External and internal cloud condensation nuclei (CCN) mixtures: controlled laboratory studies of varying mixing states

Abstract

Changes in aerosol chemical mixtures modify cloud condensation nuclei (CCN) activity. Previous studies have developed CCN models and validated changes in external and internal mixing state with ambient field data. Here, we develop an experimental method to test and validate the CCN activation of known aerosol chemical composition with multicomponent mixtures and varying mixing states. CCN activation curves consisting of one or more activation points are presented. Specifically, simplified two-component systems of varying hygroscopicity were generated under internal, external, and transitional mixing conditions. κ-Köhler theory predictions were calculated for different organic and inorganic mixtures and compared to experimentally derived kappa values and respective mixing states. This work employs novel experimental methods to provide information on the shifts in CCN activation data due to external to internal particle mixing from controlled laboratory sources. Results show that activation curves consisting of single and double activation points are consistent with internal and external mixtures, respectively. In addition, the height of the plateau at the activation points is reflective of the externally mixed concentration in the mixture. The presence of a plateau indicates that CCN activation curves consisting of multiple inflection points are externally mixed aerosols of varying water-uptake properties. The plateau disappears when mixing is promoted in the flow tube. At the end of the flow tube experiment, the aerosols are internally mixed and the CCN activated fraction data can be fit with a single-sigmoid curve. The technique to mimic externally to internally mixed aerosol is applied to non-hygroscopic carbonaceous aerosol with organic and inorganic components. To our knowledge, this work is the first to show controlled CCN activation of mixed nonhygroscopic soot with hygroscopic material as the aerosol population transitions from externally to internally mixed states in laboratory conditions. Results confirm that CCN activation analysis methods used here and in ambient data sets are robust and may be used to infer the mixing state of complex aerosol compositions of unknown origin.

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

“…Furthermore, 85 nm is a reasonable lower limit for CCN measurements of low-hygroscopicity aerosols. Low-hygroscopicity aerosols (predominantly organics with κ ≤ 0.2; Xu et al, 2014;Vu et al, 2019;Wang et al, 2019) do not activate readily at smaller particle sizes and atmospherically relevant supersaturations (< 1 %). The laboratory number size distribution measurements for such aerosols are reliable at low to midrange supersaturations with the AAC-CCNC setup.…”

“…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). SMCA works well for a variety of organic and inorganic aerosols to estimate their CCN activity (e.g., but not limited to Moore et al, 2010;Padró et al, 2012;Giordano et al, 2015;Fofie et al, 2018;Barati et al, 2019;Vu et al, 2019;Dawson et al, 2020;C. Peng et al, 2021).…”

Comment on amt-2021-258

“…Petters and Kreidenweis (2007), Xu et al (2014), Vu et al (2019), Wang et al (2019)) do not activate readily at smaller particle sizes and atmospherically relevant supersaturations (<1%). The laboratory number size distribution measurements for such aerosols are reliable at low to mid-range supersaturations with the AAC-CCNC setup.…”

Enhancement of secondary aerosol formation by reduced anthropogenic emissions during Spring Festival 2019 and enlightenment for regional PM&lt;sub&gt;2.5&lt;/sub&gt; control in Beijing

“…Over the past a few years, many emission control measures have been taken in China to mitigate air pollution. As a response, the mass concentration of PM2.5 has decreased in most cities in China since 2013, especially in the BTH region (Q. Zhang et al, 2019;Vu et al, 2019;Zhai et al, 2019). Organics and black carbon (BC) concentrations largely decreased during these years thanks to the reduction in coal combustion and biomass burning (H. Li et al, 2019a;Xu et al, 2019).…”