the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 29 Nov 2024
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.
Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(1204 KB)
-
Supplement
(1018 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1204 KB) - Metadata XML
-
Supplement
(1018 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3584', Anonymous Referee #3, 03 Dec 2024
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 - AC1: 'Reply on RC1', Guangjie Zheng, 08 Jan 2025
-
RC2: 'Comment on egusphere-2024-3584', Anonymous Referee #2, 17 Dec 2024
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 Cni 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.
Citation: https://doi.org/10.5194/egusphere-2024-3584-RC2 -
AC2: 'Reply on RC2', Guangjie Zheng, 08 Jan 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3584/egusphere-2024-3584-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Guangjie Zheng, 08 Jan 2025
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-3584', Anonymous Referee #3, 03 Dec 2024
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 - AC1: 'Reply on RC1', Guangjie Zheng, 08 Jan 2025
-
RC2: 'Comment on egusphere-2024-3584', Anonymous Referee #2, 17 Dec 2024
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 Cni 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.
Citation: https://doi.org/10.5194/egusphere-2024-3584-RC2 -
AC2: 'Reply on RC2', Guangjie Zheng, 08 Jan 2025
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-3584/egusphere-2024-3584-AC2-supplement.pdf
-
AC2: 'Reply on RC2', Guangjie Zheng, 08 Jan 2025
Peer review completion
Journal article(s) based on this preprint
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 221 | 65 | 15 | 301 | 34 | 9 | 11 |
- HTML: 221
- PDF: 65
- XML: 15
- Total: 301
- Supplement: 34
- BibTeX: 9
- EndNote: 11
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Xiaolin Duan
Guangjie Zheng
Chuchu Chen
Qiang Zhang
Kebin He
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(1204 KB) - Metadata XML
-
Supplement
(1018 KB) - BibTeX
- EndNote
- Final revised paper