The manuscript titled “Distinct aerosol populations and their vertical gradients in central Amazonia revealed by optical properties and cluster analysis” by Valiati et al. presents multi-instrument datasets (optical, chemical, and size-resolved measurements) at two different heights and the application of clustering algorithm to characterize of Amazonian aerosol dynamics and sources. The study leverages five years of vertically resolved in-situ data (2018–2023) from the Amazon Tall Tower Observatory (ATTO), applying unsupervised machine learning (k-means clustering) to optical intensive parameters and black carbon concentrations. By stratifying observations seasonally and vertically (60 m and 325 m), the authors identify different aerosol populations associated with background conditions, long-range transport (LRT) events (e.g., Saharan dust and African biomass burning), and regional biomass-burning episodes. The methodology is well based in the literature, it extends the field by linking aerosol intensive properties to source and transformation processes and this study provide a valuable optical characterization of different aerosol populations at the analyzed site. The manuscript deserves publication since it is methodologically sound, comprehensive, and clearly written. However, I recommend minor revisions before acceptance.

General comments:

• The manuscript explains the limitations of the clustering method using intensive optical parameters, such as increased uncertainty in Ångström exponents at low scattering/absorption, sensitivity to instrument calibration, and the subjective weighting applied in k-means. Please motivate a bit more why you chose this input aerosol parameter for the k-means clustering and why do you prefer to perform clustering with optical parameters instead of PNSD and ACMS data. It has been demonstrated the utility and effectiveness of clustering methods to characterize different aerosol population according to PNSD data. In this sense, other parameter (like intensive optical parameters) can be used as ancillary information for the clustering interpretation. Explain why SAE, AAE, and eBC were chosen as clustering inputs instead of, or alongside, PNSD metrics. What specific insights do these optical parameters provide about aerosol composition and source attribution that PNSD and chemical speciation do not capture?

• Throughout the whole manuscript (where applicable) verify the statistical significance between the averaged parameters. Several sections report differences between clusters or vertical levels (e.g., in SSA, refractive index, BrC fraction, MSE), but the manuscript does not consistently demonstrate that these differences are statistically robust. I recommend the authors to select appropriate statistical tests. For example, use non-parametric tests (e.g., Wilcoxon rank-sum or Kruskal–Wallis) for distributions that are not Gaussian, or t-tests/ANOVA when normality holds.

Specific comments:

Line 135: PNSDs measurements were performed using a TSI SMPS in the range between 10-400 nm. Why did you choose this range without considering a wider range which would allow you to characterize a more realistic accumulation mode (up to 500 – 800 nm, for example)? Considering a wider range in the PNSD data will provide very useful information for the aerosol population characterization.

Lines 166-170: Why did you consider only two inorganic species? What about the contribution of H₂SO₄ or NH₄HSO₄? Probably, you expect a negligible effect of this species, I recommend supporting this statement with previous publication in the same area.

Lines 169-171: The plots and regression coefficients of the comparison of ACSM and SMPS-derived mass concentration should be included in the manuscript (maybe in the supplement) to support the statement mentioned in these lines.

Lines 225-228: In the manuscript, it’s pointed out that the main driver of the aerosol population at this site is the dry/wet condition. Since it’s mentioned that the k-means clustering is applied to two different datasets: the dry and wet condition seasons. As a quality check for the input variables choice, have you tried to perform the clustering with k=2 for the whole dataset? According to this analysis, we expect that each cluster represents one of the main seasons. If this quality check is not satisfactory, I am a bit skeptical in the decision of the input variables of the algorithm. I’m curious to see a figure like Fig. S2b with the cluster frequency of each two cluster over it.  

Line 460: “Section 3.5 Aerosol mass scattering efficiency”. In my opinion, I found that the last section (on aerosol mass scattering efficiency) reads somewhat independently of the earlier parts of the study. In the introduction of the manuscript all the optical parameters (SSA, refractive index, MSE, etc…) are presented at the same time showing their usefulness for characterizing aerosol population properties. If this section were more clearly related to the objectives of the document, the cohesion of the entire document would be improved. I recommend, for example, to clarify at the begging of this section the purpose of this analysis within the manuscript main goals.

Manuscript by Valiati et al. presents long-term in situ data (5 years) from atmospheric aerosol measurements at tall tower using state-of-the-art aerosol instrumentation in the Amazon region. The study provides a unique data set investigating changes in aerosols optical properties in combination with their chemical composition, vertical gradient and size distribution as well as aerosol origin.

From this perspective, I find this study suitable for publication in the ACP. I have only a few minor comments that would be worth to address.

• Fig. 2, matrix: Could you add to this Ångström matrix a classification scheme presented e.g. by Cappa et al. (2016) (in their Fig. 8d) for better orientation of possible aerosol composition?  And can you provide a same matrix for 60 m data? Considering the different year-variations of SAE (Fig. 1a), certain changes could also be reflected in this image. It can be included in the supplement.
• The same eBC trend at both heights (60 vs. 325 m, Fig. 1c) is an interesting result. Some shorter time measurements on towers show that eBC concentrations usually decrease with height - e.g. Sun et al. (2020). The vertical distribution of other components in individual clusters is also similar (60 vs. 325 m, Fig. 4e), suggesting that atmospheric mixing at both levels is similar. Could this indicate that the mixed planetary boundary layer (PBL) is much higher than 325 m? Can you compare this with other studies where the concentration gradient changes with elevation?
• 167: „…and ρOrg calculated based on Kuwata et al.“ Please, provide average organic matter density + standard deviation you calculated for the measurement period.
• 170: you write: „Lastly, the scatter and linear regression between ACSM daily concentrations and SMPS-derived daily concentrations minus eBC indicate that the methodology could accurately represent ACSM aerosol concentrations at both heights.“ Can you prove this statement, for example with a graph in the supplement?
• Some parts of the manuscript are too long and should be shortened or moved to the supplement. An example is section 2.4. (Clustering procedure), but in general, I think the entire methodology could be shortened appropriately.
• Fig. 6: Can you add correlations of individual fits to scatter plots?
• References, l.607, Andreae et al. (2015): Citation to preprint is provided, please update with link to final article.

Refrences:

Cappa, C. D., Kolesar, K. R., Zhang, X., Atkinson, D. B., Pekour, M. S., Zaveri, R. A., Zelenyuk, A. and Zhang, Q.: Understanding the optical properties of ambient sub-and supermicron particulate matter: Results from the CARES 2010 field study in northern California, Atmos. Chem. Phys., 16(10), 6511–6535, doi:10.5194/acp-16-6511-2016, 2016.

Sun, T., Wu, C., Wu, D., Liu, B., Sun, J. Y., Mao, X., Yang, H., Deng, T., Song, L., Li, M., Li, Y. J. and Zhou, Z.: Time-resolved black carbon aerosol vertical distribution measurements using a 356-m meteorological tower in Shenzhen, Theor. Appl. Climatol., 140(3–4), 1263–1276, doi:10.1007/s00704-020-03168-6, 2020.