The temporal distribution characteristics of the ambient particulate matter in beijing

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http://www.ivypub.org/fes

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 Jiao

1,2, Yaqin Ji

1,2#, Jing Zhang

1,2, Zhenyu Zhu

1,2, Shijian Zhang

1,2, Yafei Zhang

1,2, Leibo Zhang

1,2

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: [email protected]

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

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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)

Year Date Sampling

site Al S K Ca Ti V Cr Mn Fe Ni Cu Zn As Pb Reference

1983 01/05-01/15 MT 350 2039 1506 1183 89.6 9.2 19.9 29.2 505.2 2.8 7.5 132.8 9.7 97.5 (Wang et al. 1986)

1987 Jan. & Mar. BNU – 6854 1132 614 76 6 24 55 779 9 17 26.4 23 220 (Wang et al. 1990)

1992 12/14-01/13 MT 1039.8 4783 928.4 1166 91.3 – 11.6 76.5 844.1 10.4 19.3 237.3 – 133.4 (Zhang et al. 1998)

1999 12/21-12/24 MT 3386.5 3594.8 633.3 634.8 47.2 0.7 9.3 77.6 574.8 13.9 35.7 165.4 24.1 143 (Zhang et al. 2000)

2000 12/01-12/30 CQ 874 4485 1194 2772 80 37 44 59 1017 71 77 236 80 218 (Wang et al. 2002a)

2001 12/01-02/28 CGZ – 5100 2500 1340 90 – – – 1380 – 50 550 70 210 (Duan et al. 2006)

2006 11/16-11/30 MT 546.5 – 2995 2578 – 26.7 120.2 95.2 1309.1 16.8 53.1 452 25.3 305 (Yang et al. 2008)

2008 11/11-12/12 BNU 1746 1933 853 1645 37 – – 37 761 13 75 295 23 57 (Yu et al. 2010)

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/m3)

Year Date Sampling

site Mg Al S K Ca Ti V Cr Mn Fe Ni Cu Zn As Pb Reference

1983 01/05-01/15 MT – 6653 3090 4204 9591 526.9 46.5 20.32 105.5 3356.2 8.1 19.3 175.8 14.6 133.3 (Wang et al. 1986)

1987 Jan. & Mar. BNU – – 9224 4376 18642 1166 64 42 2090 9113 410 100 430 28 50 (Wang et al. 1990)

1999 l2/21-01/24 MT 764 6015 1545 890 3883 239 3 27 99 2671 59 169 174 56 53 (Zhang et al. 2002)

2000 12/18-12/28 MT 516 2486 3845 1973 4144 128 47 68 81 3689 41 72 152 – 196 (Xie et al. 2003)

2003 03/15-03/14 TU – 3463 28556 – – 296 11.5 20.2 295 5889 22.3 157 1121 63 694 (Okuda et al. 2008)

2006 11/15-11/30 MT 2561.7 2830 – 3824.7 12508 – 59.7 292.5 169 5272.9 50.9 76.8 583.6 30.3 366.1 (Yang et al. 2008)

2008 11/11-12/12 BNU 3999 4213 3062 1420 4679 125 – – 69 2052 55 108 357 32 101 (Yu et al. 2010)

TU: Tsinghua University

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TABLE 1 COMPONENT SPECTRA OF PM10 FROM DIFFERENT PERIODS (ng/m3)

Year Date Sampling site Al S K Ca Ti V Cr Mn Fe Ni Cu Zn Sr Pb Reference

2000 12/08-12/28 MT 1177 2568 1392 2126 69 33 49 56 1906 29 45 120 19 139 (Xie et al. 2003)

2002 12/01-02/28 BNU 4050 10000 – 4570 240 7.36 40 110 2620 110 110 680 60 370 (Sun et al. 2004)

2004 01/01-01/12 MT 2372 2821 2017 5542 290 36 169 103 3308 26 106 503 79 265 (Wang et al. 2006)

2006 03/03-03/14 CQ 1020 – – 5320 – 5.05 4.18 210 – 5.15 68.65 840 – 230 (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 y-

axis 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:

2

1

1( )

pij ik

jki

ij ik

x xCD

p x x

(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:

i n

i n

/

/

sample

background

C CEF

C C (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.

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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.

Al S K Ca Fe Zn Pb0

1000

2000

3000

4000

5000

6000

7000

1983 1987

1992 1999

2000 2001

2006 2008

ng/m3

Ti V Cr Mn Ni Cu As

0

20

40

60

80

100

120

140

ng/m3

1983 1987

1992 1999

2000 2001

2006 2008

FIG. 1 TEMPORALLYDYNAMICCHANGES OF THE ELEMENTAL CONCENTRATIONS INPM2

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Mg Al S K Ca Ti Mn Fe Zn

100

1000

10000

100000

1983 1987

1999 2000

2003 2006

2008

ng/m3

V Cr Ni Cu As Pb0

100

200

300

400

500

600

700

ng/m3

1983 1987

1999 2000

2003 2006

2008

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

Al S K Ca Fe Zn0

2000

4000

6000

8000

10000

ng/m3

2000 2002

2004 2006

Ti V Cr Mn Ni Cu Sr Pb0

100

200

300

400

2000 2002

2004 2006

ng/m3

FIG 3 TEMPORALLY DYNAMIC CHANGES OF THE ELEMENTAL CONCENTRATIONS INPM10

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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

1987 0.372 0

1992 0.345 0.302 0

1999 0.483 0.377 0.244 0

2000 0.507 0.483 0.357 0.486 0

2001 0.482 0.436 0.291 0.390 0.232 0

2006 0.529 0.499 0.422 0.540 0.337 0.231 0

2008 0.432 0.445 0.317 0.284 0.377 0.398 0.381 0

Values less than 0.30 are in bold.

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1 10 100 1000 100001

10

100

1000

10000

Al S

KCa

Ti

Cr

Mn

Fe

Ni

Cu

ZnPb

concentration in 1992 as ng·m-3

conce

ntr

atio

n i

n 1

999 a

s ng·m

-3

CD=0.244

10 100 1000 1000010

100

1000

10000

Al

S

K

Ca

Ti

V CrMn

Fe

Ni Cu

Zn

As

Pb


con

cen

trat

ion

in

20

00

as

ng

·m-3

CD=0.486

10 100 1000 1000010

100

1000

10000

S

K

Ca

Ti

Fe

Cu

Zn

As

Pb


con

cen

trat

ion

in

20

01

as

ng

·m-3

CD=0.232

10 100 1000 1000010

100

1000

10000

KCa

Fe

Cu

Zn

As

Pb


con

cen

trat

ion

in

20

06

as

ng

·m-3

CD=0.231

10 100 1000 1000010

100

1000

10000

Al

K

Ca

Mn

Fe

Ni

Cu

Zn

As

Pb


con

cen

trat

ion

in

20

08

as

ng

·m-3

CD=0.381

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

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TABLE 5 COEFFICIENTS OF DIVERGENCE FOR THE ELEMENTAL COMPONENT SPECTRA OF TSP

Year 1983 1987 1999 2000 2003 2006 2008

1983 0

1987 0.521 0

1999 0.493 0.603 0

2000 0.394 0.559 0.392 0

2003 0.555 0.587 0.510 0.515 0

2006 0.463 0.489 0.545 0.388 0.435 0

2008 0.429 0.565 0.297 0.305 0.509 0.342 0

Values less than 0.30 are bold.

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.

1 10 100 1000 100001

10

100

1000

10000

Mg

AlS

KCa

Ti

VCr Mn

Fe

NiCuZnPb

CD=0.392

con

cen

trat

ion

in

20

00

as

ng

·m-3


10 100 1000 1000010

100

1000

10000

Al

V

CrMn

Fe

NiCu

Zn

As

Pb

CD=0.435

conce

ntr

atio

n i

n 2

006 a

s ng·m

-3


10 100 1000 1000010

100

1000

10000

MgAl

K

Ca

Mn

Fe

NiCu

Zn

As

Pb

CD=0.342

con

cen

trat

ion

in

20

08

as

ng

·m-3


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

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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

2002 0.490 0

2004 0.397 0.360 0

2006 0.570 0.483 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.

10 100 1000 10000

10

100

1000

10000

Al

S

Ca

Ti

V

Cr

Mn

Fe

NiCu

Zn

Sr

Pb

CD=0.520

conce

ntr

atio

n i

n 2

002 a

s ng·m

-3


10 100 1000 10000

10

100

1000

10000

Al S

Ca

Ti

V

CrMn

Fe

Ni

Cu

Zn

Sr

Pb

CD=0.360co

nce

ntr

atio

n i

n 2

00

4 a

s n

g·m

-3


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

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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

K Ca Ti V Cr Mn Fe Ni Cu Zn As Pb0.1

1

10

100

1000

10000

1983 1992

1999 2000

2006 2008E

nri

chm

ent

fact

or

FIG. 7 ENRICHMENT FACTORS OF THE ELEMENTS IN PM2

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

1

10

100

1000

1983 1999

2000 2003

2006 2008

Enri

chm

ent

fact

or

FIG. 8 ENRICHMENT FACTORS OF THE ELEMENTS IN TSP

K Ca Ti V Cr Mn Fe Ni Cu Zn Sr Pb1

10

100

1000

2000 2002

2004 2006

En

rich

men

t fa

cto

rs

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.

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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 1Jiao JIAO(1989- ), female, Chief field of

research is atmospheric particulate matter

pollution controlling theory and

technology. And now as a current Master

students at Nankai University.

Email: [email protected]

2Yaqin JI(1971- ),Associate Professor, Doctor of environmental

sciences, Master Tutor. Mainly engaged in atmospheric

particulate matter pollution controlling theory and technology.

Email: [email protected]

Documents

The temporal distribution characteristics of the ambient particulate matter in beijing