The characteristics, seasonal variation and source apportionment of VOCs at Gongga Mountain, China

Atmospheric Environment 88 (2014) 297e305

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The characteristics, seasonal variation and source apportionment of VOCs at Gongga Mountain, China Junke Zhang a, b, Yang Sun a, Fangkun Wu a, Jie Sun a, Yuesi Wang a, * a State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China b University of Chinese Academy of Sciences, Beijing 100049, China

h i g h l i g h t s
This is the first time to study the VOCs in the remote station in southwest China.
Aromatics and alkanes are the major components of VOC.
The seasonal variation shows higher value in spring and lower value in autumn.
Anthropogenic sources are the most important sources in the remote area.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 November 2012 Received in revised form 4 March 2013 Accepted 18 March 2013 Available online 29 March 2013

The mixing ratio, composition and variability of volatile organic compounds (VOCs) were measured from 2008 through 2011 at Gongga Mountain Forest Ecosystem Research Station (102 000 E, 29 330 N, elevation 1640 m), a remote station in southwest China. Weekly samples were collected in the Gongga Mountain area and were analyzed using a three-stage preconcentration method coupled with GCeMS. An advance receptor model positive matrix factorization (PMF) was applied to identify and apportion the sources of VOCs. The results show that the measured VOC mixing ratio at Gongga Mountain is dominated by aromatics (35.7%) and alkanes (30.8%), followed by halocarbons (21.6%) and alkenes (11.9%). The general trend of seasonal variation shows higher mixing ratios in spring and lower mixing ratios in autumn. The effect of alkanes and aromatics on the seasonal variation of total volatile organic compounds (TVOCs) is significant. Five sources were resolved by the PMF model: (1) gasoline-related emission (the combination of gasoline exhaust and gas vapor), which contributes 35.1% of the measured VOC mixing ratios; (2) solvent use, contributing 21.8%; (3) fuel combustion, contributing 29.1%; (4) biogenic emission, contributing 5.2%; and (5) industrial, commercial and domestic sources, contributing 8.7%. The effect on this area of the long-range transport of air pollutants from highly polluted areas is significant. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: VOC sources at Gongga Mountain Seasonal variation PMF receptor model Source apportionment

1. Introduction There is no doubt of the importance of volatile organic compounds (VOCs) in the atmosphere, because they are the important precursors of secondary air pollutants and secondary organic aerosols in photochemical processes. Moreover, most of VOCs are also impact the environment and human health directly (Leuchner and Rappenglück, 2010; Pérez-Rial et al., 2010). The study of VOCs is still very limited in China because most such studies have focused on mega-cities or city clusters, such as Beijing (Song et al., 2007; Yuan et al., 2009; Su et al., 2011), the Pearl

* Corresponding author. Tel.: þ86 (0)1082080530; fax: þ86 (0)1062362389. E-mail addresses: [email protected], [email protected] (Y. Wang). 1352-2310/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.atmosenv.2013.03.036

River Delta (Chan et al., 2006; Guo et al., 2006, 2011; Ling et al., 2011) and the Yangtze River Delta (Cai et al., 2010; Geng et al., 2010; Huang et al., 2011). However, information about the source characteristics of VOCs in remote areas is insufficient because it is more difficult and more expensive to observe these areas than observe areas in cities. However, the study of VOC emissions from remote areas is crucial because these areas occupy more area than cities worldwide. Thus, the results of such studies are highly useful for studying the global temporal and spatial variation of VOCs. Unfortunately, this type of study is rare in China, especially in the remote area of the underdeveloped southwestern region of China. Furthermore, it is not sufficient simply to measure the mixing ratios of VOCs to develop of an effective control strategy. We also need to obtain and understand accurate information about the sources of VOCs. One effective method for studying VOC sources

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involves the use of receptor models (Song et al., 2007; Lanz et al., 2009; Chan et al., 2011; Ling et al., 2011), such as positive matrix factorization (PMF). This receptor model has been successfully tested in comparison with other receptor models for VOCs (Song et al., 2007; Sauvage et al., 2009; Yuan et al., 2009) and particulate matter (PM) (Leuchner and Rappenglück, 2010; Chan et al., 2011). A very effective statistical method, it can apportion ambient concentration data to sources by identifying the intrinsic characteristics of the data while limiting all of the elements in the factor score (source profiles) and the factor loading (source contributions) matrix to positive values, making this method especially useful in environmental source analysis (Yuan et al., 2009). In this paper, the mixing ratios, composition and seasonal variations of VOCs at Gongga Mountain, a station in a remote area of southwest China, are first presented. We then use the PMF receptor model to identify VOC source types and to apportion their contributions. To the best of our knowledge, this paper is the first to study VOCs at a remote station in southwest China. 2. Experiment and methods 2.1. VOC sampling and analysis The measurements were performed at the standard meteorological field of the Gongga Mountain Forest Ecosystem Research Station (102 000 E, 29 330 N, elevation 1640 m) in Hailuogou Scenic Area, a remote site located in southeast Ganzi Tibetan Autonomous Prefecture in Sichuan province. This scenic area is famous for its large areas of glaciers and virgin forest and its large number of rare animal and plant resources. The forest area in Hailuogou is approximately 70 km2 and the total resort area is approximately 200 km2. The principal tourist seasons are spring and summer. There are approximately 30,000 inhabitants in the vicinity of the scenic area. During the tourist seasons, the population is several times more numerous than the local residents. This station is approximately 130 km to the northeast of Ya’an and 250 km from Chengdu, the capital of Sichuan province (Fig. 1). There are two major roads in the north and east, 500 m and 400 m from the station, respectively. A gas station is located 1000 m to the north of the station. Air samples were collected twice a day every Tuesday from Jan. 2008 to Dec. 2011. A total of 380 samples were collected during the measurement period (approximately 40 samples were excluded for various reasons). The monitoring site is shown in Fig. 1. A pump was used to draw, ambient air samples from a gas inlet through a PFATeflon tube (OD: ¼ in). These samples were collected in preevacuated 1 L electropolished canisters. A 3 min integrated

sample was taken for each canister sample at 8:00 and 14:00. Ambient air was collected at a flow rate of 1 L min1. After the canisters were pressurized to 60 psig, the valve was closed and the pump turned off. The samples were returned to Beijing for analysis within 7 days of collection. Measurements of VOCs were made using an Entech 7100A preconcentrator (Entech Inc., USA) followed by a GCeMS system (Finnigan Trace GC/Trace DSQ). The details of the VOC analysis procedures have been described previously by Mao et al. (2009) and will be described briefly here. A 500 mL sample was concentrated in an Entech 7100A preconcentrator, a three-stage preconcentration was utilized to remove the water, carbon dioxide, nitrogen and oxygen in air samples. The VOCs were further focused with a capillary focusing trap for rapid injection prior to the analytical column. A DB-5 MS fused-silica capillary column (60 m  0.25 mm  0.25 mm, Agilent Technologies Inc.) coupled with a quadrupole mass spectrometer detector (Finnigan Trace 2000/DSQ. Thermofisher Inc. USA) was used for qualification and quantification. Forty VOCs were identified and quantified. Each target species was identified by its retention time, mass spectrum and USEPA standard gases. The quantification of the target VOCs was performed with multi-point external standard curves and modified using Relative Responsible Factors (RRFs). The calibration curves were prepared using 100 ppbv external standard gases (Scott Specialty, TO14 standard; alkanes and alkenes) and 100 ppbv internal standard gases of dibromomethane at five different diluted concentrations plus nitrogen (0e100 ppbv). Internal standard gas was added to each sample to trace the analytical procedure (Mao et al., 2009). Five sets of replicate samples were collected to check the precision and reliability of the sampling and analysis methods. The RSD was within 10% for the target compounds in all five replicates. For most samples, the VOC species were above the detection limit of 5e10 pptv. 2.2. Positive matrix factorization (PMF) 2.2.1. Principles and application of PMF The PMF method is comprehensively described by Paatero and Tapper (1994) and Paatero (1997) and has been used in many VOC source identification studies (Song et al., 2007; Sauvage et al., 2009; Yuan et al., 2009). In this paper, PMF 3.0 (USEPA, 2008) was used to apportion the contributions from emission sources. Several concepts that are relevant to the understanding of this work are briefly described here. For additional details about the method, the reader is referred to the publications cited above and to the PMF 3.0 user manual. An ambient data set can be viewed as an i by j data matrix X in which i samples and j chemical species are represented. The goal of multivariate receptor modeling is to identify a number of sources p, the species profile f of each source and the amount of mass g contributed by each source to each individual sample as well as the residuals eij:

Xij ¼

p X

gjk fkj þ eij

k¼1

where eij is the residual for each sample/species. The PMF solution minimizes the objective function Q based on these uncertainties (u):

Q ¼ Fig. 1. Location of the sampling site and some important cities around it.

" #2 Pp n X m x  X g f ij k¼1 ik kj uij i¼1 j¼1

J. Zhang et al. / Atmospheric Environment 88 (2014) 297e305

To find the number of sources, it is necessary to test different numbers of sources and to find the optimal value corresponding to the most reasonable results. If the number of sources is estimated properly, the theoretical Q value should be approximately the number of degrees of freedom or approximately the total number of data points. However, the Q value may deviate from the theoretical value if the number of sources is not well determined, (Guo et al., 2011). The PMF 3.0 model also provides the rotational freedom parameter (Fpeak) function, which can control whether more extreme values are assumed for the factor loadings (by assigning positive Fpeak values) or for the factor scores (by assigning negative Fpeak values) (Chan et al., 2011). In this analysis, an average of approximately 98% of the scaled residuals calculated by PMF was between 3 and 3, indicating a good fit of the modeled results. The factors also showed oblique edges, a property that has been proposed as an additional check of the rotation (Paatero et al., 2005). 2.2.2. Data pre-processing and the choice of species Not all VOC species were used in this source apportionment analysis. There are three principles for choosing the right species for the model. (1) In the mixing ratios file, species with more than 25% of samples missing or below the minimum detection limits (MDLs) were rejected. (2) Species that are highly reactive were excluded because they react and are lost quickly in the ambient atmosphere. Including them may bias the model. An exception to this approach is the inclusion of special species that are important tracers of certain emission sources, even though they react quickly and become lost in the ambient atmosphere, such as isoprene, an important biogenic tracer (Brown et al., 2007). (3) Certain species at low mixing ratios that are not typically tracers of emission sources were also rejected. Eventually, 28 VOC species were selected for the source apportionment analysis because they are the most abundant species and/or are typical tracers of various emission sources. The PMF 3.0 model requires two input files: one for the measured mixing ratios of the species and one for the estimated uncertainty of the mixing ratios. Data below the MDL were replaced by half the MDL values, and their uncertainties were set as 5/6 of the MDL. Missing data were replaced by the geometric mean of the

299

measured mixing ratios of that species, and their uncertainties were set at four times the geometric mean (Polissar and Hopke, 1998). The uncertainties for the normal data points were substituted with 20% of the mixing ratio values, as suggested by Buzcu and Fraser (2006). 3. Result and discussion 3.1. General characteristics of the VOCs Forty VOC (C4eC12) species were measured in the samples collected at Gongga Mountain from Jan. 2008 to Dec. 2011, including alkanes, aromatics, alkenes and halocarbons. The means and standard deviations of the mixing ratios of these compounds are listed in Table 1. Benzene, chloromethane, isopentane, toluene and isoprene were the most strongly dominant compounds, with average mixing ratios of 0.72  0.64 ppbv, 0.62  0.34 ppbv, 0.47  0.50 ppbv, 0.44  0.33 ppbv and 0.40  0.59 ppbv, respectively. The mixing ratio for the total volatile organic compounds (TVOCs) was 8.75  5.76 ppbv. This value is higher than that of Mount Tai (Table 2), where the mixing ratio observed was 6.95  5.71 ppbv (Mao et al., 2009). However, this value is much lower than the result obtained in Shanghai (32.35  19.76 ppbv), a mega-city in China. The NMHC mixing ratio was 6.33  4.63 ppbv at Gongga Mountain. This value is higher than that at Jianfeng Mountain (4.78  1.85 ppbv), a background site in Hainan province, and is lower than that at Dinghu Mountain (23.40  9.84 ppbv), a remote station in Guangdong province (Tang et al., 2007). Therefore, the TVOCs or NMHCs at Gongga Mountain were at an intermediate level compared with other background or remote sites but were much lower than the emissions of large cities. The contributions of the four main hydrocarbon groups to the TVOC mixing ratio at Gongga Mountain are given in Fig. 2. Aromatics (35.7%) provided the largest contribution to the TVOCs, followed by alkanes (30.8%), halocarbons (21.6%) and alkenes (11.9%). The contributions of the four main hydrocarbon groups to the TVOCs are similar to the results for Mount Tai. The contribution of aromatics was largest (34%), and the percentage of alkenes was the lowest, at only 11% (Mao et al., 2009).

Table 1 Average mixing ratios of VOCs samples in the air of Gongga Mountain. Name Alkanes Butane Isopentane Cyclopentane Hexane 2-methylhexane 3-methylhexane Methylcyclohexane 3-methylheptane Alkenes 1-butene Isoprene c-2-pentene Limonene Aromatics Benzene Ethylbenzene Styrene Isopropylbenzene 1,3,5-trimethylbenzene Halocarbons Chloromethane CFC-11 (trichlorofluoromethane) Dichloromethane 1,2-dichloroethane

Mixing ratio (ppbv)

Name

Mixing ratio (ppbv)

0.20 0.47 0.11 0.09 0.13 0.18 0.14 0.14

       

0.16 0.50 0.19 0.12 0.36 0.42 0.32 0.35

Isobutane Pentane 2-methylpentane Methylcyclopentane 2,3-dimethylpentane Heptane 2-methylheptane

0.27 0.22 0.08 0.17 0.18 0.14 0.16

      

0.38 0.40 0.02 0.06

   

0.47 0.59 0.04 0.08

1-pentene t-2-pentene a-pinene

0.05  0.07 0.04  0.06 0.10  0.18

0.72 0.28 0.23 0.07 0.12

    

0.64 0.42 0.41 0.14 0.24

Toluene m/p-xylene o-xylene Propylbenzene 1,2,4-trimethylbenzene

0.44 0.28 0.31 0.29 0.39

    

0.33 0.42 0.43 0.44 0.50

0.62 0.28 0.36 0.25

   

0.34 0.25 0.42 0.36

CFC-114 (tetrafluorodichloroethane) CFC-113 (trichlorotrifluoroethane) Chloroform Chlorobenzene

0.02 0.09 0.20 0.08

   

0.03 0.08 0.35 0.15

0.26 0.23 0.10 0.38 0.35 0.30 0.36

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Table 2 Comparison of TVOCs or NMHCs measured at Gongga Mountain and at other stations. Station name

Mount Taia

Jianfeng Mountainb

Dinghu Mountainb

Shanghaic

Gongga Mountain

Station Background Background Background City Background type TVOCs 6.95  5.71 32.35  19.76 8.75  5.76 (ppbv) NMHCs 4.78  1.85 23.40  9.84 6.33  4.63 (ppbv) a b c

Mao et al. (2009). Tang et al. (2007). Cai et al. (2010).

3.2. Seasonal variation patterns of VOCs The seasonal trend characteristics of VOCs are valuable for understanding important processes in atmospheric transport and chemistry. In this study, winter is defined as the three-month period from December to February; spring, from March to May; summer, from June to August; and autumn, from September to November. The seasonal variations of the mixing ratios of the total and the four main hydrocarbon groups are shown in Fig. 3. As shown, there is a large and statistically significant variation in VOC levels (only considering the mean values). The mixing ratios were generally high in spring (12.9 ppbv) and low in autumn (6.44 ppbv). This result is different from those of studies in several cities, which all found that the mixing ratios of most VOCs were high in winter and low in summer (Ho et al., 2004; Parra et al., 2009). In addition, note that the error range for the mixing ratios of the total and the four main hydrocarbon groups are wide. The significant difference may disappear if these errors are considered. The following discussion will only focus on the difference in mean values. Several factors affect the seasonal variation of VOCs in the atmosphere. These factors include the following: (1) Photochemical removal (primarily by the hydroxyl (OH) radical). The chemical removal of VOCs by OH radicals is faster in warmer seasons than in cooler seasons. Because more sunlight and higher temperatures in warmer seasons will result in higher chemical removal reaction rates (Ho et al., 2004). (2) The dilution due to atmospheric mixing. The mixing layer in warmer seasons is much higher than in cooler seasons. The dilution of airborne pollutants from ground source emissions in warmer seasons is stronger than in cooler seasons. However, another, more important factor influences the seasonal variation of VOCs at Gongga Mountain. Because the site is a tourist attraction, the principal sources of VOC emissions will change with the tourism seasonal variations significant. The highest and the lowest values appeared in spring and autumn as the result of all of the above factors. Spring is a favorable season for tourism. Many local sources could introduce VOCs into the air. Moreover,

Halocarbons 21.6%

Aromatic 35.7%

Alkanes 30.8%

Alkenes 11.9%

Fig. 2. Contributions of four main hydrocarbon groups to TVOC in the air at Gongga Mountain.

photochemical removal and dilution are weak due to the lower temperature. Therefore, the VOCs will accumulate. The highest value appeared in this season. Although emission sources are also present in autumn, they are lower than in summer, and the effects of photochemical removal and the dilution are remain strong. For this reason, the lowest values of VOCs appeared in autumn. In winter, although the emission sources are less than in autumn, the accumulative effect of meteorological factors is strongest. The VOC emissions from non-tourism sources will accumulate, and the total in winter will higher than that in autumn. However, the long-term presence of air masses could result in the transport of VOCs from large cities to this area in spring than in other seasons, whereas this effect could be negligible in autumn. We will discuss this topic in detail in Section 3.4. Among the four main hydrocarbon groups, the mixing ratios of alkanes and aromatics were dominant throughout the year and showed a pattern of variation similar to that of the TVOCs. A correlation analysis found that the relationship between TVOCs and alkanes or aromatics was significant (P < 0.01). Consequently, the seasonal variation of the TVOCs was primarily affected by that of the alkanes and aromatics. The decisive role of these two hydrocarbon groups has also been identified by other studies (Saito et al., 2009; Geng et al., 2010). 3.3. Source identification Five factors were resolved at Gongga Mountain through the application of the PMF model. Fig. 4 shows the explained variations (EVs) for all the identified sources. The EV quantity indicates the importance of each factor element in explaining the total mass of the element and is particularly powerful for identifying tracer species if the absolute amounts of chemical species show a significant difference (Yuan et al., 2009). Source 1 is identified as gasoline-related emissions (the combination of gasoline exhaust and gas vapor). Gasoline is the dominant vehicle fuel, and VOC emissions from gasoline are transported along the following three pathways: (1) gasoline vapor emitted from headspace emissions at gas stations and bulk terminals and from vehicles as diurnal emissions and resting loss; (2) liquid gasoline arising from spillage, leakage and vehicle operations; and (3) exhaust released from the tailpipes of gasoline-powered vehicles during gasoline combustion (Watson et al., 2001; Choi and Ehrman, 2004). Isobutene, isopentane and hexane are the main constituents of gasoline and are thus good tracers of gasoline evaporation (Xie and Berkowitz, 2006; Brown et al., 2007). Additionally, BTEX and 2-methylhexane are important component species of vehicular exhaust, as shown by many studies (Watson et al., 2001; Guo et al., 2006, 2007). The Hailuogou scenic area attracts large numbers of tourists due to the attractive natural environment and the favorable climate. The number of visitors reached to 141,000 between January and July in 2009, and the number of visitors per day can reach approximately 10,000 on Labor Day, a traditional festival in China. The two roads cited above are the principal routes into the scenic area. For this reason, the gasoline exhaust emissions from the traffic sources on the two main roads and the gas vapor from the gas station are very important contributors to this source at the study site. High percentages were found for trimethylbenzene and aromatic hydrocarbons, especially for TEX (toluene, ethylbenzene, m/ p-xylene and o-xylene) in Source 2. It is known that TEX is the primary constituent of solvents (Guo et al., 2004a; Choi et al., 2011). TEX is often used as a solvent in paints, coatings, synthetic fragrances, adhesives, inks and cleaning agents, in addition to its use in fossil fuel (Borbon et al., 2002; Chan et al., 2006). 1,2,4trimethylbenzene and 1,3,5-trimethylbenzene are also typical

J. Zhang et al. / Atmospheric Environment 88 (2014) 297e305

18

10

Mixing ratio(ppbv)

301

9

16

8

14

7

12

6

TVOCs Alkanes Aromatics Alkenes Halocarbons

10

5

8

4 3

6

2

4

1

2

0

0

-1 Jan.Feb.Mar.Apr. MayJun. Jul. Aug.Sep.Oct.Nov.Dec.

Spring

Summer

Autumn

Winter

Fig. 3. Monthly and seasonal average mixing ratios of the total and the four chemical groups of Gongga Mountain.

development of the Gongga area has been rapid. For example, the number of visitors in the first seven months of 2009 was 123% greater than that in the same period in 2008. The tourism zone requires more hotels, restaurants and other related infrastructure

tracers that are used as solvents (Borbon et al., 2002; Guo et al., 2004a). In the PMF-derived source profile, 1,2,4-trimethylbenzene and 1,3,5-trimethylbenzene all account for 75% of the total mass. This source is therefore assigned to solvent use. In recent years, the

100

Gasoline-related emission

80 60 40 20 0 100 80

Solvent use

60 20 0 100 Fuel combustion 80 60 40 20 0 100 Biogenic emission 80 60 40 20 0 100 Industrial, commercial and domestic 80 60 40 20

Fig. 4. Explained variation of five identified sources at Gongga Mountain.

-pinene

Limonene

1.2.4-trimethyl-benzene

propyl-benzene

1.3.5-trimethyl-benzene

Isopropylbenzene

Styrene

o-xylene

m/p-xylene

Ethylbenzene

Toluene

Chlorobenzene

Heptane

2,3-dimethyl-pentane

Benzene

2-methyl-hexane

Hexane

1,2-Dichloroethane

Dichloromethane

2-methyl-Pentane

t-2-Pentene

c-2-pentene

Pentane

Isoprene

1-Pentene

Isobutane

Isopentane

0 Chloromethane

% of species

40

302

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to meet the rapid growth in visitor numbers. Solvent widely are used extensively in the construction industry. They are the primary components of coatings, adhesives, paints and cleaning agents as previously stated. Source 3 was characterized by a significant presence of chloromethane (91%) and benzene (41%) (Guo et al., 2004b, 2007). Chloromethane is a typical species from biomass burning (Ling et al., 2011). Barletta et al. (2009) also considered chloromethane a biomass/coal burning tracer, which is consistent with the results of Wang et al. (2005). Moreira dos Santos et al. (2004) found that coal combustion could release significant amounts of benzene into the atmosphere. Source 3 has, therefore, been assigned to fuel combustion. At Gongga Mountain, local residents burn large amounts of straw after the harvest. Moreover, the development of the tourism industry has promoted the rapid development of many related industries, and coal is used as the principal fuel by many of these industries. Therefore, the widespread use of these two types of fuel is the key reason for their identification as the fuel combustion source. Source 4 was distinguished by a strong presence of isoprene, apinene and limonene, the indicators of biogenic emissions. In the derived source profile, isoprene, a-pinene and limonene represent 75%, 77% and 83% of their EVs, respectively. Isoprene can also be characterized by industrial emissions if it is emitted with other industrial VOCs, such as 2-methylpentane, 3-methylpentane and 1,3butadiene (Buzcu and Fraser, 2006), and if it if emitted by heavy vehicular traffic (Song et al., 2007). This possible contribution can be neglected because the correlation coefficient R2 between isoprene and two important tracers of industrial emissions (1-pentene and c2-pentene) and two important tracers of gasoline-related emissions (isopentane and hexane) were low (0.03 and 0.158, 0.154 and 0.151). Therefore, this source was identified as a biogenic emission. Guo et al. (2004a) found that 1-pentene and cis-2-pentene are emitted into the air from industrial, commercial and domestic processes that either use or manufacture the materials or that they are emitted from the sites where they are formed as byproducts. 1pentene is a product of oil pyrolysis. It can be used for organic synthesis, dehydrogenation for producing isoprene, and as an additive in high-octane gasoline. Cis-2-pentene is used in organic synthesis and as a polymerization inhibitor. This source is also associated with 70% of the total pentane used in the manufacture of artificial ice, anesthetic agents and the synthesis of pentanol and ipentane. In source 5, 1-pentene and cis-2-pentene represent 83% and 90% of their EVs, respectively. Buzcu and Fraser (2006) also found that 2-methylpentane is an important emission component from industrial processes. This species is an important chemical raw material and can be used as a rubber solvent and a vegetable oil extraction solvent. It is also an intermediate in organic syntheses. Therefore, this source was identified as industrial, commercial and domestic. The sources resolved by PMF were the results of the interaction of local emissions and long-term transport from areas around the site. The topic of local emissions was addressed at the end of each of the preceding discussions of the sources. A discussion of long-term transport must involve information on other air masses. For this reason, long-term transport will be discussed in Section 3.4. The individual contributions of the five major VOC sources to the measured VOC mixing ratios are shown in Fig. 5. This figure shows that gasoline-related, industrial, commercial and domestic sources and solvent use are estimated to be the major contributors to the TVOCs at Gongga Mountain, representing approximately 86% of the total source. Biogenic emissions play only a minor role, contributing only 5% of the total VOC mass. The basis of this phenomenon is that there were only three species of emissions from the biogenic we studied and that the mixing ratios of a-pinene and

industrual, commercial and domestic, 9% biogenic emission, 5%

fuel combustion, 29%

gasoline-related emission, 35%

solvent use, 22%

Fig. 5. Individual contributions of the five major sources to the measured VOCs.

limonene were only 0.10  0.18 ppbv and 0.06  0.08 ppbv, respectively. This finding is similar to those of previous studies in remote or background sites, such as those at Lin’an (Guo et al., 2004b) and Yufa (Yuan et al., 2009). 3.4. The effect of long-range transport The long-range transport of air pollutants (clean air) from highly polluted areas (clean areas) could increase (decrease) the VOC mixing ratios at the study site. This transport will affect the sources at the sampling site in conjunction with the local sources. The Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model is a useful air trajectory model, especially for studying the long-range transport of air masses (Mao et al., 2009; Cheng et al., 2010). This model was used to calculate 36 h back-trajectories for each day during different seasons in 2011. The back-trajectories were calculated every 6 h. The air mass direction can be divided into four categories in terms of the original direction, namely, 0 e 90 , 90 e180 , 180 e270 and 270 e360 . Only one air mass in the 0 e90 category was present in spring (Fig. 6a). It originated from Xi’an, a large city in central China, and passed over Hanzhong and several cities of Sichuan, including Guangyuan, Mianyang, Deyang and Chengdu, before ultimately arriving at Gongga Mountain. The transport of pollutants from these cities via the air masses could significantly promote the level of VOCs in the air at the study site. Two air masses were associated with the 180 e270 direction. The air masses originated from the northeastern part of India and passed over northern Myanmar and the clean air area in Yunnan province, China. Two air masses were associated with the 270 e360 direction. These air masses originated from several underdeveloped areas of China, including Qinhai province and the Tibet Autonomous Region. The effect on the sampling site of the air masses that originate from the latter two directions was to dilute the mixing ratio of VOCs in the atmosphere. In summer, two air masses were associated with the 0e90 direction (Fig. 6b). The original and effect of the longer of these two air masses (7th) was similar to that of the 0e90 air mass in spring. The shorter air mass originated from and passed several small counties or towns. Only one air mass was associated with the 90 e 180 direction. This air mass originated from Guizhou province, an underdeveloped province in China. Three air masses were associated with the 180 e270 direction. The third of these air masses originated from Yunnan province, a clean area in China. The other two air masses originated from northern Myanmar, also a clean area. The effect of the air masses originating from the latter two directions was to dilute the VOCs in the atmosphere. One air mass originated from the east of China in autumn (Fig. 6c). This air mass was very short and may have transported small amounts of pollutants from nearby towns. However, its effect was very limited. The major air masses in autumn were associated

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303

Fig. 6. The main air masses of different seasons at Gongga Mountain in 2011. a: spring; b: summer; c: autumn; d: winter.

with the 180 e270 and 270 e360 directions. The origins and effects of these air masses were similar to those of the air masses identified in spring and summer and associated with the same directions. In winter, the air masses were associated with the 180 e270 and 270 e360 directions (Fig. 6d). The origins and effects of these air masses were the same as those of the air masses associated with the same directions in spring and autumn. The air masses can be divided into the following two categories: (1) contaminated air masses, it only appearing in spring and summer (the 5th air mass in spring and the 7th air mass in summer) which could transport air pollutants from highly polluted areas and increase the level of VOCs in the Gongga Mountain area; and (2) the other air masses, which originated from other directions and diluted the VOCs at the sampling site. The heights associated with these two categories of air masses differed significantly. The height of the air masses belonging to the first category was always less than 500 m. This characteristic was not conducive to the dilution of pollutants in the vertical direction during the transport process. In contrast, the height of the air masses belonging to the second

category was not stable. It ranged from more than 3000 m to less than 500 m for different directions or seasons. The differences between air masses in different seasons can also be used to explain the seasonal variation at Gongga Mountain. Air masses carrying high mixing ratios of pollutants only appeared in spring and summer, and the associated proportions were all 10% (the 5th air mass in spring and the 7th air mass in summer). While the scavenging effect is stronger in summer than spring, due to the higher temperature and mixing layer. For this reason, the effect of long-term transport on the VOCs in the Gongga Mountain area is stronger in spring than in summer. All of the air masses appearing in autumn and winter can be viewed as dilute air masses. Therefore, the mixing ratio of VOCs in autumn and winter is lower than in spring (especially) and summer. Note that the station at which this study was conducted would be considered a remote station in a relative sense. The air masses traveling to the site from several large cities have some effects, as discussed above. Therefore, the source tracers cannot be used as the source apportionment if these tracers experienced a loss during transport. Accordingly, we analyzed the correlations between

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several important tracers belonging to the same source. For gasoline-related emissions, we analyzed isopentane, hexane and 2methylhexane; for solvent use, we analyzed toluene, ethylbenzene, xylene, 1,3,5-trimethylbenzene and 1,2,4-trimethylbenzene; for fuel combustion, we analyzed chloromethane and benzene; and for industrial, commercial and domestic sources, we analyzed 1pentene, c-2-pentene and 2-methylpentane. The ranges of the R2 statistic for the tracers for gasoline-related emissions, solvent sources, fuel combustion sources and industrial, commercial and domestic sources were 0.43e0.78, 0.51e0.89, 0.34 and 0.45e0.60, respectively. The R2 values for chloromethane and benzene were lower. The reason for this difference is that chloromethane and benzene primarily represent emissions from biomass and coal burning, respectively, although they are the tracers for the combustion source. As the above discussion indicates, we found that the correlations for the important tracers corresponding to every source were still reliable although some of them represented transport from certain cities. Therefore, these data could be used in the PMF analysis. 4. Conclusions Field measurements of volatile organic compounds (VOCs) were conducted at the Gongga Mountain Forest Ecosystem Research Station, an important remote station in southwest China, from 2008 to 2011. The VOC mixing ratios at Gongga Mountain were dominated by alkanes and aromatics, followed by halocarbons and alkenes. The VOCs showed obvious seasonal variation, with higher mixing ratios during spring and lower mixing ratios during autumn. The effect of alkanes and aromatics on the seasonal variation of the TVOCs was significant. A positive matrix factorization (PMF) model was used to investigate the contributions of the VOC sources. Five stable sources were identified on the basis of fingerprint species in the source profiles by the PMF model: gasolinerelated emissions (the combination of gasoline exhaust and gas vapor), solvent use, fuel combustion, biogenic emissions and industrial, commercial and domestic sources. The first three sources were found to be the strongest VOC contributors in this area, representing approximately 86% of the total source material. The effect on this area of the long-range transport of air pollutants from highly polluted areas should not be ignored. This process has a significant effect on sources and seasonal variations. Note that this study reports the results of preliminary research on this topic. We need more precise studies in the future, such as more intensive sampling and analysis of the sources in different seasons, because the effect of air masses on the sampling site differs over the four seasons. Acknowledgements This work was funded by the CAS Strategic Priority Research Program Grant NO. XDA05100100 and the National Natural Science Foundation of China Grants No. 41021004, 41175107 and 41275139. We gratefully acknowledge this financial support. The authors also acknowledge the additional support provided by all members of the Gongga Mountain campaign science team. References Barletta, B., Meinardi, S., Simpson, I.J., Atlas, E.L., Beyersdorf, A.J., Baker, A.K., Blake, N.J., Yang, M., Midyett, J.R., Novak, B.J., McKeachie, R.J., Fuelberg, H.E., Sachse, G.W., Avery, M.A., Campos, T., Weinheimer, A.J., Rowland, F.S., Blake, D.R., 2009. Characterization of volatile organic compounds (VOCs) in Asian and north American pollution plumes during INTEX-B: identification of specific Chinese air mass tracers. Atmospheric Chemistry and Physics 9, 5371e 5388.

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