The study employs a novel Lagrangian modeling framework (SOSAA-FP) to investigate the impact of anthropogenic aerosols on cloud condensation nuclei (CCN) concentrations in a boreal forest environment. By combining global emission datasets, backward trajectories, and detailed aerosol dynamics, the work provides insights into the relative contributions of primary emissions and secondary aerosol formation to CCN, highlighting the importance of anthropogenic influences even in rural settings. This approach advances understanding of aerosol-cloud interactions and their implications for climate modeling. Yet several questions should be clarified before its publication.

Major:

1. Mechanisms Behind Weak Sensitivity to Cluster Formation Rates: The study finds that changes in atmospheric cluster formation rates have a relatively weak impact on CCN concentrations, which is unexpected given the importance of new particle formation (NPF) in aerosol dynamics. What specific mechanisms or feedbacks within the model could explain this weak sensitivity? For instance, how do the interactions between primary particles and newly formed clusters influence the overall CCN population? Understanding these mechanisms is crucial for refining models and improving predictions of aerosol impacts on climate.
2. Role of Primary Emissions in CCN Formation: The results indicate that primary particle emissions play a substantial role in CCN concentrations even in a rural environment like SMEAR II. How do these findings reconcile with previous studies that emphasize the importance of secondary organic aerosols (SOA) in CCN formation? Specifically, what are the potential trade-offs between primary and secondary sources in different environmental conditions, and how might these findings influence the development of emission reduction strategies?
3. Impact of BVOC Emissions on CCN Seasonality: The study demonstrates that BVOC emissions significantly affect CCN concentrations, especially during the growing season. However, the sensitivity of CCN to BVOC emissions is found to be highest in the largest CCN sizes. What are the implications of this finding for the role of BVOCs in cloud formation and climate feedback mechanisms? Additionally, how do seasonal variations in BVOC emissions influence the overall aerosol size distribution and CCN activity, and what are the potential feedbacks on regional climate?
4. While the model shows reasonable agreement with observations for some components (e.g., organic aerosols and sulfates), significant discrepancies exist for others (e.g., nucleation-mode particles and gas-phase species like SO₂ and H₂SO₄). The paper does not thoroughly address the potential sources of these biases, such as uncertainties in emission inventories, simplified deposition schemes, or missing chemical pathways (e.g., aromatic oxidation). A more rigorous discussion of model limitations and their impact on CCN predictions is needed.

Minor:

1. The text mentions the use of Welch’s t-test and Mood’s median test, but the results (e.g., p-values) are not included. Please provide the specific p-values or significance levels in the text to support the conclusions drawn from these tests.
2. The color scheme used in Figure 7 for different supersaturation levels is not very distinct. Please use a more contrasting color palette to enhance readability.
3. The reference list includes some inconsistencies in formatting (e.g., some references have full journal names while others are abbreviated). Please standardize the reference formatting to ensure consistency.
4. Line 565: please change “backed with” to “supported by”.

This study employs a novel Lagrangian modeling framework  to investigate the origins and history of gas and aerosol components observed at the boreal forest measurement site, and their potential to act as CCN. I am impressed by the efforts the authors made for very detailed analysis and evaluation of the simulation results, with hourly, diurnal, and also seasonal variations. This study is methodologically rigorous, supported by extensive data, and provides significant scientific insights, particularly in understanding the relative contributions of anthropogenic and biogenic emissions to CCN formation. I recommend the publication of this work once the authors address my comments below.

Major Comments:

Page 10, Line 260-263, for the parameterizations estimating the volatility of organic compounds, as there are several other methods, such as Li et al., (2016) and  Yang et al., (2023), I am curious why the authors picked the method of Stolzenburg et al. (2022)? Isaacman-Vanwertz and Aumont (2021) concluded that considering the average and distribution of error, the combined Daumit-Li method (modified to consider nitrates) represented a nearly optimal approach to estimating vapor pressure from a molecular formula. In addition, did the simulated organic compounds in the model in this study include CHOS compounds? If CHOS compounds were available, I recommend the authors re-examine whether Stolzenburg et al. (2022) could predict the volatility of compounds containing sulfur. 

Minor comments:

    (1) The manuscript is written very long. I recommend the authors could somehow shorten it and highlight the significance of this study. For Section 4, the Discussion and Conclusions, I recommend separate discussion and conclusions. Section 4 is too long and too detailed. The readers would like to have a more straight conclusion and significance/ implication of this study.

   (2) In the discussion, the authors had emphasized the importance of the heterogeneous chemistry and particle-phase chemistry in CCN formation. Besides it, the phase state of organic aerosols may also play an important role in CCN formation pathways (Reid et al., 2018; Shiraiwa et al., 2017), and online simulation of organic aerosol phase state has been coupled in several chemical transport models (Rasool et al., 2021; Zhang et al., 2024) which could be applied to examine its effect in CCN. The potential effect of phase state on cluster formation or gas-particle partitioning is recommended to be discussed.

(3) Page 25, Line 521, supersaturation classes of 0.4 % should be 0.6 % as Figure 9 showed. 

References:

1.    Isaacman-VanWertz, G. and Aumont, B.: Impact of organic molecular structure on the estimation of atmospherically relevant physicochemical parameters, Atmos. Chem. Phys., 21, 6541-6563, 10.5194/acp-21-6541-2021, 2021.

2.    Li, Y., Pöschl, U., and Shiraiwa, M.: Molecular corridors and parameterizations of volatility in the chemical evolution of organic aerosols, Atmos. Chem. Phys., 16, 3327-3344, 10.5194/acp-16-3327-2016, 2016.

3.    Rasool, Q. Z., Shrivastava, M., Octaviani, M., Zhao, B., Gaudet, B., and Liu, Y.: Modeling Volatility-Based Aerosol Phase State Predictions in the Amazon Rainforest, ACS Earth and Space Chemistry, 5, 2910-2924, 10.1021/acsearthspacechem.1c00255, 2021.

4.    Reid, J. P., Bertram, A. K., Topping, D. O., Laskin, A., Martin, S. T., Petters, M. D., Pope, F. D., and Rovelli, G.: The viscosity of atmospherically relevant organic particles, Nat. Commun., 9, 956, 10.1038/s41467-018-03027-z, 2018.

5.    Shiraiwa, M., Li, Y., Tsimpidi, A. P., Karydis, V. A., Berkemeier, T., Pandis, S. N., Lelieveld, J., Koop, T., and Pöschl, U.: Global distribution of particle phase state in atmospheric secondary organic aerosols, Nat. Commun., 8, 15002, 10.1038/ncomms15002, 2017.

6.    Yang, X., Ren, S., Wang, Y., Yang, G., Li, Y., Li, C., Wang, L., Yao, L., and Wang, L.: Volatility Parametrization of Low-Volatile Components of Ambient Organic Aerosols Based on Molecular Formulas, Environmental Science & Technology, 57, 11595-11604, 10.1021/acs.est.3c02073, 2023.

7.    Zhang, Z., Li, Y., Ran, H., An, J., Qu, Y., Zhou, W., Xu, W., Hu, W., Xie, H., Wang, Z., Sun, Y., and Shiraiwa, M.: Simulated phase state and viscosity of secondary organic aerosols over China, Atmos. Chem. Phys., 24, 4809-4826, 10.5194/acp-24-4809-2024, 2024.