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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 analysis 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 can propagate through droplet activation, and the subsequent uncertainties with respect to the single-hygroscopicity parameter (κ) are reported. For a known aerosol such as sucrose, the κ derived 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.
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Comparison of three essential sub-micrometer aerosol measurements: Mass, size and shape
An instrumental trifecta now exists for aerosol separation and classification by aerodynamic diameter (Dae), mobility diameter (Dm) and mass (m) utilizing an aerodynamic aerosol classifier (AAC), differential mobility analyzer (DMA) and aerosol particle mass analyzer (APM), respectively. In principle, any combination of two measurements yields the third. These quantities also allow for the derivation of the particle effective density (ρeff) and dynamic shape factor (χ). Measured and/or derived deviations between tandem measurements are dependent upon the configuration but are generally less than 10 %. Notably, non-physical values of χ (< 1) and ρeff (> bulk) were determined by the AAC-APM. Harmonization of the results requires the use of χ in the determination of m and Dm from the AAC-DMA and AAC-APM requiring either a priori assumptions or determination from another method. Further errors can arise from assuming instead of measuring physical conditions – e.g. temperature and pressure affect the gas viscosity, mean free path and the Cunningham slip correction factor therefore impacting Dm, Dae – but are expected to have a smaller impact than χ. Utilizing this triplet of instrumentation in combination allows for quantitative determination of χ and the particle density (ρp). If the bulk density is known or assumed, then the packing density can be determined. The χ and ρp were determined to be 1.10 ± 0.03 and (1.00 ± 0.02) g cm-3, respectively, for a water stabilized black carbon mimic that resembles aged (collapsed) soot in the atmosphere. Assuming ρbulk = 1.8 g cm 3, a packing density of 0.55 ± 0.02 is obtained.
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- Award ID(s):
- 1708337
- PAR ID:
- 10184492
- Date Published:
- Journal Name:
- Aerosol Science and Technology
- ISSN:
- 0278-6826
- Page Range / eLocation ID:
- 1 to 13
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1080/02786826.2020.1763248
