We propose a novel method which is accurate in identification of micro-dynamics phase transition processes for ammonium sulfate aerosols by using two-dimensional correlation spectroscopy. we explore more sophisticated structural evolution patterns and the precise sequence of hydrogen-bonding rearrangements. These findings deepen the mechanistic understanding of aerosol phase transitions at the molecular scale, which could inspire new research directions in atmospheric heterogeneous chemistry.

The hygroscopic growth of single nanoparticles is important for hygroscopic characteristic analysis of atmospheric particles and for scientific studies involving atmospheric particles. Based on the hygroscopicity difference of subgroups of atmospheric nanoparticles, the classification and proportion analysis of atmospheric nanoparticles has been completed, which has potential significance in predicting the contribution of the atmospheric particulate hygroscopicity and particle growth mechanism.

We demonstrated the usage of the Fourier transform infrared (FTIR) spectroscopic technique to characterize in real time the hygroscopic growth properties of nanoparticles and their phase transition micro-dynamics at the molecular level. We first realize real-time measurements of water content and dry nanoparticle mass to characterize hygroscopic growth factors. We then identify in real time the hydration interactions and the dynamic hygroscopic growth process of the functional groups.

We quantified the variability, source, and transport of urban CO over the Himalayas and Tibetan Plateau (HTP) by using measurement, model simulation, and the analysis of meteorological fields. Urban CO over the HTP is dominated by anthropogenic and biomass burning emissions from local, South Asia and East Asia, and oxidation sources. The decreasing trends in surface CO since 2015 in most cities over the HTP are attributed to the reduction in local and transported CO emissions in recent years.

For the first time, a vertical observation network consisting of 13 aerosol lidars and more than 1000 ground observation stations were combined with a data assimilation technique to reveal key processes driving the 3-D dynamic evolution of PM_2.5 concentrations during extreme heavy aerosol pollution on the North China Plain.