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 their intrinsic complexity. Here, we propose a new framework to quantify factor importance, which incorporated an interpretive structural modeling (ISM) approach and time series analysis. In particular, a hierarchical influencing factor relationship is established based on the multiphase buffer theory with ISM. A long-term (2018-2023) 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 of the driving factors of aerosol acidity at different levels, which is important in acidity-related process studies and policy-making.