The spatial and temporal distribution of aerosol optical depths in China

Caspar J. Chung Abstract

China’s economy is rapidly growing, and it is suspected that this has caused an increase in

aerosols among East Asian countries. Every early spring, dust storms containing mass amounts of

aerosols occur in China and move eastward, eventually reaching Korea, Japan, and even the western

region of North America. Aerosols are a complex mixture of organic and inorganic chemicals that can

be classified as air pollutants. They can affect human health seriously. They may cause lower respiratory

symptoms, chronic obstructive pulmonary disease, lowering lung function, and even decreasing life

expectancy. Most aerosols in China are generated by anthropogenic activities including industrialization

and urbanization in the east and southeast region and desertification in the Gobi desert caused by land

overuse and increase in population. Monitoring the spatial and temporal distribution of aerosols is

essential for predicting future trend and setting suitable policies for regulating further increase of

aerosols. In this study, mean monthly aerosol optical depth was measured (from March 2000 to

September 2005) using MISR (Multi-angle SpectroRadiometer) in order to observe aerosol distribution

in China.

Keywords: Aerosol Optical Depth (AOD), PM, MISR

1. Introduction

Every year in the spring, yellow dust clouds, or Asian dust, occur in China’s arid land

and moves eastward, over the Yellow Sea, Korea, Japan, and further to the Pacific Ocean

[Duce et al., 1980; Jaffe et al., 2003]. In addition, a mass of chemicals in the atmosphere,

emitted from industries and other human activities, are constantly affecting this region. Both

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Asian dust and these chemicals can be categorized as aerosols, or particulate matter (PM).

Particulate matter (PM), sometime called aerosols, is a complex mixture of organic

and inorganic particles and can also be classified as an air pollutant1. PM originates from

various sources; volcanoes, dust storms, forest and grassland fires, vegetation, erosion and

anthropogenic activities (burning fossil fuels, land cover change)2. Especially, aerosols caused

by anthropogenic activities are contributing about 10 % of the total amount of aerosols in the

air3. Due to a rapid increase of human activity, this estimation may also increase.

PM breaks down into two groups based on its size; PM10 and PM2.5. PM10, larger

aerosols mostly from smoke and dust, are particulate matter smaller than 10 micrometers in

diameter, and for this size is possible to infiltrate into lungs and airways’ upper part4. PM2.5,

which is less than 2.5 micrometers of diameter and refers to smaller aerosols mostly from

toxic compounds and heavy metal5 is more dangerous than PM10 since they can reach to

deeper part of the lung and even the alveolar region6.

As its tiny size makes possible to reach to the human’ respiratory organs, PM affects

human health seriously. There is a case study showing that an exposure to PM in the long-

term may cause increasing lower respiratory symptoms and chronic obstructive pulmonary

disease, lowering children and adults’ lung function, and even decreasing life expectancy due

to lung cancer and cardiopulmonary disease6.

The issue that PM contributes to climate change is still inconclusive and controversial.

With the current level of technology, experts admit that they still have a lack of skill to

1http://www.euro.who.int/document/mediacentre/fs0405e.pdf 2http://earthobservatory.nasa.gov/Library/Aerosols/ 3http://earthobservatory.nasa.gov/Library/Aerosols/ 4http://www.euro.who.int/document/mediacentre/fs0405e.pdf 5http://www.airinfonow.org/html/ed_particulate.html 6http://www.euro.who.int/document/mediacentre/fs0405e.pdf

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measure the relative impacts of aerosols on climate and have an insufficient observation over

earth’s atmosphere based on global scale in terms of trends of aerosols7. However, a recent

study shows that an increase of aerosols in the air can lead to increasing the number of

droplets within the clouds, which works to reduce rainfall8. The consequence of this event

would significantly affect on the condition of the biomes and landscape which may contribute

to a degradation of the ecosystem.

As a magnitude of China’s economy is rapidly increasing, there is a strong suspicion

that this may greatly correlated with an increase of aerosols from anthropogenic activities

including an increase of industrial and agricultural activities [Li et al., 1995]. As long as a

current phenomenon continues in China, a possible climate change (although it is still

controversial) and an increase of health risk in this area (including Korea and Japan) may

become a possible threat to human and nature. Considering a significance of an increase of

aerosol and its possible influence over this area, a monitoring a spatial and temporal

distribution of aerosol and analyzing its result is essential for setting a suitable policy for

regulating human activities

2. China

An increase amount of aerosols in China contributes to desertification and industrialization

mainly caused by humans. Although recently the group of scientist have found that human activity has a

less contribution to dust storm compared to the factor of meteorology and climate in China [Gong et al,

7http://earthobservatory.nasa.gov/Library/Aerosols/ 8http://earthobservatory.nasa.gov/Study/Pollution/pollution.html

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2004], still the academic world in general believes that human activity has a potential effects on this

region, especially in the Gobi desert (Figure 1). Soil erosion, overuse of the land for farming, and

increase of population are known as causing factors of desertification, which significantly related to

human factor. Some research indicates that every year in the northern area of China, nearly 900 square

miles (about 2,300 km2) of the farmland transforms into desert, which its size of the area is greater than

twice the size of Honk Kong9.

An increase of aerosols caused by anthropogenic activities strongly correlates to the fast-paced

industrialization of China. Air pollution, a largest contributor of aerosols along with dust from the desert

in China has been worsening since a rapid growth of China’s economy. The typical sources of air

pollution are coal usage which is a major source of energy, gas emissions from vehicles around in

Gobi Desert

Figure 1.

9http://news.nationalgeographic.com/news/2001/06/0601_chinadust.html

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populated major cities and provinces10. Coal combustions accounts for 80% of total energy [Gu et al,

2006] and its emission contains SO2 which is possible to go through a transformation of SO2 into sulfate

and black carbon [Lefohn et al., 1999; Xu, 2001]. Another major contributor of aerosols is NOx

(nitrogen oxides), which comes from motor vehicles, electric utilities, factories and houses burning gas10.

A rapid economic development and urbanization for last decades in the east and southeastern regions

including Beijing, Shanghai, Guangzhou, the Pearl River delta, and the Yangtze delta have increased the

numbers of vehicles11 and usage of energy in industrial, commercial and residential areas (Figure 2).

The influence of aerosols, contributed by the northern area where experiencing desertification

in progress and heavily industrialized area in east and southeast region in China, becomes severe in

Beijing

Shanghai & the Yangtze delta

Guangzhou & the Pearl River Delta

Figure 2.

10http://www.epa.gov/oar/urbanair/nox/what.html 11http://www.iupac.org/publications/pac/2004/pdf/7606x1227.pdf

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winter and early spring as a Siberian cold front passes northwestern of China, Inner Mongolia region and

Gansu province12. The cold front triggers dust storms which move from west to east, extending its

influence from China to Korea and Japan. The dust storm reduces visibility and quality of air which

provoke the global conflicts between the East Asian nations.

3. Data and Methods

The purpose of this observation is to analyze the spatial and temporal distribution of aerosols in

China. In this observation, mean monthly aerosols optical depth of six years (from March 2000 to

September 2005) derived from MISR (Multi-angle SpectroRadiometer) were used and acquired from

the Atmospheric Science Data Center at NASA Langley Research Center13.

MISR is one of the instruments aboard NASA's Terra spacecraft launched in August 199913.

With cameras viewing earth at nine different angles, it provides imagery of earth observation in four

different wavelengths (blue, green, red, and near infrared). These wavelengths and different angled

cameras allow MISR to measure the brightness, contrast, and colors of sunlight reflected from earth14.

Aerosol optical depth (AOD) is one of the MISR products addressed by the change of reflection due to

atmospheric particles14.

With acquired images of mean monthly AOD of six years (total 67 images) the mean annual

AOD, the mean seasonal AOD, and the mean AOD averaged for 67 months from 2000 to 2005 were

calculated and used to generate images (Figure 3 and Figure 4). The mean annual AOD was generated

by averaging total months for each year. The mean seasonal AOD was generated by averaging relevant

12http://www-airs.jpl.nasa.gov/News/Features/FeaturesChinaDustStorm/ 13http://eosweb.larc.nasa.gov/ 14http://www-misr.jpl.nasa.gov/

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months for each season between 2000 and 2005, for example, spring (March, April, May), summer

(June, July, August), fall (September, October, November), and winter (December, January, February).

The mean AOD over six years (2000-2005) was acquired by averaging all images.

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2000-2005 Annual AOD

Figure 3.

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Averaged Seasonal AOD from 2000 to 2005

The mean annual and seasonal AOD of China and each of administrative divisions were

generated by applying Zonal Statistics in ArcMap. The boundaries of China and

administrative divisions were used in this task and the result was rendered in maps and tables

(Figure 5 and 6, Table 1 and 2). In order to find a significance of statistical correlation

between the continent and each administrative division of China, paired t-test was performed

based on the mean annual and seasonal AOD (Table 3 and 4).

Figure 4.

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2000-2005 Annual AOD (Zonal Statistics)

Figure 5.

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Averaged Seasonal AOD from 2000 to 2005 (Zonal Statistics)

Figure 6.

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Annual Means and Standard Deviations of AOD (2000-2005)

2000 2001 2002 2003 2004 2005

x σ x σ x σ x σ x σ x σ

China 0.23 0.14 0.22 0.15 0.25 0.16 0.22 0.15 0.23 0.16 0.30 0.19

Anhui 0.44 0.12 0.43 0.15 0.44 0.09 0.38 0.08 0.46 0.10 0.65 0.35

Beijing 0.30 0.09 0.28 0.09 0.35 0.10 0.37 0.10 0.31 0.09 0.51 0.10

Fujian 0.26 0.09 0.31 0.10 0.28 0.13 0.21 0.06 0.27 0.10 0.27 0.08

Gansu 0.18 0.09 0.18 0.12 0.17 0.09 0.17 0.08 0.16 0.07 0.27 0.15

Guangdong 0.30 0.07 0.39 0.10 0.43 0.09 0.43 0.16 0.44 0.15 0.48 0.20

Guangxi 0.31 0.09 0.42 0.14 0.36 0.13 0.40 0.09 0.53 0.14 0.46 0.17

Guizhou 0.33 0.10 0.33 0.09 0.28 0.08 0.24 0.06 0.40 0.15 0.43 0.17

Hainan 0.36 0.13 0.52 0.11 0.35 0.20 0.37 0.08 0.46 0.10 0.41 0.14

Hebei 0.35 0.18 0.31 0.14 0.36 0.19 0.42 0.21 0.28 0.14 0.49 0.22

Heilongjiang 0.15 0.04 0.12 0.03 0.17 0.06 0.14 0.04 0.12 0.04 0.17 0.06

Henan 0.44 0.11 0.44 0.10 0.51 0.14 0.50 0.13 0.42 0.09 0.66 0.24

Hong Kong 0.29 0.00 0.51 0.00 0.51 0.00 0.33 0.00 0.43 0.00 0.48 0.00

Hubei 0.36 0.17 0.43 0.16 0.40 0.17 0.48 0.21 0.39 0.14 0.54 0.23

Hunan 0.38 0.15 0.42 0.12 0.42 0.14 0.46 0.14 0.48 0.09 0.56 0.16

Jiangsu 0.43 0.09 0.32 0.07 0.43 0.07 0.36 0.05 0.49 0.14 0.55 0.08

Jiangxi 0.28 0.06 0.33 0.06 0.42 0.10 0.36 0.11 0.38 0.09 0.43 0.15

Jilin 0.14 0.03 0.12 0.03 0.23 0.05 0.16 0.04 0.15 0.04 0.23 0.07

Liaoning 0.16 0.05 0.16 0.05 0.24 0.10 0.23 0.14 0.21 0.13 0.24 0.07

Nei Mongol 0.17 0.05 0.16 0.08 0.17 0.06 0.16 0.05 0.13 0.05 0.22 0.08

Ningxia 0.19 0.04 0.22 0.08 0.18 0.04 0.22 0.06 0.20 0.03 0.36 0.09

Qinghai 0.22 0.10 0.16 0.09 0.19 0.09 0.15 0.08 0.17 0.07 0.25 0.12

Shaanxi 0.26 0.14 0.25 0.15 0.23 0.12 0.19 0.12 0.22 0.11 0.33 0.17

Shandong 0.35 0.10 0.39 0.16 0.43 0.15 0.46 0.13 0.43 0.14 0.56 0.21

Shanghai 0.37 0.11 0.42 0.15 0.47 0.03 0.41 0.22 0.88 0.39 0.49 0.13

Shanxi 0.29 0.08 0.29 0.11 0.27 0.10 0.24 0.09 0.22 0.09 0.36 0.12

Sichuan 0.26 0.22 0.25 0.20 0.32 0.27 0.25 0.24 0.30 0.27 0.30 0.25

Tianjin 0.52 0.07 0.33 0.04 0.39 0.06 0.61 0.09 0.46 0.08 0.59 0.08

Xinjiang 0.25 0.11 0.22 0.11 0.24 0.12 0.22 0.11 0.22 0.12 0.30 0.13

Xizang 0.16 0.14 0.10 0.06 0.15 0.16 0.12 0.09 0.12 0.07 0.17 0.11

Yunnan 0.13 0.10 0.16 0.11 0.18 0.12 0.13 0.10 0.17 0.12 0.22 0.16

Zhejiang 0.30 0.13

0.32 0.12

0.40 0.14

0.28 0.22

0.49 0.24

0.41 0.14

Table 1

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Seasonal Means and Standard Deviations of AOD (2000-2005)

MAM JJA SON DJF x σ x σ x σ x σ

China 0.34 0.18 0.34 0.20 0.23 0.17 0.36 0.30

Anhui 0.49 0.10 0.59 0.13 0.34 0.17 0.86 0.78

Beijing 0.43 0.08 0.62 0.12 0.31 0.12 0.67 0.15

Fujian 0.30 0.11 0.33 0.09 0.28 0.10 0.26 0.11

Gansu 0.27 0.11 0.25 0.10 0.17 0.09 0.28 0.23

Guangdong 0.40 0.21 0.44 0.17 0.32 0.11 0.45 0.20

Guangxi 0.40 0.12 0.53 0.19 0.38 0.11 0.53 0.34

Guizhou 0.33 0.08 0.35 0.13 0.36 0.17 0.49 0.24

Hainan 0.22 0.03 0.39 0.22 0.39 0.10 0.31 0.13

Hebei 0.50 0.17 0.57 0.21 0.36 0.22 0.60 0.44

Heilongjiang 0.28 0.11 0.23 0.08 0.11 0.05 0.24 0.15

Henan 0.70 0.27 0.56 0.11 0.37 0.10 0.73 0.67

Hong Kong 0.53 0.00 0.54 0.00 0.28 0.00 0.49 0.00

Hubei 0.62 0.35 0.49 0.15 0.41 0.26 0.46 0.25

Hunan 0.56 0.27 0.51 0.12 0.51 0.26 0.49 0.21

Jiangsu 0.51 0.10 0.75 0.29 0.34 0.09 0.54 0.26

Jiangxi 0.45 0.26 0.41 0.10 0.28 0.07 0.44 0.23

Jilin 0.29 0.06 0.32 0.09 0.10 0.04 0.45 0.23

Liaoning 0.30 0.09 0.43 0.15 0.12 0.03 0.35 0.15

Nei Mongol 0.29 0.08 0.27 0.08 0.15 0.07 0.35 0.17

Ningxia 0.27 0.03 0.33 0.05 0.17 0.06 0.47 0.22

Qinghai 0.36 0.12 0.28 0.14 0.19 0.15 0.34 0.25

Shaanxi 0.29 0.15 0.25 0.11 0.26 0.15 0.32 0.18

Shandong 0.46 0.10 0.69 0.22 0.31 0.14 0.61 0.34

Shanghai 0.54 0.02 1.18 0.37 0.23 0.00 0.70 0.34

Shanxi 0.49 0.13 0.34 0.10 0.32 0.09 0.35 0.13

Sichuan 0.28 0.19 0.24 0.18 0.29 0.28 0.34 0.38

Tianjin 0.54 0.05 0.88 0.04 0.54 0.18 0.36 0.08

Xinjiang 0.36 0.16 0.38 0.19 0.24 0.13 0.41 0.25

Xizang 0.25 0.16 0.26 0.22 0.14 0.15 0.19 0.16

Yunnan 0.19 0.10 0.19 0.14 0.20 0.17 0.11 0.08

Zhejiang 0.47 0.18 0.64 0.24

0.27 0.21

0.40 0.41

Table 2

Seasons: MAM – March through May, JJA – June through August, SON – September through November DJF-December through February

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Annual Paired t-test (2000-2005)

China Administrative Divisions t p r

Anhui -8.42 0.00 0.90

Beijing -5.10 0.00 0.85

Fujian -1.46 0.20 0.01

Gansu 9.32 0.00 0.87

Guangdong -7.75 0.00 0.24

Guangxi -5.47 0.00 0.03

Guizhou -4.05 0.01 0.36

Hainan -5.10 0.00 0.05

Hebei -5.51 0.00 0.56

Heilongjiang 10.49 0.00 0.47

Henan -9.91 0.00 0.85

Hong Kong -5.00 0.00 0.09

Hubei -9.22 0.00 0.46

Hunan -11.43 0.00 0.56

Jiangsu -7.33 0.00 0.63

Jiangxi -7.27 0.00 0.44

Jilin 5.95 0.00 0.65

Liaoning 2.49 0.05 0.27

Nei Mongol 11.71 0.00 0.77

Ningxia 0.74 0.49 0.75

Qinghai 6.34 0.00 0.75

Shaanxi -0.50 0.64 0.66

Shandong -9.55 0.00 0.66

Shanghai -3.39 0.02 0.00

Shanxi -2.93 0.03 0.64

Sichuan -3.02 0.03 0.33

Tianjin -5.98 0.00 0.18

Xinjiang 0.30 0.78 0.89

Xizang 12.83 0.00 0.59

Yunnan 10.98 0.00 0.75

Zhejiang 4.05 0.01 0.13

Table 3

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Seasonal Paired t-test (2000-2005)

China Administrative

Divisions Spring (MAM)

Summer (JJA)

Fall (SON)

Winter (DJF)

t p r t p r t p r t p r

Anhui -7.04 0.00 0.04 -8.98 0.00 0.33 -11.71 0.00 0.15 -8.00 0.00 0.84

Beijing -2.61 0.02 0.50 -5.56 0.00 0.48 -3.69 0.00 0.54 -1.16 0.27 0.61

Fujian 1.62 0.12 0.04 0.97 0.35 0.11 -1.49 0.16 0.14 0.73 0.48 0.22

Gansu 1.94 0.07 0.16 5.91 0.00 0.55 9.77 0.00 0.66 8.92 0.00 0.82

Guangdong 0.29 0.78 0.00 -0.19 0.85 0.27 -6.91 0.00 0.46 -2.30 0.04 0.21

Guangxi 3.03 0.01 0.01 -0.55 0.59 0.01 -4.58 0.00 0.17 -0.47 0.65 0.11

Guizhou 2.19 0.04 0.03 3.53 0.00 0.00 -1.37 0.19 0.06 0.28 0.78 0.02

Hainan -0.93 0.36 0.03 1.17 0.26 0.16 -4.67 0.00 0.15 -3.99 0.00 0.02

Hebei -4.17 0.00 0.43 -8.13 0.00 0.71 -6.52 0.00 0.51 -2.83 0.01 0.45

Heilongjiang 3.27 0.00 0.60 4.77 0.00 0.44 4.83 0.00 0.02 0.36 0.73 0.32

Henan -10.51 0.00 0.08 -9.23 0.00 0.37 -12.31 0.00 0.38 -7.57 0.00 0.27

Hong Kong -1.74 0.10 0.00 -1.27 0.22 0.36 -6.27 0.00 0.08 -3.48 0.00 0.12

Hubei -6.20 0.00 0.10 -5.74 0.00 0.39 -7.99 0.00 0.36 -7.20 0.00 0.13

Hunan -2.10 0.05 0.02 -3.68 0.00 0.36 -6.74 0.00 0.38 -2.67 0.02 0.12

Jiangsu -9.11 0.00 0.35 -6.95 0.00 0.28 -7.26 0.00 0.02 -13.93 0.00 0.78

Jiangxi -2.54 0.02 0.00 -4.22 0.00 0.29 -7.83 0.00 0.30 -2.71 0.02 0.16

Jilin 1.03 0.32 0.49 1.93 0.07 0.40 3.98 0.00 0.18 -2.30 0.04 0.86

Liaoning -0.75 0.46 0.59 -2.33 0.03 0.44 0.64 0.53 0.07 -2.96 0.01 0.66

Nei Mongol 0.31 0.76 0.82 1.05 0.31 0.80 8.70 0.00 0.50 1.39 0.18 0.93

Ningxia -1.99 0.06 0.04 -1.48 0.16 0.30 0.56 0.58 0.44 -0.15 0.88 0.89

Qinghai 1.33 0.20 0.25 7.56 0.00 0.24 9.94 0.00 0.61 6.30 0.00 0.92

Shaanxi 0.56 0.58 0.02 0.37 0.72 0.29 1.01 0.33 0.62 1.86 0.08 0.43

Shandong -8.08 0.00 0.58 -8.06 0.00 0.58 -8.88 0.00 0.34 -6.90 0.00 0.42

Shanghai -0.87 0.40 0.15 -2.51 0.02 0.05 -2.42 0.03 0.22 -5.80 0.00 0.30

Shanxi -2.45 0.03 0.01 -4.19 0.00 0.62 -2.60 0.02 0.67 -1.43 0.18 0.22

Sichuan 4.51 0.00 0.02 4.09 0.00 0.05 -0.12 0.90 0.24 1.10 0.29 0.18

Tianjin -4.85 0.00 0.35 -8.29 0.00 0.24 -4.93 0.00 0.36 -2.80 0.01 0.04

Xinjiang -7.76 0.00 0.46 -5.93 0.00 0.51 0.26 0.80 0.70 -2.57 0.02 0.92

Xizang 11.46 0.00 0.37 16.02 0.00 0.49 18.27 0.00 0.60 22.44 0.00 0.84

Yunnan 8.83 0.00 0.00 14.33 0.00 0.08 10.07 0.00 0.43 10.13 0.00 0.18

Zhejiang -1.94 0.07 0.01 -2.03 0.06 0.04 -4.07 0.00 0.14 -3.48 0.00 0.09

Table 4

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Despite the advantage of using zonal statistics when describing a general AOD trend

based on administrative divisions, a relatively large size of each divisions works as a

disadvantage when observing the target area in detail. The variation of AOD within the

division may exist which indicates a possibility of misleading the viewer to generalize the

outcome as a single value when using zonal statistics which yields only an averaged value

within the zone. In order to make up for the weak points in using zonal statistics, the map of

the Aerosols Distribution of China (Figure 7) was made with using a method of Unsupervised

Classification. Unsupervised classification accumulates the pixels based on the similarities of

the class, and then regenerates the image according to the newly specified classes15. The

mean AOD image over six years (2000-2005) was used as a target feature for performing this

method. Figure 7, an outcome of using unsupervised classification, provides a good reference

for analyzing a general trend of mean AOD in China from 2000 to 2005.

Figure 7.

15http://geog.hkbu.edu.hk/virtuallabs/rs/classification3.htm

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4. Results

Operating zonal statistics yielded an annual means of AOD of administrative

divisions in China from March 2000 to September 2005 (Table 1) and some divisions

revealed interesting outcomes compared to other divisions. According to the result, Shanghai,

Henan, Tianjin, Anhui, and Hunan were ranked within top five highest mean values.

Particularly, Shanghai had a record of 0.88 in 2004, which indicates the highest value among

any other divisions during this study period. Guangdong, Hong Kong, Hubei, Jiangsu, and

Shandong also ranked next to the previous top five groups. These divisions correspond to the

target area revealed in Figure 2, which indicates the east, and southeastern regions where

rapid economic development and urbanization is in progress. This trend also can be identified

in Figure 7, which shows a heavy concentration of high AOD around east and southeast

region of China. Regarding that the Gobi desert contributing to forming dust storms, the

divisions near the desert including Gansu, Nei Mongol and Xinjiang were expected to show a

high mean AOD, however, the result indicated that their means are around the China mean or

much lower. Overall mean AOD of China maintained between 0.23 and 0.25 except in 2005

(0.30).

The averaged seasonal mean AOD values (Table 2) indicated seasonal patterns based

on the geographical location. The divisions adjacent to Yellow Sea, East China Sea, and

South China Sea except Hebei and Guangdong revealed the highest AOD in summer. These

divisions are known to have a rapid and active economic development where we can

normally expect to see large amounts of chemical gas emissions. Shanghai was ranked first

among other divisions in terms of mean AOD (1.18) in summer. On the other hand, the

divisions around Gobi desert including Gansu, Nei Mongol and Xinjiang revealed to have the

highest AOD in winter, although their values were relatively lower than those divisions in

east and south coast of China. This result may imply the movement of dust storm caused by

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Siberian cold front during winter. Figure 4 reveals a seasonal pattern that corresponds to

result from Table 2. Circular shape of AOD concentration can be found around in the east and

southeast area in the image of summer (JJA). Overall, winter image (DJF) reveals a

somewhat spread-out pattern. However, a partial concentration of AOD is noticeable around

Nei Mongol and northwest of China.

For assessing a significance of statistical difference between the continent of China

and each administrative division, the annual and seasonal paired t-test was performed based

on the mean annual and seasonal AOD (Table 3 and 4). The t-value indicates whether the

mean of China as a whole is greater or less than each division. Negative t-value indicates that

the division has a higher AOD than China mean. The p-value less than 0.05 indicates that

there is a statistical difference in mean AOD between China and division. The r-value implies

a degree of correlation between two study groups. If the r-value is positive and closer to one,

it indicates a very strong positive correlation. The r-value around zero reveals a very weak

correlation or no correlation. While Shanghai, one of the heavily developed area in the east

coast, stood out with its very high annual mean AOD throughout the years in Table 1 and its

negative t-value (-3.39) proved it is greater than China mean , its r-value was near 0 which

indicates a non-directional correlation. However, still Shanghai revealed to have a negative t-

value and a zero p-value indicating the statistical difference exist compared to national

average. Some parts of east and southeast divisions in general showed high negative t-values

with low p-values (p<0.05) and r-values near to 1, which indicates they have significantly

greater mean AOD than China mean and have strong positive correlation. Hunan (-11.43) has

the highest negative t-value, followed by Henan (-9.91) and Shandong (-9.55). The rest of the

divisions that were ranked within top 10 in terms of high mean annual AOD also revealed

high t-values, though r-values were low. Except Anhui (-8.42) and Jiangsu (-7.33),

Guangdong (-7.75), Tianjin (-5.98), Hong Kong (-5.00), Hubei (-9.22) had r-values that are

Chung 18

lower than 0.5 which indicates a weak correlation. Unlike the east and southeast regions

where the industrialization is accelerating, the divisions around the Gobi desert showed an

opposite trend. Gansu (9.32), Nei Mongol (11.71), and Xinjiang (0.30) have positive t-values

which indicate that the surrounding regions of the Gobi desert has lower mean AOD than the

China mean. Overall, negative t-values throughout the east and southeast areas corresponded

to a rapid economic development and urbanization in this area16, which we can infer that high

AOD is correlated to the gas emission from major cities and industrial complexes. The

surrounding areas of Gobi desert showed positive t-values. Considering that the area contains

great amounts of dust and it is well known as where the dust storm forms, the positive t-value

was not expected result.

The result of seasonal paired t-test (Table 4) reveals a similar trend to that of Annual

paired t-test, which shows high t-values in east and southeast regions. Compared to other

divisions, Henan stood out throughout the season in terms of high negative t-values. Except

winter (-7.57) when ranked third, it had the highest negative t- value for spring (-10.51),

summer (-9.23), and fall (-12.31). Anhui continuously ranked second from summer to winter,

which also remarkably stood out. Shandong also showed consistency with high negative t-

values which was placed within top five throughout the seasons. Xinjiang, located in

northwestern area of China and near to Gobi desert, brought my attention with showing

negative t-values in spring (-7.76) and winter (-2.57). The result may support a frequent

occurrence of ‘dust storm’ in early spring around in Gobi desert.

5. Summary

The observation illustrates the advantage of using MISR data for measuring and

monitoring the trend of spatial and temporal distribution of aerosol optical depth over China.

Using techniques of zonal statistics, paired t-test, and unsupervised classification yielded

16http://www.iupac.org/publications/pac/2004/pdf/7606x1227.pdf

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tables and images, indicating east and southeast region of China is experiencing a high

concentration of AOD. Seasonal pattern was also found in northwestern and Gobi desert

regions which showed the highest AOD in winter compared to the other seasons. The increase

of aerosols throughout China is expected in long term since an industrialization coupled with

desertification mostly caused by human factors looks evident and it is continuously

intensifying. In order to prevent the further influence of aerosols accumulation over China

and East Asia in the future, monitoring the spatial and temporal variability of AOD should be

continued and integrated to the later study.

Acknowledgements

I would like to thank Professor Thomas Frank for advising my undergraduate

research. I would also like to acknowledge the advice received from Dr. Dave Diner, Chief

Scientist, MISR Mission, Jet Propulsion Lab with regard to problems with MISR AOD values

over the Sea of Japan.

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Reference

Duce, R. A., C. K. Unni, B. J. Ray, J. M. Prospero, and J. T. Merill (1980), Long-range atmospheric transport of soil dust from Asia to the tropical North Pacific: Temporal variability, Science, 209, 1522– 1524. Gong, S.L., Zhang, X.Y., Zhao, T.L., and Barrie, L. A. (2004), Sensitivity of Asian dust storm to natural and anthropogenic factors, Geophysical Research Letters, VOL. 31, L07210, doi:10.1029/2004GL019502, 2004 Gu, Y., Liou, K. N., Xue, Y., Mechoso, C.R., Li, W., Luo, Y.(2006), Climatic effects of different aerosol types in China simulated by the UCLA general circulation model, Journal of Geophysical Reseach, VOL. 111, D15201, doi:10.1029/2005JD006312, 2006 Lefohn, A. S., J. D. Husar, and R. B. Husar (1999), Estimating historical anthropogenic global sulfur emission patterns for the period 1850–1990, Atmos. Environ., 33, 3435– 3444. Li, X., X. Zhou, W. Li, and L. Chen, The cooling of Sichuan province in recent 40 years and its probable mechanisms, Acta Met. Sinica, 9, 57– 68, 1995. Xu, Q. (2001), Abrupt change of the mid-summer climate in central east China by the influence of atmospheric pollution, Atmos. Environ., 35, 5029– 5040.

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