Abstract
The Aerodynamic Aerosol Classifier (AAC) is a novel instrument that size-selects aerosol particles based on their mechanical mobility. So far, the application of an AAC for Cloud Condensation Nuclei (CCN) activity analysis of aerosols has yet to be explored. Traditionally, a Differential Mobility Analyzer (DMA) is used for aerosol classification in a CCN experimental setup. A DMA classifies particles based on their electrical mobility. Substituting the DMA with an AAC can eliminate multiple charging artifacts as classification using an AAC does not require particle charging. In this work, we describe an AAC-based CCN experimental setup and CCN analysis method. We also discuss and develop equations to quantify the uncertainties associated with aerosol particle sizing. To do so, we extend the AAC transfer function analysis and calculate the measurement uncertainties of the aerodynamic diameter from the resolution of the AAC. The analyses framework has been packaged into a Python-based CCN Analysis Tool (PyCAT 1.0) open-source code, which is available on GitHub for public use. Results show that the AAC size-selects robustly (AAC resolution is 10.1, diffusion losses are minimal and particle transmission is high) at larger aerodynamic diameters (≥∼85 nm). The size-resolved activation ratio is ideally sigmoidal since no charge corrections are required. Moreover, the uncertainties in the critical particle aerodynamic diameter at a given supersaturation canpropagate through droplet activation and the subsequent uncertainties with respect to the single-hygroscopicity parameter (κ) are reported. For a known aerosol such as sucrose, theκderived from the critical dry aerodynamic diameter can be up to ∼50 % different from the theoretical κ. In this work, we do additional measurements to obtain dynamic shape factor information and convert the sucrose aerodynamic to volume equivalent diameter. The volume equivalent diameter applied to κ- Köhler theory improves the agreement between measured and theoretical κ. Given the limitations of the coupled AAC-CCN experimental setup, this setup is best used for low hygroscopicity aerosol (κ ≤ 0.2) CCN measurements.
Highlights
20 Cloud Condensation Nuclei (CCN) activity is defined as the ability of an aerosol particle to facilitate the condensation of water vapor on its surface; the condensation occurs in supersaturated ambient conditions resulting in the formation of droplets
The Aerodynamic Aerosol Classifier (AAC)-CCNC measurements of sucrose were reported at varying CCNC instrument supersaturations (0.2 to 0.6%)
The aerosol particles classified with the AAC were counted using a Condensation Particle Counter (CPC) and CCNC
Summary
20 Cloud Condensation Nuclei (CCN) activity is defined as the ability of an aerosol particle to facilitate the condensation of water vapor on its surface; the condensation occurs in supersaturated ambient conditions resulting in the formation of droplets. One previous study (Barati et al (2019)) published results for the CCN analysis of low-hygroscopicity aerosols but did not investigate the uncertainties in AACCCN size resolved measurements and CCN activity predictions. This work explains the AAC-CCNC coupling for CCN activity measurements and uncertainties associated with size-selection, number size distributions and CCN activity estimates employing the AAC transfer function. The Scanning Mobility CCN Analysis (SMCA) (Moore et al (2010)) package is widely 70 used to calculate the CCN activity of aerosols using their electrical mobility-classified number size distribution data. SMCA has been shown to efficiently perform functions that include inversion of time series measurements to obtain size-resolved data (Wang and Flagan (1990)), and multiple charge correction using the algorithm given by Wiedensohler (1988). We discuss the uncertainties associated with aerodynamic size selection and the propagated error into the CCN activity analysis, as well as the impact on the subsequently derived single-hygroscopicity parameter (κ) values
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