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Single particle characterization of spring and summer aerosols in Beijing: Formation of composite sulfate of calcium and potassium
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Atmospheric Environment 39 (2005) 6909–6918 www.elsevier.com/locate/atmosenv
Single particle characterization of spring and summer aerosols in Beijing: Formation of composite sulfate of calcium and potassium Xiande Liua, Jia Zhub, P. Van Espenb, F. Adamsb,, Rui Xiaoa, Shuping Dongc, Yuwu Lic a
Chinese Research Academy of Environmental Sciences, Beijing 100012, China University of Antwerp, Department of Chemistry, B-2610 Antwerp, Belgium c National Research Center for Environmental Analysis and Measurements, Beijing 100029, China b
Received 16 December 2004; received in revised form 26 July 2005; accepted 6 August 2005
Abstract Scanning electron microscopy–energy dispersive X-ray analysis (SEM–EDX) was used for the analysis of 2500 single particles in five atmospheric aerosol samples collected during the spring and summer of 2000 in Beijing, China. Mineral dust appeared to be the dominant particles during an Asian dust episode, while in other circumstances mineral dust and Scontaining particles constituted the major particle components. During anthropogenic pollution episodes in the summer, a large abundance of S-containing particles featured the atmospheric aerosol. Chemical and size distribution characteristics are discussed for Ca–S, K–S and Ca–K–S particle classes. Formation of Ca–K–S and other S-containing particle classes with high abundance was closely related to meteorological conditions such as relative humidity and cloud coverage. Simple and composite sulfate particles with an elongated crystalline morphology were detected which appear to be indicative of aqueous phase oxidation, such as in-cloud processing for sulfate formation pathway. r 2005 Elsevier Ltd. All rights reserved. Keywords: Atmospheric aerosol; Single-particle analysis; Scanning electron microscopy–energy dispersive X-ray analysis; Sulfate formation; In-cloud processing; Beijing
1. Introduction The application of single particle analysis with methods such as scanning electron microscopy–energy dispersive X-ray analysis (SEM–EDX) provides complementary and more detailed information than that available through bulk analysis. In Corresponding author. Tel.: +32 3 820 2010; fax: +32 3 820 2376. E-mail address: [email protected] (F. Adams).
1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.08.007
recent years SEM–EDX has been used to study aerosol particles collected over the North Sea (Van Malderen et al., 1996a; Hoornaert et al., 1996), the Atlantic Ocean (Posfai et al., 1995; Anderson et al., 1996), the Antarctic (Artaxo et al., 1992), and Siberia (Van Malderen et al., 1996b). Urban areas around the world were also investigated by such single particle analysis methods as e.g., in Phoenix, Arizona, USA (Katrinak et al., 1995), Antwerp, Belgium (Van Borm and Adams, 1989), Seoul and other cities in Korea (Ro et al., 2002). The study of
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urban aerosols is important due to their complexity; many anthropogenic sources are involved. Their study is also important for health effect studies tracing the effects of atmospheric pollution. In general, there appears to be a similarity in aerosol composition and size distribution for urban areas around the world but specific compositional characteristics occur according to population size, energy consumption pattern, industrial structure, geographic location and topography/economic situation. The Beijing site is featured by its rapidly expanding population, increasing traffic density, a high consumption of coal and flourishing construction activities. Beijing is situated in a semi-arid region in North China and surrounded by mountains in the west and north. The weather conditions usually do not favor dispersion and transport of air pollutants. At present, the concentration of inhalable particles exceeds the national air quality standards during extended periods of time each year. The municipal government recently implemented pollution control measures, policies and various action plans. An in-depth understanding of air pollution and aerosol chemistry in Beijing is urgently needed, justifying intensive aerosol research in the area. The urban aerosol in Beijing has been the topic of several recent studies (He et al., 2001; Shi et al., 2003; Yao et al., 2002, 2003). In their study Yao et al. (2002) focused on the ionic composition of PM2.5 particles. The major route of sulfate formation in Beijing was identified as gas-phase oxidation of SO2 in winter and incloud processing in summer, but insufficient ammonium was present to totally neutralize the aerosol. Based on the size distribution of ionic species and the mole ratio of sulfate to SO2, more evidence on the formation mechanisms of secondary aerosol in Beijing was provided by Yao et al. (2003). During the summer, sulfate was mainly in the fine particles with a mass median aerodynamic diameter (MAD) of 0.770.1 mm. Sulfate formation was attributed to in-cloud processing. Sulfate formation in the spring was attributed to non-cloud heterogeneous processes, as sulfate had a MAD of 0.4570.05 mm (Yao et al., 2003). Shi et al. (2003) examined a variety of particles collected in Beijing in 2001 by using SEM and image analysis. These included mineral dust, coal fly ash, soot aggregates as well as sulfates. Some sulfate particles were crystalline and contained S, Ca, K and Al and there were indications that two or more phases of sulfates were present.
In this study, aerosol samples collected in the spring and summer in Beijing were analyzed with SEM–EDX. The aerosol composition, pollution characteristics and the formation mechanism of secondary sulfate aerosol were investigated. For the spring samples, the difference between Asian dust and normal urban aerosols was investigated. For the summer samples, the number variation of major particle classes and their relation with aerosol pollution situation and meteorological conditions was observed. The focus of the study was largely put on the formation of sulfate particle classes, which showed close relationship with the urban air quality in the summer. 2. Experimental 2.1. Aerosol samples The sampling site is located in an urban area, under the influence of the traffic, of the north part of the urban fourth ring of Beijing. PM10 aerosol samples were collected for 24 h on the roof of the 11 floor main building of the Sino-Japan Friendship Center for Environmental Protection on 25 April , 15 May (spring samples) and on 21, 24 and 25 July (summer samples), 2000. PM10 concentration was 386, 70, 153, 70 and 124 mg m 3, respectively. An Asian dust event occurred on 25 April, while air quality was reasonably good on 15 May and 24 July, and showed light pollution on 21 and 25 July, all this according to National Standard of Ambient Air Quality and the Air Quality Index System in China. Nuclepore (polycarbonate) membrane with 0.4 mm pores was used as aerosol sampling filter as it provides a flat surface for SEM observation. The particulate aerosol sampler with flow rate of 16.7 l min 1 was the stacked filter unit (SFU) of Gent type recommended by IAEA for a number of projects (Maenhaut et al., 1994), but only the PM10 size particles (with aerodynamic size less than or equal to 10 mm) were collected. In this work, particles with sizes from 2.5 to 10 mm are defined as coarse particles, those less than 2.5 mm are defined as fine particles, and those less than 1 mm are listed as sub-micrometer. 2.2. SEM– EDX measurements Sections of the 47-mm diameter filter sample were mounted on electron microprobe stubs and vacuum coated with a carbon layer of about 40 nm. The individual particle analyses were carried out with a
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JEOL JXA-733 superprobe (Tokyo, Japan) equipped with an annular backscattered electron detector and a TN-2000 EDX detection system (Tracor Northern, Middleton, USA). The X-ray detector consisted of a conventional Si(Li) with Be window that was able to detect elements from Na upwards in atomic number. An electron accelerating voltage of 20 kV was used at a beam current of 1.0 nA with magnification of 2000. Automated analyses were done using the particle recognition and characterization software package 733B developed at the University of Antwerp in which a particle is recognized when its backscattered electron intensity is larger than a threshold, and its diameter larger than a preset minimum size of 0.13 mm. For each recognized particle, the diameter, area and shape factor were computed and an X-ray spectrum was accumulated for 30 s. A simple, but fast on-line spectral analysis method was used to detect the characteristic peaks of the various elements and to determine their intensity. The elemental intensities and the size and shape data of each particle were stored for further off-line analysis. More details on the automated particle analysis procedures and software package can be found elsewhere (Raeymaekers et al., 1988; Van Borm and Adams, 1989). A preset number of at least 500 particles were analyzed for each sample. 2.3. Data analysis Cluster analysis was used for the reduction and interpretation of the data sets obtained by the automated SEM–EDX measurements. As in previous studies (Shattuck et al., 1991; Hoornaert et al., 1996), the particles were classified into ‘particle groups’ or ‘particle classes’ on the basis of their normalized characteristic X-ray intensities to the sum of all X-ray intensities expressed as %. Cluster analysis was performed with the integrated data analysis system (IDAS) software package (Bondarenko et al., 1996). Hierarchical cluster analysis was applied first, after which the results thus obtained were used as the initial seed points for non-hierarchical cluster analysis. The selection of the most appropriate number of clusters was based on the Akaike information criterion (Bondarenko et al., 1994). Cluster analyses were first applied to the data of each sample, subsequently the results for all combined samples were subjected to a second cluster analysis in which the average normalized X-ray intensities of the particle classes were used as
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input data. A two-dimensional table was made to sort out different particle classes over the 2500 particles (see Table 1). Many particles in a class showed similar composition. Excel software was used for a regression analysis to detect the scatter of the intensities for the elements. A program, Quanta2 is adapted for ZAF correction using first-order corrections for backscattering, absorption and secondary fluorescence effects, converting X-ray intensity data of particles into weight or atomic percentage for elements detected (Van Borm and Adams, 1991). The method is applicable to K- and L-lines for the elements Na to Bi, as the characteristic lines of elements lighter than Na are absorbed by the beryllium window of the Si (Li) detector used. As for sulfur-containing particles, elements detected such as Na, Mg, K, Ca and S are proposed with stoichiometry as oxide; however, ammonium ions are not detectable. The method is implemented in a FORTRAN computer program written for PC. It is capable of processing the massive amount of data produced by automated analysis of large particle collections. 2.4. Position tagged spectra (PTS) measurements Elemental mapping was carried out at 20 keV and 1.0 nA current with a JEOL JSM-6300 scanning microscope equipped with a PGT X-ray analysis system PGT EDX detector and IMIX-PC software (Princeton Gamma-Tech, USA). The magnification was 2500 and the total acquisition time was 5 h and the image size 128 102 pixels. Scanning was performed with a dwell-time of 1.38 s per pixel and a step size of 0.47 mm. The elements Al, Si, S, K, Ca and Fe were selected for mapping on the basis of the data obtained in single particle analysis. The results of the X-ray mapping are done via PTS, resulting in a file containing beam coordinates (x, y) and X-ray energy (channel number) for each X-ray photon detected. The data can be displayed as a spatial map of selected X-ray energies (elemental maps) (Mott and Friel, 1999). X-ray spectra of marked areas corresponding to minimum nine pixels could also be generated. 3. Results and discussion 3.1. Particle classes and their abundance Particles were classified into particle classes on the basis of their normalized characteristic X-ray
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Table 1 Number abundance percentage and size of particle classes in aerosol samples Particle class
Minerals Aluminosilicates Si-rich Fe-rich Ca-rich
Number abundance percentage (%) 25 April
15 May
21 July
24 July
25 July
76.7 (1.3271.45) 14.6 (1.4571.31) 2.0 (0.8470.48) 5.2 (1.5971.88)
28.3 (0.7971.29) 0.2 (1.4570.31) 4.6 (0.4170.29)
12.4 (1.0971.68) 3.8 (0.7571.14) 2.0 (0.3670.29)
43.6 (1.2271.58) 9.9 (1.0571.75) 7.4 (0.4870.67) 15.4 (1.0971.44)
18.0 (1.2571.43) 3.4 (1.9071.64) 19.3 (0.3970.34) 5.5 (1.2871.64)
6.1 (0.2070.16)
3.2 (0.2570.35)
0.9 (0.1870.08) 5.6 (0.3470.28)
3.4 (0.5570.98) 6.8 (0.3670.41) 41.7 (0.3270.23)
S-containing S-rich Na–S
0.9 (1.2471.52)
Mg–S K–S
10.8 (0.2070.12) 24.3 (0.3270.32) 6.1 (0.1670.0.04) 2.2 (0.4070.30) 2.4
Ca–S Ca–K–S Zn–S Other S-containing Minor class Mn-containing
1.3 (0.2070.05) 6.4 (0.5070.97)
P-containing Zn-containing
37.3 (0.3470.42) 27.9 (0.5170.64)
2.8 0.9 (0.4170.21)
2.3 (0.2670.12)
1.8 (0.3570.23) 2.0 (0.4070.30)
Pb-containing Carbonaceous Others
4.7 (0.6470.95) 0.3
0.6
3.3
0.5
11.0
3.9
0.5
4.0
1.8
The mean geometric diameter and standard deviation data are given in the parenthesis for a particle class.
intensities using the clustering software IDAS. Clustering results are listed in Table 1. Particle classes were sorted into four categories, namely, mineral dust, sulfur containing, minors and carbonaceous. Mineral dusts are of nature origin and consist of mainly aluminosilicates and quartz (Si-rich) particles. It accounted for 97.8% of the particles in the sample of 25 April 2000, a sample of Asian Dust event, while the other samples contain a much lower percentage of mineral dust, but considerable num-
bers of S-containing particles. Iron-rich particles could originate from iron and steel production and steel product erosion, but coal combustion can also not be excluded as a source. As shown in Table 1, high abundances occur for the 24 and 25 July samples, when the prevailing wind direction was southwest and south-southwest. Hence, the source might be related to an iron and steel manufactory complex situated in the southwest of the sampling site. Calcium-rich particles can be attributed to wind-blown soil and construction dust.
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3.2. Composition of sulfate particles Sulfates, as sulfur-containing particles, are present with high abundance (see Table 1) and are closely related to anthropogenic emission sources. Sulfate particles are mainly secondary in nature, formed by a variety of atmospheric reactions. Ammonia often accompanies the sulfate formed as the major neutralizing cation (Yao et al., 2002, 2003). Other sulfates such as calcium and magnesium salts are frequently found in Chinese aerosols, as alkaline dusts are abundant. Potassium sulfate particles were reported in high numbers in previous studies (Liu et al., 2002). The atomic percentage of sulfur and other major elements are listed in Table 2 for sulfate particles, but ammonium ions were not detectable due to the limitation of the Si (Li) detector. MgSO4 and CaSO4 have the same
July 21, 2000 0.3 S CA K
Atomic percent
0.25 0.2 0.15 0.1 0.05 0 0.1
0.3
1 Particle diameter in micron
10
July 25, 2000
0.6 S CA K
0.25 Atomic percent
Different kinds of sulfur-containing particles can be distinguished on the basis of the elements detected. On the other hand, sulfur-rich particles occur with only sulfur detected. In the Asian dust sample of 25 April, sulfur-containing particles were nearly absent, while in the 15 May, 21 and 25 July samples sulfur-containing particles accounted for 50% or more of all the particles detected. Apparently, such particles are an important component of the urban aerosol in Beijing. Among the three samples of July 2000, they appeared with low abundance on 24 July, but abundance was high on 25 July, and even higher on 21 July. There was an increasing trend of sulfur-containing particles when air pollution was high. There is a positive correlation between the abundance of sulfur-containing particles and the PM10 concentration in July. Carbonaceous particles are those detected, which do not provide any characteristic X-rays. The diameter of nearly all those particles is less than 0.3 mm. Such particles contain only low atomic number elements and are carbonaceous in composition. They are an abundant component of urban aerosols. SEM–EDX is not optimal for the detection of these particles; hence the data for this particle class in Table 1 are very much underestimated. The mean geometric diameter and standard deviation data are also presented for particle classes in Table 1. Mineral dust particles fall in a size range larger than that of sulfur-containing particles. As an example, compositional data were illustrated against particle size for Ca–K–S particles in Fig. 1.
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0.2
0.5 0.4
0.15
0.3
0.1
0.2
0.05
0.1
0 0.1
1 Particle diameter in micron
0 10
Fig. 1. Scatter plot of atomic percentage of Ca, K, and S over particle size for individual Ca–K–S particles in aerosol samples of 21 July and 25 July 2000. (Note: K data in lower plot to the right axis.)
theoretical atomic percentage values, 0.167 for Mg or Ca, 0.167 for S and 0.667 for O, respectively; however, when 1:1 mixed with ammonium sulfate, the atomic percent change to 0.091 for Mg or Ca, 0.182 for S and 0.727 for O, respectively. It means that mixing with ammonium sulfate leads to lower percentage for metallic cations, while higher values for S and O. As shown in Table 2, Ca–S particles of 15 May 2000 had a composition close to theoretical CaSO4 values, while those of three aerosol samples in July 2000 seemed to mix with ammonium sulfate to some extent. Composition of Mg–S particles of 21 July 2000 was similar with MgSO4 (NH4)2SO4 rather than MgSO4. CaSO4 was frequently detected in atmospheric aerosols in many urban areas around the world (Van Borm and Adams, 1989; Hoornaert et al., 1996; Liu et al., 1994). CaCO3 particles originated as soil or road dust could react
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Table 2 The atomic percentage of S-containing particle classes in aerosol samplesa 15 May Mean7SD
N
Mean7SD
24 July N
25 July
Mean7SD
N
Mean7SD
N
S Mg O
MgSO4, MgSO4 (NH4)2SO4b 0.20370.006 0.07270.021 0.70470.007
S K O
K2SO4, K2SO4 (NH4)2SO4, K2SO4 2((NH4)2SO4)b 0.20370.014 49 0.12470.037 49 0.67270.020 49
0.16870.008 0.21870.022 0.61470.012
4 4 4
0.18270.009 0.17970.025 0.63870.013
4 4 4
S Ca O
CaSO4, CaSO4 (NH4)2SO4b 0.16770.013 77 0.18470.015 0.16570.028 77 0.13070.026 0.66770.013 77 0.68570.014
358 358 358
0.17170.022 0.15870.046 0.67170.02
4 4 4
0.16970.010 0.15270.012 0.67070.013
42 42 42
S K Ca O
CaSO4 K2SO4, CaSO4 K(NH4)SO4b 0.14970.021 3 0.18470.011 0.13570.008 3 0.02570.011 0.10170.007 3 0.10770.017 0.61570.006 3 0.67970.014
363 363 363 363
0.17670.011 0.07570.017 0.08770.016 0.65970.013
239 239 239 239
Mg–S
K–S
Ca–S
Ca–K–S
a
21 July
33 33 33
The atomic percent mean value and standard deviation for each element are presented. N is the number of particles in the particle class. Some possible speciation forms for sulfates.
b
with SO2 ,or sulfuric acid, or acidic sulfates such as ammonium sulfate to generate CaSO4 (Hoornaert et al., 1996). Some CaSO4 particles are primary particles such as those from desulfurization process in coal combustion power stations or from soiling of calcite in building construction materials. K2SO4 has the theoretical atomic percentage values, 0.286 for K, 0.143 for S and 0.571 for O, respectively. However, when K2SO4 (1:1) mixed with ammonium sulfate, it is 0.167 for K, 0.167 for S and 0.667 for O, respectively; it seems the case of K–S particles of 25 July 2000. The composition of the K–S particles of 24 July 2000 falls in between the two cases proposed above. It is possible that K2SO4 was mixed with less (NH4)2SO4 than 1:1 ratio. Further more, if K2SO4 was 1:2 mixed with (NH4)2SO4 , it will be 0.118 for K, 0.176 for S and 0.706 for O. This set of values describes better the composition of K–S particles of 15 May 2000. K–S particles were reported as major particle class in biomass burning aerosols (Liu et al., 2000). It could be also be possible that they are converted from K2CO3. CaSO4 and K2SO4 mixed at 1:1 ratio would lead to the theoretical atomic percentage values, 0.077 for Ca, 0.154 for K and S, and 0.615 for O, respectively. It seems comparable with that of
Ca–K–S particles on 15 May 2000. When CaSO4 and K(NH4)SO4 mixing was proposed, it would be 0.084 for Ca and K, 0.167 for S and 0.667 for O, respectively. It fits well with that of Ca–K–S particles on 25 July 2000. The composition of Ca–K–S particles on 21 July 2000 is with low percents of Ca and K, and high values of S and O, indicating possible involvement of ammonium sulfate. In summary, a variety of sulfates were found in this study based on compositional data of individual particles. They could be simple sulfates or composite sulfates as internal mixture of ammonium sulfate and metallic sulfates. 3.3. Formation of composite sulfate particles of calcium and potassium Ca–K–S particles are present in high numbers in two of the summer samples. Fig. 1 shows data with respect to chemical composition and particle size. Particles with size below 0.3 mm account for nearly 60% number abundance for the 21 and 25 July samples. They show, however, a different size distribution, as particles with size larger than 1 mm account for 15% in the 21 July sample, while they are almost absent in the 25 July sample. The chemical composition of Ca–K–S class in those
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two summer samples is also different (Table 2). Sulfate particles are mainly converted from gaseous SO2 in the atmosphere. According to Seinfeld and Pandis (1996) aqueous phase oxidation is one of the major mechanisms for sulfate formation. In-cloud processes have been postulated which is important for sulfate formation in certain circumstances (McHenry and Dennis, 1994; Yao et al., 2002, 2003). Ca–K–S particles, on the other hand, are composite sulfates and since calcium and potassium should be generated by different kinds of emission sources, it is difficult to explain the formation of Ca–K–S particles by the same simple and straightforward atmospheric reactions as proposed for the Ca–S and K–S particles. When comparing Ca–S, K–S particles with Ca–K–S particles, the latter with more complex composition need both calcium and potassium in raw materials and an efficient mixing. There is a negative correlation between the abundance of the two types of sulfur-containing particles, competing for common raw materials. As shown in Table 1, Ca–K–S particles were detected only in the three samples obtained in typical urban pollution conditions. Ca–S and K–S particles were more abundant (24% and 11%) than the Ca–K–S particles (6%) in the 15 May sample. Hence, it seems that the effect of in-cloud formation was limited, which was obviously related to the dry weather in spring. The situation was different for the summer samples when in-cloud processes exerted profound effects. In the 21 July sample, Ca–S particle abundance was 37% whereas K–S particles were almost absent and Ca–K–S particles were abundantly present. It seemed that K–S particles were exhausted, restricting further formation of Ca–K–S particles. In the 25 July sample, both Ca–S and K–S particle abundances were at 3.4% and 6.8%, while the Ca–K–S particle abundance is up to 42%. The potassium content of the Ca–K–S particles in the sample of 25 July is three times higher than on 21 July. The atomic percent of potassium is 2.571.1% on 21 July and 7.571.7% on 25 July (see Table 2) and it reveals the compositional dissimilarity between aerosol samples and the similarity within an aerosol sample. It seems that the content of potassium in Ca–K–S particles depends on the availability of potassium containing particles in air, a high abundance of those particles leading to Ca–K–S particles with higher potassium content. The mass transfer and mixing in the incloud processes may lead to homogenization of the
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chemical composition of cloud droplets and thus the particles generated afterwards from those droplets. The occurrence of Ca–K–S particles in high numbers was not reported in previous studies. It is difficult for bulk analytical methods such as ion chromatography to distinguish Ca–K–S particles from Ca–S and K–S particles. Its formation is related to the hot, humid, cloudy weather during summer in Beijing facilitating the in-cloud formation processes. The average monthly precipitation in Beijing is 21 and 34 mm in April and May, while it is 185 mm in July 2000. In Fig. 2, some meteorological data are presented for July 2000. The total cloud coverage was 85%, 32% and 65% and low cloud coverage was 50%, 0% and 15%, respectively, for 21, 24 and 25 July 2000. There appears a positive correlation between number abundance of sulfate particles and the cloud coverage, indicating the importance of the in-cloud process for SO2 oxidation and sulfate aerosol formation. Ca–K–S particles have consistent composition within an aerosol sample, but a different composition between samples (see Table 2 and Fig. 1). From this, it is inferred that Ca–K–S particles do not correspond to one specific compound with constant composition but are a composite of different sulfates formed as internal mixture in in-cloud processes. 3.4. X-ray mapping measurements As a support for the results obtained by single particle analysis, X-ray maps were collected. As an example, Fig. 3 presents the secondary electron image of one field of the 21 July sample whereas Fig. 4 shows X-ray maps for selected elements for the same field of view. In addition to the 0.4 mm pores from the polycarbonate membrane, the figure shows four mineral particles that (see Fig. 4) contain Si, Al, Fe and K, typical elements for mineral dust. Three of those particles have an irregular shape, one spherical particle is typical for coal fly ash. Also, sulfur-containing particles are abundantly present. Most of these also show up in the Ca map, while some are also discernable in the K map. The spectral data here match well with the major particle classes summarized in Table 1. Some particles with crystalline morphology were observed in the summer samples. Three elongated crystalline particles for the 21 July sample are shown in Fig. 3 and in the mappings of S and Ca in Fig. 4. These particles have a width of about 1 mm
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10
100 90 80 70 60 50 40 30 20 10 0
R.H.(%) 8 Total cloud (%) 6 4 2
Low cloud (%) Temperature (°C) Wind speed (m/s)
0 19
20
21
22 23 24 Date in July 2000
25
26
27
300
100
250
80
200
60
PM10
40
Sulfate particles %
150 100
20
50 0
0 19
20
21
22 23 24 Date in July 2000
25
26
27
Fig. 2. Meteorological data during late July 2000 (upper plot) and aerosol mass concentration in mg m 3 and number abundance percentage of sulfate particles for three aerosol samples in July 2000 (lower plot). (Note: Wind speed data in m s 1 in upper plot and number abundance data of sulfate particles in lower plot to the right axis.)
Fig. 3. Secondary electron image of some aerosol particles collected on 21 July 2000 (magnification of 2500).
and a length from 5 to 10 mm, equivalent to an average diameter of 2.2–3.2 mm in the automated measurements. These elongated particles are similar to those illustrated by Shi et al. (2003). The
crystalline morphology implies a mechanism of aqueous phase formation and supports the hypothesis of in-cloud processes as a major sulfate formation route during summer in Beijing. As the
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Fig. 4. X-ray maps for selected elements (Al, Si, Fe, S, Ca, and K) of the same field as in Fig. 3 (sample 21 July 2000).
water evaporates, the crystal seeds are then formed in cloud droplets and finally turn out as airborne particles. 4. Conclusion As microscopic analytical method, SEM–EDX is suitable for chemical characterization of mineral dusts and sulfur-containing particles. Mineral dusts are dominant in windy days in spring in Beijing, and become overwhelming during Asian dust events. On the other hand, mineral dust, as a basic component of background aerosol in Northern China, is relatively abundant in periods with improved air quality. Sulfur-containing particles, mainly sulfates, feature aerosol pollution in urban Beijing where coal is the main energy source. The number abundance of sulfur-containing particles is closely correlated with the aerosol mass concentration levels in summer. Although the SO2 emission is lower in summer than in winter, the conversion of SO2 into sulfates is fast and efficient in summer due to the high temperature and humidity. Sulfate to SO2 ratio as well as size-distribution pattern of sulfates were used as experimental evidence for the aqueous phase oxidation of sulfate formation pathway postulated in previous studies. The occurrences of a great number particles of composite sulfates of calcium and potassium as well as the crystalline morphology of sulfate particles were found as supporting experimental phenomena in this work. It is indicative of aqueous phase oxidation in general and in-cloud processing in particular for sulfate formation pathway. This explains how calcium and potassium originated from different
sources turn out as composite sulfates in Beijing aerosol during the summer. Among possible aqueous phase oxidation pathways, in-cloud processing is most likely, as mass transfer is more active and mixing is more efficient in clouds. Aqueous phase oxidation facilitates not only the formation of sulfates, but also the transformation of ammonium sulfate into sulfate of calcium, or potassium, or internal mixture of both. The observations of this study justify the pollution control measures that come into effect in Beijing, including mandatory use of coal with low content of sulfur, promotion of clean energy alternatives such as nature gas, the ban of coal burning boilers in downtown Beijing and the installation of desulfurification utilities for new power stations. All these measures reduces SO2 emission and lead to less sulfate aerosol. Acknowledgments Financial support from Flemish–Chinese Bilateral Cooperation Program (BIL01/57), the China NSFC (20177036, 20477042) and The National Basic Research Program of China, no. 2003CB415 003 is gratefully acknowledged. References Anderson, J.R., Buseck, P.R., Patterson, T.L., Arimoto, R., 1996. Characterization of the Bermuda tropospheric aerosol by combined individual particle and bulk-aerosol analysis. Atmospheric Environment 30, 319–338. Artaxo, P., Rabello, M.L.C., Maenhaut, W., Van Grieken, R., 1992. Trace elements and individual particle analysis of atmospheric aerosols from the Antarctic peninsula. Tellus 44B, 318–334.
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Bondarenko, I., Van Malderen, H., Treiger, B., Van Espen, P., Van Grieken, R., 1994. Hierarchical cluster analysis with stopping rules built on Akaike’s information criterion for aerosol particle classification based on electron probe X-ray microanalysis. Chemometrics and Intelligent Laboratory Systems 22, 87–95. Bondarenko, I., Treiger, B., Van Grieken, R., et al., 1996. IDAS: a windows based software package for cluster analysis. Spectromchimica Acta B 51, 441–456. He, K.B., Yang, F.M., Ma, Y.L., Zhang, Q., Yao, X.H., Chan, C.K., Cadle, T., Mulawa, P., 2001. The characterization of PM2.5 in Beijing, China. Atmospheric Environment 35, 4959–4970. Hoornaert, S., Van Malderen, H., Van Grieken, R., 1996. Gypsum and other calcium-rich aerosol particles above the North Sea. Environmental Science and Technology 30 (5), 1515–1520. Katrinak, K.A., Anderson, J.R., Buseck, P.R., 1995. Individual particle types in the aerosol of Phoenix, Arizona. Environmental Science and Technology 29, 321–329. Liu, X.D., Jia, H., Qi, J.B., Zhang, J.Q., 1994. Characterization and source identification of Qingdao aerosol with SEM–EDX method. Research of Environmental Sciences 7 (3), 10–17 (Chinese with English abstract). Liu, X.D., Van Espen, P., Adams, F., Cafmeyer, J., Maenhaut, W., 2000. Biomass burning in southern Africa: individual particle characterization of atmospheric aerosols and savanna fire sample. Journal of Atmospheric Chemistry 36, 135–155. Liu, X.D., Li, Y.W., Dong, S.P., Van Espen, P., Adams, F., 2002. Quantitative analysis and elemental speciation of biomass burning particles. Environmental Chemistry 21 (3), 209–217 (Chinese with English abstract). Maenhaut, W., Franc- ois, F., Cafmeyer, J., 1994. Regional and global atmospheric aerosol studies using the Gent stacked filter unit and other aerosol collectors, with multi-elemental analysis of the samples by nuclear-related analytical techniques. In: Applied Research on Air Pollution using NuclearRelated Analytical Techniques, Report NAHRES-19, IAEA, Vienna, pp. 249–263. McHenry, J.N., Dennis, R.L., 1994. The relative importance of oxidation pathways and clouds to atmospheric ambient sulfate production as predicted by the regional acid deposition model. J. Applied Meteorology 33, 890–905. Mott, R.B., Friel, J.J., 1999. Saving the photons: mapping X-rays by position-tagged spectrometry. Journal of Microscopy 193 (1), 2–14.
Posfai, M., Anderson, J.R., Buseck, P.R., Sievering, H., 1995. Compositional variations of sea-salt-mode aerosol particles from the North Atlantic. Journal of Geophysical Research 100, 23063–23074. Raeymaekers, B., Demuynck, M., Dorrine´, W., Adams, F., 1988. Characterization of oil fly ash particles by electron probe microanalysis. Internationl Journal of Environmental Analytical Chemistry 32, 291–312. Ro, C.U., Kim, H., Oh, K.Y., Yea, S.K., Lee, C.B., Jang, M., Van Grieken, R., 2002. Single-particle characterization of urban aerosol particles collected in three Korean cities using low-Z electron probe X-ray microanalysis. Environmental Science and Technology 36, 4770–4776. Seinfeld, J.H., Pandis, S.N., 1996. Atmospheric Chemistry and Physics: from Air Pollution to Climate Change. Wiley, New York, pp. 812–823. Shattuck, T.W., Germani, M.S., Buseck, P.R., 1991. Multivariate statistics for large data sets: applications to individual aerosol particles. Analytical Chemistry 63, 2646–2656. Shi, Z.B., Shao, L.Y., Jones, T.P., Whittaker, A.G., Lu, S.L., Berube, K.A., He, T.O., Richards, R.J., 2003. Characterization of airborne individual particles collected in an urban area, a satellite city and a clean air area in Beijing, 2001. Atmospheric Environment 37, 4097–4108. Van Borm, W.A., Adams, F.C., 1989. Characterization of individual particles in the Antwerp aerosol. Atmospheric Environment 23 (5), 1139–1151. Van Borm, W.A., Adams, F.C., 1991. A standardless ZAF correction for semi-quantitative electron probe microanalysis of microscopical particles. X-ray Spectrometry 20, 51–62. Van Malderen, H., Van Grieken, R., Bufetov, N.V., Koutzenogii, K.P., 1996a. Chemical characterization of individual aerosol particles in central Siberia. Environmental Science and Technology 30, 312–321. Van Malderen, H., Hoornaert, S., Van Grieken, R., 1996b. Identification of individual aerosol particles containing Cr, Pb, and Zn above the North Sea. Environmental Science and Technology 30, 489–498. Yao, X.H., Chan, C.K., Fang, M., Cadle, S., Chan, T., Mulawa, P., He, K.B., Ye, B.M., 2002. The water-soluble ionic composition of PM2.5 in Shanghai and Beijing, China. Atmospheric Environment 36, 4223–4234. Yao, X.H., Lau, A.P.S., Fang, M., Chan, C.K., Hu, M., 2003. Size distribution and formation of ionic species in atmospheric particulate pollutants in Beijing, China: inorganic ions. Atmospheric Environment 37, 2991–3000.
Atmospheric Environment 39 (2005) 6909–6918 www.elsevier.com/locate/atmosenv
Single particle characterization of spring and summer aerosols in Beijing: Formation of composite sulfate of calcium and potassium Xiande Liua, Jia Zhub, P. Van Espenb, F. Adamsb,, Rui Xiaoa, Shuping Dongc, Yuwu Lic a
Chinese Research Academy of Environmental Sciences, Beijing 100012, China University of Antwerp, Department of Chemistry, B-2610 Antwerp, Belgium c National Research Center for Environmental Analysis and Measurements, Beijing 100029, China b
Received 16 December 2004; received in revised form 26 July 2005; accepted 6 August 2005
Abstract Scanning electron microscopy–energy dispersive X-ray analysis (SEM–EDX) was used for the analysis of 2500 single particles in five atmospheric aerosol samples collected during the spring and summer of 2000 in Beijing, China. Mineral dust appeared to be the dominant particles during an Asian dust episode, while in other circumstances mineral dust and Scontaining particles constituted the major particle components. During anthropogenic pollution episodes in the summer, a large abundance of S-containing particles featured the atmospheric aerosol. Chemical and size distribution characteristics are discussed for Ca–S, K–S and Ca–K–S particle classes. Formation of Ca–K–S and other S-containing particle classes with high abundance was closely related to meteorological conditions such as relative humidity and cloud coverage. Simple and composite sulfate particles with an elongated crystalline morphology were detected which appear to be indicative of aqueous phase oxidation, such as in-cloud processing for sulfate formation pathway. r 2005 Elsevier Ltd. All rights reserved. Keywords: Atmospheric aerosol; Single-particle analysis; Scanning electron microscopy–energy dispersive X-ray analysis; Sulfate formation; In-cloud processing; Beijing
1. Introduction The application of single particle analysis with methods such as scanning electron microscopy–energy dispersive X-ray analysis (SEM–EDX) provides complementary and more detailed information than that available through bulk analysis. In Corresponding author. Tel.: +32 3 820 2010; fax: +32 3 820 2376. E-mail address: [email protected] (F. Adams).
1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2005.08.007
recent years SEM–EDX has been used to study aerosol particles collected over the North Sea (Van Malderen et al., 1996a; Hoornaert et al., 1996), the Atlantic Ocean (Posfai et al., 1995; Anderson et al., 1996), the Antarctic (Artaxo et al., 1992), and Siberia (Van Malderen et al., 1996b). Urban areas around the world were also investigated by such single particle analysis methods as e.g., in Phoenix, Arizona, USA (Katrinak et al., 1995), Antwerp, Belgium (Van Borm and Adams, 1989), Seoul and other cities in Korea (Ro et al., 2002). The study of
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urban aerosols is important due to their complexity; many anthropogenic sources are involved. Their study is also important for health effect studies tracing the effects of atmospheric pollution. In general, there appears to be a similarity in aerosol composition and size distribution for urban areas around the world but specific compositional characteristics occur according to population size, energy consumption pattern, industrial structure, geographic location and topography/economic situation. The Beijing site is featured by its rapidly expanding population, increasing traffic density, a high consumption of coal and flourishing construction activities. Beijing is situated in a semi-arid region in North China and surrounded by mountains in the west and north. The weather conditions usually do not favor dispersion and transport of air pollutants. At present, the concentration of inhalable particles exceeds the national air quality standards during extended periods of time each year. The municipal government recently implemented pollution control measures, policies and various action plans. An in-depth understanding of air pollution and aerosol chemistry in Beijing is urgently needed, justifying intensive aerosol research in the area. The urban aerosol in Beijing has been the topic of several recent studies (He et al., 2001; Shi et al., 2003; Yao et al., 2002, 2003). In their study Yao et al. (2002) focused on the ionic composition of PM2.5 particles. The major route of sulfate formation in Beijing was identified as gas-phase oxidation of SO2 in winter and incloud processing in summer, but insufficient ammonium was present to totally neutralize the aerosol. Based on the size distribution of ionic species and the mole ratio of sulfate to SO2, more evidence on the formation mechanisms of secondary aerosol in Beijing was provided by Yao et al. (2003). During the summer, sulfate was mainly in the fine particles with a mass median aerodynamic diameter (MAD) of 0.770.1 mm. Sulfate formation was attributed to in-cloud processing. Sulfate formation in the spring was attributed to non-cloud heterogeneous processes, as sulfate had a MAD of 0.4570.05 mm (Yao et al., 2003). Shi et al. (2003) examined a variety of particles collected in Beijing in 2001 by using SEM and image analysis. These included mineral dust, coal fly ash, soot aggregates as well as sulfates. Some sulfate particles were crystalline and contained S, Ca, K and Al and there were indications that two or more phases of sulfates were present.
In this study, aerosol samples collected in the spring and summer in Beijing were analyzed with SEM–EDX. The aerosol composition, pollution characteristics and the formation mechanism of secondary sulfate aerosol were investigated. For the spring samples, the difference between Asian dust and normal urban aerosols was investigated. For the summer samples, the number variation of major particle classes and their relation with aerosol pollution situation and meteorological conditions was observed. The focus of the study was largely put on the formation of sulfate particle classes, which showed close relationship with the urban air quality in the summer. 2. Experimental 2.1. Aerosol samples The sampling site is located in an urban area, under the influence of the traffic, of the north part of the urban fourth ring of Beijing. PM10 aerosol samples were collected for 24 h on the roof of the 11 floor main building of the Sino-Japan Friendship Center for Environmental Protection on 25 April , 15 May (spring samples) and on 21, 24 and 25 July (summer samples), 2000. PM10 concentration was 386, 70, 153, 70 and 124 mg m 3, respectively. An Asian dust event occurred on 25 April, while air quality was reasonably good on 15 May and 24 July, and showed light pollution on 21 and 25 July, all this according to National Standard of Ambient Air Quality and the Air Quality Index System in China. Nuclepore (polycarbonate) membrane with 0.4 mm pores was used as aerosol sampling filter as it provides a flat surface for SEM observation. The particulate aerosol sampler with flow rate of 16.7 l min 1 was the stacked filter unit (SFU) of Gent type recommended by IAEA for a number of projects (Maenhaut et al., 1994), but only the PM10 size particles (with aerodynamic size less than or equal to 10 mm) were collected. In this work, particles with sizes from 2.5 to 10 mm are defined as coarse particles, those less than 2.5 mm are defined as fine particles, and those less than 1 mm are listed as sub-micrometer. 2.2. SEM– EDX measurements Sections of the 47-mm diameter filter sample were mounted on electron microprobe stubs and vacuum coated with a carbon layer of about 40 nm. The individual particle analyses were carried out with a
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JEOL JXA-733 superprobe (Tokyo, Japan) equipped with an annular backscattered electron detector and a TN-2000 EDX detection system (Tracor Northern, Middleton, USA). The X-ray detector consisted of a conventional Si(Li) with Be window that was able to detect elements from Na upwards in atomic number. An electron accelerating voltage of 20 kV was used at a beam current of 1.0 nA with magnification of 2000. Automated analyses were done using the particle recognition and characterization software package 733B developed at the University of Antwerp in which a particle is recognized when its backscattered electron intensity is larger than a threshold, and its diameter larger than a preset minimum size of 0.13 mm. For each recognized particle, the diameter, area and shape factor were computed and an X-ray spectrum was accumulated for 30 s. A simple, but fast on-line spectral analysis method was used to detect the characteristic peaks of the various elements and to determine their intensity. The elemental intensities and the size and shape data of each particle were stored for further off-line analysis. More details on the automated particle analysis procedures and software package can be found elsewhere (Raeymaekers et al., 1988; Van Borm and Adams, 1989). A preset number of at least 500 particles were analyzed for each sample. 2.3. Data analysis Cluster analysis was used for the reduction and interpretation of the data sets obtained by the automated SEM–EDX measurements. As in previous studies (Shattuck et al., 1991; Hoornaert et al., 1996), the particles were classified into ‘particle groups’ or ‘particle classes’ on the basis of their normalized characteristic X-ray intensities to the sum of all X-ray intensities expressed as %. Cluster analysis was performed with the integrated data analysis system (IDAS) software package (Bondarenko et al., 1996). Hierarchical cluster analysis was applied first, after which the results thus obtained were used as the initial seed points for non-hierarchical cluster analysis. The selection of the most appropriate number of clusters was based on the Akaike information criterion (Bondarenko et al., 1994). Cluster analyses were first applied to the data of each sample, subsequently the results for all combined samples were subjected to a second cluster analysis in which the average normalized X-ray intensities of the particle classes were used as
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input data. A two-dimensional table was made to sort out different particle classes over the 2500 particles (see Table 1). Many particles in a class showed similar composition. Excel software was used for a regression analysis to detect the scatter of the intensities for the elements. A program, Quanta2 is adapted for ZAF correction using first-order corrections for backscattering, absorption and secondary fluorescence effects, converting X-ray intensity data of particles into weight or atomic percentage for elements detected (Van Borm and Adams, 1991). The method is applicable to K- and L-lines for the elements Na to Bi, as the characteristic lines of elements lighter than Na are absorbed by the beryllium window of the Si (Li) detector used. As for sulfur-containing particles, elements detected such as Na, Mg, K, Ca and S are proposed with stoichiometry as oxide; however, ammonium ions are not detectable. The method is implemented in a FORTRAN computer program written for PC. It is capable of processing the massive amount of data produced by automated analysis of large particle collections. 2.4. Position tagged spectra (PTS) measurements Elemental mapping was carried out at 20 keV and 1.0 nA current with a JEOL JSM-6300 scanning microscope equipped with a PGT X-ray analysis system PGT EDX detector and IMIX-PC software (Princeton Gamma-Tech, USA). The magnification was 2500 and the total acquisition time was 5 h and the image size 128 102 pixels. Scanning was performed with a dwell-time of 1.38 s per pixel and a step size of 0.47 mm. The elements Al, Si, S, K, Ca and Fe were selected for mapping on the basis of the data obtained in single particle analysis. The results of the X-ray mapping are done via PTS, resulting in a file containing beam coordinates (x, y) and X-ray energy (channel number) for each X-ray photon detected. The data can be displayed as a spatial map of selected X-ray energies (elemental maps) (Mott and Friel, 1999). X-ray spectra of marked areas corresponding to minimum nine pixels could also be generated. 3. Results and discussion 3.1. Particle classes and their abundance Particles were classified into particle classes on the basis of their normalized characteristic X-ray
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Table 1 Number abundance percentage and size of particle classes in aerosol samples Particle class
Minerals Aluminosilicates Si-rich Fe-rich Ca-rich
Number abundance percentage (%) 25 April
15 May
21 July
24 July
25 July
76.7 (1.3271.45) 14.6 (1.4571.31) 2.0 (0.8470.48) 5.2 (1.5971.88)
28.3 (0.7971.29) 0.2 (1.4570.31) 4.6 (0.4170.29)
12.4 (1.0971.68) 3.8 (0.7571.14) 2.0 (0.3670.29)
43.6 (1.2271.58) 9.9 (1.0571.75) 7.4 (0.4870.67) 15.4 (1.0971.44)
18.0 (1.2571.43) 3.4 (1.9071.64) 19.3 (0.3970.34) 5.5 (1.2871.64)
6.1 (0.2070.16)
3.2 (0.2570.35)
0.9 (0.1870.08) 5.6 (0.3470.28)
3.4 (0.5570.98) 6.8 (0.3670.41) 41.7 (0.3270.23)
S-containing S-rich Na–S
0.9 (1.2471.52)
Mg–S K–S
10.8 (0.2070.12) 24.3 (0.3270.32) 6.1 (0.1670.0.04) 2.2 (0.4070.30) 2.4
Ca–S Ca–K–S Zn–S Other S-containing Minor class Mn-containing
1.3 (0.2070.05) 6.4 (0.5070.97)
P-containing Zn-containing
37.3 (0.3470.42) 27.9 (0.5170.64)
2.8 0.9 (0.4170.21)
2.3 (0.2670.12)
1.8 (0.3570.23) 2.0 (0.4070.30)
Pb-containing Carbonaceous Others
4.7 (0.6470.95) 0.3
0.6
3.3
0.5
11.0
3.9
0.5
4.0
1.8
The mean geometric diameter and standard deviation data are given in the parenthesis for a particle class.
intensities using the clustering software IDAS. Clustering results are listed in Table 1. Particle classes were sorted into four categories, namely, mineral dust, sulfur containing, minors and carbonaceous. Mineral dusts are of nature origin and consist of mainly aluminosilicates and quartz (Si-rich) particles. It accounted for 97.8% of the particles in the sample of 25 April 2000, a sample of Asian Dust event, while the other samples contain a much lower percentage of mineral dust, but considerable num-
bers of S-containing particles. Iron-rich particles could originate from iron and steel production and steel product erosion, but coal combustion can also not be excluded as a source. As shown in Table 1, high abundances occur for the 24 and 25 July samples, when the prevailing wind direction was southwest and south-southwest. Hence, the source might be related to an iron and steel manufactory complex situated in the southwest of the sampling site. Calcium-rich particles can be attributed to wind-blown soil and construction dust.
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3.2. Composition of sulfate particles Sulfates, as sulfur-containing particles, are present with high abundance (see Table 1) and are closely related to anthropogenic emission sources. Sulfate particles are mainly secondary in nature, formed by a variety of atmospheric reactions. Ammonia often accompanies the sulfate formed as the major neutralizing cation (Yao et al., 2002, 2003). Other sulfates such as calcium and magnesium salts are frequently found in Chinese aerosols, as alkaline dusts are abundant. Potassium sulfate particles were reported in high numbers in previous studies (Liu et al., 2002). The atomic percentage of sulfur and other major elements are listed in Table 2 for sulfate particles, but ammonium ions were not detectable due to the limitation of the Si (Li) detector. MgSO4 and CaSO4 have the same
July 21, 2000 0.3 S CA K
Atomic percent
0.25 0.2 0.15 0.1 0.05 0 0.1
0.3
1 Particle diameter in micron
10
July 25, 2000
0.6 S CA K
0.25 Atomic percent
Different kinds of sulfur-containing particles can be distinguished on the basis of the elements detected. On the other hand, sulfur-rich particles occur with only sulfur detected. In the Asian dust sample of 25 April, sulfur-containing particles were nearly absent, while in the 15 May, 21 and 25 July samples sulfur-containing particles accounted for 50% or more of all the particles detected. Apparently, such particles are an important component of the urban aerosol in Beijing. Among the three samples of July 2000, they appeared with low abundance on 24 July, but abundance was high on 25 July, and even higher on 21 July. There was an increasing trend of sulfur-containing particles when air pollution was high. There is a positive correlation between the abundance of sulfur-containing particles and the PM10 concentration in July. Carbonaceous particles are those detected, which do not provide any characteristic X-rays. The diameter of nearly all those particles is less than 0.3 mm. Such particles contain only low atomic number elements and are carbonaceous in composition. They are an abundant component of urban aerosols. SEM–EDX is not optimal for the detection of these particles; hence the data for this particle class in Table 1 are very much underestimated. The mean geometric diameter and standard deviation data are also presented for particle classes in Table 1. Mineral dust particles fall in a size range larger than that of sulfur-containing particles. As an example, compositional data were illustrated against particle size for Ca–K–S particles in Fig. 1.
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0.2
0.5 0.4
0.15
0.3
0.1
0.2
0.05
0.1
0 0.1
1 Particle diameter in micron
0 10
Fig. 1. Scatter plot of atomic percentage of Ca, K, and S over particle size for individual Ca–K–S particles in aerosol samples of 21 July and 25 July 2000. (Note: K data in lower plot to the right axis.)
theoretical atomic percentage values, 0.167 for Mg or Ca, 0.167 for S and 0.667 for O, respectively; however, when 1:1 mixed with ammonium sulfate, the atomic percent change to 0.091 for Mg or Ca, 0.182 for S and 0.727 for O, respectively. It means that mixing with ammonium sulfate leads to lower percentage for metallic cations, while higher values for S and O. As shown in Table 2, Ca–S particles of 15 May 2000 had a composition close to theoretical CaSO4 values, while those of three aerosol samples in July 2000 seemed to mix with ammonium sulfate to some extent. Composition of Mg–S particles of 21 July 2000 was similar with MgSO4 (NH4)2SO4 rather than MgSO4. CaSO4 was frequently detected in atmospheric aerosols in many urban areas around the world (Van Borm and Adams, 1989; Hoornaert et al., 1996; Liu et al., 1994). CaCO3 particles originated as soil or road dust could react
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Table 2 The atomic percentage of S-containing particle classes in aerosol samplesa 15 May Mean7SD
N
Mean7SD
24 July N
25 July
Mean7SD
N
Mean7SD
N
S Mg O
MgSO4, MgSO4 (NH4)2SO4b 0.20370.006 0.07270.021 0.70470.007
S K O
K2SO4, K2SO4 (NH4)2SO4, K2SO4 2((NH4)2SO4)b 0.20370.014 49 0.12470.037 49 0.67270.020 49
0.16870.008 0.21870.022 0.61470.012
4 4 4
0.18270.009 0.17970.025 0.63870.013
4 4 4
S Ca O
CaSO4, CaSO4 (NH4)2SO4b 0.16770.013 77 0.18470.015 0.16570.028 77 0.13070.026 0.66770.013 77 0.68570.014
358 358 358
0.17170.022 0.15870.046 0.67170.02
4 4 4
0.16970.010 0.15270.012 0.67070.013
42 42 42
S K Ca O
CaSO4 K2SO4, CaSO4 K(NH4)SO4b 0.14970.021 3 0.18470.011 0.13570.008 3 0.02570.011 0.10170.007 3 0.10770.017 0.61570.006 3 0.67970.014
363 363 363 363
0.17670.011 0.07570.017 0.08770.016 0.65970.013
239 239 239 239
Mg–S
K–S
Ca–S
Ca–K–S
a
21 July
33 33 33
The atomic percent mean value and standard deviation for each element are presented. N is the number of particles in the particle class. Some possible speciation forms for sulfates.
b
with SO2 ,or sulfuric acid, or acidic sulfates such as ammonium sulfate to generate CaSO4 (Hoornaert et al., 1996). Some CaSO4 particles are primary particles such as those from desulfurization process in coal combustion power stations or from soiling of calcite in building construction materials. K2SO4 has the theoretical atomic percentage values, 0.286 for K, 0.143 for S and 0.571 for O, respectively. However, when K2SO4 (1:1) mixed with ammonium sulfate, it is 0.167 for K, 0.167 for S and 0.667 for O, respectively; it seems the case of K–S particles of 25 July 2000. The composition of the K–S particles of 24 July 2000 falls in between the two cases proposed above. It is possible that K2SO4 was mixed with less (NH4)2SO4 than 1:1 ratio. Further more, if K2SO4 was 1:2 mixed with (NH4)2SO4 , it will be 0.118 for K, 0.176 for S and 0.706 for O. This set of values describes better the composition of K–S particles of 15 May 2000. K–S particles were reported as major particle class in biomass burning aerosols (Liu et al., 2000). It could be also be possible that they are converted from K2CO3. CaSO4 and K2SO4 mixed at 1:1 ratio would lead to the theoretical atomic percentage values, 0.077 for Ca, 0.154 for K and S, and 0.615 for O, respectively. It seems comparable with that of
Ca–K–S particles on 15 May 2000. When CaSO4 and K(NH4)SO4 mixing was proposed, it would be 0.084 for Ca and K, 0.167 for S and 0.667 for O, respectively. It fits well with that of Ca–K–S particles on 25 July 2000. The composition of Ca–K–S particles on 21 July 2000 is with low percents of Ca and K, and high values of S and O, indicating possible involvement of ammonium sulfate. In summary, a variety of sulfates were found in this study based on compositional data of individual particles. They could be simple sulfates or composite sulfates as internal mixture of ammonium sulfate and metallic sulfates. 3.3. Formation of composite sulfate particles of calcium and potassium Ca–K–S particles are present in high numbers in two of the summer samples. Fig. 1 shows data with respect to chemical composition and particle size. Particles with size below 0.3 mm account for nearly 60% number abundance for the 21 and 25 July samples. They show, however, a different size distribution, as particles with size larger than 1 mm account for 15% in the 21 July sample, while they are almost absent in the 25 July sample. The chemical composition of Ca–K–S class in those
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two summer samples is also different (Table 2). Sulfate particles are mainly converted from gaseous SO2 in the atmosphere. According to Seinfeld and Pandis (1996) aqueous phase oxidation is one of the major mechanisms for sulfate formation. In-cloud processes have been postulated which is important for sulfate formation in certain circumstances (McHenry and Dennis, 1994; Yao et al., 2002, 2003). Ca–K–S particles, on the other hand, are composite sulfates and since calcium and potassium should be generated by different kinds of emission sources, it is difficult to explain the formation of Ca–K–S particles by the same simple and straightforward atmospheric reactions as proposed for the Ca–S and K–S particles. When comparing Ca–S, K–S particles with Ca–K–S particles, the latter with more complex composition need both calcium and potassium in raw materials and an efficient mixing. There is a negative correlation between the abundance of the two types of sulfur-containing particles, competing for common raw materials. As shown in Table 1, Ca–K–S particles were detected only in the three samples obtained in typical urban pollution conditions. Ca–S and K–S particles were more abundant (24% and 11%) than the Ca–K–S particles (6%) in the 15 May sample. Hence, it seems that the effect of in-cloud formation was limited, which was obviously related to the dry weather in spring. The situation was different for the summer samples when in-cloud processes exerted profound effects. In the 21 July sample, Ca–S particle abundance was 37% whereas K–S particles were almost absent and Ca–K–S particles were abundantly present. It seemed that K–S particles were exhausted, restricting further formation of Ca–K–S particles. In the 25 July sample, both Ca–S and K–S particle abundances were at 3.4% and 6.8%, while the Ca–K–S particle abundance is up to 42%. The potassium content of the Ca–K–S particles in the sample of 25 July is three times higher than on 21 July. The atomic percent of potassium is 2.571.1% on 21 July and 7.571.7% on 25 July (see Table 2) and it reveals the compositional dissimilarity between aerosol samples and the similarity within an aerosol sample. It seems that the content of potassium in Ca–K–S particles depends on the availability of potassium containing particles in air, a high abundance of those particles leading to Ca–K–S particles with higher potassium content. The mass transfer and mixing in the incloud processes may lead to homogenization of the
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chemical composition of cloud droplets and thus the particles generated afterwards from those droplets. The occurrence of Ca–K–S particles in high numbers was not reported in previous studies. It is difficult for bulk analytical methods such as ion chromatography to distinguish Ca–K–S particles from Ca–S and K–S particles. Its formation is related to the hot, humid, cloudy weather during summer in Beijing facilitating the in-cloud formation processes. The average monthly precipitation in Beijing is 21 and 34 mm in April and May, while it is 185 mm in July 2000. In Fig. 2, some meteorological data are presented for July 2000. The total cloud coverage was 85%, 32% and 65% and low cloud coverage was 50%, 0% and 15%, respectively, for 21, 24 and 25 July 2000. There appears a positive correlation between number abundance of sulfate particles and the cloud coverage, indicating the importance of the in-cloud process for SO2 oxidation and sulfate aerosol formation. Ca–K–S particles have consistent composition within an aerosol sample, but a different composition between samples (see Table 2 and Fig. 1). From this, it is inferred that Ca–K–S particles do not correspond to one specific compound with constant composition but are a composite of different sulfates formed as internal mixture in in-cloud processes. 3.4. X-ray mapping measurements As a support for the results obtained by single particle analysis, X-ray maps were collected. As an example, Fig. 3 presents the secondary electron image of one field of the 21 July sample whereas Fig. 4 shows X-ray maps for selected elements for the same field of view. In addition to the 0.4 mm pores from the polycarbonate membrane, the figure shows four mineral particles that (see Fig. 4) contain Si, Al, Fe and K, typical elements for mineral dust. Three of those particles have an irregular shape, one spherical particle is typical for coal fly ash. Also, sulfur-containing particles are abundantly present. Most of these also show up in the Ca map, while some are also discernable in the K map. The spectral data here match well with the major particle classes summarized in Table 1. Some particles with crystalline morphology were observed in the summer samples. Three elongated crystalline particles for the 21 July sample are shown in Fig. 3 and in the mappings of S and Ca in Fig. 4. These particles have a width of about 1 mm
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10
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Fig. 2. Meteorological data during late July 2000 (upper plot) and aerosol mass concentration in mg m 3 and number abundance percentage of sulfate particles for three aerosol samples in July 2000 (lower plot). (Note: Wind speed data in m s 1 in upper plot and number abundance data of sulfate particles in lower plot to the right axis.)
Fig. 3. Secondary electron image of some aerosol particles collected on 21 July 2000 (magnification of 2500).
and a length from 5 to 10 mm, equivalent to an average diameter of 2.2–3.2 mm in the automated measurements. These elongated particles are similar to those illustrated by Shi et al. (2003). The
crystalline morphology implies a mechanism of aqueous phase formation and supports the hypothesis of in-cloud processes as a major sulfate formation route during summer in Beijing. As the
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Fig. 4. X-ray maps for selected elements (Al, Si, Fe, S, Ca, and K) of the same field as in Fig. 3 (sample 21 July 2000).
water evaporates, the crystal seeds are then formed in cloud droplets and finally turn out as airborne particles. 4. Conclusion As microscopic analytical method, SEM–EDX is suitable for chemical characterization of mineral dusts and sulfur-containing particles. Mineral dusts are dominant in windy days in spring in Beijing, and become overwhelming during Asian dust events. On the other hand, mineral dust, as a basic component of background aerosol in Northern China, is relatively abundant in periods with improved air quality. Sulfur-containing particles, mainly sulfates, feature aerosol pollution in urban Beijing where coal is the main energy source. The number abundance of sulfur-containing particles is closely correlated with the aerosol mass concentration levels in summer. Although the SO2 emission is lower in summer than in winter, the conversion of SO2 into sulfates is fast and efficient in summer due to the high temperature and humidity. Sulfate to SO2 ratio as well as size-distribution pattern of sulfates were used as experimental evidence for the aqueous phase oxidation of sulfate formation pathway postulated in previous studies. The occurrences of a great number particles of composite sulfates of calcium and potassium as well as the crystalline morphology of sulfate particles were found as supporting experimental phenomena in this work. It is indicative of aqueous phase oxidation in general and in-cloud processing in particular for sulfate formation pathway. This explains how calcium and potassium originated from different
sources turn out as composite sulfates in Beijing aerosol during the summer. Among possible aqueous phase oxidation pathways, in-cloud processing is most likely, as mass transfer is more active and mixing is more efficient in clouds. Aqueous phase oxidation facilitates not only the formation of sulfates, but also the transformation of ammonium sulfate into sulfate of calcium, or potassium, or internal mixture of both. The observations of this study justify the pollution control measures that come into effect in Beijing, including mandatory use of coal with low content of sulfur, promotion of clean energy alternatives such as nature gas, the ban of coal burning boilers in downtown Beijing and the installation of desulfurification utilities for new power stations. All these measures reduces SO2 emission and lead to less sulfate aerosol. Acknowledgments Financial support from Flemish–Chinese Bilateral Cooperation Program (BIL01/57), the China NSFC (20177036, 20477042) and The National Basic Research Program of China, no. 2003CB415 003 is gratefully acknowledged. References Anderson, J.R., Buseck, P.R., Patterson, T.L., Arimoto, R., 1996. Characterization of the Bermuda tropospheric aerosol by combined individual particle and bulk-aerosol analysis. Atmospheric Environment 30, 319–338. Artaxo, P., Rabello, M.L.C., Maenhaut, W., Van Grieken, R., 1992. Trace elements and individual particle analysis of atmospheric aerosols from the Antarctic peninsula. Tellus 44B, 318–334.
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