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Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China
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Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China Gang Liu ∗ , Jiuhai Li, Dan Wu, Hui Xu School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
a r t i c l e
i n f o
Article history: Received 20 December 2013 Received in revised form 3 March 2014 Accepted 5 March 2014 Keywords: PM2.5 Water-soluble ion Metal element TC Source apportionment Chemical composition
a b s t r a c t To identify and apportion the sources of the ambient PM2.5 in the urban area of Hangzhou, China, PM2.5 samples were collected at three sites in the city from April 2004 to March 2005. Water-soluble ions, metal elements, and total carbon (TC) in PM2.5 samples were analyzed. The results indicated that the 24-h mean concentrations of PM2.5 ranged from 17.1 to 267.0 g/m3 , with an annual average value of 108.2 g/m3 . Moreover, the seasonal mean values for PM2.5 in spring, summer, autumn, and winter were 116, 73.1, 114.2, and 136.0 g/m3 , respectively. According to the Chinese ambient quality standard, at least 70% of the monitoring data exceeded the limit value. The total contribution of water-soluble ions, including F− , Cl− , NO3 − , SO4 2− , NH4 + , K+ , and Na+ , to PM2.5 mass varied from 32.3% to 36.7%. SO4 2− , NO3 − , and NH4 + were the main constituents of the ions, with contributions to PM2.5 varying from 14.1% to 14.7%, 6.0% to 7.8%, and 6.4% to 7.7%, respectively. In addition, the annual mean mass fraction of TC in PM2.5 was 27.8%. The annual average total contribution of the group of elements of Zn, Pb, Cu, Mn, Cr, Ni, Se, Mo, Cd, Sb, and Ag to the aerosol was in the range of 1.7–2.0%. Furthermore, positive matrix factorization was applied to analyze the PM2.5 data collected from the central area, and five factors were identified. The factor contributions to PM2.5 mass were 12.8%, 31.9%, 10.1%, 17.2%, and 27.9%, respectively. Iron/steel manufacturing and secondary aerosol were the main sources for the fine particles. These findings may have significance for controlling the atmospheric contamination in the city. © 2014 Published by Elsevier B.V. on behalf of Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences.
1. Introduction Ambient PM2.5 pollution has various effects on human health (Jacobs et al., 2012; Mcdonnell, Nishino-Ishikawa, Petersen, Chen, & Abbey, 2000; Rohr & Wyzga, 2012). PM2.5 is composed of metal elements, water-soluble ions, elemental carbon (EC), and organic compounds. Metal elements in PM2.5 can be divided into crustal and anthropogenic elements. The former, including Fe, Al, Ca, and Mg, contribute 20% to the PM2.5 mass, while the latter, including Zn, Pb, Cu, Mn, Cr, Ni, Se, Mo, Cd, Sb, Ag, and Hg, contribute 1–2% to the aerosol content (Lonati, Giugliano, Butelli, Romele, & Tardivo, 2005; Vecchi, Marcazzan, Valli, Ceriani, & Antoniazzi, 2004; Viana et al., 2005; Wang et al., 2004). F− , Cl− , NO2 − , NO3 − , SO4 2− , NH4 + , K+ , Na+ , Ca2+ , and Mg2+ are common constituents of the water-soluble ions in PM2.5 , of which NO3 − , SO4 2− , Cl− , and NH4 + are the major components. The contribution of these ions to the PM2.5 mass varies
∗ Corresponding author. Tel.: +86 2558731090; fax: +86 2558731090. E-mail address: [email protected] (G. Liu).
from 30% to 50% (Giugliano et al., 2005; Kim et al., 2006; Lonati et al., 2005; Viana et al., 2005; Wang et al., 2004; Wang, Bi, Sheng, & Fu, 2006). The contents of the total carbon (TC) in PM2.5 in Beijing ranges from 20% to 40%, more than 70% of which is organic carbon (OC). In addition, the concentrations of both OC and EC are all greater in winter than in summer (Dan, Zhuang, Li, Tao, & Zhuang, 2004). In Milan (Italy), the average contents of EC in PM2.5 collected in summer and winter vary from 4.0% to 8.8% and 3.1% to 4.8%, respectively. In addition, the mean contents of OC collected in summer and winter vary from 19.3% to 37.3% and 26.6% to 46.8%, respectively (Giugliano et al., 2005; Lonati et al., 2005; Vecchi et al., 2004). Furthermore, the TC contents in PM2.5 in European urban backgrounds are in the range of 20–35% (Querol et al., 2004). Chemical composition discrimination for PM2.5 with different dynamical diameters exists. In Meptitz (Germany), nitrate is distributed in both coarse and fine PM2.5 , whereas most of the sulfate is distributed in fine PM2.5 (Maenhaut, Cafmeyer, Dubtsov, & Chi, 2002). The content peaks for sulfates and nitrates are found in particles with a diameter of 1.1 m in the rural and urban regions of Leeds (Clarke, Azsdi-Boogar, & Andrews, 1999). Moreover, more
http://dx.doi.org/10.1016/j.partic.2014.03.011 1674-2001/© 2014 Published by Elsevier B.V. on behalf of Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences.
Please cite this article in press as: Liu, G., et al. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology (2014), http://dx.doi.org/10.1016/j.partic.2014.03.011
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Fig. 1. Location of the sampling sites.
than fifty percent of chloride is in the coarse aerosol (Clarke et al., 1999). There are some differences in the temporal distribution for water-soluble salts in PM2.5 . For example, most of the sulfate exists in fine particles all of the time, whereas the distribution modes of chloride are distinct in PM2.5 collected at day and night (Fang et al., 1999). In addition, crustal elements generally concentrate in coarse PM2.5 , whereas anthropogenic elements tend to be enriched in fine PM2.5 (Balachandran, Meena, & Khillare, 2000). Atmospheric PM2.5 could be emitted from different sources. Iron and steel manufacturing, diesel-powered motor vehicles, coal combustion, road re-suspension dust, solid waste incineration, and open biomass burning are all emission sources of the aerosol. Furthermore, gaseous contaminants, such as SO2 , NOx , and NH3 , are likely to transform into PM2.5 through photochemical reactions. Chemical mass balance, positive matrix factorization (PMF), principal component analysis, and multivariable linear regression are often used to identify and apportion sources for PM2.5 (Begum, Hopke, & Zhao, 2005; Hwang & Hopke, 2007; Marmur, Mulholland, & Russell, 2007; Pancras, Landis, Norris, Vedantham, & Dvonch, 2013; Viana et al., 2007; Zheng et al., 2005). Since the policy of economic reform was put into practice in China since the late seventies of the last century, the Chinese economy has developed quickly. However, the air quality in the country has simultaneously declined, particularly in urban areas. Hangzhou is one of the most developed cities in China, but the ambient pollution level in the city is relatively high as well. PM10 is the primary pollutant in the city. However, less research work has been performed on PM2.5 contamination. Zhu, Chen, Wang, and Shen (2004) studied the composition of PAHs in particulate matter. Okuda, Kumata, Naraoka, and Takada (2002) analyzed the stable carbon isotope ratio values of PAHs. Furthermore, the relationship between meteorological factors and the concentration of ambient PM2.5 has also been studied (Hong, Jiao, & Ma, 2013). The aims of this paper are as follows: (1) to investigate the temporal and spatial distributions of ambient PM2.5 in urban area of Hangzhou; (2) to determine the chemical compositions of water-soluble ions, metal elements and TC in PM2.5 ; and (3) to identify and apportion the emission sources of ambient PM2.5 .
2. Sampling and analytical methods 2.1. Sample collection Ambient PM2.5 samples were collected at three sites in the urban area of Hangzhou from April 2004 to March 2005. One sampling site, HJ was located in the division campus of Zhejiang University,
Huajiachi (Fig. 1). Another, MZ was located in the second habitation area of Zhaohui. The third, JK lay south of Wulin Square, the urban center. Air samplers (PM2.5-2, Beijing Geological Instrument Factory, China) were placed on the roofs of buildings, which were ten to twenty meters above the ground and thirty meters away from nearest roads. Two samples were collected simultaneously at each site per week at the airflow rate of 77 L/min. The collection duration per sample was 24 h (from 9:00 a.m. to next 9:00 a.m.). Before sampling, glass fiber filters were baked for 2 h at 500 ◦ C and then placed in a desiccator to reach humidity equilibrium for 24 h at room temperature. To assess sampling error, all air samplers were placed together at a site and used to collect experimental aerosol samples at one time. The relative errors for 24 h sampling between the three samplers varied from −2.6% to +2.3%. 2.2. Analytical methods 2.2.1. Mass and TC The PM2.5 mass was determined by gravimetry. Before and after sampling, the glass filters were weighed on an electronic balance with a reading precision of 0.01 mg. The samples were allowed to reach humidity equilibrium in the weighing room for 24 h before weighing. The aerosol samples were wrapped in aluminum foil and stored in a freezer under −18 ◦ C until performing the chemical analysis after weighing. The TC in PM2.5 samples was analyzed using an elemental analyzer (Flash EA-1112, ThermoFinnigan Italia S.P.A.). A filter sample weighing 2 mg was burned under an oxygen environment in a quartz tube at 800 ◦ C. The carbon dioxide produced was then carried by helium to a gas chromatograph and analyzed. Five blank filters were measured in the same process, and the average background was subtracted from all of the aerosol samples. 2.2.2. Water-soluble ions To measure the water-soluble ions in PM2.5 , a quarter of a filter sample was cut into pieces and then placed into a polypropylene tube with screw cap. Afterwards, 13 mL of deionized water (electrical conductivity was equal to 18.2 M cm) was added to the tube. The sample was extracted for 30 min under ultrasonic conditions. Finally, the liquor was centrifuged at 3000 rpm for 5 min and filtrated using a hydrophilic membrane with a pore diameter of 0.45 m. Five blank filters were treated in the same way to subtract the background from the aerosol samples. An ion chromatograph (Metrohm-732, Metrohm, Switzerland) was used to analyze the water-soluble ions in the aerosol. Anions were eluted using an aqueous solution with concentration of (336 mg NaHCO3 + 106 mg
Please cite this article in press as: Liu, G., et al. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology (2014), http://dx.doi.org/10.1016/j.partic.2014.03.011
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Table 1 Annual mean concentration for PM2.5 and its components. Sampling site Sampling number Unit PM2.5 F− Cl− NO3 − SO4 2− Na+ K+ NH4 + TC Cr Ni Se Mo Ag Cd Sb Mn Cu Zn Pb a
g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 g/m3 g/m3
HJ 89 (47a ) Mean ± SD 112.5 0.1 2.0 7.6 16.8 1.6 2.4 7.8
± ± ± ± ± ± ± ±
46.0 0.1 1.9 5.0 8.0 0.3 1.2 3.4
4.2 2.9 11.7 6.9 0.8 6.7 7.0 26.3 88.8 1.3 0.5
± ± ± ± ± ± ± ± ± ± ±
3.7 1.9 2.9 6.4 0.4 5.6 4.1 29.1 103.7 1.2 0.3
MZ 90 (48) Mean ± SD 108.4 0.1 1.7 8.1 16.5 2.4 3.4 9.9 30.8 6.6 2.1 5.7 8.1 0.7 6.2 4.0 43.2 66.1 1.3 0.8
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
45.7 0.1 1.5 5.1 7.6 0.5 1.7 4.5 11.7 5.1 1.9 4.3 12.1 0.4 4.6 2.2 41.5 44.6 1.3 0.3
JK 93 (48) Mean ± SD 104.0 0.1 1.7 9.1 17.7 2.8 3.3 10.5 30.3 8.3 4.1 9.3 6.5 0.9 8.7 7.3 44.9 58.5 1.2 0.6
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
37.8 0.1 1.3 7.2 9.0 0.4 1.4 4.3 10.4 4.8 3.9 5.8 4.1 0.5 6.1 5.1 38.8 27.6 0.6 0.2
Sum or average 272 (143) Mean ± SD 108.2 0.1 1.8 8.3 17.0 2.2 3.0 9.3 30.6 6.4 3.0 8.9 7.2 0.8 7.2 6.1 38.2 71.0 1.3 0.6
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
43.2 0.1 1.5 5.9 8.2 0.6 1.5 4.2 11.0 4.9 2.9 5.1 8.2 0.5 5.5 4.2 37.6 67.5 1.0 0.3
Number of analyzed samples.
Na2 CO3 )/L at a flow rate of 1.2 mL/min. Cations were eluted with a solution with concentration of (167 mg 2,6-pyridinedicarboxylic acid +600 mg tartaric acid)/L at a flow rate of 0.8 mL/min. The columns for separating the anions and cations were Metrosep A SUPP 4 (250 mm × 4 mm) and Metrosep C 2 250 (250 mm × 4 mm), respectively. Water-soluble ions in the aerosol samples were quantified using authoritative standard solutions purchased from the National Center for Reference Materials of China. Four aerosol samples were experimentally extracted and determined three times each using the procedure described above. The results indicated that the recovery rates were greater than 85%.
2.2.3. Metal elements A modified microwave digestion method was utilized to treat samples for metal element analysis on the basis of the American EPA method (USEPA, 1999). A quarter of a filter sample was first placed into a Teflon digestion vessel. 8 mL of nitric acid (6%, v/v) and 2 mL of hydrogen peroxide were then added to the vessel. Finally, the vessel was covered and placed in a microwave digestive system (MK-III, Sinco Institute of Microwave Dissolving and Testing Techniques, China) to dissolve the sample. Only one sample was digested at a time to ensure the dissolution of the metal elements in PM2.5 . A sample was digested initially under 0.5 MPa for 1 min; then, the digestion was completed under 1.5 MPa for 15 min. The sample solution and filter residue mixture was transferred into a polypropylene test tube after cooling. The inside of the vessel was rinsed with 3 mL deionized water, and then, the liquid was added to the mixture. The solution was centrifuged at 3000 rpm for 5 min. Finally, the clear fraction was diluted with water and then used to determine the metal elements. Five blank filters were also treated with the same process to subtract the background. The solution was measured using an inductively coupled plasma mass spectrometry (ICP-MS, VG PQ ExCell, Thermo Fisher Scientific Inc., USA). Before the sampled filters were analyzed, a 0.1 g/mL mixture of standard solutions of eleven metal elements was added to eight blank filters; then, the solutions were digested and tested using the method described above. The results indicated that the recovery rates were in the range of 76–110%. In addition, 10% extracted samples in a batch were measured twice to control the analytical quality. The relative errors were between −20% and +20%.
2.3. Data analysis EPA PMF 3.0 (USEPA, 2008) was used to identify the major emission sources for ambient PM2.5 in the urban area of Hangzhou. In addition to the concentration data for each species in PM2.5 , uncertainty data were also required to give the model an estimate of the confidence in each value. The uncertainties provided should encompass errors such as sampling and analytical errors. However, they were not available in this work and had to be estimated. There was a ten percent uncertainty in the concentration estimate of each PM2.5 value and a twenty percent uncertainty in the concentration estimate for each species value, based on the relative errors and recovery rates mentioned above. IBM SPSS Statistics 21 was used to analyze the correlations between the components in the PM2.5 and gaseous contaminants in the air.
3. Results and discussion 3.1. Ambient concentrations of PM2.5 The 24-h average concentrations of PM2.5 at three sites varied from 17.1 to 267.0 g/m3 . However, the annual mean values at the three sites were close to each other (104.0–112.5 g/m3 ) (Table 1). The average levels in the eastern and northeastern areas were 8.2% and 4.2% greater than in the center, respectively, implying the spatial differences of PM2.5 concentration were small. According to the ambient air quality standards issued by the Ministry of Environmental Protection of China in 2012, the limit values for the 24-h and the annual mean concentrations for PM2.5 are 75 and 35 g/m3 , respectively (MEPC, 2012). Thus, 70.0–75.5% of the 24-h average data exceeded the limit. Furthermore, the annual mean values at the three sites were 3.0–3.2 times the relevant limit. The annual average for the entire urban area was 3–13 times the PM2.5 concentrations in Milan, New York, San Francisco, Washington, and Los Angeles (Chow et al., 1994; Lonati et al., 2005), and 2.7, 2.6, and 1.3 times of those in Hong Kong, Bangkok, and Karachi, respectively (Chuersuwan, Nimrat, Lekphet, & Kerdkumrai, 2008; Mansha, Ghauri, Rahman, & Amman, 2012; So, Guo, & Li, 2007). However, the annual average for the entire
Please cite this article in press as: Liu, G., et al. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology (2014), http://dx.doi.org/10.1016/j.partic.2014.03.011
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Fig. 2. Variability of the monthly mean concentrations for PM2.5 at the three sites.
urban area was approximately equal to that in Beijing (Wang et al., 2004). Obviously, the PM2.5 level in the urban area of Hangzhou was extremely high. 3.2. Temporal distribution of PM2.5 The variability for the monthly average concentrations of PM2.5 at all sites was extremely similar (Fig. 2). The values were the lowest in July and the highest in November. Hangzhou has a subtropical monsoon climate with four distinct seasons. Spring, summer, autumn, and winter included months from March to May, June to August, September to November, and December to February, respectively, in the city. The quarterly mean concentrations of PM2.5 in spring, summer, autumn, and winter at the three sites ranged from 105.8 to 121.8, 70.9 to 75.4, 110.4 to 119.0, and 127.9 to 144.9 g/m3 , respectively (Table 2). The PM2.5 levels in summer were the lowest, and the ones in winter were the highest. The average concentrations in the summer and winter were 1.7 and 1.9 times of those in Chennai (India), respectively (Srimuruganandam & Nagendra, 2011). 3.3. Composition and seasonal variation of the water-soluble ions F− , Cl− , NO3 − , SO4 2− , NH4 + , K+ , and Na+ were measured in PM2.5 . Ca2+ and Mg2+ were not determined due to their high blank values in the glass filters. At each sampling site, the annual mean concentrations of anions or cations decreased in the same order, namely, SO4 2− > NO3 − > Cl− > F− , and NH4 + > K+ > Na+ (Table 1). In addition, the annual average contribution of all ions to PM2.5 mass varied from 32.3% to 36.7%, which was much greater than the value in Kanpur (India) (Ram & Sarin, 2011). SO4 2− , NO3 − , and NH4 + were the Table 2 Quarterly mean concentrations of ambient PM2.5 (g/m3 ). Sampling site
Season
Sampling number
Mean ± SD
HJ
Spring Summer Autumn Winter
22 24 25 18
121.8 72.9 119.0 144.9
± ± ± ±
26.1 31.4 49.8 42.0
MZ
Spring Summer Autumn Winter
22 24 26 18
120.7 70.9 113.3 136.0
± ± ± ±
29.8 31.8 51.4 39.5
JK
Spring Summer Autumn Winter
23 24 26 20
105.8 75.4 110.4 127.9
± ± ± ±
22.0 30.3 39.5 38.7
major constituents of the water-soluble ions in PM2.5 in Hangzhou. This result was in agreement with the results obtained in Nanjing, Shanghai, Beijing, and Fuzhou (Wang, Huang, Gao, Gao, & Wang, 2002; Yao et al., 2002; Zhang et al., 2013). The annual contributions for the three ions to PM2.5 at three sites of HJ, MZ, and JK ranged from 14.1% to 14.7%, 6.0% to 7.8%, and 6.4% to 7.7%, respectively. Although the concentrations for each ion were the lowest in summer, some differences still existed between the seasonal distributions of the ions (Table 3). In the central region (JK), the highest level of Cl− was present in winter, whereas the highest ones for NO3 − , SO4 2− , NH4 + , and K+ occurred in autumn. Moreover, Na+ had the highest level in spring. In the northeast region (MZ), the temporal distribution of NH4 + was the same as Cl− in the central region; the variations of the other ions were similar to the corresponding ions in the central area. In the eastern region (HJ), the highest levels occurred in spring for SO4 2− and in winter for NH4 + . In addition, the variability of the other ions was similar to the relevant ones in the central region as well. 3.4. Correlation analysis of water-soluble ions The statistical results indicated that significant linear correlations existed between NO3 − , SO4 2− , and NH4 + in spring (Table 4), suggesting that NO3 − and SO4 2− were likely derived from the same sources to some extent and that most of the NH4 + was probably in the form of ammonium nitrate and ammonium sulfate. There were evident distinctions between the statistical results in spring and summer. In summer, the correlations were significant between Cl− and NO3 − and between NO3 − and NH4 + . However, the correlation coefficient for the latter two ions (i.e. 0.56) was less than the relevant one in spring (i.e. 0.79), suggesting that NH4 + might exist mainly as nitrate rather than sulfate. Ammonium nitrate and sodium chloride were the two most common components in the atmospheric PM2.5 . Nitrate volatilizes and decomposes into nitrate acid and ammonia at high temperatures. A part of the acid might react with sodium chloride in the aerosol and form hydrochloric acid, resulting in Cl− volatilization in the form of HCl (Viana et al., 2005). The existence of nitrate in ambient PM2.5 was possibly favorable for the conservation of chloride. The chemical reactions probably occurred in the aerosol in summer in Hangzhou. More correlations were identified between the other ions in autumn, in addition to those found both between Cl− and NO3 − in summer and between NO3 − , SO4 2− , and NH4 + in spring. Correlations between Na+ and NH4 + , K+ and SO4 2− (NH4 + , Na+ ) were all significant (Table 5). This result implies that the increase of nitrate content in PM2.5 was also helpful for stabilizing chloride in autumn. In addition, it was possible that NH4 + was mainly in the forms of
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Table 3 Seasonal mean concentrations of the water-soluble ions in PM2.5 (g/m3 ). F−
NO3 −
Cl−
SO4 2−
Na+
NH4 +
K+
Sampling site
Sampling number
Season
HJ
11 12 13 11
Spring Summer Autumn Winter
0.09 0.05 0.10 0.14
± ± ± ±
0.05 0.04 0.06 0.09
1.95 0.58 1.78 3.86
± ± ± ±
1.53 0.42 0.98 2.50
7.78 3.40 8.02 11.44
± ± ± ±
3.71 2.63 5.05 4.91
19.55 13.80 16.87 17.07
± ± ± ±
9.35 6.92 6.31 9.38
1.64 1.45 1.53 1.61
± ± ± ±
0.32 0.25 0.19 0.45
2.35 2.04 2.61 2.76
± ± ± ±
1.19 1.49 1.08 1.13
8.48 5.01 7.86 10.00
± ± ± ±
3.28 2.31 3.60 2.55
MZ
12 12 13 11
Spring Summer Autumn Winter
0.08 0.05 0.10 0.17
± ± ± ±
0.06 0.04 0.08 0.08
1.63 0.55 1.57 3.28
± ± ± ±
1.02 0.39 0.92 1.79
9.03 4.76 8.16 10.75
± ± ± ±
4.13 4.78 5.20 5.08
16.99 15.61 17.99 15.48
± ± ± ±
7.59 8.10 8.02 7.51
2.61 2.00 2.46 2.34
± ± ± ±
0.31 0.67 0.36 0.33
3.44 2.18 4.07 3.82
± ± ± ±
1.69 1.30 1.67 1.36
10.80 6.99 10.29 11.92
± ± ± ±
3.45 4.20 5.25 3.46
JK
12 12 13 11
Spring Summer Autumn Winter
0.10 0.05 0.11 0.12
± ± ± ±
0.04 0.06 0.07 0.08
1.41 0.67 2.00 2.62
± ± ± ±
0.61 0.52 1.32 1.49
8.69 5.53 11.77 10.31
± ± ± ±
5.92 6.14 9.60 5.37
18.12 14.24 22.31 14.82
± ± ± ±
9.78 8.39 9.58 5.06
3.01 2.41 2.77 2.85
± ± ± ±
0.47 0.27 0.25 0.34
2.83 2.41 3.92 3.66
± ± ± ±
1.57 0.95 1.37 1.43
10.81 6.98 11.62 11.55
± ± ± ±
4.13 3.61 4.96 3.45
Table 4 Correlations of water-soluble ions in spring and summer (p < 0.05). Spring (n = 31)
−
Cl NO3 − SO4 2− NH4 + Na+
Summer (n = 30)
Cl−
NO3 −
SO4 2−
NH4 +
Na+
K+
Cl−
NO3 −
SO4 2−
NH4 +
Na+
K+
1.00
0.16 1.00
0.26 0.69 1.00
0.16 0.79 0.66 1.00
0.12 0.09 −0.15 0.15 1.00
−0.18 −0.03 0.25 0.14 0.35
1.00
0.50 1.00
0.05 0.22 1.00
0.00 0.56 0.32 1.00
0.19 −0.30 −0.23 −0.31 1.00
−0.04 −0.07 0.08 0.13 0.08
Bold figures represent significant correlations.
nitrate and sulfate, and K+ was likely in the form of sulfate in the aerosol. There may be a relationship in the origins between Na+ , NH4 + , and K+ . In winter, correlations were only found between NH4 + and NO3 − , NH4 + and Na+ , and K+ and Na+ (Table 5). This result suggests that most of the NH4 + might occur as ammonium nitrate, and there were correlations between NH4 + and Na+ , K+ and Na+ in the sources.
northeastern areas were statistically analyzed together. No significant correlation was found between SO2 and the ions in spring (Table 6). The situation was the same between NO2 and the ions. The gaseous pollutants had positive correlations with SO4 2− and NH4 + in summer and with NO3 − and NH4 + in autumn, respectively (Table 7). In winter, SO2 was correlated with NO3 − , and NO2 was correlated with NO3 − , NH4 + , and SO4 2− . The statistical results above suggest that no direct originating relationship existed between nitrate and NO2 or between sulfate and SO2 in the atmosphere in spring. In summer, a considerable amount of SO4 2− in the ambient PM2.5 was likely derived from SO2 through photochemical reactions at a faster rate due to the high air temperature. However, NO2 did not affect the concentration of NO3 − due to the instability of the nitrate during the season (Viana et al., 2005). The rise of the NO2 concentration in the atmosphere would lead to an increase of nitrate in autumn and winter, whereas
3.5. Correlations between water-soluble ions and gaseous pollutants Daily monitoring data on ambient SO2 and NO2 (supplied by the Hangzhou Central Station for Environmental Monitoring) and the concentration values for NO3 − , NH4 + , and SO4 2− in the easternand Table 5 Correlations of the water-soluble ions in autumn and winter (p < 0.05). Autumn (n = 34)
−
Cl NO3 − SO4 2− NH4 + Na+
Winter (n = 27)
−
−
Cl
NO3
1.00
0.44 1.00
SO4
2−
NH4
+
+
Na
−0.07 0.44 0.50 1.00
0.06 0.42 1.00
0.20 0.07 0.32 0.47 1.00
+
K
−0.20 0.06 0.51 0.69 0.53
Cl−
NO3 −
SO4 2−
NH4 +
Na+
K+
1.00
0.32 1.00
0.11 0.34 1.00
−0.01 0.54 −0.12 1.00
0.13 0.14 −0.17 0.68 1.00
0.24 0.05 0.07 0.20 0.58
Bold figures represent significant correlations.
Table 6 Correlations between ions and gaseous pollutants in spring and summer (p < 0.05). Spring (n = 23) NO3 −
NO3 NH4 + SO4 2− SO2
−
1.00
Summer (n = 22) NH4
+
0.79 1.00
SO4
2−
0.66 0.58 1.00
SO2
NO2
0.07 −0.26 −0.02 1.00
0.04 −0.14 −0.17 0.63
NO3 − 1.00
NH4 +
SO4 2−
SO2
NO2
0.80 1.00
0.62 0.83 1.00
0.31 0.46 0.46 1.00
0.37 0.56 0.63 0.47
Bold figures represent significant correlations.
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Table 7 Correlations between the ions and the gaseous pollutants in autumn and winter (p < 0.05). Autumn (n = 26) NO3 NO3 − NH4 + SO4 2− SO2
−
1.00
Winter (n = 22) NH4
+
SO4
0.82 1.00
2−
SO2
0.37 0.22 1.00
NO2
0.69 0.64 0.06 1.00
0.84 0.72 0.26 0.85
NO3 − 1.00
NH4 +
SO4 2−
SO2
NO2
0.48 1.00
0.57 0.24 1.00
0.58 0.40 0.30 1.00
0.55 0.51 0.51 0.78
Bold figures represent significant correlations.
SO2 possibly had no effect on the concentrations of sulfate in the two seasons. 3.6. Concentrations of TC in PM2.5 The PM2.5 samples collected at MZ and JK were selected to determine the TC. In the northeastern and central areas, the seasonal mean concentrations for TC ranged from 19.9 to 36.5 g/m3 and 21.5 to 35.8 g/m3 , respectively, with the variability similar to PM2.5 . The annual averages at the two sites were approximately equal to each other, and the mean value for the entire urban area was 30.6 g/m3 (Table 1). The seasonal average contributions of TC to the PM2.5 mass at each site varied from 25.2% to 29.1% for MZ area, and 27.3% to 31.0% for JK area, which were greater in summer and autumn than in spring and winter. The quarterly mean values in Hangzhou were evidently greater in summer and smaller in winter than in Beijing (Dan et al., 2004), whereas they were close to the corresponding ones in Milan (Italy) (Giugliano et al., 2005). Furthermore, the annual mean content of TC in the aerosol in the center was 5.5% higher than in the northeast. For the entire urban region of Hangzhou, the annual average was 27.8%, which was 3.5% greater than the annual average in Fuzhou (Zhang et al., 2013). 3.7. Relationship between TC and water-soluble ions The positive correlation between TC and Cl− was significant in both spring and autumn (Table 8). There were negative correlations between TC and both NO3 − and NH4 + in summer. Moreover, TC had a negative correlation with NO3 − in winter. The TC in ambient PM2.5 consisted of OC and EC, and the OC/EC ratios were generally greater than two (Dan et al., 2004; Giugliano et al., 2005; Querol et al., 2004). The majority of the TC in fine particles in Hangzhou was probably OC. Chloride in the aerosol could be derived from soil, sea salt, coal combustion, and solid waste burning. The correlation between TC and Cl− mentioned above implies that coal combustion in the city was their main emission source in spring and autumn. The relationship between TC and both NO3 − and NH4 + in summer and winter suggests that the increase of organic matter in PM2.5 might favor the transformation of nitrate or ammonium salt to other compounds. Table 8 Correlations between TC and water-soluble ions (p < 0.05). Cl−
TC
Spring (n = 21) 0.59 Summer (n = 18) 0.09 Autumn (n = 23) 0.46 Winter (n = 20) 0.02
Bold figures represent significant correlations.
NO3 −
SO4 2−
NH4 +
−0.29
−0.11
−0.21
−0.60
−0.10
−0.83
−0.03
−0.02
−0.22
−0.53
−0.41
−0.33
3.8. Concentrations of metal elements in PM2.5 One of two samples collected per week at each site was analyzed to investigate the composition of the metal elements. The annual mean concentrations for Cr, Ni, Se, Mo, Ag, Cd, and Sb in PM2.5 were in the range 0.8–8.9 ng/m3 , and the corresponding ones for Mn and Cu, and Pb and Zn varied from 38.2 to 71.0 ng/m3 and 0.6 to 1.3 g/m3 , respectively (Table 1). Mn, Cu, Pb, and Zn were apparently the main constituents of the metal elements. The annual average total concentration in the northeastern region (i.e. MZ) was 14.6% higher than in the eastern region (i.e. HJ). However, the relevant values in the eastern and central areas were almost equivalent to each other. In addition, the annual average total contents of the eleven elements in PM2.5 ranged from 1.74% to 2.04%. Most of the elements had the highest concentrations in spring or winter, whereas the lowest concentrations were in summer (Table 9). The seasonal mean concentrations for Cu, Zn, Se, and Pb in Hangzhou in summer were 3–22 times those in Milan, and the average values for Cr, Mn, Cu, Zn, Se, and Pb in winter 1.8–11 times those in the Milan city (Vecchi et al., 2004).
3.9. Source apportionment of ambient PM2.5 The concentration data on PM2.5 in the central region were analyzed with EPA PMF (version 3.0), and a five-factor solution was obtained. Factor 1 in the solution primarily consisted of Mn and Cl− (Fig. 3). HCl is a common component of smoke from coal combustion. HCl could react with other pollutants, such as NH3 , in the atmosphere to form the corresponding salt. Mn was one of metal elements with high concentrations in fly ash from coal combustion (Bhangare, Ajmal, Sahu, Pandit, & Puranik, 2011). In China, desulfurization practice was performed for many years in coal combustion related enterprises. This desulfurization probably was the main reason for the lack of contribution of SO4 2− to this factor. Consequently, factor 1 was closely associated with the aerosols derived from coal combustion. There was a thermal power plant in the northwest seven kilometers away from urban center. This power plant was possibly the major emission source. Factor 2 had the highest loadings of Ni and Cr. These elements were generally related with iron/steel and other metal manufacturing (Mansha et al., 2012; Mooibroek, Schaap, Weijers, & Hoogerbrugge, 2011; Morishita et al., 2011; Yatkin & Bayram, 2008). Accordingly, this factor was likely associated with metal production sources. Hanggang Steel-making Corporation was located in the northeast 12 kilometers away from urban center (Wulin Square). This corporation was probably the primary emission source for factor 2. Factor 3 was marked by the highest loadings of Sb and Se. Antimony can be released from many sources. For example, it could be derived from brake wear and iron/steel production (Mansha et al., 2012; Morishita et al., 2011; Querol et al., 2007). Therefore, factor 3 probably was a mixture of several anthropogenic emission sources.
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Fig. 3. Factor profiles for ambient PM2.5 in the urban center.
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0.6 0.5 0.9 0.8 ± ± ± ± 2.0 1.4 2.2 2.0 0.2 0.2 0.2 0.2 ± ± ± ± 0.7 0.5 0.6 0.6 0.4 0.4 0.8 0.6 ± ± ± ± 1.2 0.9 1.5 1.3 20.3 15.6 28.8 34.4 ± ± ± ± 21.4 22.3 28.7 53.3 ± ± ± ± 8.7 7.5 8.1 10.6 0.3 0.2 0.6 0.8 ± ± ± ± 2.2 5.7 3.7 4.3 7.4 6.2 9.0 10.9
12 12 13 11
12 12 13 11
Spring Summer Autumn Winter
Spring Summer Autumn Winter
MZ
JK
6.0 3.6 3.2 3.7
± ± ± ±
5.3 4.4 2.2 2.7 2.3 3.5 5.3 6.4 ± ± ± ±
± ± ± ± 5.5 5.7 7.2 7.6 4.3 4.4 6.4 6.9 ± ± ± ± 7.1 7.7 11.5 11.0
0.7 0.7 1.1 1.1
6.1 7.5 8.6 6.9
± ± ± ±
3.1 3.6 7.2 5.3 5.7 7.5 2.9 7.9 ± ± ± ±
37.1 17.9 48.8 78.3
62.2 40.8 57.8 74.9
2.7 0.3 0.5 0.7 ± ± ± ± 2.9 1.4 2.2 2.6 0.3 0.2 0.3 0.2 ± ± ± ± 0.9 0.6 0.9 0.8 2.4 0.3 0.4 0.5 ± ± ± ± 1.8 0.8 1.2 1.5 67.4 11.4 29.7 32.0 ± ± ± ± 34.3 4.3 27.6 35.8 ± ± ± ± ± ± ± ± 5.0 2.8 3.7 4.4 6.1 5.3 5.9 7.5 0.5 0.4 0.4 0.4 ± ± ± ± 0.6 0.5 0.8 0.9 7.3 15.9 16.1 5.5 ± ± ± ± 7.2 3.7 5.3 10.7
11 12 13 11 Spring Summer Autumn Winter HJ
2.7 0.9 1.5 3.5
5.2 2.5 3.4 5.7
Samplingnumber Season
4.0 1.5 2.1 4.1
2.4 1.1 1.0 1.9 4.3 4.5 3.4 5.7 ± ± ± ±
± ± ± ± 6.0 9.2 9.5 7.4 4.8 3.2 2.8 4.5 ± ± ± ± 8.3 3.6 4.2 7.2
7.4 6.9 5.9 6.9 ± ± ± ± 4.8 2.7 3.6 3.0 ± ± ± ±
1.5 1.7 1.7 1.6
Ni (ng/m3 ) Cr (ng/m3 )
8.5 8.1 3.4 4.7
References
± ± ± ± 9.2 6.1 5.8 6.8
Acknowledgements
1.6 2.4 3.4 3.1
0.4 0.4 0.4 0.3
The ambient PM2.5 pollution in Hangzhou was severe during the study period, particularly in winter. The annual mean concentrations of PM2.5 decreased from east to northeast as well as toward the central region. SO4 2− , NO3 − , and NH4 + were the major water-soluble ions in the aerosol. The annual average contribution of TC to PM2.5 mass was 27.8%. Zn, Pb, Cu, and Mn were the main metal elements in the fine particles. The most significant sources for atmospheric PM2.5 in the central area were iron/steel manufacturing and secondary aerosol.
± ± ± ±
± ± ± ±
4. Conclusions
13.4 10.3 11.2 11.9
0.9 0.7 0.9 0.9
7.7 7.7 6.2 6.4
2.8 1.6 2.0 1.8 3.7 6.0 3.0 5.5 ± ± ± ±
42.2 2.0 41.5 91.1
80.4 35.6 60.0 91.2
2.5 0.4 0.7 1.1 ± ± ± ± 2.7 1.3 1.9 2.1 0.2 0.2 0.1 0.5 ± ± ± ± 0.6 0.4 0.5 0.5 2.1 0.3 0.6 0.6 ± ± ± ± 1.9 0.8 1.2 1.4 195.9 23.1 51.3 41.7 ± ± ± ± 137.3 44.1 74.9 105.5 27.4 0.8 22.9 31.0 ± ± ± ± 28.0 0.2 27.1 52.0 ± ± ± ± 3.0 9.6 3.1 4.7 ± ± ± ±
4.1 3.5 4.0 5.0
Sb (ng/m3 )
Factor 4 was represented by high contributions of Pb, Zn, Cu, Na+ , and TC. EC was the main constituent in soot released from gasoline- and diesel-powered vehicles. Pb, Zn, Cu, and Na were found in the particles as well (Cheng et al., 2010). In addition, dust originated from motor brake wears contained Cu and Pb (Denier van der Gon, Hulskotte, Visschedijk, & Schaap, 2007; Garg et al., 2000). Zn was identified in the dust from tire wear (Adachi & Tainosho, 2004; Blok, 2005). Thus, factor 4 probably had a relationship with the tailpipe emission of motor vehicles and brake/tire wear. Factor 5 had the highest loadings of F− , NO3 − , Cl− , SO4 2− , and NH4 + . Factor 5 was easily identified as secondary particles (Santacatalina et al., 2010; Srimuruganandam & Nagendra, 2012). The contributions of factors 1, 2, 3, 4, and 5 described above to the ambient PM2.5 mass in the central area were 12.8%, 31.9%, 10.1%, 17.2%, and 27.9%, respectively. In brief, iron/steel manufacturing and secondary aerosols were the major sources for fine particles; motor vehicle and road resuspension dust were the next major sources.
This work was financially supported by the National Natural Science Foundation of China (41073019). The authors thank Tianxiang Zhong and Weilin Teng for their participating in the sampling campaign.
Samplingsite
Table 9 Seasonal mean concentrations of the metal elements in PM2.5 .
Se (ng/m3 )
Mo (ng/m3 )
Ag (ng/m3 )
Cd (ng/m3 )
Mn (ng/m3 )
Cu (ng/m3 )
Zn (g/m3 )
Pb (g/m3 )
Sum (g/m3 )
8
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Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China Gang Liu ∗ , Jiuhai Li, Dan Wu, Hui Xu School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
a r t i c l e
i n f o
Article history: Received 20 December 2013 Received in revised form 3 March 2014 Accepted 5 March 2014 Keywords: PM2.5 Water-soluble ion Metal element TC Source apportionment Chemical composition
a b s t r a c t To identify and apportion the sources of the ambient PM2.5 in the urban area of Hangzhou, China, PM2.5 samples were collected at three sites in the city from April 2004 to March 2005. Water-soluble ions, metal elements, and total carbon (TC) in PM2.5 samples were analyzed. The results indicated that the 24-h mean concentrations of PM2.5 ranged from 17.1 to 267.0 g/m3 , with an annual average value of 108.2 g/m3 . Moreover, the seasonal mean values for PM2.5 in spring, summer, autumn, and winter were 116, 73.1, 114.2, and 136.0 g/m3 , respectively. According to the Chinese ambient quality standard, at least 70% of the monitoring data exceeded the limit value. The total contribution of water-soluble ions, including F− , Cl− , NO3 − , SO4 2− , NH4 + , K+ , and Na+ , to PM2.5 mass varied from 32.3% to 36.7%. SO4 2− , NO3 − , and NH4 + were the main constituents of the ions, with contributions to PM2.5 varying from 14.1% to 14.7%, 6.0% to 7.8%, and 6.4% to 7.7%, respectively. In addition, the annual mean mass fraction of TC in PM2.5 was 27.8%. The annual average total contribution of the group of elements of Zn, Pb, Cu, Mn, Cr, Ni, Se, Mo, Cd, Sb, and Ag to the aerosol was in the range of 1.7–2.0%. Furthermore, positive matrix factorization was applied to analyze the PM2.5 data collected from the central area, and five factors were identified. The factor contributions to PM2.5 mass were 12.8%, 31.9%, 10.1%, 17.2%, and 27.9%, respectively. Iron/steel manufacturing and secondary aerosol were the main sources for the fine particles. These findings may have significance for controlling the atmospheric contamination in the city. © 2014 Published by Elsevier B.V. on behalf of Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences.
1. Introduction Ambient PM2.5 pollution has various effects on human health (Jacobs et al., 2012; Mcdonnell, Nishino-Ishikawa, Petersen, Chen, & Abbey, 2000; Rohr & Wyzga, 2012). PM2.5 is composed of metal elements, water-soluble ions, elemental carbon (EC), and organic compounds. Metal elements in PM2.5 can be divided into crustal and anthropogenic elements. The former, including Fe, Al, Ca, and Mg, contribute 20% to the PM2.5 mass, while the latter, including Zn, Pb, Cu, Mn, Cr, Ni, Se, Mo, Cd, Sb, Ag, and Hg, contribute 1–2% to the aerosol content (Lonati, Giugliano, Butelli, Romele, & Tardivo, 2005; Vecchi, Marcazzan, Valli, Ceriani, & Antoniazzi, 2004; Viana et al., 2005; Wang et al., 2004). F− , Cl− , NO2 − , NO3 − , SO4 2− , NH4 + , K+ , Na+ , Ca2+ , and Mg2+ are common constituents of the water-soluble ions in PM2.5 , of which NO3 − , SO4 2− , Cl− , and NH4 + are the major components. The contribution of these ions to the PM2.5 mass varies
∗ Corresponding author. Tel.: +86 2558731090; fax: +86 2558731090. E-mail address: [email protected] (G. Liu).
from 30% to 50% (Giugliano et al., 2005; Kim et al., 2006; Lonati et al., 2005; Viana et al., 2005; Wang et al., 2004; Wang, Bi, Sheng, & Fu, 2006). The contents of the total carbon (TC) in PM2.5 in Beijing ranges from 20% to 40%, more than 70% of which is organic carbon (OC). In addition, the concentrations of both OC and EC are all greater in winter than in summer (Dan, Zhuang, Li, Tao, & Zhuang, 2004). In Milan (Italy), the average contents of EC in PM2.5 collected in summer and winter vary from 4.0% to 8.8% and 3.1% to 4.8%, respectively. In addition, the mean contents of OC collected in summer and winter vary from 19.3% to 37.3% and 26.6% to 46.8%, respectively (Giugliano et al., 2005; Lonati et al., 2005; Vecchi et al., 2004). Furthermore, the TC contents in PM2.5 in European urban backgrounds are in the range of 20–35% (Querol et al., 2004). Chemical composition discrimination for PM2.5 with different dynamical diameters exists. In Meptitz (Germany), nitrate is distributed in both coarse and fine PM2.5 , whereas most of the sulfate is distributed in fine PM2.5 (Maenhaut, Cafmeyer, Dubtsov, & Chi, 2002). The content peaks for sulfates and nitrates are found in particles with a diameter of 1.1 m in the rural and urban regions of Leeds (Clarke, Azsdi-Boogar, & Andrews, 1999). Moreover, more
http://dx.doi.org/10.1016/j.partic.2014.03.011 1674-2001/© 2014 Published by Elsevier B.V. on behalf of Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences.
Please cite this article in press as: Liu, G., et al. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology (2014), http://dx.doi.org/10.1016/j.partic.2014.03.011
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Fig. 1. Location of the sampling sites.
than fifty percent of chloride is in the coarse aerosol (Clarke et al., 1999). There are some differences in the temporal distribution for water-soluble salts in PM2.5 . For example, most of the sulfate exists in fine particles all of the time, whereas the distribution modes of chloride are distinct in PM2.5 collected at day and night (Fang et al., 1999). In addition, crustal elements generally concentrate in coarse PM2.5 , whereas anthropogenic elements tend to be enriched in fine PM2.5 (Balachandran, Meena, & Khillare, 2000). Atmospheric PM2.5 could be emitted from different sources. Iron and steel manufacturing, diesel-powered motor vehicles, coal combustion, road re-suspension dust, solid waste incineration, and open biomass burning are all emission sources of the aerosol. Furthermore, gaseous contaminants, such as SO2 , NOx , and NH3 , are likely to transform into PM2.5 through photochemical reactions. Chemical mass balance, positive matrix factorization (PMF), principal component analysis, and multivariable linear regression are often used to identify and apportion sources for PM2.5 (Begum, Hopke, & Zhao, 2005; Hwang & Hopke, 2007; Marmur, Mulholland, & Russell, 2007; Pancras, Landis, Norris, Vedantham, & Dvonch, 2013; Viana et al., 2007; Zheng et al., 2005). Since the policy of economic reform was put into practice in China since the late seventies of the last century, the Chinese economy has developed quickly. However, the air quality in the country has simultaneously declined, particularly in urban areas. Hangzhou is one of the most developed cities in China, but the ambient pollution level in the city is relatively high as well. PM10 is the primary pollutant in the city. However, less research work has been performed on PM2.5 contamination. Zhu, Chen, Wang, and Shen (2004) studied the composition of PAHs in particulate matter. Okuda, Kumata, Naraoka, and Takada (2002) analyzed the stable carbon isotope ratio values of PAHs. Furthermore, the relationship between meteorological factors and the concentration of ambient PM2.5 has also been studied (Hong, Jiao, & Ma, 2013). The aims of this paper are as follows: (1) to investigate the temporal and spatial distributions of ambient PM2.5 in urban area of Hangzhou; (2) to determine the chemical compositions of water-soluble ions, metal elements and TC in PM2.5 ; and (3) to identify and apportion the emission sources of ambient PM2.5 .
2. Sampling and analytical methods 2.1. Sample collection Ambient PM2.5 samples were collected at three sites in the urban area of Hangzhou from April 2004 to March 2005. One sampling site, HJ was located in the division campus of Zhejiang University,
Huajiachi (Fig. 1). Another, MZ was located in the second habitation area of Zhaohui. The third, JK lay south of Wulin Square, the urban center. Air samplers (PM2.5-2, Beijing Geological Instrument Factory, China) were placed on the roofs of buildings, which were ten to twenty meters above the ground and thirty meters away from nearest roads. Two samples were collected simultaneously at each site per week at the airflow rate of 77 L/min. The collection duration per sample was 24 h (from 9:00 a.m. to next 9:00 a.m.). Before sampling, glass fiber filters were baked for 2 h at 500 ◦ C and then placed in a desiccator to reach humidity equilibrium for 24 h at room temperature. To assess sampling error, all air samplers were placed together at a site and used to collect experimental aerosol samples at one time. The relative errors for 24 h sampling between the three samplers varied from −2.6% to +2.3%. 2.2. Analytical methods 2.2.1. Mass and TC The PM2.5 mass was determined by gravimetry. Before and after sampling, the glass filters were weighed on an electronic balance with a reading precision of 0.01 mg. The samples were allowed to reach humidity equilibrium in the weighing room for 24 h before weighing. The aerosol samples were wrapped in aluminum foil and stored in a freezer under −18 ◦ C until performing the chemical analysis after weighing. The TC in PM2.5 samples was analyzed using an elemental analyzer (Flash EA-1112, ThermoFinnigan Italia S.P.A.). A filter sample weighing 2 mg was burned under an oxygen environment in a quartz tube at 800 ◦ C. The carbon dioxide produced was then carried by helium to a gas chromatograph and analyzed. Five blank filters were measured in the same process, and the average background was subtracted from all of the aerosol samples. 2.2.2. Water-soluble ions To measure the water-soluble ions in PM2.5 , a quarter of a filter sample was cut into pieces and then placed into a polypropylene tube with screw cap. Afterwards, 13 mL of deionized water (electrical conductivity was equal to 18.2 M cm) was added to the tube. The sample was extracted for 30 min under ultrasonic conditions. Finally, the liquor was centrifuged at 3000 rpm for 5 min and filtrated using a hydrophilic membrane with a pore diameter of 0.45 m. Five blank filters were treated in the same way to subtract the background from the aerosol samples. An ion chromatograph (Metrohm-732, Metrohm, Switzerland) was used to analyze the water-soluble ions in the aerosol. Anions were eluted using an aqueous solution with concentration of (336 mg NaHCO3 + 106 mg
Please cite this article in press as: Liu, G., et al. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology (2014), http://dx.doi.org/10.1016/j.partic.2014.03.011
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Table 1 Annual mean concentration for PM2.5 and its components. Sampling site Sampling number Unit PM2.5 F− Cl− NO3 − SO4 2− Na+ K+ NH4 + TC Cr Ni Se Mo Ag Cd Sb Mn Cu Zn Pb a
g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 g/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 ng/m3 g/m3 g/m3
HJ 89 (47a ) Mean ± SD 112.5 0.1 2.0 7.6 16.8 1.6 2.4 7.8
± ± ± ± ± ± ± ±
46.0 0.1 1.9 5.0 8.0 0.3 1.2 3.4
4.2 2.9 11.7 6.9 0.8 6.7 7.0 26.3 88.8 1.3 0.5
± ± ± ± ± ± ± ± ± ± ±
3.7 1.9 2.9 6.4 0.4 5.6 4.1 29.1 103.7 1.2 0.3
MZ 90 (48) Mean ± SD 108.4 0.1 1.7 8.1 16.5 2.4 3.4 9.9 30.8 6.6 2.1 5.7 8.1 0.7 6.2 4.0 43.2 66.1 1.3 0.8
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
45.7 0.1 1.5 5.1 7.6 0.5 1.7 4.5 11.7 5.1 1.9 4.3 12.1 0.4 4.6 2.2 41.5 44.6 1.3 0.3
JK 93 (48) Mean ± SD 104.0 0.1 1.7 9.1 17.7 2.8 3.3 10.5 30.3 8.3 4.1 9.3 6.5 0.9 8.7 7.3 44.9 58.5 1.2 0.6
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
37.8 0.1 1.3 7.2 9.0 0.4 1.4 4.3 10.4 4.8 3.9 5.8 4.1 0.5 6.1 5.1 38.8 27.6 0.6 0.2
Sum or average 272 (143) Mean ± SD 108.2 0.1 1.8 8.3 17.0 2.2 3.0 9.3 30.6 6.4 3.0 8.9 7.2 0.8 7.2 6.1 38.2 71.0 1.3 0.6
± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±
43.2 0.1 1.5 5.9 8.2 0.6 1.5 4.2 11.0 4.9 2.9 5.1 8.2 0.5 5.5 4.2 37.6 67.5 1.0 0.3
Number of analyzed samples.
Na2 CO3 )/L at a flow rate of 1.2 mL/min. Cations were eluted with a solution with concentration of (167 mg 2,6-pyridinedicarboxylic acid +600 mg tartaric acid)/L at a flow rate of 0.8 mL/min. The columns for separating the anions and cations were Metrosep A SUPP 4 (250 mm × 4 mm) and Metrosep C 2 250 (250 mm × 4 mm), respectively. Water-soluble ions in the aerosol samples were quantified using authoritative standard solutions purchased from the National Center for Reference Materials of China. Four aerosol samples were experimentally extracted and determined three times each using the procedure described above. The results indicated that the recovery rates were greater than 85%.
2.2.3. Metal elements A modified microwave digestion method was utilized to treat samples for metal element analysis on the basis of the American EPA method (USEPA, 1999). A quarter of a filter sample was first placed into a Teflon digestion vessel. 8 mL of nitric acid (6%, v/v) and 2 mL of hydrogen peroxide were then added to the vessel. Finally, the vessel was covered and placed in a microwave digestive system (MK-III, Sinco Institute of Microwave Dissolving and Testing Techniques, China) to dissolve the sample. Only one sample was digested at a time to ensure the dissolution of the metal elements in PM2.5 . A sample was digested initially under 0.5 MPa for 1 min; then, the digestion was completed under 1.5 MPa for 15 min. The sample solution and filter residue mixture was transferred into a polypropylene test tube after cooling. The inside of the vessel was rinsed with 3 mL deionized water, and then, the liquid was added to the mixture. The solution was centrifuged at 3000 rpm for 5 min. Finally, the clear fraction was diluted with water and then used to determine the metal elements. Five blank filters were also treated with the same process to subtract the background. The solution was measured using an inductively coupled plasma mass spectrometry (ICP-MS, VG PQ ExCell, Thermo Fisher Scientific Inc., USA). Before the sampled filters were analyzed, a 0.1 g/mL mixture of standard solutions of eleven metal elements was added to eight blank filters; then, the solutions were digested and tested using the method described above. The results indicated that the recovery rates were in the range of 76–110%. In addition, 10% extracted samples in a batch were measured twice to control the analytical quality. The relative errors were between −20% and +20%.
2.3. Data analysis EPA PMF 3.0 (USEPA, 2008) was used to identify the major emission sources for ambient PM2.5 in the urban area of Hangzhou. In addition to the concentration data for each species in PM2.5 , uncertainty data were also required to give the model an estimate of the confidence in each value. The uncertainties provided should encompass errors such as sampling and analytical errors. However, they were not available in this work and had to be estimated. There was a ten percent uncertainty in the concentration estimate of each PM2.5 value and a twenty percent uncertainty in the concentration estimate for each species value, based on the relative errors and recovery rates mentioned above. IBM SPSS Statistics 21 was used to analyze the correlations between the components in the PM2.5 and gaseous contaminants in the air.
3. Results and discussion 3.1. Ambient concentrations of PM2.5 The 24-h average concentrations of PM2.5 at three sites varied from 17.1 to 267.0 g/m3 . However, the annual mean values at the three sites were close to each other (104.0–112.5 g/m3 ) (Table 1). The average levels in the eastern and northeastern areas were 8.2% and 4.2% greater than in the center, respectively, implying the spatial differences of PM2.5 concentration were small. According to the ambient air quality standards issued by the Ministry of Environmental Protection of China in 2012, the limit values for the 24-h and the annual mean concentrations for PM2.5 are 75 and 35 g/m3 , respectively (MEPC, 2012). Thus, 70.0–75.5% of the 24-h average data exceeded the limit. Furthermore, the annual mean values at the three sites were 3.0–3.2 times the relevant limit. The annual average for the entire urban area was 3–13 times the PM2.5 concentrations in Milan, New York, San Francisco, Washington, and Los Angeles (Chow et al., 1994; Lonati et al., 2005), and 2.7, 2.6, and 1.3 times of those in Hong Kong, Bangkok, and Karachi, respectively (Chuersuwan, Nimrat, Lekphet, & Kerdkumrai, 2008; Mansha, Ghauri, Rahman, & Amman, 2012; So, Guo, & Li, 2007). However, the annual average for the entire
Please cite this article in press as: Liu, G., et al. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology (2014), http://dx.doi.org/10.1016/j.partic.2014.03.011
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Fig. 2. Variability of the monthly mean concentrations for PM2.5 at the three sites.
urban area was approximately equal to that in Beijing (Wang et al., 2004). Obviously, the PM2.5 level in the urban area of Hangzhou was extremely high. 3.2. Temporal distribution of PM2.5 The variability for the monthly average concentrations of PM2.5 at all sites was extremely similar (Fig. 2). The values were the lowest in July and the highest in November. Hangzhou has a subtropical monsoon climate with four distinct seasons. Spring, summer, autumn, and winter included months from March to May, June to August, September to November, and December to February, respectively, in the city. The quarterly mean concentrations of PM2.5 in spring, summer, autumn, and winter at the three sites ranged from 105.8 to 121.8, 70.9 to 75.4, 110.4 to 119.0, and 127.9 to 144.9 g/m3 , respectively (Table 2). The PM2.5 levels in summer were the lowest, and the ones in winter were the highest. The average concentrations in the summer and winter were 1.7 and 1.9 times of those in Chennai (India), respectively (Srimuruganandam & Nagendra, 2011). 3.3. Composition and seasonal variation of the water-soluble ions F− , Cl− , NO3 − , SO4 2− , NH4 + , K+ , and Na+ were measured in PM2.5 . Ca2+ and Mg2+ were not determined due to their high blank values in the glass filters. At each sampling site, the annual mean concentrations of anions or cations decreased in the same order, namely, SO4 2− > NO3 − > Cl− > F− , and NH4 + > K+ > Na+ (Table 1). In addition, the annual average contribution of all ions to PM2.5 mass varied from 32.3% to 36.7%, which was much greater than the value in Kanpur (India) (Ram & Sarin, 2011). SO4 2− , NO3 − , and NH4 + were the Table 2 Quarterly mean concentrations of ambient PM2.5 (g/m3 ). Sampling site
Season
Sampling number
Mean ± SD
HJ
Spring Summer Autumn Winter
22 24 25 18
121.8 72.9 119.0 144.9
± ± ± ±
26.1 31.4 49.8 42.0
MZ
Spring Summer Autumn Winter
22 24 26 18
120.7 70.9 113.3 136.0
± ± ± ±
29.8 31.8 51.4 39.5
JK
Spring Summer Autumn Winter
23 24 26 20
105.8 75.4 110.4 127.9
± ± ± ±
22.0 30.3 39.5 38.7
major constituents of the water-soluble ions in PM2.5 in Hangzhou. This result was in agreement with the results obtained in Nanjing, Shanghai, Beijing, and Fuzhou (Wang, Huang, Gao, Gao, & Wang, 2002; Yao et al., 2002; Zhang et al., 2013). The annual contributions for the three ions to PM2.5 at three sites of HJ, MZ, and JK ranged from 14.1% to 14.7%, 6.0% to 7.8%, and 6.4% to 7.7%, respectively. Although the concentrations for each ion were the lowest in summer, some differences still existed between the seasonal distributions of the ions (Table 3). In the central region (JK), the highest level of Cl− was present in winter, whereas the highest ones for NO3 − , SO4 2− , NH4 + , and K+ occurred in autumn. Moreover, Na+ had the highest level in spring. In the northeast region (MZ), the temporal distribution of NH4 + was the same as Cl− in the central region; the variations of the other ions were similar to the corresponding ions in the central area. In the eastern region (HJ), the highest levels occurred in spring for SO4 2− and in winter for NH4 + . In addition, the variability of the other ions was similar to the relevant ones in the central region as well. 3.4. Correlation analysis of water-soluble ions The statistical results indicated that significant linear correlations existed between NO3 − , SO4 2− , and NH4 + in spring (Table 4), suggesting that NO3 − and SO4 2− were likely derived from the same sources to some extent and that most of the NH4 + was probably in the form of ammonium nitrate and ammonium sulfate. There were evident distinctions between the statistical results in spring and summer. In summer, the correlations were significant between Cl− and NO3 − and between NO3 − and NH4 + . However, the correlation coefficient for the latter two ions (i.e. 0.56) was less than the relevant one in spring (i.e. 0.79), suggesting that NH4 + might exist mainly as nitrate rather than sulfate. Ammonium nitrate and sodium chloride were the two most common components in the atmospheric PM2.5 . Nitrate volatilizes and decomposes into nitrate acid and ammonia at high temperatures. A part of the acid might react with sodium chloride in the aerosol and form hydrochloric acid, resulting in Cl− volatilization in the form of HCl (Viana et al., 2005). The existence of nitrate in ambient PM2.5 was possibly favorable for the conservation of chloride. The chemical reactions probably occurred in the aerosol in summer in Hangzhou. More correlations were identified between the other ions in autumn, in addition to those found both between Cl− and NO3 − in summer and between NO3 − , SO4 2− , and NH4 + in spring. Correlations between Na+ and NH4 + , K+ and SO4 2− (NH4 + , Na+ ) were all significant (Table 5). This result implies that the increase of nitrate content in PM2.5 was also helpful for stabilizing chloride in autumn. In addition, it was possible that NH4 + was mainly in the forms of
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Table 3 Seasonal mean concentrations of the water-soluble ions in PM2.5 (g/m3 ). F−
NO3 −
Cl−
SO4 2−
Na+
NH4 +
K+
Sampling site
Sampling number
Season
HJ
11 12 13 11
Spring Summer Autumn Winter
0.09 0.05 0.10 0.14
± ± ± ±
0.05 0.04 0.06 0.09
1.95 0.58 1.78 3.86
± ± ± ±
1.53 0.42 0.98 2.50
7.78 3.40 8.02 11.44
± ± ± ±
3.71 2.63 5.05 4.91
19.55 13.80 16.87 17.07
± ± ± ±
9.35 6.92 6.31 9.38
1.64 1.45 1.53 1.61
± ± ± ±
0.32 0.25 0.19 0.45
2.35 2.04 2.61 2.76
± ± ± ±
1.19 1.49 1.08 1.13
8.48 5.01 7.86 10.00
± ± ± ±
3.28 2.31 3.60 2.55
MZ
12 12 13 11
Spring Summer Autumn Winter
0.08 0.05 0.10 0.17
± ± ± ±
0.06 0.04 0.08 0.08
1.63 0.55 1.57 3.28
± ± ± ±
1.02 0.39 0.92 1.79
9.03 4.76 8.16 10.75
± ± ± ±
4.13 4.78 5.20 5.08
16.99 15.61 17.99 15.48
± ± ± ±
7.59 8.10 8.02 7.51
2.61 2.00 2.46 2.34
± ± ± ±
0.31 0.67 0.36 0.33
3.44 2.18 4.07 3.82
± ± ± ±
1.69 1.30 1.67 1.36
10.80 6.99 10.29 11.92
± ± ± ±
3.45 4.20 5.25 3.46
JK
12 12 13 11
Spring Summer Autumn Winter
0.10 0.05 0.11 0.12
± ± ± ±
0.04 0.06 0.07 0.08
1.41 0.67 2.00 2.62
± ± ± ±
0.61 0.52 1.32 1.49
8.69 5.53 11.77 10.31
± ± ± ±
5.92 6.14 9.60 5.37
18.12 14.24 22.31 14.82
± ± ± ±
9.78 8.39 9.58 5.06
3.01 2.41 2.77 2.85
± ± ± ±
0.47 0.27 0.25 0.34
2.83 2.41 3.92 3.66
± ± ± ±
1.57 0.95 1.37 1.43
10.81 6.98 11.62 11.55
± ± ± ±
4.13 3.61 4.96 3.45
Table 4 Correlations of water-soluble ions in spring and summer (p < 0.05). Spring (n = 31)
−
Cl NO3 − SO4 2− NH4 + Na+
Summer (n = 30)
Cl−
NO3 −
SO4 2−
NH4 +
Na+
K+
Cl−
NO3 −
SO4 2−
NH4 +
Na+
K+
1.00
0.16 1.00
0.26 0.69 1.00
0.16 0.79 0.66 1.00
0.12 0.09 −0.15 0.15 1.00
−0.18 −0.03 0.25 0.14 0.35
1.00
0.50 1.00
0.05 0.22 1.00
0.00 0.56 0.32 1.00
0.19 −0.30 −0.23 −0.31 1.00
−0.04 −0.07 0.08 0.13 0.08
Bold figures represent significant correlations.
nitrate and sulfate, and K+ was likely in the form of sulfate in the aerosol. There may be a relationship in the origins between Na+ , NH4 + , and K+ . In winter, correlations were only found between NH4 + and NO3 − , NH4 + and Na+ , and K+ and Na+ (Table 5). This result suggests that most of the NH4 + might occur as ammonium nitrate, and there were correlations between NH4 + and Na+ , K+ and Na+ in the sources.
northeastern areas were statistically analyzed together. No significant correlation was found between SO2 and the ions in spring (Table 6). The situation was the same between NO2 and the ions. The gaseous pollutants had positive correlations with SO4 2− and NH4 + in summer and with NO3 − and NH4 + in autumn, respectively (Table 7). In winter, SO2 was correlated with NO3 − , and NO2 was correlated with NO3 − , NH4 + , and SO4 2− . The statistical results above suggest that no direct originating relationship existed between nitrate and NO2 or between sulfate and SO2 in the atmosphere in spring. In summer, a considerable amount of SO4 2− in the ambient PM2.5 was likely derived from SO2 through photochemical reactions at a faster rate due to the high air temperature. However, NO2 did not affect the concentration of NO3 − due to the instability of the nitrate during the season (Viana et al., 2005). The rise of the NO2 concentration in the atmosphere would lead to an increase of nitrate in autumn and winter, whereas
3.5. Correlations between water-soluble ions and gaseous pollutants Daily monitoring data on ambient SO2 and NO2 (supplied by the Hangzhou Central Station for Environmental Monitoring) and the concentration values for NO3 − , NH4 + , and SO4 2− in the easternand Table 5 Correlations of the water-soluble ions in autumn and winter (p < 0.05). Autumn (n = 34)
−
Cl NO3 − SO4 2− NH4 + Na+
Winter (n = 27)
−
−
Cl
NO3
1.00
0.44 1.00
SO4
2−
NH4
+
+
Na
−0.07 0.44 0.50 1.00
0.06 0.42 1.00
0.20 0.07 0.32 0.47 1.00
+
K
−0.20 0.06 0.51 0.69 0.53
Cl−
NO3 −
SO4 2−
NH4 +
Na+
K+
1.00
0.32 1.00
0.11 0.34 1.00
−0.01 0.54 −0.12 1.00
0.13 0.14 −0.17 0.68 1.00
0.24 0.05 0.07 0.20 0.58
Bold figures represent significant correlations.
Table 6 Correlations between ions and gaseous pollutants in spring and summer (p < 0.05). Spring (n = 23) NO3 −
NO3 NH4 + SO4 2− SO2
−
1.00
Summer (n = 22) NH4
+
0.79 1.00
SO4
2−
0.66 0.58 1.00
SO2
NO2
0.07 −0.26 −0.02 1.00
0.04 −0.14 −0.17 0.63
NO3 − 1.00
NH4 +
SO4 2−
SO2
NO2
0.80 1.00
0.62 0.83 1.00
0.31 0.46 0.46 1.00
0.37 0.56 0.63 0.47
Bold figures represent significant correlations.
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Table 7 Correlations between the ions and the gaseous pollutants in autumn and winter (p < 0.05). Autumn (n = 26) NO3 NO3 − NH4 + SO4 2− SO2
−
1.00
Winter (n = 22) NH4
+
SO4
0.82 1.00
2−
SO2
0.37 0.22 1.00
NO2
0.69 0.64 0.06 1.00
0.84 0.72 0.26 0.85
NO3 − 1.00
NH4 +
SO4 2−
SO2
NO2
0.48 1.00
0.57 0.24 1.00
0.58 0.40 0.30 1.00
0.55 0.51 0.51 0.78
Bold figures represent significant correlations.
SO2 possibly had no effect on the concentrations of sulfate in the two seasons. 3.6. Concentrations of TC in PM2.5 The PM2.5 samples collected at MZ and JK were selected to determine the TC. In the northeastern and central areas, the seasonal mean concentrations for TC ranged from 19.9 to 36.5 g/m3 and 21.5 to 35.8 g/m3 , respectively, with the variability similar to PM2.5 . The annual averages at the two sites were approximately equal to each other, and the mean value for the entire urban area was 30.6 g/m3 (Table 1). The seasonal average contributions of TC to the PM2.5 mass at each site varied from 25.2% to 29.1% for MZ area, and 27.3% to 31.0% for JK area, which were greater in summer and autumn than in spring and winter. The quarterly mean values in Hangzhou were evidently greater in summer and smaller in winter than in Beijing (Dan et al., 2004), whereas they were close to the corresponding ones in Milan (Italy) (Giugliano et al., 2005). Furthermore, the annual mean content of TC in the aerosol in the center was 5.5% higher than in the northeast. For the entire urban region of Hangzhou, the annual average was 27.8%, which was 3.5% greater than the annual average in Fuzhou (Zhang et al., 2013). 3.7. Relationship between TC and water-soluble ions The positive correlation between TC and Cl− was significant in both spring and autumn (Table 8). There were negative correlations between TC and both NO3 − and NH4 + in summer. Moreover, TC had a negative correlation with NO3 − in winter. The TC in ambient PM2.5 consisted of OC and EC, and the OC/EC ratios were generally greater than two (Dan et al., 2004; Giugliano et al., 2005; Querol et al., 2004). The majority of the TC in fine particles in Hangzhou was probably OC. Chloride in the aerosol could be derived from soil, sea salt, coal combustion, and solid waste burning. The correlation between TC and Cl− mentioned above implies that coal combustion in the city was their main emission source in spring and autumn. The relationship between TC and both NO3 − and NH4 + in summer and winter suggests that the increase of organic matter in PM2.5 might favor the transformation of nitrate or ammonium salt to other compounds. Table 8 Correlations between TC and water-soluble ions (p < 0.05). Cl−
TC
Spring (n = 21) 0.59 Summer (n = 18) 0.09 Autumn (n = 23) 0.46 Winter (n = 20) 0.02
Bold figures represent significant correlations.
NO3 −
SO4 2−
NH4 +
−0.29
−0.11
−0.21
−0.60
−0.10
−0.83
−0.03
−0.02
−0.22
−0.53
−0.41
−0.33
3.8. Concentrations of metal elements in PM2.5 One of two samples collected per week at each site was analyzed to investigate the composition of the metal elements. The annual mean concentrations for Cr, Ni, Se, Mo, Ag, Cd, and Sb in PM2.5 were in the range 0.8–8.9 ng/m3 , and the corresponding ones for Mn and Cu, and Pb and Zn varied from 38.2 to 71.0 ng/m3 and 0.6 to 1.3 g/m3 , respectively (Table 1). Mn, Cu, Pb, and Zn were apparently the main constituents of the metal elements. The annual average total concentration in the northeastern region (i.e. MZ) was 14.6% higher than in the eastern region (i.e. HJ). However, the relevant values in the eastern and central areas were almost equivalent to each other. In addition, the annual average total contents of the eleven elements in PM2.5 ranged from 1.74% to 2.04%. Most of the elements had the highest concentrations in spring or winter, whereas the lowest concentrations were in summer (Table 9). The seasonal mean concentrations for Cu, Zn, Se, and Pb in Hangzhou in summer were 3–22 times those in Milan, and the average values for Cr, Mn, Cu, Zn, Se, and Pb in winter 1.8–11 times those in the Milan city (Vecchi et al., 2004).
3.9. Source apportionment of ambient PM2.5 The concentration data on PM2.5 in the central region were analyzed with EPA PMF (version 3.0), and a five-factor solution was obtained. Factor 1 in the solution primarily consisted of Mn and Cl− (Fig. 3). HCl is a common component of smoke from coal combustion. HCl could react with other pollutants, such as NH3 , in the atmosphere to form the corresponding salt. Mn was one of metal elements with high concentrations in fly ash from coal combustion (Bhangare, Ajmal, Sahu, Pandit, & Puranik, 2011). In China, desulfurization practice was performed for many years in coal combustion related enterprises. This desulfurization probably was the main reason for the lack of contribution of SO4 2− to this factor. Consequently, factor 1 was closely associated with the aerosols derived from coal combustion. There was a thermal power plant in the northwest seven kilometers away from urban center. This power plant was possibly the major emission source. Factor 2 had the highest loadings of Ni and Cr. These elements were generally related with iron/steel and other metal manufacturing (Mansha et al., 2012; Mooibroek, Schaap, Weijers, & Hoogerbrugge, 2011; Morishita et al., 2011; Yatkin & Bayram, 2008). Accordingly, this factor was likely associated with metal production sources. Hanggang Steel-making Corporation was located in the northeast 12 kilometers away from urban center (Wulin Square). This corporation was probably the primary emission source for factor 2. Factor 3 was marked by the highest loadings of Sb and Se. Antimony can be released from many sources. For example, it could be derived from brake wear and iron/steel production (Mansha et al., 2012; Morishita et al., 2011; Querol et al., 2007). Therefore, factor 3 probably was a mixture of several anthropogenic emission sources.
Please cite this article in press as: Liu, G., et al. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology (2014), http://dx.doi.org/10.1016/j.partic.2014.03.011
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Fig. 3. Factor profiles for ambient PM2.5 in the urban center.
Please cite this article in press as: Liu, G., et al. Chemical composition and source apportionment of the ambient PM2.5 in Hangzhou, China. Particuology (2014), http://dx.doi.org/10.1016/j.partic.2014.03.011
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0.6 0.5 0.9 0.8 ± ± ± ± 2.0 1.4 2.2 2.0 0.2 0.2 0.2 0.2 ± ± ± ± 0.7 0.5 0.6 0.6 0.4 0.4 0.8 0.6 ± ± ± ± 1.2 0.9 1.5 1.3 20.3 15.6 28.8 34.4 ± ± ± ± 21.4 22.3 28.7 53.3 ± ± ± ± 8.7 7.5 8.1 10.6 0.3 0.2 0.6 0.8 ± ± ± ± 2.2 5.7 3.7 4.3 7.4 6.2 9.0 10.9
12 12 13 11
12 12 13 11
Spring Summer Autumn Winter
Spring Summer Autumn Winter
MZ
JK
6.0 3.6 3.2 3.7
± ± ± ±
5.3 4.4 2.2 2.7 2.3 3.5 5.3 6.4 ± ± ± ±
± ± ± ± 5.5 5.7 7.2 7.6 4.3 4.4 6.4 6.9 ± ± ± ± 7.1 7.7 11.5 11.0
0.7 0.7 1.1 1.1
6.1 7.5 8.6 6.9
± ± ± ±
3.1 3.6 7.2 5.3 5.7 7.5 2.9 7.9 ± ± ± ±
37.1 17.9 48.8 78.3
62.2 40.8 57.8 74.9
2.7 0.3 0.5 0.7 ± ± ± ± 2.9 1.4 2.2 2.6 0.3 0.2 0.3 0.2 ± ± ± ± 0.9 0.6 0.9 0.8 2.4 0.3 0.4 0.5 ± ± ± ± 1.8 0.8 1.2 1.5 67.4 11.4 29.7 32.0 ± ± ± ± 34.3 4.3 27.6 35.8 ± ± ± ± ± ± ± ± 5.0 2.8 3.7 4.4 6.1 5.3 5.9 7.5 0.5 0.4 0.4 0.4 ± ± ± ± 0.6 0.5 0.8 0.9 7.3 15.9 16.1 5.5 ± ± ± ± 7.2 3.7 5.3 10.7
11 12 13 11 Spring Summer Autumn Winter HJ
2.7 0.9 1.5 3.5
5.2 2.5 3.4 5.7
Samplingnumber Season
4.0 1.5 2.1 4.1
2.4 1.1 1.0 1.9 4.3 4.5 3.4 5.7 ± ± ± ±
± ± ± ± 6.0 9.2 9.5 7.4 4.8 3.2 2.8 4.5 ± ± ± ± 8.3 3.6 4.2 7.2
7.4 6.9 5.9 6.9 ± ± ± ± 4.8 2.7 3.6 3.0 ± ± ± ±
1.5 1.7 1.7 1.6
Ni (ng/m3 ) Cr (ng/m3 )
8.5 8.1 3.4 4.7
References
± ± ± ± 9.2 6.1 5.8 6.8
Acknowledgements
1.6 2.4 3.4 3.1
0.4 0.4 0.4 0.3
The ambient PM2.5 pollution in Hangzhou was severe during the study period, particularly in winter. The annual mean concentrations of PM2.5 decreased from east to northeast as well as toward the central region. SO4 2− , NO3 − , and NH4 + were the major water-soluble ions in the aerosol. The annual average contribution of TC to PM2.5 mass was 27.8%. Zn, Pb, Cu, and Mn were the main metal elements in the fine particles. The most significant sources for atmospheric PM2.5 in the central area were iron/steel manufacturing and secondary aerosol.
± ± ± ±
± ± ± ±
4. Conclusions
13.4 10.3 11.2 11.9
0.9 0.7 0.9 0.9
7.7 7.7 6.2 6.4
2.8 1.6 2.0 1.8 3.7 6.0 3.0 5.5 ± ± ± ±
42.2 2.0 41.5 91.1
80.4 35.6 60.0 91.2
2.5 0.4 0.7 1.1 ± ± ± ± 2.7 1.3 1.9 2.1 0.2 0.2 0.1 0.5 ± ± ± ± 0.6 0.4 0.5 0.5 2.1 0.3 0.6 0.6 ± ± ± ± 1.9 0.8 1.2 1.4 195.9 23.1 51.3 41.7 ± ± ± ± 137.3 44.1 74.9 105.5 27.4 0.8 22.9 31.0 ± ± ± ± 28.0 0.2 27.1 52.0 ± ± ± ± 3.0 9.6 3.1 4.7 ± ± ± ±
4.1 3.5 4.0 5.0
Sb (ng/m3 )
Factor 4 was represented by high contributions of Pb, Zn, Cu, Na+ , and TC. EC was the main constituent in soot released from gasoline- and diesel-powered vehicles. Pb, Zn, Cu, and Na were found in the particles as well (Cheng et al., 2010). In addition, dust originated from motor brake wears contained Cu and Pb (Denier van der Gon, Hulskotte, Visschedijk, & Schaap, 2007; Garg et al., 2000). Zn was identified in the dust from tire wear (Adachi & Tainosho, 2004; Blok, 2005). Thus, factor 4 probably had a relationship with the tailpipe emission of motor vehicles and brake/tire wear. Factor 5 had the highest loadings of F− , NO3 − , Cl− , SO4 2− , and NH4 + . Factor 5 was easily identified as secondary particles (Santacatalina et al., 2010; Srimuruganandam & Nagendra, 2012). The contributions of factors 1, 2, 3, 4, and 5 described above to the ambient PM2.5 mass in the central area were 12.8%, 31.9%, 10.1%, 17.2%, and 27.9%, respectively. In brief, iron/steel manufacturing and secondary aerosols were the major sources for fine particles; motor vehicle and road resuspension dust were the next major sources.
This work was financially supported by the National Natural Science Foundation of China (41073019). The authors thank Tianxiang Zhong and Weilin Teng for their participating in the sampling campaign.
Samplingsite
Table 9 Seasonal mean concentrations of the metal elements in PM2.5 .
Se (ng/m3 )
Mo (ng/m3 )
Ag (ng/m3 )
Cd (ng/m3 )
Mn (ng/m3 )
Cu (ng/m3 )
Zn (g/m3 )
Pb (g/m3 )
Sum (g/m3 )
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