The paper “ Challenges of parameterizing CCN due to changes in particle physicochemical properties : implications from observations at a suburban site in China ”

This discussion paper is/has been under review for the journal Atmospheric Chemistry and Physics (ACP). Please refer to the corresponding final paper in ACP if available. Abstract This study is concerned with the challenges of parameterizing cloud condensation nu-clei (CCN) when changes in particle physicochemical properties occur, based on field measurements made at two distinct locations in China. The CCN nucleation efficiency of aerosols produced by local biomass burning was low. This is because the particles 5 were freshly emitted with low oxidation level organics and thus are less hygroscopic. The CCN activation efficiency was enhanced significantly when the site was under the influence of air transported from far away, during which aerosol properties changed with more hygroscopic secondary organic and inorganic components. The influence of the variation in particle number size distribution (PSD) on estimating CCN number con

were freshly emitted with low oxidation level organics and thus are less hygroscopic.The CCN activation efficiency was enhanced significantly when the site was under the influence of air transported from far away, during which aerosol properties changed with more hygroscopic secondary organic and inorganic components.The influence of the variation in particle number size distribution (PSD) on estimating CCN number concentrations (N CCN ) was examined, showing poor correlation (slope = 0.8, R 2 = 0.35) of predicted and measured N CCN .While the PSD is found to play a dominant role in predicting (N CCN ), a strong dependence of N CCN on the mass fraction of organics (x org ) was also noted.N CCN was underestimated by 52 and 13 % at supersaturation levels of 0.13 and 0.76 %, respectively, when x org = 66 %.N CCN was slightly overestimated, or in good agreement, with observations when x org was reduced to 35 % (R 2 = 0.94).The applicability of the CCN activation spectrum obtained at Xinzhou to the Xianghe site, about 400 km to the northeast of Xinzhou, was investigated, with the goal of further examining the sensitivity of CCN to aerosol type.Overall, the mean CCN efficiency spectrum derived from Xinzhou performs well at Xianghe when the supersaturation levels are > 0.2 % (overestimation of 2-4 %).However, N CCN was overestimated by ∼ 20 % at supersaturation levels of < 0.1 %.This suggests that the overestimation is mainly due to the smaller proportion of aged and oxidized organic aerosols present at Xianghe compared with Xinzhou.

Introduction
To reduce the uncertainty of aerosol indirect effects on the radiative balance of the atmosphere, it is important to gain a good knowledge of the ability of aerosol particles to form cloud condensation nuclei (CCN) at the typical supersaturations found in the atmosphere.The CCN activity of aerosol particles is governed by the Köhler theory (Köhler, 1936).This theory determines CCN from aerosol particle size and physicochemical properties, which include the molar volume, activity coefficient, and effect on surface tension (McFiggans et al., 2006).These properties, however, are difficult to measure.
Researchers have proposed single-parameter models to parameterize the CCN activation and hygroscopicity of multi-component aerosols (Hudson and Da, 1996;Rissler et al., 2006;Petters and Kreidenweis, 2007;Wex et al., 2007).Field experiments have been conducted with the aim of better characterizing particle physicochemical parameters influencing cloud CCN activation.Due to the large spatial variability of aerosol types and compositions, the CCN activation efficiency varies greatly over different regions.CCN number concentrations (N CCN ) can often be better predicted in the background atmosphere (Chuang et al., 2000;Dusek et al., 2003;VanReken et al., 2003;Rissler et al., 2004;Gasparini et al., 2006;Stroud et al., 2007;Bougiatioti et al., 2009).
The largest errors are associated with urban emissions (Sotiropoulou et al., 2007).This is likely due to the organics component of aerosol particles, which have the largest uncertainty and are not fully understood.Biomass burning aerosols and secondary organics formed from the oxidation of common biogenic emissions are often more difficult to activate (Mircea et al., 2005;VanReken et al., 2005;Lee et al., 2006;Varutbangkul et al., 2006;Clarke et al., 2007;Rose et al., 2010;Engelhart et al., 2012;Paramonov et al., 2013;Lathem et al., 2013;Mei et al., 2013b;Zhang et al., 2014).Particles with aged/oxidized secondary organic components (e.g., organic acids) have been shown to be more hygroscopic (Raymond and Pandis, 2002;Hartz et al., 2006;Bougiatioti et al., 2011), but still much less hygroscopic than inorganic species.The sensitivity of Figures

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Full predicted N CCN to organics have been examined in a number of recent studies (Wang et al., 2008;Reutter et al., 2009;Ervens et al., 2010;Kammermann et al., 2010;Ward et al., 2010;Zhang et al., 2012;Mei et al., 2013a).It is widely known that the predicted N CCN is sensitive to changes in organics due to the latter's complex components.The amounts and hygroscopicity parameter of organics (κ org ) vary substantially and lead to significant biases in predicting CCN concentrations and aerosol indirect forcing (Sotiropoulou et al., 2007;Hings et al., 2008;Liu and Wang, 2010).Therefore, field investigations regarding CCN activity and organics impacts, especially in heavily polluted regions, are pivotal to better parameterize CCN in climate models.Northern China is a fast developing and densely populated region of China, where aerosol loading is high (Li et al., 2007(Li et al., , 2011)), the particle composition is complex, and severe haze pollution episodes are common (Guo et al., 2014).In recent years, CCN measurements have been collected during field campaigns carried out in the region (Wiedensohler et al., 2009;Gunthe et al., 2011;Yue et al., 2011;Deng et al., 2011Deng et al., , 2013;;Zhang et al., 2014).These studies have presented different perspectives on the influence of particle size and composition on CCN activity.instrumentation and data used in the study are described in Sect. 2. The method for calculating the hygroscopicity parameter (κ chem ) is introduced in Sect.3. The sensitivity of aerosol particle size distribution and the mass fraction of organics (x org ) to CCN activity, as well as the ability of the CCN efficiency spectrum observed at the Xinzhou site to represent CCN at the Xianghe site, are presented and discussed in Sect. 4.
Conclusions from the study are given in Sect. 5.

Measurements and data
An intensive observation period field campaign similar to the Aerosol-CCN-Cloud Closure Experiment (Zhang et al., 2014) Sitting between two mountains (Taihang Mountain to the east and Lüliang Mountain to the west), the site experiences frequent pollution plumes from Xinzhou City to the north and from Taiyuan City to the south, the capital of Shanxi Province.Air masses from the northeast and southwest dominate over the site during summer.Depending on the wind direction, measurements at the Xinzhou site can detect pollutants of urban, rural, or mixed origins, including both fresh biomass burning emissions and aged aerosols from advection.

Instruments and measurements
During the field campaign, a Scanning Mobility Particle Sizer (SMPS), combined with a Droplet Measurement Technologies-Cloud Condensation Nuclei Counter (DMT-CCNc) (Lance et al., 2006), was used for size-resolved CCN measurements as well as Figures

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Full size distribution measurements.The measured aerosol PSD is within the size range of 14-600 nm.Aerosol chemical composition was measured simultaneously by an Aerodyne Aerosol Chemical Speciation Monitor (ACSM) (Sun et al., 2012).The aerosol inlet for the size distribution measurements was equipped with a TSI Environmental Sampling System (Model 3031200), which consists of a sharp-cut PM 1 cyclone and a bundled nafion dryer.The size-resolved CCN efficiency spectra were measured by coupling the DMT-CCN C used with the SMPS (Rose et al., 2008).In this step, the particles are rapidly dried with RH < 30 % upon entering the Differential Mobility Analyzer (DMA).Thus, size selection is effectively performed under dry conditions.Relative deviations in particle diameter should be < 1 % except for potential kinetic limitations (Mikhailov et al., 2009).The sample flow exiting the DMA was split into two parts: 0.3 L min −1 for the CPC and 0.5 L min −1 for the CCN counter (CCN C ).The DMA, controlled by TSI-AIM software, scanned one size distribution every five minutes.The CCN C was operated at a total flow rate of 0.5 L min −1 with a sheath-to-aerosol flow ratio of 10.The inlet RH for CCNc was < 30 %.During the field campaign, the mean sample temperature and pressure measured by CCN C sensors was (24.3±1.4)• C and (898.4 ± 11.7) hPa.The supersaturations levels of CCN C were calibrated with ammonium sulfate before and after the field campaign, following the procedures outlined in Rose et al. (2008).During each CCN measurement cycle, calibrated effective supersaturations were set at 0.075, 0.13, 0.17, 0.39, and 0.75 %.The overall error (1σ) for the supersaturation levels was estimated to be < 3.5 %.The completion of a full measurement cycle took 50 min (10 min for each supersaturation level).
The measurement of non-refractory submicron aerosol species including organics, sulfate, nitrate, ammonium, and chloride were made with an ACSM.During the field campaign, ambient aerosols were drawn inside through a 1/2 inch (outer diameter) desiccant.The residence time in the sampling tube was ∼ 5 s.The ACSM was operated at a time resolution of ∼ 15 min with a scan rate of mass spectrometer at 500 ms amu −1 from m/z 10 to 150.Regarding the calibration of the ACSM, mono-dispersed, sizeselected 300 nm ammonium nitrate particles within a range of concentrations were sampled into both the ACSM and a condensation particle counter (CPC).The ionization efficiency (IE) was then determined by comparing the response factors of the ACSM to the mass calculated with known particle size and number concentrations from the CPC.More detailed descriptions of the operation and calibration of the ACSM are given in Sun et al. (2012) and Ng et al. (2011).
In addition to the ACSM, the black carbon (BC) in PM 2.5 was simultaneously measured at a time resolution of 5 min by a seven-wavelength aethalometer (Model AE31, Magee Scientific Corporation).The campaign averaged mass concentration of BC is ∼ 2.5 µg m −3 .During the experiment, the campaign area was generally hot and dry, with an average temperature of 21.6 • C and an average ambient RH of 69.5 %.

Data
The raw CCN data were first filtered according to instrument recorded parameters (e.g., temperature and flow).For example, if the relative difference between the actual and preset sample flows was larger than 4 %, the data are flagged as invalid.The data is also excluded if the temperature stability was zero.These flagged data are not used for further analysis.A multiple charge correction and transfer function (Deng et al., 2011) is applied to each aerosol number (CN) size distribution spectrum as well as to the CCN efficiency spectrum.The CCN activation ratio (AR) is the ratio of N CCN to CN concentration (N CN ).Size-resolved CCN and PSD data, measured with a DMT-CCNc and a SMPS (with a particle size range of 10-700 nm) on 7-21 July 2013 at Xianghe (Zhang et al., 2014), are used in this study for comparisons with CCN activity at the Xinzhou site.To estimate N CCN at the Xianghe site, CCN size distributions were calculated by multiplying the fitted campaign-averaged CCN efficiency spectrum obtained using the three-parameter Figures

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Full cumulative distribution function method (Rose et al., 2008) with the CN size distribution.The total N CCN was then obtained by integrating the size-resolved N CCN over the whole size range.Aerosol mass concentrations were processed using the ACSM standard data analysis software (version 1.5.3.0).Detailed procedures for the data analysis have been described by Ng et al. (2011) and Sun et al. (2012).

Derivation of κ chem
As proposed by Petters and Kreidenweis (2007), κ can be used to describe the ability of particles to absorb water vapor and act as CCN.Based on Köhler theory (Köhler, 1936), κ relates the dry diameter of aerosol particles to the critical water vapor supersaturations.According to measurements and thermodynamic models, κ is zero for insoluble materials like soot or mineral dust.However, their hygroscopicity changes due to the aging process, so the κ value then becomes > 0. The magnitude of κ is ∼ 0.1 for secondary organic aerosols, ∼ 0.6 for ammonium sulfate and nitrate, 0.95-1 for sea salt (Niedermeier et al., 2008), and 1.28 for sodium chloride aerosols.In this study, we calculate κ chem based on bulk chemical composition observations made during the field campaign.The method to derive κ chem is described below.
For a given internal mixture, κ chem can be predicted using a simple mixing rule based on chemical volume fractions, ε i (Petters and Kreidenweis, 2007): where κ i and ε i are the hygroscopicity parameter and volume fraction, respectively, for the individual (dry) components in the mixture and i is the number of components in the mixture.We derived ε i from the particle chemical composition measured by the ACSM.
Measurements from the ACSM show that the composition of submicron particles was dominated by organics, followed by sulfate, ammonium, and nitrate.The contribution of chloride was negligible (volume fraction of about < 2 %).The analysis of the anion Introduction

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Full 4 over the relevant size range.For refractory species, BC represented a negligible fraction of the total submicron aerosol volume (< 3 %).Sea salt and dust are usually coarse mode particles with particle sizes > 1 µm (Whitby, 1978).The contribution of such types of aerosols is thus expected to be negligible for sizes < 1000 nm.
Therefore, the submicron particles measured by the ACSM mainly consisted of organics, (NH 4 ) 2 SO 4 , and NH 4 NO 3 .The particle hygroscopicity is thus the volume average of the three participating species: The values of κ are 0.67 and 0.61 for (NH 4 ) 2 SO 4 and NH 4 NO 3 , respectively, which have been derived from previous laboratory experiments (Petters and Kreidenweis, 2007).The following linear function derived by Mei et al. (2013) was used to estimate κ Org in our study: κ Org = 2.10 × f 44 − 0.11, where f 44 is the fraction of m/z 44 in total organics.The mean value of κ Org during the field campaign is 0.115 ± 0.019.The volume fractions of species were derived from mass concentrations and densities of participating species.The densities of (NH 4 ) 2 SO 4 and NH 4 NO 3 are 1770 and 1720 kg m −3 , respectively.The density of organics is 1200 kg m −3 (Turpin et al., 2001).

CCN efficiency spectra
During the field campaign at the Xinzhou site, ∼ 790 size-resolved CCN efficiency spectra at five supersaturation levels ranging from 0.075 to 0.76 % were measured.(bottom panel) during their respective observation periods.Significant differences in size-resolved CCN efficiency spectra at the two sites are seen.Aerosol particles at Xinzhou activate more efficiently (higher values of AR) at a given particle diameter (D p ) for the same supersaturation level.In the other words, a larger D p was required to reach the same activation efficiency at Xianghe.This suggests that aerosol properties at each site differ.
The slope of AR with respect to diameters near D p when AR = 50 % (defined here as the cut-off diameter, D cut ) provides information about the heterogeneity of the composition for size-resolved particles.For an ideal case when all CCN-active particles have the same composition and size, a steep change in AR from 0 to 1 would be observed as D p reached D cut .A gradual increase in size-resolved AR with D p suggests that aerosol particles have different hygroscopicities.The steeper slopes of AR around D cut observed at Xinzhou suggest that the particle composition was less heterogeneous with more hygroscopicity than particles at the Xianghe site.This can be partially explained by the magnitudes of the mean κ chem at the two sites (0.42 at Xinzhou and 0.38 at Xianghe).The f 44 is greater at Xinzhou than at Xianghe.The m/z 44 signal is mostly due to acids (Takegawa et al., 2007;Duplissy et al., 2011) or acid-derived species, such as esters.f 44 is closely related to the organic oxidation level (Aiken et al., 2008).Oxidized/aged acids are generally more hygroscopic and easily activated.Moreover, the primary inorganic particles at the Xinzhou site are sulfates, with a mass fraction that is two times greater than that measured at Xianghe.Therefore, particles at the Xinzhou site consist of more hygroscopic sulfate-dominant inorganics and aged/oxidized secondary organics and can thus be more efficiently activated at a given D p , as shown in Fig. 1.

Air mass influences on aerosol activity: a case study
Because air mass back trajectories combined with ambient air measurements can be used for analyzing large-scale air pollutant transport and source identification at a receptor site (Stohl, 1996;Rousseau et al., 2004), in this study, we calculated five-day 16151 Introduction

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Full (120 h) back trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Hess, 1998) with National Centers for Environmental Prediction (NCEP) reanalysis data.TrajStat software (Wang et al., 2009) has been used to calculate trajectories.The arrival height of the trajectories at the Xinzhou site was at the surface.
In Case 1, air trajectories (red line in Fig. 3) originated from the southwest and passed through northern Shaanxi Province and northwestern Shanxi Province, then rounded back to the site from the north/northeast.So, aerosols in this case are closely associated with air parcels north/northeast of the site.The trajectories were very short, suggesting that the air flow was slow during the observational period.Under these circumstances, aerosol loading would be largely impacted by local sources around the site.
A high mass fraction of organics (> 60 %) with low f 44 (∼ 10 %) and κ chem (< 0.3) values was measured during the observational period.Furthermore, the PSD showed one peak mode with D p = 56 nm and a high N CN (∼ 1.7 × 10 4 cm −3 ), but low mass concentration of PM 1 (28.36 µg m −3 ).This suggests that particles were mainly composed of freshly emitted biomass burning primary organic aerosols.This type of aerosol usually originates from local emissions and is less hygroscopic with a single peak mode primarily composed of fine particles (Whitby, 1978;Hussein et al., 2005).These aerosols cannot activate efficiently.The maximum size-resolved AR shown in the top right panel of Fig. 2 is less than 0.6 at all supersaturation levels for particles with D p > 300 nm.This illustrates the impact of local primary particle sources (e.g., emissions generated during cooking time) on CCN activation.Introduction

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Full In Case 2 (blue line in Fig. 3), air parcels moved rapidly from the west to the site.The site should then be influenced by the large-scale transportation of air masses.For this case, aerosols contain a small amount of organics (< 30 %), but have high f 44 (∼ 14 %) and κ chem values (∼ 0.5).The PSD showed a double peak mode with an N CN of ∼ 1.3 × 10 4 cm −3 and a relatively high mass concentration of PM 1 (81.45 µg m −3 ).
The double peak mode suggests that aerosols in this case are a mixture of aerosols from local sources and from other regions (Whitby, 1978;Dal Maso et al., 2007).Because aerosols are aged and oxidized during long-distance transport, these particles are usually composed of secondary organic and inorganic components with more hygroscopicity (Weber et al., 1999;Verver et al., 2000).These aerosols can activate efficiently.The maximum AR is close to 1 and the slopes of AR around D cut are steep at all supersaturation levels (middle right panel of Fig. 2).This CCN efficiency spectrum is similar to the ideal spectrum of pure ammonium sulfate.
In Case 3 (green line in Fig. 3), air parcels travelled from the northwest to the site.Air masses arriving at the site in this case had passed over densely populated regions with more heavy pollution.A gradual increase in size-resolved AR with D p is seen (bottom right panel of Fig. 2).This is attributed to the diversity in aerosol hygroscopicity because of the complex nature of the chemical composition of aerosol particles.

Estimation of CCN
Precise quantification of N CCN is crucial for understanding aerosol indirect effects and characterizing these effects in models.A CCN closure study is useful to examine the controlling physical and chemical factors and to help verify experimental results.N CCN is usually derived from measured aerosol properties, such as particle number size distribution and composition or hygroscopicity based on the Köhler theory.The closure between measured and estimated N CCN is often achieved under background atmospheric conditions without heavy pollution (Stroud et al., 2007;Bougiatioti et al., 2009).Achieving such closure under heavily polluted conditions is more challenging.In this Introduction

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Full section, we investigate the influences of particle physical and chemical properties on N CCN prediction with a focus on particles containing large amounts of organics.

Can we use N CN to parameterize N CCN ?
Different parameterizations for estimating CCN have been proposed.A parameterization with very few constants that uses only CCN supersaturation spectra is a simple way to predict CCN number concentrations (Twomey, 1959;Ji and Shaw, 1998;Mircea et al., 2005).These schemes assume a uniform aerosol chemical composition and do not take into account any variation in CCN loading.Bulk ARs have also been used to parameterize CCN number concentrations (Pruppacher and Klett, 1997).However, this method has its limitations because of the spatial variation in AR for maritime and continental aerosols.These variations are primarily attributed to variations in aerosol particle size, i.e., the shape of the PSD as well as particle composition.Empirical formulas and bulk CCN ARs cannot represent CCN spectra in models focused on the North China Plain where there is heavy pollution (Deng et al., 2013).The study by Zhang et al. (2014) demonstrated that the relationship between bulk ARs and N CN is complex under polluted conditions and is heavily dependent on the physicochemical properties of atmospheric aerosols.
Figure 4 shows N CN as a function of N CCN for different supersaturation levels at the Xinzhou and Xianghe sites.As expected, there is a moderate correlation at high supersaturation levels (e.g., R 2 = 0.51 at Xinzhou and R 2 = 0.85 at Xianghe at a supersaturation level of 0.8 %) and a poor correlation at low supersaturation levels.The negative correlation at low supersaturation levels suggests that CCN spectra are not well represented at low supersaturation levels.

Influence of the PSD on N CCN estimation
To investigate the influence of the variation in size distribution on N CCN , the measured CCN efficiency spectrum is multiplied by the mean measured CN size distribution, Introduction

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Full which yields the CCN size distribution.This is then integrated over the whole size range (14-600 nm) to estimate N CCN .Figure 5 shows estimated N CCN as a function of observed N CCN .The correlation is poor at all supersaturation levels, with mean slope of 0.80 and R 2 of 0.35.There is little dependency of changes in predicted N CCN at a given supersaturation level to changes in observed N CCN , suggesting the significant influence of the variation in PSD on estimating N CCN .

Impact of x org on N CCN
For the purpose of examining the sensitivity of calculated N CCN to x org , N CCN at three supersaturation levels (0.13, 0.17, and 0.76 %) and three values of x org (35, 52, and 66 %) were computed from CCN efficiency spectra and measured dry particle size distributions.Calculated N CCN were more sensitive to x org at lower supersaturation levels (Fig. 6a and b).At the supersaturation level of 0.13 %, the predicted N CCN was underestimated by 52 % when x org = 66 %, while N CCN was slightly overestimated (∼ 3 %) when x org = 35 %.At the supersaturation level of 0.76 % (Fig. 6c), the predicted N CCN was underestimated by 13 % when x org = 66 %.A good agreement is seen when x org = 35 % (R 2 = 0.94).This is likely because a large fraction of particles was already CCN-active.Also, particle composition has relatively less influence on CCN activation at high supersaturation levels (Twohy and Anderson, 2008).The sensitivity of organics to N CCN increased with increasing x org (Fig. 7).A nearzero bias in the estimate of N CCN is seen when x org = ∼ 35 %.This is because this optimal x org is closest in value to the average x org measured during the campaign.If x org is less than the optimal x org , N CCN is overestimated, and vice versa.The bias is more negative as x org increases, suggesting that N CCN is increasingly underestimated, especially at low supersaturations.
When x org is less than 30 %, the overall hygroscopicity of internally mixed particles is dominated by inorganic species such as sulfate and nitrate, which are more hygroscopic than organic compounds with an overall κ chem of 0.46 (κ org = 0.19).As a result, Introduction

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Full a larger fraction of particles can be activated.Calculated N CCN would then be greater than measured N CCN .If x org is greater than 60 %, organics will dominate the overall particle hygroscopicity.Particles with a large x org are less hygroscopic and thus are difficult to activate, leading to an overestimation of N CCN .Mei et al. (2013a) observed that N CCN at an urban site was underestimated by ∼ 40 % when 60 % < x org < 68 % at a given supersaturation level.This underestimation is less than that observed in this study, i.e., an underestimation in N CCN of 52 % when x org = 66 %.This may be because the measurements described by Mei et al. (2013a) were made in an urban area (∼ 60 km downwind of Sacramento, California) where hydrophobic particles would quickly become internal mixtures and hydrophilic by condensation of secondary hygroscopic species (Riemer et al., 2004;Moffet and Prather, 2009;Mei et al., 2013b).The aerosol composition in this case would become increasingly more homogeneous as particles age than those particles observed in suburban areas like Xinzhou.The discrepancy between our estimation and the result reported by Mei et al. (2013a) indicates that increasing the proportion of secondary organics may decrease the uncertainties in predicting N CCN and would lead to a more accurate estimation of N CCN .

Applicability of CCN efficiency spectra
As a means of testing the applicability of the CCN activation spectra, campaign mean CCN efficiency spectra observed at the Xinzhou site is used to estimate N CCN at the Xinzhou and Xianghe sites, which helps to further examine the sensitivity of CCN to aerosol type.Data from the two sites were measured during the warm season so that the effect of temporal variations in aerosols on CCN levels is reduced.Figure 8 shows estimated N CCN as a function of measured N CCN for different supersaturation levels at the two sites.N CCN at Xinzhou was underestimated by 4-5 % at supersaturation levels of 0.39 and 0.76 %, and was slightly overestimated (∼ 2 %) at Xianghe for the same supersaturation levels.Good agreement is seen at the 0.39 and 0.76 % supersaturation levels for data from both sites (R 2 > 0.92).N CCN at Xinzhou was underestimated by ∼ 7 % at supersaturation levels < 0.1 % (R 2 = 0.87).At Xianghe, however, N CCN was overestimated by 19-23 % at supersaturation levels < 0.1 % although the correlation between calculated and measured N CCN was good.Because size-resolved CCN efficiency spectra were applied here, excluding the impact of particle size, the influence of chemical composition on CCN activation can be investigated.
The poor estimates of CCN at low supersaturation levels could be attributed to the high sensitivity of N CCN to chemical composition.Because the mass fractions of inorganics and organics measured at the two sites are similar (Fig. 1) and the hygroscopicity for inorganic components is fixed, this overestimation is attributed to the smaller proportion of aged and oxidized organic aerosols at Xianghe compared with aerosols at Xinzhou (f 44 = 17 and 11 % at Xinzhou and Xianghe, respectively).

Summary and conclusions
In this study, we have investigated the complexity of parameterizing CCN when changes in particle physicochemical properties occur based on field measurements obtained from 22 July to 26 August of 2014 in the suburb of Xinzhou, China.Five-day back trajectories combined with measurements were analyzed to examine air mass influences on CCN activity.CCN efficiency was largely reduced by local primary biomass burning events during which high N CN (∼ 1.7 × 10 4 cm −3 , PM 1 = 28.36 µg m −3 ) and high x org (> 60 %) were measured.f 44 was equal to ∼ 10 % and κ chem < 0.3.The PSD showed a single peak mode at D p = 56 nm.Aerosol particles were efficiently activated when the site was influenced by regional air masses, but less efficiently activated when aerosols were mainly composed of organics.Figures

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Full The relationship between N CN and N CCN was generally poor.Large errors would arise if using the former to estimate the latter, especially under low supersaturation conditions.The PSD was found to play a dominant role in N CCN , which also showed a strong dependence on x org .This dependence weakens as the supersaturation level increases.N CCN was underestimated by 52 and 13 % at supersaturation levels of 0.13 and 0.76 %, respectively, when x org = 66 %.The agreement is better when x org is reduced to 35 % (R 2 = 0.94).
The applicability of the CCN efficiency spectrum measured at the Xinzhou site to the Xianghe site was examined and a good agreement was found when the supersaturation level was > 0.2 %.However, N CCN at the Xianghe site was overestimated by 19-23 % when the supersaturation level was < 0.1 %.Because of the similar mass fractions of inorganics and organics measured at the two sites, we conclude that this overestimation was mainly caused by the smaller proportion of aged and oxidized organic aerosols at Xianghe compared with aerosols at Xinzhou.  , 45, 6070-6077, 2011. Zhang, F., Li, Y., Li, Z., Sun, L., Li, R., Zhao, C., Wang, P., Sun, Y., Liu, X., Li, J., Li, P., Ren, G., and  Full Discussion Paper | Discussion Paper | Discussion Paper | For example, Deng et al. (2013) evaluated various schemes for CCN parameterization and recommended that the particle number size distribution (PSD) together with inferred mean sizeresolved activation ratios can be used to predict CCN number concentrations without considering the impact of particle composition.However, Zhang et al. (2014) demonstrated that the 30-40 % uncertainties in N CCN are mainly associated with changes in particle composition.None of the above-mentioned studies have investigated the impact of organics on estimating N CCN in Northern China.Zhang et al. (2012) noted a more significant influence of organics on CCN activity, but the campaign average mass fraction of organics in their study was < 20 %.The aim of this paper is to examine the sensitivity of changes in aerosol physicochemical properties (especially aerosols containing large amounts of organics) to CCN activity, and to see how much uncertainty is incurred by applying the CCN efficiency spectra measured at one site to another site in a heavily polluted region.The Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | stainless steel tube at a flow rate of ∼ 3 L min −1 , of which ∼ 84 cc min −1 was subsampled into the ACSM.An URG cyclone (Model: URG-2000-30ED) was also positioned in front of the sampling inlet to remove coarse particles with a cut-off size of 2.5 mm.Before sampling into the ACSM, aerosol particles were dried using a silica gel Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | and cation balance suggests that anionic species (NO − essentially neutralized by NH + Figure 1 shows campaign averaged spectra of the measured CCN efficiency at Xinzhou for supersaturation levels of 0.075, 0.13, 0.17, 0.39, and 0.76 %.The observed CCN efficiency at the Xianghe site is also shown.The right panels show the mass concentration fraction of particle chemical compositions at Xinzhou (top panel) and Xianghe Introduction Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | First, mean CCN efficiency spectra derived from size-resolved CCN measurements made from 22 July to 26 August 2014 at Xinzhou (corresponding to spectra in Fig. 1) is multiplied by dry CN size distributions measured from 22 July to 26 August 2014 at the Xinzhou site and from 7-22 July 2013 at the Xianghe site.This generates CCN size distributions at the two sites.They are then integrated over the whole size range (14-600 and 10-700 nm at the Xinzhou and Xianghe sites, respectively) to estimate total CCN concentrations.Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Figure 1 .Figure 3 .Figure 4 .Figure 5 .Figure 6 .
Figure 1.Mean CCN efficiency spectra at the Xinzhou site (black lines with asterisks) measured from 22 July-26 August 2014 and at the Xianghe site (grey lines with circles) site measured from 7-21 July 2013 for different supersaturation levels.Error bars representing one standard deviation are shown.Right panels show particle chemical composition in terms of mass concentration fractions at Xinzhou (top panel) and Xianghe (bottom panel) during their respective observation periods.Note that the preset supersaturation levels were 0.07, 0.1, 0.2, 0.4 and 0.8 % at both sites, but effective supersaturation levels were slightly different after calibration.

Figure 7 .Figure 8 .
Figure 7. Biases (estimated minus observed N CCN ) as a function of mass fraction of organics (x org ) at different supersaturation levels.
Fan, T.: Aerosol hygroscopicity and cloud condensation nuclei activity during the AC 3 Exp campaign: implications for cloud condensation nuclei parameterization, Atmos.Chem.