University of California, Davis

Department of Economics

Honors Thesis Paper

Air Pollution and Infant Mortality

Has Tokyo Reached a Harmless Level of Air Pollution?

Akio Yamazaki

June 2009

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Table of Contents
I.

Introduction

1

II.

Air Pollution and the Impacts on Health

4

1. Types of Air Pollution 4

2. The Impact on Health 5

III.

Literature review

6

IV.

Air Quality in Tokyo

11

1. Background 11

2. Diesel-Powered Vehicle Control Regulations 12

3.

15

4. Post-Diesel Powered Vehicles Control Regulations 16

V.

Methodology

17

VI. Data Sources

19

VII.

Regression Results

27

1. Cross-Sectional Estimate Results 27

2. Fixed-effects and Random-effects Estimates Results 30

VIII.

Discussions and Conclusion

33

References

37

Appendices

40

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List of Tables

Table 1. Discriptive Statistics

25

Table 2.

Discriptive Statistis with CO

26

Table 3. Pooled Cross-sectional Estimates (Both w/o CO and with CO)

41

Table 4.

Fixed-effects and Random-effects Estimates

43

Table 5.

Fixed-effects and Random-effects Estimates with CO

43

Table 6.

Fixed-effects and Random-effects Estimates by Grouping

44

Table 7.

Fixed-effects and Random-effects Estimates by Grouping with CO

44

List of Figures

Figure 1. Excise difference between diesel fuel and gasoline

14

Figure 2. Comparison of emission characteristics by fuel

15

Figure 3. Transition in the number of LPG and CNG vehicles

16

Figure 4. Maps of Tokyo

21

Figure 5. A Trend of Air Pollutants (Mean Value)

23

Figure 6. A Trend of Infant Mortality

24

Figure 7. List of Other Studies

45

Figure 8. Transition of PM and NOx regulations

46

Figure 9. Ration of gasoline and diesel models among cargo vehicles

47

Acknowledgements

Many thanks to Douglas Miller, my principle advisor on this thesis project, for his support and

assistance; from discussing the theoretical implications to running the regressions.

Also thanks to Guido Kuersteiner of UC Davis in economics department for answering my many

email questions regarding my econometrics modeling and STATA operations. And also thanks

to Y. Hossein Farzin of UC Davis, whose specialty is environmental economics, in agriculture

resources economics department for discussing many potential thesis ideas with me.

Last but not least, thanks to Tsutomu Suzuki from National Institute Environmental Studies,

Kazuhiko Takemoto from Ministry of the Environment, and Sayaka Takemoto1 for helping me to

obtain data on air pollution.

1 Sayaka Takemoto is a student of Waseda University in Tokyo whose father is Kazuhiko

Takemoto from Ministry of the Environment.

iii

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Air Pollution and Infant Mortality

~ Has Tokyo Reached a Harmless level of Air Pollution? ~

Akio Yamazaki

I. Introduction

Among many of environmental issues we have today such as water pollution, nuclear

waste, resources depletion and many others, air pollution is widely discussed publicly by people

these days. One of many discussions about air pollution is the problems of increasing level of

emission from automobiles releasing tremendous amount of carbon dioxide and others pollutants.

Many countries in the world have been trying to reduce the amount of air pollution in order to

provide cleaner air to its citizens. From current actions towards these issues taken by many

countries in the world, people are aware that the air quality has been worsened and causing

various health problems. However, the magnitude of risks to our health is not considered as

much. Depends on the condition of individuals health, some are more vulnerable to possible

adverse effects of air pollution. Air pollution can be severely dangerous and even can lead to a

fatal disease or death.

The effects of air pollution on mortality have been intensively studies since the London

pollution episodes in the 1950s (Logan, 1953). Since then, many of epidemiologic studies show a

variety of severe effects among adults and children exposed to current levels of air pollution

(Pope, 1989, Seaton, et al, 1995). Most studies of air pollution have been conducted in locations

where the level of pollution is high such as China, Mexico, Brazil, US, and so on. In those areas,

even before examining the associations, it is expected to find the definite associations between

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air pollution and infant mortality due to the high level of air pollution. However, not many

studies of air pollution-related health have conducted in the area with a moderate level of air

pollution such as Tokyo in recent years, particularly since the end of 1980s. Therefore, this paper

addresses this issue by examining the impact of air pollution on infant death in Tokyo from 1997

to 2006.

This study is based on a quantitative analysis with comprehensive data of air pollution

and infant mortality from Tokyo Metropolitan Government (TMG) and National Institute of

Environment Studies (NIE). The air pollution data is consisted of yearly-averaged level of four

important air pollutants which many other studies typically considered in their studies, Nitrogen

Oxide (NOx), Nitrogen Dioxide (NO2), Suspended Particulate Matter (SPM), and Carbon

Monoxide (CO), obtained by 44 districts over 10 years period of time.

Many studies use time-series, cross section, or natural experimental methods, but only a

few uses multiple methods together. My paper will use panel (time-series and cross section)

model to examine changes in air pollution across Tokyo over time. Tokyo is broken into

relatively small districts, which allow me to apply the similar methodology as ones of leading air

pollution-infant mortality literature by Currie and Neidell (2004) and Chay and Greenston (2003).

The comprehensive analysis of the associations is considered by applying various econometric

models such as cross section with and without time effects, and fixed- and random-effects.

Detailed description of my methodology is explained in Section V.

As similar to study of Currie and Neidell (2004), my paper offers several innovations

over the existing literatures. First, many studies only examine a few pollutants because of data

limitations, while I examine four pollutants mentioned above that are commonly monitored in

Japan. Thus it may allow me to analyze which pollutants are more harmful to infants.

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Second, since Tokyo is believed to be relatively cleaner city than other metropolitan

cities in the world, examining the associations between air pollution and infant mortality may

give a signal to the existing literatures that even with a moderate level of air pollution, air

pollution are still harmful to our health. From my knowledge, my paper would be the first study

to answer this important question.

Third, many studies have looked at the effect of air pollution on health of adults more

than that of infants. Recent epidemiologic studies point out that infant has unique characteristics

that are clearer to show the association with air pollution than adults. In the case of infant death

the link between cause and effect is immediate, whereas for adults, diseases today may reflect

pollution exposure that occurred many years ago (Currie and Neidell, 2004). Also, Tokyo is a

city where enormous numbers of people commute every day from neighboring cities. People are

coming from different area with different level of air pollution. Therefore, it is hard to find the

direct association between air pollution and deaths by focusing on adults death. Therefore,

looking at infants death rather than adults would be able to give me better and possibly more

accurate associations between air pollution and health.

Overall, my finding supports the conclusions that air pollution has possible causal effects

on infant mortality. The findings are less compelling than the existing literatures because the

estimated results are very sensitive to specifications. Yet, the cross-sectional estimates imply that

reduction in NOx over the time interval in my sample saved roughly 500 infants lives and the

panel fixed-effects estimates suggests that reduction in SPM saved about 200 infants in Tokyo.

The rest of paper is laid out as follows: Section II provides necessary background

information about air pollution and the way in which pollution may affect health. Section III

introduces previous literatures and Section IV provides background information about the air

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quality in Tokyo. Section V describes methods of my studies while my data are described in

Section VI. Section VII offers results, and Section VIII ends with discussion and conclusions.

II. Air Pollution and the Impacts on Health

Before getting into any examinations on air pollution and infant mortality, I discuss in

this section the general information about air pollution and its possible impacts on our heath.

Types of Air Pollution

Air pollution is made up of many kinds of gases, droplets and particles that reduce the

quality of the air, and can be found in both urban and rural settings. In the city, cars, buses and

airplanes, as well as industry and construction may cause air pollution. While in the country, this

pollution is caused by dust from tractors plowing fields, trucks and cars driving on dirt or gravel

roads, rock quarries and smoke from wood and crop fires. Among pollution, there are several

main types of and various well-known effects. These adverse effects include smog, acid rain, and

the greenhouse effect, each of which have serious implications for our health and well-being as

well as for the whole environment.

First, we can categorize air pollution into two groups; outdoor and indoor pollution each

of which within contain various subgroups. In outdoor pollution, there are two main types; the

release of particles and noxious gas. Particles are released into the air from burning fuel for

energy. A good example of this particulate matter is diesel smoke, often referred it as PM. These

particles are very small pieces of matter measuring about 2.5 microns or about .0001 inches

( What is Air Pollution ). Another type of outdoor pollution is the release of noxious gases, such

as Sulfur dioxide (SO2), CO, NOx, and chemical vapors. These gases are the main sources of

environmental issue such as smog, acid rain, and ground-level ozone by chemical reactions once

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they are in the atmosphere. Furthermore, pollution can also be considered inside our homes,

offices, and schools.

We work, study, eat, drink and sleep in enclosed environments where air circulation may

be restricted. For these reasons, some experts feel that more people suffer from the effects of

indoor air pollution than outdoor pollution some of pollutants that I mentioned above can be

created by indoor activities such as tobacco smoke, cooking and heating appliances, and vapors

from building materials, paints, furniture. In the United States, we spend about 80-90% of our

time inside buildings, and so our exposure to harmful indoor pollutants can be serious. ( Indoor

Air Pollution ) It is therefore important to consider both indoor and outdoor air pollution. The

negative effects of air pollution to environments are commonly known and discussed widely;

however, many implications for our health are less known. Now let us discuss the impact of air

pollution on our health.

The Impact on Health

Air pollution can affect our health in many ways with both short-term and long-term

effects. Different groups of individuals are affected by air pollution in different ways. Some

individuals are much more sensitive to pollutants than others. Young children and elderly people

often suffer more from the effects of air pollution. People with health problems such as asthma,

heart and lung disease may also suffer more when the air is polluted. The extent to which an

individual is harmed by air pollution usually depends on the total exposure to the damaging

chemicals, i.e., the duration of exposure and the concentration of the chemicals must be taken

into account.

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Examples of short-term effects include irritation to the eyes, nose and throat, and upper

respiratory infections such as bronchitis and pneumonia ( Health Effects ). Other symptoms can

include headaches, nausea, and allergic reactions. Short-term air pollution can aggravate the

medical conditions of individuals with asthma and emphysema. Extreme example of short-term

effects is the great "Smog Disaster" in London in 1952. Four thousand people died in a few days

due to the high concentrations of pollution ( Health Effects ).

Long-term health effects can include chronic respiratory disease, lung cancer, heart

disease, and even damage to the brain, nerves, liver, or kidneys ( Health Effects ). Continual

exposure to air pollution affects the lungs of growing children and may aggravate or complicate

medical conditions in the elderly. It is estimated that half a million people die prematurely every

year in the United States as a result of smoking cigarettes ( Health Effects ). In any case, both

short and long-term effects can bring out serious health problems.

It may be already everyone s common knowledge that air pollution causes various

adverse effects to our health. In the next section, to make these consensuses certain, I summarize

many studies that empirically show the associations between air pollution and infants health.

III. Literature review

Epidemiological studies have shown an association between air pollution and not only

exacerbate of illness in people with respiratory diseases but also rise in the numbers of deaths

from cardiovascular and respiratory diseases. Different studies have attempted to investigate the

effect of air pollution on human health from different angles. Looking at the changes in mortality

rate on adults and infant, numbers of respiratory diseases hospital admissions and postneonatal

health conditions such as its birth weight seem to be the common methods. Many studies have

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looked at the effect of air pollution on health of adults more than that of infants. Most studies of

air pollution have agreed upon that. However, in recent years, the effect on infant or children

have been considered as much as adults in many studies. In fact, focusing on infants have

become dominant ways of looking at the effects of air pollution because those studies that look

specifically at infant mortality all agree that infant has unique characteristics that are clearer to

show the association with air pollution than adults. In the case of infant death the link between

cause and effect is immediate, whereas for adults, diseases today may reflect pollution exposure

that occurred many years ago (Currie and Neidell, 2004). However, depends on the location of

studies that are conducted, finding the definite associations between air pollution and infant

mortality can still be difficult especially in developing countries.

Many of studies of air pollution and mortality had been conducted in North America and

Western European cities, while most of world s children live in developing countries in extreme

poverty and under conditions of health and environment quality distinctly different from those in

high income countries. A few recent studies suggest that air pollution may pose greater risks for

children in less affluent industrialized countries (Loomies et al, 1999). Studies in Brazil, Mexico,

Taiwan, and the Czech Republic have reported associations between infant or child mortality and

various measures of air pollution. It is plausible that children in less affluent countries, and poor,

urban children, in particular, may be especially sensitive to the effects of air pollution. Numerous

countries in Latin America, Asia, and Eastern Europe have undergone rapid industrialization

without having fully resolved the environmental hazards of earlier stages of development.

Children whose health is already compromised by infection and other conditions resulting from

poor sanitation, inadequate nutrition, and deficient housing may be more likely to suffer acute,

life-threatening events when the insult of air pollution is added. If they experience a life-

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threatening event, these children may be more likely to die from lack of rapid, high-quality

medical attention. Poverty and living conditions may interact with air pollution to exacerbate the

risk of mortality among urban children exposed to high levels of air pollution. Therefore, when I

attempt to find out the association between air pollution and infant mortality, other factors come

into place significantly so that results may not be able to clearly show the direct and definite

associations. However, children in Tokyo are relatively healthier than those in less affluent

countries. Therefore, it may be somewhat easier to find the definite associations between air

pollution and infant mortality. Hence, if my study appears to show the association, then it can

add more confidences to those existing literatures.

Here are many different types of studies that look at the associations between air

pollution and infants health. The summary tables of these studies are shown in Figure 7 in

appendix. A time-series study of sudden infant death syndrome in Taiwan by Knobel, Chien-Jen,

and Liang (1995; 96), suggested an association with visibility, a surrogate measure of air

pollution that largely reflects fine particles. Another study in Sao Paulo, Brazil, by Saldiva at el

(1994),

also using a daily time-series design, showed associations of total and respiratory

mortality among children under 5 years old with NOx and ozone, but not with inhalable particles

or SO2. In an earlier time-series study of Mexico City in the years 1990 to 1992, by Loomis et al

(1999), they observed patterns suggestive of an association of peak ambient ozone lagged 3 to 4

days with mortality among children under 5 years old, but in that study they did not examine

other air pollution indices separately from ozone. Other studies based on analysis of geographical

patterns of pollution and infant mortality have yielded additional supporting results. An ecologic

analysis in Rio de Janeiro by Penna and Duchiade (1991) suggested an association between

infant deaths from pneumonia and total suspended particles, but did not examine other air

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pollutants. Annual infant mortality rates in the Czech Republic by Bobak and Leon (1999) were

associated with annual average concentrations of inhalable particles, SO2, and NOx in an eco-

logic study based on 45 administrative districts. The effect of particles was observed for neonatal

morality, total postneonatal mortality, and neonatal respiratory mortality, while the other

pollutants were associated primarily with neonatal respiratory mortality. A study by Woodruff,

Grillo, and Schoendorf (1997), using data from 86 cities in the United States reported an

association of postneonatal infant mortality with the mean level of inhalable particles in the first

2 months of life. This latter study is important because it is the first to show that infant mortality

may be associated with present-day air pollution in an affluent country with an established air

pollution control program.

In general, ecologic studies are more susceptible than time-series designs to confounding

by geographical differences in nonpollution factors that affect mortality, such as social class and

medical attention; because they use average community- or neighborhood-level pollution levels

as an estimate of chronic exposure to pollutants (Loomies et al, 1999). In contrast, time-series

studies consider day-to-day changes in pollution within a geographic area, greatly reducing the

potential for confounding by in essence allowing a single area to serve as its own control. This

existing epidemiologic literature falls short of linking excess child deaths with a specific

pollutant. The London air pollution episodes when excess infant deaths were observed in the past

were characterized by high levels of particles and sulfates and some recent studies also point

toward these pollutants, together or separately. Other studies suggest associations with NOx,

SO2, or ozone. Few studies, however, have examined multiple pollutants simultaneously except

the study of Currie and Neidell, and Lipfert, Zhang, and Wyzga.

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In addition, some studies have looked at the exogenous shocks that might have altered the

level of air pollution (Chay and Greenstone, Pope, Pope, Schwartz, and Ransom) such as

economic shocks and environmental regulations and studied the before-after effects. In their

analysis, they were able to conclude that those shocks had a significantly reduce the level of air

pollution. For my analysis, No Diesel-powered Vehicle Campaign , which would be discussed

in detail in next section, would be the equivalent shocks as their studies; however, the results

from the campaign were not significant enough to be appeared.

Overall, most studies proves that there exists an association between air pollution and

infant mortality. Some even shows ones are more harmful than the other. Most existing

literatures and studies are conducted at the location where its air pollution level is relatively high.

Then, they all seem to want to prove that there indeed exists an association. There are few

studies that disprove the existence of associations. It may not make sense to conduct the studies

at a location where the level of air pollution is low since the chances of having the association

would be low. However, conducting these studies at a location like Tokyo where its air pollution

level is moderate might shed more lights on the magnitude of effect of air pollution on infant

mortality. If my study does not show the association, it can begin to have a sense of what level of

air pollution is harmful or not harmful to our health. Not only to show the association between

air pollution and infant mortality, finding out the threshold of concentration would be helpful to

policymaker for a sake of setting the efficient regulations. Now before examining any

associations between air pollution and infant mortality in Tokyo, it is important to understand the

trend in air pollution and characteristics of Tokyo. In next section, I provide the information on

an important regulation called Diesel-Powered Vehicle Control Regulations .

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IV. Air Quality in Tokyo

Background

Tokyo is the largest city in the world. An estimated 12.79 million people live in Tokyo

with 8.653 million living within Tokyo′s 23 wards. ("The Principal Agglomerations of the

World.") Thirteen million might not sounds too exciting; however, considering the fact that

Tokyo is the capital of Japan where millions of people commute from neighboring cities to work,

the population increases to nearly 33 million once we include cities like Yokohama and

Kawasaki2. ("The Principal Agglomerations of the World.") Tokyo is known to have the best

public transportation system in the world. Therefore, there must be fewer cars in Tokyo, which

would lead to cleaner air quality. However, the numbers of cars in Tokyo are just as many or in

fact more than that in other metropolitan cities in the world such as New York City or London.

("Tokyo Environment White Paper 2006.") It is known fact that the one of the main source of air

pollution is automobile. Specifically, in Tokyo releasing significant amount of NOx and PM are

coming from diesel powered vehicles. Since the late 1960s, various measures including control

on factory emission of dust and smoke, have significantly improved the level of air pollution

caused by SO2 and CO. As for SPM and NOx, the national government′s delay in regulating

diesel vehicles, which emit these kinds of pollutants, has kept their average concentrations high,

leaving the nation with a low level of compliance to the national environmental quality standards.

According to a study by the Environment Ministry, five of the ten air pollution monitoring

stations that registered the worst PM concentrations, and 7 of those with the worst NO2

concentrations, were located in Tokyo, indicating that Tokyo′s air pollution remains to be in a

serious situation. ("Diesel Vehicle Control in Tokyo.")

2 Yokohama and Kawasaki are in Kanagawa prefecture.

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Diesel-Powered Vehicle Control Regulations (DPVC regulations)

Within a type of automobiles, large trucks contribute more to the release of air pollution

than other types of automobile. In Tokyo, a number of diesel-powered trucks exceed that of

gasoline truck since early 1990s. In order to achieve targeted reduction of NOx and PM,

regulating and enforcing the Diesel Vehicle Control is important to the improvement of air

quality in Tokyo. Tokyo Metropolitan Government (TMG) has compiled the brochure3 to

indicate the main six failures of national government towards improvement of air quality. Below

I briefly summarize each failure.

1. Japan′s PM regulation seriously lagging behind that of the U.S. and Europe4

Japan introduced PM regulations in 1994, six years behind the United States and two years

behind Europe. Yet, the regulation standard was over 5 times more relaxed than equivalent

standards in the west. The current Japanese PM standard (Long-Term Regulation introduced in

1998) has only just caught up with the level western standards reached in the early 90s, a real-

term delay of almost ten years. This national government′s delay in introducing appropriate

regulations has allowed diesel vehicles spreading a massive amount of PM to stay on roads

throughout Japan.

2. Failure to ensure early distribution of "low sulfur diesel fuel", essential for PM reduction

Low-sulfur diesel fuel is essential to ensure effective functioning of PM reducing devices (Diesel

Particulate Filter or DPF). While EU, in preparation for a tight diesel emission control to go into

effect in 2005, set out the schedule of introducing low sulfur diesel fuel in December 1998,

around the same time, the Japanese Central Environment Council failed to even define the target

year for introducing low sulfur diesel fuel in its set of recommendations to the government. EU

3 http://www2.kankyo.metro.tokyo.jp/jidousya/six-result/index.htm (In Japanese)

4 Graph for transactions of NOx and PM are in Figure 8 in Appendix.

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countries have even provided tax incentives and other measures for low sulfur diesel fuel,

thereby achieving its early distribution ahead of target years defined in their respective

regulations. On the other hand, Japan has so far failed to even attempt to take such measures.

3. Turning its back on the primary source of air pollution

-

.

Many diesel vehicles, manufactured during the days of insufficient emission control, still remain

on the road today, continuously spreading a large amount of PM and NOx. Action on such

vehicles is an urgent task in order to improve air pollution in Tokyo. However, the national

government has been reluctant to develop a DPF, which could effectively reduce the amount of

PM in-use diesel vehicles emit. What′s worse, the national vehicle registration system only

checks for black exhaust smoke, does not even measure the level of PM / NOx emission, which

is crucial in determining whether the vehicle has maintained its engine performance since it was

new. In order to prevent diesel emission of existing vehicles from worsening further, it is

imperative to improve the current vehicle registration / regular inspection systems.

4. Postponement of the revised Automotive NOx PM Law and letting off old diesel

vehicles on the road.

The national government revised laws in 2001 at long last to make PM subject to control in

addition to NOx released from in-use diesel vehicles. However, despite TMG′s repeated

opposition, it delayed the revision′s enforcement by up to two and a half years from originally

planned. The move consequently allowed approximately one million old diesel cargo trucks, not

subject to PM control, to continue to drive in three major cities, releasing a massive amount of

PM in their paths. This doubled the number of vehicles subject to TMG′s diesel vehicle control

from 94,000 to 202,000.

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5. Preferential excise on diesel fuel increase diesel-powered vehicles5

For compact and mid-sized vehicles, diesel models are more popular than petrol-fueled models

because, although manufacturing costs are roughly the same between diesel fuel and gasoline,

prices", i.e. cheaper diesel fuel and more expensive gasoline. Figure 1 demonstrates the price

differences in detail. The popularity and the expansion in fuel price differences increased the

number of diesel vehicles. In view of the trend, TMG has consistently demanded that the

preferential excise system be rectified, but the national government has not even explored any

possibility.

Figure 1

6. Lack of action on "illicit diesel fuel", which facilitates serious tax evasion and threatens

the health of Tokyo citizens.

"Illicit diesel fuel", prepared by mixing kerosene, diesel fuel, etc. with heavy oil, provides a

hotbed for tax evasion, emits more PM and NOx than usual, and generates, in its manufacturing

process, a substance called "sulfate pitch" that threatens health and the environment. TMG has

actively cracked down on illicit diesel fuel, but the national government has taken almost no

5 Graph of Ratio of gasoline and diesel models among cargo vehicles is in Figure 9 in Appendix

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action, leaving it to circulate freely. The national government must take fundamental measures,

e.g. introducing a legal ban on the manufacturing of illicit diesel fuel.

TMG s Achievements

Those are the six failures of national government towards air quality measures. On the

other hand, TMG has been taking initiative before the national government to reduce the amount

of NOx and PM by correcting those failures of national government. I will not discuss all of

TMG s achievements in detail here; however, there are two that are worth mentioning. First is, as

Figure 2

of October 1st, 2003, TMG implemented and

enforced the ban of diesel vehicles that do

not meet PM emission standards in Tokyo,

Saitama, Chiba, and Kanagawa. Those four

neighboring prefectures and four others

have cooperated together to bring about this

successful diesel vehicles control. The

diesel vehicle control targeting 34 million

people in the Greater Metropolitan Area is an unprecedented initiative; unseen anywhere else in

the world. The joint action of eight major prefectures and cities provides a model for pioneering

environmental administration under the initiative of local governments. Second is promoting the

proliferation of clean and low-pollution trucks. Starting 1999, as part of the "Say No to Diesel

Vehicles" campaign, TMG has advocated replacing diesel vehicles with low-pollution vehicles.

In 2000, the "New Market Creation Strategy Council" was established together with auto

manufacturers, gas station operators and corporate diesel users in a bid to promote the

proliferation of extremely low-pollution vehicles powered with LPG and CNG. Figure 2

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demonstrates the significant differences of level of emissions among these vehicles. As a result,

the number of LPG vehicles in Tokyo has increased 2.3 times to 4,000, while that of CNG

vehicles has surged 13 folds to 3,100 over the past four years. Figure 3 shows the increase trend

of LPG and CNG powered vehicles since 1999. It is apparent that Say No to Diesel Vehicles

Campaign had a successful contribution to a shift of vehicles.

Figure 3

Post-Diesel Powered Vehicles Control Regulations

The air quality improved remarkably and steadily due to the compliance with

environment standards for SPM and NO2 in fiscal 2004, according to an announcement on June

24, 2005 by the TMG. The SPM level met the standard6 at 33 out of 34 Roadside Air Pollution

Monitoring Stations (RAPMS7), as well as all of 47 Ambient Air Pollution Monitoring Stations

6 The Basic Environmental Law defines air quality environmental standards as those levels

desirable for the protection of human health and conservation of living environment. For SPM,

daily average of hourly values shall not be more than 0.10mg/cubic meter, and no single hourly

value shall be more than 0.20mg/cubic meter. For NOx, daily average of hourly values shall be

in a range between 0.04ppm and 0.06ppm, or less.

7 RAPMS refer to measuring stations set up along roads where large amounts of automobile

emissions are present.

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(AAPMS8) in residential areas. The achievement rates in fiscal 2002 and 2003 were 0 and 12

percent for RAPMS, and 40 and 51 percent for AAPMS, respectively. Even at one station9 where

the level did not meet the standard this time, a remarkable improvement was seen. The NO2

level met the standard at 16 RAPMSs (47 percent) and all of the AAPMSs. The achievement

rates in fiscal 2002 and 2003 were 37 and 53 percent for RAPMS, 93 and 98 percent for AAPMS,

respectively. Since the No Diesel Vehicle Campaign began in Tokyo in August 1999, the

TMG has been taking many pioneering initiatives in various fields to prevent air pollution in

Tokyo, calling for cooperation of the national government and related industry groups. Tokyo

attributes the above results to its diesel vehicle emission control regulations enforced in October

2003.

Due to the slow action of national government, the level of air pollution might have been

even higher than other metropolitan cities in the world. However, as diesel-powered vehicles

control regulations being strictly enforced, the dramatic improvements were seen.

V. Methodology

Now, I discuss the econometric models used to estimates the air pollution and infant

mortality associations. I will use the methodology of Chay and Greenston (2003) as my role

model and make adjustment so that it will fit into my data. Their analysis is predominantly cross-

sectional, but I would consider time-series and cross-sectional in order to take an advantages of

my long panel data. A general cross-sectional model of infant mortality can be written as

(1) y t = f xn , w

t

t

+

t

8 AAPMS refer to measuring stations set up in residential areas.

9 The one station is at Matsubara Bashi measuring station in Ota-ku

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where y t is the infant mortality rate in district in year , xn is the average air pollutants

t

reading across all monitors in the districts ( n represents the air pollutant that are considered in a

specification), w t is a vector of the other determinants of district-level infant mortality rates (such

as district geographical characteristics and year dummies, etc), and is nonparametric

conditional mean function of y t. Thus,

and w t are defined so that [ t| x t,w t] = 0.

Empirical implementation of equation (1) is not practical for two reasons. First, w t could

be of extremely high dimension, and many of its elements may not be observable to the researcher

(e.g., maternal behaviors). Second,

is a general conditional expectation that is a function of the

joint distribution of y t, xn , and w

t

t . Consequently, estimation of the effects of interest is

A common approach to reducing the dimensionality of the problem is to assume that the

effects of the covariates are additive and linear. This results in the linear regression model:

(2) y t = xn t n + w t + t, t =

+ u t

This model would be my first approach to the analysis. The coefficient n is the true effects of

air pollutants on infant mortality. For consistent estimation, the least squares estimator of

n requires that

E

[xn t

t ] = 0. If there are omitted permanent (

t ) or transitory (u t ) factors

that covary with air pollutants levels, then the cross-sectional estimator will be biased. Any

nonlinearities or interactions in the true effects will also lead to bias. The ideal research design is

a controlled experiment in which different levels of pollution exposure are randomly assigned

across mothers/infants. Random assignment ensures that variation in air pollution exposure is

independent of other factors (i.e.,

wit

) that impact infant mortality. This reduces the

dimensionality of the inference problem. However, using randomized clinical trials to study the

effects of air pollutants exposure on infant health would be unethical.

18

---

My next approach would be considering a model using a fixed-effects estimator applied

to the pooled ten years of data, then compared that to cross-sectional estimator. .

(3) dyjt = dx1jt 1 + dx2jt 2 +

+ dxnjt n + d jt, d jt = sj + dujt

, where si are district-fixed effects in infant mortality changes. Since (3) is based on first-

differences, any permanent unobserved differences across districts,

t , is controlled for.

Then, I would consider a model using a random-effects estimator.

(4) yjt =

+ Xj 1 + dxjt 2 + vj + jt

Before proceeding, I note the potential for censoring bias in my estimates of the effects of

air pollutants. The analysis is based on the population of live births. Since air pollution may

damage the fetus before birth, it may also affect the likelihood of a miscarriage or stillbirth. In

this case, our analysis will

understate

the impact of air pollutants on infant mortality by

conditioning on fetuses that survive to a live birth. As Chay and Greenstone (2003), ideally I

would like to consider Quasi-experiments with Say No to Diesel Vehicles Campaign;

however, with limitations of controlled group data, I decided to forgo the model.

VI. Data Sources

To implement my evaluation strategy, I obtained data for 23 wards and 21 cities in

Tokyo10 on 1997

2006 period (once per a year), the most recent period for which the data were

available. I combined infant mortality11 data from TMG Bureau of General Affairs with air

pollution data from National Institute for Environmental Studies (NIE). Statistical information on

10 Tokyo is consisted of 23 wards, 26 cities, 5 towns, and 8 villages

http://www.metro.tokyo.jp/ENGLISH/LINKS/links1.htm

11 Infant mortality data are calculated by numbers of infant death divided by live birth in the

same district and year.

19

---

infant death was obtained from vital statistics in Tokyo Statistical Yearbook. Infant is defined as

a baby of between a birth and 1 year of age and new born as a baby of less than 4 weeks since

birth. Infants born in some cities and towns were excluded from the analysis because for those

areas air pollution data were not available. The vital statistics contained, not only the data on

infant mortality, but also on overall birth/ death, stillbirth, total fertility rate, and

marriage/divorce. Besides data on infant mortality rate, other earlier studies consider other

important variables such as birth weight of the same infant from data, education level of mothers

who gave birth to those infant they have data on and so forth. However, within the vital statistics,

those information were not available; therefore, I was not able to find those data that match with

my infant data. One of the difficulties with data on air pollution is inconsistency of monitoring

stations over time. Monitoring stations tend to be located in the most densely populated areas of

the districts, and also in those that are most polluted, which might potentially produce biased

results, and the location of monitors may also change over time. Hence, in this analysis, I use

only those monitors that existed continuously throughout the period. Air pollution data from NIE

contained data sets for not only NO2, NOx, SPM, and CO, but also Ox and SOx; however, due

to the limitations of monitoring stations, SOx and Ox are excluded from my analysis. NIE

monitors the air pollutants at monitoring stations for at least 6,000 hours a year and disclose the

year averaged value in their database. They also provide information on each monitoring

station s geographical characteristics, which would be included in my analysis.

20

---

Figure 4. Source: Bureau of Environment TMG

These maps in Figure 4 present the hourly updated concentration of air pollutants12 in

Tokyo. In the map for NOx and CO, their unit of measure is in ppb13 as opposed to ppm which

NOx, NO2, and CO in my data are measured in. The east side of Tokyo in the map is where 23

wards are located while west side is where the rest of cities are. As you can see, the levels of air

pollution are relative low in the west side of Tokyo in all cases.

Since CO was one of two most significant air pollutants in Currie and Neidell s study, I

decided to make another set of data with NO2, NOx, SPM and CO from 16 wards, 8 cities, and a

village. However, a data set for CO is limited compared to the data sets of the rest of pollutants;

therefore, I was not able to combine a data of CO to my master database to run regression

together. Therefore, I separately run regression with those limited data.

Descriptive statistics for the pollution variables are shown in the Table 1 and 2 below.

Clearly, from both tables numbers of infant deaths in Tokyo are slightly lower than that of other

12 I excluded NO2 from the map, since NOx includes NO2.

13 ppb is parts per billion.

21

---

studies. Infant mortality in Currie and Neidell s study is 5.5 per 1000 births while mine is 3.17

per 1000 births. From 1997 to 2006, the numbers of births are relatively constant over time while

the numbers of infant death varies moderately. Due to the limitations of CO data, there are more

variations in both the numbers of birth and infant death overtime in Table 2 than that in Table 1.

As far as the level of air pollution concern, the levels of all pollutants are almost the same level

as Currie and Neidell s. In their summary, they measure its level by ppb and g/m3, so it might

be hard to compare mine to theirs. For example, for PM, the total mean of Currie and Neidell s

PM is 39 g/m3, while mine is also 0.039 mg/m3, which is equivalent to 39 g/m314. The level of

CO is largely smaller in my data than theirs. This information is interesting since I originally

thought that the level of both air pollution and infant mortality would be much lower than that of

other countries like US. However, I would still assume that on average the level of air pollution

in Tokyo is rather moderate level.

If I look at the table more closely, Table 2 shows more variations in all air pollutants than

that in Table 1. CO and SPM seem to vary larger than NOx and NO2 overtime. These two tables

do not show the variations across districts; however, it is feasible to assume some variations exist

across districts as well. Aside from variations, correlations among air pollutants are presented in

both tables. As can be seen, all air pollutants are positively correlated with each others. From

Table 1, as expected, NOx and NO2 have the highest correlation due to the way how NOx is

calculated. However, from Table2, CO and NOx have a relative higher correlation than that of

NOx and NO2. Since correlation coefficients for most air pollutants are higher than 0.5, it is a bit

difficult to tell which one is not highly correlated with any other pollutants; however, NO2 seems

to be the one with lowest correlations with all the pollutants.

141 g/m3 = 0.001 mg/m3 and 1ppb = 0.001ppm.

22

---

Figure 5. Source: National Institute Environmental Studies, graphed by author

Mean Value

0.08

1.2

M

0.07

P

1

0.06

)

S

0.8

0.05

m

p

)

&

0.04

0.6

m

Nox

)

(

p

p

3

0.03

0.4

O

(

p

NO2

/

m

0.02

C

2

g

0.2

O

0.01

SPM

(

m

N

0

0

d

n

CO

a

x

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

O

N

Year

Figure 5 presents the trends of yearly and district-wise averaged concentration of air pollutants

from 1997 to 2006 on all the districts. Although the unit scale for CO is a lot bigger than the rest

of air pollutants, all of them show decreasing trends in ambient air quality. Besides NO, they all

seem to have the similar variations over time. They have relative larger reductions between 1998

and 1999, when No Diesel Vehicle Campaign has started. Considering the fact that most of air

pollutants have met its standard level which TMG has set in most of monitoring station, ambient

air quality in Tokyo might have reached the level which might be considered as clean. From

Currie and Neidell s study, the level is defined as a threshold for harmless air pollution level.

Therefore, if my analysis ends up showing statistically insignificancy on air pollutants, it might

be either due to the quality of data or the threshold level of concentration being already reached.

Figure 6 shows the trend of infant mortality rate in Tokyo from 1997 to 2006. Slightly

decreasing trends is shown; however, considering the unit scale, decreasing from 0.4% to 0.2%

might be better to assume that the

23

---

change in infant mortality rate is constant over time, which is a problem since I would need some

variations on infant mortality to link with the variations of level of air pollution to show an

association. Therefore, hopefully this 0.2% decrease in infant mortality would give me some

interesting results.

Infant Mortality Rate

0.5

e

g

0.4

t

a

0.3

n

0.2

r

c

e

e

0.1

P

InfantD

0

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

Year

Figure 6. [Data from Bureau of General Affair TMG, graphed by author]

24

---

)

2)

3)

s

1

0.29

le

0.0289

0.0502

0.0307

SPM

a

riab

2006

96,851

v

) (0.0185

)

(0.0028

3) (0.0061

ts

n

0.27

0.054

uta

1

0.0295

0.0305

(0.003

ll

NO2

0.512

2005

91,849

(0.0062

i

r po

)

(0.0198

)

8)

0.27

een a

0.0300

0.0564

(0.021

0.0307

t

w

1

(0.0031

t

be

2004

94,409

(0.0059

7)

6)

9)

ien

0.8302

0.6055

ic

0.30

ff

0.0318

0.0587

0.0352

(0.0222

coe

2003

- - - - -

93,774

tion

3) (0.0064

6)

3) (0.0043

la

280 280 251 251 282

2002

0.29

0.032

NOx

95,140

0.0624

0.0374

Corre

NOx

NO2

SPM

(0.0061

(0.0231

(0.0051

-

)

)

9)

ean.

281

2001

,

599

0.30

93

0.0326

0.0664

0.0419

(0.0064

(0.0058

(0.0236

-

)

8)

9)

Min

341

0.36

25

0.015

0.018

0.023

95,329

0.0324

0.0668

0.0432

(0.0061

viation for its m

2000

-

(0.0240

)

(0.0059

2)

6)

Max

0.138

0.068

315

0.34

0.0453

ndard de

93,208

0.0319

0.0678

0.0424

(0.0059

l

)

)

)

1999

(0.0061

(0.0244

ta

44

-

o

8)

9)

9)

3.17

is the sta

2,988

342

0.36

0.0317

0.0392

941,190

0.06325

(0.024

(0.009

94,074

0.0335

0.0742

0.0498

(0.0064

1998

-

) (0.0065

) (0.0258

) (0.0072

e

nthesis

T

365

1997

0.39

0.075

92,957

0.0346

(0.027

0.0506

)

) )

(0.0063

(0.0089

e

of par

le

3

-

2006

p

00

n

le

/

m

)

)

)

p

i

o

g

le

3

insid

sam

r 1,0

lut

p

n

/

m

,

1997

le

(ppm

(ppm

g

in

sam

p

i

o

le

pol

NO2 (ppm

NOx (ppm

in

sam

te pe

lut

(m

stics

cts

p

el

SPM (m

sam

ti

ra

in

NO2

NOx

stri

pol

sam

ath

t

y

le

p

in

cts

el

SPM

15

e

Sta

in

t

de

rtali

stri

l

i

t

y

sam

ath

iv

t

r

i

c

t

-

l

ev

rth

fan

o

rta

in

t

de

o

LE. 1

bers of di

l bi

l in

t

r

i

c

t

-

l

ev

rth

fan

t

m

The values that are

ta

ta

o

o

fant m

15

TAB

Descript

Num

T

T

I

n

Mean dis

bers of di

l bi

l in

fan

ta

ta

in

o

o

Num

T

T

%

Mean dis

---

)

)

)

)

1

CO

0.29

0.608

0.0290

0.0518

0.0304

(0.021

les

2006

61,323

1

) (0.0066

)

)

(0.1938

7) (0.0032

a

riab

SPM

0.28

0.627

0.6675

0.0297

0.0561

0.0303

(0.228

ion v

ut

(0.0032

ll

2005

57,906

)

(0.0067

)

(0.0222

)

)

i

r po

0.27

0.685

0.0300

0.0586

0.0309

een a

t

w

2004

59,283

)

(0.0064

)

(0.0237

)

(0.0033

)

(0.2398

t

be

1

0.497

0.6166

1

0.29

ien

0.744

0.0317

0.0612

0.0348

ic

NOx NO2

(0.026

(0.282

ff

0.7089

0.6311

0.8417

2003

58,662

coe

- - - - -

) (0.0066

)

) (0.0048

)

175 173 158 164 175

0.29

tion

2002

0.767

la

59,596

0.0316

0.0644

0.0368

(0.005

(0.244

(0.0064

(0.0267

Corre

NOx

NO2

SPM

CO

-

)

)

)

)

179

2001

0.31

0.873

58,484

0.0327

(0.07

0.0687

0.0415

(0.0275

(0.0061

(0.3452

-

)

)

)

)

200

0.34

26

Min

0.4

0.915

0.018

0.015

0.023

59,523

0.0326

0.0698

0.0431

(0.0276

(0.3418

2000

-

)

(0.0066

)

)

(0.0065

)

2

205

0.35

0.894

Max

0.138

0.068

58,175

0.0319

(0.007

0.0709

0.0416

(0.344

0.0453

1999

-

)

)

(0.028

)

(0.0064

)

l

)

)

)

)

ta

25

208

0.35

o

3.14

1.002

1,855

0.039

58,811

0.0335

0.0765

0.0486

0.0658

0.0317

0.8130

(0.32

589,421

(0.027

(0.0068

(0.0091

1998

-

) (0.0072

) (0.029

) (0.0068

) (0.34

218

1997

0.38

-

2006

1.019

T

57,658

0.0342

(0.007

0.0796

0.0510

(0.329

(0.0318

(0.0091

)

)

)

)

le

3

)

)

)

)

p

00

le

3

CO, 1997

n

le

/

m

p

n

h

p

i

o

g

le

/

m

g

sam

i

o

(ppm

(ppm

(ppm

r 1,0

lut

p

sam

wit

lut

(m

in

sam

CO (ppm

CO

le

pol

NO2 (ppm

NOx (ppm

in

sam

NO2

NOx

cts

p

in

te pe

pol

el

SPM (m

le

cts

p

in

t

i

s

tics

el

SPM

stri

sam

ath

t

y ra

stri

l

i

t

y

sam

ath

e Sta

in

t

de

rta

rtali

t

de

o

iv

t

r

i

c

t

-

l

ev

in

rth

fan

o

t

r

i

c

t

-

l

ev

rth

fan

t

m

LE. 2

bers of di

l bi

l in

bers of di

ta

ta

l bi

l in

fan

o

o

fant m

ta

ta

in

TAB

Descript

Num

T

T

I

n

Mean dis

o

o

Num

T

T

%

Mean dis

---

VI. Regression Results

1. Cross-Sectional Estimate Results

I begin by replicating the conventional cross-sectional approach to estimate the

association between air pollution and infant mortality across districts in Tokyo. For each cross

section from 1997 2006, Table 3 shows estimates of model (2), where the dependent variables is

the infant mortality rate per 100 livebirths. For comparison with previous work, I first estimate

pooled cross sectional models for each pollutant separately and also together. In Table 3, mainly

there are two parts, the one without CO16 (Table 3.1 ~ 3.2) and the other with CO (Table 3.3 ~

3.4). Then, there is subcategory within these models, which are Standard and Grouping . I

define Standard as a normal regression with variables which contains true values of air

pollutants. Grouping represents the separation of the level of pollutants concentration. I used

data sets of Standard model and break the level of each air pollutants into two groups, low and

high, using the information from descriptive statistics about 50th percentile. Then I run new

regressions with those dummy variables instead of using the variables that contains the actual

concentration level. In addition, as can be seen in Table 3 I also created specifications with year

effects, and then compared them with non-year effects specifications.

Let us first take a look at Standard case without CO in Table 3.1. In terms of statistical

significance of each coefficient, regardless of the sign of the coefficients, they all seem to have

relatively large significances. Especially, NOx s coefficients might actually be meaningful. The

single pollutant models shown in column (5) ~ (10) of Table 3.1 indicate that only exposure to

NOx increases the infant mortality rate regardless of year-effects. Moreover, one ppm increase in

NOx, holding other stuff constant, leads to an increase in 2.18 deaths per 100,000 live births17.

16 As stated in section V. specifications with CO has less districts.

17 Usually air pollutants increases by 0.01ppm instead of 1ppm; therefore, 1ppm increases in any

air pollution leads to X deaths per 100,000 livebirths.

27

---

On the other hand, from column (7), exposure to SPM reduces the infant mortality rate, which is

counterintuitive. Column (1) ~ (4) shows that if I run regression on some combinations of air

pollutants, all of their magnitude of estimates becomes larger than that of single pollutants

model. The estimated effects are much larger if I include all with year-effects, shown in column

(2). Moreover, given the 941,190 births in areas where pollution could be assigned over my

sample period, the estimated of 5.23 deaths per 100,000 suggests that the one unit decline in

NOx that took place over the sample period saved 47 infant lives. (Note that I do not consider

possible live saved in areas without pollution monitors. If these areas did not have monitors

because they had little pollution and /or were sparely populated, then reductions in pollution

could be expected to have relatively little effect, assumption established by Currie and Neidell)

Also, if I consider year-effects for each specification, most of their estimates improve. Same

inferences apply to the model with CO; however, the magnitude of those estimates is much

larger, shown in Table 3.3. With this specification, reduction of one unit of NOx leads to 5018

infant lives saved in column (10) of Table 3.3.

Now let us look at the same models, but with different angels, looking at Grouping

model. This model reveals the statistics about how much of each air pollutants from those

districts that have high concentration contribute to infant mortality compared to those with low

concentrations. From column (5) ~ (10) in Table 3.2, the effects on infant mortality rate from

exposure to NOx in high concentration districts have statistically significant differences from

that in low concentration districts. More precisely, high concentration districts have additional 5

deaths per 10,000 livebirths more than that of low concentration districts if I consider year-

18 The coefficient is much larger in Table 3.3, but with CO specifications, total birth is smaller

than the one without CO, so the numbers of infant that were saved is not proportionate to the

increase in the coefficient.

28

---

effects. Estimated 5 deaths per 10,000 suggest if a high concentration district converges to low

concentration district, it can save 470 infants. Same inference applies to NO2 case except that for

NO2, estimates are significant regardless of year-effects. Once again like Standard case,

magnitude of all estimates become larger if I run regression with some combinations of

pollutants shown in column (1) ~ (4). A specification with all pollutants in column (1) show the

largest magnitude, but only SPM and NO2 are significant. NOx shows significant only with SPM.

Once again, SPM has counterintuitive estimated effects, meaning high concentration districts

have lower infant mortality rate than that of low concentration districts. With CO specifications

shown in Table 3.4, NOx and NO2 show statistically significances; however, CO shows

counterintuitive results and SPM lost its significant that were there in without-CO model. With

year-effects model, only exposure to NO2 in high concentration districts has significant

difference from its low concentration districts. Overall, in CO specification many of coefficients

lose its significances, which is opposite results from standard regression. The magnitudes of

coefficients become smaller than that of Non-CO specifications.

To summarize, NOx appears to have the most significant effects on infant mortality. The

estimated effect of NOx is remarkably robust to many changes in specification. The magnitude

of coefficient becomes larger as being considered with other pollutants. It is quite new results

that NOx have the most significant effects on infant mortality compared to other studies. The

coefficient on SPM is also somewhat robust to many chances in specifications; however, it has

negative effects. Unlike the results from the Currie and Neidell estimates, CO is not as

significant as NOx or SPM. Not only CO is not significant, but also the coefficients on CO show

negative effects. Also many other studies conclude to say that PM or TSP has a strong

associations with infant mortality, while my data fail to support those results. However, it must

29

---

be kept in mind that the Currie and Neidell and others include many more control variables than

mine, which could change the estimate results dramatically. Therefore, it is reasonable to assume

that my cross sectional estimates might have many omitted variables that are not considered in

the models but might affect the level of infant mortality rate. This raises the problems of biasness

of cross sectional estimates. To eliminate this bias, in next section, I consider fixed-effects and

random-effects estimates to see how the results changes.

2. Fixed-effects and Random-effects Estimates Results

Table 4 ~ 7 presents fixed-effects (FE columns) and random-effects (RE columns)

regression estimates of the effect of air pollutants on the infant mortality rate within a year of

birth per 100 live births. All specifications include year-effects, districts-effects, and

geographical characteristic dummies but not shown in tables for the sake of limited spaces.

As can be seen in Table 4, first all the significances that are there with cross sectional

estimates are totally gone with both fixed and random effects. Single pollutant model in

column (3) ~ (6) shows the opposite estimated effects, meaning now SPM has positive effect and

NOx and NO2 have negative effects. From column (1), 1 mg/m3 increase in SPM leads to an

increase in 1.5 deaths per 100,000 live births, while 1 ppm increase in NOx leads to a reduction

in almost 3 deaths per 100,000. Regardless of its statistically significance, the magnitude of their

coefficients is much smaller than cross sectional estimates. With random-effects shown in

column (6) ~ (10), all coefficients are now positive regardless of their significant except when I

include all pollutants shown in column (6); however, the magnitude of all the estimates are even

smaller than that of fixed-effects estimates. Since all the coefficients lose their significant, it is

hard for me to say which pollutants have stronger effects on infant mortality. However, what I

30

---

can say from these results is that the results from cross sectional estimates could highly be biased.

Therefore, it is important to control for omitted variables. The estimated results on geographical

characteristics19 are excluded from the tables; however, in random-effects in Table 4, it shows

that residential and commercial districts are relatively healthier than industrial districts as

expected. They both show negative effects, meaning if a district is either residential or

commercial, it will reduce the infant mortality rate. However, if a district is industrial, then it

increases the infant mortality rate. Unfortunately, with random-effects estimates in Table 5 do

not show such results.

Even though, all coefficients lost its significance, with fixed-effects I can discuss about

the economical significances. From column (1) in Table 4 shows the reductions in SPM and NO2

can save 71 infant lives, which is still relatively large compared to cross-sectional analysis.

However, with random-effects, most coefficients get so small that it loose economically

significances as well.

Similarly, I run Grouping regression with fixed and random-effects, shown in Table 6

and 7. The interpretations on these results are the same as those on cross sectional estimates.

NOx shows statistically significance at 90% level as opposed to 95 or 99%. In Table 6, in both

fixed and random effects, only exposure to NOx has the significant different effects on infant

mortality rate between high and low concentration districts. The magnitude of coefficient is

about the same level as the cross sectional estimates with year effects. Table 6 shows that

residential and commercial districts are once again healthier than industrial districts. Table 7

shows the estimated results with the specifications with CO. As can be seen in Table 7, in both

fixed and random effects NOx gain more statistically significance than that in Table 6. NOx by

19 Geographical characteristics dummies get dropped when I run fixed-effects regression due to

the multicolinearlity.

31

---

itself does not gain the significance; however, with all pollutants together, its magnitude

increases so that it gains its significance. CO shows its significances for all specifications.

However, the estimated effects are negative, which are similar results from cross sectional

estimates. When I consider pooled cross section with Grouping , CO lost its significant when I

include year-effects. However, with fixed and random effects, CO does not lose its significant at

all. Comparing with cross section estimates, coefficients in Table 6 and 7 show that their

magnitudes are much larger than that shown in Table 3.4.

To summarize, with standard regressions, all coefficients show statistically

insignificances, which prevents me from rejecting the null hypothesis that those coefficients are

zero, meaning air pollution has no effects on infant mortality. With grouping regressions,

estimated results are a slightly better than that of cross sectional estimate. This might proves that

although I cannot say much about overall effects of air pollution on infant mortality since the

estimated effects for all pollutants are sensitive to specifications and types of regressions, the

high concentration districts have slightly higher infant mortality rate than that of low

concentration districts. Even though the results from fixed- and random-effects are not

statistically significant, in fixed-effects most coefficients maintain the same level of economical

significance as cross sectional estimates.

Since the results differ a lot between cross sectional and fixed/random-effects estimates,

it is hard for me to decide which results I should believe in. However, fixed/random-effects

estimated results might be more believable as many literatures confirm that there exists bias due

to omitted variables from cross sectional estimates. The results from fixed/random-effects are

somewhat disappointing due to its inconsistency with existing literatures, yet the results might

still be meaningful considering the economical significant of its estimated results.

32

---

VII. Discussions and Conclusion

This paper examined the effects of air pollution on infant mortality in Tokyo, using

recent data from 44 districts on time period of 1997~2006. My models are consisted of three

econometric models: cross-sectional, fixed effects, and random effects. Using within district

level variation in pollution, I am able to control for unobservable fixed characteristics of districts

a well as for detailed group of observable time-varying characteristics. (Currie and Neidell,

2003) The estimated results from each model are quite different; however, especially cross-

sectional and fixed-effects estimates show some economical significance of the associations

between air pollution and infant mortality. In my cross section estimates, I find that only NOx

increases infant mortality. Unfortunately, I cannot say much about the effect of SPM and CO

since the results show the estimated effects of SPM and CO are more sensitive to specifications,

and show negative effects on infant mortality. The cross section estimates imply that reduction in

NOx over the time interval I study saved about 500 infant lives in Tokyo. However, my preferred

model is fixed-effects estimates, which possibly eliminate the biasness of cross section estimates.

Fixed-effects estimates show that SPM and NO2 increase infant mortality, while the estimated

effects of NOx and CO show negative effects. Even though fixed-effects estimates do not show

statistical significances, yet it can still provide meaningful interpretations as some economical

significances exist on some of pollutants. Although the magnitudes are still much a bit lower

than cross sectional estimates, I find that reduction in SPM saved about 200 infants in Tokyo

within 10 years.

My findings are interesting for two reasons. One is the finding can be useful in terms of

policymaking. It can provide policymakers great motivations to establish air pollution related

policies. For example, in a case of TMG, according to TMG s

Tokyo Environment Master Plan,

33

---

based on data obtained in fiscal year 2006, the concentrations of NO2 measured at all 43 AAPMS

met the Environmental Quality Standards, while attainment was confirmed at 21 out of 34

RAPMS in the same year. The achievement of the Environmental Quality Standards regarding

SPM has been confirmed at all 47 AAPMS and all 34 RAPMS for the first time since monitoring

began. On the other hand, according to annual average data, the concentration of NO2 has

remained flat, while that of SPM has shown a downward trend over recent years. This decreasing

tend has been clearly observed since 2003 when DPVC regulations took an effect. The

Environmental Quality Standards regarding CO and SO2 has been fulfilled at all monitoring

stations; however, the standards regarding Ox have not yet been met at any of the stations.

Clearly, DPVC regulations have brought a successful contribution to the air quality of Tokyo.

However, DPVC is only targeted towards NOx and SPM; therefore, still some other air

pollutants remained unattained in some monitoring stations. TMG believes necessary actions to

obtain better air quality for Tokyo is needed. Considering what came out of TMG s effort to

obtain cleaner air quality in Tokyo for ensuring safe environment for the residents of Tokyo,

TMG has set up several goals established in their

Tokyo Environment Master Plan

.

An environmental standard of a SPM and a NO2 is achieved at ALL monitoring stations

by fiscal year 2010 and obtain more stable lower concentration status by 2016.

High concentration pollution requires more dramatic improvement by fiscal year 2010.

Establish a new standard level for NO2 and PM2.5

Set a photochemical smog warning announcement day to 0 days by 2016.

Secondly, my finding can contribute more certainty to the findings of existing academic

literatures and also a new possible compelling results. Even though my estimates are a bit

disappointing because some of estimates are not consistent with previous studies such as Currie

34

---

and Neidell (2003) or Chay and Greenstone (2004), yet it is not entirely hopeless. Considering

the fact that my estimates are based on the best currently available information, but without

many of important control variables which many other studies considered in their studies, the

estimated results can be improved. The finding supports that the reduction on NOx and SPM

possibly can save roughly 200 ~ 500 infants over my sample time period. This findings would

mean a lot to the existing literatures because first not many studies proved that the associations

of the level of NOx and infant mortality and second it confirms the finding of Chay and

Greenstone and many others that SPM increases infant mortality. If overall statistical

significances are a bit stronger and the estimated results are robust to specifications, my findings

can say a lot more about the associations between air pollution and infant mortality with

certainty. Yet, I believe that my findings are reasonable.

Currently,

TMG s concern is the level of PM2.5. The concentration of PM2.5 in Tokyo is

higher than standards set by the United States and the World Health Organization; however,

environmental quality standards have not yet been set in Japan. TMG has called on the national

government to set environmental quality standards and carry out pertinent measures as soon as

possible. With the objectives of further improving air quality in Tokyo and protecting the health

of local residents, conducting a study in the effect of PM2.5 on health would be required as a next

step. Many other epidemiological literatures confirm that PM2.5 is harmful to our health; however,

to determine the appropriate standard level of PM2.5, it is important to understand in detail how

much PM2.5 can potentially affects the resident of Tokyo. I conclude that air pollution-related

infant mortality is a major public health problem. Particularly, PM2.5 is much smaller particulate

matter than SPM, which can be more harmful to the health. Therefore, the health damage to

infants may be greater than my estimated results obtained by SPM. Therefore, based on my

35

---

estimated results and many other epidemiological literatures, TMG should consider the

vulnerability of infant when it comes to setting policies regarding air pollution.

Finally, to answer the question of

Has Tokyo Reached a Harmless Level of Air

Pollutions?

, from my estimated results and the successful contribution from the DPVC it is

apparent that the level of air pollution has been converging to a harmless level. I cannot say for

sure that the level has already reached that level at this moment. However, considering the fact

that I was unable to find a strong association between air pollution and infant mortality as

existing literature, I may deduce that the level of air pollution in Tokyo might be less harmful

than other metropolitan cities and more polluted locations. Yet, further research with more

control variable added to regression or on new problematic pollutants in Tokyo such as PM2.5

would shed lights to answering the question even more.

36

---

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---

---

Tokyo Environment Master Plan

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Tokyo Metropolitan Government. 6 Mar 2009

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taikiosen/files/bisyouryuusi.pdf>. [in Japanese]

Penna M-L F, Duchiade MP. Air Pollution and Infant Mortality from Pneumonia in the Rio de

Janeiro Metropolitan Area. Bulletin of PAHO 1991;25:47-54

Saldiva PHN, Lichtenfels AJFC, Paiva PSO, Barone IA, Martins MA, Massad E, Pereira JCR,

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38

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Seaton, Anthony et al. Particulate Air Pollution and Acute Health Effects,The Lancet,

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.

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Statistical data

The monthly value and the year value data on Air Quality in Environment numeric database at

National Institute Environmental Studies

http://www.nies.go.jp/igreen/index.html

Vital Statistics in TOKYO STATISTICAL YEARBOOK at Tokyo Metropolitan Government

Bureau of General Affairs, Statistic Division Management and Coordination Section

http://www.toukei.metro.tokyo.jp/tnenkan/tn-index.htm

39

---

Appendices

40

---

*

)

)

)

**

Y

Y

436

440

)

(10)

2.097

0.128

(1.718

0.116

(10

0.0693

(0.0258

(9)

N

1.595

249

(3.061

0.031

)

)

**

)

)

0.475

(2.051

(9

0.0596

(0.0263

-

0.0171

(0.0660

)

)

0.101

0.111

(9)

)

Y N

)

0.030

(8)

435 436

(8)

0.448

(1.547

0.114

(8

0.0270

(0.0267

(7)

N N

250 242

-

2.354

(2.175

0.035

)

)

0.116

**

)

N

N Y N

(7)

435

440 440 440

)

0.109

0.105

**

-

2.552

(1.297

(7

-

0.0373

(0.0234

1.679

(0.775

*

)

)

***

)

)

)

0.048

(6)

2.176

(0.822

(6

0.0511

(0.0293

(5)

N N

242 250

-

3.101

(2.953

0.0427

0.031

(0.0872

)

)

0.146

0.123

(6)

**

)

N Y

41

(5)

437 437

)

)

1.327

(0.526

0.113

0.0292

***

(0.0250

***

(5

(4)

N

242

0.105

6.454

(1.419

-

0.465

(0.117

)

)

)

)

**

0.103

***

)

Y N Y

440 440 440

)

)

3.185

(1.105

-

6.618

(2.797

(4

0.0511

0.121

(0.0336

***

***

0.0000701

(0.0324

(3)

N

250

0.080

3.466

(0.959

(2.671

)

)

0.157

)

-

8.210

**

)

***

(4)

***

)

**

N Y

(3)

435 435

0.153

3.119

(0.646

-

7.165

(1.585

(3

0.0566

)

*

)

)

s

(0.0275

-

0.0600

(0.0258

***

***

te

N

a

(2)

242

0.114

)

)

)

)

)

)

(1.432

-

5.456

(2.864

(0.123

**

**

0.112

6.918

-

0.392

***

)

**

Estim

5.237

(1.678

-

5.994

(2.674

-

10.31

(4.048

(2

0.0117

(0.0376

-

0.00653

(0.0316

0.0627

(0.0283

*

ional

)

)

)

)

*

***

**

ct

N

s
e

)

)

)

0.174

)

)

)

(1)

5.269

241

***

***

0.116

s-

5.086

**

**

0.125

(2)

***

(1.612

(2.892

0.400

(0.126

(4.087

(1)

N Y

)

434 434

N Y N

0.165

440 440 440

CO

(0.923

6.998

(1.582

9.074

(3.304

0.119

h

(0.0357

0.0632

(0.0257

(0.0363

ed Cros

wit

t

s

(1

ool

fec

P

dard

t

s

t

s

-

2

r

ef

ing

fec

fec

j

.

R

2

LE 3.

dard

2

NOx 7.844

SPM

CO -

NO2 -

Yea

N

ad

-

r

ef

r

ef

3, Stan

j

.

R

j

.

R

TAB

NOx_H 0.00711

SPM_H -

NO2_H 0.0782

NOx 4.944

SPM

NO2 -

Yea

ad

N

ad

1, Stan

N

2, Group

Yea

---

)

)

**

)

***

Y

N

(18)

(9)

2.510

249

0.049

(2.261

0.013

0.0948

(0.0426

0.100

(0.0372

)

)

)

(18)

0.034

Y

242

0.0201

0.010

(0.0507

-

0.0452

(0.0421

-

0.0284

(0.0423

)

0.034

(17)

)

(8)

)

(17)

0.013

Y

(16)

250

-

1.643

(2.535

0.013

-

0.0341

(0.0388

0.00545

(0.0500

.

ants

)

)

0.033

(7)

)

0.012

l

l

ut

Y

250

0.042

of po

2.274**

(0.998

0.0364

(0.0424

0.0530

(0.0403

e

st

)

)

)

(15)

)

0.033

(6)

)

)

0.018

ns to r

(15) (16)

Y

N N N N

tio

(

14)

242

(5)

Y Y Y Y Y

-

3.422

(3.561

0.0572

0.008

0.031

250 250 250 250 250

(0.0704

-

0.0189

(0.0439

-

0.0356

(0.0477

(

14)

0.0213

0.010

(0.0566

-

0.0340

(0.0465

r

e

ta

e

rp

42

)

)

)

)

)

)

e int

**

**

Y

N

(13)

242

(4)

0.085

0.047

(13)

6.406***

(2.176

0.112

0.113

s

.

Sam

-

0.437***

(0.147

(0.0549

-

0.117

(0.0547

(0.0748

-

0.100

(0.0771

ct

stri

< 0.01

)

)

)

)

)

)

p

0.026 *

**

on di

Y

(12)

250

N

0.066

(3)

(12)

a

ti

3.883**

(1.541

-

11.74**

(5.184

0.0685

0.037

(0.0477

-

0.0633

(0.0437

0.0655

(0.0493

-

0.0367

(0.0611

entr

< 0.05,

p

)

)

)

**

conc

)

)

)

)

)

)

0.015

h

*

**

i

g

Y

(11)

242

N

0.105

(2)

(11)

0.046

0.121

7.675***

(2.507

-

11.12**

(4.973

-

0.0267

-

0.0984

-

0.407***

(0.136

0.125

(0.0566

-

0.0429

(0.0449

-

0.103

(0.0564

(0.0799

(0.0617

(0.0766

< 0.10,

)

p

*

:

f

NOx in h

)

)

)

)

0.023

t

i

nued

)

)

)

)

***

e

ses

)

)

)

)

**

**

e

l o

Y Y Y Y

250 250 250 250

Y

(1)

N

0.0447

0.109

0.039

lev

241

0.065

(0.0736

(0.0608

(0.0769

(0.0395

-

4.952

0.105

ping

0.0455

(0.0654

(0.0445

0.113

(0.0560

(0.0564

a

renth

he

8.561***

(2.838

-

10.39**

(4.921

(4.304

-

0.424***

(0.144

CO (Con

h

in p

rs

e

ans t

nd Grou

o

t

s

t

s

err

m

fec

fec

dard wit

f

f

e

cts

rd

2

2

r

ef

r

ef

j

.

R

j

.

R

e

ar e

j

.

R2

NOx_H 0.0435

SPM_H -

CO_H -

NO2_H 0.136

Yea

ad

(10)

NOx_H 0.0468

SPM_H -

CO_H -

NO2_H 0.131

Yea

N

ad

Standa

te: NOx_H

3, Stan

(10)

NOx

SPM

CO

NO2

Y

N

ad

4, with CO a

No

---

)

)

)

(9)

(9)

(10)

436

2.344

249

0.009

2.367

(3.574

1.167

(3.897

(2.558

2006

)

)

)

(9)

(8)

0.586

435

242

(2.271

-

0.142

(0.161

0.010

(0.120

1997 to

-

0.0884

om

)

fr

)

)

0.596

(8)

Rate

(1.000

06

1.847

(2.832

(7)

1.380

l

ity

(3.616

rta

)

)

o

(8)

)

0.009

(7)

(7)

0.675

435 437

(1.059

0.0358

(2.425

)

1997 to 20

(6)

250 250

fant M

-

4.318

0.024

n

(4.383

(6)

-

0.837

(1.616

nd I

)

)

)

t

s a

(6)

Rate from

)

)

0.773

434

-

0.402

)

)

tan

(1.431

-

0.0260

(2.438

(5.271

(5)

242

lu

ality

2.722

0.008

rt

(3.318

-

0.171

(0.177

(5)

2.126

)

(3.964

-

0.112

(0.128

ous Pol

(5)

436

-

0.169

)

)

(5.322

0.051

fant Mo

n

)

)

Vari

(4)

242

43

(4)

en

-

4.338

0.019

)

a

nd I

(4.919

-

0.0281

(0.153

-

0.247

(2.068

(0.147

-

0.0727

t

s

(4)

435

tan

Betwe

0.617

(2.023

0.050

llu

)

)

)

)

)

s Po

3.391

-

4.722

(4.594

(3.260

(3)

-

0.863

(1.651

1.890

(3.794

(3)

riou

437

0.055

Associations

-

1.691

(2.505

Va

)

)

)

0.024

(3)

the

en

)

)

)

)

)

(2)

3.284

242 250

0.018

s

of

-

4.506

(5.012

(3.487

-

0.0581

(0.160

(2)

1.916

ate

(2)

435

s

Betwe

-

0.0108

(2.050

(4.026

-

0.0968

(0.150

-

1.858

(2.681

1.398

(2.323

0.051

tim

Es

iation

)

)

)

)

)

)

)

)

cts

)

)

)

soc

ffe

(1)

241

0.440

-e

2.734

4.446

0.0907

0.01

(3.478

(2.380

(8.170

(5.027

(3.501

0.0562

(0.163

(2.906

0.014

(2.100

(4.031

(0.153

(3.692

m

the As

heses

s

<

nt

p

a
te

***

a
t
e
s

of

m

pare

Rando

s
ti

t
i
m

t
s E

rs in

< 0.05,

t
s and

ts Es

p

**

ffec

0.051

-
effec

1.488

2

e
ffec

LE 4

LE 5

3.295

2

m

1.996

ed-e

434

j

.

R

j

.

R

(1)

NOx -

SPM

NO2 6.142

N

ad

< 0.10,

TAB

Fix

TAB

Fixed-

NOx -

SPM

CO -

NO2 1.914

N

ad

Rando

(1)

NOx -

SPM

CO -

NO2 2.823

Standard erro

p*

---

)

)

*

**

*

)

RE

(10)

(9)

250

0.022

0.0605

(0.0343

0.0822

(0.0380

(9)

0.0847

(0.0440

)

)

*

6

)

RE

(9)

440 440

**

200

0.00987

(0.0285

o

-

0.111

(0.0576

(8)

-

0.1000

(0.0450

1997 t

*

)

)

0.033

)

from

RE

(8)

440

(8)

0.0561

(0.0318

(7)

250 250

-

0.0199

0.009

Rate

06

(0.0450

-

0.0179

(0.0464

ty

a

li

*

)

)

)

)

Mort

RE

(7)

440

(7)

0.0564

(0.0327

1997 to 20

-

0.000963

(0.0291

(6)

(6)

fant

250

0.016

n

0.0769

(0.0501

0.0723

(0.0524

)

)

)

and I

ts

Rate from

RE

(6)

)

)

)

)

**

t

an

0.0421

0.0448

*

u

(0.0347

-

0.00477

(0.0292

(0.0372

ality

rt

(5)

-

0.111

0.000221

(0.0467

-

0.100

(0.0459

s

Poll

)

-

0.00240

(0.0458

(0.0585

44

fant Mo

FE

(5)

n

440 440

)

)

Variou

0.0567

0.056

(0.0366

)

)

0.029

(5)

**

***

en

a

nd I

*

**

t

s

(4)

(4)

0.115

(0.0540

(0.0466

e

twe

)

-

0.129

tan

0.112

(0.0585

-

0.132

(0.0616

llu

FE

(4)

440

0.051

0.00733

(0.0263

s Po

)

)

a

tions B

)

)

riou

*

0.046

soci

(3)

*

)

Va

(3)

250 250 250

0.0827

(0.0542

-

0.0361

(0.0477

e As

0.015

FE

(3)

en

440

0.0865

(0.0514

-

0.0359

(0.0462

0.057

f

th

0.0580

(0.0335

o

)

)

)

s Betwe

a

tes

**

)

)

)

***

*

)

)

**

**

(2)

s

tim

iation

(2)

FE

(2)

250

0.120

-

0.0214

440

(0.0553

(0.0473

-

0.126

(0.0470

0.117

-

0.0210

0.043

t
s

E

0.0582

0.055

(0.0588

(0.0462

-

0.130

(0.0619

(0.0339

soc

-

0.000976

(0.0267

0.01

the As

<

)

)

)

)

*

)

)

)

)

m-effec

)

)

)

**

*

*

s

e

ses

p

a
te

(1)

***

440

250

0.101

0.0270

0.046

m

0.0980

0.0281

(0.0574

(0.0475

-

0.119

(0.0472

(0.0459

a

renth

(0.0331

0.00433

(0.0270

(0.0367

a
t
e
s

of

(0.0590

(0.0460

-

0.121

(0.0629

(0.0388

s
ti

d Rando

t
i
m

t
s E

in p

< 0.05,

rs

p

ts an

ts Es

o

**

err

0.056

-
effec

LE 6

e
ffec

2

LE 7

e
ffec

2

m

rd

j

.

R

j

.

R

< 0.10,

TAB

p

Fixed-

FE

(1)

NOx_H 0.0491

SPM_H -

NO2_H 0.0445

N

ad

TAB

(1)

NOx_H

SPM_H -

CO_H

NO2_H 0.0582

N

ad

NOx_H

SPM_H -

CO_H

NO2_H 0.0637

Fixed-

Rando

Standa

*

---

y

t

a

st

o

rnal

e

uld

e

e d

,

g

r

i

thin 3

o

w

th

entered

w

mate

w

in

on

y

or SO2.

r

ession,

effects

o to thre

to increased

f

1970.

vascula

die

of lo

ilit

greatest for

f

fetal death

i

sib

the average

re tw

ly to

v

e

pollutants

f

mortality

y related to

er the 1990s in

asonal

in the reg

fant mortality from

w

PM10 was lar

ach pollutant

v

of

in 1997 than

t

hs was

y

se

een

sed.

increased mortality

ed

s

like

f

preterm birth

d

, but not PM

in

n.

f

e

or

.

tw

and

h. Effects

an Air Act o

y

en all

risk o

sed risk o

creases risk

e

bir

or

ns

s clo

nancy related

r

llution measured did n

pollutants when

egnanc

t

io

a

n

e o

sk o

fant lives.

i

v

clud

be

and

spitals

a

t

h, next cardio

.

TSP

e

o

g

n

s die

d

h

more

c

ateg

e

s

i

nters when the mill was

a

d PM10 o

to

s wh

f

pr

categ

r

v

w

preg

g. po

d.

ure

fant

ather and

s

west

e

to ho

o

avera

an

f

the Cle

1000 l

est

s

e

b

s

en it w

i

th hig

p

g

er 100 i

r in

h

associatio

n

ases de

cause

nships t

x

d.

v

e

e lo

i

g

s

ariable in

i

th mortality

e

posure

ual av

th retarda

i

n

h

for w

ng the

during

preterm birt

th

x

t trimester i

ence o

e

I

DS per

ow

ted w

latio

gh e

in

h

n w

dis

controlle

mov

th

f

particulate

missio

o

and

ved o

S

s

n was o

hi

ys

d

a

in

3

h

a

e

300 few

om

ared to wh

vel ann

g

r

eater

a

n

O

her duri

l

s

s are

sociated with incre

g

ed with increased ri

1,

fr

nia

ts

wit

y

sociati

.

f

LBW

de

la

hly

the income v

mo

10 associa

ruterine grow

ual level o

hs

posure in 1st month o

posure in las

talit

x and

u

i

thin 5 d

death th

i

th

at

Effec

Mean exposure

risk o

exposure in first trimester

Mean lifetime e

due to respiratory

in mo

PM

to 5 day

County le

have robust re

variable

Infant

postneonatal perio

Ex

inte

Index as

w

Ex

7-day

associat

The reductions in CO

California s

Roug

have in the abs

Mor

3.3 times g

of

After controlling

NO

W

an associatio

ann

pne

Children′s ad

times hig

open comp

The as

for respiratory

de

x

x

2,

2,

2,

2.5

nts

,

NO

,

NO

, SO

a

P

2.5

M

, PM10,

TSP

TSP

x

TS

O

10

, PM10, NO

10

3, N

Pollut

SO2, TSP

SO2,

PM10

PM10, CP

SO4, PM

PM

PM10, P

PM10

SO2 &

SO2 &

CO

O3

TSP

PSI, PM10, SO

CO

SO2, CO

O

TSP

PM

PM10

n

ases

e

45

y

ight

ight

i

s

e

e

D

mortality

mortality

mortality

mortalit

mortality

mortality

death

mortality

th Retardatio

Mortality

birth w

th retard

birth w

fant

fant

r

ow

f

ant

aily

Outcomes

Low

preterm birth

grow

Infant

In

Infant

G

Fetal death

Low

Preterm birth

Infant

Infant

Infant

syndrome

Children mortality

In

Respiratory

D

In

88

ars

e

Y

1991

1993-96

1990

1989-91

1994-96

1991,92, 95

1988-91

1988

1990-91

1980

1985-89

1985-

1989-91

c

a

n

na

, U.S

, U.S.

public

SAs

Bohemia,

City

, Chi

, Chi

1981-91

Janeiro

alley

alley

ico

f

US

e

i
es

.

S 1971-72

t

ah V

t

ah V

d

Czech Republi

Czech Re

Mex

All o

86 U.S. M

Europe

Sao Paulo, Brazil

Beijing

Beijing

California 1990s

U

Taiwan

Sao Paulo, Brazil

Rio d

U

U

d

r Stu

)

Northern

d

g

de

n

n,

an, an

a

f
Othe

, an

y

chia

g

W

o

n-Je

u

, R

r

eenstone

1995)

Leon (1999)

t

al (1997)

g

Neidell

G

D

el at (1999)

t

al. (1999

, and

g (

t

al (1992)

.
List

g

d

l

, Chie

a e

t

al (1992)

a

ruff e

t

al (1998)

, Din

ert, Zhan

g

d

g

ure7

z

o

n

Lian

o

jmek e

obe

g

e

a

n

Study Location

Bobak (2000)

Bobak and

Loomies

Lipf

Wy

(2000)

W

D

Luiz e

W

Xu (1997)

Xu, Din

(1995)

.

Currie and

(2004)

Chay an

(2003)

K

and

Saldiv

Penna and

(1991)

Pope (1989)

Pope e

Fi

---

l

a

tions

u

e

g

) r

(NOx

ide

n ox

46

2, Transition of nitroge

ent TMG
m

nviron

of E

ulations

reau
Bu

Source:

Figure 8

1, Transition of PM Reg

---

-ku

a

-shi

m

ma

shi

a

o

i

T

-ku T

a-sh

-ku

i

-ku

awa

aw

nam

ik

-ku

nag

c

h

i

t

o

a

a

Shibuya

Shi

Shinjuku-ku

Sugi

Sumida-ku

T

T

-

shi

-shi

o

n

-ku

-ku

-ku

a

shi

no

ma

-shi

ka

ri

-ku

a

e

Musa

N

N

Nishitokyo

Ome

Ota

Setagay

chi

-

shi

-

shi

a

-shi

d

ka

chi

ta

zuho-ma

47

Koto-ku

Ma

Meguro-ku

Mi

tion)

-ku Kunitachi

Minato-ku

Mi

ifica

-shi

i

-ku

-shi

ra-shi

e

spec

-ku

a

tsushika

ta

yose-shi

CO

Itabash

Ka

Ki

Koganei

Kom

Ki

d. (Without

-

shi Kodai

-shi

dere

ama

-

shi

i

amato

j

i

-shi

u-sh

o

h

chi

ist of Districts Consi

Fuc

Ha

Higashimuray

I

.

L

-ku Fussa-shi

-ku Higashikurume

Higashiy

-ku Hino-shi

-ku

-ku

ndix

hi

u-shi

ure 9
g

ac

akawa

ogawa

Fi

Appe

Ad

Ar

Bunkyo

Chiyoda

Chof

Chuo-ku

Ed

---

-

ku

a

-

ku

w

i

-

ku

-

shi

nam

-

ku

a

i

t

o

m

Shinaga

Shinjuku

Sugi

Ta

Ta

u

-ku

-

k

a

-ku

48

i

ma

-ku

Ner

Ome-shi

Ota

Setagay

Shibuya

ations)

ific

u

-

k

o

gur

e

M

Mizuho-machi

Musashino-shi

d (With CO spec

i Nakano-ku

u

Minato-ku

-

sh

i

-

k

-shi

ricts Considere

yose-shi

ist of Dist

L

Itabash

Kunitachi

u

I

I

,

-

k

-ku Ki

-ku Koganei

-shi

ndix

awa

ak

ogawa

Appe

Ar

Chiyoda

Ed

Fussa-shi Koto-ku

Hachioji

---