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Characteristics and source apportionment of VOCs measured in Shanghai, China
Atmospheric Environment 44 (2010) 5005e5014
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Characteristics and source apportionment of VOCs measured in Shanghai, China Changjie Cai a, b, Fuhai Geng a, *, Xuexi Tie c, Qiong Yu a, Junlin An b a
Shanghai Meteorological Bureau, Shanghai, China Key Laboratory for Atmospheric Physics & Environment, Nanjing University of information Science and Technology, Nanjing, China c National Center for Atmospheric Research, Boulder, CO, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 June 2010 Received in revised form 26 July 2010 Accepted 27 July 2010
Volatile organic compounds (VOCs) were measured from 2007 to 2010 at the center of Shanghai, China. Because VOCs are important precursors for ozone photochemical formation, detailed information of VOC sources needs to be investigated. The results show that the measured VOC concentrations in Shanghai are dominated by alkanes (43%) and aromatics (30%), following by halo-hydrocarbons (14%) and alkenes (6%). Based on the measured VOC concentrations, a receptor model (PMF; positive matrix factorization) coupled with the information related to VOC sources (the distribution of major industrial complex, meteorological conditions, etc.) is applied to identify the major VOC sources in Shanghai. The result shows that seven major VOC sources are identified by the PMF method, including (1) vehicle related source which contributes to 25% of the measured VOC concentrations, (2) solvent based industrial source to 17%, (3) fuel evaporation to 15%, (4) paint solvent usage to 15%, (5) steel related industrial production to 12%, (6) biomass/biofuel burning to 9%, and (7) coal burning to 7%. Furthermore, ozone formation potential related to VOC sources is calculated by the MIR (maximum incremental reactivity) technique. The most significant VOC source for ozone formation potential is solvent based industrial sources (27%), paint solvent usage (24%), vehicle related emissions (17%), steel related industrial productions (14%), fuel evaporations (9%), coal burning (6%), and biomass/biofuel burning (3%). The weekend effect on the VOC concentrations shows that VOC concentrations are generally higher in the weekdays than in the weekends at the sampling site, suggesting that traffic conditions and human activities have important impacts on the VOC emissions in Shanghai. Ó 2010 Published by Elsevier Ltd.
Keywords: VOC sources in Shanghai Ozone formation potential PMF receptor model Source apportionment
1. Introduction The Yangtze River Delta (YRD) with an area of 210,700 km2 and a population of 147 million located in eastern coast of China is an important economic region in China. Shanghai is the largest city in the YRD region with an area of 6340 km2 and a population of 19 million. In the past two decades, Shanghai is undergoing a rapid increase in economic development. For example, the gross domestic production (GDP) of Shanghai is over 1.49 trillion RMB, accounting for about 21% of the total GDP in the YRD region. Industrial gross output (IGO) increases from 0.51 to 2.56 trillion RMB from 1996 to 2008, and the numbers of automobiles increases from 0.47 to 2.61 million between 1996 and 2008 (SMSB, 1997, 2009). Accompanying the rapid economic development, the air quality is deteriorated during recent years. For example, high particular matter (PM) concentrations and poor visibility were
* Corresponding author. Tel.: þ1 13901912572. E-mail address: [email protected] (F. Geng). 1352-2310/$ e see front matter Ó 2010 Published by Elsevier Ltd. doi:10.1016/j.atmosenv.2010.07.059
often occurred (Tie et al., 2006; Streets et al., 2008). The concentrations of O3 are increasing and could be another important atmospheric pollutant in the YRD region (Geng et al., 2007, 2008; Tie et al., 2009a). Geng et al. (2007, 2008, 2009) and Tang et al. (2007, 2008b) also reported that O3 chemical production is limited by the concentrations of VOCs (VOC-sensitive regime) in Shanghai, and different VOC species (e.g., aromatics, alkenes, alkanes, etc.) have different contributions to the ozone photochemical formation. Thus, identification of the originations (emission sources) of VOCs and their contributions to various VOC concentrations become a crucial issue for the development of an effective O3 control strategy in Shanghai. One of the effective methods to study VOC sources is the use of receptor models. At the present, there are two main multivariate receptor models, including principal component analysis (PCA) and positive matrix factorization (PMF) that are widely used for VOC source studies (Guo et al., 2004a,b, 2006, 2007; Jorquera and Rappenglück, 2004; Buzcu and Fraser, 2006; Brown et al., 2006; Song et al., 2007, 2008; Liu et al., 2008a,b; Yuan et al., 2009). Geng et al. (2010) used the PCA model for identifying the major
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sources of VOCs at the city center of Shanghai, and their result suggested that fuel evaporations, vehicle related emissions, solvent usage, industrial productions, and biomass/biofuel/coal burning þ other nature sources are the main sources for VOC emissions in Shanghai. Their study also suggested that some VOC sources which have strong correlation and similarity cannot be clearly distinguished. For example, the fuel evaporation source cannot be separated between LPG/NG leakage and gasoline evaporation. In addition, the PCA method cannot properly handle missing and below-detection-limit (BDL) data. In order to better analyze VOC sources in Shanghai, a more advanced receptor model (PMF) is applied in this study. The PMF method does not require any priori knowledge about the number of VOC sources and emission source profiles, and has better detection for low value data. Thus, the PMF method produces more accurate result than the PCA method. More details about the PMF method were described by several studies (Paatero and Tapper, 1994; Paatero, 1997; Reff et al., 2007). For different VOC species, they are associated with different sources, and can be used as “tracers” to identify VOC sources. For example, isopentane is a tracer of gasoline evaporation (Barletta et al., 2005), propene is the characteristic product of internal combustion engines and was reported as a good indicator of vehicle exhaust in Shanghai (Scheff and Wadden, 1993; Geng et al., 2010). According to the study by Barletta et al. (2009), coal burning is important source for 1,2-dichloroethane due to the large consumption of coal burning in Shanghai. Based upon these studies, the PMF receptor model is used to identify major VOC sources and to evaluate the individual contribution of VOC sources to the measured VOC concentrations in Shanghai. In order to better identify VOC sources, in addition to the application of the PMF model, other important information which is related to VOC sources, is also applied in this study, including the locations of industrial complexes, the chemical lifetime of various VOC species, and the meteorological conditions (temperature, wind speed and direction) around the sampling site. Because ozone photochemical formation is initiated by reactions of VOCs with hydroxyl radical (OH), the product of VOC concentrations and the OH reaction coefficients is often called the reactivity of VOCs. The VOC reactivity is an important factor to determine the ozone formation due to various VOC species in large cities (Chameides et al., 1992; Ran et al., 2009; Tie et al., 2009b). In this study, a propylene-equivalent concentration method suggested by Chameides et al. (1992) and a maximum incremental reactivity
(MIR) method proposed by Carter (1994) were used to calculate the VOC reactivity. The paper is organized as the follows. In Section 2, we describe the experimental method; including the instruments, measurements, the propylene-equivalent concentration and MIR method, and the PMF receptor model. In Section 3, the results from measurements and the model result are discussed. The summary of the results are given in Section 4. 2. Measurement and method 2.1. Description of sampling site, measurements and instruments The sampling site is located at Xujiahui (XJH) commercial center of Shanghai (shown as a star in Fig. 1). VOCs were sampled at 6:00e9:00 for 3 h using a 6 L silonite canister with silonite valve (model 29-10622, Entech Instruments Inc., USA) from Jan. 2007 to Mar. 2010. In order to study the diurnal variations, VOCs were intensively measured (8 samples a day with a 3 h interval) from August 25 to September 20, 2009. Gas samples were pre-processed using Model 7100 VOC pre-concentrator (Entech Instruments Inc., USA) and analyzed by a gas chromatography system (Agilent GC6890) equipped with a mass-selective detection (Agilent MSD5975N). More details about the sampling site, measurements, and instruments were described by Geng et al. (2010). In order to get insights of the influence of industrial factories to the sampling site, the distribution of industrial factories (smelters, steel) and chemical factories in the surrounding area of Shanghai is shown in Fig. 1. Fig. 1 shows that the large smelter and steel factories are mainly located in Baoshan and Jiading districts which are in the north and northwest of Shanghai (Fig. 1a), and the main chemical industrial complex are mainly located in the west, southwest and south of the sampling site (Fig. 1b). Since there are no major industrial factories nearby the sampling site and the different industrial pollution sources are widely distributed in different directions from the site, wind directions become a crucial factor for the influence of the different factory sources to the measured VOC concentrations at the sampling site. Fig. 2 shows the prevailing winds during different seasons in the Shanghai region. The result suggests that the prevailing winds are southeast/northwest (spring), southeast (summer), northeast (fall), and northwest (winter), respectively. Table 1 shows the wind speeds and air temperature during different seasons. The mean
Fig. 1. Location of sampling site (yellow star) and the distribution of large smelter and steel factories (a) and large chemical industrial complex (b) in Shanghai.
C. Cai et al. / Atmospheric Environment 44 (2010) 5005e5014
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Summer
Spring
N
N NNW NW
NNW NW
NNE NE
WNW
ENE
NNE NE
WNW
ENE
W
E
W
E
WSW
ESE
WSW
ESE
SW SSW
SE
SW SSW
SSE
SE SSE
S
S
Autumn
Winter N
N NNW
NNW
NNE
NW WNW
NNE
NW
NE ENE
NE
WNW
ENE
W
E
W
E
WSW
ESE
WSW
ESE
SW
SW
SE SSW
SE SSW
SSE
SSE S
S
Fig. 2. The prevailing winds during different seasons in Shanghai.
wind speeds are relatively constant during different seasons (about 2 m s1), but the mean temperature changes significantly from winter to summer (from 6 C in winter to 28 C in summer). Because the main chemical industrial factories are located in the west, southwest and south of the sampling site, those factories have largest effects on measured VOC concentrations at the sampling site in summer and in winter and less effects in fall and spring. In addition to the wind directions, the chemical lifetimes (Brasseur et al., 1999) of VOC species (CT) and transport time constant from the factories to the site (TT) also need to be considered. Geng et al. (2010) shows that the TT from the largest chemical industrial complex located at Jinshan district to the sampling site is about 7 h under northward wind condition. Compared to the typical chemical lifetimes of the 25 VOC species (see Table 2), the CTs of alkanes (32e253 h) are much longer than the TT, and can be easily transported from the chemical complex to the sampling site. By contrast, the CTs of alkenes (3e10 h) are shorter or close to the TT (dependent upon the variability of wind speeds). As a result, the alkenes
from the large chemical complex from the Jinshan District may be or may not be transported to the sampling site. The above information will be considered when the PMF method is used for identifying the VOC sources. 2.2. PMF receptor model PMF is an advanced algorithm in various receptor modeling methods. The data used by PMF are directly from measured data. By contrast, PCA uses normalized data. PMF solves a constrained and weighted least-squares optimization equation to convert data to a set of profile and score matrices (see equation (1)):
xik ¼
p X
gij fjk þeik ði ¼ 1;2;:::;m; j ¼ 1;2;;p; k ¼ 1;2;:::;nÞ
(1)
j¼1
where xik represents the concentration of VOC compound i at kth sampling; gij represents the profile matrice of compound i at jth
Table 1 Meteorological conditions in different seasons at the sampling site. Spring (Mar., Apr., and May)
Summer (Jun., Jul, and Aug.)
Min
Max
Mean
Median
Min
Max
Mean
Median
Wind speed (m s1) Temperature ( C)
0 0.2
7.9 28.8
2 17.8
1.9 17.8
0 19.6
6.1 35.3
2 27.5
1.9 28.2
Wind speed (m s1) Temperature ( C)
Autumn (Sep., Oct., and Nov.) Min 0 5
Median 1.7 19.2
Winter (Dec., Jan., Feb.) Min 0 2.3
Max 6.8 16
Mean 1.6 6.2
Median 1.5 6.3
Max 9.8 31.2
Mean 1.9 19.3
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Table 2 Calculated chemical lifetimes (298 K) of VOC species by setting OH radical to an average value of 2.0 106 molecule cm3. Alkanes
Lifetime Alkenes (h)
Propane 253 iso-Butane 119 Butane 121 iso-Pentane 71 2-Methyl pentane 50 3-Methyl pentane 49 n-Hexane 50 2,4-Dimethyl 54 pentane n-Heptane 39 n-Nonae 29 n-Decane 25
Lifetimes Aromatics (h) and others
Propene 10 1-Butene 9 cis-2-Butene 5 Tran-2-Butene 4 1-Pentene 9 Isoprene 3
Lifetimes (h)
Styrene 28 Benzene 226 Toluene 47 Ethyl benzene 39 m-Xylene 12 p-Xylene 19 o-Xylene 20 MTBE 98
OFPðiÞ ¼ concðiÞ MIRcoefficient ðiÞ
m X n X
ðeik =uik Þ2
ði ¼ 1; 2; :::; m; k ¼ 1; 2; :::; nÞ
(6)
where OFP(i) is defined as the ozone formation potential of individual hydrocarbon i and MIRcoefficient(i) the maximum incremental reactivity coefficient of compound i, which is defined by Carter (1994).
sources; fjk represents the score matrice at jth source and kth sampling; and eik is the residual factor for compound i at kth sampling. Using the PMF method, users need to choose a number of factors (p). The value of p is chosen based on several parameters, including the normalized sum of error squares in individual VOC concentration (Q-value), the normalized residual distribution for individual VOC compound, and the factor scores of the measured VOC concentrations (Anderson, 2001; Anderson et al., 2002). The PMF method gives a minimum of object function Q (see equation (2)), based upon the calculation of uncertainties (u) (see equations (3) and (4)):
Q ¼
propylene; conc(i) is the concentration of a VOC compound i; kOH(i) is the rate constant for the reaction of VOC compound i with OH radical; and kOH(C3H6) is the rate constant for the reaction of C3H6 with OH radical. The rate constants were given by Atkinson and Arey (2003). The MIR method, proposed by Carter (1994), is a good indicator for comparing the ozone formation potential of each individual VOC species, and it is defined by the following equation:
(2)
3. Results and discussions 3.1. General characteristics of ambient VOCs at XJH Thirty-two VOC species, which were accounted for 72% of the total mass of the 93 detected VOC species from Jan. 2007 to Mar. 2010, were selected in this study. The measured VOC concentrations at the XJH site were listed in Table 3. The result indicates that propane and toluene are the most abundant hydrocarbons, with averaged concentrations of 4.2 and 3.2 ppbv, especially. The averaged total VOC concentration is about 32.4 ppbv, in which alkanes, alkenes, aromatics, and halo-hydrocarbons accounted for 43, 6, 30, and 14% of the total VOC concentration, respectively. The general characteristics of the measured VOC concentrations in Shanghai are
i¼1 k¼1
where uik represents uncertainty of compound i at kth sampling. The uncertainty (U) is calculated using the following equation (Polissar et al., 1998):
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U ¼ ðEF concÞ2 þðMDLÞ2
5 MDL 6
Species
2007e2010 (n ¼ 284) Unit: ppbv Median Mean S.D.
ðconc > MDLÞ
(3)
where EF represents an error fraction (EF ¼ the percent uncertainty 100), and MDL represents the method detection limit. If the concentration is less than or equal to the MDL, the calculation is:
U ¼
Table 3 Measured VOC concentrations (ppbv) measured at the sampling site in Shanghai.
ðconc MDLÞ
(4)
There is a rotational ambiguity in the result of the PMF method, and one way for choosing from different possible solutions is to use “Fpeak” parameter. The “Fpeak” parameter is used to rotate an incorrect solution back to the real solution, which is described by Jorquera and Rappenglück (2004). 2.3. The propylene-equivalent concentration and the MIR method In this study, the ozone chemical formation related different VOC compounds are also studied according to a propylene-equivalent concentration and a MIR method. The propylene-equivalent concentration method was proposed by Chameides et al. (1992), and the following equation is used to calculate the propyleneequivalent concentration for each individual VOC species:
Propy equivðiÞ ¼ concðiÞ kOH ðiÞ=kOH ðC3 H6 Þ
(5)
where Propy-equiv(i) is defined as a VOC compound i on an OH reactivity-based scale, which is normalized to the reactivity of
Propene 0.70 1-Butene 0.23 cis-2-Butene 0.16 tran-2-Butene 0.18 1-Pentene 0.11 Isoprene 0.10 Propane 4.24 iso-Butane 1.16 Butane 1.71 iso-Pentane 1.86 2-Methyl pentane 0.53 3-Methyl pentane 0.35 n-Hexane 0.54 2,4-Dimethyl pentane 0.17 Methyl cyclopentane 0.21 2-Methyl hexane 0.15 3-Methyl hexane 0.17 n-Heptane 0.18 n-Nonane 0.08 n-Decane 0.07 Chloromethane 0.92 Dichloromethane 0.73 1,2-Dichloroethane 0.76 Styrene 011 Benzene 1.42 Toluene 3.18 Ethyl benzene 0.94 m/p-Xylene 1.11 o-Xylene Ethyl acetate Methyl tertbutyl ether Total (VOCs) a b
0.84 0.26 0.22 0.24 0.13 0.12 4.81 1.43 2.03 2.29 0.67 0.48 0.84 0.21 0.27 0.18 0.21 0.23 0.09 0.09 2.04 0.95 1.56 0.14 1.81 4.70 1.23 1.40
0.57 0.20 0.17 0.20 0.07 0.09 2.14 0.92 1.17 1.44 0.48 0.41 0.82 0.15 0.19 0.13 0.15 0.15 0.06 0.07 2.85 0.64 1.99 0.12 1.19 4.21 1.02 1.11
0.38 0.49 0.36 1.21 2.09 2.53 0.22 0.29 0.24 25.83 32.35 19.76
Rang
k298a
MIRb
0.06e3.14 0.01e1.19 0.01e1.00 0.02e1.36 0.01e0.35 0.01e0.50 1.48e14.67 0.19e4.79 0.53e7.30 0.47e9.52 0.07e2.69 0.03e2.69 0.03e5.09 0.03e0.96 0.04e1.25 0.02e0.84 0.01e0.94 0.03e0.79 0.02e0.29 0.01e0.33 0.17e19.05 0.20e3.24 0.09e14.45 0.01e0.79 0.36e6.57 0.47e17.55 0.16e7.04 0.18e8.79
2.63 1011 3.14 1011 5.64 1011 6.40 1011 3.14 1011 1.00 1010 1.09 1012 2.12 1012 2.36 1012 3.60 1012 5.20 1012 5.20 1012 5.20 1012 4.77 1012 e e e 6.76 1012 9.70 1012 11.0 1012 e e e 5.80 1011 1.22 1012 5.93 1012 7.00 1012
9.4 8.9 10 10 6.2 9.1 0.48 1.21 1.02 1.38 1.5 1.5 0.98 1.5 2.8 1.08 1.4 0.81 0.54 0.46 e e e 2.2 0.42 2.7 2.7
2:311011 ðmÞ 8:2ðmÞ 1:431011 ðpÞ 6:6ðpÞ
0.08e2.41 1.36 1011 6.5 0.02e15.21 e e 0.02e1.16 2.94 1012 0.62 8.16e111.10
Unit: cm3 molecule1 s1; Atkinson, 2003; Atkinson and Arey, 2003. Unit: O3/VOCs; Carter, 1994.
C. Cai et al. / Atmospheric Environment 44 (2010) 5005e5014
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Table 4 Measured VOC concentrations (ppbv) in different urban areas.
Mixing ratio (ppbv)
a
Beijing China
Guangzhou China
Mexico city Mexico
Nagoya Japan
This work
Song et al., 2008
Tang et al., 2008a
Apel et al., 2010
Saito et al., 2009
0.84 0.26 0.22 0.24 0.13 0.12 4.81 1.43 2.03 2.29 0.67 0.48 0.84 0.21 0.27 0.18 0.21 0.23 0.09 0.09 0.14 1.81 4.70 1.23 1.40 0.49 0.29
1.97 2.21 0.75 0.86 0.34 1.12 6.24 5.36 6.36 11.84 e e 2.22 e e e e e e 1.50 e 5.43 11.14 4.08 8.54 3.91 3.07
1.79 0.60 0.43 0.54 0.33 0.26 5.39 3.11 4.60 3.00 1.28 1.02 1.13 e 0.54 e e 1.22 0.22 e e 2.80 14.09 2.21 5.16 2.63 e
1.765 e 0.330 0.311 0.264 0.134 37.536 8.266 20.332 8.380 2.894 2.057 4.493 0.301 0.960 e e 0.679 0.123 0.224 e 1.703 10.649 0.938 1.218 0.404 e
0.705 0.214 0.141 0.143 0.147 0.656 3.339 1.404 2.661 1.331 0.371 0.290 0.555 0.058 0.128 0.121 0.143 0.156 0.129 0.187 0.133 0.519 2.544 0.524 0.675 0.253 e
28 24 20 16 12 8 4 0
c
24 Working days Weekends
Aromatics
16 12 8 4
d
80 60
03
06
09
12
15
21
24
Working days Weekends
Total VOCs
70
18
50 40 30 20 10 0
03
06
09
12
15
18
21
3600 Working days Weekends
3200 2800
00
24
2400 2000 1600 1200 800 400
f
45
Mixing ratio (ppbv)
00
Vehicle flow
Working days Weekends
Alkenes
00
0
e
4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5
00 03 06 09 12 15 18 21 24
20 Mixing ratio (ppbv)
b
Working days Weekends
Alkanes
Mixing ratio (ppbv)
Propene 1-Butene cis-2-Butene tran-2-Butene 1-Pentene Isoprene Propane iso-Butane Butane iso-Pentane 2-Methyl pentane 3-Methyl pentane n-Hexane 2,4-Dimethyl pentane Methyl cyclopentane 2-Methyl hexane 3-Methyl hexane n-Heptane n-Nonane n-Decane Styrene Benzene Toluene Ethyl benzene m/p-Xylene o-Xylene MTBE
Shanghai China
Mixing ratio (ppbv)
VOC species
35
40
03
06
09
12
15
18
21
24
Working days Weekends
30 25 20 15 10 5 0
0 00
03
06
09
12
15
18
21
24
Alkanes
Alkenes
Aromatics Total VOCs
Fig. 3. Weekend effects of measured alkanes, alkenes, aromatic, and total VOC concentrations at the sampling site.
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compared to other measurements in different large cities (see Table 4). Table 4 shows the mean concentrations of VOCs measured in other four urban areas (Beijing, Guangzhou, Mexico, and Nagoya) as well as Shanghai. The comparison shows that the concentrations of VOCs in Shanghai are similar to Nagoya, but are much lower than Mexico, Guangzhou, and Beijing. 3.2. Weekend effects of VOCs Before the application of the PMF method to VOC source study, the weekend effects on VOC concentrations are studied. The result of the weekend effects can provide some insight that the effect of traffic on the measured VOC concentrations. Averaged diurnal variations of VOC concentrations in different groups (alkanes, alkenes and aromatics) and the total VOC concentrations measured at the sampling site are shown in Fig. 3. It shows that VOC concentrations are higher in working days/weekdays (Monday to Friday) than in weekends (Saturday to Sunday). For example, the alkane concentration is about 35% higher in weekdays than
weekends, especially during high traffic time (07:00e19:00). However, the concentrations of alkanes are lower in weekdays than weekends during low traffic time (01:00e06:00). Compared to the numbers of automobile on streets, the vehicle numbers are lower in weekdays than weekend during the low traffic time (01:00e06:00) and are higher during the high traffic time (07:00e19:00). Fig. 3 also suggests that alkane has the largest weekend effect, following by alkenes, and aromatics, suggesting that automobile emissions have important impact on VOC emissions, especially on alkene emission. 3.3. Source identification (PMF results) In this study, the PMF method is applied to analyze the measured result at the sampling site (the center of Shanghai). Table 6 shows the source profiles derived by the PMF model. Eight factors were selected according to the resulted stable Q values. In order to better classify the automobile sources, three important components associated with vehicular emissions of VOCs are
benzene
toluene N
N NNW NW
NNW NW
NNE NE
WNW
ENE
W
E
WSW
ESE
SW SSW
WNW
E
WSW
ESE
SW SSW
S
SE SSE S
m/p-xylene
ethylbenzene
N
N NNW NW
NNE NE
WNW
ENE
W
E
WSW
ESE
SW SSW
ENE
W
SE SSE
NNW NW
NNE NE
SE SSE
NNE NE
WNW
ENE
W
E
WSW
ESE
SW SSW
S
SE SSE S
the total VOCs N NNW NW
NNE NE
WNW
ENE
W
E
WSW
ESE
SW SSW
SE SSE S
Fig. 4. The prevailing winds for BTEX (benzene, toluene, ethyl benzene, m/p-xylene) and total VOCs.
C. Cai et al. / Atmospheric Environment 44 (2010) 5005e5014
analyzed in this study, including vehicular exhaust (about 40%), fuel evaporation from tank (about 40%), and internal engine burned and unburned emissions from crankcase ventilation (about 20%). Table 6 shows that the factor 1 has high values of propene and C3eC6 alkanes. Because propene is reported as the characteristic products of internal combustion engines and C3eC6 alkanes are associated with unburned vehicular emissions (Scheff and Wadden, 1993; Grosjean et al., 1999; Guo et al., 2004a), the factor 1 mainly contains vehicular engine burned and unburned emissions. The factor 2 in Table 6 is characteristic by high chloromethane values. Since chloromethane is normally considered as a typical tracer of biomass/biofuel burning (Lobert et al., 1991; Liu et al., 2008a), VOC species in the factor 2 are mainly considered as the emissions of biomass/biofuel burning. In the factor 3, the BTEX (including benzene, toluene, ethyl benzene and xylenes) value is high. Since BTEX is a major constituent of paints (Brocco et al., 1997), VOC species in the factor 3 are primarily resulted from paint solvent usage. As shown above, there are no large factories or industrial activities in center of Shanghai, the transport of BTEX from the surrounding areas could be important factors for the measured BTEX concentrations at the sampling site. Fig. 4 shows the BTEX concentrations from different wind directions. As shown in Fig. 4, the wind directions from west and southwest had largest effects on the BTEX concentrations at the sampling site, which is consistent to the fact that largest paint factories are located in west and southwest directions (see Fig. 1). As a result, the BTEX species in the factor 3 are mainly resulted from the paint factories located at the surrounding areas of the sampling site. The factor 4 is dominated by C3eC5 alkenes (propene, 1-butene, t/c-2-butene and 1-pentene), C3eC5 alkanes (propane, isobutane, butane, isopentane) and aromatics. From the weekend effect analysis, these species are closely related to automobile emissions. Thus, the factor 4 is primarily related to vehicular emissions. The factor 5 is dominated by C3eC5 alkenes (propene, 1-butene, t/c-2-butene, 1-pentene and isoprene), C3eC6 alkanes (propane, i/n-butanes, isopentane and 2/3-methyl pentanes) and MTBE (methyl tertbuty ether). In Shanghai, in addition to vehicular exhaust, C3eC5 alkenes and alkanes are also emitted from fuel evaporations (gasoline, LPG/NG and diesel) (Geng et al., 2010), and MTBE is an additive of gasoline in China. For example, isopentane is a typical tracer for gasoline evaporation (Liu et al., 2008a,b). Propane, propene and n/i-butanes are mainly released from the use of LPG and NG (Na and Kim, 2001). LPG and NG are two main usages as household fuel, and they are also used for a large number of small motorcycles in Shanghai. For example, the number of household users of LPG and NG reached 2.9 and 3.1 million in 2008 (SMSB, 2009). Thus, the VOC species in the factor 5 are mainly resulted from fuel evaporations (gasoline and LPG/NG leakage). In the factor 6, large values of benzene and toluene are presented. In addition to solvent usage, benzene and toluene are also emitted from industrial sources (ATSDR, 1997; Guo et al., 2004a; Liu et al., 2008a,b). The B/T (benzene/toluene) ratio is also higher in the factor 6 than in other factors. In order to better understand the high ratio of B/T, the B/T ratio from different wind directions is studied (see Fig. 5). Fig. 5 shows that the B/T ratio is high (>0.8) when the winds are in northwest, north and northeast wind directions. In the north of the sampling site, there is a large industrial complex (the Baoshan smelter and steel company). This result suggests that the high benzene and toluene concentrations in the factor 6 could be due to steel related industrial emissions. In the factor 7, the highest values are toluene and ethyl acetate. In addition to paint solvent usage, vehicular emissions and steel related productions, solvent based industrial emissions are also important sources for toluene (U.S. EPA, 1993; Fujita et al., 1995). According to the study by Geng et al. (2010), industrial emission is
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Fig. 5. The prevailing winds for the high B/T ratio.
the important source of ethyl acetate in Shanghai. As a result, the VOC species in the factor 7 are mainly attributed to solvent industrial emissions. In the factor 8, 1,2-dichloroethane values are the highest. According to studies by Fernandez-Martinez et al. (2001) and Barletta et al. (2009), a large emission of 1,2-dichloroethane is due to coal burning. Because the coal burning is a very large anthropogenic source in Shanghai as the usage of power plants, the high values of 1,2-dichloroethane are mainly attributed to coal burning in the factor 8. According to the analysis above, eight factors are identified as main VOC sources in Shanghai by using the PMF method. Among these factors, the factor 1 (vehicle internal engine burned and unburned emissions) and the factor 4 (vehicular exhaust) can be combined to be one source (vehicular emissions). As a result, seven major VOC sources are identified by the PMF method. Fig. 6a shows the individual contributions of the seven major VOC sources to the measured VOC concentrations. It shows that the most important source is vehicle related emissions, contributing to 25% of the measured VOC concentrations. The second significant source is the solvent based industrial productions, contributing to 17% of the measured VOC concentrations. Fuel evaporations (gasoline, LPG.NG and diesel) and paint solvent usage are also play
Fig. 6. a, Individual contributions of VOC sources to the measured VOC concentrations. b, Same to a, except for ozone formation potential.
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Table 5 Highest propylene-equivalent concentrations and their contributions to ozone formation potential. Rank
1 2 3 4 5 6 7 8 9 10
Propylene-equivalent concentration (units: ppbC)
Ozone formation potential (units: ppbv)
Species
Conc.
(%)
Species
OFP (%)
(%)
Toluene m/p-Xylene Propene Styrene tran-2-Butene Isoprene o-Xylene Ethyl benzene cis-2-Butene iso-Pentane
8.48 6.97 2.52 2.47 2.34 2.28 2.03 1.96 1.89 1.57
20.81 17.10 6.19 6.06 5.73 5.60 4.98 4.82 4.63 3.85
Toluene m/p-Xylene Ethyl benzene o-Xylene Propene iso-Pentane tran-2-Butene 1-Butene cis-2-Butene Butane
24.32 22.88 7.33 7.03 6.91 4.74 2.80 2.70 2.57 2.50
24.03 22.61 7.25 6.95 6.83 4.68 2.77 2.67 2.54 2.47
important roles for the measured VOC concentrations (contributing to about 15% of the measured VOC concentrations). The fuel evaporations, steel related industrial sources, biomass/biofuel burning and coal burning contribute to15, 12, 9 and 7% of the measured VOC concentrations, respectively.
the ozone formation potential (OFP) proposed by Carter (1994) were used to compare the reactivity and the contribution to photochemical ozone formation among different VOCs. The top 10 VOCs according to their propylene-equivalent concentrations and ozone formation potentials are summarized in Table 5. These 10 VOCs accounted for about 80% of the total propylene-equivalent concentration and about 83% of the total OFP in Shanghai. Toluene, m/p-xylene and propene have highest the PE concentrations, which accounted for about 21, 17, and 6% of the total PE concentrations, respectively. With the knowledge of the individual contribution of VOC sources to the measured VOC concentrations obtained from the above analysis, the contributions of the seven VOC sources to photochemical ozone formation are calculated (Fig. 6b). The result suggests that the five most significant sources for ozone chemical formation are solvent based industrial productions (accounting for about 27%,), paint solvent usage (accounting for about 24%), vehicle related emissions (accounting for about 17%), steel related productions (accounting for about 14%), and fuel evaporations (accounting for about 9%). 4. Summary
3.4. VOC reactivity and ozone formation potential Photochemical formation of ozone is initiated by reactions of OH radical with VOCs, and the magnitudes of ozone formation due to the VOC reactions vary in a large range. A propylene-equivalent (PE) concentration method suggested by Chameides et al. (1992) and
In this study, the measured VOC concentrations at center of Shanghai are analyzed. The results show that the measured VOC concentrations in Shanghai are dominated by alkanes (43%) and aromatics (30%), following by halo-hydrocarbons (14%) and alkenes (6%). The measured concentrations of VOCs are also compared to
Table 6 Source profiles calculated by PMF model. Sources
Propene 1-Butene cis-2-Butene Tran-2-Butene 1-Pentene Isoprene Propane Isobutane Butane Isopentane 2-Methyl pentane 3-Methyl pentane n-Hexane 2,4-Dimethyl pentane Methyl cyclopentane 2-Methyl hexane 3-Methyl hexane n-Heptane n-Nonane n-Decane Chloromethane Methylene chloride 1,2Dichloroethane Styrene Benzene Toluene Ethylbenzene m/p-Xylene o-Xylene Ethyl acetate Methyl tertbutyl ether
1
2
7
4
5
6
Vehicle internal Biomass/ engine burned and biofuel unburned burning emissions
Paint solvent usage
Vehicle exhaust
Fuel evaporation (gasoline, LPG/NG Leakage)
Steel related Industrial industrial sources productions (solvent based)
Coal burning
0.127 0.013 0.020 0.002 0.013 0.002 0.009 0.002 0.006 0.002 0.002 0.001 0.330 0.121 0.055 0.018 0.081 0.031 0.093 0.033 0.222 0.018 0.258 0.022 0.512 0.044 0.086 0.007
0.075 0.012 0.005 0.003 0.010 0.003 0.005 0.003 0.008 0.002 0.002 0.002 0.164 0.142 0.032 0.021 0.081 0.036 0.210 0.041 0.033 0.008 0.017 0.007 0.012 0.015 0.008 0.002
0.001 0.002 0.035 0.002 0.037 0.002 0.038 0.003 0.017 0.002 0.024 0.002 0.661 0.174 0.102 0.017 0.141 0.027 0.279 0.035 0.083 0.015 0.049 0.018 0.051 0.038 0.024 0.005
0.225 0.017 0.030 0.005 0.030 0.007 0.029 0.007 0.023 0.005 0.006 0.005 2.660 0.177 0.345 0.031 0.653 0.048 0.468 0.094 0.049 0.016 0.015 0.011 0.006 0.019 0.022 0.005
0.195 0.024 0.072 0.004 0.069 0.004 0.076 0.004 0.053 0.003 0.054 0.002 0.333 0.272 0.482 0.025 0.574 0.049 1.022 0.053 0.184 0.021 0.081 0.024 0.037 0.046 0.039 0.009
0.070 0.019 0.016 0.003 0.007 0.002 0.008 0.003 0.002 0.001 0.003 0.003 0.230 0.280 0.136 0.025 0.211 0.053 0.035 0.032 0.040 0.016 0.024 0.021 0.062 0.047 0.010 0.007
0.033 0.012 0.013 0.002 0.013 0.003 0.015 0.003 0.005 0.002 0.003 0.001 0.212 0.159 0.067 0.021 0.078 0.038 0.094 0.039 0.007 0.007 0.019 0.009 0.067 0.021 0.009 0.003
0.112 0.009
0.009 0.003 0.031 0.007
0.028 0.006
0.045 0.011
0.014 0.009 0.012 0.009
0.013 0.004 0.97
0.006 0.002 0.007 0.002 0.008 0.002 0.003 0.001 0.002 0.001 0.023 0.032 0.009 0.011 0.016 0.021
0.007 0.002 0.006 0.002 0.012 0.002 0.006 0.001 0.008 0.001 1.582 0.052 0.058 0.007 0.192 0.178
0.021 0.005 0.021 0.006 0.028 0.006 0.027 0.001 0.028 0.002 0.047 0.062 0.079 0.032 0.059 0.060
0.012 0.003 0.002 0.003 0.013 0.003 0.009 0.002 0.004 0.002 0.041 0.059 0.049 0.030 0.068 0.067
0.042 0.005 0.063 0.007 0.071 0.006 0.021 0.002 0.026 0.002 0.089 0.101 0.214 0.041 0.029 0.039
0.042 0.004 0.046 0.005 0.047 0.005 0.011 0.002 0.011 0.003 0.095 0.123 0.287 0.023 0.118 0.133
0.042 0.005 0.052 0.006 0.039 0.006 0.007 0.002 0.010 0.002 0.082 0.093 0.172 0.031 0.062 0.089
0.002 0.002 0.003 0.003 0.003 0.002 0.002 0.001 0.000 0.000 0.071 0.064 0.035 0.016 1.021 0.063
0.87 0.90 0.88 0.82 0.66 1.00 0.76 1.00
0.001 0.002 0.106 0.038 0.021 0.034 0.033 0.011 0.026 0.016 0.011 0.004 0.040 0.021 0.016 0.004
0.000 0.000 0.087 0.029 0.094 0.054 0.028 0.016 0.036 0.024 0.017 0.007 0.061 0.045 0.005 0.005
0.044 0.006 0.163 0.098 0.926 0.096 0.673 0.022 0.818 0.032 0.237 0.008 0.011 0.028 0.013 0.008
0.003 0.004 0.265 0.139 0.422 0.099 0.035 0.051 0.218 0.069 0.049 0.018 0.028 0.053 0.009 0.012
0.015 0.007 0.106 0.128 0.074 0.127 0.184 0.047 0.087 0.060 0.050 0.016 0.123 0.084 0.184 0.005
0.049 0.005 0.943 0.051 0.892 0.087 0.083 0.040 0.024 0.030 0.027 0.010 0.073 0.070 0.007 0.006
0.020 0.006 0.068 0.091 1.963 0.117 0.130 0.058 0.132 0.076 0.060 0.020 1.552 0.052 0.036 0.009
0.003 0.003 0.060 0.068 0.100 0.069 0.032 0.020 0.043 0.027 0.031 0.008 0.085 0.051 0.010 0.005
0.72 0.98 0.93 0.94 0.96 0.96 0.92 0.81
0.085 0.016 0.060 0.003 0.024 0.002 0.051 0.002 0.008 0.001 0.014 0.002 0.233 0.155 0.153 0.021 0.171 0.034 0.013 0.027 0.052 0.020 0.021 0.022 0.083 0.043 0.013 0.007
8
R2
3
0.75 0.76 0.67 0.75 0.77 0.58 0.95 0.91 0.95 0.90 0.96 0.99 0.97 0.97
C. Cai et al. / Atmospheric Environment 44 (2010) 5005e5014
the values in other four urban areas (Beijing, Guangzhou, Mexico, and Nagoya). The comparison shows that the concentrations of VOCs in Shanghai are similar to Nagoya, but are much lower than Mexico, Guangzhou, and Beijing. Based on the measured VOC concentrations, a receptor model (PMF; positive matrix factorization) coupled with VOC source related information (the distribution of major industrial complex, the meteorological conditions, etc.) is applied to identify the major VOC sources in Shanghai. This study shows that by using the PMF model alone is not sufficient to provide detailed information of the VOC sources in Shanghai. The additional information related to the VOC sources (mentioned above) is necessary and useful when the PMF model is applied for identification of VOC sources in large cities. Because VOCs are important precursors for the chemical formation of ozone, identifying the contributions of individual VOCs to ozone formation becomes an important issue for better understanding the ozone chemical production in the Shanghai region. Seven important VOC sources are found in this study, including (1) vehicle related sources which contribute to 25% of the measured VOC concentrations, (2) solvent based industrial sources to 17%, (3) fuel evaporation to 15%, (4) paint solvent usage to 15%, (5) steel related industrial productions to 12%, (6) biomass/biofuel burning to 9%, and (7) coal burning to 7%. Furthermore, ozone formation potential related to the VOC sources is calculated by the MIR (maximum incremental reactivity) technique. The result suggests that the five VOC sources play important roles in controlling ozone chemical formation in Shanghai, such as solvent based industrial productions (accounting for about 27%,), paint solvent usage (accounting for about 24%), vehicle related emissions (accounting for about 17%), steel related productions (accounting for about 14%), and fuel evaporations (accounting for about 9%). The results gained from this study provide useful information for ozone pollutant control strategy in Shanghai. Acknowledgements This work is funded by the National Natural Science Foundation of China (NSFC) under Grant No. 40705046; The Shanghai Science and Technology Commission under Grant No. 08230705200; The National Center for Atmospheric Research is sponsored by the National Science Foundation. References Anderson, M.J., 2001. Source Apportionment of Toxic Volatile Organic Compounds. M.S. thesis, Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, CO. Anderson, M.J., Daly, E.P., Miller, S.L., Milford, J.B., 2002. Source apportionment of exposure to volatile organic compounds: II. Application of receptor models to TEAM study data. Atmos. Environ. 39, 3642e3658. Apel, E.C., Emmons, L.K., Karl, T., Flocke, F., Hills, A.J., Madronich, S., Lee-Taylor, J., Fried, A., Weibring, P., Walega, J., Richter, D., Tie, X., Mauldin, L., Campos, T., Weinheimer, A., Knapp, D., Sive, B., Kleinman, L., Springston, S., Zaveri, R., Ortega, J., Voss, P., Blake, D., Baker, A., Warneke, C., Welsh-Bon, D., Gouw, J., de, Zheng, J., Zhang, R., Rudolph, J., Junkermann, W., Riemer, D.D., 2010. Chemical evolution of volatile organic compounds in the outflow of the Mexico City Metropolitan area. Atmos. Chem. Phys. 10, 2353e2376. Atkinson, R., 2003. Kinetics of the gas-phase reactions of OH radicals with alkanes and cycloalkanes. Atmos. Chem. Phys. 3, 2233e2307. Atkinson, R., Arey, J., 2003. Atmospheric degradation of volatile organic compounds. Chem. Rev. 103, 4605e4638. ATSDR (Agency for Toxic Substance and Disease Registry), 1997. Toxicological Profile for Benzene, Update. Public Health Service, U.S. Department of Health and Human Services, Atlanta, GA. Barletta, B., Meinardi, S., Rowland, F.S., Chan, C., Wang, X., Zou, S., Chan, L.Y., Blake, D., 2005. Volatile organic compounds in 43 Chinese cities. Atmos. Environ. 39, 5979e5990. Barletta, B., Meinardi, S., Simpson, I.J., Atlas, E.L., Beyersdorf, A.J., Baker, 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
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Characteristics and source apportionment of VOCs measured in Shanghai, China Changjie Cai a, b, Fuhai Geng a, *, Xuexi Tie c, Qiong Yu a, Junlin An b a
Shanghai Meteorological Bureau, Shanghai, China Key Laboratory for Atmospheric Physics & Environment, Nanjing University of information Science and Technology, Nanjing, China c National Center for Atmospheric Research, Boulder, CO, USA b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 June 2010 Received in revised form 26 July 2010 Accepted 27 July 2010
Volatile organic compounds (VOCs) were measured from 2007 to 2010 at the center of Shanghai, China. Because VOCs are important precursors for ozone photochemical formation, detailed information of VOC sources needs to be investigated. The results show that the measured VOC concentrations in Shanghai are dominated by alkanes (43%) and aromatics (30%), following by halo-hydrocarbons (14%) and alkenes (6%). Based on the measured VOC concentrations, a receptor model (PMF; positive matrix factorization) coupled with the information related to VOC sources (the distribution of major industrial complex, meteorological conditions, etc.) is applied to identify the major VOC sources in Shanghai. The result shows that seven major VOC sources are identified by the PMF method, including (1) vehicle related source which contributes to 25% of the measured VOC concentrations, (2) solvent based industrial source to 17%, (3) fuel evaporation to 15%, (4) paint solvent usage to 15%, (5) steel related industrial production to 12%, (6) biomass/biofuel burning to 9%, and (7) coal burning to 7%. Furthermore, ozone formation potential related to VOC sources is calculated by the MIR (maximum incremental reactivity) technique. The most significant VOC source for ozone formation potential is solvent based industrial sources (27%), paint solvent usage (24%), vehicle related emissions (17%), steel related industrial productions (14%), fuel evaporations (9%), coal burning (6%), and biomass/biofuel burning (3%). The weekend effect on the VOC concentrations shows that VOC concentrations are generally higher in the weekdays than in the weekends at the sampling site, suggesting that traffic conditions and human activities have important impacts on the VOC emissions in Shanghai. Ó 2010 Published by Elsevier Ltd.
Keywords: VOC sources in Shanghai Ozone formation potential PMF receptor model Source apportionment
1. Introduction The Yangtze River Delta (YRD) with an area of 210,700 km2 and a population of 147 million located in eastern coast of China is an important economic region in China. Shanghai is the largest city in the YRD region with an area of 6340 km2 and a population of 19 million. In the past two decades, Shanghai is undergoing a rapid increase in economic development. For example, the gross domestic production (GDP) of Shanghai is over 1.49 trillion RMB, accounting for about 21% of the total GDP in the YRD region. Industrial gross output (IGO) increases from 0.51 to 2.56 trillion RMB from 1996 to 2008, and the numbers of automobiles increases from 0.47 to 2.61 million between 1996 and 2008 (SMSB, 1997, 2009). Accompanying the rapid economic development, the air quality is deteriorated during recent years. For example, high particular matter (PM) concentrations and poor visibility were
* Corresponding author. Tel.: þ1 13901912572. E-mail address: [email protected] (F. Geng). 1352-2310/$ e see front matter Ó 2010 Published by Elsevier Ltd. doi:10.1016/j.atmosenv.2010.07.059
often occurred (Tie et al., 2006; Streets et al., 2008). The concentrations of O3 are increasing and could be another important atmospheric pollutant in the YRD region (Geng et al., 2007, 2008; Tie et al., 2009a). Geng et al. (2007, 2008, 2009) and Tang et al. (2007, 2008b) also reported that O3 chemical production is limited by the concentrations of VOCs (VOC-sensitive regime) in Shanghai, and different VOC species (e.g., aromatics, alkenes, alkanes, etc.) have different contributions to the ozone photochemical formation. Thus, identification of the originations (emission sources) of VOCs and their contributions to various VOC concentrations become a crucial issue for the development of an effective O3 control strategy in Shanghai. One of the effective methods to study VOC sources is the use of receptor models. At the present, there are two main multivariate receptor models, including principal component analysis (PCA) and positive matrix factorization (PMF) that are widely used for VOC source studies (Guo et al., 2004a,b, 2006, 2007; Jorquera and Rappenglück, 2004; Buzcu and Fraser, 2006; Brown et al., 2006; Song et al., 2007, 2008; Liu et al., 2008a,b; Yuan et al., 2009). Geng et al. (2010) used the PCA model for identifying the major
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sources of VOCs at the city center of Shanghai, and their result suggested that fuel evaporations, vehicle related emissions, solvent usage, industrial productions, and biomass/biofuel/coal burning þ other nature sources are the main sources for VOC emissions in Shanghai. Their study also suggested that some VOC sources which have strong correlation and similarity cannot be clearly distinguished. For example, the fuel evaporation source cannot be separated between LPG/NG leakage and gasoline evaporation. In addition, the PCA method cannot properly handle missing and below-detection-limit (BDL) data. In order to better analyze VOC sources in Shanghai, a more advanced receptor model (PMF) is applied in this study. The PMF method does not require any priori knowledge about the number of VOC sources and emission source profiles, and has better detection for low value data. Thus, the PMF method produces more accurate result than the PCA method. More details about the PMF method were described by several studies (Paatero and Tapper, 1994; Paatero, 1997; Reff et al., 2007). For different VOC species, they are associated with different sources, and can be used as “tracers” to identify VOC sources. For example, isopentane is a tracer of gasoline evaporation (Barletta et al., 2005), propene is the characteristic product of internal combustion engines and was reported as a good indicator of vehicle exhaust in Shanghai (Scheff and Wadden, 1993; Geng et al., 2010). According to the study by Barletta et al. (2009), coal burning is important source for 1,2-dichloroethane due to the large consumption of coal burning in Shanghai. Based upon these studies, the PMF receptor model is used to identify major VOC sources and to evaluate the individual contribution of VOC sources to the measured VOC concentrations in Shanghai. In order to better identify VOC sources, in addition to the application of the PMF model, other important information which is related to VOC sources, is also applied in this study, including the locations of industrial complexes, the chemical lifetime of various VOC species, and the meteorological conditions (temperature, wind speed and direction) around the sampling site. Because ozone photochemical formation is initiated by reactions of VOCs with hydroxyl radical (OH), the product of VOC concentrations and the OH reaction coefficients is often called the reactivity of VOCs. The VOC reactivity is an important factor to determine the ozone formation due to various VOC species in large cities (Chameides et al., 1992; Ran et al., 2009; Tie et al., 2009b). In this study, a propylene-equivalent concentration method suggested by Chameides et al. (1992) and a maximum incremental reactivity
(MIR) method proposed by Carter (1994) were used to calculate the VOC reactivity. The paper is organized as the follows. In Section 2, we describe the experimental method; including the instruments, measurements, the propylene-equivalent concentration and MIR method, and the PMF receptor model. In Section 3, the results from measurements and the model result are discussed. The summary of the results are given in Section 4. 2. Measurement and method 2.1. Description of sampling site, measurements and instruments The sampling site is located at Xujiahui (XJH) commercial center of Shanghai (shown as a star in Fig. 1). VOCs were sampled at 6:00e9:00 for 3 h using a 6 L silonite canister with silonite valve (model 29-10622, Entech Instruments Inc., USA) from Jan. 2007 to Mar. 2010. In order to study the diurnal variations, VOCs were intensively measured (8 samples a day with a 3 h interval) from August 25 to September 20, 2009. Gas samples were pre-processed using Model 7100 VOC pre-concentrator (Entech Instruments Inc., USA) and analyzed by a gas chromatography system (Agilent GC6890) equipped with a mass-selective detection (Agilent MSD5975N). More details about the sampling site, measurements, and instruments were described by Geng et al. (2010). In order to get insights of the influence of industrial factories to the sampling site, the distribution of industrial factories (smelters, steel) and chemical factories in the surrounding area of Shanghai is shown in Fig. 1. Fig. 1 shows that the large smelter and steel factories are mainly located in Baoshan and Jiading districts which are in the north and northwest of Shanghai (Fig. 1a), and the main chemical industrial complex are mainly located in the west, southwest and south of the sampling site (Fig. 1b). Since there are no major industrial factories nearby the sampling site and the different industrial pollution sources are widely distributed in different directions from the site, wind directions become a crucial factor for the influence of the different factory sources to the measured VOC concentrations at the sampling site. Fig. 2 shows the prevailing winds during different seasons in the Shanghai region. The result suggests that the prevailing winds are southeast/northwest (spring), southeast (summer), northeast (fall), and northwest (winter), respectively. Table 1 shows the wind speeds and air temperature during different seasons. The mean
Fig. 1. Location of sampling site (yellow star) and the distribution of large smelter and steel factories (a) and large chemical industrial complex (b) in Shanghai.
C. Cai et al. / Atmospheric Environment 44 (2010) 5005e5014
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Summer
Spring
N
N NNW NW
NNW NW
NNE NE
WNW
ENE
NNE NE
WNW
ENE
W
E
W
E
WSW
ESE
WSW
ESE
SW SSW
SE
SW SSW
SSE
SE SSE
S
S
Autumn
Winter N
N NNW
NNW
NNE
NW WNW
NNE
NW
NE ENE
NE
WNW
ENE
W
E
W
E
WSW
ESE
WSW
ESE
SW
SW
SE SSW
SE SSW
SSE
SSE S
S
Fig. 2. The prevailing winds during different seasons in Shanghai.
wind speeds are relatively constant during different seasons (about 2 m s1), but the mean temperature changes significantly from winter to summer (from 6 C in winter to 28 C in summer). Because the main chemical industrial factories are located in the west, southwest and south of the sampling site, those factories have largest effects on measured VOC concentrations at the sampling site in summer and in winter and less effects in fall and spring. In addition to the wind directions, the chemical lifetimes (Brasseur et al., 1999) of VOC species (CT) and transport time constant from the factories to the site (TT) also need to be considered. Geng et al. (2010) shows that the TT from the largest chemical industrial complex located at Jinshan district to the sampling site is about 7 h under northward wind condition. Compared to the typical chemical lifetimes of the 25 VOC species (see Table 2), the CTs of alkanes (32e253 h) are much longer than the TT, and can be easily transported from the chemical complex to the sampling site. By contrast, the CTs of alkenes (3e10 h) are shorter or close to the TT (dependent upon the variability of wind speeds). As a result, the alkenes
from the large chemical complex from the Jinshan District may be or may not be transported to the sampling site. The above information will be considered when the PMF method is used for identifying the VOC sources. 2.2. PMF receptor model PMF is an advanced algorithm in various receptor modeling methods. The data used by PMF are directly from measured data. By contrast, PCA uses normalized data. PMF solves a constrained and weighted least-squares optimization equation to convert data to a set of profile and score matrices (see equation (1)):
xik ¼
p X
gij fjk þeik ði ¼ 1;2;:::;m; j ¼ 1;2;;p; k ¼ 1;2;:::;nÞ
(1)
j¼1
where xik represents the concentration of VOC compound i at kth sampling; gij represents the profile matrice of compound i at jth
Table 1 Meteorological conditions in different seasons at the sampling site. Spring (Mar., Apr., and May)
Summer (Jun., Jul, and Aug.)
Min
Max
Mean
Median
Min
Max
Mean
Median
Wind speed (m s1) Temperature ( C)
0 0.2
7.9 28.8
2 17.8
1.9 17.8
0 19.6
6.1 35.3
2 27.5
1.9 28.2
Wind speed (m s1) Temperature ( C)
Autumn (Sep., Oct., and Nov.) Min 0 5
Median 1.7 19.2
Winter (Dec., Jan., Feb.) Min 0 2.3
Max 6.8 16
Mean 1.6 6.2
Median 1.5 6.3
Max 9.8 31.2
Mean 1.9 19.3
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Table 2 Calculated chemical lifetimes (298 K) of VOC species by setting OH radical to an average value of 2.0 106 molecule cm3. Alkanes
Lifetime Alkenes (h)
Propane 253 iso-Butane 119 Butane 121 iso-Pentane 71 2-Methyl pentane 50 3-Methyl pentane 49 n-Hexane 50 2,4-Dimethyl 54 pentane n-Heptane 39 n-Nonae 29 n-Decane 25
Lifetimes Aromatics (h) and others
Propene 10 1-Butene 9 cis-2-Butene 5 Tran-2-Butene 4 1-Pentene 9 Isoprene 3
Lifetimes (h)
Styrene 28 Benzene 226 Toluene 47 Ethyl benzene 39 m-Xylene 12 p-Xylene 19 o-Xylene 20 MTBE 98
OFPðiÞ ¼ concðiÞ MIRcoefficient ðiÞ
m X n X
ðeik =uik Þ2
ði ¼ 1; 2; :::; m; k ¼ 1; 2; :::; nÞ
(6)
where OFP(i) is defined as the ozone formation potential of individual hydrocarbon i and MIRcoefficient(i) the maximum incremental reactivity coefficient of compound i, which is defined by Carter (1994).
sources; fjk represents the score matrice at jth source and kth sampling; and eik is the residual factor for compound i at kth sampling. Using the PMF method, users need to choose a number of factors (p). The value of p is chosen based on several parameters, including the normalized sum of error squares in individual VOC concentration (Q-value), the normalized residual distribution for individual VOC compound, and the factor scores of the measured VOC concentrations (Anderson, 2001; Anderson et al., 2002). The PMF method gives a minimum of object function Q (see equation (2)), based upon the calculation of uncertainties (u) (see equations (3) and (4)):
Q ¼
propylene; conc(i) is the concentration of a VOC compound i; kOH(i) is the rate constant for the reaction of VOC compound i with OH radical; and kOH(C3H6) is the rate constant for the reaction of C3H6 with OH radical. The rate constants were given by Atkinson and Arey (2003). The MIR method, proposed by Carter (1994), is a good indicator for comparing the ozone formation potential of each individual VOC species, and it is defined by the following equation:
(2)
3. Results and discussions 3.1. General characteristics of ambient VOCs at XJH Thirty-two VOC species, which were accounted for 72% of the total mass of the 93 detected VOC species from Jan. 2007 to Mar. 2010, were selected in this study. The measured VOC concentrations at the XJH site were listed in Table 3. The result indicates that propane and toluene are the most abundant hydrocarbons, with averaged concentrations of 4.2 and 3.2 ppbv, especially. The averaged total VOC concentration is about 32.4 ppbv, in which alkanes, alkenes, aromatics, and halo-hydrocarbons accounted for 43, 6, 30, and 14% of the total VOC concentration, respectively. The general characteristics of the measured VOC concentrations in Shanghai are
i¼1 k¼1
where uik represents uncertainty of compound i at kth sampling. The uncertainty (U) is calculated using the following equation (Polissar et al., 1998):
qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U ¼ ðEF concÞ2 þðMDLÞ2
5 MDL 6
Species
2007e2010 (n ¼ 284) Unit: ppbv Median Mean S.D.
ðconc > MDLÞ
(3)
where EF represents an error fraction (EF ¼ the percent uncertainty 100), and MDL represents the method detection limit. If the concentration is less than or equal to the MDL, the calculation is:
U ¼
Table 3 Measured VOC concentrations (ppbv) measured at the sampling site in Shanghai.
ðconc MDLÞ
(4)
There is a rotational ambiguity in the result of the PMF method, and one way for choosing from different possible solutions is to use “Fpeak” parameter. The “Fpeak” parameter is used to rotate an incorrect solution back to the real solution, which is described by Jorquera and Rappenglück (2004). 2.3. The propylene-equivalent concentration and the MIR method In this study, the ozone chemical formation related different VOC compounds are also studied according to a propylene-equivalent concentration and a MIR method. The propylene-equivalent concentration method was proposed by Chameides et al. (1992), and the following equation is used to calculate the propyleneequivalent concentration for each individual VOC species:
Propy equivðiÞ ¼ concðiÞ kOH ðiÞ=kOH ðC3 H6 Þ
(5)
where Propy-equiv(i) is defined as a VOC compound i on an OH reactivity-based scale, which is normalized to the reactivity of
Propene 0.70 1-Butene 0.23 cis-2-Butene 0.16 tran-2-Butene 0.18 1-Pentene 0.11 Isoprene 0.10 Propane 4.24 iso-Butane 1.16 Butane 1.71 iso-Pentane 1.86 2-Methyl pentane 0.53 3-Methyl pentane 0.35 n-Hexane 0.54 2,4-Dimethyl pentane 0.17 Methyl cyclopentane 0.21 2-Methyl hexane 0.15 3-Methyl hexane 0.17 n-Heptane 0.18 n-Nonane 0.08 n-Decane 0.07 Chloromethane 0.92 Dichloromethane 0.73 1,2-Dichloroethane 0.76 Styrene 011 Benzene 1.42 Toluene 3.18 Ethyl benzene 0.94 m/p-Xylene 1.11 o-Xylene Ethyl acetate Methyl tertbutyl ether Total (VOCs) a b
0.84 0.26 0.22 0.24 0.13 0.12 4.81 1.43 2.03 2.29 0.67 0.48 0.84 0.21 0.27 0.18 0.21 0.23 0.09 0.09 2.04 0.95 1.56 0.14 1.81 4.70 1.23 1.40
0.57 0.20 0.17 0.20 0.07 0.09 2.14 0.92 1.17 1.44 0.48 0.41 0.82 0.15 0.19 0.13 0.15 0.15 0.06 0.07 2.85 0.64 1.99 0.12 1.19 4.21 1.02 1.11
0.38 0.49 0.36 1.21 2.09 2.53 0.22 0.29 0.24 25.83 32.35 19.76
Rang
k298a
MIRb
0.06e3.14 0.01e1.19 0.01e1.00 0.02e1.36 0.01e0.35 0.01e0.50 1.48e14.67 0.19e4.79 0.53e7.30 0.47e9.52 0.07e2.69 0.03e2.69 0.03e5.09 0.03e0.96 0.04e1.25 0.02e0.84 0.01e0.94 0.03e0.79 0.02e0.29 0.01e0.33 0.17e19.05 0.20e3.24 0.09e14.45 0.01e0.79 0.36e6.57 0.47e17.55 0.16e7.04 0.18e8.79
2.63 1011 3.14 1011 5.64 1011 6.40 1011 3.14 1011 1.00 1010 1.09 1012 2.12 1012 2.36 1012 3.60 1012 5.20 1012 5.20 1012 5.20 1012 4.77 1012 e e e 6.76 1012 9.70 1012 11.0 1012 e e e 5.80 1011 1.22 1012 5.93 1012 7.00 1012
9.4 8.9 10 10 6.2 9.1 0.48 1.21 1.02 1.38 1.5 1.5 0.98 1.5 2.8 1.08 1.4 0.81 0.54 0.46 e e e 2.2 0.42 2.7 2.7
2:311011 ðmÞ 8:2ðmÞ 1:431011 ðpÞ 6:6ðpÞ
0.08e2.41 1.36 1011 6.5 0.02e15.21 e e 0.02e1.16 2.94 1012 0.62 8.16e111.10
Unit: cm3 molecule1 s1; Atkinson, 2003; Atkinson and Arey, 2003. Unit: O3/VOCs; Carter, 1994.
C. Cai et al. / Atmospheric Environment 44 (2010) 5005e5014
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Table 4 Measured VOC concentrations (ppbv) in different urban areas.
Mixing ratio (ppbv)
a
Beijing China
Guangzhou China
Mexico city Mexico
Nagoya Japan
This work
Song et al., 2008
Tang et al., 2008a
Apel et al., 2010
Saito et al., 2009
0.84 0.26 0.22 0.24 0.13 0.12 4.81 1.43 2.03 2.29 0.67 0.48 0.84 0.21 0.27 0.18 0.21 0.23 0.09 0.09 0.14 1.81 4.70 1.23 1.40 0.49 0.29
1.97 2.21 0.75 0.86 0.34 1.12 6.24 5.36 6.36 11.84 e e 2.22 e e e e e e 1.50 e 5.43 11.14 4.08 8.54 3.91 3.07
1.79 0.60 0.43 0.54 0.33 0.26 5.39 3.11 4.60 3.00 1.28 1.02 1.13 e 0.54 e e 1.22 0.22 e e 2.80 14.09 2.21 5.16 2.63 e
1.765 e 0.330 0.311 0.264 0.134 37.536 8.266 20.332 8.380 2.894 2.057 4.493 0.301 0.960 e e 0.679 0.123 0.224 e 1.703 10.649 0.938 1.218 0.404 e
0.705 0.214 0.141 0.143 0.147 0.656 3.339 1.404 2.661 1.331 0.371 0.290 0.555 0.058 0.128 0.121 0.143 0.156 0.129 0.187 0.133 0.519 2.544 0.524 0.675 0.253 e
28 24 20 16 12 8 4 0
c
24 Working days Weekends
Aromatics
16 12 8 4
d
80 60
03
06
09
12
15
21
24
Working days Weekends
Total VOCs
70
18
50 40 30 20 10 0
03
06
09
12
15
18
21
3600 Working days Weekends
3200 2800
00
24
2400 2000 1600 1200 800 400
f
45
Mixing ratio (ppbv)
00
Vehicle flow
Working days Weekends
Alkenes
00
0
e
4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 -0.5
00 03 06 09 12 15 18 21 24
20 Mixing ratio (ppbv)
b
Working days Weekends
Alkanes
Mixing ratio (ppbv)
Propene 1-Butene cis-2-Butene tran-2-Butene 1-Pentene Isoprene Propane iso-Butane Butane iso-Pentane 2-Methyl pentane 3-Methyl pentane n-Hexane 2,4-Dimethyl pentane Methyl cyclopentane 2-Methyl hexane 3-Methyl hexane n-Heptane n-Nonane n-Decane Styrene Benzene Toluene Ethyl benzene m/p-Xylene o-Xylene MTBE
Shanghai China
Mixing ratio (ppbv)
VOC species
35
40
03
06
09
12
15
18
21
24
Working days Weekends
30 25 20 15 10 5 0
0 00
03
06
09
12
15
18
21
24
Alkanes
Alkenes
Aromatics Total VOCs
Fig. 3. Weekend effects of measured alkanes, alkenes, aromatic, and total VOC concentrations at the sampling site.
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compared to other measurements in different large cities (see Table 4). Table 4 shows the mean concentrations of VOCs measured in other four urban areas (Beijing, Guangzhou, Mexico, and Nagoya) as well as Shanghai. The comparison shows that the concentrations of VOCs in Shanghai are similar to Nagoya, but are much lower than Mexico, Guangzhou, and Beijing. 3.2. Weekend effects of VOCs Before the application of the PMF method to VOC source study, the weekend effects on VOC concentrations are studied. The result of the weekend effects can provide some insight that the effect of traffic on the measured VOC concentrations. Averaged diurnal variations of VOC concentrations in different groups (alkanes, alkenes and aromatics) and the total VOC concentrations measured at the sampling site are shown in Fig. 3. It shows that VOC concentrations are higher in working days/weekdays (Monday to Friday) than in weekends (Saturday to Sunday). For example, the alkane concentration is about 35% higher in weekdays than
weekends, especially during high traffic time (07:00e19:00). However, the concentrations of alkanes are lower in weekdays than weekends during low traffic time (01:00e06:00). Compared to the numbers of automobile on streets, the vehicle numbers are lower in weekdays than weekend during the low traffic time (01:00e06:00) and are higher during the high traffic time (07:00e19:00). Fig. 3 also suggests that alkane has the largest weekend effect, following by alkenes, and aromatics, suggesting that automobile emissions have important impact on VOC emissions, especially on alkene emission. 3.3. Source identification (PMF results) In this study, the PMF method is applied to analyze the measured result at the sampling site (the center of Shanghai). Table 6 shows the source profiles derived by the PMF model. Eight factors were selected according to the resulted stable Q values. In order to better classify the automobile sources, three important components associated with vehicular emissions of VOCs are
benzene
toluene N
N NNW NW
NNW NW
NNE NE
WNW
ENE
W
E
WSW
ESE
SW SSW
WNW
E
WSW
ESE
SW SSW
S
SE SSE S
m/p-xylene
ethylbenzene
N
N NNW NW
NNE NE
WNW
ENE
W
E
WSW
ESE
SW SSW
ENE
W
SE SSE
NNW NW
NNE NE
SE SSE
NNE NE
WNW
ENE
W
E
WSW
ESE
SW SSW
S
SE SSE S
the total VOCs N NNW NW
NNE NE
WNW
ENE
W
E
WSW
ESE
SW SSW
SE SSE S
Fig. 4. The prevailing winds for BTEX (benzene, toluene, ethyl benzene, m/p-xylene) and total VOCs.
C. Cai et al. / Atmospheric Environment 44 (2010) 5005e5014
analyzed in this study, including vehicular exhaust (about 40%), fuel evaporation from tank (about 40%), and internal engine burned and unburned emissions from crankcase ventilation (about 20%). Table 6 shows that the factor 1 has high values of propene and C3eC6 alkanes. Because propene is reported as the characteristic products of internal combustion engines and C3eC6 alkanes are associated with unburned vehicular emissions (Scheff and Wadden, 1993; Grosjean et al., 1999; Guo et al., 2004a), the factor 1 mainly contains vehicular engine burned and unburned emissions. The factor 2 in Table 6 is characteristic by high chloromethane values. Since chloromethane is normally considered as a typical tracer of biomass/biofuel burning (Lobert et al., 1991; Liu et al., 2008a), VOC species in the factor 2 are mainly considered as the emissions of biomass/biofuel burning. In the factor 3, the BTEX (including benzene, toluene, ethyl benzene and xylenes) value is high. Since BTEX is a major constituent of paints (Brocco et al., 1997), VOC species in the factor 3 are primarily resulted from paint solvent usage. As shown above, there are no large factories or industrial activities in center of Shanghai, the transport of BTEX from the surrounding areas could be important factors for the measured BTEX concentrations at the sampling site. Fig. 4 shows the BTEX concentrations from different wind directions. As shown in Fig. 4, the wind directions from west and southwest had largest effects on the BTEX concentrations at the sampling site, which is consistent to the fact that largest paint factories are located in west and southwest directions (see Fig. 1). As a result, the BTEX species in the factor 3 are mainly resulted from the paint factories located at the surrounding areas of the sampling site. The factor 4 is dominated by C3eC5 alkenes (propene, 1-butene, t/c-2-butene and 1-pentene), C3eC5 alkanes (propane, isobutane, butane, isopentane) and aromatics. From the weekend effect analysis, these species are closely related to automobile emissions. Thus, the factor 4 is primarily related to vehicular emissions. The factor 5 is dominated by C3eC5 alkenes (propene, 1-butene, t/c-2-butene, 1-pentene and isoprene), C3eC6 alkanes (propane, i/n-butanes, isopentane and 2/3-methyl pentanes) and MTBE (methyl tertbuty ether). In Shanghai, in addition to vehicular exhaust, C3eC5 alkenes and alkanes are also emitted from fuel evaporations (gasoline, LPG/NG and diesel) (Geng et al., 2010), and MTBE is an additive of gasoline in China. For example, isopentane is a typical tracer for gasoline evaporation (Liu et al., 2008a,b). Propane, propene and n/i-butanes are mainly released from the use of LPG and NG (Na and Kim, 2001). LPG and NG are two main usages as household fuel, and they are also used for a large number of small motorcycles in Shanghai. For example, the number of household users of LPG and NG reached 2.9 and 3.1 million in 2008 (SMSB, 2009). Thus, the VOC species in the factor 5 are mainly resulted from fuel evaporations (gasoline and LPG/NG leakage). In the factor 6, large values of benzene and toluene are presented. In addition to solvent usage, benzene and toluene are also emitted from industrial sources (ATSDR, 1997; Guo et al., 2004a; Liu et al., 2008a,b). The B/T (benzene/toluene) ratio is also higher in the factor 6 than in other factors. In order to better understand the high ratio of B/T, the B/T ratio from different wind directions is studied (see Fig. 5). Fig. 5 shows that the B/T ratio is high (>0.8) when the winds are in northwest, north and northeast wind directions. In the north of the sampling site, there is a large industrial complex (the Baoshan smelter and steel company). This result suggests that the high benzene and toluene concentrations in the factor 6 could be due to steel related industrial emissions. In the factor 7, the highest values are toluene and ethyl acetate. In addition to paint solvent usage, vehicular emissions and steel related productions, solvent based industrial emissions are also important sources for toluene (U.S. EPA, 1993; Fujita et al., 1995). According to the study by Geng et al. (2010), industrial emission is
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Fig. 5. The prevailing winds for the high B/T ratio.
the important source of ethyl acetate in Shanghai. As a result, the VOC species in the factor 7 are mainly attributed to solvent industrial emissions. In the factor 8, 1,2-dichloroethane values are the highest. According to studies by Fernandez-Martinez et al. (2001) and Barletta et al. (2009), a large emission of 1,2-dichloroethane is due to coal burning. Because the coal burning is a very large anthropogenic source in Shanghai as the usage of power plants, the high values of 1,2-dichloroethane are mainly attributed to coal burning in the factor 8. According to the analysis above, eight factors are identified as main VOC sources in Shanghai by using the PMF method. Among these factors, the factor 1 (vehicle internal engine burned and unburned emissions) and the factor 4 (vehicular exhaust) can be combined to be one source (vehicular emissions). As a result, seven major VOC sources are identified by the PMF method. Fig. 6a shows the individual contributions of the seven major VOC sources to the measured VOC concentrations. It shows that the most important source is vehicle related emissions, contributing to 25% of the measured VOC concentrations. The second significant source is the solvent based industrial productions, contributing to 17% of the measured VOC concentrations. Fuel evaporations (gasoline, LPG.NG and diesel) and paint solvent usage are also play
Fig. 6. a, Individual contributions of VOC sources to the measured VOC concentrations. b, Same to a, except for ozone formation potential.
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Table 5 Highest propylene-equivalent concentrations and their contributions to ozone formation potential. Rank
1 2 3 4 5 6 7 8 9 10
Propylene-equivalent concentration (units: ppbC)
Ozone formation potential (units: ppbv)
Species
Conc.
(%)
Species
OFP (%)
(%)
Toluene m/p-Xylene Propene Styrene tran-2-Butene Isoprene o-Xylene Ethyl benzene cis-2-Butene iso-Pentane
8.48 6.97 2.52 2.47 2.34 2.28 2.03 1.96 1.89 1.57
20.81 17.10 6.19 6.06 5.73 5.60 4.98 4.82 4.63 3.85
Toluene m/p-Xylene Ethyl benzene o-Xylene Propene iso-Pentane tran-2-Butene 1-Butene cis-2-Butene Butane
24.32 22.88 7.33 7.03 6.91 4.74 2.80 2.70 2.57 2.50
24.03 22.61 7.25 6.95 6.83 4.68 2.77 2.67 2.54 2.47
important roles for the measured VOC concentrations (contributing to about 15% of the measured VOC concentrations). The fuel evaporations, steel related industrial sources, biomass/biofuel burning and coal burning contribute to15, 12, 9 and 7% of the measured VOC concentrations, respectively.
the ozone formation potential (OFP) proposed by Carter (1994) were used to compare the reactivity and the contribution to photochemical ozone formation among different VOCs. The top 10 VOCs according to their propylene-equivalent concentrations and ozone formation potentials are summarized in Table 5. These 10 VOCs accounted for about 80% of the total propylene-equivalent concentration and about 83% of the total OFP in Shanghai. Toluene, m/p-xylene and propene have highest the PE concentrations, which accounted for about 21, 17, and 6% of the total PE concentrations, respectively. With the knowledge of the individual contribution of VOC sources to the measured VOC concentrations obtained from the above analysis, the contributions of the seven VOC sources to photochemical ozone formation are calculated (Fig. 6b). The result suggests that the five most significant sources for ozone chemical formation are solvent based industrial productions (accounting for about 27%,), paint solvent usage (accounting for about 24%), vehicle related emissions (accounting for about 17%), steel related productions (accounting for about 14%), and fuel evaporations (accounting for about 9%). 4. Summary
3.4. VOC reactivity and ozone formation potential Photochemical formation of ozone is initiated by reactions of OH radical with VOCs, and the magnitudes of ozone formation due to the VOC reactions vary in a large range. A propylene-equivalent (PE) concentration method suggested by Chameides et al. (1992) and
In this study, the measured VOC concentrations at center of Shanghai are analyzed. The results show that the measured VOC concentrations in Shanghai are dominated by alkanes (43%) and aromatics (30%), following by halo-hydrocarbons (14%) and alkenes (6%). The measured concentrations of VOCs are also compared to
Table 6 Source profiles calculated by PMF model. Sources
Propene 1-Butene cis-2-Butene Tran-2-Butene 1-Pentene Isoprene Propane Isobutane Butane Isopentane 2-Methyl pentane 3-Methyl pentane n-Hexane 2,4-Dimethyl pentane Methyl cyclopentane 2-Methyl hexane 3-Methyl hexane n-Heptane n-Nonane n-Decane Chloromethane Methylene chloride 1,2Dichloroethane Styrene Benzene Toluene Ethylbenzene m/p-Xylene o-Xylene Ethyl acetate Methyl tertbutyl ether
1
2
7
4
5
6
Vehicle internal Biomass/ engine burned and biofuel unburned burning emissions
Paint solvent usage
Vehicle exhaust
Fuel evaporation (gasoline, LPG/NG Leakage)
Steel related Industrial industrial sources productions (solvent based)
Coal burning
0.127 0.013 0.020 0.002 0.013 0.002 0.009 0.002 0.006 0.002 0.002 0.001 0.330 0.121 0.055 0.018 0.081 0.031 0.093 0.033 0.222 0.018 0.258 0.022 0.512 0.044 0.086 0.007
0.075 0.012 0.005 0.003 0.010 0.003 0.005 0.003 0.008 0.002 0.002 0.002 0.164 0.142 0.032 0.021 0.081 0.036 0.210 0.041 0.033 0.008 0.017 0.007 0.012 0.015 0.008 0.002
0.001 0.002 0.035 0.002 0.037 0.002 0.038 0.003 0.017 0.002 0.024 0.002 0.661 0.174 0.102 0.017 0.141 0.027 0.279 0.035 0.083 0.015 0.049 0.018 0.051 0.038 0.024 0.005
0.225 0.017 0.030 0.005 0.030 0.007 0.029 0.007 0.023 0.005 0.006 0.005 2.660 0.177 0.345 0.031 0.653 0.048 0.468 0.094 0.049 0.016 0.015 0.011 0.006 0.019 0.022 0.005
0.195 0.024 0.072 0.004 0.069 0.004 0.076 0.004 0.053 0.003 0.054 0.002 0.333 0.272 0.482 0.025 0.574 0.049 1.022 0.053 0.184 0.021 0.081 0.024 0.037 0.046 0.039 0.009
0.070 0.019 0.016 0.003 0.007 0.002 0.008 0.003 0.002 0.001 0.003 0.003 0.230 0.280 0.136 0.025 0.211 0.053 0.035 0.032 0.040 0.016 0.024 0.021 0.062 0.047 0.010 0.007
0.033 0.012 0.013 0.002 0.013 0.003 0.015 0.003 0.005 0.002 0.003 0.001 0.212 0.159 0.067 0.021 0.078 0.038 0.094 0.039 0.007 0.007 0.019 0.009 0.067 0.021 0.009 0.003
0.112 0.009
0.009 0.003 0.031 0.007
0.028 0.006
0.045 0.011
0.014 0.009 0.012 0.009
0.013 0.004 0.97
0.006 0.002 0.007 0.002 0.008 0.002 0.003 0.001 0.002 0.001 0.023 0.032 0.009 0.011 0.016 0.021
0.007 0.002 0.006 0.002 0.012 0.002 0.006 0.001 0.008 0.001 1.582 0.052 0.058 0.007 0.192 0.178
0.021 0.005 0.021 0.006 0.028 0.006 0.027 0.001 0.028 0.002 0.047 0.062 0.079 0.032 0.059 0.060
0.012 0.003 0.002 0.003 0.013 0.003 0.009 0.002 0.004 0.002 0.041 0.059 0.049 0.030 0.068 0.067
0.042 0.005 0.063 0.007 0.071 0.006 0.021 0.002 0.026 0.002 0.089 0.101 0.214 0.041 0.029 0.039
0.042 0.004 0.046 0.005 0.047 0.005 0.011 0.002 0.011 0.003 0.095 0.123 0.287 0.023 0.118 0.133
0.042 0.005 0.052 0.006 0.039 0.006 0.007 0.002 0.010 0.002 0.082 0.093 0.172 0.031 0.062 0.089
0.002 0.002 0.003 0.003 0.003 0.002 0.002 0.001 0.000 0.000 0.071 0.064 0.035 0.016 1.021 0.063
0.87 0.90 0.88 0.82 0.66 1.00 0.76 1.00
0.001 0.002 0.106 0.038 0.021 0.034 0.033 0.011 0.026 0.016 0.011 0.004 0.040 0.021 0.016 0.004
0.000 0.000 0.087 0.029 0.094 0.054 0.028 0.016 0.036 0.024 0.017 0.007 0.061 0.045 0.005 0.005
0.044 0.006 0.163 0.098 0.926 0.096 0.673 0.022 0.818 0.032 0.237 0.008 0.011 0.028 0.013 0.008
0.003 0.004 0.265 0.139 0.422 0.099 0.035 0.051 0.218 0.069 0.049 0.018 0.028 0.053 0.009 0.012
0.015 0.007 0.106 0.128 0.074 0.127 0.184 0.047 0.087 0.060 0.050 0.016 0.123 0.084 0.184 0.005
0.049 0.005 0.943 0.051 0.892 0.087 0.083 0.040 0.024 0.030 0.027 0.010 0.073 0.070 0.007 0.006
0.020 0.006 0.068 0.091 1.963 0.117 0.130 0.058 0.132 0.076 0.060 0.020 1.552 0.052 0.036 0.009
0.003 0.003 0.060 0.068 0.100 0.069 0.032 0.020 0.043 0.027 0.031 0.008 0.085 0.051 0.010 0.005
0.72 0.98 0.93 0.94 0.96 0.96 0.92 0.81
0.085 0.016 0.060 0.003 0.024 0.002 0.051 0.002 0.008 0.001 0.014 0.002 0.233 0.155 0.153 0.021 0.171 0.034 0.013 0.027 0.052 0.020 0.021 0.022 0.083 0.043 0.013 0.007
8
R2
3
0.75 0.76 0.67 0.75 0.77 0.58 0.95 0.91 0.95 0.90 0.96 0.99 0.97 0.97
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the values in other four urban areas (Beijing, Guangzhou, Mexico, and Nagoya). The comparison shows that the concentrations of VOCs in Shanghai are similar to Nagoya, but are much lower than Mexico, Guangzhou, and Beijing. Based on the measured VOC concentrations, a receptor model (PMF; positive matrix factorization) coupled with VOC source related information (the distribution of major industrial complex, the meteorological conditions, etc.) is applied to identify the major VOC sources in Shanghai. This study shows that by using the PMF model alone is not sufficient to provide detailed information of the VOC sources in Shanghai. The additional information related to the VOC sources (mentioned above) is necessary and useful when the PMF model is applied for identification of VOC sources in large cities. Because VOCs are important precursors for the chemical formation of ozone, identifying the contributions of individual VOCs to ozone formation becomes an important issue for better understanding the ozone chemical production in the Shanghai region. Seven important VOC sources are found in this study, including (1) vehicle related sources which contribute to 25% of the measured VOC concentrations, (2) solvent based industrial sources to 17%, (3) fuel evaporation to 15%, (4) paint solvent usage to 15%, (5) steel related industrial productions to 12%, (6) biomass/biofuel burning to 9%, and (7) coal burning to 7%. Furthermore, ozone formation potential related to the VOC sources is calculated by the MIR (maximum incremental reactivity) technique. The result suggests that the five VOC sources play important roles in controlling ozone chemical formation in Shanghai, such as solvent based industrial productions (accounting for about 27%,), paint solvent usage (accounting for about 24%), vehicle related emissions (accounting for about 17%), steel related productions (accounting for about 14%), and fuel evaporations (accounting for about 9%). The results gained from this study provide useful information for ozone pollutant control strategy in Shanghai. Acknowledgements This work is funded by the National Natural Science Foundation of China (NSFC) under Grant No. 40705046; The Shanghai Science and Technology Commission under Grant No. 08230705200; The National Center for Atmospheric Research is sponsored by the National Science Foundation. References Anderson, M.J., 2001. Source Apportionment of Toxic Volatile Organic Compounds. M.S. thesis, Department of Civil, Environmental, and Architectural Engineering, University of Colorado, Boulder, CO. Anderson, M.J., Daly, E.P., Miller, S.L., Milford, J.B., 2002. Source apportionment of exposure to volatile organic compounds: II. Application of receptor models to TEAM study data. Atmos. Environ. 39, 3642e3658. Apel, E.C., Emmons, L.K., Karl, T., Flocke, F., Hills, A.J., Madronich, S., Lee-Taylor, J., Fried, A., Weibring, P., Walega, J., Richter, D., Tie, X., Mauldin, L., Campos, T., Weinheimer, A., Knapp, D., Sive, B., Kleinman, L., Springston, S., Zaveri, R., Ortega, J., Voss, P., Blake, D., Baker, A., Warneke, C., Welsh-Bon, D., Gouw, J., de, Zheng, J., Zhang, R., Rudolph, J., Junkermann, W., Riemer, D.D., 2010. Chemical evolution of volatile organic compounds in the outflow of the Mexico City Metropolitan area. Atmos. Chem. Phys. 10, 2353e2376. Atkinson, R., 2003. Kinetics of the gas-phase reactions of OH radicals with alkanes and cycloalkanes. Atmos. Chem. Phys. 3, 2233e2307. Atkinson, R., Arey, J., 2003. Atmospheric degradation of volatile organic compounds. Chem. Rev. 103, 4605e4638. ATSDR (Agency for Toxic Substance and Disease Registry), 1997. Toxicological Profile for Benzene, Update. Public Health Service, U.S. Department of Health and Human Services, Atlanta, GA. Barletta, B., Meinardi, S., Rowland, F.S., Chan, C., Wang, X., Zou, S., Chan, L.Y., Blake, D., 2005. Volatile organic compounds in 43 Chinese cities. Atmos. Environ. 39, 5979e5990. Barletta, B., Meinardi, S., Simpson, I.J., Atlas, E.L., Beyersdorf, A.J., Baker, 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
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