Relating particle hygroscopicity and CCN activity to chemical composition during the HCCT-2010 field campaign
Particle hygroscopic growth at 90% RH (relative humidity), cloud condensation nuclei (CCN) activity, and size-resolved chemical composition were concurrently measured in the Thuringer Wald mid-level mountain range in central Germany in the fall of 2010. The median hygroscopicity parameter values, kappa, of 50, 75, 100, 150, 200, and 250 nm particles derived from hygroscopicity measurements are respectively 0.14, 0.14, 0.17, 0.21, 0.24, and 0.28 during the sampling period. The closure between HTDMA (Hygroscopicity Tandem Differential Mobility Analyzers)-measured (kappa(HTDMA)) and chemical composition-derived (kappa(chem)) hygroscopicity parameters was performed based on the Zdanovskii-Stokes-Robinson (ZSR) mixing rule. Using size-averaged chemical composition, the kappa values are substantially overpredicted (30 and 40% for 150 and 100 nm particles). Introducing size-resolved chemical composition substantially improved closure. We found that the evaporation of NH4NO3, which may happen in a HTDMA system, could lead to a discrepancy in predicted and measured particle hygroscopic growth. The hygroscopic parameter of the organic fraction, kappa(org), is positively correlated with the O:C ratio (kappa(org) = 0.19 x (O:C) - 0.03). Such correlation is helpful to define the kappa(org) value in the closure study. kappa derived from CCN measurement was around 30% (varied with particle diameters) higher than that determined from particle hygroscopic growth measurements (here, hydrophilic mode is considered only). This difference might be explained by the surface tension effects, solution non-ideality, gas-particle partitioning of semivolatile compounds, and the partial solubility of constituents or non-dissolved particle matter. Therefore, extrapolating from HTDMA data to properties at the point of activation should be done with great care. Finally, closure study between CCNc (cloud condensation nucleus counter)-measured (kappa(CCN)) and chemical composition (kappa(CCN, chem)) was performed using CCNc-derived kappa values for individual components. The results show that the kappa(CCN) can be well predicted using particle size-resolved chemical composition and the ZSR mixing rule.