Aerosol acidity is one of the core parameters in atmospheric chemistry. In recent years, much interesting work has been done on the trends and driving factors of aerosol acidity changes. This article is pioneering in decomposing the trends of aerosol acidity changes into long-term, seasonal, diurnal, and random components, and decoupling the driving factors into meteorological and emission drivers. This research framework greatly simplifies the interpretation of the results from complex multiphase buffering theory. I believe that with minor revisions, this article can be published in Atmospheric Chemistry and Physics.

Here are my specific recommendations:

Lines 35-36: The expression here is not very precise. Andrew Ault et al have focused on directly measuring aerosol pH. Although their methods have limitations and have not yet been widely applied in practical measurements, direct measurement methods do exist.

Lines 57-64: I hope to use highly concise language to summarize the differences between this study and previous research on chemical profiles and meteorological parameters driven pH changes, as well as highlight the most innovative and distinctive features of this article.

Line 87: Can mathematical formulas be provided here? For example, linear fitting and Fourier curve fitting, as well as how to use mathematical methods to decouple the trends of 4 components.

Line 142: It is recommended to use percentiles for RH.

Lines 144-145: The decomposition of pH into the three factors can be understood mathematically. However, is it appropriate to plot these three factors as time series? From the perspective of aerosol physicochemical properties, especially the meaning of H⁺, plotting them as time series may not be easily interpretable.

Lines 149-152: The colors are unclear. It is recommended to increase the thickness of the lines in the legend.

Lines 178-179: There seems to be an error here.

Lines 239-241: What are the percentage changes in pKa^*, c_ni and X_gp relative to? The difference in pH or the original pH? This could be expressed more clearly in the figure caption.

Understanding the driving factors of aerosol pH is important, as aerosol pH drives many multiphase processes in the atmosphere. Duan et al. developed a new framework to decouple the main factors that influence aerosol pH. The results have important implications for atmospheric chemistry and air quality studies. The manuscript can be recommended for publication after the following concerns are addressed.

Specific comments:

Lines 84-86: As shown in Fig. S1, the highest gas-phase HNO₃ concentration was around 6 µg/m³, which is not a low value. Are there any other reasons leading to the measurement uncertainty of HNO₃? A related question is how the uncertainty of HNO₃ would bias the main conclusion.

Lines 90-92: Does this mean that the sum of long-term trend, season variation, diurnal cycle, and random residues is aerosol pH? Can the authors elaborate on the rationale?

Lines 120-125: Please elaborate on the connection between middle-level and top-level factors. For example, how f(NO₃^-) would affect C_ni is unclear.

Line 259: Can the authors provide more comparisons with other studies in the YRD region?

Technical comments:

Lines 41-42: Please correct the reference format.

Line 43: “which” should be “while”.

Line 179: Please correct the reference format.

The quality of the figures should be improved.