Measurement report: Hygroscopic growth of ambient fine particles measured at five sites in China

The aerosol hygroscopic growth describes the interaction between aerosols and water vapor, which varies largely, depending on the chemical composition, types, and emissions of gas precursors under diverse environments. In this study, we analyzed size-resolved hygroscopic growth measured at five field sites of China by a hygroscopic tandem differential mobility analyzer (H-TDMA). Results show that the probability density function of hygroscopic growth factor (GF-PDF) at the megacity sites of Guangzhou (GZ), Shanghai (SH), and Beijing (BG) was generally with bimodal hydrophobic and hydrophilic modes, while a unimodal hydrophilic mode was dominated at the suburb sites of Xinzhou (XZ) and Xingtai (XT) throughout the measured particle size of 40-200ĝ€¯nm. As a result, the more hygroscopic (MH) mode accounts for a number fraction of >80ĝ€¯% at the suburb sites, compared to only 20ĝ€¯%-40ĝ€¯% for 40ĝ€¯nm particles at the megacity sites. Further analysis shows that the GF value increases with the aggravated PM2.5 pollution at the sites (BG, XZ, and XT) in northern China, but that is not the case for GZ and SH, which are located in the southern regions. The distinct dependence of GF on the variations in PM2.5 concentrations among the sites suggests the spatial variability in particle composition with the evolution of pollution events in different regions of China. Moreover, different particle hygroscopic behaviors during new particle formation (NPF) events were observed at the five sites, reflecting the distinct mechanisms of NPF in diverse atmospheric environments. By including results from more sites, we find that the aerosol particles observed at those suburb sites are basically more hygroscopic than those in megacities. However, a large variability in the hygroscopic parameter κ at a given particle size among different sites is also observed, suggesting a complex impact from local sources and atmospheric processes. The measured dataset is helpful for improving the understanding of the formation of fine particles and the regional environmental and climate change.