Analysis of the characteristics of single atmospheric particles in Chengdu using single particle mass spectrometry

Accepted Manuscript Analysis of the characteristics of single atmospheric particles in chengdu using single particle mass spectrometry Junke Zhang, Bin Luo, Jianqiang Zhang, Ouyang Feng, Hongyi Song, Peichuan Liu, Pan Cao, Klaus Schäfer, Shigong Wang, Xiaojuan Huang, Yongfu Lin PII:

S1352-2310(17)30136-X

DOI:

10.1016/j.atmosenv.2017.03.012

Reference:

AEA 15223

To appear in:

Atmospheric Environment

Received Date: 24 December 2016 Revised Date:

16 February 2017

Accepted Date: 7 March 2017

Please cite this article as: Zhang, J., Luo, B., Zhang, J., Feng, O., Song, H., Liu, P., Cao, P., Schäfer, K., Wang, S., Huang, X., Lin, Y., Analysis of the characteristics of single atmospheric particles in chengdu using single particle mass spectrometry, Atmospheric Environment (2017), doi: 10.1016/ j.atmosenv.2017.03.012. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Analysis of the characteristics of single atmospheric particles in Chengdu using single particle mass spectrometry Junke Zhanga*, Bin Luob, Jianqiang Zhanga, Feng Ouyanga, Hongyi Songa, Peichuan Liub, Pan Caob, Klaus Schäferc, Shigong Wangc, Xiaojuan Huangc*, Yongfu Lind Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

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a

Sichuan Environmental Monitoring Center, Chengdu 610074, China

c

Plateau Atmosphere and Environment Key Laboratory of Sichuan Province, College of Atmospheric

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b

Sciences, Chengdu University of Information Technology, Chengdu 610225, China Chengdu branch, Beijing Everise Technology Co., Ltd. HQ, Chengdu 610091, China

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d

Corresponding authors: Tel.: +86 2866367459; Fax.: +86 2885966389

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E-mail: J. K. Zhang ([email protected]) and X. J. Huang ([email protected])

ACCEPTED MANUSCRIPT Abstract: Chengdu, the capital of Sichuan Province and the main city in Sichuan basin, is one of the heavily polluted cities in China. The characteristics of single particles in the atmosphere over Chengdu are critical for the in-depth understanding of their sources, formation mechanisms, and effects. In this study, a continuous ambient aerosol measurement was performed in summer in Chengdu with a single particle aerosol mass spectrometer (SPAMS) and other monitoring instruments. The particulate matter (PM) mass concentrations were low during our study period: PM2.5 and PM10 (aerosol particles with an aerodynamic diameter of less than 2.5 or 10 µm) were 40.5±23.6 µg m-3 and 67.0±38.1 µg m-3,

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respectively. This was mainly caused by the favorable meteorological conditions during the summer season. Twelve particle types were identified and classified as dust particles (Dust), four types of carbonaceous particles, organic nitrogen and potassium containing particles (KCN), four types of secondary particles, Na-K-containing particles (NaK), and metal-containing particles (Metal). The highest contribution of particles was from potassium with elemental carbon (KEC; 23.0%), and the

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lowest from elemental carbon (EC; 0.2%). All types of particles showed different diurnal variations and size distributions, which were closely related to their sources and reactions in the atmosphere. The eastern and southern air masses corresponded with high PM2.5 mass concentrations. The contributions of KEC and K-sulfate (KSO4) particles to PM2.5 were clearly higher than those in air masses from the

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southeast. During polluted days, the contributions of KEC and KSO4 particles increased, while the contributions of organic carbon (OC), combined OC and EC particles (OCEC), and K-nitrate (KNO3) particles decreased. This shows the importance of biomass burning and industrial emissions for the PM2.5 pollution in Chengdu. These results will be useful for the in-depth understanding of the PM2.5 pollution in Chengdu, even in Sichuan basin.

Keywords: Single particles; Single particle aerosol mass spectrometer (SPAMS); Chemical composition; Chengdu

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1. Introduction

In recent years, atmospheric PM2.5 pollution has become a serious issue that has raised great public attention in most cities in China. The pollution originates from anthropogenic and natural sources, and significantly affects air quality, public health, and global climate change (Harrison and Yin, 2000; Poschl, 2005; Watson, 2002; Zelenyuk et al., 2008). To date, most of the related studies

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have focused on Beijing-Tianjin-Hebei, and the Yangtze and Pearl River Delta areas. This research has produced many useful results, which play a key role in controlling and reducing PM2.5 pollution in these areas and, as a result, PM2.5 pollution has decreased gradually over recent years. For example, the annual mean PM2.5 mass concentrations in Beijing were 89.5, 85.9, and 80.6 µg m−3 in 2013, 2014, and

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2015, respectively (http://www.bjepb.gov.cn/). However, many studies have shown that Sichuan basin has become another high PM2.5 pollution

area in addition to Beijing-Tianjin-Hebei, and the Yangtze and Pearl River Delta areas. At times, the PM2.5 mass concentration in the basin has been higher than that in these three regions. For example, Tao et al. (2013) found that the PM2.5 concentration in Chengdu reached an annual mean of 165 µg m−3 during 2009 and 2010. Although this value decreased in 2011 (119 µg m−3) (Tao et al., 2014), it is still much higher than the new national ambient air quality standard (NAAQS; 35 µg m−3) and the limit value of the air quality guideline (10 µg m−3) recommended by the WHO. High PM2.5 concentrations in Chengdu were also reported by Tian et al. (2013), Chen and Xie (2014), and Shi et al. (2015). However, air pollution studies in Chengdu, and even in Sichuan basin, are far behind the other three regions. Previous studies in the Sichuan basin area have mostly been based on filter sampling followed by laboratory analyses (Tao et al., 2014; Tian et al., 2013). Reported drawbacks of this method include

ACCEPTED MANUSCRIPT low resolution, the experiments are limited by the small amount of material collected, and organic aerosols cannot be analyzed in-depth (Bi et al., 2011; Fu et al., 2015; Liu et al., 2016; Zhang et al., 2014). Some advanced aerosol on-line measurement devices with high temporal resolution, such as aerosol mass spectrometers (AMS) (Canagaratna et al., 2007), aerosol time-of-flight mass spectrometry (ATOFMS) (Prather et al., 1994), and single particle aerosol mass spectrometer (SPAMS) (Li et al., 2011), are now more popular for measuring aerosols in the atmosphere. These devices can measure basic characteristics, such as chemical composition and size distribution, and more detailed information,

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such as the aging processes, oxidation states, and the mixing state of the aerosols (Wang et al., 2016; Zhang et al., 2015a; Zhang et al., 2016a). However, to the best of our knowledge, there are almost no reported results based on this type of instruments in Chengdu. The only study that used an AMS in Sichuan basin was reported by Hu et al. (2016). Their observation site was located at Ziyang, which is a suburban site in Sichuan basin. Therefore, there is a lack of in-depth research on the PM2.5 pollution

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in Sichuan basin, especially in some typical cities, which further hinders the improvement of the air quality in this area.

Chengdu, as the capital of Sichuan Province, is the most important city in Sichuan basin with a population of more than 14.4 million and a vehicle fleet of approximately 4 million in 2014

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(http://www.sc.stats.gov.cn/). An in-depth study of the PM2.5 pollution in Chengdu is therefore important for the improvement of the air quality in Sichuan basin. In this paper, we present real-time single particle analysis of Chengdu ambient air during the summer using SPAMS. To date, SPAMS has been used widely in cities (Bi et al., 2011; Ma et al., 2016; Wang et al., 2016; Zhang et al., 2015a), at background sites (Chen et al., 2014), and marine areas (Fu et al., 2015) in China. Because only a few air pollution studies have been carried out in the region, we first summarize the characteristics of the meteorological factors and some key pollutants identified during the measurements. We also perform

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an in-depth analysis of the characteristics of 12 particle types and their chemical composition. Finally, the effects of air masses and pollution at different air quality levels are discussed. To the best of our knowledge, this is the first study of aerosols using SPAMS in Chengdu and even in Sichuan basin and these results will be useful to understand the PM2.5 pollution in this ares. 2. Experimental methods

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2.1. Sampling

Ambient aerosol measurements of single particles were performed from July 16 to August 16, 2016. The observation site was located on the roof (25 m above ground) of the School of Civil Engineering building on the Nine Mile campus of Southwest Jiaotong University. The building is

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located between the north 2nd Ring Road and the north 3rd Ring Road, and is approximately 300 m from the 2nd Ring Road (Fig. 1).

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Fig. 1 Location (red star) of sampling sites in this study 2.2 Instrumentation

SPAMS was developed by Hexin Analytical Instrument Co., Ltd. (Guangzhou, China), and was described in detail by Li et al. (2011). Briefly, particles are introduced into SPAMS through a critical

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orifice (~100 µm), then focused and accelerated to specific velocities that are determined by two continuous diode Nd:YAG (neodymium-doped yttrium aluminum garnet) laser beams operated at 532 nm and located 6 cm apart. The velocity of a single particle is determined and converted into its aerodynamic diameter. Finally, the sized particles reach the laser desorption/ionization region, and positive and negative ions are produced by a 266 nm Nd:YAG laser (hit particle). The ions are then analyzed by a bipolar mass spectrometer. The standard SPAMS commercial instrument can analyze particles ranging from ～200 to 2000 nm. Particles were sampled with a flow rate of about 80 ml/min.

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Polystyrene latex sphere (PSL) particles with aerodynamic diameters of 0.2, 0.3, 0.5, 0.72, 1.0, 1.3, and 2.0 µm were used for size calibration and a small amount of ions of Li, Na, K, V, Ba, and Pb were used for mass spectrum calibration.

Meteorological data, such as temperature (T), relative humidity (RH), precipitation (Pre.), wind speed/direction (WS/WD), and pressure (P) were available from an automatic meteorological

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observation instrument (FRT X09A, Fronttech, China). The planetary boundary layer (PBL) height and visibility (V) were measured by EV-lidar (Everise, China) and OWI 430 DSP-WIVISTM (OSI, USA), respectively. Trace gases, including carbon monoxide (CO), sulfur dioxide (SO2), nitride oxide (NOx), and ozone (O3), were measured by carbon monoxide, sulfur dioxide, nitride oxide, and ozone analyzers

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(CO12M, AF22M, AC32M, and O342M, from Environnement S.A., France), respectively. The concentrations of PM2.5 and PM10 were continuously measured using a Beta attenuation mass monitor (BAM 1020, Met One Instruments, Inc., USA). 2.3 Data analysis

Particle size and mass spectra information were imported into MATLAB 7.1.2 (The Math Works

Inc., Natick, Massachusetts, USA) and analyzed with YAADA (www.yaada.org). Individual particles were classified into different groups or clusters using an adaptive resonance theory neural network algorithm (ART-2a) based on the similarity of mass spectra (presence and intensity of ions) with a vigilance factor of 0.75, learning rate of 0.05, and 20 iterations (Hopke and Song, 1997; Song et al., 1999). 3 Results and discussion 3.1 Meteorological conditions and pollution level

ACCEPTED MANUSCRIPT Figure 2 presents the time-resolved variation of PM2.5 and PM10 mass concentrations and of the meteorological parameters (e.g., T, RH, Pre., WS, WD, V, PBL, and P) from 16 July to 16 August 2016. The time series of some gaseous pollutants (such as CO, NOx, SO2, and O3) are presented in Fig. S1 and the diurnal variations of all pollutants and meteorological parameters in Fig. S2. During the study, the temperature varied from 21ºC to 36ºC with an average of 28±3ºC with higher values observed during daytime. Relative humidity was in the range of 45% to 100% with an average of 78±13 % with peak values during the night. Wind speed varied from 0.3 m s−1 to 6.1 m s−1 with an average of 1.7±1.0 m s−1. The higher wind speed occurred in general during the daytime. In addition,

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frequent rainfall occurred during the observation period, with more rainfall at night (Figs. 2 and S2).

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Fig. 2 Time series of (a) T, RH, and Pre.; (b) WS and WD; (c) V, P, and PBL height; and (d) PM2.5 and

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PM10 mass concentrations

The average mass concentrations of PM2.5 and PM10 were 40.5±23.6 µg m-3 and 67.0±38.1 µg m-3, respectively. This is much lower than the annual average observations in some previous studies. For

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example, Shi et al. (2015) reported average values of 130.5 ± 43.7 µg m-3 and 206.7 ± 69.9 µg m-3 for PM2.5 and PM10, respectively. This is consistent with seasonal observations, for example, by Li et al. (2015), which showed a concave parabolic shape for the annual variation of pollution levels in Sichuan basin: PM1.0, PM2.5, and PM10 maxima occurred during winter, reaching 77 µg m-3, 83 µg m-3, and 98 µg m-3, respectively. Minimum PM1.0, PM2.5, and PM10 values of 40 µg m-3, 43 µg m-3, and 49 µg m-3, respectively, occurred during summer. This is because more intense convective air transport and rainfall occur during the summer, and therefore PM is more effectively removed, leading to minimum PM concentrations. The mixing ventilation is greater in summer, resulting in the rapid dispersion of air pollutants (Li et al., 2015). Similar results were also reported by Tao et al. (2014). The diurnal variation characteristics of PM2.5 and PM10 were almost identical, with the highest value occurring at 9:00 in the morning, corresponding with the rush hour. It then decreased rapidly together with the increase of the PBL height in the afternoon, and gradually increased again after 19:00 (Fig. S2).

ACCEPTED MANUSCRIPT SO2, NOx, and O3 are important precursors for particle components, such as sulfate, nitrate, and secondary organic aerosols (SOA). Their diurnal variations showed clear trends. SO2 was mainly emitted from fossil fuel combustion from industry, which is mainly located around Chengdu. Therefore, the higher daytime wind speed can carry SO2 to the site producing peak values in the afternoon. NOx was mostly emitted from traffic in the urban and industrial areas around Chengdu. Therefore, the peak values occurred during rush hour in the morning. O3 was a typical product of photochemical reactions, resulting in high O3 values in the afternoon (Fig. S2).

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3.2 Size distribution and particle signatures

In this study, the time series of particle number and PM2.5 mass concentrations showed a strong correlation (R2 = 0.78; Fig. S3), and a total of 2121 clusters were classified by ART-2a. The top 634 clusters, which contributed more than 96% of the total particles, were further grouped manually into 12 general types based on the major ion peak in the mass spectra: dust particles (Dust), potassium with

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elemental carbon (KEC), elemental carbon (EC), organic carbon (OC), combined OC and EC particles (OCEC), organic nitrogen and potassium containing particles (KCN), K-nitrate (KNO3), K-sulfate (KSO4), K-Secondary (KS), secondary particles (containing sulfate and nitrate, S), Na-K-containing particles (NaK), and metal-containing particles (Metal). Fig. 3 shows the average positive and negative

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mass spectra of these groups of particles, and Fig. 4 shows their relative contribution with particle size and time of day. 3.2.1 Dust particles

The mass spectra of dust particles showed typical m/z values of crustal element oxides, i.e., 24Mg+, 27

Al+, 40Ca+, 56Fe+/56CaO+, 60SiO2−, 76SiO3−, 79PO3−, and other characteristic crustal element peaks (Fig.

3a). Si was the most abundant mineral element in both mass and number concentrations during dusty periods (Li and Shao, 2012). The peaks of

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NO2− and

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NO3– were also strong in the negative mass

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spectrum, but with a lower contribution of 97HSO4−. The common occurrence of secondary components demonstrates the chemical transformation of the dust particles in the ambient air of Chengdu. More nitrate peaks suggest that the heterogeneous reaction of dust particles with NOx was stronger than that with SO2 during long distance transport, which is consistent with the results of Li et al. (2014) and Liu et al. (2016). However, Yuan et al. (2008) reported that dust brought large amount of sulfate to Beijing,

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which is different from the results of this study. Sullivan et al. (2007) suggested that the difference was caused by different transport pathways, which is important in determining the species that accumulate on the mineral dust. The size distribution showed peaks in the coarse part (> 1 µm) of the size distribution (Fig. 4a). Li et al. (2014) also found that the size distribution of dust particles was mainly

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in the coarse particle range.

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+ + K+ Fe /CaO

SiO3 SiO2 NO2NO 3 PO3C4HSO4-

(b) KEC

-

Ca+

Al+ Mg+

NO3C6-

(c) EC C5-

C3+ C2-

C4+

C5+

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2 NO3NO

CNO3- 6

K+ C3H+ C H O+ 2 3 C5+ C3+ C+ C4+ C5H+ 7 + C7H

C3NO2-

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(g) KNO3

K+

NO3-NO

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(h) KSO4

HSO4-

NO3-

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(i) KS

K+

C5H3+ C4H7+

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HSO4-

C2H3O+ C4H3+

C3H2+

(f) KCN

C4-

C5+

K+

+

(e) OCEC

C5-

C4+ C2-

-

C6+ + C7 C

HSO4-

C3-

C5-

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HSO4C7-

C3-

C6-

C3+

(d) OC

HSO4-

C4-

K+

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(a) Dust

(j) S

NO2-

HSO4-

SO4HSO4-

NO3NO2-

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(k) NaK

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Cr+ Mn+ V+ Fe+ Cu+ Zn+

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Fig. 3 Average mass spectra for particle types identified in this study 3.2.2 Carbonaceous particles

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Four types of particles (KEC, EC, OC, and OCEC) were defined as carbonaceous. Their contributions showed large differences, e.g., KEC was the most abundant (23.0%) and EC the least abundant (0.2%) particles (Fig. 5a). This reflects the differences in their sources and the reaction

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processes in the atmosphere.

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Dva (µm)

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Hour of Day

ACCEPTED MANUSCRIPT Fig. 4 Relative contribution of (a) the size-resolved distributions of the particle types (in 100 nm resolution) and (b) diurnal variation throughout the study period The KEC particle mass spectra contained 39K+ and dual polarity carbon cluster ions (12C , 24C2 , ±

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±

±

some peaks at m/z −45, −59, and −73 due to levoglucosan from biomass-burning particles were also detected in the negative mass spectrum in this particle type, although they were low in intensity (Bi et al., 2011; Moffet et al., 2008). The peaks corresponding to nitrate and, especially, sulfate were

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commonly observed in the negative mass spectrum, which means that the fresh particles that were emitted from sources, such as biomass burning, were subjected to atmospheric aging and acquired a large amount of sulfate and nitrate during transport (Pratt et al., 2011). Decesari et al. (2002) concluded that once emitted into the air, the irregular geometry and complex microstructure of EC may provide active surfaces for further aging. The size distribution of KEC particles showed a clear contribution in

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the range of 0.5 to 0.9 µm (Fig. 4a). This supports the idea that the addition of sulfate through in-cloud processing and nitrate via heterogenic reactions causes the transition into the condensation mode (Yu et al., 2010). This distribution is very similar to KEC in the study of Chen et al. (2014) and biomass burning particles reported by Moffet et al. (2008) and Zhai et al. (2015). Many previous studies have

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showed that this type of particle is mainly emitted from biomass burning and industrial processes (Moffet et al., 2008; Pratt and Prather, 2009). Similar to the mass spectra of KEC particles, the mass spectra of EC particles were dominated by dual polarity EC cluster ions (12C , However, the peak of

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K+ almost disappeared and the peak of

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±

24

C2 , ±

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C3 ,..., Cn ). ±

±

HSO4– also decreased significantly.

This is likely to be the fresh EC in Guangzhou (Zhang et al., 2013) or the “pure” EC in Shanghai (Yang et al., 2012; Zhai et al., 2015), which were mainly from primary combustion sources, such as vehicle emissions, coal combustion, and biomass burning, suggesting that these were fresh EC particles and not influenced by any aging processes (Moffet et al., 2008; Zhai et al., 2015). The contribution of EC particles was only 0.2%. This means that the fresh EC from local sources was mixed with other species and aged rapidly after entering the surroundings. Although its contribution was very low, this type of particle was mostly distributed in the smallest particle size segment (0.2 to 0.3 µm) in this study (Fig. 4a).

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C3 ,..., Cn ) without OC marker ions, or with very low intensity of OC marker ions. In addition,

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Dust OC EC OCEC KEC KSO4 KNO3 KS KCN NaK S Metal

ACCEPTED MANUSCRIPT Fig.5 Compositions of the particle types (%) (a) during the whole study period; and on (b) excellent air quality; (c) good air quality, and (d) polluted days The mass spectra of OC particles were dominated by intense signals of e.g., 41

C3H5+, 43C2H3O+, 51C4H3+,

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27

C2H3+,

38

C3H2+,

C4H7+, and 63C5H3+. Strong secondary ions peaked in the negative mass

spectrum, especially 97HSO4−, indicating that aged OC particles were most likely mixed with sulfate. Moffet et al. (2008) found similar higher inorganic peaks in the OC mass spectra and concluded that the majority of the OC particles contained both secondary organic and inorganic species. This particle

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type is typically correlated with secondary organic aerosols, as measured by other on-line techniques, such as an aerosol mass spectrometer (Salcedo et al., 2006). In addition, a large peak at m/z +39 was also observed in the mass spectra of OC, which can be explained by coagulation between OC and 39K+, or condensation of organic species onto a biomass seed (Moffet et al., 2008). The sizes of OC were distributed almost over the full size range, which reflects the extensive sources of OC particles, such as

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the primary emission of OC and the formation of secondary aerosols (Fig. 4a). Bi et al. (2011) attributed the stable contribution in larger size ranges to secondary aerosol formation. OCEC exhibited a signature indicative of internally mixed OC marker ions (e.g., 27C2H3+, 29C2H5+, 37

C3H+, and

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C2H3O+) and EC cluster ions (12C , ±

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C2 ,…, ±

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Cn ) as well as a strong peak of 39K+. ±

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Similar to the results in Beijing (Li et al., 2014), the secondary ion species of nitrate and sulfate indicated aging processes in the atmosphere and the intensity of sulfate was much higher than that of nitrate. Li et al. (2014) concluded that sulfate was mainly mixed in carbon-related particles, including biomass-K and carbonaceous particles, while nitrate was more associated with direct industrial emissions and dust particles. Median peaks at m/z +51 and +63, which are attributed to aromatic compounds, were also observed (Zhang et al., 2013). Most OCEC particles were distributed at diameters less than 0.5 µm (Fig. 4a) and the fraction of OCEC was 4.6% of the total particles (Fig. 5a).

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Except for the EC, there was a clear and intense K signal in the other three carbonaceous particle types, which may indicate the biomass/biofuel origin of these particles (Zhang et al., 2013). Moffet and Prather (2009) observed

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HSO4– in the urban area of Mexico City, and suggest that the sulfate

originated from the oxidation of sulfur in fuel and rapidly condensed onto existing EC particles in the plume. Nitrate can be formed through heterogeneous reactions, including nitrogen oxides on the

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surface of aerosol particles besides the absorption of nitric acid (Zhang et al., 2000). 3.2.3 Organic nitrogen and potassium containing particles KCN was represented by the m/z of 39K+ and 26CN−. 39K+ was the highest ion peak in the positive spectrum, which is often used as a biomass burning/wood smoke marker (Bi et al., 2011). 26CN− was

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the highest ion peak in the negative spectrum, which may have an origin in coke-making emissions and motor vehicles, waste treatment facilities, and agricultural animal operations (Ho et al., 2016; Liu et al., 2016; Malloy et al., 2009; Taiwo et al., 2014). In addition to the two most obvious peaks, the peaks of 46

NO2−, 62NO3−, and 97HSO4− were also significant, indicating that the KCN particles were the subject

of atmospheric aging and transport processes. The KCN particle fraction made up 8.8% of the total (Fig. 5a) and was mainly concentrated at particle diameters <1 µm (Fig. 4a). 3.2.4 Secondary particles There were four types of secondary particles: KNO3, KSO4, KS, and S.

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K+ was the common

peak in the positive ion peaks of the KNO3 and KSO4 particles. Their main negative ion peaks were 46

NO2− and

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NO3−, and

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HSO4−, respectively. KS particles were marked by strong peaks at

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NO2−,

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NO3−, and 97HSO4− at the same time. KNO3, KSO4, and KS made up 14.4%, 16.7%, and 8.0% of the

total analyzed particles, respectively (Fig. 5a), which may have been related to the local and regional

ACCEPTED MANUSCRIPT pollution sources. The nitrate may be related to traffic emissions or residential wood smoke processes. Sulfate has been linked to coke-making emissions, the sinter, and blast furnace plants (Konieczynski et al., 2012). Nitrate and sulfate in China originate mainly from the transformation of SO2, NO2, and other primary gas pollutants through heterogeneous reactions (Huang et al., 2016). Burning of fossil fuels (coal), industrial emissions, and vehicle emissions also contribute (Yan et al., 2016; Zhang et al., 2015b). Therefore, KNO3, KSO4, and KS particles may have originated from direct emissions of vehicle exhaust, industrial processes, biomass burning, and secondary aerosol formation. The S 97

HSO4− and nitrate at

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NO2− and

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particles were characterized by low ions in the positive spectra, while strong sulfate occurred at NO3− in the negative spectra. Yan et al. (2016) found that the

secondary aerosols were characterized by the high abundance of secondary species of NO2−, NO3−, and SO42−. The nitrate of particles was mainly generated through the oxidation of nitrogen oxide on particle surfaces, which indicates that this type of particle was aged by atmospheric processes. 3.2.5 Na-K-containing particles spectrum, and of nitrate (46NO2− and

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NaK particles were characterized by the dominant contribution of 23Na+ and

K+ in the positive

NO3−) in the negative spectrum. This is very similar to the

results in Mexico City (Moffet et al., 2008), where a large fraction (87%) of these particles also 62

NO3−) as major peaks in the negative ion mass spectra. This may

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contained nitrate (46NO2− and

indicate an aging process via reaction with HNO3 and the release of hydrogen chloride to the gas phase (Li et al., 2014), as suggested by the detection of the m/z of 79PO3− and 35,37Cl−. The average fraction of NaK particles was 6.8% (Fig. 5a). The highest contribution of NaK appeared in the smallest size range (200–300 nm), and the lowest contribution occurred in the size range of 500–700 nm. The NaK contribution increased with increasing particle size in the large size ranges ( ＞700 nm). This distribution is very close to that of the NaK particles in the study by Chen et al. (2014). In addition,

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results from a study based in Guangzhou showed the particles divided into two types (NaK-EC and NaK), and their size distributions corresponded to the smallest range (less than 300 nm) and a larger size (greater than 800 nm) (Zhang et al., 2015a). Therefore, the NaK particles in this study may have been a combination of NaK-EC and NaK. These particles may have originated from industrial combustion, biomass burning smoke, local traffic emissions, and sea salt particles (Lin and Pan, 1998;

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Moffet et al., 2008; Schmidl et al., 2008; Wang et al., 2015). However, when considering the long distance from the sea to Sichuan basin, the particles may have been mainly emitted from biomass burning, local traffic emissions, and industrial combustion. 3.2.6 Metal-containing particles

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The particle types containing large signals of metals, such as

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VO+, and

206,207,208

Pb+, were classified as metal particles.

56

51

V+,

52

Cr+,

55

Mn+,

56

Fe+,

63,65

Cu+,

Fe+ had the highest peak of all metals,

which was thought to be a tracer for blast furnace emissions, the metal industry, and coal combustion (Oravisjärvi et al., 2003). Flament et al. (2008) reported that the Fe-containing particles emitted from the iron/steel industry were commonly oxidized through industrial high temperature processes. In addition, road dust particles are also a potential source of iron in urban areas. However, such iron-dominated particles are unlikely to be of natural origin and the absence of Al in the mass spectra suggests that this type of particle was not from dust and more likely originated from neighboring industry. V-containing particles have been detected in ambient aerosols, and are attributed to residual fuel, oil combustion, and vehicles exhaust (Ault et al., 2010; Moffet et al., 2008; Zhang et al., 2013). Mn is a notable marker for the steel industry from iron production units (Mazzei et al., 2008). Cu usually originates from industry, foundries, and traffic processes (Fernandez et al., 2000).

206,207,208

Pb+

ACCEPTED MANUSCRIPT and

40

Ca+ in the positive spectrum, together with intense peaks of

46

NO2−,

62

NO3−, and

79

negative spectrum, were found to originate from vehicle emissions (Ondov et al., 1982). 39

K+, and

23

Na+ were signatures of steel manufacturing facilities (Reinard et al., 2007).

PO3− in the

206,207,208

Pb+,

206,207,208

Pb+

occurred with almost all of these particle compounds, which indicates that both the steel industry and vehicle emissions were the sources of 206,207,208Pb+ particles. Strong negative ion peaks occurred for 46NO2−, 62NO3−, and 97HSO4− in the mass spectra of metal particles (Fig. 3l). Metal particles originated mainly from industrial emissions, coal- and oil-fired

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power plant emissions, fossil fuel combustion, and steel plant processes (Feng et al., 2014; Li et al., 2014; Moffet et al., 2008). These processes are important sources of nitrate, sulfate, and their precursors. In addition, many metals play an important role in the formation of some PM2.5 components. For example, the oxidation of S(IV) with dissolved oxygen can be significantly catalyzed by Fe(III) and Mn(II). This catalytic effect is inhibited by the ionic strength and sulfate. Thus, the oxidation of

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S(IV) by O2 catalyzed by Fe and Mn may be important under high-pH conditions (Zhang et al., 2015b). Metals change the evolution profiles of carbon, catalyzing the oxidation of EC and the charring of OC, and generally reduce the EC/OC ratio (Wang et al., 2010). The metal particles accounted for 5.1% of the total number of particles and their contribution increased with increasing particle size.

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Fig. 4b shows the diurnal patterns of the contribution of all particles in this study. Sulfate and OC were typical secondary species in ambient air, and photochemical reactions were one of their main formation pathways. Therefore, the corresponding particles (KSO4 and OC) shared similar patterns with a high contribution during the afternoon. They accounted for about 43% of the total particles in the afternoon (15:00). Although nitrate was also an important product of photochemical reactions, the high daytime temperature (higher than 30ºC) resulted in its rapid decomposition in the atmosphere. Another important formation pathway that dominates the formation of nitrate is heterogeneous

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hydrolysis of N2O5 during the night. The low temperature and high relative humidity during the night produce favorable conditions for the mass generation of nitrate. Therefore, a high contribution of KNO3 particles occurred at nighttime in this study (21%; Fig. 4b). Similar results have been reported by Zhang et al. (2015a). The lowest contribution of KEC appeared in the morning and increased during the afternoon, with a significant increase after 16:00. This is consistent with the biomass burning intensity

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in this area, which usually occurs in the afternoon and reaches its peak after dinner time. The emitted particles will then be transmitted to the center of the urban area. The particles will mix with sulfate, which is formed at daytime during the transmission process. The particles will continue to accumulate and reach their peak contribution with the decrease of the PBL height at night. Therefore, the highest

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contribution of KEC particles was about 30% between 20:00 and 23:00. In addition, the diurnal contributions of other particles with several different sources also showed a stable trend. For example, S particles were mainly affected by biomass burning, the formation of sulfate at daytime, and nitrate during nighttime. OCEC was also the result of the synthesis of biomass burning after dinner time and photochemical reactions in the afternoon. 3.3 Influence of back trajectory on the distribution of particle types To explore the influence of pollutant transport on PM2.5 loading and particle type patterns in Chengdu,

a

back

trajectory

(BT)

analysis

with

the

HYSPLIT

4.9

transport

model

(http://ready.arl.noaa.gov/HYSPLIT.php) (Draxler and Rolph, 2003) was performed. Forty-eight-hour air mass back trajectories were simulated every 6 h, ending at 200 m above the ground level of the site. A total of 129 trajectories were classified into three clusters, as shown by different colors in Fig. 6.

ACCEPTED MANUSCRIPT Three different colors represent different origins of air masses, i.e., 30% are from the south (cluster 1), 38% from the east (cluster 2), and 32% from the southeast (cluster 3). 40.0 µg m-3 5.2 1.6 10.6 7.5 0.3 0.2 4.6

m-3

0.2 6.7

50.2 µg 1.4 5.0 10.2 0.2 4.7

8.7 6.8

9.4 7.3

25.4

13.8

17.0

17.8

C3 100

17.9

12.3

40 20 0

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Day types

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13.7

60

C1

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7.5

80

%

32.5 µg m-3 0.6 5.0 1.4 7.8 14.3 0.2 6.0 9.9

C2

Fig. 6 Back trajectories for each of the identified clusters, corresponding to PM2.5 mass concentration and particle compositions (%) during the study; and the composition of the clusters during different types of air quality day (bar chart). E, G, P: excellent, good, and polluted days, respectively; C1, C2, C3: cluster 1, cluster 2, and cluster 3, respectively The mass concentration of PM2.5 decreased from clusters 1 to 3 (50.2 µg m-3, 40.0 µg m-3, and 32.5 µg m-3, respectively) and the particle composition was also different between clusters (Fig. 6).

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Compared with the other clusters, cluster 1 had the greatest contribution from KEC (25.4%), which is the typical particle type from biomass burning and industry. This is consistent with the fact that the more frequent biomass burning occurs in this direction (Fig. S4). The KSO4 particles were more abundant in cluster 1 (17.8%) and 2 (17.0%) than in cluster 3 (13.7%). This type of particle is mainly emitted from biomass burning and industrial processes. The air masses in cluster 1 passed a biomass

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burning region and two cities in Sichuan Province (Meishan and Leshan), while the air masses in cluster 2 passed over several important industrial cities in Sichuan (e.g., Suining and Nanchong), and other cities also located in this direction (e.g., Mianyang, Guangyuan, and Bazhong), which are important contributors of KSO4 particles. The air masses of cluster 3 showed the highest speed of all

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Dust OC EC OCEC KEC KSO4 KNO3 KS KCN NaK S Metal

21.5

14.7

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340 341

clusters. Biomass burning was also low and fewer cities were located along the travel path of cluster 3. Therefore, the amount of KEC and KSO4 particles was much less than for the other two clusters. The high contribution of OC and OCEC particles in cluster 3 may have mainly originated from local emissions and photochemical reactions. Therefore, the air masses of clusters 1 and 2 carried the particles from biomass burning and industrial areas, and the clusters can be defined as “polluted”. The main effect of the cluster 3 air masses was to dilute the fine particle pollution in Chengdu, and cluster 3 can therefore be defined as “clean”. 3.4 Particle characteristics of different pollution levels According to the latest air quality standards, the air quality of days with a PM2.5 mass concentration less than 35 µg m-3 is defined as “excellent (E)”, the air quality of days with a PM2.5 mass concentration between 35 µg m-3 and 75 µg m-3 is defined as “good (G)”, and days with PM2.5 mass concentrations higher than 75 µg m-3 are defined as “polluted (P)” and will have adverse effects on

ACCEPTED MANUSCRIPT human health and the environment. In this study, the measurement period was also divided into three types of air quality days, and the corresponding totals were 13, 17, and 2 days, respectively. The PM2.5 mass concentrations during these three types of air quality days were 25.6 µg m-3, 47.0 µg m-3, and 81.9 µg m-3, respectively. The corresponding meteorological factors and pollutants for the three types of days are compared in Fig. 7. T, RH, and P were not that different during the three types of days, ranging from 28–29ºC, 77–78%, and 943–944hPa, respectively (Fig. 7a to c). Therefore, these three parameters were not

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critical for the change of air quality during this study period. This is different from the results from Beijing. For example, T and RH increased from unpolluted to polluted days, and were more stable during polluted days. P decreased during polluted days (Wang et al., 2013; Zhang et al., 2016a; Zhang et al., 2014). Therefore, the formation mechanisms are diverse in different regions. Looking at other meteorological parameters, WS and PBL height were high during excellent days, which are critical

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factors for the dilution of pollutants in the atmosphere. The most critical factor for air quality in this study was Pre.. The average daily Pre. was approximately 10.7 mm during excellent days, whereas it was much lower during good days (4.0 mm) and there was almost no Pre. during polluted days (Fig. 7f). The cumulative Pre. was 139.4, 68.3, and 0 mm during these three types of days, respectively. For

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gaseous pollutants, the highest values of the two key precursors of nitrate and sulfate (i.e., NOx and SO2) and O3 occurred during polluted days, which ensured that a higher amount of PM2.5 species was generated during this type of days (Fig. 7i to k). In addition, the typical primary combustion exhaust (i.e., CO) also showed an increasing trend with the deterioration of air quality, indicating that the primary combustion processes, such as biomass burning, have a critical impact on the air quality in summer in Chengdu (Fig. 7h). The contribution of each cluster during the three types of day was also different. According to the statistical results in Fig. 6 (bar chart), the clean cluster 3 was the most

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important cluster during excellent air quality days, accounting for 47% of the total back trajectories (BT), followed by clusters 2 (34 %) and 1 (19%). During the good air quality days, the contribution of cluster 1 decreased to 25%, while the contribution of the other two clusters (corresponding to high PM2.5 mass concentration) accounted for 75%. When high pollution occurred, there was no contribution from the clean cluster (cluster 3). Therefore, the long-term transmission of pollutants from pollution

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areas is an important factor for air pollution in Chengdu. These differences resulted in the highest visibility during excellent air quality days, and the lowest visibility during polluted days (Fig. 7e).

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ACCEPTED MANUSCRIPT (b) 1000 750 500 250 0

80 60 40 20 0

20 10 0 E

G

P

E

(e) V(km)

E

(i)

G

90 60 30 0 G

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CO(mg m-3)

0.4 0.0

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G

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E

(k)

NOx(µg m-3)

80 60 40 20 0

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0.8

E

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(j)

SO2(µg m-3)

E

(h)

2.5 2.0 1.5 1.0 0.5 0.0

0 P

P

PBL(km)

Pre.(mm)

4

E

0 G

(g)

8

G

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E

G

P

(l)

O3(µg m-3)

90 60 30

PM2.5(µg m-3)

0

E

G

P

E

G

P

Fig. 7 Comparison of meteorological parameters and pollutants during different types of air quality

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days. E, G, P: excellent, good, and polluted days, respectively

Fig. 5b to d compares the composition of particles during the three types of air quality days. With the deterioration of air quality, the trends of all particle types can be attributed to three categories. First, the contributions of particles, mainly KEC and KSO4, increase. From “excellent” to “polluted” days, their contribution increased by 8.4% and 3.8%, respectively. The two types of particle were mainly emitted from biomass burning and industrial sources. Therefore, these two sources were the critical factors for the fine particle pollution in summer in Chengdu. Biomass burning is an important source of

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K particles, which exist in the form of potassium chloride (KCl) in young smoke. The amount of K2SO4 and KNO3 increases as the smoke ages due to the rapid substitution of chloride by sulfate and nitrate during transport (Zauscher et al., 2013). However, the temperature was almost consistent and high during the three types of air quality days, which is not conducive to nitrate particles in the atmosphere. KEC is the typical particle compound emitted from the biomass burning process. Therefore, the fine

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particle pollution in this study was mainly caused by biomass burning and industry around Chengdu. In addition, these two types of particle strongly absorb solar radiation and visibility therefore decreased with their increasing contribution during polluted days. The second particle characteristic was that the contribution of dust, OC, OCEC, KNO3, KS, and

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400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429

G

2

(f)

4 3 2 1 0

(d) WS(m s-1)

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30

20 15 10 5 0

(c) P(hPa)

RH(%)

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(a) T(℃ ℃)

NaK decreased gradually. With the increase of the PM2.5 mass concentration, WS decreased and the weather systems were more stable. The contribution of dust particles therefore decreased. The decrease in the contribution of OC and OCEC is also consistent with many previous results from polluted areas in China (Sun et al., 2014; Zhang et al., 2016b). The decrease of KNO3 can be attributed to a higher temperature during the entire study period, followed by evaporation from the particle phase. Accordingly, the contribution of KS also decreased because it contains a lot of nitrate. Other types of particulate matter did not show significant changes with the increase of the PM2.5 mass concentration. Therefore, particles emitted from different sources play different roles in the air quality of Chengdu. Biomass burning and industry should be focused on to improve the air quality in this area. 4 Conclusions Sichuan basin has become an important PM2.5 pollution area in China. In this study, we investigated the composition and possible sources of PM2.5 particles based on SPAMS measurements in

ACCEPTED MANUSCRIPT Chengdu during the summer of 2016. Other observations (meteorological parameters, gaseous pollutants, and PM2.5 and PM10 mass concentrations) were also used for an in-depth understanding of the PM2.5 pollution in this area. The major conclusions are: (1) The PM (PM2.5 and PM10) mass concentrations were low during our field study, which can be explained by frequent precipitation events and other meteorological conditions that favored diffusion of air pollutants. (2) Particles with both positive and negative spectra were categorized as 12 particle types. They

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showed different mass spectrum and size distributions, which were determined by their respective sources, as well as physical and chemical processes after emission from the sources.

(3) Air mass transport from different directions around Chengdu provides different pollution levels in Chengdu. The particle composition for the different clusters showed large differences; in particular, the eastern and southern air masses corresponded to high PM2.5 mass concentrations and

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higher contributions of KEC and KSO4 particles.

(4) The monitoring period could be divided into three types of air quality day. T, RH, and P were not key parameters; however, WS/WD, PBL height, and Pre. were critical parameters for the PM2.5 pollution in this area. The contribution of KEC and KSO4 increased during polluted days, which

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indicates that biomass burning and industrial emissions are two important sources of the PM2.5 pollution in Chengdu. Acknowledgements

This study was supported by the National Natural Science Foundation of China (No. 91644226) and the ambient air quality comprehensive monitoring station of ambient air automatic monitoring network of Sichuan Province. We gratefully acknowledge their support. The authors also thank the reviewers for

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their helpful and valuable comments. Finally, we acknowledge the NOAA Air Resources Laboratory for providing the HYSPLIT transport and dispersion model used in this publication. References

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ACCEPTED MANUSCRIPT Figures: Fig. 1 Location (red star) of sampling sites in this study Fig. 2 Time series of (a) T, RH, and Pre.; (b) WS and WD; (c) V, P, and PBL height; and (d) PM2.5 and PM10 mass concentrations Fig. 3 Average mass spectra for particle types identified in this study Fig. 4 Relative contribution of (a) the size-resolved distributions of the particle types (in 100 nm resolution) and (b) diurnal variation throughout the study period

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Fig.5 Compositions of the particle types (%) (a) during the whole study period; and on (b) excellent air quality; (c) good air quality, and (d) polluted days

Fig. 6 Back trajectories for each of the identified clusters, corresponding to PM2.5 mass concentration and particle compositions (%) during the study; and the composition of the clusters during different types of air quality day (bar chart). E, G, P: excellent, good, and polluted days,

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respectively; C1, C2, C3: cluster 1, cluster 2, and cluster 3, respectively

Fig. 7 Comparison of meteorological parameters and pollutants during different types of air quality

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days. E, G, P: excellent, good, and polluted days, respectively

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(a)

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Fig. 1 Location (red star) of sampling sites in this study

(b)

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(c)

(d)

Fig. 2 Time series of (a) T, RH, and Pre.; (b) WS and WD; (c) V, P, and PBL height; and (d) PM2.5 and PM10 mass concentrations

ACCEPTED MANUSCRIPT HSO4-

+ + K+ Fe /CaO

SiO3 SiO2 NO2NO 3 PO3C4HSO4-

(b) KEC

-

Ca+

Al+ Mg+

NO3C6-

(c) EC C5-

C3+ C2-

C4+

C5+

8

2 NO3NO

K+ C3H+ C H O+ 2 3 C5+ C3+ C+ C4+ C5H+ 7 + C7H

C3NO2-

CNO3- 6

K+

(g) KNO3

K+

NO3-NO

2

K+

(h) KSO4

HSO4-

NO3-

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K+

C5H3+ C4H7+

CN-

CN-

HSO4-

C2H3O+ C4H3+

C3H2+

(f) KCN

C4-

C5+

K+

+

(e) OCEC

C5-

C4+ C2-

-

C6+ + C7 C

HSO4-

C3-

C5-

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C3-

C6-

C3+

(d) OC

HSO4-

C4-

K+

C4-

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(a) Dust

(j) S

NO2-

HSO4-

SO4HSO4-

NO3NO2-

NO3-

-120 -90

(k) NaK

K+

HSO4-

C3C4NO3- NO2- C2-

-120 -90

-60

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0

30

60

90

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(l) Metal

NO3- NO2-

-120 -90 -60 -30 0

0

30

60

90

120

K+

Cr+ Mn+ V+ Fe+ Cu+ Zn+

Pb+

30 60 90 120 150 180 210

m/z

m/z

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Fig. 3 Average mass spectra for particle types identified in this study

(b)

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Dva (µm)

Hour of Day

Fig. 4 Relative contribution of (a) the size-resolved distributions of the particle types (in 100 nm resolution) and (b) diurnal variation throughout the study period

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(b)

1.4

5.1 6.8 0.4

0.2 4.6

10.7 8.8

0.5 5.4

1.6

7.9

11.2

0.3

5.0

9.9

8.0

7.7

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(c) 0.4 5.5 7.0

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(d) 0.1 5.3 1.0 6.0 8.4

1.4 0.2 4.7

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Dust OC EC OCEC KEC KSO4 KNO3 KS KCN NaK S Metal

0.2 4.4

7.3

5.8

22.7

11.1

14.3

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Fig.5 Compositions of the particle types (%) (a) during the whole study period; and on (b) excellent air quality; (c) good air quality, and (d) polluted days

m-3

50.2 µg 1.4 5.0 10.2 0.2 4.7

8.7 6.8

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25.4

13.8

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C3 100

32.5 µg m-3

Dust OC EC OCEC KEC KSO4 KNO3 KS KCN NaK S Metal

21.5

14.7

0.2 6.0

60 40 20 0

Day types

Fig. 6 Back trajectories for each of the identified clusters, corresponding to PM2.5 mass concentration and particle compositions (%) during the study; and the composition of the clusters during different types of air quality day (bar chart). E, G, P: excellent, good, and polluted days, respectively; C1, C2, C3: cluster 1, cluster 2, and cluster 3, respectively

ACCEPTED MANUSCRIPT (b) 1000 750 500 250 0

80 60 40 20 0

20 10 0 E

G

P

E

(e)

G

V(km)

E

(i)

G

90 60 30 0 G

P

CO(mg m-3)

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G

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E

(k)

NOx(µg m-3)

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80 60 40 20 0

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1.2

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SO2(µg m-3)

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0 G

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1

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(d) WS(m s-1)

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O3(µg m-3)

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PM2.5(µg m-3)

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(c) P(hPa)

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E

G

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E

G

P

Fig. 7 Comparison of meteorological parameters and pollutants during different types of air quality

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days. E, G, P: excellent, good, and polluted days, respectively

ACCEPTED MANUSCRIPT Highlights: The first SPAMS study results in Chengdu were reported There were 12 types of particles were identified through ART-2a The PM at a low level due to the frequent precipitation process

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KEC and KSO4 are critical particles for the formation of PM2.5 pollution