Articles | Volume 25, issue 7
https://doi.org/10.5194/acp-25-3919-2025
https://doi.org/10.5194/acp-25-3919-2025
Research article
 | 
08 Apr 2025
Research article |  | 08 Apr 2025

Driving factors of aerosol acidity: a new hierarchical quantitative analysis framework and its application in Changzhou, China

Xiaolin Duan, Guangjie Zheng, Chuchu Chen, Qiang Zhang, and Kebin He

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This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Cited articles

Ault, A. P.: Aerosol Acidity: Novel Measurements and Implications for Atmospheric Chemistry, Accounts Chem. Res., 53, 1703–1714, https://doi.org/10.1021/acs.accounts.0c00303, 2020. 
Bae, M., Kang, Y.-H., Kim, E., Kim, S., and Kim, S.: A multifaceted approach to explain short- and long-term PM2.5 concentration changes in Northeast Asia in the month of January during 2016–2021, Sci. Total Environ., 880, 163309, https://doi.org/10.1016/j.scitotenv.2023.163309, 2023. 
Bloomfield, P.: Fourier Analysis of Time Series: An Introduction, John Wiley & Sons, 285 pp., https://doi.org/10.1002/0471722235, 2004. 
Changzhou Ecological Environment Bureau: The Changzhou Air Quality Automatic Monitoring Station Data Platform, http://192.168.100.25/czems/MainFrame.aspx (last access: 11 January 2024), 2024. 
Cheng, Y., Zheng, G., Wei, C., Mu, Q., Zheng, B., Wang, Z., Gao, M., Zhang, Q., He, K., Carmichael, G., Pöschl, U., and Su, H.: Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China, Sci. Adv., 2, e1601530, https://doi.org/10.1126/sciadv.1601530, 2016. 
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Aerosol acidity is an important parameter in atmospheric chemistry, while its driving factors, especially chemical profiles versus meteorological conditions, are not yet fully understood. Here, we established a hierarchical quantitative analysis framework to understand the driving factors of aerosol acidity on different timescales. Its application in Changzhou, China, revealed distinct driving factors and corresponding mechanisms of aerosol acidity from annual trends to random residuals.
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