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Seasonal variations in PM2.5 composition and their
effects on CCN activation properties

Yongwei Lu, Yukun Chen, Xin Wang, Bo Ren, Biao Li, Eric Lichtfouse,

Yueshe Wang

► To cite this version:

Yongwei Lu, Yukun Chen, Xin Wang, Bo Ren, Biao Li, et al. Seasonal variations in PM2.5 composition and their effects on CCN activation properties. Atmospheric Environment, 2025, 349, pp.121129. 10.1016/j.atmosenv.2025.121129. hal-04975153

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Submitted on 4 Mar 2025

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# Seasonal variations in PM₂.₅ composition and their effects on CCN activation properties

Yongwei Lu<sup>a</sup>, Yukun Chen<sup>b</sup>, Xin Wang<sup>c,*</sup>, Bo Ren<sup>a</sup>, Biao Li<sup>a</sup>, Eric Lichtfouse<sup>a,●</sup>,
Yueshe Wang<sup>a,**</sup>

* As particle size decreases, the gap between $\kappa_{ccn}$ and $\kappa_{chem}$ widens significantly.

* The input of summer forest air masses leads to lower $\kappa$ compared to winter.

* The $\kappa$-closure approach fits winter better, with correlations above 0.53.

* $NO_3^-$ and OM are the main causes of discrepancies between $\kappa_{ccn}$ and $\kappa_{chem}$.

<figure><img src="image_2.png" /></figure>

## ABSTRACT

PM₂.₅ samples were collected in Xi'an during the summer and winter of 2019 to analyze seasonal, diurnal, and pollution-level variations in particle chemical composition. Scanning Mobility CCN Analysis was used to investigate the impact of chemical composition on cloud condensation nuclei (CCN) activation. Results showed increased particle concentrations in winter, with significant rises in SO₄²⁻, OM, NO₃⁻, and Cl⁻. Motor vehicle exhaust was the primary pollution source, while biomass burning air masses further exacerbated winter pollution. Xi'an aerosols exhibited continental characteristics, with $\kappa_{ccn}$ = 0.26 ± 0.07 in summer and $\kappa_{ccn}$ = 0.33 ± 0.09 in winter. Seasonal $\kappa_{ccn}$ differences were mainly attributed to the prevalence of weakly hygroscopic air masses in summer, while higher $\kappa_{ccn}$ values were observed in winter and during severe pollution episodes. The $\kappa$-closure scheme proposed in this study performed poorly at higher supersaturations (the nucleation mode), with nitrate evaporation losses and the surface tension reduction effect of organics being key factors. A fixed $\kappa$-closure scheme could not accurately characterize CCN activity across different seasons, but after appropriate corrections, the closure error was controlled within 6%, achieving a good agreement.

Keywords:

Aerosol

Chemical composition

Cloud condensation nuclei

Activation characteristics

Chemical composition closure

<footnote><sup>a</sup> State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China</footnote>

<footnote><sup>b</sup> Science and Technology on Aerospace Chemical Power Laboratory, Hubei Institute of Aerospace Chemotechnology, Xiangyang, 441003, Hubei, China</footnote>

<footnote><sup>c</sup> School of Chemistry, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>** Corresponding author.</footnote>

<footnote>E-mail addresses: xinwang@xjtu.edu.cn (X. Wang), wangys@mail.xjtu.edu.cn (Y. Wang).</footnote>
# 1. Introduction

Aerosols are a mixture of air, particles, and droplets, with increased particle concentrations being a key driver of air pollution. These particles not only directly affect the climate by altering radiative forcing (Chung et al., 2005; Yu et al., 2006), but can also carry toxic substances, such as bacteria and viruses, contributing to diseases and premature deaths (Pope III & Dockery, 2006; Pöschl, 2005). China is one of the countries with relatively high aerosol concentrations. Despite the government's active efforts to mitigate this issue, air pollution events still occur occasionally (Ma et al., 2019; Wang et al., 2019; Wong and Karplus, 2017).

Cloud condensation nuclei (CCN) are particles that can be activated into cloud droplets (10–20 µm) at specific supersaturation (SS) levels (Royer et al., 2023). Studies have shown that the uncertainty in aerosol-induced radiative forcing is ±0.70 W m⁻², which is double that of carbon dioxide (±0.35 W m⁻²) (IPCC, 2013). Therefore, investigating the activation characteristics of CCN is essential to enhancing the understanding of the interactions between atmospheric particles and climate change.

The particle size, chemical composition, and mixing state significantly influence the activation of CCN (Cerully et al., 2011; Padró et al., 2012). However, whether the particle size or chemical composition has a greater impact on CCN activation is debatable (Che et al., 2017; Schmale et al., 2018). Nevertheless, variations in chemical compositions can significantly impact CCN concentrations. The hygroscopic parameter κ, derived using Köhler theory, links particle hygroscopicity (chemical composition) with activation diameter (size) (Petters and Kreidenweis, 2007). Studies have shown that insoluble particles, such as soot or mineral dust, have a κccn of approximately 0 (Cerully et al., 2011), while secondary organic aerosols have a κccn of approximately 0.1 (Sihto et al., 2011). Highly hygroscopic substances, such as ammonium sulfate ((NH₄)₂SO₄; approximately 0.6) and sea salt (approximately 1.28), exhibit substantially higher κccn values (Petters and Kreidenweis, 2007).

The Zdanovskii–Stokes–Robinson (ZSR) theory posits that the overall hygroscopic parameter κ of a particle can be directly calculated from the hygroscopicity of its chemical components (Chan et al., 2000; Svensningsson et al., 2006; Zobrist et al., 2008). This implies that under specific thermodynamic conditions, the CCN activity of a particle with known components is predictable. However, because of the complexity of atmospheric particle sources, sinks, and their interactions with meteorological conditions, accurately modeling CCN in actual environments is challenging (Burkart et al., 2011). Consequently, extensive field measurements are required.

Recent investigations on CCN in China have examined a range of environments, including urban and rural areas, as well as various terrains. These studies have indicated notable regional and seasonal differences in CCN concentrations and their properties. Research on the Yangtze River Delta and Pearl River Delta, for instance, has revealed notable seasonal and regional differences (An et al., 2018; Yue et al., 2016). Urbanization has been demonstrated to significantly increase CCN concentrations (Liu et al., 2023; Yu et al., 2022), while the impact of air pollution, such as that from biomass burning and aerosol particles, has been extensively studied (Ding et al., 2021; Liu et al., 2020a,b; Sun et al., 2024). Despite these insights, most studies have focused on southeastern coastal areas, with limited data on inland urban regions (Zhang et al., 2023).

Additionally, certain long-term studies have found that κccn displays minimal seasonal variation (Salma et al., 2021). However, these findings are generally based on online observations. Although online measurement techniques, which can provide high-resolution aerosol information, are considered a significant advancement in atmospheric science research, the variations in κccn can be masked by fluctuations in meteorological conditions, such as atmospheric stability, mixing processes, and particle aging (Elmes and Gasparon, 2017). Research suggests that

aging can reduce the activation diameter of biomass burning particles by 22.8% and increase the κccn by 42% (SS = 0.2%) (Li et al., 2018). Although offline measurements are more time-consuming and costly, they allow for long-term sample preservation, making them better suited for tracking and comparing CCN activity over time (Kawana et al., 2022). Additionally, through offline measurements, particles with different mixing states and size distributions in the atmospheric environment are transformed into internally mixed aerosols and introduced into the CCN counter. A size distribution is scanned with a certain gradient to obtain CCN activity information for the particles (Section 2.4 and Fig. S4). This method effectively eliminates the interference of complex atmospheric conditions on CCN activity measurements, which offers advantages in studying the relationship between chemical composition and CCN activity (Lacher et al., 2024).

At the same time, offline measurement techniques can better reflect the characteristics of atmospheric particles. Moore et al. (2010) used an offline measurement method to obtain CCN number concentrations while simultaneously measuring the CCN concentration distribution in situ with another CCN counter. Their results showed good consistency within the measurement variability range. Padró et al. (2010) collected atmospheric PM₂.₅ and measured the CCN activity of samples at supersaturation levels ranging from 0.2% to 1.2% using a CCNc. The results indicated that the κ values obtained from Köhler theory were consistent with those measured in situ by the CCNc. Current research suggests that when combined with thermodynamic theory, offline sampling techniques can be used to parameterize the effects of aerosol composition on cloud droplet formation (Deng et al., 2024; Dusek et al., 2010; Rose et al., 2010).

Particulate matter (PM₂.₅), with its small size and complex composition, is a key concern in atmospheric monitoring due to its role as a cloud condensation nucleus and its connection to human activities. This study aims to: (1) identify the components and sources of PM₂.₅ during the summer and winter of 2019, (2) monitor the spatiotemporal variations in CCN activity under different meteorological conditions, (3) investigate the impact of seasonal differences on the closure of κ chemical components, and (4) introduce a numerical model that accurately predicts CCN activity.

# 2. Methodology

## 2.1. Observation site

Xi'an, the capital of Shaanxi Province, is a megacity with a permanent population of more than ten million individuals. Located in the Guanzhong Plain, between the northern Shaanxi Plateau and the Qinling Mountains (Fig. S1), the region faces challenges with pollutant dispersion. The sampling site was situated on the roof of the Energy Building at Xi'an Jiaotong University (34° 15' N, 108° 59' E), approximately 21 m above ground, in the city center.

## 2.2. Component analysis

This study used an atmospheric sampler with a PM₂.₅ impactor (KC-1000, Qingdao Laoshan Electronic Factory) to collect PM₂.₅ samples at a flow rate of 1050 L/min during the summer (July 31–August 31, 2019) and winter (December 10, 2019–January 9, 2020). The samples were collected during two periods each day: daytime (8:00–18:00) and nighttime (19:00–7:00). Ambient particles were captured on Whatman quartz filters (approximately 518 cm²; effective area: approximately ~414 cm²). The filters were heated at 450 °C for 6 h to eliminate impurities prior to sampling. A precision balance (WZA26-NC, Sartorius) was used to measure the mass change of the filters before and after sampling, with repeated weighings ensuring a relative deviation of less than 2%. The filters were stabilized for 24 h in a controlled environment (CR-M, approximately 23 °C and 48% relative humidity) before weighing. The daily average PM₂.₅ concentration shows good
consistency with the national monitoring online data (Fig. S2). The samples were then stored at -20 °C to preserve their properties for further analysis.

Approximately 1.554 cm² of the filter membrane was dissolved in 40 mL of ultrapure water and exposed to ultrasound for 1 h to extract water-soluble substances. Insoluble impurities were eliminated by filtering through a 0.22-µm polyethersulfone membrane to isolate the water-soluble components (WSM). Ion chromatography (ICS-5000, Thermo Fisher Scientific) was utilized to analyze the ion components, while inductively coupled plasma mass spectrometry (NexION 350D) was employed to detect metal components.

An organic carbon/elemental carbon (OC/EC) analyzer (DRI, 2001A, Atmoslytic) was employed to measure the OC and EC levels in the PM2.5 samples. A 0.518-cm² sample was cut for each test. The instrument was calibrated three times using a 4.28 µg/mL sucrose standard, ensuring that the relative deviation between measurements was less than 10%. Ten percent of the samples were reanalyzed (the first sample after every tenth sample was remeasured) to ensure accuracy. The methodology used in the OC/EC analyzer has been thoroughly described in previous studies (Cao et al., 2003, 2013). Organic matter (OM) was estimated as OC × 1.6 (Liu et al., 2020a,b).

## 2.3. PM2.5 source analysis

Backward trajectory simulations are widely used to trace the sources of atmospheric particles (Wang et al., 2023; Xiao et al., 2023; Zeng et al., 2024). In this study, the Hybrid Single-Particle Lagrangian Integrated Trajectory model was utilized to trace air masses backward at 1-h intervals over 24 h to analyze the external particle input. Simulations were conducted for the summer (July 31–August 31, 2019) and winter (December 10–January 9, 2020), with air masses tracked at an altitude of 500 m. Trajectories were grouped into four primary categories based on angular distance. The model was run using the TrajStat plugin in Meteoinfo 3.8 (Wang, 2014), and meteorological data were obtained from the Global Data Assimilation System (GDAS) (https://www.read.noaa.goaa.gov/data/archives/gds1). More detailed methodological explanations are provided in the supporting materials (Fig. S3).

Remote sensing of fire is a key tool for monitoring agricultural straw burning and wildfires (Lin et al., 2022; Zhang et al., 2019). The fire hotspot data used in this study were obtained from the Fire Information for Resource Management System (FIRMS) (https://firms.modaps.eosdis.nasa.gov/map/).

## 2.4. CCN activation analysis

The CCN activity measurement scheme based on offline sampling technology is referred to as Scanning Mobility CCN Analysis (SMCA) (Moore et al., 2010; Padró et al., 2010). The CCN activation characteristics were measured using a system combining a differential mobility analyzer (DMA), a condensation particle counter (CPC), and a continuous-flow thermal gradient CCN counter (CCNc-100, DMT). Aerosols were generated using a collision-type atomizer, dried via a silica gel tube, filtered to obtain large particles, and charged using an impactor and an ionizer. Monodisperse particles were selected by adjusting the DMA voltage. One stream was sent to the CPC to measure the total particle count (CN), whereas the other was sent to the CCNc to measure the activated CCN at various supersaturations. The ratio of CCN to CN provided the activation ratio (AR) for specific diameters and supersaturation levels. Measurements were conducted at five supersaturation levels (0.2, 0.4, 0.6, 0.8, and 1.0%). During the measurement process, the DMA voltage was manually adjusted to increase the initial dry aerosol particle diameter from 20 nm to 140 nm in increments of 2–20 nm, obtaining the particle size-activation ratio curve. The curve was modeled using a Gaussian cumulative distribution function:

$$f_{N_{CCN}/N_{CN}} = a \left( 1 + \text{erf} \left( \frac{D - D_a}{\sigma_a \sqrt{2}} \right) \right) \quad (1)$$

where a is half the maximum value, erf is the error function, and $D_a$ is the critical or "activation diameter." $\sigma_a$ is the standard deviation of the Gaussian cumulative distribution function. For additional details, please refer to related studies (Rose et al., 2008, 2010).

Particle hygroscopicity significantly influences activation ability. Petters and Kreidenweis (2007) introduced the $\kappa$ parameter to describe the CCN activity of substances with varying hygroscopic properties:

$$\kappa_{ccn} = \frac{4A^3}{27D_a^3 \ln^2 S_c} \quad (2)$$

$$A = \frac{4\sigma_{s/a} M_w}{RT\rho_w} \quad (3)$$

where $\kappa_{ccn}$ is the experimentally measured value, $D_a$ is the critical diameter (m), $S_c$ is the critical supersaturation, and $\sigma_{s/a}$ is the surface tension at the solution/air interface (0.072 J m⁻²). $M_w$ and $\rho_w$ are the molecular weight (18 g mol⁻¹) and density (1 g cm⁻³) of water, respectively. R is the gas constant (8.315 J K⁻¹·mol⁻¹), and T is the temperature (298.15 K).

The complete experimental analysis technical roadmap is shown in Fig. S4. The flow calibration of the instruments and the verification of the DMA particle size selection performance are shown in Table S1 and Fig. S5 of the supplementary materials, respectively. Pure (NH₄)₂SO₄ was used for the instrument supersaturation calibration, with deviations between the fitted and measured activation diameters remaining within 3%. Fig. S6 and Table S2 present the ARs and fitted activation diameters for (NH₄)₂SO₄.

## 2.5. $\kappa$ closure of particle chemical components

Particles are assumed to be internally mixed. $\kappa_{chem}$ is calculated using the ZSR mixing rule based on the volume fraction of each chemical component (Petters and Kreidenweis, 2007; Stokes and Robinson, 1966; Zdanovskii, 1948).

$$\kappa_{chem} = \sum_{i} \epsilon_{i} \kappa_{i} \quad (4)$$

where $\kappa_{chem}$ represents the theoretical value based on the chemical components. i denotes the chemical components in the particles, with $\epsilon$ and $\kappa$ indicates the volume fraction and hygroscopic parameter, respectively. Although CCN activity is closely linked to chemical components, not all substances can be accounted for due to the complexity of particle chemistry. Our analysis identified the main components for summer and winter 2019 as OM, NO₃⁻, SO₄²⁻, and NH₄⁺. The ion balance results from the aerosol thermodynamic model ISORROPIA-II show that not all samples are neutral (Fig. S7). According to the ion pairing scheme proposed by Gysel et al. (2007), the main inorganic species are NH₄NO₃, NH₄HSO₄, and (NH₄)₂SO₄. As shown in Equation (5), the scheme applies piecewise linear interpolation to the hygroscopicity between pure substances (NH₄)₂SO₄ and NH₄HSO₄, as well as between NH₄HSO₄ and H₂SO₄, using H₂SO₄ to balance the ion equilibrium.

$$\begin{align*}
n\text{NH}_4\text{NO}_3 &= n_{\text{NO}_3^-} \\
n\text{H}_2\text{SO}_4 &= \max(0, n_{\text{SO}_4^{2-}} - n_{\text{NH}_4^+} + n_{\text{NO}_3^-}) \\
n\text{NH}_4\text{HSO}_4 &= \min(2n_{\text{SO}_4^{2-}} - n_{\text{NH}_4^+} + n_{\text{NO}_3^-}, n_{\text{NH}_4^+} - n_{\text{NO}_3^-}) \quad (5) \\
n(\text{NH}_4)_2\text{SO}_4 &= \max(n_{\text{NH}_4^+} - n_{\text{NO}_3^-} - n_{\text{SO}_4^{2-}}, 0) \\
n\text{HNO}_3 &= 0
\end{align*}$$

The OM was categorized into secondary organic matter (SOM), primary organic matter, and elemental carbon to minimize uncertainties
(Mei et al., 2013). $κ_{chem}$ was calculated using Equation (6), with the component densities and $κ$ values provided in Table S3.

$$κ_{chem} = ε_{NH_4NO_3}κ_{NH_4NO_3} + ε_{(NH_4)_2SO_4}κ_{(NH_4)_2SO_4} + ε_{NH_4HSO_4}κ_{NH_4HSO_4} + ε_{H_2SO_4}κ_{H_2SO_4} + ε_{SOM}κ_{SOM} + ε_{POM}κ_{POM} + ε_{EC}κ_{EC} \quad (6)$$

# 3. Results and discussion

## 3.1. Overview of air pollutants

Based on China's Ambient Air Quality Standard (GB 3095-2012), the sampling site in this study was a combined commercial, traffic, and residential area, classified as Category II, with a daily PM$_{2.5}$ limit of 75 µg/m$^3$. The collected samples were categorized into three groups: Clean (C, ≤75 µg/m$^3$), Pollution (P, 75-150 µg/m$^3$), and Heavy Pollution (HP, >150 µg/m$^3$). As shown in Fig. 1, during the 63 observation days in the summer and winter of 2019, 35 d (55.6%) were categorized as C, 20 d (31.8%) as P, and 8 d (12.7%) as HP, with over 40% of the days exceeding the national PM$_{2.5}$ limit. Table 1 lists the average PM$_{2.5}$ concentrations. Seasonal fluctuations had a significant impact, with winter levels being nearly three times higher than those in summer. Daytime concentrations were slightly higher than nighttime concentrations in both seasons.

## 3.2. Chemical composition of PM$_{2.5}$

During the summer and winter of 2019, OM, NO$_3$, and SO$_4$ were the three most abundant components in PM by mass (Fig. 1). The chloride ion ($Cl^−$), which serves as a marker for biomass burning, was also tracked for variations (Zhang et al., 2020). Fig. 2 highlights the changes in the concentrations of these four components across different seasons, periods, and pollution levels. Compared to summer concentrations, the winter concentrations of all four components increased, with SO$_4$ and NO$_3$ rising by 110% and 355%, respectively. This trend is consistent with observations in northern Chinese cities that require heating (Ge et al., 2017; Wang et al., 2023). Additionally, the unique topography of Xi'an can create temperature inversions, lowering the atmospheric

boundary layer and enhancing the transformation rates of these ions. Seasonal changes caused NO$_3$ concentrations to increase approximately three times more than SO$_4$ $^−$, indicating a greater impact on NO$_3$ particle formation. This may be partly attributed to the implementation of national policies in recent years, which have caused NO$_2$ to become the primary gaseous pollutant in Xi'an (Fig. S8). On the other hand, it may be related to the higher sensitivity of the conversion rate of NO$_3$ to seasonal changes compared to SO$_4$ $^−$. Our results show that the impact of seasonal changes in meteorological conditions on the accumulation of NO$_3$ is highly significant (248.4%), while the contribution to the accumulation of SO$_4$ $^−$ is only 110% (Fig. S9). $Cl^−$ saw the most significant increase at 2270%, suggesting ongoing biomass burning issues around Xi'an in winter. The OM levels increased by 159%, likely owing to fossil fuel combustion, such as vehicle-related emissions and coal burning. Xu et al. (2022) found that precursor substances from fossil fuel combustion contributed 55-67% of the organic compounds (oxalic acid, glyoxal, and methylglyoxal) in continental aerosols. Additionally, the correlation analysis in Table S4 demonstrates a strong relationship between OM and $Cl^−$ in winter. The back-trajectory analysis indicated that 20.35% of the airflows passed over the fire points (Fig. 3), suggesting that the external transport of biomass-burning particles may significantly contribute to the increase in OM.

Fig. 2 shows that in summer, particulates tend to accumulate at night, whereas in winter, accumulation is more pronounced during the day. As pollution levels rise, the concentrations of all four components increase, with $Cl^−$ and NO$_3$ showing the sharpest increases of 1214% and 583%, respectively. This suggests that biomass burning and vehicle-related emissions significantly contribute to the winter smog in Xi'an.

Studies employ OC/EC ratios to estimate sources of PM: 1.0-4.2 for vehicle-related emissions, 2.5-10.5 for coal burning, 3.8-13.2 and 16.8-40.0 for biomass burning (For example, the OC/EC ratios for eucalyptus, oak, and pine are approximately 16.8, 18.5, and 40.0, respectively), 32.9-81.6 for food industry-related emissions, and 12.0-13.1 for emissions from long-range transport and resuspended dust (Chow et al., 2004; Schauer et al., 1999, 2002a, 2002b). The OC/EC ratios for the summer and winter of 2019 were 3.78 ± 1.16 and 4.56 ± 2.66, respectively. Considering NO$_2$ as the main gaseous pollutant in

<figure><img src="image_3.png" /><figcaption>Fig. 1. Particulate matter (PM)$_{2.5}$ concentrations and chemical compositions during the summer and winter of 2019.</figcaption></figure>
<table><caption>**Table 1**
Average PM<sub>2.5</sub> concentrations and standard deviations during the observation period.</caption><thead><tr><th>Items</th><th>S</th><th>SD</th><th>SN</th><th>W</th><th>WD</th><th>WN</th></tr></thead><tbody><tr><td>PM<sub>2.5</sub> (µg/m<sup>3</sup>)</td><td>44.7 ± 15.3</td><td>44.4 ± 16.1</td><td>45.0 ± 20.5</td><td>124.3 ± 47.4</td><td>129.8 ± 57.6</td><td>118.8 ± 49.1</td></tr></tbody></table>

(S: Summer, SD: Summer daytime, SN: Summer nighttime, W: Winter, WD: Winter daytime, WN: Winter nighttime).

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Variation of major components in PM (black dots represent daily averages, arrows indicate change ratios; SD: Summer daytime; SN: Summer nighttime; WD: Winter daytime; WN: Winter nighttime; C: Clean; P: Pollution; HP: Heavy pollution).</figcaption></figure>

Xi'an and its higher conversion potential (Figs. S8 and S9), this suggests that motor vehicle-related emissions and biomass burning have a significant impact on the formation of PM<sub>2.5</sub>. The ratios remained consistent across varying pollution levels: 4.03 ± 1.10 for C, 3.90 ± 1.00 for P, and 4.07 ± 0.96 for HP (Table S5). This stability suggests that no new emission sources contributed to the worsening pollution, with vehicle-related emissions remaining the primary factor. Moreover, in recent years, the congestion index in Xi'an has not shown significant seasonal variation, indicating that the motor vehicle emissions source strength is not controlled by seasonal changes (Figure S10). Therefore, biomass burning may have a significant impact on the deterioration of air quality.

### 3.3. CCN activation characteristics

In general, particles with higher hygroscopic substance content exhibit stronger hygroscopicity (Mikhailov et al., 2009), enabling them to activate as CCN at smaller diameters and resulting in higher κ<sub>ccn</sub> values. As listed in Table 2, winter particles have smaller activation diameters and higher κ<sub>ccn</sub> values at the same supersaturation, indicating greater hygroscopicity. The proportion of strongly hygroscopic components (SO<sub>4</sub><sup>2-</sup>, NO<sub>3</sub><sup>-</sup>, and NH<sub>3</sub><sup>-</sup>) in winter particles was 55.21% during the day and 56.32% at night, which was noticeably higher than the 47.98% and 48.72% values observed in summer (Fig. S11), respectively. As pollution increases, CCN activity also increases. For example, at an SS of 0.2%, the activation diameter of HP particles decreases by 10.13% (from 111.61 to 100.30 nm), while κ<sub>ccn</sub> increases by 46.2% (from 0.26 to 0.35).

During both summer and winter, higher supersaturation results in smaller particle activation diameters, with κ<sub>ccn</sub> remaining relatively stable. κ<sub>ccn</sub> is useful for identifying particle sources: 0.1–0.4 for forest aerosols (Cerully et al., 2011), ~0.3 for continental aerosols (Orikasa et al., 2020), and ~0.7 for marine aerosols (Andreae and Rosenfeld, 2008). As Xi'an lies inland with minimal marine aerosol input, its winter particles have typical continental aerosol κ<sub>ccn</sub> values (Table 2). Summer particles have a lower κ<sub>ccn</sub> of ~0.25 (SS = 0.2%), indicating a higher presence of less hygroscopic organic components (Fig. S12), likely originating from southern forest reserve air masses (Fig. S13).

Fig. 4 compares the κ<sub>ccn</sub> values across different sites. During winter, Xi'an's κ<sub>ccn</sub> values are similar to those in Guangzhou, likely due to motor vehicle-related emissions being a major source in both cities (Rose et al., 2010). In Guangzhou, κ<sub>ccn</sub> decreased significantly during biomass burning periods, whereas the summer κ<sub>ccn</sub> values in Xi'an were even lower, predominantly due to particle transport from the southern mountains. Interestingly, the marine sites showed opposite trends. In the North Atlantic, particles were internally mixed, with weakly hygroscopic organic layers surrounding the highly hygroscopic components. Low supersaturation resulted in low κ<sub>ccn</sub> values, similar to those observed for forest particles. As humidity increased, the weakly hygroscopic surface layers dissolved, exposing the highly hygroscopic components and causing κ<sub>ccn</sub> to rise rapidly. Conversely, in the South China Sea, particles were mixed externally. Low supersaturation dissolved only the highly hygroscopic components, leading to initially high κ<sub>ccn</sub> values. As supersaturation increased, weakly hygroscopic components also dissolved, causing κ<sub>ccn</sub> to decrease. This indicates that mixed-state
<figure><img src="image_5.png" /><figcaption>Fig. 3. Winter 2019 fire points and air mass trajectories in Xi'an (red dots indicate thermal anomalies).</figcaption></figure>

<table><caption>**Table 2**
Activation diameters and $\kappa_{ccn}$ of particles across different seasons, periods, and pollution levels.</caption><thead><tr><th>Supersaturation</th><th>D<sub>c</sub>-0.2%</th><th>D<sub>c</sub>-0.4%</th><th>D<sub>c</sub>-0.6%</th><th>D<sub>c</sub>-0.8%</th><th>D<sub>c</sub>-1.0%</th></tr></thead><tbody><tr><td>SD</td><td>112.41 ±<br>9.67</td><td>67.23 ±<br>4.74</td><td>53.30 ±<br>4.02</td><td>44.87 ±<br>3.43</td><td>38.67 ±<br>3.11</td></tr><tr><td>SN</td><td>111.54 ±<br>11.66</td><td>67.37 ±<br>5.75</td><td>53.28 ±<br>5.10</td><td>44.60 ±<br>4.39</td><td>39.17 ±<br>5.38</td></tr><tr><td>WD</td><td>105.07 ±<br>12.56</td><td>65.18 ±<br>6.62</td><td>51.78 ±<br>4.65</td><td>43.36 ±<br>3.77</td><td>37.24 ±<br>3.38</td></tr><tr><td>WN</td><td>99.96 ±<br>7.55</td><td>62.83 ±<br>3.82</td><td>49.66 ±<br>2.99</td><td>41.91 ±<br>2.81</td><td>38.47 ±<br>15.00</td></tr><tr><td>C</td><td>111.61 ±<br>10.47</td><td>67.16 ±<br>5.14</td><td>53.25 ±<br>4.53</td><td>44.67 ±<br>3.88</td><td>38.85 ±<br>4.32</td></tr><tr><td>P</td><td>103.03 ±<br>11.97</td><td>64.71 ±<br>6.18</td><td>51.35 ±<br>4.19</td><td>43.14 ±<br>3.49</td><td>37.03 ±<br>2.87</td></tr><tr><td>HP</td><td>100.30 ±<br>7.95</td><td>62.23 ±<br>3.88</td><td>48.94 ±<br>3.00</td><td>41.28 ±<br>2.75</td><td>39.49 ±<br>19.24</td></tr><tr><td>Supersaturation</td><td>κ<sub>ccn</sub>-0.2%</td><td>κ<sub>ccn</sub>-<br>0.4%</td><td>κ<sub>ccn</sub>-<br>0.6%</td><td>κ<sub>ccn</sub>-<br>0.8%</td><td>κ<sub>ccn</sub>-1.0%</td></tr><tr><td>SD</td><td>0.25 ±<br>0.07</td><td>0.29 ±<br>0.06</td><td>0.26 ±<br>0.06</td><td>0.25 ±<br>0.06</td><td>0.25 ±<br>0.06</td></tr><tr><td>SN</td><td>0.26 ±<br>0.08</td><td>0.29 ±<br>0.08</td><td>0.27 ±<br>0.09</td><td>0.26 ±<br>0.09</td><td>0.25 ±<br>0.10</td></tr><tr><td>WD</td><td>0.31 ±<br>0.10</td><td>0.32 ±<br>0.09</td><td>0.28 ±<br>0.07</td><td>0.27 ±<br>0.07</td><td>0.27 ±<br>0.09</td></tr><tr><td>WN</td><td>0.35 ±<br>0.07</td><td>0.35 ±<br>0.06</td><td>0.32 ±<br>0.05</td><td>0.30 ±<br>0.06</td><td>0.30 ±<br>0.06</td></tr><tr><td>C</td><td>0.26 ±<br>0.07</td><td>0.29 ±<br>0.07</td><td>0.26 ±<br>0.07</td><td>0.25 ±<br>0.08</td><td>0.25 ±<br>0.08</td></tr><tr><td>P</td><td>0.33 ±<br>0.09</td><td>0.33 ±<br>0.08</td><td>0.29 ±<br>0.06</td><td>0.28 ±<br>0.06</td><td>0.27 ±<br>0.08</td></tr><tr><td>HP</td><td>0.35 ±<br>0.08</td><td>0.36 ±<br>0.07</td><td>0.33 ±<br>0.06</td><td>0.31 ±<br>0.06</td><td>0.32 ±<br>0.06</td></tr></tbody></table>

(SD: Summer daytime, SN: Summer nighttime, WD: Winter daytime, WN: Winter nighttime, C: Clean, P: Pollution, HP: Heavy Pollution).

variations significantly affect the assessment of particle CCN activation during field observations (e.g., location, time, and emission sources).

### 3.4. κ-closure of particle chemical components

#### 3.4.1. Seasonal effects on κ-closure

As shown in Fig. 5, chemical closure is significantly better in winter than in summer, with the gap between $\kappa_{chem}$ and $\kappa_{ccn}$ increasing as supersaturation rises. In summer, the reliability of $\kappa_{chem}$ declines between SS = 0.4% and 0.8%, while in winter, the slope of the fitted line decreases sharply between SS = 0.2% and 0.4%. This implies that winter particles are more responsive to variations in supersaturation. While activation diameters exhibited similar trends and variability across both seasons, the summer $\kappa_{ccn}$ vs. $\kappa_{chem}$ points were more scattered. The correlation between $\kappa_{ccn}$ and $\kappa_{chem}$ decreased after SS = 0.6% in summer but remained stable in winter. Despite poor closure in both seasons, the winter $\kappa_{chem}$ better reflects the trends in $\kappa_{ccn}$. These findings indicate that a single approach for estimating $\kappa$ across seasons may not be appropriate.

Additionally, the closure of $\kappa$ exhibits a clear particle size dependence. As supersaturation increases from 0.2% to 1.0%, the critical diameter decreases from the accumulation mode (>100 nm) to the nucleation mode (<100 nm), leading to a noticeable deterioration in $\kappa$ closure. Two key factors influencing the critical supersaturation are the curvature effect and the solute effect. The curvature effect (Kelvin effect) depends on droplet surface tension and tends to increase the saturation ratio, while the solute effect (Raoult effect) lowers the saturation ratio by accounting for the solute's contribution to droplet growth. According to Köhler theory, the critical supersaturation of CCN varies with $D^{-3/2}$ (Padró et al., 2007). At smaller particle sizes, high concentrations of water-soluble organic carbon can suppress surface tension, leading to a greater reduction in supersaturation than expected based solely on the solute effect. However, at larger particle sizes, organic matter concentrations are insufficient to induce this effect (Moore et al., 2008). Asa-Awuku et al. (2008) found that the difference in CCN activity
<figure><img src="image_6.png" /><figcaption>Fig. 4. $\kappa_{ccn}$ measurements in different areas (a. Salma et al. (2021); b. Rose et al. (2010); c. Pöhlker et al. (2016); d. Gunthe et al. (2009); e. Cheung et al. (2020) and f. Cheng et al. (2023)).</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 5. Seasonal comparison of $\kappa_{ccn}$ and $\kappa_{chem}$ (A: Summer; B: Winter; $D_c$: Activation Diameter; SS: Supersaturation; Black dashed line: $\kappa_{ccn} = \kappa_{chem}$; Colored dashed lines: fitted lines for $\kappa_{ccn}$ and $\kappa_{chem}$ at different SS values. Numbers in parentheses: slope and the correlation coefficient).</figcaption></figure>
between multicomponent particles and pure $(NH_4)_2SO_4$ particles is more pronounced at smaller sizes. Based on Köhler theory, as particle size decreases, the concentration of solutes (organic and inorganic) at the activation point increases, and surface tension decreases accordingly. Conversely, at larger particle diameters, solute concentration at the activation point is lower, causing the CCN activity curves of mixed and pure $(NH_4)_2SO_4$ particles to converge. This also explains why the $\kappa$ closure in Fig. 5 deteriorates as particle size decreases.

To further investigate the impact of seasonal variation on $\kappa$ parameterization, the activation diameter ($D_c$) and $\kappa_c$ were plotted on the x- and y-axes. $\kappa_c$ represents the value when $\kappa_{chem}$ equals $\kappa_{ccn}$, indicating the influence of supersaturation changes under optimal $\kappa$ parameterization. As shown in Fig. 6, the seasonal difference in $\kappa_c$ decreased with lower supersaturation, becoming negligible at $SS < 0.6\%$. At $SS = 0.2\%$, the $\kappa_c$ values for summer and winter were equal, suggesting that seasonal effects no longer influence $\kappa$ parameterization.

This shift occurs at approximately $D_c = 50$ nm ($SS = 0.6\%$), which marks the boundary between the Aitken and accumulation modes. Seasonal effects notably impact the parameterization of small particles in the Aitken mode, but have minimal effect on larger particles. The Kelvin effect likely plays a role, as it begins influencing the hygroscopicity of particles smaller than 100 nm, with stronger effects below 20 nm, but weakens as the particles become larger (e.g., 15–30% for $D = 50$ nm and 7–16% for $D = 100$ nm) (Bilde et al., 2015). Since larger particles are typically concentrated near the ground, while smaller ones are found at higher altitudes where humidity increases near clouds, these findings highlight the need to account for seasonal variations in climate models based on $\kappa_{chem}$.

### 3.4.2. Effect of diurnal and pollution level variations on $\kappa$ closure

As shown in Fig. 7, the impact of supersaturation on $\kappa$ closure is consistent across both day-night and seasonal variations, with higher supersaturation leading to poorer closure. In summer, the day-night effect was significant (Fig. 7A and B). Daytime $\kappa$ closure (Fig. 7A) is significantly stronger than at night (Fig. 7B), indicating differences in particle composition. This may be because intense sunlight drives distinct photochemical and gas-phase reactions. In winter, with heavier particle loads and weaker photochemical activity, there was minimal difference between the day (Fig. 7C) and night (Fig. 7D). These results highlight that day-night variations in summer should be carefully considered in $\kappa$-closure studies.

Pollution levels significantly impact $\kappa$ closure (Fig. 8). At the same

supersaturation, $\kappa$ chemical closure follows the order $HP > C > P$, indicating that this study's $\kappa$ closure scheme is more suitable for high-pollution environments. HP occurred only in winter, C occurred mostly in summer, and P occurred during both seasons. This suggests that a single $\kappa$ closure scheme is unsuitable for evaluating CCN activity across different seasons.

### 3.5. Seasonal sources of deviation and corrections for $\kappa_{ccn}$ and $\kappa_{chem}$

(Under: Underestimate deviation ($\sigma < -10\%$), Normal: Chemical components are well matched ($-10\% \le \sigma \le +10\%$), Over: Overestimate deviation ($\sigma > +10\%$). The ratio represents the deviation proportions at the SS. The chemical composition value denotes the average proportion and standard deviation of the particles)

To investigate the seasonal differences between $\kappa_{ccn}$ and $\kappa_{chem}$, $\sigma$ ($\sigma = (\kappa_{chem} - \kappa_{ccn})/\kappa_{chem}$) was divided into three categories: underestimation ($\sigma < -10\%$), normal ($-10\% \le \sigma \le +10\%$), and overestimation ($\sigma > +10\%$) (Fig. S14). Table 3 lists the $\sigma$ distribution and corresponding chemical component ratios for summer and winter. In summer, as the proportion of $NO_3^-$ increases significantly, the deviation type shifts from underestimation to overestimation (Fig. S15A). Meanwhile, the proportion of overestimation deviations in winter samples increases significantly with supersaturation (Fig. S15B). $NH_4NO_3$ particles can cause negative (evaporation) or positive (absorption) artifacts during aerosol sampling. Mikhailov et al. (2004) observed an evaporation loss of up to 27% for $NH_4NO_3$ in a hygroscopicity-tandem differential mobility analyzer (HTDMA) over 11 s. Gysel et al. (2007) estimated a loss of approximately 61% during hygroscopic analysis. As supersaturation increases to 1.0%, the overestimation deviation rises sharply (Fig. S16). Notably, numerous offline analysis studies have demonstrated that low-temperature preservation can maintain the integrity of particulate matter components (Chen et al., 2023; Duan et al., 2023). Kawana et al. (2022) measured the hygroscopicity of particulate matter samples collected from the Arctic in 1991 and found good consistency in $\kappa$ values under both subsaturated and supersaturated conditions, with differences not exceeding 8%. Although offline sampling does result in some degree of nitrate loss compared to online measurements, the nitrate loss observed in the CCN measurement process in this study is different. Therefore, this phenomenon may be attributed to the increase in instrument temperature during CCN measurements, which accelerates the evaporation loss of $NO_3^-$ under higher supersaturation conditions.

Underestimation deviations may have resulted from changes in the proportion of OM. A constant OM density was employed in the calculation of $\kappa_{chem}$. Although this value has been widely adopted in several studies (Jiang et al., 2025; Ray et al., 2023), it may introduce deviations compared to the dynamic OM densities, such as those estimated by O:C or m/z 44 (Mei et al., 2013). Additionally, the effect of organic surfactants on surface tension is difficult to quantify using either approach, because atmospheric aerosols may contain hundreds of organic species. These species can reduce the surface tension by 10% and increase the $\kappa_{ccn}$ by 30%, leading to underestimation deviations (Petters and Kreidenweis, 2013). According to the results shown in Table 3, the deviation distribution in summer is balanced across the three categories at different supersaturation levels (Fig. S17). The concentrations of $NO_3^-$ and OM in summer samples are highly correlated with the $\kappa_{chem}$ closure deviation (Table S6). This suggests that the evaporation loss of $NO_3^-$ and changes in OM proportion are significant factors contributing to the deterioration of $\kappa_{chem}$ closure. In contrast, in winter, $\sigma$ primarily represents overestimation deviations, and thus the proportion of organic matter in the $\sigma$ distribution shows little variation.

Researchers worldwide aim to use $\kappa_{chem}$ as a replacement for the labor-intensive $\kappa_{ccn}$ observations to predict aerosol hygroscopicity and its impact on climate. However, this process is hindered by several factors, such as weather conditions, mixing states, particle size, chemical composition, and shape. Wu et al. (2016) found a weak correlation ($r \approx$

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Impact of seasonal variation on $\kappa$ estimation ($D_c$: activation diameter; $\kappa_c$: $\kappa$ value at $\kappa_{ccn} = \kappa_{chem}$; solid line: summer; dashed line: winter).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 7. Comparison of $\kappa_{ccn}$ and $\kappa_{chem}$ under diurnal variation (A: summer day; B: summer night; C: winter day; D: winter night; $D_c$ is the activation diameter. The black dashed line represents $\kappa_{ccn} = \kappa_{chem}$, and the colored dashed lines indicate fit lines for different supersaturation levels. Numbers in parentheses correspond to the slope and correlation coefficient).</figcaption></figure>

0.4) between chemical and HTDMA-derived hygroscopicity. Fan et al. (2020) observed a correlation below 0.3 between $\kappa_{HTDMA}$ and $\kappa_{chem}$ in Beijing during 2016–2017. Ray et al. (2023) noted that as particle size decreased, the correlation weakened, with an r-value of 0.15 for 75 nm particles. In this study, winter $\kappa_{chem}$ estimates were marginally acceptable (r > 0.53) but did not account for seasonal variations. This highlights the need for refining $\kappa_{chem}$ estimation methods and establishing a more universal approach.

This study assumed that particles are spherical, with only over-

estimation and underestimation deviations considered in the estimation of $\kappa_{chem}$. The total deviation is expressed as:

$$ \sigma = \kappa_{chem} - \kappa_{ccn} = \varepsilon_{NH_4NO_3}\sigma_1 + \varepsilon_{SOM}\sigma_2 \quad (7) $$

where $\varepsilon$ is the volume fraction, and $\sigma_1$ and $\sigma_2$ represent the over-estimation and underestimation deviation factors, respectively. When $\kappa_{chem}$ equals $\kappa_{ccn}$, $\sigma_2$ is correlated with the proportions of $NO_3^-$ and SOM:

$$ \sigma_2 = - \frac{\varepsilon_{NH_4NO_3}}{\varepsilon_{SOM}} \times \sigma_1 \quad (8) $$
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Comparison of $\kappa_{ccn}$ and $\kappa_{chem}$ across pollution levels (A: Clean, C: B: Pollution, P: C: Heavily Pollution, HP; $D_c$ is the activation diameter. The black dashed line represents $\kappa_{ccn} = \kappa_{chem}$, and the colored dashed lines indicate fitted lines at various supersaturation levels. Numbers in parentheses correspond to the slope and correlation coefficient).</figcaption></figure>

$\sigma_1$ is considered dependent only on supersaturation (temperature). Starting with a 5% $NO_3^-$ loss gradient, the actual $NO_3^-$ loss value is gradually approached (slope = 1:1), as shown in Fig. S18 (winter, SS = 0.2%). Once $\sigma_1$ is determined, $\sigma_2$ is fitted to correct $\sigma$ (Fig. S19). This method allows for correcting $\kappa_{chem}$ for both summer and winter, aligning it well with $\kappa_{ccn}$ (Fig. S20). $NO_3^-$ loss in both seasons exhibited a parabolic relationship with supersaturation, showing similar growth trends at 0.4, 0.6, and 0.8%, but significant differences at extreme supersaturation levels (0.2% and 1.0%) (Fig. 9A). This may be attributed to the lower $NO_3^-$ loss concentrations in summer (Fig. 9B). The fitting results for $\sigma_1$ and $\sigma_2$ are presented in Table 4.

Most studies on atmospheric particle hygroscopicity rely on the ZSR theory under the internal mixing assumption, including online (Fan et al., 2020; Ren et al., 2018) and offline measurements (Royer et al., 2023; Wang et al., 2022; Zhang et al., 2023). Ren et al. (2018) found this assumption suitable for CCN predictions in continental regions, but Moore et al. (2011) showed it tends to overestimate CCN activity, while assuming external mixing with soluble organics improves consistency (±0–20%). This study also evaluates external mixing (Fig. S21). The impact on summer particles is minimal (~2% deviation), but in winter, internal mixing overestimates $\kappa$ by ~22%, highlighting the importance

of mixing state in $\kappa$ closure studies.

4. Conclusions

Aerosol particle activation plays a crucial role in determining CCN, influencing cloud microphysics, precipitation, radiation, and climate. Long-term observations of aerosol activation characteristics are essential, yet studies remain limited. Based on long-term offline observations conducted in Xi'an in 2019, this study systematically analyzes the seasonal variations in aerosol composition, activation properties, and CCN activity predictions. Results show that seasonal changes significantly impact particle composition, with winter $SO_4^{2-}$, OM, $NO_3^-$, and $Cl^-$ levels increasing by 110%, 159%, 355%, and 2270%, respectively. Local emissions, especially vehicle exhaust, are the primary pollution sources, while biomass burning exacerbates pollution in winter. CCN activity exhibits typical continental aerosol characteristics, with $\kappa_{ccn} = 0.26 \pm 0.07$ in summer and $\kappa_{ccn} = 0.33 \pm 0.09$ in winter (SS = 0.2%). The variations in CCN activity are closely linked to chemical composition, with the input of weakly hygroscopic organic air masses contributing to seasonal differences. As supersaturation increases, the activation diameter decreases, leading to significant differences in $\kappa$ closure
<table><caption>**Table 3**
Distribution of deviation (σ) for κ<sub>ccn</sub> vs. κ<sub>chem</sub> and major chemical components.</caption><thead><tr><th>Summer</th><th>SS</th><th>ratio</th><th>σ</th><th>SO<sub>4</sub><sup>2-</sup></th><th>NO<sub>3</sub><sup>-</sup></th><th>NH<sub>4</sub><sup>+</sup></th><th>OM</th><th>EC</th></tr></thead><tbody><tr><td rowspan="6">Under</td><td>0.2%</td><td>36%</td><td>-0.25 ± 0.11</td><td>0.19 ± 0.06</td><td>0.08 ± 0.03</td><td>0.05 ± 0.02</td><td>0.51 ± 0.06</td><td>0.08 ± 0.03</td></tr><tr><td>0.4%</td><td>58%</td><td>-0.27 ± 0.12</td><td>0.20 ± 0.05</td><td>0.09 ± 0.05</td><td>0.05 ± 0.02</td><td>0.49 ± 0.07</td><td>0.08 ± 0.03</td></tr><tr><td>0.6%</td><td>42%</td><td>-0.28 ± 0.12</td><td>0.19 ± 0.05</td><td>0.09 ± 0.06</td><td>0.05 ± 0.02</td><td>0.50 ± 0.07</td><td>0.08 ± 0.03</td></tr><tr><td>0.8%</td><td>38%</td><td>-0.29 ± 0.12</td><td>0.18 ± 0.04</td><td>0.09 ± 0.06</td><td>0.05 ± 0.02</td><td>0.50 ± 0.07</td><td>0.08 ± 0.03</td></tr><tr><td>1.0%</td><td>41%</td><td>-0.29 ± 0.12</td><td>0.19 ± 0.04</td><td>0.09 ± 0.06</td><td>0.05 ± 0.02</td><td>0.51 ± 0.07</td><td>0.08 ± 0.03</td></tr><tr><td>0.2%</td><td>31%</td><td>0.003 ± 0.058</td><td>0.20 ± 0.04</td><td>0.14 ± 0.1</td><td>0.06 ± 0.02</td><td>0.43 ± 0.09</td><td>0.08 ± 0.03</td></tr><tr><td rowspan="4">Normal</td><td>0.4%</td><td>22%</td><td>0.031 ± 0.057</td><td>0.18 ± 0.04</td><td>0.19 ± 0.09</td><td>0.07 ± 0.02</td><td>0.40 ± 0.07</td><td>0.07 ± 0.03</td></tr><tr><td>0.6%</td><td>27%</td><td>0.0003 ± 0.06</td><td>0.20 ± 0.04</td><td>0.14 ± 0.08</td><td>0.06 ± 0.02</td><td>0.43 ± 0.08</td><td>0.07 ± 0.02</td></tr><tr><td>0.8%</td><td>25%</td><td>0.016 ± 0.052</td><td>0.21 ± 0.04</td><td>0.10 ± 0.06</td><td>0.06 ± 0.01</td><td>0.45 ± 0.03</td><td>0.08 ± 0.03</td></tr><tr><td>1.0%</td><td>19%</td><td>0.005 ± 0.055</td><td>0.23 ± 0.05</td><td>0.09 ± 0.04</td><td>0.06 ± 0.01</td><td>0.44 ± 0.07</td><td>0.08 ± 0.03</td></tr><tr><td rowspan="5">Over</td><td>0.2%</td><td>33%</td><td>0.44 ± 0.27</td><td>0.22 ± 0.06</td><td>0.26 ± 0.13</td><td>0.09 ± 0.04</td><td>0.29 ± 0.16</td><td>0.06 ± 0.03</td></tr><tr><td>0.4%</td><td>21%</td><td>0.45 ± 0.26</td><td>0.25 ± 0.06</td><td>0.32 ± 0.1</td><td>0.11 ± 0.03</td><td>0.22 ± 0.16</td><td>0.04 ± 0.01</td></tr><tr><td>0.6%</td><td>33%</td><td>0.51 ± 0.42</td><td>0.22 ± 0.06</td><td>0.27 ± 0.12</td><td>0.09 ± 0.04</td><td>0.28 ± 0.16</td><td>0.06 ± 0.03</td></tr><tr><td>0.8%</td><td>39%</td><td>0.36 ± 0.11</td><td>0.23 ± 0.07</td><td>0.26 ± 0.12</td><td>0.09 ± 0.04</td><td>0.29 ± 0.15</td><td>0.06 ± 0.03</td></tr><tr><td>1.0%</td><td>43%</td><td>0.70 ± 0.66</td><td>0.21 ± 0.06</td><td>0.26 ± 0.12</td><td>0.09 ± 0.03</td><td>0.31 ± 0.15</td><td>0.06 ± 0.03</td></tr><tr><td rowspan="6">Winter</td><td>SS</td><td>ratio</td><td>σ</td><td>SO<sub>4</sub><sup>2-</sup></td><td>NO<sub>3</sub><sup>-</sup></td><td>NH<sub>4</sub><sup>+</sup></td><td>OM</td><td>EC</td></tr><tr><td>0.2%</td><td>13%</td><td>-0.15 ± 0.09</td><td>0.13 ± 0.08</td><td>0.26 ± 0.07</td><td>0.06 ± 0.04</td><td>0.39 ± 0.08</td><td>0.06 ± 0.01</td></tr><tr><td>0.4%</td><td>10%</td><td>-0.22 ± 0.13</td><td>0.12 ± 0.10</td><td>0.23 ± 0.03</td><td>0.06 ± 0.03</td><td>0.43 ± 0.09</td><td>0.06 ± 0.01</td></tr><tr><td>0.6%</td><td>2%</td><td>-0.15</td><td>0.13</td><td>0.26</td><td>0.09</td><td>0.37</td><td>0.05</td></tr><tr><td>0.8%</td><td>0%</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td><td>/</td></tr><tr><td>1.0%</td><td>2%</td><td>-0.1</td><td>0.13</td><td>0.26</td><td>0.09</td><td>0.37</td><td>0.05</td></tr><tr><td rowspan="4">Normal</td><td>0.2%</td><td>36%</td><td>0.001 ± 0.060</td><td>0.15 ± 0.07</td><td>0.30 ± 0.10</td><td>0.06 ± 0.05</td><td>0.34 ± 0.10</td><td>0.05 ± 0.02</td></tr><tr><td>0.4%</td><td>53%</td><td>0.016 ± 0.063</td><td>0.14 ± 0.06</td><td>0.27 ± 0.09</td><td>0.06 ± 0.04</td><td>0.36 ± 0.09</td><td>0.06 ± 0.02</td></tr><tr><td>0.6%</td><td>26%</td><td>0.007 ± 0.067</td><td>0.13 ± 0.09</td><td>0.22 ± 0.06</td><td>0.06 ± 0.03</td><td>0.42 ± 0.09</td><td>0.06 ± 0.01</td></tr><tr><td>0.8%</td><td>20%</td><td>0.004 ± 0.067</td><td>0.13 ± 0.10</td><td>0.22 ± 0.04</td><td>0.06 ± 0.03</td><td>0.42 ± 0.09</td><td>0.06 ± 0.01</td></tr><tr><td rowspan="5">Over</td><td>1.0%</td><td>26%</td><td>0.008 ± 0.067</td><td>0.13 ± 0.09</td><td>0.23 ± 0.06</td><td>0.06 ± 0.04</td><td>0.41 ± 0.1</td><td>0.06 ± 0.01</td></tr><tr><td>0.2%</td><td>51%</td><td>0.30 ± 0.29</td><td>0.13 ± 0.06</td><td>0.27 ± 0.09</td><td>0.09 ± 0.04</td><td>0.36 ± 0.18</td><td>0.05 ± 0.02</td></tr><tr><td>0.4%</td><td>38%</td><td>0.30 ± 0.25</td><td>0.14 ± 0.06</td><td>0.31 ± 0.08</td><td>0.1 ± 0.05</td><td>0.33 ± 0.2</td><td>0.04 ± 0.01</td></tr><tr><td>0.6%</td><td>72%</td><td>0.32 ± 0.22</td><td>0.15 ± 0.06</td><td>0.30 ± 0.09</td><td>0.08 ± 0.05</td><td>0.34 ± 0.15</td><td>0.05 ± 0.02</td></tr><tr><td>0.8%</td><td>80%</td><td>0.38 ± 0.26</td><td>0.14 ± 0.06</td><td>0.29 ± 0.09</td><td>0.08 ± 0.05</td><td>0.34 ± 0.15</td><td>0.05 ± 0.02</td></tr><tr><td>1.0%</td><td>72%</td><td>0.84 ± 2.75</td><td>0.14 ± 0.06</td><td>0.30 ± 0.09</td><td>0.08 ± 0.05</td><td>0.34 ± 0.15</td><td>0.05 ± 0.02</td></tr></tbody></table>

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Nitrate (NO₃⁻) loss fraction (A) and loss concentration (B) at different supersaturation levels.</figcaption></figure>

<table><caption>**Table 4**
Fitted values of σ₁ and σ₂.</caption><caption>σ₁ = a + b × SS + c × SS² + d × SS³</caption><thead><tr><th></th><th>a</th><th>b</th><th>c</th><th>d</th><th>R²</th></tr></thead><tbody><tr><td>Summer</td><td>76.8</td><td>-2118.21</td><td>458.9286</td><td>-250</td><td>0.96</td></tr><tr><td rowspan="2">Winter</td><td>a</td><td>b</td><td>c</td><td>d</td><td>R²</td></tr><tr><td>27.6</td><td>-24.7619</td><td>236.6071</td><td>-197.917</td><td>0.96</td></tr><tr><td colspan="6">σ₂ = e × NO₃⁻ wt%/SOM<sub>win</sub>% + f</td></tr><tr><td>SS</td><td>0.20%</td><td>0.40%</td><td>0.60%</td><td>0.80%</td><td>1.00%</td></tr><tr><td>e<sub>sum</sub></td><td>0.60</td><td>0.55</td><td>0.20</td><td>0.30</td><td>0.16</td></tr><tr><td>f<sub>sum</sub></td><td>2.0</td><td>2.7</td><td>2.4</td><td>2.4</td><td>2.4</td></tr><tr><td>e<sub>win</sub></td><td>0.35</td><td>0.88</td><td>0.60</td><td>0.70</td><td>0.36</td></tr><tr><td>f<sub>win</sub></td><td>1.3</td><td>1.8</td><td>1.5</td><td>1.4</td><td>1.3</td></tr></tbody></table>

between summer and winter, particularly in the Aitken mode. This suggests that the vertical distribution of atmospheric particles (supersaturation) affects the accurate prediction of CCN activity. Poor κ<sub>ccn</sub> chemical closure in both seasons is primarily attributed to NO₃⁻ evaporation losses and the impact of organic surfactants on particle surface tension. After correction, κ<sub>ccn</sub> and κ<sub>chem</sub> show good agreement. Finally, for accurate CCN activity predictions in atmospheric environments, considering different mixing states and determining the surface tension of organic components are crucial.

CRediT authorship contribution statement

Yongwei Lu: Writing – original draft, Methodology, Data curation.
**Yukun Chen:** Data curation, Conceptualization. **Xin Wang:** Writing – review & editing, Visualization, Investigation. **Bo Ren:** Resources, Investigation. **Biao Li:** Methodology, Investigation. **Eric Lichtfouse:** Writing – review & editing, Validation, Software. **Yueshe Wang:** Visualization, Supervision, Funding acquisition.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgement

This work was supported simultaneously by the Fundamental Research Funds for the Central University of Xi'an Jiaotong University (Grant No. Xpt022022012), and the Regular Project of National Natural Science Foundation of China (Grant No. 51576160)

We acknowledge the use of data and/or imagery from NASA's Fire Information for Resource Management System (FIRMS) (https://earthdata.nasa.gov/firms), part of NASA's Earth Observing System Data and Information System (EOSDIS).

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.atmosenv.2025.121129.

## Data availability

Data will be made available on request.

## References

An, J., Cao, Q., Zou, J., Wang, H., Duan, Q., Shi, Y., Chen, C., Wang, J., 2018. Seasonal variation in water-soluble ions in airborne particulate deposition in the suburban Nanjing area, Yangtze River Delta, China, during haze days and normal days. Arch. Environ. Contam. Toxicol. 74, 1–15.

Andreae, M.O., Rosenfeld, D., 2008. Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols. Earth Sci. Rev. 89 (1), 13–41. https://doi.org/10.1016/j.earscirev.2008.03.001.

Asa-Awuku, A., Sullivan, A.P., Hennigan, C.J., Weber, R.J., Nenes, A., 2008. Investigation of molar volume and surfactant characteristics of water-soluble organic compounds in biomass burning aerosol. Atmos. Chem. Phys. 8 (4), 799–812. https://doi.org/10.5194/acp-8-799-2008.

Bilde, M., Barsanti, K., Booth, M., Cappa, C.D., Donahue, N.M., Emanuelsson, E.U., McGiggans, G., Krieger, U.K., Marcolli, C., Topping, D., Ziemann, P., Barley, M., Clegg, S., Dennis-Smither, B., Hallquist, C., Hallquist, Å.M., Khlystov, A., Kulmala, M., Mogensen, D., Riipinen, I., 2015. Saturation vapor pressures and transition enthalpies of low-volatility organic molecules of atmospheric relevance: from dicarboxylic acids to complex mixtures. Chem Rev 115 (10), 4115–4156. https://doi.org/10.1021/cr5005502.

Burkart, J., Steiner, G., Reischl, G., Hitzenberger, R., 2011. Long-term study of cloud condensation nuclei (CCN) activation of the atmospheric aerosol in Vienna. Atmos. Environ. 45 (32), 5751–5759. https://doi.org/10.1016/j.atmosenv.2011.07.022.

Cao, J.J., Lee, S.C., Ho, K.F., Zhang, X.Y., Zou, S.C., Fung, K., Chow, J.C., Watson, J.G., 2003. Characteristics of carbonaceous aerosol in Pearl River Delta region, China during 2001 winter period. Atmos. Environ. 37 (11), 1451–1460. https://doi.org/10.1016/S1352-2310(02)01002-6.

Cao, J.J., Zhu, C.S., Tie, X.X., Geng, F.H., Xu, H.M., Ho, S.S.H., Wang, G.H., Han, Y.M., Ho, K.F., 2013. Characteristics and sources of carbonaceous aerosols from Shanghai, China. Atmos. Chem. Phys. 13 (2), 803–817. https://doi.org/10.5194/acp-13-803-2013.

Cerully, K.M., Raatikainen, T., Lance, S., Tkacik, D., Tiitta, P., Petäjä, T., Ehn, M., Kulmala, M., Worsnop, D.R., Laaksonen, A., Smith, J.N., Nenes, A., 2011. Aerosol hygroscopicity and CCN activation kinetics in a boreal forest environment during the 2007 EUCAARI campaign. Atmos. Chem. Phys. 11 (23), 12369–12386. https://doi.org/10.5194/acp-11-12369-2011.

Chan, C.K., Ha, Z., Choi, M.Y., 2000. Study of water activities of aerosols of mixtures of sodium and magnesium salts. Atmos. Environ. 34 (28), 4795–4803.

Che, H.C., Zhang, X.Y., Zhang, L., Wang, Y.Q., Zhang, Y.M., Shen, X.J., Ma, Q.L., Sun, J. Y., Zhong, J.T., 2017. Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics. Sci. Rep. 7 (1), 5819. https://doi.org/10.1038/s41598-017-05998-3.

Chen, Y., Wang, X., Dai, W., Wang, Q., Guo, X., Liu, Y., Qi, W., Shen, M., Zhang, Y., Li, L., Cao, Y., Wang, Y., Li, J., 2023. Particle number size distribution of wintertime Alpine

aerosols and their activation as cloud condensation nuclei in the Guanzhong Plain, northwest China. J. Geophys. Res. Atmos. 128 (6), e2022JD037877. https://doi.org/10.1029/2022JD037877.

Cheng, Z., Morgenstern, M., Henning, S., Zhang, B., Roberts, G.C., Fraund, M., Marcus, M.A., Lata, N.N., Fialho, P., Mazzoleni, L., Leu, Wheyer, B., Mazzoleni, C., China, S., 2023. Cloud condensation nuclei activity of internally mixed particle populations at a remote marine free troposphere site in the North Atlantic Ocean. Sci. Total Environ. 904, 166865. https://doi.org/10.1016/j.scitotenv.2023.166865.

Cheung, H.C., Chou, C.C.K., Lee, C.S.L., Kuo, W.C., Chang, S.C., 2020. Hygroscopic properties and cloud condensation nuclei activity of atmospheric aerosols under the influences of Asian continental outflow and new particle formation at a coastal site in eastern Asia. Atmos. Chem. Phys. 20 (10), 5911–5922. https://doi.org/10.5194/acp-20-5911-2020.

Chow, J.C., Watson, J.G., Kuhns, H., Etyemezian, V., Lowenthal, D.H., Crow, D., Kohl, S. D., Engelbrecht, J.P., Green, M.C., 2004. Source profiles for industrial, mobile, and area sources in the big bend regional aerosol visibility and observational study. Chemosphere 54 (2), 185–208. https://doi.org/10.1016/j.chemosphere.2003.07.004.

Chung, C.E., Ramanathan, V., Kim, D., Podgorny, I., 2005. Global anthropogenic aerosol direct forcing derived from satellite and ground-based observations. J. Geophys. Res. Atmos. 110 (D24).

Deng, Z., Ran, L., Xu, X., Yan, P., Tian, P., Lin, W., Wu, Y., Zhang, R., Pan, W., Lu, D., 2024. Size-resolved cloud condensation nuclei activity of aerosol particles sampled above the mixing layer. Aerosol. Sci. Technol. 58, 485–497.

Duan, J., Chen, Y., Zhang, X., Wang, W., Zhong, S., Li, J., Lu, G., Fang, C., Guo, L., Fu, P., 2023. Influence of aerosol physicochemical properties on CCN activation during the Asian winter monsoon at the summit of Mt. Lu, China. Atmos. Environ. 296, 119592. https://doi.org/10.1016/j.atmosenv.2023.119592.

Dusek, U., Frank, G., Curtius, J., Drewnick, F., Schneider, J., Kürten, A., Rose, D., Andreae, M.O., Borrmann, S., Pöschl, U., 2010. Enhanced organic mass fraction and decreased hygroscopicity of cloud condensation nuclei (CCN) during new particle formation events. Geophys. Res. Lett. 37.

Ding, K., Huang, X., Ding, A., Wang, M., Su, H., Kerminen, V.-M., Petäjä, T., Tan, Z., Wang, Z., Zhou, D., 2021. Aerosol-boundary-layer-monsoon interactions amplify semi-direct effect of biomass smoke on low cloud formation in Southeast Asia. Nat. Commun. 12 (1), 6416.

Elmes, M., Gasparon, M., 2017. Sampling and single particle analysis for the chemical characterisation of fine atmospheric particulates: a review. J. Environ. Manag. 202, 137–150. https://doi.org/10.1016/j.jenvman.2017.06.067.

Fan, X., Liu, J., Zhang, F., Chen, L., Collins, D., Xu, W., Jin, X., Ren, J., Wang, Y., Wu, H., Li, S., Sun, Y., Li, Z., 2020. Contrasting size-resolved hygroscopicity of fine particles derived by HTDMA and HR-ToF-AMS measurements between summer and winter in Beijing: the impacts of aerosol aging and local emissions. Atmos. Chem. Phys. 20 (2), 915–929. https://doi.org/10.5194/acp-20-915-2020.

Ge, X., He, Y., Sun, Y., Xu, J., Wang, J., Shen, Y., Chen, M., 2017. Characteristics and formation mechanisms of fine particulate nitrate in typical urban areas in China. Atmosphere 8 (3), 62. https://www.mdpi.com/2075-4333/8/3/62.

Gunthe, S.S., King, S.M., Rose, D., Chen, Q., Roldin, P., Farmer, D.K., Jimenez, J.L., Artaxo, P., Andreae, M.O., Martin, S.T., Pöschl, U., 2009. Cloud condensation nuclei in pristine tropical rainforest air of Amazonia: size-resolved measurements and modeling of atmospheric aerosol composition and CCN activity. Atmos. Chem. Phys. 9 (19), 7551–7575. https://doi.org/10.5194/acp-9-7551-2009.

Gysel, M., Crosier, J., Topping, D.O., Whitehead, J.D., Bower, K.N., Cubison, M.J., Williams, P.I., Flynn, M.J., McGiggans, G.B., Coe, H., 2007. Closure study between chemical composition and hygroscopic growth of aerosol particles during TORCH2. Atmos. Chem. Phys. 7 (24), 6131–6144. https://doi.org/10.5194/acp-7-6131-2007.

IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp, doi:10.1017/CBO9781107415324.

Jiang, Y., Ma, Y., Zheng, J., Ye, N., Yuan, C., 2025. Characterization of size-resolved aerosol hygroscopicity and liquid water content in Nanjing of the Yangtze River Delta. Journal of Environmental Sciences 151, 26–41. https://doi.org/10.1016/j.jes.2024.03.035.

Kawana, K., Kawamura, K., Fu, P., Barrie, L.A., 2022. Hygroscopicity and CCN activity of water-soluble extracts from the arctic aerosols in winter to early summer. J. Geophys. Res. Atmos. 127 (19), e2021JD035979. https://doi.org/10.1029/2021JD035979.

Lacher, L., Adams, M.P., Barry, K., Bertozzi, B., Bingemer, H., Boffo, C., Bras, Y., Büttner, N., Castarede, D., Cziczo, D.J., DeMott, P.J., Fösig, R., Goodell, M., Höhler, K., Hill, T.C.J., Jentzsch, C., Ladino, L.A., Levin, E.J.T., Mertes, S., Freney, E., 2024. The Puy de Dôme Ice Nucleation Intercomparison Campaign (PICNIC): comparison between online and offline methods in ambient air. Atmos. Chem. Phys. 24 (4), 2651–2678. https://doi.org/10.5194/acp-24-2651-2024.

Li, Y., Tasoglou, A., Liangou, A., Cain, K.P., Jahn, L., Gu, P., Kostenidou, E., Pandis, S.N., 2018. Cloud condensation nuclei activity and hygroscopicity of fresh and aged cooking organic aerosol. Atmos. Environ. 176, 103–109. https://doi.org/10.1016/j.atmosenv.2017.11.035.

Lin, Y., Rong, Y., Yu, J., Zhang, H.C., Li, L., 2022. An optimized remote sensing recognition approach for straw burning in Henan Province, China. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences 43, 127–134.

Liu, L., Cheng, Y., Wang, S., Wei, C., Pöhlker, M.L., Pöhlker, C., Artaxo, P., Shrivastava, M., Andreae, M.O., Pöschl, U., 2020a. Impact of biomass burning
aerosols on radiation, clouds, and precipitation over the Amazon: relative importance of aerosol-cloud and aerosol-radiation interactions. Atmos. Chem. Phys. 20 (21), 13283-13301.

Liu, L., Zhang, J., Du, R., Teng, X., Hu, R., Yuan, Q., Tang, S., Ren, C., Huang, X., Xu, L., Zhang, Y., Zhang, X., Song, C., Liu, B., Lu, G., Shi, Z., Li, W., 2020b. Chemistry of atmospheric fine particles during the COVID-19 pandemic in a megacity of eastern China. Geophys. Res. Lett. 48.

Liu, Q., Shen, X., Li, L., Sun, J., Liu, Z., Zhu, W., Zhong, J., Zhang, Y., Hu, X., Liu, S., 2023. Impacts of aerosol chemical composition on cloud condensation nuclei (CCN) activity during wintertime in Beijing. China. Remote Sens. 15 (17), 4119.

Ma, Z., Liu, R., Liu, Y., Bi, J., 2019. Effects of air pollution control policies on PM2.5 pollution improvement in China from 2005 to 2017: a satellite-based perspective. Atmos. Chem. Phys. 19 (10), 6861-6877.

Mei, Setyan, A., Zhang, Q., Wang, J., 2013. CCN activity of organic aerosols observed downwind of urban emissions during CARES. Atmos. Chem. Phys. 13 (24), 12155-12169. https://doi.org/10.5194/acp-13-12155-2013.

Mikhailov, E., Vlasenko, S., Martin, S.T., Koop, T., Pöschl, U., 2009. Amorphous and crystalline aerosol particles interacting with water vapor: conceptual framework and experimental evidence for restructuring, phase transitions and kinetic limitations. Atmos. Chem. Phys. 9 (24), 9491-9522. https://doi.org/10.5194/acp-9-9491-2009.

Mikhailov, E., Vlasenko, S., Niessner, R., Pöschl, U., 2004. Interaction of aerosol particles composed of protein and saltwater with water vapor: hygroscopic growth and microstructural rearrangement. Atmos. Chem. Phys. 4 (2), 323-350. https://doi.org/10.5194/acp-4-323-2004.

Moore, R.H., Nenes, A., Medina, J., 2010. Scanning mobility CCN analysis—a method for fast measurements of size-resolved CCN distributions and activation kinetics. Aerosol. Sci. Technol. 44, 861-871.

Moore, R.H., Bahreini, R., Brock, C.A., Froyd, K.D., Cozic, J., Holloway, J.S., Middlebrook, A.M., Murphy, D.M., Nenes, A., 2011. Hygroscopicsity and composition of alaskan arctic CCN during april 2008. Atmos. Chem. Phys. 11 (22), 11807-11825. https://doi.org/10.5194/acp-11-1807-2011.

Orikasa, N., Saito, A., Yamashita, K., Tajiri, T., Zaizen, Y., Kuo, T.-H., Kuo, W.-C., Murakami, M., 2020. Seasonal variations of atmospheric aerosol particles focused on cloud condensation nuclei and ice nucleating particles from ground-based observations in tsukuba, Japan. SOLA 16, 212-219. https://doi.org/10.2151/sola.2020-036.

Padró, L.T., Asa-Awuku, A., Morrison, R., Nenes, A., 2007. Inferring thermodynamic properties from CCN activation experiments: single-component and binary aerosols. Atmos. Chem. Phys. 7 (19), 5263-5274. https://doi.org/10.5194/acp-7-5263-2007.

Padró, L.T., Moore, R.H., Zhang, X., Rastogi, N., Weber, R.J., Nenes, A., 2012. Mixing state and compositional effects on CCN activity and droplet growth kinetics of size-resolved CCN in an urban environment. Atmos. Chem. Phys. 12 (21), 10239-10255. https://doi.org/10.5194/acp-12-10239-2012.

Padró, L.T., Tkacik, D., Latham, T., Hennigan, C.J., Sullivan, A.P., Weber, R.J., Huey, L. G., Nenes, A., 2010. Investigation of cloud condensation nuclei properties and droplet growth kinetics of the water-soluble aerosol fraction in Mexico City. J. Geophys. Res. Atmos. 115 (D9). https://doi.org/10.1029/2009JD013195.

Petters, M.D., Kreidenweis, S.M., 2007. A single parameter representation of hygroscopic growth and cloud condensation nucleus activity. Atmos. Chem. Phys. 7 (8), 1961-1971. https://doi.org/10.5194/acp-7-1961-2007.

Petters, M.D., Kreidenweis, S.M., 2013. A single parameter representation of hygroscopic growth and cloud condensation nucleus activity – Part 3: including surfactant partitioning. Atmos. Chem. Phys. 13 (2), 1081-1091. https://doi.org/10.5194/acp-13-1081-2013.

Pöhlker, M.L., Pöhlker, C., Ditas, F., Klimach, T., Hrabe de Angelis, I., Araújo, A., Brito, J., Carbone, S., Cheng, Y., Chi, X., Ditz, K., Gunthe, S.S., Kesselmeier, J., Könemann, T., Lavrić, J.V., Martin, S.T., Mikhailov, E., Moran-Zuloaga, D., Rose, D., Pöschl, U., 2016. Long-term observations of cloud condensation nuclei in the Amazon rain forest – Part 1: aerosol size distribution, hygroscopicsity, and new model parametrizations for CCN prediction. Atmos. Chem. Phys. 16 (24), 15709-15740. https://doi.org/10.5194/acp-16-15709-2016.

Pope III, C.A., Dockery, D.W., 2006. Health effects of fine particulate air pollution: lines that connect. Journal of the air & waste management association 56 (6), 709-742.

Pöschl, U., 2005. Atmospheric aerosols: composition, transformation, climate and health effects. Angew. Chem. Int. Ed. 44 (46), 7520-7540.

Ray, A., Pandithurai, G., Mukherjee, S., Kumar, V.A., Hazra, A., Patil, R.D., Waghmare, V., 2023. Seasonal variability in size-resolved hygroscopicity of sub-micron aerosols over the Western Ghats, India: closure and parameterization. Sci. Total Environ. 869, 161753. https://doi.org/10.1016/j.scitotenv.2023.161753.

Ren, J., Zhang, F., Wang, Y., Collins, D., Fan, X., Jin, X., Xu, W., Sun, Y., Cribb, M., Li, Z., 2018. Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing. Atmos. Chem. Phys. 18 (9), 6907-6921. https://doi.org/10.5194/acp-18-6907-2018.

Rose, D., Gunthe, S.S., Mikhailov, E., Frank, G.P., Dusek, U., Andreae, M.O., Pöschl, U., 2008. Calibration and measurement uncertainties of a continuous-flow cloud condensation nuclei counter (DMT-CCNC): CCN activation of ammonium sulfate and sodium chloride aerosol particles in theory and experiment. Atmos. Chem. Phys. 8 (5), 1153-1179. https://doi.org/10.5194/acp-8-1153-2008.

Rose, D., Nowak, A., Achtert, P., Wiedensohler, A., Hu, M., Shao, M., Zhang, Y., Andreae, M.O., Pöschl, U., 2010. Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China – Part 1: size-resolved measurements and implications for the modeling of aerosol particle hygroscopicity and CCN activity. Atmos. Chem. Phys. 10 (7), 3365-3383. https://doi.org/10.5194/acp-10-3365-2010.

Royer, H.M., Pöhlker, M.L., Krüger, O., Blades, E., Sealy, P., Lata, N.N., Cheng, Z., China, S., Ault, A.P., Quinn, P.K., Zuidema, P., Pöhlker, C., Pöschl, U., Andreae, M.,

Gaston, C.J., 2023. African smoke particles act as cloud condensation nuclei in the wintertime tropical North Atlantic boundary layer over Barbados. Atmos. Chem. Phys. 23 (2), 981-998. https://doi.org/10.5194/acp-23-981-2023.

Salma, I., Thén, W., Vörösmarty, M., Gyöngyösi, A.Z., 2021. Cloud activation properties of aerosol particles in a continental Central European urban environment. Atmos. Chem. Phys. 21 (14), 11289-11302. https://doi.org/10.5194/acp-21-11289-2021.

Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R.T., 1999. Measurement of emissions from air pollution sources. 2. C1 through C30 organic compounds from medium duty diesel trucks. Environmental Science & Technology 33 (10), 1578-1587. https://doi.org/10.1021/es980081n.

Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R., 2002a. Measurement of emissions from air pollution sources. 4. C1-C27 organic compounds from cooking with seed oils. Environ. Sci. Technol. 36 (4), 567-575. https://doi.org/10.1021/es002053m.

Schauer, J.J., Kleeman, M.J., Cass, G.R., Simoneit, B.R.T., 2002b. Measurement of emissions from air pollution sources. 5. C1–C32 organic compounds from gasoline-powered motor vehicles. Environmental Science & Technology 36 (6), 1169–1180. https://doi.org/10.1021/es0108077.

Schmale, J., Henning, S., Decesari, S., Henzing, B., Keskinen, H., Sellegri, K., Ovadnevaite, J., Pöhlker, M.L., Brito, J., Bougiatioti, A., Kristensson, A., Kalivitis, N., Stavroulas, I., Carbone, S., Jefferson, A., Park, M., Schlag, P., Iwamoto, Y., Aalto, P., Gysel, M., 2018. Long-term cloud condensation nuclei number concentration, particle number size distribution and chemical composition measurements at regionally representative observatories. Atmos. Chem. Phys. 18 (4), 2853-2881. https://doi.org/10.5194/acp-18-2853-2018.

Sihto, S.L., Mikkilä, J., Vanhanen, J., Ehn, M., Liao, L., Lehtipalo, K., Aalto, P.P., Duplissy, J., Petäjä, T., Kerminen, V.M., Boy, M., Kulmala, M., 2011. Seasonal variation of CCN concentrations and aerosol activation properties in boreal forest. Atmos. Chem. Phys. 11 (24), 13269-13285. https://doi.org/10.5194/acp-11-13269-2011.

Stokes, R., Robinson, R., 1966. Interactions in aqueous nonelectrolyte solutions. I. Solute-solvent equilibria. The Journal of Physical Chemistry 70 (7), 2126-2131.

Sun, J., Hermann, M., Weinhold, K., Merkel, M., Birmili, W., Yang, Y., Tuch, T., Flentje, H., Briel, B., Ries, L., 2024. Measurement Report: Contribution of Atmospheric New Particle Formation to Ultrafine Particle Concentration, Cloud Condensation Nuclei and Radiative Forcing: Results from Five-Year Observations in Central Europe, vol. 2024. EGUSphere, pp. 1-34.

Svenningsson, B., Rissler, J., Swietlicki, E., Mircea, M., Bilde, M., Facchini, M., Decesari, S., Fuzzi, S., Zhou, J., Monster, J., 2006. Hygroscopic growth and critical supersaturations for mixed aerosol particles of inorganic and organic compounds of atmospheric relevance. Atmos. Chem. Phys. 6 (7), 1937-2052.

Wang, H., Lu, K., Tan, Z., Chen, X., Liu, Y., Zhang, Y., 2023. Formation mechanism and control strategy for particulate nitrate in China. Journal of Environmental Sciences 123, 476-486. https://doi.org/10.1016/j.jes.2022.09.019.

Wang, Y., Henning, S., Poulain, L., Lu, C., Stratmann, F., Wang, Y., Niu, S., Pöhlker, M.L., Herrmann, H., Wiedensohler, A., 2022. Aerosol activation characteristics and prediction at the central European ACTRIS research station of Melpitz, Germany. Atmos. Chem. Phys. 22 (24), 15943-15962. https://doi.org/10.5194/acp-22-15943-2022.

Wang, Y., Zhang, S., Hao, J., 2019. Air pollution control in China: progress, challenges and future pathways. Research of Environmental Sciences 32 (10), 1755-1762.

Wang, Y.Q., 2014. MeteoInfo: GIS software for meteorological data visualization and analysis. Meteol. Appl. 21 (2), 360-368. https://doi.org/10.1002/met.1345.

Wong, C., Karplus, V.J., 2017. China's war on air pollution: can existing governance structures support new ambitions? China Q. 231, 662-684.

Wu, Z.J., Zheng, J., Shang, D.J., Du, Z.F., Wu, Y.S., Zeng, L.M., Wiedensohler, A., Hu, M., 2016. Particle hygroscopicsity and its link to chemical composition in the urban atmosphere of Beijing, China, during summertime. Atmos. Chem. Phys. 16 (2), 1123-1138. https://doi.org/10.5194/acp-16-1123-2016.

Xiao, K., Yao, X., Ren, X., Zhang, X., Fu, N., Shi, Q., 2023. Transmission characteristics, source apportionment and health risk assessment of PM2.5 in a typical industrial city in Northwest China. Urban Clim. 47, 101367. https://doi.org/10.1016/j.uclim.2022.101367.

Xu, B., Zhang, G., Gustafsson, Ö., Kawamura, K., Li, J., Andersson, A., Bikkina, S., Kunwar, B., Pokhrel, A., Zhong, G., Zhao, S., Li, J., Huang, C., Cheng, Z., Zhu, S., Peng, P., Sheng, G., 2022. Large contribution of fossil-derived compounds to aqueous secondary organic aerosols in China. Nat. Commun. 13 (1), 5115. https://doi.org/10.1038/s41467-022-32863-3.

Yu, C., Liu, D., Hu, K., Tian, P., Wu, Y., Zhao, D., Wu, H., Hu, D., Guo, W., Li, Q., 2022. Aerodynamic size-resolved composition and cloud condensation nuclei properties of aerosols in a Beijing suburban region. Atmos. Chem. Phys. 22 (7), 4375-4391.

Yu, H., Kaufman, Y., Chin, M., Feingold, G., Remer, L., Anderson, T., Balkanski, Y., Bellouin, N., Boucher, O., Christopher, S., 2006. A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmos. Chem. Phys. 6 (3), 613–666.

Yue, D., Zhong, L., Zhang, T., Shen, J., Yuan, L., Ye, S., Zhou, Y., Zeng, L., 2016. Particle growth and variation of cloud condensation nucleus activity on polluted days with new particle formation: a case study for regional air pollution in the PRD region, China. Aerosol Air Qual. Res. 16 (2), 323-335.

Zdanovskii, A., 1948. New methods for calculating solubilities of electrolytes in multicomponent systems. Zh. Fiz. Khim. 12 (22), 1478-1485.

Zeng, Y., Jiang, Y., Li, Y., Xu, X., Zhang, X., Yu, W., Yu, R., Liu, X., 2024. Early warning of urban heavy metal pollution based on PMF- MeteoInfo model combined with physicochemical properties of dust. Stoch. Environ. Res. Risk Assess. https://doi.org/10.1007/s00477-023-02644-5.
Zhang, J., Liu, L., Xu, L., Lin, Q., Zhao, H., Wang, Z., Guo, S., Hu, M., Liu, D., Shi, Z., Huang, D., Li, W., 2020. Exploring wintertime regional haze in northeast China: role of coal and biomass burning. Atmos. Chem. Phys. 20 (9), 5355–5372. https://doi.org/10.5194/acp-20-5355-2020.

Zhang, S., Shen, X., Sun, J., Che, H., Zhang, Y., Liu, Q., Xia, C., Hu, X., Zhong, J., Wang, J., Liu, S., Lu, J., Yu, A., Zhang, X., 2023. Seasonal variation of particle hygroscopicity and its impact on cloud-condensation nucleus activation in the

Beijing urban area. Atmos. Environ. 302, 119728. https://doi.org/10.1016/j.atmosenv.2023.119728.

Zhang, W., Wang, S., Li, Z., Huang, Y., Yuan, Y., 2019. Research on fire detection based on landsat-8 remote sensing image. Spacecraft Engineering 28 (6), 114–119. https://doi.org/10.3969/j.issn.1673-8748.2019.06.016.

Zobrist, B., Marcolli, C., Pedernera, D., Koop, T., 2008. Do atmospheric aerosols form glasses? Atmos. Chem. Phys. 8 (17), 5221–5244.
Seasonal variations in PM2.5 composition and their effects on CCN activation properties

Yongwei Lu¹, Yukun Chen², Xin Wang³,*, Bo Ren¹, Biao Li¹, Eric Lichtfouse¹, Yueshe Wang¹,**

1. State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China

2. Science and Technology on Aerospace Chemical Power Laboratory, Hubei Institute of Aerospace Chemotechnology, Xiangyang, 441003, Hubei, China

3. School of Chemistry, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China

(* corresponding author: wangxin@mail.xjtu.edu.cn, +8615389037539; ** corresponding author: wangys@mail.xjtu.edu.cn, +86-29-82668767)

1. Supplementary Figures (Figure S1 – S21)

2. Supplementary Tables (Table S1 – S6)

3. Supplementary References

<figure><img src="image_12.png" /><figcaption>**Figure S1. Observation Site Overview**</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure S2.** PM<sub>2.5</sub> Concentrations in Summer and Winter of 2019. Black dots represent offline data, while white dots represent online data from https://www.mee.gov.cn/</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>**Figure S3.** The air mass trajectories in Xi'an during the winter of 2019 (A) and the total spatial variance (TVS) of the number of air mass trajectories (B)</figcaption></figure>

The method for air mass classification is as follows: This study used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to track air mass trajectories backward for 24 hours at 1-hour intervals. The simulation period covered the summer of 2019 (July 31 - August 31, 2019) and the winter of 2019 (December 10, 2019 - January 9, 2020), with air mass heights set at 500 m. The model's altitude limit was 1000 m. We merged the .tgs trajectory files of the two months into a single seasonal air mass trajectory file and converted it into a .shp image file, such as the winter air mass trajectory in Xi'an shown in Figure S2(A). Subsequently, all air mass trajectories were subjected to cluster analysis. The Meteoinfo software offers two clustering methods: Euclidean, which classifies the air mass trajectories based on their length, and Angle, which classifies them by angle. In this study, we merged the air mass trajectory files using the angle-based method. Based on the spatial variation rate of the air mass trajectories shown in Figure S2(B), we observed a sharp increase in the spatial variation rate when the air mass trajectories were clustered into 4 categories, and further merging led to unreliable results. Therefore, the winter air mass trajectories in Xi'an for 2019 were classified into 4 categories.
<figure><img src="image_15.png" /><figcaption>**Figure S4.** Experimental Analysis Technical Route</figcaption></figure>

In the manuscript, the CCN activity measurement scheme based on offline sampling technology is referred to as Scanning Mobility CCN Analysis (SMCA) (Moore et al., 2010). This scheme consists of three main parts: aerosol generation, size classification, and CCN measurement. Polydisperse aerosols are generated by nebulizing filter extracts. The droplets are then dried as they pass through multiple diffusion dryers and are charged using a Kr-85 bipolar charger (TSI Model 3077). A Differential Mobility Analyzer (DMA, TSI Model 3081L) is then used to select dried particles based on their electrical mobility. The classified aerosol stream is split into two: one stream is directed to a Condensation Particle Counter (CPC, TSI Model 3022A) to measure the total particle concentration (CN), while the other is sent to a continuous-flow thermal gradient CCN chamber to measure the cloud condensation nuclei concentration (CCN) at a given supersaturation (Padró et al., 2010). During the measurement process, the DMA voltage is manually adjusted to gradually increase the initial dry particle diameter from 20 nm to 140 nm in increments of 2–20 nm, obtaining the particle size-activation ratio curve. This curve exhibits an S-shape and follows a Gaussian cumulative distribution function. The critical diameter is determined when the activation ratio reaches 50%, allowing the calculation of the sample's hygroscopicity parameter (κ).

<figure><img src="image_16.png" /><figcaption>**Figure S5.** Verification of DMA Particle Size Selection Performance</figcaption></figure>
In this study, polystyrene spheres with a standard particle size of 100 nm were used to verify the DMA selection effectiveness. Two to four drops of the standard solution were added to 500 mL of ultrapure water. After atomization, the water vapor evaporated, leaving only the dry standard particles, which were counted using a Condensation Particle Counter (CPC). The obtained results were fitted with a log-normal distribution, as shown in Figure S3. Measurements were taken three times, with the maximum deviation in particle size not exceeding 0.15%, proving that the DMA selection effectiveness is satisfactory.

<figure><img src="image_17.png" /><figcaption>**Figure S6.** Ammonium sulfate activation curves at various supersaturation levels. Scatter points: measured values, solid lines: Gaussian cumulative distribution function fit</figcaption></figure>

Due to particle losses entering the CCNc and CPC, and differences in the counting efficiency of the two devices, the activation ratio of ammonium sulfate did not reach 100%. The measurement data were not normalized, but this does not affect the fitting of the particle activation diameter (critical diameter) (Rose et al., 2008).

<figure><img src="image_18.png" /><figcaption>**Figure S7.** pH of the samples from the summer and winter of 2019</figcaption></figure>
The aerosol thermodynamic model ISORROPIA-II is commonly used to calculate the equilibrium components of Na⁺-K⁺-Ca²⁺-Mg²⁺-NH₄⁺-Cl⁻-NO₃⁻-SO₄²⁻-H₂O. This model operates in both forward and backward modes. The forward mode calculates the aerosol species based on the given gas concentrations and total concentrations of gases, while the backward mode calculates based on temperature, humidity, and aerosol concentration. In this study, we used the backward mode for the calculation. Additionally, ISORROPIA-II provides two prediction scenarios, "metastable" and "solid+liquid." Considering the relatively high humidity during the sampling period, the "metastable" option was chosen.

<figure><img src="image_19.png" /><figcaption>**Figure S8.** Variation of atmospheric SO₂ and NO₂ concentrations in Xi'an during the last 10 years. Source of data from the Ministry of Ecology and Environment of the People's Republic of China: https://www.mee.gov.cn/</figcaption></figure>

SO₄²⁻, NO₃⁻, and NH₄⁺ in particulate matter are generally considered secondary reaction products, formed through the conversion of gaseous pollutants. The concentrations of gaseous pollutants in Xi'an are shown in Figure S6. As seen in Figure S6, over the past decade, due to the continuous implementation of the city's coal-to-gas and centralized heating policies, SO₂ concentrations in the atmosphere have been significantly controlled. Additionally, with the continuous increase in per capita car ownership, NO₂ has become the primary gaseous pollutant. Seasonal variations have gradually had a greater impact on the concentration changes of NO₂. This may lead to a higher proportion of NO₃⁻ in particulate matter, with a more significant seasonal influence.

<figure><img src="image_20.png" /><figcaption>**Figure S9.** Secondary particulate matter generation (SD: Summer daytime, SN: Summer nighttime, WD: Winter daytime, WN: Winter nighttime, C: Clean, P: Pollution, HP: Heavy pollution)</figcaption></figure>
Meteorological conditions (such as aging time, mixing state, atmospheric boundary layer height, etc.) may influence the secondary transformation rate of particulate matter. EC (elemental carbon) generally comes from direct emissions of pollution sources, is stable in nature, and is not easily subject to secondary transformation. Changes in EC characteristics can represent the influence of environmental conditions. In many studies, the species/EC ratio is used to exclude the interference of meteorological conditions. In Figure S7, excluding the influence of meteorological conditions, the SO₄²⁻/EC concentration is approximately equal across different seasons, periods, and pollution levels. It is noteworthy that the results for NO₃⁻/EC differ completely from those for SO₄²⁻/EC. After excluding the interference of meteorological conditions, high NO₃⁻/EC concentrations still appear in winter and in particulate matter with higher pollution levels. Compared to summer, the concentration increase of NO₃⁻/EC in winter is 106.6%, while the concentration increase of NO₃⁻ is 355% (Figure 2 in the manuscript). This may suggest that seasonal changes leading to altered meteorological conditions have a very significant effect (248.4%) on the accumulation of NO₃⁻, while contributing only 110% to the accumulation of SO₄²⁻.

In addition, SOR and NOR are commonly used to estimate the secondary transformation rates of SO₄²⁻ and NO₃⁻, with the following calculation formulas:

$$ \text{SOR} = \frac{\text{SO}_4^{2-}}{\text{SO}_4^{2-} + \text{SO}_2} \qquad (I) $$

$$ \text{NOR} = \frac{\text{NO}_3^-}{\text{NO}_3^- + \text{NO}_2} \qquad (II) $$

Both summer and winter have higher SOR values than NOR, which aligns with the findings of other studies (Chu et al., 2023; Guo et al., 2023; Nirmalkar et al., 2023). However, the seasonal variation causes a much larger increase in NOR compared to SOR (ΔSOR = 11%, ΔNOR = 112%). This indicates that meteorological conditions indeed contribute significantly to the secondary formation of NO₃⁻.

<figure><img src="image_21.png" /><figcaption>**Figure S10.** Traffic congestion index of Xi'an from 2017 to 2023 for summer and winter seasons. Sum: Summer, Win: Winter. The data is from https://huiyan.baidu.com/reports.</figcaption></figure>

The traffic congestion index is defined as travel time / free-flow travel time. As shown in Figure S10, except for 2020, the congestion index in Xi'an exhibited very little variation between summer and winter. This suggests that, with respect to vehicle emissions, seasonal changes may not have a significant
impact. Seasonal changes are more likely to alter regional meteorological conditions, such as atmospheric boundary layer height and secondary transformation rates, which in turn affect particulate matter concentration and composition, rather than changing the emission source strength of vehicle exhaust.

<figure><img src="image_22.png" /><figcaption>**Figure S11.** Component Analysis of Particulate Matter</figcaption></figure>

<figure><img src="image_23.png" /><figcaption>**Figure S12.** Correlation analysis between OM and $\kappa$ for summer 2019 aerosols</figcaption></figure>
CCN activity is closely related to the chemical composition of aerosols, as different chemical components have varying hygroscopic properties, directly affecting whether particles can act as CCN under a given supersaturation condition. Highly hydrophilic components such as SO₄²⁻, NO₃⁻, and NH₄⁺ typically have high κ values (~0.5–1.0) and effectively promote CCN formation. In contrast, organic matter (OM) is generally more hydrophobic, with lower κ values (<0.1), contributing less to CCN activity. Correlation analysis of OM fraction and κ in the summer 2019 samples also indicates that a higher OM fraction corresponds to a lower κ value, as shown in Figure S12. The aerosol composition analysis further reveals that the OM fraction in summer is approximately 5% higher during the daytime and 4% higher at night compared to winter (Figure S11).

<figure><img src="image_24.png" /><figcaption>Figure S13. Summer 2019 Air Mass Backward Trajectories</figcaption></figure>

Generally, marine aerosols have the highest κ values (~0.7) due to their high inorganic content. Continental aerosols, containing mineral dust (natural sources) and black carbon (anthropogenic sources), have slightly lower κ values (~0.3). Forest aerosols, rich in biogenic organic matter, have the lowest κ values (~0.1–0.4). Xi’an is bordered to the south by the Qinling Mountains, which have an average elevation of about 2,000 meters and serve as an important nature reserve in China, as well as a significant source of natural biomass (Figure S1). Additionally, Xi’an’s summer climate is influenced by the East Asian monsoon, with prevailing southeasterly and southerly winds. Air mass trajectory simulations in Figure S13 show that approximately 31% of summer 2019 air masses originated from the Qinling Mountains. Therefore, we infer that weakly hygroscopic air masses from southern forests contribute to the lower summer κ values.
<figure><img src="image_25.png" /><figcaption>**Figure S14.** Time series of $\kappa_{ccn}$, $\kappa_{chem}$, and $\sigma$ ($\sigma$: relative deviation; Underestimation: $< -10\%$; Normal: $\pm 10\%$; Overestimation: $> +10\%$)</figcaption></figure>

<figure><img src="image_26.png" /><figcaption>**Figure S15.** (A) Proportion of $NO_3^-$ in the three deviation types at different supersaturation levels in summer; (B) Distribution ratio of the three deviation types at different supersaturation levels in winter</figcaption></figure>
<figure><img src="image_27.png" /></figure>

**Figure S16.** Absolute deviations of κ<sub>ccn</sub> and κ<sub>chem</sub> at different supersaturation levels (A) Summer; (B) Winter

<figure><img src="image_28.png" /></figure>

**Figure S17.** The distribution ratio of the three deviation types for summer samples at different supersaturation levels

<figure><img src="image_29.png" /></figure>

**Figure S18.** NO<sub>3</sub><sup>-</sup> loss correction (winter, SS = 0.2%, numbers in parentheses represent slope, intercept, and correlation coefficient)
<figure><img src="image_30.png" /><figcaption>**Figure S19.** Winter 2019, SS = 0.2%: (A) $\sigma_2$ fitting; (B) $\sigma$ correction (numbers in parentheses represent the slope, intercept, and correlation coefficient of the fitted line)</figcaption></figure>
<figure><img src="image_31.png" /><figcaption>**Figure S20.** $\kappa$ closure correction for summer and winter 2019: (A) Nitrate evaporation loss in summer; (B) Summer $\sigma_2$ fitting; (C) Summer corrected $\kappa$ closure; (D) Nitrate evaporation loss in winter; (E) Winter $\sigma_2$ fitting; (F) Winter corrected $\kappa$ closure (numbers in parentheses represent the slope, intercept, and correlation coefficient of the fitted line)</figcaption></figure>
<figure><img src="image_32.png" /><figcaption>Figure S21. κ values under the assumptions of internal and external mixing in summer and winter of 2019</figcaption></figure>

In an externally mixed state, different types of aerosol particles exist independently, with each type having distinct chemical compositions and hygroscopicity parameter κ. The overall cloud condensation nuclei (CCN) activity is weighted based on the mass or number distribution of each particle type. The κ calculation for the external mixing state is as follows:

$$ \kappa_{\text{ext}} = \sum_{i=1}^{N} f_i \times \kappa_i $$

Where κext is the effective κ value of the externally mixed aerosol system, fᵢ is the volume or number fraction of the ith particle type, and κᵢ is the intrinsic κ value of that particle type. Given that the proportion of each particle type in the total aerosol is unknown under external mixing conditions, we follow the estimation approach proposed by Padró et al. (2012). This approach categorizes particles into three types: (1) All ionic components, assuming the κ value of (NH₄)₂SO₄, the most common inorganic salt in continental aerosols, which is 0.6. (2) All organic matter (OM) components, assigned a κ value of 0.1. (3) Other components, including mineral dust and black carbon, with a κ value of 0.05.

<table><caption>Table S1. Calibration and Error of Sample Flow and Sheath Flow</caption><thead><tr><th rowspan="2">Measurement<br>Location</th><th colspan="5">Number of Measurements (LPM)</th><th rowspan="2">Average<br>Value</th><th rowspan="2">Standard Deviation<br>(%)</th></tr><tr><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th></tr></thead><tbody><tr><td>CPC</td><td>0.305</td><td>0.307</td><td>0.299</td><td>0.298</td><td>0.304</td><td>0.303</td><td>0.40</td></tr><tr><td>CCNc</td><td>0.501</td><td>0.503</td><td>0.499</td><td>0.498</td><td>0.501</td><td>0.500</td><td>0.20</td></tr><tr><td>Sheath Flow</td><td>8.053</td><td>8.002</td><td>8.010</td><td>8.008</td><td>7.882</td><td>7.991</td><td>6.42</td></tr></tbody></table>

<table><caption>Table S2. Fitted activation diameter of (NH₄)₂SO₄.</caption><thead><tr><th rowspan="2">Supersaturation (%)</th><th colspan="3">Activation diameter (nm)</th></tr><tr><th>This study</th><th>Peng et al. (2022)</th><th>Malek et al. (2022)</th></tr></thead><tbody><tr><td>0.2</td><td>78.9 ± 1.0</td><td>/</td><td>/</td></tr><tr><td>0.4</td><td>48.9 ± 0.2</td><td>48.9 ± 0.3</td><td>46.9 ± 0.6</td></tr><tr><td>0.6</td><td>38.2 ± 0.2</td><td>37.3 ± 0.7</td><td>36.8 ± 0.4</td></tr><tr><td>0.8</td><td>31.9 ± 0.2</td><td>33.9 ± 0.1</td><td>31.6 ± 0.3</td></tr><tr><td>1.0</td><td>27.1 ± 0.1</td><td>28.8 ± 0.9</td><td>26.9 ± 0.3</td></tr></tbody></table>
<table><caption>**Table S3.** Density and κ of major species in particles</caption><thead><tr><th>ρ(NH<sub>4</sub>NO<sub>3</sub>)<sup>a</sup></th><th>ρ((NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>)<sup>a</sup></th><th>ρ(NH<sub>4</sub>HSO<sub>4</sub>)<sup>a</sup></th><th>ρ(H<sub>2</sub>SO<sub>4</sub>)<sup>d</sup></th><th>ρ(SOM)<sup>b</sup></th><th>ρ(POM)<sup>b</sup></th><th>ρ(EC)<sup>c</sup></th></tr></thead><tbody><tr><td>1720</td><td>1769</td><td>1780</td><td>1830</td><td>1400</td><td>1400</td><td>1700</td></tr><tr><td>κ(NH<sub>4</sub>NO<sub>3</sub>)<sup>d</sup></td><td>κ((NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>)<sup>d</sup></td><td>κ(NH<sub>4</sub>HSO<sub>4</sub>)<sup>a</sup></td><td>κ(H<sub>2</sub>SO<sub>4</sub>)<sup>d</sup></td><td>κ(SOM)<sup>b,e</sup></td><td>κ(POM)<sup>b,e</sup></td><td>κ(EC)<sup>c</sup></td></tr><tr><td>0.67</td><td>0.61</td><td>0.65</td><td>0.9</td><td>0.15</td><td>0.1</td><td>0</td></tr></tbody></table>

(a. (Ray et al., 2023); b.(Gysel et al., 2007); c.(Jiang et al., 2025); d.(Petters & Kreidenweis, 2007); e.(Ren et al., 2018))

<table><caption>**Table S4.** Correlation of major ions in particulate matter for summer and winter</caption><thead><tr><th colspan="5">Summer</th><th colspan="5">Winter</th></tr><tr><th></th><th>SO<sub>4</sub><sup>2-</sup></th><th>NO<sub>3</sub><sup>-</sup></th><th>Cl<sup>-</sup></th><th>OM</th><th></th><th>SO<sub>4</sub><sup>2-</sup></th><th>NO<sub>3</sub><sup>-</sup></th><th>Cl<sup>-</sup></th><th>OM</th></tr></thead><tbody><tr><th>SO<sub>4</sub><sup>2-</sup></th><td>1</td><td></td><td></td><td></td><th>SO<sub>4</sub><sup>2-</sup></th><td>1</td><td></td><td></td><td></td></tr><tr><th>NO<sub>3</sub><sup>-</sup></th><td>0.62****</td><td>1</td><td></td><td></td><th>NO<sub>3</sub><sup>-</sup></th><td>0.90****</td><td>1</td><td></td><td></td></tr><tr><th>Cl<sup>-</sup></th><td>0.54****</td><td>0.75****</td><td>1</td><td></td><th>Cl<sup>-</sup></th><td>0.67****</td><td>0.82****</td><td>1</td><td></td></tr><tr><th>OM</th><td>ns</td><td>ns</td><td>ns</td><td>1</td><th>OM</th><td>0.50****</td><td>0.72****</td><td>0.91****</td><td>1</td></tr></tbody></table>

(****: P < 0.0001, ***: P < 0.001, **: P < 0.01, *: P < 0.05, ns: irrelevant)

<table><caption>**Table S5.** OC, EC concentrations and OC/EC ratio in Xi'an's atmospheric particles</caption><thead><tr><th>Type</th><th>OC</th><th>EC</th><th>OC/EC</th></tr></thead><tbody><tr><td>Summer Daytime</td><td>6.44± 1.76</td><td>1.74± 0.79</td><td>4.16± 1.32</td></tr><tr><td>Summer Nighttime</td><td>6.96± 3.04</td><td>2.16± 1.19</td><td>3.48± 1.16</td></tr><tr><td>Average</td><td>6.7± 2.46</td><td>1.97± 1.01</td><td>3.82± 1.08</td></tr><tr><td>Winter Daytime</td><td>15.91± 7.50</td><td>3.82± 1.86</td><td>5.09± 3.62</td></tr><tr><td>Winter Nighttime</td><td>15.72± 6.65</td><td>3.96± 1.50</td><td>4.03± 0.86</td></tr><tr><td>Average</td><td>15.82± 7.03</td><td>3.89± 1.72</td><td>4.56± 2.66</td></tr><tr><td>Clean</td><td>7.63± 4.24</td><td>2.12± 1.20</td><td>3.88± 1.25</td></tr><tr><td>Pollution</td><td>16.03± 7.72</td><td>3.85± 1.80</td><td>4.88± 3.30</td></tr><tr><td>Heavy Pollution</td><td>14.42± 5.90</td><td>3.85± 1.74</td><td>3.92± 0.92</td></tr></tbody></table>

<table><caption>**Table S6.** Correlation between NO<sub>3</sub><sup>-</sup>, OM, and κ closure deviations in summer</caption><thead><tr><th></th><th>NO<sub>3</sub><sup>-</sup></th><th>OM</th><th>0.20%</th><th>0.40%</th><th>0.60%</th><th>0.80%</th><th>1.00%</th></tr></thead><tbody><tr><th>NO<sub>3</sub><sup>-</sup></th><td>1</td><td>-0.883<sup>**</sup></td><td>0.709<sup>**</sup></td><td>0.700<sup>**</sup></td><td>0.712<sup>**</sup></td><td>0.717<sup>**</sup></td><td>0.712<sup>**</sup></td></tr><tr><th>OM</th><td>-0.883<sup>**</sup></td><td>1</td><td>-0.703<sup>**</sup></td><td>-0.716<sup>**</sup></td><td>-0.736<sup>**</sup></td><td>-0.742<sup>**</sup></td><td>-0.733<sup>**</sup></td></tr></tbody></table>

(κ closure deviations: κ<sub>ccn</sub>-κ<sub>chem</sub>; **: P < 0.01)

## References

Chu, W., Li, L., Li, H., Zhang, Y., Chen, Y., Zhi, G., Yang, X., Ji, Y., & Chai, F. (2023). Atmospheric Oxidation Capacity and Its Impact on the Secondary Inorganic Components of PM2.5 in Recent Years in Beijing: Enlightenment for PM<sub>2.5</sub> Pollution Control in the Future. Atmosphere, 14(8), 1252. https://www.mdpi.com/2073-4433/14/8/1252
Guo, J., Wang, H., Liu, S., & Wang, Z. (2023). Chemical Characteristics and Source Apportionment of PM<sub>2.5</sub> in Western Industrial Region of Jinan. Atmosphere, 14(5), 864. https://www.mdpi.com/2073-4433/14/5/864

Gysel, M., Crosier, J., Topping, D. O., Whitehead, J. D., Bower, K. N., Cubison, M. J., Williams, P. I., Flynn, M. J., McFiggans, G. B., & Coe, H. (2007). Closure study between chemical composition and hygroscopic growth of aerosol particles during TORCH2. *Atmos. Chem. Phys.*, 7(24), 6131-6144. https://doi.org/10.5194/acp-7-6131-2007

Jiang, Y., Ma, Y., Zheng, J., Ye, N., & Yuan, C. (2025). Characterization of size-resolved aerosol hygroscopicity and liquid water content in Nanjing of the Yangtze River Delta. *Journal of Environmental Sciences*, 151, 26-41. https://doi.org/https://doi.org/10.1016/j.jes.2024.03.035

Malek, K. A., Gohil, K., Al-Abadleh, H. A., & Asa-Awuku, A. A. (2022). Hygroscopicity of polycatechol and polyguaiacol secondary organic aerosol in sub- and supersaturated water vapor environments [10.1039/D1EA00063B]. *Environmental Science: Atmospheres*, 2(1), 24-33. https://doi.org/10.1039/D1EA00063B

Moore, R. H., Nenes, A., & Medina, J. (2010). Scanning Mobility CCN Analysis—A Method for Fast Measurements of Size-Resolved CCN Distributions and Activation Kinetics. *Aerosol Science and Technology*, 44, 861-871.

Nirmalkar, J., Jung, J., Han, S., Dong, Z., Xu, Z., Fu, P., & Pavulari, C. M. (2023). Chemistry of PM<sub>2.5</sub> in haze events in two East Asian cities during winter-spring 2019. *Atmospheric Environment*, 293, 119457. https://doi.org/10.1016/j.atmosenv.2022.119457

Peng, C., Malek, K. A., Rastogi, D., Zhang, Y., Wang, W., Ding, X., Asa-Awuku, A. A., Wang, X., & Tang, M. (2022). Hygroscopicity and cloud condensation nucleation activities of hydroxyalkylsulfonates. *Science of The Total Environment*, 830, 154767. https://doi.org/https://doi.org/10.1016/j.scitotenv.2022.154767

Petters, M. D., & Kreidenweis, S. M. (2007). A single parameter representation of hygroscopic growth and cloud condensation nucleus activity. *Atmos. Chem. Phys.*, 7(8), 1961-1971. https://doi.org/10.5194/acp-7-1961-2007

Padró, L. T., Tkacik, D., Lathem, T., Hennigan, C. J., Sullivan, A. P., Weber, R. J., Huey, L. G., & Nenes, A. (2010). Investigation of cloud condensation nuclei properties and droplet growth kinetics of the water-soluble aerosol fraction in Mexico City. *Journal of Geophysical Research: Atmospheres*, 115(D9). https://doi.org/https://doi.org/10.1029/2009JD013195

Padró, L. T., Moore, R. H., Zhang, X., Rastogi, N., Weber, R. J., & Nenes, A. (2012). Mixing state and compositional effects on CCN activity and droplet growth kinetics of size-resolved CCN in an urban environment. *Atmos. Chem. Phys.*, 12(21), 10239-10255. https://doi.org/10.5194/acp-12-10239-2012

Ray, A., Pandithurai, G., Mukherjee, S., Kumar, V. A., Hazra, A., Patil, R. D., & Waghmare, V. (2023). Seasonal variability in size-resolved hygroscopicity of sub-micron aerosols over the Western Ghats, India: Closure and parameterization. *Science of The Total Environment*, 869, 161753. https://doi.org/https://doi.org/10.1016/j.scitotenv.2023.161753

Ren, J., Zhang, F., Wang, Y., Collins, D., Fan, X., Jin, X., Xu, W., Sun, Y., Cribb, M., & Li, Z. (2018). Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing. *Atmos. Chem. Phys.*, 18(9), 6907-6921. https://doi.org/10.5194/acp-18-6907-2018

Rose, D., Gunthe, S. S., Mikhailov, E., Frank, G. P., Dusek, U., Andreae, M. O., & Pöschl, U. (2008). Calibration and measurement uncertainties of a continuous-flow cloud condensation nuclei counter (DMT-CCNC): CCN activation of ammonium sulfate and sodium chloride aerosol particles in theory and experiment. *Atmos. Chem. Phys.*, 8(5), 1153-1179. https://doi.org/10.5194/acp-8-1153-2008