We sincerely appreciate your thorough review of our manuscript. Your insightful comments have provided valuable insights for further improving the clarity and robustness of our work. We have carefully considered each of your points and have made corresponding revisions to address these critical issues.

1. UAAS Tilt and Wind Speed Calculation: Your concern regarding the limitations of the kinematic model used to infer wind velocity is well-noted. The kinematic model does not account for the payload carried underneath the hexacopter, likely resulting in wind speed estimation errors as wind conditions increase since the tilt range of aircraft is limited by the added weight. Wind direction estimates obtained from the kinematic model, on the other hand, are not as much affected by the aircraft payload. Future work will explore how higher-fidelity rigid-body models like the ones characterized by Gonzalez-Rocha et al. (2019, 2020), which do account for aircraft mass, can improve the reliability of UAS-based wind estimates.

González-Rocha, J., Woolsey, C.A., Sultan, C. and De Wekker, S.F., 2019. Sensing wind from quadrotor motion. Journal of Guidance, Control, and Dynamics, 42(4), pp.836-852.

González-Rocha, J., De Wekker, S.F., Ross, S.D. and Woolsey, C.A., 2020. Wind profiling in the lower atmosphere from wind-induced perturbations to multirotor UAS. Sensors, 20(5), p.1341.

1. AirCore Sample Collection and Analysis: We acknowledge the need for a more detailed account of the AirCore sample collection and analysis procedure. The diffusion effect certainly plays a role in understanding plume structures, but we'd like to highlight that the integrated concentration comparison as outlined in Table 3. Since the emission rate is computed by integration, the diffusion effect won’t influence the total emission rate calculation in real deployment. We performed an experiment involving storage time by leaving samples with spike signals overnight, and the results demonstrated minimal smearing of signals. We didn’t include this test in the manuscript because we connected the Aircore to the analyzers immediately to the analyzers on site. The AirCore's Teflon material minimally influences the release or absorption of CO2 and CH4.

1. Flow Pattern during AirCore Sampling: We appreciate your input regarding the flow pattern during AirCore sampling. We assumed the pressure gradient is existing to be in steady state and the flow inside the Aircore to be turbulent flow. This article’s focus is in combination with the model on interpreting the Aircore’s measurements. Detailed discussion about the filling process inside the Aircore can be found in the reference below.

Tans, P.: Fill dynamics and sample mixing in the AirCore, Atmos. Meas. Tech., 15, 1903–1916, https://doi.org/10.5194/amt-15-1903-2022, 2022.

Replies to the detailed comments:

Line 127: what is the flow rate of micro diaphragm pump when pumping air vs. pumping AirCore? Since AirCore is a long, thin tubing, it may create some resistance to the pump. It is also important to make sure that air is entering the AirCore without too much turbulence. Also, how do you control the on/off of the pump?

Flow control was achieved by using a metal orifice that effectively constrained the flow rate as long as the upstream vacuum pressure remained below its specific threshold (Refer to the provided flow chart for comprehensive details). Under the vacuum conditions provided by the micro-diaphragm pump at 16" Hg, an inlet flow rate of approximately 0.45 LPM was registered within the Aircore. The operational modulation of the pump was executed by employing a remote relay connected with the pump's power cable.

Line 135: here the authors introduced the laboratory test of AirCore-CRDS system, however, the results of such tests are not reported in detail. Section 3.3 do not have figures to show the real-time measurements of CH4. In addition, as mentioned above, the “cleanness” of AirCore sampling system need to be carefully checked before measuring real-world samples.

Thank you for catching the miss. The real-time CH4 measurements have been visually represented in the figure below. The intended procedure involves preconditioning the Aircore with zero air before starting the sampling process. Unfortunately, due to challenges in preparing zero air source and conducting consecutive measurements, this protocol could not be executed during this deployment. Nevertheless, we ensured that the pump continuously drew in ambient air from the ground for an adequate duration between measurements. Given the generally low ambient concentrations of CH4 and CO2, this approach was expected to yield a consistent and uncontaminated baseline.

Line 166: in real flights, will the AirCore payload affect the b3 Vector?

Yes, the weight of AirCore is likely to limit how the hexacopter adjusts its attitude in the presence of a wind gust, resulting in a smaller inflow angles and more significant wind speed prediction errors as wind conditions increase. However, the estimates of wind direction obtained by projecting of the b3 vector onto the i1-i2 plane are not as much affected by the weight-induced attenuation of the vehicle’s response to wind velocity variations. We have expanded our discussion of the wind estimation results to clarify these two points for the readers.

Line 223: how long did it take between AirCore landing and analysis during each flight?

On average, it took less than 5 minutes.

Line 253: how do you define the start of ascent and end of descend? Is there a special gas that distinguish sample air vs. air left inside the AirCore? Will a variable wind speed condition affect your sample collection?

We placed an ignited lighter in front of the Aircore’s inlet before the drone took off. By doing so, a CO₂ spike was identified as the start of ascent. The end of descend was identified based on the start of ascend plus the flight time. A variable wind speed condition would not affect the sample collection process.