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 PM_2.5, 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 K_a^*, non-ideality c_ni and gas-particle partitioning. Random variations are dominated by the aerosol water contents through K_a^* and chemical profiles through c_ni. 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.