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Source apportionment to PM10 in different air quality conditions for Taichung urban and coastal areas, Taiwan
ARTICLE IN PRESS AE International – Asia Atmospheric Environment 38 (2004) 6893–6905 www.elsevier.com/locate/atmosenv
Source apportionment to PM10 in different air quality conditions for Taichung urban and coastal areas, Taiwan Chia-Pin Chio, Man-Ting Chenga,, Chu-Fang Wangb a
Department of Environmental Engineering, National Chung Hsing University 250, Kuokuang Road, Taichung 402, Taiwan, ROC b Department of Atomic Science, National Tsing Hua University, 101, Section 2, Kuang Fu Rd., Hsinchu 300, Taiwan, ROC Received 5 April 2003; accepted 13 August 2004
Abstract PM2.5 and PM2.5–10 samples were collected between August 1998 and March 1999 at Taichung urban and coastal sites. The major objective in this study was to identify the source contributions to PM10 in different air quality conditions. Principal component analysis with varimax rotation and a chemical mass balance model were used to qualify and quantify, respectively, the source contributions to PM10. Vehicle emissions was the most important source of PM10 at the urban site, followed by crustal materials, secondary aerosols, biomass burning, industrial emissions and marine spray. There was a similar pattern of sources at the coastal site, except that marine spray was found more significantly than the urban site. Although biomass burning and secondary aerosols were not main sources during clean air quality periods, they were the influential sources causing the increase of PM10 to ‘‘episodic’’ levels at both sites. r 2004 Elsevier Ltd. All rights reserved. Keywords: Aerosol compositions; Urban; Coastal; Principal component analysis; CMB source apportionment
1. Introduction Taichung urban area, located in the Taichung basin surrounded by the Central Ranges and Tatu Hill, extends to 163.4 km2 and has a population of over 0.9 million people, giving a population density of over 5500 people km2. To the west, located between the Taiwan Strait and Tatu Hill, Taichung coastal area extends to 95.1 km2 and has a population of 45,000 people, giving a population density of about 460 people km2. Inventory data suggests that in the urban area, populated by about 700,000 vehicles and 4200 factories, Corresponding author. Tel: +886 4 22851984;
fax: +886 4 22862587. E-mail address: [email protected] (M.-T. Cheng).
about 80% of PM10 is emitted from construction activity and paved road dust, and about 40% of SO2, 80% of NOx, and 98% of CO is emitted from vehicles. The same data suggests that only 50% of PM10 in the coastal area is emitted from construction activity and paved road dust, while 80% of CO is emitted from 150,000 vehicles there. A 4400 MW coal-fired power plant located in the coastal area is estimated to contribute about 10% of that area’s atmospheric PM10, 97% of its SO2, 75% of its NOx, and 10% of its CO. Finally, agricultural areas account for 18% and 25% of the total urban and coastal area, respectively. Tsai and Cheng (1999) studied how the chemical composition of aerosols varied in different air quality conditions in the Taichung coastal area, and found that visibility showed a strong negative correlation with sulfate, ammonium and nitrate. Meanwhile, Cheng
1352-2310/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2004.08.041
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(2001) focused on the synoptic weather patterns’ relationship to ozone episodes in the Taichung urban area, and highlighted the interactive influences between micrometeorological factors and air pollutants. Chen et al. (1997) applied the CMB7 model to evaluate the impact of a new petrochemical complex at Meliao, located in the south of the Taichung area, and indicated that the combustion of agricultural wastes was a significant contributory source of fine and coarse particles. In this study, seven intensive aerosol samplings were conducted simultaneously at two sites with dichotomous samplers between August 1998 and March 1999. Only samples collected during the northerly wind prevailing seasons were selected for analyzing metallic elements, soluble ionic species, and carbons. The purpose was to investigate the source contributions to PM10 in different air quality conditions in these seasons. The characteristics of PM10 aerosol at the two sites were compared, and principal component analysis (PCA) was used to find and apportion source types to PM10. The source contributions were quantitatively analyzed using a chemical mass balance (CMB) receptor model.
2. Methods 2.1. Experimental program Chunglun junior high school and Wuchi elementary school were selected as sampling sites to represent the Taichung urban and coastal areas, respectively. The urban site is about 18 km from the aforementioned power plant, and the coastal site about 8 km from the plant (Cheng et al., 1999). The locations of the sampling sites, the air quality monitoring stations, the weather service stations, and the local main pollutant source in central Taiwan (the power plant) are shown in Fig. 1. Simultaneous weather data from the Taichung and Wuchi weather service stations (including pressure, temperature, relative humidity, and wind profile information) were available for data analyses. Gaseous pollutants data (including SO2, CO, O3, NO and NO2,) were also available, reported from Chungmin and Shalu air quality monitoring sites. Sierra & Andersen dichotomous samplers (Dichot, model 241), fitted with both quartz fiber filters (US Pallflex type 2500 QAT-UP) and Teflon membrane filters (Gelman type R2PJ037), were used to collect
Fig. 1. The positions of sampling sites, air quality monitoring stations, weather service stations and the local main pollutant source in central Taiwan.
ARTICLE IN PRESS C.-P. Chio et al. / Atmospheric Environment 38 (2004) 6893–6905
coarse aerosol (PM2.5–10) and fine aerosol (PM2.5) at both sites. Each single aerosol sample was collected in a 12-h period, from 8 a.m. to 8 p.m. or from 8 p.m. to 8 a.m. A total of 110 samples were collected at the urban site and 92 at the coastal site. For the purposes of this study, the year was divided into summer (June–August), autumn (September–November), winter (December–February) and spring (March–May). The collected aerosols were intensively analyzed for water-soluble ions, carbon contents, and metallic elements. 2.2. Chemical composition analyses Dionex ion chromatography (model DX100) was employed to analyze the concentrations of Cl, NO 3, + + + 2+ SO2 and Ca2+. The detection 4 , Na , NH4 , K , Mg limits for these ionic species were 0.021, 0.006, 0.009, 0.003, 0.012, 0.007, 0.005 and 0.013 mg m3, respectively. Average recoveries appeared to range from 88% to 104%. The carbon content of aerosol samples were analyzed using an elemental analyzer (EA, F002Heraeus CHN-O-Rapid elemental analyzer). CO2 was elicited from the aerosol samples via a thermal process of oxidation at 950 1C for a heating time of 1.5 min and reduction at 600 1C, and then a thermal conductivity detector (TCD) was used to quantify the CO2 content in the samples. As pre-treatment for elemental carbon (EC), the quartz filter was heated in an oven at 340 1C for 10 min (Cadle and Groblicki, 1982; Tsai and Cheng, 1999). Total carbon (TC) was obtained by putting the samples without any pre-treatment directly into the analyzer. The quantity of organic carbon (OC) was calculated as the difference between TC and EC. The method detection limit of carbon content in this study was 0.4 mg m3. Microwave digestion (Wang et al., 1995, 1996) was used as pre-treatment for Teflon filters loaded with airborne particulate matter. The metallic elements magnesium (Mg), aluminum (Al), potassium (K), calcium (Ca), vanadium (V), manganese (Mn), iron (Fe), nickel (Ni), zinc (Zn), arsenic (As), selenium (Se) and lead (Pb) were determined for partial aerosol samples in this study, using inductively coupled plasma mass spectrometry (ICP-MS, Perkin–Elmer SCIEX ELAN 5000). The method detection limits were 0.017, 0.13, 0.08, 0.409, 0.0002, 0.003, 0.155, 0.001, 0.018, 0.0005, 0.0007 and 0.011 mg m3, respectively. 2.3. Principal component analysis PCA, as a technique which attempts to explain the statistical variance in a number of original variances by a minimum number of significant components, has often been employed in the source apportionment of air pollutants measured at a receptor site. (Harrison et al., 1997) The original data matrix X that consists of the
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normalized variables of the aerosol constituent’s concentrations and the air quality data as well as the meteorological data. R is the n n sample correlation matrix showing the intercorrelations among all variables, where n is the number of variables. The solutions of the characteristic equation, RlI=0, are the eigenvalues with l1 4l2 4 4ln 40; and the associated eigenvectors are q1 ; q2 ; . . . qn The relationship between the eigenvector matrix Q and R could be presented as 2 3 l1 0 6 . . 7 0 . . ... 7 R ¼ Q6 4 .. 5Q : 0 ln Principal components (PC) are the orthogonal projections of the matrix X onto new axes. Therefore, X is linearly dependent on a few principal factors F 1 ; F 2 ; . . . ; F m : m is the number of the PC selected. In factor analysis, the proportion of the total population variance due to the ith principal component can be written as li =ðl1 þ l2 þ þ ln Þ: Usually the m PC account for at least 80% of the variance (Johnson and Wichern, 1992). The communalities of PCs are calcuP 2 lated using the equation m i;j¼1 F ij ; where F ij is the factor loading of the ith variable on the jth PC. The factor loading with absolute values larger than 0.25 are presented in this study, with those larger than or equal to 0.6 are considered as influential factors.
2.4. Chemical mass balance (CMB) receptor model The great advantage of using PCA as a receptor model is that there is no need for a priori knowledge of emission inventories. But the PCA method only identify the probable source types, the quantitative source contributions can only be solved by using the CMB receptor model (Watson, 1979). The fundamental relation between the concentration at a receptor site and source information can be expressed as Ci ¼
p X
aij S j ;
j¼1
where Ci is the concentration of elemental component i at a receptor site, p represents the number of sources, aij is the fraction of ith element from jth source, and Sj is the contributing concentration from jth source to the receptor site. The CMB model provided several goodness-of-fit indicators of the results. The criteria of t-statistic values should be 42. Reduced chi square value should be o4. R2 required greater than 0.8. Percentage mass of aerosols explained by the source ranged between 80% and 120%. Degree of freedom should be 45. The
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solutions should not present the collinearity (Watson et al., 1990).
3. Results and discussions 3.1. Aerosol mass concentrations Taichung urban and coastal areas are part of the Central Taiwan Air Basin (CTAB) by geographical environment and living space (EPA/Taiwan, 2000). Table 1 shows PM2.5, PM2.5–10 and PM10 measurements for central Taiwan between 1992 and 1999. The Dacheng, Meliao and Taihsi sites are located in the southernmost region of the CTAB and are considered as coastal sites (Chen et al., 1997). In the period 1992–1999, higher average PM10 and a higher ratio of PM2.5 to PM10 were found at the coastal sites in spring. Tsai and Cheng (1999) reported a high PM10 episodic period in late fall of 1997, and identified PM2.5 as the major pollutant at the coastal site. In the same time, the high PM10 and PM2.5 concentrations were also observed in the Taichung urban area (Cheng and Tsai, 2000). In this study, the higher concentrations of PM2.5, PM2.5–10 and PM10 were observed in fall 1998, and were especially highest in October 1998. Fig. 2 shows the relationship between wind speed (WS), wind direction, PM10 concentrations and ratios of
PM2.5 to PM10 measured at Taichung urban and coastal sites. Since more population and factories were located in the north of the studying area, the higher PM10 always occurred with seasons when the northerly wind prevailed. During the seasons of fall, winter and spring, the WSs were relatively low in the urban area during fall and in the coastal area during spring. A previous study also pointed out that lower WS resulted in less vertical and horizontal mixing in this study (Tsuang et al., 2003). As a result, the highest PM10 concentration occurred in fall at the urban site and in spring at the coastal site. 3.2. Qualitative PCA Table 2 shows the PCA of PM10 species, gaseous pollutants and meteorological parameters measured at Taichung urban and coastal sites. In order to identify the major source affecting Taichung urban and coastal areas, the samples measured with the northerly prevailing wind were selected. Because the aerosol samples measured in summer were omitted, only 96 and 79 samples were selected in Taichung urban and coastal areas, respectively. There were five PC factors were identified by PCA method. PC1 represented the mixing factor of vehicle and industry, as there were high factor loadings in PC1 for SO2, CO and NO2. In PC2, the most prominent species were SO2 and NH+ 4 4 , hence this factor was considered as secondary inorganic (i.e.
Table 1 PM2.5, PM2.5–10 and PM10 measurements for central Taiwan between 1992 and 1999 Avg. PM2.5 (mg m3)
Avg. PM2.5–10 (mg m3)
Avg. PM10 (mg m3)
Ratios of PM2.5 to PM10
Author (Year)
Sampling site
Seasons of year
Chen et al. (1997)
Dacheng, Meliao and Taihsi (coastal site)
Early winter 1992
42.1
31.5
73.6
0.58
45.2 25.6 14.5 24.5
47.5 37.6 30.9 19.8
92.7 63.2 45.4 44.3
0.54 0.43 0.35 0.56
71.9 71.2
39.2 37.9
111.1 109.1
0.62 0.64
Tsai and Cheng (1999)
Wuchi (coastal site)
Early spring 1993 Early summer 1993 Early fall 1993 Middle summer 1997
Cheng and Tsai (2000)
Chunglun (urban site)
Late fall 1997 Fall and winter 1997
This study
Chunglun (Urban site)
Summer 1998 (N=14) Fall 1998 (N=28) Winter 1998 (N=25) Spring 1999 (N=29) October 1998a (N=14)
34.4 79.8 43.4 57.1 119.8
19.9 37.7 26.7 30.5 50.7
54.3 117.5 70.0 87.7 170.5
0.62 0.66 0.62 0.66 0.69
Wuchi (Coastal site)
Summer 1998 (N=13) Fall 1998 (N=28) Winter 1998 (N=24) Spring 1999 (N=27)
19.6 39.1 32.8 53.7
17.0 30.2 26.1 29.3
36.7 69.3 58.9 83.0
0.54 0.56 0.55 0.62
a
The sampling was conducted only at Taichung urban site.
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0.75 Chunglun
WS*10
PM10
Ratio
100
0.7
80
0.6 40 0.55
20 0 100
S SW
N NNW
N NNE NNW
NNW N
0.5 0.7
Wuchi
N NNE
80
Ratio of PM2.5 to PM10
PM10 Concentrations (µg m-3) and Wind Speed (m s-1)
0.65 60
0.65 60
NNE NE N
N NNE
40
20
SSE S
0.6
0.55
0
0.5 Summer'98
Fall'98
Winter'98
Spring'99
Fig. 2. The relationship among average WS, wind directions, PM10 concentrations and ratios of PM2.5 to PM10 measured in Taichung urban and coastal sites during sampling periods. (Note: The prevailing wind directions are presented only if the sum of their occurences was over 300 h in a season.).
ammonium sulfate and nitrate). PC3 clearly represented an ozone-related factor for temperature, relative humidity (RH) and O3. PC4 was considered as the crustal dust, there were high factor loading in PC4 for Na+, Mg2+ and Ca2+. PC 5 presented the major contributions from Cl, K+, TC and NO. These species were tracers of biomass burning. Approximately 80.9% of total variance were explained in these five factors. The com2+ munalities of the factor loadings, such as SO2 4 , Ca and RH, were X0.9. The mixing factor (PC1) of vehicle and industry was the source contributing the maximum explained variance, but the secondary inorganic (PC2) was significant source to affect the PM10 mass concentration. The total sources affecting air quality in the urban areas extracted by PCA were vehicle emissions, industry, secondary aerosols, crustal dust and biomass burning. The ozone related factor correlated well with the meteorological parameters (i.e. temperature and RH). Although only 79 samples were taken in the coastal site, 6 PCs were extracted. PC1 was identified as the mixing factor of ammonium sulfate and nitrate from photochemical reactions. The major species contributing
2 + to the factor were NO 3 , SO4 , NH4 and WS. PC2 could be considered as a mixing factor of marine spray and combustion, because there were high factor loadings for Cl, Na+, TC and NO species. PC3 had significant loadings for temperature, RH and O3, representing ozone-related source. PC4 clearly represented the crustal dust for high factor loadings of Mg2+ and Ca2+. PC5 can be considered the mixing factor of vehicle and industry, as there were high factor loadings in PC5 for SO2, CO and NO2. Finally, biomass burning was the probable source in PC6. Approximately 85.2% of total variance were 2+ + explained in these six factors. Cl, NO , 3 , NH4 , Mg 2+ Ca and temperature were explained well, because their communality in the factor loadings was X0.9. The secondary inorganic (PC1) was the most significant source to PM10, also contributing most of the explained variance. In summary, using PCA, source factors were found to be similar in the urban and coastal areas.
3.3. Ambient data and source profiles In Taiwan, an episodic period refers to a PM10 concentration 4150 mg m3. When PM10 concentration
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Table 2 PCA of PM10 species, gaseous pollutants and meteorological parameters measured at Taichung urban and coastal sites Taichung urban area (N=96) PC 1 0.35 0.31 0.50
PC 3
PC 4
0.74 0.28 0.25 0.31 0.74 0.37
0.36 0.69 0.26 0.28 0.74
0.36
0.45 0.56 0.88 0.77 0.34 0.89 3.26 18.1 18.1 Vehicle+ industry
0.29 0.72 0.89
0.75 0.96 0.26
0.69
0.43 0.47
3.04 16.9 35.0 Secondary inorganic
Factor loading values 40.60 are in bold. a TC: total carbon (i.e. EC+OC). b RH: relative humidity. c WS: wind speed.
0.80 0.46
0.34
2.96 16.5 51.5 Ozone related
2.75 15.3 66.8 Crustal
0.62 2.54 14.1 80.9 Biomass burning
Communality 0.86 0.87 0.77 0.92 0.81 0.89 0.68 0.76 0.93 0.72 0.74 0.90 0.54 0.86 0.89 0.83 0.74 0.84
PC 1 0.78 0.34 0.88 0.76
PC 2 0.28 0.77 0.25
PC 3
PC 4
PC 5
PC 6
0.32 0.37 0.40 0.48
0.77
0.26
0.93 0.91 0.96 0.94 0.70
0.57 0.85 0.83
0.33
0.77 0.46
0.60 0.71 0.29
0.36
0.53 0.72 0.27 0.36 4.13 22.9 22.9 Secondary inorganic
0.46 0.37 0.27
0.81 2.78 15.4 38.3 Marine+ combustion
2.72 15.1 53.4 Ozone related
2.40 13.4 66.8 Crustal
0.75 1.77 9.9 76.7 Vehicle+ industry
1.52 8.5 85.2 Biomass burning
Communality 0.81 0.91 0.91 0.84 0.89 0.93 0.89 0.96 0.95 0.83 0.91 0.77 0.83 0.84 0.85 0.71 0.80 0.71
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0.42 0.88 0.37 0.81
0.46 0.47
PC 5
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Mass Cl NO 3 SO2 4 Na+ NH+ 4 K+ 2+ Mg Ca2+ TCa Temperature RHb WSc SO2 CO O3 NO NO2 Eigenvalues % variance Cumulative % variance Sources
PC 2
Taichung coastal area (N=79)
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was o50 mg m3, we defined this as a ‘‘clear’’ period. Finally, the aerosol sample indicated a ‘‘moderate’’ period if the PM10 concentration ranged between 50 and 150 mg m3. Tsai and Cheng (1999) reported that the levels of chemical species were significantly different in clear and hazy periods, especially those of SO2 4 , NO3 + and NH4 . In this paper, 18 and 12 samples were selected in Taichung urban and coastal areas, respectively, for analyzing the metallic elements, soluble ionic species, and carbons. The data were used to evaluate the source apportionment by CMB receptor model. Temporal variation of PM10 and PM2.5 concentrations in Taichung urban and coastal areas had been reported in previous study (Cheng et al., 1999). The episodic events often occurred in September, October and November 1998 at the urban site. However, only one episodic event was observed in March 1999 at the coastal site. The sampling dates of those data sets were on 25 September 1998; 12–14 October 1998; 24–27 November 1998; and 24 March 1999, respectively. Table 3 shows the averaged mass and chemical compositions in different air quality conditions of aerosols at Taichung urban and coastal sites. Most samples were collected during moderate periods in the coastal area, with a few collected in episodic or clear conditions. The only one sample collected at ‘‘nighttime’’ (on 24 March 1999) was collected during an episodic period in the coastal area. There was a significant difference in the concen2 + trations of TC (i.e. OC+EC), NO 3 , SO4 and NH4 in fine fraction aerosols collected in clear and episodic periods in the Taichung urban area. Comparing the concentrations of above species, they were about 3 to 11 times higher in the episodic period than those in the clear period. However, in the Taichung coastal area, only the concentrations of secondary aerosols 2 (NO and NH+ 3 , SO4 4 ) appear much higher in the episodic event. According to the results of PCA, vehicle emissions, industry, secondary aerosols, crustal dust, biomass burning, marine spray and combustion are sources of particulate matter in both Taichung urban and coastal areas. Twelve source profiles that included the above seven source types were selected and used as the input data for CMB modelling. Fig. 3 shows the source profiles selected using this model. The coal-fired power plant (CFPPT) was the specific source of combustion in this study. The profile of biomass (or agricultural waste) burning (BIOBN) was published in a previous study (Chen et al., 1997). Oil-fired food plant (OFFPT) and coal-fired steel plant (CFSPT) were selected to represent the sources emitted from industry. The construction dust at a Taichung site (CONST) and paved road dust at Wuchi site (PRDWC) were the two sources of crustal materials. Two kinds of vehicle emission were selected, one (PH7525) included 75% diesel- and 25% gasoline-
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fuelled vehicles, and the other (PH2575) included 25% diesel- and 75% gasoline-fuelled vehicles (Watson et al., 1994). Fresh marine spray (MARIN), ammonium sulfate (AMSUL) and ammonium nitrate (AMNIT) were defaulted in the CMB7 model (Watson et al., 1990). In order to decrease the collinearity between the simulated sulfate and nitrate aerosols, a new profile (Nitrate, NO3) was also added into the CMB7 model (Chan et al., 1999). 3.4. Quantitative analyses using the CMB model Fig. 4 shows the results of CMB calculations for individual samples from the urban and coastal areas. The industrial source contributions in the figure represent contributions from the coal-fired power plant, coal- and oil-fired boilers. Meanwhile, the combustion source indicates emissions from biomass burning. Four pollution types were classified according to the patterns of air pollution. The first type was named the cumulative high pollution type, marked with a dashed line in Fig. 4 (e.g. 10/12D, 10/12N and 10/13D). Geographical and micrometeorological factors were both influential in decreasing the diffusion effect in the urban area. The major pollutants were emitted from vehicle emissions, and secondary aerosols were produced in photochemical reactions with the precursor gas. In general, vehicle emissions and secondary aerosols were the significant sources in the first pollution type. The second type was named the occasional high pollution type, marked with a dotted line in Fig. 4 (e.g. 11/25D, 11/25N, 11/26D and 11/26N). Biomass burning usually occurs in late autumn in the urban area, and was the key factor in this second pollution type. The third type was named the low pollution type, marked with an unbroken line in Fig. 4 (e.g. 11/24D, 11/24N, 11/27D and 11/27N). Crustal material and vehicle emissions were the major sources in this type. The last type was named normal pollution type, and is unmarked in Fig. 4. Vehicle emissions were the significant contributory source in this pollution type, with other sources varying with season and local micrometeorology. Only three of these pollution types were found in the coastal area: cumulative high pollution type, occasional high pollution type, and low pollution type. Fig. 5 shows the results of PM10 source apportionment at Chunglun (urban) and Wuchi (coastal) sites in different air quality conditions. Source apportionment and average source apportionment for samples collected during the three air quality conditions, clear periods (PM10o50 mg m3), moderate periods (50oPM10o150 mg m3), and episodic periods (PM104150 mg m3), were estimated. At the urban site (Figs. 5(a)–(d)), vehicle emissions, crustal materials, secondary aerosols, biomass burning, industrial emissions and marine spray were the sources in sequence.
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Table 3 Mass and chemical compositions in different air quality at Taichung urban and coastal sites Aerosol measured from Taichung urban site
a
Moderate periods (N=5) 50oPM10o150 mg m3
Episodic periods (N=10) PM104150 mg m3
Clear Periods (N=2) PM10o50 mg m3
Moderate periods (N=9) 50oPM10o150 mg m3
Episodic periods (N=1) PM104150 mg m3
Coarse
Fine
Coarse
Fine
Coarse
Fine
Coarse
Fine
Coarse
Fine
Coarse
Fine
17.774.70 0.6070.18 1.1370.62 1.1170.23 1.4370.72 0.0070.00 0.0570.01 1.1170.03 0.0170.00 0.1370.05 0.0070.00 0.0070.00 0.3370.39 2.3071.51 6.1972.56 0.1170.03 0.2070.05 0.1470.15 0.1970.03 0.2570.04 0.0970.06 0.0970.05 0.3770.23
20.976.10 0.1070.03 0.1870.09 0.9771.10 0.2970.18 0.0170.01 0.0870.08 0.2470.10 0.0170.02 0.1270.06 0.0070.01 0.0070.00 0.3370.18 2.5071.12 9.7872.66 0.2670.13 0.8970.52 2.8073.24 0.5070.13 1.3171.16 0.4770.23 0.1170.09 0.3070.29
34.2711.5 0.6270.46 0.7170.47 0.7970.76 0.6170.09 0.0170.02 0.1070.16 0.8770.63 0.0370.03 0.1770.12 0.0070.00 0.0070.00 0.4370.69 3.2372.46 6.1072.65 0.7570.57 2.5872.57 1.6372.59 0.8670.50 0.9571.24 0.2070.21 0.1870.08 1.1370.92
75.4726.8 0.2670.20 0.1770.17 0.9171.07 0.2370.10 0.0370.02 0.1870.17 0.4770.32 0.0570.04 0.3870.17 0.0170.01 0.0070.00 0.7970.80 8.8175.30 8.4473.06 0.9870.97 6.5476.26 12.0076.15 0.8770.50 6.1773.27 0.9070.13 0.2270.07 0.8570.31
60.2718.2 0.4170.39 0.7470.34 0.4270.42 1.0470.31 0.0170.01 0.0370.03 0.8170.24 0.0170.01 0.1270.07 0.0070.00 0.0170.01 0.3870.51 5.1371.95 7.8872.29 0.5770.44 3.8273.01 2.1476.14 0.7570.32 1.1771.79 0.1170.13 0.1770.06 0.6870.31
144.1734.3 0.2470.22 0.5470.25 1.1870.86 0.2470.14 0.0270.02 0.1170.10 0.3870.22 0.0470.03 0.2470.13 0.0170.01 0.0170.02 0.6571.04 20.0278.40 20.1377.63 2.3872.49 9.7677.24 17.83711.6 1.2670.54 8.2872.30 1.7670.99 0.3870.11 1.2270.36
20.375.40 0.8670.48 0.8370.03 0.7170.26 0.4570.06 0.0170.01 0.0370.04 0.1770.24 0.0170.02 0.1970.26 0.0070.00 0.0070.00 0.2870.40 1.6671.64 4.7270.95 1.0570.02 0.3170.10 0.2670.09 0.7370.17 0.1770.23 0.1170.05 0.1170.01 0.2170.04
21.273.00 0.4670.27 0.3570.16 1.0870.72 0.2770.12 0.0570.03 0.0770.03 0.1670.02 0.0870.06 0.1970.15 0.0070.00 0.0070.00 0.6270.79 4.9472.53 5.9170.42 0.5370.23 0.7770.31 3.1371.63 0.9070.01 1.2570.27 0.4270.01 0.2370.06 0.5770.27
33.8713.0 0.5770.50 0.8370.50 0.7370.89 2.0771.29 0.0970.26 0.0470.03 0.7170.17 0.0370.04 0.2270.31 0.0670.17 0.2770.80 0.3070.59 3.6572.45 5.8173.75 2.1471.62 1.1070.75 0.7170.87 1.5270.85 0.5070.29 0.2370.21 0.2270.10 0.6370.40
39.578.50 0.1970.24 0.3570.21 0.8871.21 0.7170.70 0.0370.02 0.0370.02 0.1770.11 0.0370.04 0.2970.23 0.0170.01 0.0270.05 0.3070.40 6.8973.27 10.1673.52 1.4171.24 2.0271.57 6.7772.85 1.1170.43 2.8071.14 0.7370.19 0.1870.12 0.7070.21
47.074.70a 0.4370.02a 1.5970.16a 0.5670.06a 1.1970.12a 0.0470.01a 0.2070.02a 1.5070.15a 0.0570.01a 0.2970.03a 0.0070.00a 0.0070.00a 0.0870.01a 3.1170.31a 5.7070.57a 2.0770.21a 5.3670.54a 2.7870.28a 1.0070.10a 1.9370.19a 0.3270.03a 0.1770.02a 1.0870.11a
131.4713.1a 0.2270.02a 0.8970.09a 0.5370.05a 0.2170.02a 0.0870.01a 0.1770.02a 0.5470.06a 0.1170.01a 0.6570.07a 0.0470.01a 0.0170.01a 0.6270.06a 6.3770.64a 5.8470.58a 8.0670.81a 20.6372.06a 9.8670.99a 1.7270.17a 9.9270.99a 0.7270.07a 0.3270.03a 1.0670.11a
Only one sample was measured, the standard deviation was estimated by 10% of mean value.
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Mass Mg Al K Ca V Mn Fe Ni Zn As Se Pb OC EC Cl NO 3 SO2 4 Na+ NH+ 4 K+ 2+ Mg Ca2+
Clear periods (N=3) PM10o50 mg m3
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Size
Aerosol measured from Taichung coastal site
10-2
10-1
10-2
10 1
10 0
10-1
10-2 CFSPT-F CONST-F PRDWC-F
CFPPT-C OFFPT-C CFSPT-C CONST-C PRDWC-C
PH7525-F PH2575-F BIOBN-C BIOBN-F
MARIN-C
Species Species
Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
10 2
Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
10 0 OFFPT-F
10 1
10 0
10-1
Species
Fig. 3. Source profiles selected using the CMB model.
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Species Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
Abundance 10 1 CFPPT-F
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Species Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
Abundance 10 2
Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
Abundance 10 2
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Fig. 4. The CMB results from individual samples at Taichung urban and coastal areas.
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Fig. 5. (a–h) PM10 source apportionment in the Taichung urban and coastal areas during different air quality conditions.
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The source contributory pattern of the overall averaged sample was similar to that of the samples during episodic periods in the urban area, and there was an underestimated uncertainty (about 10% by mass). Although vehicle emissions was the biggest contributory source, biomass burning and secondary aerosols were the influential sources causing the increase of PM10 at the urban site during episodic periods. At the coastal site, results (in Figs. 5(e)–(h)) show that vehicle emissions, crustal materials, secondary aerosols, marine spray, biomass burning and industry emissions were the sources in sequence. The ranking of marine spray increased to fourth due to higher WS in the coastal area. At this site, the source contributory pattern of the overall averaged sample was similar to that of the samples during moderate periods. As was the case at the urban site, biomass burning and secondary aerosols were the influential sources causing the increase of PM10 at the coastal site during episodic periods. These episodic events always occurred in fall and winter in the urban area, but they occurred only in spring in the coastal area. 4. Conclusions In this study, the characteristics and source apportionment of aerosols in different air quality conditions were investigated, and the major findings were as follows: 1. The results of PCA indicate that the vehicle emissions, industry, secondary aerosols, crustal dust, biomass burning, marine spray and combustion were the contributory sources to particulate matter in both Taichung urban and coastal areas. 2. Four pollution types were found in the urban area: cumulative high, occasional high, low, and normal. Only the first three types were found in the coastal area. 3. Vehicle emissions, crustal materials, secondary aerosols, biomass burning, industry emissions and marine spray were the sources in sequence at the Taichung urban site. There was a similar pattern of sources at the Taichung coastal site, except that the ranking of marine spray increased to the fourth position. 4. The contribution emitted from vehicle emission was indeed the major sources during episodic periods. But focusing on the increments of the contributory percentage, the biomass burning and secondary aerosols were the influential sources causing the increase of PM10 at both sites during episodic periods. These episodic events always occurred in fall and winter in the urban area, but they could only happen in spring in the coastal area.
Acknowledgement This work was supported by the National Science Council, ROC through a Grant number NSC88-EPA-Z005-003.
References Cadle, S.H., Groblicki, P.J., 1982. An evaluation of methods for the determination of organic and elemental carbon in particulate samples. In: Wolff, G.T., Klimisch, R.L. (Eds.), Particulate Carbon-Atmospheric Life Cycle. Plenum Press, New York, pp. 89–109. Chan, Y.C., Simpson, R.W., Mctainsh, G.H., Vowles, P.D., Cohan, D.D., Bailey, G.M., 1999. Source apportionment of PM2.5 and PM10 aerosols in Brisbane (Australia) by receptor modelling. Atmospheric Environment 33, 3251–3268. Chen, W.C., Wang, C.S., Wei, C.C., 1997. An assessment of source contributions to ambient aerosols in Central Taiwan. Journal of the Air and Waste Management Association 47, 501–509. Cheng, W.L., 2001. Synoptic weather patterns and their relationship to high ozone concentrations in Taichung Basin. Atmospheric Environment 35, 4971–4994. Cheng, M.T., Tsai, Y.I., 2000. Characterization of visibility and atmospheric aerosols in Urban, Suburban and Remote areas. The Science of the Total Environment 263, 101–114. Cheng, M.T., Tsai, Y.I., Chio, C.P., 1999. The characteristics of urban and suburban atmospheric particulates in Central Taiwan. First Asia Aerosol Conference, Nagoya, Japan, July 27–29. Environmental Protection Agency, Taiwan (EPA/Taiwan), 2000. Air quality management in Taiwan in the past 25 years (1975–2000). Environmental Protection Agency, Taiwan (in Chinese), http://www.epa.gov.tw. Harrison, R.M., Deacon, A.R., Jones, M.R., Appleby, R.S., 1997. Sources and process affecting concentrations of PM10 and PM2.5 particulate matter in Birmingham (UK). Atmospheric Environment 31, 4103–4117. Johnson, R.A., Wichern, D.W., 1992. Applied Multivariate Statistical Analysis, third ed. Prentice-Hall, Englewood Cliffs, pp. 356–458. Tsai, Y.I., Cheng, M.T., 1999. Visibility and aerosol chemical compositions near the Coastal area in Central Taiwan. The Science of the Total Environment 231, 37–51. Tsuang, B.J., Chen, C.L., Lin, C.H., Cheng, M.T., Tsai, Y.I., Chio, C.P., Pan, R.C., Kuo, P.H., 2003. Quantification on the source/receptor relationship of primary pollutants and secondary aerosols by a Gaussian plume trajectory model: part II. case study. Atmospheric Environment 37, 3993–4006. Wang, C.F., Chen, W.H., Yang, M.H., Chiang, P.C., 1995. Microwave decomposition for airborne particulate matter for the determination of trace elements by inductively coupled plasma mass spectrometry. Analyst 120, 1681–1686. Wang, C.F., Yang, J.Y., Ke, C.H., 1996. Multi-element analysis of airborne particulate matter by various spectrometric
ARTICLE IN PRESS C.-P. Chio et al. / Atmospheric Environment 38 (2004) 6893–6905 methods after microwave digestion. Analytica Chemica Acta 320, 207–216. Watson, J.G., 1979. Chemical element balance receptor model methodology for source of fine and total suspended particulate matter in Portland. Oregon Ph.D. Thesis, Oregon Graduate Center. Watson, J.G., Robinson, N.F., Chow, J.C., Henry, R.C., Kim, B.M., Pace, T.G., Meyer, E.L., Nguyen, Q., 1990. The
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USEPA/DRI chemical mass balance receptor model, CMB 7.0. Environmental Software 5 (1), 38–49. Watson, J.G., Chow, J.C., Lowenthal, D.H., Pritchett, L.C., Frazier, C.A., Neuroth, G.R., Robbins, R., 1994. Differences in the carbon composition of source profiles for dieseland gasoline-powered vehicles. Atmospheric Environment 28, 2493–2505.
Source apportionment to PM10 in different air quality conditions for Taichung urban and coastal areas, Taiwan Chia-Pin Chio, Man-Ting Chenga,, Chu-Fang Wangb a
Department of Environmental Engineering, National Chung Hsing University 250, Kuokuang Road, Taichung 402, Taiwan, ROC b Department of Atomic Science, National Tsing Hua University, 101, Section 2, Kuang Fu Rd., Hsinchu 300, Taiwan, ROC Received 5 April 2003; accepted 13 August 2004
Abstract PM2.5 and PM2.5–10 samples were collected between August 1998 and March 1999 at Taichung urban and coastal sites. The major objective in this study was to identify the source contributions to PM10 in different air quality conditions. Principal component analysis with varimax rotation and a chemical mass balance model were used to qualify and quantify, respectively, the source contributions to PM10. Vehicle emissions was the most important source of PM10 at the urban site, followed by crustal materials, secondary aerosols, biomass burning, industrial emissions and marine spray. There was a similar pattern of sources at the coastal site, except that marine spray was found more significantly than the urban site. Although biomass burning and secondary aerosols were not main sources during clean air quality periods, they were the influential sources causing the increase of PM10 to ‘‘episodic’’ levels at both sites. r 2004 Elsevier Ltd. All rights reserved. Keywords: Aerosol compositions; Urban; Coastal; Principal component analysis; CMB source apportionment
1. Introduction Taichung urban area, located in the Taichung basin surrounded by the Central Ranges and Tatu Hill, extends to 163.4 km2 and has a population of over 0.9 million people, giving a population density of over 5500 people km2. To the west, located between the Taiwan Strait and Tatu Hill, Taichung coastal area extends to 95.1 km2 and has a population of 45,000 people, giving a population density of about 460 people km2. Inventory data suggests that in the urban area, populated by about 700,000 vehicles and 4200 factories, Corresponding author. Tel: +886 4 22851984;
fax: +886 4 22862587. E-mail address: [email protected] (M.-T. Cheng).
about 80% of PM10 is emitted from construction activity and paved road dust, and about 40% of SO2, 80% of NOx, and 98% of CO is emitted from vehicles. The same data suggests that only 50% of PM10 in the coastal area is emitted from construction activity and paved road dust, while 80% of CO is emitted from 150,000 vehicles there. A 4400 MW coal-fired power plant located in the coastal area is estimated to contribute about 10% of that area’s atmospheric PM10, 97% of its SO2, 75% of its NOx, and 10% of its CO. Finally, agricultural areas account for 18% and 25% of the total urban and coastal area, respectively. Tsai and Cheng (1999) studied how the chemical composition of aerosols varied in different air quality conditions in the Taichung coastal area, and found that visibility showed a strong negative correlation with sulfate, ammonium and nitrate. Meanwhile, Cheng
1352-2310/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2004.08.041
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(2001) focused on the synoptic weather patterns’ relationship to ozone episodes in the Taichung urban area, and highlighted the interactive influences between micrometeorological factors and air pollutants. Chen et al. (1997) applied the CMB7 model to evaluate the impact of a new petrochemical complex at Meliao, located in the south of the Taichung area, and indicated that the combustion of agricultural wastes was a significant contributory source of fine and coarse particles. In this study, seven intensive aerosol samplings were conducted simultaneously at two sites with dichotomous samplers between August 1998 and March 1999. Only samples collected during the northerly wind prevailing seasons were selected for analyzing metallic elements, soluble ionic species, and carbons. The purpose was to investigate the source contributions to PM10 in different air quality conditions in these seasons. The characteristics of PM10 aerosol at the two sites were compared, and principal component analysis (PCA) was used to find and apportion source types to PM10. The source contributions were quantitatively analyzed using a chemical mass balance (CMB) receptor model.
2. Methods 2.1. Experimental program Chunglun junior high school and Wuchi elementary school were selected as sampling sites to represent the Taichung urban and coastal areas, respectively. The urban site is about 18 km from the aforementioned power plant, and the coastal site about 8 km from the plant (Cheng et al., 1999). The locations of the sampling sites, the air quality monitoring stations, the weather service stations, and the local main pollutant source in central Taiwan (the power plant) are shown in Fig. 1. Simultaneous weather data from the Taichung and Wuchi weather service stations (including pressure, temperature, relative humidity, and wind profile information) were available for data analyses. Gaseous pollutants data (including SO2, CO, O3, NO and NO2,) were also available, reported from Chungmin and Shalu air quality monitoring sites. Sierra & Andersen dichotomous samplers (Dichot, model 241), fitted with both quartz fiber filters (US Pallflex type 2500 QAT-UP) and Teflon membrane filters (Gelman type R2PJ037), were used to collect
Fig. 1. The positions of sampling sites, air quality monitoring stations, weather service stations and the local main pollutant source in central Taiwan.
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coarse aerosol (PM2.5–10) and fine aerosol (PM2.5) at both sites. Each single aerosol sample was collected in a 12-h period, from 8 a.m. to 8 p.m. or from 8 p.m. to 8 a.m. A total of 110 samples were collected at the urban site and 92 at the coastal site. For the purposes of this study, the year was divided into summer (June–August), autumn (September–November), winter (December–February) and spring (March–May). The collected aerosols were intensively analyzed for water-soluble ions, carbon contents, and metallic elements. 2.2. Chemical composition analyses Dionex ion chromatography (model DX100) was employed to analyze the concentrations of Cl, NO 3, + + + 2+ SO2 and Ca2+. The detection 4 , Na , NH4 , K , Mg limits for these ionic species were 0.021, 0.006, 0.009, 0.003, 0.012, 0.007, 0.005 and 0.013 mg m3, respectively. Average recoveries appeared to range from 88% to 104%. The carbon content of aerosol samples were analyzed using an elemental analyzer (EA, F002Heraeus CHN-O-Rapid elemental analyzer). CO2 was elicited from the aerosol samples via a thermal process of oxidation at 950 1C for a heating time of 1.5 min and reduction at 600 1C, and then a thermal conductivity detector (TCD) was used to quantify the CO2 content in the samples. As pre-treatment for elemental carbon (EC), the quartz filter was heated in an oven at 340 1C for 10 min (Cadle and Groblicki, 1982; Tsai and Cheng, 1999). Total carbon (TC) was obtained by putting the samples without any pre-treatment directly into the analyzer. The quantity of organic carbon (OC) was calculated as the difference between TC and EC. The method detection limit of carbon content in this study was 0.4 mg m3. Microwave digestion (Wang et al., 1995, 1996) was used as pre-treatment for Teflon filters loaded with airborne particulate matter. The metallic elements magnesium (Mg), aluminum (Al), potassium (K), calcium (Ca), vanadium (V), manganese (Mn), iron (Fe), nickel (Ni), zinc (Zn), arsenic (As), selenium (Se) and lead (Pb) were determined for partial aerosol samples in this study, using inductively coupled plasma mass spectrometry (ICP-MS, Perkin–Elmer SCIEX ELAN 5000). The method detection limits were 0.017, 0.13, 0.08, 0.409, 0.0002, 0.003, 0.155, 0.001, 0.018, 0.0005, 0.0007 and 0.011 mg m3, respectively. 2.3. Principal component analysis PCA, as a technique which attempts to explain the statistical variance in a number of original variances by a minimum number of significant components, has often been employed in the source apportionment of air pollutants measured at a receptor site. (Harrison et al., 1997) The original data matrix X that consists of the
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normalized variables of the aerosol constituent’s concentrations and the air quality data as well as the meteorological data. R is the n n sample correlation matrix showing the intercorrelations among all variables, where n is the number of variables. The solutions of the characteristic equation, RlI=0, are the eigenvalues with l1 4l2 4 4ln 40; and the associated eigenvectors are q1 ; q2 ; . . . qn The relationship between the eigenvector matrix Q and R could be presented as 2 3 l1 0 6 . . 7 0 . . ... 7 R ¼ Q6 4 .. 5Q : 0 ln Principal components (PC) are the orthogonal projections of the matrix X onto new axes. Therefore, X is linearly dependent on a few principal factors F 1 ; F 2 ; . . . ; F m : m is the number of the PC selected. In factor analysis, the proportion of the total population variance due to the ith principal component can be written as li =ðl1 þ l2 þ þ ln Þ: Usually the m PC account for at least 80% of the variance (Johnson and Wichern, 1992). The communalities of PCs are calcuP 2 lated using the equation m i;j¼1 F ij ; where F ij is the factor loading of the ith variable on the jth PC. The factor loading with absolute values larger than 0.25 are presented in this study, with those larger than or equal to 0.6 are considered as influential factors.
2.4. Chemical mass balance (CMB) receptor model The great advantage of using PCA as a receptor model is that there is no need for a priori knowledge of emission inventories. But the PCA method only identify the probable source types, the quantitative source contributions can only be solved by using the CMB receptor model (Watson, 1979). The fundamental relation between the concentration at a receptor site and source information can be expressed as Ci ¼
p X
aij S j ;
j¼1
where Ci is the concentration of elemental component i at a receptor site, p represents the number of sources, aij is the fraction of ith element from jth source, and Sj is the contributing concentration from jth source to the receptor site. The CMB model provided several goodness-of-fit indicators of the results. The criteria of t-statistic values should be 42. Reduced chi square value should be o4. R2 required greater than 0.8. Percentage mass of aerosols explained by the source ranged between 80% and 120%. Degree of freedom should be 45. The
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solutions should not present the collinearity (Watson et al., 1990).
3. Results and discussions 3.1. Aerosol mass concentrations Taichung urban and coastal areas are part of the Central Taiwan Air Basin (CTAB) by geographical environment and living space (EPA/Taiwan, 2000). Table 1 shows PM2.5, PM2.5–10 and PM10 measurements for central Taiwan between 1992 and 1999. The Dacheng, Meliao and Taihsi sites are located in the southernmost region of the CTAB and are considered as coastal sites (Chen et al., 1997). In the period 1992–1999, higher average PM10 and a higher ratio of PM2.5 to PM10 were found at the coastal sites in spring. Tsai and Cheng (1999) reported a high PM10 episodic period in late fall of 1997, and identified PM2.5 as the major pollutant at the coastal site. In the same time, the high PM10 and PM2.5 concentrations were also observed in the Taichung urban area (Cheng and Tsai, 2000). In this study, the higher concentrations of PM2.5, PM2.5–10 and PM10 were observed in fall 1998, and were especially highest in October 1998. Fig. 2 shows the relationship between wind speed (WS), wind direction, PM10 concentrations and ratios of
PM2.5 to PM10 measured at Taichung urban and coastal sites. Since more population and factories were located in the north of the studying area, the higher PM10 always occurred with seasons when the northerly wind prevailed. During the seasons of fall, winter and spring, the WSs were relatively low in the urban area during fall and in the coastal area during spring. A previous study also pointed out that lower WS resulted in less vertical and horizontal mixing in this study (Tsuang et al., 2003). As a result, the highest PM10 concentration occurred in fall at the urban site and in spring at the coastal site. 3.2. Qualitative PCA Table 2 shows the PCA of PM10 species, gaseous pollutants and meteorological parameters measured at Taichung urban and coastal sites. In order to identify the major source affecting Taichung urban and coastal areas, the samples measured with the northerly prevailing wind were selected. Because the aerosol samples measured in summer were omitted, only 96 and 79 samples were selected in Taichung urban and coastal areas, respectively. There were five PC factors were identified by PCA method. PC1 represented the mixing factor of vehicle and industry, as there were high factor loadings in PC1 for SO2, CO and NO2. In PC2, the most prominent species were SO2 and NH+ 4 4 , hence this factor was considered as secondary inorganic (i.e.
Table 1 PM2.5, PM2.5–10 and PM10 measurements for central Taiwan between 1992 and 1999 Avg. PM2.5 (mg m3)
Avg. PM2.5–10 (mg m3)
Avg. PM10 (mg m3)
Ratios of PM2.5 to PM10
Author (Year)
Sampling site
Seasons of year
Chen et al. (1997)
Dacheng, Meliao and Taihsi (coastal site)
Early winter 1992
42.1
31.5
73.6
0.58
45.2 25.6 14.5 24.5
47.5 37.6 30.9 19.8
92.7 63.2 45.4 44.3
0.54 0.43 0.35 0.56
71.9 71.2
39.2 37.9
111.1 109.1
0.62 0.64
Tsai and Cheng (1999)
Wuchi (coastal site)
Early spring 1993 Early summer 1993 Early fall 1993 Middle summer 1997
Cheng and Tsai (2000)
Chunglun (urban site)
Late fall 1997 Fall and winter 1997
This study
Chunglun (Urban site)
Summer 1998 (N=14) Fall 1998 (N=28) Winter 1998 (N=25) Spring 1999 (N=29) October 1998a (N=14)
34.4 79.8 43.4 57.1 119.8
19.9 37.7 26.7 30.5 50.7
54.3 117.5 70.0 87.7 170.5
0.62 0.66 0.62 0.66 0.69
Wuchi (Coastal site)
Summer 1998 (N=13) Fall 1998 (N=28) Winter 1998 (N=24) Spring 1999 (N=27)
19.6 39.1 32.8 53.7
17.0 30.2 26.1 29.3
36.7 69.3 58.9 83.0
0.54 0.56 0.55 0.62
a
The sampling was conducted only at Taichung urban site.
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0.75 Chunglun
WS*10
PM10
Ratio
100
0.7
80
0.6 40 0.55
20 0 100
S SW
N NNW
N NNE NNW
NNW N
0.5 0.7
Wuchi
N NNE
80
Ratio of PM2.5 to PM10
PM10 Concentrations (µg m-3) and Wind Speed (m s-1)
0.65 60
0.65 60
NNE NE N
N NNE
40
20
SSE S
0.6
0.55
0
0.5 Summer'98
Fall'98
Winter'98
Spring'99
Fig. 2. The relationship among average WS, wind directions, PM10 concentrations and ratios of PM2.5 to PM10 measured in Taichung urban and coastal sites during sampling periods. (Note: The prevailing wind directions are presented only if the sum of their occurences was over 300 h in a season.).
ammonium sulfate and nitrate). PC3 clearly represented an ozone-related factor for temperature, relative humidity (RH) and O3. PC4 was considered as the crustal dust, there were high factor loading in PC4 for Na+, Mg2+ and Ca2+. PC 5 presented the major contributions from Cl, K+, TC and NO. These species were tracers of biomass burning. Approximately 80.9% of total variance were explained in these five factors. The com2+ munalities of the factor loadings, such as SO2 4 , Ca and RH, were X0.9. The mixing factor (PC1) of vehicle and industry was the source contributing the maximum explained variance, but the secondary inorganic (PC2) was significant source to affect the PM10 mass concentration. The total sources affecting air quality in the urban areas extracted by PCA were vehicle emissions, industry, secondary aerosols, crustal dust and biomass burning. The ozone related factor correlated well with the meteorological parameters (i.e. temperature and RH). Although only 79 samples were taken in the coastal site, 6 PCs were extracted. PC1 was identified as the mixing factor of ammonium sulfate and nitrate from photochemical reactions. The major species contributing
2 + to the factor were NO 3 , SO4 , NH4 and WS. PC2 could be considered as a mixing factor of marine spray and combustion, because there were high factor loadings for Cl, Na+, TC and NO species. PC3 had significant loadings for temperature, RH and O3, representing ozone-related source. PC4 clearly represented the crustal dust for high factor loadings of Mg2+ and Ca2+. PC5 can be considered the mixing factor of vehicle and industry, as there were high factor loadings in PC5 for SO2, CO and NO2. Finally, biomass burning was the probable source in PC6. Approximately 85.2% of total variance were 2+ + explained in these six factors. Cl, NO , 3 , NH4 , Mg 2+ Ca and temperature were explained well, because their communality in the factor loadings was X0.9. The secondary inorganic (PC1) was the most significant source to PM10, also contributing most of the explained variance. In summary, using PCA, source factors were found to be similar in the urban and coastal areas.
3.3. Ambient data and source profiles In Taiwan, an episodic period refers to a PM10 concentration 4150 mg m3. When PM10 concentration
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Table 2 PCA of PM10 species, gaseous pollutants and meteorological parameters measured at Taichung urban and coastal sites Taichung urban area (N=96) PC 1 0.35 0.31 0.50
PC 3
PC 4
0.74 0.28 0.25 0.31 0.74 0.37
0.36 0.69 0.26 0.28 0.74
0.36
0.45 0.56 0.88 0.77 0.34 0.89 3.26 18.1 18.1 Vehicle+ industry
0.29 0.72 0.89
0.75 0.96 0.26
0.69
0.43 0.47
3.04 16.9 35.0 Secondary inorganic
Factor loading values 40.60 are in bold. a TC: total carbon (i.e. EC+OC). b RH: relative humidity. c WS: wind speed.
0.80 0.46
0.34
2.96 16.5 51.5 Ozone related
2.75 15.3 66.8 Crustal
0.62 2.54 14.1 80.9 Biomass burning
Communality 0.86 0.87 0.77 0.92 0.81 0.89 0.68 0.76 0.93 0.72 0.74 0.90 0.54 0.86 0.89 0.83 0.74 0.84
PC 1 0.78 0.34 0.88 0.76
PC 2 0.28 0.77 0.25
PC 3
PC 4
PC 5
PC 6
0.32 0.37 0.40 0.48
0.77
0.26
0.93 0.91 0.96 0.94 0.70
0.57 0.85 0.83
0.33
0.77 0.46
0.60 0.71 0.29
0.36
0.53 0.72 0.27 0.36 4.13 22.9 22.9 Secondary inorganic
0.46 0.37 0.27
0.81 2.78 15.4 38.3 Marine+ combustion
2.72 15.1 53.4 Ozone related
2.40 13.4 66.8 Crustal
0.75 1.77 9.9 76.7 Vehicle+ industry
1.52 8.5 85.2 Biomass burning
Communality 0.81 0.91 0.91 0.84 0.89 0.93 0.89 0.96 0.95 0.83 0.91 0.77 0.83 0.84 0.85 0.71 0.80 0.71
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0.42 0.88 0.37 0.81
0.46 0.47
PC 5
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Mass Cl NO 3 SO2 4 Na+ NH+ 4 K+ 2+ Mg Ca2+ TCa Temperature RHb WSc SO2 CO O3 NO NO2 Eigenvalues % variance Cumulative % variance Sources
PC 2
Taichung coastal area (N=79)
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was o50 mg m3, we defined this as a ‘‘clear’’ period. Finally, the aerosol sample indicated a ‘‘moderate’’ period if the PM10 concentration ranged between 50 and 150 mg m3. Tsai and Cheng (1999) reported that the levels of chemical species were significantly different in clear and hazy periods, especially those of SO2 4 , NO3 + and NH4 . In this paper, 18 and 12 samples were selected in Taichung urban and coastal areas, respectively, for analyzing the metallic elements, soluble ionic species, and carbons. The data were used to evaluate the source apportionment by CMB receptor model. Temporal variation of PM10 and PM2.5 concentrations in Taichung urban and coastal areas had been reported in previous study (Cheng et al., 1999). The episodic events often occurred in September, October and November 1998 at the urban site. However, only one episodic event was observed in March 1999 at the coastal site. The sampling dates of those data sets were on 25 September 1998; 12–14 October 1998; 24–27 November 1998; and 24 March 1999, respectively. Table 3 shows the averaged mass and chemical compositions in different air quality conditions of aerosols at Taichung urban and coastal sites. Most samples were collected during moderate periods in the coastal area, with a few collected in episodic or clear conditions. The only one sample collected at ‘‘nighttime’’ (on 24 March 1999) was collected during an episodic period in the coastal area. There was a significant difference in the concen2 + trations of TC (i.e. OC+EC), NO 3 , SO4 and NH4 in fine fraction aerosols collected in clear and episodic periods in the Taichung urban area. Comparing the concentrations of above species, they were about 3 to 11 times higher in the episodic period than those in the clear period. However, in the Taichung coastal area, only the concentrations of secondary aerosols 2 (NO and NH+ 3 , SO4 4 ) appear much higher in the episodic event. According to the results of PCA, vehicle emissions, industry, secondary aerosols, crustal dust, biomass burning, marine spray and combustion are sources of particulate matter in both Taichung urban and coastal areas. Twelve source profiles that included the above seven source types were selected and used as the input data for CMB modelling. Fig. 3 shows the source profiles selected using this model. The coal-fired power plant (CFPPT) was the specific source of combustion in this study. The profile of biomass (or agricultural waste) burning (BIOBN) was published in a previous study (Chen et al., 1997). Oil-fired food plant (OFFPT) and coal-fired steel plant (CFSPT) were selected to represent the sources emitted from industry. The construction dust at a Taichung site (CONST) and paved road dust at Wuchi site (PRDWC) were the two sources of crustal materials. Two kinds of vehicle emission were selected, one (PH7525) included 75% diesel- and 25% gasoline-
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fuelled vehicles, and the other (PH2575) included 25% diesel- and 75% gasoline-fuelled vehicles (Watson et al., 1994). Fresh marine spray (MARIN), ammonium sulfate (AMSUL) and ammonium nitrate (AMNIT) were defaulted in the CMB7 model (Watson et al., 1990). In order to decrease the collinearity between the simulated sulfate and nitrate aerosols, a new profile (Nitrate, NO3) was also added into the CMB7 model (Chan et al., 1999). 3.4. Quantitative analyses using the CMB model Fig. 4 shows the results of CMB calculations for individual samples from the urban and coastal areas. The industrial source contributions in the figure represent contributions from the coal-fired power plant, coal- and oil-fired boilers. Meanwhile, the combustion source indicates emissions from biomass burning. Four pollution types were classified according to the patterns of air pollution. The first type was named the cumulative high pollution type, marked with a dashed line in Fig. 4 (e.g. 10/12D, 10/12N and 10/13D). Geographical and micrometeorological factors were both influential in decreasing the diffusion effect in the urban area. The major pollutants were emitted from vehicle emissions, and secondary aerosols were produced in photochemical reactions with the precursor gas. In general, vehicle emissions and secondary aerosols were the significant sources in the first pollution type. The second type was named the occasional high pollution type, marked with a dotted line in Fig. 4 (e.g. 11/25D, 11/25N, 11/26D and 11/26N). Biomass burning usually occurs in late autumn in the urban area, and was the key factor in this second pollution type. The third type was named the low pollution type, marked with an unbroken line in Fig. 4 (e.g. 11/24D, 11/24N, 11/27D and 11/27N). Crustal material and vehicle emissions were the major sources in this type. The last type was named normal pollution type, and is unmarked in Fig. 4. Vehicle emissions were the significant contributory source in this pollution type, with other sources varying with season and local micrometeorology. Only three of these pollution types were found in the coastal area: cumulative high pollution type, occasional high pollution type, and low pollution type. Fig. 5 shows the results of PM10 source apportionment at Chunglun (urban) and Wuchi (coastal) sites in different air quality conditions. Source apportionment and average source apportionment for samples collected during the three air quality conditions, clear periods (PM10o50 mg m3), moderate periods (50oPM10o150 mg m3), and episodic periods (PM104150 mg m3), were estimated. At the urban site (Figs. 5(a)–(d)), vehicle emissions, crustal materials, secondary aerosols, biomass burning, industrial emissions and marine spray were the sources in sequence.
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Table 3 Mass and chemical compositions in different air quality at Taichung urban and coastal sites Aerosol measured from Taichung urban site
a
Moderate periods (N=5) 50oPM10o150 mg m3
Episodic periods (N=10) PM104150 mg m3
Clear Periods (N=2) PM10o50 mg m3
Moderate periods (N=9) 50oPM10o150 mg m3
Episodic periods (N=1) PM104150 mg m3
Coarse
Fine
Coarse
Fine
Coarse
Fine
Coarse
Fine
Coarse
Fine
Coarse
Fine
17.774.70 0.6070.18 1.1370.62 1.1170.23 1.4370.72 0.0070.00 0.0570.01 1.1170.03 0.0170.00 0.1370.05 0.0070.00 0.0070.00 0.3370.39 2.3071.51 6.1972.56 0.1170.03 0.2070.05 0.1470.15 0.1970.03 0.2570.04 0.0970.06 0.0970.05 0.3770.23
20.976.10 0.1070.03 0.1870.09 0.9771.10 0.2970.18 0.0170.01 0.0870.08 0.2470.10 0.0170.02 0.1270.06 0.0070.01 0.0070.00 0.3370.18 2.5071.12 9.7872.66 0.2670.13 0.8970.52 2.8073.24 0.5070.13 1.3171.16 0.4770.23 0.1170.09 0.3070.29
34.2711.5 0.6270.46 0.7170.47 0.7970.76 0.6170.09 0.0170.02 0.1070.16 0.8770.63 0.0370.03 0.1770.12 0.0070.00 0.0070.00 0.4370.69 3.2372.46 6.1072.65 0.7570.57 2.5872.57 1.6372.59 0.8670.50 0.9571.24 0.2070.21 0.1870.08 1.1370.92
75.4726.8 0.2670.20 0.1770.17 0.9171.07 0.2370.10 0.0370.02 0.1870.17 0.4770.32 0.0570.04 0.3870.17 0.0170.01 0.0070.00 0.7970.80 8.8175.30 8.4473.06 0.9870.97 6.5476.26 12.0076.15 0.8770.50 6.1773.27 0.9070.13 0.2270.07 0.8570.31
60.2718.2 0.4170.39 0.7470.34 0.4270.42 1.0470.31 0.0170.01 0.0370.03 0.8170.24 0.0170.01 0.1270.07 0.0070.00 0.0170.01 0.3870.51 5.1371.95 7.8872.29 0.5770.44 3.8273.01 2.1476.14 0.7570.32 1.1771.79 0.1170.13 0.1770.06 0.6870.31
144.1734.3 0.2470.22 0.5470.25 1.1870.86 0.2470.14 0.0270.02 0.1170.10 0.3870.22 0.0470.03 0.2470.13 0.0170.01 0.0170.02 0.6571.04 20.0278.40 20.1377.63 2.3872.49 9.7677.24 17.83711.6 1.2670.54 8.2872.30 1.7670.99 0.3870.11 1.2270.36
20.375.40 0.8670.48 0.8370.03 0.7170.26 0.4570.06 0.0170.01 0.0370.04 0.1770.24 0.0170.02 0.1970.26 0.0070.00 0.0070.00 0.2870.40 1.6671.64 4.7270.95 1.0570.02 0.3170.10 0.2670.09 0.7370.17 0.1770.23 0.1170.05 0.1170.01 0.2170.04
21.273.00 0.4670.27 0.3570.16 1.0870.72 0.2770.12 0.0570.03 0.0770.03 0.1670.02 0.0870.06 0.1970.15 0.0070.00 0.0070.00 0.6270.79 4.9472.53 5.9170.42 0.5370.23 0.7770.31 3.1371.63 0.9070.01 1.2570.27 0.4270.01 0.2370.06 0.5770.27
33.8713.0 0.5770.50 0.8370.50 0.7370.89 2.0771.29 0.0970.26 0.0470.03 0.7170.17 0.0370.04 0.2270.31 0.0670.17 0.2770.80 0.3070.59 3.6572.45 5.8173.75 2.1471.62 1.1070.75 0.7170.87 1.5270.85 0.5070.29 0.2370.21 0.2270.10 0.6370.40
39.578.50 0.1970.24 0.3570.21 0.8871.21 0.7170.70 0.0370.02 0.0370.02 0.1770.11 0.0370.04 0.2970.23 0.0170.01 0.0270.05 0.3070.40 6.8973.27 10.1673.52 1.4171.24 2.0271.57 6.7772.85 1.1170.43 2.8071.14 0.7370.19 0.1870.12 0.7070.21
47.074.70a 0.4370.02a 1.5970.16a 0.5670.06a 1.1970.12a 0.0470.01a 0.2070.02a 1.5070.15a 0.0570.01a 0.2970.03a 0.0070.00a 0.0070.00a 0.0870.01a 3.1170.31a 5.7070.57a 2.0770.21a 5.3670.54a 2.7870.28a 1.0070.10a 1.9370.19a 0.3270.03a 0.1770.02a 1.0870.11a
131.4713.1a 0.2270.02a 0.8970.09a 0.5370.05a 0.2170.02a 0.0870.01a 0.1770.02a 0.5470.06a 0.1170.01a 0.6570.07a 0.0470.01a 0.0170.01a 0.6270.06a 6.3770.64a 5.8470.58a 8.0670.81a 20.6372.06a 9.8670.99a 1.7270.17a 9.9270.99a 0.7270.07a 0.3270.03a 1.0670.11a
Only one sample was measured, the standard deviation was estimated by 10% of mean value.
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Mass Mg Al K Ca V Mn Fe Ni Zn As Se Pb OC EC Cl NO 3 SO2 4 Na+ NH+ 4 K+ 2+ Mg Ca2+
Clear periods (N=3) PM10o50 mg m3
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Size
Aerosol measured from Taichung coastal site
10-2
10-1
10-2
10 1
10 0
10-1
10-2 CFSPT-F CONST-F PRDWC-F
CFPPT-C OFFPT-C CFSPT-C CONST-C PRDWC-C
PH7525-F PH2575-F BIOBN-C BIOBN-F
MARIN-C
Species Species
Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
10 2
Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
10 0 OFFPT-F
10 1
10 0
10-1
Species
Fig. 3. Source profiles selected using the CMB model.
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Species Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
Abundance 10 1 CFPPT-F
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Species Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
Abundance 10 2
Na Mg Al S K Ca Ti V Mn Fe Ni Zn As Se Pb OC EC ClNO3SO4= Na+ NH4+ K+ Mg2+ Ca2+
Abundance 10 2
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Fig. 4. The CMB results from individual samples at Taichung urban and coastal areas.
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Fig. 5. (a–h) PM10 source apportionment in the Taichung urban and coastal areas during different air quality conditions.
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The source contributory pattern of the overall averaged sample was similar to that of the samples during episodic periods in the urban area, and there was an underestimated uncertainty (about 10% by mass). Although vehicle emissions was the biggest contributory source, biomass burning and secondary aerosols were the influential sources causing the increase of PM10 at the urban site during episodic periods. At the coastal site, results (in Figs. 5(e)–(h)) show that vehicle emissions, crustal materials, secondary aerosols, marine spray, biomass burning and industry emissions were the sources in sequence. The ranking of marine spray increased to fourth due to higher WS in the coastal area. At this site, the source contributory pattern of the overall averaged sample was similar to that of the samples during moderate periods. As was the case at the urban site, biomass burning and secondary aerosols were the influential sources causing the increase of PM10 at the coastal site during episodic periods. These episodic events always occurred in fall and winter in the urban area, but they occurred only in spring in the coastal area. 4. Conclusions In this study, the characteristics and source apportionment of aerosols in different air quality conditions were investigated, and the major findings were as follows: 1. The results of PCA indicate that the vehicle emissions, industry, secondary aerosols, crustal dust, biomass burning, marine spray and combustion were the contributory sources to particulate matter in both Taichung urban and coastal areas. 2. Four pollution types were found in the urban area: cumulative high, occasional high, low, and normal. Only the first three types were found in the coastal area. 3. Vehicle emissions, crustal materials, secondary aerosols, biomass burning, industry emissions and marine spray were the sources in sequence at the Taichung urban site. There was a similar pattern of sources at the Taichung coastal site, except that the ranking of marine spray increased to the fourth position. 4. The contribution emitted from vehicle emission was indeed the major sources during episodic periods. But focusing on the increments of the contributory percentage, the biomass burning and secondary aerosols were the influential sources causing the increase of PM10 at both sites during episodic periods. These episodic events always occurred in fall and winter in the urban area, but they could only happen in spring in the coastal area.
Acknowledgement This work was supported by the National Science Council, ROC through a Grant number NSC88-EPA-Z005-003.
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