(748e) CCN Activity Analysis of Low-Hygroscopicity Aerosols Using the Aerodynamic Aerosol Classifier (AAC)

In this paper we describe the methodology for Cloud Condensation Nuclei (CCN) activity analysis of aerosol particles involving an Aerodynamic Aerosol Classifier (AAC). The AAC is a novel instrument that size-selects aerosol particles based on their mechanical mobility. While moving through a rotating AAC column an aerosol particle is subjected to a centrifugal force that varies according to particle mass and is balanced by the drag force acting on it. The drag force defines the mechanical mobility of the particle, which is related to its relaxation time within the AAC column. The relaxation time depends on the rotational speed of the AAC column and determines the “aerodynamic diameter” for which the aerosol particles can be size-selected/classified. So far, previous studies have examined the use of the AAC for numerous applications. These include calibration of Optical Particle Counter (OPC), determination of particle mass, effective density and dynamic shape factor, and separation of particles from a polydisperse stream. In addition to this, theoretical models to represent the classifying characteristics of an AAC have also been studied in vast detail. However, the utility of an AAC for CCN activity analysis of aerosols has not yet been explored. Traditionally, a Differential Mobility Analyzer (DMA) is used for aerosol classification in a CCN analysis setup. A DMA size-selects/classifies particles based on their electrical mobility. This requires the particles to acquire a unit charge before being subjected to an external electrostatic field while they move along the DMA column. Despite the utility of a DMA-based setup, there are issues related to particle multiple charging artifacts that can introduce uncertainties in CCN measurements and subsequent CCN activity predictions. Substituting the DMA with an AAC can help with eliminating these uncertainties as classification using an AAC does not require particle charging. This can improve classification for aerosol particles and has the potential to then improve the CCN activity predictions, particularly of low-hygroscopicity species. In this work, we present the results for CCN analysis of a moderately aqueous soluble and weakly hygroscopic organic aerosol (sucrose) using an AAC-based experimental setup. We show how the uncertainties associated with aerosol particle sizes can be removed with the help of extensive transfer function analysis. Previously, the AAC transfer function and uncertainties associated with particle relaxation times have been combined for expressing the resolution for the AAC classification. Since the particle relaxation times are translated into aerodynamic diameters, their uncertainties will also carry over. We extend the transfer function analysis to examine the variability AAC resolution in terms of the aerosol aerodynamic diameter instead of relaxation time. This facilitates quantification of the size-dependent uncertainties in the particle measurements corresponding to specific aerodynamic diameter of aerosols being classified using the AAC. We conclude that this is effective for reducing the disparities in size-resolved aerosol measurement, therefore increasing the accuracy in the predictions related to CCN activity and droplet nucleation.