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

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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;

/persons/resource/persons191530

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

/persons/resource/persons101196

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

/persons/resource/persons127588

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

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Citation

Zheng, G., Su, H., Wang, S., Pozzer, A., & Cheng, Y. (2021). Impact of non-ideality on reconstructing spatial and temporal variations of aerosol acidity with multiphase buffer theory. Atmospheric Chemistry and Physics Discussions, 21. doi:10.5194/acp-2021-55.


Cite as: http://hdl.handle.net/21.11116/0000-0008-4FFC-5
Abstract
Aerosol acidity is a key parameter in atmospheric aqueous chemistry and strongly influence the interactions of air pollutants and ecosystem. The recently proposed multiphase buffer theory provides a framework to reconstruct long-term trends and spatial variations of aerosol pH based on the effective acid dissociation constant of ammonia (Ka,NH3*). 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 NO3− in aqueous-phase anions, due to different NH4+ activity coefficients between (NH4)2SO4− and NH4NO3-dominated aerosols. A parameterization method is thus proposed to estimate cni at given RH, temperature and NO3− fraction, and is validated against long-term observations and global simulations. In the ammonia-buffered regime, with cni correction the buffer theory can well reproduce the Ka,NH3* predicted by comprehensive thermodynamic models, with root-mean-square deviation ~0.1 and correlation coefficient ~1. Note that, while cni is needed to predict Ka,NH3* levels, it is usually not the dominant contributor to its variations, as ~90 % of the temporal or spatial variations in Ka,NH3* is due to variations in aerosol water and temperature.