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Characterization of ambient PM2.5 concentrations
Atmospheric Environment 44 (2010) 2902e2912
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Characterization of ambient PM2.5 concentrations Tai-Yi Yu* Department of Risk Management and Insurance, Ming Chuan University, 250 Zhong Shan N. Rd., Sec. 5, Taipei 111, Taiwan
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 September 2009 Received in revised form 17 April 2010 Accepted 19 April 2010
Statistical spatial and temporal analysis of PM2.5 concentrations in ambient air using principal component analysis may provide health risk information for air-quality management. This investigation simultaneously determines and interprets spatial variations and features of PM2.5 concentrations using ambient air-quality monitoring data during 2006e2008 and emission inventory. Daily mean values of PM2.5 and PM10 and maximum hourly data of SO2, CO, O3 and NO2 were calculated as sampling data for year 2006e2008. Therefore, principal component analysis and descriptive statistics of ambient air pollutants were utilized to assess the spatial features and variations of PM2.5 concentrations. This study also provides PM2.5/PM10 ratios and the rates at which 24-h particulate matter exceed air-quality standards over Taiwan. Analytical results indicate that four rotational components cumulatively explain 87% and 84% of concentration variances for PM10 and PM2.5 and form a delineation of four “influence regimes.” The separated districts of the four “influence regimes” for PM10 and PM2.5 were the same. With the rate at which PM2.5 24-h concentrations were above 65 mg m3, 36% of air-quality stations, is higher than 10%; 24% of air-quality stations is higher than 15%. Based on analytical results, if the PM2.5 limit would be considered as National Ambient Air-Quality Standard in the future, the priority of reducing PM10 or O3 concentrations in the past decade could be replaced with PM2.5. The novel methodologies presented in this study can spatially assess adequate boundaries of atmospheric carrying capacity for particulate matter. Local governments located at the same influence regime characterized by factor loading isopleths should implement trans-boundary air pollution control programs. Box plots, the rates of particulate matter exceeding air-quality standards and PM2.5/PM10 ratios in distinct “influenced regimes” were also examined. Continued study of spatial and temporal variations in airborne PM2.5 concentrations will provide sufficient information about health risks and for air-quality control programs. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Principal component analysis Spatial variation PM2.5 Taiwan
1. Introduction Exposure to airborne particulate matter is strongly associated with adverse health effects, affecting the respiratory and cardiovascular systems (Englert, 2004; WHO, 2005). The fine particulates, small particles less than 2.5 mm in diameter (PM2.5), can get deep into the lungs and cause serious health problems. Each 10 mg m3 increase in PM2.5 was associated with approximately a 4%, 6%, and 8% increase in all cause, cardiopulmonary, and lung cancer mortality respectively (Pope et al., 2002). Hence, the spatial information and characteristics of PM2.5 concentrations are crucial data for epidemiological researches to estimate the health effects associated with fine particulate matter. These data are also needed for human exposure risk models and decision making processes for airquality goals. Many researchers (Chen et al., 1999, 2001; Lin, 2002; Wu et al., 2002; Fang et al., 2003a, 2003b; Lai et al., 2004; Horng * Tel.: þ886 2 28824564; fax: þ886 2 28809577. E-mail address: [email protected] 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.04.034
et al., 2007) investigated the speciation of PM2.5 concentrations and contributed sources of PM2.5 emissions in Taiwan. However, these studies could not simultaneously supply the spatial variations and long term measurements of PM2.5 concentrations over Taiwan. Principal component analysis (PCA), one manner of multivariate statistical techniques, can reduce the numbers of variables and simplified dimensions with several dominant factors in complexly environmental problems. PCA is an effective method for classifying meteorological patterns (Maheras, 1984; Eder et al., 1994; Cheng and Lam, 2000), simplifying the emission sources that affect concentrations of air pollutants (Statheropopulos et al., 1998; Tsai, 2000; Stella et al., 2002; Lengyel et al., 2004; Yu and Yu, 2004; Yu and Chang, 2006; Sakihama et al., 2008; Wang et al., 2008) and providing spatial patterns of interrelated air pollutants (Juang et al., 1996; Yu and Chang, 2001; Yang et al., 2008). PCA is an effective method for finding underlying components and attributed contributions of possible emitted sources for highly correlated variables on environmental problems. Verbeke et al. (1984) utilized PCA technique to determine the correlations
T.-Y. Yu / Atmospheric Environment 44 (2010) 2902e2912
among 26 VOCs; three of 26 components were extracted and at least accounted for 54% of concentration variances over four sites in Netherlands. Statheropoulos et al. (1996) adopted PCA manner on GC/MS data sets for resolving coeluted substances in doping control analysis. Manoli et al. (2002) applied PCA method and trace elements (Fe, Zn) to identify possible emission sources of coarse and fine aerosols. Tsai (2005) used PCA to establish the relationship among atmospheric visibility, major air pollutants and meteorological parameters in the urban area and demonstrated that increased vehicular emissions, road traffic dust and industrial activity markedly impacted visibility. Abdul-Wahab et al. (2005) selected high loadings of rotated principal components to obtain adequate predictor variables in the regression model. Data on the concentrations of seven air pollutants and five meteorological variables were employed to predict the ozone concentration with multiple linear and principal component regression methods. Almeida et al. (2005) recognized seven main sources and determined their mass contribution for particulate matter with aerosol chemical composition data. Seven possible sources were identified as soil, sea, secondary aerosols, road traffic, fuel-oil combustion, coal combustion and a Se/Hg emission source. Lynam and Keeler (2006) develop source receptor relationships for mercury species in urban Detroit. Liu et al. (2007) sampled road dusts and analyzed 16 polycyclic aromatic hydrocarbons (PAHs) with GC/MS from central Shanghai in January and August, respectively. The contribution percentages of pyrogenic and petrogenic sources were respectively 71% and 11% in January; 65% and 14% in August, respectively. Vardoulakis and Pavlos (2008) used PCA and regression analysis to quantify the contribution of non-combustion sources to the observed PM10 background levels and the contribution ranged 45e70% in Birmingham and 41e74% in Athens. PCA also provides division of spatial distribution and reduction of variables for environmental problems. Eder (1989) and Eder et al. (1993) presented the spatial features of SO2 4 and O3 concentrations
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with several principal components; six of 40 and seven of 77 principals were extracted and accounted for 74% and 64% of concentration variance, respectively, over eastern United States. Astel et al. (2007) demonstrated three classification techniques, PCA, cluster analysis and self-organizing maps, on a large environmental data set of chemical indicators of river water quality and revealed different patterns of monitoring sites conditionally named “tributary”, “urban”, “rural” or “background”. Pires et al. (2007) applied PCA and cluster analysis, two multivariate statistical techniques, to identify city areas with similar air pollution behaviors and locate emission sources. Zhou et al. (2007a) investigated the spatial distribution patterns of 12 heavy metals in marine sediments at 59 sites from 1998 to 2004 and identified spatial human impacts on global and local scales. PCA method further subdivided human impacts and their three affected areas, explaining 84e87% of the total variances, respectively. Zhou et al. (2007b) evaluated the spatiotemporal patterns and source apportionment of coastal water pollution in eastern Hong Kong with 14 variables at 27 sites. Five potential pollution sources were identified for each part by rotated principal component analysis, explaining 71% and 68% of the total variances for JuneeSeptember and the remaining months, respectively. This investigation aims to present the use of PCA, ambient airquality monitoring data and emission inventory of air pollutants to supply useful spatial and temporal information of PM2.5 concentrations. The following three issues are addressed using the multivariate statistical manners: 1. Typical, seasonal and spatial features of PM2.5 concentrations; 2. Spatial patterns of high PM2.5 concentrations; 3. Spatial distributions of ambient PM2.5/PM10 ratios and PM2.5 emissions. Analytical results will provide spatial characteristics and the current status of ambient PM2.5 concentrations in Taiwan.
Fig. 1. Locations of monitoring stations and PM2.5 emissions (tons year1) over Taiwan.
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Table 1 Emission inventory of air pollutants over Taiwan (TEPA, 2006). Types of Sources
PM2.5
PM10 Tons/y
%
Tons/y
SOx %
NOx
Tons/y
%
NMHC
Tons/y
%
Tons/y
CO %
Tons/y
%
A. Stationary sources 1. Combustion 1.1 Fuel combustion Power generation Industries Commercial Residential 1.2 Non-fuel combustion 2. Nonecombustion Fugitive Industrial Process B. Mobile sources 1. On road Gasoline vehicles Diesel vehicles Motorcycles 2. Off road
304,646 29,368 15,910 4328 10,563 818 201 13,458 275,279 189,678 85,465 31,929 31,348 7764 17,903 5681 581
90.5 8.7 4.7 1.3 3.0 0.2 0.1 4.0 81.8 56.4 25.4 9.5 9.3 2.3 5.3 1.7 0.2
148,356 21,462 9098 2643 5952 315 188 12,364 126,894 74,360 52,441 26,608 26,201 5742 15,929 4529 407
84.8 12.4 5.2 1.5 3.5 0.2 0.1 7.1 72.5 42.5 30.0 15.2 15.0 3.3 9.1 2.6 0.2
173,679 136,470 135,732 63,234 59,122 12,611 766 737 37,209 40 36,692 15,453 7226 833 6206 187 8228
91.8 72.2 71.8 33.4 31.3 6.7 0.4 0.4 19.7 0.3 19.4 8.2 3.8 0.4 3.3 0.1 4.3
255,040 192,601 80,410 88,445 77,796 9513 4656 12,191 62,439 50 62,247 386,735 364,163 96,215 247,540 20,408 22,573
39.7 30.0 28.1 13.8 12.1 1.5 0.7 1.9 9.7 0.0 9.7 60.3 56.7 15.0 38.6 3.2 3.5
605,338 29,520 11,089 1415 9295 256 122 18,431 575,818 420,427 155,391 258,682 257,404 134,529 23,859 99,015 1278
70.1 3.4 1.3 0.2 1.0 0.0 0.0 2.1 66.7 48.7 18.0 30.0 29.8 15.6 2.8 11.5 0.1
248,687 177,891 95,177 46,919 45,896 1349 1012 82,713 70,796 0 70,795 1,423,131 1,417,277 1,111,136 93,233 212,909 5853
14.9 10.6 5.7 2.8 2.7 0.1 0.1 4.9 4.2 0.0 4.2 85.1 84.8 66.5 5.6 12.7 0.3
Sum
336,575
100.0
174,964
100.0
189,132
100.0
641,776
100.0
864,021
100.0
1,671,818
100.0
TEPA utilized the rate of Pollutant Standards Index (PSI) > 100 and the National Ambient Air-Quality Standard (NAAQS) as airquality management goals. Notably, PM10 and O3 are the two dominant air pollutants that violate these limits frequently. The TEPA set the 24-h PM10 standard at 125 mg m3. The PM2.5 concentration was first included in the NAAQS by the U.S. EPA in 1997 (U.S. EPA, 1996, 2004, 2009), followed by Canada, the World Health Organization (WHO) and the European Union. This study calculated the rate measurements at air-quality stations exceed 24-h PM2.5 standards established by U.S. EPA. Both standards of 65 and 35 mg m3 (effective December 17, 2006) were considered. If the rate at which 24-h PM2.5 concentrations exceeded 35 or 65 mg m3 was discussed, the first priority in controlling criteria air pollutants for local governments may change. Clean Air Act requires EPA to set National Ambient Air Quality Standards (NAAQS) for six common air pollutants. These commonly found air pollutants, also known as “criteria pollutants”, are found all over the United States. They are particle pollution (often referred to as particulate matter), groundlevel ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. EPA calls these pollutants “criteria” air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The six air pollutants, PM10, PM2.5, SO2, CO, O3 and NO2, were performed with PCA method from 2006 to 2008. Sampling data for PM10 and PM2.5 were daily arithmetic mean values; other air pollutants sample maximum hourly values within a day. Quality Assurance (QA) is implemented to ensure the accuracy and precision of monitoring data provided and achieve data quality objectives. Based on the QA program, the observation stations conduct monthly checks and an annual performance check on its pollutant analysis instruments and particulate analysis instruments.
2. Materials and methods Since June 1993, the Air-quality Observation and Forecast Network (AQOFN), established by Taiwan’s Environmental Protection Administration (TEPA), has continuously monitored ambient air pollutants using 66 stations spread throughout Taiwan. To address issues related to PM2.5 concentrations and provide information to those making regulations, the TEPA has introduced instruments for monitoring PM2.5 concentrations in the AQOFN in 2004. At the end of 2006, the TEPA had 76 air-quality observation stations nationwide that measured concentrations of such air pollutants as PM10, PM2.5, CO, SO2, NO, NO2, NOx, O3 and hydrocarbons. Fig. 1 shows Taiwan’s topography, locations of monitoring stations and political boundaries in Taiwan. Fig. 1 also presents the PM2.5 spatial emissions (tonsyear1) estimated with the Taiwan Emission Database System (TEDS) model developed by the TEPA (TEPA, 2006). The area of each cell is 1 km2 (1 1 km). Table 1 show the emission inventory of six air pollutants, PM2.5, PM10, SOx, NOx, NMHC and CO over Taiwan (TEPA, 2006). Three major sources, fugitive, industrial processes and on road mobiles sources, present high contribution to PM10 and PM2.5. Fugitive emission includes construction sites and road dusts. Fugitive emission accounts for 56.4% of PM10 and 42.5% of PM2.5; industrial processes account for 25.4% of PM10 and 30.0% of PM2.5; on road mobile sources account for 9.3% of PM10 and 15.0% of PM2.5. The contribution percentages of SOx emission for power generation, industries (fuel of combustion) and industrial processes contribute are 66.5%, 12.7% and 5.6%. Diesel vehicles, gasoline vehicles and power generation account for 66.5%, 12.7% and 5.6% of NOx emissions. Gasoline vehicles, motorcycles and diesel vehicles contribute 66.5%, 12.7% and 5.6% of CO emissions.
Table 2 Eigenvalues and explained variances of rotated principal components for air pollutants. Components
1 2 3 4 5
PM2.5
PM10
SO2
CO
O3
NO2
l
Var
Cum
l
Var
Cum
l
Var
Cum
l
Var
Cum
l
Var
Cum
l
Var
Cum
26.4 22.5 9.7 4.0 1.5
37 31 14 6 2
37 68 81 87 89
24.9 19.3 12.0 4.1 1.1
35 27 17 6 2
35 62 78 84 86
6.4 5.1 4.5 4.1 3.6
9 7 6 6 5
9 16 22 28 33
15.8 13.8 10.7 5.5 2.6
22 19 15 8 4
22 41 56 64 67
16.8 14.9 14.6 7.0 4.7
23 21 20 10 7
23 44 64 74 81
22.7 15.6 10.7 3.3 1.7
32 22 15 5 2
32 53 68 73 75
l: Eigenvalues Var: Percentage of variance (%) Cum: Cumulative percentage (%).
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Fig. 2. Separated subregions for PM10 and PM2.5. The first (a), second (b), third (c) and fourth (d) subregions for PM10; the first (e), second (f), third (g) and fourth (h) subregions for PM2.5.
Fig. 3. Separated subregions for SO2 and CO. The first (a), second (b), third (c) and fourth (d) subregions for SO2; the first (e), second (f), third (g) and fourth (h) subregions for CO.
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Fig. 4. Separated subregions for O3 and NO2. The first (a), second (b), third (c) and fourth (d) subregions for O3; the first (e), second (f), third (g) and fourth (h) subregions for NO2.
Fig. 5. Four homogenous subregions for PM10 and PM2.5 concentrations over Taiwan.
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Daily value calculates arithmetic average of effective hourly concentrations within a day. Effective daily value should at least have 16 effective hourly values. The completeness of hourly PM10 concentrations is 98.3%, 97.7% and 98.3% in 2006, 2007 and 2008. The average values at individual stations were substituted for lost data. The normalized value is (1)
Zik ¼
Cik mi Si
Zik ¼
n X
Lij Pjk
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(2)
i¼1
where n is the number of stations, Lij is the factor loading of the ith station on the jth rotational component, and Pjk is the component score of the kth station for the jth rotational component. The rotational principal component can be derived by inverting Equation (2).
(1) 3. Results and discussion
where Zik is the normalized value of the kth observation at the ith station, Cik is the kth values at the ith station, mi is the mean value at the ith station, and Si is the standard deviation at the ith station. A correlation matrix presents the isopleths of component loadings and can be regarded as correlation coefficients between rotational components and individual stations. The Varimax rotational technique can increase the segregation between component loadings and facilitate an easy spatial interpretation of correlated data. The model for rotational principal component analysis is (2)
Table 2 demonstrates eigenvalues and explained variances of rotated principal components for six air pollutants. The eigenvalues of first five rotational components for six air pollutants were >1; the cumulative explained variances of the first four rotational components were 87% and 84% for PM10 and PM2.5, respectively. The first rotational component accounts for 37% and 35% of explained variances for PM10 and PM2.5; the second rotational component accounts for 31% and 27%. Comparing explained percentages of the every
Fig. 6. Box plots of PM2.5 concentrations for Regions (a) I, (b) II, (c) III and (d) IV.
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component for the first five rotated principal components, the differences between PM10 and PM2.5 are the smallest among six air pollutants. The explained variances of rotational components after the fourth rotational component for PM10 and PM2.5 were <2% and the factor loading for one station between PM10 (PM2.5) and the fifth rotational component is greater than 0.45. Therefore, components after the fourth component were not considered dominant factors in this study. Factor loadings reveal the correlations between a rotational component and monitoring stations and provide the hidden physical meaning of principal components. The Varimax rotational technique was utilized to confirm the relative contribution and spatial features of individual subregions. Each rotational component identified an “influence regime” that existed consistent trend of air pollutant’s concentration. The factor loadings of the first four components were plotted as Fig. 2 for PM10 and PM2.5; Fig. 3 for SO2 and CO; Fig. 4 for O3 and NO2. A high factor loading indicates a strong correlation between an air-quality station and rotational component. The cutting value of factor loading for each rational component could be set as 0.55e0.65 to isolate an “influence regime” and remove possible overlaps among rotational components. With the isopleths of loading factors, the first four components show high loading factors over specific and separated regions for PM10 and PM2.5. The four “influence regimes” of PM10 (Fig. 2aed) and PM2.5 (Fig. 2eeh) are similar in shape and size. Four “influence regimes” of SO2 (Fig. 3aed) demonstrate several stationary sources were clustered at separated locations. The dominant sources of SOx emissions are stationary sources (91.8%) and stationary sources are situated at scattering locations. Then, each size of “influence regime” for the first three rotated components of SO2 is not larger
than other five air pollutants. The first three rotational components of CO (Fig. 3eeh) identified specific cities with high emissions of mobile sources. Taipei metropolis could be identified by the first component; Taoyuan, Hsinchu and Taichung Cities by the second component; Tainan and Kaoshiung Cities by the third component. Four “influence regimes” of O3 (Fig. 4aed) demonstrate the specific region with homogenous O3 concentrations, and the shapes of four “influence regimes” for O3 were not the same as PM10. The contribution percentage of NOx is 39.7% for stationary source and 56.7% for on road mobile source. Then, every separated subregion for NO2 (Fig. 4eeh) is not easily to identify its physical meaning. Fig. 5 integrates the four “influence regimes” into one graph for PM10 and PM2.5 concentrations; subregions aed for PM10 and Regions IeIV for PM2.5. The cutting value of factor loading was set at 0.60e0.62 for PM10 and 0.59e0.62 for PM2.5. According to analytical results for PM10 and PM2.5 concentrations, four districts with homogenous concentrations were separated. Clearly, the analytical results after separating the four “influence regimes” for PM10 and PM2.5 concentrations were almost the same. Emission sources, topographical and meteorological conditions were parameters affecting concentration variations of particulate matter. The air-quality monitoring stations grouped in the same “influence regime” have the same concentration variation features of PM10 and PM2.5. The first factor to categorize Taiwan into eastern and western sides could be topographical features (terrain height > 2000 m), then southeastern part of Taiwan (Region IV) is isolated as an unique region. The northern Taiwan (Region I) is isolated because of the factors of topographical features (terrain height <2000 m) and emission sources (located at Taipei metropolis). The difference between Regions II and III would be discussed in the following section.
Fig. 7. Monthly average concentrations of ambient air pollutants.
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Fig. 8. The (a) probability and (b) cumulative density function of PM2.5 concentrations over four separated regions.
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The box plots present monthly PM2.5 concentrations for four Regions (Fig. 6). Regions II and VI had the highest and lowest PM2.5 concentrations in Taiwan, respectively. March had the highest median PM2.5 concentration in Regions I and III; January in Region II; October in Region IV. July had the lowest median PM2.5 concentration in Regions I and June in other regions. Considering the seasonal variance in PM2.5 concentrations, summer (June to Aug.) had the lowest PM2.5 concentrations in Taiwan. Spring (Mar. to May) had the highest PM2.5 concentrations in Regions I and IV; winter (Dec. to Feb.) in Regions II and III. Fig. 7 show the monthly values of SO2, CO, O3 and NO2 for separated regions. Region IV has the lowest concentrations of SO2, O3, PM10, PM2.5 and NO2. Region II has the highest concentrations of SO2, PM10, PM2.5 and O3. The concentration trends and values for CO and NO2 are similar between Regions II and III. Major differences of SO2 concentration exist between Regions II and III. Then, the reason for the delineation of Regions II and III would be the geographic locations and emission intensity of stationary sources. Fig. 8 depicts the probability and cumulative distributions of PM2.5 concentrations over four separated regions. The PM2.5 concentrations were equally divided into twenty-four intervals between 0 and 120 mg m3, and the concentrations greater than 120 mg m3 were categorized as one interval. Probability distribution of PM2.5 concentrations demonstrates that the large probability values exist at 20e30 mg m3 for Regions I, II and III, and at 10e15 mg m3 for Region IV. The rates at which PM2.5 and PM10 concentrations exceeded the 24-h particulate matter limits (Fig. 9)dPM2.5 > 35 mg m3, PM2.5 > 65 mg m3 and PM10 > 125 mg m3dindicate that southwestern Taiwan had the highest particulate matter concentrations. For 87% of air-quality stations, the rates at which PM2.5 concentrations > 65 mg m3 are higher than the rates at which PM10 concentrations > 125 mg m3. The exceeding rates of PM2.5 concentrations > 35 mg m3 were less than 2% for zero air-quality station; >65 mg m3 for 19% of air-quality stations. The mean exceeding percentage of PM2.5 concentration are 26%, 57%, 40% and 5% for stations located in Regions I, II, III and IV for PM2.5 standard of 35 mg m3; 3%, 18%, 7% and 0% for PM2.5 standard of 65 mg m3. Meanwhile, the percentage of exceeding PM10 standard of 125 mg m3 is 5.0%, and O3 standard of 120 ppb is 2.4%. Therefore, PM2.5 standard of 65 mg m3 is
Fig. 9. The rates (%) at which 24-h particulate matter exceed air-quality standards. (a) PM2.5 > 35 mg m3(b) PM2.5 > 65 mg m3 (c) PM10 > 125 mg m3.
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Fig. 10. Seasonal values of the rate (%) at which 24-h PM2.5 were >35 mg m3.
stricter than PM10 standard of 125 mg m3 and O3 standard of 120 ppb for current ambient air-quality over Taiwan. Therefore, if the rate at which PM2.5 concentrations exceed 35 or 65 mg m3 is considered as the NAAQS in the near future, the priority of reducing PM10
concentrations would be substituted by PM2.5 concentrations; furthermore, the first priority in controlling criteria air pollutants would be to reduce PM2.5 concentrations in urban regions and southwestern Taiwan.
T.-Y. Yu / Atmospheric Environment 44 (2010) 2902e2912 0.35 Region I Region II Region III Region IV
0.30
Probability
0.25 0.20 0.15 0.10 0.05 0.00
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Fig. 11. Frequency distribution of the PM2.5/PM10 ratios.
Fig. 9a shows the rate at which PM2.5 concentrations were >35 mg m3; this rate was 10e40% for Region I, 36e68% for II, 26e52% for III, and 4e14% for VI. Considering the rate at which PM2.5 24-h concentrations were above 35 mg m3, 35% of air-quality stations, 94% of stations in Region II, is higher than 50%; 26% of air-quality stations, all stations in Region II, is higher than 55%. Fig. 9b presents rates at which PM2.5 24-h concentrations were >65 mg m3; these rates were 0e7%, 7e34%, 2e13% and 0e2% for Regions I, I, III and IV. With the rate at which PM2.5 24-h concentrations were above 65 mg m3, 36% of air-quality stations, 89% of stations in Region II, is higher than 10%; 24% of air-quality stations, 62% of stations in Region II, is higher than 15%. The rates at which 24-h PM10 concentrations > 125 mg m3 (Fig. 9c) were 0e3%, 2e23%, 0e4% and 0e1% at Regions I, II, III and IV. Considering the rate at which PM10 24-h concentrations were above 125 mg m3, 44% of air-quality stations, 84% of stations in Region II, is higher than 3%; 31% of airquality stations, all stations in Region II, is higher than 5%. The seasonal values of the rate (%) at which 24-h PM2.5 were >35 mg m3(Fig. 10) present that Regions I and IV have the highest exceeding rates in spring, Regions II and III in winter; Taiwan has the low exceeding rates in summer. In winter, the rate at which 24-h PM2.5 concentrations above 35 mg m3 is greater than 75% for the air-quality stations located at Region II. This result could supply useful information for performing seasonal PM2.5 control strategies. The PM2.5/PM10 mass ratios were computed and the mean (standard deviation) values is 0.56 (0.14) for Region I, 0.50 (0.09) for II, 0.54 (0.13) for III and 0.52 (0.14) for IV. The probabilities of PM2.5/ PM10 ratios for varied regions are plotted in Fig. 11 and the ratios appear to be normally distributed. Region II has the highest mean PM2.5 and PM10 concentrations and the lowest mean PM2.5/PM10 ratios for the four regions. While the PM2.5/PM10 ratio is 0.5e0.6, the probability has peak value (0.27e0.32). The PM2.5/PM10 ratio is 0.6e0.8, Region II has the highest probability than other regions. The PM2.5/PM10 ratio ranges from 0.1 to 0.5, Region II has the lowest probability than other areas. The PM2.5/PM10 ratio is 0.5e1.0, Region IV has the lowest probability. 4. Conclusions This paper demonstrates the use of emission inventory, concentrations of ambient air pollutants and PCA approach to provide new insights for the discussion of PM2.5 concentrations. Using principal component analysis, this investigation analyzed ambient air-quality measured data and the spatiotemporal variability of PM2.5 and other ambient air pollutants over Taiwan. Four components of 76
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rotational components were cited as major factors and explained 87% and 84% of PM2.5 and PM10 concentration variance. The critical loading factors for rotational components were set at 0.59e0.62 and then categorized into four “influence regimes” for PM10 and PM2.5 concentrations. The four “influence regimes” based on particulate matter of different diameters, PM10 and PM2.5, were the same. This study also finds physical interpretations of the delineation of four “influence regimes”. This delineation provides the TEPA with a spatial assessment of atmospheric carrying capacity for particulate matter. Local governments located at the same “influence regimes” with unique PM2.5 concentrations should implement the same trans-boundary air pollution control programs. Southwestern and eastern Taiwan had the highest and lowest PM2.5 concentrations, respectively. In terms of seasonal trends for PM2.5 concentrations, summer had the lowest PM2.5 concentrations, eastern and northern Taiwan had the highest PM2.5 concentrations in spring; other regions in winter. Considering the rate at which PM2.5 24-h concentrations were above 35 mg m3, 26% of air-quality stations, all stations in Region II, is higher than 55%. With the rate at which PM2.5 24-h concentrations were above 65 mg m3, 24% of airquality stations, 62% of stations in Region II, is higher than 15%. If PM2.5 standard is considered in the NAAQS, the priority of reducing PM10 concentrations can be replaced with PM2.5; moreover, the first priority for controlling criteria air pollutants (SO2, CO, O3, PM10 and NO2) in urban areas and southwestern Taiwan would be PM2.5. Future studies should provide data and contributed rates of secondary air pollutants and trans-boundary effects of particulate matter emissions. Investigations of source apportionments for PM2.5 concentrations should be conducted to identify the rates at which anthropogenic sources contribute to PM2.5 concentrations.
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Characterization of ambient PM2.5 concentrations Tai-Yi Yu* Department of Risk Management and Insurance, Ming Chuan University, 250 Zhong Shan N. Rd., Sec. 5, Taipei 111, Taiwan
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 September 2009 Received in revised form 17 April 2010 Accepted 19 April 2010
Statistical spatial and temporal analysis of PM2.5 concentrations in ambient air using principal component analysis may provide health risk information for air-quality management. This investigation simultaneously determines and interprets spatial variations and features of PM2.5 concentrations using ambient air-quality monitoring data during 2006e2008 and emission inventory. Daily mean values of PM2.5 and PM10 and maximum hourly data of SO2, CO, O3 and NO2 were calculated as sampling data for year 2006e2008. Therefore, principal component analysis and descriptive statistics of ambient air pollutants were utilized to assess the spatial features and variations of PM2.5 concentrations. This study also provides PM2.5/PM10 ratios and the rates at which 24-h particulate matter exceed air-quality standards over Taiwan. Analytical results indicate that four rotational components cumulatively explain 87% and 84% of concentration variances for PM10 and PM2.5 and form a delineation of four “influence regimes.” The separated districts of the four “influence regimes” for PM10 and PM2.5 were the same. With the rate at which PM2.5 24-h concentrations were above 65 mg m3, 36% of air-quality stations, is higher than 10%; 24% of air-quality stations is higher than 15%. Based on analytical results, if the PM2.5 limit would be considered as National Ambient Air-Quality Standard in the future, the priority of reducing PM10 or O3 concentrations in the past decade could be replaced with PM2.5. The novel methodologies presented in this study can spatially assess adequate boundaries of atmospheric carrying capacity for particulate matter. Local governments located at the same influence regime characterized by factor loading isopleths should implement trans-boundary air pollution control programs. Box plots, the rates of particulate matter exceeding air-quality standards and PM2.5/PM10 ratios in distinct “influenced regimes” were also examined. Continued study of spatial and temporal variations in airborne PM2.5 concentrations will provide sufficient information about health risks and for air-quality control programs. Ó 2010 Elsevier Ltd. All rights reserved.
Keywords: Principal component analysis Spatial variation PM2.5 Taiwan
1. Introduction Exposure to airborne particulate matter is strongly associated with adverse health effects, affecting the respiratory and cardiovascular systems (Englert, 2004; WHO, 2005). The fine particulates, small particles less than 2.5 mm in diameter (PM2.5), can get deep into the lungs and cause serious health problems. Each 10 mg m3 increase in PM2.5 was associated with approximately a 4%, 6%, and 8% increase in all cause, cardiopulmonary, and lung cancer mortality respectively (Pope et al., 2002). Hence, the spatial information and characteristics of PM2.5 concentrations are crucial data for epidemiological researches to estimate the health effects associated with fine particulate matter. These data are also needed for human exposure risk models and decision making processes for airquality goals. Many researchers (Chen et al., 1999, 2001; Lin, 2002; Wu et al., 2002; Fang et al., 2003a, 2003b; Lai et al., 2004; Horng * Tel.: þ886 2 28824564; fax: þ886 2 28809577. E-mail address: [email protected] 1352-2310/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2010.04.034
et al., 2007) investigated the speciation of PM2.5 concentrations and contributed sources of PM2.5 emissions in Taiwan. However, these studies could not simultaneously supply the spatial variations and long term measurements of PM2.5 concentrations over Taiwan. Principal component analysis (PCA), one manner of multivariate statistical techniques, can reduce the numbers of variables and simplified dimensions with several dominant factors in complexly environmental problems. PCA is an effective method for classifying meteorological patterns (Maheras, 1984; Eder et al., 1994; Cheng and Lam, 2000), simplifying the emission sources that affect concentrations of air pollutants (Statheropopulos et al., 1998; Tsai, 2000; Stella et al., 2002; Lengyel et al., 2004; Yu and Yu, 2004; Yu and Chang, 2006; Sakihama et al., 2008; Wang et al., 2008) and providing spatial patterns of interrelated air pollutants (Juang et al., 1996; Yu and Chang, 2001; Yang et al., 2008). PCA is an effective method for finding underlying components and attributed contributions of possible emitted sources for highly correlated variables on environmental problems. Verbeke et al. (1984) utilized PCA technique to determine the correlations
T.-Y. Yu / Atmospheric Environment 44 (2010) 2902e2912
among 26 VOCs; three of 26 components were extracted and at least accounted for 54% of concentration variances over four sites in Netherlands. Statheropoulos et al. (1996) adopted PCA manner on GC/MS data sets for resolving coeluted substances in doping control analysis. Manoli et al. (2002) applied PCA method and trace elements (Fe, Zn) to identify possible emission sources of coarse and fine aerosols. Tsai (2005) used PCA to establish the relationship among atmospheric visibility, major air pollutants and meteorological parameters in the urban area and demonstrated that increased vehicular emissions, road traffic dust and industrial activity markedly impacted visibility. Abdul-Wahab et al. (2005) selected high loadings of rotated principal components to obtain adequate predictor variables in the regression model. Data on the concentrations of seven air pollutants and five meteorological variables were employed to predict the ozone concentration with multiple linear and principal component regression methods. Almeida et al. (2005) recognized seven main sources and determined their mass contribution for particulate matter with aerosol chemical composition data. Seven possible sources were identified as soil, sea, secondary aerosols, road traffic, fuel-oil combustion, coal combustion and a Se/Hg emission source. Lynam and Keeler (2006) develop source receptor relationships for mercury species in urban Detroit. Liu et al. (2007) sampled road dusts and analyzed 16 polycyclic aromatic hydrocarbons (PAHs) with GC/MS from central Shanghai in January and August, respectively. The contribution percentages of pyrogenic and petrogenic sources were respectively 71% and 11% in January; 65% and 14% in August, respectively. Vardoulakis and Pavlos (2008) used PCA and regression analysis to quantify the contribution of non-combustion sources to the observed PM10 background levels and the contribution ranged 45e70% in Birmingham and 41e74% in Athens. PCA also provides division of spatial distribution and reduction of variables for environmental problems. Eder (1989) and Eder et al. (1993) presented the spatial features of SO2 4 and O3 concentrations
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with several principal components; six of 40 and seven of 77 principals were extracted and accounted for 74% and 64% of concentration variance, respectively, over eastern United States. Astel et al. (2007) demonstrated three classification techniques, PCA, cluster analysis and self-organizing maps, on a large environmental data set of chemical indicators of river water quality and revealed different patterns of monitoring sites conditionally named “tributary”, “urban”, “rural” or “background”. Pires et al. (2007) applied PCA and cluster analysis, two multivariate statistical techniques, to identify city areas with similar air pollution behaviors and locate emission sources. Zhou et al. (2007a) investigated the spatial distribution patterns of 12 heavy metals in marine sediments at 59 sites from 1998 to 2004 and identified spatial human impacts on global and local scales. PCA method further subdivided human impacts and their three affected areas, explaining 84e87% of the total variances, respectively. Zhou et al. (2007b) evaluated the spatiotemporal patterns and source apportionment of coastal water pollution in eastern Hong Kong with 14 variables at 27 sites. Five potential pollution sources were identified for each part by rotated principal component analysis, explaining 71% and 68% of the total variances for JuneeSeptember and the remaining months, respectively. This investigation aims to present the use of PCA, ambient airquality monitoring data and emission inventory of air pollutants to supply useful spatial and temporal information of PM2.5 concentrations. The following three issues are addressed using the multivariate statistical manners: 1. Typical, seasonal and spatial features of PM2.5 concentrations; 2. Spatial patterns of high PM2.5 concentrations; 3. Spatial distributions of ambient PM2.5/PM10 ratios and PM2.5 emissions. Analytical results will provide spatial characteristics and the current status of ambient PM2.5 concentrations in Taiwan.
Fig. 1. Locations of monitoring stations and PM2.5 emissions (tons year1) over Taiwan.
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Table 1 Emission inventory of air pollutants over Taiwan (TEPA, 2006). Types of Sources
PM2.5
PM10 Tons/y
%
Tons/y
SOx %
NOx
Tons/y
%
NMHC
Tons/y
%
Tons/y
CO %
Tons/y
%
A. Stationary sources 1. Combustion 1.1 Fuel combustion Power generation Industries Commercial Residential 1.2 Non-fuel combustion 2. Nonecombustion Fugitive Industrial Process B. Mobile sources 1. On road Gasoline vehicles Diesel vehicles Motorcycles 2. Off road
304,646 29,368 15,910 4328 10,563 818 201 13,458 275,279 189,678 85,465 31,929 31,348 7764 17,903 5681 581
90.5 8.7 4.7 1.3 3.0 0.2 0.1 4.0 81.8 56.4 25.4 9.5 9.3 2.3 5.3 1.7 0.2
148,356 21,462 9098 2643 5952 315 188 12,364 126,894 74,360 52,441 26,608 26,201 5742 15,929 4529 407
84.8 12.4 5.2 1.5 3.5 0.2 0.1 7.1 72.5 42.5 30.0 15.2 15.0 3.3 9.1 2.6 0.2
173,679 136,470 135,732 63,234 59,122 12,611 766 737 37,209 40 36,692 15,453 7226 833 6206 187 8228
91.8 72.2 71.8 33.4 31.3 6.7 0.4 0.4 19.7 0.3 19.4 8.2 3.8 0.4 3.3 0.1 4.3
255,040 192,601 80,410 88,445 77,796 9513 4656 12,191 62,439 50 62,247 386,735 364,163 96,215 247,540 20,408 22,573
39.7 30.0 28.1 13.8 12.1 1.5 0.7 1.9 9.7 0.0 9.7 60.3 56.7 15.0 38.6 3.2 3.5
605,338 29,520 11,089 1415 9295 256 122 18,431 575,818 420,427 155,391 258,682 257,404 134,529 23,859 99,015 1278
70.1 3.4 1.3 0.2 1.0 0.0 0.0 2.1 66.7 48.7 18.0 30.0 29.8 15.6 2.8 11.5 0.1
248,687 177,891 95,177 46,919 45,896 1349 1012 82,713 70,796 0 70,795 1,423,131 1,417,277 1,111,136 93,233 212,909 5853
14.9 10.6 5.7 2.8 2.7 0.1 0.1 4.9 4.2 0.0 4.2 85.1 84.8 66.5 5.6 12.7 0.3
Sum
336,575
100.0
174,964
100.0
189,132
100.0
641,776
100.0
864,021
100.0
1,671,818
100.0
TEPA utilized the rate of Pollutant Standards Index (PSI) > 100 and the National Ambient Air-Quality Standard (NAAQS) as airquality management goals. Notably, PM10 and O3 are the two dominant air pollutants that violate these limits frequently. The TEPA set the 24-h PM10 standard at 125 mg m3. The PM2.5 concentration was first included in the NAAQS by the U.S. EPA in 1997 (U.S. EPA, 1996, 2004, 2009), followed by Canada, the World Health Organization (WHO) and the European Union. This study calculated the rate measurements at air-quality stations exceed 24-h PM2.5 standards established by U.S. EPA. Both standards of 65 and 35 mg m3 (effective December 17, 2006) were considered. If the rate at which 24-h PM2.5 concentrations exceeded 35 or 65 mg m3 was discussed, the first priority in controlling criteria air pollutants for local governments may change. Clean Air Act requires EPA to set National Ambient Air Quality Standards (NAAQS) for six common air pollutants. These commonly found air pollutants, also known as “criteria pollutants”, are found all over the United States. They are particle pollution (often referred to as particulate matter), groundlevel ozone, carbon monoxide, sulfur oxides, nitrogen oxides, and lead. EPA calls these pollutants “criteria” air pollutants because it regulates them by developing human health-based and/or environmentally-based criteria (science-based guidelines) for setting permissible levels. The six air pollutants, PM10, PM2.5, SO2, CO, O3 and NO2, were performed with PCA method from 2006 to 2008. Sampling data for PM10 and PM2.5 were daily arithmetic mean values; other air pollutants sample maximum hourly values within a day. Quality Assurance (QA) is implemented to ensure the accuracy and precision of monitoring data provided and achieve data quality objectives. Based on the QA program, the observation stations conduct monthly checks and an annual performance check on its pollutant analysis instruments and particulate analysis instruments.
2. Materials and methods Since June 1993, the Air-quality Observation and Forecast Network (AQOFN), established by Taiwan’s Environmental Protection Administration (TEPA), has continuously monitored ambient air pollutants using 66 stations spread throughout Taiwan. To address issues related to PM2.5 concentrations and provide information to those making regulations, the TEPA has introduced instruments for monitoring PM2.5 concentrations in the AQOFN in 2004. At the end of 2006, the TEPA had 76 air-quality observation stations nationwide that measured concentrations of such air pollutants as PM10, PM2.5, CO, SO2, NO, NO2, NOx, O3 and hydrocarbons. Fig. 1 shows Taiwan’s topography, locations of monitoring stations and political boundaries in Taiwan. Fig. 1 also presents the PM2.5 spatial emissions (tonsyear1) estimated with the Taiwan Emission Database System (TEDS) model developed by the TEPA (TEPA, 2006). The area of each cell is 1 km2 (1 1 km). Table 1 show the emission inventory of six air pollutants, PM2.5, PM10, SOx, NOx, NMHC and CO over Taiwan (TEPA, 2006). Three major sources, fugitive, industrial processes and on road mobiles sources, present high contribution to PM10 and PM2.5. Fugitive emission includes construction sites and road dusts. Fugitive emission accounts for 56.4% of PM10 and 42.5% of PM2.5; industrial processes account for 25.4% of PM10 and 30.0% of PM2.5; on road mobile sources account for 9.3% of PM10 and 15.0% of PM2.5. The contribution percentages of SOx emission for power generation, industries (fuel of combustion) and industrial processes contribute are 66.5%, 12.7% and 5.6%. Diesel vehicles, gasoline vehicles and power generation account for 66.5%, 12.7% and 5.6% of NOx emissions. Gasoline vehicles, motorcycles and diesel vehicles contribute 66.5%, 12.7% and 5.6% of CO emissions.
Table 2 Eigenvalues and explained variances of rotated principal components for air pollutants. Components
1 2 3 4 5
PM2.5
PM10
SO2
CO
O3
NO2
l
Var
Cum
l
Var
Cum
l
Var
Cum
l
Var
Cum
l
Var
Cum
l
Var
Cum
26.4 22.5 9.7 4.0 1.5
37 31 14 6 2
37 68 81 87 89
24.9 19.3 12.0 4.1 1.1
35 27 17 6 2
35 62 78 84 86
6.4 5.1 4.5 4.1 3.6
9 7 6 6 5
9 16 22 28 33
15.8 13.8 10.7 5.5 2.6
22 19 15 8 4
22 41 56 64 67
16.8 14.9 14.6 7.0 4.7
23 21 20 10 7
23 44 64 74 81
22.7 15.6 10.7 3.3 1.7
32 22 15 5 2
32 53 68 73 75
l: Eigenvalues Var: Percentage of variance (%) Cum: Cumulative percentage (%).
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Fig. 2. Separated subregions for PM10 and PM2.5. The first (a), second (b), third (c) and fourth (d) subregions for PM10; the first (e), second (f), third (g) and fourth (h) subregions for PM2.5.
Fig. 3. Separated subregions for SO2 and CO. The first (a), second (b), third (c) and fourth (d) subregions for SO2; the first (e), second (f), third (g) and fourth (h) subregions for CO.
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Fig. 4. Separated subregions for O3 and NO2. The first (a), second (b), third (c) and fourth (d) subregions for O3; the first (e), second (f), third (g) and fourth (h) subregions for NO2.
Fig. 5. Four homogenous subregions for PM10 and PM2.5 concentrations over Taiwan.
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Daily value calculates arithmetic average of effective hourly concentrations within a day. Effective daily value should at least have 16 effective hourly values. The completeness of hourly PM10 concentrations is 98.3%, 97.7% and 98.3% in 2006, 2007 and 2008. The average values at individual stations were substituted for lost data. The normalized value is (1)
Zik ¼
Cik mi Si
Zik ¼
n X
Lij Pjk
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(2)
i¼1
where n is the number of stations, Lij is the factor loading of the ith station on the jth rotational component, and Pjk is the component score of the kth station for the jth rotational component. The rotational principal component can be derived by inverting Equation (2).
(1) 3. Results and discussion
where Zik is the normalized value of the kth observation at the ith station, Cik is the kth values at the ith station, mi is the mean value at the ith station, and Si is the standard deviation at the ith station. A correlation matrix presents the isopleths of component loadings and can be regarded as correlation coefficients between rotational components and individual stations. The Varimax rotational technique can increase the segregation between component loadings and facilitate an easy spatial interpretation of correlated data. The model for rotational principal component analysis is (2)
Table 2 demonstrates eigenvalues and explained variances of rotated principal components for six air pollutants. The eigenvalues of first five rotational components for six air pollutants were >1; the cumulative explained variances of the first four rotational components were 87% and 84% for PM10 and PM2.5, respectively. The first rotational component accounts for 37% and 35% of explained variances for PM10 and PM2.5; the second rotational component accounts for 31% and 27%. Comparing explained percentages of the every
Fig. 6. Box plots of PM2.5 concentrations for Regions (a) I, (b) II, (c) III and (d) IV.
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component for the first five rotated principal components, the differences between PM10 and PM2.5 are the smallest among six air pollutants. The explained variances of rotational components after the fourth rotational component for PM10 and PM2.5 were <2% and the factor loading for one station between PM10 (PM2.5) and the fifth rotational component is greater than 0.45. Therefore, components after the fourth component were not considered dominant factors in this study. Factor loadings reveal the correlations between a rotational component and monitoring stations and provide the hidden physical meaning of principal components. The Varimax rotational technique was utilized to confirm the relative contribution and spatial features of individual subregions. Each rotational component identified an “influence regime” that existed consistent trend of air pollutant’s concentration. The factor loadings of the first four components were plotted as Fig. 2 for PM10 and PM2.5; Fig. 3 for SO2 and CO; Fig. 4 for O3 and NO2. A high factor loading indicates a strong correlation between an air-quality station and rotational component. The cutting value of factor loading for each rational component could be set as 0.55e0.65 to isolate an “influence regime” and remove possible overlaps among rotational components. With the isopleths of loading factors, the first four components show high loading factors over specific and separated regions for PM10 and PM2.5. The four “influence regimes” of PM10 (Fig. 2aed) and PM2.5 (Fig. 2eeh) are similar in shape and size. Four “influence regimes” of SO2 (Fig. 3aed) demonstrate several stationary sources were clustered at separated locations. The dominant sources of SOx emissions are stationary sources (91.8%) and stationary sources are situated at scattering locations. Then, each size of “influence regime” for the first three rotated components of SO2 is not larger
than other five air pollutants. The first three rotational components of CO (Fig. 3eeh) identified specific cities with high emissions of mobile sources. Taipei metropolis could be identified by the first component; Taoyuan, Hsinchu and Taichung Cities by the second component; Tainan and Kaoshiung Cities by the third component. Four “influence regimes” of O3 (Fig. 4aed) demonstrate the specific region with homogenous O3 concentrations, and the shapes of four “influence regimes” for O3 were not the same as PM10. The contribution percentage of NOx is 39.7% for stationary source and 56.7% for on road mobile source. Then, every separated subregion for NO2 (Fig. 4eeh) is not easily to identify its physical meaning. Fig. 5 integrates the four “influence regimes” into one graph for PM10 and PM2.5 concentrations; subregions aed for PM10 and Regions IeIV for PM2.5. The cutting value of factor loading was set at 0.60e0.62 for PM10 and 0.59e0.62 for PM2.5. According to analytical results for PM10 and PM2.5 concentrations, four districts with homogenous concentrations were separated. Clearly, the analytical results after separating the four “influence regimes” for PM10 and PM2.5 concentrations were almost the same. Emission sources, topographical and meteorological conditions were parameters affecting concentration variations of particulate matter. The air-quality monitoring stations grouped in the same “influence regime” have the same concentration variation features of PM10 and PM2.5. The first factor to categorize Taiwan into eastern and western sides could be topographical features (terrain height > 2000 m), then southeastern part of Taiwan (Region IV) is isolated as an unique region. The northern Taiwan (Region I) is isolated because of the factors of topographical features (terrain height <2000 m) and emission sources (located at Taipei metropolis). The difference between Regions II and III would be discussed in the following section.
Fig. 7. Monthly average concentrations of ambient air pollutants.
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Fig. 8. The (a) probability and (b) cumulative density function of PM2.5 concentrations over four separated regions.
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The box plots present monthly PM2.5 concentrations for four Regions (Fig. 6). Regions II and VI had the highest and lowest PM2.5 concentrations in Taiwan, respectively. March had the highest median PM2.5 concentration in Regions I and III; January in Region II; October in Region IV. July had the lowest median PM2.5 concentration in Regions I and June in other regions. Considering the seasonal variance in PM2.5 concentrations, summer (June to Aug.) had the lowest PM2.5 concentrations in Taiwan. Spring (Mar. to May) had the highest PM2.5 concentrations in Regions I and IV; winter (Dec. to Feb.) in Regions II and III. Fig. 7 show the monthly values of SO2, CO, O3 and NO2 for separated regions. Region IV has the lowest concentrations of SO2, O3, PM10, PM2.5 and NO2. Region II has the highest concentrations of SO2, PM10, PM2.5 and O3. The concentration trends and values for CO and NO2 are similar between Regions II and III. Major differences of SO2 concentration exist between Regions II and III. Then, the reason for the delineation of Regions II and III would be the geographic locations and emission intensity of stationary sources. Fig. 8 depicts the probability and cumulative distributions of PM2.5 concentrations over four separated regions. The PM2.5 concentrations were equally divided into twenty-four intervals between 0 and 120 mg m3, and the concentrations greater than 120 mg m3 were categorized as one interval. Probability distribution of PM2.5 concentrations demonstrates that the large probability values exist at 20e30 mg m3 for Regions I, II and III, and at 10e15 mg m3 for Region IV. The rates at which PM2.5 and PM10 concentrations exceeded the 24-h particulate matter limits (Fig. 9)dPM2.5 > 35 mg m3, PM2.5 > 65 mg m3 and PM10 > 125 mg m3dindicate that southwestern Taiwan had the highest particulate matter concentrations. For 87% of air-quality stations, the rates at which PM2.5 concentrations > 65 mg m3 are higher than the rates at which PM10 concentrations > 125 mg m3. The exceeding rates of PM2.5 concentrations > 35 mg m3 were less than 2% for zero air-quality station; >65 mg m3 for 19% of air-quality stations. The mean exceeding percentage of PM2.5 concentration are 26%, 57%, 40% and 5% for stations located in Regions I, II, III and IV for PM2.5 standard of 35 mg m3; 3%, 18%, 7% and 0% for PM2.5 standard of 65 mg m3. Meanwhile, the percentage of exceeding PM10 standard of 125 mg m3 is 5.0%, and O3 standard of 120 ppb is 2.4%. Therefore, PM2.5 standard of 65 mg m3 is
Fig. 9. The rates (%) at which 24-h particulate matter exceed air-quality standards. (a) PM2.5 > 35 mg m3(b) PM2.5 > 65 mg m3 (c) PM10 > 125 mg m3.
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Fig. 10. Seasonal values of the rate (%) at which 24-h PM2.5 were >35 mg m3.
stricter than PM10 standard of 125 mg m3 and O3 standard of 120 ppb for current ambient air-quality over Taiwan. Therefore, if the rate at which PM2.5 concentrations exceed 35 or 65 mg m3 is considered as the NAAQS in the near future, the priority of reducing PM10
concentrations would be substituted by PM2.5 concentrations; furthermore, the first priority in controlling criteria air pollutants would be to reduce PM2.5 concentrations in urban regions and southwestern Taiwan.
T.-Y. Yu / Atmospheric Environment 44 (2010) 2902e2912 0.35 Region I Region II Region III Region IV
0.30
Probability
0.25 0.20 0.15 0.10 0.05 0.00
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0.3
0.4
0.5
0.6
0.7
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0.9
1.0
Fig. 11. Frequency distribution of the PM2.5/PM10 ratios.
Fig. 9a shows the rate at which PM2.5 concentrations were >35 mg m3; this rate was 10e40% for Region I, 36e68% for II, 26e52% for III, and 4e14% for VI. Considering the rate at which PM2.5 24-h concentrations were above 35 mg m3, 35% of air-quality stations, 94% of stations in Region II, is higher than 50%; 26% of air-quality stations, all stations in Region II, is higher than 55%. Fig. 9b presents rates at which PM2.5 24-h concentrations were >65 mg m3; these rates were 0e7%, 7e34%, 2e13% and 0e2% for Regions I, I, III and IV. With the rate at which PM2.5 24-h concentrations were above 65 mg m3, 36% of air-quality stations, 89% of stations in Region II, is higher than 10%; 24% of air-quality stations, 62% of stations in Region II, is higher than 15%. The rates at which 24-h PM10 concentrations > 125 mg m3 (Fig. 9c) were 0e3%, 2e23%, 0e4% and 0e1% at Regions I, II, III and IV. Considering the rate at which PM10 24-h concentrations were above 125 mg m3, 44% of air-quality stations, 84% of stations in Region II, is higher than 3%; 31% of airquality stations, all stations in Region II, is higher than 5%. The seasonal values of the rate (%) at which 24-h PM2.5 were >35 mg m3(Fig. 10) present that Regions I and IV have the highest exceeding rates in spring, Regions II and III in winter; Taiwan has the low exceeding rates in summer. In winter, the rate at which 24-h PM2.5 concentrations above 35 mg m3 is greater than 75% for the air-quality stations located at Region II. This result could supply useful information for performing seasonal PM2.5 control strategies. The PM2.5/PM10 mass ratios were computed and the mean (standard deviation) values is 0.56 (0.14) for Region I, 0.50 (0.09) for II, 0.54 (0.13) for III and 0.52 (0.14) for IV. The probabilities of PM2.5/ PM10 ratios for varied regions are plotted in Fig. 11 and the ratios appear to be normally distributed. Region II has the highest mean PM2.5 and PM10 concentrations and the lowest mean PM2.5/PM10 ratios for the four regions. While the PM2.5/PM10 ratio is 0.5e0.6, the probability has peak value (0.27e0.32). The PM2.5/PM10 ratio is 0.6e0.8, Region II has the highest probability than other regions. The PM2.5/PM10 ratio ranges from 0.1 to 0.5, Region II has the lowest probability than other areas. The PM2.5/PM10 ratio is 0.5e1.0, Region IV has the lowest probability. 4. Conclusions This paper demonstrates the use of emission inventory, concentrations of ambient air pollutants and PCA approach to provide new insights for the discussion of PM2.5 concentrations. Using principal component analysis, this investigation analyzed ambient air-quality measured data and the spatiotemporal variability of PM2.5 and other ambient air pollutants over Taiwan. Four components of 76
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rotational components were cited as major factors and explained 87% and 84% of PM2.5 and PM10 concentration variance. The critical loading factors for rotational components were set at 0.59e0.62 and then categorized into four “influence regimes” for PM10 and PM2.5 concentrations. The four “influence regimes” based on particulate matter of different diameters, PM10 and PM2.5, were the same. This study also finds physical interpretations of the delineation of four “influence regimes”. This delineation provides the TEPA with a spatial assessment of atmospheric carrying capacity for particulate matter. Local governments located at the same “influence regimes” with unique PM2.5 concentrations should implement the same trans-boundary air pollution control programs. Southwestern and eastern Taiwan had the highest and lowest PM2.5 concentrations, respectively. In terms of seasonal trends for PM2.5 concentrations, summer had the lowest PM2.5 concentrations, eastern and northern Taiwan had the highest PM2.5 concentrations in spring; other regions in winter. Considering the rate at which PM2.5 24-h concentrations were above 35 mg m3, 26% of air-quality stations, all stations in Region II, is higher than 55%. With the rate at which PM2.5 24-h concentrations were above 65 mg m3, 24% of airquality stations, 62% of stations in Region II, is higher than 15%. If PM2.5 standard is considered in the NAAQS, the priority of reducing PM10 concentrations can be replaced with PM2.5; moreover, the first priority for controlling criteria air pollutants (SO2, CO, O3, PM10 and NO2) in urban areas and southwestern Taiwan would be PM2.5. Future studies should provide data and contributed rates of secondary air pollutants and trans-boundary effects of particulate matter emissions. Investigations of source apportionments for PM2.5 concentrations should be conducted to identify the rates at which anthropogenic sources contribute to PM2.5 concentrations.
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