47

# Air pollution and its threat to public health in Asia

Authored by: Lee Liu

# Routledge Handbook of Sustainable Development in Asia

Print publication date:  June  2018
Online publication date:  May  2018

Print ISBN: 9781138182189
eBook ISBN: 9781351008204

10.4324/9781351008204-3

#### Abstract

Section 2 begins with Lee Liu in “Air pollution and its threat to public health in Asia.” Dr. Liu writes that air pollution presents a severe threat to environmental, social, and economic sustainability in Asia, and contributes to climate change, resulting in both desertification and rising sea levels in Asia. Access to clean air is essential to human life, but threatens sustainability in many parts of the world, especially Asia. In this chapter, Dr. Liu examines air pollution in Asian countries and then takes China as a case study on variations within a country. Liu explores how air pollution threatens sustainability in Asia in terms of its impact on human health and then uses Asia as an example to challenge the notion that developing countries cannot combat pollution at earlier stages.

#### Introduction

Sustainable development is development that meets the needs of the present without sacrificing the ability of future generations to meet their own needs (World Commission on Environment and Development 1987). It has been illustrated as having three overlapping dimensions: the simultaneous pursuit of economic prosperity, environmental quality, and social equity, also known as the “three pillars” of sustainability (United Nations General Assembly 2005; Adams 2006; Liu 2009). Recent holistic and inclusive thinking of sustainability emphasizes overlapping dimensions and the interaction among them (Liu 2009). The question of how to achieve sustainability in Asia has long been contested, as different schools of thought exist in the interpretation of the relationship between economic development and environmental conditions. On the one hand, it is argued that the “grow (pollute) first, clean up later” path is unavoidable in some developing countries (Azadi et al. 2011). Some fast-growing Asian economies have followed that path, such as Japan, South Korea, Taiwan, and China (Rock 2002; Rock and Angel 2007). The theoretical support of the “grow first” path is provided by the environmental Kuznets curve (EKC). The EKC suggests that environmental quality first decreases and then improves with economic growth. The implication of the EKC is that economic growth is the key to achieve both economic and environmental goals (Beckerman 1992; Panayotou 1993; Ekins 2000; Weber and Allen 2010).

On the other hand, the “grow first, clean up later” approach has been long criticized and the applicability of the EKC disputed (Liu 2008, 2012, 2013a). EKC studies do not support the existence of a simple, predictable relationship between pollution and per capita income because multiple factors are involved (Stern 2004; Dasgupta et al. 2006). Harbaugh et al. (2002) conclude that there is little empirical support for an EKC relationship between important air pollutants and national income. However, governments in many Asian countries tend to promote rapid economic growth at the cost of the environment and social equity. The result is worsening environmental pollution and degradation in these countries.

Among the many environmental challenges, air pollution is a severe threat to environmental, social, and economic sustainability in Asia. Air pollution is a major cause of climate change that contributes to rising sea levels and intensification of extreme weather. Consequently, many Asian countries are losing their precious land due to desertification and rising sea levels. The loss of biodiversity has been disastrous to wildlife and ecosystems. It is the less developed countries or poorer parts of a country that are often affected the most by climate change. This causes worsening environmental injustice. Furthermore, environmental hazards threaten public health in both more and less developed countries. The World Health Organization (WHO 2014a, 2014b) estimated that indoor and outdoor air pollution exposure killed about 7 million people (one eighth of total deaths globally) in 2012. Of the 7 million deaths, 5.9 million were in the WHO’s Southeast Asia and Western Pacific Regions. Lelieveld et al. (2015) estimated that outdoor air pollution (mainly PM2.5) was responsible for 3.3 million premature deaths globally, concentrated in Asia. PM2.5 refers to particulate matter (PM) 2.5 micrometers in diameter or smaller. PM2.5 can lodge deep into human lung and blood tissue. They may cause stroke, lung cancer, and even death, particularly among children and the elderly (WHO 2014c). Access to clean air is a basic human need and a human right, and comprises an important part of sustainability.

In recent years, there have been an increasing number of global and country-specific studies on air pollution and its impact on sustainability. However, Asia-specific studies are lacking. Such studies can potentially contribute to the understanding of global air pollution and sustainability. As the world’s most populated region with diverse environmental, social, and economic processes, Asia is vitally important to global development and sustainability. This study will first provide an overview of air pollution in Asian countries, and its current situation with historical and regional comparison. It will provide an overview and comparison of all major countries in Asia. Variations within a country will be examined using China as a case study. The study will also explore how air pollution threatens sustainability in Asia in terms of its impact on human health. Furthermore, it will use the Asian experience to challenge the traditional approach to development such as the “grow first, clean up later” approach and the EKC, in order to promote sustainable policies.

#### Criteria for inclusion of countries/regions

This study intends to include all countries/regions in East, South, and Southeast Asia. However, some entities such as Macao, Fuji, and Brunei are small in size, and possess a unique economic structure, or incomplete data. These entities were excluded to enhance comparability among the countries/regions. Quantitative data were mainly from the WHO (2016a, 2016b, 2016c) and Yale University’s Environmental Performance Index (EPI) Report (Yale University 2016a, 2016b, 2016c, 2016d, 2016e). We also note that the WHO and Yale’s EPI Regions are a little different from the common geographic divisions. The Asian countries/regions included in this study belong to two different WHO and EPI Regions. The WHO classified Asia into a South-East Asia Region and a Western Pacific Region. The EPI categorizes Asian into an East Asia and Pacific Region and a South Asia Region, according to which EPI regional peer comparisons are conducted. In particular, this study excludes some Pacific countries such as Australia, New Zealand and some island countries in the western Pacific, which are in the EPI Regional comparison.

#### Data quality

The WHO data were based on reports by governments to the United Nations. In this case, the quality is variable because different countries/regions may use different data collection methods and guidelines. Air quality data from monitoring stations are regarded as accurate in the case of China (Rohde and Muller 2015). However, the placement of monitoring stations may be subject to various political considerations. The data quality is affected by the number of PM10 and PM2.5 stations and whether the data were measured or converted. The location of the stations in the cities matters. Some cities may place them near areas with the worst possible pollution so people will be alerted when pollution is at high levels. Chinese city officials tend to place them away from the most polluted spots because they want to use typical or representative spots for the city, or they may place some in highly polluted areas and others in less polluted areas so that the averages for the city may look “representative.” This practice usually results in underestimated city averages.

Furthermore, the WHO city data on Indonesia and Sri Lanka were based on only a single station, while the number of stations for Nepal was unavailable (WHO 2016a). For Indonesia, data based on one station in the city of Bandung may not be representative of such a large country. Hong Kong uses 15 and Singapore uses 22 monitoring stations to directly measure both PM10 and PM2.5. Most cities in Bangladesh and Pakistan measure both PM10 and PM2.5 through monitoring stations. For other countries, one set of data is directly measured and the other is converted using the directly measured data. In India, Malaysia, South Korea, and Thailand, it is usually the case that PM10 is directly measured and PM2.5 is converted from PM10 measurements, In Japan and Taiwan, PM2.5 is directly measured while PM10 is converted from PM2.5 data. In China, PM2.5 is directly measured in all cities, while PM10 is directly measured in some and converted from PM2.5 measurements in other cities. Despite these variations and limitations, the data obtained were the best available for this study. Both the WHO and Yale University follow their own data collection standards and criteria and employ different methods to enhance data accuracy, completeness, and comparability across countries/regions in the published datasets.

#### Terminology

This study will focus on air pollution in terms of particulate matter (PM). PM is a complex mixture of extremely small particles and liquid droplets. Such a mixture may be made up of acids, organic chemicals, metals, soil, and dust. In addition to PM2.5 just discussed, another category is PM10, referring to particles larger than 2.5 micrometers and smaller than 10 micrometers in diameter. Such particles can pass through the throat and nose and enter the lungs. Thus they can cause heart and lung diseases.

#### PM2.5 guidelines (targets)

World organizations and countries have set different guidelines (targets) for small particulate pollution (Table 3.1). The guidelines by the WHO are the strictest. The WHO (2006) argues that small particulate pollution has health impacts even at very low concentrations – indeed no threshold has been identified below in which no damage to health is observed. Therefore, the WHO 2005 guideline limits aimed to achieve the lowest concentrations of PM possible. Few Asian countries/regions have set up these guidelines for annual averages and daily (24 hours) averages. Those published guidelines are compared to the WHO, USA, and European Union standards. Singapore, Japan, and Taiwan are quite compatible with such standards while other countries, such as China and India, have lower standards. Those Asian countries not listed in Table 3.1 do not yet have any official standards available, which are very important in fighting air pollution.

### Table 3.1   Guidelines (targets) in Asian countries/regions for PM2.5 annual average and daily (24 hours) average (μg/m3) as compared to the WHO, USA, and European Union standards

Annual mean

Daily average

WHO

10

25

USA

12

35

European Union

25

na

Singapore

12

37.5

Japan

15

35

Taiwan

15

35

South Korea

25

50

Thailand

25

50

China

35

75

India

40

60

#### Current air pollution in Asian cities

The WHO (2016a, 2016b) just updated its urban air quality database, primarily based on government reporting. Annual mean concentrations of particulate matter (PM10 and/or PM2.5) were based on daily measurements, or data which could be aggregated into annual means (WHO 2016a). The database includes PM10 and PM2.5 levels in selective monitored cities in 15 Asian countries or regions (Table 3.2). The number of cities in each country or region varied from just one to 194. The data indicate that South Asian countries, Pakistan, Bangladesh, and India, tend to have very high PM10 and PM2.5 levels. The highest annual average PM10 was 540 ug/m3 in Peshawar, Pakistan. The highest annual average PM2.5 was 176 ug/m3, which is 17.6 times the WHO limit, in Gwalior, India. China is fourth in terms of urban PM2.5 pollution with an annual average at 55 ug/m3, 5.5 times the WHO limit. Urban Japan is the least polluted with an annual average PM2.5 at 15 ug/m3 and PM10 at 28 ug/m3. Malaysia, Singapore, Taiwan, South Korea, and Thailand are also among the least polluted. The means of PM10 and PM2.5 in Table 3.2 were derived by this study from averaging values among the cities regardless of city population size. WHO (2016c) also published country-wide urban PM2.5 means (Table 3.3). These means are higher than the means shown in Table 3.2 because larger urban areas tend to have higher PM2.5 pollution. Table 3.3 better reflects PM2.5 pollution in urban areas in a country than Table 3.2 does. It also shows that Asia was among the most PM2.5 polluted regions of the world. Table 3.4 presents Asia’s most polluted cities in terms of PM2.5 levels 10 times the WHO limit. Of the 27 cities, 18 are found in India including the top four most polluted cities. China is a distant second with six cities, followed by Pakistan with two cities and Bangladesh with one. The top 14 most polluted cities include 10 from India and four from China.

### Table 3.2   Particulate matter (PM10 and PM2.5) levels in Asian cities

Country or region

# of cities covered

Data

PM10 annual means (µg/m3)

PM2.5 annual means (µg/m3)

Year

Maximum

Minimum

Mean

Maximum

Minimum

Mean

8

2014

191

64

140

106

37

78

China

194

2014

305

23

89

128

15

55

Hong Kong

1

2014

49

29

India

122

2012

329

11

107

176

6

58

Indonesia

1

2014

59

33

Japan

15

2012

35

19

28

19

10

15

Malaysia

6

2014

47

20

31

25

10

16

Myanmar

14

2012*

140

31

95

78

17

53

Nepal

1

2013

88

49

Pakistan

5

2010*

540

217

339

111

66

88

South Korea

16

2014

54

38

47

28

22

25

Singapore

1

2014

30

18

Sri Lanka

1

2011

64

36

Taiwan

19

2014

51

16

31

34

11

24

Thailand

23

2014

57

23

46

32

13

25

Source: Compiled from WHO (2016a). Means were derived by author from averaging values among the cities regardless of city population size.

Notes

Notes:

#  of cities: Number of cities included in the WHO database.

*  = mode year. The year when the data were measured in different cities was 2009, 2012, and 2013 in Myanmar and 2009-2011 in Pakistan. A mode year is used here for the two countries.

### Table 3.3   Annual mean concentrations of fine particulate matter (PM2.5) in urban areas (µg/m3) in Asian countries and WHO regions, 2014

Country/WHO region

PM2.5 (µg/m3)

89.7

Nepal

75.7

India

73.6

Pakistan

68.7

China

61.8

Myanmar

56.7

Bhutan

39

Laos

33.6

Mongolia

33.5

North Korea

31.6

Viet Nam

28.7

Sri Lanka

28.6

South Korea

27.9

Philippines

27.6

Thailand

27.5

Cambodia

25

Indonesia

18.1

Singapore

17

Malaysia

16.7

Japan

13

African Region

36.7

Region of the Americas

14.5

South-East Asia Region

60.2

European Region

18.4

Eastern Mediterranean Region

62.9

Western Pacific Region

49.2

Global

38.4

Source: Compiled from WHO (2016a).

### Table 3.4   Most polluted Asian cities in terms of PM2.5 pollution 10 or more times the WHO limit

Country

City

PM2.5 annual means (µg/m3)

PM10 annual means (µg/m3)

India

Gwalior

176

329

India

170

317

India

Patna

149

167

India

Raipur

144

268

China

Xingtai

128

193

China

Baoding

126

190

India

Delhi

122

229

India

Ludhiana

122

228

China

Shijiazhuang

121

305

India

Kanpur

115

215

India

Khanna

114

213

India

113

212

India

Lucknow

113

211

China

Handan

112

169

Pakistan

Peshawar

111

540

India

Amritsar

108

202

India

Gobindgarh

108

201

Pakistan

Rawalpindi

107

448

China

Hengshui

107

161

Narayangonj

106

191

India

Agra

105

196

China

Tangshan

102

153

India

Jodhpur

101

189

India

100

188

India

100

83

India

Jaipur

100

187

India

Howrah

100

186

Source: Compiled from WHO (2016a).

#### Variations within a country: the case of Chinese cities

The national annual average of PM2.5 and PM10 levels is an important indicator of air pollution in a country. In addition, it is important to understand the regional variations within a country, which may be substantial for a large country such as China. While the annual average of PM2.5 is 55 ug/m3 in China, there is a large variation among the provinces (Table 3.5). The following examines such variations at the province level in China. The WHO database covers 194 Chinese cities with a total population of 863.2 million. While air pollution in Beijing has been well known, 85.9 million people in nearby Hebei and Tianjin live in worse air pollution, with Hebei’s PM2.5 level being 8 percent higher than Beijing’s. About 162.1 million or 19 percent Chinese live in PM2.5 pollution seven times or more of the WHO limit, 353.5 million or about 41 percent live in six times or more of the WHO limit, 656.4 or over 76 percent live in five times or more of the WHO limit, and 856.6 million or over 99 percent Chinese live in three times or more of the WHO limit. Only Tibet and Hainan with less than 1 percent of the Chinese population live in PM2.5 levels compatible to that of Japan. At the city level, variations are even greater. About 44.8 million Chinese in six cities live in PM2.5 levels 10 to 12.8 times the WHO limit (Table 3.6). Nearly 74.6 million people in nearby Hebei, Tianjin, and Henan cities live in air pollution worse than Beijing. Nearly 94.2 million or 11 percent of the Chinese population live in air quality at or worse than the air in Beijing. Xingtai with PM2.5 at 128 is 50 percent worse than Beijing. About 402.5 million or 47 percent of Chinese live in cities where PM2.5 levels are six to 12.8 times the WHO limit.

### Table 3.5   Population and particulate matter (PM2.5 and PM10) levels in Chinese provinces, 2014

Province level region

Population

Number of stations

PM2.5 Annual mean (µg/m3)

PM10 Annual mean (µg/m3)

Hebei

73

55

92

149

Tianjin

12.9

15

87

150

Beijing

19.6

14

85

108

Hubei

20.9

24

73

112

Henan

35.7

37

70

112

Subtotal

162.1

Anhui

16.9

21

64

96

Shaanxi

33.1

51

64

120

Chongqing

28.8

17

61

106

Hunan

26.3

39

60

88

Jiangsu

86.1

92

60

92

Subtotal

353.5

Jilin

12.1

17

57

103

Liaoning

35.2

62

56

86

Zhejiang

57.4

57

54

90

Guangxi

16.7

22

52

83

Shanghai

23

10

52

84

Shanxi

7.6

12

52

78

Sichuan

41.1

41

52

83

Xinjiang

4.1

15

52

94

Shandong

105.7

98

51

82

Subtotal

656.4

Guizhou

10.4

15

49

80

Jiangxi

9.8

17

45

88

Ningxia

2.7

10

44

90

Heilongjiang

22.1

29

41

65

Gansu

5.8

10

40

83

Neimenggu

11.8

23

40

81

Guangdong

94

91

39

61

Shenzhen

10.4

11

34

61

Fujian

18.8

14

33

58

Yunnan

14.6

12

32

58

Subtotal

856.6

Tibet

0.6

6

24

64

Hainan

2.7

7

19

35

Subtotal

3.3

China

863.2

Sources: PM10 and PM2.5 data were compiled from WHO (2016a). Population data are from National Bureau of Statistics of China (2012).

### Table 3.6   Most polluted Chinese cities, 2014

Province level region

City

2010 census population (million)

PM2.5 Annual mean, µg/m3

PM10 Annual mean, µg/m3

Hebei

Xingtai

7.1

128

193

Hebei

Baoding

11.2

126

190

Hebei

Shijiazhuang

10.2

121

305

Hebei

Hengshui

4.3

112

169

Hebei

Tangshan

7.6

107

161

Hebei

Langfang

4.4

102

153

Subtotal

44.8

Hebei

Cangzhou

7.1

96

144

Hebei

Shouguang

1.1

88

133

Tianjin

Tianjin

12.9

87

150

Henan

Zhengzhou

8.6

86

171

Subtotal

74.6

Beijing

Beijing

19.6

85

108

Subtotal

94.2

Hubei

Wuhan

9.8

80

124

Henan

Anyang

5.2

79

119

Anhui

Hefei

5.7

79

115

Hebei

Shouguang

1.1

78

117

Hubei

Jingzhou

5.7

74

112

Hunan

Changsha

7

74

94

Jiangsu

Nanjing

8

72

137

Liaoning

Shenyang

8.1

72

129

Sichuan

Chengdu

14

71

150

Jilin

Harbin

10.6

71

119

Henna

Kaifeng

4.7

70

106

Hubei

Yichang

4.1

70

106

Hubei

Yangquan

1.4

70

105

Henan

Pingdingshan

4.9

70

105

Hunan

Xiangtan

2.8

70

105

Shaanxi

Xi’an

8.5

70

189

Hunan

Zhuzhou

3.9

69

105

Shandong

Laiwu

1.3

68

103

Henan

Jiaozuo

3.5

68

103

Jiangsu

Jiangyin

1.6

68

102

Jiangsu

Suqian

4.7

68

102

Shaanxi

Weinan

5.3

68

102

Shaanxi

Changzhi

3.3

67

101

Shaanxi

Taiyuan

4.2

67

157

Jiangsu

Wuxi

6.4

67

101

Jiangsu

Xuzhou

8.6

66

100

Jiangsu

Zhenjiang

3.1

66

99

Jiangsu

Changzhou

4.6

65

99

Shaanxi

Xianyang

5.1

65

98

Guangxi

Liuzhou

3.8

65

98

Liaoning

Anshan

3.6

65

98

Jiangsu

Huai’an

4.8

65

98

Shaanxi

Baoji

3.7

65

98

Jiangsu

Jurong

0.6

65

97

Jiangsu

Yangzhou

4.5

65

97

Xinjiang

Urumqi

3.1

64

146

Sichuan

Zigong

2.7

64

97

Zhejiang

Shaoxing

4.9

64

105

Jiangsu

Suzhou

10.5

64

97

Shaanxi

Tongchuan

0.8

64

97

Henan

Sanmenxia

2.2

64

96

Liaoning

Changchun

7.7

64

130

Shandong

Liaocheng

5.8

63

96

Zhejiang

Jinhua

5.4

63

99

Zhejiang

Huzhou

2.9

63

111

Shanxi

Linfen

4.3

62

94

Qinghai

Xining

2.2

62

163

Chongqing

Chongqing

28.8

61

106

Zhejiang

Taizhou

6

61

82

Anhui

Wuhu

2.3

61

92

Jiangsu

Lianyungang

4.4

61

92

Zhejiang

Hangzhou

8.7

61

106

Shandong

Heze

8.3

60

91

Hebei

Qinhuangdao

3

60

91

Jiangsu

Zhangjiagang

1.2

60

91

Jiangsu

Nantong

7.3

60

90

Anhui

Fuyang

7.6

60

90

Total

402.5

Sources: PM10 and PM2.5 data were compiled from WHO (2016a). Population data are from National Bureau of Statistics of China (2012).

None of the 194 Chinese cities met the WHO guidelines for PM10 or PM2.5. Sanya, Hainan, had the lowest PM10 at 23 μg/m3 followed by Yifan, Heilongjiang, at 24 μg/m3. Both cities also had the lowest PM2.5 level at 15 μg/m3 and 16 μg/m3 respectively. The 17 worst cities had PM10 levels over seven to 15 times exceeding WHO limit ranging from 144 to 305 μg/m3 were Shijiangzhuan, Jinan, Xingtai, Baoding, Xi’an, Zhengzhou, Handan, Xining, Hengshui, Taiyuan, Tangshan, Lanzhou, Tianjin, Chengdu, Urumqi, Hohhot, and Langfang. In terms of PM2.5 air pollution, six cities had levels over 10 to 12 times exceeding the WHO limit. They were Xingtai, Baoding, Shijiangzhuan, Handan, Hengshui, and Tangshan. The PM2.5 levels in Langfang, Cangzhou, Tianjin, Zhengzhou, Beijing, and Wuhan were from eight to nine times the WHO limit.

The most polluted tend to be lower and medium income manufacturing centers such as those in Hebei Province. This agrees with WHO findings that populations in less-developed cities are the most impacted by air pollution (WHO 2016b). On the other hand, less polluted areas tend to be more-developed cities such as those in Guangdong, Zhejiang, and Fujian. WHO (2016b) finds that 44 percent of cities in high-income countries meet the WHO air quality guidelines. However, none of the high-income Chinese cities do. Some of them are as wealthy as cities in high-income countries but severely polluted, such as Beijing, Tianjin, Wuhan, Tangshan, Zhengzhou, Nanjing, and Chengdu. Larger population centers also tend to be more likely to have higher pollution. However, there are many exceptions. Beijing and Tianjin more among the most developed and most polluted. Some of the least polluted Chinese cities are also less-developed, such as Zhanjiang, Sanya, Yilan, Haikou, Yuxi, Maoming, Yangjiang, Jiujiang, Chifeng, and Lhasa. Geographic factors also influence level of pollution. These factors include climate, particularly precipitation and wind direction and speed, topography, and distance to the coast.

#### National air quality performance in Asian countries

The above discussion was based on outdoor air pollution data mainly from ground-based monitoring stations in selective spots in selective cities. Yale University (2016a) provides national level information on air quality including both urban and rural areas. It “ranks how well countries perform on protection of human health from environmental harm and protection of ecosystems.” The Air Quality category is based on different indicators. They include average exposure to PM2.5, health risk exposure to PM2.5, percentage of the population exposed to PM2.5 levels above WHO air quality guidelines, indoor solid fuel usage, and average concentration of NO2 (Yale University 2016b). The Health Impacts indicator “assesses human health risks associated with unsafe water and sanitation as well as household and outdoor air quality” (Yale University 2016c). Different from the WHO data, the outdoor Air Quality indicators are mainly based on satellite-derived estimates (Yale University 2016d). Countries/regions are ranked by their performances and ten-year changes in each indicator as well as compared to their peers in terms of GDP per capita and their geographic neighbors.

Asian countries/regions tend to have lower rankings in air quality indicators (Table 3.7). Among the worst 21 countries/regions in PM2.5 exposure and exceedance, 12 are in Asia, with China, Bangladesh, India, Nepal, and Pakistan as the worst five. Other countries do not rank high except for Mongolia and the Philippines in PM2.5 exposure and Singapore and Mongolia being number one in PM2.5 exceedance. Asian countries/regions do not rank well in terms of exposure to NO2 which tends to be associated with more developed economies. It is not surprising that South Korea, Japan, Singapore, and Taiwan were among the worst. However, it is a surprise that China as a developing country ranked 176, the fourth worst in the world and second only to South Korea in Asia. Myanmar and Bhutan had the highest ranking in Asia, in consistence with their level of economic development. Household air quality rankings are closely related to level of economic development. Japan, South Korea, and Singapore were the world best while most Asian countries rank poorly, with Laos, Myanmar, Cambodia, Bangladesh, Nepal, and Sri Lanka being the worst in Asia. The air quality category ranking was based on the above indicators. The world’s worst eight countries in air quality are all in Asia. Thailand, Bhutan, and Taiwan are not doing well either with their rankings above 160. Only four of the 20 countries/regions were ranked above 100. Singapore, the Philippines, and Mongolia are better than their Asian neighbors but still ranked below the world’s top 50. The poor air quality rankings reflect the low rankings in PM2.5 exposure and exceedance, except for exceedance in Singapore which is ranked number 1. Since the Health Impacts indicator refers to impacts by both air and water pollution, it may not agree with air pollution rankings. For example, Malaysia and Japan were ranked higher in water pollution performance so its Health Impacts ranking is better than its air quality ranking. The worst rankings are Bangladesh, Myanmar, Nepal, Cambodia, and India.

### Table 3.7   Air quality and health impacts rankings of selective Asian countries/regions among 180 countries/regions worldwide, 2014

Country or Region

Exposure to PM2.5

PM2.5 Exceedance*

Exposure to NO2

Household Air Quality

Air Quality

Health Impacts**

China

180

179

176

116

179

95

179

178

107

151

180

150

India

178

178

110

135

178

134

Nepal

177

177

61

146

177

141

Pakistan

176

176

105

125

175

123

South Korea

174

174

178

1

173

103

Laos

173

174

75

162

176

127

Viet Nam

170

165

103

119

170

93

Myanmar

168

168

40

156

174

143

Thailand

166

170

118

104

167

85

Taiwan

162

160

159

101

161

84

Bhutan

160

173

40

112

163

91

Cambodia

114

127

61

153

148

137

Malaysia

110

155

133

54

117

42

Sri Lanka

109

140

52

141

140

114

Japan

95

133

172

1

104

57

Singapore

93

1

163

1

54

63

Indonesia

74

122

101

113

92

78

Philippines

30

78

75

123

61

108

Mongolia

23

1

61

131

65

111

Source: Compiled from Yale University (2016c).

Notes

Notes:

*  = the percentage of the population exposed to PM2.5 levels above the WHO limit. 179 was the worst ranking in 2016 Report.

**  Impacts by both air and water pollution.

A country/region’s level of economic development is commonly believed to be associated with certain level of air pollution. Such a belief calls for comparison among countries/regions at the same level of development in order to be fair. The result shows that majority Asian countries/regions compare rather unfavorably to their GDP peer set (Table 3.8). China appears to be the worst in the outdoor air quality comparison, followed by Bangladesh and India. In terms of Exposure to PM2.5, China and Bangladesh were 97.23 percent and 92.79 percent below their peers. India, Nepal, and Pakistan were all over 80 percent below their peers. On the other hand, six of the 20 countries/regions compare favorably with their GDP peers, such as Mongolia and the Philippines. In terms of PM2.5 exceedance, Bangladesh, China, and India were each 100 percent below their peers, followed by Nepal and Pakistan. Only three countries compared favorably to their GDP peers, including Singapore, Mongolia, and the Philippines. It was a surprise that Bhutan is compared unfavorably by 66.94 percent to its peers. The country has little manufacturing at low level of economic development. It is reasonable to assume that it has been affected by pollution from its southern neighbors such as India and Bangladesh. In the matter of Exposure to NO2, South Korea was 100 percent below its GDP peers, followed by China at 80.68 percent. Six countries compared favorably including Myanmar, Bhutan, and Sri Lanka. With regard to Household Air Quality, half of the 20 countries/regions compared favorably to their GDP peers, with Nepal at 100 percent and Cambodia at 61.27 percent. On the other hand, Laos and Myanmar were over 50 percent below their peers. Pertaining to the Air Quality category, China and Bangladesh were over 70 percent worse than countries at the same economic development level. India, Laos, Nepal, and Pakistan were all over 50 percent below their peers in the comparison. The Philippines and Mongolia compared most favorably with their peers. In reference to the Health Impacts category, Bangladesh, South Korea, and Myanmar were the least favorably compared to their GDP peers while Cambodia and Nepal had the most favorably comparison.

### Table 3.8   Air quality and health impacts of selective Asian countries/regions as compared individually to countries/regions at the same level of GDP per capita (%), 2014

Country or Region

Exposure to PM2.5

PM2.5 Exceedance

Exposure to NO2

Household Air Quality

Air Quality

Health Impacts*

China

−97.23

−100

−80.68

−18.12

−71.65

−11.34

−92.79

−100

−11.27

−37.24

−70.84

−28.8

India

−89.71

−100

−12.63

−8.72

−62.55

−12.89

Nepal

−83.49

−93.79

−1.35

100

−55.27

70.41

Pakistan

−81.7

−87.16

−10.61

−1.38

−53.88

−3.1

South Korea

−58.2

−73.52

−100

1.94

−44.26

−21.89

Laos

−52.13

−73.84

1.14

−67.17

−56.71

−7.02

Viet Nam

−41.56

−46.69

−9.95

5.94

−26.94

17.72

Thailand

−39.26

−60.65

−6.38

−8.54

−32.93

−6.39

Myanmar

−37.71

−53.32

6.78

−50.68

−40.85

−21.31

Taiwan

−28.31

−38.26

−18.13

−13.57

−25.31

−13.4

Bhutan

−20.25

−66.94

6.78

15.13

−22.57

19.51

Malaysia

−5.74

−34.56

−17.56

8.4

−11.07

11.63

Cambodia

−1.17

−17.28

−1.35

61.27

−0.68

78.38

Japan

1.51

−8.51

−54.13

1.94

−4.92

−2.69

Sri Lanka

2.17

−15.51

5.68

−16.53

−7.96

3.46

Singapore

2.45

27.55

−27.26

1.94

7.53

−4.91

Indonesia

17.83

−1.03

−8.65

12.08

7.21

27.56

Philippines

27.15

22.92

1.14

0.75

15.79

7.57

Mongolia

28.59

26.03

4

−6.39

15.65

5.54

Source: Compiled from Yale University (2016c).

Notes

Notes:

*  Impacts by both air and water pollution. The percentages range from positive 100 to negative 100.

With regard to PM2.5 exposure, 12 of the 20 countries/regions compared unfavorably to their neighbors in the same EPI Regions (Table 3.9). China was the worst, 97.13 percent below its neighbors. Bangladesh and India were over 80 percent worse than their neighbors. On the other hand, Sri Lanka, Bhutan, Mongolia, and the Philippines compared very favorably to their neighbors. The worst countries China, Bangladesh, and India were all 100 percent worse than their neighbors in the PM2.5 exceedance comparison. Nepal, South Korea, Laos, and Pakistan were over 70 percent worse than their neighbors. On the opposite side, Sri Lanka, Mongolia, Singapore, and the Philippines compared very favorably to their neighbors. South Korea was the worst when compared to its neighbors in Exposure to NO2, followed by China, Japan, Singapore, and Taiwan. Half of the countries/regions, all of them less-developed, compared favorably to their neighbors. Yet, the less-developed economies tended to compare very unfavorably to their neighbors in Household Air Quality, except for Bhutan. More-developed economies tended to do better in Household Air Quality. In the overall Air Quality category, China was the worst, 68.28 percent worse than its neighbors, followed by Laos, Bangladesh, and India. On the other hand, Sri Lanka was nearly 37 percent higher compared to its neighbors. In terms of Health Impacts, most less-developed economies did not compare well with their neighbors. The exceptions were Bhutan, Malaysia, and Sri Lanka.

### Table 3.9   Air quality and health impacts of selective Asian countries/regions as compared to their neighbors in the EPI Regions (%), 2014

Country or Region

Exposure to PM2.5

PM2.5 Exceedance

Exposure to NO2

Household Air Quality

Air Quality

Health Impacts*

China

−97.13

−100

−79.78

1.26

−68.28

−1.96

−87.75

−100

−11.96

−28.92

−56.62

−25.59

India

−82.52

−100

−13.31

3.38

−44.3

−8.96

Nepal

−71.52

−84.44

3.19

−12.9

−40.79

−15.69

Pakistan

−68.92

−71.61

−11.31

11.71

−31.4

1.28

South Korea

−57.46

−72.43

−100

37.12

−39.38

−6.17

Laos

−54.23

−72.43

18.17

−69.59

−56.78

−21.75

Viet Nam

−44.12

−43.82

5.22

−1.88

−27.06

−0.93

Thailand

−37.06

−56.05

−1.99

13.12

−24.98

3.52

Taiwan

−27.03

−35.7

−41.29

16.25

−18.77

4.02

Cambodia

−4

−1.22

21.52

−43.28

−11.75

−28.94

Malaysia

−2.34

−26.91

−13.69

34.07

−0.52

23.45

Japan

3.32

−4.73

−67.1

37.12

3.41

16.89

Singapore

4.28

32.82

−47.83

37.12

16.95

14.23

Myanmar

5.81

3.21

5.95

−44.14

−12.01

−17.76

Indonesia

12.68

4.3

6.74

3.81

7.04

7.35

Philippines

21.59

29.54

18.17

−6.69

15.61

−9.47

Mongolia

22.97

32.82

21.52

−13.3

15.48

−11.18

Bhutan

35.47

−26.91

5.95

30.4

15.17

24.91

Sri Lanka

73.55

86.81

4.85

−5.46

36.9

8.14

Source: Compiled from Yale University (2016c).

Note

Notes:

*  Impacts by both air and water pollution.

#### Historical trends

From 2005 to 2014, PM2.5 Exposure increased in all countries/regions except for Japan and the Philippines (Table 3.10). The largest increases were by 68 percent to 84 percent in China, India, and Bangladesh. However, PM2.5 Exceedance increased in only five countries while most countries/regions experienced a declining trend in the 10 years. China’s Exposure to NO2 increased by 54.36 percent in ten years, the largest increase in Asia. The increases in other countries were small. Japan had a 58 percent decrease in ten years followed by Taiwan with a 45 percent decrease. Most countries improved their Household Air Quality by 90 percent to 100 percent in the 10 year period. The overall Air Quality increased in most countries/regions also, possibly benefiting from Household Air Quality improvement. Myanmar and Bangladesh experienced the largest decrease in overall Air Quality. The same trend also happened to Health Impacts with most countries/regions experienced improvement. Yet, Singapore, Malaysia, and Japan suffered some losses.

### Table 3.10   Ten-year change (%) in air quality and health impacts of selective Asian countries/regions, 2014

Country or Region

Exposure to PM2.5

PM2.5 Exceedance

Exposure to NO2

Household Air Quality

Air Quality

Health Impacts*

−83.57

0

−7.37

49.48

−18

−1.29

India

−72.84

0

−1.94

74.74

4.97

5.1

China

−67.79

0

−54.36

100

53.22

20

Nepal

−58.73

100

−0.77

95.82

13.98

30.36

Laos

−45.42

100

−1.97

−6.14

−9.38

14.73

Myanmar

−45.13

−36.18

−2.09

38.42

−25.73

12.45

Viet Nam

−36.51

83.51

−5.99

100

20.64

25.31

Thailand

−36.29

90.5

1.66

100

18.94

−0.63

Bhutan

−27.56

−42.64

−1.13

100

−1.04

15.93

Pakistan

−20.6

100

−4.92

100

51.36

20.99

Cambodia

−18.29

−2.27

−1.77

90.28

−0.02

24.61

Malaysia

−15.29

−0.7

3.49

98.56

15.71

−7.38

Sri Lanka

−15.18

−32.96

−0.19

87.64

−8.4

11.07

Taiwan

−10.86

100

44.97

−0.89

28.65

7.01

Singapore

−8.54

0

13.14

95

16.46

−13.66

South Korea

−6.1

100

95

77.15

−1.2

Indonesia

−3.93

15.5

7.21

100

20.7

6.86

Mongolia

−0.2

0

−2.28

96.28

11.28

18.56

Philippines

1.41

24.51

0.74

83.59

21.38

−0.45

Japan

5.03

56.86

57.9

95

45.1

−4.29

Source: Compiled from Yale University (2016e).

Note

Notes:

*  Impacts by both air and water pollution.

Historical data on pollution measurements from Yale University (2016e) reflect changing patterns in different countries/regions. Based on the data, means were derived for the historical periods (Table 3.11). With 78.4 percent of its population exposed to PM2.5 levels above the WHO limit, China is the highest in the world from 2000 to 2014. South Asian countries also had high levels except for Sri Lanka and Bhutan. Only six countries met the WHO annual limit. Furthermore, the worst seven countries in the world are in Asia, from China to North Korea. Again, South Asian countries also had a very depressing situation with Sri Lanka and Bhutan as the exceptions. Singapore and Mongolia were very successful in dealing with this issue. Similar situation is true in regard to PM2.5 Exceedance, with Asian countries took the worst seven places. The situation is a little better with PM2.5 Health Risk Exposure from 1990 to 2013. China is the worst in Asia but the third worst in the world. South Asian countries followed, except for Sri Lanka and Bhutan. Mongolia and the Philippines had the lowest risk exposure. With regard to NO2 Exposure, more-developed economies such as South Korea, Hong Kong, and Japan suffered high level of exposure from 1997 to 2011. The data again indicate that China experienced higher level of NO2 pollution than would have been expected at its level of economic development. Most developing countries, such as Bhutan and Myanmar, had low levels of exposure, constant with their level of economic development.

### Table 3.11   Asian countries/regions ranked in the world by their means of air pollution indicators

Country or region

PM2.5 Exposure 2000–2014

Country or region

PM2.5 Exceedance 2000–2014

Country or region

PM2.5 Health Risk* 1990–2013

Country or region

NO2 Exposure 1997–2011

1. China

45.1

1. China

0.784

3. China

0.656

3. South Korea

7.06

2. India

28.4

2. Pakistan

0.686

7. Pakistan

0.621

4. Hong Kong

6.37

3. Pakistan

27.9

3. Nepal

0.656

0.6

9. Japan

4.2

4. Nepal

27.3

4. India

0.642

15. India

0.579

16. China

3.29

24.8

0.614

16. Nepal

0.576

18. Taiwan

3.19

6. South Korea

21.5

6. South Korea

0.529

22. South Korea

0.527

59. Malaysia

1.03

7. North Korea

19.8

7. North Korea

0.482

40. North Korea

0.451

70. Thailand

0.71

10. Laos

16.7

10. Hong Kong

0.409

52. Bhutan

0.417

82. India

0.51

11. Hong Kong

16.6

15. Laos

0.379

59. Viet Nam

0.408

0.41

14. Viet Nam

15.9

24. Taiwan

0.333

65. Singapore

0.397

91. Pakistan

0.4

26. Taiwan

14.5

28. Thailand

0.32

70. Taiwan

0.391

93. Viet Nam

0.38

32. Thailand

13.7

29. Viet Nam

0.319

73. Myanmar

0.388

101. Philippines

0.28

39. Bhutan

12.6

34. Bhutan

0.288

83. Laos

0.372

120. Laos

0.21

45. Japan

12.3

51. Japan

0.2

85. Thailand

0.367

123. Nepal

0.19

53. Myanmar

11.4

53. Myanmar

0.192

87. Japan

0.362

129. Cambodia

0.17

72. Malaysia

9.6

65. Malaysia

0.14

111. Sri Lanka

0.301

134. Sri Lanka

0.15

84. Indonesia

8.5

77. Indonesia

0.11

114. Cambodia

0.29

157. Myanmar

0.11

85. Cambodia

8.5

87. Cambodia

0.062

116. Indonesia

0.285

158. Bhutan

0.1

91. Sri Lanka

7.8

89. Sri Lanka

0.055

119. Malaysia

0.276

113. Philippines

6.4

98. Philippines

0.035

149. Philippines

0.151

153. Singapore

4.6

147. Mongolia

0.001

157. Mongolia

0.121

211. Singapore

0

Source: Compiled from Yale University (2016e).

Note

Notes:

*  PM2.5 Health Risk Exposure is a unitless measurement from 0 to 1 with 1 being the highest risk.

Eight countries/regions were selected from Table 3.11 to illustrate the historical trends in each air quality indicator. In order to pay attention to the relationship between level of development and air quality, the figures included four more-developed economies: Japan, South Korea, Taiwan, and Hong Kong. In the case of PM2.5 Health Risk Exposure for which Hong Kong had no data, Singapore was used instead. They also include four less-developed economies: China, India, Pakistan, and Bangladesh. It should be noted that the more-developed economies are all Island countries/regions. Their air quality should have been favorably affected by their geographic location that is associated with stronger wind, ocean influence, and more precipitation.

Pertaining to annual PM2.5 Exposure, the less-developed economies all experienced an increasing trend from 2000 to 2014 (Figure 3.1). China experienced the fastest increasing trend with leveling off and slight decline in recent years. Leveling off and a slight decline also happened to Pakistan, while India and Bangladesh had a steady growing trend. The more-developed economies tend to have lower levels than the less-developed. South Korea, Taiwan, and to some lesser extent, Japan experienced a slight rise first, followed by a slight decline. Hong Kong was a little different with a recent rise. In regard to the proportion of population exposed to PM2.5 levels exceeding the WHO limit, the four less-developed countries had a higher rate than the more-developed (Figure 3.2). China and Pakistan had a leveling off while India and Bangladesh continue to rise. The more-developed economies experienced a rise and fall while maintaining low levels, with Hong Kong and South Korea having an increase in 2014. Japan’s trend was rather flat, indicating a sustained low level. Comparing Figures 3.1 and 3.2, it may be argued that more proportion of Chinese suffered from PM2.5 pollution while the intensity of the pollution had leveled off in recent years. The pollution has become more widely spread while intensity slightly lowered.

Figure 3.1   Annual mean PM2.5 Exposure in Asian countries/regions 2000–2014

Source: Compiled from Yale University (2016c).

Figure 3.2   Annual mean PM2.5 Exceedance in Asian countries/regions 2000–2014

Source: Compiled from Yale University (2016c).

In respect to PM2.5 Health Risk Exposure, the four less-developed countries had a high risk with a rising trend (Figure 3.3). The more-developed economies had a low risk with a slight declining trend, except for Singapore with an inverted shape. Its risk was higher than any other countries in the early 1990s but quickly bottomed to the lowest level and then increased again. Trends in NO2 Exposure were very different among the countries/regions (Figure 3.4). China’s NO2 pollution caught up very quickly, overtaking Taiwan’s in the early 2000 and Japan’s in the late 2000s. The other less-developed countries all had very low level of NO2 pollution with a slight increase. The more-developed economies experienced a declining trend starting in the late 1990s to mid-2000s. China was a less-developed country with NO2 pollution at the level of more-developed economies. Its increasing trend leveled off from 2010 to 2011.

Figure 3.3   Annual mean PM2.5 Health Risk in Asian countries/regions 1990–2014

Source: Compiled from Yale University (2016c).

Figure 3.4   Annual mean NO2 Exposure in Asian countries/regions 1997–2011

Source: Compiled from Yale University (2016c).

#### Air pollution in Asia may be the worst globally and historically

The above discussion indicates that the exact extent of pollution varies by sources of data, possibly due to different methods of measurement by the same or different organizations. For example, the WHO reported that annual mean PM2.5 for urban China was 61.8 μg/m3 (Table 3.3). That was higher than what Rohde and Muller (2015) reported, noting 52 μg/m3 as the population-weighted average based on station-measured data in 190 cities. Yale University reported 45.13 μg/m3 for both urban and rural areas. However, both the WHO and Yale University data suggested similar patterns and trends. Asia has been the worst hit by air pollution, particularly in terms of deadly PM2.5 exposure, despite the fact that some Asian countries such as Japan and Singapore have much lower levels of pollution. India, China, Pakistan, and Bangladesh are the worst in Asia. Furthermore, the country-wide averages may hide disparities in air pollution. Levels of pollution also varied greatly within large countries such as China, where about 44.8 million people live in PM2.5 polluted air 10 times or more the WHO limit. Eastern China, particularly areas around Beijing and Tianjin, has been the worst hit by PM2.5 pollution. Geographic factors such as climate and topography affect the level of air pollution, in addition to population and type of economy. Air pollution has also been reported to be the worst in the northern parts of India, Pakistan, and Bangladesh (WHO 2016d).

National level data from Yale University provided additional insight into air quality and pollution in Asia in a global context. Asian countries/regions tended to receive lower rankings in PM2.5 Exposure and Exceedance with China, Bangladesh, India, Nepal, and Pakistan ranked as the worst in the world. This generally coincides with the findings based on the WHO data. Furthermore, Asian countries/regions also ranked low in NO2 Exposure, which tends to be associated with more-developed economies. This means that these countries/regions suffer more from NO2 pollution than expected at their level of development. China is the worst in this aspect. Asia is also worst in terms of overall air quality based on outdoor and indoor pollution. Asia is home to the world’s worst eight countries in terms of air quality. Asian countries/regions compare rather poorly to countries at similar levels of economic development, with China as the worst. As air pollution is not restricted by national borders, it is reasonable to assume that some less-developed countries such as Bhutan have been negatively affected by air pollution from their neighbors.

From 2005 to 2014, China, India, and Bangladesh led the increase in PM2.5 Exposure. Indeed, the worst seven countries in the world in terms of PM2.5 Exposure are located in Asia. There were also substantial increases in other Asian countries/regions except for Japan and the Philippines. The good news is that most Asian countries/regions experienced a decrease in PM2.5 Exceedance. Historical data from 2000 to 2014 indicate that China is the worst in the world in PM2.5 Exceedance, as 78.4 percent of its population was exposed to PM2.5 above the WHO limit. The historical patterns appear to be different between more and less-developed economies. Less-developed economies tended to experience high levels and an increasing trend in PM2.5 Exposure while the more-developed economies tended to have lower levels with a rising and falling trend. Similar trends exist in terms of PM2.5 Exceedance and PM2.5 Health Risk Exposure.

#### Air pollution is a threat to public health in Asia

Air pollution is a threat to sustainability in several aspects, including its impact on climate change, human health, social justice, and economic equality and well-being. This study focuses on its threat to public health. The WHO just published its global assessment of disease burden due to environmental risks in 2012 (Prüss-Ustün et al. 2016). The WHO report and accompanying dataset provide a rare opportunity to compare environmental health in Asian countries. China and India each lost nearly three million people to environmental risks, contributing to 30 percent of all deaths in the two countries (Table 3.12). That was 30 percent higher than the world average of 23 percent. Only a few Asian countries were below the world average, including Japan, Singapore, South Korea, Malaysia, Thailand, and the Philippines. Laos was the worst at 32 percent. In addition, environmental risks contributed to lost years of healthy life as indicated by Disability-Adjusted Life Years (DALYs). The percentages of DALYs varied among the Asian countries but were closely associated with percentages of deaths. The age-standardized death rates attributable to environmental risks were highest in Laos, India, North Korea, and Mongolia, with over 300 per 100,000. Japan, Singapore, and South Korea had the lowest rates of death due to environmental risks. The ratio of such deaths between Laos and Japan is nearly eight times, which could be used to argue for an association between economic development and environmental health. However, there is no justification to degrade environmental health in the name of development.

### Table 3.12   Burden of disease from environmental risks, Asian countries, 2012

Country

Total (000s)

% deaths

Total (000s)

% DALYs

Age-standardized deaths/100,000

Age-standardized DALYs/100,000

201.53

23

11346.44

22

189

8,520

Bhutan

1.26

26

70.26

25

225

10,574

Cambodia

21.01

25

1263.56

22

173

9,051

China

2986.68

30

95968.22

26

199

6,408

India

2911.67

30

133618.4

25

315

12,119

Indonesia

349.87

23

16163.07

21

198

7,479

Japan

131.28

11

4222.25

13

41

2,110

Laos

14.91

32

927.29

31

321

14,524

Malaysia

25.94

18

1427.05

19

123

5,504

Mongolia

5.17

27

238.72

24

309

10,665

Myanmar

109.24

25

5271.27

23

277

11,255

Nepal

46.69

25

2369.45

23

251

10,129

North Korea

70.45

31

2454.7

27

310

10,122

Pakistan

331.18

25

19468.4

23

258

11,385

Philippines

123.46

22

7024.14

21

206

8,809

Singapore

3.11

13

120.46

13

47

1,900

South Korea

37.96

14

1482.86

14

58

2,461

Sri Lanka

34.92

25

1337.63

22

169

6,265

Thailand

93.82

19

3941.21

18

124

5,389

Viet Nam

129.27

25

5748.16

23

158

6,764

Total

7629.42

596412.2

Source: Compiled from Prüss-Ustün et al. (2016).

Note

Notes:

DALY refers to Disability-Adjusted Life Year. One DALY can be thought of as one lost year of “healthy” life. The sum of these DALYs across the population, or the burden of disease, can be thought of as a measurement of the gap between current health status and an ideal health situation where the entire population lives to an advanced age, free of disease and disability. DALYs for a disease or health condition are calculated as the sum of the Years of Life Lost (YLL) due to premature mortality in the population and the Years Lost due to Disability (YLD) for people living with the health condition or its consequences (Prüss-Ustün et al. 2016).

Environmental health risks include more than air pollution. Yet air pollution, especially PM2.5 pollution, is one of the most deadly risks to human health (Table 3.13). Beelen et al. (2014) found that naturally-caused mortality was associated with long-term exposure to PM2·5 in European countries, even if the air pollution level was well below the mean annual limit of 25 μg/m3. Developing countries have been suffering from severe air pollution which is a major cause of health problems, resulting in between 1.2 to 2 million premature deaths a year in China alone (Yang et al. 2013; WHO 2014b). Rohde and Muller (2015) found that Eastern China, where most of the population resides, was the hardest hit by air pollution. They estimated that unhealthy air affected 92 percent of China’s population, if US standards were applied.

### Table 3.13   Mortality rate (per 100,000 population) and total deaths attributed to household and ambient air pollution versus total environment attributable deaths, Asian countries, WHO regions, and the world total, 2012

Country

Deaths/100,000

Total deaths attributed to air pollution* (000s)

As % of total environment attributable deaths**

North Korea

234.1

58.89

83.59

China

163.1

2257.18

75.57

Mongolia

132.2

3.91

75.62

India

130

1704.37

58.54

Myanmar

127.4

68.66

62.86

Sri Lanka

119.4

24.73

70.84

Laos

107.6

7.32

49.08

Nepal

104.2

29.71

63.64

Pakistan

88.8

167.77

50.66

Viet Nam

84

78.5

60.72

Indonesia

83.9

216.1

61.76

Philippines

82.7

83.28

67.45

Cambodia

71.4

11.12

52.93

68.2

109.8

54.48

Thailand

65.3

44.38

47.3

Bhutan

59.9

0.46

36.87

Japan

24.2

30.63

23.33

South Korea

23.7

11.92

31.4

Malaysia

22.4

6.79

26.19

Singapore

20.5

1.15

36.94

Subtotal

125

4916.67

64.44

African Region

77.4

765.62

Region of the Americas

21.7

214.11

South-East Asia Region

117.1

2257.89

European Region

64.9

590.62

Eastern Mediterranean Region

59.3

381.76

Western Pacific Region

134.8

2500.71

World

91.7

6706.03

53.12

Source: Compiled from WHO (2016c) and Prüss-Ustün et al. (2016).

Notes

Notes:

*  Calculated by author based on the 2015 population published by WHO (2016c).

**  Calculated by author based on Table 3.12.

Among the 21 countries for which data were available, air pollution is the most deadly in North Korea, contributing to 83.59 percent of all environment-attributed deaths (Table 3.13). China is second only to North Korea, with air pollution being responsible for 163.1 deaths per 100,000 population and over three-fourths of all environmentally-attributed deaths. Air pollution also causes over 70 percent of all environmentally attributed fatalities in Mongolia and Sri Lanka. On the other hand, four Asian countries, Singapore, Malaysia, South Korea, and Japan, are doing well, with air-pollution causing fewer than 25 deaths per 100,000 population and less than 37 percent of all environmentally attributed deaths. The total population for the 21 Asian countries is 3,931.8 million, about 54 percent of the global population. However, these countries contribute 4.917 million deaths, with 73 percent of the global deaths attributed to air pollution. China alone contributes 34 percent of the world deaths attributed to air pollution. The mortality rate of the 21 Asian countries is 125 per 100,000 population. That is 5.76 times the rate for the Americas, 2.1 times the rate for the Eastern Mediterranean Region, 93 percent higher than the rate for the European Region, and 61 percent higher than that of the African Region.

#### Is there an environmental Kuznets curve for air pollution in Asia?

Yale’s 2016 EPI Report suggests that the relationship between Environmental Health and GDP per capita is strongly positive, possibly due to improvement in public health as countries develop (Yale University 2016a). It also pointed out that something other than economic development alone may also be critical in achieving environmental results. An EKC relationship was found in emissions in 14 Asian countries (Apergis and Ozturk 2015) and in NO2 emissions in Indian cities (Sinha and Bhattacharya 2016). A few papers have attempted to explore if an EKC exists in PM2.5 pollution. Keene and Deller (2015) found such an EKC for the United States with the turning point occurring between US $27,100 and US$28,200 per capita income for PM2.5 emissions and US $24,000 and US$25,500 for PM2.5 concentration. However, Stern and van Dijk (2016) found that economic growth had relatively small effects on the variation in PM2.5 pollution globally. Han et al. (2016) was unable to find such an EKC for Beijing, which they believed had not reached the turning point of an EKC.

To test the relationship between economic development and air pollution, the annual mean PM2.5 and PM10 measurements were plotted against GDP per capita in 131 cities in China. The results indicated no EKC or any relationship in either PM2.5 pollution (Figure 3.5) or PM10 pollution (Figure 3.6). The relationship was also tested using the EPI data for the Asian countries/regions in Table 3.11, with GDP per capita data for the Asian countries/regions obtained from the World Bank (2016) and CIA (2016). No associations were detected for the PM2.5 Exposure (Figure 3.7), PM2.5 Exceedance (Figure 3.8), or PM2.5 Health Risk (Figure 3.9). However, an EKC was found for NO2 pollution (Figure 3.10).

Figure 3.5   Annual mean PM2.5 Exposure and GDP per capita in Chinese cities, 2014

Source: Compiled from Yale University (2016e).

Figure 3.6   Annual mean PM210 exposure and GDP per capita in Chinese cities, 2014

Source: Compiled from Yale University (2016e).

Figure 3.7   Annual mean PM2.5 Exposure and GDP per capita in Asian countries/regions, 2000–2014

Source: Compiled from Yale University (2016e).

Figure 3.8   Annual mean PM2.5 Exceedance and GDP per capita in Asian countries/regions, 2000–2014

Source: Compiled from Yale University (2016e).

Figure 3.9   Annual mean PM2.5 Health Risk Exposure and GDP per capita in Asian countries/regions, 1990–2013

Source: Compiled from Yale University (2016e).

Figure 3.10   Annual mean NO2 Exposure and GDP per capita in Asian countries/regions, 1997–2011

Source: Compiled from Yale University (2016e).

The literature is inconsistent in the existence of an EKC for air pollution in Asia. As pollution rises to dangerous levels, Asian governments are under pressure to take measures. This is true in almost all countries in Asia, including the most polluted countries such as China, India, Bangladesh, and Pakistan. Some progress has been reported. For example, a newly released UNEP (2015) review found that Beijing was effective in controlling air pollution from coal-fired plants and vehicle emissions. It is possible that major cities have made progress in controlling air pollution in their urban areas. However, it is important to note how this progress has been made in China. To avoid impacting economic growth, city governments often relocate polluting factories from the urban centers to nearby suburban and rural areas or to neighboring cities (Liu 2010, 2012, 2013a, 2013b). Some of these factories have caused severe pollution in their new locations. Some Model Cities have improved their environmental conditions at the expense of surrounding areas. This is supported by Yale’s EPI data discussed earlier. Exposure to PM2.5 may have been leveled off or declined in recent years, but an increased proportion of the population has been exposed to PM2.5 pollution (Figures 3.1 and 3.2). Population in rural and suburban areas who benefit the least from polluting industries now suffer more from the pollution (Liu 2012, 2013a, 2013b).

#### Policy implications

The EKC and the “grow first, clean up later” approach may be extremely harmful to the powerless and poor (Liu 2012, 2013a, 2013b). Developed countries such as Japan took this path and were able to achieve better environmental conditions and some degree of sustainability. At that time, there was insufficient knowledge of the tremendous environmental, social, and economic costs of unsustainable development practices. Today, the importance of sustainability is common knowledge, and science and technology make sustainable practices possible. Political pressure, rather than economic growth, determines when the turning point of the EKC will occur. In an undemocratic political system such as China or some other Asian countries, political pressure may not be large enough to force governments and industries to switch to sustainable practices until much later. The turning point may be delayed if there even is one. Developing countries should avoid this approach and adopt a sustainable path to development and environmental management. Social determinants of health, such as poverty, access, and inequality, are the very determinants that make populations more vulnerable to environmental risk factors and environmental change (Kovats 2012). On the other hand, protecting the environment may bring health benefits and economic benefits from health-care savings, in addition to help with fighting global climate change (WHO 2014a).

Even if an EKC exists in PM2.5 concentration, less-developed countries in Asia are unlikely to be able to afford it, as the turning point requires such high income levels reported by Keene and Deller (2015). Irreversible damage to climactic conditions and human health would be disastrous. As populations in less-developed cities are the most impacted by air pollution (WHO 2016b), the poor in polluted countries suffer the most from air pollution because they do not have the resources to protect themselves and to treat their illnesses. To the millions of people who have died from air pollution, an EKC does not mean anything, even if there is one. Scientific evidence shows that air pollution poses greater risks to human health than we previously realized, particularly in causing strokes and heart diseases (WHO 2014a). In addition, it is projected that the impact on deaths from outdoor air pollution could double by 2050, if the current unsustainable practices continue (Lelieveld et al. 2015). That means the traditional unsustainable approach of “grow first, clean up later” must stop in order to avoid devastating the environment and people’s livelihoods.

#### References

Adams, W.M. (2006) “The Future of Sustainability: Re-thinking Environment and Development in the Twenty-first Century,” In Proceedings of the IUCN Renowned Thinkers Meeting, Gland, Switzerland, January 29–31.
Air Korea. (2016) “Air Quality Information,” www.airkorea.or.kr/.
Apergis, N. and Ozturk, I. (2015) “Testing Environmental Kuznets Curve Hypothesis in Asian Countries,” Ecological Indicators. 52:16–22.
Azadi, H. , Verheijke, G. , and Witlox, F. (2011) “Pollute First, Clean Up Later?” Global & Planetary Change. 78:77–82. www.dst.unipi.it/dst/rocchi/SR/GG_files/sdarticle.pdf.
Beckerman, W. (1992) “Economic Growth and the Environment,” World Development. 20:481–496.
Beelen, R. , Raaschou-Nielsen, O. , Stafoggia, M. , Andersen, Z.J.O. , Weinmayr, G. , Hoffmann, B. , … Hoek, G. (2014) “Effects of Long-term Exposure to Air Pollution on Natural-Cause Mortality: An Analysis of 22 European Cohorts within the Multicentre ESCAPE Project,” The Lancet. 383:785–795. doi: 10.1016/S0140-67361362158-3
CIA (Central Intelligent Agency, USA). (2016) “World factbook. Taiwan,” www.cia.gov/library/publications/the-world-factbook/geos/tw.html.
Dasgupta, S. , Hamilton, K. , Pandey, K.D. , and Wheeler, D. (2006) “Environment During Growth: Accounting for Governance and Vulnerability,” World Development. 34:1597–1611.
Ekins, P. (2000) Economic Growth and Environmental Sustainability: The Prospects for Green Growth. London/New York: Routledge.
Environmental Protection Administration, ROC. (2015) “PM2.5 Control (in Chinese),” http://air.epa.gov.tw/Public/suspended_particles.aspx.
European Commission. (2016) “Air Quality Standards,” http://ec.europa.eu/environment/air/quality/standards.htm.
Han, L. , Zhou, W. , and Li, W. (2016) “Fine Particulate PM2.5 Dynamics During Rapid Urbanization in Beijing, 1973–2013,” Scientific Reports. 6, Article number: 23604. doi: 10.1038/srep23604.
Harbaugh, W.T. , Levinson, A. , and Wilson, D.M. (2002) “Reexamining the Empirical Evidence for an Environmental Kuznets Curve,” The Review of Economics and Statistics (MIT Press). 843:541–551.
IES. (2016) “Ambient Air Quality Standards in India,” www.arthapedia.in/index.php?title=Ambient_Air_Quality_Standards_in_India.
Keene, A. and Deller, S. (2015) “Evidence of the Environmental Kuznets’ Curve among US Counties and the Impact of Social Capital,” International Regional Science Review. 38:358–387.
Kovats, R.S. (2012) “Global Health and Global Environmental Governance: Research for Policy,” Global Environmental Change. 221:1–2. www.sciencedirect.com/science/article/pii/S0959378011001981.
Lelieveld, J. , Evans, J.S. , Fnais, M. , Giannadaki, D. , and Pozzer, A. (2015) “The Contribution of Outdoor Air Pollution Sources to Premature Mortality on a Global Scale,” Nature. 525:367–371.
Liu, L. (2008) “Sustainability Efforts in China: Reflections on the Environmental Kuznets Curve Through a Locational Evaluation of ‘Eco-Communities,’” Annals of the Association of American Geographers. 983:604–629.
Liu, L. (2009) “Sustainability: Living Within One’s Own Ecological Means,” Sustainability. 14:1412–1430.
Liu, L. (2010) “Made in China: Cancer Villages,” Environment: Science and Policy for Sustainable Development. 522:8–21.
Liu, L. (2012) “Environmental Poverty, a Decomposed Environmental Kuznets Curve, and Alternatives: Sustainability Lessons from China,” Ecological Economics. 73:86–92.
Liu, L. (2013a) “Geographic Approaches to Resolving Environmental Problems in Search of the Path to Sustainability: The Case of Polluting Plant Relocation in China,” Applied Geography. 45:138–146. doi: 10.1016/j.apgeog.(2013)08.011.
Liu, L. (2013b) “Chinese Model Cities and Cancer Villages: Where Environmental Policy is Social Policy,” In: Isidor Wallimann ed., Environmental Policy is Social Policy – Social Policy is Environmental Policy: Toward Sustainability Policy. pp. 121–134. New York: Springer.
MEP (Ministry of Environmental Protection of China). (2012) “Ambient Air Quality Standards (in Chinese),” GB 3095–(2012) http://210.72.1.216:8080/gzaqi/Document/gjzlbz.pdf.
National Bureau of Statistics of China. (2012) Tabulation on the 2010 Population Census of People`s Republic of China by County, compiled by the Population Census Office under the State Council and Department of Population and Employment Statistics. China Statistics Press, Beijing.
National Environment Agency of Singapore. (2016) “Air Quality and Targets,” www.nea.gov.sg/anti-pollution-radiation-protection/air-pollution-control/air-quality-and-targets.
Panayotou, T. (1993) “Empirical Tests and Policy Analysis of Environmental Degradation at Different Stages of Economic Development,” World Employment Program Research Working Paper WEP 2-22/WP 238 International Labour Office, Geneva.
Prüss-Ustün, A. , Wolf, J. , Corvalán, C. , Bos, R. , and Neira, M. (2016) Preventing Disease through Healthy Environments: A Global Assessment of the Burden of Disease from Environmental Risks. WHO. Report and age stand by country spreadsheet downloaded from www.who.int/quantifying_ehimpacts/publications/preventing-disease/en/.
Rock, M. (2002) Pollution Control in East Asia. Washington, DC: Resources for the Future.
Rock, M. and Angel, D. (2007) “Grow First, Clean Up Later? Industrial Transformation in East Asia,” Environment: Science and Policy for Sustainable Development. 494:8–19.
Rohde, R.A. and Muller, R.A. (2015) “Air Pollution in China: Mapping of Concentrations and Sources,” PLoS ONE. 108:e0135749.
Sinha, A. and Bhattacharya, J. (2016) “Environmental Kuznets Curve Estimation for NO2 Emission: A Case of Indian Cities,” Ecological Indicators. 67:1–11.
Stern, D.I. (2004) “The Rise and Fall of the Environmental Kuznets Curve,” World Development. 32:1419–1439.
Stern, D.I. and van Dijk, J. (2016) “Economic Growth and Global Particulate Pollution Concentrations,” CCEP Working Paper 1604, Feb (2016) Crawford School of Public Policy. The Australian National University.
Transport Policy Net. (2016a) “Japan: Air Quality Standards,” http://transportpolicy.net/index.php?title=Japan:_Air_Quality_Standards.
Transport Policy Net. (2016b) “Thailand: Air Quality Standards,” http://transportpolicy.net/index.php?title=Thailand:_Air_Quality_Standards.
UNEP (United Nations Environment Program). (2015) “A Review of Air Pollution Control in Beijing: 1998–2013 ,” www.unep.org/roap/Portals/96/Documents/Air_Pollution.pdf.
United Nations General Assembly. (2005) World Summit Outcome, Resolution A/60/1, Adopted on September 15, 2005; New York, http://daccessdds.un.org/doc/UNDOC/GEN/N05/487/60/PDF/N0548760.pdfOpenElement/.
USEPA (United States Environmental Protection Agency). (2012) “The National Ambient Air Quality Standards for Particle Pollution. Revised Air Quality Standards for Particle Pollution and Updates to the Air Quality Index AQI,” www3.epa.gov/airquality/particlepollution/2012/decfsstandards.pdf.
Weber, D.J. and Allen, D.O. (2010) Environmental Kuznets Curves: Mess or Meaning? International Journal of Sustainable Development & World Ecology. 173:198–207.
World Bank. (2016) “GDP Per Capita Current US \$,” http://data.worldbank.org/indicator/NY.GDP.PCAP.CD.
World Commission on Environment and Development. (1987) Our Common Future. Oxford: Oxford University Press.
World Health Organization (WHO). (2006) “WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide. Global Update 2005,” http://apps.who.int/iris/bitstream/10665/69477/1/WHO_SDE_PHE_OEH_06.02_eng.pdf.
World Health Organization (WHO). (2014a) “7 Million Premature Deaths Annually Linked to Air Pollution,” News release. www.who.int/mediacentre/news/releases/2014/air-pollution/en/.
World Health Organization (WHO). (2014b) Burden of Disease from the Joint Effects of Household and Ambient Air Pollution for (2012). WHO Technical Report. (2012) www.who.int/phe/health_topics/outdoorair/databases/AP_jointeffect_BoD_results_March(2014)pdf.
World Health Organization (WHO). (2014c) “Ambient Outdoor Air Quality and Health,” www.who.int/mediacentre/factsheets/fs313/en/.
World Health Organization (WHO). (2016a) “WHO Global Urban Ambient Air Pollution Database update (2016),” www.who.int/phe/health_topics/outdoorair/databases/cities/en/.
World Health Organization (WHO). (2016b) “Air Pollution Levels Rising in Many of the World’s Poorest Cities,” News release. www.who.int/mediacentre/news/releases/2016/air-pollution-rising/en/.
World Health Organization (WHO). (2016c) “World Health Statistics 2016: Monitoring Health for the SDGs, Annex B: tables of health statistics by country, WHO region and globally,” www.who.int/gho/publications/world_health_statistics/2016/Annex_B/en/.
Yale University. (2016a) “Global Metrics for the Environment,” www.epi.yale.edu/.
World Health Organization (WHO). (2016d) “Global Ambient Air Pollution,” http://maps.who.int/airpollution/.
Yale University. (2016b) “Environmental Performance Index. Air Quality,” http://epi.yale.edu/chapter/air-quality.
Yale University. (2016c) “Environmental Performance Index. Health Impacts,” http://epi.yale.edu/chapter/health-impacts.
Yale University. (2016d) “Environmental Performance Index. Methods,” http://epi.yale.edu/chapter/methods.
Yale University. (2016e) “Environmental Performance Index 2016 Report,” 2016 EPI Raw Data. Air Quality. http://epi.yale.edu/downloads.
Yang, G. , Wang, Y. , Zeng, Y. , Gao, G.F. , Liang, X. , Zhou, M. , et al. (2013) “Rapid Health Transition in China, 1990–2010: Findings from the Global Burden of Disease Study 2010,” The Lancet. 381:1987–2015. doi: 10.1016/S0140-67361361097-1.pmid:23746901.

## Use of cookies on this website

We are using cookies to provide statistics that help us give you the best experience of our site. You can find out more in our Privacy Policy. By continuing to use the site you are agreeing to our use of cookies.