The study provides a multi-year (2022–2024) dataset on aerosol and ion number size distributions, focusing on new particle formation (NPF) in the Arctic, thereby extending existing measurements from Svalbard (e.g., Lee et al., 2020). This dataset is valuable, as multi-year, high-resolution ion–particle observations from the Arctic are crucial for improving the limited representation of aerosols in this remote region. However, although the dataset is important, the roles of solar insolation, CS, and DMS in driving Arctic NPF have already been extensively explored (e.g., Tunved et al., 2013; Lee et al., 2020; Beck et al., 2021). The authors have discussed these studies in the introduction and referred to them again in the discussion. The novelty of this study, therefore, lies primarily in the temporal extension of the data record rather than in a fundamental advancement in understanding Arctic NPF processes.
For instance, the reported correlation between the ratio of solar radiation to CS and the frequency of NPF events is already well known and represents an oversimplified approach to predicting or characterising NPF. A more process-based, multivariate analysis that includes DMS, wind speed, wind direction, and relative humidity could strengthen the robustness of NPF predictions. Alternatively, the ratio of CS to growth rate can also serve as a predictive criterion for assessing NPF likelihood, as it characterises particle growth dynamics and the survival probability of new particles against coagulation scavenging (Kulmala et al., 2017; Cai et al., 2017).
Further, the manuscript covers a broad range of topics related to Arctic NPF, such as seasonality and interannual variability, nighttime NPF events, NPF characteristics, the role of ions in NPF, geospatial extent, NPF likelihood criteria, and NPF contributions to potential CCN. However, a mechanistic understanding of the underlying processes is largely missing. For example, the relative contributions of various precursor vapors (e.g., H₂SO₄, MSA, HOMs, and iodic acid) to nucleation and growth are mentioned but not quantitatively disentangled. Similarly, while the influence of DMS and marine biogenic activity is acknowledged, there is limited discussion of the chemical pathways or kinetics explaining the observed temporal shifts in NPF intensity. Furthermore, it is unclear how the authors differentiated background Aitken-mode particles from those originating from NPF events, either upwind or downwind of the site. It is extremely challenging to estimate the fraction of Aitken-mode particles from NPF that grow to CCN-active sizes based on a single observational location. The decadal DMS emission trends are increasing across the Arctic due to decreasing sea-ice coverage, especially near the marginal ice zone. However, observations of DMS oxidation products do not show a uniform trend, highlighting the complexity of sulfur aerosol processes. If available, please include direct observations of DMS oxidation products during the study period. The paper could be significantly strengthened by providing a more integrated, mechanistic interpretation linking precursor chemistry, meteorology, and aerosol dynamics.
The introduction also underemphasizes recent modeling and global aerosol–climate coupling studies (both laboratory- and field-based) that address biogenic sulfur and iodine pathways and Arctic aerosol processes in general. Including these would better contextualise the implications of the findings (e.g., He et al., 2023; Baccarini et al., 2020; Cala et al., 2020; Hoffmann et al., 2016; Finkenzeller et al., 2022; Xavier et al., 2024). The discussion of general aerosol processes could be shortened to make space for this.
The authors acknowledge the subjective nature of manual NPF classification and the potential observer bias. However, it is unclear why authors still rely primarily on manual classification. The purpose of using nanoparticle-ranking analysis is to quantitatively identify NPF days on a continuous scale (e.g., 0–1). In this study, more than 50% of the days were classified as “undefined.” I strongly recommend using the nanoparticle-ranking analysis and correlating the NPF rank with DMA, CS, Cv, solar radiation, and other meteorological parameters. Also, why did the authors choose the 2.8–5 nm size range instead of 2.5–5 nm, as suggested by Aliaga et al. (2023)?
The authors claim that NPF events “lead to an increase in Aitken-mode particles contributing to CCN” is plausible but not directly demonstrated. No CCN closure or hygroscopicity data are presented. I recommend avoiding such strong statements unless supported by direct evidence or case studies.
If I understand correctly, the authors claim that polar-night NPF events occur under descending air masses from higher altitudes that originated in higher latitudes. I suggest plotting airmass back trajectory cross-sections (time/distance versus altitude) for the polar-night NPF event days (see Fig. 1b in Zha et al., https://doi.org/10.1093/nsr/nwad138).
Previous studies are cited excessively in the results, which makes it difficult to follow the paper’s unique contribution. This also weakens the perceived novelty. I suggest separating the results and discussion.
A mere change in diameter during an NPF event does not necessarily imply active CCN or activated CCN. What is the background median or modal aerosol diameter? How did the authors separate background particles exceeding the threshold diameter? From Fig. 16, the number concentration of particles >70 nm does not differ significantly between cases. The authors could calculate survival probabilities for CCN-active particles (25 nm, 50 nm, 100 nm) following Pierce & Adams (2007) and other methods in the literature in the absence of CCN measurements. This could be a separate paper, if I may suggest so, investigating NPF's potential to produce CCN-active particles.
Figure 1: Clarify what “Total” represents—is it the total number of days or total valid observation days? For instance, in April 2022, were there no measurements for approximately 18 days?
Figure 2: The black dots are hardly visible, and the legend is not fully explained. The caption is too long and should be more concise. Avoid scaling factors where possible; use dual axes if needed. Define “recent 6 hours”—from sunrise or noon? And what is the basis for using a 6-hour accumulated solar flux? How was the mixed-layer depth used? The quality of the figure is very poor.
Improve figure quality: ensure self-explanatory legends, avoid abbreviations in figure captions, and maintain consistent font type and size. For example, in Fig. S11, clarify what “AHX,” “SUM,” and “SBU” represent. Captions should be sufficiently detailed to allow interpretation without referring to the main text, and at the same time, not too long. Figure S7: The x-axis lacks tick labels. Please show exact hours here and in other figures.
Several figure captions (e.g., Fig. 2) are overly long; shorten them for readability.
Abstract: Replace “encourages” with “favors” or “promotes.”
Specify whether the PC version of the HYSPLIT model or tools like ZeFir were used for back trajectories.
Show both ion-polarity datasets from ~0.8 nm, but exclude particle data below 2 nm, as already justified.
Use consistent units throughout both the main text and the Supplementary Information. For example, in Figs. S2 and S4, particle diameter (Dp) is given in nm and m, respectively.
Clarify whether the CS was calculated for dry or ambient particle sizes, as hygroscopic growth affects surface area.
Eq. 3 - Verify the units of the constant “A” and specify which molecular properties of sulfuric acid it represents, and provide a citation. Clarify whether dDp/dt refers to total GR (over the full size range) or nucleation mode (<25 nm). Each equation should be self-contained with the necessary details and proper citations.
Ensure that all figures (e.g., Fig. S17 and others) are cited correctly in the main text.
Over 30 supplementary figures are excessive. Combine related figures where appropriate to reduce redundancy.
Correct typographical inconsistencies (e.g., missing spaces in “solar insolationand” or “Condensation sink,CS”).
Maintain consistent language (choose either British or American English throughout).
Format all units correctly (e.g., “nm h⁻¹” instead of “nm hr⁻¹”).
References.
Baccarini, A., Karlsson, L., Dommen, J. et al. Frequent new particle formation over the high Arctic pack ice by enhanced iodine emissions. Nat Commun 11, 4924 (2020). https://doi.org/10.1038/s41467-020-18551-0
Bianchi, F., Junninen, H., Bigi, A., Sinclair, V. A., Dada, L., Hoyle, C. R., Zha, Q., Yao, L., Ahonen, L. R., Bonasoni, P., Buenrostro Mazon, S., Hutterli, M., Laj, P., Lehtipalo, K., Kangasluoma, J., Kerminen, V. M., Kontkanen, J., Marinoni, A., Mirme, S., Molteni, U., Petäjä, T., Riva, M., Rose, C., Sellegri, K., Yan, C., Worsnop, D. R.,
Kulmala, M., Baltensperger, U., and Dommen, J.: Biogenic particles formed in the Himalaya as an important source of free tropospheric aerosols, Nat. Geosci., 14, 4-9, https://doi.org/10.1038/s41561-020-680 00661-5, 2021.
Cala, B. A., Archer-Nicholls, S., Weber, J., Abraham, N. L., Griff iths, P. T., Jacob, L., Shin, Y. M., Revell, L. E., Woodhouse, M., and Archibald, A. T.: Development, intercomparison, and evaluation of an improved mechanism for the oxidation of dimethyl sulfide in the UKCA model, Atmos. Chem. Phys., 23, 1473514760, https://doi.org/10.5194/acp-23-14735-2023, 2023.
Carlton Xavier, Robin Wollesen de jonge, Tuija Jokinen, Lisa Beck, Mikko Sipilä, Tinja Olenius, and Pontus Roldin Environmental Science & Technology 2024 58 (17), 7314-7324, DOI: 10.1021/acs.est.3c09103
Cai, R., D. Yang, Y. Fu, X. Wang, X. Li, Y. Ma, J. Hao, J. Zheng, and J. Jiang. 2017. Aerosol surface area concentration: a governing factor in new particle formation in Beijing. Atmos. Chem. Phys. 17 (20):12327–40. doi: 10.5194/acp-17-12327-2017
Finkenzeller, H., Iyer, S., He, XC. et al. The gas-phase formation mechanism of iodic acid as an atmospheric aerosol source. Nat. Chem. 15, 129–135 (2023). https://doi.org/10.1038/s41557-022-01067-z
Hoffmann, E. H., Tilgner, A., Schrödner, R., Bräuer, P., Wolke, R., and Herrmann, H.: An advanced modeling study on the impacts and atmospheric implications of multiphase dimethyl sulf ide chemistry, P. Natl. Acad. Sci. USA, 113, 11776–11781, https://doi.org/10.1073/pnas.1606320113, 2016.
Kulmala, M., V. M. Kerminen, T. Petaja, A. J. Ding, and L. Wang. 2017. Atmospheric gas-to-particle conversion: Why NPF events are observed in megacities? Faraday Discuss. 200:271–88. doi: 10.1039/C6FD00257A.
Pierce, J. R., & Adams, P. J. (2007). Efficiency of cloud condensation nuclei formation from ultrafine particles. Atmospheric Chemistry and Physics, 7(5), 1367–1379.
Xu-Cheng He et al., Iodine oxoacids enhance nucleation of sulfuric acid particles in the atmosphere.Science382,1308-1314(2023).DOI:10.1126/science.adh2526 |
