The temporal distribution characteristics of the ambient particulate matter in beijing

Frontier of Environmental Science September 2014, Volume 3, Issue 3, PP.84-96

The Temporal Distribution Characteristics of the Ambient Particulate Matter in Beijing Jiao Jiao1,2, Yaqin Ji1,2#, Jing Zhang1,2, Zhenyu Zhu 1,2, Shijian Zhang 1,2, Yafei Zhang 1,2, Leibo Zhang1,2 1. College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China 2. State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin 300071, China #

Email: jiyaqin@nankai.edu.cn

Abstract From data on the chemical composition of atmospheric particulate matter from the 1980s to the present and from the result of logarithmic concentration diagrams, coefficients of divergence and enrichment factors, the temporal distribution characteristics of the components of ambient particulate matter in Beijing and their controlling factors were comprehensively analyzed. The temporally dynamic changes of the elemental concentrations of atmospheric particulate matter showed that most of the elements presented a peak or a type of double peak. The elemental concentrations were relatively low in the early 1980s. With the growth of population and the development of industry, the air quality in Beijing decreased until the 21st century, when China successfully bid for the 29th Olympic Games. At that time, the city of Beijing took a series of measures to control air pollution, and environmental protection gradually improved, leading to significant decreases in the elemental concentrations. Coefficient divergence analysis showed great dissimilarities in the elemental component spectra of different-sized atmospheric particulate matter during the years analyzed. The elemental enrichment factors demonstrated that elements in atmospheric particulate matter mainly came from anthropogenic sources, namely industrial production, heating and vehicle exhaust. In addition, most of the elements coming from anthropogenic sources were found to be enriched easily in the fine particles.

Keywords: Atmospheric Particulate Matter; Features of Distribution; Coefficient of Divergence; Enrichment Factor

1 INTRODUCTION With the acceleration of social development and urbanization, there has been increasing attention to urban atmospheric aerosols. Aerosol is a peptization state system formed by solid particles and tiny liquid droplets suspended in the atmosphere, with a particle size of 1~10-6 mm. Many effects of aerosols have been recognized: they have a major impact on the climate, for example, and can reduce the solar energy reaching the earth’s surface by 15%, which will lead directly to surface cooling and atmospheric warming (Charlson et al. 1992); a significant increase in aerosol particles in the atmosphere will result in decreased visibility (Schwartz et al. 1996; Xie et al. 2001; Xie et al. 2009; Yang and Ma 2000), thereby affecting the quality of life; aerosol particles, especially fine particles, can act as a carrier of a variety of pathogens (Zhao et al. 1988), thus directly and indirectly affecting human health, as shown by increased mortality and morbidity. Because of all these impacts, urban aerosol particles have become an important research topic in today's universities and have increasingly aroused public attention.

Beijing, the capital of China and the host city for the 2008 Olympic Games, has undergone rapid development in recent years. Therefore, there have been some previous studies of the characteristics of aerosols in Beijing. For example, there has been research on the distribution characteristics of airborne particles (Lang et al. 2013; Li et al.

2008), on the seasonal and regional variations of polycyclic aromatic hydrocarbons in atmospheric particles (Huang et al. 2007; Wang et al. 2010; Zhou et al. 2005), on the seasonal variations and sources of monocarboxylic acids in atmospheric PM10 and PM2.5 (Liu et al. 2009), on the concentration levels and seasonal variations of organic carbon and elemental carbon (Chi et al. 2000), on the characteristics of elements contained in suspended particles of - 84 http://www.ivypub.org/fes

different sizes (Wang et al. 2002), and on the sources of inhalable pollutant particles (Charlson et al. 1992; Tian and Wang 2011).

Atmospheric particulate matter is one of the major pollutants in Beijing. Its chemical composition during different periods is variable, and it also has different effects on the environment and human health. Although there has been much research on the chemical composition of atmospheric particulate matter in Beijing since the 1980s, there has been little comprehensive analysis of its temporal variation. Therefore, the present study was undertaken to collect data on the chemical composition of the atmospheric particulate matter from the 1980s to the present and to analyse the data with the divergence coefficient and the enrichment factors. The objective of this study was a preliminary analysis of the characteristics of the temporal distribution and the causes of pollution that would provide the basis for the control of air pollution and the formulation of government policy.

2 METHODOLOGY An easy way to comply with the journal paper formatting requirements is to use this document as a template and simply type your text into it.

2.1 Data Collection Investigators from the Institute of Atmospheric Physics, the Institute of Low Energy Nuclear Physics of Beijing Normal University and some other academic research institutes collected aerosol samples in Beijing and studied their chemical composition. Data on the chemical composition of atmospheric particulate matter were collected from a number of published papers. The component spectrum of PM2 was monitored in 1983 (Wang et al. 1986), 1987 (Wang et al. 1990), 1992 (Zhang et al. 1998), 1999 (Zhang et al. 2000), 2000 (Wang et al. 2002a), 2001 (Duan et al.

2006), 2006 (Yang et al. 2008) and 2008 (Yu et al. 2010). The component spectra of TSP were obtained for 1983 (Wang et al. 1986), 1987 (Wang et al. 1990), 1999 (Zhang et al. 2002), 2000 (Xie et al. 2003), 2003 (Okuda et al.

2008), 2006 (Yang et al. 2008) and 2008 (Yu et al. 2010). The component spectra of PM10 were acquired for 2000 (Xie et al. 2003), 2002-2003 (Sun et al. 2004), 2004 (Wang et al. 2006) and 2006 (Cui et al. 2008). The data and their sources were listed in Tables 1-3.

TABLE 1 COMPONENT SPECTRA OF PM2FROM DIFFERENT PERIODS (ng/m3) Sampling site

Al

S

1983 01/05-01/15

MT

350

2039

1506 1183 89.6 9.2

1987 Jan. & Mar.

BNU

–

6854

1132

1992 12/14-01/13

MT

1039.8

4783

928.4 1166 91.3

1999 12/21-12/24

MT

3386.5 3594.8 633.3 634.8 47.2 0.7

2000 12/01-12/30

CQ

2001 12/01-02/28 2006 11/16-11/30 2008 11/11-12/12

Year

Date

K

Ca

Ti

614

V

76

874

4485

1194 2772

80

CGZ

–

5100

2500 1340

90

MT

546.5

–

2995 2578

–

BNU

1746

1933

853

37

1645

Cr

Fe

19.9 29.2 505.2

6 –

Mn

24

Cu

2.8

779

9

Zn

As

Pb

Reference

7.5 132.8 9.7

97.5

(Wang et al. 1986)

17

26.4

23

220

(Wang et al. 1990)

11.6 76.5 844.1 10.4 19.3 237.3

–

133.4

(Zhang et al. 1998)

9.3

55

Ni

143

(Zhang et al. 2000)

37

44

77.6 574.8 13.9 35.7 165.4 24.1 59

1017

71

77

236

80

218

(Wang et al. 2002a)

–

–

–

1380

–

50

550

70

210

(Duan et al. 2006)

305

(Yang et al. 2008)

57

(Yu et al. 2010)

26.7 120.2 95.2 1309.1 16.8 53.1 452 25.3 –

–

37

761

13

75

295

23

MT: Meteorological Tower, BNU: Beijing Normal University, CGZ: Chegongzhuang, CQ: urban area including suburbs–: no data TABLE 1 COMPONENT SPECTRA OF TSP FROM DIFFERENT PERIODS (ng/m 3) Sampling site

Mg

1983 01/05-01/15

MT

–

1987 Jan. & Mar.

BNU

–

1999 l2/21-01/24

MT

2000 12/18-12/28 2003 03/15-03/14

Year

Date

Al

S

Ca

Ti

V

Cr

Mn

Fe

Ni

Cu

Zn

As

Pb

Reference

4204

9591 526.9 46.5 20.32 105.5 3356.2 8.1 19.3 175.8 14.6 133.3

9224

4376

18642 1166

64

42

2090

9113

410 100

430

28

50

(Wang et al. 1990)

764

6015 1545

890

3883

239

3

27

99

2671

59

169

174

56

53

(Zhang et al. 2002)

MT

516

2486 3845

1973

4144

128

47

68

81

3689

41

72

152

–

196

(Xie et al. 2003)

TU

–

3463 28556

–

–

296

11.5 20.2

295

5889

22.3 157 1121

63

694

(Okuda et al. 2008)

–

59.7 292.5

169

2006 11/15-11/30

MT

2008 11/11-12/12

BNU

6653 3090

K

–

2561.7 2830 3999

–

4213 3062

3824.7 12508 1420

4679

125

–

–

69

TU: Tsinghua University - 85 http://www.ivypub.org/fes

5272.9 50.9 76.8 583.6 30.3 366.1 2052

55

108

357

32

101

(Wang et al. 1986)

(Yang et al. 2008) (Yu et al. 2010)

TABLE 1 COMPONENT SPECTRA OF PM10 FROM DIFFERENT PERIODS (ng/m3) Year 2000 2002 2004 2006

Date Sampling site 12/08-12/28 MT 12/01-02/28 BNU 01/01-01/12 MT 03/03-03/14 CQ

Al S K Ca 1177 2568 1392 2126 4050 10000 – 4570 2372 2821 2017 5542 1020 – – 5320

Ti 69 240 290 –

V 33 7.36 36 5.05

Cr 49 40 169 4.18

Mn Fe Ni Cu 56 1906 29 45 110 2620 110 110 103 3308 26 106 210 – 5.15 68.65

Zn 120 680 503 840

Sr 19 60 79 –

Pb 139 370 265 230

Reference (Xie et al. 2003) (Sun et al. 2004) (Wang et al. 2006) (Cui et al. 2008)

2.2 Analysis Methods 1)

Concentration Diagram and the Coefficient of Divergence

The concentration diagram is a log-log plot of the concentration or mass fraction of the chemical components of one site at a time against that at another time. The diagonal line of unit slope represents the hypothetical case in which the concentrations of the chemical components for the reference location (x axis) and comparison site (y axis) are equal. The diagonal divided the diagram into regions termed x-axis-barren or y-axis-rich (above the diagonal) and yaxis barren or x-axis-rich (below the diagonal) for individual chemical components.

The scatter diagrams provided the most easily visualized images for both quantitative and qualitative purposes, but deeper investigation into the similarity of two databases required a quantitative method, such as the coefficient of divergence (CD).

The coefficient of divergence (CD) was first used in biology (Clark 1952; Hu and Wei 2002) and has been widely used to measure the spread of the data points for two datasets. In the present study, CD, as a self-normalizing parameter, was used to calculate similarities between two sampling years. The formula is as follows: CD jk 

1 p xij  xik ) ( p i 1 xij  xik

2

(1)

where j and k stand for the two profiles for sampling times or fractions, p is the number of investigated components, and xij and xik represent the average mass concentrations of chemical component i for j and k (Feng et al. 2007; Han et al. 2010; Wongphatarakul et al. 1998; Zhang and Friedlander 2000). If CD approaches zero, the two sampling sites are similar. If CD approaches one, the two sampling sites are significantly different (Hu and Wei 2002).

Generally, the CD value 0.3 was the demarcation point of similarity. By use of CD, aerosol databases could be compared even if the numbers of chemical components measured for each fraction are different.

2)

Enrichment Factor

The enrichment factor was mainly used to analyze enrichment of the elements in atmospheric aerosol particles and atmospheric pollution status, then to judge qualitatively the contribution of natural and anthropogenic sources to air pollution.

The following formula (Zoller et al. 1974) was applied: EF 

C

i

C

i

/ Cn sample

/ C n background

(2)

where Ci is the element under consideration, Cn is the chosen reference element, and the subscripts sample and background indicate the medium that the concentration refers to.

Five contamination categories can be recognized on the basis of the enrichment factor (Sutherland 2000), and higher pollution levels represent the greater impact by human activities: EF<2——depletion to minimal enrichment; EF=2-5——moderate enrichment; EF=5-20——significant enrichment; EF=20-40——very high enrichment; EF>40——extremely high enrichment.

- 86 http://www.ivypub.org/fes

For the choice of reference elements, Schiff and Weisberg (Schiff and Weisberg 1999) considered that the reference elements should meet the following conditions: a smaller influence from other metals; minor contribution of anthropogenic pollution; chemical stability; abundance in crustal matter. Fe (Schiff and Weisberg 1999), Al (Reimann and De Caritat 2000), Ti (Teng and Hu 1999) and Si (Wang et al. 2000) have been the most frequently used reference elements in previous studies (Tang et al. 2006). Because it has a lower migration rate than Fe or Ti and more stable chemical properties and because it can be analyzed with greater precision, Al has been the most widely used reference element in China (Ji 2006). So in the present study, Al was chosen as the reference element.

The background values of the other elements (Wei et al. 1990) were used in EF calculations.

3 RESULTS AND DISCUSSION 3.1 Analysis of Component Spectra of Atmospheric Particulate Matter The temporally dynamic changes of the elemental concentrations in PM2, TSP and PM10 were shown in Figs. 1-3.

The concentrations of Ni and As in PM2 (Fig. 1), Cu and As in TSP (Fig. 2), and Al, S, Ca, V, Cr, Ni, Cu and Pb in PM10 (Fig. 3) all had peak values. The concentrations of these chemical elements in the early 1980s were relatively low because the level of industrialization was low. With sustained and rapid economic growth, environmental pollution became increasingly serious. Then in the 21st century, China successfully bid for the 29th Olympic Games, and the city of Beijing took a series of measures to control air pollution, bringing about significant decreases in the elemental concentrations.

The concentrations of Al and Cr in PM2 (Fig. 1) and the concentrations of Mg, Al, V, Cr, Ni and Sr in TSP (Fig. 2) showed minimum values at around the year 2000. The main reason was that the city of Beijing had implemented, in three stages, a total of 68 measures to control air pollution since the end of 1998 and had achieved preliminary success. Lack of data was another reason.

3

ng/m 7000 6000 5000

1983 1992 2000 2006

1987 1999 2001 2008

Fe

Zn

4000 3000 2000 1000 0 Al

S

K

Ca

Pb

3

ng/m 140

1983 1992 2000 2006

120 100

1987 1999 2001 2008

80 60 40 20 0 Ti

V

Cr

Mn

Ni

Cu

As

FIG. 1 TEMPORALLYDYNAMICCHANGES OF THE ELEMENTAL CONCENTRATIONS INPM2 - 87 http://www.ivypub.org/fes

3

ng/m 100000

1983 1999 2003 2008

1987 2000 2006

10000

1000

100 Mg

ng/m

Al

S

K

Ca

Ti

Mn

Fe

Zn

3

700

1983 1999 2003 2008

600 500

1987 2000 2006

400 300 200 100 0 V

Cr

Ni

Cu

As

Pb

FIG. 2 TEMPORALLY DYNAMIC CHANGES OF THE ELEMENTAL CONCENTRATIONS IN TSP 3

ng/m 10000

2000 2004

2002 2006

8000 6000 4000 2000 0 Al

ng/m 400

S

K

Ca

Fe

Zn

3

2000 2004

2002 2006

300

200

100

0 Ti

V

Cr

Mn

Ni

Cu

Sr

Pb

FIG 3 TEMPORALLY DYNAMIC CHANGES OF THE ELEMENTAL CONCENTRATIONS INPM10 - 88 http://www.ivypub.org/fes

The concentrations of S, K, Ca, Fe, Zn, Pb, Ti, V, Mn and Sr in PM2 showed double peaks with a trough at around the year 2000 (Fig. 1); for S, K, Ca, Fe, Zn, Pb, Ti, and Mn, there were also double peaks. These phenomena were related to the three stages of 68 measures that were implemented. Subsequently, the increasing population, the demand for economic growth and other factors led to slight increases in the elemental concentrations. Finally, the elemental concentrations decreased as a result of a series of temporary measures taken by the city of Beijing during the 2008 Olympic Games.

The concentration of Cu in PM2 steadily increased during the period studied (Fig. 1). Figure 3 appeared to show that the concentrations of K, Fe, Zn, Ti, Mn and Sr in PM10 also continued to grow, but the data were insufficient to show the trend clearly.

On the whole, it was clear that the concentrations, most notably of those elements related to anthropogenic sources, had decreased in recent years. The reasons could be summarized as follows: industrial pollution had been reduced by wider adoption of clean fuels and low-sulfur coal; local dust emission had been reduced because construction activities had been supervised by the government and vegetation coverage of bare ground had increased; emissions from vehicle exhausts had been reduced by adoption of new emission standards and conversion from diesel buses to those fueled by compressed natural gas. Doubtlessly, the environmental pollution control measures taken by the government had been effective and should continue to be implemented. In addition, inflation, business failures, economic difficulties and reduced consumption caused by the economic crisis of 2008 may have contributed to the reduction of pollutant discharges.

3.2 Analysis of the differences Between Component Spectra of Atmospheric Particulate Matter using the Coefficient of Divergence 1)

Comparison of the Component Spectra of PM2

For the elemental components of PM2 during eight separate years, the coefficients of divergence (Table 4) ranged from 0.231 to 0.540. The smallest coefficient of divergence, 0.231, appeared between 2001 and 2006, while the largest one, 0.540, occurred between 1999 and 2006. Except for 1992-1999, 1992-2001, 1999-2008, 2000-2001, 2001-2006, the coefficients were greater than 0.3, revealing significant differences in the coefficients of divergence of the component spectra of PM2 among these years.

TABLE 4COEFFICIENTS OF DIVERGENCE FOR THE ELEMENTAL COMPONENT SPECTRA OF PM2 Year 1983 1987 1992 1999 2000 2001 2006 2008

1983 0 0.372 0.345 0.483 0.507 0.482 0.529 0.432

1987

1992

1999

2000

2001

2006

2008

0 0.302 0.377 0.483 0.436 0.499 0.445

0 0.244 0.357 0.291 0.422 0.317

0 0.486 0.390 0.540 0.284

0 0.232 0.337 0.377

0 0.231 0.398

0 0.381

0

Values less than 0.30 are in bold.

- 89 http://www.ivypub.org/fes

-3

S

Al

CD=0.244 1000

KCa Fe PbZn Mn Cu Ti Ni Cr

100

10

1

1

10

100

V

1000

10000

m concentration in 2000 as ng·

-3

m concentration in 1999 as ng·

10000

CD=0.486

100

-3

K Fe Ca

Ti As Cu

1000

10000

CD=0.231

10000 -3

Ca K Fe

1000

Pb

Zn

100

Cu As 10 10

-3

concentration in 2000 as ng· m

100

1000

10000 -3

concentration in 2001 as ng· m

10000

m concentration in 2008 as ng·

-3

1000

10000

m concentration in 2006 as ng·

-3

m concentration in 2001 as ng·

S

100

100

concentration in 1999 as ng· m

Pb

10 10

Ti NiAsCu Mn Cr

10 10

-3

Zn 100

Al

Zn Pb

10000

10000

1000

K Fe

1000

concentration in 1992 as ng· m

CD=0.232

S Ca

CD=0.381 Al 1000

Ca Fe K

Zn 100

10 10

Cu Pb Mn As Ni 100

1000

10000 -3

concentration in 2006 as ng· m

FIG. 4 LOGARITHMIC DIAGRAMS OF CONCENTRATIONS INPM2IN DIFFERENT YEARS

Figure 4 showed logarithmic diagrams of concentrations for each pair of successive years. It could visually explain the divergences and the temporally dynamic changes of the concentration of each element. Concentrations of Sr, V, Zn, Ca and K had a reduction between 1983 and 1987. In 1992, concentrations of S, Pb and Cr had a reduction. In 1999, most of the elemental concentrations declined, with the exception of Cu, Ni and Al, and in 2000, concentrations of the majority of the elements rose, Al and Mn excepted. From 2000 to 2001, concentrations of S, K, Zn, and Fe had an increase, while Ca and Cu had a reduction. In 2006, the concentrations of most of the elements, with the exception of As and Sr, declined. In 2008, concentrations of Pb, K and Cr declined while Ca, Zn, Mn and Ti increased. These observations were consistent with the results shown in Fig. 1.

2)

Comparison of the Component Spectra of TSP - 90 http://www.ivypub.org/fes

TABLE 5 COEFFICIENTS OF DIVERGENCE FOR THE ELEMENTAL COMPONENT SPECTRA OF TSP Year 1983 1987 1999 2000 2003 2006 2008

1983 0 0.521 0.493 0.394 0.555 0.463 0.429

1987

1999

2000

2003

2006

2008

0 0.603 0.559 0.587 0.489 0.565

0 0.392 0.510 0.545 0.297

0 0.515 0.388 0.305

0 0.435 0.509

0 0.342

0

Values less than 0.30 are bold.

m concentration in 2000 as ng·

-3

There were great differences among the coefficients of divergence, which ranged from 0.297 to 0.603 (Table 5). The smallest coefficient of divergence, 0.297, was between 1999 and 2008, while the largest, 0.603, was between 1987 and 1999. The largest value of CD was twice the smallest one.

CD=0.392

10000

Ca S Fe Al K

1000

Mg Pb Zn Ti Cu Cr Mn Ni

100

V 10 1

1

10

100

1000 10000 -3

m concentration in 2008 as ng·

m concentration in 2006 as ng·

-3

-3

concentration in 1999 as ng· m

CD=0.435

10000

1000

Zn Pb Mn Cu

Cr 100

V Ni

10 10

As 100

1000

CD=0.342

10000

Fe Al

10000

-3

concentration in 2003 as ng· m

Al Ca Mg Fe K

1000

Zn Cu Pb Ni Mn As

100

10 10

100

1000

10000

-3

concentration in 2006 as ng· m

FIG. 5LOGARITHMIC DIAGRAMS OF CONCENTRATIONS IN TSP IN DIFFERENT PERIODS

Figure 5 showed logarithmic diagrams of concentrations of TSP for each pair of successive years. Concentrations of - 91 http://www.ivypub.org/fes

Pb declined from 1983 to 1987; in 1999, concentrations of As and Cu decreased, while others had an increase; in 2000, concentrations of most of the elements declined, with the exception of V, Cr, Pb, K, Fe and S; concentrations of V, Cr and Ni declined from 2000 to 2003; and in 2006, concentrations of a great majority of the elements declined, with the exception of V, Cr and Ni; in 2008, concentrations of most of the elements declined, while those of Mg, Al and Cu increased.

3)

Comparison of the Component Spectra of PM10 TABLE 6 COEFFICIENTS OF DIVERGENCE FOR THE ELEMENTAL COMPONENT SPECTRA OFPM10 Year 2000 2002 2004 2006

2000 0 0.490 0.397 0.570

2002

2004

2006

0 0.360 0.483

0 0.507

0

There were great differences among the coefficients of divergence, which ranged from 0.360 to 0.570 (Table 6). The smallest coefficient of divergence, 0.360, was between 2002 and 2004, while the largest, 0.570, was between 2000 and 2006.

1000

100

10

10000

CD=0.360

-3

S AlCa Fe

CD=0.520

m concentration in 2004 as ng路

m concentration in 2002 as ng路

-3

10000

Ca Fe S Al

1000

Zn Pb

Ti NiCMn u Sr Cr

V 10 100 1000 10000 -3 concentration in 2000 as ng路 m

Zn TiPb

Cr Mn Sr Cu

100 V

Ni

10 10 100 1000 10000 -3 concentration in 2002 as ng路 m

FIG. 6LOGARITHMIC DIAGRAMS OF CONCENTRATIONS IN PM10 IN DIFFERENT PERIODS

Figure 6 showed clearly that the concentrations of V and Cr declined while others rose from 2000 to 2002; in 2004, concentrations of the majority of the elements declined with the exception of V and Cr; in 2006, concentrations of most of the elements declined, except Zn and Mn. In conclusion, the elemental component spectra of different-sized particulate material during the period studied showed significant differences from year to year. The CD values for the elemental component spectra of PM2 over the years were smaller than those of PM10 and TSP. From close examination of the logarithmic diagrams it can be seen that the elemental concentrations have changed significantly over the years, suggesting that some extensive - 92 http://www.ivypub.org/fes

social and technological changes have occurred. For example, population, coal use, number of vehicles have changed gradually in Beijing. Unfortunately, we have not been able to find any evidence to verify this idea in the statistical data available.

3.3 Using Enrichment Factors to Study the Temporally Dynamic Changes of Individual Elements 10000

Enrichment factor

1000

1983 1999 2006

1992 2000 2008

100 10 1 0.1 K Ca Ti

V

Cr Mn Fe Ni Cu Zn As Pb

FIG. 7 ENRICHMENT FACTORS OF THE ELEMENTS IN PM2

Enrichment factor

1000

1983 2000 2006

1999 2003 2008

100

10

1

Mg K Ca Ti V Cr Mn Fe Ni Cu Zn As Pb

FIG. 8 ENRICHMENT FACTORS OF THE ELEMENTS IN TSP 1000

Enrichment factors

2000 2004

2002 2006

100

10

1 K Ca Ti

V

Cr Mn Fe Ni Cu Zn Sr Pb

FIG. 9 ENRICHMENT FACTORS OF THE ELEMENTS IN PM10

The data showed that the enrichment factors of Ca, Ti and Fe in PM2 (Fig. 7), Mg, K, Ca, Ti, Mn and Fe in TSP (Fig. - 93 http://www.ivypub.org/fes

8), and K, Ti, and Fe in PM10 (Fig. 9) were small. The values were basically less than 5, placing these elements in the categories of depletion to minimal enrichment or moderate enrichment and showing that the level of anthropogenic pollution of crustal elements, such as Al, Ti, Ca, and Fe, was low. The enrichment factors of Cu, Zn, As and Pb in PM2 were very high, as were those in TSP. The values were generally greater than 40, placing these elements in the category of extremely high enrichment and indicating that they came mainly from anthropogenic sources of pollution.

The enrichment factors of Cr, Ni, Cu, Zn and Pb in PM10 were also in the category of extremely high enrichment. Pb, one of the main components of atmospheric pollution and easier to accumulate than remove, comes from road dust, winter heating with coal and industrial production. Zn is related to coal combustion, metallurgy, the chemical industry and automobile exhaust. As comes mainly from coal combustion. In summary, the results demonstrated that in Beijing this group of elements in particulate matter came mainly from anthropogenic sources that included industrial production, heating and vehicle exhaust.

In addition, comparison of the enrichment factors in different-sized particles showed that the values in fine particles were significantly higher than in coarse particles. This indicateed that these metals were more easily enriched in fine particles.

4 CONCLUSIONS In this investigation, concentration diagrams, coefficients of divergence and enrichment factors were used to study the concentrations of elements in atmospheric particulate matter in Beijing during different periods.

Concentrations of elements from anthropogenic sources have decreased in recent years. Measures taken prior to the Beijing Olympic Games and the economic crisis of 2008 were possible explanations for the reduction of the amount of atmospheric pollution.

The elemental component spectra of different-sized particulate material during the period studied showed large dissimilarities when the coefficients of divergence of the elemental component spectra were compared. Logarithmic diagrams of concentration comparing different years suggested that concentrations of elements changed significantly because of some extensive social and technological changes.

The enrichment factor values of Cu, Zn, As and Pb were very high. These elements in atmospheric particulate matter come mainly from anthropogenic sources. Most of the enrichment factors in TSP were smaller than those in PM2, showing that most of the elements coming from anthropogenic sources were concentrated mostly in the fine particles.

ACKNOWLEDGMENT The authors are extremely grateful to the authors of the documents from which the data were taken for this article.

The study could not have been finished without their support. This study was supported financially by the Special Environmental Research Fund for Public Welfare of China (No. 201409004) and the National Key Scientific Instrument and Equipment Development Projects (No. 2011YQ060111).

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AUTHORS 1

2

research is atmospheric particulate matter

sciences, Master Tutor. Mainly engaged in atmospheric

pollution

particulate matter pollution controlling theory and technology.

Jiao JIAO(1989- ), female, Chief field of controlling

theory

and

technology. And now as a current Master students at Nankai University.

Yaqin JI(1971- ),Associate Professor, Doctor of environmental

Email: jiyaqin@nankai.edu.cn

Email: jaojoe@foxmail.com

- 96 http://www.ivypub.org/fes