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THE STATUS OF AIR POLLUTANT PM10 FROM A HUMAN
HEALTH PERSPECTIVE IN TAICHUNG CITY, TAIWAN

by
Su-Miao Lai

A Thesis: Essay of Distinction
Submitted in partial fulfillment
of the requirements for the degree
Master of Environmental Study
The Evergreen State College
June 2009

 2009 by Su-Miao Lai. All rights reserved.

This Thesis for the Master of Environmental Study Degree
by
Su-Miao Lai
has been approved for
The Evergreen State College
by

________________________
Maria Bastaki, PhD.
Member of the Faculty

________________________
Date

ABSTRACT
The Status of Air Pollutant PM10 from A Human Health Perspective in
Taichung City, Taiwan
Su-Miao Lai
The association of air pollution with health effects has been studied for decades. Many
researches have shown that ambient air pollution is associated with respiratory disease
and cardiovascular diseases. The Central Taiwan Science Park (CTSP) established in
Taichung City raised residents’ attention in terms of potential health effects caused by
increased air pollution. The aim of this study attempts to analyze the status of PM10, one
of the major air pollutants, in Taichung City, Taiwan and estimate the likelihood of health
effects. This research examined air quality data from the air monitoring stations, obtained
from Taiwan’s EPA. Specifically, PM10 concentrations from two air monitoring stations
in Taichung City, in proximity to the CTSP and one control air monitoring station in
Taichung County were first examined through time-trend graphs, descriptive statistics. A
student’s t-test was conducted to compare PM10 levels between the two years prior CTSP
construction and two years after, in order to investigate whether CTSP played a role in
terms of PM10 levels. The results of this test showed that after CTSP was built there was a
small but statistically significant increase in the average PM10 levels. To conclude, this
study compares the current status of PM10 to other large cities and estimates the health
risk of long-term exposure to air pollution.

Table of Contents
FRONT PAGE……………………………………………………………….…………i
COPY RIGHT PAGE………………………………………………………….……….ii
APPROVAL PAGE……………………………………………………………………iii
TABLE OF CONTENTS…………………………………………………….……….. iv
LIST OF FIGURES…………………………………………………………………... vi
LIST OF TABLES…………………………….……………..……………………….viii
ACKNOWLEDGEMENTS……………………………………………..……………. ix
Chapter 1 Introduction………………………………………………………………….1
Chapter 2 Health Effect Literature Reviews……………………………………………3
2.1 Air Pollution and Health Effects
2.2 Epidemiological Studies Associated with Air Pollution
Chapter 3 Air Pollution in Taichung City, Taiwan…………………………………....12
3.1 Geology
3.2 Climate
3.3 Demographics
3.4 Economics
3.5 Air Monitoring Stations
3.6 Air Quality Regulation
3.7 Central Taiwan Science Park
Chapter 4 Methods ……………………………………………………….…………..21
4.1 PM10 Measuring Technology
4.2 Data Sources
4.3 Data Analysis
Chapter 5 Results……………………………………………………………….….....25
5.1 Time Trend Comparisons of Air Pollutants in Two Air Monitoring
Stations in Taichung City
5.2 Comparisons for PM10 Concentration between Seasons and Air
Monitoring Stations

iv

Table of Contents - Continue
5.3 Comparison for PM10 Concentrations before and after the CTSP
5.4 Multiple Regression Model for PM10
5.5 Public Health - Current Situation in Taichung City
Chapter 6 Discussions and Conclusions………………………………………………44
6.1 Review of Research Findings
6.2 Limitations of the Study
6.3 Recommendations for the Future Research
6.4 Conclusions
Reference ……………………………………………………………………………...55

v

List of Figures
Figure 1. Pyramid of the impact of particulate pollution
Figure 2. How big particle pollution is
Figure 3. The parts of the respiratory system
Figure 4. Relative risk estimates for associations between long-term exposure to PM and
mortality
Figure 5. Epidemiologic studies of air pollution in Asia published from 1980 to 2003
Figure 6. The location of Taiwan in Asia
Figure 7. The location of Taichung City in Taiwan
Figure 8. The eight districts and three Taiwan EPA air monitoring stations in Taichung
City
Figure 9. Central Taiwan Science Park
Figure 10. Taichung coal-fired power plant
Figure 11. Da-Li air monitoring station
Figure 12. Chung-Ming air monitoring station
Figure 13. Si-Tun air monitoring station
Figure 14. Beta attenuation principle
Figure 15. O3 levels in Chung-Ming and Si-Tun station 1994-2007
Figure 16. PM10 levels in Chung-Ming and Si-Tun station 1994-2007
Figure 17. NO2 levels in Chung-Ming and Si-Tun station 1994-2007
Figure 18. SO2 levels in Chung-Ming and Si-Tun station 1994-2007
Figure 19. NO levels in Chung-Ming and Si-Tun station 1994-2007
Figure 20. 1994-2007 Da-Li station PM10 concentration
Figure 21. 1994-2007 Chung-Ming station PM10 concentration
Figure 22. 1994-2007 Si-Tun station PM10 concentration
Figure 23. Seasonal PM10 levels in Da-Li station
Figure 24. Seasonal PM10 levels in Chung-Ming station
Figure 25. Seasonal PM10 levels in Si-Tun station
Figure 26. Spring PM10 levels in three air monitoring stations
Figure 27. Summer PM10 levels in three air monitoring stations
Figure 28. Fall PM10 levels in three air monitoring stations
Figure 29. Winter PM10 levels in three air monitoring stations
Figure 30. PM10 Frequency between seasons and air monitoring stations
Figure 31. Top 12 mortality rates of diseases (per 100,000 persons) in Taichung City
2001-2007
Figure 32. Average death rate of cancer in Taichung City 2001-2007
Figure 33. Lung cancer death rates in Taichung City 2001-2007
Figure 34. Mortality rate of disease associated with PM10
Figure 35. Effects of fine (•) and coarse (◦) particles on respiratory admissions in
published time series studies (Brunekreef and Forsberg, 2005)
Figure 36. Effects of fine (•) and coarse (◦) particles on chronic obstructive pulmonary
disease (COPD) admissions in published time series studies (Brunekreef and
Forsberg, 2005)

vi

List of Figures - Continue
Figure 37. Effects of fine (•) and coarse (◦) particles on cardiovascular admissions in
published time series studies. CVD: cardiovascular disease; HF: heart failure;
IHD: ischaemic heart disease (Brunekreef and Forsberg, 2005)
Figure 38. Age-Adjusted Cancer Death Rates for the 10 Primary Sites with the Highest
Rates within State- and Sex-Specific Categories (United States Cancer
Statistics, CDC)
Figure 39. PM10 and Cardiorespiratory deaths no. in 20 largest US cities, 1987–1994
(Daniels et al., 2000)

vii

List of Tables
Table 1. European cities, demographic and environmental data (Ballester et al, 2008)
Table 2. Comparison of PM10 standards in western countries
Table 3. Comparison of PM10 standards in Asia countries
Table 4. Annual Average concentrations of five air pollutants at the Chung-Ming Station
Table 5. Annual Average concentrations of five air pollutants at the Si-Tun Station
Table 6. Five pollutants correlation at the Chung-Ming station in 2007
Table 7. Five pollutants correlation at the Si-Tun station in 2007
Table 8. Pearson correlation coefficients between Chung-Ming station and Si-Tun station
Table 9. Descriptive Statistics about PM10 before and after CTSP
Table 10. Descriptive Statistics of PM10 parameter in the multiple regression model
Table 11. Estimates of percent increase (95% confidence intervals) in mortality risk
across selected studies of short-term exposure (Pope III, 2007)

viii

Acknowledgements
I would like to thank my reader, Maria Bastaki, for all of her time and
extensive aid in finishing this thesis essay. Because of her help, this work
became clear and organized. This thesis essay can not be done in this short
time period without her help. I want to thank Ben-Jei Tsuang, a professor at
Chung-Hsing University, Taiwan, for the data he provided and the help from
his student, Pei-Hsuan Kuo. I also want to thank environmentalist, BingHeng Chen, for discussing the current air pollution issues with me.
I also want to thank for the support and encouragement from my family,
friends and former colleagues in Taiwan.

ix

Chapter 1
Introduction

According to environmental history, we have known of two major smog incidents around
the 1950s. One had occurred in Donora, Pennsylvania, USA, 1948. Twenty people died,
approximately six hundred were hospitalized, and thousands more were affected in that
particular environmental disaster. The other one happened in London, UK in 1952, which
was known as a killer smog and caused approximately four thousands deaths. These
deaths drew worldwide attention to the relationship between air pollution and public
health effects. Since then, there has been much research in this field.
Over the past few decades, research on air pollution has shown that it has significant
impact on human health mainly on the respiratory system but also some evidence shows
cardiovascular effects as well. The data from hospitalized patients and clinic visiting
patients have been adopted as health indicators of the impact of air pollution. Many
studies in western countries have been carried out due to the growing interest in
particulate matter (PM) among the types of air pollutants, especially in United States and
Europe. The research was conducted with data from many cities with prospective study
or cohort study design. Several prospective epidemiological studies have been preformed
with large cohorts in many cities.
Air pollution has been a big issue in central Taiwan. Residents have complained about air
pollution and have expressed concerns about health effects related to environmental
factors. In recent years, the government built a Central Taiwan Science Park in the suburb
area of Taichung city. It is important to evaluate the concentration of air pollution of the
surrounding environment with the goal to prevent respiratory illness and to ensure the
safety of community. The present study will analyze the air pollution data over a period
of time including the time of the Park construction, obtained from the air monitoring
stations established by Environment Protection Agency of Taiwan.

1

There are six criteria pollutants included in the National Ambient Air Quality Standards
(NAAQS), Ozone (O3), particulate matter (PM), NO, CO, SO2 and lead. In this study,
PM10 will be addressed as a pollutant indicator. The primary research question to be
addressed in this paper is as follows: “What is the status of air pollutants, specifically
PM10, from a human health perspective in Taichung City, Taiwan?”

2

Chapter 2
Health Effects Literature Reviews

2.1 Air Pollution and Health Effects
To date, there are many studies have confirmed that there is an association between PM10
concentration and health effects, both at low-dose and high-exposure levels. The health
impact of particulate matter pollution is shown in Figure 1. There are many literature
sources that provide some evidence for health outcome. Some of the major studies are
summarized below.

Figure 1 - Pyramid of the impact of particulate pollution (Ministry for the
Environment, 2003)
2.1.1 Type of Air Pollution

3

According to the Clean Air Act (CAA), U.S. EPA selected six common air pollutants,
which are known as criteria pollutions. They are particulate matter (PM), ozone (O3), lead
(Pb), carbon monoxide (CO), sulfur oxides (SO2) and nitrogen oxides (NO and NO2,
together named NOx). PM is the term that describes a mixture of solid particles and
liquid droplets found in the air. Different particles size has different health effects. PM2.5
and PM10 are two major categories of particulate matter for which research has been
done. PM2.5 is the particle with diameter less than 2.5 micrometers and PM10 is the
particle with diameter less than 10 micrometers. In this study, PM10 is the pollutant of
interest that will be investigated.

Figure 2 - Relative size of particulate matter pollutants
(http://www.epa.gov/air/particlepollution/basic.html)
2.1.2 Physiology of Respiratory System
There are many particles in the air. Some sizes of particles are easy to inhale into the lung
and cause health effects. The particles with diameter > 10μm usually precipitate within a
short time and do not have high impact as health hazards, except for eye and upper
respiratory irritation (nose, throat). The diameter of particles < 10μm are considered as

4

particulate matter (PM10). The main sources are traffic dust, vehicle, burning, industry
and second pollutant from other air pollution. If the diameter is smaller, the chance of
getting into the lung becomes bigger.
PM10 is particulate matter less than 10μm in aerodynamic diameter and PM2.5 is
particulate matter less than 2.5μm in aerodynamic diameter. General speaking, most the
diameter of particles > 4.7μm will deposit on the nasal cavity and pharynx. The diameter
of particles between 3.3μm and 4.7μm will deposit on trachea and bronchus. The
diameter of particles between 2.1μm and 3.3μm will deposit on bronchiole. The diameter
of particles between 1.1μm and 2.1μm will deposit on terminal bronchiole. The diameter
of particles < 1.1μm will deposit on alveolar or alveoli. (EPA, Taiwan) Figure 3
illustrates the parts of the respiratory system.

Figure 3 - The parts of the respiratory system (Canadian centre for Occupational
and health Safety) (http://www.ccohs.ca/oshanswers/chemicals/how_do.html)
2.2 Studies of Human Health
Previous epidemiological studies have shown an association between the levels of
ambient air pollutants and daily mortality ((Joel Schwartz, 1994; Klea Katsouyanni, et al.,
2001), daily clinic and emergence room visits and hospitalization rate for respiratory

5

disease and cardiovascular disease (B. Brunekreef & B. Forsberg, 2005; C. Arden Pope
III, et al., 2004). Air pollutants had been found to contribute to both increased mortality
and hospital admissions (Francesca Dominici, et al., 2006).

Figure 4 - Relative risk estimates (and 95% confidence intervals) for associations
between long-term exposure to PM (per 10 PM10–2.5) and mortality. *Note the
second result presented for Laden et al. (2006) is for the intervention study results
(U.S. EPA, 2006a).

6

2.2.1 North America studies
The Harvard six cities study (SCS) (Moolgavkar, Dockery, & Pope, 1994) and American
Cancer Society (ACS) Study (Pope, et al., 1995) have been particularly influential in
contributing insights into particulate matter research as the leading studies. Both studies
indicate particulate matter is associated with chronic health effects, such as
cardiovascular, respiratory, and mortality (Pope, et al., 1995). Besides these two studies,
many studies estimate relative risk for associations between long-term exposure to PM
and mortality (Figure 4).
2.2.2 Europe studies
Air Pollution and Health: A European Approach (APHEA 2 project) (N. Kunzli, et al.,
2000) investigated short-term health effects of particles in eight European cities. The
study provides the evidence that particulate concentrations in European cities are
associated with increased numbers of hospital admissions for respiratory diseases.
A collaborative study titled “Air Pollution and Health: A Combined European and North
American Approach” shows the results of multicity time-series data on the effect of air
pollution on respiratory disease. The study includes data from the European APHEA and
the U.S. NMMAPS (National Morbidity, Mortality and Air Pollution Study) projects, as
well as Canadian data (Evangelia Samoli, et al., 2008). This study found the health risk
estimates of PM10 are similar in Europe and United States, but higher in Canada. Also,
the risk for older age population is higher.
A cohort study conducted in the Netherlands found long-tern exposure to traffic-related
air pollution may shorten life expectancy (Gerard Hoek, Bert Brunkreef, Sandra
Goldbohm, Pual Fischer, & Piet A van den Brandt, 2002). Health and exposure data
among 10 European cities were available for 2001 and 2002 (Ferran Ballester, et al.,
2008) (Table 1).

7

Table 1 - European cities, demographic and environmental data (Ferran Ballester, et al.,
2008)
City

Year of

Mortality and demographic data
Annual
Population, Mortality

PM10
Measurement

PM10 measured

the data

deaths, 30

30 years

rate, 30 years

method

levels (annual

years and

and over

and over

over

average, μg/m3)

(x1000)

Athens

2001

28407

2023945

14.04

 -attenuation

52.1

Barcelona

2002

16385

1033376

15.86

gravimetric

39.7

Budapest

2001

24291

1137019

21.36

TSP,  -ray

22.2

Hamburg

2001

17651

1176425

15.00

TEOM,

19.1

 -absorption
Lisbon

2002

17895

1215742

14.72

 -attenuation

28.8

London

2001

54576

4166772

13.10

TEOM

13.1

Madrid

2002

25692

1952919

13.16

 -attenuation

33.3

Paris

2001

42983

3664892

11.73

TEOM

22.4

Rome

2001

21439

1754427

12.22

 -gauge

47.3

monitor
Vienna

2002

16652

1052083

15.83

gravimetric

30.0

2.2.3 New Zealand studies
Two epidemiological studies addressed the relationship between particulate matter and
health in New Zealand (Ministry for the Environment, 2003). One is the mortality impact
of PM10 concentration and temperature (Simon Hales, Clare Salmond, G. Ian Town, Tord
Kjellstrom, & Alistair Woodward, 1999), which is not like previous studies because it
shows no relationship between deaths from cardiovascular disease and PM10
concentration. The other study indicated an increase in respiratory hospital admission and
cardiac admissions with increased air pollution (J.A. McGowan, P.N. Hider, E. Chacko,
& G.I. Town, 2002).
2.2.4 Asia studies
Unlike other countries, Asian countries do not have many studies of human health
associations with ambient particulate matter. There are only a few studies that have

8

addressed the association of exposure to PM10 and health outcomes and all are singlecity studies in Asia. However, there are data showing that Asia has higher air pollution
than western countries. The level of air pollution may be higher because most of the
Asian countries are still developing and have fewer if any restrictions on environmental
emissions. To date, there are many cities that still suffer from very high PM10 level in
Asia (e.g. cities in China and India). China and India have been rapidly developing
countries for the last two decades. Public health has been threatened by industry pollution
in many ways, in addition to air pollution. Some western studies (e.g. APHEA) have
served as a resource of methodology to help Asian countries to get on board with similar
efforts to study air pollution health effects. Public Health and Air Pollution in Asia
(PAPA) is an ongoing project based on APHEA (Evangelia Samoli, et al., 2008).
PAPA is a project supported through HEI (Health Effects Institute) for understanding the
effects of air pollution on human health in Asia. Seven countries participated in this
project, China, India, Malaysia, Philippines, Indonesia, Korea, and Vietnam. This project
lasted four years from 2002 to 2006. It reviewed all of existing studies of air pollution
and health outcomes in Asia, conducted new high quality studies in representative cities
on health effects of air pollution, and provided local scientists more scientific and
technical capacity (http://www.cleanairnet.org/caiasia/1412/article-48844.html).
An HEI report titled “The Health Effects of outdoor Air Pollution in Developing
Countries of Asia: A literature Review” (Health Effects Institute, 2004) presents the
epidemiological research of health effects associated with ambient air pollution in Asia
from 1980 to 2003, with a total of 138 papers published in peer-reviewed literatures.
Those studies were conducted in 9 countries, China, Taiwan, South Korea, Japan, India,
Thailand, Malaysia, Singapore and Indonesia.

9

Figure 5 - Epidemiologic studies of air pollution in Asia published from 1980 to
2003. Numbers in parentheses are total studies/time-series studies conducted. (HEI
Special Report 15 Executive Summary, Health Effects of Outdoor Air Pollution in
Developing Countries of Asia, 2004)
2.2.5 Taiwan Studies
Like other Asian countries, Taiwan used to be a developing country and has very high
concentration of PM level. It contributes a lot of research related to the association
particulate matter with health effects. However, even though Taiwan is not a member of
PAPA, it should be included in the global public health perspective. Taiwan has carried
out short-term research to provide detailed information about the impact of air pollution
on the Taiwanese population.
The association between air pollution and asthma is known world-wide. Asthma is
characterized by a combination of inflammatory process and bronchial constriction that
prevents the person from taking a deep enough breath. During the past several years,
asthma has become a severe health issue and a public health challenge in Taiwan.
Asthma prevalence in Taiwan was found to be 1.34%, 5.04% and 5.82% in 1974, 1985
and 1990, respectively (KH Hsieh & JJ Shen, 1988). Traffic-related air pollutants have

10

been associated with asthma in school children (B-F Hwang, Y-L Lee, Y-C Lin, J J K
Jaakkola, & Y L Guo, 2005; N. Kunzli, et al., 2000) .
Another study from Taiwan showed evidence of an association between PM10 and
mortality from respiratory and cardiovascular disease among elderly people during the
winter season (Wen-Miin Liang, Hsing-Yu Wei, & Hsien-Wen Kuo, 2009).

11

Chapter 3
Air Pollution in Taichung City, Taiwan
Air pollution has been raising serious public health concerns in Taiwan for several years,
particularly with regards to respiratory illness, such as childhood asthma (B-F Hwang, et
al., 2005). The association between ambient air pollution and health effects should be
addressed in terms of public health. It has become a significant public health issue. Also,
it is important to understand the current trends of air pollution in Taiwan and the
projections to future pollution levels in light of new industry development and population
increases. Excess health effects from increased air pollution would result in increased
hospital visits that would greatly burden the national health care system budget. This
study will be focused on the PM10 concentration in Taichung City and its associated
health effects.
3.1 Geography
Taiwan is an island located in East Asia and has an area of 35,801 km2 (13,822.8 sq mi).
Given the small area and large population, the air pollution issue becomes more serious.

Figure 6 - The location of Taiwan in Asia
(http://www.jccp.or.jp/english/country/img/asia_taiwan_map.gif)

12

Taichung city is a third largest city in Taiwan and is located in the Taichung Basin. The
city is located just north of the 24° latitude and about 120.5° east longitude. Taichung city
is composed of eight executive districts (Figure 8). In this study, the data collected are
from three air monitoring stations located in Si-Tun District, West District and the
southern boundary of South District.

Figure 7 - The location of Taichung City in Taiwan (commons.wikimedia.org)

Figure 8 - The eight districts and three Taiwan EPA air monitoring stations in Taichung
City

13

3.2 Climate
According to weather statistics, Taichung city’s annual average temperature is 23℃
(73°F), annual average rainfall is about 1,708 mm, and the average humidity is 80%.
Taichung city usually has South-East and North-West wind in the summer and North and
North-East wind in the spring, fall and winter (Source: Taichung Weather Station,
Central Weather Bureau; data collected period: 1897-1995).
3.3 Demographics
The population in Taichung is now just over one million (January. 2009 = 1,066,843).
The ratio of male and female is 95.31 (male/female*100). Population density is 6,524
persons per square kilometers.
3.4 Activities in Taichung City
Taichung City is the third largest city in Taiwan. The economy has been growing
tremendously. Since its location is in the center of Taiwan, Taichung City has become an
important transit center in Taiwan. Lately, Taichung Port has become a major port for
airplanes and ships between Taiwan and China following some transportation policy
changes. Also, many manufactures have moved into the area for work related to the
Central Taiwan Science Park (CTSP) (Figure 9) and got involved in the construction that
started since 2003. These activities have stimulated economic growth and development.
On the other hand, they may be contributing to increased air pollution and impact on
Taichung City residents’ health.

14

Figure 9 - Central Taiwan Science Park (Image from http://www.ctsp.gov.tw/)
An additional big concern in Central Taiwan is the Taichung coal-fired power plant. It’s
located in the area of Taichung port and generates the most electricity in the summer.
According to a survey of CARMA (Carbon Monitoring for Action), Taichung coal-fired
power plant had been ranked first in the world in terms of CO2 emissions. It emitted
39,700,000 tons CO2 and generated 39,200,000 MWh energy and 2,022 intensity
presently (Data from CARMA (www.CARMA.org)).

15

Figure 10 - Taichung coal-fired power plant (image from
http://tpctcps.myweb.hinet.net/)
3.5 Air Monitoring Stations
The air monitoring station in Da-Li monitors SO2, CO, O3, PM10, NOx (NO, NO2), spell
this out before you abbreviate: THC, NMHC, CH4, PM2.5, CO2. The monitor machine is
established in a residential area. Da-Li station is located on the third floor of Da-Li City
Hall. The average height of surrounding buildings is about 20 meters. To the North is DaLi Elementary school. There is no building or structure in the area that could influence
the accuracy of the data. In Figure 11 the panel number 5 shows the air monitoring
machine. Panel numbers 1, 2, 3, 4, 6, 7, 8, and 9 show the surrounding environment.

16

Figure 11 - Da-Li air monitoring station (EPA)
The Chung-Ming air monitor station is located on the third floor of Chung-Ming
Elementary School. To the east, about 100 meters, is the Chung-Ming South Road that is
5 meters wide. To the south, about 150 meters, is the Chung-Kang Road that is 35 meters
width. The traffic load is heavy because of these two roads. In Figure 12 the panel 5
shows the air monitoring machine. Number 1, 2, 3, 4, 6, 7, 8, and 9 of pictures are the
surrounding environment.

17

Figure 12 - Chung-Ming air monitoring station (EPA)
Si-Tun station is in rural. It is located on the second floor of the administration building
at National Taichung School for The Deaf. There are new school building constructions
in front of building, farms in school’s backyard, and northwest is Taichung Industry Park.
Also, this station is about one kilometer away from waste water treatment plant. Si-Tun
station monitors SO2, CO, O3, PM10, NOx, NO, NO2, THC, NMHC, CH4. In Figure 13
the panel 5 shows air monitoring machine. Panel 1, 2, 3, 4, 6, 7, 8, and 9 of pictures are
the surrounding environment.

18

Figure 13 - Si-Tun air monitoring station (EPA)
3.6 Air Quality Regulation
In the United States, The Clean Air Act (CAA) was launched in 1990. It has been
improving air quality in the states since then. In 2001, the Canadian federal government
also declared PM10 as a toxic substance under the Canadian Environmental Protection
Act (CEPA). The European Union also has standards for air pollutant criteria. Table 2
and Table 3 give a comparison of PM10 standard in some western and Asian countries.
Table 2 - Comparison of PM10 standards in western countries
Averaging
Pollution (μg/m3)
PM10
1 day (24-h)
1 year
─ No value

U.S. California
150
50
─
20

Mexico Canada
150
50
50
─

E.U.
50
40

19

Table 3 - Comparison of PM10 standards in Asia countries
Averaging
Pollution (μg/m3)
1 day
(24-h)
PM10
1 year
─ No value

Japan Korea
100
─

<100
<70

Hong
India China Kong Taiwan
100
60

150
100

180
55

125
65

3.7 Central Taiwan Science Park
Compared to Taipei (first big city) and Kaohsiung (second big city), Taichung has less
population and pollution. However, in recent years the Taiwan government intends to
develop a plan for Taichung as a third business/industry city. Many city plans have been
undergoing, and the Central Taiwan Science Park has the most impact in terms of growth.
The CTSP was planned to increase economic growth in Central Taiwan area. The CTSP
Preparation Plan was approved by Executive Yuan on September 23, 2002 and broke
ground on July 28, 2003. At the same time, firms started to move in and build the Park’s
facilities. CTSP includes six business types, which are precision machinery,
optoelectronics, integrated circuits, biotechnology, communications, and computer
accessories. Since the CTSP intends to be a big cluster of high technology business in
central Taiwan, pollution concerns have been raising among the public, particularly for
local residents.

20

Chapter 4
Methods
4.1 PM10 Measuring Technology
The Environmental Protection Agency (EPA) in Taiwan has three air quality monitoring
stations (Figure 8) in Taichung area. They are located in Da-Li (Figure 11), Chung-Ming
(Figure 12), and Si-Tun (Figure 13). These monitoring stations are fully automated and
provide readings of PM10 levels using a technology called beta-ray absorption. The
Figure 14 illustrates the Beta Attenuation Principle.
Beta-ray Absorption monitors can efficiently analyze the concentration of the particles in
the air, and automatically and continually monitor particles in a long period of time. The
principle of the design of beta-ray absorption monitors is to calculate the concentration of
particles systematically based on the difference of radiant intensity on filter paper. The air
sample from the atmosphere gets into the system by an air pump. Before the air enters the
analytical system, the air goes through a sampling system. This system includes a
sampling door for the collected air, filter to determine the size of particles, such as PM10
or PM2.5, and heater equipment to eliminate water interaction.
When air sampling goes into the system, it goes through a specific material filter paper
which will collect the particle in the air. There is an equipment that contains radioactive
elements, such as Carbon-14, that emit electrons during the nuclear decay of these
radioactive elements. When the radiation goes through the filter paper it loses intensity
and it is counted on an acceptor to calculate radiation paused intensity.
The radiation intensity will be measured on the filter paper before and after collecting the
particles. By doing so, we can recognize the difference. The difference of intensity
reflects the ratio of the particulate matter. Thus, the system can calculate the
concentration of PM10 or PM2.5 in the air.
21

Figure 14 - Beta attenuation principle
(http://www.esmonline.de/andersen/product/group6/betaa.htm).
4.2 Data Sources
EPA in Taiwan has 123 regular air monitoring stations around the nation. They are used
to measure basic air pollution criteria. In Taichung city, there are three stations, which are
Da-Li station, Chung-Ming station and Si-Tun station.
For the purposes of this study, the data of PM10 concentration in Da-Li, Chung-Ming and
Si-Tun stations were collected for fourteen years, from 1994 to 2007. These data were
accessible and downloaded from the EPA air monitoring database.

22

Weather data, such as average annual temperature, average annual humidity and
precipitation were collected from Taichung Weather Station, Central Weather Bureau.
Population data were collected from Taichung City Hall.
Health effect data were provided by Department of Health, Executive Department,
Taiwan. The data include mortality rate of lung cancer and cardiovascular disease and
respiratory disease. Health effects from literature reviews will be used as well.
4.3 Data Analysis
I used SPSS Statistics 17.0 software package and Microsoft Excel to conduct statistical
analysis. First descriptive statistics of PM10, temperature, humidity and precipitation were
computed.
The establishment of the CTSP 2003 drew the public’s attention because of the concern
that it would lead to increased air pollution in Taichung City area. To find out whether
the CTSP had an impact on PM10 levels, annual average levels of PM10 before and after
2003, the year that the CTSP was established, were compared using a student t-test for
two independent means. Time trends graphs were prepared to observe the trend of PM10
levels over time for the 14 years of study.
There are several possible factors that are associated with PM10 as shown in previous
research, such as temperature (Cizao Ren, Gial M. Williams, & Shilu Tong, 2006; Cizao
Ren & Shilu Tong, 2006; Steven Roberts, 2004), humidity (Wen-Chao Ho, et al., 2007)
and traffic (Joachim Heinrich & Heinz-Erich Wichmann, 2004). In order to clarify the
relative contribution of these factors, they were used as explanatory variables in a
multiple regression analysis, with PM10 levels as the dependent variable.
In addition to the above, a regression analysis was performed to analyze the relationship
between PM10 and some environmental factors which are temperature, humidity,

23

precipitation and population. The annual PM10 value and seasonal PM10 value of three air
monitoring stations are also presented.
After presenting the status of PM10 in Taichung City over time, I looked at the resident’s
health conditions. Time trends graphs were prepared to observe the trend of PM10 levels
over time during the same time period for which air monitoring data were collected. I
also prepared graphs of health conditions over the same time period for which air
monitoring data were collected. The diseases with the top ten mortality rates show the
primary health outcomes of Taichung City. I will estimate the impact of health effect
based on current PM10 levels and statistical information of their relationship from other
published studies.

24

Chapter 5
Results
5.1 Time Trend Comparisons of Air Pollutants in Two Air Monitoring Stations in
Taichung City
To get a general idea about air pollutants in Taichung City, I compiled data from
Taiwanese EPA air monitoring database to present annual average values of five
pollutants, which are NO2, NO, SO2, PM10, and O3 from 1994 to 2007. In Taichung City,
there are two air monitoring stations in Chung-Ming (Table 4) and Si-Tun (Table 5) that
monitor and represent air quality.
Table 4 presents the average concentrations of NO2, NO, SO2, PM10 and O3 from 1994 to
2007 at the Chung-Ming station. Table 5 presents the average concentrations of NO2, NO
SO2, PM10 and O3 from 1994 to 2007 at the Si-Tun station.
Table 4 - Annual Average concentrations of five air pollutants at
the Chung-Ming Station
Air Pollutants
Chung- Year NO2(ppb) NO(ppb) SO2(ppb) PM10(μg/m3) O3(ppb)
Ming
1994
36.17
19.30
6.48
81.72
19.35
1995
30.17
15.96
5.80
74.21
21.41
1996
28.36
13.64
4.88
65.95
26.88
1997
32.87
17.07
5.16
69.36
20.58
1998
29.28
16.09
3.35
60.01
17.82
1999
29.44
13.85
3.51
67.07
19.55
2000
29.80
14.38
3.26
64.57
21.04
2001
30.08
12.72
2.88
59.83
21.94
2002
27.55
10.63
3.09
62.37
24.80
2003
25.42
9.45
3.24
62.64
26.86
2004
25.82
8.89
3.34
66.29
21.91
2005
22.10
10.15
3.47
64.16
23.70
2006
23.25
9.34
3.24
59.60
23.40
2007
22.86
8.00
3.39
58.12
25.10

25

Table 5 - Annual Average concentrations of five air pollutants at
the Si-Tun Station
Air Pollutants
Si- Year NO2(ppb) NO(ppb) SO2(ppb)
PM10(μg/m3) O3(ppb)
Tun
1994
24.68
12.56
5.78
60.05
22.47
1995
21.09
10.25
4.88
61.04
20.85
1996
22.05
10.54
4.98
60.32
23.04
1997
23.40
12.74
4.70
63.43
21.99
1998
22.14
12.65
3.75
54.73
15.24
1999
19.90
10.91
3.19
66.24
19.32
2000
21.49
12.96
2.73
72.68
21.84
2001
19.98
10.46
2.57
66.42
22.53
2002
19.00
11.10
2.64
62.87
25.64
2003
17.33
17.96
2.97
63.20
27.27
2004
20.57
7.07
3.15
74.07
28.66
2005
17.65
6.85
3.77
71.70
27.84
2006
18.27
6.77
3.70
60.44
28.15
2007
18.10
6.13
3.44
59.70
29.18
Among five pollutants in Taichung City, the trend of O3 (Figure 15) levels are increasing
in both Chung-Ming station and Su-Tun station. According to the measurements from the
Si-Tun station, the O3 levels were relatively stable from 1994 to 1997, decreased from
1997 to 1998 and have been increasing from 1999 to 2007. During the period of 1996 to
1998 and 2003 to 2004, the O3 levels in Chung-Ming station decreased, otherwise
increased.

Chung-Ming

Si-Tun

35

Average O3 level

30
25
20
15
10
5
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 15 - O3 levels in Chung-Ming and Si-Tun station 1994-2007
26

According to PM10 measurements in Chung-Ming station and Si-Tun station (Figure 16),
PM10 concentration was decreasing from 1994 to 1998 and did not change a lot from
1999 to 2007. The trend of PM10 in Si- Tun did not have much change from 1994 to
2007.

Average PM10 level

Chung-Ming

Si-Tun

90
80
70
60
50
40
30
20
10
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 16 - PM10 levels in Chung-Ming and Si-Tun station 1994-2007
According to NO2 measurements in Chung-Ming station and Si-Tun station (Figure 17),
the trend of NO2 concentration has been decreasing. Also, Chung-Ming station has
reported consistently higher NO2 concentration than Si-Tun station from 1994 to 2007.

Chung-ming

Si-Tun

40

Average NO2 level

35
30
25
20
15
10
5
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 17 - NO2 levels in Chung-Ming and Si-Tun station 1994-2007

27

The SO2 levels reported from Chung-Ming and Si-Tun stations were close each year
(Figure 18). It appears that SO2 levels in both stations decreased from 1994 to 1998 and
SO2 levels have very slightly changed between 1999 and 2007.

Chung-Ming

Si-Tun

7

Average SO2 level

6
5
4
3
2
1
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 18 - SO2 levels in Chung-Ming and Si-Tun station 1994-2007
Figure 19 shows that in general the Chung-Ming station has reported higher NO levels
than Si-Tun station with the exception of the years 2002 when the NO level at Si-Tun
station was equal or slight higher than Chung-Ming and particularly in 2003 when the
NO level measured at Si-Tun peaked at much higher level than Chung-Ming station.
Overall, the trend of NO recorded has been steadily decreasing over time.

Chung-Ming

Si-Tun

25

Average NO level

20
15
10
5
0
1994

1995 1996

1997 1998 1999 2000 2001 2002
Year

2003 2004

2005 2006

2007

Figure 19 - NO levels in Chung-Ming and Si-Tun station 1994-2007

28

The sources of these air pollutants may be common among some of them, and this may
result in similar changes in their levels. In order to see whether there is a relationship
among them, I compared the correlation of air pollutants in both air monitoring stations
in 2007. The results for Chung-Ming station showed that the relationship among air
pollutants, while statistically significant, was moderate in strength besides the
relationship of NO2 and O3 that was very low. The levels of PM10 were positively
correlated with NO (r=0.30), NO2 (r=0.66), SO2 (r=0.71) and O3 (r=0.42) (Table 6). The
levels of NO were positively correlated with NO2 (r=0.65), and SO2 (r=0.23). The
correlation of NO and O3 was negative (r=-0.34). The correlation between NO2 and SO2
(r=0.51) was positive. SO2 and O3 were weakly but positively correlated (r=0.26).
Table 6 - Five pollutants correlation at the Chung-Ming station in
2007
PM10
NO
NO2
SO2
O3
PM10
1
0.30**
0.66**
0.71**
0.42**
NO
1
0.65**
0.23**
-0.34**
NO2
1
0.51**
0.03
SO2
1
0.26**
** Correlation is significant at the 0.01 level (2-tailed).
Similar relationships among air pollutants were shown for the results from Si-Tun station.
While statistically significant, the correlations were moderate in strength with the
exception of the non-significant relationship of NO2 and O3. The levels of PM10 were
positively correlated with NO (r=0.27), NO2 (r=0.62), SO2 (r=0.53) and O3 (r=0.35)
(Table 7). The levels of NO were positively correlated with NO2 (r=0.66) and SO2
(r=0.26). The correlation of NO and O3 was negative (r= -0.36). The correlation between
NO2 and SO2 (r=0.47) is positive. SO2 and O3 are positively correlated (r=0.07).

29

Table 7 - Five pollutants correlation at the Si-Tun station in 2007
PM10
NO
NO2
SO2
O3
PM10
1
0.27**
0.62**
0.53**
0.35**
NO
1
0.66**
0.26**
-0.36**
NO2
1
0.51**
0.06
SO2
1
0.07**
** Correlation is significant at the 0.01 level (2-tailed).

The air pollutant levels between Chung-Ming and Si-Tun stations showed significant
positive correlations (Table 8). PM10 levels (r=0.63) between the Chung-Ming and SiTun stations are more strongly correlated than O3(r=0.48), NO(r=0.39), NO2(r=0.43) and
SO2 (r=0.26).
Table 8 - Pearson correlation coefficients between
Chung-Ming station and Si-Tun station
PM10
0.63**
O3
0.48**
SO2
0.43**
NO
0.39**
NO2
0.43**
** Correlation is significant at the 0.01 level (2-tailed).
5.2 Comparisons for PM10 Concentration between Seasons and Air Monitoring
Stations
One of the difficulties with describing the air pollution in any location is the accurate
representation of the peaks of air pollution and the number of times that the levels exceed
air quality standards. This information may be lost if levels are averaged over the entire
year and it is not easily seen if only the average levels of each pollutant are reported. I
analyzed the air monitoring data on PM10 and I compared the levels to the EPA PM10 24
hour (24-h) standard. Besides the data of Chung-Ming station and Si-Tun station, I added
the data from Da-Li station as a control. Da-Li station is located in a rural area in the
border of Taichung City and is not close to Chung-Ming and Si-Tun stations. I found that
the three air monitoring stations had different situations with regards to the incidence of

30

daily PM10 level being above the 24-h standard (125μg/m3). The data from the Da-Li
station show that the number of days when PM10 levels were above the PM10 standard
decreased from 1994 to 2002, started to increase from 2003 to 2005 and after 2005 the
days decreased again to 2007 (Figure 20). The annual average concentration also had the
same trend.

Annual average PM10 Concentration

50

90
80
70
60
50
40
30
20
10
0

Days

40
30
20
10
0

PM10 Concentration
(μg/m3)

Days above PM10 Standard

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 20 - 1994-2007 Da-Li station PM10 concentration
Figure 21 shows that the annual average PM10 value in Chung-Ming station ranged
between 60μg/m3 and 80μg/m3. Before 2000 Chung-Ming station recorded more days
with PM10 levels above the 24-h standard (125μg/m3). After 2000 the days above
standard decreased with the exception of the year 2004. This year follows the start of the
Science Park construction and may indicate a temporary increase in pollution that didn’t
continue after completion of construction.
Days above PM10 standard

annual average PM10 concentration
90
80

Days

40

70
60

30

50
40

20

30
20

10

10
0

PM Concentration (μg/m3)

50

0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

31

Figure 21 - 1994-2007 Chung-Ming station PM10 concentration
In contrast, the records from Si-Tun station show fewer days of PM10 above standard
before 2000, but more days in 2000. After 2000, the days above PM10 standard decreased
until 2004 and increased in 2005 (Figure 22). Just as with the Da-Li station, after 2005
the days exceeding the 24-h standard decreased again to 2007. The annual average PM10
concentration was between 55μg/m3 and 75μg/m3 from 1994 to 2007. Pollution recorded
in 2004 at both stations was higher although it was also high in 2005 at the Si-Tun
station. It appears that each station is influenced by different sources of PM10. The reason
for the high PM10 levels recorded at the Si-Tun station in 2000 is not clear.
Days above PM10 standard

Annual average PM10 concentration

50

90

Days

70
60

30

50
40

20

30
20

10

PM10 Concentration
(μg/m3)

80
40

10
0

0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 22 - 1994-2007 Si-Tun station PM10 concentration
I used one-way ANOVA to analyze the average PM10 among three air monitoring
stations from 1994 to 2007. To satisfy the requirement of normal distribution of the data
before using ANOVA, I had to transform PM10 data into ln(PM10) to meet the normality
criterion. The homogeneity of variances test show unequal variance (p=0.005). Post-hoc
test under unequal variance showed the average PM10 measured in Dali was significantly
higher from that in Chung-Ming (p<0.000) and in Si-Tun (p<0.000).
Air pollution is likely affected by the seasonal weather patterns. Besides looking at
annual average of PM10, I integrated the daily PM10 into four seasons and considered the
trend of seasonal PM10 average. March, April and May are determined as spring. June,
32

July and August are determined as summer. September, October and November are
determined as fall. December, January and February are determined as winter. Figure 23,
Figure 24 and Figure 25 show that the average PM10 levels appear to be lower in the
summer compared to spring, fall and winter according to measurements from all three air
monitoring stations.

Spring

Summer

Fall

Winter

PM10 value (μg/m3)

100
90
80
70
60
50
40
30
20
10
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 23 - Seasonal PM10 levels in Da-Li station

Spring

Summer

Fall

Winter

PM10 level (μg/m3)

100
90
80
70
60
50
40
30
20
10
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 24 - Seasonal PM10 levels in Chung-Ming station

33

Spring

Summer

Fall

Winter

PM10 level (μg/m3)

100
90
80
70
60
50
40
30
20
10
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 25 - Seasonal PM10 levels in Si-Tun station
The average PM10 values in spring for all three stations ranged between 59.4μg/m3 and
99μg/m3 (Figure 26); in summer between 32.3μg/m3 and 66.8μg/m3 (Figure 27); in fall
between 49μg/m3 and 94.8μg/m3 (Figure 28) and in winter between 60μg/m3 and
91.7μg/m3 (Figure 29). Among all three of the air monitoring stations, the seasonal
average PM10 levels indicated that summers had consistently lower PM10 levels.

Chung-Ming

Si-Tun

Da-Li

PM10 level (μg/m3)

100
80
60
40
20
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 26 - Spring PM10 levels in three air monitoring stations

34

Chung-Ming

Si-Tun

Da-Li

PM10 level (μg/m3)

100
80
60
40
20
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 27 - Summer PM10 levels in three air monitoring stations

Chung-Ming

Si-Tun

Da-Li

PM10 level (μg/m3)

100
80
60
40
20
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 28 - Fall PM10 levels in three air monitoring stations

35

Chung-Ming

Si-Tun

Da-Li

PM10 level (μg/m3)

100
80
60
40
20
0
1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Year

Figure 29 - Winter PM10 levels in three air monitoring stations
While the average PM10 levels present a summarized picture of this air pollutant, a more
complete description must include the full range of PM10 measurements. Figure 30 shows
the frequencies of a range of PM10 levels between seasons and air monitoring stations. It
appears that the PM10 levels present a moderate right skew and similar distribution
among spring, fall and winter in the period from 1994 to 2007, but a more pronounced
skewed distribution to the right in the summers.
Also, the range of PM10 distribution was from 0μg/m3 to 400μg/m3. This indicates that
residents had higher exposure on those high PM10 level days, substantially higher than the
standard.

36

Figure 30 - PM10 Frequency between seasons and air monitoring stations
5.3 Comparison for PM10 Concentrations before and after the CTSP
The construction of the CTSP has attracted residents’ attention and raised their concerns
in terms of increasing air pollution in the surrounding area. I conducted t-test to see if
there is a statistically significant difference on PM10 between before and after the Central
Taiwan Science Park was built. I employed monitoring data on daily PM10 levels for the
twenty-four months before and twenty-four months after the CTSP construction began.
The data were obtained from the Si-Tun station because this is the closest to the location
of the CTSP and reflects the air quality of that area.
Since the CTSP construction began in July 2003, the daily PM10 value between July 2001
and June 2003 will be used as a variable for before the CTSP was built. The daily PM10
value between July 2003 and June 2005 will be used as variable for during and shortly
after the CTSP construction. The hypothesis is that daily PM10 levels have had a
significant increase after the CTSP construction began. After removing the null data, I
had 696 PM10 daily observations before the CTSP construction started and 725 PM10
daily observations during and after the CTSP construction phase (Table 9). The t-test for

37

two sample means was preformed. The average PM10 level before the CTSP built was
63.42μg/m3 with standard deviation 33.84 μg/m3. The average PM10 level after CTSP
built was 68.16μg/m3 with a Standard deviation 37.09μg/m3. The result suggested that
PM10 levels are significantly higher after the CTSP was built (t= -2.52, p=0.012,
CI=(-1.04, -8.44)).
Table 9 - Descriptive Statistics about PM10 before and after CTSP
N Mean Std.Dev
Std. error
PM10 (μg/m3) Before CTSP 696 63.42
33.84
1.28
After CTSP
725 68.16
37.09
1.38
Since it appears that the PM10 levels were higher on average following CTSP
construction and that health effects are more pronounced on days of peak pollution when
the standard is exceeded, I used t-test method to see if there was a difference in the
number of days exceeding the standard before and after the CTSP. I organized the data as
two groups. One group included the number of days exceeding the standard in each
month for 24 months before CTSP was built (total of 43 days). The other group included
the number of days exceeding the standard in each month for 24 months after CTSP was
built (total of 52 days). Then a two samples t-test method was conducted. The result
revealed that there was no statistically significant difference in the number of days
exceeding the PM10 standard between before and after CTSP was built (p=0.65).
5.4 Multiple Regression Model for PM10
Many studies indicated that temperature, population, precipitation and humidity could be
factors that affect ambient PM10 levels. In this study, Multiple Linear Regression (MLR)
will be preformed to analyze the relationship between PM10 levels and temperature,
population, precipitation and humidity in Taichung City from 1994 to 2007. Table 10
presents the descriptive statistics for temperature, population, precipitation, humidity,
PM10.

38

Table 10 - Descriptive Statistics of PM10 parameter in the multiple regression
model
N
Min.
Max.
Mean
Std.
Annual average temperature (F)
14
73.2
75.7
74.49
0.66
Annual average humidity (%)
14
72.4
77.1
74.7
1.66
Annual average rainfall (mm)
14
930.6
2574.5 1859.13 474.92
Population (persons)
14 832654 1055898 959466.7 73320.5
Annual average PM10 level (μg/m3) 14
60.1
74.3
66.3
4.716

Temperature, population, precipitation and humidity are the independent variables, and
PM10 is the dependent variable. In order to conduct multiple regression method, all of the
variables should be quantitative variables and data should satisfy all of assumptions,
which are linearity, normality and homoscedasticity. From examination of the Residuals
Plots, it is indicated that the assumptions of linearity, normality and homoscedasticity are
satisfied. Also, in the scatter plot matrix of each independent variable with the dependent
variable, linearity and normality are confirmed.
There is no missing data and no outliers. The χ2 critical value is 18.47 (df=4) at α=0.001.
All of the cases with MAH_1 are less than 18.47. Thus, we do not need to eliminate any
case.
After all assumptions are fulfilled, I conduct a multiple linear regression to investigate if I
can use temperature, population, precipitation and humidity to predict PM10 in Taiwan
City. The model shows a statistically significant prediction of PM10 in Taiwan City at the
5% significance level (R2=0.641, F (4,9) = 4.022, p=0.039).
In the coefficients table, each parameter is tested for the hypothesis that it has no
relationship with the PM10 levels or that its coefficient is equal to zero. The t-values and
p-values provide the results of testing the null hypothesis for each parameter. Because
none of the p-values is <.05, we can not rejected the null hypothesis that they are equal to
zero and therefore the coefficients do not provide any evidence that there is linear
relationship between each independent variable and PM10. In other words, the

39

coefficients for temperature (-4.622), humidity (-0.011), precipitation (0.001) and
population (-1.92x10-5) are not significantly different from 0 (p>0.001). Even though the
overall model is valid and it is statistically significant for the set of parameters, each
parameter is not

significantly associated with PM10 levels. This model accounts for

64.1% of variance on the PM10. Tolerance for all variables is greater than 0.1, so
multicollinearity is not a problem in this study.
The regression equation in natural units is
PM10  428.84  4.62Temperature  0.01Humidity  0.001Percipitation  1.92  105 Population

Indeed, the interpretation of the coefficients would not describe the relationships in a
meaningful way. The coefficient of temperature is -4.622. So, for every increase of one
unit on temperature, the PM10 is predicted to be lower by 4.622. The coefficient of
humidity is -0.011. So, for every increase of one unit on humidity, PM10 is predicted to be
lower by 0.011. The coefficient of precipitation is 0.001. So, for every increase of one
unit on precipitation, PM10 is predicted to be higher by 0.001. The coefficient of
population is -1.92x10-5. So, for every increase of one unit on population, PM10 is
predicted to be lower by 1.92x10-5.

This indicates that these parameters are not enough by themselves to predict PM10, as
expected, and that other important factors that affect PM10 levels, such as sources of PM10
must be included. Unfortunately, no related data were available. However, there is some
correlation between the parameters used and PM10.
The correlation coefficient of annual average temperature with annual PM10 level is 0.756, indicating a strong correlation. The correlation coefficients of annual average
humidity, annual average precipitation and population with annual PM10 level are 0.075,

40

0.018 and -0.515, respectively. This indicates that annual average humidity, annual
average precipitation and population have little correlation with annual PM10 level.
5.5 Public Health - Current Situation in Taichung City
Figure 31 presents the top 12 diseases in Taichung City that have high mortality.
Approximately 131.96 deaths per 100,000 persons were cancer patients, 36.93 deaths per
100,000 persons were cerebrovascular disease patients, 35.55 deaths per 100,000 persons
were heart disease patients, and 4.37 deaths per 100,000 persons were related to
bronchitis, chronic and unspecified, emphysema and asthma.
Bronchitis, chronic and unspecified,
emphysema and asthma

4.37

Hypertensive disease

6.98

Suicide

10.49

Sepsis

4.66

Pneumonia

14.44

Chronic liver disease and cirrhosis

14.71

Nephrits, nephrotic syndrome, and nephrosis

18.54

Diabetes mellitus

28.53

Injury and poisoning

23.66

Heart disease

35.55

Cerebrovascular disease

36.93

Malignant neoplasm

131.96
0

20

40

60

80

100

120

140

Figure 31 - Top 12 mortality rates of diseases (per 100,000 persons) in Taichung City
2001-2007
From 2001 to 2007, cancer prevalence rate was highest among the morality rates of
diseases. After stratifying the data, Figure 32 shows lung cancer had highest death rate in
the malignant neoplasm category, followed closely by liver cancer. Figure 33 shows that
lung cancer death rate slightly increases from 2001 to 2007.

41

25.0

24.1

24.0

20.0
12.9
8.7

7.5

6.9

6.5

Cervix uteri
cancer

10.0

Prostate
cancer

13.4

15.0

4.6

5.0

4.4

Pancreas
cancer

Oesophagus
cancer

Stomach
cancer

Oral cavity
cancer

0.0
Trachea,
bronchus,
and lung
Liver and
intrahepatic
bile ducts
Female
breast
cancer
Colon and
rectum
cancer

Death Rate per 100,000 persons

30.0

Malignant neoplasms

Figure 32 - Average death rate of cancer in Taichung City 2001-2007
30

per 100,000 people

25

25.05

23.34

23.03
20

24.1

26

26.3

20.93

15
10
5
0
2001

2002

2003

2004

2005

2006

2007

Year

Figure 33 - Lung cancer death rates in Taichung City 2001-2007
As shown in the Figure 34, cerebrovascular disease declined from 2001 to 2004 and
increased slightly from 2004 to 2007. The mortality rate of heart disease also showed an
overall increasing trend with more variation. The mortality rate of the category including
bronchitis, chronic and unspecified, emphysema and asthma was observed much less than
the other two diseases associated with PM10.

42

Cerebrovascular disease
Heart disease

Mortality rate (per 100,000
persons)

Bronchitis, chronic and unspecified, emphysema and asthma
45
40
35
30
25
20
15
10
5
0
2001

2002

2003

2004

2005

2006

2007

Year

Figure 34 - Mortality rate of disease associated with PM10

43

Chapter 6
Discussion and Conclusion
6.1 Review of Research Findings
6.1.1 Interpretation for the Results
There are many research studies on air pollution and its associated health effects over the
world. Epidemiology-based health outcomes were used to quantify the effect of air
pollution (Kunzli et al., 2000). However, it is difficult to measure precisely how air
pollution impacts human health. Even if we measure the pollutants concentration in a
certain area, many uncertainties still exist regarding the health effects impact for the
residents. Air pollution existing in the air could be transported anywhere, depending on
the wind direction. This study used two air monitoring stations in Taichung City to
address the air pollution levels in the area and one air monitoring station in the Taichung
County as a control. The results suggested the levels of pollutants in both Chung-Ming
and Si-Tun were positively correlated and statistically significant. It shows air pollution
levels in both Chung-Ming and Si-Tun were consistent.
The associations of particulates with daily mortality have been reported at lower
concentration in locations which were humid and had high air pollution in cold weather
(J. Schneider & A. Marcus, 1990). The extreme temperature is associated with
cardiovascular mobility/mortality (Rupa Basu & Jonathan M. Samet, 2002). I found PM10
concentration in the cold weather appeared higher PM10 concentration in Taichung City.
Wong et al. (T W Wong, W S Tam, T S Yu, & A H Wong, 2002) found that several
pollutants had a statistically significant impact on daily mortality in the cool weather.
Liang et al. (2009) had found the evidence of an association between PM10 and mortality
from respiratory and cardiovascular diseases, especially among elderly people during the
winter. However, there are many studies have addressed that temperature might be the
confounder (Klea Katsouyanni, et al., 2001) or modify (Cizao Ren, et al., 2006; M.
Stafoggia, J. Schwartz, F. Forastiere, C. A. Perucci, & Group, 2008) the association
44

between PM10 and mortality. Other studies however, found little evidence for an
interaction between ambient particulate matter and temperature on mortality (Samet,
1998; Roberts, 2004). Data on the health outcomes associated with ambient air pollutants
were not available for this study. The relationship between PM10 and mortality in this
study has not been addressed. However, based on the literature, the PM10 levels in
Taichung City are higher than other cities and could be a factor for increasing respiratory
diseases and heart diseases.
Five air pollutants in both Chung-Ming and Si-Tun have a strong positive correlation.
The CTSP seems to play a role for increasing PM10 levels in Taichung City. The CTSP
construction started in July 2003. I examined the levels of PM10 over time and I showed
that in 2004 both Chung-Ming and Si-Tun stations recorded more days with PM10 levels
above the standard. Si-Tun station had higher daily PM10 concentration than Chung-Ming
station. This may be explained by the fact that Si-Tun station is located in the same
district as the CTSP and had more impact from the CTSP during the time of construction.
Although the multiple linear regression model significantly predict PM10 with population,
annual average temperature, annual average humidity and annual average precipitation
(R2=0.641, F (4,9) = 4.022, p=0.039), the coefficients of factors are too small to explain
the relationship. This lack of strength can be explained because I used annual average
value of temperature, humidity, precipitation and PM10 value not daily values.
Even before the CTSP was established, a coal-fired power plant in Taichung City has
been running since 1998. This might explain the reason why Taichung City has high
PM10 levels. More energy demand might happen because of the CTSP operation and
development. Ambient air quality regulation should follow to address this issue.
6.1.2 Comparing Results in term of Health Effects in other Countries

45

Although this research did not study the association of PM10 levels and health effects
directly, the past 10-20 years research have confirmed that ambient air pollution
contributes to mobility and mortality (R. Wilson & J. Spengler, 1996).
Health effects from PM exposure have been widely discussed. Health effects can be
defined into two categories – those that result from acute exposure and those that are
related to chronic exposure. The health effects from acute exposure include mortality,
hospitalization, increased respiratory symptoms, decreased lung function, heart rate
variability, and pulmonary inflammation. Chronic PM exposure includes increased
mortality rates, reduced survival times, chronic cardiopulmonary disease, and reduced
lung function. Many studies had shown elderly, infants, children and persons with
chronic cardiopulmonary or respiratory diseases have more risk under particulate matter
exposure (Chun-Yuh Yang, Hui-Ju Hsieh, Sang-Shyue Tsai, Trong-Neng Wu, & Hui-Fen
Chiu, 2006; D E Abbey, B L Hwang, R L Burchette, T Vancuren, & P K Mills, 1995; T J
Woodruff, J Grillo, & K C Schoendorf, 1997).
The annual average PM10 level in two air monitoring stations in 2007 were 58.12μg/m3
and 59.7μg/m3, respectively, in Taichung City. Compared with European cities (Figure
39), residents in Taichung City might have higher risk on respiratory diseases and heart
disease. Pope et al. (C. Arden Pope III, 2007) reviewed selected studies of short-time
exposure and reported estimation of percent increase in mortality risk (Table 11).
Approximate 0.4% ~1.3% mortality risk increases per 20 μg/m3 PM10 exposure
increment. Schwartz et al. (1997) presented that the health effects from PM10 depends on
the amount of fine particles. Kuo et al. (Hsien W. Kuo, Jim S. Lai, Mon C. Lee, Ru C.
Tai, & Ming C. Lee, 2002) reported the prevalence rates of asthma were correlated
significantly with NO2 (r=0.63) and O3 (r=0.51) concentrations. He also reported the
levels of NO2 and PM10 were correlated significantly with monthly hospital admissions.

46

Table 11 - Estimates of percent increase (95% confidence intervals) in
mortality risk across selected studies of short-term exposure (Pope III, 2007)
(partial)
Short-time exposure

Study area and type

Primary sources

Meta-estimate from
Single-city studies
U.S. 10-cities

Anderson et al. (2005)

U.S. 14-cities
case-crossover
NMMAPS
20-100 U.S. cities
APHEA-2
15-29 European cities

Exposure
increment
20μg/m3 PM10

Percent increases
in mortality risk
(95%)
All causes
1.2 (1.0, 1.4)

Schwartz
(2000c,2003b)
Schwartz (2004)

20μg/m3 PM10

1.3 (1.0, 1.6)

20μg/m3 PM10

0.7 (0.4, 1.0)

Dominici et al. (2003a)

20μg/m3 PM10

0.4 (0.2, 0.8)

Katsouyanni et al. (2003)
Analisis et al. (2006)

20μg/m3 PM10

1.2 (0.8, 1.4)

Guo et al (1999) and Hwang et al. (B-F Hwang, et al., 2005) both found an association
between traffic related air pollution concentrations and the risk of asthma in school
children. The long term exposure to traffic-related out door air pollutants, such as NOx,
CO and O3, increases the risk of asthma in children were confirmed (Hwang et al., 2005).
Bates et al. (DV Bates, M. Baker-Anderson, & R Sizto, 1990) also found the correlation
between emergency visits caused by asthmatic symptoms and PM10 concentrations. The
hours between 6:30am-8:30am and 5:00pm-7:00pm are traffic peak periods in Taichung
City. Many people travel to work or school with motor scooters during traffic peak
periods. The scenario of long time exposure should be of high concern.
Brunekreef and Forsberg reviewed published time series studies about the association
between fine and coarse particles and hospital admissions for respiratory problems and
indicated many studies had found hospital admissions increase when particulate matter
level increases (Figure 33). The average of respiratory admissions is approximate 5.1%
increase per 10μg/m3. He also indicated chronic obstructive pulmonary disease (COPD)
admissions (Figure 34) and cardiovascular admissions (Figure 35) increase when
particulate matter is increasing. The average of CODP admissions and cardiovascular
admissions are approximate 7.1% and 2.5% increase per 10μg/m3, respectively.

47

According to the population of Taichung City, with 10 μg/m3 PM10 increase,
approximately 54,409 residents will be admitted in the hospital because of respiratory
problems, approximately 74,679 residents will be admitted in the hospital because of
CODP problems, and approximately 26,671 residents will be admitted in the hospital
because of cardiovascular problems. Schwarts et al. (1993) found the daily emergency
visits for people under age 65 years were associated significantly with PM10 exposure on
the previous day. In this study I found that PM10 level increased after CTSP was built
(t=-2.52, p=0.012, CI=(-1.04, -8.44)). The results indicate residents in the Taichung City
exposure higher PM10 level since the CTSP started construction.

Figure 35 - Effects of fine (•) and coarse (◦) particles on respiratory admissions in
published time series studies (Brunekreef and Forsberg, 2005).

48

Figure 36 - Effects of fine (•) and coarse (◦) particles on chronic obstructive
pulmonary disease (COPD) admissions in published time series studies
(Brunekreef and Forsberg, 2005).

Figure 37 - Effects of fine (•) and coarse (◦) particles on cardiovascular
admissions in published time series studies. CVD: cardiovascular disease; HF:
heart failure; IHD: ischaemic heart disease (Brunekreef and Forsberg, 2005).

49

The correlation between PM10 exposure and increasing risk of lung cancer has been
observed (Pope et al., (C.A.r. Pope & D.W. Dockery, 2006). Y. Sanchez-Perez et al.
(Yesennia Sanchez-Perez, et al., 2009) also suggested that DNA damage could be the
way by which particulate matter exposure increases the risk of lung cancer. Lung cancer
has been the top one or two cancers in Taichung City last decade. The average death rate
of lung cancer was 24.1 per 100,000 people during 2001-2007. The time trend showed
the prevalence of lung cancer in Taichung has been increasing (Figure 32). The death rate
of lung cancer in 2007 was 26.3 per 100,000 people. In the Washington State and US
nation, the death rate of lung and bronchus cancer were 51.5 and 52.8 per 100,000
people, respectively (Figure 38). It seems like more research are needed to investigate the
association between PM10 exposure and lung cancer risk. The scenario is still unclear,
especially since smoking is a critical and significant confounding factor for lung cancer.
The prevalence of smoking in Taichung that may be responsible for lung cancer has also
been increasing.

Figure 38 - Age-Adjusted Cancer Death Rates for the 10 Primary Sites with the Highest
Rates within State- and Sex-Specific Categories (United States Cancer Statistics, CDC)

50

Daniels et al. (Michael J. Daniels, Francesca Dominici, Jonathan M. Samet, & Scott L.
Zeger, 2000) reported PM10 and cardiorespiratory deaths numbers in 20 largest cities in
the US (Figure 39). It showed the PM10 concentrations in most of cities were between
20μg/m3 and 40μg/m3. The PM10 concentrations in big cities range from 3.6 μg/m3 to 46
μg/m3, in San Diego and Los Angeles respectively. Compared to the PM10 concentration
in Taichung was 66.3μg/m3, there is reason for concern for higher cardiorespiratory
disease risk in Taiwan. However, from the figure it seems that there is no good
relationship between deaths and PM10 levels. Specifically, with the exception of NYC,
LA and Chicago all other cities seem to have low death rates despite differences in PM10
levels, so it is clear that other factors contribute too.

Los Angeles

New York

Chicago

Dallas-Ft.Worth

Houston

San Diego

Santa Ana-Anaheim

Phoenix

Detroit

Miami

Philadelphia

Minneapolis

Seattle

San Jose

Cleveland

San Bernadino

Pittsburgh

Oakland

Atlanta

San Antonio

Cardiorespiratory deaths (no.)

120
100
80
60
40
20
0
0

10

20

30

40

50

PM10(μg/m3)

Figure 39 - PM10 and Cardiorespiratory deaths no. in 20 largest US cities, 1987–
1994 (Daniels et al., 2000)
6.2 Limitations of the Study
The first limitation concerns health effects associated with air pollution in this study. Due
to lack of accurate health data for Taichung city specifically, I cannot illustrate the
relationship between PM10 and its associated diseases directly for Taichung City. The
second limitation is that there is no access to the data of daily temperature, precipitation
and humidity to properly conduct multiple linear regression method. Using average
51

values increased uncertainty because the daily levels cannot be parsed out. The third
limitation is that I did not have well-designed cohort study. I used existing air monitoring
data from Taiwan EPA and the mortality rates of air pollution associated diseases to
estimate the health outcome. I also extrapolated the health effects based on previous
epidemiological research results conducted in western countries, which may involve
other confounding factors such as climate differences. Thus, it is important to emphasize
that methodological problems in the research design limit its interpretations.
Although the present study cannot show a strong association between air pollution and
health effects, it does seem to demonstrate that high air pollution level in Taichung city is
very likely to cause respiratory and cardiovascular health effects, based on the overall
weight of evidence from published studies.
6.3 Recommendations for the Future Research
Park et al. (Eun-Jung Park, Dae-Seon Kim, & Kwangsik Park, 2008) found heavy metals
detected in PM2.5 as well as in PM10 in Seoul, Korea. With high PM10 values, the
concentration of heavy metals in PM10 should be also addressed in Taichung City. Future
work will hopefully clarify the concern of PM10 with heavy metals in Taichung City.
For the purpose of economic growth, the Taiwan government has opened many
opportunities to import goods via ships and airplanes. This may in turn increase the heavy
load of truck traffic in Taichung City. Many products come from other countries could
arrive in Taichung Port and transport to other locations by trucks. Increased truck traffic
might become a driver for future increase in PM10 levels. Many Cities, such as Los
Angeles and Seattle have studies on the relationship on port and air pollution. Air
monitoring stations in Los Angeles port detected annual average PM10 levels in 2005,
2006 and 2007 were 27.1 μg/m3, 27.8μg/m3 and 29.3μg/m3, respectively
(http://www.portoflosangeles.org/environment/air_quality.asp). The annual average PM10
levels in Si-Tun station in 2005, 2006 and 2007 were 71.70μg/m3, 60.44μg/m3 and
59.7μg/m3, respectively. It appears that the annual average PM10 level in Taichung City is

52

more than twice of annual average PM10 levels in Los Angeles. Therefore health effects
related to air pollution may be expected to be double than those in LA. More research
should be conduct to address the issue of Taichung Port and air quality and its health
effect as well.
Recent years, many researches have raised the attention on the toxicity of O3. The longer
time or the higher O3 concentration that people are exposed to, the more impact on
human health it has. This study found O3 is the only pollutant which is increasing over
time in Taichung city. It is important to study what the driver is for increasing O3 levels
and find a way to reduce its levels. O3 is a photochemical pollutant driven by automobile
exhaust hydrocarbons and NO2 in the presence of sunlight. Therefore increased traffic
can be reasonably expected to lead to increased O3 levels. Asthma prevalence rates are
increasing in Taiwan because there is increased exposure to ambient air pollution,
harmful indoor sources of allergens and exposure to passive smoking. A positive
association between the risk of childhood asthma and exposure to O3 has been identified
(David B Peden, 2002). The prevalence of childhood asthma has been associated with O3
(Hwang et al., 2009). Since O3 levels have been increasing over time in Taichung City,
more studies are needed to address this public health concern.
6.4 Conclusions
This study was designed to investigate the status of air pollution in Taichung City. The
results showed air pollutants (PM10, NO, NO2 and SO2) were decreasing over years
except O3. Two air monitoring stations in Taichung City showed similar pollution levels,
which indicated air pollutants levels in Taichung City were consistent. Although PM10 is
decreasing, its concentration is still much higher than western countries. The overall
pollution in Taichung City is higher than in other cities and should be improved to protect
public. healthSchindler et al. (Christian Schindler, et al., 2009) had found that reductions
in particle levels in Switzerland over the 11-year follow-up period had a beneficial effect
on respiratory symptoms among adults. The results imply that it is important to control
and reduce the emission of particles in Taichung City, Taiwan.

53

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