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Atmospheric Research

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Wintertime aerosol chemistry in Beijing during haze period: Significantcontribution from secondary formation and biomass burning emission

Xingru Lia,b,c, Lei Jiangd, Yu Baib, Yang Yangb, Shuiqiao Liuc, Xi Chenb, Jing Xub, Yusi Liub,Yingfeng Wangb, Xueqing Guob, Yuesi Wange,f,⁎, Gehui Wanga,f,⁎⁎

a Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinabDepartment of Chemistry, Analytical and Testing Center, Capital Normal University, Beijing 100048, Chinac College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, Chinad Beijing Municipal Research Institute of Environmental Protection, Beijing 100037, Chinae State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Chinaf Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China

A R T I C L E I N F O

Keywords:HazePM2.5

Chemical compositionsOrganic tracersSource apportionment

A B S T R A C T

Air pollution in north China is still severe, although Chinese government has exerted great efforts to cut downpollutant emissions in the past decade. To understand the chemistry and sources of the haze particles in northChina, PM2.5 aerosols were collected in Beijing from January 7 to February 27, 2014, during which two hazeepisodes (haze I and haze II) occurred. The samples were analyzed for water soluble inorganic ions, trace ele-ments, sugars, n-alkane, polycyclic aromatic hydrocarbons (PAHs), fatty acids, dicarboxylic acid, EC (elementalcarbon) and OC (organic carbon). Our results showed that relative abundances of primary species such as sugars,PAHs and EC were more abundant on the clean days and the haze I period in Beijing, while secondary aerosolsespecially secondary inorganic aerosol (SIA)were more abundant in the haze II period. Source apportionmentfurther showed that emissions from cooking (21.6%), road-dust (19.4%), coal combustion (17.8%) and biomassburning (15.4%) were the main sources of PM2.5 during the clean days, while biomass burning (20.1%) and coalcombustion (17.1%) were the main sources during haze I period. For the haze II period, vehicle exhausts(26.9%), secondary aerosol formation (26.6%) and industrial emission (19.1%) were the dominant sources ofPM2.5. Our work suggests that it is indispensable to reduce the emissions from biomass burning and vehicleexhaust in Beijing in order to further mitigate the haze pollution.

1. Introduction

Severe haze episodes have frequently occurred in North China Plain(NCP) especially in winter (Liu et al. 2015b; Sun et al. 2016a; Zhenget al. 2015), which is characterized by high loadings of fine particulatematters (PM2.5) and low visibility (Luan et al. 2018; Ma et al. 2017;Zhang et al. 2016). Haze pollution is of significant impact on humanhealth (Chen et al. 2013b), and solar radiation in the atmosphere(Milton et al. 2008). PM2.5 is the major pollutant during winter hazeperiods in North China Plain, which is a complex mixture of inorganicsubstances (e.g., heavy metals, sulfate, ammonium, and nitrate) andhundreds of organic compounds (e.g., n-alkanes, PAHs, carboxylic acid,

and plasticizers) (Barros et al. 2010; Holzinger et al. 2010; Spenceret al. 2008). In general, inorganic species comprise 25–50% of theaerosol mass with sulfate, ammonium and nitrate as the dominant ions(Wang et al. 2017). Organic compounds are the other major compo-nents of PM2.5 in urban area (Chai et al. 2005; Rushdi et al. 2006),accounting for 20–90% of the fine aerosol mass (He et al. 2001; Zhuanget al. 2004).

Many studies have been conducted in recent years to investigate thesources and formation mechanisms of severe haze episodes in NCP(Chen et al. 2017; Lu et al. 2016; Sun et al. 2016b; Yang et al. 2015).The haze development in NCP including Beijing is often characterizedby a sharp increase in PM2.5 concentration from a few μg m−3 to

https://doi.org/10.1016/j.atmosres.2018.10.010Received 16 May 2018; Received in revised form 16 October 2018; Accepted 16 October 2018

⁎ Correspondence to: Y. Wang, State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, ChineseAcademy of Sciences, Beijing 100029, China

⁎⁎ Correspondence to: G. Wang, Key Laboratory of Geoscience Information of the Ministry of Education, School of Geographic Sciences, East China NormalUniversity, Shanghai 200241, China

E-mail address: wys@dq.cern.ac.cn (Y. Wang).

Atmospheric Research 218 (2019) 25–33

Available online 24 October 20180169-8095/ © 2018 Published by Elsevier B.V.

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hundreds of μg m−3 within a few hours (Wang et al. 2014b; Zheng et al.2015). It is widely considered that the formation of haze episode istightly associated with stagnant meteorological conditions (Sun et al.2014; Wang et al. 2014b). However, the sources of air pollutants andthe formation mechanisms of haze particles in Beijing are controversial(Wang et al. 2016, 2018).

In order to improve our understanding on the sources and formationmechanism of the haze pollution in NCP, PM2.5 samples from Beijing inthe winter of 2014 were collected. During the campaign several severehaze events occurred in Beijing and its surrounding areas, during whichconcentrations of PM10 and PM2.5 separately maximized at 626 and606 μgm−3 on 16 January. In the current work aerosol chemistry of theabove PM2.5 samples were investigated on a molecular level. We firstidentified the differences in chemical compositions of PM2.5 aerosolsunder clean and haze conditions, and finally quantitatively analyzedthe sources of the haze particles.

2. Experimental details

2.1. Sample collection

PM2.5 samples were collected from January 7 to February 27, 2014on the rooftop of a two-story building in the courtyard of CapitalNormal University (39°58′N, 116°22′E). A total of 98 PM2.5 sampleswere obtained including the aerosols that were collected every 4 h fromJanuary7 to 19, and the others that were collected on a day/night basis.Meteorological data were recorded concurrently. A high-volume sam-pler with an airflow rate of 1.03m3min−1 was used for the samplecollection (Anderson, USA). Before sampling, the quartz fiber filters(Whatman Inc., Maidstone, UK) were wrapped in aluminum foil andpre-heated at 450 °C for approximately 4 h to remove potential organiccontamination, then conditioned in a constant temperature and hu-midity desiccator (temperature: 25 °C; RH: 50%) for 24 h, and weighed.After sampling, the quartz fiber filters were wrapped with aluminumfoil and returned to the desiccator (temperature: 25 °C; RH: 50%) for24 h. At last the filters were weighed and stored in a− 20 °C freezerprior to analysis. Six field blank samples were collected before and afterthe sampling by mounting the filters onto the sampler for approxi-mately 10min without pumping any air. Gaseous pollutants (NO2, SO2)and meteorological parameters (Fig. 1), including air temperature (°C),relative humanity (%), wind velocity (WV, m s−1) and wind direction,were measured simultaneously using automated observation instru-ments during the sampling period.

2.2. Extraction and derivatization

2.2.1. Organic compoundsDetailed methods for the extraction, derivatization and gas chro-

matography/mass spectrometry (GC–MS) analyses were describedelsewhere (Chen et al. 2013a; Li et al. 2017). Briefly, one eighth of eachfilter was put into a glass vessel, spiked with six isotopically labeledcompounds (i.e., D-Tetracosane, D-anthracene, C13-levoglucosan, D-Benzo [a] pyrene, D-glutaric acid, D-palmitic acid) and ultrasonicallyextracted three times each for 20min with 20mL of a dichloromethane(DCM) (HPLC grade,> 99.8%) and methanol (HPLC grade,> 99.8%)mixture (1:2, v/v). After extraction the combined solution was filteredthrough a glass fiber filter, concentrated to 1.5mL at 35 °C by a rotaryevaporator (Buchi, Sweden), and blow down to dryness using a gentlenitrogen stream. After reaction with a mixture of 50 μLN, O-bis-(tri-methylsilyl) trifluoroacetamide (BSTFA) and 10 μL pyridine (5:1, v/v)at 70 °C for 3 h, the derivatives were diluted with DCM and internalstandard substance to 400 μL and analyzed by GC–MS (Thermo DSQIIFinnigan, USA).

The GC column was a HP-5MS capillary column (30m length,0.25mm diameter, 0.25 μm film thickness), and the carrier gas washigh-purity helium, which had a velocity of 1.0mLmin−1. A total of

1 μL of sample was injected into the GC on a splitless mode. The GCtemperature was programmed as follows: 50 °C for 2min, increased to120 °C at 15 °Cmin−1, isothermal hold for 5min; ramped to 290 °C at5 °Cmin−1, and isothermal hold for 10min. The injector temperature is250 °C. The MS was operated on electron impact (EI) mode at 70 eV,and the full scan ranged from 50 to 550 amu. Determinations wereperformed by using selected ion monitoring (SIM) mode and internalstandard method was used in the quantification. Hexamethylbenzenewas used as the internal standard. The mass spectra of the targetcompounds were compared with standards from the National Instituteof Standards and Technology (NIST) 2014. All the detected compoundswere determined using the peak area of the individual characteristicions. In addition, the GC–MS response factors were estimated using theauthentic standards.

All analytical procedures were monitored using strict quality as-surance and control measures. Lab blanks, field blanks and solventblanks were used to determine the potential contamination. The pu-rities of DCM, n-hexane, methanol, and acetonitrile exceeded 99.8%.Phthalate esters were the main contaminants found in the blanks. Thesetrace contaminants did not interfere with the identification or quanti-fication of interested compounds. The average recoveries of the targetcompounds were better than 72%. The method detection limit (MDL) ofall the target compounds were determined by three times the standarddeviation of the spiked standards. In this study, the MDL for n-alkanes,PAHs, sugars, fatty acids, and dicarboxylic acids were 0.092, 0.033,0.252, 0.938 and 0.896 ngm−3, respectively.

2.2.2. Elemental carbon (EC), organic carbon (OC), water-soluble organiccarbon (WSOC), inorganic ions

Detailed methods for the analysis of EC, OC, WSOC and water so-luble ions in aerosols were reported in our previous studies (Li et al.2017; Li et al. 2012). Briefly, EC and OC in the PM2.5 samples weredetermined by using DRI Model 2001 Carbon analyzer following the

Fig. 1. Evolution of SO2, NO2, PM2.5, visibility (km), wind velocity (m s−1),wind direction, relative humidity (%), and temperature (T) in Beijing fromJanuary 7 to February 28, 2014.

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Interagency Monitoring of Protected Visual Environments (IMPROVE)thermal/optical reflectance (TOR) protocol (Chow et al. 2007). WSOCand water soluble ions in the samples were extracted with Milli-Q purewater (conductivity: 18.2 MΩ﹒cm) and measured by using a ShimadzuTOC-L CPH analyzer and Dionex-1100 ion chromatography, respec-tively. OM, SOC, SOA, POA were estimated using the following equa-tions (Han et al. 2015; Xing et al. 2013):

= ×SOC OC–EC (OC/EC)min (1)

= ×OM 1.6 OC (2)

= ×SOA 1.6 SOC (3)

= −POA OM SOA (4)

2.3. Tracer elements

The methods applied for the mass determination and chemicalanalyses had already been described elsewhere (Li et al. 2012). Briefly,an eighth of each filter was digested in an 8mL mixture of 2mL HCl,5 mL HNO3 and 1mL HF by using MARS (CEM), then analyzed by In-ductively Coupled Plasma Mass Spectrometry (ICP-MS, 7500a, Agilent).Laboratory and field blanks were also determined and were subtractedfrom the samples. 16 trace element concentrations of Na, Mg, Al, K, Ca,V, Cr, Mn, Fe, Ni, Cu, Zn, As, Ba, Tl, and Pb were determined.

2.4. Backward trajectories

Backward trajectory analysis was conducted by Hybrid SingleParticle Lagrangian Integrated Trajectory (HYSPLIT) model developedby the National Oceanic and Atmosphere Administration (NOAA), usingthe archived Global Data Assimilation System (GDAS) meteorologicaldata (http://www.arl.noaa.gov/HYSPLIT.php). In this study, 24-day airmass backward trajectories were calculated every 4 h arriving at theheight of 500m (AGL) in Beijing. Based on the results of the backwardtrajectory analysis, a total of 157 trajectories were used for the clusteranalysis by the trajectory paths in the HYSPLIT.

3. Results and discussion

Two haze episodes were observed during the campaign. The firstevent occurred from January 15 to 17, 2014, during which PM2.5 masssharply increased from 84 μgm−3 at 5 pm to 428 μgm−3 at 7 pm onJanuary15 and reached the maximum concentration of 602 μgm−3 onJanuary 16 (Fig. 1 and Table S1). The second episode had persisted fora longer time, occurring from February 14 to 26 with a short-term re-vival on February 17 and 18, during which the hourly PM2.5 increasedto 502 μgm−3 with the visibility down to 0.8 km.

Fig. 2 compares the differences in chemical compositions of PM2.5 inBeijing under different pollution conditions. Water soluble ions andorganic matter were the main components of PM2.5 for all the samples;the sum of both accounted for 70% and 81% of PM2.5 mass on the cleanand hazy days, respectively. With the increase of PM2.5 concentrations,water soluble ions significantly increased from 36% on the clean days,

50% during the haze I to 61% during the haze II. In contrast, the re-lative abundance of OM to PM2.5 decreased from 34% in the cleanperiod to 31% and 20% during haze I and haze II, respectively, al-though the mass concentration of OM on the clean days(35.2 ± 21.6 μgm−3) was the lowest compared to those on the haze I(71.7 ± 48.5 μgm3) and the haze II (49.7 ± 20.0 μgm−3) (Table S1)phases, indicating that the hygroscopic inorganic ions are the key to thehaze developmemt in Beijing.

3.1. Water soluble ions

A total of eight water soluble inorganic ions were detected in thiswork. As shown in Fig. 3 and Table S1, secondary inorganic aerosol,i.e., SO4

2−, NO3−, and NH4

+ and Cl− were the major components ofwater soluble ions. Average concentrations of the four ions were 8.76,9.98, 4.95 and 4.84 μgm−3 during the clearn period, respectively, ac-counting for 25%, 28%, 14% and 14% of the total ions. SO4

2−, NO3−

and Cl− were 21.95, 23.68 and 7.51 μgm3 in the haze I phase andsharply increased to 53.9, 65.2 and 9.52 μgm3 in the haze II phase.Relative abundance (35%) of SO4

2− to the total ions during the haze Iperiod was similar to that (36%) in the haze II, but NO3

− (44%) washigher and Cl− (7%) was lower in the haze II period compared to therelative abudances (38% for NO3

− and 12% for Cl−) of the two ions inthe haze I period. Such high relative abundances of SO4

2− and NO3− on

the haze days can be explained by an efficient aqueous phase formationof sulfate and nitrate in Beijing (Wang et al. 2014a; Wang et al. 2016).On the contrary, concentrations of Ca2+ and Mg2+, which are mainlyoriginated from the suspended road-dust(Wang et al. 2014a), sig-nificantly reduced during the haze periods in comparison with those inthe clean period, mainly due to the decrease of dust emission caused bythe stagnant conditions.

3.2. Major organic compounds

Five classes of organic compounds (i.e., n-alkanes, fatty acids, su-gars, dicarboxylic acids, polycyclic aromatic hydrocarbons (PAHs)) inthe PM2.5 samples were detected with the total concentrations being1666 ± 940, 2553 ± 946 and 1950 ± 874 ngm−3 during the clean,haze I and haze II periods, respectively (Fig. 4 and Table S2). OC, ECand POA showed the similar variation patterns during the observationperiod, which were highest in the haze I phase and lowest in the cleanperiods (Fig. 4a). In contrast, concentrations of SOC, SOA and di-carboxylic acids were highest in the haze II event and lowest in theclean periods (Fig. 4a and b). Such differences in POA and SOA abun-dances suggest that primary emission was probably more significant inthe haze I formation process, while during the haze II formation processsecondary oxidation may be more important.

3.2.1. Sugar compoundsSugars are one of major water-soluble organic aerosols in the at-

mosphere (Fu et al. 2012; Pietrogrande et al. 2014; Scaramboni et al.2015; Wang et al. 2011). Studies showed that biogenic particles such asplant debris, leaf litter, pollen, fungi, bacteria, soil particles and

Fig. 2. Chemical composition of PM2.5 in Beijing during the sampling campaign.

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biomass burning emissions are the major sources of sugars (Bauer et al.2008; Medeiros and Simoneit 2007; Nolte et al. 2001; Simoneit et al.2004). In this study, eight sugars (glycerol, arabitol, levoglucosan, xy-litol, glucose, sorbitol, myo-Inositol, and trehalose) were detected withthe concentrations of 121–2413 ngm−3 (average 551 ngm−3, TableS2) on the clean days, 86.1–2021 ngm−3, (average 899 ngm−3) in thehaze I and 10.2–1568 ngm−3(averaged 651 ngm−3) in the haze II,respectively. Among the sugar class levoglucosan is the most abundantcompound during the three phases with averaged concentrations ran-ging from 400 ngm−3 in the clean period to 680 ngm −3 in the haze Iperiod (Fig. 5a and Table S2). Levoglucosan is a key tracer for biomassburning plume. The highest concentration of levoglucosan in the haze Iperiod indicated that the contribution of POA from biomass burning toPM2.5 was most significant in Beijing during this phase.

3.2.2. n-alkanesHighest concentrations of n-alkanes were found in the haze I phase,

which were 483 ± 259 ngm−3 and 1.28 and 1.24 times more thanthose during the clean and haze II periods. C23 was the most abundantcongener for all the samples (Fig. 5b and Table S2), indicating theimportance of fossil fuel combustion, because n-alkanes from thecombustion of wheat, straw and other herbages are in general domi-nated by higher molecular weight (HMW) (e.g., C29 or C31), while thoseemitted from fossil fuel combustion are dominated by lower molecularweight (LMW) (e.g., C21 or C23) (Wang et al. 2009a). Carbon preferenceindex (CPI), which is the relative abundance of n-alkanes containingodd number carbon atoms to those containing even numbers carbonatoms, is indicative of different n-alkane sources. CPI is close to unityfor n-alkanes derived from fossil fuel combustion and larger than 5when n-alkanes are emitted from biogenic sources (Cheng et al. 2006;

Feng et al. 2006). The mean CPI values were 1.24, 1.18 and 1.20 in theclean, haze I and haze II phases, respectively (Table S2), indicating thatemissions from automobile exhaust and coal combustion are the majorsources of organic aerosols in Beijing during the cold season(Kalaitzoglou et al. 2004).

3.2.3. PAHsA total of eight PAHs were quantified including phenanthrene

(PHE), anthracene (ANT), fluoranthene (FLT), pyrenen(PYR), benzo(a)anthracene(BaA), chrysene (CHR), benzo[b+ k] fluoranthrene (BbkF),benzo(a)pyrene(BaP). The average concentrations of the individualPAHs in the different pollution stages in Beijing are shown in Fig. 5c.Total PAHs during the clean, haze I and haze II periods were188 ± 83.2, 276 ± 158.2 and 206 ± 39.7 ngm−3, respectively.PAHs are derived from incomplete combustion of carbon-containingmaterials such as biomass, coal and petroleum (Katsoyiannis et al.2007; Rogge et al. 1993; Simoneit 2002). Molecular composition pro-files and characteristic ratios of PAHs have been used to recognize theirdifferent sources (Kavouras et al. 2001; Ladji et al. 2009; Zhang and Tao2008). According to previous studies, FLT/(FLT+PYR) ratios of< 0.4,0.4–0.5 and > 0.5 are for petroleum emissions, natural gas combustionand biomass and coal combustion, respectively (Yadav et al., 2018;Yunker et al., 2002). In this study, the ratios of FLT/ (FLT+PYR)during the clean, haze I and haze II phases are 0.6 (0.49–0.75), 0.54(0.51–0.58) and 0.53(0.50–0.62), respectively, indicating that biomassand coal combustion are the important sources of PAHs in Beijing inwinter.

3.2.4. Fatty acidsA homologue of fatty acids (C8:0-C25:0) in the aerosol samples were

Fig. 3. Comparison of inorganic ions chemistry of PM2.5 in Beijing during the clean, haze I and haze II periods ((a) mass concentrations, (b) relative abundances).

Fig. 4. Comparison of carbonaceous species of PM2.5 in Beijing during the clean, haze I and haze II periods ((a) carbonaceous fractions, (b) organic compounds)).

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determined with C16:0 and C18:0 being the dominant species (Fig. 5d).The total concentrations of fatty acids during the clean, haze I and hazeII were 434 ± 277, 658 ± 259 and 438 ± 375 ngm−3, respectively(Fig. 5d and Table S2).

Higher CPIfatty acid values indicate the biological sources such asmicrobial activities and epicuticular waxes of vascular plants (Simoneit1984; Simoneit and Mazurek 1982). In this study, the CPIfatty acid valuesare 4.33, 3.55 and 5.03 for the clean, haze I and haze II samples, re-spectively, indicating more input of biological sources during haze IIthan that during the clean and haze I periods. Studies have shown thatfatty acid homologues≥C20 are derived from vegetative waxes (Li et al.2013), while the homologues<C20 is thought to be derived frompetroleum related emissions, such as gasoline/diesel-powered vehicle

exhausts, distilled fuel oil, tire wear debris, and road dust (Rogge et al.1991; Simoneit 1986) and microbial sources (BinAbas and Simoneit1996). Meat cooking has also been reported as a source of short chainfatty acids with C14:0, C16:0 and C18:0 being the dominant species (Roggeet al. 1991). Besides saturated fatty acids, unsaturated fatty acids (e.g.,oleic acid C18:1), which can be rapidly degraded in the atmosphere,were also observed. Mass ratio of C18:0 to C18:1 is often used as an in-dicator of aerosol ageing (Li et al. 2013). In this study, the ratios ofC18:1/C18:0 in PM2.5 sample are 0.15, 0.22 and 0.18 in the clean, haze Iand haze II phases, respectively. The highest ratio of C18:1/C18:0 in hazeI suggests that organic aerosols in this period was less oxidized, which isin good agreement with the lower abundances of sulfate, nitrate, SOCand SOA discussed above. A few studies reported that the ratio of C18:0/

Fig. 5. Molecular distributions of (a) sugars, (b) fatty acids, (c) n-alkanes, (d) PAHs, (e) dicarboxylic acids, and (f) relative abundance of organics to the total detectedin PM2.5.

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C16:0 can be used as a qualitative tool for source assessment (Oliveiraet al. 2007); they found that the ratios of C18:0/C16:0 of< 0.25,0.25–0.5 and > 0.5 are attributed to biomass burning, vehicle ex-hausts and road dust, respectively (Rogge et al. 2006). In the Beijingsamples the averaged ratios of C18:0/C16:0 are 0.34, 0.47 and 0.36 in thethree phases, respectively, indicating that the contribution of vehicleexhaust to fatty acids of PM2.5 in the city is significant, which is con-sistent with that of n-alkanes.

3.2.5. Dicarboxylic acidsA homologue of dicarboxylic acids (diC3 to diC9) in the samples

were determined. As seen in Fig. 5e, azelaic acid (diC9) is the mostabundant compound, followed by malonic acid (diC3). Most of themeasured dicarboxylic acids are higher in the haze periods than in theclean period. Dicarboxylic acids in the atmosphere are largely derivedfrom photochemical oxidation of various organic precursors and areimportant secondary organic compounds (Kawamura et al., 2016; Wanget al., 2005, 2006). Azelaic acid (diC9) is originated from oxidation ofbiogenic unsaturated fatty acid C18:1 containing a double bond at theC9– position. C18:1 is very reactive under the atmospheric conditionsbecause its electron-rich double bond is easily attacked by atmosphericoxidants like ozone or OH radical oxidation to form oxo- and di-carboxylic acids (Ho et al. 2007; Jeong et al. 2008; Wang et al. 2009b).Adipic (diC6) are produced by the oxidation of anthropogenic cyclo-hexene (Fu et al. 2013; Seinfeld and Pankow 2003). Consequently, themass ratio of diC6/diC9 can be used to qualitatively evaluate the sourcestrength of anthropogenic versus biogenic precursors for dicarboxylicacids. The mean ratios of diC6/ diC9 were 0.71, 0.75 and 1.01 duringthe clean, Haze I and Haze II periods, respectively, suggesting thatcontribution of anthropogenic emission to SOA was more significant inthe Haze II phase.

3.3. Impact of biomass burning and secondary formation on PM2.5

Mass ratio of levoglucosan to OC measured in the source samples ofbiomass burning has been widely used to quantitatively estimate thecontribution of biomass burning to the aerosol OC loading (Puxbaumet al. 2007; Zhang et al. 2010). The contributions of biomass burning toOC can be inferred as follows (Wan et al. 2017):

=

∗( )( )

Contributions of biomass burning to OC (%)aerosol

source100

[Levoglucosan][OC]

[Levoglucosan][OC]

(5)

In biomass burning source emission tests for three major types ofcereal straw (corn, wheat and rice) in China, Zhang et al. 2007b re-ported a levoglucosan to OC ratio of 0.082 for PM2.5. Here we use thisratio to estimate the contributions of biomass burning smoke to am-bient OC in Beijing. The levoglucosan-tracer based method shows thatcontribution of biomass burning to OC is 30% & 16% in haze I & haze II,respectively, indicating the importance of the contribution of biomassburning to PM2.5 especially during the haze I period.

Several studies have reported that open burning of agricultural re-sidues and domestic usage of biofuel are not only the important sourcesof sugars, but also the sources of EC, PAHs, CO, NOx and VOCs (Duan

et al. 2004; Streets et al. 2003). As shown in Table 1, the correlationcoefficients of levoglucosan with K+, PAHs, POA, WSOC and EC in theclean and haze I periods were comparable but much higher than thosein the haze II period, demonstrating that biomass burning is a moreimportant source for primary pollutants in Beijing during this twophases in comparison with that in the haze II period.

Fig. 6 shows the averaged mass proportion of chemical componentsin PM2.5 during the sampling period. It should be noted that the residualcomponents in Fig. 6 refer to the sum of the measured composition (i.e.,Na+, Mg2+, NO2

−) and the undetected components. The mass con-centrations of secondary aerosol (SO4

2−+NO3−+NH4

+ +SOA) ac-counted for 42%, 32%, and 65% of PM2.5 in the clean, haze I and hazeII, respectively. Meanwhile, the primary aerosols(POA+EC+Ca2++K++Cl−) accounted for 34%, 39%, and 22% ofthe mass concentration of PM2.5 during the clean, haze I and haze IIdays, respectively. These results again indicate that secondary aerosoltransformation played an important role in the haze II formation pro-cess, while primary source especially biomass burning emissions aremore important in other two phases in Beijing.

3.4. Source apportionment by PMF model

Here we apportion the PM2.5 sources in the whole campaign byusing the PMF model based on 36 chemical species. Factor 1 is re-presented by the high loadings of Lev (76.1%), Cl−(39.6%) and EC(35.9%) (Fig. 7a), and identified as biomass burning, because thesetracers are closely associated with biomass burning emissions(Scaramboni et al. 2015; Tao et al. 2016).The PMF results showed thatcontribution of biomass burning emissions to PM2.5 is 15.4%& 20% &11% in clean & haze I & II periods, respectively (Fig. 8). The chemicalprofile of factor 2 is characterized by fatty acid C16:0 (65.5%) and C18:0

(87.7%) (Fig. 7b), which are generally considered as cooking emissiontracers (Faber et al. 2013; Zhao et al. 2007). Therefore, factor 2 isidentified as cooking emission with the contribution to PM2.5 rangingfrom 2.4% in the haze periods to 21.6% in the clean period (Fig. 8). Thechemical profile of factor 3 is mainly associated with SO4

2− (65.4%),NO3

− (64.5%), Cl− (44.6%), NH4+ (37.6%), and WSOC (36.8%)

(Fig. 7c) and thus identified as secondary oxidation (Contini et al. 2010;Liu et al. 2015a). The contributions of this factor to PM2.5 varied from4.8% on the clean days, 10.6% in the haze I period to 26.6% in the hazeII period (Fig. 8). Factor 4 is highly related to Cr (90.2%) and Ba(61.0%) (Fig. 7d). Cr is mainly from metal manufacturing. Ba is usuallyused as an additive in lubricating oil, in addition, is mainly emittedfrom fugitive dust. So factor 4 is conceived to be the emission frommetal manufacturing industries (Almeida et al. 2015; Pan et al. 2013;Voutsa and Samara 2002) and traffic-road dust (Zhang et al. 2007a).The contributions of factor 4 to PM2.5 in the three phases variedfrom14.2% on the clean days to 19.1% in the haze II period. Factor 5 ischaracterized by the high loadings of Zn (54.7%), Pb (50.8%), V(45.5%) and Fe (43.1% (Fig. 7e)). These factors are associated to trafficemissions (Almeida et al., 2009b; Zhang et al., 2018), because Zn isusually used as an additive in lubricating oil in engines and Pb is relatedto vehicle exhaust emission (Yang et al. 2013). The contributions of thisfactor to PM2.5 were 6.8%, 11.4% and to 26.9% in the clean, haze I andhaze II periods, respectively (Fig. 8). Factor 6 is identified as soil dust,since this factor shows high loading with Ca (59.0%), Mg (55.1%), Al

Table 1Pearson correlations of levoglucosan with K+, PAHs, POA,WSOC and EC during the clean, haze I and haze II phases.

Lev vs. K+ Lev vs. ∑PAHs Lev vs. POA Lev vs. WSOC Lev vs.EC

r P r P r P r P r P

Clean 0.59 **** 0.84 **** 0.63 **** 0.64 **** 0.80 ****Haze I 0.76 *** 0.74 *** 0.77 *** 0.72 *** 0.69 **Haze II −0.29 ns 0.43 * −0.01 ns 0.18 ns 0.03 ns

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(52.6%) and Fe (47.5%) (Fig. 7f), which are typically emitted from soildust (Begum et al. 2004; Gu et al. 2011; Liu et al. 2015a). As shown inFig. 8, the contributions of soil dust to PM2.5 decreased from 19.4% inthe clean period, 10.2% in the haze I period to only 8.7% in the haze IIperiod. Factor 7 shows the high loading with As (46.7%), C30 (43.5%)and PAHs (Fig. 7g. These compositions are closely associated with di-rect emissions from coal combustion (Bhangare et al. 2011; Liu et al.2015a). Thus, factor 7 is identified as coal combustion emissions; ofwhich the contributions to PM2.5 in the three phases were 17.8% (cleandays), 17.1% (haze I) and 5.2% (haze II), respectively (Fig. 8).

3.5. Backward trajectory

Wintertime air masses in Beijing during the clean period were lar-gely transported from the northwest direction (Fig. 9(a)). Relative cleanair masses transported from the northwest lead to relative lower che-mical compounds. During haze I period, the air masses were mainlytransported from the northwest (51%) and the southwest (49%) di-rections (Fig. 9(b)). High concentrations of ions and organic compounds(Table S3) were observed from south Hebei, and proposed as the sourceregion of the air pollutants. There were four air masses in Beijing duringthe hazeII period, which were largely transported from south Hebei(49%). Higher concentrations of chemical compounds in the haze IIperiod were found from the south (air mass of NO.1) and the localemission (air mass of NO. 2). The third air mass originated fromLiaoning Provence, passed through Bohai Bay and Tianjin and ulti-mately arrived in Beijing, which led to higher organic matters inBeijing. In general, the samples with south trajectories are character-ized by higher pollutant levels compared to those from the northwestdirection.

Fig. 6. Averaged mass proportion of the main chemical components inPM2.5 during the ampling period.

Fig. 7. Factor profile (% of species) of each source in Beijing for the wholecampaign.

Fig. 8. Contribution of each source to the ambient PM2.5 loadings during the clean, haze I and haze II periods in Beijing.

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4. Conclusions

In this study, chemical compositions of PM2.5 in Beijing fromJanuary 7 to February 27, 2014 were determined on a molecular level.Relative abundances of compounds in PM2.5 showed that the fine par-ticles in the clean and haze I periods were dominated by primary spe-cies such as POA, EC, levoglucosan, n-alkanes, PAHs and fatty acids,while in the haze II period secondary species such as sulfate, nitrate,SOC and dicarboxylic acids were dominant, suggesting that primaryemissions from biomass burning and coal combustion were the majorsources of PM2.5 in the clean and haze I phases. In contrast, secondaryphotochemical oxidation was the more important source of PM2.5 in thehaze II period. Source apportionment by using PMF model furthershowed that emissions from cooking (21.6%) and dust (19.4%) werethe two most important sources of PM2.5 in the clean period, emissionsfrom biomass (20.1%) burning and coal combustion (17.1%) were thetwo most important sources in the haze I period, and secondary oxi-dation (26.6%) and vehicle exhaust emission (26.9%) were the majorcontributors to PM2.5 in the haze II period. Such results revealed thesignificant roles of vehicle exhaust emission, secondary formation andbiomass burning emission in the haze formation process in Beijing.

Acknowledgements

This work was financially supported by National Key R&D Programof China (No: 2017YFC0210000), the Ministry of Science andTechnology of China (No: 2016YFC0202001), China National NaturalScience Founds for Distinguished Young Scholars (No. 41325014), andNational Nature Science Foundation of China (No. 41773117). Theauthors would like to thank Dr. Liu Xingang for providing visibilitydata.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.atmosres.2018.10.010.

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