Preprints
https://doi.org/10.5194/amt-2021-258
https://doi.org/10.5194/amt-2021-258

  30 Sep 2021

30 Sep 2021

Review status: this preprint is currently under review for the journal AMT.

Cloud Condensation Nuclei (CCN) Activity Analysis of Low-hygroscopicity Aerosols Using the Aerodynamic Aerosol Classifier (AAC)

Kanishk Gohil1 and Akua Asa-Awuku1,2 Kanishk Gohil and Akua Asa-Awuku
  • 1Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, United States
  • 2Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, United States

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.

Kanishk Gohil and Akua Asa-Awuku

Status: open (until 04 Nov 2021)

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Kanishk Gohil and Akua Asa-Awuku

Kanishk Gohil and Akua Asa-Awuku

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Short summary
This work develops a methodology and software to study and analyze the cloud droplet forming ability of aerosols with an Aerodynamic Aerosol Classifier (AAC). This work quantifies the uncertainties in size-resolved measurements and subsequent uncertainties propagated to cloud droplet parameterizations. Lastly, we present the best practices for AAC cloud droplet measurement.