This manuscript presents a novel algorithm for retrieving black carbon (BC) surface concentrations by synergizing MODIS and AERONET data, integrating K-means clustering, Maxwell-Garnett effective medium approximation (MG-EMA), 6SV2.1 radiative transfer modeling, and MERRA-2-based vertical conversion. Seasonal aerosol modeling via K-means clustering and the MG-EMA significantly improves the representation of internally mixed aerosols. Validation against AE33 in-situ measurements (R = 0.727, RMSE = 0.353) demonstrates strong agreement, while comparison with MERRA-2 BC data effectively highlights the algorithm’s superior performance. Besides, the uncertainty analysis confirms that the algorithm performs better in conditions of high AOD.

Overall, the method used in this manuscript is unique and innovative. The manuscript is well-structured and scientifically sound. Due to MODIS having continuous long-term observational data (1999 to present), the results of this study contribute to obtaining a more accurate and long-term series of BC surface concentration datasets, providing richer reference information for climate change and air quality research. Therefore, I suggest accepting the manuscript for publication with minor modifications. Please find my specific suggestions below.

1. In section 1, the introduction on satellite platform inversion of BC only lasts until 2023. It is recommended to supplement the latest research progress in this field.
2. In the Introduction, it is important to mention (with suitable references) the following: BC  contributes to the corrosion and soiling of materials by carrying acidic compounds that accelerate metal degradation and stain surfaces. It darkens buildings, especially light-colored and porous materials like stone, leading to aesthetic and structural damage. BC also traps moisture, promoting biological growth and further decay. These effects increase maintenance costs and pose risks to cultural heritage and urban infrastructure, making emission control and protective measures essential (e.g., https://doi.org/10.5194/acp-17-439-2017, 2017). Also, mention that black carbon is ultrafine  and thus an important indicator of air quality that requires continuous observations (e.g., https://doi.org/10.1016/j.atmosenv.2012.05.015)
3. In Fig.3(a), it isn't easy to distinguish the particle size distribution of each season in the fine mode region using equidistant radius r scales. Logarithmic scales are recommended to increase discrimination.
4. In Fig.4, the spelling of 'reteieval' is incorrect and needs to be changed to 'retrieval'.
5. In section 3.4, there are aerosol optical property data for DJF, MAM, JJA, and SON. Why choose DJF for sensitivity analysis?
6. In section 3.4, only the sensitivity of surface reflectance less than 0.1 is analyzed, and most bright surfaces (such as deserts) are not included. It is recommended to expand the numerical range and set the threshold below 0.3.
7. In section 4.1, “This trend is likely related to the region's high population density, developed industry, and low temperatures, which hinder the timely dispersion of emitted BC. From April to June, the overall BC concentration in the study area remains at a relatively low level”. I suggest adding references here to prove that these factors do indeed affect the high values.
8. In section 4.2, “It is evident that the fluctuation trends of MODIS BC and AE33 BC are generally consistent, although MODIS BC tends to be lower than AE33 BC most of the time.” Why is there a trend of underestimation?
9. In Fig.7, the author only presented data from 3 out of 6 AE33 sites, and it may be considered to display data from other sites.
10. In section 4.3, “suggesting that the algorithm’s applicability in bright surface areas still needs improvement. However, it is worth noting that due to the lack of data from AE33 stations in high brightness areas, the surface reflectance of the AE33 stations in this study is below 0.12, and DT AOD accuracy is better in dark surface, so further research is needed to determine the applicability of bright surface reflectance.” The surface reflectance here is relatively low, making it difficult to reveal the characteristics of bright surfaces. It is recommended to revise the statement to make it more rigorous.

The authors developed a novel algorithm to retrieve BC surface concentration using satellite data through several steps, including determined the background aerosol model, the Maxwell–Garnett effective medium approximation model, lookup tables, and a conversion coefficient. The retrieved BC surface concentrations were validated by using ground measurements, and the statistical parameters outperform those obtained from MERRA-2 BC data. In general, this manuscript is well organized and written. Some explanations are needed for a clear understanding. Generally, this manuscript can be accepted for publication after minor revisions.

As for the proposed algorithms and models, it is suggested to introduce the physical and chemical mechanisms, and how to consider and apply them in model designs and improvements. What are the advantages and disadvantages of the algorithms and models in this study? They should be discussed somewhere in the text.

L11-16, “In this study, we developed a novel algorithm for retrieving BC surface concentration using MODIS and AERONET data. The algorithm first determined the seasonal background aerosol model using the K-means clustering method, based on AERONET V3 daily products. It then employed the Maxwell–Garnett effective medium approximation model to calculate the complex refractive index of the internally mixed aerosols and used the 6SV2.1 radiative transfer code to establish lookup tables for optimal BC fraction and column concentration estimation”, this paragraph should be rewritten.

L77, These datasets can be obtained from this website (https://ladsweb.modaps.eosdis.nasa.gov/), the accessed date should be added. It is same for all websites used in the text.

L137-138, which is associated with the frequent occurrence of dust aerosols from North Africa during this period (Meloni et al., 2008), please explain the time period (e.g., year) and the sites, as well as in Tables 2 and 3.

L167-168, since the proportion of BC in mixture aerosol particles generally does not exceed 6%, please explain where it is come from for 6%.

L200-201, Furthermore, at high aerosol loading (AOD ≥ 1.0), the model estimates a higher TOA standard deviation at lower surface reflectance, indicating better performance in BC estimation under these conditions. It is confused for that the model estimates a higher TOA standard deviation and better performance, more explanations are suggested.

L203-204, Fig. 5. Sensitivity analysis of BC inversion based on 6SV2.1. (a)-(b) represent 𝜌𝜌𝑠𝑠 at 0.66μm change steps are 0.02 and 0.10, respectively, should be rewritten.

Fig. 9. BC surface concentration bias (MODIS - AE33) dependence analysis on the (a) AOD and (b) surface reflectance, please indicate the meanings of the bars.

L224-236, it is recommended to make more comparisons with other studies, including the main statistical parameters shown in the text.

L219, transit time. (Remer et al., 2005), please remove the dot after time.