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.
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.
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.
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.
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.
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.
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 |
Sources: WHO: WHO (2006); USA: USEPA (2012); European Union: European Commission (2016); Singapore: National Environment Agency of Singapore (2016); Japan: Transport Policy Net (2016a); Taiwan: Environmental Protection Administration, ROC (2015); South Korea: Air Korea (2016); Thailand: Transport Policy Net (2016b); China: MEP (2012); India: IES (2016).
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.
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 |
||
Bangladesh |
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:
# 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.
Country/WHO region |
PM2.5 (µg/m3) |
---|---|
Bangladesh |
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).
Country |
City |
PM2.5 annual means (µg/m3) |
PM10 annual means (µg/m3) |
---|---|---|---|
India |
Gwalior |
176 |
329 |
India |
Allahabad |
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 |
Firozabad |
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 |
Bangladesh |
Narayangonj |
106 |
191 |
India |
Agra |
105 |
196 |
China |
Tangshan |
102 |
153 |
India |
Jodhpur |
101 |
189 |
India |
Dehradun |
100 |
188 |
India |
Ahmedabad |
100 |
83 |
India |
Jaipur |
100 |
187 |
India |
Howrah |
100 |
186 |
Source: Compiled from WHO (2016a).
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.
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).
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.
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.
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 |
Bangladesh |
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:
* = 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.
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 |
Bangladesh |
−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:
* 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.
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 |
Bangladesh |
−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).
Notes:
* Impacts by both air and water pollution.
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.
Country or Region |
Exposure to PM2.5 |
PM2.5 Exceedance |
Exposure to NO2 |
Household Air Quality |
Air Quality |
Health Impacts* |
---|---|---|---|---|---|---|
Bangladesh |
−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).
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.
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 |
10. Bangladesh |
0.6 |
9. Japan |
4.2 |
4. Nepal |
27.3 |
4. India |
0.642 |
15. India |
0.579 |
16. China |
3.29 |
5. Bangladesh |
24.8 |
5. Bangladesh |
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 |
90. Bangladesh |
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).
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
Figure 3.2 Annual mean PM2.5 Exceedance in Asian countries/regions 2000–2014
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
Figure 3.4 Annual mean NO2 Exposure in Asian countries/regions 1997–2011
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 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.
Country |
Total (000s) |
% deaths |
Total (000s) |
% DALYs |
Age-standardized deaths/100,000 |
Age-standardized DALYs/100,000 |
---|---|---|---|---|---|---|
Bangladesh |
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).
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.
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 |
Bangladesh |
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:
* 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.
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
Figure 3.6 Annual mean PM210 exposure and GDP per capita in Chinese cities, 2014
Figure 3.7 Annual mean PM2.5 Exposure and GDP per capita in Asian countries/regions, 2000–2014
Figure 3.8 Annual mean PM2.5 Exceedance and GDP per capita in Asian countries/regions, 2000–2014
Figure 3.9 Annual mean PM2.5 Health Risk Exposure and GDP per capita in Asian countries/regions, 1990–2013
Figure 3.10 Annual mean NO2 Exposure and GDP per capita in Asian countries/regions, 1997–2011
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).
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.