Articles | Volume 15, issue 4
https://doi.org/10.5194/amt-15-1007-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-15-1007-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Feb 2022
Research article |
| 25 Feb 2022
| 25 Feb 2022
Cloud condensation nuclei (CCN) activity analysis of low-hygroscopicity aerosols using the aerodynamic aerosol classifier (AAC)
Kanishk Gohil
Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, United States
Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD 20742, United States
Department of Chemistry and Biochemistry, University of Maryland, College Park, MD 20742, United States
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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.
This work develops a methodology and software to study and analyze the cloud-droplet-forming...
