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Impact of non-ideality on reconstructing spatial and temporal variations in aerosol acidity with multiphase buffer theory

MPG-Autoren
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Zheng,  Guangjie
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Su,  Hang
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Wang,  Siwen
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Pozzer,  Andrea
Atmospheric Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Cheng,  Yafang
Multiphase Chemistry, Max Planck Institute for Chemistry, Max Planck Society;

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Zitation

Zheng, G., Su, H., Wang, S., Pozzer, A., & Cheng, Y. (2022). Impact of non-ideality on reconstructing spatial and temporal variations in aerosol acidity with multiphase buffer theory. Atmospheric Chemistry and Physics, 22(1), 47-63. doi:10.5194/acp-22-47-2022.


Zitierlink: https://hdl.handle.net/21.11116/0000-0009-CB8D-4
Zusammenfassung
Aerosol acidity is a key parameter in atmospheric aqueous chemistry and strongly influences the interactions of air pollutants and the ecosystem. The recently proposed multiphase buffer theory provides a framework to reconstruct long-term trends and spatial variations in aerosol pH based on the effective acid dissociation constant of ammonia (). However, non-ideality in aerosol droplets is a major challenge limiting its broad applications. Here, we introduced a non-ideality correction factor (cni) and investigated its governing factors. We found that besides relative humidity (RH) and temperature, cni is mainly determined by the molar fraction of NO in aqueous-phase anions, due to different NH activity coefficients between (NH4)2SO4- and NH4NO3-dominated aerosols. A parameterization method is thus proposed to estimate cni at a given RH, temperature and NO fraction, and it is validated against long-term observations and global simulations. In the ammonia-buffered regime, with cni correction, the buffer theory can reproduce well the predicted by comprehensive thermodynamic models, with a root-mean-square deviation ∼ 0.1 and a correlation coefficient ∼ 1. Note that, while cni is needed to predict levels, it is usually not the dominant contributor to its variations, as ∼ 90 % of the temporal or spatial variations in are due to variations in aerosol water and temperature.