Cloud condensation nuclei (CCN) activity analysis of low-hygroscopicity aerosols using the aerodynamic aerosol classifier (AAC)

Abstract. The aerodynamic aerosol classifier (AAC) is a novel instrument that size-selects particles based on their mechanical mobility. So far, the application of an AAC for cloud condensation nuclei (CCN) activity analysis aerosols has yet to be explored. Traditionally, differential mobility analyzer (DMA) used classification in CCN experimental setup. A DMA classifies electrical Substituting with can eliminate multiple-charging artifacts as using does not require particle charging. In this work, we describe AAC-based setup and method. We also discuss develop equations quantify uncertainties associated sizing. To do so, extend transfer function calculate measurement diameter from resolution AAC. framework been packaged into Python-based Analysis Tool (PyCAT 1.0) open-source code, which available GitHub public use. Results show robustly (AAC 10.1, diffusion losses are minimal, transmission high) at larger diameters (?? 85 nm). size-resolved activation ratio ideally sigmoidal since no charge corrections required. Moreover, critical given supersaturation propagate through droplet activation, subsequent respect single-hygroscopicity parameter (?) reported. For known such sucrose, ? derived dry up ? 50 % different theoretical ?. additional measurements obtain dynamic shape factor information convert sucrose volume equivalent diameter. applied ?-Köhler theory improves agreement between measured Given limitations coupled AAC–CCN setup, best low-hygroscopicity (??0.2) measurements.