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

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.

On the evolution of sub- and super-saturated water uptake of secondary organic aerosol in chamber experiments from mixed precursors

To better understand the chemical controls of sub- and super-saturated aerosol water uptake, we designed and conducted a series of chamber experiments to investigate the evolution of aerosol physicochemical properties during SOA formation from the photochemistry of single or mixed biogenic (α-pinene, isoprene) and anthropogenic (o-cresol) volatile organic compounds (VOCs) in the presence of ammonium sulphate seeds. During the six-hour experiments, the cloud condensation nuclei (CCN) activity at super-saturation of water (0.1 ~ 0.5 %), hygroscopic growth factor at 90 % RH, and non-refractory PM1 chemical composition were recorded concurrently. The hygroscopicity parameter κ was used to represent water uptake ability below and above water saturation, and the κ-Kӧhler approach was implemented to predict the CCN activity from the sub-saturated hygroscopicity. The sub- and super-saturated water uptake (in terms of κHTDMA and κCCN) were mainly controlled by the SOA mass fraction which depended on the SOA production rate of the precursors, and the SOA composition played a second-order role. For the reconciliation of κHTDMA and κCCN, the κHTDMA / κCCN ratio increased with the SOA mass fraction and this was observed in all investigated single and mixed VOC systems, independent of initial VOC concentrations and sources. For all VOC systems, the mean κHTDMA of aerosol particles was ~ 25 % lower than the κCCN at the beginning of the experiments with inorganic seeds. With the increase of condensed SOA on seed particles throughout the experiments, the discrepancy of κHTDMA and κCCN became weaker (down to ~ 0 %) and finally the mean κHTDMA was ~ 60 % higher than κCCN on average when the SOA mass fraction approached ~ 0.8. This is possibly attributable to the non-ideality of solutes at different RH or the different co-condensation of condensable organic vapours within the two instruments. As a result, the predicted CCN number concentrations from the κHTDMA and particle number size distribution were ~ 10 % lower than CCN counter measurement on average at the beginning, and further even turned to an overestimation of ~ 20 % on average when the SOA mass fraction was ~ 0.8. This chemical composition-dependent performances of κ-Kӧhler approach on CCN prediction can introduce a variable uncertainty in predicting cloud droplet numbers from the sub-saturated water uptake.

Calibration and evaluation of broad supersaturation scanning (BS2) cloud condensation nuclei counter for rapid measurement of particle hygroscopicity and CCN activity

For understanding and assessing aerosol-cloud interactions and their impact on climate, reliable measurement data of aerosol particle hygroscopicity and cloud condensation nuclei (CCN) activity are required. The CCN activity of aerosol particles can be determined by scanning particle size and supersaturation (S) in CCN measurements. Compared to the existing differential mobility analyzer (DMA)-CCN activity measurement, a broad supersaturation scanning CCN (BS2-CCN) system, in which particles are exposed to a range of S simultaneously, can measure the CCN activity with a high time-resolution. Based on a monotonic relation between the activation supersaturation of aerosol particles (Saerosol) and the activated fraction (Fact) of the BS2-CCN measurement, we can derive κ, a single hygroscopicity parameter, directly. Here, we describe how the BS2-CCN system can be effectively calibrated and which factors can affect the calibration curve (Fact – Saerosol). For calibration, size-resolved CCN measurements with ammonium sulfate and sodium chloride particles are performed under the three different thermal gradient (dT) conditions (dT = 6, 8, and 10 K). We point out key processes that can affect the calibration curve and thereby need to be considered as follows: First, the shape of the calibration curve is primarily influenced by Smax, the maximum S in the activation tube. We need to determine appropriate Smax depending on particle size and κ to be investigated. To minimize the effect of multiply charged particles, small geometric mean diameter (𝐷𝑔) and 𝜎𝑔 geometric standard deviation (𝜎𝑔) in number size distribution are recommended when generating the calibration aerosols. Last, Fact is affected by particle number concentration and has a decreasing rate of 0.02/100 cm−3 due to the water consumption in the activation tube. For evaluating the BS2-CCN system, inter-comparison experiments between typical DMA-CCN and BS2-CCN measurement were performed with the laboratory-generated aerosol mixture and ambient aerosols. Good agreements of κ values between DMA-CCN and BS2-CCN measurements for both experiments show that the BS2-CCN system can measure CCN activity well compared to the existing measurement, and can measure a broad range of hygroscopicity distribution with a high time-resolution (~1 second vs. few minutes for a standard CCN activity measurement). As the hygroscopicity can be used as a proxy for the chemical composition, our method can also serve as a complementary approach for fast and size-resolved detection/estimation of aerosol chemical composition.

Properties and emission factors of cloud condensation nuclei from biomass cookstoves – observations of a strong dependency on potassium content in the fuel

Residential biomass combustion is a significant source of aerosol particles on regional and global scales influencing climate and human health. The main objective of the current study was to investigate the properties of cloud condensation nuclei (CCN) emitted from biomass burning of solid fuels in different cookstoves mostly of relevance to sub-Saharan east Africa. The traditional three-stone fire and a rocket stove were used for combustion of wood logs of Sesbania and Casuarina with birch used as a reference. A natural draft and a forced-draft pellet stove were used for combustion of pelletised Sesbania and pelletised Swedish softwood alone or in mixtures with pelletised coffee husk, rice husk or water hyacinth. The CCN activity and the effective density were measured for particles with mobility diameters of ∽65, ∽100 and ∽200 nm, respectively, and occasionally for 350 nm particles. Particle number size distributions were measured online with a fast particle analyser. The chemical composition of the fuel ash was measured by application of standard protocols. The average particle number size distributions were by number typically dominated by an ultrafine mode, and in most cases a soot mode was centred around a mobility diameter of ∽150 nm. The CCN activities decreased with increasing particle size for all experiments and ranged in terms of the hygroscopicity parameter, κ, from ∽0.1 to ∽0.8 for the ultrafine mode and from ∽0.001 to ∽0.15 for the soot mode. The CCN activity (κ) of the ultrafine mode increased (i) with increasing combustion temperature for a given fuel, and (ii) it typically increased with increasing potassium concentration in the investigated fuels. The primary CCN and the estimated particulate matter (PM) emission factors were typically found to increase significantly with increasing potassium concentration in the fuel for a given stove. In order to link CCN emission factors to PM emission factors, knowledge about stove technology, stove operation and the inorganic fuel ash composition is needed. This complicates the use of ambient PM levels alone for estimation of CCN concentrations in regions dominated by biomass combustion aerosol, with the relation turning even more complex when accounting for atmospheric ageing of the aerosol.

Secondary aerosol formation alters CCN activity in the North China Plain

Secondary aerosols (SAs, including secondary organic and inorganic aerosols, SOAs and SIAs) are predominant components of aerosol particles in the North China Plain (NCP), and their formation has significant impacts on the evolution of particle size distribution (PNSD) and hygroscopicity. Previous studies have shown that distinct SA formation mechanisms can dominate under different relative humidity (RH). This would lead to different influences of SA formation on the aerosol hygroscopicity and PNSD under different RH conditions. Based on the measurements of size-resolved particle activation ratio (SPAR), hygroscopicity distribution (GF-PDF), PM2.5 chemical composition, PNSD, meteorology and gaseous pollutants in a recent field campaign, McFAN (Multiphase chemistry experiment in Fogs and Aerosols in the North China Plain), conducted during the autumn–winter transition period in 2018 at a polluted rural site in the NCP, the influences of SA formation on cloud condensation nuclei (CCN) activity and CCN number concentration (NCCN) calculation under different RH conditions were studied. Results suggest that during daytime, SA formation could lead to a significant increase in NCCN and a strong diurnal variation in SPAR at supersaturations lower than 0.07 %. During periods with daytime minimum RH exceeding 50 % (high RH conditions), SA formation significantly contributed to the particle mass and size changes in a broad size range of 150 to 1000 nm, leading to NCCN (0.05 %) increases within the size range of 200 to 500 nm and mass concentration growth mainly for particles larger than 300 nm. During periods with daytime minimum RH below 30 % (low RH conditions), SA formation mainly contributed to the particle mass and size and NCCN changes for particles smaller than 300 nm. As a result, under the same amount of mass increase induced by SA formation, the increase of NCCN (0.05 %) was stronger under low RH conditions and weaker under high RH conditions. Moreover, the diurnal variations of the SPAR parameter (inferred from CCN measurements) due to SA formation varied with RH conditions, which was one of the largest uncertainties within NCCN predictions. After considering the SPAR parameter (estimated through the number fraction of hygroscopic particles or mass fraction of SA), the relative deviation of NCCN (0.05 %) predictions was reduced to within 30 %. This study highlights the impact of SA formation on CCN activity and NCCN calculation and provides guidance for future improvements of CCN predictions in chemical-transport models and climate models.