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Activation of biomass burning aerosols (BBA) and fossil fuel combustion aerosols (FFA) in fogs and clouds significantly impact regional air quality through aqueous chemistry and climate by affecting cloud microphysics. However, we lack direct observations of how these aerosols behave in fogs and clouds. Using a newly developed aerosol‐cloud sampling system, we conducted observations during fog events and found that BBA, despite their high organic content, effectively contributed to super‐micron interstitial aerosols and fog droplets in low supersaturation fogs. In contrast, FFA, predominantly externally mixed organic, did not grow beyond the super‐micron size in fogs due to their near‐hydrophobic nature. Measurements conducted under supersaturations relevant for cloud formation revealed that portions of FFA could serve as cloud condensation nuclei, but only when supersaturation exceeded ∼0.14%. These findings have broad implications for future investigations into the influence of BBA and FFA on fog and cloud chemistry and their interactions with clouds. Plain Language Summary: Tiny particles, known as aerosols, emitted from combustion of biomass and fossil fuels, can impact air quality and global climate by interacting with fog and clouds. However, we lack direct observational evidence of how these aerosols behave in these conditions. In our study, we developed an advanced aerosol‐cloud sampling system to observe aerosol activation during fog events. Our findings highlight a crucial factor of aerosol activation: the mixing of these aerosols. Biomass burning aerosols with an internal mixture of organic and inorganic components activate more easily, even in conditions with low supersaturation like fog. In contrast, fossil fuel combustion aerosols are often externally mixed and almost water‐repellent, requiring higher supersaturation to become cloud condensation nuclei. Understanding these distinctions has significant implications for regional air quality and the intricate interactions between aerosols and clouds. By gaining insights into how various aerosols interact with fog and clouds, we can enhance our understanding of their impact on our environment and climate. Key Points: Advanced Aerosol‐Cloud sampling system was developed to characterize aerosol activation in fogsBiomass burning aerosols efficiently form fog droplets, while fossil fuel combustion aerosols are almost hydrophobic and don't contributeFossil fuel combustion organic aerosols can serve as CCN in high supersaturation conditions (>0.14%) [ABSTRACT FROM AUTHOR]

The commercial continuous‐flow streamwise thermal‐gradient cloud condensation nuclei (CCN) counter is widely used in aerosol CCN activity measurements which are critical for investigating aerosol‐cloud interactions. In CCN measurements, a critical threshold is needed for distinguishing interstitial aerosols and activated droplets, and a default threshold of 0.75 μm was set in CCN counter. Theoretically, interstitial aerosols could also grow larger than 0.75 μm in CCN counter at low supersaturations, thus theoretical thresholds derived from the Köhler theory were suggested. Here, we report that droplet growth in the CCN counter is kinetically limited and CCN‐active droplets does not reach their theoretical diameters under low supersaturations. Neglecting the kinetic growth limitation in CCN identification would count less CCN and underestimate the aerosol hygroscopicity parameter κ $\kappa $ by up to 50% for supersaturations lower than 0.1%. We recommend that thresholds considering kinetic limitations should be used as new criteria for CCN identification under low supersaturations. Plain Language Summary: Accurate Cloud Condensation Nuclei (CCN) predictions in climate and regional models are crucial for the assessment of climate effects of aerosol‐cloud interactions. Direct measurements of CCN activity, varied by aerosol size, hygroscopicity, and supersaturation, are important for model verifications and improvements. The commercial continuous‐flow streamwise thermal‐gradient CCN counter (CCNC) is widely used in CCN activity measurements. We found that the droplet growth in the CCNC is kinetically limited under low supersaturations (<0.14%), thus not reaching the theoretical critical activation diameter, which significantly affect CCN identification. Kinetic model results revealed that the wet diameter thresholds of CCN identification that took the kinetic limitations of droplet growth into account were independent of dry aerosol size and hygroscopicity thus could be directly used as new criteria for CCN identification. Neglecting the kinetic growth limitation in CCN identification would result in significant bias in CCN activity measurements and thus supersaturated aerosol hygroscopicity retrievals. Findings of this research have significant impacts on CCN activity measurements and supersaturated aerosol hygroscopicity investigations. Key Points: Limited hygroscopic growth of cloud condensation nuclei (CCN) in CCN counter is recognized and evaluated based on model analysisKinetic limitations in CCN counter impacts significantly on size‐resolved CCN activity measurementsKinetic limitations in CCN counter impacts significantly on aerosol hygroscopicity measurements under low supersaturation [ABSTRACT FROM AUTHOR]

Biomass burning organic aerosol (BBOA) impacts significantly on climate directly through scattering and absorbing solar radiation and indirectly through acting as cloud condensation nuclei. However, fundamental parameters in the simulation of BBOA radiative effects and cloud activities such as size distribution and refractive index remain poorly parameterized in models. In this study, biomass burning events with high combustion efficiency characterized by a high black carbon (BC) to BBOA ratio (0.22 on average) were frequently observed during autumn in the Pearl River Delta region, China. An improved absorption Ångström exponent (AAE) ratio method considering both variations and spectral dependence of black carbon AAE was proposed to differentiate brown carbon (BrC) absorptions from total aerosol absorptions. BBOA size distributions, mass scattering and absorption efficiency were retrieved based on the changes in aerosol number size distribution, scattering coefficients and derived BrC absorptions that occurred with BBOA spikes. Geometric mean diameter of BBOA volume size distribution Dgv depended largely on combustion conditions, ranging from 245 to 505 nm, and a linear relationship between Dgv and ΔBC/ΔBBOA ​​​​​​​ was achieved. The retrieved real part of the BBOA refractive index ranges from 1.47 to 1.64, with evidence showing that its variations might depend largely on combustion efficiency, which is rarely investigated in existing literature but which however requires further comprehensive investigations. Retrieved imaginary parts of BBOA refractive index (mi,BBOA) correlated highly with ΔBC/ΔBBOA (R>0.88) but differ a lot from previous parameterization schemes. The reason behind the inconsistency might be that single formula parameterizations of mi,BBOA over the whole BC/BBOA range were used in previous studies which might deviate substantially for specific BC/BBOA ranges. Thus, a new scheme that parameterizes wavelength-dependent mi,BBOA was presented, which filled the gap for field-based BBOA absorptivity parameterizations of ​​​​​​​BC/BBOA >0.1. These findings have significant implications for simulating BBOA climate effects and suggest that linking both BBOA refractive index and BBOA volume size distributions to BC content might be a feasible and a good choice for climate models. [ABSTRACT FROM AUTHOR]

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), PM 2.5 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. [ABSTRACT FROM AUTHOR]

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The number concentration of cloud condensation nuclei (CCN) plays a fundamental role in cloud physics. Instrumentations of direct measurements of CCN number concentration (NCCN) based on chamber technology are complex and costly; thus a simple way for measuring NCCN is needed. In this study, a new method for NCCN calculation based on measurements of a three-wavelength humidified nephelometer system is proposed. A three-wavelength humidified nephelometer system can measure the aerosol light-scattering coefficient (σsp) at three wavelengths and the light-scattering enhancement factor (fRH). The Ångström exponent (Å) inferred from σsp at three wavelengths provides information on mean predominate aerosol size, and hygroscopicity parameter (κ) can be calculated from the combination of fRH and Å. Given this, a lookup table that includes σsp, κ and Å is established to predict NCCN. Due to the precondition for the application, this new method is not suitable for externally mixed particles, large particles (e.g., dust and sea salt) or fresh aerosol particles. This method is validated with direct measurements of NCCN using a CCN counter on the North China Plain. Results show that relative deviations between calculated NCCN and measured NCCN are within 30 % and confirm the robustness of this method. This method enables simplerNCCN measurements because the humidified nephelometer system is easily operated and stable. Compared with the method using a CCN counter, another advantage of this newly proposed method is that it can obtain NCCN at lower supersaturations in the ambient atmosphere. [ABSTRACT FROM AUTHOR]

Parameterization of the size-resolved particle activation ratio (AR) is useful for the prediction and analysis of the cloud condensation nuclei (CCN) number concentration ( N CCN ). Critical issues for the application of AR parameterizations in models are (1) the consistency of the different parameterization schemes in terms of fitting AR curves and (2) the applicability of the estimate of N CCN under different aerosol conditions. These issues are discussed in this study based on summertime measurements of the size-resolved AR in the North China Plain. By comparing parameterized AR curves, variations in the fitting parameters and application to N CCN calculations, the consistency of the three existing parameterization schemes is confirmed. Based on an analysis using representative AR fitting parameters, the method commonly used to calculate N CCN with a fixed AR was found to be accurate, except during periods affected by strong black carbon (BC) emissions or new particle formation (NPF) events. The N CCN value was overestimated by approximately 10% when BC aerosol emissions were abundant and deviated from the 1:1 line by 15% during NPF events. The bias of the calculated N CCN due to these significant emission or aerosol production events cannot be eliminated by using the appropriate representative fitting parameters. Under these circumstances, accurate prediction of N CCN requires real-time aerosol hygroscopicity data based on CCN measurements. This research furthers our understanding of the relationship between aerosol cloud activation and aerosol spectra. [ABSTRACT FROM AUTHOR]

Size-resolved Particle Activation Ratio (SPAR), defined as the size-dependence of CCN activity of aerosol particles, is generally parameterized by a scheme that neglects the contribution of nearly-hydrophobic particles. In this paper, it was found that nearly-hydrophobic particles can contribute up to 20% to the CCN activity for large particles and the estimation of particle hygroscopicity would be significantly biased using the original parameterization scheme. An improved parameterization scheme considering the contribution of nearly-hydrophobic particles was proposed and applied on measurements conducted at a rural site in the North China Plain during summer 2013. For hygroscopic particles in range of 50–70 nm diameter, which might be dominated by organic compounds, their number fraction (f n) and midpoint activation diameter (D a) fitted with the improved scheme were up to 20% lower than the parameters fitted with the original scheme. Both parameterization schemes of SPAR were further applied in a comparison study of aerosol hygroscopicity measurements between Cloud Condensation Nuclei (CCN) Counter coupled with Differential Mobility Analyzer (DMA-CCNC) and Hygroscopicity Tandem Differential Mobility Analyzer (HTDMA) during the Xianghe Campaign. Applying the improved scheme, the correlations of derived f n for hygroscopic particles between measurements of DMA-CCNC and HTDMA were improved (R increased by up to 0.2 with the uncertainty of the regression to be about 10%). In addition, for particles smaller than 100 nm, the size-dependence of hygroscopicity parameter and the diurnal cycle of f n derived using the improved scheme, based on the DMA-CCNC measurements were more consistent with those derived with the HTDMA measurements. These results highlight the importance of considering nearly-hydrophobic particles in SPAR curves fitting and in the calculation of aerosol hygroscopicity based on DMA-CCNC measurements. • A new parameterization scheme for SPAR considering the contribution of the nearly-hydrophobic particles is proposed. • The fitting parameters using the improved scheme were up to 20% lowered for hygroscopic particles. • The derived aerosol hygroscopicity using the improved scheme agrees better with aerosol hygroscopicity derived from HTDMA. [ABSTRACT FROM AUTHOR]

Size-resolved particle activation ratio (SPAR), the size-dependent ability of aerosol particles to form cloud condensation nuclei (CCN), is generally parameterized by a formula with sigmoidal shape with three key parameters. However, our understanding about the intrinsic relationship between the sigmoidal shape function and aerosol hygroscopicity distribution remains incomplete. In this study, the relationship between SPAR and particle hygroscopicity distribution is mathematically derived and relationships between key parameters for SPAR and particle hygroscopicity distribution are manifested, making it clear for the physical understanding of key CCN spectral characteristics. On the basis of the mathematically derived SPAR parameterization, a simplified method to calculate SPAR parameters (including maximum activation fraction (MAF), critical diameter and its heterogeneity of CCN-active particles) based on hygroscopicity distribution data is proposed. This simplified method is applied to Hygroscopicity Tandem Differential Mobility Analyzer (HTDMA) field measurement datasets around the world for the meta-analysis of regional and seasonal characteristics of CCN activity. It was found that, for CCN-active particles in polluted regions, the MAF can reach lower values, and the variations of both hygroscopicity and its heterogeneity can be larger, compared with those in clean regions. In summer, the MAF and the hygroscopicity of CCN can reach higher values than those in winter. In addition, the variations of SPAR parameters among different campaigns are large and thus measurements of CCN Counter or HTDMA are needed to specify the SPAR parameters at a specific site during a specific period. The bulk CCN activity at different SSs are also calculated based on the calculated SPAR by considering the measured particle number size distribution. The mathematical derivation and physical interpretation of SPAR parameterization in this study can further the understanding of CCN activity based on particle hygroscopicity distribution. The proposed simplified method to calculate SPAR parameters based on HTDMA measurements allows meta-analysis of SPAR parameters and helps expand the CCN activity datasets around the world. • Relationship between Size-resolved particle activation ratio and hygroscopicity distribution is mathematically derived. • A simplified method to calculate SPAR parameters based on hygroscopicity distribution data is proposed. • Meta-analysis of CCN activity is conducted based on HTDMA field measurement datasets in references. [ABSTRACT FROM AUTHOR]

Cloud condensation nuclei (CCN) spectra, that indicate the dependence of CCN number concentrations (N CCN) on supersaturation, are important factors in aerosol–cloud interactions. Owing to the lack of direct measurements of N CCN , calculating the CCN spectra based on conventional observational data of aerosols is important to obtain N CCN ; however, this is challenging owing to the complex relationship between aerosol properties and CCN activity. Machine learning techniques have recently been applied to estimate N CCN and found to be a promising method for CCN prediction. In this study, the random forest (RF) model was applied to predict CCN spectral parameters using the observation data measured in four campaigns in the North China Plain, and the effects of chemical and optical properties of aerosol on the estimation of CCN spectra were investigated. The results show that the RF model trained with the data of one campaign can be used to estimate CCN spectral parameters in another campaign, with the coefficient of determination between the estimated and measured CCN spectral parameters being approximately 0.5. The deviations of the estimation by the RF model may result from the difference in both the aerosol properties and measurement uncertainties among different campaigns, whose influence on the deviations can be further magnified by overfitting the RF model. Further analysis revealed that the major aerosol properties among the input variables of the RF model were the mass concentration of black carbon and aerosol hemispheric backscattering fraction at a wavelength of 450 nm. In addition, the roles of chemical compositions of aerosol in estimating CCN spectra parameters are different among different campaigns because the poor correlation between aerosol chemical compositions and CCN spectra parameter can affect the performance of the RF model. For estimating CCN spectra, using aerosol optical properties, including the hemispheric backscattering fraction and absorption coefficient, as model inputs is more recommended than using aerosol chemical properties. • Random forest model is used to estimate CCN spectra based on observation data in the North China Plain. • Mass concentration of black carbon and hemispheric backscattering fraction are the most important input variables. • Aerosol optical properties data are recommended in estimation of CCN spectra parameters. [ABSTRACT FROM AUTHOR]