the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Retrieval and analysis of the composition of an aerosol mixture through Mie-Raman-Fluorescence lidar observations
Abstract. In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie-Raman-fluorescence lidar, with the primary input information being the aerosol backscattering coefficient, particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (bF, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France.
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RC1: 'Comment on amt-2024-17', Anonymous Referee #1, 15 Mar 2024
Veselovksii, AMT, 2024
The paper is well written and appropriate for AMT. It is an excellent contribution the lidar literature.
I have only minor points. Since, many publications have been written and published by the Lille lidar team on the application of polarization/fluorescence lidar in atmospheric aerosol research during the last few years, the authors should make very clear (in the Introduction) what is the step forward here (not covered by the foregoing papers). Only by 1:1 comparison with the paper of Veselovskii et al. (AMT, 2022), I saw the difference. The layout of many figures in the new publication (2024) has not changed compared to the 2022 paper. The motivation is given in the Introduction, but not well enough. A more contrasting wording would be helpful. Maybe, start to review the 2022 paper and then state what was missing so far, and this gap is now filled with the 2024 publication.
Individual remarks:
Section 2.2 is new, and that must be better highlighted in the Introduction.
After line 142 it becomes quite complicated (without a nice flow chart of all the steps…). One could even start to explain (step by step) the respective three-aerosol-component separation approach before one continues with the discussion of the methodology in case of the four-component system.
Table 1: The numbers now differ a bit from the ones in Table 1 in Veselovskii et al. (2022). Should be explained! Smoke depol values from 2.0-8.0! Does that cover the full range of values. Baars et al. (2019) or Ohneiser et al (2020) show values up to 20% at 532 nm. Depol values of 2-8% in the case of urban aerosol! Does this range of values (up to 8%) include road dust? Why should there be a depol ratio of >5% in the case of a sulfate-aerosol dominating aerosol?
Page 8, lines 209-210: Please provide reference to Veselovskii et al. (2022).
Page 8, line 223: Spain? I do not see that! You mean: Italy?
Page 8, line 244: eta-S = 0.1 and not 1.0
Page 9, line 250 … from the free troposphere … By the way, the 1 October 2023 smoke event was a UNIQUE event. It is almost impossible to find North American smoke so close to the ground. I hope there will be another paper on this UNIQUE observation.
Page 10, line 281-284: I would step forward to mass concentration! Particle densities are 1.15 g/cm3 (smoke), 2.6 g/cm3 (mineral dust), and 1.5g/cm3 (sulfate aerosol). These numbers are given in the referenced papers.
Table 2: regarding aged smoke, I would cite own papers as well (Hu et al., …..)
Section 4: Why is that an extra section and not simply a subsection of section 3? Please provide a small introduction why you present and discuss this episode separately.
Page 12, line 340: When was the heat wave over? Should be mentioned! And then, please provide mass concentrations instead of volume concentrations in Fig. 17.
Achnowledgement: A statement concerning ACTRIS is missing, but required to my opinion.
Fig. 2, caption: 350-2800 m.
Fig. 12, caption: Maybe in line 589: … depict the total particle volume….
General remark to the figures: There should be always (a) (b) (c) when there are several panels. Sometimes it is written (a) … and (b) … in the caption, but no indication of panels in terms of (a) and (b).
Citation: https://doi.org/10.5194/amt-2024-17-RC1 -
RC2: 'Comment on amt-2024-17', Sergei Bobrovnikov, 28 Mar 2024
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2024-17/amt-2024-17-RC2-supplement.pdf
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RC3: 'Comment on amt-2024-17', Anonymous Referee #3, 04 Apr 2024
General comment
The authors have developed the partitioning method of smoke, urban, and dust aerosols based on Mie-Raman-fluorescence lidar measurements and have shown excellent performance. Classification of aerosol types and quantification of their respective components is very important in atmospheric environment and climate change. In particular, the partitioning of smoke and urban aerosols is a significant contribution to remote sensing methods. The methods, results, and suggestions are reasonable and clearly described. I recommend that this paper can be published with some minor modifications.
Specific comments
Lines 143-144: How did you introduce the non-negativity constraint to the least squares method?
Lines 154-156 and 162-163: The partitioning method would be helpful for atmospheric environment monitoring and data assimilation. The calculations of the ATS method for the four triplets seem time consuming. Is the method applicable to the quasi-real-time analysis?
Lines 177-129: What are the ranges of fluorescence capacities and depolarization ratios for smoke, pollen, urban, and dust aerosols above 60 % relative humidity? If several studies exist, their ranges should be noted for reference.
Table 1: Why is the fluorescence capacities of smoke and pollen so large? A brief explanation is in the best interest of the reader.
Citation: https://doi.org/10.5194/amt-2024-17-RC3
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