Source Apportionment of Ambient Volatile Organic Compounds in

Read OnlinePDF (132 KB) ... Comparison of Machine Learning Approaches with a General Linear Model To Predict Personal Exposure ..... Non-methane volat...
4 downloads 0 Views 132KB Size
Download PDF
Environ. Sci. Technol. 2007, 41, 4348-4353

Source Apportionment of Ambient Volatile Organic Compounds in Beijing Y U S O N G , † M I N S H A O , * ,‡ Y I N G L I U , ‡ SIHUA LU,‡ WILLIAM KUSTER,§ PAUL GOLDAN,§ AND SHAODONG XIE† Department of Environmental Sciences, Peking University, Beijing 100871, China, State Joint Key Laboratory of Environmental Simulation and Pollution Control, Peking University, Beijing 100871, China, and Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, Colorado 80305

The ambient air quality standard for ozone is frequently exceeded in Beijing in summer and autumn. Source apportionments of volatile organic compounds (VOCs), which are precursors of ground-level ozone formation, can be helpful to the further study of tropospheric ozone formation. In this study, ambient concentrations of VOCs were continuously measured with a time resolution of 30 min in August 2005 in Beijing. By using positive matrix factorization (PMF), eight sources for the selected VOC species were extracted. Gasoline-related emissions (the combination of gasoline exhaust and gas vapor), petrochemicals, and liquefied petroleum gas (LPG) contributed 52, 20, and 11%, respectively, to total ambient VOCs. VOC emissions from natural gas (5%), painting (5%), diesel vehicles (3%), and biogenic emissions (2%) were also identified. The gasoline-related, petrochemical, and biogenic sources were estimated to be the major contributors to ozone formation potentials in Beijing.

1. Introduction Volatile organic compounds (VOCs) are important for urban air quality because, combined with nitrogen oxides, they can lead to the production of secondary air pollutants through complex photochemical cycles, resulting in tropospheric ozone, peroxyacetyl nitrates, and secondary organic aerosols (1). Furthermore, some species such as benzene, ethylbenzene, and n-decane are toxic to human beings (2). In Beijing, photochemical smog is a serious air quality problem. The national ozone standard was exceeded by 110, 109, 77, 45, 57, and 67 days in the respective years from 1999 to 2004 (3). High ozone concentrations frequently occurred in summer and autumn. Ground-level ozone abatement is needed in Beijing to protect public health and is an urgent issue because the Olympic Games will be held in Beijing in August 2008. Understanding VOCs sources can be helpful to the study of tropospheric ozone formation. Gridded VOC emission inventories have been established in Beijing for the purpose * Corresponding author phone: (86-10) 62757973; fax: (86-10) 62751927; e-mail: [email protected]. † Department of Environmental Sciences, Peking University. ‡ State Joint Key Laboratory of Environmental Simulation and Pollution Control, Peking University. § NOAA Earth System Research Laboratory. 4348

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 41, NO. 12, 2007

of atmospheric modeling (4, 5) based on the bottom-up approach. One inventory was developed in 2000 by Streets et al. (4); however, it is difficult to obtain source apportionment results for Beijing because of coarse spatial and temporal resolutions of the raw datasets. Another monthly VOC inventory with a grid resolution of 1 km was developed for Beijing in recent years (5), indicating that gasolinepowered vehicles contributed 53% to ambient VOCs, emissions from gasoline stations, solvents, and area industrial sources contributed 21%, and petrochemical emissions from point sources contributed 15%. VOC source apportionments were also done by applying the chemical mass balance (CMB) model in Beijing: vehicle exhaust was determined to be the major source, accounting for almost 60% of all VOCs in summer (6, 7). However, some source profiles used in those studies were measured in U.S. cities and may not be directly applicable in Beijing. As a powerful factor analysis model, the positive matrix factorization (PMF) model has been widely used for source apportionment of particulate matter and VOCs lacking local source profile measurements (8-11). In the present study, the concentrations of ambient VOCs in Beijing were measured continuously from August 1 to 27, 2005 using an on-line system packing gas chromatography(GC) with flame ionization detector (FID) and mass spectrometry (MS) together from the NOAA Aeronomy Laboratory and collecting samples at a 30-min interval. The PMF model was used to extract the VOC sources for this time period, and the VOCs losses due to the photochemical reactions during the daytime were estimated. Finally, the ozone formation potentials contributed from PMF-extracted sources were discussed.

2. Materials and Methods 2.1. Sampling and Chemical Analysis. The sampling site was on the roof of a five-story building (∼20 m above ground) on the campus of Peking University (PKU) (Figure 1). The campus is surrounded by roads with a high density of traffic. The sampling site faces one main road to the east, which is oriented north-south. Another main road is close to the site, to the south. This site is also surrounded by residential apartments and electronics companies. The concentrations of VOC species were quantified using a custom-made GC-FID/MS system with 2 channels: samples were collected through a dual coaxial Teflon line system; C2-C5 alkanes, C2-C4 alkenes, and acetylene were separated on an Al2O3/KCl column and quantified with a flame ionization detector. The C5-C10 alkanes, C5-C9 alkenes, C6-C9 aromatics, C1-C5 alcohols, C2-C7 aldehydes and ketones, C1-C5 alkylnitrates, isoprene, monoterpenes, and several chlorofluorocarbons were separated on a semi-polar stationary phase column (DB624) on the second channel, and a subset of these were quantified with a linear quadrupole mass spectrometer. The detection limit for most of the compounds measured was near 0.5 pptv (signal-to-noise ratio of 2). More detailed descriptions of the sampling preconcentration procedure and instrument parameters can be found elsewhere (12). Up to 45 VOC species were quantified at a time resolution of 30 min. The sampling period was from August 1 to 27, 2005, during which the gaseous pollutants O3, NO, and NO2 were also continuously measured. The data were averaged over 1 h. Winds were measured by the Department of Atmospheric Science at Peking University with an automatic weather station installed on the top of another building, about 100 m from the VOC sampling site. 10.1021/es0625982 CCC: $37.00

 2007 American Chemical Society Published on Web 05/17/2007

FIGURE 1. Locations of the sampling sites for the measurement of ambient volatile organic compounds (VOCs) at Peking University (PKU) and industrial point sources. The black square indicates the location of PKU, the receptor site, and the black dots represent (1) Yanshan Petrochemical Corporation Limited, (2) Beijing municipal chemical plants, which mainly produce coke and coal gas for the city, (3) Beijing Municipal Eastern Chemical Works, which mainly produces chemicals for daily use (e.g., detergents, solvents, cosmetics, etc.); and (4) and (5) clusters of other chemical plants. 2.2. Positive Matrix Factorization Model and Conditional Probability Function. In general, the ordinary bilinear receptor-model (13) can be written as p

xij )

∑

gik fkj + eij

(1)

k)1

where xij is the concentration of the jth element in the i th sample (i ) 1, ..., n samples; j ) 1, ..., m elements), gik is the source contribution from the k th source to the i th sample (k ) 1, ..., p sources), fkj is the element fraction of the j th element in the k th source, and eij is residuals. To decrease the rotational freedom, PMF applies non-negative constraints to the parameters (14). The objective function Q(E) is based on the uncertainties inherent in each observation that are to be minimized and is defined as

[ ] p

m

Q(E) )

n

∑∑ i)1 j)1

xij -

∑

2

gik fkj

k)1

sij

(2)

where sij is the uncertainty estimate of the j th element measured in the ith sample. The standard deviations for each data point, sij, are calculated at each iteration using p

sij ) c1 + c2 max(|xij|,

∑ |g

ik fkj|)

(3)

k)1

where c1 is measurement uncertainties and c2 is adjusted to 0.01 for this study, because Q(E) is close to the number of individual elements of X. Although some oxygenated VOCs such as alcohols, aldehydes, and ketones were measured, few were listed in

TABLE 1. Measurement Statistics from August 2005, Beijing (Data in ppbv, Total Number of Samples ) 1257) compound

mean

standard deviation

missing

ethane ethylene acetylene propane propylene 2M-propene i-butane n-butane t-2-butene 1-butene c-2-butene 2M-1-butene 3M-1-butene 2M-2-butene i-pentane n-pentane 1-pentene c-2-pentene t-2-pentene n-hexane n-decane benzene toluene ethylbenzene m,p-xylene o-xylene isoprene R-pinene β-pinene limonene MTBE

3.75 4.59 5.41 3.59 1.16 0.57 2.31 2.75 0.37 0.98 0.33 0.31 0.05 0.3 4.11 1.7 0.12 0.1 0.18 0.65 0.26 1.76 3.03 0.98 2.04 0.93 0.41 0.09 0.02 0.02 0.88

1.67 2.95 2.91 2.18 0.73 0.38 1.36 1.65 0.36 0.69 0.3 0.22 0.02 0.36 2.5 0.97 0.06 0.09 0.15 0.37 0.19 0.89 1.72 0.65 1.25 0.56 0.28 0.11 0.02 0.02 0.5

167 167 167 167 167 167 167 167 167 167 167 201 206 201 167 167 201 201 201 201 209 211 218 201 201 201 201 201 201 202 209

the VOC source profiles reported in previous studies. In our study, we selected 31 of the 45 species for source apportionment. Table 1 lists the statistics of the mixing ratios for these species. The average mixing ratio of all 31 species VOL. 41, NO. 12, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4349

TABLE 2. Source Profiles (Mass Fraction, in ppbv/ppbv) Obtained from Positive Matrix Factorization compound

natural gas

biogenic

paint

petrochemical

LPG

diesel exhaust

liquid/evaporated/ exhaust gasoline

gasoline exhaust

ethane ethylene acetylene propane propylene 2M-propene i-butane n-butane t-2-butene 1-butene c-2-butene 2M-1-butene 3M-1-butene 2M-2-butene i-pentane n-pentane 1-pentene c-2-pentene t-2-pentene n-hexane n-decane benzene toluene ethylbenzene m,p-xylene o-xylene isoprene R-pinene β-pinene limonene MTBE

0.385 ( 0.015 0.000 ( 0.002 0.095 ( 0.021 0.017 ( 0.012 0.023 ( 0.005 0.027 ( 0.003 0.002 ( 0.009 0.001 ( 0.008 0.000 ( 0.001 0.022 ( 0.004 0.007 ( 0.001 0.043 ( 0.001 0.016 ( 0.000 0.000 ( 0.000 0.029 ( 0.016 0.036 ( 0.007 0.023 ( 0.001 0.010 ( 0.000 0.016 ( 0.001 0.017 ( 0.003 0.007 ( 0.001 0.057 ( 0.005 0.094 ( 0.009 0.000 ( 0.001 0.055 ( 0.005 0.003 ( 0.003 0.006 ( 0.001 0.000 ( 0.000 0.001 ( 0.000 0.000 ( 0.000 0.006 ( 0.004

0.002 ( 0.008 0.005 ( 0.028 0.000 ( 0.001 0.000 ( 0.000 0.090 ( 0.007 0.026 ( 0.003 0.020 ( 0.013 0.000 ( 0.003 0.009 ( 0.001 0.017 ( 0.004 0.005 ( 0.001 0.004 ( 0.002 0.002 ( 0.000 0.014 ( 0.001 0.029 ( 0.028 0.000 ( 0.001 0.000 ( 0.001 0.001 ( 0.001 0.000 ( 0.000 0.007 ( 0.004 0.008 ( 0.001 0.026 ( 0.008 0.035 ( 0.013 0.036 ( 0.003 0.052 ( 0.007 0.018 ( 0.003 0.580 ( 0.004 0.002 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.009 ( 0.005

0.001 ( 0.008 0.001 ( 0.007 0.001 ( 0.007 0.000 ( 0.000 0.000 ( 0.002 0.000 ( 0.000 0.000 ( 0.000 0.002 ( 0.007 0.012 ( 0.002 0.000 ( 0.005 0.018 ( 0.002 0.012 ( 0.002 0.003 ( 0.000 0.046 ( 0.001 0.023 ( 0.023 0.069 ( 0.010 0.005 ( 0.001 0.006 ( 0.001 0.012 ( 0.001 0.015 ( 0.003 0.002 ( 0.001 0.059 ( 0.007 0.071 ( 0.012 0.125 ( 0.003 0.311 ( 0.009 0.106 ( 0.004 0.018 ( 0.001 0.066 ( 0.000 0.010 ( 0.000 0.008 ( 0.000 0.000 ( 0.001

0.000 ( 0.001 0.174 ( 0.007 0.060 ( 0.009 0.000 ( 0.001 0.049 ( 0.002 0.014 ( 0.001 0.016 ( 0.004 0.010 ( 0.004 0.000 ( 0.000 0.027 ( 0.001 0.002 ( 0.000 0.004 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.013 ( 0.006 0.026 ( 0.003 0.002 ( 0.000 0.001 ( 0.000 0.001 ( 0.000 0.024 ( 0.001 0.010 ( 0.000 0.028 ( 0.002 0.128 ( 0.004 0.091 ( 0.001 0.209 ( 0.002 0.087 ( 0.001 0.003 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.024 ( 0.001

0.023 ( 0.006 0.071 ( 0.008 0.009 ( 0.008 0.179 ( 0.005 0.071 ( 0.002 0.029 ( 0.001 0.160 ( 0.004 0.142 ( 0.004 0.046 ( 0.001 0.122 ( 0.002 0.033 ( 0.001 0.003 ( 0.001 0.000 ( 0.000 0.000 ( 0.000 0.031 ( 0.009 0.004 ( 0.003 0.002 ( 0.000 0.003 ( 0.000 0.006 ( 0.000 0.006 ( 0.001 0.000 ( 0.000 0.003 ( 0.003 0.027 ( 0.005 0.000 ( 0.000 0.018 ( 0.003 0.009 ( 0.001 0.001 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.003 ( 0.002

0.210 ( 0.011 0.119 ( 0.012 0.179 ( 0.013 0.046 ( 0.007 0.022 ( 0.003 0.004 ( 0.001 0.029 ( 0.005 0.031 ( 0.005 0.001 ( 0.000 0.002 ( 0.002 0.000 ( 0.000 0.000 ( 0.001 0.000 ( 0.000 0.005 ( 0.001 0.071 ( 0.010 0.022 ( 0.004 0.000 ( 0.000 0.000 ( 0.000 0.001 ( 0.000 0.017 ( 0.002 0.116 ( 0.002 0.046 ( 0.003 0.046 ( 0.005 0.002 ( 0.002 0.001 ( 0.003 0.010 ( 0.002 0.004 ( 0.001 0.000 ( 0.000 0.000 ( 0.000 0.004 ( 0.000 0.010 ( 0.002

0.017 ( 0.005 0.116 ( 0.006 0.185 ( 0.008 0.000 ( 0.002 0.021 ( 0.002 0.033 ( 0.001 0.013 ( 0.003 0.025 ( 0.004 0.021 ( 0.001 0.005 ( 0.002 0.019 ( 0.001 0.027 ( 0.001 0.001 ( 0.000 0.040 ( 0.000 0.218 ( 0.007 0.063 ( 0.003 0.005 ( 0.000 0.010 ( 0.000 0.017 ( 0.000 0.013 ( 0.001 0.000 ( 0.000 0.038 ( 0.002 0.058 ( 0.003 0.000 ( 0.001 0.000 ( 0.001 0.007 ( 0.001 0.001 ( 0.000 0.001 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.046 ( 0.001

0.117 ( 0.002 0.099 ( 0.002 0.168 ( 0.003 0.120 ( 0.001 0.009 ( 0.001 0.003 ( 0.000 0.062 ( 0.001 0.084 ( 0.001 0.000 ( 0.000 0.004 ( 0.000 0.000 ( 0.000 0.001 ( 0.000 0.000 ( 0.000 0.001 ( 0.000 0.119 ( 0.002 0.047 ( 0.001 0.000 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.013 ( 0.000 0.000 ( 0.000 0.052 ( 0.001 0.066 ( 0.001 0.009 ( 0.000 0.000 ( 0.001 0.005 ( 0.000 0.001 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.000 ( 0.000 0.019 ( 0.000

was 43.4 ppbv, or 102 µg/m3 in mass concentration. The alkanes, alkenes, and aromatics contributed 43.7, 21.6, and 20.0%, respectively, to the total of the selected VOC species in our study (in ppbv). Samples were selected when no concentration data was missing for each compound, and 1019 samples in total were used for PMF analysis. The analytical uncertainties were estimated as 10% of the quantification for benzene, toluene, ethylbenzene, m,pxylene, o-xylene, isoprene, R-pinene, β-pinene, and limonene, and as 15% of the measurements for the other chemical species. The conditional probability function (CPF) (15) was used to identify the orientation of sources based on PMF source contributions and the wind direction data. The absolute concentrations were often low when advection and diffusion were strong, and the fractional VOC volume contribution from each source relative to the total of all sources could then be a better indicator representing the source contribution. The CPF is defined as

CPF )

m∆θ n∆θ

(4)

where m∆θ is the number of occurrences from wind sector ∆θ that exceed the threshold criterion and n∆θ is the total number of data from the same wind sector. In this study, ∆θ was set to 30°. Calm winds (less than 0.5 m/s) were excluded from this analysis because of the difficulty in defining wind direction under calm conditions. A threshold criterion of the upper 25% is selected to show clear directionality.

3. Results The parameter FPEAK and the matrices FKEY in the PMF model can be used to control the rotation in order to obtain a more reasonable solution (14). FPEAK is often used to determine the range within which the values of the objective 4350

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 41, NO. 12, 2007

TABLE 3. Average Source Contributions (µg/m3) to the Total Volatile Organic Compound (VOC) Levels (the Total Average Mass Concentration Was 102 µg/m3)

source

average source contribution (standard error)

percentage

natural gas biogenic painting petrochemical LPG diesel exhaust gasoline 1 gasoline 2

4.7 (0.7) 1.6 (0.2) 4.7 (0.4) 20.2 (0.9) 11.2 (0.8) 3.2 (0.3) 12.0 (0.6) 40.4 (0.9)

5 2 5 20 11 3 12 40

function Q(E) remain relatively constant. An entry with an unreasonably large value in the F matrices (species concentrations) can be pulled down by FKEY when a priori information indicates that the value should be small or zero. PMF was run many times with different FPEAK values, and no rotation was selected (FPEAK ) 0). Eight possible VOC sources were identified: natural gas, biogenic emissions, painting sources, petrochemical sources, liquefied petroleum gas (LPG), diesel exhaust, and two gasoline-related sources. Ethane was high in biogenic emissions and butane was high in the painting profile based on the primary PMF run; however, such values should be low according to abundant measurements of these sources (6, 7, 16, 17). FKEY was set to 7 for ethane in the biogenic profile and to 9 for butane in the painting profile. The source profiles (matrix F) and the average source contributions (in µg/m3) are listed in Tables 2 and 3, respectively (the time series of daily source contributions are shown in Figure S1 in the Supporting Information). 98% of the VOC mass concentration was resolved by the PMF method. The natural gas source was identified as rich in ethane (18). Compressed natural gas (CNG) is the dominant

FIGURE 2. Conditional probability function (CPF) plots for the highest 25% of the relative volume concentrations (wind sector is 30°, and calm winds are excluded). residential energy source in Beijing, and the ambient VOCs from natural gas could be caused by leakage during CNG consumption. The contribution of natural gas to the mass concentration of total VOCs at the PKU site was 5%. It was not derived by CMB model (6, 7). The biogenic source was represented by a high abundance of isoprene (19). The contribution of biogenic sources to the total VOC concentration was only 2%, very close to the CMB results (6). It should be noted that the biogenic contribution calculated by the receptor model may be an underestimate because isoprene is very active and therefore rapidly oxidized in photochemical reactions (18). Unfortunately, other possible biogenic emission tracers such as R-pinene, β-pinene, and limonene did not show distinct enrichment in this study, probably because of significant chemical loss of these monoterpenes between the emission source and the sampling site. The painting source was characterized by high benzene, toluene, ethylbenzene, m,p-xylene, and o-xylene (BTEX) (18, 20), and can be explained in part by construction underway in Beijing, especially for Olympic stadiums and housing. This source contributed 5% to the total mass of VOCs measured at the PKU site, less than the CMB results, 6% and 9% (6, 7). The petrochemical source was described by high levels of ethylene, acetylene, and BTEX (21, 22). Although BTEX can be emitted from industrial solvents and vehicular exhaust, the level of i/n-pentane, an important indicator for evaporated and liquid gasoline (18, 20), was relatively low in this profile. Three important chemical plants, Yanshan Petrochemical Corporation Limited (YSPCL) in Fangshan, Beijing Chemical Plants in Chaoyang, and Eastern Chemical Works in Tongzhou (see Figure 1), are located in Beijing at about 31, 26, and 34 km, respectively, from the sampling site. Their products include ethylene, polyethylene, polystyrene, and biphenyl. High BTEX concentrations were found in Tongzhou (7). The petrochemical source contributed 20% to the total VOC. It was higher than the emission inventory results, 15% (5) and the CMB results, 11% (6). The LPG source was rich in propane, butane, propylene, and butene (10, 23). High propane, and i/n-butane were also found in the samples collected from two LPG stations in Beijing (6). These results agree with the LPG profile measurements from the Pearl River Delta in China (24); however, propane was dominant in North American LPG (18). In Beijing, LPG is used for some buses and taxis and for cooking in restaurants and some households. Its contribution to the VOC total was about 11%. CMB model suggested 11% (6)

and 3% (7) respectively, probably due to the different sampling sites. The sixth source was rich in ethane, ethylene, acetylene, n-decane, and BTEX. Ethane, ethylene, and acetylene can be formed during fuel combustion (18, 20, 25), and a high level of n-decane is a good marker for diesel emission (20). Thus, this source may be diesel exhaust, and it contributed only 3% to the mass concentrations. In Beijing, the number of diesel vehicles has been stringently controlled in recent years owing to concern about emissions of fine particles and air toxins. The number of diesel vehicles in 2005 was 120 000 or less than 5% of the total number of vehicles in Beijing (2.6 million). Gasoline is the dominant vehicle fuel in Beijing, and VOC emissions from gasoline come from three pathways (20, 26): (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 such as running loss and hot soak; and (3) exhaust released from the tailpipes of gasoline-powered vehicles during gasoline combustion. The i/n-pentane level has been reported to be high in source profiles related to gasoline (20), and i-pentane is especially abundant in gasoline vapor, as it has a higher vapor pressure than n-pentane. Ethane and acetylene levels are high in vehicular exhaust but are not found in gasoline evaporation. Both the seventh and eighth sources had high i/n-pentane, ethylene, acetylene, and MTBE. MTBE is a common gasoline additive in Beijing. Thus both of the sources contained gasoline exhaust. The differences between the sources were that higher levels of i-pentane and MTBE were present in the seventh source, and higher levels of ethane, propane, and i/n-butane were present in the eighth source. Therefore, the seventh source may be a mixture of evaporated or liquid gasoline and gasoline exhaust, and the eighth source may be the exhaust from gasoline powered vehicles. We denoted these two gasoline-related sources as gasoline 1 and gasoline 2, and their contributions to the VOC mass concentration were 12% and 40%, respectively (52% total). It was less than the CMB results: 63% (6) and 78% (7). We realized that BTEX seemed low in the PMF gasoline-related profiles, or some of them were mixed in the petrochemical profile. This could lead to the underestimated contributions by the gasolinerelated emissions. As mentioned in Section 2.1., the sampling site was surrounded by busy roads and residential buildings, and the CPF plots indicated that the painting sources, gasoline and VOL. 41, NO. 12, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4351

TABLE 4. Estimated Source Contributions to Ozone Formation (ppbv) from the Reacted and Measured Volatile Organic Compounds (VOCs) during the Daytime (07:00-19:00) source

reacted VOCs to OFP

measured VOCs to OFP

natural gas biogenic painting petrochemical LPG diesel exhaust gasoline 1 + gasoline 2 total

2.8 7.1 2.3 9.5 3.5 0.8 10.4 36.4

2.9 3.3 2.9 17.9 3.6 1.2 16.5 48.3

diesel emissions, natural gas, and LPG emissions could come from all directions. However, the CPF plots for biogenic sources indicated that the source could be located to the northwest (Figure 2), where nature reserves with flourishing vegetation are located, and to the south. The southern biogenic source is not clear, and the isoprene may be emitted by the heavy vehicular traffic. CPF plots for the petrochemical source clearly indicated that the sources were located to the south or west of the PKU site (Figure 2). In Beijing, summer winds often come from the south during the day (27) and could transport the VOC emissions from the three chemical plants, especially from YSPCL, to the sampling site.

4. Discussion Both VOCs and oxides of nitrogen (NOx, sum of NO and NO2) are important precursors to tropospheric ozone. From nearly 1 month of data measured in this study, the VOC/NOx ratio (ppbC/ppbv) was close to 6.8, reflecting the urban atmospheric chemistry at the PKU site (28, 29). Chemical reactivity in terms of hydroxyl (OH) loss rate is an indicator of the potential role of VOCs and NOx in photochemical reactions. The calculated reactivity of the total measured non-methane hydrocarbon (NMHC), oxygenated VOCs (acetaldehyde, propanal, n-butanal, acetone, MEK, methanol, ethanol, i-propanol, methacrolein, methyl vinyl ketone), and NOx during daytime (07:00-19:00) were 8.6, 3.3, and 5.0 per second, respectively (the rate constants for OH-VOCs reactions are listed in Table S1 in the Supporting Information). The calculated VOCs reactivity is often significantly underestimated because some short-lived, highly reactive VOCs, including long straight- and branched-chain alkanes, internal and external alkenes, and the secondary products of photochemical reactions, are not fully quantified (30, 31, 32). This is also true for this study. Understanding the contributions of ambient VOCs and their sources to ozone formation potential (OFP) would be helpful in developing atmospheric ozone control measures. These contributions can be estimated by multiplying the concentration of each VOC species by its incremental reactivity (IR) value, which is defined as the amount of ozone formed per quantity of VOC added to the VOC mixture of a given air parcel (28, 33). However, we tended to underestimate most reactive VOCs, because we were measuring in summer when photochemical processes were active. Consequently, the source profiles extracted by PMF consist of “aged” VOCs, which differ from “fresh” emissions. To realize the initial OFPs of VOCs, the daytime losses of reactive VOCs must be corrected. There is no simple way to do so, because the magnitude of VOC loss would depend on each specific VOC emitted, the time required for the species to reach the receptor site, and the specific meteorological conditions (temperature, radiation levels, etc.). It may be possible to make such estimates using state-of-the-science air quality models, but the requirements of the inputs, e.g., emission inventory and meteorological conditions, could present other difficulties. 4352

9

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 41, NO. 12, 2007

An approach was developed by Wiedinmeyer et al. (29). First, reacted isoprene (∆isoprene) is estimated from its primary photo-oxidation, methacrolein (MACR), and methyl vinyl ketone (MVK) (29, 34). Fortunately, both MACR and MVK were measured in this study. The concentration of reacted VOCi (or the photochemical loss, ∆VOCi) can then be roughly estimated from the extent of the isoprene reaction:

∆VOCi ) ∆isoprene ×

kVOCi kisoprene

×

[VOCi] [isoprene]

(5)

where [isoprene] and [VOCi ] are the measured concentrations of isoprene and VOCi, respectively, and kisoprene and kVOCi are the rate constants for the reaction of isoprene and VOCi with the hydroxyl radical, respectively. Assumptions to this approach include that the relative source strengths of VOCs are constant in the area immediately surrounding the site and that atmospheric transport and mixing are non-limiting factors compared with chemistry. The concentration ratio of VOC to isoprene and the ratio of hydroxyl radical rate constants are used to account for differences in emission rates and differences in atmospheric reactivity, respectively. The sum of the reacted VOCs and the measured VOCs would reflect the initial emission without considering photochemical reactions. As the VOC/NOx ratio in this study was 6.8, it was safe to use the IR values taken from the literature for a VOC/NOx ratio of 8.2 (29). Table 4 lists the apportionments of OFPs for both reacted and measured VOCs of each source. As with the source contributions to VOC concentrations, gasoline emissions and petrochemical emissions were the two main sources contributing to ozone formation. Alkenes (e.g., 2-methyl1-butene, 2-methyl-2-butene, and ethylene) and aromatics are the major ozone-forming compounds in the gasoline source. The OFP values from the reacted parts in the two sources were 10.4 and 9.5 ppbv. If all the measured VOCs were reacted completely, the additional ozone concentrations attributable to gasoline source and petrochemical source could be 16.5 and 17.9 ppbv, respectively. A reactive VOC compound emitted from gasoline-powered vehicles, 1,3butadiene, was not measured in this study, although it may have evident contribution to ozone formation. BTEX and ethylene from petrochemical sources were the dominant contributors to the OFP values. Although the contribution from biogenic sources to ambient VOC levels was low, biogenic sources contributed significantly to the ambient ozone (7.1 ppbv) because of the high IR value of isoprene. The OFP value for measured isoprene was not high, however, because most isoprene had already reacted before reaching the sampling site. As the 2008 Beijing Olympic Games approach, the need for abatement of ground-level ozone is growing quite urgent. VOCs are one of the major precursors to the formation of ozone. Further study of VOC sources and their contributions to ozone formation is required. The PMF model together with the CPF method can be used to apportion VOC sources in Beijing. Gasoline-related emissions were the dominant source, accounting for 52% of VOCs. The petrochemical industry, which contributed 20% of the VOCs in this study, was the second most important source. These two sources also emitted large quantities of reactive VOCs, such as alkenes and air toxics (e.g., toluene and xylenes), and together with biogenic emission may be the major contributors to ozone formation potential in Beijing. Future atmospheric simulations are needed to determine if the control of VOCs or NOx could reduce ground-level ozone levels in Beijing.

Acknowledgments This study was funded by the China National Natural Science Foundation program (Grants 40575059 and 20637001) and

the National Key Basic Research and Development Program of China (Grant 2005CB422204 and 2002CB410801).

Supporting Information Available Time series plot of source contributions, and table of reactivity species with their rate constants for the reaction with hydroxyl radical. This material is available free of charge via the Internet at http://pubs.acs.org.

Literature Cited (1) Seinfeld, J. H.; Pandis, S. N. Atmospheric Chemistry and Physics; Wiley: New York, 1998; 533 pp. (2) United States Environmental Protection Agency. The Clean Air Act Amendments of 1990; Washington, DC, 2006; http:// www.epa.gov/air/caa/. (3) Beijing Environmental Protection Bureau. Annual Report; Beijing, 1999-2004; http://www.bjepb.gov.cn. (4) Streets, D. G.; Bond, T. C.; Carmichael, G. R.; Fernandes, S. D.; Fu, Q.; He, D.; Klimont, Z.; Nelson, S. M.; Tsai, N. Y.; Wang, M. Q.; Woo, J. H.; Yarber, K. F. An inventory of gaseous and primary aerosol emissions in Asia in the year 2000. J. Geophys. Res. 2003, 108 (D21), Art. No. 8809. (5) Beijing Environmental Protection Bureau. The Air Pollution Status and Emission Inventory; Beijig, 2005. (6) Lu, S. H. Source apportionment of anthropogenic emissions of volatile organic compounds; MS Thesis, Peking University, 2004. (7) Liu, Y.; Shao, M.; Zhang, J.; Fu, L.; Lu, S. Distributions and source apportionment of ambient volatile organic compounds in Beijing city, China. J. Environ. Sci. Health 2005, 40, 1843-1860. (8) Hopke, P. K. Recent developments in receptor modeling. J. Chemometr. 2003, 17, 255-265. (9) Miller, S. L.; Anderson, M. J.; Daly, E. P.; Milford, J. B. Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data. Atmos. Environ. 2002, 36, 3629-3641. (10) Jorquera, H.; Rappengluck, B. Receptor modeling of ambient VOC at Santiago, Chile. Atmos. Environ. 2004, 38, 4243-4263. (11) Zhao, W. X.; Hopke, P. K.; Karl, T. Source identification of volatile organic compounds in Houston, Texas. Environ. Sci. Technol. 2004, 38, 1338-1347. (12) Goldan, P. D.; Kuster W. C.; Williams E.; Murphy P. C.; Fehsenfeld F. C.; Meagher J. Nonmethane hydrocarbon and oxy-hydrocarbon measurements during the 2002 New England Air Quality Study. J. Geophys. Res. 2004, 109, D21309; doi:10.1029/ 2003JD004455. (13) Paatero, P.; Tapper, U. Positive matrix factorization: a nonnegative factor model with optimal utilization of error estimates of data values. Envirometrics 1994, 5, 111-126. (14) Paatero, P. User’s guide for positive matrix factorization programs PMF2 and PMF3, Part 1: Tutorial; 2004. (15) Ashbaugh, L. L.; Malm, W. C.; Sadeh, W. Z. A residence time probability analysis of sulfur concentrations at Grand Canyon National park. Atmos. Environ. 1985, 19, 1263-1270. (16) Rudolph, J. The tropospheric distribution and budget of ethane. J. Geophys. Res. 1995, 100, 11369-11381. (17) Guenther, A.; Geron, C.; Pierce, T.; Lamb, B.; Harley, P.; Fall, R. Natural emissions of non-methane volatile organic compounds, carbon monoxide, and oxides of nitrogen from North America. Atmos. Environ. 2000, 34, 2205-2230. (18) Fujita, E. M. Hydrogen Source Apportionment for the 1996 Paso del Norte Ozone Study. Sci. Total. Environ. 2001, 276, 171-184. (19) Guenther, A.; Baugh, W.; Davis, K.; Hampton, G.; Harley, P.; Klinger, L.; Vierling, L.; Zimmerman, P.; Allwine, E.; Dilts, S.;

(20)

(21)

(22)

(23)

(24)

(25)

(26)

(27) (28)

(29)

(30)

(31)

(32)

(33)

(34)

Lamb, B.; Westberg, H.; Baldocchi, D.; Geron, C.; Pierce, T. Isoprene fluxes measured by enclosure, relaxed eddy accumulation, surface-layer gradient, mixed-layer gradient, and mass balance techniques. J. Geophys. Res. 1996, 101 (D15), 18555-18567. Watson, J. G.; Chow, J. C.; Fujita, E. M. Review of volatile organic compound source apportionment by chemical mass balance. Atmos. Environ. 2001, 35, 1567-1584. Harley, R. A.; Hannigan, M. P.; Cass, G. R. Respeciation of organic gas emissions and the detection of excess unburned gasoline in the atmosphere. Environ. Sci. Technol. 1992, 26, 2395-2408. Brocco, D.; Fratarcangeli, R.; Lepore, L.; Petricca, M.; Ventrone, I. Determination of aromatic hydrocarbons in urban air of Rome. Atmos. Environ. 1997, 31, 557-566. Blake, D. R.; Rowland, F. S. Urban leakage of liquefied petroleum gas and its impact on air quality in Mexico city. Science 1995, 269, 953-956. Tsai, W. Y.; Chan, L. Y.; Blake, D. R.; Chu, K. W. Vehicular fuel composition and atmospheric emissions in South China: Hong Kong, Macau, Guangzhou, and Zhuhai. Atmos. Chem. Phys. 2006, 6, 3281-3288. Vega, E.; Mugica, V.; Carmona, R.; Valencia, E. Hydrocarbon source apportionment in Mexico City using the chemical mass balance receptor model, Atmos. Environ. 2000, 34, 4121-4129. Choi, Y. J.; Ehrman, S. H. Investigation of sources of volatile organic carbon in Baltimore area using highly time-resolved measurements. Atmos. Environ. 2004, 38, 775-791. Beijing Meteorological Center. Climatography of Beijing; Beijing Press, 1987; 185 pp. Bowman, F.; Seinfeld, J. H. Fundamental basis of incremental reactivities of organics in ozone formation in VOC/NOX mixtures. Atmos. Environ. 1994, 28, 3359-3368. Wiedinmeyer, C.; Friedfeld, S.; Baugh, W.; Greenberg, J.; Guenther, A.; Fraser, M.; Allen, D. Measurement and analysis of atmospheric concentrations of isoprene and its reaction products in central Texas. Atmos. Environ. 2001, 35, 10011013. Kovacs, T. A.; Brune, W. H.; Harder, H.; Martinez, M.; Simpas, J. B.; Frost, G. J.; Williams, E.; Jobson, T.; Stroud, C.; Young, V.; Fried, A.; Wert, B. Direct measurements of urban OH reactivity during Nashville SOS in summer 1999. J. Environ. Monit. 2003, 5, 68-74. Sadanaga, Y.; Yoshino, A.; Kato, S.; Kajii Y. Measurements of OH reactivity and photochemical ozone production in the urban atmosphere. Environ. Sci. Technol. 2005, 39, 8847-8852. Yoshino, A.; Sadanaga, Y.; Watanabe, K.; Kato, S.; Miyakawa, Y.; Matsumoto, J.; Kajii, Y. Measurement of total OH reactivity by laser-induced pump and probe techniquescomprehensive observations in the urban atmosphere of Tokyo. Atmos. Environ. 2006, 40, 7869-7881. Carter, W. P. L.; Atkinson, R. An experimental study of incremental hydrocarbon reactivity. Environ. Sci. Technol. 1987, 21, 670-679. Duane, M.; Poma, B.; Rembges, D.; Astorga, C.; Larsen, B. R. Isoprene and its degradation products as strong ozone precursors in Insubria, Northern Italy. Atmos. Environ. 2002, 36, 38673879.

Received for review October 30, 2006. Revised manuscript received March 7, 2007. Accepted April 5, 2007. ES0625982

VOL. 41, NO. 12, 2007 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

9

4353