Testing the near-field Gaussian plume inversion flux quantification technique using unmanned aerial vehicle sampling

Shah, Adil; Pitt, Joseph R.; Ricketts, Hugo; Leen, J. Brian; Williams, Paul I.; Kabbabe, Khristopher; Gallagher, Martin W.; Allen, Grant

doi:https://doi.org/10.5194/amt-13-1467-2020

Articles | Volume 13, issue 3

https://doi.org/10.5194/amt-13-1467-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/amt-13-1467-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 13, issue 3

Research article

|

30 Mar 2020

Research article |

| 30 Mar 2020

Testing the near-field Gaussian plume inversion flux quantification technique using unmanned aerial vehicle sampling

Adil Shah, Joseph R. Pitt, Hugo Ricketts, J. Brian Leen, Paul I. Williams, Khristopher Kabbabe, Martin W. Gallagher, and Grant Allen

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Final revised paper (published on 30 Mar 2020)
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Preprint (discussion started on 19 Aug 2019)
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Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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RC1: 'Review of Shah et al.', Anonymous Referee #1, 06 Nov 2019
- AC1: 'Response to Reviewer 1', Adil Shah, 06 Dec 2019
RC2: 'Review Shah et al.', Anonymous Referee #2, 14 Nov 2019
- AC2: 'Response to Reviewer 2', Adil Shah, 06 Dec 2019

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Adil Shah on behalf of the Authors (06 Dec 2019) Author's response Manuscript

ED: Referee Nomination & Report Request started (02 Jan 2020) by Thomas Röckmann

RR by Anonymous Referee #3 (15 Jan 2020)

Suggestions for revision or reasons for rejection

General Comments:
1) In S3 and S4 you derived the [H2O] offset by measuring two or three different concentrations. I think two points alone does not make a strong enough correlation for a calibration curve (typically you need at least 4-5 for this practice). This however has created a very large spread on the ends of your distribution, and it doesn’t necessarily tell you much about if the behavior is linear in between the points. For example, in S3, your point between the extremes lies under your line of best fit. So perhaps it would be best to present your endpoints as points with error bars to better represent the uncertainty. If possible, I would recommend running the pMGGA with the 2.167 cylinder and both with one more standard. You may also consider adding a blank (0 ppm), as is common in most spectroscopic practices. However, I think the method you used of drying the gas is acceptable. I also agree with the assertion that this will stay relatively constant over time.
The same thinking goes to S6 and S7. This treatment may help determine if the exponential truly is the line of best fit in S9 and S10.

2) For UAV2, where was the airflow being pulled into the pMGGA from? Did it have a boom or some other inlet that brough air into the instrument from a location undisturbed from the rotor wash? Same goes for the UAV1 and its tubing – was it located on a boom away from the propellers? Several groups have determined that sensor placement matters a great deal, so you may want to at least discuss any considerations you did make, even if this was not the aim of this work. Including a schematic of UAV 1 and 2 in section 3.1 could be beneficial to help readers see where the sensors were placed, including the wind sensor you did describe. This was very good, and I think you just need to add the inlets for the pMGGA / MGGA tubing to this section.

Detailed Comments:
L33-39: This paragraph does an excellent job elucidating why the methane budget is poorly constrained and how we can work to reduce our uncertainties. However, what could really put the finishing touch here I believe is a sentence at the beginning highlighting the importance of the methane budget to the climate system (i.e. GHG forcing, implications on hydroxyl radicals etc.).

L309: Be advised the numbering in section 3 skips from 3.1 to 3.3. Please correct this to 3.2 then 3.3.

S17-18: If the authors are referring to the mole fraction intercept in S2, 4.3014 ppm is incorrect. The y-axis in the plot is the same in both S1 and in S2, so either S2 needs to be corrected of the text needs corrected.

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RR by Anonymous Referee #1 (19 Jan 2020)

ED: Publish subject to minor revisions (review by editor) (28 Jan 2020) by Thomas Röckmann

AR by Adil Shah on behalf of the Authors (04 Feb 2020) Author's response Manuscript

ED: Publish subject to technical corrections (15 Feb 2020) by Thomas Röckmann

Dear authors,
I am happy to inform you that your manuscript is now accepted for publication in AMT subject to a few technical corrections that you find below. Please incorporate them and submit the final manuscript to the editorial office (you will receive instructions).
Best regards
Thomas Röckmann

Technical corrections:

Introduction, second sentence: I think you can leave out the comment in the bracket. If you decide to leave it, please change "average annualized" to "annual average". Also leave out "even".

page 11, last sentence before 2.2: ... in THE future...
Section 2.2. second sentence: Water vapour influences measurements of dry mole fraction [X] or three main reasons

page 13, end of first paragraph, change to:
However, any water correction may be systematically influenced by cell temperature and cell pressure. This was not investigated in detail in this work as these effects are likely small under typical near-surface environmental changes compared to the large methane elevations that were measured (see SI for further discussion).

page 15, section 2.4, paragraph 3, change to: We adopt the same non-linearity correction for the pMGGA as they use identical spectroscopic techniques.

Section 2.5: Leave out: "Although it is beyond the scope of this manuscript,"

Rewrite sentence: "This is supported by environmental variations observed during the Allan variance tests (see sect. 2.1)."
This is not clear. Neither what is supported (that p and T affect G or that this effect is small?) nor how the environmental variations support this.

page 17, second last sentence before 3.2: ...the impact of such a geospatial positioning error is expected to the small.

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AR by Adil Shah on behalf of the Authors (15 Feb 2020) Author's response Manuscript

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Short summary

Methane is a potent greenhouse gas, with large flux uncertainties from facility-scale sources, such as natural gas extraction infrastructure. A recently developed flux quantification method was successfully tested by flying an unmanned aerial vehicle (UAV) downwind of 22 controlled atmospheric methane releases. The UAVs were used to derive high-precision atmospheric methane measurements. The UAV methodology was successful in both detecting the release and providing a rough flux estimate.