be reworked to improve the case studies and their interpretation, and to be considered for submission as a dataset description paper than as an ACP measurement report.
In our opinion, scarcity of this type of data, collected regularly over an entire year in an urban area, and on top of that, in the city which is located in a concave terrain, makes them more than desired by the modelling community. They provide a mean to verify modeled BL dynamics in complex urban areas. The unexpected added value of encountered plumes was analyzed in detail. While this is true that the extensive dataset description does not belong to a research paper, the detailed analysis of cases which is supported by atmospheric modelling, does not belong to a dataset description paper either. This is why a measurement report is the best choice in this case.
Specific comments
L7 Abstract: indicate maximum altitude reached for each platform?
Max altitude information added.
Introduction: I find the introduction theoretical and generic. It could be reinforced on developing a more solid argument for the vertical measurements reported here and lay the ground for the selected case studies (e.G. CO2 point sources in a mixture of urban emissions).
The introduction is supposed to introduce the reader, who is not necessarily familiar with the field, into the research problem. It is impossible to do without theoretical and generic sentences. The section was supplemented by a justification on the need of vertical profiles of GHGs.
L31 I suggest to replace ‘development’ by ‘validation’.
Changed
L31 : ‘and also in the calibration and validation of satellite-borne measurements’ – this is true only for profiles that span full troposphere and lower stratosphere
This aspect is included in the discussion.
L34-47 It is not immediately clear what is the point of this paragraph. Please highlight to the reader the relevance of this paragraph
The paragraph was extended.
L38 ‘The variability… ‘ - this is relevant for CO2, but other GHG (CH4) deserve a significantly different description.
The sentence “variability of GHG fluxes is linked to land use and land cover” is true for both CO2 and CH4. Human activities releasing GHGs is also true for both gases. The differences are highlighted in following sentences. The paragraph was rewritten.
L50-52: please also include vertical profiles from tall towers, and aircores in the discussion
Profiles from tall towers were already included – see Richardson et al. 2017, aircore systems were added to the discussion.
L55 in this case UAV with discrete sampling should be differentiated from aircore sampling in their ability to achieve detailed vertical resolution
corrected
L140 please provide this literature review and the other reason for this choice
(McKinney et al. 2019, doi:10.5194/amt-12-3123-2019, Hedworth et al. 2022, doi: 10.3390/drones6090253) The text has been extended “Furthermore, since the air inlet was located above the balloon gondola, during the descent measured air might be contaminated with the passengers breathing”.
L157 the paper should briefly mention how is the WMO calibration scale propagated to the working standards
A sentence was added: For maintaining the WMO scale the laboratory is participating in a periodic intercomparison exercises.
L159 for the 200 m tubing is a flush pump used or is the Picarro pump sufficient?
modified
L254+ what are the meaningful spatial resolution (if applicable) of land surface databases? Is it relevant and justified to use 200m/1s resolution for the simulation? Given the reason for modelling given at L77 this complex model configuration is maybe an oversize solution?
Modified. The resolution of emission databases is insufficient to be able to resolve point sources, especially in highly heterogeneous urban environment. Parametrization of unresolved features usually leads to increased uncertainty of the model. The complex model configuration is a part of extensive analysis that should not be in a data description paper, but it certainly does belong to a measurement report, as was argued in the answer to general comments. The modeling system was designed as a part of other task and is the subject of a separate publication. Here we used this system as designed to support the interpretation of observed CO2 enhancements observed at high altitudes.
Figure 2 could be more interesting to replace daily means in panels B by an envelope extending from daytime mean to nighttime means daily values.
The figure has been modified
Figure 3. Maybe averaged profiles per period of the day and per season could be more explicit?
The authors carefully considered the different methods of presentation of the profile data to to make them as readable as possible. The averaging option was also considered, but due to different flights altitude and inhomogeneous temporal coverage the averaging was not possible (it produces artifacts and make the data not representative). We realize that figures can be difficult to interpret at first glance, but in our opinion this method is the most appropriate.
L292 I would not describe the interpretation of this figure as ‘easily’ identified – given the density of the figure the wording sounds a little bit ironic.
The paragraph was rewritten, the sentence in question was rephrased.
L293 please define SBL determination in this context and describe the data stratification that was performed to arrive at these mean values.
The text has been modified
Section 3.2 is interesting as academic illustrations but it is unclear what is the added value of this description. The selected academic examples are not put in the perspective of new information that would be expected from such a paper.
Establishing a background BL dynamics for the dataset is important for the following case studies. On top of that, observation of “academic” weather features in real atmosphere is not that common as it may appear.
Figure 4 – too many profiles: the visual clutter and the colors make it challenging to follow the interpretation. Given the title of section 3.2.1, why show all day’s measurements? Top row in the figure do not bring information.
The colors for all the figures were adjusted according to ACP accessibility guidelines. Small daytime variations in measured parameters are still variations and not showing them would be an omission. In this case, also valuable information can be obtained: for example, the highest wind speed in the whole profile was measured at 14:56UTC (around 17:00 local time), late afternoon when a short-time increase of wind speed is often reported in the area by aviation users and weather office forecasters. This is a time when daytime turbulence starts to decline and a gradual decrease of wind speed should be observed. This feature is worth investigating further and may potentially be beneficial for the local weather forecasting (information obtained directly from the aviation forecasting office).
L326-327 : are these the only two flights where this enhancement has been observed? Or are they just selected examples? How are they selected? How representative are they?
Our intention is not a full analysis of all of the events, as this is a measurement report, and not a comprehensive research article. These two events presented in the main text are the most clearly observed plumes that have been encountered – in two out of 12 measurement campaigns. Others were not that clear, but still noticeable – for example, CO2 peaks at 19:41UTC in April (Fig. S7), or CH4 peaks at 250 m agl at 02:15 UTC in June (Fig. S9).
L345 remind what different tagged tracer were included.
See section 2.4 line 270
Figure 6 why not also show CH4?
CH4 plume was not detected in the campaign 5, see Figure S13. Campaign 7 CH4 plumes are presented in Figure 8.
Figure 7 so the likelihood of source receptor relationship is fairly well established for this case, but how useful is it? it would be interesting to show also the simulation using total CO2 emissions next to the power plant tagged tracer
The purpose of presenting the figure 7 was to confirm the origin of CO2 enhancement observed in the vertical profiles. While the manuscript is focused on the presentation of the dataset, the additional model analysis (being the subject of the separate paper) were not consider by authors here.
L349 So what may have caused this enhancement, comparable in importance to the other one associated to the power plant? How would that be processed? What other important sources are influencing the measurements in this dataset?
There are several possible CO2 emission sources located in the urban areas which may influence the observed CO2 molar fraction. Observed CO2 enhancements located far above the inversion layer clearly seen in the profiles had to be attributed to one of industrial high emission sources existing nearby. It has been confirmed by numerical simulation presented on fig.7.
L350-355: is it possible that the choice of 200m spatial resolution is not appropriate or running out of control? How sensitive is the simulation to injection height? Injection velocity and gas temperature?
The detailed investigation of model configuration and performance is out of the scope of this measurement report.
L360 could the same information be obtained with a simpler approach?
Probably yes, although the results of simplified analyses based on e.g. Gaussian plum models could also be questioned due to a number of simplifications in this method. Since the authors had the opportunity to use a high-resolution model developed in other studies, they used it to demonstrate the origin of the observed plume.
L364 what is a quasi gaussian plume here?
We agree that quasi Gaussian term is not precisely used here (it is rather referring to the approximation of the probability distribution rather than gas plum shape. The term was removed from the text.
Fig 8 why not also show CO2 here?
The CO2 was presented and discussed on previous section (Fig. 6). It was decided to separate CO2 and CH4 cases into separate sections to make the text more clear and better organized.
Section 3.3.1: there is no articulated information in this section, I suggest to remove it completely.
The information we convey here is that while for CO2 it was relatively easy to locate the plume sources, it is not that obvious for CH4 – this GHG is more complicated in this regard as there are numerous possible sources, and more sophisticated data are required to identify them in the specific events.
L375-378 I find this statement poorly supported by the current text. However I assume that deeper work on the interpretation of the dataset may ultimately support such a finding.
Observational data, especially these describing “standard” dynamics of the weather parameters, are extremely valuable for validating of model performance.
L380-381 Same as above, I find this statement poorly supported by the text.
The sentence was supplemented by highlighting a need for more sophisticated data in case of methane plumes source identification.
Conclusion: I suggest to finish the conclusion with some opening toward potential applications and suggestions rather than on an 'impossible' measurement (I agree that reporting negative findings is useful for the community but maybe elsewhere in the conclusion). Maybe further research using different anemometer would solve the problem? or ground based lidar if available?
A recommendation of using different wind direction measurement methods was added. “Impossible” was rephrased to “unreliable”. This report describes in detail the methodology of the measurements, hence any recommendations, including those coming from a negative experience, should be emphasized.
Editorial
L32 : all ‘these’ components- not ‘the’?
done
L57ff : this paragraph is challenging to read. I encourage the authors to review the logic, and to simplify with shorter sentences
Paragraph modified
L361 maybe this section should be numbered 3.4? why is the CH4 case study numbering one level lower than CO2?
done
Citation: https://doi.org/10.5194/egusphere-2024-1167-AC1
Peer review completion
Journal article(s) based on this preprint
Data sets
Observational datasets of urban CO2 fluxes, atmospheric vertical profiles of CO2 and CH4 and 14CO2, and isotopic composition of atmospheric CO2 at Krakow, Poland; period 2021-2023; part of the CoCO2 project Mirosław Zimnoch, Piotr Sekuła, Alina Jasek-Kamińska, Alicja Skiba, Michał Gałkowski, Łukasz Chmura, Jakub Bartyzel, Paweł Jagoda, Michał Kud, and Jarosław Nęcki https://doi.org/10.18160/8DSK-R4JS
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Michał Gałkowski
Piotr Sekuła
Łukasz Chmura
Jakub Bartyzel
Alina Jasek-Kamińska
Alicja Skiba
Jarosław Nęcki
Przemysław Wachniew
Paweł Jagoda
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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General comments
The paper is a Measurement report on urban vertical profiles of atmospheric CO2 and CH4 in situ measurements. The measurements were collected during 11 intensive period covering days, roughly every month. The platforms used alternate UAV (sampling air directly injected in a Picarro) and touristic tethered balloon. The data is commented through a general overview of the dataset, through examples of daily boundary layer development and its link to vertical profiles of trace gas species, and finally through a case study of the influence of nearby coal plant exploiting high resolution WRF simulation.
The collection of vertical profiles in repeated intensive periods is certainly valuable and such measurements could ultimately contribute to a better quantification of urban emissions.
Here, a few case studies are selected and analyzed. The strategy behind the selection of the case studies is not really explained. The results on BLH development are in essence short pedagogical illustrations of expected atmospheric behavior; they do not bring novel information. The profiles with CO2 enhancements are left half unexplained, even after mobilizing a high-resolution simulation. The couple of CH4 profiles are simply introduced in a figure but no fair analysis is offered.
The shallow and descriptive analysis of the case studies falls short of demonstrating the value of vertical profiles for urban GHG emission research.
Given the lack of novelty extracted from the data reported here, this paper could be reworked to improve the case studies and their interpretation, and to be considered for submission as a dataset description paper than as an ACP measurement report.
Specific comments
L7 Abstract: indicate maximum altitude reached for each platform?
Introduction: I find the introduction theoretical and generic. It could be reinforced on developing a more solid argument for the vertical measurements reported here and lay the ground for the selected case studies (e.G. CO2 point sources in a mixture of urban emissions).
L31 I suggest to replace ‘development’ by ‘validation’.
L31 : ‘and also in the calibration and validation of satellite-borne measurements’ – this is true only for profiles that span full troposphere and lower stratosphere
L34-47 It is not immediately clear what is the point of this paragraph. Please highlight to the reader the relevance of this paragraph
L38 ‘The variability… ‘ - this is relevant for CO2, but other GHG (CH4) deserve a significantly different description.
L50-52: please also include vertical profiles from tall towers, and aircores in the discussion
L55 in this case UAV with discrete sampling should be differentiated from aircore sampling in their ability to achieve detailed vertical resolution
L140 please provide this literature review and the other reason for this choice
L157 the paper should briefly mention how is the WMO calibration scale propagated to the working standards
L159 for the 200 m tubing is a flush pump used or is the Picarro pump sufficient?
L254+ what are the meaningful spatial resolution (if applicable) of land surface databases? Is it relevant and justified to use 200m/1s resolution for the simulation? Given the reason for modelling given at L77 this complex model configuration is maybe an oversize solution?
Figure 2 could be more interesting to replace daily means in panels B by an envelope extending from daytime mean to nighttime means daily values.
Figure 3. Maybe averaged profiles per period of the day and per season could be more explicit?
L292 I would not describe the interpretation of this figure as ‘easily’ identified – given the density of the figure the wording sounds a little bit ironic.
L293 please define SBL determination in this context and describe the data stratification that was performed to arrive at these mean values.
Section 3.2 is interesting as academic illustrations but it is unclear what is the added value of this description. The selected academic examples are not put in the perspective of new information that would be expected from such a paper.
Figure 4 – too many profiles: the visual clutter and the colors make it challenging to follow the interpretation. Given the title of section 3.2.1, why show all day’s measurements? Top row in the figure do not bring information.
L326-327 : are these the only two flights where this enhancement has been observed? Or are they just selected examples? How are they selected? How representative are they?
L345 remind what different tagged tracer were included.
Figure 6 why not also show CH4?
Figure 7 so the likelihood of source receptor relationship is fairly well established for this case, but how useful is it? it would be interesting to show also the simulation using total CO2 emissions next to the power plant tagged tracer
L349 So what may have caused this enhancement, comparable in importance to the other one associated to the power plant? How would that be processed? What other important sources are influencing the measurements in this dataset?
L350-355: is it possible that the choice of 200m spatial resolution is not appropriate or running out of control?
How sensitive is the simulation to injection height? Injection velocity and gas temperature?
L360 could the same information be obtained with a simpler approach?
L364 what is a quasi gaussian plume here?
Fig 8 why not also show CO2 here?
Section 3.3.1: there is no articulated information in this section, I suggest to remove it completely.
L375-378 I find this statement poorly supported by the current text. However I assume that deeper work on the interpretation of the dataset may ultimately support such a finding.
L380-381 Same as above, I find this statement poorly supported by the text.
Conclusion: I suggest to finish the conclusion with some opening toward potential applications and suggestions rather than on an 'impossible' measurement (I agree that reporting negative findings is useful for the community but maybe elsewhere in the conclusion). Maybe further research using different anemometer would solve the problem? or ground based lidar if available?
Editorial
L32 : all ‘these’ components- not ‘the’?
L57ff : this paragraph is challenging to read. I encourage the authors to review the logic, and to simplify with shorter sentences
L361 maybe this section should be numbered 3.4? why is the CH4 case study numbering one level lower than CO2?