the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Driving factors of aerosol acidity: a new hierarchical quantitative analysis framework and its application in Changzhou, China
Abstract. Aerosol acidity (or pH) plays a crucial role in atmospheric chemistry, influencing the interaction of air pollutants with ecosystems and climate. Aerosol pH shows large temporal variations, while the driving factors of chemical profiles versus meteorological conditions are not fully understood due to the intrinsic complexity. Here, we propose a new framework to quantify the factor importance, which incorporated interpretive structural modelling approach (ISM) and time series analysis. Especially, a hierarchical influencing factor relationship is established based on the multiphase buffer theory with ISM. Long-term (2018 to 2023) observation dataset in Changzhou, China is analyzed with this framework. We found the pH temporal variation is dominated by the seasonal and random variations, while the long-term pH trend varies little despite the large emission changes. This is an overall effect of decreasing PM2.5, increasing temperature, and increased alkali-to-acid ratios. Temperature is the controlling factor of pH seasonal variations, through influencing the multiphase effective acid dissociation constant Ka*, non-ideality cni and gas-particle partitioning. Random variations are dominated by the aerosol water contents through Ka* and chemical profiles through cni. This framework provides quantitative understanding on the driving factors of aerosol acidity at different levels, which is important in acidity-related process studies and policy-making.
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Status: open (until 10 Jan 2025)
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RC1: 'Comment on egusphere-2024-3584', Anonymous Referee #3, 03 Dec 2024
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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*, cni and Xgp relative to? The difference in pH or the original pH? This could be expressed more clearly in the figure caption.
Citation: https://doi.org/10.5194/egusphere-2024-3584-RC1
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