Remarks to the Author:

Guo et al. present a comprehensive analysis of a three-year sulfuric acid concentration dataset at an urban site and put forward three daytime concentration proxies. Compared to proxies presented in earlier research, the three proxies in this study exhibit better results in reproducing the daytime sulfuric acid concentration. Furthermore, the authors discuss the possibility of applying the three proxies in this study to other environments.

In the atmospheric chemistry research area, especially for atmospheric new particle formation research, sulfuric acid is a critical substance as it is the major oxidation product from sulfur dioxide and possesses non-volatile characteristics. However, measurement of sulfuric acid based on mass spectrometer instruments is not yet wide and continuous enough for us to generate a big picture of new particle formation in different environments and periods.

The study from Guo et al. not only makes a valuable complement to the current sulfuric acid concentration dataset but also gives an attempt on sulfuric acid concentration estimation based on the work of predecessors. I think once the questions listed below are addressed, this paper is adequate to be published.

--1. I do not agree with the “applicable to various sites” description in the title. The three proxies in this study only consider the source of sulfur dioxide oxidation by OH radical and the authors only verify these proxies against the dataset in Hyytiälä. As mentioned in the introduction, for example, sulfuric acid may arise from dimethyl disulfide or dimethyl sulfide oxidation in coastal areas, and this has caused pronounced underestimation using a similar proxy (k [SO₂] [OH]/CS).

--2. According to Fig 1C, Fig S2, Fig 6 and Fig S10, the current sulfur dioxide concentration is relatively low in Beijing (e.g., the median concentration from May to Nov. is all close to or lower than 0.5 ppb in 2021 even without precipitation data (Fig 1C and Fig S2), which can also be seen from the frequency plot of sulfur dioxide concentration in Fig 6 and Fig S10). While the three proxies in this study exhibit negative bias when sulfur dioxide concentration lies in this range (Fig 6 and Fig S10).

As far as I know, new particle formation events (NPF) often correspond to scavenging conditions in polluted urban environments, which means that NPF are most likely to stay in the low CS and low sulfur dioxide concentration range. Despite the aforementioned negative bias in the low sulfur dioxide concentration range, the three proxies in this study still behave well for the lowest CS bin which corresponds to scavenging conditions. So, this present method confused me when it comes to how well these proxies work exactly during NPF since the authors highlight the importance of these proxies for NPF analysis.

If you could break the individual bins in the frequency plot into accumulated columns classified by NPF and non-NPF, I think the plot will be more straightforward and valuable for others. Or you can find other ways to sort out the inherent co-occurrence relationship between these parameters (UVB, SO₂ concentration, CS, RH).

--3. In Fig 5, as the dataset becomes larger, more points fall into the measured sulfuric acid concentration >> proxy calculated sulfuric acid concentration regime. What is the parameter condition of these deviated points? Does the parameter condition of these deviated points match the results you found “When OH radical, UVB and SO₂ are too low, when CS and PM_2.5 are too high, or when RH exceeds 60%, estimated sulfuric acid concentration may deviate from the actual concentration to a larger extent”? And why are there fewer points deviating into the measured sulfuric acid concentration << proxy calculated sulfuric acid concentration regime?

--4. In equation (6), why is there no term (T/300)^-0.7 anymore?

--5. Some details in writing. For example, in Table 3, there is no parameter “f” in the Proxy_{Lu et al.}. Please check again.

This study presents long-term measurements of sulfuric acid (H₂SO₄) in urban Beijing (2019–2021) and develops three formation- and loss-based proxies (OH–CS, UVB–CS, and UVB–PM₂.₅). All three proxies reproduced observed concentrations well, and the UVB–PM₂.₅ proxy was successfully applied to a boreal forest site, indicating its potential for broader global application in studying atmospheric nucleation and aerosol growth. This paper presents innovative and valuable findings; however, some revisions are needed to improve clarity before publication.

Major Comments

1. The manuscript would benefit from improved organization. It currently appears to have been written by two different contributors (modeling and observation parts), resulting in inconsistent flow and style. The overall readability should be improved by polishing the language and avoiding vague expressions.

2. The modeled concentrations of key pollutants should be evaluated against available observations (e.g., O₃, SO₂, and other relevant species) to validate model performance and strengthen the credibility of the results.

Minor Comments

1. Please avoid vague wording such as “scarce.”

2. Abbreviations (e.g., OH–CS, UVB–CS, UVB–PM₂.₅) and variables (e.g., J(NO₂)) in the abstract should be spelled out at first mention for clarity.

3. Lines 101–109: Please present each equation on a separate line and number them sequentially.

4. Line 139: Ensure that all equations in the manuscript are numbered consistently and in order.

5. Line 174: Revise the sentence to remove the repeated name in parentheses — it should read: “The modelling domain was the same as in Zheng et al. (2020).”

6. Line 197: In the sentence “Thus, the yearly decline of sulfuric acid is mainly attributed to the decrease of SO₂ (by ~25% per year),” please provide appropriate references to support this statement.