The manuscript is overall good, scientifically robust and well written. Nevertheless, the following minor comments should be addressed before publication.

Page (P) 1, Line (L) 17. I do not think it is correct to associate marine aerosol WSOC to Ice Nucleating Particles in such a straightforward way. Usually, water soluble aerosol components are not considered good ice nucleators (Kanji et al., 2017) and there is no proof that WSOC contributes to the ice nucleating properties of sea-spray aerosol. On the other hand, it is likely that the ice nucleating properties of sea-spray is related to cell fragments or algal exudates (McCluskey et al., 2017; 2018; Wilson et al., 2015), which are typically insoluble (Orellana et al., 2011). I do not have the same problem with other parts of the text, like P2L3, where ice nuclei are associated to marine aerosol organics more in general (i.e., without a direct reference to the water soluble fraction).

Kanji, Z. A.et al., Measurement and Modeling Challenges, 58, 10.1175/amsmonographs-d-16-0006.1, 2017.

McCluskey, C. S. et al., Journal of the Atmospheric Sciences, 74, 151-166, 10.1175/jas-d-16-0087.1, 2017.

McCluskey et al., Journal of the Atmospheric Sciences, 75, 2405-2423, 10.1175/jas-d-17-0155.1, 2018a.

Orellana, M. et al., PNAS, 108, 3313612–13617, 201.

Sect. 2. The manuscript would benefit of some extra information. For instance, I noticed that the sampling times are not the same for day and night samples. This affects the detection limits (LOD) in terms of concentration per cubic meter. LODs will correspond to lower atmospheric concentrations in day samples than in night ones. To evaluate the impact of this difference on the reported concentrations, the authors should provide more information: how many samples have been analyzed in total? How many are MBL, FT, wet and dry? How many samples have concentrations below LOD per each analysed species? What is the detection limit (in atmospheric concentration units) per species, in day and night samples? How the samples below detection limits have been treated to calculate the averages and st. deviations reported in Table 1?

For instance, Br^- represents an extreme case. Br^- presents always concentrations below LOD in night samples. Without more information, it is impossible to understand which of the following situation is represented by the data:

• there is a huge concentration gap between day and night samples for Br^-
• Br^- concentrations are pretty similar but in night samples they fall below LOD as an effect of the lower sampling times.

P3L10. This sentence needs revision: subject and verb are not consistent. Removing “The term” at the beginning of the sentence may solve it.

P4L8. What does “significant” mean? This paper is based on comparing different conditions: MBL vs FT, wet vs dry season; therefore, the statistical significance of the described differences should be addressed in a quantitative way. I invite the authors to report the result of the appropriate statistic tests, when presenting and discussing these differences (number of samples in the compared subsets, confidence interval, …).

P4L23-24. “The variation of the mass fractions was similar in MBL and FT in both seasons”: the meaning of this sentence is not clear.

P4L30. Maybe the difference in WSOC concentration is indicative of some level of local contamination. This is an important issue and I invite the authors to discuss it. If possible, the authors should demonstrate that their measurements are representative of uncontaminated marine conditions. For instance, Fig. 3 shows a couple of samples with important NO3 contribution. According to my experience, NO3 indicates potential anthropogenic influence in marine aerosol.

P5L1-5. Provide a reference that links bromide emissions to sea-spray aerosol and biological productivity.

P5L5-8. A correlation analysis between the different aerosol species would make this part more quantitative.

Sect. 3.4. From the plots in Figure 9, I noticed a weird finding, which is never discussed in the manuscript. The contribution of “Marine PA” to WSOC is higher in FT samples than in MBL ones, both during the wet and dry seasons. This result should be addressed and discussed.

First of all, this seems in disagreement with the Br^- results. Br^- was presented by the authors as a tracer for biogenic organic matter from primary sea-spray and, accordingly, it presents higher concentration in MBL samples than in FT ones. How can this result be reconciled with the PMF output?

Absolute concentrations data for the WSOC contributing sources are not presented, so it is difficult to judge properly, but I have the impression that this high PA contribution to WSOC is driven by a few samples, while on the rest of the time series the signal is almost negligible. I invite the authors to analyse this aspect more in detail. I think this is an indication of the limits of the WSOC source apportionment performed by PMF, with such a limited number of samples and analysed species. I am not discussing the general PMF results, but maybe the authors are overconfident on the WSOC source attribution potential of this approach. For instance, I notice that the most consolidated organic aerosol source apportionment technique by PMF, the one based on Aerosol Mass Spectrometry data, makes use of much larger input matrixes, both in terms of number of samples and analytic information (mass spectra peaks) to resolve the organic aerosol sources. Considerations on these aspects should be added in the text. I do not think this spoils the work, as the WSOC source attribution is supported soundly by the carbon isotope results, but still, I would like to see the limits of the PMF clearly stated in the text.

P6L33-36. This would be clearer by showing a regression analysis.

P713-14. Please provide a reference. According to my experience, “models” do not consider either marine POA.

Figure 8. Change the colour palette. “Marine SOA” and “Terrestrial source” are too similar to each other.

Data availability statement. To me, this seems not in line with the journal policy:

“The output of research is not only journal articles but also data sets, model code, samples, etc. Only the entire network of interconnected information can guarantee integrity, transparency, reuse, and reproducibility of scientific findings. Moreover, all of these resources provide great additional value in their own right. Hence, it is particularly important that data and other information underpinning the research findings are "findable, accessible, interoperable, and reusable" (FAIR) not only for humans but also for machines.

Therefore, Copernicus Publications requests depositing data that correspond to journal articles in reliable (public) data repositories, assigning digital object identifiers, and properly citing data sets as individual contributions. Please find your appropriate data repository in the registry for research data repositories: re3data.org. A data citation in a publication resembles a bibliographic citation and needs to be included in the publication's reference list. To foster the accessibility as well as the proper citation of data, Copernicus Publications requires all authors to provide a statement on the availability of underlying data as the last paragraph of each article (see section data availability)”.

General comments:

This study presents chemical analyses of submicron aerosols sampled at Maïdo observatory on La Réunion Island in the Indian Ocean during March – May 2018. This period covers part of the wet and dry season. There is a clear need for improved knowledge on the physicochemical properties of aerosols in remote locations, especially the undersampled Southern Hemisphere. I therefore support eventual publication of this manuscript in ACP, however comments detailed below should be addressed first. Overall, the manuscript is well written, and the analytical procedures appear sound, with the exception of PMF, where more details on the choice of factors should be given.

Specific comments:

P. 1, L. 23 – 24; P. 2., L. 33 - 34: The separation into FT and MBL is usually not that simple (see e.g. Collaud Coen et al., 2018), and also the references given (Guilpart et al., 2017, seems to be missing in the reference list) state that no observational evidence is available to confirm the assumption of FT conditions at night. A short discussion and more information about the uncertainties related to the potential influence of MBL or residual layer on the night-time samples need to be given.

P. 1, L. 26; P. 2, L. 31 – 32; P. 4, L. 13 - 26: The separation between wet and dry season is described in “methods” as "[…] wet (typically from November to April) and dry seasons (from May to October).” How do you justify the separation of your data into “dry” and “wet” with the change taking place within one day? More information needs to be given, e.g. a time series of the water vapor mixing ratios should be shown. Also, in section 3.1 you mention “significant” differences in water vapor mixing rations at several instances, however, are the differences really statistically significant? If not please use another word than “significant”. Also, without more context and analyses the values do not prove or confirm the the separation into FT and MBL. Please revise.

P. 1, L. 26, P. 5, L. 5: The outbreak of the volcano coincides with the start of the dry season. The sulfate is therefore not due to a change in season, but due to the volcano. This needs to be stated very clearly, otherwise the interpretation is very misleading.

P. 1, L. 33: “[…] which may be responsible for cloud formation […]” - This is too strong of a statement, I suggest toning it down, similar for P. 7, L. 38 - 39

P. 2, L. 8: The bubble bursting process also produces particles larger than 1 micrometer

P. 3, L. 5 – 8: How were the filters stored after sampling and before analysis? Please add this information.

P. 4, L. 3 – 10, section 3.4: These sections need more information for the reader to be able to understand the PMF results; e g. how were the analytical measurement uncertainties calculated? Based on what criteria did you choose a six-factor-solution, especially given the uncertainties related to F5 and F6? You state that PMF reproduced 86% of the measured WSOC mass – what about the other aerosol components that you used as input in PMF? You state in L. 4 - 5 that you investigate the sources of WSOC using PMF, however your input also contains inorganic ions. Please clarify.

P. 5, L. 1 -2: Why? Can you give some reasoning here?

P. 5, L. 26, 29: Given the stated uncertainty range, these values are very close to the limit value. Please comment.

P. 5, L. 31 – 34: Give details here on the mass balance equation. Also, the d13C values you used in the equation – how well do they represent your dataset? Please discuss.

P. 6, L. 38 - 39: “[…] attributable to local terrestrial biogenic emissions of VOCs during the transport from the ocean to the observatory […]” – unclear, how can terrestrial emissions be transported from the ocean?

P. 7, L. 11 – 12: I don´t understand how you come to this conclusion.

Technical comments:

P. 1, L. 16 – 17: Revise sentence; CCN and IN are aerosol particles, and “important factors relevant to” therefore is unclear

P. 2, L. 2: The ocean is a source of aerosols, the ocean surface is simply the interface to the atmosphere. Suggest deleting.

P. 3, L. 19: Should read “were transferred”

P. 4, L. 1: ECMWF: Give full name

P. 5, L. 11 – 12: “Substantially larger” – compared to what?

P. 5, L. 19 – 22: Give a short explanation here how this is done, and the accuracy and uncertainty range of the limit values in L. 24

P. 4, L. 38: “typical” for what?

P. 6, L. 10: “Enrichment” compared to what?

P. 7, L. 15: “additional” to what?

Caption Figure 2: “Extreme data”?

Figure 5: Dotted lines are not visible

Figure 6: Specify 10-day period. Color scale is missing.

References:

Collaud Coen, M., Andrews, E., Aliaga, D., Andrade, M., Angelov, H., Bukowiecki, N., Ealo, M., Fialho, P., Flentje, H., Hallar, A. G., Hooda, R., Kalapov, I., Krejci, R., Lin, N.-H., Marinoni, A., Ming, J., Nguyen, N. A., Pandolfi, M., Pont, V., Ries, L., Rodríguez, S., Schauer, G., Sellegri, K., Sharma, S., Sun, J., Tunved, P., Velasquez, P., and Ruffieux, D.: Identification of topographic features influencing aerosol observations at high altitude stations, Atmos. Chem. Phys., 18, 12289–12313, https://doi.org/10.5194/acp-18-12289-2018, 2018.