Articles | Volume 17, issue 10
https://doi.org/10.5194/essd-17-5209-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.Special issue:
Data collected by a drone backpack for air quality and atmospheric state measurements during Pallas Cloud Experiment 2022 (PaCE2022)
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- Final revised paper (published on 09 Oct 2025)
- Preprint (discussion started on 18 Feb 2025)
Interactive discussion
Status: closed
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
| : Report abuse
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RC1: 'Comment on essd-2025-61', Anonymous Referee #1, 17 Mar 2025
- AC1: 'Reply on RC1', David Brus, 03 Jun 2025
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RC2: 'Comment on essd-2025-61', Anonymous Referee #2, 20 Mar 2025
- AC2: 'Reply on RC2', David Brus, 03 Jun 2025
Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by David Brus on behalf of the Authors (05 Jun 2025)
Author's response
Author's tracked changes
Manuscript
ED: Referee Nomination & Report Request started (05 Jul 2025) by Gholamhossein Bagheri
RR by Simon Thivet (10 Jul 2025)

RR by Anonymous Referee #2 (14 Jul 2025)
ED: Publish subject to technical corrections (14 Jul 2025) by Gholamhossein Bagheri

AR by David Brus on behalf of the Authors (22 Jul 2025)
Manuscript
This study presents an extensive dataset collected using a drone-based backpack system for air quality and atmospheric state measurements during the Pallas Cloud Experiment 2022 (PaCE2022). The authors detail the instrumentation, calibration/validation, and data collection methods used during the campaign, emphasizing the advantages of drone-based measurements for atmospheric studies, especially in subarctic areas. The dataset includes information on aerosol concentrations, and meteorological parameters, providing insights into the atmospheric conditions of the studied area.
Strong points:
The dataset has been rigorously validated through comparisons with reference measurements, which enhances the credibility and usability of the collected data.
The dataset offers valuable information for the atmospheric science community, particularly regarding the use of UAV-based measurement techniques in complex and/or under-studied atmospheric conditions.
Suggested improvements:
While the introduction provides a solid background on the importance of UAV-based measurements, it lacks a clear structure that outlines the research objectives and the organization of the paper. Providing a more structured introduction would enhance readability and help guide the reader through the study.
Although the dataset is well-documented, the discussion on its potential applications and future uses is relatively limited. Expanding the conclusion to explicitly address how this dataset could be utilized by the scientific community and integrated into broader atmospheric research (e.g., CCN, INP) would strengthen the impact of the study.
Minor comments line by line:
L10: "against the reference" - Please explain the meaning.
L11: "meteorological parameters" - Which ones?
L12-14 - The provided links include the coma at the end, thus are not working when direct click on them.
L24 - "our previous research" - Please specify and cite.
L42 - Also check https://doi.org/10.5194/amt-2024-162. Can be also interesting to consider as this study used the same OPC in a different drone system to study volcanic aerosols.
L42 - "FMI" - Please specify it properly the first time for people that does not know the Finish Meteorological Institute.
L59 - "minimize the propeller airflow" - Based on what ? You could add some references that indeed show propeller airflow is minimal in this drone area (e.g., https://doi.org/10.2514/6.2018-1266, https://doi.org/10.2514/1.C032828, https://doi.org/10.3390/drones6110329).
L110 - Might be useful to specify here the size range of the measured particles.
L128 - I suppose that PM are calculated from the raw particle counts of the OPC, but based on which particle density?
Figure 5 - Not really clear why you have such errors/uncertainties on the OPC measurements.