the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Sep 2024
A novel aerosol filter sampler for measuring the vertical distribution of ice-nucleating particles via fixed-wing uncrewed aerial vehicles
Abstract. A mobile sampler for the collection of aerosol particles on an uncrewed aerial vehicle (UAV) was developed and deployed during three consecutive Pallas Cloud Experiment campaigns in the vicinity of the Sammaltunturi Global Atmosphere Watch site (67°58’ N, 24°7’ E, 565 m above sea level). The sampler is designed to collect aerosol particles onto Nuclepore filters, which are subsequently analysed for the temperature-dependent number concentration of ice-nucleating particles of the sampled aerosol with the Ice Nucleation Spectrometer of the Karlsruhe Institute of Technology (INSEKT). This setup is an easy and flexible way to connect INP concentration measurements with cloud microphysics. The sampler was flown with a fixed-wing UAV in different altitudes up to 1000 m above ground level. The total flight time ranges from 1 hour to more than 1.5 hours, depending on environmental conditions. Pressure, temperature and relative humidity are also measured to provide information about the meteorological flight conditions. The flow over the filter was maintained by a micro-diaphragm pump, providing around 10 standard litres per minute over a small filter (diameter of 25 mm) and around 11 standard litres per minute over a larger filter (diameter of 47 mm) at a pressure corresponding to 500 m above sea level. For a typical flight time of 1.5 hours, this results in a sampled air volume of about 930 to 1000 standard litres per flight, giving an INP detection limit of approximately 1.1 × 10−3 and 1.0 × 10−3 INPs per standard litre, respectively. For comparison to the flight results, a similar setup was deployed at ground level. The comparison shows a clear distinction from the water and handling blank background for both setups, proving the technical feasibility of the setups. Furthermore, for some flights, a shift between the two INP populations can be seen, indicating that ground-based INP measurements deviate from the samples collected on-board the UAV.
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RC1: 'Comment on amt-2024-120', Anonymous Referee #2, 05 Nov 2024
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The manuscript describes the application of a newly deployed mobile filter sampling system, operating on an uncrewed aerial vehicle (UAV). The development and improvement of the sampling technique over three subsequent measurement campaigns is well documented. Also, the text is well written, everything is well described.
However, occasionally statements need correction, and in some parts additional clarifications are needed, as specified in the general comments. Also, there is one mayor criticism I have that may well go beyond this study: For testing new equipment, test flights were done in northern Finland. It is not clear why they were not done at the home institute, and instead a lot of travel and transport of both people and equipment was done to get these results. This does not diminish the quality of this work, but should at least be considered in future developmental work.
After below comments will have been addressed, I can support publication in AMT.
General comments:
Line 69: “regions with low aerosol concentrations, such as northern Finland” sounds wrong. An annual cycle of the Arctic aerosol has been understood for some time now. Concerning size distributions or number concentrations, you can check out e.g. Tunved et al. (2013) or Freud et al. (2017). Arctic haze contributes particles in winter time, new particle formation causes elevated particle concentrations in summer time. Concerning INP concentrations, it has also been understood that there is an annual cycle with high concentrations possible already in spring and advancing well into autumn (see e.g., Creamean et al., 2018, Wex et al., 2019; Porter et al., 2023; Schneider et al., 2021, with the latter one from your institute). Therefore, the assumption of low INP concentrations in norther Finland is somewhat amazing and wrong. Please correct. (BTW: Check your literature list. For Schneider et al. (2021), I saw that the doi refers to the discussion paper. – I did not check the rest.)
Line 134-135: Why do you subtract the water background, but not, instead, the background from the field handling blank? By this, you create some in-between state with the background partially, but not fully, subtracted.
Line 169: Why are you talking about campaign 2 here at the beginning of the description of campaign 1? Did you not take field handling blanks during campaign 1? Then better mention that explicitly.
Line 174 and Fig. 6: Do you have any idea why the handling blank on the UAV is higher than that on ground? And is it right that there was only one handling blank for UAV and one for the ground? Then please mention this explicitly.
Line 146 ff: About the detection limit: The argument in this paragraph is too complicated and can be simplified a lot. The complication is by first assuming that in the whole batch there should be one INP, then getting a value for that, but then saying (correctly), that it is not the whole batch that is examined, but only one droplet. The number given in the text for the whole batch is useless, and no number (or range) is given here for the results of equation (3), which really is the lower detection limit. (I only later on found one value in line 214.)
Instead, it could just be said that the detection limit comes from equation (2) with the assumption of one frozen droplet (i.e., one of the 64 droplets frozen, or f_l = 63/64 = 0.984). That will be the minimum value you get as a concentration, and is in the same order of magnitude than the value you give in line 214. Modify / simplify this paragraph!
Figure 7: In the caption, you refer to the steepness of the sampling curve such that the impression arises that the steepness of the curve depends on the sampling time. But such a dependency does not exist! The steepness of the curve reflects which INPs are there in which concentrations! Each INP has its characteristic freezing (or ice-activation) temperature (in the non-stochastic approach you are using here). And the curve shows how many INPs are there that are ice-active at the different temperature. So the shape of the curve has nothing to do with the sampling time! You can only argue, as you do, that by increasing the volume of sampled air you also increase the maximum temperature at which you get data (by decreasing the minimum detection limit), and that you may see different parts of the curve. But the curve at a certain temperature range will be the same, independent how long you sample. Otherwise the whole measurement approach would not make sense! And that increase in maximum temperature is not much, as doubling the sampling time only halves concentration, which is not much gain in terms of temperature at which data is available.
Bottom line: Modify the caption of Fig. 7 (and elsewhere in the text if needed) such that this wrong impression of a connection between sampling time and steepness of the curve does not appear any more.
Line 187: Again, as above, the Arctic is not on its own a region with clean air (see e.g., Arctic haze) or low INP concentrations (see my remark for line 69). Please reword this sentence.
Line 191: “This difference highlights the importance of measuring the vertical distribution.” While I agree that differences in INP concentrations are to be expected with altitude, it becomes less clear in the presented data than what I would have expected. It would be more correct to state that while you do not present data showing big differences between ground and UVB, stronger differences can be expected and measuring the vertical distribution is important.
Line 203: Referring to Chapter 4 and the overall work:
Given that we are living in times of climate change and should rather watch our resources, a question arises, overall: Why were these tests not done at home, but instead, for a few research fights, a lot of travel and transport was done?
Also: How did you realize that the different inlets performed differently? Only by modeling? Please add this information to the text.
Line 215: You claim that INP concentrations are “usually showing a decrease of INP concentrations at higher altitudes”. This cannot be seen from the data in Fig. 7, given the agreement within measurement uncertainty in both panels and the fact that data obtained from the UAV is even higher than data collected at ground in the left panel. Either show different data in Fig. 7 if you have them, or correct the text (here and throughout the manuscript) accordingly.
Line 218-222: In this part, you give future plans instead of highlighting why your approach was a useful addition. The impression arises that you found that your new approach is not very good. You can leave that as it is, but maybe it diminishes the value of your work too much.
Line 223: You can already use backwards trajectories for your own UAV measurements, so the question arises why you did not do that if you deem them so valuable, here.
Line 224: Why especially in the Arctic? Please justify (half a sentence can be enough) if you want to make this claim.
Specific and editorial comments:
Line 3: Add “in Finland” to the site description.
Line 26-28: This sentence is totally correct, but not needed in the context of this work. Consider deleting it, although I will not force you to do it.
Line 111: wrong format for citing Schneider et al. (2020). Try using \citep[text preceeding citation][text following citation]{label} . (If this won’t work for AMT, there will be a similar way, mayby with <…> instead of […] ).
Line 183-184: Which weight are you talking about, here? And is this important?
Line 187: Names are always capitalized -> “Arctic”.
Equation A2: Explicitly state also here, that Ddn is a specific dilution.
Line 265: As this part is quite separate from the main text, specify here which flow meter you are referring to here, with “the flow meter”.
Literature:
Creamean, J. M., R. M. Kirpes, K. A. Pratt, N. J. Spada, M. Maahn, G. de Boer, R. C. Schnell, and S. China (2018), Marine and terrestrial influences on ice nucleating particles during continuous springtime measurements in an Arctic oilfield location, Atmos. Chem. Phys., 18, 18023–18042, doi:10.5194/acp-18-18023-2018.
Freud, E., R. Krejci, P. Tunved, R. Leaitch, Q. T. Nguyen, A. Massling, H. Skov, and L. Barrie (2017), Pan-Arctic aerosol number size distributions: seasonality and transport patterns, Atmos. Chem. Phys., 17(13), 8101-8128, doi:10.5194/acp-17-8101-2017.
Porter, G. C. E., M. P. Adams, I. M. Brooks, L. Ickes, L. Karlsson, C. Leck, M. E. Salter, J. Schmale, K. Siegel, S. N. F. Sikora, M. D. Tarn, J. Vüllers, H. Wernli, P. Zieger, J. Zinke, and B. J. Murray (2022), Highly active ice-nucleating particles at the summer North Pole, J. Geophys. Res.-Atmos., in print, doi:10.1029/2021JD036059.
Schneider, J., K. Hohler, P. Heikkila, J. Keskinen, B. Bertozzi, P. Bogert, T. Schorr, N. S. Umo, F. Vogel, Z. Brasseur, Y. S. Wu, S. Hakala, J. Duplissy, D. Moisseev, M. Kulmala, M. P. Adams, B. J. Murray, K. Korhonen, L. Q. Hao, E. S. Thomson, D. Castarede, T. Leisner, T. Petaja, and O. Mohler (2021), The seasonal cycle of ice-nucleating particles linked to the abundance of biogenic aerosol in boreal forests, Atmos. Chem. Phys., 21(5), 3899-3918, doi:10.5194/acp-21-3899-2021.
Tunved, P., J. Strom, and R. Krejci (2013), Arctic aerosol life cycle: Linking aerosol size distributions observed between 2000 and 2010 with air mass transport and precipitation at Zeppelin station, Ny-Alesund, Svalbard, Atmos. Chem. Phys., 13(7), 3643-3660, doi:10.5194/acp-13-3643-2013.
Wex, H., L. Huang, W. Zhang, H. Hung, R. Traversi, S. Becagli, R. J. Sheesley, C. E. Moffett, T. E. Barrett, R. Bossi, H. Skov, A. Hünerbein, J. Lubitz, M. Löffler, O. Linke, M. Hartmann, P. Herenz, and F. Stratmann (2019), Annual variability of ice nucleating particle concentrations at different Arctic locations, Atmos. Chem. Phys., 19, 5293–5311, doi:10.5194/acp-19-5293-2019.
Citation: https://doi.org/10.5194/amt-2024-120-RC1
Model code and software
as_tools Alexander Böhmländer https://codebase.helmholtz.cloud/alexander.boehmlaender/as_tools
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