Thermodynamic properties of nanoparticles during new particle formation events in the atmosphere of North China Plain

Atmospheric Research 188 (2017) 55–63

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Thermodynamic properties of nanoparticles during new particle formation events in the atmosphere of North China Plain Z.J. Wu a,⁎, N. Ma b, J. Größ b, S. Kecorius b, K.D. Lu a, D.J. Shang a, Y. Wang a, Y.S. Wu a, L.M. Zeng a, M. Hu a,⁎, A. Wiedensohler b, Y.H. Zhang a a b

State Key Joint Laboratory of Environmental Simulation and Pollution Control, College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China Leibniz Institute for Tropospheric Research, 04318 Leipzig, Germany

a r t i c l e

i n f o

Article history: Received 14 August 2016 Received in revised form 3 January 2017 Accepted 18 January 2017 Available online 19 January 2017 Keywords: New particle formation Particle hygroscopicity Air pollution CCN Particle formation and growth Particle volatility

a b s t r a c t To better understand the sources, formation, and the transport of air pollutants over North China Plain (NCP), a four-week intensive campaign during summertime in 2014 was conducted in a central NCP rural site. In this study, particle hygroscopicity and volatility measurements were focused to characterize the thermodynamic properties of nanoparticles and gain insight into chemical composition of nanoparticles during the new particle formation (NPF) events. The water-soluble fractions of 30 and 50 nm newly formed particles were respectively 0.64 ± 0.06 and 0.61 ± 0.06, indicating that the water-soluble chemical compounds, most likely ammonium sulfate, dominated the condensational growth of newly formed particles over the NCP. Due to containing higher water-soluble fraction, nanoparticles can be activated as cloud condensation nuclei (CCN) at lower supersaturation in the atmosphere of NCP in contrast to cleaner environments, such as Melpitz (Central European background) and Hyytiälä (boreal forest) during the NPF events. Our observations showed that the NPF and subsequent growth significantly resulted in an enhancement in CCN number concentration. The ranges of enhancement factors of CCN number concentration for supersaturation (SS) = 0.2, 0.4, 0.8% were respectively 1.9–7.0, 2.7–8.4, and 3.6–10.1. After being heated to 300 °C, the shrink factors for 30 and 50 nm particles were respectively 0.35 and 0.38. This indicated that non-volatile compounds could be produced during the growth process of newly formed particles. © 2017 Published by Elsevier B.V.

1. Introduction Atmospheric new particle formation (NPF) and subsequent growth are of a great interest because it is an important source of cloud condensation nuclei (CCN) (Kazil et al., 2010; Kerminen et al., 2012; Laakso et al., 2012; Sotiropoulou et al., 2006; Spracklen et al., 2008; Wang and Penner, 2009; Wiedensohler et al., 2009). In addition, the newly formed particles may significantly deteriorate air quality via their rapid and long-term growth in the atmospheric environment with rich in gaseous precursors, like in a Mega-city, Beijing (Guo et al., 2014; Wiedensohler et al., 2009), Shanghai (Xiao et al., 2015), Nanjing (An et al., 2015), and suburban site of Yangtze River Delta of China (Qi et al., 2015). Freshly formed particles must grow tens of nanometers in order to serve as a CCN (Dusek et al., 2006; Kerminen et al., 2012). Therefore, it is important to understand the growth processes of newly formed particles for determining their roles in the atmosphere. Detection of the chemical composition of newly formed particles is a key factor for quantitatively understanding the particle formation and ⁎ Corresponding authors. E-mail addresses: [email protected] (Z.J. Wu), [email protected] (M. Hu).

http://dx.doi.org/10.1016/j.atmosres.2017.01.007 0169-8095/© 2017 Published by Elsevier B.V.

subsequent growth processes. The tandem differential mobility analyzer is often employed to measure the hygroscopicity and volatility of nanoparticles to provide indirect information on condensing vapor properties and chemical composition of newly formed particles (e.g. Ehn et al., 2007a; Hämeri et al., 2001; Ristovski et al., 2010). Over the past decades, the determination of particle hygroscopicity and volatility during the NPF events was deployed in various atmospheric environments, such as urban (Sakurai et al., 2005), rural (Wu et al., 2015), forest (Ehn et al., 2007a), and coastal areas (Väkevä et al., 2002). These observations show that the hygroscopicity and volatility of newly formed particles varied with the sampling atmospheres, indicating that condensed vapors driving the particle growth varied too. Up to now, no similar study was performed in the North China Plain (NCP). Such a scarcity of information on the nanoparticle thermodynamic properties during the NPF events in a polluted area has motivated the current study. The NCP, including the Beijing City, often suffers from severe air pollution, especially due to long-range transport from southerly directions under calm weather conditions (Wehner et al., 2008). To better understand the sources, formation, and the transport of air pollutants, an intensive atmospheric field campaign during summertime 2014 was

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conducted near the city Wangdu located at about 150 km southwest of Beijing. During the four-week intensive study, NPF events occurred N50% of the sampling days. These frequent NPF events gave us the opportunity to investigate not only the hygroscopicity and volatility of nanoparticles but also the changes in chemical composition of newly formed particles during the NPF events. In this study, we will characterize the thermodynamic properties of nanoparticles during the NPF events to gain some insights into the particle growth processes in a sulfur-rich environment and make comparisons with other environments. 2. Experiment

Table 1 The summary of instrumentation and parameters. Instruments

Parameters

High humidity tandem differential mobility analyzer (HH-TDMA) Volatility TDMA (V-TDMA) Twin differential scanning mobility particle sizer (TDSMPS) LIF Thermo Fisher Scientific

Particle hygroscopic growth at RH = 98% Particle volatility at 300 °C Particle number size distribution (3–800 nm) OH radical Sulfur dioxygen (SO2) concentration Meteorological parameters

Weather station

The field campaign was conducted at Wangdu County (38.666N, 115.210E), located in the central NCP, as displayed in Fig. 1. Wangdu County is to the southwest and 150 km away from Beijing. The measurement site is located in an ecological park in the rural area of Wangdu County. The surrounding area was wheat field without significant traffic and industry emissions. Table 1 summarizes the instruments and the parameters involved in this study. The core instruments are briefly described in the following sections. All aerosol instruments were installed in a temperature-controlled container (25 °C) and shared one sampling line with PM10 cutoff. The relative humidity (RH) of the sampling air was kept below 30% using an automatic silica gel dryer system (Tuch et al., 2009). 2.1. Particle hygroscopic growth measurements The particle hygroscopicity was investigated using a high humidity tandem differential mobility analyzer (HH-TDMA). The HH-TDMA was designed to measure the size-resolved particle hygroscopicity at RH up to 99% (Hennig et al., 2005). It consists of two DMAs connected in series with a humidity conditioning section between the two DMAs. The humidity conditioning section and the second DMA are placed in two different temperature controlled water baths to keep the fluctuation

of the temperatures within ±0.1 K in the HH-TDMA system. The final RH up to 98.5% is produced by decreasing the temperature of the second DMA relative to the humidity conditioning section. During the measurement, a constant temperature difference of approximately 2.5 K is maintained between the water bath for the second DMA (operated at 20 °C) and the water bath for the humidity conditioning section (operated at 22.5 °C). A dew point mirror (DPM) is located in the excess airline of the second DMA to measure the RH inside. Temperature and RH stability tests conducted by Hennig et al. (2005) showed that the absolute temperatures in HH-TDMA system can be maintained within ± 0.1 K with a stability of ±0.02 K, and the RH in the second DMA reaches an absolute accuracy of ±1.2% for 98%. More detailed information regarding the HH-TDMA system is provided by Hennig et al. (2005) and Liu et al. (2011). Hygroscopicity scans with 100 nm standard ammonium sulfate particles were performed every late afternoon to calibrate the stability of the relative humidity of 98% in the second DMA. The TDMAinv algorithm developed by Gysel et al. (2009) was used to process the HH-TDMA data and derive the probability density function of the hygroscopic growth factor at measured sizes. The hygroscopic growth factor (HGF) is defined as the ratio of the particle mobility diameter at a given RH (Dp(RH)) to the dry diameter (Dpdry): HGFðRHÞ ¼

Beijing


1=3 ∑ εi HGF3i : i

ð2Þ

Here, we assume that aerosol particles consist of two components including soluble and insoluble fractions (also refer to Ehn et al., 2007a; Swietlicki et al., 1999). The soluble fraction is assumed as ammonium sulfate. Then, ε of soluble fraction can be calculated by: εsoluble ¼

Fig. 1. The location of the measuring site (red square). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

ð1Þ

Based on the Zdanovskii-Stokes-Robinson (ZSR) method (Stokes and Robinson, 1966; Zdanovskii, 1948), the HGF of a mixture can be estimated from the HGF of pure components and their respective volume fractions, εi (Malm and Kreidenweis, 1997): HGFmixed ¼

Wangdu site

DpðRH Þ : Dpdry

HGF3measured −1 HGF3ðHN4Þ2 SO4 −1

;

ð3Þ

where HGFmeasured is the HGF of particle measured by HH-TDMA, and HGF(NH4)2SO4 is the HGF of pure (NH4)2SO4 particle with the same size. When calculating HGF(NH4)2SO4 in different diameters, the parameterizations for (NH4)2SO4 water activity developed by Potukuchi and Wexler (1995) and the density reported by Tang and Munkelwitz (1994) are used. The Kelvin term was considered in the calculation. One should keep in mind that the assumption of insoluble organic fraction may lead to overestimate the soluble fraction because atmospherically relevant secondary organics typically have a growth factor larger than 1 (e.g. Varutbangkul et al., 2006). This implies that in the presence of several classes of hygroscopic substances, ε derived from Eq. (3) is only an “equivalent” soluble fraction (i.e. assuming

Z.J. Wu et al. / Atmospheric Research 188 (2017) 55–63

ammonium sulfate as the only soluble substance). The uncertainty of the water soluble fraction originates from the uncertainty of the HGFmeasured. The HGFmeasured of our TDMA system is around 2.5% (Maßling et al., 2003). Considering the propagation of error, the uncertainty of the estimated water soluble fraction is 8.3%. 2.2. Particle volatility measurement Refractory aerosol fractions were measured using a volatility tandem differential mobility analyzer (V-TDMA) (Philippin et al., 2004). The system consists of two DMAs, two CPCs (TSI model 3010) and a heating cell. The V-TDMA technique can be concluded in three steps: quasi-monodisperse particles defined by the first DMA are alternating passed through 25 °C and heated (300 °C) columns, where volatile fraction is removed by thermal desorption. Afterwards, the residual particle number size distribution is measured by a combination of the second DMA and a CPC. The shrink factor (SF(T)) at a certain temperature is defined as: SFðTÞ ¼

DpðTÞ : DpðT0Þ

ð4Þ

where Dp(T0) is the original particle diameter at a room temperature (T = 25 °C) and Dp(T) is the diameter of particles being heated to temperature T. In this study, T is 300 °C. The volume fraction remaining (VFR), which is the ratio of particle volume at 25 °C (VT0) to that at temperature T (VT), can be estimated from SF(T): VFR ¼

VT ¼ SFðTÞ3 : VT0

ð5Þ

Similar to HH-TDMA data inversion, the TDMAinv algorithm developed by Gysel et al. (2009) was also used to invert the V-TDMA data. 2.3. Particle number size distribution A twin scanning mobility particle size spectrometer (TSMPS) (Birmili et al., 1999) (based on the TROPOS scanning design; Wiedensohler et al., 2012) was deployed to measure particle number size distributions from 3 to 800 nm mobility diameter with a time resolution of 10 min. The system consists of two Differential Mobility Analyzers (DMA, Hauke-type) and two Condensation Particle Counters (CPC, TSI model 3010 and TSI model 3025). Evaluation of particle number size distributions includes a multiple charge inversion, corrections for the CPC efficiency and diffusional losses in the DMA and all internal and external sampling lines according to the recommendations in Wiedensohler et al. (2012). 2.4. Calculation of formation rate (FR) and growth rate (GR) The particle formation rate and growth rate for the NPF events were calculated on the basis of particle number size distribution. The particle formation rate (J3–25 nm) can be expressed as (Dal Maso et al., 2005):

Observed particle GR can be expressed as: GR ¼

ΔDm ; Δt

J3−25

nm

2.5. Estimation of H2SO4 concentration and condensation sink A zero-dimensional chemical box model based on the Regional Atmospheric Chemical Mechanism 2 (Goliff et al., 2013) was utilized to calculate the H2SO4 concentration. The model run was constrained by the measured time series of O3, HONO, NO, NO2, CO, VOCs, photolysis frequencies, water vapor, ambient temperature, pressure, ambient aerosol surface area concentrations, and assumed deposition loss of model-generated species (mimicked by a lifetime of 24 h). The accommodation coefficient of H2SO4 (alpha) on aerosol particles was assumed to be unity. The uptake rate coefficient was accounted by free molecular collisions with alpha × w × A/4, of which w is the mean molecular speed of H2SO4 and A is the observed surface area concentrations. The condensational sink (CS) determines how rapidly molecules can condense onto the preexisting aerosols (Dal Maso et al., 2005). It is calculated from the particle number size distribution adjusted to ambient relative humidity. The particle number size distributions at ambient RH were calculated based on the measured dry-state number size distributions and the calculated size-dependent hygroscopic growth factors at ambient RH. Hygroscopic growth factor at ambient RH was determined using the parameterization of Laakso et al. (2004):   HGF Dp ; RH ¼


 RH γ 1− : 100

ð8Þ

Here, HGF is the hygroscopic growth factor of a particle of size Dp at a relative humidity RH. γ is a parameter derived by a least squares fit to the hygroscopicity data. Here, the hygroscopicity data mean that the hygroscopic growth factor for particles with diameter of 30, 50, 100, 150, 200, 250 nm at RH = 98% measured during the entire campaign. 2.6. Estimation of CCN number concentration The CCN number concentration (NCCN) at a certain supersaturation (SS) can be simply estimated as:   D n logDp dlogDp ; NCCN ¼ ∫ Dmax crit

ð9Þ

where n(logDp) is aerosol particle number size distribution, and Dmax and Dcrit are respectively the maximum diameter detected by TSMPS and the critical diameter at given supersaturation. According to Petters and Kreidenweis (2007), the critical diameter (Dcrit), at which 50% of the particles activate at a certain supersaturation (Sc) can be estimated:

ð6Þ

Nnuc is the number concentration of nucleation mode particles. Fcoag represents a loss of formed particles due to coagulation to the preexisting particle population. Fgrowth is the flux of particles out of the specified size range. The diameter range of Nnuc is from 3 to 25 nm for calculating particle formation rate in this study. The newly formed particles rarely grew beyond 25 nm before the formation event ended, and Fgrowth can be neglected.

ð7Þ

where Dm is a geometric mean diameter (GMD) of log-normal ultrafine particle mode, which has been fitted to the number size distribution (Heintzenberg, 1994). GR means evolution of the mean diameter within a time period Δt.

Dcrit ¼ dNnuc þ Fcoag þ Fgrowth : ¼ dt

57

A¼

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 4A3 3 2

27kln Sc

4σ s=a Mw ; RTρw

ð10Þ

ð11Þ

where κ is the single hygroscopicity parameter. σs/a is the droplet surface tension (assumed to be that of pure water, σs/a = 0.0728 N m−2), Mw the molecular weight of water, ρw the density of liquid water, R the universal gas constant, and T the absolute temperature. For estimating Dcrit, the κ values derived from CCN counter measurements should be taken. In our study, κ value in equation is replaced by κHHTDMA (see

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Eq. (10)) derived from HH-TDMA. In previous studies, the inconsistency between CCN and H-TDMA derived κ values has been reported (Cerully et al., 2011; Good et al., 2010; Irwin et al., 2010; Petters et al., 2009; Wex et al., 2009). Wex et al. (2009) and Petters et al. (2009) found a gap between the hygroscopicity parameter κ derived from hygroscopic growth factor (at RH = 99.6%) and activation measurements. The solution non-ideality and surface effects of organics may play key roles in such discrepancy. Therefore, the critical diameter predicted by κΗHTDMA at RH = 98% may be larger compare to that predicted by CCNc measurements. As a result, the CCN number concentration calculated by integrating the particle number size distribution from the critical diameter to the maximum diameter detected by TSMPS (800 nm, above which the particle number concentration is generally negligible) might be underestimated. κHHTDMA at RH = 98% is calculated from HGF measured by HHTDMA:

κHHTDMA


 1 0 A  B exp D C Pdry  HGF ¼ HGF3 −1 B −1C A; @ RH

ð12Þ

where DPdry and HGF are the initial dry particle diameter and the hygroscopic growth factor (HGF) at 98% RH measured by HH-TDMA, respectively.

were sunny mornings that experienced decreasing ambient RH and increasing temperature (see Fig. 2B) over NCP. We used the ratio ([H2SO4]/CS) of H2SO4 concentration to CS to define the competition between nucleation precursor (here represented as H2SO4) and the pre-existing particles. It is well-recognized that the H2SO4 is a key precursor for atmospheric nucleation (Kulmala and Kerminen, 2008). Pre-existing particles may suppress the new particle formation by scavenging not only the precursors but also the newly formed tiny particles (Kerminen et al., 2001). As shown in Fig. 2, the burst in 3–10 nm particles associated with an enhancement of [H2SO4]/CS. On the contrary, the NPF events did not occur when [H2SO4]/CS was at a low level. Table 2 summarizes the FRs and GRs for six NPF events, which were selected for further analysis. The ranges of FRs spanned from 5.8 to 68.3 cm−3 s−1, which was comparable with the new particle formation rates in the atmosphere of Beijing (J3–25nm = 3.3–81.4 cm−3 s−1) and in other cities summarized by Kulmala et al. (2004), such as in Atlanta (20–70 cm− 3 s− 1) and St. Louis (1–80 cm− 3 s− 1). The mean FRs in this study is higher than that in urban Shanghai during wintertime (mean J3 = 8.7 cm−3 s−1) (Xiao et al., 2015). The observation in suburban Nanjing in the western part of the YRD, eastern China showed that the formation rate of 6 nm particles (J6) in summer is 2.1 ± 1.4 cm− 3 s−1, which is much lower than our observations. The GRs spanned from 4.4 to 13 nm h−1 in our study. The upper bound is close to those observed in PRD (12.8 ± 4.4 nm h−1) (Qi et al., 2015) and in Shanghai (11.4 ± 9.7 nm h−1) (Xiao et al., 2015). The following analysis will focus on the hygroscopicity and volatility of nanoparticles.

3. Results and discussion 3.2. Hygroscopicity of nanoparticles 3.1. New particle formation events Fig. 2 displays the time series of particle number size distributions, meteorological parameters, 3–10 nm particle number concentration, and the ratio of sulfuric acid concentration [H2SO4] to condensation sink (CS) during the entire field campaign. Here, the criterion for discerning new particle formation events is the burst in nucleation mode (3–10 nm) particle number concentration and the subsequent growth of newly formed particles over several hours (Wu et al., 2007). As shown in Fig. 2(A), NPF events took place frequently, accounting for 54% of the sampling days. The typical weather conditions for NPF events

The particle hygroscopicity at RH = 98% was measured by the HHTDMA. In total, six NPF event days were chosen for deeper investigation considering available TDMA data and the duration of new particle growth. As examples, Fig. 3(B1 and B2) displays the growth factor probability density function (GF-PDF) for 30 nm and 50 nm particles on 9th and 10th, June. The water-soluble fraction for 30 nm and 50 nm particles is given in Fig. 4. Obvious changes in particle hygroscopicity were observed during the NPF events. At the onset of the NPF event marked with vertical gray dashed lines in Fig. 3, the HGF values of 30 nm and 50 nm particles enhanced gradually. For example, the HGF of 30 nm

Fig. 2. Time series of (A) particle number size distribution, (B) relative humidity (RH), ambient temperature, and sulfuric acid concentration and (C) number concentration of 3–10 nm particles and ratio of H2SO4 to CS (H2SO4/CS). The black arrow in panel (B) indicates the wet deposition.

Z.J. Wu et al. / Atmospheric Research 188 (2017) 55–63

59

Table 2 The summary of water-soluble fraction and non-volatility fraction. Here, FR, GR, HGR, εsoluble SF, VFR are formation rate, growth rate, hygroscopic growth factor, water-soluble fraction, shrink factor, and volume fraction remaining, respectively. Date

FR

GR

30 nm

50 nm εsoluble

HGF 9 June 10 June 13 June 23 June 27 June 28 June Mean

16.1 15.9 68.3 5.8 28.7 12.5 24.6 ± 22.7

4.6 7.4 13 8.8 4.4 5.7 7.3 ± 3.3

1.79 1.90 1.87 1.88 1.80 1.80 1.84

± ± ± ± ± ± ±

0.02 0.03 0.04 0.04 0.03 0.06 0.06

0.58 0.71 0.67 0.68 0.59 0.59 0.64

± ± ± ± ± ± ±

SF 0.02 0.04 0.05 0.05 0.03 0.07 0.06

0.37 0.32 0.35 0.36 0.36 0.35 0.35

VFR ± ± ± ± ± ± ±

particles increased step-wisely from 1.5 to 1.9 on June 9, 2014. Correspondingly, the water-soluble fraction of 30 nm particles increased from 0.29 to 0.71 during the course of the event, as shown in Fig. 4. The gradual enhancement of particle hygroscopicity could be caused by the condensable vapors that enable nucleation condensed onto the pre-existing particles, and further affecting their hygroscopicity before the newly formed particles grew to 30 and 50 nm. The GF-PDF in Fig. 3(B1 and B2) shows that the mixing state of nanoparticles changed as well during the NPF events. Before the starting of the events, 30 nm and 50 nm particles with diverse hygroscopicity were external mixed. After nucleation, the pre-existing nanoparticles possibly grew out of the 30–50 nm size ranges via condensational and coagulation growth in a condensable vapors-rich environment and were replaced by the newly formed particles. The GF-PDF of newly formed particles during the events showed a perfect single hygroscopic mode, meaning that the particles were internally well-mixed. The similar phenomenon were also observed during the NPF events in urban Atlanta by Sakurai et al. (2005).

0.01 0.02 0.02 0.04 0.00 0.02 0.02

0.05 0.03 0.04 0.05 0.05 0.04 0.04

εsoluble

HGF ± ± ± ± ± ± ±

0.00 0.00 0.01 0.02 0.00 0.01 0.01

2.01 2.10 2.03 1.93 1.95 1.95 1.99

± ± ± ± ± ± ±

0.04 0.01 0.06 0.12 0.05 0.06 0.08

0.61 0.72 0.64 0.60 0.55 0.55 0.61

± ± ± ± ± ± ±

0.04 0.02 0.06 0.11 0.05 0.06 0.06

SF

VFR

0.38 ± 0.01 0.36 ± 0.00

0.05 ± 0.00 0.05 ± 0.00

0.39 0.38 0.40 0.38

0.06 0.06 0.06 0.05

± ± ± ±

0.02 0.01 0.01 0.02

± ± ± ±

0.01 0.00 0.01 0.01

Table 2 summarizes the HGF and water-soluble fraction (εsoluble) of the newly formed particles when the GMD of nucleation mode particle reached 30 and 50 nm (also see the black circle in Fig. 3(A)). The particle diameters selected for TDMA measurements were not exactly the same as the target diameters (i.e. GMD = 30 and 50 nm). Hence, TDMA data for diameters within the range GMD ± 10 nm were assembled for representing the HGF of freshly formed particles having the diameters of 30 and 50 nm. On average, the HGFs of 30 and 50 nm newly formed particles are respectively 1.84 ± 0.06 and 1.99 ± 0.08 at RH = 98%. Correspondingly, the water-soluble fraction is 64 ± 6% and 61 ± 6%, respectively. Compared to 30 nm particles, the water-soluble fraction of 50 nm particles decreased slightly. These observations indicate that the newly formed particles consist of around 60% of water-soluble materials. The most likely candidate of these water-soluble materials were H2SO4 and its ammonium salt ((NH4)2SO4), which are highly hygroscopic at RH = 98%. In addition to (NH4)2SO4, aminium salts were other possible water-soluble compounds contributing to atmospheric nanoparticle

Fig. 3. (A) particle number size distribution, (B1 and B2) growth factor probability density distributions (GF-PDF) and (C1 and C2) shrink factor probability density distributions (SF-PDF) for 30 nm and 50 nm particles. The black circles in the panel (A) means the geometric mean diameter (GMD)s of particle number size distribution. The black dashed lines indicate the 30 nm and 50 nm in diameter. The pink and black dashed lines indicate the starting time and ending time of NPF events, respectively. Higher GF-PDF (SF-PDF) indicates a higher probability of hygroscopic growth factor (shrink factor) within a certain bin (here bin width = 0.005 for HGF and SF, no unit). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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The sulfuric acid, the oxidation product of SO2 may be the dominated contributor to particle growth. By analyzing one-year NPF events observed in South Africa, Vakkari et al. (2015) found that in rural conditions the growth is dominated by organic compounds, whereas in sulfur-rich atmosphere sulfate can dominate the growth. Their findings are similar to our observations. We should also note that at least 40% of new particle growth is contributed by water-insoluble fraction, which the most possible is organics, even in the atmosphere of NCP. This means that secondary organic aerosol plays a key role in particle growth to CCN sizes even in a sulfur-rich atmosphere environment, such as NCP.

3.3. Volatility of nanoparticles The particle volatility at T = 300 °C was measured by V-TDMA. As examples, Fig. 3(C1 and C2) displays the shrink factor probability density function (SF-PDF) for 30 nm and 50 nm particles on 9th and 10th, June. A clear increase in particle volatility at 300 °C was observed at the onset of the events. For example, the SF of 30 nm particle decreased from 0.48 to 0.38 between 9:00 and 11:20 (local time (LT)) on June 9, 2014. After being heated, the core size of newly formed particles is smaller than those of the pre-existing particles, indicating that the non-volatile residual fraction of newly formed particles did not come from the pre-existing particles. The VFR was calculated and shown in Fig. 4. The VFRs for both 30 nm and 50 nm particles were around 5% during the course of the events. The shrink factor and non-volatile volume fraction remaining are given in Table 2. The SFs for 30 nm and 50 nm particles are 0.35 and 0.38, respectively. Correspondingly, the VFRs are respectively 0.04 and 0.05 at 300 °C. This means that non-volatile materials were produced during the particle growth process. Similar observations were also reported by Wehner et al. (2005) and Ehn et al. (2007b). They applied volatility analysis to the number size distribution of sub-micrometer particles at the observation sites in Melpitz, Eastern Germany and in Hyytiälä, Southern Finland, respectively and found that newly formed particles contain non-volatile residuals which are not volatile at temperature higher than 280 °C at both sites. As above-mentioned, in our study, the volume fraction of non-volatile materials increased slightly with increasing size of newly formed particles. Previous studies showed that the inorganic compounds including ammonium sulfate and ammonium nitrate evaporate completely after being heated to around 180 °C (Huffman et al., 2008). SOA from the ozonolysis of monoterpenes evaporated below 100 °C (Lee et al., 2011). This means that the residuals of newly formed particle after being heated to 300 °C are not ammonium sulfate or SOA. A certain fraction of these organics are converted continuously to polymers as already demonstrated in laboratory experiments (Kalberer et al., 2004). These polymers were found to be non-volatile at 300 °C. Recently, Wang et al. (2010) exposed nanoparticles to a range of organic vapors and found that organic species enhance the growth of nanoparticles, producing non-volatile oligomers, polymers, and alkylaminium sulfates in the particle phase. These materials could be candidates of the non-volatile core in newly formed particles.

Fig. 4. The water-soluble fraction and volume fraction remaining of 30 nm (A) and 50 nm (B) particles during the new particle formation.

growth (Barsanti et al., 2009; Smith et al., 2010). Laboratory measurements of alkylammonium carboxylate salt nanoparticles showed only slightly lower hygroscopicity than ammonium sulfate nanoparticles (Smith et al., 2010). The hydrophobic fraction of newly formed particles was most possibly secondary organic aerosol (SOA). The typical hygroscopic growth factor of SOA is 1.1 (±0.05) at RH = 90%. At RH up to 98%, the SOA generated from the dark ozonolysis of α-pinene with hygroscopic growth factors b 1.05 was still hydrophobic (Wex et al., 2009). Wex's study showed that SOA hygroscopic growth starts to increase significantly at RH N 98%. Table 3 summarizes the observations on the basis of H-TDMA technique at RH = 90% in various atmospheric environments in the literature. In Melpitz (Wu et al., 2015) and Hyytiälä (Ehn et al., 2007a), the water-soluble fraction of newly formed particles decreased obviously with increasing particle size due to an increasing fraction of organic matter. Differently, our observations showed that the water-soluble fraction is comparable for 30 nm and 50 nm newly formed particles. For 30 nm particles, the water-soluble fraction accounts for 30% in both Melpitz and Hyytiälä. In this study, the 30 nm newly formed particles consists of 64%, which significantly higher than other environments. For 50 nm particles, the water-soluble fraction is smaller than 20% in Melpitz and Hyytiälä, which obviously smaller than that of at Wangdu site. At Melpitz and Hyytiälä, lots of biogenic volatile organic compounds (BVOCs) produced by biological activities produced may lead to an organic-rich environment during summertime. The oxidation products of BVOCs may be responsible for the new particle growth. However, the polluted atmosphere of NCP is a sulfur-rich environment.

Table 3 Summary of water-soluble fraction of newly formed particles measured in various atmospheric environments. Location

Environment

Size

Water-soluble fraction

Reference

Wangdu, NCP

Rural site Rural site

Hyytiälä, Southern Finland

Forest site

0.64 ± 0.06 0.61 ± 0.06 0.30 0.20 0.55 0.46 0.30 0.16

This study

Melpitz, Germany

30 50 35 50 10 20 30 50

nm nm nm nm nm nm nm nm

Wu et al. (2015) Ehn et al. (2007a)

Z.J. Wu et al. / Atmospheric Research 188 (2017) 55–63

3.4. CCN activity of nanoparticles during NPF events The CCN number concentrations at different supersaturations were estimated using the method introduced in Section 2.6. Here, we should remind that the CCN number concentration was estimated from Eq. (9) in which the critical diameter was calculated from κHHTDMA at RH = 99.6%. As above-mentioned, this estimation may underestimate the CCN number. As examples, the time series of CCN number concentration at SS = 0.2%, 0.4%, and 0.8% during the selected NPF events is shown in Fig. 5. The left panel shows the CCN number concentration during the NPF events occurring on 9th and 10th, June. The particle growth processes were interrupted at around 18:00 LT probably caused by air mass change before new particles grew to 80 nm. The CCN number concentration increased significantly for SS = 0.8% with the critical diameter of around 45 nm. No enhancement in CCN number concentration at SS = 0.2% and 0.6% with the critical diameter of 113 and 71 nm was observed. One of reasons was that newly formed particles did not grow up to the critical sizes at SS = 0.2% and 0.6% before the ending of NPF event. Another reason was the dilution caused by boundary layer expansion during the day. As displayed in the right panel of Fig. 5, the growth of newly formed particles continues until the morning on the next day during the NPF event on 27th, June. The GMD of the particles formed by NPF reached to around 100 nm in diameter. The increase in the CCN number concentration was pronounced for all the supersaturations and occurred first for the highest supersaturation (SS = 0.8%). Associating with particle growth, the increase can be seen consecutively at 0.4 and 0.2% supersaturations. The increase in the CCN number concentration can mainly be attributed to the particles produced during the NPF events. One should also note that the pre-existing particles rapidly aged in a photochemically active environment during NPF events. The ageing processes increased the particle hygroscopicity, and further increased the ability of pre-existing particles to act as CCN. The enhancement factor of CCN number concentration is defined as the ratio between the minimum and the maximum of CCN number concentration during the NPF events. A similar method was also used in other studies (Laaksonen et al., 2005; Wang et al., 2013). One should note that the number concentration of the pre-existing particles serving as CCN might change during the NPF events. This could lead to a bias in the enhancement factor. In addition, the uncertainty in calculated CCN number concentration may also cause the errors in the enhancement factor. As examples, the minimum and the maximum of CCN number concentration, as marked (“Min” and “Max”) in Fig. 5, during the NPF events are marked in Fig. 5. The enhancement factor for each event was calculated by dividing the maximum by the minimum of CCN

61

number. A summary of the enhancement factors is given in Table 4. The enhancement factor on June 13 was not considered because of missing particle number size distribution data (see Fig. 2). The enhancement factors at SS = 0.2 and 0.4% are not calculated during the NPF events on June 9 and 10, 2014 because no enhancement in CCN number concentration was observed during these two events. Overall, the ranges of enhancement factors for SS = 0.2, 0.4, 0.8% are respectively 1.9–7.0, 2.7–8.4, and 3.6–10.1. Few earlier studies reported the enhancement in CCN concentration during the NPF events in China, up to now. Wang et al. (2013) observed that the NPF and the subsequent condensable growth increased the CCN number concentration in the North China Plain by factors in the range from 5.6 to 8.7, which varied with supersaturations. In Beijing, the NPF events increased CCN by 0.4–6 times (Yue et al., 2011). In urban Shanghai, China, NPF enhanced CCN number concentration by a factor of 1.2–1.8 (Leng et al., 2014). At the Finnish sub-Arctic Pallas station, a 210 ± 110% increase in the number concentration of 80 nm particles was observed from the beginning to the end of the a nucleation event (Asmi et al., 2011). At a forested site (SMEAR II station in Hyytiälä) in Southern Finland, nucleation enhanced CCN number concentration by 70 to 110%, varying with the supersaturation level (Sihto et al., 2011). In Boulder, CO, Atlanta, GA, and Tecamac, Mexico, the pre-existing CCN number concentration increased by a factor of 3.8 on average as a result of new particle formation (Kuang et al., 2009). Overall, the enhancement in CCN number concentration associated with atmospheric nucleation varied significantly in different environments. Please note that the methods for defining the enhancement factors used in the existing literature were very different. As a result, the estimated enhancement factors may be different by using different methods. We should also note that the present studies in the literature reached the agreement on the increase in CCN number concentration caused by nucleation and subsequent growth, no matter which methods were taken to estimate the enhancement factors. Considering the comparability, a harmonization of the methodology for estimating the enhancement factor is required in the future. In order to make comparisons in the CCN activity of newly formed particles in different atmospheric environments, the supersaturation at which 50 nm particles in different atmospheric environments can be activated, is calculated according to: qffiffiffiffiffiffiffiffiffiffiffi 3 Sc ¼ e

4A 27Dp3 κ crit

ð13Þ

Here κ is estimated on the basis of κ-Köhler theory proposed by (Petters and Kreidenweis, 2007) and ZSR method. The κ can simply

Fig. 5. The variations in the CCN number concentration during the NPF events. The markers “Min” and “Max” are used to determine the enhancement factors (see the texts).

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Table 4 The summary of the enhancement factor at different supersaturation on NPF days. Date 9 June 10 June 23 June 27 June 28 June

SS = 0.2%

2.7 8.4 3.8

SS = 0.4%

SS = 0.8%

3.7 10.1 3.6

1.9 2.0 3.7 7.0 3.6

much higher in North China Plain. This indicates that the water-soluble chemical compounds, most likely ammonium sulfate were mainly responsible for particle growth. We also note that a significant fraction (about 40%) in newly formed particles was insoluble materials, most likely secondary organics. The nanoparticles (here, 50 nm) during the new particle formation events can be activated as CCN at low supersaturation over NCP in contrast to clean atmosphere (such as Melpitz and Hyytiälä). This implies that newly formed particles (50 nm here) in polluted environments can activate to CCN at lower supersaturation than clean environments.

present as: κ ¼ κsoluble  εsoluble þ κinsoluble  εinsoluble

ð14Þ

where εsoluble and εinsoluble are respectively water-soluble and insoluble volume fractions defined by HH-TDMA measurements. κsoluble is the κ value of water-soluble fraction and taken as 0.61 (ammonium sulfate). The κ of water-insoluble fraction is considered as 0. Considering the uncertainty of the estimated water soluble fraction is 8.3%, the uncertainty of the parameters including κ (calculated from Eq. (14)), Sc (calculated from Eq. (13)), and estimated CCN number could be below 10%. The water-soluble fraction of 50 nm newly formed particles in selected measurements in different environments is given in Table 5. Correspondingly, the κ and Sc are estimated and listed in the Table 3 as well. The Scs are 0.56%, 0.99%, and 1.11%, respectively for the NCP, Melpitz, and Hyytiälä. This means that the newly formed particles with the same size can be activate as CCN at lower supersaturation in the NCP. Conversely, the newly formed particles need higher supersaturation to be activated in clean continental atmosphere with rich in organic gaseous precursors. 4. Summary and conclusion New particle formation events were frequently observed during the Wangdu field campaign performed in North China Plain during summertime. The NPF events took place in the sunny morning with relatively clean air masses coming in and associating with an increasing ratio of sulfuric acid concentration and condensation sink. By using the HHTDMA and V-TDMA, the nanoparticle hygroscopic growth at RH = 98% and volatility at T = 300 °C were characterized during the NPF events. The observations showed that particle thermodynamic properties changed significantly during the particle formation and growth processes. On average, the HGF (RH = 98%) of 30 and 50 nm newly formed particles are 1.84 ± 0.06 and 1.99 ± 0.08, respectively. Correspondingly, the water-soluble fractions are respectively 0.64 ± 0.06 and 0.61 ± 0.06. The volatility measurements showed that a non-volatile core formed during the new particle formation and growth. The shrink factors for 30 and 50 nm particles are respectively 0.35 and 0.38. Correspondingly, the remaining volume fractions are 0.04 and 0.05, respectively. The CCN number concentration increased during the NPF events. The ranges of enhancement factors of CCN number concentration for SS = 0.2, 0.4, 0.8% are respectively 1.9–7.0, 2.7–8.4, and 3.6–10.1. These enhancement factors present the upper limit for the NPF effect on CCN because the CCN from the pre-existing particles were not subtracted when from calculations of CCN. Compared to the measurements performed in clean atmospheric environments, the water-soluble fraction of nanoparticle during new particle formation events was

Table 5 The water-soluble fraction and κ for 50 nm newly formed particles in different environments. Location

εsoluble

κ

Sc

NCP Melpitz Hyytiälä

0.61 0.20 0.16

0.37 0.12 0.10

0.56 0.99 1.11

Acknowledgement This work is supported by the following projects: National Natural Science Foundation of China (21190052, 41475127, 41571130021, 91544214, 21190052, and 41121004), the Non-profit Research Projects of Environmental Protection Department of China (201409010), the National Basic Research Program of China (2013CB228503), Special Fund of State Key Joint Laboratory of Environment Simulation and Pollution Control (14L02ESPC), Sino-German Science Center project (GZ663), and the Collaborative Innovation Center for Regional Environmental Quality. References An, J., Wang, H., Shen, L., Zhu, B., Zou, J., Gao, J., Kang, H., 2015. Characteristics of new particle formation events in Nanjing, China: effect of water-soluble ions. Atmos. Environ. 108, 32–40. Asmi, E., Kivekäs, N., Kerminen, V.M., Komppula, M., Hyvärinen, A.P., Hatakka, J., Viisanen, Y., Lihavainen, H., 2011. Secondary new particle formation in Northern Finland Pallas site between the years 2000 and 2010. Atmos. Chem. Phys. 11, 12959–12972. Barsanti, K.C., McMurry, P.H., Smith, J.N., 2009. The potential contribution of organic salts to new particle growth. Atmos. Chem. Phys. 9, 2949–2957. Birmili, W., Stratmann, F., Wiedensohler, A., 1999. Design of a DMA-based size spectrometer for a large particle size range and stable operation. J. Aerosol Sci. 30, 549–553. 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, 12369–12386. Dal Maso, M., Kulmala, M., Riipinen, I., Wagner, R., Hussein, T., Aalto, P.P., Lehtinen, K.E., 2005. Formation and growth of fresh atmospheric aerosols: eight years of aerosol size distribution data from SMEAR II, Hyytiala, Finland. Boreal Environ. Res. 10, 323. Dusek, U., Frank, G.P., Hildebrandt, L., Curtius, J., Schneider, J., Walter, S., Chand, D., Drewnick, F., Hings, S., Jung, D., Borrmann, S., Andreae, M.O., 2006. Size matters more than chemistry for cloud-nucleating ability of aerosol particles. Science 312, 1375–1378. Ehn, M., Petäjä, T., Aufmhoff, H., Aalto, P., Hämeri, K., Arnold, F., Laaksonen, A., Kulmala, M., 2007a. Hygroscopic properties of ultrafine aerosol particles in the boreal forest: diurnal variation, solubility and the influence of sulfuric acid. Atmos. Chem. Phys. 7, 211–222. Ehn, M., Petäjä, T., Birmili, W., Junninen, H., Aalto, P., Kulmala, M., 2007b. Non-volatile residuals of newly formed atmospheric particles in the boreal forest. Atmos. Chem. Phys. 7, 677–684. Goliff, W.S., Stockwell, W.R., Lawson, C.V., 2013. The regional atmospheric chemistry mechanism, version 2. Atmos. Environ. 68, 174–185. Good, N., Topping, D.O., Allan, J.D., Flynn, M., Fuentes, E., Irwin, M., Williams, P.I., Coe, H., McFiggans, G., 2010. Consistency between parameterisations of aerosol hygroscopicity and CCN activity during the RHaMBLe discovery cruise. Atmos. Chem. Phys. 10, 3189–3203. Guo, S., Hu, M., Zamora, M.L., Peng, J., Shang, D., Zheng, J., Du, Z., Wu, Z., Shao, M., Zeng, L., Molina, M.J., Zhang, R., 2014. Elucidating severe urban haze formation in China. Proc. Natl. Acad. Sci. U. S. A. 111, 17373–17378. Gysel, M., McFiggans, G.B., Coe, H., 2009. Inversion of tandem differential mobility analyser (TDMA) measurements. J. Aerosol Sci. 40, 134–151. Hämeri, K., Väkevä, M., Aalto, P.P., Kulmala, M., Swietlicki, E., Zhou, J., Seidl, W., Becker, E., O'Dowd, C.D., 2001. Hygroscopic and CCN properties of aerosol particles in boreal forests. Tellus Ser. B Chem. Phys. Meteorol. 53, 359–379. Heintzenberg, J., 1994. Properties of the log-normal particle size distribution. Aerosol Sci. Technol. 21, 46–48. Hennig, T., Massling, A., Brechtel, F.J., Wiedensohler, A., 2005. A tandem DMA for highly temperature-stabilized hygroscopic particle growth measurements between 90% and 98% relative humidity. J. Aerosol Sci. 36, 1210–1223. Huffman, J.A., Ziemann, P.J., Jayne, J.T., Worsnop, D.R., Jimenez, J.L., 2008. Development and characterization of a fast-stepping/scanning thermodenuder for chemically-resolved aerosol volatility measurements. Aerosol Sci. Technol. 42, 395–407. Irwin, M., Good, N., Crosier, J., Choularton, T.W., McFiggans, G., 2010. Reconciliation of measurements of hygroscopic growth and critical supersaturation of aerosol particles in central Germany. Atmos. Chem. Phys. 10, 11737–11752.

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