This paper describes a method that estimates the aerosol components by calculating the refractive index of a aerosol mixture. Then the algorithm was applied to ground-based remote sensing measurements to retrieve the aerosol components in China. The information of aerosol component is important for the understanding of climate change, air quality, the interaction between aerosols and cloud, chemical transport model estimation, etc. Meanwhile, the concentration of aerosol components in the atmospheric column are quite difficult to measured. Therefore, the efforts on retrieval of aerosol component in this study are commendable and the work is meaningful. However, I have some comments on the current manuscript.
1. I think the authors should highlight the improvements of aerosol component retrieval in their study rather than some results that are well-known in many previous studies in the abstract, such as “the atmospheric columnar DU component is dominant in the northern region of China, whereas the AW is higher in the southern coastal region”. Because the title of “improved inversion of aerosol components in the atmospheric column from remote sensing data” emphasizes the new development of algorithm. I also suggest to show some comparisons of aerosol component retrievals between the improved algorithm and the previous algorithm. As Referee #3 mentioned in “Major comments 3: a comparison to a previous method could be presented, if, of course, such comparison could be done. For example, comparison with OM from Zhang 2018 could be performed to illustrate improvements (if any)”
2. Line 217-223: You mentioned that “The improved algorithm described here is more suitable for the calculation of the properties of a mixture of multiple water-soluble components …” and the previous algorithm had some limits. Could you provide the comparisons of aerosol component retrieval derived by these two algorithms? For example, aerosol water fraction. Because I wonder if these two approaches can obtain similar results for aerosol water fraction that is considered in both of two algorithms. The aerosol water fraction should have a good agreement between the improved algorithm and previous algorithm (Zhang, 2018).
3. Could you provide a table to show the statistics of fitting for each aerosol component in Figure 5?
4. I also read the paper (Zhang et al., 2018), which is you cited and mentioned in the current manuscript. The values of aerosol component density used in Zhang et al. (2018) (Table 1) are quite different to that used in the current manuscript (Table 2), but with same values of complex refractive index. Why? I suggest to use same values of density in the current manuscript as that in Zhang et al. (2018), because more uncertainty could be induced from the density. For example, WIOM and WSOM density in Zhang et al. (2018) is 1.0, whereas it is 1.547 in current manuscript. The uncertainty could be up to more than 50%. In this case, the results in Figure 8,9 and 10 may have some changes.
5. Could you provide some validation of aerosol component retrievals? For example, the validation for black carbon concentration with in situ measurements.
1. The definition of “aerosol water” is not appropriate and it cannot describe exactly the aerosol component. I suggest to use “aerosol water content” in the paper.
2. Line 23: “aerosol particles scatter and absorb solar radiation” It is imprecise.
3. Please reword the sentence of “the detail of information depends on the technique used” (Line 27)
4. Line 217: “In that algorithm” should be “In previous algorithm”
5. Line 234: “in good agreement” should be “in a good agreement”
6. Line 230-231: “…the volume fraction of BC was constrained between 0 to 3.0%...”. Why?