Investigation of the sources and seasonal variations of secondary organic aerosols in PM2.5 in Shanghai with organic tracers

Atmospheric Environment 79 (2013) 614e622

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Investigation of the sources and seasonal variations of secondary organic aerosols in PM2.5 in Shanghai with organic tracers Jialiang Feng a, *, Man Li a, Pan Zhang a, Shiyi Gong a, Mian Zhong a, Minghong Wu a, Mei Zheng b, Changhong Chen c, Hongli Wang c, Shengrong Lou c a b c

School of Environmental and Chemical Engineering, Shanghai University, Shanghai 200444, China College of Environmental Science and Engineering, Peking University, Beijing 100871, China Shanghai Academy of Environmental Sciences, Shanghai 200233, China

h i g h l i g h t s
SOA tracers from biogenic and anthropogenic precursors in urban Shanghai were investigated.
WSOC-based method gave more reasonable estimation of SOC than EC-based method.
Biomass burning contributed w50% of the WSOC in autumn while less than one third in other seasons.
More than half of the SOA in Shanghai was from anthropogenic precursors.
A large part of the SOC was associated with sulfate and nitrate but not with the SOA tracers.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 9 February 2013 Received in revised form 30 May 2013 Accepted 11 July 2013

One hundred and forty seasonal PM2.5 samples were collected from January 2010 to January 2011 at one urban site and one suburban site simultaneously in a Chinese megacity, Shanghai, to study the concentrations and seasonal variation of secondary organic aerosols (SOA). Concentrations of water-soluble organic carbon (WSOC) were determined together with organic and elemental carbons. Thirteen organic tracers, including the tracer for biomass burning and tracers for SOA from isoprene, a-pinene, b-caryophyllene and toluene, were measured. EC-based method, WSOC-based method, tracer-based method and PMF modeling were used to estimate the seasonal contributions of secondary organic carbon (SOC) in Shanghai, and the results from the different methods were compared and evaluated. Biomass burning was the major contributor to the measured WSOC in the autumn sampling period, while SOA was the major contributor in the other seasons. The concentrations of the SOA tracers in summer were obviously higher than that in other seasons. It was found that SOC estimated with the tracer-based method accounted for only a small part of the SOC from the WSOC-based method in Shanghai, especially for the winter and spring sampling periods. PMF results showed that a large part of the SOC was associated with sulfate and nitrate but not with the SOA tracers. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: PM2.5 Secondary organic aerosol WSOC Organic tracer Shanghai

1. Introduction PM2.5 in China is drawing more and more attention in recent years both in China and around the world because of its effect on human health, atmospheric visibility and climate change (Jacobson, 2001; Menon et al., 2002; Pope et al., 2002). It is well recognized that carbonaceous aerosols are the important components of the urban PM2.5 particles in China, accounting for up to 40% of the PM2.5 mass (He et al., 2001; Ye et al., 2003; Cao et al., 2004; Feng et al., * Corresponding author. E-mail address: [email protected] (J. Feng). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.07.022

2009). Previous studies have shown that secondary organic aerosols (SOA) dominate even in urban areas (Cao et al., 2004; Duan et al., 2005; Volkamer et al., 2006), and the occurring of atmospheric haze in many Chinese cities was closely related with the formation of SOA (Wang et al., 2006; Tan et al., 2009). To estimate the contribution of SOA to organic carbon (OC), several indirect approaches have been developed. Kondo et al. (2007) found that major part of the SOA was found to be watersoluble and hygroscopic, so water-soluble organic carbon (WSOC) could be used as an estimation of the SOA mass when the contribution of biomass burning is negligible (Weber et al., 2007). In normal cases, the concentration of secondary organic carbon (SOC)

J. Feng et al. / Atmospheric Environment 79 (2013) 614e622

could be roughly estimated as the difference between the total WSOC and the WSOC from biomass burning (Weber et al., 2007; Ding et al., 2008a). But large uncertainty may exist because the WSOC to tracer (levoglucosan) ratio may vary with the types of vegetation and the burning conditions. The elemental carbon (EC) tracer method is another way to estimate the contribution of SOC and has been widely applied, which take EC as the tracer for primary emissions and use the OC/EC ratio obtained from primary emission to estimate the primary organic carbon (POC) in the ambient samples, and the difference between OC and POC is considered to be SOC (Turpin and Huntzicker, 1995). In recent years, several molecular markers of SOA have been found. For example, 2-methylthreitol and 2-methylerythritol were found to be tracers for SOA from isoprene (Claeys et al., 2004; Edney et al., 2005); 3-hydroxyglutaric acid, 3-hydroxy-4,4dimethylglutaric acid and 3-methyl-1,2,3-butane-tricarboxylic acid for a-pinene (Offenberg et al., 2007; Kourtchev et al., 2008a; Szmigielski et al., 2007); b-caryophyllinic acid for b-caryophyllene (Jaoui et al., 2007); and 2,3-dihydroxy-4-oxopentanoic acid for toluene (Jaoui et al., 2007; Kleindienst et al., 2007). Based on the mass ratios of the tracers to SOC derived from the smog chamber experiments, Kleindienst et al. (2007) developed a tracer-based method to estimate the contributions of biogenic and anthropogenic volatile organics to ambient SOC in PM2.5. The method has been applied successfully to the ambient PM2.5 samples (Kleindienst et al., 2007; Hu et al., 2008; Ding et al., 2012). Kleindienst et al. (2010) studied the consistencies of the tracerbased method by comparing with other approaches including multiple regressions, CMB, carbon isotope and EC-based method in the southeastern United States, and the results showed that these approaches matched reasonably well. Ding et al. (2012) compared SOC from the tracer-based method with that from the EC-based method, the results showed that SOC levels estimated by the tracer-based method was consistent and correlated well with those by the EC-based method during summer, however, SOC from the tracer-based method was only one third of that by EC-based method during fallewinter, and the gaps were significantly correlated with levoglucosan, the biomass burning tracer. Although many chamber experiments had been carried out, the uncertainty of the tracer-based method should be considerably large due to the differences between the laboratory and atmospheric conditions (Edney et al., 2005).

615

Published studies on SOA tracers in China focus mainly on the rural sites in Pearl River Delta (sub-tropicaletropical areas in south China) and the forestry areas and mainly in summer. Wang et al. (2008) analyzed the biogenic SOA tracers in four forested areas in China in summer. Their study confirmed the importance of biogenic VOCs in the formation of SOA. Fu et al. (2010) measured the biogenic SOA tracers at the summit of Mountain Tai, Central East China, in summer, and estimated the contribution of biogenic precursors to SOA with the tracer-based method. Ding et al. (2011) measured the biogenic SOA tracers at a rural site in the central Pearl River Delta region in south China, and investigated the enhancement of SOA formation by the aerosol acidity. Hu et al. (2008, 2010) measured SOA tracers in the summer PM2.5 samples in Hong Kong and used these tracers together with other inorganic and organic constituents in the source apportionment of primary and secondary aerosols. There is still no report on the SOA tracers and their seasonal variations in the urban areas of China. As large uncertainties may exist in the laboratory analysis of the tracers and due to the difference between the laboratory and atmospheric conditions, more field studies are needed to get better application of the tracer-based method to identify the contributing sources of SOA. Shanghai is the most important mega-city in China, and the role of SOA in the formation of urban haze is drawing more and more attention (Feng et al., 2009). Shanghai is also an important starting point of the outflow of continental pollutants to the East China Sea and the Pacific Ocean. In this study, seasonal PM2.5 samples were collected in Shanghai and the SOA tracers from biogenic and anthropogenic precursors measured together with OC, EC and WSOC. Different methods of SOA estimation such as EC-based method, WSOC-based method and tracerbased PMF method were applied and compared. The purposes of the study are 1) to provide the levels of SOA tracers and their seasonal variations in the urban areas of Shanghai; 2) to get better understanding of the contributing sources of SOA in PM2.5 by studying the distribution of the SOA tracers. 2. Experimental 2.1. Sampling PM2.5 samples were collected simultaneously at an urban site and a suburban site in Shanghai (Fig. 1) using high-volume samplers (Thermo, USA) at a flow rate of 1.13 m3 min1. The urban site (XJH,

Fig. 1. Map of sampling sites (stars in the right sub-figure).

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100 mL N,O-bis-(trimethylsilyl)-trifluoroacetamide (BSTFA, with 1% trimethylchlorosilane as catalyst) and 20 mL pyridine at 75  C for 45 min. Hexamethylbenzene was added before injection as internal standard to check the recovery of the surrogates. GCeMS analysis was conducted with an Agilent 6890 GC/5975 MSD. The GC was equipped with a DB-5MS capillary column (30 m  0.25 mm  0.25 mm film thickness, J&W Scientific), and the oven temperature was initially held at 60  C for 2 min, increased to 300  C at 5  C min1 and held for 10 min. The MS was operated in EI mode at 70 eV with a scan range of 50e550 amu. Thirteen target compounds were quantified in the GCeMS analysis, as shown in Fig. 2. The SOA tracers were identified by comparing their mass spectra and GC retention times with previously reported data (Claeys et al., 2004, 2007; Hu et al., 2008; Jaoui et al., 2007; Kleindienst et al., 2007; Szmigielski et al., 2007; Wang et al., 2005, 2008). In this study, levoglucosan and cis-pinonic acid were quantified by authentic standards. While the SOA tracers without available commercial standards were quantified using KPA as the surrogate (Kleindienst et al., 2007). Areas of typical ions were used in quantification instead of the total ions, which is the method adopted by Kleindienst et al. (2007), due to the impact of co-elution in the ambient samples. To reduce the uncertainties in quantification, relative response factors of the target tracers to KPA were estimated by comparing the target to KPA area ratio of typical ions with that of total ions in the selected summer samples with high concentrations of target tracers.

E121250 4700, N31100 4200 ) is located in Xujiahui district (Shanghai Academy of Environmental Sciences) with a sampling height of about 15 m, representing the typical urban environment in Shanghai. Traffic, commercial and residential activities are the main local emissions at XJH. The suburban site (BS, E121230 4200 , N31180 5400 ) is on the rooftop of a 20 m-high building on the campus of Shanghai University in Baoshan district, representing the residential environment impacted by industrial activities. The distance between the two sampling sites is about 16 km. Industrial and agriculture emissions had stronger impact on BS than XJH. The sampling started at 9 A.M. and lasted nominally 24 h for each sample. A total of 140 samples were collected during January 13e24, April 21eMay 10, July 10e24, October 20eNovember 11 of 2010, and January 4e14 of 2011. The average daily temperatures were 5  C, 18  C, 29  C, 15  C and 1  C in each sampling periods, respectively. As a coastal city influenced by the Asian Monsoon, the prevailing wind in Shanghai is southeasterly (oceanic) in spring and summer, while northwesterly in winter. All samples were collected on quartz fiber filters (20.3 cm  25.4 cm, Whatman QM-A) which were baked at 450  C for 5 h before use. Field blanks were collected for each sampling period at both sites. 2.2. Chemical analysis The concentrations of OC and EC in each sample was analyzed with a DRI 2001A thermal/optical carbon analyzer (Atmoslytic Inc., Calabasas, CA, USA) with the IMPROVE temperature program. To measure the concentration of WSOC, part of the sampled filters (6 cm2) were ultrasonically extracted with 10 ml Milli-Q water for 30 min at room temperature, the extract was filtered and analyzed using a total organic carbon (TOC) analyzer (Multi N/C 2100, Analytikjena, Germany). The method detection limit (MDL) was determined as three times of the standard deviation of the concentrations in the field blanks, and it was 0.2 mgC m3 based on the filter area of 6 cm2 and the volume of water of 10 mL. The analyzing procedure for polar tracers was adapted from that of Kleindienst et al. (2007). About 20 cm2 of each quartz filter was ultrasonically extracted with 20 mL of dichloromethane/methanol (1:1, v/v) at room temperature for three times and the extracts combined. Prior to solvent extraction, surrogate mixture of methylb-D-xylanopyranoside (MXP) and cis-ketopinic acid (KPA) were spiked into the samples as internal/recovery standards. The extracts were filtered with quartz wool, blown to dryness under a gentle stream of ultrapure nitrogen and then derivatized with

2.3. Quality assurance/quality control Field blanks and laboratory blanks (every ten samples) were extracted and analyzed in the same way as the ambient samples. Target compounds were not detected in the field and laboratory blanks during the tracer analysis. Recoveries of the surrogates (MXP and KPA) were 70%e110%. The reported results were recovery corrected assuming that the target compounds had the same recovery as the surrogates. Duplicate analysis showed that the deviation was less than 15%. 3. Results and discussion 3.1. Concentrations of OC, EC and WSOC The average mass concentrations of OC, EC and WSOC at the two sampling sites during the sampling periods were summarized in Table 1. The concentrations of OC and EC at the BS sampling site

Abundance 2000000

B1

1800000 1600000 I6

1400000 1200000 1000000 800000 600000 I3 400000

I1

I4

IS T1

I2

I5

A3

A2

A4

A1

200000 Time-->

16.0

18.0

20.0

22.0

24.0

26.0

28.0

30.0

32.0

Fig. 2. Total ion chromatogram for silylated sample. I1. 2-methylglyceric acid; I2. cis-2-methyl-1,3,4-trihydroxy-1-butene; I3. 3-methyl-2,3,4-trihydroxy-1-butene; I4. trans-2methyl-1,3,4-trihydroxy-1-butene; I5. 2-methylthreitol; I6. 2-methylerythritol; A1. cis-pinonic acid; A2. 3-hydroxyglutaric acid; A3. 3-hydroxy-4,4-dimethylglutaric acid; A4. 3methyl-1,2,3-butanetricarboxylic acid; T1. 2,3-dihydroxy-4-oxopentanoic acid; b1. b-caryophyllinic acid; B1. levoglucosan; IS. KPA.

J. Feng et al. / Atmospheric Environment 79 (2013) 614e622

617

Table 1 Concentrations of OC, EC, WSOC and polar organic tracers in PM2.5 in Shanghai. XJH

BS

Jan 3

OC (mg m ) EC (mg m3) WSOC (mg m3) Organic compounds (ng m3): Levoglucosan 2-Methylglyceric acid cis-2-Methyl-1,3,4-trihydroxy1-butene 3-Methyl-2,3,4-trihydroxy-1-butene trans-2-Methyl-1,3,4-trihydoxy1-butene 2-Methylthreitol 2-Methylerythritol P 2-Methyltetrol P Isoprene oxidation products cis-Pinonic acid 3-Hydroxyglutaric acid 3-Hydroxy-4,4-dimethylglutaric acid 3-Methyl-1,2,3-butanetricarboxylic acid P a-Pinene oxidation products b-Caryophyllinic acid 2,3-Dihydroxy-4-oxopentanoic acid

ApreMay

Jul

OcteNov

Jan

ApreMay

Jul

OcteNov

13.3  6.7 3.8  3.9 5.1  2.1

8.6  3.0 2.2  1.1 3.2  0.8

6.3  2.4 1.8  0.4 2.9  1.5

13.7  11.6 2.7  1.9 7.8  6.6

13.7  6.2 3.8  2.3 5.4  2.2

8.5  3.0 2.4  1.2 3.2  1.0

6.4  2.5 1.4  0.5 2.8  1.2

17.1  15.1 3.5  2.4 7.4  6.9

151.6  79.6 1.0  0.5 0.1  0.1

58.3  27.5 0.8  0.3 0.1  0.1

42.6  35.0 2.3  2.5 4.3  7.3

339.4  348.1 2.5  2.7 0.3  0.4

214.2  102.8 0.9  0.5 0.1  0.1

88.8  57.2 1.2  0.8 0.2  0.2

77.7  70.1 2.9  3.1 4.3  6.5

485.7  456.7 2.0  2.2 0.2  0.3

0.1  0.1 0.2  0.2

0.2  0.2 0.3  0.4

6.2  9.9 14.0  22.7

0.3  0.2 0.9  1.1

0.1  0.1 0.2  0.1

0.2  0.3 0.5  0.5

7.0  10.7 11.4  16.6

0.4  0.4 1.3  2.0

0.3 0.8 1.1 2.5 0.6 0.1

     

0.3 0.6 0.9 1.6 0.4 0.1

nd nd 0.7  0.4 nd 0.2  0.2

0.3 0.8 1.2 2.5 1.0 0.5 0.8 0.4

       

0.3 0.7 1.0 1.7 0.7 0.3 0.7 0.3

2.7  1.3 0.4  0.3 0.6  0.4

5.7 16.7 22.5 49.2 0.3 0.7 3.1 3.8

       

8.7 25.1 33.7 75.5 0.3 0.5 3.2 4.5

8.0  8.1 0.7  0.4 1.9  1.7

were 1.8e58.3 mg m3 and 0.7e9.7 mg m3 respectively, with the average concentrations of 11.9 and 2.9 mg m3, slightly higher than that found at XJH (averaged at 11.2 mg m3 and 2.8 mg m3 for OC and EC), but the differences were not statistically significant. The concentration of WSOC was 0.8e26.9 mg m3 with an average of 4.9 mg m3 at BS, and 0.9e22.6 mg m3 with an average of 5.0 mg m3 at XJH. Averaged for the whole sampling periods, WSOC contributed 33% and 37% of the total carbon (TC, sum of OC and EC) at BS and XJH, higher than the reported values for the year of 2002e2003 (Feng et al., 2006). The higher relative contribution of WSOC in this study should be resulted from the decreasing contribution of primary organic aerosols achieved by the fulfillment of emission reduction measures in recent years. Significant correlation was found between the WSOC concentrations at BS and XJH (R2 ¼ 0.92, slope ¼ 1.04), suggesting that WSOC in Shanghai had little spatial variation. From Table 1, it can be seen that the concentrations of OC and WSOC showed a seasonal variation of autumn (OcteNov) > winter (Jan) > spring (ApreMay) > summer (Jul), while EC had a trend of winter > autumn > spring > summer at both sites. The seasonal variation of higher pollutant concentrations in winter/fall than summer in Shanghai were well recognized and can be attributed to the seasonal changes of meteorological conditions such as temperature and wind direction (Ye et al., 2003; Feng et al., 2006, 2009). Other factors besides accumulation should be the cause of the highest concentration of WSOC in autumn because the EC concentration in the autumn sampling period was lower than that in winter. Burning of crop residues is believed to be the main reason, and will be discussed later.

0.8 2.3 3.1 7.1 2.1 1.1 1.4 0.5

       

0.9 2.4 3.2 7.3 3.2 1.1 1.6 0.7

5.1  5.4 1.2  1.4 2.6  3.1

0.3 0.7 1.0 2.2 0.8 0.1

     

0.2 0.5 0.7 1.3 0.3 0.1

nd nd 0.9  0.4 nd 0.3  0.2

0.5 1.3 1.7 3.8 1.7 0.8 2.3 1.4

       

0.6 1.6 2.3 3.9 1.4 0.5 2.0 1.5

6.1  3.7 1.0  0.7 1.1  0.7

8.5 24.4 32.9 58.6 0.8 1.0 3.5 4.7

       

12.2 35.5 47.7 83.9 0.5 0.7 3.5 5.6

10.0  9.6 1.1  0.9 2.0  1.6

0.6 1.6 2.2 6.1 1.0 1.1 1.2 0.4

       

0.5 1.3 1.8 6.4 0.7 1.2 1.2 0.4

3.7  2.5 1.2  1.6 1.9  2.0

Strong correlation was found between the levoglucosan concentration at BS and XJH (R2 ¼ 0.94, slope ¼ 1.38), and the levoglucosan concentration at BS was significantly higher than that at XJH, in accordance with the fact that BS is closer to the farm lands. Strong correlations were found between levoglucosan and WSOC at both BS and XJH (with r values of 0.90 and 0.95 respectively), showing that biomass burning was an important contributor to the WSOC in PM2.5 in Shanghai. The major part of OC from biomass burning was found to be water-soluble (Mayol-Bracero et al., 2002), and the OC/levoglucosan ratio (mgC mg1) in the fine particles from biomass burning experiments in China was about 8e 16 (Zhang et al., 2007). Ding et al. (2008a) used WSOC/levoglucosan ratio of 10 when estimating the biomass burning contribution to WSOC in PM2.5 in southeastern US. In this study, the lowest WSOC/ levoglucosan ratio was found to be w9 for the ambient PM2.5 (occurred in autumn), so WSOC/levoglucosan ratio of 8 for source emission was used to estimate the biomass burning contribution to WSOC. It should be noted that using a fixed WSOC/levoglucosan ratio in different seasons would cause considerable uncertainty because the WSOC/levoglucosan ratio would vary with the types of vegetation and the burning conditions. The results showed that the contributions of biomass burning was highest in the autumn sampling period (averaged at 3.9 mg m3 and 2.7 mg m3 at BS and XJH respectively), accounting for 54% of the measured WSOC at BS, followed by winter (31%), spring (22%) and summer (21%). Comparing with BS, the contributions of biomass burning to WSOC were lower at XJH (35%, 23%, 14% and 11% for autumn, winter, spring and summer respectively). 3.3. Estimation of SOC with EC-based and WSOC-based method

3.2. Contribution of biomass burning to WSOC Levoglucosan was the dominant polar organic compounds in nearly all samples, with the average concentration of 230 ng m3 and 167 ng m3 in the whole sampling periods at BS and XJH. Autumn (OcteNov) had the highest average concentration of levoglucosan (Table 1), about 2 and 6 times of that in winter and summer, indicating the high contribution of biomass burning in autumn as farmers burned the crop residues in the harvest season.

Though the importance of SOA has long been recognized, there is still no direct way to separate SOC from primary OC (POC). Several indirect methods, such as EC-based method (Turpin and Huntzicker, 1995), WSOC-based method (Weber et al., 2007), CMB method (Ding et al., 2008b) and AMS method (Xiao et al., 2011), have been developed to estimate the SOC concentration. EC-based method has been applied widely, which uses EC as the tracer for POC because EC is directly emitted from combustion

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sources and will not change during the atmospheric transport (Turpin and Huntzicker, 1995). But it is usually impossible to get the OC/EC ratio for primary sources, and the primary OC/EC ratio should be different for different types of emission sources and will be affected by many factors such as ambient temperature and carbon determination method. So the observed minimum ratio ((OC/EC)min) was used in stead of the primary OC/EC ratio as discussed in detail by Castro et al. (1999), and the estimation equation as follow:

SOCECbased ¼ OC  EC  ðOC=ECÞmin The (OC/EC)min in this study was 3.20, 2.64, 3.40 and 2.37 in the January, AprileMay, July and OctobereNovember sampling periods at BS, and 1.68, 2.43, 2.43 and 3.14 at XJH respectively. Previous studies found that major part of the SOA is watersoluble (Kondo et al., 2007), and WSOC in fine particles is mainly derived from SOC and biomass burning (Weber et al., 2007). So SOC concentration can be estimated as following:

SOCWSOCbased ¼ WSOCtotal  WSOCBB where WSOCBB was the WSOC from biomass burning, which was already discussed in Section 3.2. The estimated SOC concentrations from the EC-based and WSOC-based method were listed in Table 2. It can be seen from Table 2 that the SOC concentrations estimated with the EC-based and WSOC-based method in winter, spring and summer were comparable, and the average SOCWSOC-based was usually higher than SOCEC-based. However, SOCEC-based in the autumn sampling period was obviously higher than SOCWSOC-based. The ECbased method gave the highest SOC to OC ratio in autumn (44% at BS and 46% at XJH), similar phenomena was also reported for the PM2.5 in Shanghai by Feng et al. (2009). This is somehow contrary to the well accepted concept that higher SOC/OC ratio would be found in summer (Szidat et al., 2006; Ding et al., 2011). Meanwhile, the SOCWSOC-based/OC trend was summer > autumn > spring > winter, in accordance with the meteorological conditions during the sampling periods. As mentioned earlier, episodic crop residue burning happened from time to time in autumn, causing high OC/EC value in the ambient PM2.5 because biomass burning emit mainly organic matter. It would thus be a plausible explanation that the SOCEC-based in autumn contained considerable amounts of primary OC from biomass burning. Significant correlation was found between SOCWSOC-based and SOCEC-based when the samples with minimum OC/EC ratios were excluded. The regressing equation at XJH was SOCWSOC-based ¼ 0.49* SOCEC-based þ 1.48 with a R2 value of 0.85 (similar equation was got at BS). The positive intercept suggested that SOCEC-based suffered from the incorrectly estimated primary OC/EC ratio because part of the OC in the samples with minimum OC/EC ratios could also be secondary. Though SOCWSOC-based might be underestimated as some of the oxidation products with high carbon/oxygen ratio would be more soluble in organic phase than aqueous phase, we believe that the WSOC-based method could give reasonable estimation of the

Table 2 Estimated SOC concentrations in Shanghai based on EC and WSOC (unit: mg m3, percentage in the brackets was the contribution of SOC to OC). Jan SOCEC-based: BS 2.8  1.9 XJH 4.8  3.1 SOCWSOC-based: BS 3.7  1.6 XJH 3.9  1.6

ApreMay

Jul

OcteNov

(25%) (41%)

2.2  1.1 (29%) 1.8  1.1 (23%)

1.5  1.2 (25%) 1.9  2.2 (25%)

8.8  9.8 (44%) 7.2  7.8 (46%)

(28%) (31%)

2.5  0.9 (30%) 2.7  0.6 (33%)

2.1  1.1 (35%) 2.5  1.2 (39%)

3.8  3.8 (24%) 5.0  4.0 (37%)

SOC contribution (Weber et al., 2007). So SOCWSOC-based was used in the following discussions. 3.4. Concentrations and seasonal variations of SOA tracers 3.4.1. Isoprene oxidation products The concentrations of the 12 quantified SOA tracers in different sampling periods were listed in Table 1. 2-methylglyceric acid, three C5-alkene triols and two 2-methyltetrols (I1 w I6 in Fig. 2) were identified as tracers of SOA from isoprene according to the previous chamber experiments (Claeys et al., 2004; Edney et al., 2005). The total concentration of the measured isoprene SOA tracers at BS ranged 1.9e292.8 ng m3 with an average of 58.6 ng m3 in summer, and 0.8e23.5 ng m3 with an average of 4.0 ng m3 in other three sampling periods. 2-methyltetrols (sum of 2-methylthreitol and 2-methylerythritol) accounted for about half of the total amount of the measured isoprene tracers (Table 1). The summer concentration of 2-methyltetrols were ten times higher than the other sampling periods, and the seasonal trend of summer > autumn > spring > winter in this study was in agreement with the dependence of isoprene emission and photochemical oxidation on the ambient temperature (Xia and Hopke, 2006; Kleindienst et al., 2007; Ding et al., 2008a, 2011; Lewandowski et al., 2008; Hu et al., 2008). The summer concentration of 2-methyltetrols in Shanghai was lower than that in North Carolina of the US (Kleindienst et al., 2007), but comparable with that found in Hong Kong and Guangzhou (Hu et al., 2008; Ding et al., 2011). C5-alkene triols were found to have comparable concentrations with 2-methyltetrols, averaged at 0.8, 22.7, 1.9 and 0.3 ng m3 in spring, summer, autumn, and winter, respectively at BS. The average concentration of C5-alkene triols in summer in Shanghai was lower than which reported in sub-tropical Hong Kong (w50 ng m3), and much higher than those reported for a Scots pine dominated forest in southern Finland (w4.5 ng m3) and western Germany (w2.7 ng m3) (Kourtchev et al., 2008a,b). 3.4.2. a-pinene oxidation products The measured tracers for SOA from a-pinene included cispinonic acid, 3-hydroxyglutaric acid (HGA), 3-hydroxy-4,4dimethylglutaric acid (HDMGA), 3-methyl-1,2,3-butane-tricarboxylic acid (MBTCA) (A1 w A4 in Fig. 2). The total concentration of the a-pinene SOA tracers ranged from 0.4 to 34.9 ng m3, with an average of 4.6 ng m3. HDMGA and MBTCA were the dominant compounds in summer (Table 1), accounting for about 75% of the total a-pinene tracers, but they were not detected in the winter samples. The concentration of HDMGA was obviously higher than that of MBTCA in the spring and autumn sampling periods (average HDMGA/MBTCA ratio was 2 and 3 respectively), while they had similar concentrations in the summer samples. One reason for this phenomenon should be the seasonal change of monoterpene emissions, and another possible explanation could be that the higher temperature and stronger solar radiation in summer favored the formation of MBTCA. The concentration of HGA was less than 2 ng m3 even in the summer samples, similar to what found in the Midwestern US cities (Lewandowski et al., 2008). Comparing with the isoprene tracers, the seasonal variations for the a-pinene tracers were complicated. HDMGA and MBTCA had highest concentration level in summer, while HGA had comparable level in spring, summer and autumn. Cis-pinonic acid had higher concentrations in the spring and autumn sampling periods than that in winter and summer. It was found that cis-pinonic acid was the intermediate compound during the oxidation of a-pinene to HGA and MBTCA (Szmigielski et al., 2007). The lowest average concentration of cis-pinonic acid in summer suggested its further oxidation to form other compounds under high temperature and

J. Feng et al. / Atmospheric Environment 79 (2013) 614e622

strong solar radiation. Shift of gas/particle partitioning also contributed to the lower concentration of cis-pinonic acid in summer.

619

Table 4 Correlations of tracers at XJH with that at BS during different sampling periods.

Isoprene tracer a-Pinene tracer b-Caryophyllene tracer Toluene tracer

3.4.3. b-Caryophyllene oxidation products b-caryophyllinic acid (b1 in Fig. 2) was the photooxidation product of b-caryophyllene, a biogenic sesquiterpene, and had been detected as a SOA tracer in several studies (Jaoui et al., 2007; Lewandowski et al., 2007; Fu et al., 2009). In this study, b-caryophyllinic acid was detected in the spring, summer and autumn samples with the highest concentration of w6 ng m3 (<2 ng m3 for most of the samples). Due to its high molecular weight, sesquiterpene is not likely emitted in the cold season. No distinct seasonal variation was found for b-caryophyllinic acid, suggesting the different emission pattern of sesquiterpene with that of isoprene or monoterpene.

Jan

ApreMay

Jul

OcteNov

All periods

0.80 0.22 e 0.28

0.71 0.24 0.66 0.29

0.89 0.92 0.94 0.77

0.88 0.44 0.56 0.57

0.92 0.70 0.46 0.24

product of ozone oxidation of cis-pinonic acid (Szmigielski et al., 2007; Surratt et al., 2010), the significant mutual correlations of these tracers suggested that the emission pattern of the precursors (isoprene and a-pinene) were similar and/or the photochemical formation of these tracers achieved states of equilibrium. The correlations between 2-methylglyceric acid (I1) and the other isoprene tracers were weaker. It’s quite interesting that 2-methylglyceric acid, 3-hydroxyglutaric acid, b-caryophyllinic acid and 2,3Dihydroxy-4-oxopentanoic acid (I1, A2, b1 and T1) were significantly correlated with each other, but their correlations with other SOA tracers were weak, suggesting their different formation pathways with other SOA tracers. It’s noteworthy that all the above mentioned SOA tracers were significantly correlated in the summer samples. As expected, cis-pinonic acid (A1) was not (sometimes negatively) correlated with other species, confirming that it is the intermediate product during the oxidation process. To investigate if the spatial distribution of the SOA tracers in Shanghai were homogeneous, correlations between the two sampling sites were made (Table 4). Concentrations of isoprene tracers at BS were significantly correlated with that at XJH in all the sampling periods (r > 0.7), suggesting that isoprene SOA in Shanghai had regional character. For the tracers of a-pinene, b-caryophyllene and toluene, strong correlations between the two sites were also found in the summer, but the correlations were weak in other sampling periods, suggesting the impact of local emissions. As mentioned earlier, the main source of anthropogenic aromatics was industry activities, and would change from site to site. So the poor correlation for the toluene tracer was expected and was due to the impact of local emissions. O3 concentration was monitored at XJH, and the correlation between the SOA tracers and O3 were analyzed. No correlation between isoprene tracers and O3 was found. For the a-pinene tracers, moderate correlations (r value of about 0.5) were found only in the spring sampling period. While the SOA tracers for bcaryophyllene and toluene showed moderate correlations with O3 in the spring and summer sampling periods. As O3 concentrations at the urban site was influenced/controlled by local emissions, the correlation results between SOA tracers and O3 suggested that isoprene SOA in Shanghai was regional, while the sesquiterpene and toluene SOA had local contributions.

3.4.4. Toluene oxidation products 2,3-Dihydroxy-4-oxopentanoic acid (DHOPA, T1 in Fig. 2) was detected in controlled laboratory chamber experiments with toluene/NOx and also in the field samples (Kleindienst et al., 2004), and was confirmed to be the tracer for SOA from anthropogenic aromatics. The concentration of DHOPA in this study ranged from 0.04 to 10.0 ng m3 (average of 1.2 ng m3, Table 1), comparable with the results in Hong Kong (Hu et al., 2008). Higher average concentrations were found in summer and autumn, followed by spring and winter. Previous studies revealed that the major sources of anthropogenic aromatics in Shanghai (Yangtze River Delta) were chemical industry, industrial coating, domestic paint/printing and gasoline vehicles, and only very small seasonal change existed in the total amount (Liu et al., 2010; Huang et al., 2011). So the observed seasonal variation in DHOPA concentration reflected the high oxidative power in summer and the accumulation of pollutants in autumn. 3.4.5. Correlation between the SOA tracers Correlations between the measured SOA species were conducted to evaluate if these species were formed in similar pathway, and the results for all sampling periods were listed in Table 3. Significant mutual correlations were found between the isoprene SOA tracers of C5-alkene triols and 2-methyltetrols (I2 w I6 in Table 3) and the a-pinene SOA tracers of HDMGA and MBTCA (A3 and A4). Fu et al. (2010) also found that MBTCA and the isoprene tracers were in the same cluster when performing hierarchical cluster analysis on dataset of aerosols over Mt. Tai of China. Chamber test suggested that the formation pathways of MBTCA and the isoprene tracers could be different, say, 2-methyltetrols are formed by oxidation of isoprene with OH radical while MBTCA is a

Table 3 Correlations between the SOA tracers in PM2.5 in Shanghai (The compound names of the tracer ID are the same as that in Fig. 2. Correlation coefficients larger than 0.7 are listed in bold to highlight the strong correlations.).

I1 I2 I3 I4 I5 I6 A1 A2 A3 A4 b1 T1

I1

I2

I3

I4

I5

I6

A1

A2

A3

A4

b1

T1

1 0.56 0.53 0.54 0.59 0.58 0. 30 0.81 0.66 0.57 0.76 0.82

1 0.99 0.99 0.95 0.96 0.06 0.33 0.80 0.93 0.32 0.38

1 0.98 0.96 0.97 0.07 0.35 0.80 0.94 0.34 0.38

1 0.93 0.93 0.07 0.37 0.77 0.91 0.35 0.40

1 0.99 0.04 0.38 0.75 0.92 0.38 0.40

1 0.04 0.34 0.74 0.93 0.37 0.38

1 0.41 0.14 0.06 0.44 0.42

1 0.65 0.46 0.84 0.84

1 0.86 0.67 0.64

1 0.46 0.43

1 0.80

1

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J. Feng et al. / Atmospheric Environment 79 (2013) 614e622

3.5. SOC estimation with SOA tracers The SOC contributions of isoprene, a-pinene, b-caryophyllene and toluene precursors to PM2.5 in Shanghai were estimated with the measured tracer concentrations in different seasons and the laboratory-derived tracer mass fraction (fsoc) factors of 0.155, 0.231, 0.0230 and 0.0079 for the above precursors respectively (Kleindienst et al., 2007). The results were listed in Table 5. The total concentration of SOC estimated from SOA tracers (SOCtracer-based) ranged from 0.02 to 2.76 mg m3 at BS, with an average of 0.30 mg m3. The urban site (XJH) had similar SOCtracerbased concentrations. The average concentration of SOCtracer-based showed distinct seasonal variation as expected, highest during the summer and lowest during the winter (Table 5). SOC from anthropogenic aromatics (SOCtoluene in Table 5) accounted for more than half of the SOCtracer-based (except the summer sampling period at BS). The relative contribution of SOC from aromatics in this study was comparable with that found in Hong Kong (76e79%, Hu et al., 2008), indicating that anthropogenic VOCs is the main source of SOA in Shanghai. Among the estimated SOC from biogenic VOCs (sum of SOCisoprene, SOCa-pinene and SOCb-caryophyllene in Table 5), isoprene contributed more than 80% during the summer sampling period, while about half of the biogenic SOC was SOCb-caryophyllene in spring and autumn, suggesting that isoprene was the biggest contributor of biogenic SOA in summer, and sesquiterpene was also an important biogenic SOA contributor in Shanghai. Large discrepancy was found between SOCtracer-based and SOCWSOC-based in Shanghai, especially in the winter and spring sampling periods. SOCtracer-based accounted for 2%, 10%, 30% and 16% of the SOCWSOC-based in the winter, spring, summer and autumn sampling periods at BS, while 2%, 5%, 19% and 8% respectively at XJH. Correlation analysis showed that SOCtracer-based and SOCWSOCbased were significantly correlated in summer and autumn (r > 0.75), while no significant correlation (r < 0.45) could be found in winter and spring. The discrepancy found could be partly ascribed to two possible reasons. Firstly, tracers measured in this study were not the same as that of Kleindienst et al. (2007), thus adoption of the fsoc values from chamber test directly would cause uncertainty. We evaluated this uncertainty by adjusting the fsoc value for isoprene and a-pinene with the mass fraction of the tracers measured in both studies in the total tracer mass from the chamber tests, and then re-calculated the SOC contribution based only on the common tracers. The deviation between the two estimations was found to be 30% or less for most of the samples. Secondary, the tracers were measured with chemical ionization (CI) mode mass spectrometer in the study of Kleindienst et al. (2007),

while electronic ionization (EI) mode in this study. The sensitivities of CI and EI are different for the compounds without commercial standards, and would cause considerable uncertainty in the estimation of SOA. Despite of the large uncertainties in both of the tracer-based method and the WSOC-based method, the low percentage of SOCtracer-based in SOCWSOC-based suggested that part of the SOA in PM2.5 in Shanghai could not be explained by the tracerbased method. These un-explained SOC could be from two possible sources: Firstly, other chemical mechanisms besides what happened in the chamber test, such as cloud-processing, were important for the formation of SOA. Secondary, oxidation products of other precursors contributed to SOA, but not identified and included in this study.

3.6. Source apportionment of WSOC with PMF To better understand the sources of the SOA in PM2.5 in Shanghai, and to evaluate the estimation results in the earlier sections, positive matrix factorization (PMF, U.S. EPA version 3.0) was conducted to resolve the sources of WSOC. Besides the above discussed species, NO3  , SO4 2 and five saccharides (arabitol, 2 fructose, 2 glucose and mannitol) were included in the PMF model to cover as many possible contributors of WSOC in fine particles in Shanghai (data for ions and saccharides will be discussed in other papers). Six factors/sources were obtained and the source profiles listed in Fig. 3. According to the high loading species in each factor,

100

F1: WSOC from soil

50

0 100

F2: SOA from isoprene

50

0 100

F3: SOA from terpene and toluene

50

0 100

F4: SOA associated with pinonic Acid

50

Table 5 Seasonal average concentrations of SOC at XJH and BS estimated with the tracerbased method (unit: mg m3). Jan

Jul

F5: Biomass burning

OcteNov 50

0.05 0.02 0.05 0.33 0.45 71

     

0.05 0.02 0.06 0.40 0.51 10

0.02 0.03 0.04 0.14 0.24 57

     

0.02 0.02 0.03 0.09 0.15 15

0.38 0.04 0.05 0.26 0.73 45

     

0.54 0.04 0.04 0.20 0.76 15

0.04 0.02 0.05 0.24 0.35 66

     

0.04 0.01 0.07 0.25 0.35 15

0 100

F6: SOA associated with nitrate and sulfate

50

0

Fig. 3. PMF source profiles (% of species) of WSOC in PM2.5 in Shanghai.

T1

0.49 0.04 0.02 0.21 0.74 14

I5

     

I6 M 1 M 2 M 3 M 4

0.32 0.03 0.03 0.24 0.62 51

I3

 0.02  0.03  10

0.01 0.01 0.01 0.06 0.06 14

I4

 0.01  0.00

     

I1

 0.02  0.03  12

0.02 0.01 0.02 0.07 0.12 58

I2

0.01 0.00 e 0.04 0.05 65

 0.01  0.00

SO C NO 3SO 42 Le ar vo ab fru ito ct l fru ose ct 1 o gl s e 2 uc os m e1 an n gl ito uc l os e2

0.02 0.00 e 0.03 0.05 58

W

XJH SOCisoprene SOCa-pinene SOCb-caryophyllene SOCtoluene Total SOCtoluene% BS SOCisoprene SOCa-pinene SOCb-caryophyllene SOCtoluene Total SOCtoluene%

ApreMay

0 100

J. Feng et al. / Atmospheric Environment 79 (2013) 614e622

F1eF6 were recognized as WSOC from soil (Simoneit et al., 2004), SOA from isoprene, SOA from terpene and anthropogenic aromatics, SOA associated with pinonic acid (from monoterpene), WSOC from biomass burning, and SOA associated with nitrate and sulfate, respectively. Biomass burning (F5) contributed about 50% of the WSOC in the autumn and winter sampling periods, while w15% and w10% in the spring and summer sampling periods, comparable with the estimation in Section 3.2. It can be seen from Fig. 3 that SOA from isoprene could be undoubtedly recognized as a single source, while SOA from monoterpene (a-pinene), sesquiterpene (b-caryophyllene) and anthropogenic aromatics could not be separated clearly. WSOC associated with F2, F3, F4 and F6 were considered to be SOC (SOCPMF), and SOCPMF had a range 2.1e2.8 mg m3 with no obvious seasonal variation. SOCPMF in autumn and winter were lower than the SOCWSOC-based (Table 2), while they were comparable in summer and spring. The PMF results showed that SOC from isoprene only accounted for w10% of the SOCPMF in summer and negligible in other sampling periods, and SOC from terpenes and toluene contributed w60% of the SOCPMF in spring, summer and autumn, suggesting that the contribution of SOA from terpenes and aromatics might be underestimated in the tracer-based method used in this study. Linear regression analysis showed that SOCtracer-based was significantly correlated with the SOC estimated by PMF which was associated with the SOA tracers (sum of WSOC in F2, F3 and F4, SOC(F2 þ F3 þ F4)), at a R2 of 0.68, but SOCtracer-based only accounted for 34% of the SOC(F2 þ F3 þ F4). The mass fraction of tracers to WSOC in the PMF source profiles were 0.22 for F2, 0.006 for F3 and 0.0003 for F4, also suggesting the underestimation of SOC in the tracerbased method using fSOC from chamber test (Kleindienst et al., 2007), due to the uncertainties mentioned in the previous section. Meanwhile, 77%, 47%, 14% and 40% of the SOCPMF in the winter, spring, summer and autumn sampling periods was from F6, which was associated with nitrate and sulfate but not with the SOA tracers, suggesting that these part of SOC might be formed via similar mechanism as that for sulfate and nitrate, say, cloudprocessing (Yao et al., 2002), but from different pathway than the SOA tracers. More research are needed to identify the precursors and tracers for the SOC associated with nitrate and sulfate. 4. Conclusions High concentrations of WSOC (averaged at 5 mg m3 for the whole sampling periods) were found in PM2.5 in Shanghai. About half of the WSOC at BS should be contributed by biomass burning in autumn, while less than one third in other seasons, and the contribution of biomass burning to WSOC was lower at XJH (35%, 23%, 14% and 11% for autumn, winter, spring and summer respectively). Most part of the WSOC should thus be SOC, and the SOC/OC ratio was higher at the urban area in Shanghai than the suburban. The WSOC-based method could give more reasonable estimation of SOC than the EC-based method in case of Shanghai, due to the impact of episodic biomass burning and the difficulty in getting representative primary OC/EC ratio. The summer concentration of isoprene SOA tracers (w50 ng m3) was significantly (more than seven times) higher than the other sampling periods, while the tracers for b-caryophyllene and toluene had moderate seasonal variations, showing the different seasonal emissions of the precursors. The average SOC concentrations estimated with tracer-based method were less than 1.0 mg m3 in all the sampling periods, and more than half of the SOC was from anthropogenic aromatics. Large discrepancies were found between the SOC estimated with tracer-based method and WSOC-based method in Shanghai especially in the winter and spring sampling periods, possibly due to the uncertainties in the

621

analytical methods. PMF analysis with tracers indicated that a large part of the SOC in PM2.5 in Shanghai was associated with nitrate and sulfate but not with the measured SOA tracers. As commercial standards for many of the tracers are not available, large uncertainty might be induced in the quantification of the SOA tracers, PMF method with SOA tracers should be a practical way to make out the SOA contributions from different sources. Acknowledgments This research was financially supported by the National Natural Science Foundation of China (Grant No: 41173097), the Innovation Program of Shanghai Municipal Education Commission (12YZ027) and the Shanghai Leading Academic Disciplines (S30109) for which the authors are grateful. References Cao, J.J., Lee, S.C., Ho, K.F., Zou, S.C., Fung, K., Li, Y., Watson, J.G., Chow, J.C., 2004. Spatial and seasonal variations of atmospheric organic carbon and elemental carbon in Pearl River Delta Region, China. Atmospheric Environment 38, 4447e4456. 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