COPD and Urban Air Pollution

GIS Based Spatial Pattern and the Geostatistical Analysis of Izmir

Cigdem Tarhan

and Nur Sinem Ozcan

Department of Management Information Systems, Dokuz Eylul University, Buca, Izmir, Turkey

Department of City and Regional Planning, Dokuz Eylul University, Buca, Izmir, Turkey

Keywords: Air Pollution, COPD, Environmental Health, GIS based Spatial Pattern, Multivariate Regression.

Abstract: In Turkey, starting from the 1950’s air pollution has been increasing because of rapid population increase,

rapid urbanization and industrialization. These cause intense energy usage in settlement areas and this

brings some problems in environmental health. The aim of the study is the examination of whether there is

any statistical relationship between the level of air pollution with the number of COPD cases and incidence

between 2006 and 2010 in Izmir City Center. The study area has six districts of Izmir City Center: Konak,

Bornova, Buca, Karsiyaka, Cigli and Balcova. There are in total 89,776 COPD cases between 2006 and

2010. The spatial pattern of these cases is mapped via the GIS environment. Then, multivariate linear

regression analysis is performed in the study. Additionally, the questionnaire was realized with 25 COPD

inpatients in Dr.S.Seren Chest Diseases Hospital in Izmir in March - April 2014. The results show that there

is a significant and positive relationship between the level of air pollution (PM and SO

) and the number of

COPD cases and incidence. In general, the increasing of the level of air pollutant and population cause an

increase in the number of COPD cases and incidence. It is observed that the level of air quality in

Karsiyaka, Bornova and Konak districts is lower than and incidence rates are higher than the other case

districts. According to questionnaire results, there is a significant relationship between the inpatients’ age

and their period of smoking. Also, there are significant relationships among the diagnosis, sex, job, risk

factor, genetic predisposition, smoking habits, environment lived in and heating preferences.

1 INTRODUCTION

Environment is quite important for human health. It

has been stated that there have been various agents

which can affect human health either directly or

indirectly (The Ministry of Health of Turkey, 2008).

Conducted studies generally show inequalities in

health are emerging according to socioeconomic

status, education level, geographic location, gender,

ethnic groups and age groups. The World Health

Organization Regional Office for Europe (WHO/

EURO) has started a major new project known as

“Healthy Cities” at the local level to adopt a policy

leading to ‘Health for all’. Izmir Metropolitan

Municipality also applied to be a member of Turkish

Healthy Cities Association on Jun 02, 2006 by

Resolution of the City Council. Within the studies of

healthy cities and environmental impacts on health,

some diseases among populations become noticeable

such as chronic obstructive pulmonary disease

(COPD).

COPD is defined as a clinical condition

characterized by chronic obstructive disease and

progressed on the basis of chronic bronchitis and/or

emphysema (Koç et al., 2002). The cause of COPD

in the literature is classified as smoking, air

pollution, viral respiratory infections during

childhood, organic and inorganic particles around

people and inherited changes (URL1, 2014). In

epidemiological studies, it has been determined that

air pollution causes an increase in total mortality,

and morbidity of respiratory diseases in adults and

respiratory symptoms in children. Especially, it has

been proved that the intensity of air pollutants

(sulfur dioxide (SO

) and particulate matter (PM))

causes COPD cases to increase (Koç et al., 2002;

Chen et al., 2000; Zanobetti et al., 2000).

In Turkey, from the 1950’s to today air pollution

has been increasing because of rapid population

growth, rapid urbanization and industrialization

rates. The increasing use of fossil fuels, unplanned

urbanization, inappropriate and inadequate

223

Tarhan C. and Ozcan N..

COPD and Urban Air Pollution - GIS Based Spatial Pattern and the Geostatistical Analysis of Izmir.

DOI: 10.5220/0005160402230230

In Proceedings of the International Conference on Health Informatics (HEALTHINF-2015), pages 223-230

ISBN: 978-989-758-068-0

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

combustion techniques, lack of green areas and

increase of motor vehicles cause a significant

increase in urban air pollution.

Urban air pollution is an inevitable component of

modern life especially in urban areas. Therefore, it

exposes a contradiction between the biological and

economic requirements of human beings. More than

half of the total population in Turkey are living in

the settlement areas classified as “cities” that have a

population greater than 10,000 people. The level of

air pollutants in the atmosphere has been increasing

continuously because of dense population in these

settlement areas, in total comprising 1% of the land

area of the country, and increasing demand of

quality of life standards (Müezzinoğlu, 2000;

İncecik, 1994; Godish, 1997).

The aim of the study is the examination of

whether there is any statistical relationship between

the level of air pollution (PM and SO

) with the

number of COPD cases and incidence in the years

2006-2010 in Izmir City Center. The study area is

Konak, Bornova, Buca, Karsiyaka, Cigli and

Balcova districts of Izmir City Center. In 2009,

Konak district has been divided as Konak and

Karabaglar, also Karsiyaka district has been divided

as Karsiyaka and Bayrakli. COPD cases have been

recorded in the districts of Konak and Karsiyaka

until 2009 and have been started being recorded in

the districts of Konak, Karabaglar, Bayrakli and

Karsiyaka. In this study, for 2009 and 2010 COPD

cases, Karabaglar cases have been counted in Konak

district and also Bayrakli cases have been counted in

Karsiyaka district. There are total 89,776 COPD

cases between 2006 and 2010 (The Ministry of

Health, Izmir Provincial Directorate of Health,

Department of Statistics, The Distribution of Patient

and Deaths by Gender, 2006-2010 Years). SPSS

software is used to perform statistical analysis and

ArcGIS is used to realize spatial analysis. Spatial

patterns of these cases are mapped via a GIS

environment. Multivariate regression analysis is

performed in the study.

2 LITERATURE

Environmental health studies are related to the

effects of environmental factors, such as air

pollution, on human health and the effective health

policies to handle their effects (Maantay and

McLafferty, 2011).

In the literature, there exist a lot of studies thet

the relationship and either positive or negative

influence between air quality and respiratory system.

These studies are differentiated in terms of the type

of disease. Generally, respiratory system related

diseases studies are performed by Tagil and

Mentese, 2012; Cengiz et al., 2013; Unsal et al.,

1999; Zhang et al., 2013; Darçın, 2013; Jerrett et al.,

2009; Dockery et al., 1993 and Wong et al., 2001.

Besides, some studies are performed just using

COPD cases by Chen et al., 2000; Zanobetti et al.,

2000; Faustini et al., 2012; Lingdren et al., 2009;

and Cinarka et al., 2011.

A GIS is used as a common tool to be equipped

with an electronic environment which links the

exposure model with the demographic, migration

and health data of the exposed population. The

integration of the model in a GIS together with

individual data and information from routine health

statistics proved its usefulness in demarking the

exposed population (Poulstrup and Hansen, 2004).

GISs have been applied in assessment of

accessibility to opportunities such as education,

employment, goods and services, recreation and

health care services in urban environments. Related

researches have examined the relationship between

urban life and health levels. Additionally, GIS

makes it possible to combine survey based data on

COPD studies at the individual level with spatial

objective data of the urban environment (Marans and

Stimson, 2011).

In Turkey, despite the past and ongoing research

and studies on spatial distribution of COPD and

other diseases from the point of view of

epidemiology, the amount of research is quite

limited compared to studies in developed countries

(Schikowski et al., 2005; Nuvolone et al., 2011).

3 THE STUDY AREA AND DATA

The study area has six districts of Izmir City Center:

Konak, Bornova, Buca, Karsiyaka, Cigli and

Balcova. There are in total 89,776 COPD cases

between 2006 and 2010. In 2009, Konak district has

been divided as Konak and Karabaglar. Also

Karsiyaka district has been divided as Karsiyaka and

Bayrakli. COPD cases have been recorded in the

districts of Konak and Karsiyaka until 2009, and

have been started being recorded in the districts of

Konak, Karabaglar, Bayrakli and Karsiyaka. In this

study, for 2009 and 2010 COPD cases, Karabaglar

cases have been counted in Konak district and also

Bayrakli cases have been counted in Karsiyaka

district. The study area is represented in Figure 1,

the distribution of the number of COPD cases

between 2006 and 2010 at district level is shown in

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Table 1 and the spatial distribution of them

presented in Figure 3.

Figure 1: The Study Area (Turkey, Izmir, Districts of

Study Area) (Google Earth, 2014).

As seen in Table 1 and Figure 3, Konak, Buca,

Karsiyaka and Bornova districts have more intensive

COPD cases in 2006-2010 in Izmir Province. The

reason of this intensity can be explained by density

of population and motor vehicles in these districts,

also having more urban study areas than other

districts. Additionally, the main transportation axes

are in these districts; because of this, the level of air

quality is lower than the other districts.

In this study, the number of COPD cases are

related with the level of air quality. The level of air

quality is measured as air pollutants (PM and SO

)

and these measurements are shown in Table 2

(URL2, 2014). Additionally, the spatial distributions

of PM and SO

are presented in Figure 5 and Figure

6. In Table 2 and Figure 5, it is clearly observed that

the level of PM has the highest values in Karsiyaka,

Bornova, Konak and Balcova. As seen in Table 2

and Figure 6, the level of SO

has the highest values

in Konak, Karsiyaka and Bornova.

Table 1: Total population, COPD cases and COPD

incidence rate at district level (2006-2010).

Table 1 shows total population of the case districts

and COPD incidence rates. Additionally, Figure 2

and Figure 4 present these data spatially. As

presented in Table 1, the most intense districts are

Konak, Karsiyaka, Bornova and Buca in terms of

total population.

Also as shown in Table 1 and Figure 4, the

highest incidence rates are in Balcova, Buca,

Bornova and Karsiyaka. The difference between

total population and incidence rate is based on high

population density, but low COPD cases in districts.

The incidence rate is the number of new cases per

population at risk in a given time period, in this

study this rate represented per 100,000 persons. For

example, Konak has the most crowded district;

however its COPD incidence rate is lower than the

other districts in Izmir Province. Therefore, the

study considers both the number of COPD cases and

COPD incidence rates in statistical analysis.

Figure 2: The spatial distribution of population at district

level (2006-2010).

Figure 3: The spatial distribution of COPD cases at district

level (2006-2010).

Figure 4: The spatial distribution of COPD incidence at

district level (2006-2010).

2006 2007 2008 2009 2010

Population

470 645 470 211 392 631 402 453 412 275

Case

1 342 1 815 1 920 1 472 5 614

Inci de nce

285 386 489 366 1 362

Population

522 698 514 917 903 375 917 074 925 586

Case

3 874 4 003 2 478 2 095 6 746

Inci de nce

741 777 274 228 729

Population

376 189 393 934 404 472 412 639 419 693

Case

2 580 5 643 5 535 5 456 8 422

Inci de nce

686 1 432 1 368 1 322 2 007

Population

885 399 847 409 853 449 859 958 863 579

Case

307 2 194 3 587 4 268 12 527

Inci de nce

35 259 420 496 1 451

Population

75 497 74 837 76 219 77 915 77 767

Case

917 272 2 540 210 1 646

Inci de nce

1 215 363 3 333 270 2 117

Population

137 847 141 769 153 508 154 397 157 530

Case

235 298 52 44 1 684

Inci de nce

170 210 34 28 1 069

Bornova

Karsiyaka

Buca

Konak

Balcova

Cigli

COPDandUrbanAirPollution-GISBasedSpatialPatternandtheGeostatisticalAnalysisofIzmir

225

Figure 5: The spatial distribution of the level of PM at

district level (2006-2010).

Figure 6: The spatial distribution of the level of SO

district level (2006-2010).

Table 2: The levels of PM and SO

at district level (2006-

2010).

4 METHODOLOGY

Multivariate linear regression analysis is used in

order to measure the relationship between dependent

and independent variables. The number of COPD

cases and COPD incidence are dependent variables;

the level of air quality (PM and SO

) and total

population are independent variables in this study.

The independent variables are denoted as:

2006pm: PM value of 2006, 2007pm: PM value of

2007, 2008pm: PM value of 2008, 2009pm: PM

value of 2009, 2010pm: PM value of 2010, 2006so2:

value of 2006, 2007so2: SO

value of 2007,

2008so2: SO

value of 2008, 2009so2: SO

value of

2009, 2010so2: SO

value of 2010, 2006tn: Total

population of 2006, 2007tn: Total population of

2007, 2008tn: Total population of 2008, 2009tn

Total population of 2009 and denoted 2010tn: Total

population of 2010.

The dependent variables are denoted as:

2006t: The number of COPD cases (2006), 2007t:

The number of COPD cases (2007), 2008t: The

number of COPD cases (2008), 2009t: The number

of COPD cases (2009), 2010t: The number of COPD

cases (2010), i2006: COPD incidence rate (2006),

i2007: COPD incidence rate (2007), i2008: COPD

incidence rate (2008), i2009: COPD incidence rate

(2009), and i2010: COPD incidence rate (2010).

Table 3 presents descriptive statistics of the

number of COPD cases, PM values, SO

values,

total population and COPD incidence rate.

Table 3: The descriptive statistics of parameters (2006-

2010) (n=6).

The number of COPD cases vary between 235

2006 2007 2008 2009 2010

PM 61 66 26 45 51

15 12 11 15 15

PM 50 47 30 57 93

20 18 24 11 30

PM0 25117651

0 0 2116

PM407255436

37 33 41 26 11

PM 49 59 29 43 43

106201113

PM0 14126839

0031014

Bornova

Karsiyaka

Buca

Konak

Balcova

Cigli

Variable Minimum Maximum Mean

Standard

Deviation

2006t

235 3 874 1 542.50 1 426.868

2007t

272 5 643 2 370.83 2 117.215

2008t

52 5 535 2 685.33 1 817.575

2009t

44 5 456 2 257.50 2 191.245

2010t

1 646 12 527 6 106.50 4 163.903

2006pm

0 61 33.33 26.666

2007pm

14 72 47.17 23.250

2008pm

5 30 18.83 10.759

2009pm

43 76 57.17 12.891

2010pm

36 93 52.17 20.923

2006so2

0 37 13.67 13.952

2007so2

0 33 11.50 12.645

2008so2

2 41 16.83 14.770

2009so2

10 26 14.00 6.132

2010so2

6 30 14.83 8.085

2006tn

75 497 885 399 411 379 293 078.197

2007tn

74 837 847 409 407 179 279 446.619

2008tn

76 219 903 375 463 942 346 449.822

2009tn

77 915 917 074 470 739 350 179.356

2010tn

77 767 925 586 476 072 352 019.613

i2006

35 1 215 522.00 440.987

i2007

210 1 432 571.17 466.671

i2008

34 3 333 986.33 1 235.714

i2009

28 1 322 451.67 453.720

i2010

729 2 117 1 455.83 534.356

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and 3,874 people, with a mean of 1,543 in 2006,

between 272 and 5,643, with a mean of 2,371 in

2007, between 52 and 5,535 people, with a mean of

2,685 in 2008, between 44 and 5,456 people, with a

mean of 2,258 in 2009 and between 1,646 and

12,527 people, with a mean of 6,107 in 2010. As

seen in Table 3, it is clearly observed that there is a

continuous increase in the number of COPD cases

between 2006 and 2010.

PM values vary between 0 and 61 µg/m

, with a

mean of 33.33 µg/m

in 2006, between 14 and 72

µg/m

, with a mean of 47.17 µg/m

in 2007, between

5 and 30 µg/m

, with a mean of 18.83 µg/m

2008, between 43 and 76 µg/m

, with a mean of

57.17 µg/m

in 2009 and between 36 and 93 µg/m

with a mean of 52.17 µg/m

in 2010. As shown in

Table 3, although there is a slight decrease in PM

value in 2008, the PM value trend is increase

between 2006 and 2010.

values vary between 0 and 37 µg/m

, with a

mean of 13.67 µg/m

in 2006, between 0 and 33

µg/m

, with a mean of 11.50 µg/m

in 2007, between

2 and 41 µg/m

, with a mean of 16.83 µg/m

2008, between 10 and 26 µg/m

, with a mean of

14.00 µg/m

in 2009, and between 6 and 30 µg/m

with a mean of 14.83 µg/m

in 2010. As seen in

Table 3, although there is a slight increase in SO

value in 2008, generally any significant increase or

decrease SO

value is not observable between 2006

and 2010.

The total population of districts vary between

75,497 and 885,399 people, with a mean of 411,379

in 2006, between 74,837 and 847,409 people, with a

mean of 407,180 in 2007, between 76,219 and

903,375 people, with a mean of 463,942 in 2008,

between 77,915 and 917,074 people, with a mean of

470,739 in 2009, and between 77,767 and 925,586

people, with a mean of 476,072 in 2010. As seen in

Table 3, there is a continuous increase in the total

population between 2006 and 2010.

COPD incidence rates vary between 35 and

1,215 people, with a mean of 522 in 2006, between

210 and 1,432 people, with a mean of 571 in 2007,

between 34 and 3,333 people, with a mean of 986 in

2008, between 28 and 1,322 people, with a mean of

452 in 2009, and between 729 and 2,117 people,

with a mean of 1,456 in 2010. As shown in Table 3,

COPD incidence rate increases approximately three

times from 2006 to 2010.

In Table 4 and 5, according to the results

surveyed a total of 25 patients diagnosed with

COPD are almost all men, the average age is 65 and

mainly work in very risky occupations (steam

earnings, weavers, etc.). Inpatients reside mostly in

Konak and Karabağlar Districts and the average

residence period varies between 10 – 55 years. The

time since diagnosis of COPD ranges from 1 to 3

years. The average one person in each household

was found to consume a daily average of 1.5 packs

of cigarettes and inpatients have been active smokers

approximately 39 years. Residents in the districts

mainly prefer heating type of wood – coal and air

conditioning and the average residence time is 38

years. While travelling patients mainly prefer public

transport and they often live in an open environment

of green areas, not industrial areas.

Table 4: The descriptive statistics of the questionnaire

(n=25).

Table 5: The frequency distribution of the questionnaire.

The results of the survey show that there is a

significant and positive relationship between the

ages of inpatients diagnosed with COPD.

Additionally, there is a significant relationship

between the inpatients’ age and their period of

smoking. Also, there are significant relationships

Variables Minimum Maximum Mean

Standard

Deviation

Age

45 85 65.04 10.964

Sex

1 2 1.92 0.277

Residence Period (year)

3 85 37.96 25.538

Occupation Risk

1 2 1.32 0.476

Smoking Habit (package)

0 3 1.56 0.154

Smoking Person in Residence

0 10 1.28 2.151

Smoking Habit (year)

0 65 39.12 2.666

Heating System at house (Coal)

0 1 0.52 0.510

Heating System at house (Natural Gas)

0 1 0.08 0.277

Heating System at house (AC)

0 1 0.36 0.490

Heating System at environment (Coal)

0 1 0.52 0.510

Heating System at environment (Natural Gas)

0 1 0.12 0.332

Heating System at environment (AC)

0 1 0.28 0.458

Travelling Preferences

1 3 1.60 0.707

Recreation Area Existing

0 1 0.52 0.510

Industrial Area Existing

0 1 0.16 0.374

Variables Value Fre

uenc

Percent

(

)

Residence

(

district

)

Konak 7 28

Karaba

lar 7 28

Othe

1 4

Residence Period

ear

)

10 - 15 2 8

20 - 25 2 8

30 - 35 2 8

50 - 55 2 8

Othe

1 4

Occu

ation Risk

More Risk

17 68

Less Risk

8 32

Dia

nosis

ear

)

1 10 40

2 2 8

3 3 12

Othe

1 4

Smokin

Habit

ear

)

30 5 20

40 4 16

50 6 24

Othe

1 4

Travellin

Preferences

Public Trans

ort 13 52

Private Car 8 32

Othe

3 12

COPDandUrbanAirPollution-GISBasedSpatialPatternandtheGeostatisticalAnalysisofIzmir

227

among the diagnosis, sex, job, risk factor, genetic

predisposition, smoking habits, lived in environment

and heating preferences (natural gas) (p <0.05). On

the other hand, no significant relationship was

determined between the diagnosis of COPD and

preferred coal home and environment, smoking

period and residence period (p>0.05).

5 RESULTS AND DISCUSSION

Multivariate linear regression analysis is performed

in order to estimate the statistical relationship

between the independent (explanatory) and the

dependent variables.

The result of multivariate linear regression

analysis is interpreted on the basis of years. The

model summary, which is done by using the number

of COPD cases and independent variables (PM, SO

and total population) is presented in Table 6a-6b. In

Table 6a, the selected model explains about 63% of

the proportion of the variability in the dependent

variables in 2006 (R

=0.632); about 88% in 2007

=0.882); about 67% in 2008 (R

=0.670); about

85% in 2009 (R

=0.850); and about 99% in 2010

=0.988). In Table 6b, the relationship between

the dependent variable (the number of COPD cases)

and the independent variables (PM, SO

and total

population) is statistically significant at the 0.05

level (p<0.05). In other words, PM, SO

and total

population are found to be significant at the 0.05

level for the number of COPD cases.

In Table 8, according to the models’ parameter

estimations, the increasing trend of the level of air

quality (PM and SO

) and total population between

2007 and 2010 caused also an increase trend for the

number of COPD cases. On the other hand, in 2006,

it is observed that there exists a decrease for the

number of COPD cases. Similarly, the increasing

trend of the level of air quality (PM and SO

) and

total population between 2006 and 2009 caused also

an increase trend for COPD incidence rate.

Table 6a: Model summary I (2006 - 2010).

However, in 2010, it is observed that there exists a

decrease for COPD incidence rate.

Table 6b: Model summary II (2006 - 2010).

Table 7a: Model summary I (2006 - 2010).

Table 7b: Model summary II (2006 - 2010).

The estimated model suggests that an increase in the

level of urban air pollution (PM and SO

) and

Year R R

Adjuste d R

Square

Std. Error

of the

Estimate

2006

0.795 0.632* 0.263 0.920132

2007

0.939 0.882* 0.765 0.549404

2008

0.818 0.670* 0.34 0.995425

2009

0.922 0.850* 0.699 0.757914

2010

0.994 0.988* 0.976 0.148554

* The R

coefficient of determination is a statistical measure of

how well the regression line approximates the real data points.

Sum of

Squares

Mean

Square

FSig.

Regre ssi on

4.354 3 1.451 1.714 0.025

Resi du al

2.540 3 0.847

Total

6.894 6

Regre ssi on

6.789 3 2.263 7.497 0.050

Resi du al

0.906 3 0.302

Total

7.694 6

Regre ssi on

6.031 3 2.010 2.029 0.028

Resi du al

2.973 3 0.991

Total

9.003 6

Regre ssi on

9.738 3 3.246 5.651 0.050

Resi du al

1.723 3 0.574

Total

11.461 6

Regre ssi on

5.371 3 1.790 81.128 0.002

Resi du al

0.066 3 0.022

Total

5.437 6

Year / Model

2006

2007

2008

2009

2010

Year R R

usted R

Square

Std. Error

of the

Estimate

2006

0.914 0.836* 0.671 1.464.763

2007

0.966 0.934* 0.867 0.973653

2008

0.931 0.866* 0.732 1.411.254

2009

0.975 0.951* 0.902 0.778403

2010

0.932 0.868* 0.737 1.610.722

*TheR

coefficient of determination is a statistical measure of how wellthe regression line

approximates the real data points..

Sum of

Squares

Mean

Square

FSig.

Regression

32.737 3 10.912 5.086 0.025

Resi du al

6.437 3 2.146

Total

39.174 6

Regression

39.937 3 13.312 14.043 0.029

Resi du al

2.844 3 0.948

Total

42.781 6

Regression

38.672 3 12.891 6.472 0.008

Resi du al

5.975 3 1.992

Total

44.647 6

Regression

35.350 3 11.783 19.447 0.018

Resi du al

1.818 3 0.606

Total

37.168 6

Regression

51.370 3 17.123 6.600 0.008

Resi du al

7.783 3 2.594

Total

59.153 6

Year / Model

2006

2007

2008

2009

2010

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Table 8: Parameter Estimations (2006 - 2010).

population causes a decrease in number of COPD

cases and an increase in incidence in 2006. An

additional 1% population and amount of pollutants

will cause 2,072 less number of COPD cases and

6,113 people more incidences for 2006. The model

suggests that an increase in the level of urban air

pollution (PM and SO

) and population causes in

number of COPD cases and incidence in 2007. An

additional 1% population and amount of pollutants

will cause 1,706 more COPD cases and 5,066 more

incidences for 2007. The model suggests that an

increase in the level of urban air pollution (PM and

) and population causes an increase in number of

COPD cases and incidence in 2008. An additional

1% population and amount of pollutants will cause

1,922 more COPD cases and 6,759 more incidences

for 2008. An increase in the level of urban air

pollution (PM and SO

) and population causes an

increase in number of COPD cases and incidence in

2009. An additional 1% population and amount of

pollutants will cause 1,656 people more COPD cases

and 1,618 more incidence for 2009. An increase in

the level of urban air pollution (PM and SO

) and

population causes an increase in number of COPD

cases and a decrease in incidence in 2010. An

additional 1% population and amount of pollutants

will cause 1,115 more COPD cases and 9,334 less

incidence for 2010.

According to the findings of the analysis, the

increasing rate of total population and the decreasing

rate of air quality in the study area cause an

important increase for the number of COPD cases

and COPD incidence rate. In Karsiyaka, Konak and

Bornova Districts, the level of air pollution is lower

than the other districts because of the density of

population and motor vehicles in these districts, also

having more urban study areas than other districts

and the location of these districts according to the

main transportation axes.

In metropolitan cities, it is vitally important to

minimize the level of air pollutant in the atmosphere

for improving quality of life. Recently, several

precautions have been introduced for the

improvement of urban air quality such as the

dissemination of the usage of natural gas for

domestic heating, the emission controls for the

reduction of the level of air pollutants because of

dense motor vehicle in traffic and monitoring of coal

sales. Despite these precautions, the level of air

pollutants is still above the acceptable level

especially during the winter months.

There will be important steps in order to solve air

pollution problem especially in urban settlement

areas, such as the dissemination of renewable and

clean energy resources (natural gas, thermal energy,

etc.) in domestic heating and industrial processes,

more frequent controls for the measurements for air

quality in terms of spatial and time tables, improving

the control strategies, developing plan decisions in

consideration of air corridors, the dissemination of

open and green areas, encouragement of public

transportation and railway transportation type by

local governments and realizing studies to minimize

carbon emissions and to improve the air quality and

controlling over facilities of industrial areas.

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Variable

Es t i m a t e d

Unstandardized

Coefficients

tSig.

2006t

-207.2126 -0.984 0.025

2007t

170.5796 1.457 0.05

2008t

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i2010

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