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Source Apportionment of Fine-Particle Pollution in Beijing

YUANHANG ZHANG, XIANLEI ZHU, and LIMIN ZENG

College of Environmental Sciences

Peking University

WEI WANG

China Research Academy of Environmental Sciences

Airborne particulates, especially fine particulates, have serious effects on visibility, climate, and human health. Fine particles can penetrate the human respiratory tract and lungs, and several epidemiological studies have reported a link between elevated particle concentration and increased mortality and morbidity (e.g., Abbey et al., 1998; Ostro et al., 1999; Wilson and Suh, 1997). In addition, aerosols influence both atmospheric visibility and climate through the scattering and absorption of solar radiation (Schwartz, 1996).

China, which is undergoing rapid economic development and population growth, is facing serious and complicated air pollution problems that have resulted in a unique chemical transformation and transport process. In the past several decades, air pollution in China was caused mainly by the burning of coal as fuel for industrial and domestic purposes; sulfur dioxide (SO2) and particulate matter were among the major pollutants. Unfortunately, owing to rapid urbanization and industrialization, before the existing problems caused by coal combustion could be resolved, other emission sources have become increasingly important. The rapid increase in the number of vehicles in some Chinese megacities and economically developed regions, such as Beijing, Shanghai, Guangzhou, Pearl River delta, and Yangtze delta, has led to a sharp increase in concentrations of nitrogen oxides (NOx), volatile organic compounds (VOCs), particulate matter (PM), and ozone (Tang et al., 1995; Zhang et al., 1997, 1998). The combination of coal smog and traffic exhaust results in serious pollution characterized by high levels of photochemical smog, high concentration of fine particles, and poor visibility. Air pollution of this kind causes both local and regional problems.



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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium Source Apportionment of Fine-Particle Pollution in Beijing YUANHANG ZHANG, XIANLEI ZHU, and LIMIN ZENG College of Environmental Sciences Peking University WEI WANG China Research Academy of Environmental Sciences Airborne particulates, especially fine particulates, have serious effects on visibility, climate, and human health. Fine particles can penetrate the human respiratory tract and lungs, and several epidemiological studies have reported a link between elevated particle concentration and increased mortality and morbidity (e.g., Abbey et al., 1998; Ostro et al., 1999; Wilson and Suh, 1997). In addition, aerosols influence both atmospheric visibility and climate through the scattering and absorption of solar radiation (Schwartz, 1996). China, which is undergoing rapid economic development and population growth, is facing serious and complicated air pollution problems that have resulted in a unique chemical transformation and transport process. In the past several decades, air pollution in China was caused mainly by the burning of coal as fuel for industrial and domestic purposes; sulfur dioxide (SO2) and particulate matter were among the major pollutants. Unfortunately, owing to rapid urbanization and industrialization, before the existing problems caused by coal combustion could be resolved, other emission sources have become increasingly important. The rapid increase in the number of vehicles in some Chinese megacities and economically developed regions, such as Beijing, Shanghai, Guangzhou, Pearl River delta, and Yangtze delta, has led to a sharp increase in concentrations of nitrogen oxides (NOx), volatile organic compounds (VOCs), particulate matter (PM), and ozone (Tang et al., 1995; Zhang et al., 1997, 1998). The combination of coal smog and traffic exhaust results in serious pollution characterized by high levels of photochemical smog, high concentration of fine particles, and poor visibility. Air pollution of this kind causes both local and regional problems.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium Beijing, the capital of the People’s Republic of China, is located on the North China Plain at an elevation of 44 meters above mean sea level. Beijing is a typical Chinese megacity with a population of more than 11 million. The city, which has undergone rapid development since the 1980s, has high concentrations of particulate matter and poor visibility in spite of the adoption of numerous measures to control particle pollution. Measured annual mean mass concentrations of fine particles (PM10) exceed the Grade II (100 µg/m3) National Ambient Air Quality Standard (NAAQS) (Song et al., 2002a,b). Concentrations of PM2.5, particles with aerodynamic diameters of less than 2.5 µg/m3, are also much higher than the recommended standard for annual average ground-level PM2.5 in the United States (15 µg/m3). The ratio of PM2.5 concentration to PM10 concentration (PM2.5/PM10 ratio) ranges from 0.5 to 0.7, with an average of 0.6, which is about the same as the ratio observed in Europe and the United States. Measurements of PM2.5 performed along wind direction suggest that anthropogenic pollution in urban areas extends to a regional scale under some meteorological conditions and that the whole Beijing-Tianjin area is sometimes covered with a large polluted air mass. Studies in Beijing showed an inverse correlation between elevated concentrations of PM2.5 and visibility based on hourly measurements in June 1999 and January 2000 (Bergin et al., 2001). High levels of particulate matter and adverse effects have inevitably increased concerns about how fine particles can be controlled. As a basis for developing effective control strategies for fine-particle pollution and improving air quality in Beijing, we must first determine the relative importance of the various sources that contribute to PM2.5. The goals of this study were: (1) to quantify the source contributions to PM2.5 in Beijing by source inventory and chemical mass balance (CMB) model; (2) to compare the results of these two methods; and (3) to investigate the spatial and seasonal variations of source contributions. EXPERIMENTAL METHODS Sampling Sites The samples of airborne PM2.5 used in this study were collected at three sites: College of Chemistry of Beijing Union University (BUU), Chinese Academy of Preventive Medicine (CAPM), and Chinese Research Academy of Environmental Sciences (CRAES). The College of Chemistry of BUU, located at Fatou town in the southeast of Beijing, is 2 kilometers from the Eastern Fourth Ring Road and 500 meters west of a chemical-industry zone. The sampling location was on the roof of the Main Teaching Building; there were no high-rise buildings nearby. The two roads, to the east and north, had little traffic. CAPM, situated in the south downtown area of Beijing, is west of the Eastern Secondary Ring Road and 300 meters from Guangming Bridge. Sampling

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium instruments were placed on the southern side of the roof of the three-story office building. The sampling site is adjacent to Panjiayuan East Street (to the east), with high-rise buildings to the south and north. CRAES is located in the northeast suburbs of Beijing, about 100 meters north of the Lishui Bridge. The sampling location was on the northern side of the roof of the four-story Science and Research Building. The building is 100 meters east of Beiyuan Street, a busy main roadway connecting the urban area with Changping and the residential area in the northern part of the city. Trees between the building and the street decrease the effects of vehicle exhaust. Ambient Sampling and Analysis Atmospheric samples were taken at the three sites during April 25–30, 2000 (spring); August 18–25, 2000 (summer); October 30–November 4, 2000 (autumn); and January 9–14, 2001 (winter). Because there were not enough identical instruments for sampling in the three sites simultaneously, different instruments were used, including a MOUDI-100 impactor (MSP Company), an A-245 dichotomous sampler (CIREE Company of USA), a PM2.5 sampler (Beijing Geological Instrument Factory), and a self-developed sampler. Teflon filters and quartz-fiber filters were used to collect samples to determine chemical species. The meteorological conditions varied during the four sampling periods. On April 25, 2000, air quality had deteriorated because of a sandstorm, but sunny days followed. In the summer sampling period, the weather was mostly rainy or cloudy, unlike the typical summer weather, which is characterized by high temperatures and strong sunshine. During November 1–4, the weather was mild and winds were light. During the January sampling period, it snowed but then remained sunny or partly sunny. The chemical analysis for ambient samples has been described extensively elsewhere (Tang, 2001). Briefly, inorganic elements and ions in PM2.5 collected by teflon filters were quantified by induced, coupled, plasma-atomic emission spectrometry (ICP-AES) and ion chromatography (IC), respectively. Organic and elemental carbons (OC and EC) in PM2.5 collected on quartz-fiber filters were determined by means of optical/thermal techniques. Organic compounds, including polycyclic aromatic hydrocarbons (PAHs), were determined by gas chromatography/mass spectrometry (GC/MS). Source Tests and Source Profiles The source emission profiles used in the present study were obtained from source tests in Beijing and literature that provided emission rates of OC and EC, particle-phase PAHs, elements, and ions for the major sources (Table 1) (Hildemann et al., 1991; Watson et al., 2001). Soil dust, road dust, and wind-blown dust, which have similar profiles, were combined as fugitive dust.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium TABLE 1 Source Profiles of PM2.5 (µg/g)   Coal Combustion Vehicle Exhaust Construction Dust Fugitive Dust Biomass Burning Secondary Sulfates Secondary Nitrates Fluoranthene 1,186 2,828.4a 157.33 44.66 0.0 0.0 0.0 Pyrene 490 6,512.3a 93.20 29.58 0.0 0.0 0.0 Benz[a]anthracene 1,083 645.31 27.02 9.81 0.0 0.0 0.0 Chrysene 1,794 1,053.1 66.15 26.78 0.0 0.0 0.0 Benzo [k+b]fluoranthene 3,755 456.66 93.00 34.22 0.0 0.0 0.0 Benzo[e]pyrene 627 504.17 84.13 30.44 0.0 0.0 0.0 Benzo[a]pyrene 1,048 617.05 31.68 8.34 0.0 0.0 0.0 Perylene 365 85.03 3.38 2.36 0.0 0.0 0.0 Indeno [1,2,3-cd]pyrene 1,061 223.01 28.07 8.84 0.0 0.0 0.0 Benzo [ghi]perylene 836 232.61 30.45 9.40 0.0 0.0 0.0 Dibenzo [a,h]anthracene 240a 39.96 1.12 0.27 0.0 0.0 0.0 Organic carbon 24,800 390,000 0.0 186,800 484,000 0.0 0.0 Elemental carbon 10,700 365,000a 0.0 15,700 28,600 0.0 0.0 Aluminum 47,800a 2,160 42,600 73,100a 110 0.0 0.0 Calcium 4,400 3,373 300,000a 46,500 100 0.0 0.0 Potassium 11,000 2,111 16,100 12,100 16,700a 0.0 0.0 Nitrate 3,147 64,400 0.0 1,100 2,500 0.0 775,000a Sulfate 263,600 28,000 0.0 11,000 2,500 727,000a 0.0 Ammonium 200 15,500 0.0 100 1,500 273,000 226,000 aTracer. Sources: Bergin et al., 2001; Hildemann et al., 1991; Tang, 2001.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium Documented emission rates of potassium (K), OC and EC, aluminum (Al), calcium (Ca), nitrate (NO3−), sulfate (SO42−), and ammonium (NH4+) for wood combustion (Hildemann et al., 1991) were used as the source profile for biomass burning because no study of fine-particle emissions from crop straw burning in Beijing was available. The source profiles of secondary sulfate and nitrate, which are formed in the atmosphere, were assumed to be ammonium sulfate and ammonium nitrate, respectively. METHODOLOGY Three approaches can be used to evaluate source contributions to airborne particles: source inventory; dispersion models; and receptor models. The source inventory method is based on a compilation of measured emissions from given source types and an integration of all activities by those sources in the studied region. The method of constructing a source inventory is well defined, and source inventories are widely available in many countries. However, some components of particulate matter, such as re-suspended soil and dust, are extremely difficult to quantify accurately and are subject to seasonal variations that are not well understood. Thus, although source inventories can be used confidently for the major primary sources, such as traffic and power plant emissions, the quantification of fugitive emissions and estimates of secondary sources of sulfate and nitrate formed in the atmosphere are highly problematic. In addition, because emissions from different categories of sources often occur at different heights, their impacts on airborne particle concentrations are not directly proportional to their relative source strengths; this circumstance is not given full consideration in source inventories (Harrison et al., 1997). Dispersion models use emissions data and transport calculations to predict pollutant concentrations at specific air-monitoring locations (Bencala and Seinfeld, 1979; Liu and Seinfeld, 1975). Unlike receptor models, dispersion models are definitive enough to identify contributions of individual sources within a class. Dispersion models have been used for a long time to develop control strategies for air pollutants. However, as mentioned above, estimations based on dispersion models sometimes contain considerable uncertainty because the inventories they rely upon are often inaccurate for some sources (Gordon, 1980). Receptor models assess contributions from various sources based on observations at sampling sites, the “receptors” (Gordon, 1980; Watson, 1984). Techniques include CMB and some purely statistical approaches, such as principle component analysis (PCA). CMB requires a detailed knowledge of the composition of emissions from individual sources of all types, which is rarely available in an urban area with many potential and diverse sources. One of the limitations of multivariate-factor analyses is that they require the collection and analysis of large numbers of ambient air samples and statistically independent source tracers for each major source type.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium The CMB approach used in this study infers source contributions by determining the best-fit linear combination of emission source profiles to reconstruct the measured chemical composition (elements, ions, OC/EC, PAHs) of ambient PM2.5 samples. This can be expressed by the following equation: where Cik is the concentration of chemical species i in fine particles at receptor site k; αij is the relative concentration of chemical constituent i in the fine-particle emissions from source j; and Sjk is the mass contributed to total fine particulate at receptor site k originating from source j. RESULTS AND DISCUSSION Chemical Composition of PM2.5 The complicated chemical composition of fine particles gives some clues about their sources. Elements such as lead, arsenic, vanadium, nickel, and cadmium originate from anthropogenic sources. Crustal elements, such as aluminum, silicon, and iron, often originate in natural sources (e.g., soil dust). Some inorganic ions, such as sulfate and nitrate, are the results of atmospheric transformations of SO2 and NOx. EC derives from some primary sources (e.g., combustion). OC derives from both primary sources (e.g., cooking and biomass burning) and the formation of gas-phase organic compounds in the atmosphere. The typical chemical composition of PM2.5 in Beijing is shown in Figure 1. OC is the largest component, accounting for 31.8 percent. Crustal components, sulfate, and nitrate constitute 9.6, 7.2, and 4.7 percent, respectively. Ammonium, EC, and other elements account for relatively small proportions. Compared with some cities in other countries, Beijing has much higher concentrations of OC, sulfate, and nitrate (Castanho and Artaxo, 2001; Harrison et al., 1997; Lin and Tai, 2001; Yantin et al., 2000). This may be because secondary aerosols formed as a result of strong local emissions of VOCs, SO2, and NOx and the high oxidation capacity of the atmosphere account for a large fraction of the PM2.5 concentrations in Beijing. The high concentration of mineral elements indicates another important source of PM2.5—soil dust. Fine-Particle Sources Source Contributions from Source Inventories The pollution sources of PM2.5 in Beijing were classified into three categories: stationary sources (e.g., fuel combustion for various purposes); mobile sources (e.g., vehicle exhaust); and fugitive sources (e.g., construction dust, paved

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium FIGURE 1 Chemical composition of PM2.5 in Beijing. and unpaved road dust, industrial dust, etc.). After sources had been characterized and emissions measured, and using 1999 as a reference year, a source inventory was developed for the eight districts of Beijing (1,040 km2). All pollution sources discharge a combined 53,266 tons of PM2.5 per year. As shown in Figure 2, stationary sources account for 46.1 percent, mobile sources 15.4 percent, and combined fugitive sources 38.5 percent. Thus, stationary and fugitive sources are the major sources of PM2.5 in Beijing. In the stationary source category, point sources and area sources contribute 18.0 percent and 28.2 percent, respectively, to total emissions. In the fugitive source category, road dust and industrial dust contribute 18.7 percent and 12.4 percent, respectively; construction dust and dust from unpaved roads make only minor contributions. Source Contributions from Chemical Mass Balance Models Seven major sources of PM2.5 are identified by the CMB model (Figure 3): coal combustion; vehicle exhaust; construction dust; fugitive dust; biomass

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium FIGURE 2 Source inventory for PM2.5 in Beijing. FIGURE 3 Sources of PM2.5 in Beijing identified by CMB model.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium burning; secondary sulfate and nitrate; and organic matter. These are consistent with the results computed by positive matrix factorization (PMF) (Song et al., 2002a). Together these seven sources contributed 72.5 percent of PM2.5 mass concentration. Fugitive dust and coal combustion, with contributions of 18.1 and 16.4 percent, respectively, are largely responsible for PM2.5 in Beijing. The higher contribution of fugitive dust in Beijing than in some other cities overseas is attributable to the dry climate and lack of vegetative coverage (Schauer et al., 1996; Zheng et al., 2002). Because coal is by far the most important fuel in China, the substantial effect of coal combustion on urban air pollution is not surprising. The large contribution of coal combustion in Beijing suggests there is a long way to go to control this source effectively. To estimate the contribution of vehicle exhaust, PAHs are used as tracers; the PAH source profiles for vehicle exhaust and road dust have less linearity than profiles of inorganic elements. The CMB model shows that the contribution from vehicle emissions is 5.6 percent, much lower than the contribution of 15.5 percent obtained by using the vehicle/road dust source profile of inorganic elements. If the difference between 15.5 and 5.6 percent is a rough estimate of the contribution of road dust, it is apparent that road dust has a significant effect on PM2.5. Construction dust and biomass burning contribute a small amount to PM2.5, with contributions of 3.3 and 4.5 percent, respectively. Besides primary sources, about 9.6 percent of PM2.5 mass concentration is attributable to secondary formation of sulfate and nitrate in the atmosphere. Organic matter in fine particles derives from both primary sources and organic compounds formed in the atmosphere (i.e., secondary organic aerosol [SOA]). The contribution of 15 percent from organic matter computed by the CMB model from both primary sources and SOA. According to studies, the difference between total fine-particle OC and OC from primary sources, which accounts for 19.2 to 34.1 percent of OC in PM2.5, is related to SOA (Schauer and Cass, 2000; Schauer et al., 1996; Zheng et al., 2002). Based on this percentage and measured OC concentrations in Beijing (22.4 percent annual average), the contribution of SOA to PM2.5 is about 6.0 to 10.6 percent. The combined contributions of secondary sulfate, secondary nitrate, and SOA is a remarkable 15.6 to 20.2 percent, suggesting that limiting secondary sources might be decisive in improving air quality in Beijing. Comparison of Results Table 2 compares the average source contributions calculated by the source inventory and CMB model. Because the source inventory only provides information on primary sources, the contributions of primary sources computed by CMB have been normalized in the last row of the table for easy comparison. Both the source inventory and the CMB model suggest that the major

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium TABLE 2 Comparison of Results from the Source Inventory and Chemical Mass Balance Model   Source Contribution (percentage) Source Source Inventory CMB CMB (normalized)a Coal combustion 46.1 16.4 37.8 Mobile sources 15.4 5.6 12.9 Fugitive sources 38.5 21.4 49.3 Industrial dust 12.4 NA   Road dust 18.7 NA   Construction dust 3.7 NA   Pile-up dust 1.3 NA   Unpaved road dust 2.4 NA   Biomass burning NA 4.5   Secondary sulfate and nitrate NA 9.6   Organic matter NA 15.0   Unknown NA 27.5   Total 100 100 100 aNormalization of the contributions from coal combustion, mobile sources, and fugitive sources. primary sources of PM2.5 in Beijing are coal combustion and fugitive dust; mobile sources contribute a minor fraction of PM2.5. The two methods also provide comparable contributions of about 13 to 15 percent for vehicle exhaust. However, for coal combustion, the source inventory estimates a higher contribution than the CMB model. This is probably because the source inventory does not take into account the reduced pollution effects on ambient PM2.5 from high chimney emissions. Differences in the estimates for fugitive dust can be attributed in part to the uncertainty of the fugitive dust inventory, which needs further improvement; the higher contribution from CMB might also mean that it includes the effect of dust from the local area and beyond. Spatial Variations of Source Contributions The sampling sites of BUU, CAPM, and CRAES in this study are far away from each other and in different parts of Beijing. Comparing source contributions at these sites could provide an outline of the spatial variations of PM2.5 origins. As shown in Figure 4, comparable PM2.5 mass concentrations and similar source contributions were observed at the three sampling locations; similar results were found in a study of São Paulo, a megacity in Brazil (Castanho and Artaxho, 2001). The similarity of PM2.5 source contributions over a large area suggests that the air pollution caused by fine particles tends to be a regional problem and quite different from pollution caused by coarse particles.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium FIGURE 4 Source contributions at the three sampling sites in Beijing. Seasonal Variations of Source Contributions Figure 5 shows the seasonal pattern of contributions from major sources of PM2.5. The contributions of coal combustion and fugitive dust change remarkably from season to season. The highest source contribution is in spring from fugitive dust and in winter from coal combustion. The dry weather in Beijing is the main reason for the severe pollution caused by fugitive dust in spring. The meteorological record for 2000 shows that the highest average wind velocity and lowest precipitation level occurred in the spring; days with little wind (less than 2 m/s velocity) or high humidity (higher than 60 percent) were rare (Wang and Zhang, 2002). In addition, dry weather in northern China since the 1990s and large areas of unpaved ground have increased dust pollution (Wang and Zhang, 2002). A sandstorm during the first two sampling days in spring can explain the very high contribution of dust. In winter, the contribution of coal combustion was high both because of high consumption of coal and unfavorable meteorological conditions for dispersion. The results of a study by Zheng et al. (2002) showed similar conditions contributed to high contributions from wood combustion in cities in the southeastern United States. The results of that study showed that the contribution of wood combustion to the total OC concentration in the atmosphere increased during the colder months of October and January as a result of the high level of residential wood burning. Unlike coal combustion and fugitive dust, vehicle exhaust and construction

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium FIGURE 5 Seasonal variations in source contributions in Beijing.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium dust contributions did not vary with the seasons. Emissions from biomass burning, however, varied a great deal with the seasons. The contribution of biomass burning was high in spring and autumn; the contribution in autumn increased by 8.1 percent, 2.5 times higher than in spring and 3.9 times higher than in winter. Using potassium as a tracer for biomass burning, Duan et al. (2001) investigated the seasonal variations and found that the concentration increased in Beijing in spring and autumn, which is consistent with the results in this study. The increases are attributable to crop-straw burning after the harvest in autumn and before planting in spring. The contribution of secondary sulfate and nitrate, the result of the transformation of high-level SO2 into secondary pollutants by homogeneous and heterogeneous reactions and the accumulation of pollutants in stagnant weather, rises to its highest level (17 percent) in winter, which is 1.7 times higher than in spring and 2.5 times higher than in autumn. However, because rainy or cloudy days dominated the sampling period in summer, the contribution of secondary sulfate and nitrate was not as high as expected. The variability of the contribution of organic matter, from 13.7 to 15.8 percent, was not correlated with the seasons. By contrast, in the southeastern United States, the highest contribution of organic matter was in July and was closely correlated with concentrations of sulfate, nitrate, and ammonium (Zheng et al., 2002). CONCLUSIONS The study described in this paper explored the sources of PM2.5 by developing a source inventory and applying the CMB model to ambient samples collected at three sites in Beijing at different times of the year. The primary sources responsible for most of the mass concentration of PM2.5 are fugitive dust, coal combustion, and vehicle exhaust. Construction dust and biomass burning were also detected in the CMB model, but their contributions were relatively small. Moreover, about 9.6 percent of PM2.5 mass concentration was attributed to secondary sulfate and nitrate, and 15 percent was attributed to organic matter. In addition, 6.0 to 10.6 percent of PM2.5 could be explained by SOA. Thus, the contributions from secondary sources were as high as 15.6 to 20.2 percent, the result of the high oxidation capacity of the atmosphere and the rapid formation of secondary pollutants in the air. Using the CMB model at the sampling sites provided some insight into the spatial variability of PM2.5 sources. The results showed that the source contributions to PM2.5 mass concentrations were similar at all three sampling locations. This might imply that fine particulate air pollution has extended beyond Beijing and is developing into a regional problem. In addition, distinct seasonal variations were observed for contributions of fugitive dust, coal combustion, biomass burning, and secondary sulfate and nitrate.

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Urbanization, Energy, and Air Pollution in China: The Challenges Ahead - Proceedings of a Symposium REFERENCES Abbey, D.E., R.J. Burchette, S.F. Knutsen, W.F. McDonnell, M.D. Lebowitz, and P.L. Enright. 1998. Long-term particulate and other air pollutants and lung function in nonsmokers. American Journal of Respiratory Critical Care Medicine 158: 289–298. Bencala, K.E., and J.H. Seinfeld. 1979. An air quality model performance assessment package. Atmospheric Environment 13: 1181–1185. Bergin, M.H., G.R. Cass, J. Xu, C. Fang, L. Zeng, T. Yu, L.G. Salmon, C.S. Kiang, X.Y. Tang, Y.H. Zhang, and W.L. Chameides. 2001. Aerosol radiative, physical, and chemical properties in Beijing during June 1999. Journal of Geophysical Research 106(D16): 17969–17980. Castanho, A.D.A., and P. Artaxo. 2001. Wintertime and summertime São Paulo aerosol source apportionment study. Atmospheric Environment 35: 4889–4902. Duan, F., Y. Lu, Y. Di, X. Liu, H. Zhang, X. Yang, and T. Yu. 2001. Influence of straw burning on the air quality in Beijing. Environmental Monitoring in China 17(3): 8–11. In Chinese. Gordon, G.E. 1980. Receptor models. Environmental Science and Technology 14: 792–800. Harrison, R.M., D.J.T. Smith, C.A. Pio, and L.M. Castro. 1997. Comparative receptor modeling study of airborne particulate pollutants in Birmingham (United Kingdom), Coimbra (Portugal) and Lahore (Pakistan). Atmospheric Environment 31: 3309–3321. Hildemann, L.M., G.R. Markowski, and G.R. Cass. 1991. Chemical composition of emissions from urban sources of fine organic aerosol. Environmental Science and Technology 25: 744–759. Lin, J.J., and H. Tai. 2001. Concentrations and distributions of carbonaceous species in ambient particles in Kaohsiung City, Taiwan. Atmospheric Environment 35: 2627–2636. Liu, M.K., and J.H. Seinfeld. 1975. On the validity of grid and trajectory models of urban air pollution. Atmospheric Environment 9: 555–574. Ostro, B.D., S. Hurley, and M.J. Lipsett. 1999. Air pollution and daily mortality in the Coachella Valley, California: a study of PM10 dominated by coarse particles. Environmental Research 81: 231–238. Schauer, J.J., and G.R. Cass. 2000. Source apportionment of wintertime gas-phase and particle-phase air pollutants using organic compounds as tracers. Environmental Science and Technology 34: 1821–1832. Schauer, J.J., W.F. Rogge, L.M. Hildemann, M.A. Mazurek, and G.R. Cass. 1996. Source apportionment of airborne particulate matter using organic compounds as tracers. Atmospheric Environment 30: 3837–3855. Schwartz, S.E. 1996. The whitehouse effect-shortwave radiative forcing of climate by anthropogenic aerosols: an overview. Journal of Aerosol Science 3: 359–382. Song, Y., X. Tang, C. Fang, Y. Zhang, M. Hu, and L. Zeng. 2002a. Source apportionment on fine particles in Beijing. Environment Science 23(6): 11–16. In Chinese. Song, Y., X. Tang, Y. Zhang, M. Hu, C. Fang, L. Zeng, and W. Wang. 2002b. Effect on fine particles by the continued high temperature weather in Beijing. Environment Science 23(4): 33–36. In Chinese. Tang, X., J. Li, and D. Chen. 1995. Summertime photochemical pollution in Beijing. Pure Applied Chemistry 67: 1465–1468. Wang, S., and X. Zhang. 2002. Meteorological features of PM10 pollution in Beijing. Journal of Applied Meteorological Science 13(Suppl.): 177–184. In Chinese. Watson, J.G. 1984. Overview of receptor model principles. Journal of the Air Pollution Control Association 34(6): 619–623. Watson, J.G., J.C. Chow, and J.E. Houck. 2001. PM2.5 chemical source profiles for vehicle exhaust, vegetative burning, geological material, and coal burning in northwestern Colorado during 1995. Chemosphere 43(8): 1141–1151.

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