Monitoring Spatial Patterns of Air Pollution in Karachi Metropolis

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MONITORING SPATIAL'P ATTERNS OF AIR POLLUTION IN KARACHI

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METROPOLIS: A GIS AND REMOTE SENSING PERSPECTIVE

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MUDASSAR HASSAN ARSALAN

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DEPARTMENT OF GEOGRAPHY UNIV·ERSITY OF KARACHI

KARACHI - P AKIST Ai"!

2002

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CERTIFICATE

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This is to certify that Mr. Mtidassar Hassan Arsalan, has 'well completed his dissertation

for the degree of Doctor of Philosophy, under my supervision on

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"Monitoring Spatial Patterns of Air Pollution in Karachi Metropolis: A GIS and

Remote Sensing Perspective."

The completed work is valuable and distinct for the disciplines of Geography and allied

Environmental Sciences. It is hoped that in future, more analogous multi-disciplinary

researches would be conducted on the cities of developing countries . .

I wish him success in his future endeavours.

) • -- . . r . Mn Kazw,lj

t Prof ... ~sotr

J)epartment or Geography . Olllvcraity or

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Dr. S. Jamil H. Kazmi

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DEDICATED To:

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ABST

The growing environmental degradation has exerted desperate burden on resources,

therefore, environmental monitoring has become imperative. There is a serious need to •

evaluate the quality regularly. Remote Sensing technology has been providing multi

dimensional information, which is utilized in lots of environmental investigations.

Geographical Infonnation Systems (GIS) have been accepted as a tumkey solution for the

complex world due to its magnanimous breath of functionalities and cost effectiveness.

Karachi is one of the worst effected cities of the world due to unchecked and still •

uncontrolled air pollution. Spatial variation within metropolis have been largely ignored

mainly due to less comprehension, under estimation of spatial techniques as well as

difficulties in collecting, processing and analysing the data at micro geographic scales.

The main goals of this study are to modulate the infom1ation pertains to air quality and its •

adverse effects on human health and find out their spatial pattems all over Karachi. The

research has covered different parameters: assessment of land cover / land use classes, .

human settlement growth, temporal traffic patterns, population distributions, current level

of air pollutants, health implications and public perceptions .

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The developed GIS evaluation combined the data sets, various analyses and the resultant

maps with the capability to integrate further parameters for future risk assessments.

Multi-criteria decision analysis was successfully employed. Micro-geographic appraisals .

of the metropolis were perf0l111ed by· considering 58 zones outlined by the local •

development authority. Each zonal assessment included area, popUlation density, •

distribution of land cover classes,. split of land use categories, frequency of airborne

diseases, their prevalence scenario and temporal variations in CO concentrations within

the zone. MUltiple regression models for predicting carbon monoxide (CO) enrichment at

the olden region of Karachi metropolis have been formulated in which traffic and land use

parameters act as independent variables.

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ACKNOWLEDGMENTS

It is my pleasure to present my deep gratitude to Dr. S. JamB H. Kazmi, for his energetic ,

valuable guidance in completion of this dissertation. Without his scientific zeal, ever

willing, and detemlined assistance, his kind solicitude for my tribulations and amiable

appreciations of my limitations, it \vould have never been viable to bring these pages to

the light of the day. I will remain grateful to him ever for his valuable guidance and

intellectual competence. My profound appreciations are to an important member of my

supervisor's research team, Engineer Mohammed Raza Mehdi for his continued help in

data collection, analysis and shaping up of this thesis.

I extend my continuing indebtedness to th~ Chairperson of the Department of Geography

University of Karachi, Professor Mrs. Birjies Talat. Her continuous instructions and •

support have been illuminable through out my studentship .

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Gratefully I acknowledge my personal obligation to Ms. Sheeba Afsar for her valuable

cooperation for shaping up of the dissertation. I extend my indebtednesses for my

colleagues and respectable educationalists Mrs. Khalida Mahmood, Dr. Farkhunda Burke, ,

Mrs. Azra Parveen Azad, Mr. S. Shahid Ali, Mr. Shamshad Akhtar, Department of

Geography; and Mr. Muhammad Shahid, Department of Social Work, provided their

support and assistance.

I will remain ever thankful to Dr. Badar Ghauri, Dr. Ishaq Mirza and Mr. Shafiq Ahmed,

SUPARCO; Prof. Dr. Waseem Akhtar and Prof. Engr. Zia ur Rehman, Department of

Environmental Engineering - NEDUET; Prof. Dr. Mohsin Raza, HEJ Institute; Prof. Dr.

Ahsanullah, Mr. Jamil Ahmed Khan, and Dr. Farooq Ahmed, Department of Geography,

. University of Karachi to provide their precious ideas and suggestions for the work

My thanks to Mr. Mohammad Taufiq and Mr. Mujahid Raza Hameedi, Blue Chip ,

International; Mr. Asif Kazmi and Tahir Munir, Population Census of Pakistan; Mr.

Akhtar, Statistical Division - JPMC; Mr. Muhammad Ayoub, Statistical Division

Karachi Civil Hospital; Mr. Shahab Afrooz Alvi, Master Plan Department - KDA;

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Engr. Hyder Ali, Traffic Engineering Bureau - KDA, Mr. Arif Mahmood, Meteorological

Department; Mr. Tahir Abbas and Ms. Durdana Rais, PCSIR; Mr. Ishtiaq Ali Mehkri and

Ms. Sabahat Sherwani, daily Dawn, to provide their indispensable resources and

information.

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With pleasure I acknowledge the debt of affection to my parents, sisters and brother, for

their various considerations and generous encouragement during the preparation of this

work.' ,

In the end it would not be justified if I do not acknowledge my friends Mr. Faraz Ahmed ,

Khan, Ms. Maria Qadri, Mr. Razzaq Ahmed, S. Saulat R. Zaidi, Mr. Hasan Askari, Mr. ,

M. Javed Mahmood, Mohammad Rashid, Mr. Ali Naqi and Mr. Masood Farooqi; my

supporting staff supporting staff especially, Mr. Arif Masieh, Mr. Murtaza Khan, Mr.

Murtaza Gabol, Mr. Tariq Najamuddin, Mr. Nasar Alam and Mr. Mohammad Atiq; and ,

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my students on their heartfelt cooperation.

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TABLE OF CONTENTS

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1.1 1.2 1.3 1.4 I .4. 1 1.4.2 1.4.3 1.4.4 1.4.5 1.5

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2. I 2. 1 . 1 2.1.2 2.1.2.1 2.1.2.2 2.1.2.3 2.1.2.4 2.1.2.5 2.1.2.6 2.2 2.2.1 2.2.2 2.2.2.1 2.2.2.2 2.3

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3.1 3.1.1 3.1.2 3.1.2.1

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ABSTRACTS

ACK1'IJOWLEDGEMENTS

INTRODUCTION

Background The Problem The Study •

Study Area Physiography Climate Socio - Economic Structure Urban Settings

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Historical Sprawl Significance of the Study

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REVIEW OF LITERATURE

Air Pollution: Effects, Sources and Spatial Disseminations Global and Regional Scenarios Biotic Upshots Sulphur Dioxide (S02) Nitrogen Oxides (NOx)

Carbon Monoxide (CO) Ozone (03) Suspended Particulate Matters (SPM) Lead (Pb)

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Development of Teclmologies Remote Sensing (RS) Geographic Information Systems (GIS)

• Air Pollution Dispersion Modelling Multi Criteria Evaluation Literature Synopsis

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METHODOLOGICAL CONSTITUTION

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Remote Sensing Techniques .. Data Acquisition . Satellite Image Processing Environmental Attenuation Corrections

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3.1.2.1.1 3.1.2.1.2 3.1.2.2 3.1.2.2.1 3.1.3 3.1.4 3.1.4.1 3.1.4.2 3.1.5 3.1.6 3.1.6.1 3.1.6.2 3.1.6.3 3.1.6.4 3.1.6.5 3.1.6.6 3.2 3.2.1 3.2.2 -3.2.3 3.2.3.1 3.2.3.2 3.2.3.2.1 3.2.4 3.2.5 3.3 3.3.1 3.3.1.1 3.3.1.2 3.3.1.3 3.3.1.4 3.3.2 3.3.3 3.3.3.1 3.3.3.2 3.3.3.2.1 3.3.3.3 3.3.3.3.1 3.3.3.4 3.3.3.5 3.3.3.6 3.3.3.6.1 3.3.3.6.2 3.3.3.6.3 3.3.3.6.4 3.3.3.7 3.4 3.5 3.5.1 3.6

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Compensation for Seasonal Differences Haze Compensation Instrumental Error Correction . Line Drop Noise Correction Image Geometric Correction Image Enhancement Direct Application Indirect Application Study Area Development (Mosaicking/Subsetting) Land Cover Classification Land Cover Classification Scheme Supervised Classification Method Mining of Training Sites Classification Algorithm Classification Result Editing and Aggregation Accuracy Assessment Ground Realities Traffic Flow Information Land Use Infonnation Air Pollution Concentrations S02. NOx, 0 3, and Particulate Matters Carbon Monoxide Levels Monitoring Sites Demographic Information Epidemiological Infonnation Geographic Information Systems Cartographic Techniques Base Map Development

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Mapping of Monitoring Stations Analysis Zones' Map Cartographic Layouts Development Database Integration Analyses

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Change Detection: Growth of Settlements Air Pollution Spatial Variations Development of Spatially Continuous Patterns Air Pollution Temporal Variations Statistical Deviations Road Density Index: Massiveness Measure Population Disfributions Epidemiological Investigations Morbidity Treated by Physicians Disease Grading by Professionals Morbidity and Mortality: Vital Indices Airborne Diseases Prevalence Public Perception Evaluation Zonal Appraisals Multi Criteria Evaluation of Risk Neighbourhoods Weights Reckoning

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Predictive ModelliIlg: Estimation of Carbon Monoxide (CO) •

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4.1

RESULTS AND DISCUSSION

4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.8.1 4.8.2 4.8.3 4.8.4 4.8.5

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4.10 4.10.1 4.10.2 4.10.3 4.10.4 4. 1 I 4.12 4.12.1 4.12.2 4.12.3 4.12.4 4.12.5 4.12.6 4.12.7 4.12.8 4.12.9

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4.12.10 4.12.11 4.12.12 4.12.13 4.12.14 4.12.15 4.12.16 4.12.17 4.12.18 4.12.19 4.12.20 4.12.21 4.12.22 4.12.23 4.l2.24 4.12.25

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Rectified Satellite Product Land Cover Change Detection: Settlem«nts Growth Land Use . Population Distribution Roads Network Widespread Assessment of Traffic Patterns Spatial Patterns of Air Pollutants

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Suspended Particulate Matter (SPM) Sulphur Dioxide (S02) Nitrogen Oxides (NOx)

Surface Ozone (03)

Carbon Monoxide (CO) Temporal Variations in Carbon Monoxide (CO) Enrichment Patterns Airborne Epidemics Disease Grading by Professionals Morbidity Treated by Physicians Morbidity and Mortality: Vital Indices Spatial Distribution' of Airborne Diseases Perception Critique Zonal Appraisal Zone # 1: luna Market, Old Town area Zone # 2: Ranchore Line & Ramsawami Zone # 3: Saddar & Artillery Maidan Zone # 4: Civil Lines Area Zone # 5: 1.1. Chundrigar Road & New Queens Road Zone # 6: Port Area Zone # 7: Nawabad, Baghdadi Lane, Kharadar Zone # 8: Agra Taj, Bihar Colony Zone # 9: Lea Market, Gul Mohammad Lane Zone # 10: Chakiwara, Kalakot Zone # 11: Garden, Soldier Bazaar, lamshed Quarters Zone # 12: Lines Area & Khudadad Colony

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Zone # 13: Naval H.ospital, lPMC and Liaquat Barracks Zone # 14: Bath Island, Frere Town, Defense Society (Part) Zone # 15: Gizri Area, Delhi Colony Zone # 16: Clifton Zone # 17: Baba Bhit Islands Zone # 18: Shetshah, S.I.T.E. (Part) Zone # 19: S.I.T.E. (Sindh Industrial Trading Estate) Zone # 20: Asif, Pak Colony & T.P.T. Zone # 21: Rizvia, Firdous Colony, Golimar Zone # 22: Liaquatabad ' Zone # 23: Gulshan-e-Iqbal (Part), P.LB. Colony Zone # 24: Gulshan~e-Iqbal, Chandni Chowk, Society Area Zone # 25: Akhtar & Baloch Colony, Chanesar Goth

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4.12.26 4.12.27 4.12.28 4.12.29 4.12.30 4.12.31 4.12.32 4.12.33 4.12.34 4.12.35 4.12.36 4.12.31 4.12.38 4.12.39 4.12.40 4.12.41 4.12.42 4.12.43 4.12.44 4.12.45 4.12.46 4.12.47 4.12.48 4.12.49 4.12.50 4.12.51 4.12.52 4.12.53 4.12.54 4.12.55 4.12.56 4.12.57 4.12.58 4.13 4.13.1 4.13.2 4.13.3 4.13.4 4.13.5 4.13.6 4.14 4.14.1 4.14.2 4.14.3 4.14.4

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5. 1 5.2

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Zone # 26: Drigh Cantonment, 9th Mile Zone # 27: Gulshan-e-Iqbal, National Cement Factory Zone # 28: F.e. Area and Mansoora Zone # 29: Nazimabad, Paposhnagar Zone # 30: North Nazimabad Zone # 31: North Karachi Zone # 32: Qasba, Manghopir Area Zone # 33: Orangi, Metroville-I Zone # 34: Baldia Zone # 35: Masroor (Mauripur) Zone # 36: Hawkesbay and Adjoining Area Zone # 37: Deh Moach, Naval Depot Zone # 38: Deh Lal Bhakhar& Hawkesbay Scheme Zone # 39: Korangi (Part) Zone # 40: Landhi Colony Zone # 41: Landhi Industrial, Scheme 3 & 4 Zone # 42: Shah Latif, Deh Khanto Zone # 43: Model and Malir Colonies Zone # 44: Karachi Airport Zone # 45: Drigh Colony & Malir Zone # 46: Korangi Industrial Area - East Zone # 47: Korangi Industrial Area - West Zone # 48: Korangi Creek and Refinery Zone # 49: Steel Mill and Port Qasim Zone # 50: Deh in the East Zone # 51: Malir Cantonment Zone # 52: Scheme 33 Zone # 53: Defence Society Zone # 54:Surjani Town Zone # 55: Taisar Town Zone # 56: Halkani Scheme Zone # 57: Dehs in the West along Hub Rivet Zone # 58: Dehs along Super Highway Multi Criteria Risk Weight Extraction . Very High Risk High Risk Moderate Risk Low Risk Safe Zone

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Carbon Monoxide (CO) Prediction Prediction Models Evaluation of Models Applications of Models Constraints of Models

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CONCLUSIONS AND RECOMMENDATIONS

Conclusions •

Further Research A venues • •

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5.3 Recommendations ,

REFERENCES

Annexure A

Annexure B

Annexure C

Almexure D Annexure E Annexure F Annexure G

Almexure H

Annexure I

Annexure J

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Monitoring Sample Locations around Karachi Metropolis Carbop Monoxide Concentrations at Monitoring Sample Locations around Karachi Observed Traffic Load at Monitoring Sample Locations around Karachi Metropolis Specifications of Carbon Monoxide Analyser Health Statistics

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QuestiOlmaires (English Translation) Zone-wise Land Use and Land Cover Appraisals' Land Use and Traffic Statistics around Monitoring Stations in Old City Area within 100-Meter Diameter Pictorial Illustrations of the Problem Plume Dispersion from Industry in Karachi: Monitoring through Optical Imageries .

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LIST OF TABLES

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2.1

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2.12· 3.1

3.2

3.3

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Population of Karachi: 1931 - 2002 .

Hazardous Air Pollutants

Particulate Matters: Sizes and Identifications

Summary of Key Greenhouse Gases Affected by Human Activities

Estimated CO2 from Various Economic Sectors in Pakistan

Human Response to Different levels of S02 for Different Period

Estimated S02 from Various Economic Sectors in Pakistan

Human Response to Different Levels ofN02 for 2 Hours

N02 Exposure Effects on Human Health

Predicted Carboxyhaemoglobin in Levels for People Engaged in Different Types of Work in Different Concentrations of Carbon Monoxide Effects of CO on Humans and the Accompanying Carboxyhaemoglobin Blood (COHb) Concentrations

Methods for Integration of Air Pollution Dispersion Models with GIS

GIS Integrated Air Pollution Dispersion Models •

Satellite Imageries: Sensors, Resolution and Acquisition

Daily Traffic of Sixteen Hours at Major Intersections of Karachi

Categorization of Observed Average Hourly Traffic at Monitoring Stations

Air Pollution Assessment in Karachi Metropolis

Metadata of Base Map

Analysis Zones •

Input Data Tables Integrated with Map Objects

Target Groups, Criteria and Description Of Questionnaires

KDA Land Use 2000

Land Use Groups ofKDA Defined Categories

Zone-wise Population Distribution of Karachi Metropolis

Population Density: Descriptive Statistics

Indoor Morbidity and Mortality Statistics 2001

Indoor Morbidity and Mortality Statistics of Airborne Diseases 2000 and 2001

Disease Occurrence: Descriptive Statistics

Disease Prevalence: Descriptive Statistics

CO Concentrations across the Juna Market, Old Town area

4.10 CO Concentrations across the Ranchore Line & Ramsawami

. 4.11 CO Concentrations across the Saddar & Artillery Maidan

4.12 CO Concentrations aeross the Civil Lines Area

4.13 CO Concentrations across the I.I. Chundrigar Road & New Queens Road

4.14 CO Concentrations across the Port Area

4.15 CO Concentrations across the Nawabad, Baghdadi Lane, Kharadar .

4.16 CO Concentrations across the Agra Taj, Bihar Colony

4.17 CO Concentrations across the Lea Market, Gul Mohammad Lane

4.18 CO Concentrations across the Chakiwara, Kalakot

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4.20 4.21

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CO Concentrations across the Garden, Soldier Bazaar, lamshed Quarters

CO Concentrations across the Lines Area & Khudadad Colony

CO Concentrations across the Naval Hospital, JPMC and Liaquat Barracks

CO Concentrations across the Bath Island, Frere Town, Defense Society (part)

4.23 CO Concentrations across Gizri Area, Delhi Colony

4.24 CO Concentrations across the Clifton

4.25 CO Concentrations acrpss the Shershah, S.LT.E. (part)

4.26 CO Concentrations across the S.I.T.E. (Sindh Industrial Trading Estate)

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4.30 4.31

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4.33 4.34

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4.40 4.41

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4.45

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4.52 4.53

4.54 4.55

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4.57 4.58

4.59

CO Concentrations across the Asif, Pak Colony & T.P.1.

CO Concentrations across the Rizvia, Firdous Colony, Golimar

CO Concentrations across the Liaquatabad

CO Concentrations across the Gulshan-e-Iqbal (part), P.I.B. Colony

CO Concentrations across the Gulshan-e-Iqbal, Chandni Chowk, Society Area

CO Concentrations across the Akhtar & Baloch Colony, Chanesar Goth

CO Concentrations across the Drigh Cantonment, 9th Mile

CO Concentrations across the Gulshan-e-Iqbal, National Cement Factory

CO Concentrations across the F.C. Area and Mansoora

CO Concentrations across the Nazimabad, Paposhnagar

CO Concentrations across the North Nazimabad

CO Concentrations across the North Karachi

CO Concentrations across the Orangi, Metroville-I

CO Concentrations across the Korangi (Part)

CO Concentrations across the Landhi Colony

CO Concentrations across the Landhi Industrial, Scheme 3 & 4

CO Concentrations across the Shah Latif; Deh Khanto

CO Concentrations across the Model and Malir Colonies

CO Concentrations across the Karachi Airport

CO Concentrations across the Drigh Colony & Malir

CO Concentrations across the Korangi Industrial Area - East

CO Concentrations across the Korangi Industrial Area - West •

CO Concentrations across the Malir Cantonment

CO Concentrations across the Scheme 33

CO Concentrations across the Defence Society

Factor Analysis Communalities •

Factor Analysis Total Variance Explained

Factor Analysis Component Matrix

Correlation Matrix

Correlation Matrix (Modulus)

Extracted Weights for Variables

Multi-Criteria Overlay Template

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Constants and Coefficients of Predictive Models

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Monitoring Sample Locations around Karachi Metropolis

Carbon Monoxide Concentrations at Monitoring Sample Locations around Karachi

Criterion of Traffic Flow Observation

Observed Traffic Load at Monitoring Sample Locations around Karachi Metropolis Indoor Morbidity and Mortality Statistics 2001, Medical Records and Statistical Office, civil Hospital, Karachi .

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Indoor Morbidity and Mortality Statistics of Airborne Diseases, Statistical Records, Jinnah Postgraduate Medical Centre, Karachi: 2000 and 2001 Zone Wise Epidemiological Statistics (Questionnaire Based)

Land Use and Traffic Statistics around Monitoring Stations in Old City Area within 100-Meter Diameter

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LIST OF FIGURES

1.1

1.2

1.3

1.4

1.5

1.6

1.7

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1.9

3.1

3.2

3.3

3.4

3.5

3.6

3.7

3.8

3.9

3.10 .

3.11

3.12

3.13

3.14

4.1

4.2

4.3

4.4

4.5

4.6

4.7

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4.9

4.10

4.11

4.12

4.13

4.14 •

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Karachi Metropolis: Study Area

Karachi Metropolis: Administrative

Physiography of Karachi Metropolis

Meteorological Characteristics of Karachi Metropolis

Urban Land Cover of Karachi

Conceptual Geometrical Shape of Karachi Division

Karachi Urban Sprawl (1947 - 2002) •

Karachi Road Network

Succession of Environmental Studies

Methodological Framework: Flow diagram ·

Variations in Solar Elevation Angle

Image / Map Geometric Correction Model

Karachi Landsat 5 - TM Subset

Land Cover Classification Model

Land Cover Classification Scheme

Conceptual Diagram of MDC Algorithm

Old City Area of Karachi

Distribution of Monitoring Stations

GIS Organizational Diagranl ,

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Detailed Base Map of Karachi Metropolis •

Base Map Development Procedure Diagram

Karachi Metropolis: Analysis Zones

Weight Extraction Model ·

Karachi and Its Environs from Space (Landsat TM FCC)

Karachi and Its Environs: Geometrically Corrected Image

Selected Subsets of Pre and Post Enhanced Imageries

Classified Land Cover of Karachi Metropolis

Appraisal of Land Cover .

Growth of Settlements in Karachi Metropolis

Projected Land Use 2000 of Karachi Metropolis

Housing Distribution in Karachi

Karachi Population Distribution ·

Population Densities in Analysis Zones •

Karachi Population Density Gradient

Road Density Index

Road Proximity Buffers , ·

Karachi Traffic Concentrations Weekend Mornings

• •

XVI •

•

-

•

8

9

1 1

13

16

17

18

19

22

95

98

100

103

104

105

106

112

115

120

122

123

126

139

145

146

147

149

151

153

155

158

160

162

164

166

167

169

• •

•

•

4.15

4.16

4.17

4.18

4.19

4.20

4.21

4.22

4.23

4.24

4.25

4.26

4.27

4.28

4.29

4.30 •

4.31

4.32

4.33

4.34

4.35

4.36

4.37

4.38 4.39

4.40

4.41

4.42

4.43

4.44

4.45

4.46

4.47

4.48

4.49

4.50

4.51

4.52

4.53

4.54

Karachi Traffic Concentrations Weekend Afternoons

Karachi Traffic Concentrations Weekend Evenings

Karachi Traffic Concentrations Working Day Mornings

Karachi Traffic Concentrations Working Day Afternoons

Karachi Traffic Concentrations Working Day Evenings

Spatial Patterns of SPM Emission

Spatial Patterns ofPM IO Emission

Spatial Patterns of S02 Emission Maximums

Spatial Patterns of S02 Emission Averages

Spatial Patterns of NO x Emission Maximums

Spatial Patterns of NO x Emission Averages

Spatial Patterns of 0 3 Emission Maximums

Spatial Patterns of 0 3 Emission Averages

Spatial Patterns of CO Emission Weekend Mornings (3')

Spatial Patterns of CO Emission Weekend Mornings (4 Y])

Spatial Patterns of CO Emission Weekend Afternoons (3')

Spatial Patterns of CO Emission Weekend Afternoons (4 Y])

Spatial Patterns of CO Emission Weekend Evenings (3')

Spatial Patterns of CO Emission Weekend Evenings (4 Y])

Spatial Patterns of CO Emission Working Day Mornings (3') •

Spatial Patterns of CO Emission Working Day Mornings (4 Y])

Spatial Patterns of CO Emission Working Day Afternoons (3')

Spatial Patterns of CO Emission Working Day Afternoons (4 Y])

Spatial Patterns of CO Emission Working Day Evenings (3')

Spatial Patterns of CO Emission Working Day Evenings (4 Y])

Spatial Patterns of CO Emission Diurnal Average (3')

Spatial Patterns of CO Emission Diurnal Average (4 Y])

Spatial Patterns of CO Emission Diurnal Average

Spatial Patterns of CO Emission Diurnal Deviation (3 ')

Spatial Patterns of CO Em'ission Diurnal Deviation (4 Y])

Spatial Patterns of CO Emission Diurnal Deviation

Epidemics of Diseases in Karachi, 200 1

•

Comparison of Prevailing Diseases between General Public and Population at Risk

Occurrence of Airborne Diseases in Analysis Zones

Point Prevalence of Airborne Diseases in Analysis Zones

Perceived Rush Hours in Karachi •

Comparison between Level of Education and Perceived Effects of Air Pollution on Human Health

Zonal Aggregates of CO Emission Weekend Mornings 3 feet

Zonal Aggregates of CO Emission Weekend Afternoons 3 feet

Zonal Aggregates of CO Emission Weekend Evenings 3 feet

•

• • XVll

170

171

173

174

175

177

178

180

181

184

185

187

188

191

192

194

195

197

198

200

201

203

204

206

207

210

211

212

215

216

217

221

230

232

233

235

237

272

273

274

•

•

•

•

4.55

4.56

4.57

4.58

4.59

4,60

4.61 '

4.62

4.63 ,

4.64

4.65

4,66

4,67

4,68

4.69

4.70

4.71

4.72

4.73

4.74

4.75

D,l

Gx.l

Gy.1

Gx.2

Gy.2

Gx.3

Gy.3

Gx.4

Gy.4

Gx.5

Gy.5

Gx.6

Gy.6

Gx.7

Gy.7

Gx.8

Gy.8

Gx.9

Gy.9

Gx.l0

Gy.lO

,

,

Zonal Aggregates of CO Emission Weekend Mornings 4 Y:! feet

Zonal Aggregates of CO Emission Weekend Afternoons 4 Y:! feet

Zonal Aggregates of CO Emission Weekend Evenings 4 Y:! feet

•

Zonal Aggregates of CO Emission Working Day Mornings 3 feet

Zonal Aggregates of CO Emission Working Day Afternoons 3 feet

Zonal Aggregates of CO Emission Working Day Evenings 3 feet

Zonal Aggregates of CO Emission Working Day Mornings 4 Y2 feet

Zonal Aggregates of CO Emission Working Day Afternoons 4 Y:! feet

Zonal Aggregates of CO Emission Working Day Evenings 4 Yfeet

Zonal Aggregates of CO Emission 3 feet Averages ,

Zonal Aggregates of CO Emission 4 Y2 feet Averages

Zonal Aggregates of CO Emission Total Averages

Zonal Aggregates of SPM Emission

Zonal Aggregates ofPM IO Emission

Zonal Aggregates of S02 Emission Averages

Zonal Aggregates of S02 Emission Maximums •

Zonal Aggregates of NO x Emission Averages

Zonal Aggregates of NO x Emission Maximums

Zonal Aggregates of 0 3 Emission Averages

Zonal Aggregates of 0 3 Emission Maximums

Karachi: Multi Criteria Risk

Carbon Monoxide Analyser

Analysis Zone 1: Land Use ,

Analysis Zone 1: Land Cover

Analysis Zone 2: Land Use








Analysis Zone 6: Land Use '



Analysis Zone 7: Land Cover ,






Analysis Zone 10: Land Cover •

•

•

,

,

,

• •• XVlll

•

275

276

277

278 279

280

281

282

283 284

285

286 287

288

289

290

291

292

293

294

305

394

404

404 404

404

404 404

405

405

405

405

405

405

406

406

406 406

406

406

407

407

, "

•

,

.'

Gx.ll Analysis Zone 11: Land Use 407 Gy.ll Analysis Zone II: Land Cover 407

• Gx.l2 Analysis Zone 12: Land Use 407

Gy.l2 Analysis Zone 12: Land C~)Ver 407 Gx.13 Analysis Zone 13: Land Use 408

Gy.l3 Analysis Zone 13: Land Cover 408

Gx.14 Analysis Zone 14: Land Use 408

Gy.14 Analysis Zone 14: Land Cover 408




Analysis Zone 16: Land Cover • Gy.16 . 409




Gy.18 Analysis Zone 18: Land Cover 409 •

Gx.19 Analysis Zone 19: Land U.se 410



.. Gy.20 Analysis Zone 20: Land Cover 410




Gy.22 Analysis Zone 22: Land Cover , 411 •









Gx.27 Analysis Zone 27: Land Use' 412 --•

• ~-.




Gx.29 Analysis Zone 29: Land Use 413 ,




Gx.31 Analysis Zone 31: Land US,e 414


,

• XIX

• • •

• •


Gy.32 Analysis Zone 32: Land Cover 414 • Gx.33 Analysis Zone 33: Land Use 414
















Gx.41 Analysis Zone 41: Land Use • 417 .. Gy.41 Analysis Zone 41: Land Cover 417




Gy.43 Analysis Zone 43: Land Cover • 418







Gx.47 Analysis Zone 47: Land Use 419 •

Gy.47 Analysis Zone 47: Land Cover 419 =rt Gx.48 Analysis Zone 48: Land Use' 419 -'- .. ~


Gx.49 Analysis Zone 49: Land Use 420 •



Gy.50 Analysis Zone 50: Land Cover 420 • •

Gx.51 Analysis Zone 51: Land Use • 420


GX.52 Analysis Zone 52: Land Use 421

Gy.52 Analysis Zone 52: Land Cover • 421

•

xx

•

Gx.53

Gy.53

Gx.54

Gy.S4

Gx.55

Gy.55

Gx.56

Gy.56

Gy.57

Gy.58

l.l

1.2

1.3

1.4

1.1

1.2

1.3

•











Traffic Congestion Forming Very High Risk

"

Visibility Problem nearby Empress Market (Very High Risk Zone) ,

Typical Old City Housing (Very High Risk Zone)

Black Smoke Emission in Congested Streets of Very High Risk Zone

Landsat 5 - TM, 1992, Bands 2, 5, 7

Landsat 5 - TM, 1998, Bands 2, 3, 4 •

KVR, 1998, Very High Spatial Resolution (2 meter)

,

,

,

•

,

,

• • XXI

•

•

,

421 421

421

421

422

422

422 422 422

422

426

426

427 427

428 428

429

,

, '.'

•

•

_ _ Ie . ~, ~

•

•

ACRONYMS AND LOCAL TE S

ACRONYMS

3-S ADB APP ATSDR BERG CBD

. CDIAC DHA EC EPA EPW ESRl GIS GOP EUAD GOP GPS HE! HRV ICA IEEE IPCC IUCN JPMC KCHS KCR KDA KESC KITE KMC KMTP KPT LBA LDCs LIDAR MAGIS MBBS MDCs MIC MSS NAS NASA NIPA NOAA NSIEM OECD PCI PCSIR PC YO PECHS

•

Three integrated technologies, i.e. RS, GIS and GPS· Asian Development Bank Associated Press of Pakistan' Agency for Toxic Substances and Diseases Registry Buildings Effects Review Group Central Business District Carbon Dioxide Information Analysis Centre of ORNL Defence Housing Authority, Karachi - Pakistan European Commission Environmental Prot.ection Agency Environment Protection Wing of PC YO Environmental Systems Research Institute, Inc. Geographic Information Systems Environment & Urban Affairs Division, Government of Pakistan Government of Pakistan Global Positioning System •

Health Effect Institute, United States High Resolution Visible scanner Instituto Geografico Nacional, Spain Institute of Electrical and Electronics Engineers Intergovernmental Panel on Climate Change World Conservation Union Jinnah Post Graduate Medical Centre Karachi Cooperative Housing Society Karachi Circular Railway Karachi Development Authority . Karachi Electric Supply Corporation Korangi Industrial Trade Estate Karachi Metropolitan Corporation Karachi Mass Transit Programme Karachi Port Trust Large Scale Biosphere-Atmosphere Experiment in Amazonia Less Develop Countries Laser Radar Metropolitan Area Geographic Information Systems Bachelor of Medicine I Bachelor of Surgery Most Develop CountrJes . Map Info Corporation Multi-Spectral Scanner National Academy of Science National Aeronautics and Space Administration, United States National Institute of Public Administration, Karachi - Pakistan National Oceanic and Atmospheric Administration, United States National Swedish Institute of Environmental Medicine Organization for Economic Co-operation and Development PCI Geomatics Canada Pakistan Council for Science and Industrial Research Pakistan Crescent Youth Organization Pakistan Employees Cooperative Housing Society

• • XXll

•

•

•

•

PEPA PRB PSI RCD RS SEPA SITE SPOT SRS SUPARCO TEB TM UID UNCHS UNDP UNEP URe USEPA WB WHO WMO WRI

•

Pakistan Environmental Protection Agency •

Population Reference Bureau, United States Pakistan Standards Institution Regional Cooperation for Development Remote Sensing Sindh Environmental Protection Agency Sindh· Industrial Trade Estate Satellite Probattoire de l'Observation de la Terre (French Satellite) Satellite Remote Sensing Pakistan Space and Upper Atmosphere Research Commission Traffic Engineering Bureau, Karachi - Pakistan Thematic Mapper Urban Heat Island United Nations Centre for Human Settlements (Habitat) United Nation Development Programme United Nation Environment Programme Urban Resource Center, Karachi - Pakistan United States Environmental Protection Agency

,

World Bank World Health Organization World Meteorological Organization World Resources Institute, United States

•

LOCAL TERMS

Bagh Bazaar Chawrangi Chowk Chowki Dakkhana Dawakhana Deh Goth Gully Hakim Jhuggi Kachra Kundi Katchi Abadi Kuh Mazar Morre Patharay walas Pu/ Purana

'Qabristan Rickshaw Sarafa Bazaar Shahrah Subzi Mandi Thana

•

Garden Market Roundabout Intersection Check Post

•

Post Office Clinic

,

Rural administrative entity Village Minor Street Physician practicing with herbal medicines Temporary Settlement Neighbourhood garbage dump Squatter settlement " Village , Mausoleum Intersections Temporary slllali cabins Bridge Old Cemetery, Graveyard 2-stroke, 3-wheeled para-transit vehicle

•

Gold Market Major Arterial Vegetable Market Police Statioll

,

, • • •

XXlll

, .

•

,

•

•

•

•

• - •

from

"The Principles of Human Knowledge ", by Bishop Berkeley (1685 -1753)

,

•

•

•

•

• ,

, •

"'9 3 ,

•

•

•

•

, •

• • XXIV

•

1. INTRODUCTION

1.1 BACKGROUND

Every component of man's environment has its own significance whereas atmosphere 'as

a whole' has the greatest imponance. This significance can be appraised from the fact that

on average each person breathes 14 to 1 g Kg of air from atmosphere and man cannot

survive for more than a few minutes without air but can live for days without drinking

water and for weeks without food .

Atmosphere is very dynamic and this dynamism is changing at an alarming rate since the

advent of industrial revolution. As a result some harmful chemicals were deliberately

introduced in the environment. These substances in such quantities and of such duration

are liable to cause harm to human, plant or animal life, or damage human made materials

and structures, or bring changes in the weather / climate, or interfere with the comfons of

life or propeny or effect other human activities.

Nowadays atmospheric pollution is a major environmental health problem, affecting

developed as well as developing countries around the world. Continuous growth of

human induced unchecked sources of pollution is the pivotal point of this burgeoning

problem. Besides increasing pollution there is an increasing demand for the early and

reliable detection of adverse effects caused by this pollution so that effective monitoring

and control measures could be introduced.

Environmental monitoring enables us to identify pollutant sources, their nature and

concentrations, patterns, trends and their harmful effects. Therefore, environmental

monitoring is now recognized at global as well as national and regional levels .

Environmental modelling technique is one of the recognized tools of monitoring. Some

times these techniques determine the appropriate mean to monitor the environmental

problem. Almost all of the environmental problems possess spatial dimension. Within the

domain of environmental modelling this is addressed by spatially distributed models that

describe environmental phenomena in one, two, three or even four dimensions. The

increasing development and use of spatially distributed models replacing simple spatially

2

----------- -

aggregated or lumped parameter models is, at least in part, driven by the availability of

more and more powerful and affordable computers (Fedra, 1993).

With the changing technological paradigms some other advance tools are also coming in

the field of environment health monitoring. Remote Sensing techniques are not new as

such but development of innovative sensors and modified applications of multispectral

data is now being used in this context. Especially meso and macro scale real time smoke

plume diffusion, trends, patterns detection and its under influence fauna and flora

estimation are very common remote sensing applications of atmospheric environmental

change. Remotely sensed data is not only directly used but also it facilitates

environmental models indirectly, which may provide us spatially continuous data.

On the other hand, Geographic Information System (GIS) is a set of tools to capture,

manipulate, process and display spatial or geo-referenced data (Gatrell, 1998). They

contain both geometry (coordinates and topological information) and attributes, i.e.

information describing the properties of geometrical spatial objects such as points, lines

and area. In GIS the basic· concept is location, spatial distribution and its relationship

whereas basic elements are spatial objects. In environmental modelling, by contrast, the

essence is state, expressed in terms of numbers, mass, or energy, of interaction and

dynamics; the basic elements are "species", which may be biological, chemical, and

environmental media such as air, water or sediment.

The harmony in these techniques is proved to be very powerful and thus the integration of

these fields of research technologies, or sets of methods, that is promising us some

integrated data, the data which is essentially needed to evaluate such a pressing human

issue. Unification of fields of researches, or adding a new technology to an established

and mature field, usually leads to new and exciting developments. Air pollution IS a

multi-dimensional problem and should be tackled with multi-disciplinary approach.

There has been a growing awareness on environmental issues in the developing world

after the United Nations "Earth Summit" held in 1992 at Rio de Janeiro. In Pakistan, this

wave of awareness has been further boasted by the promulgation of "Environmental

Protection Act of 1997." However, air quality remained a low priority area both for

decision-makers and common public. Nevertheless, some agencies and individuals

3

contributed considerably on air quality studies. But most of these studies are without

spatial dimensions, in spite of the fact that air pollution by all means a spatial problem

and have spatial repercussions both at local and regional scale. Karachi in this context

ranks very high among the Mega cities and contains highest concentrations of some

pollutants like lead (Pb), carbon monoxide (CO), carbon dioxide (C<h) etc. In this study,

an attempt has been made to study air pollution geography with the most effective

monitoring tools of our times, i.e. GIS and RS.

1.2 THE PROBLEM

Sandstorms and the emission of dust and gases from volcanoes pollute the earth's

atmosphere naturally but the most serious kind of air pollution comes from human

activity, . factories, power stations and vehicular exhausts. Atmospheric Pollution IS

considered as threat for human life and well being. Urban areas of the world in

developing as well as developed countries are badly under influence of harmful effects of

air pollution.

The gravity of the situation could be realized by the fact that in the early morning, there

are no clouds in the sky but the sun does not shine until it is well above the horizon. The

sun never comes out to its full strength in the city of more than 10 million people (i.e.

Karachi Metropolis) because the atmospheric pollution (Brown, 1998).

Urbanization in the modem sens'e of the term is the embodiment of blocks of flats, traffic

jams, air pollution, noise nuisance, growth of slums and avalanches of refuse (Scholz,

1983). These environmental degradation processes continue in Karachi in which air

pollution is the major concern that is dangerously affecting the urban and rural areas of

the metropolis. Karachi is one of the leading polluted cities in the world.

Atmospheric pollution in Karachi is 40 percent higher than other cities of Pakistan that is

on alarming stage. As a colossal urban and industrial area there are three human induced

dominating air pollution sources, vehicular traffic, Industrial manufacturing units and

open air garbage burning. These sources release thousands of tons of toxic gases and

particulate matters into atmosphere of Karachi (Qureshi, 1997)

4

1.3 THE STUDY

Increasing environmental pollution is a systematic change, which starts from human

perception and behaviour; varies from nation to nation at various levels of development,

passes through the civilization, and effected by the enhancement and introduction of new

technologies, side effects, monitoring and control etc. Continued growth in industrial

sectors, number of vehicles and in the wastage from an ever-increasing number of

sources, together with the need to preserve nature, have found worldwide attention

focused on the burgeoning problems of environmental pollution. Therefore, there is an

increasing demand for the early and reliable detection of adverse effects caused by

pollution so that effective monitoring and control measures could be introduced. As

people has become more aware of the hazards associated with the pollutants that are

emitted into the atmosphere as gases or particles, interest has grown in measuring

concentration in air both in polluted environments and in 'clean' environments

(Campbell, 1995). Human beings have reached to this concept of better environment after

centuries of thought. The management of environmental pollution is a key element in

achieving sustainable development that meets the needs of the present without

compromising the ability of future generations to meet their own needs. Besides

developed countries, Third world developing nations are more affected and paymg

against pollution, much greater share of income.

Karachi is one of the worst effected cities of the world due to unchecked and still

uncontrolled air pollution. Although few governmental and private organizations have

been working on this issue but spatial variation within metropolis have been largely

ignored mainly due to less comprehension, under estimation of spalial techniques as well

as difficulties in collecting, processing and analysing the data at micro geographic scales.

The primary objective of this study is to modulate the spatial information pertains to air

quality and to visualize its adverse effects on human health ina GIS matrix . The research

is intended to cover different basic parameters of air quality. The salient objectives of this

research are envisaged as described herein:

5

I. To Assess land cover clusters in metropolitan Karachi using digital image processing

techniques

To investigate human settlement growth through change detection procedures

To appraise city-wide land use / land cover classes for the study area

2. To map mobile source (traffic) and interpretation of temporal patterns of tramc

around the study area

3. To Study population distributions across the city for further determination of human

resource at risk

4. To uncover the current level of criteria air pollutants

To conduct a field survey for measuring the level of carbon monoxide (CO) at

various locations on the basis of which a geographic evaluation of spatio-temporal

variation of Carbon monoxide across Karachi is to be presented

To create geo-database and position the monitoring stations

To present spatio-temporal variation of carbon monoxide (CO) across Karachi

5. To garner studies of air pollution of the Karachi Metropolis

To list down a comprehensive inventory of potential air-born diseases through a

detailed literature review

6. To spell out the pattern of emerging diseases and their risks out of air pollution.

Determination of the air-born diseases of high epidemics by surveys of hospitals and

physicians' experience.

7. To attempt connecting the mlssmg social link of environmental studies through a

rational review. A perception analysis to find out the level of awareness about air

pollution and its effects among the native population is envisaged.

8. Air pollution modelling through geo-statistical interpolation techniques

Formulation of Risk Criteria by statistical methods, and fusing together multi

variable to demarcate Risk zones and which may lead to an Air Quality Index of

the city for the planner.

9. Forecasting of air pollution concentration by means of other tangible parameters

6

The work would be summarized under the following hypothesis:

"The higher concentrations and spatial patterns of air pollution are in conformity with the

geographical distributions of land cover / land use, traffic and population, which resulted

in high incidence of airborne diseases and the human resource near those areas, are on

vulnerable risk ."

The results of above-mentioned objectives are subjected to the availability of data and its

quality. In third world countries like Pakistan, data resources are limited and even

available data are not continuous both temporally and spatially. Methodology IS

meticulously chosen to overcome the data errors and flaws as much as possible In

indigenous environment.

1.4 STUDY AREA

Karachi metropolis is the largest city of Pakistan, the capital of Sindh Province and

former Federal Capital. Karachi Division a larger administrative unit lying geographically

in between 24° 45' N to 25° 37' Nand 66° 42' E to 67° 34' E, comprises of five districts

Karachi East, Karachi West, Karachi South, Karachi Central and Malir Figure I. I. In

August 200 I, the Sindh Local Government Ordinance was promulgated and accordingly

Karachi City District Government was established to look after the whole Karachi

Division under the single administrative authority. Instead of former five districts Karachi

Division was divided into eighteen towns (Mahmood el al., 2001). Administratively it lies

in Sindh Province and surrounded by Lasbela District in the West (Baluchistan Province),

Dadu District in the North and North East and Thatta District in the East Figure 1.2. The

whole administrative area occupies about 3600 Sq. Km in which more than half is under

urban land use. Some businessmen and fishermen along the sea founded this city in 18th

century. At that time it was called as Kalachi-lo-Goth or Kalachi-lo-Kuh (i. e. Village of

Kalachi) and then to its present name of Karachi (GOP, 1984).

7

--------- --- - -

".'0 ' ....

8

DIVISION

KARACHI Towns

Figure 1.2

KARACHI METROPOLIS Administrative

DISTRICTS \

t

l

' //~

9

1.4.1 Physiography

This region of the world has been developed under subtropical semi arid conditions. In

Karachi basin three anticlines and three synclines, known as Malir, Layari and Hub, mark

the physical characteristics. The Malir flows in the east of Karachi, the Layari flows

through the heart of City and Hub lies 30 km to the west and flows along Karachi Lasbela

boundary. The alluvial plains of the river Layari and Malir, salty and polluted where the

sea is near at hand and now these channels, are being used as sewage water channels

especially in the urban area. Jutting from these plains are barren rocky outcrops from

ridges and low hills. In the vicinity of the coast, sea creeks and mangrove swamps are met

with. Gullying is prominent in the softer rocks of uninhabited area. North-western portion

is skirted by the Kirthar Range, which lies north-and-south as related members of the

Pabb Range, which extends northward parallel to the Kirthar Range. Extending from

Cape Monze (Extreme South west of Karachi Division) to the Manghopir area (Centre of

Karachi Division) is a series of hills and ridges, known as Jhil Range. Most of hills of this

range from 400 feet to SOO feet in height. From South east of the city to North there is

another series of low hills, which is extending north-westward. These hills are 96 feet to

200 feet high.

Distinct from the alluvial plains of the · non-perennial Malir and Layari rivers are the

planes of marine denudation. South of the city is totally connected with Arabian sea

whereas the east-south-east lies a vast expanse of mud-flats, sandbanks and mangrove

swamps, intersected by a complicated system of ramifying creeks and inlets (Pithawalla,

1946) Figure 1.3. Range, which extends northward parallel to the Kirthar Range.

Extending from Cape Monze (Extreme South west of Karachi Division) to the Manghopir

area (Centre of Karachi Division) is a series of hills and ridges, known as Jhil Range. ·

Most hills of this range are from 400 feet to SOO feet in height. From South east of the city

to North there is another series of low hills, which is extending north-westward . These

hills are 96 feet to 200 feet high. Distinct from the alluvial plains of the non-perennial

Malir and Layari rivers are the planes of marine denudation. South of the city is totally

connected with Arabian sea whereas the east-south-east lies a vast expanse of mud-flats,

sandbanks and mangrove swamps, intersected by a complicated system of ramifying

creeks and inlets (Pithawalla, 1946) Figure 1.3.

10

P HYSIOGRAPHY OF

KARACHI METROPOLIS

~ , \'.

--

. ' .--' .(

/~'£-~J,':' .) -, I

C ,

fj~n:: 1.3

11

1.4.2 Climate

Latitudinal position and Physiography of an area together determine the regional as well

as local climate. In general Karachi is a subtropical coastal lowland area. Its geographical

location is not favourable to receive even sufficient seasonal monsoonal rainfall. It is Hot

Desert Rainfall Summer Concentration i.e. BWhb region by Koppen Geiger, Arid

Megathermal- little or no surplus moisture and summer Concentration heat index < 48 .0

cm i.e. EA'da' Region by Thornthwaite whereas Tropical coast lands with low rainfall

high humidity- low diurnal and annual range of temperature and sea breeze dominant in

summer when mean maximum is 35° C while winter mean daily temperatures is 20° C i.e.

I (iii) a region by Ahmad's classification (Ahmed, 1952; Khan, 1993).

Karachi endures a long hot season from March to October. In July and August

Temperatures are moderate because of monsoon winds. Sea breeze controls the severity

of temperature but in the areas away from the coast temperatures are higher. In May and

June low pressure in interior Sindh Province the north eastern winds increase the

temperature and it soars up to 43° C or a little higher. Monthly variations in climatic

elements are shown in Figure 1.4.

Humidity in this area particularly near shore is about 50% at least through out the year.

Annual range is from 50% (December, driest month) to 85% (August, moistest month) .

Average wind velocity in winter is 6.5 miles per hours, which is overall considered as low

wind. By the end of June or in July, monsoonal winds pass through the study area and its

surroundings. These seasonal winds are strong and increase speed in local wind system up

to 11.7 miles per hours. Rainfall in Karachi is meagre as well as very variable. Average

rainfall is about 197.85 mm. Maximum is received in July and August but irregular.

There are some local afternoon disturbances due to conventional currents and contrasts of

weather conditions, such s high temperatures, diurnal ranges and differences in humidity,

which cause thunderstorms, dust storms ' and squally weather during the transition stage

between the two seasons, viz. March to May and September to November (Pithawalla,

1946).

12

1ilOnm

""" 1001\ SIl

s "" ..

Meteor.ological Characteristics of Karachi Metropolis

-

~ ~! "-

"Or <

F "

, ;,

':' -, - ,. -

I

~ t l . • • -r f • 1

~ • 1 • -- • ...... - • ~ - --0 0 0

,,., feb r: " A,. ~:~ "" .... .... S .. Oct """ Dec

- - - - -L ..... •

Figure 1,4

1

'O"e

30

20 BCoI.}tml ,Om

, . ."" ,

• 0 0

13

In addition to these there are local land and sea breezes near the coast. These sea breezes

are accompanied by fall of temperature (from about 50 F to 3.50 F), rise of humidity

(from 5% to 30% and above), shift of wind direction to west, south or southwest and an

increase of wind velocity. The transition from land breeze to sea breeze is more marked,

as the latter proceeds inland. The sea-breeze front appears to be somewhat diffuse near

the coast but by the time it reaches far inside the land it becomes quite sharp owing to

increased contrast with the land breeze.

1.4.3 Socio-Economic Status

Total population of Karachi division was 9856318 persons in 1998 with over 3 percent

annual growth rate (GOP, 2000a) that estimates 11.3647 million in 2002. Almost 94.S

percent population lives in urban area, which indicates about 17334.8 people per square

kilometre population density on an average (GOP, 2000b). Table 1.1 indicates the

population growth from 1931 to 2002.

Table 1.1: Population of Karachi

1931-2002

Karachi's population is deconcentrating from its core because of better residential and

availability of basic goods and services. Environment and urban sprawl are also major

contributors to this deconcentration. In 1972 almost sixty three percent of the population

lived with in ten kilometres of the city. By 1981 this had declined fifty two percent. as

population growth shifted to the ring located between eleven and twenty kilometres from

the centre. At present over one half of Karachi population lives more than 10 kilometre

from the city centre.

14

It is the nation's largest city its chief financial, commercial, manufacturing centre

and hence hub of transportation. Most of the international trade of Pakistan and

landlocked Afghanistan passes through the city's busy modern ports, centred on the

Kiamari and Bin Qasim. Manufacturing includes steel, textiles, chemicals, cement,

refined petroleum, and food. It is an important banking centre and has a stock

exchange.

Inside the city infrastructure is moderately planned. It was properly planned sometime in

some places but not upgraded up to the current load of population requirements. With the

pa!;sa!~e of time high-rises are growing rapidly the occupied city by replacing old,

week and high commercial land value buildings as well as newly developing areas in

surrounding of pre-developed regions. Due to political uncertainty and absence of local

government at intervals, local development authorities, civic facility providers and

municipal organizations had not been efficient and effective.

urban, commercial and industriafgrowth is remarkable.

1.4.4 Urban Settings

its population,

'urban' Karachi of today is presented in Figure 1.5, dellsely huill-lip land cover •

the prominent feature. Karachi has been one of the world's fastest growing cities the

creation of Pakistan in 1947. Its divisional shape is a little twisted equilateral triangular

with east to west base shown in Figure 1.6. The physical form of Karachi Division had

been measured using Haggett and Chorley (1969) Shape Illdex. It conveniently provides

an index of 0.41, which has been obtained with the help of the formula by

Haggett and Chorley (1969). In an ideal situation a circle would have an index of 1.00

with values ranging down towards zero (0) showing progressive elongation. Here a value

of 0.41 confirms the shape of Karachi towards progressive elongation thus minimizing

advantages of compactness, which include distance minimization and inconvenience

of transport and communication (spatial interaction) (Mahmood, 1990). dearly

points to the difficulties of administration.

15

•• '1 .. i <. • • t ",wart "' '''JlS'1 ..

~C_C -- --- • --- .... ... - --- --, • , .-- • -, - --- • --

Conceptual Ceometrical Shape of Karachi Division

E<jui1aten1 TriansJc Con<q>tual Ov<rlapped

Figure 1.6

This city has grown nearly 25 times in the last 45 years and is growing at the rate of about

6% per annwn, making Karachi one of the tastest growing cities in the world. This

situation is mainly due to the rapid industrialization process, which has caused continues

influx of people from up-country areas to Karachi.

Its Core lies near to left-base comer geometrically that is southwest geographically, near

the coast. This core is the gravitational focus of the city because of commercial centre

(Saddar). It is difficult to define it as Central Business District (CBD), owing to sufficient

night time and residential population, but that is the kernel of metropolis.

Analyses of past growth trends in Karachi suggest that the city has expended primarily

along growth corridors to the South, North, East and Northwest. Between 1931 and 2002,

growth in these places was on the order of5.4 percent per year (KDA, 1991) Figure 1.7.

Karachi is considered as transportation hub of Pakistan. It contains many of the nation's

major transport networks. Two major ports of the country: the port of Karachi and port

Qasim (container terminal) and Pakistan's largest international airport are situated in this

metropolis. The functional area or area of influence of these ports are extended upto the

neighbouring land locked states.

Karachi is the terminus of major road and rail networks, which link it to the interior of the

country. This transport infrastructure has transformed it into a cardinal national and

international commerce and banking centre and thus serves as lifeline for the whole

country.

17

KARACHI Urban Sprawl

(1947 -- 2002)

N

o &-.... l _· :;. I \~~

1"..!4,~u..-L ... .......,n..--I ......... I~

101:. .... IAIIII

--.-".. - _oJ'

Ara ian Sea

So.,.c~: A fter A fsar. 200 I

/

/'

/

/

./

/ _ t ,

Figure 1.7

18

Karachi metropolis has a major road network (Figure 1.8) of more than 7,400 kilometres

(KDA, 1991). In Planned and restructured areas, roads are wide and well carpeted as

compared to unplanned areas where such development is almost impossible. Generally

the road width is insufficient for great route flow especially in commercial and industrial

areas, which creates traffic jams, dead slow speed and choking points during rush hours.

1.4.5 Historical Sprawl

Karachi, the capital of Sindh, had a population of about 450,000 with small extensions in

1947 (GOP, 1951). [n physical terms, the city was clearly divided into two: the European

city and the native city The European City consisted of the Cantonment, Civil Lines and

the Saddar Bazaar. The native city was near the port and consisted of the old pre-British

town and its suburbs. The area between the two parts of the city consisted of Bunder

Road, McLeod Road, and Katchehry Road triangle and extended south to the port. That

area contained the Karachi PorI' Trust (KPT) warehouses and railway yard; port related

business, commercial and financial concerns; civic and municipal functions; the major

institutions of higher education, high court and provincial assembly secretariat. [n

addition to the city itself, the Karachi district also contained over 1,200 gOlhs or villages.

Fishermen, peasants and herders inhabited the coastal and inland goths.

Major changes took place in Karachi wiihin a few months after independence. Within a

few months after it became the capital of the country, Karachi received 600,000 refi.lgees

from India. [n addition, it received a large number of government servants a~d support

stalT from other provinces of Pakistan. This massive demographic change was also

accompanied by physical and cultural changes.

The major problem that Karachi faced after the refugee influx was of housing the

migrants, developing infrastructure for water and sewage, and creating space for the

further development of the capital city. Housing was developed for government servants

in or around the periphery of the city and between 1949 - 1953 over 14,000 plots were

developed by promoting cooperative housing societies. These colonies were located at

considerable distance from the city centre and transport, though inadequate, was

developed to service them (Hasan el a/., 1999).

20

Most of the industrialization took place in Karachi and hence people, displaced by the

green revolution, migrated to the city in search of jobs. As a result to accommodate the

incoming population, new townships have been established from fifteen to twenty miles

to the east and west of Karachi in Korangi and New Karachi . In the vicinity of these

townships, large industrial areas were also planned and incentives provided to the

industrialists to develop these areas. Squatter settlements were also developed along the

roads linking the city to New Karachi and Korangi . Located on or along natural drainage

channels.

In the decade of the seventies the state encouraged the construction of medium-rise and

high-rise apartment buildings. Consequently, Karachi's skyline started to change and a

nat-culture, which has now become an integral part of Karachi's sociology, began to

develop. Building bylaws were changed so as to increase building heights and covered

areas. These policies also densified the old city and overloaded its already fragile

infrastructure, and created .vertical slums in the middle and lower middle-income suburbs.

It also led to the creation of a flat culture (Meyerink, 1983). The growth of ka/chi abadies

increased and suburbs to the .north and north east of the city are densifying rapidly.

For Karachi Development Plan 2000, the monitoring and related planning exercises could

not be carried out due to scarcity of data. for which no system was proposed by the Plan.

Resultantly, fragmented development takes place, as there is no coordination among the

agencies making and implementing these development proposals (Khoro and Mooraj,

1997). The growth is still continuing but the most perilous is that, these developments

often involving high-rises commercial development, which are made without any urban

design exercise. Much of Karachi's environmental degradation is a result of atrocities.

1.5 SIGNIFICANCE OF THE STUDY

Air quality is proving detrimental to human health in many parts of the region. These

trends are likely to continue. World Health Organization reported that air pollution is a

threat to the health and well being of peoples throughout the world (WHO, 1958).

21

The uhimate purpose of air pollution monitoring is not merely to present data, but to

provide the information necessary for scientists, policy makers and planners to make

informed decisions on managing and improving the enviromnent. Monitoring fulfils a

central role in this process, providing the necessary sound scientific basis for policy and

strategy development objective setting, compliance measurement against targets and

enforcement action thus enhancing administrative output Figure 1.9.

Control and M.n8!!!l!!!!'1!t Scenarios for decision makers

Source: After WHO, 2000

Problem Idrtrtifintion MDDiloring, inventories,

sources.

PijplrCl .9:S,·_of __

Formulate Policy Modeling, Scenario evaluation

Karachi metropolis is one of the badly poUuted cities in the world. Some private as weU

as governmental organizations have tried to monitor air pollution but spatial dimensions

of air pollution and its impacts particularly on human health have been ignored. This

ignorance by aU means is not deliberate, what we believe is an under estimation of spatial

dimension by our planners. As a result any monitoring management tool has never been

used in this area, which is being conceived in a spatial matter. The spatial dimension

might store muhi-type data, query with multi-spatial criteria, analyse impacts, estimate

population at risk precisely and interpret effects visually, within one integrated system.

The essence of this study is to investigate how to monitor air pollution and to endeavour

for protecting public health from the adverse effects of air pollution thus improving

quality of life of people. Another purpose is to make recommendations to reduce to a

minimum, those air contaminants that are known to be or are likely to be, hazardous to

the health of the inhabitants residing in this biggest metropolis of Pakistan.

"Is air pollution a threat for human life" and qualitatively "How much is it dangerous" are

not questionable except quantitatively how much is it harmful for dwellers of Karachi

22

metropolis and Why? Only spatial pattern monitoring can answer this question even on

micro scale. Spatial pattern monitoring is not essential as prerequisite internationally but

it is in the local and national interest for sustainable development and welfare of

indigenous population.

Remote Sensing is a helping and data providing tool, which provides useful relevant

information related to environmental problems and their impacts. It also supplements to

comprehend the analysed results . In this work besides data collection and representation

of air pollutants, health and diseases, socio-economic and demographic structure variables

and their association were to be integrated and analysed by GIS. Geographic Information

Systems has explored and depicted spatial patterns of atmospheric pollution and spatial

epidemiology of airborne diseases and established cause and effect relationship between

pollutants and diseases respectively.

This systematic research has academic emmence of using Geographic Information

Systems and Remote Sensing tools to examine atmospheric pollution and its aftermath

monitoring. This monitoring would lead towards a cost effectual and efficient planning

and development. Geographers, environmentalists, epidemiologists, planners, decision

makers and other professionals and researchers would not only be interested in the

findings of this study but also in the research methodology.

23

2. REVIEW OF LITERATURE

2.1 AIR POLLUTION: EFFECTS, SOURCES AND SPATIAL

DISSEMINATIONS

Earth's atmosphere is a complex phenomenon formed with the interaction of

hydrosphere, lithosphere and biosphere. It is composed primarily of the gases Nitrogen

(N2) (78 % dry), Oxygen (02) (21%) and Argon (Ar) (1%), whose quantity are controlled

over geologic time-scales by uptake and release from crustal material, by degassing of the

interior and by the biosphere (Prather e/ al., 1995). Water vapour (H20) is the next

largest, though highly variable, constituent present mainly in the lower atmosphere at

concentrations as high as 3%, where evaporation and precipitation control its abundance.

The remaining gaseous constituents are considered trace gases, comprising less than I %

of the atmosphere, yet playing a disproportionately important role in the Earth's radiative

balance and these are extremely important meteorologically (Tarbuck and Lutgens, J 992).

The budget ofa trace gas is defined by its sources and sinks (Akimoto e/ al., 1994), these

chemical species are governed by the rates of production and removal (Bouwman, J 993).

A chemical that is in the wrong place at the wrong concentration is either a pollutant or a

contaminant. The distinction resides in the ability of a pollutant to damage human beings

or parts of the biosphere upon which they depend. These pollutants are emitted into the

atmosphere as gases or particulate which then, directly or indirectly, degrade or adversely

affect physical and biological sy.stems (Elsom, 1987). In the atmosphere, pollutants may

move from a dry, gaseous phase into a liquid phase before falling to the surface (e.g.

Wine and Chameides, 1990). Damage by air pollutants to metals, fabrics and materials

used by humans is very evident but the biological effects of air pollutants directly upon

humans or upon the living systems that surround them are often subtle and far more

significant (Weber, 1982).

There is growing understanding of the links between atmospheric problems such as local

air pollution, acid rain, global climate change and stratospheric ozone depletion. Isolated

25

responses to one environmental problem may in fact worsen another. For example

catalytic converters on cars decrease nitric oxide emissions and help to reduce acid rain

and urban smog but they release higher levels of nitrous oxide, which is a potent

greenhouse gas and a contributor to stratospheric ozone depletion (IPCC, I 996a).

Every year over a thousand people die from the direct affects of air pollution in Pakistan

(Waqas and Dar, 1997; Correspondent Frontier Post, 1999) and 20 to 30 percent diseases

in Pakistan result from pollution and degradation (APP, 2000a). The tremendous increase

in the use of petroleum products, particularly in motor vehicles, introduced several new

pollutants (WHO, 1972). Atmospheric pollution in Karachi, 40 percent higher than other

cities of Pakistan, was on alarming stage. Thousands of tons of toxic gases and particulate

matters were present in the atmosphere in Karachi produced by industrial activity and

traffic vehicles at large (Qureshi, 1997).

Air pollutants are usually classifIed into two broadly categories i.e. Criteria Air Pollutants

and Hazardous air pollutants (USEPA, 1985) USEPA defined Environmental Quality

Standards on the basis of six pollutants' threshold and criteria in Clean Air Act, I 970s.

Since 1970, these have been known as Criteria air pollutants.

I. Carbon Monoxide (CO)

2. Nitrogen Dioxide (N02)

3. Ozone (03)

4. Lead (Pb)

5. Particulate Matter

6. Sulphur Dioxide (S02)

Hazardous air pollutants, also known as toxic air pollutants or air toxics, are those

pollutants that cause or may cause cancer or other serious health effects, such as

reproductive effects or birth defects, or adverse environmental and ecological effects.

Table 2. I presents a list of hazardous air pollutants known:

26

Table 2.1: Hazardous Air Pollulanls

2 3 4

5 6 7 8 9 10 II 12 13 14 15 16 17 18

)9 20 21 22 23 24 25 26 27 28 29 30 31 32 13 34 35 36

Ie

2

AC!),lic acid

Ally

Aniline

lellZyl

C

Captan C.,h.rvl Carbon ' Carbon

1 sullide

Chlorine " : acid

37 ( 38 ( 39 40 ( 1 methyl other 41 C 42 :rc,ylic acid ' 43 <14 45 46 47 48 49

2.4-0, salts and esters DOE

50 C'

,Irom

,

, and ,

52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100

I ethor C

N. I aniline n Diethyl sulfate 3,3-1

[);melhvi

,1-1 '

I sulfate . and salts

1 : (1 4-C'

I (I

Ethyl ae!),late Ethyl Ethyl Elll/l

Hexane

, :(C~ 'de (Dibromo,

:(1

:.lwol : imine " : oxide

, :(1.1-['

-1

H ',acid I fluoride I sullide

l'I<thor)

: acid)

27

~. f'1oi". 102 Lindane(aJl 152 ~ , 103 Maleic ' 153 104 154 2.4-Toluene 10; 155 2, 106 Methvl bromide ill!: 156 107 Methyl ,(Ch 157 108 Methyl \0,1,1-: 158 1,I,2-TI \09 Methyl c~ 1'1 Kelonc I 159 110 Methyl ' 160 :,4,5-TI III Methvl iodid., 161 112 Melllyl,sobut\'1 ketone 162

3 Methyl 163 _4 MethYl 164 ; Metltyl terl butyl ether 16;

116 ,bis{ 166 117 , chloride ') 167

2,2.4-T: Vinyl n«\ate Viny Viny

II S ,(MDI) 168 , chloride 0, 119 169 Xylenes (isomers and ,

123 124 125 126 127 128 129 130 131 132 133 134 J:

3'

13! 139 140 141

142

N-

Phenol p-I

Phthalic

, sultone

143 : oxide

, (

, 0,2-

144 I ,c 14; 146 147 ,tyrene 148 Styrene oxide 149 2.3,7,8-T' 1;0 1.1.2,2-T,

Source: u~~. ~. 2002. Web:

170 0-) enes 171 172 173

174 175 176 177 \'

, I'

I: 181 182 183 184 1~5

186 187 188 189

Arsenic , \ (

~( Cobalt Coke Oven Cyanide G Ivcol eth.rs2 LcadC

Fine mincrallibcrs3 N :ke1

, Organic Mattcr4 :eadon)5

,AuguSI I

: orsine)

28

Apart from the USEPA classification of crileria and hazardous pollulan/s, they are

generally classified in the literature according to their physical state. These pollutants are

found in atmosphere in various states of matter. Therefore, these are usually classified

into suspended particulate matter, gaseous pollutants (gases and vapours), odours and

heat (WHO, 2000).

The term Suspended Particulate Matter refers to the wide range of finely divided solids

or liquids dispersed into the air and have a number of different names. In common

lerminology particulate matter consists of dust, aerosols, fly ash, fog, fume, mist,

particles, smoke, soot, and sprays (Parker, 1978). Some common types of particulate

matter are indicated in Table 2.2.

Table 2.2: Particulate Malter: Sizes and Identifications

Dlisl consists of solid larger particles, capable of temporary suspension in air and other

gases (Buat-Menard el al., 1983; Dulac el al., 1989, 1992). Ail aerosol consists of

dispersion in a gaseous media of solid or liquid particles of microscopic size, such as

smoke, fog or mist. In some definitions aerosols are considered to include anything from

particles of 100 !lm to 0.1 ~lm or less. Particles 5 !lm or smaller tend to form stable

suspensions. Particles larger than 5 !lm tends to settle out as dust fall. Fly ash consists of

fmely divided particles of ash entrained in flue gases arising from the combustion of fuel.

The particles of ash may contain unburned fuel and minerals. Fog consists of visible

29

.. _ - -- -----

aerosols in which the dispersed phase is liquid (Andreae, 1995). In meteorology, fog is a

dispersion of water or ice (Tarbuck and Lutgens, 1992, 1994).

Fllllle consists of particles formed by condensation, sublimation, or chemical reaction, of

which predominate part. by weight, consists of particles smaller than I J.llll. Tobacco

sllloke and condensed metal oxides are examples of fume. Fume may flocculate or

coalesce. Misl is a low concentration dispersion of relatively small liquid droplets. In

meteorology, the term mist applies to a light dispersion of water droplets(Tarbuck and

Lutgens, 1992, 1994). In enough amounts to fall from the air, Mists may result from the

condensation of gases or vapours to the liquid state. Breaking up a liquid through

splashing, or foaming can also generate mists. Sprays are liquid droplets formed by

mechanical action (Durkee el al., 1991; Stull, 2000).

The World Health Organization (1976) recolllmends the suspended particulate matter

sample to be referred to as 'smoke' (or sometimes 'soot') and latter as 'total suspended

particulate' (TSP). Each of them has a specific meaning despite being popularly used in

an interchangeable manner, and Table 2.2 distinguishes them by nature and size. In the

case of grits and most coarse dusts, only particles greater than 50 J.lm across remain

suspended for some time and, if very small (0.1-2 J.lm), act as nuclei for the condensation

of water during cloud formation. They are only removed as rain or when hit by falling

rain (washout). If they escape from the lower troposphere to upper altitudes, however,

they remain there for months or even years (Commins and Waller, 1967; Lee el at., 1972;

Ball and Hume, 1977; Parker, 1978; Pashel and Egner, 1981; Elsom, 1987). Effects of

SPM in humans depend on particle size and concentration, and can fluctuate with

tluctuations in PMIO (Concentration of particles with aerodynamic particle diameters of

less then 10 micrometers) or PM2.5 (Concentration of particles with aerodynamic particle

diameters ofless then 2.5 micrometers, respirable Particulates) levels.

VajJours include gases and compounds that in general have a boiling point below 2000 C .

. The terms vapour and gas are often used interchangeably. In a strict sense a vapour is a

substance, which, though present in the gaseous phase, generally exists as a liquid or solid

at room temperature and sea level barometric pressure such as Acetone, Benzene,

Hydrogen Sulphide, Formaldehyde etc. A gas normally exists in the gaseous phase at

30

room temperature. Gaseous pollutants include sulphur compounds (e.g. Sulphur dioxide

and Sulphur trioxide), Carbon monoxide, nitrogen compounds (e.g. Nitrogen oxide,

Nitrogen dioxide, ammonia), Organic compound (e.g. hydrocarbons, volatile organic

compounds, Polycyclic aromatic hydrocarbons and halogen derivative aldehydes etc.),

halogen compounds (HF and HCI), Ozone and etc. (Logan, 1983: Laxen, 1985: and

Novelli et al., 1992). Many industrial processes as well as the operation of machines such

as engines may either raise temperatures or lower the barometric pressure, causing a

substance to convert to the gaseous phase (Lawther, 1970).

Some odours may be dangerous for human health, these are considered as air pollutant.

During the travel on public transport, crossing the industrial odorous areas, near the

kllchra kill/dies of solid waste and near the sewage sludge many people feel Nausea and

Nasal bleeding. While some odours are known to be caused by specific chemical agents

such as hydrogen sulphide (H2S), Carbon disulphide (CS2) and mercaptans (R-SH, Rl S

R2), others are difficult t. define chemically (WHO, 2000)

The energy released by man 111 the form of "eat, called a type of air pollution

(Padmanabhamurty and Hirt, 1974). This energy causes a significant climate change in

our cities and in global climate effects. Cities are warmer than their rural surroundings

because of energy dissipation and the thermal capacitance of streets and buildings for

solar input. This concept is known as Urban Heat Island (UHI) (Quattrochi and Ridd,

1994, Lo et ai, 1997).

Transportation contributes the largest share of air pollutants to the urban environment.

The total number of registered vehicles in Asia-Pacific region in 1996 was about 127

million, 4.24 percent more than in the previous year (International Road Federation,

1997). In Seoul, car ownership doubled in a single year between 199 I and 1992 (Ministry

of Environment, Republic of Korea, 1990 and 1995). Lead pollution is a particular

problem in megacities of South and Southeast Asia. The introduction of unleaded fuels is

reducing average lead levels, although the rate of decline is slower in Asian than

elsewhere.

Pakistan has fortunately switched to unleaded gasoline. Vehicular emissions are only just

one big source of air pollution in the cities of Pakistan besides industrial emISSIons,

31

burning of solid waste and natural dust (Correspondent the Nation, 2000). Nairn (1998)

Jlcrceived that the major contributors as a source of air pollution in Karachi were tirstly

the oil tired thermal power stations, secondly the factories and thirdly the road vehicles.

Pakistani petroleum marketing and blending companies were supplying fuel to industries

and powerhouses with over 3.5 percent sulphur content. While the standard of one

pcrcent sulphur content in high-speed diesel in furnace oil by the Pakistan Standards

Institution (PSI) is very high as compare to less than 0.5 percent sulphur allowed in India

(Qureshi, 2000).

Karachi is the most populated city of Pakistan having very high traffic density. These

vehicles emit 1,813 tons of smoke daily (Khalil, 1996). Major proportion of that pollution

is caused by 15,000 rickshaws, which create multidimensional environmental problems in

which air pollution was leading (Editorial daily News, 1996; APP, 2000b). The two

stroke rickshaws are a major cause of air and noise pollution (APP, 1999). Main causes of

vehicular emitted air pollution in Karachi were supply of adulterated diesel, petrol and

engine oil, road traffic jams due to illegal encroachments at numerous places,

mismanagement of civil administration (Correspondent daily Dawn, 1997), traffic

congestion, insufficient urban road space, ineffective traffic management, low average

vehicle speed, lack of enforcement of vehicle emission standards and poorly maintained

older vehicles (Mughal, 1998).

2.1.1 Global and Regional Scenarios

Air Pollution adds harmful substances to the atmosphere resulting in damage to the

environment, human health, and quality of life. Some air pollutants return to Earth in the

form of acid rain and snow, which corrode buildings, damage crops and forests, and make

lakes and streams unsuitable for fish and other plant and animal life (Manins, 1995).

Pollution is changing Earth's atmosphere so that it lets in more harmful radiation from the

Sun. At the same time. our polluted atmosphere is becoming a better insulator, preveflting

heat from escaping back into space and leading to a rise in global average temperatures

that further affect world food supply, alter sea level, make weather more extreme, and

increase the spread of tropical disease (WHO, 1992).

32

Air pollution expands beyond a regional area to cause global effects. It is rich in ozone,

the same molecule that acts as a pollutant when found at lower levels of the atmosphere in

urban smog. Up at the stratospheric level, however, Ozone forms a protective layer that

serves a vital function: It absorbs the wavelength of solar radiation known as ultraviolet-B

(UV-B). UV-B damages deoxyribonucleic acid (DNA), the genetic molecule found in

every living cell, increasing the risk of such problems as cancer in humans (Elsom, 1987).

Several pollutants attack the ozone layer. Chief among them is the class of chemicals

known as chlorot1uorocarbons (CFCs). In these compounds the chlorine acts as a catalyst.

A single chlorine atom can destroy up to 100,000 ozone molecules in the stratosphere

(Miller, (999). Other pollutants, including nitrous oxide from fertilizers and the pesticide

methyl bromide, also attack atmospheric ozone (Keller el al., 1993; Keller and Matson,

1994). In the Antarctic region, it vanishes almost entirely for a few weeks every year

(Bowman et aI., 1995). Although eFe use has been greatly reduced in recent years and

will soon be prohibited worldwide (Montzka et al., 1993), eFe molecules already

released into the lower atmosphere are making their way to the stratosphere for decades,

and further ozone loss is expected. As a result in anticipation, an increase in the incidence

of skin cancers, more eye cataracts (clouding of the lens of the eye), and reduced yields of

some food crops are considerable effects (Liu and Trainer, 1988; Liu el al., (991).

The driving energy for weather and climate comes from the Sun. There are several

natural factors, which can change the balance between the energy, absorbed by the Earth

and that exited by it in the form of long wave infrared radiation; these factors cause the

radiative forcing on climate. One of the most important factors is the greenhouse effect

that furthers affects on human beings directly as well as indirectly. The main greenhouse

gases are water vapour {the biggest contributor (!Pee, (995)}, carbon dioxide, methane,

nitrous oxide and ozone in the troposphere and stratosphere (IPee, 1990). Aerosols in the

atmosphere can also affect climate because they can rellect and absorb radiation. The

most important natural perturbations result from explosive volcanic eruptions, which

affect concentrations in the lower stratosphere. Table 2.3 is a summarized key greenhouse

gases aft'ected by human activities and their future trends around the globe.

33

Table 2.3: Summary of Key Greenhouse Gases Affected by Human Activities

Vr.:·induslrial (11 $0 - I ~OO) 2>0 0.> 0 0 2~~

1990 333 1.72 2~0 4~4 )IU

CUINtll r;II .. · or ~h'lIIg.: I)o,:r Y":OIr I . t( (O. j ~ u) O.UIS (0.')" ,,) ').5 (4", .. ) 17 (4~,,) O.X (0.25"u)

All\1U),(JINri~ lili.:tim.: (y.:ars) 30 ·· 200 IU 63 Ilu IlO

Source: !PCC. 1 ~91l

Globally, future levels of atmospheric pollution will be governed largely by the use of

energy from fossil fuels . The Intergovernmental Panel on Climate Change (IPCC) (1995)

has predicted that global economic output may double between now and 2050, with

energy demand reaching nearly three times that in 1990. If the developing countries

tollow the conventional development path, there would be a massive increase in the

emission of atmospheric pollutants. However. this need not be the case as has been

proved by some developed countries. For example, in Europe. sulphur emissions peaked

in the 1970s and subsequently declined steadily, despite increasing energy consumption.

Similarly, the mechanisms developed fur the Kyuto Protocol could help developing

countries restrict their emissions .of greenhouse gases.

Naturally occurring greenhouse gases keep the Earth warm enough to be habitable. By

increasing their concentrations and by adding new pollutant (so called greenhouse gases)

like chlorofluorocarbons (CFCs), humankind is capable of raising the annual global mean

surface-air temperature. This is enhanced greenhouse effect as compared to that occurring

due to natural greenhouse gas concentrations. Other changes in climate are expected to

result, for example changes in precipitation, and a giobaiwllrmillg would cause sea levels

10 rise. Human made aerosols, from sulphur emitted largely in fossil fuel combustion, can

modify clouds and this may act to lower temperatures. Changes in ozone in the

stratosphere due to CFCs may also influence climate. The total release of carbon to the

atmosphere from changes in lane use, primarily deforestation, between 1850 and 1985 has

been estimated to be about 115 GtC (Houghton and Skole, 1990).

Annual global emissions of carbon dioxide from the burning of fossil fuels cement

manufacture and gas flaring reached at a peak of nearly 23,900 million tonnes in 1996

(CDIAC, 1999). This was some 400 million tonnes more than in 1995 and nearly four

times the 1950 total . Only in some countries in Europe and Central Asia has there been a

34

significant drop in emissions during the past decade, mainly a result of the economic

crises in Eastern and Central Europe. Atmospheric concentrations of COl in 1997 reached

more than 360 parts per million (ppm), the highest ever level (Keeling and Whorf 1998).

In assessing the possible impact of rising atmospheric concentrations of COl and other

grccnhouse gases (GHGs), the WMO/UNEP Intergovernmental Panel on Climate Change

(lPCC) concluded in its 1995 report that 'the balance of evidence suggests that there is a

discernible human influence on global climate' (IPCC 1996a). Recent research suggests

that climate change would have ' complex impacts on the global environment. The IPCC

mid range scenario projects an . increase in global mean temperature of 2.0 C, within a

range of 1.0 to 3.5 C by the year 2100, the largest warming in the past 10,000 years.

Average sea level is projected to rise by about 50 cm, within a range of 15 to 95 cm, by

the 2100. A 50 cm rise in sea level would lead to the displacement of million of people in

low lying delta areas and a number of small island states could be wiped out (IPCC

I 996b).

In a warmer world there would be higher agricultural production in the high latitudes of

the northern and southern hemispheres but reduced production in the tropics and sub

tropics where there is already food deficiency. The species composition of forests and

other terrestrial ecosystems is. likely to change, entire forest types may disappear .

Although forest productivity could increase, the standing biomass of forests may not

increase because of more frequent outbreaks and extended ranges of pests and pathogens,

and increasing frequency and intensity of fires. Climate change could influence lakes,

streams and wetlands through altered water temperatures, flow regimes and water levels.

Increases in the variability of water flow, particularly the frequency and duration of large

floods, and droughts, would tend to reduce water quality and biological productivity and

habitat in freshwater ecosystems (JPCC 1998).

In addition to these environmental effects, climate change may have direct and indirect

health impacts. Greater frequency and severity of heat waves, and changes in agriculture

and food production, could affect nutritional status and vector distributions (Lindsey and

Birley, 1996). Two anthropogenic processes primarily affect the concentration of COl in

the atmosphere: release of COl from fossil fuel combustion; and changes in land use such

as deforestation. The global input of COl to the atmosphere from fossil fuel combustion,

35

------ ------------.

plus minor industrial sources like cement production, has shown an exponential increase

since 1860 (about 4% per year) (Marland, 1989). The components of the flux to the

atmosphere have been: burning associated with land use change; and decay of biomass on

site (roots, stumps, slash, twigs etc.); oxidation of wood products removed from site

(paper, lumber. waste etc.); oxidation of soil carbon.

Ninety five percent of the industrial C02 emissions are from the Nonhern Hemisphere,

dominated by countries, where annual releases reach up to about 5 tC per capita (Rotty

and Marland, 1986). In contrast CO2 emission rates in most developing countries lie

between 0.2 and 0.6 tC per capita per year. However, the relative rate of increase of the

CO2 emissions is much larger in the developing countries (-6% per year), showing almost

no slowing down after 1973 in contrast to Western Europe and Nonh America where the

rate of increase decreased from about 3% per year (1945 - 72) to less than 1% per year

(1973 - 84) (lPCC, 1990).

Future GHG emissions will be a function of global energy demand, and the rate of

development and introduction of carbon-free and low carbon energy technologies.

Several variables make predictions of future emissions uncenain: economic growth rates.

energy prices, the adoption of effective energy policies and the development of efficient

industrial technologies. Meeting the targets for emission reductions agreed at Kyoto, itself

a formidable challenge for some countries, is only a first step in bringing under control

what is generally agreed to be the most critical environmental problem that the world

faces . But even meeting all the targets agreed at Kyoto will have an insignificant etTed on

the stabilization levels of carbon dioxide in the atmosphere.

With the increase in the use of relatively high carbon content fuels such as coal and oil,

emissions of C02 also increased fast at twice the average world rate of 2.6 percent a year

during 1975-95 (CDIAC, 1998). Since the 1970s, industrial emissions of CO2 have grown

60 percent faster in Asia than anywhere else (ADB, (997). China and Japan are the first

and second largest CO2 emitters respectively in the region (WRl, UNEP, UNDP, and

WB, 1998). However, CO2 emissions per capita are low, little more than half the world

average and only 11 .2 percent of the level in North America in 1995. Past land clearing

has also contributed a signifIcant proportion of C02 emissions in some countries.

36

GOP EUAD / IUCN (2000) estimated the magnitude CO2 emissions in Pakistan Irom

various economic sectors for last three decades, which is presented in Table 2.4.

Table 2.4: Estima·ted CO2 from Various Economic Sectors in Pakistan

The pollutant of Methane (CH4) is produced from a wide variety of anaerobic sources

(Cicerone and Ormland, 1988) Significant progress has been made in quantifying the

magnitude of the source of CH4 from natural wetlands (Harriss and Sebacher, 1981;

Svensson and Rosswall, 1984; Harriss e! af., 1985; Sebacher e! af., 1986; Moore and

Knowles, 1987; Mathews and Fung, 1987; Crill e! af. , 1988; Whalen and Reeburgh,

1988). Rice paddies are an important source of CH4 with estimates of the globally

averaged flux ranging from 25 - 170 Tg CH4 per year (Neue and Scharpenseel, 1984;

Holzapfel-Pschorn and Seiler, 1986). Biomass burning in tropical and sub-tropical

regions is thought to be a significant source of atmospheric CH4, with estimates of global

emission rates ranging from 20 to 80 Tg CH. per year (Crutzen e! af., 1979; Crutzen e!

a/., 1985; Bingemer and Crutzen, 1987; Andreae e! af., 1988). Methane emissions from

enteric fermentation in ruminant animals, including all cattle, sheep and wild animals, is

estimated to provide an atmospheric source of 65 - 100 Tg CH4 per year (Crutzen e! af.,

1986; Lerner e! af., 1988).

The anaerobic decay of organic wastes in landfills may be a significant anthropogenic

source of atmospheric CH., 20 - 70 Tg CH. per year (Bingemer and Crutzen, 1987).

There is a large range in the magnitude of the estimated fluxes of CH. from termites; 10-

) 00 Tg CH4 per year (Cicerone and Oremland, 1988; Zimmerman e! af., ) 982;

Rasmussen and Khalil, 198) . Oceans and freshwater; Coal mining; and Gas drilling,

venting and transmission are thought to be a minor source of atmospheric CH4 (Cicerone

and Oremland, 1988; ICF, 1990). Oxidation of CH. hy OH in the stratosphere is a

significant source of stratospheric water (H20) where it is an important greenhouse gas

(Seiler e! at., 1984; Zimmerman e! af., 1982).

J7

Halocarbons containing chlorine and bromine have been shown to deplete 0 3 in the

stratosphere. In addition, it has been recognized that they are important greenhouse gases.

Their role in perturbing stratospheric 0) and the Earth's radiative balance has been

considered significantly (WMO 1985, 1989a, 1989b). Most halo carbons, with the notable

exception of Methyl Chloride CH)Cl, are exclusively of industrial origin. Halocarbons are

used as aerosol propellants, refrigerants, foam blowing agents, solvents, and fire

retardants. The atmospheric concentration of methyl chloride is about 0.6 ppb, and is

primarily released from the oceans and during biomass burning. Methyl Bromide CH,Br

is produced by oceanic algae, and there is evidence that its atmospheric concentration has

been increasing in recent times due to a significant anthropogenic source (Wofsy ~I al.,

1975; Penkett el al., 1985).

Nitrous oxide is a chemically and radiatively active trace gas that is produced from a wide

variety of biological sources in soils and water and is primarily removed in the

stratosphere by photolysis and reaction with electronically excited oxygen atoms. The

oceans are a signifIcant, but not dominant source of N20 (McElroy and Wofsy, 1986).

Based on measurements of the concentration gradients between the atmosphere and

surface waters (Butler el al., 1990), and on estimates of the gas exchange coefficient, the

current estimate of the magnitude of the ocean source ranges from 1.4 - 2.6 Tg N peryear

(Elkins ~I al., 1978; Cohen and Gordon, 1979; Cline el al., 1987) . Denitrification in

aerobic soils is thought to be a dominant source of atmospheric N20 (Slemr ~I al., 1984;

Keller el al., 1986; Matson and Vitousek, 1987). The combustion of fossil fuels has been

thought to be an important source of atmospheric N20 (Pierotti and Rasmussen, 1976;

Weiss and Carig, 1976; Hao ~I al., 1987). Biomass burning and the use of ammonium

fertilizers are now thought to be minor but significant sources of N20 emission in

atmosphere (Muzio and Kramlich, 1988; Cmtzen 1989; Elkins ~I al., 1990; Winstead ~I

((/., 1990; Griffith el a/., 1990)

Tropospheric 0) is a greenhouse gas, of particular importance in the upper troposphere in

the tropics and sub-tropics. Its distribution is controlled by a complex interplay between

chemical, radiative, and dynamical processes. Ozone is produced by the photo-oxidation

of CO, CH4 and NMHC in the presence of reactive nitrogen oxides (NO,) and destroyed

by vegetative surfaces, uv-photolysis and by reaction with hydrogen oxide radicals (HOl )

38

(Danielsen, 1968; Crutzen, 1974; Mahlman and Moxim, 1978; Isaksen e/ al., 1978 and

GalbaJly and Roy, 1980).

Global consumption of chlorofluorocarbons (CFCs), the most prevalent ozone-depleting

substances (ODS), fell from 1.1 million tonnes in 1986 to 160,000 tonnes in 1996, thanks

10 an almost complete phase out by industrialized countries (UNEP, I 998a, 1999).

Several factors contributed to the success of policies directed at reducing the consumption

of ODS : damage to the ozone layer could be ascribed to a single group of substances,

alternative substances and processes were developed at acceptable costs, a scientific

assessment was introduced to make adjustments to the Montreal Protocol as required, the

Protocol contained flexible implementation schemes and evaluation procedures, and the

principle of 'common but differentiated' responsibilities was recognized for the

developed and developing countries.

One measure of the Protocol's success is that the ozone layer is now expected recover to

pre 1980 levels by the year 2050. Without the Protocol, levels of ODS would have been

tive times hither then than they are today, and surface UV-B radiation levels would have

doubled at mid latitudes in the northern hemisphere (UNEP 1999).

The total combined abundance of ODS in the lower atmosphere peaked in about 1994 and

is now slowly declining (WMO, UNEP, NOAA, NASA and EC 1998). While total

chlorine is declining, total bromine is still increasing, as is the abundance of CFC

substitutes. If reductions in the use of ODS continue as envisaged in the Montreal

Protocol, then concentrations of these substances in the stratosphere should gave peaked

between 1997 and 1999, and should begin to decline during the next century. The rate of

decline in stratospheric ozone levels at mid latitudes has already started to slow. The

unusually low ozone values above the Arctic in late winter/spring observed in six out of

the past nine years could have ' been accentuated by the unusually cold and prolonged

stratospheric winters experienced during those six years (WMO, UNEP, NOAA, NASA

and EC, 1998).

Despite significant progress in bringing the problem of ozone layer depletion under

control, a number of outstanding challenges remain. Past (and continuing) emissions of

ODS will result in increases in UV -8 radiation that are likely to lead to increases in the

incidence and severity of a variety of short and long term human health effects,

-----_ . . _-- -_ . . --- - - .. _ .

particularly on the eyes, the immune system and the skin. Recent evaluations of UV

related excess skin cancer risks in Europe caused by ozone depletion suggest that, even

though stratospheric ozone concentrations should reach a minimum around the year 2000

(which assumes that the measures in force are fully implemented) m excess skin cancer

incidence is not expected to begin to fall until about 2060, because of the time lags

involved .

The response of terrestrial ecosystems to increase UV -8 is evident primarily in

interactions among species rather than in the performance of individual organisms.

Recent studies indicate that increased UV-8 affects the balance of competition among

higher plants, the degree to which higher plants are consumed by insects and the

susceptibility of plants to pathogens (UNEP 1998b). Increased UV -8 can be damaging

for crop varieties but this may be offset by protective and repair processes.

In terms of overall impact, ozone depletion interacts with the climate change process.

Stratospheric loss of ozone has caused a cooling of the global lower stratosphere: changes

in stratospheric ozone since the late 1970s may have offset about 30 percent of the

warming efrect of other greenhouse gases over the same period (WMO, UNEP, NOAA,

NASA, and EC 1998). There are also complex interactions between ozone depletion,

climate change and the abundance of methane, nitrous oxide, water vapour and sulphate

aerosols in the atmosphere. For example, carbon is an important element in the absorption

of UV radiation. Climate change and acid rain have led to decreases in the dissolved

organic carbon concentration in many North American lakes (Schindler 1:1 at., \996) . As

organic carbon levels have decreased, UV radiation has been able to penetrate much more

deeply into surface waters, resulting in greater UV -8 exposure of fish and aquatic plants.

While the potential impact of stratospheric ozone depletion means there is no room for

complacency, the cooperative measures that followed the identification of the problem

remain an outstanding and encouraging example of the ability of the international

community to act in unison in protecting the global environment.

The atmospheric concentration of CO exhibits significant spatial and temporal variability

because of its short atmospheric lifetime (Heidt 1:1 at., 1980; Dianov-Klokov and

Yurganov, 1981; Seiler and Fishman, 1981; Seiler el al., 1984; Khalil and Rasmussen

40

---- -------------

1984, 1988a: Fraser el al., I 986a, 1986b: Newell el al., 1989: Zander el al., 1989;

Kirchhoff and Marinoho, 1989; Kirchhoff el aI., 1989). The total annual source of CO is

about 2400 Tg (!PCC, 1995): CO, being about equally divided between direct

anthropogenic (incomplete combustion of fossil fuels and biomass) and atmospheric

(oxidation of natural and anthropogenic CH. and NMHC) sources (Logan el aI., 1981;

Cicerone. 1988). Atmospheric concentrations of CO may have increased in the Northern

Hemisphere because of the fossil fuel source, and because of changes in the rate of

oxidation of CH., whose atmospheric concentration has increased since pre-industrial

times (USEPA. 1989).

In Latin America, the mam . anthropogenic source of atmospheric emlsstons IS

deforestation. Biomass burning and the establishment of new types of vegetation cover in

the Amazon basin have significant ecological implications for the region. the continent

and the globe (LBA. 1996). Some parts of the region also suffer from air pollution from

industry and from large cities. The situation may worsen as result of the deregulation and

privatisation of the energy sector in, for example, Argentina, Brazil and Colombia where

there may be a trend away from biomass and hydropower to more use of fossil fuels

(Rosa el al., 1996). In Japan, some pollutants were decreased nearly 40 percent between

1974 and 1987 (WRl, UNEP and UNOP, 1992). Similar air pollution problems in the

Republic of Korea have been reduced since the 1980s by increasing the use of liquefied

natural gas (Government of Republic of Korea 1998).

Non-Methane Hydrocarbons (NMHC) are also considerable pollutant for broad scale

effects. The oceans are a major source of NMHC, mainly alkenes. Estimates of the source

strength of ethane and propene range from 26 Tg C per year (Bonsang el al., 1988) to as

high as 100 Tg C per year (Penkett, 1982). Emissions of NMHC from terrestrial

vegetation are mostly dependent upon environmental factors as well as the type of

vegetation (Rasmussen and Khalil, 1988). The source strength of NMHC from

anthropogenic activities such as biomass burning, solvents and fossil fuel combustion has

been estimated to be about 100 Tg per year (Rasmussen and Khalil, 1988).

Global warming has different effects in different regions. A warmed world is expected to

have more extreme weather, with more rain during wet periods, longer droughts and more

powerful storms (Schimel and Sulzman, 1994). Although the effects of future climate

41

change are unknown, some predict that exaggerated weather conditions may translate into

better agricultural yields in areas such as the western United States where temperature

and rainfall are expected to increase. While dramatic decreases in rainfall may lead to

severe drought and plunging agricultural yields in parts of Africa (Parther, 1994).

Local and regional pollution takes place in the lowest layer of the atmosphere. the

troposphere in which most weather occur (Tarbuck and Lutgens, 1994; Christopherson,

1997). In the weather phenomenon known as thermal inversion. a layer of cooler air is

trapped near the ground by a layer of warmer air above and makes occluded front. When

this occurs, normal air mixing almost ceases and pollutants are trapped in the lower layer.

Local topography, or the shape of the land, can worsen this effect (Lyons and Scott.

1992).

Smug is intense local pollution usually trapped by a thermal inversion (Cope and

Ischtwan 1995; Cope and Hess, 1997). Before the advent of the automobile, most smog

came from burning coal. Burning gasoline in motor vehicles is the main source of smog

in most regions today. Powered by sunlight, oxides of nitrogen and volatile organic

compounds react in the atmosphere to produce photochemical smog (Lin, 1981). Smog

contains ozone in the lower atmosphere that damages vegetation, kills trees. irritates lung

tissues, and attacks rubber (Harrison, 1996). Environmentalists measure ozone to

determine the severity of smog .. When the ozone level is high, other pollutants, including

carbon monoxide, are usually present at high levels as well (Kagawa and Toyama, 1975;

Lin and Bland, 1980).

Pakistan Environment Protection Agency (PEP A) carried out air quality study during

high smog time in winter 1999, which recorded SPM in the range of 800 to 900 Mglm3

with considerable contents of soot in the air. Pakistan environmental protection agency

(PEPA) also estimated that 40 percent of the total urban population, a counting for about

16.28 million people are exposed to the risk of air pollution. Accordingly, non

compliance of World Health Organization's (WHO) air quality standards was costing

Pakistan rupees (Rs.) 25.7 billion on health accounts (Correspondent daily Dawn, 2000).

In the presence of atmospheric moisture, sulphur dioxide and oxides of nitrogen turn into

droplets of pure acid floating in smog. These airborne acids are bad for the lungs and

42

attack anything made of limestone, marble, or metal (BERG, 1989; Lal and Pati!, 2001).

III cities around the world, smog acids are eroding precious artefact, including the

Parthenon temple tn Athens, Greece, and the Taj Mahal in Agra, India (Brydges and

Wilson, 1991). by winds in the troposphere, Oxides of nitrogen and sutphur

dioxide reach distant regions where they descend in acid form, usually as rain or snow

(Cape, 1987), Such acid precipitation can burn the leaves of plants and make lakes too

acidic to support fish and other living things (Hutchinson and Havas, 1980; HoweHs,

1983, Heck 11/ aI., 1988; Last and Watling, 1991; Jager e/ ai" 1993). Because of

acidification,

many lakes

,~,.,nH" species such as the popular brook trout can 110 longer survive in

streams in the eastern States (Howells, I Wellburn, (994),

Rainwater once was the purest form of water available but now is often contaminated by

pollutants in the air (Galloway e/ ai" 1979; Legge and Krupa, 1986), Acid rain is caused

when industrial emissions mix with atmospheric moisture (Legge and Kurpa, 1986,

1990), Pollutants may be carried in clouds for long distances before falling, which mean

that and far away from factories may be damaged acid raill (Lollghurst,

1989),

Strenuous efforts have begun 10 abate atmospheric pollution in many industrialized

countries but urban air pollution problems are reaching crisis dimensions in most cities of

the developing world. Acid rain remains a problem with critical loads (the threshold at

which deposition causes frequently over large parts of North

America, Europe and South Southeast Asia (Kuylenstierna e/ aI" 1998),

precipitation of atmospheric pollutants at sea is the major source of open ocean pollution,

and the identification of processes that transport toxic chemicals from warm regions into

the Arctic shows how the atmosphere links the global environment into Ii single

integrated system,

The Korean peninsula will be seriously affected by cross border acid rain, Mongolia may

receive acid rain form its, north-western border with Russia, In addition, increasing

emissions from transport will aggravate urban pollution, A study of Nepal, for

instance, estimates that total emissions will increase fivefold by 20! 3, about two thirds of

which are likely to come from the transport sector (Shrestha and Malia, 1996),

43

Beg was cited in a news (APP, ! 997) item regarding acid in which it was stated that

massive invasion of the power plants, using sulphur containing heavy fuel, has largely

exposed Karachi to the hazards of acid rains and relevant environmental risks, Shams

(19973) the attention of towards the threat of Acid rain in Karachi. He

estimated that the use of furnace oil with very high content in power

acidity the whole environment the city would experience the same fate, which

European cities had experienced during the 1960s and 19705,

A team of Pakistan Youth Organization, Environment Protection Wing

EPW} the imminent hazard of acid in Karachi metropolis to

thousands of tons of sulphur dioxide daily emission (APP, 1998), the

corrosive effects of sulphuric acid in Karachi, Furnace oil, contains approximately 3,5

percent sulphur, was used as a fuel in thermal power plants in Pakistan, This sulphur in

form of sulphur dioxide forms sulphuric acid with combination of atmospheric moisture,

I Biotic Upshots

This review cannot encompass the entire spectrum of more than 190 known air pollutants

Nonetheless, it is endeavoured here to elaborate the effects on life due to the

pollutants,

L2.l Sulphur Dioxide (S02)

crilf!ria

Sulphur dioxide (S02) is a colourless pungent, irritating, water-soluble reactive gas. It

may react catalytically or photo-chemically with other pollutants to form sulphur trioxide,

sulphuric acid and sulphates (Hameed, 1991; WHO, 2000), Tills pollutant has great

impacts on plants (Mansfield, 1976). It the size of stomatal aperture in leaves,

which provide a plant with mechanisms to control the movement of CO2 into leaves and

H20 out wards, S02 injures the plasma membranes of guard cells and transport cells

loading the vascular elements of leaves (Winner el aI., 1985), The prominent effects of

SOlon plants are necrosis, chloroplastidic damage, early leaf fall, and increases in

numbers of dead leaves (Wellburn, 1994) whereas, invisible injury associated with

relative changes in growth and yield (Unsworth and Omrod, 1982)

44

Sulphur dioxide is the oldest recognized harmful air pollutant-affecting human (Clean Air

Year Book, 1975), recently other priorities (e.g. photochemical oxidant damage) have

tended to disguise the harmful consequences to human health of this acidic gas (Pikhart e/

al., 200 I; Harman el aI. , 200 I; Pande el al., 2002). Human studies have shown acute

ctTccts of S02 on pulmonary functions among asthmatics as well as an increase in

symptoms, such as wheezing and shortness of breath (WHO, 1995a). Petroeschevsky e/

al. (200 I), Polat e/ al. (2002) have discussed the effects of Sulphur dioxide and its

derivatives that produce strong irritation upon the eyes and also within the nasal

passageways.

Particles smaller than I lAm are the most irritating but larger particles, as well as high

concentrations of gaseous S02, also induce an involuntary coughing reflex (Pope e/ al.,

1995; WHO, 1995a; Hajat el al., 2002). Braga el al., (2001) mentioned that the eye

irritation combined with choking cough draws the attention of those affected to the

hazards of the surrounding atmosphere. Linn el al. (1987) report a 10 percent decrease in

FEY I after 15 minutes exposure to 1,144 lAg mol (0 .4 ppm), and a reduction of IS percent

after exposure to 1,716 lAg mol (0 .6 ppm) among moderate and severe asthmatics.

However, very high concentrations of S02 paralysed the sense of smell quickly (Smith

Sivertsen el al., 2001). Horstman el al. (1988), Herbarth el al., (2001), and Trenga e/ al.

(2001) have shown in their studies that when asthmatic subjects exercise in air containing

S02, symptomatic broncho-constriction and dryness can develop, which may induce

chronic bronchitis in some individuals. S02 causes local release of histamine, which then

acts as a local modulator to · cause constriction of the airways and initiates local

inflammation (Lippmann el al., 2000). Wellburn (1994) summarize the human response

to S02 for various dose levels Table 2.5.

Table 2.5: Human response to different levels of S02 for diffe.'ent period

Source: Welburn. 1994

45

During accidental non-fatal exposures, victims expenence inflammation of the eyes,

nausea, vomiting, abdominal pain, a sore throat, bronchitis, and often pneumonia (Buseck

and Poslili, 1999). Lippmann (1989c) reviewed the state of knowledge on these ell'ects.

Alteration of lung functions, particularly increase in pulmonary flow resistance due to

acculllulations in the epithelial tissues of the lungs or nasal regions, adverse localized

damage, occurs after acute exposure. As well as changes in levels of enzymes, the

properties of the surrounding cell membranes may also be altered by SOl, bisulphite or

sulphite.

Sti~b /11 al., (2002) have discussed the effects of gases and panicles and the influence of

cause of death, age, and season and mentioned that SOl reduce immune surveillance then

gives rise to the bronchitic and respiratory problems. Ballester el al. (2002) have shown

the long-term respiratory problems of humans caused by atmospheric SOl with

combination of particulates in the atmosphere such as Emphysema. The literature, which

deals with and confirms the correlation between chronic chest disease and levels of S02

in urban air is vast, but the evidence suggesting that chronic exposure to SOl and

particulate matter plays a part in the cause and development of chronic respiratory disease

has been established (e.g. Zeghnoun el al., 200 I; Tunnicliffe el al., 200 I; Wong, 2002) .

The Convention on Long Range Transboundary Air pollution has resulted in significant

reductions in emissions of acidifying gases in Developed world of Europe and North

America, between 1985 and 1994, SOl emissions in Western, Central and Eastern Europe

fell by 50 percent in line with the Convention on Long Range Transboundary Air

Pollution protocols (Olendrzynski, 1997). However, emissions in other regions, especially

in developing nations such as parts of Asia, are a major and growing problem. If current

trends continue, emissions of sulphur dioxide from coal burning in Asia has been surpass

emissions form North America and Europe combined by the year 2000 and continue to

grow thereafter, unlike emissions in North America and Europe which are expected to

tall. Impacts have already been observed (WB, 1997a). In Japan, many monitoring sites

recorded annual sulphur dioxide deposition at levels equal to or greater than those in

Europe or North America; and in the Republic of Korea winter rain acidity has been

nearly as high as pH 4 (Shrestha and Iyngararasan 1998). On global scale there is a main

problem of unchecked growing air pollution in developing nations, such as India,

Pakistan and Central Asian countries (WHO and UNEP. 1992). The Asian and Pacific

46

region has experienced significant growth in atmospheric pollution, resulting from the

heavy use of coal and high sulphur fuels, traffic growth and forest fires . The most serious

problems are in urban areas and the developing countries in the region. Japan, however,

has reduced sulphur emissions through gains in efficiency, heavier reliance on oil imd

lIuclear power. and stringent pollutioll control laws.

I n the past quarter of a century, atmospheric pollution increased significantly in much of

the region, largely as a result of escalating energy consumption due to economic growth

and greater use of motor vehicles. The use of poor quality fuels with high sulphur content

such as coal, inefficient methods of energy production and use, traffic congestion, poor

automobile and road conditions, leaded fuel and inappropriate mining methods have

exacerbated the situation. Forest fires are also contributing significantly to air pollution.

Significant health threats also exist from the use of low quality traditional solid fuels ,

such as wood, crop residues and dung, for cooking and heating in lower income urban

households and rural areas. Sulphur dioxide emissions in Asia increased from I 1.2

million tones of sulphur equivalent in 1970 to 20 million tones in 1986 at least four times

the rate of many other regions (Hameed and Dignon 1992).

Two of Asia ' s giant economies, China and India, rely heavily on coal. Ninety percent of

China's 18 million tones of SOl emitted into the atmosphere annually come from coal

burning (State Planning Commission, 1997). Overall, Asian emissions of SOl are at least

50 percent higher than those of North America, Africa and Latin America (ADB, 1997).

Three of Asia ' s II megacities exceed WHO guidelines for acceptable SOl levels (WHO

and UNEP, 1992).

With increasing SOl emissions, acidification IS an emergmg Issue. The most sensitive

areas are in south China, the southeast of Thailand, Cambodia and south Viet Nam

(Hettelingh e/ al. 1995). On the other hand, there is no evidence of significant acid

deposition in Australia, which in not subjected to emissions from neighbouring countries

and where fossil fuels have a low sulphur content (Commonwealth of Australia, 1996).

GOP EUAD / IUCN (2000) estimated the magnitude SOl emissions in Pakistan from

various economic sectors for last three decades, which is presented in Table 2.6.

47

Table 2.6: Estimated SOl from Various Economic Sectors in Pakistan

Thermal power stations m Pakistan had been a major source of atmospheric pollution

especially SOl (Correspondent the Muslim, 1996). In Karachi, oil-fired thermal power

plants of KESC burned 80 percent of the total furnace oil used in the city (Nairn, (996).

Furnace oil, contains approximately 3.5 percent sulphur, has been commonly using in

Karachi . Only Bin Qasim thermal power plant unit burns more than fifty percent of the

furnace oil consumed in the city, to produce 100 MW. It was observed that the unit

generates some 400 tones of sulphur dioxide and 450 kg of toxic metals gravely polluting

the local environments on daily basis. Massive invasion of the sulphur containing heavy

fuel has largely exposed Karachi to the hazards of acid rains and relevant environmental

risks (APP, 1997) and the city would experience the same fate, which European cities had

experienced during the I 960s and 1970s (Shams, 1997b).

2.1.2.2 Nitrogen Oxides (NO,)

Nitrogen dioxide (NOl) and nitric oxide (NO) are not the predominant nitrogen oxides of

the atmosphere but they are the ones, which appear to give most problems in the

troposphere (Grosjean, 1979). Generally, nitrogen oxides reduce rather than enhance

growth~ plants also fail to take advantage of the extra nitrogen (N) in nitrogen oxides.

Collapsed and bleached tissues increase mostly at the apex of the leaves and along the

margins due to increasing concentrations of nitrogen oxides in troposphere (Lee and

Stewart, 1978~ Rowland el al.,. 1985). Certain plants (e.g. grasses) grown on nitrogen

deficient soils and exposed to nitrogen oxides show a slight improvement in yield

(MAFF, 1969), whereas plants from the same stock but grown with an adequate supply of

N fertilizer have decreased yield and sometimes show evidence of visible damage

(Marietta, 1989~ Herman el al., 200 I) such as increased 'Ieafiness' and poorer root

development (Wellburn, 1990).

48

Nitrogen dioxide is an irritating gas and its toxicity is generally attributed to its oxidative

capabilities (Glass, 1979). It penetrates the lung periphery and is primarily deposited in

the centriacinar region. It is also absorbed into the mucosa of the respiratory tract (Vogel,

\ 984: Schwela and Zali. \(99).

Upon inhalation 80-90 percent of N02 can be absorbed, although this proportion varies

according to nasal or oral breathing (Polat el al., 2002). The maximal dose to the lung

tissue occurs at the junction of the conducting airway and the gas exchange region

(Hazucha <11 al., 1994). Because N02 is not very soluble in aqueous surfaces, the upper

airways retain only small amounts of inhaled nitrogen oxides. Nitric and nitrous acids or

their salts can be observed in the blood and urine after exposure to N02 (WHO, 1987a).

Nitrogen dioxide is a widespread contaminant of indoor as well as outdoor air, and indoor

levels can exceed those found outdoors ' (Anyanwu, 1999; Junker el al., 2000). Indoor

levels of N02 are determined by the infiltration of NOl from outdoor air and by the

presence and strength of indoor sources, such as gas cooking stoves and kerosene space

heaters, and by air exchange (Bascom el al., 1996).

Some workers associated with certain occupations, are intermittently laid open to high

concentrations of oxides of nitrogen, particularly NO and NOl (England e/ al., 200 I). The

spectrum of pathological effects in the lung resulting from occupational exposure to

nitrogen oxides range from mild inflammatory response in the mucosa of the

tracheobronchial tree at low concentrations, to bronchitis, to bronchopneumonia, and to

acute pulmonary oedema at high concentrations (WHO, 1977). In a similar fashion

cough, headaches, chest tightness, circulatory collapse or congestion and reduce blood

pressure are some of the acute symptoms of nitrogen oxides dose (Hatta el al., 2002;

Vaziri el al .. 1999). Continuous exposure increases the chronic pulmonary disorders such

as asthma and emphysema (Saldiva e/ al., 1994; Hazucha e/ aI., 1994; Leduc e/ al., 1995,

Koren, 1995; Yamamoto, 2001: Qian e/ al., 200 I). In Karachi, asthma becoming quite

common because of alarming increase in level of pollution, industrialization, unhygienic

living conditions and growing stress. During 1995 to 2000, there had been a notable

yearly increase in the incidence of asthma patients (Shahzad, 2000).

49

Some epidemiological research endeavours have emphasised on the acute effects of shon

term exposure to high levels of NO, and there are few data on long-term effects of low

level or repeated exposure at peak levels (e.g. Hyden and Varhelyi, 2000; Akimoto e/ aI.,

2000; Dzik Itl aI., 2002). Damji and Richters (1989) have shown a reduction of T

lymphocyte subpopulations following acute exposure to NO"

and this may reflect a

functional impairment of the immune response. Morrow (1984) has reported that N02 can

be toxic in cenain biological systems, and acute exposure to NO, has been accounted to

affect both the cellular and humoral immune systems. Devlin e/ al. (1992) described an

impaired phagocytic activity in human alveolar macrophages after exposure to 3,760 Ilg

m') (2.0 ppm) for 4 hour, with intermittent exercise.

Several studies revealed the effect of nitrogen dioxide on the lung functions of healthy

individuals, asthmatics and subjects with chronic bronchitis (USEPA, 1982a). Shon

exposure (10-15 minutes) to concentrations of N02 exceeding 1,300 Ilg m') (0.7ppm)

caused functional changes in healthy subjects, particularly an increased airway resistance.

Studies show conflicting results concerning respiratory effects in asthmatics and healthy

individuals at NO, concentrations in the r~nge of 190 -7,250 Ilg m') (0.1 - 4.0 ppm). The

lowest observed level to affect lung function consistently was a 30-minute exposure, with

intermittent exercise, to a N02 concentration of 380 - 560 Ilg m') (0.2 - 0.3 ppm).

Asthmatics appear to be more responsive to NO" and their lung functions may be

alfected by a concentration of ·560 - 940 Ilg m') (0.3 - 0.5 ppm) with an enhanced

reactivity to pharmacological bronchoconstrictor agents (Mohsenin, 1987a; WHO,

1995a). Wellburn (1994) has given an account on likely effects of nitrogen dioxide upon

human (Table 2.7) for a 2-hours exposure time and suggested much lower values for

asthmatics.

Neve'1heless, several studies have failed to demonstrate an adverse effect of N02 on the

respiratory health of asthmatics (Morrow and Utell, 1989; Roger el al., 1990).

Inconsistencies in the results could be due to the lack of comparability in experimental

studies and a difference in susceptibility among asthmatics.

50

Table 2.7: Human response to different levels of N02 for 2-hours

11-0.21 NoeJJects 11.11 - 0.21 SliKht odour detected 0.22 - t.1 Some metabolic effecl.\· associated with either toxicity. adaptatiun or

rC!pnir of fum! tls:,'uC!s (c. C'. in"ihi/ed melaholi .... m of prosloj!,lolldin I~'y I . I - 2 ~'·ignijicalll changcs 10 respiralU/:v rale lIud lung volume, coliallced

susceptibility /0 i~fection anel evidence of tissue repair 2.1 - 5.3 Deterioration of lung /issue (e.g. loss of cilia) not balanced by repair

mechanisms Above 5.3 Gross distortioN oj lung lissties and emphysema. possibl4t death ~r

prolofl~ed

Suurl.:C : Wdbuflt. l'.)')~

Some reviews (Dijkstra el al., 1990; Neas el al., 1991; Samet el aI., 1993) have

concentrated on indoor air pollution through N02. These studies could not establish a

consistent trend in incidence or duration of illness due to NOz exposure. In addition no

consistent effect of N02 on pulmonary function could be confirmed, and no association

was observed between symptoms such as chronic cough, persistent wheeze and shortness

of breath, with N02 concentrations. As reponed by Braun-Fahrlander el al. (1992) Ihe

duration of episodes with symptoms was associated with outdoor, but not indoor, N02

concentrations. The inconsistency in the results of epidemiological studies has 'been

commented on by Samet and Utell (1990) who pointed out the potential for

misclassification, confounding and lack of statistical power among the investigations.

Also, some factors such as exposure pattern, age, nutritional status, and interaction with

other pollutants or allergen~ may panly explain the variability observed in response to

N02 exposure.

Some studies of outdoor NO, exposure have recognized an association between ambient

air levels of NO, compounds and measurable health effects (e.g. Bobak and Leon, 1992;

Tornqvist and Ehrenben, 1992; Leikauf el al., 1995, Carna el al., 1998; Anstey el aI.,

1999; Bobak and Leon, 1999; D' Amato el aI., 2000). However, methodological

problems, such as the presence of a mixture of pollutants and a lack of control due to

parental smoking or indoor sources of N02 (Speizer el af, 1980), in these studies

preclude acceptance of any of the results as clear evidence for an increase in acute

respiratory illness due to NO} exposures. Some endeavours have been extensively,

reviewed by the United Stines Environmental Protection Agency (USEP A) (USEPA,

51

1982a). To summarise the results of studies reponed up until 1990, a meta analysis of II

epidemiological studies showed (Schwela and Zali, 1999) an increase in respiratory

illness in children of less than 12 years of age. associated with long term exposure to high

concentrations of N02, compared with children exposed to low concentrations of this

pollutant (Table 2.8). A difference in exposure of28.5 fig m-l (0.015 ppm) N02 (2-week

average) resulted in an increase of about 20 percent in the odds ratio of respiratory illness.

In these studies, the weekly N02 average ranged from 15 to 128 fig m-l (8 - 65 ppb) or

possibly higher. There was, however, insufficient epidemiological evidence to draw any

conclusions regarding the shon or long-term effects of N02 on pulmonary function

(Hasselblad e/ al., 1992).

In summary we could conclude that in spite of decades of untiring laboratory, clinical and

epidemiological research. the human health effects of N02 exposure have not been fully

documented. The toxicological evidence has indicated hypotheses to be tested in human

populations but limitations in the clinical and epidemiological studies have precluded

such definitive testing of these hypotheses. Therefore, there is an emergent need to

characterise factors that may modulate response to N02 exposure.

Table 2.8: N02 Exposure Effects on Human Health

;' ;i'~~~«~ , ~ . ' ~ -r· I~"; :{;'~;';;

,.1 ' ,. ' /" . ... -F" , I , " '":": ' 1i "';~'111;,:"' ,~ ,, " ,,', , ;,i:" ': ,," " M~_ol"", "" . ;e;t<~ .. ..

"- . .' f ;f J - ... ~ .j. .' ~ " . :I ..,:.: :-;;.' .' ~ • Il/!> ~,'!

Increased i,tlensity of rcspimtory infections Reduced cfficaC}'. of lungdefenccs Increased severity of respiratorv infections Reduced efficacy of lung defences Respiratorv sYmptoms Airways injury Reduced lung function Airways and alveolar injury Worsening of the clinical Slatus of persons wiUl Airways injury asthma. chronic obstructive pulmonary disease or other chronic rcspiratorv conditions Source: Samet and Utell . 1990

From 1970 to 1986. Nitrogen oxide (NO.) emissions from fossil fuel combustion in Asia

increased by about 70 percent (Hameed and Dignon 1992). However, total emissions

were significantly less than those of Nonh America and Europe during the same period.

Critical loads for acid deposition were still being exceeded for more than 25 percent of

ecosystems in Western and Central Europe, and emissions of nitrogen oxides in Nonh

America increased by about' 10 percent from the 1980s to the 1990s (International Joint

Commission. 1997). The situation was likely to worsen as the economics in Eastern

52

Europe and Central Asia grow stronger, and with the continuing increase in car use in

these regions in the rest of Europe and in North America.

2.1.2.3 Carbon Monoxide (CO)

Carbon monoxide is a sneaky poison, approaches without warnlllg because it is

colourless, non-irritating and without any odour. It is rapidly absorbed in the lungs and is

taken up in the blood where 80 - 90 percent of CO binds to haemoglobin (Hb) with the

formation of carboxy haemoglobin (COHb) (WHO, 2000), which impairs the oxygen

carrying capacity of blood, which may result as fatal (Seinfeld, 1986). It is formed when

carbon in fuels is not burned completely (Nadakavukaren, 1990; IPCC, 1992; Schwela

and Zali, 1999). It is a by-product of highway vehicle exhaust, which contributes about 60

percent of all CO emissions in USA (MacEachern, 1990; Seitz, 1995). In urban areas,

automobile exhaust can cause as much as 95 percent of all CO emissions (Harrison,

\996). These emissions can result in high concentrations of CO, particularly in local areas

with heavy traffic congestion (Calabrese, 199\; Coburn, 1970). Other sources of CO

emissions include industrial processes and fuel combustion in sources such as boilers and

incinerators. Despite an overall downward trend in concentrations and emissions of CO,

some metropolitan areas still experience high levels of CO (Schwela and Zali, 1999;

Miller, \999).

This criteria pollutant results from incomplete combustion of fuel and is emitted directly

from vehicle tailpipes. Incomplete combustion is most likely to occur at low air to fuel

rations in the engine. These conditions are common during vehicle starting when air

supply is restricted, when cars are not tuned properly, and at altitude, where 'thin' air

effectively reduces the amourit oxygen available for combustion (Harrison, 1996;

Seinfeld, 1986). Carbon monoxide (CO) emissions form automobiles increase

dramatically in cold weather. This is because cars need more fuel to start at cold

temperatures, and because some emission control devices operate less efficiently when

they are cold (Marsh and Grossa, 1996; Lyons and Scott, 1992).

The resultant carboxyhaemoglobin is extremely stable. CO at very low concentrations in

the blood (i.e. 0.1%) will combine with over half of the haemoglobin and immediately

reduce the O2 carrying capacity by a similar proponion. The affinity of CO for human

53

foetal haemoglobin is higher than that for normal haemoglobin, This means that unborn

babies in the womb are especially susceptible to CO poisoning (Guo el ai" 200 I;

Piantadosi, 2002), In ambient air CO rapidly diffused but its concentration approaches to

an alarming stage at proximity of source, especially, in open window low height vehicles

(Flachsbart, 1999; Alm el ai" 1999; Atimtay el ai" 2000; Mukherjee and Viswanathan,

2001),

The dissociation of oxyhaemoglobin is also altered due to the presence of COHb in the

blood thereby further impairing the oxygen supply to body tissues (Choi, 200 I), Carbon

monoxide is also bound to myoglobin (MbCO) in cardiac and skeletal muscle, which will

limit the rate of 0 1 uptake by , these tissues and impair 0 1 delivery to intercellular

contractile processes (Agostoni el al., 1980; Westphal, 2002). The main factors

conditioning the uptake of CO are its concentration in tbe inhaled air, the endogenous

production of CO, the intensity of physical effort, body size, the condition of the lungs,

and the barometric pressure whereas alcoholism, obesity, old age, heart conditions, and

lung diseases exacerbate the intensity of effect (Uasuf el aI., 1999; Antuni el al., 2000;

Easley, 2000), Table 2,9 presents the expected COHb levels after exposure to CO

concentrations between 11.5 and liS mg m') during different types of physical activity.

In absence of CO exposure, COHb concentrations are approximately 0,5 per cent; one

peak per day cigarette smokers may achieve COHb saturation of 4 - 7 percent (WHO,

1979), Wellburn (1994) haS an attempted to develop a relationship between 2 hours

exposure (CO concentrations) and effects of carbon monoxide on humans Table 2,10,

Table 2.9:

100

50

25

to

Predicted carboxyhaemoglobin in levels for people engaged in different types of work in different concentrations of carbon monoxide

tt5 15 min t.2 2.0 2,8

57 30 min l.l 1.9 2,6

29 01 h 1.1 1.7 2,2

ItS 08 II 1.5 1.7 1.7

54

Table 2.10: ElTects of CO on bumans and tbe accompanying carboxyhaemoglobill blood (COBb) concentrations

ExPl»iur;e". '.J:};;; .j;~~::,f~~€>1);>-~~~t~e~~:#~k~~~:lihl-i?;~~'!; ': (llp~i,V ' .: 01' >-. " :":~:~~~:; :tOO~ :~~mb:: ,~ .; . . .... . _ .~ .. .... t 1')

.. " h. • •

0 - 10 Nu discomfort or effect 0-2 10 - 50 Some tiredness. impaired vigilance and reduction in manual 2- 10

dexteritv 50 - 100 Sli~ht headache. tiredness and irritability to 20 100 - 200 Mild headache 20 - 30 200 - 400 Severe headache. visual impOirment, nausea, general weakness and 30-40

vomitinK ~OO - 600 As above, but with greater possibility of col/apse 40 - 50 (.UO - HOO Fn;l1l;n'lo!, increased pulse rale and convulsions 50 -60 SilO - lGOU Comu, weak pul.,e and possibility of death 60-70 1600 + Death within a short period 70 + Source. Wellburn, 1994

The major consequence of CO is to reduce the oxygen transport to the tissues (Mehta,

200 I; Aneja el a/. , 200 I). Organs, which are dependent on a large oxygen supply, are the

most vulnerable, particularly the. heart and the central nervous system, as well as the

foetus (Raub eJ al., 2000; scharte eJ al., 2000; Jaeger eJ al., 2000). Four types of health

effects are described to be associated with CO exposure: neurobehavioral effects,

cardiovascular effects, fibrnolysis effects and peri'llatal effects (Nicholls, 200 I; Zevin eJ

ul., 200 I; Motterlini el al., 2002). Carbon monoxide leads to a decreased oxygen uptake

capacity with a resultant decreased work capacity under maximum exercise conditions.

According to available data, the COHb level required to induce these effects is

approximately 5 percent (WHO,' 1979). Some authors (Beard and Wertheim, 1967) have

reported impairment ill the ability to judge correctly slight differences in given tasks in

successive short-time intervals at lower COHb levels of 3.2 to 4.2 percent. At this level,

subjects may miss signals the'y would not have missed when starting a task.

Subjects with previous cardiovascular disease (chronic angina patients) seem to be the

group most sensitive to CO exposure (Koehler and Traystman, 2002). Similarly, Allred el

al. (1989) investigated the effects of CO exposure 011 myocardial ischaemia during

exercise in 63 men with documented coronary artery disease. Results showed a decr.ease

in dose-response relationship between the length of time to the onset of angilla and COHb

level. This study shows that CO.Hb levels as low as 2 percent can exacerbate myocardial

ischemia during exercise ill subjects with coronary artery disease. Similar effects have

been demonstrated ill patients with intermittent c1audicatioll from peripheral vascular

disease (Aronow el al., 1974) .

5;

Carbon monoxide exposure may also affect the foetus directly though oxygen deficit

without elevation of COHb level in the foetal blood. During exposure to high CO levels.

the mother's Hb gives up its oxygen, less readily with a consequent lowering of the

oxygen pressure in the placenta, and hence also in the foetal blood . Research has mainly

focused on they eflects of cigarette smoking during pregnancy. The main eflects are

reduced birth weight (Hebel el al., 1988; Ash el aI., 1989; Mathai el al., 1990; Maisonet

<:1 (II., 200 I; Chen el aI., 2002) and retarded postnatal development (Campbell 1:1 01..

1988). Impairment of neurobehavioral development has also been related to maternal

smoking during pregnancy (US EPA, 1983) Ambient CO exposure has been related to

low birth weight in a study conducted in Denver, USA (Alderman .:1 al .. 1987) Mothers

who lived in neighbourhoods with a mean CO concentration below 3.4 mg m-J (3 ppm)

during the last trimester of their pregnancy had a 50 percent increase in the risk of having

an infant with low birth weight when compared with a group unexposed to CO

A cohort study conducted among bridge and tunnel officers (5,529 persons) exposed to

CO showed a 35 percent excess risk of arteriosclerotic heart disease mortality among

tunnel officers when compared with the New York City Population. Two factors

contributed to this excess risk : the exposure to CO of tunnel officers and the movement

into a critical higher age group. There was a reduction in mortality signifying a decrease

in exposure (more ventilation in the tunnel) (Stern d al., 1988). The classic symptoms of

CO poisoning are headache and dizziness at COHb levels between 10 and 30 percent and

severe headache, cardiovascular 'symptoms and malaise at above about 30 percent. Above

about 40 percent there is considerable risk of coma and death.

In summary, average COHb levels in the general population are around 1.2 - 1.5 percent

(in cigarette smokers around 3 - 4 percent). Below 10 percent COHb, it is mainly

cardiovascular and neurobehavioral effects that have been evaluated. The aggravation of

symptoms in angina pectoris patients, which is a major public health concern, may occur

at levels as low as 2 percent COHb. Decreased work capacity and neurobehavioral

function have mostly been observed at around 5 percent COHb (Schwela and Zali, 1999)

Severe hypoxia due to acute CO pOisomng may cause both reversible, short-lasting,

neurological deficits and sever, often delayed, neurological damage The neurobehavioral

56

include as low as 5. I - percent (WHO; 2000; Kourembanas, 2002)

Andersson el al., (2002) has reported Increased carbon monodixe levels in the nasal

airway of subjects with a history of seasonal allergic rhinitis and in patients with upper

respiratory tract.

A study on air pollution reported that in Karachi alone. annually about 2.705.000 tons

carbon monoxide (CO) was emitted into the air from garbage burning and traffic vehicles.

The report said that a survey as early as 1988 showed that average CO concentrations

from 8 am to 6pm in various parts of Karachi managed from 2 to mglm' The highest

levels were found at Tibet Centre, Plaza and Nagan Chowrangi in North

Karachi In these congested sites the CO concentration was as high as 107 mg/m). This is

more than 10 times the WHO limit of 10 mg/mJ (Qureshi, 1996).

GOP ECAD I IUCN (2000) furnished annual emissions of carbon monoxide (CO) for

1985 as 96 percent (123,054 tonnes) from motor vehicles and 4 percent (4,622

tonnes) wood, coal and waste. Carbon monoxide (CO) levels in the range of S

- 30 parts per millions (ppm) and 6 - 40 parts per million (ppm) have been recorded for

Lahore and Karachi respectively. Metropolitan reliance on buses and light commercial

vehicles also has various air pollution consequences. Old vehicles stay on the roads

because of the absence of emission regulations, lack of enforcement motor vehicle

fllness regulations. and the owner·s· lack of capital to purchase replacements. Thus the

average Pakistani vehicle emits 25·times as much carbon monoxide (CO) as the average

vehicle in the Cniled Slates (Haider, 1996).

2.1.2.4

Ozone is such a reactive gas towards organic molecules that it is worth examining the

consequences in detail. In plants plasma or cell membranes of plant cells (sometimes

called the plasmalemma) suffer the most injury due to surplus of surface ozone (Lawson

e/ al., 2001) damage due to oxidants arising from 0 3 lS heralded by losses of

chlorophyll, in leaf fluorescence (indicative wasted light energy), and

changes in adenylale (ATP) levels. It produces irreversible pathological damage of

sensitive cells in leaves and roots (Wellburn, 1994) Many researches have reported the

57

decreased yield of crops where the ozone (0-,) level has been high (Saitanis ef <II .. 2001 .

Gntnhage ef al., 200 I).

The observed health effects of photochemical oxidant exposure cannot be attributed

uniquely to oxidants because photochemical smog typically consists of 02, N02, acid

sulphate and other reactive agents. These pollutants may have additive or synergistic

etlects on human health, but 0, appears to be the most biologically active (WHO, 1978.

1987b).

In human, the primary target organ for OJ is the lung. High-risk groups include children,

the aged, individuals with pre-existing respiratory or immune-related diseases, and those

with coronary heart diseases (Polat ef aI., 2002). This photochemical product causes

irritation to the eyes, nose, throat, and chest. Ozone exposure produces cellular and

structural changes, the overall effect of which is a decrease in the ability of the lung to

perform normal functions. Cilaiated and Type I cells are the most sensitive to 0 .1

exposure (Gornicki and Gutsze, 2000) . Proliferation of non-ciliated bronchiolar and Type

2 alveolar cells occurs as a result of damage and death of ciliated and Type I cells (Luster,

200 I) Ozone exposure causes major lesions in the centriacinar area of the lung that

includes the end of the terminal bronchioles and the first few generations of either

rElspiratory bronchioles or alveolar ducts (Lippmann, I 989a).

Experimental studies have shown that OJ increases alJ'Way permeability and particle

clearance, causes airway inflammation and a decrease in bactericidal capacity, and causes

structural alterations in the lung (Dye ef 01., 1999). The acute morphologic response to 0 .1

involves epithelial cell injury along the entire respiratory tract, resulting in cell loss and

replacement (Gosepath ef aI., 2000; Desqueyroux and Momas, 2001; Cohen ef al., 2001)

In the lower airway, the proximal regions are most affected. In the alveolus, Type I cells

are highly sensitive to OJ while Type \I cells are resistant and appear to serve as a stem

cell for Type I cell replacement (Limppmann, I 989b). Studies have reported an increase

in numbers of neutrophils (Schelegle ef aI., 1991; Koren ef aI., 1991) and in cytokine

chemotactic for neutrophils (LDH and IL-8) (Aris ef al., 1993) Ozone induced increases

in constituents of BAL fluid such as lactate dehyudrogenase, prostaglandine E1,

interleukin-6, flbronectin and albumin, have been observed in humans (Devlin ef al. ,

1991). The increase in the content of neutrophils in nasal lavage fluid has also been

----- --- -- -- _. __ .

observed in humans after exposure to 03 (Graham and Koren, 1990; Smith et at, 1993)

It is not yet known whether repetitive inflammation has long-term consequences.

Ozone can induce increased non-specific airway sensitivity to inhalation challenge testing

with bronchoconstrictive agents (HEI, 1988) In several human studies, reductions in lung

nmction have been observed among voluntary subjects exposed to 03 concentrations

ranging from 012 ppm to 0.40 ppm (Avol el al, 1984; McDonnell el at"~ 1985; Linn eI

aI" 1986; Paige el aI., 2000; Mortimer el at"~ 2000; Declercq and iV!acquet, 2000). Recell!

research has shown that effects can be produced by exposures as short as 5 minutes, and

that various effects become progressively worse as exposures at a given concentration are

extended in time up to 6.6 hours. Exposure to 160 ~lg m'] (0.08 ppm) for 6.6 hours in a

group of healthy exercising adults led to a decrease in lung functions of more than 10

percent in the most sensitive individuals, However, repeated exposures to a given

concentration (60.6 hours to 0.08, 0 10 and 0 12 ppm) on several consecutive days

resulted in attenuation of functional changes but persistence of ainv!!y hyper

responsiveness. This suggests an ongoing action 0) on the lung (Folinsbee 1.'1 (II,

1994). Although smokers and subjects with pre-existing pulmonary disease do not appear

to be more sensitive than others to O}, within the apparently normal population there is a

range of responsiveness to O} Ihat is reproducible (VtHO, 1987b)

Most studies of the health effects of have focused on short-term (1-2 hour) exposure

and have indicated a number of acute effects of 0] and other major photochemical

oxidants (USEPA, 1986). Some studies have linked acute daily mortality with 0,

exposure in Los Angeles County and New York City (Kinney and Ozkaynat, 1991, 1992),

bllt data from a study conducted in Mexico City do 110t confirm these results (Borja

Aburto el at"~ 1995; Loomis 111 a/., 1996) Studies of hospital admissions in relation to 0)

exposure reported an increase in hospital admission rales for respiratory diseases (Tenias

el a/., 2002; Petroeschevsky el al., 20001; Burnett el aI" 2001; Bates and Sizto, 1983,

1987) and asthma attacks (Whittemore and Korn, 1980; White and Etzel, 1991; White el

at"~ 1994: Romieu el a/., 1995), Summer haze pollutants (including 0) have also been

related to hospitalisation and emergency roOIl) visits for respiratory diseases (Cody el at"~

1992; Thurston el a/., 1992, -1994),

Many endeavours have examined symptoms related to 0.1 exposure. In a clinical study,

Avol et al. (1987) studied the occurrence of symptoms in subjects to 0 ..

concentrations of 0 640 (lg mol (0 .- ppm). Symptoms were classified as upper

(nasal congestion or discharge and throat irritation), lower respiratory

(substernal irritation, cough, sputum production, dyspnea, wheeze and chest tightness)

and non-respiratory (headache, fatigue and eye irritation). SO.flfes were calculated, based

on the intensity of the symptoms presented. The results showed a

relationship between effective dose (01 concentration x time x ventilation rate)

and symptom scores. Imai e/ al (1985) reported significant increases in the symptoms or

adults during periods of increased ambient 0.1 exposure in Japan Ostro (1989) analysed

data collected for 6 1976- 81, and showed that ambient 0-, levels were associated

with days of activity due to respiratory illness in the working population, Seal e/

a/. (1996) have correlated the effects of socio-economic status and menstrual cycle

on pulmonary response to ozone. According to these analyses, the change in the number

of days of minor or restricted activity of an individual on a given day in a given

population; the proportionality factor is 0.077 (Kleinman et ai"~ 1989a)

The inhalation of 0, causes concentration-dependent decreases in average lung volumes

and flow rates during expiration; the mean value of the decrease increases with increasing

depth of breathing (Lippmann, 1989a; Aris Itf aI" 1993; Frischer 1:/ aI" 1993; Hoek e/ "I.,

1993; Putman Itt al., 1997; Bouthillier ef al.. I Chen et al., I Chistakos and

Kolovos, 1999). Decreases in the lung functions of healthy children and young adults

have been reported at hourly average OJ concentrations in the range of 160 - 300 ,lg n(1

(0.08 - O. J 50 ppm).

Though has been shown to alter macrophage function, \vhich could lead to an

increased susceptibility to respiratory infection. there is little data on the relation

exposure to 03 to acute respiratory infection in humans. In a stud\" conducted ill Mexico

among preschool children, investigators reported a 14 percent increase in school

absenteeism for respiratory infection related to an 0,1 ambiellt concentration (I hour

maximum) > 240 ).lg m-3 (0 12 ppm) 011 Ihe preceding day (lag of I day). When 0,

concentrations (I hour maximum) were high (> 240 ).lg mol) on two consecutive days a

percent was observed. interactive was between low

(,(1

temperature and high 03 exposure (Sartor el al., 1995; Romieu el al .. 1992, Cody III af,

1992)

Based on estimates from Spekror el af. (i 988), moderate physical activity for a range of

exposure between 38 and 226 llg m') (0.019 10 0.113 ppm) for I hour could lead to a

decrease of 0.5 ml per J,lg for FVC (Forced Vilal Capacity) and 0.7 ml per J,lg nfl for

FEV, (Forced Expiratory Volume in I second); this would result in a decrease in FVe,

FEV, and (Peak Expiratory Flow Rate) of 4.9 percent, 7.7 percent and 17 percent

respectively were predicted for the current USEPA standard of 240 m'l (0 12 ppm).

Further studies conducted among children have shown similar decrease in function

and have reported the persistence of a measurable functional deficit mto the following

day, even for peak concentrations below or equal to 300 /lg m" on the day before (0.15

ppm) (Lippmann, 1993, ; Friedman el aI., 1992; Lippmann el at. 1983).

A study conducted in Mexico City among school children, Castillejos el al. (1992)

reported acute and sub-acute elIects of 03 on tung functions. However, the decreases in

function were smaller than was to be expected from the regressIOn slop of Spektor .:1 01.

(1988) (08 percent for FVe and 0.8 percent for FEV l at a maximum 0) concentration of

240 ~lg (0.12 ppm) h prior to testing). Mean exposures of 48 hand 168 h (7

days) were more significant in predicting FEV, and FEF2P5 (Forced Expiratory Flow

from 2S percent to percelltof the Forced Vital Capacity). These authors suggested that

children chronically exposed to OJ might present a phenomenon of "tolerance" This

fmding supports the observation that repetitive exposures tend to produce less of a

response (Folinsbee Itt al., 1994) The potential adverse effect of such "tolerance" is not

known. Avol el al. (1998) studied asthmatic, wheezy and healthy children in Southern

California during periods of varying 03 levels in spring and late Sl.lmmeL The study fbund

that asthmatic children had the most trouble breathing, the most wheezing and the most

use of inhalers during high 0, days in the spring. Wheezy children had the most trouble

breathing and wheezed more during low 0 3 days in summer.

Susceptibility to OJ exposure does not appear to vary by ethic (Seal el al., 1993) or

demographic characteristics (Messineo and Adams, 1990). Dietary antioxidant levels

have been shown to modulate the response to 03 exposure ill animals (Slade "I af., 1985,

61

Eisayed ef al., 1988), but data for humans are still sparse. In controlled clinical studies,

asthmalics do not appear to be more sensitive 10 OJ as shown by their FEV I response

(Koenig e/ al., 1989). However. short-term follow up of asthmatic subjects (panel sllldy)

exposed to 0., have shown a decrease in lung fullction and an increase in respiratory

symploms among adults (Lebowitz el ai., 1987; Holguin el al., 199.:1) and children

(Krzyzanowski el al., 1992; Hoek 1:1 aI., 1993; Neas el aI., 1995; OSlro el aI., 1995;

Romieu 1:1 al, 1996). Other studies were unable 10 find any adnlrse effect on respiratory

health (Vedal el aI., 1987; Roemer ell/I., 1993). However, ill these studies, ambient 0,

concentrations were low and tllis could partly explain tile results Ozone may exacerbate

asthma by facilitating the entry of allergens or because of the inflammation it induces.

There is some evidence that may act synergistically with other pollutants, slich as

sulphate and NO, (Kleinman el ClI., 1989b). Koenig eI ClI. (1989) showed that inhaling 10\\

concentrations of 0, might potentiate the bronchial hyper-responsiveness of people with

asthma to SOl exposure. A similar potentiating effect has been observed wilh exposures

to OJ and sensitivity to NOz (Hazucha el al., 1994)

The chronic effects of OJ are still blurred, but there are ample evidences to believe tilal

repeated exposure could lead to persistent impairment of lung development and

malfunctioning. Animal studies have demonstrated progressive epithelial damage and

inflammatory changes that appear to cumulative and persistent, even in animals that

adapted to exposure by modifying their respiratory mechanisms (Tepper e/ ai,

1989) at 0, concentrations slightly higher than those that produce effects in humans.

Furthermore, for some chronic effects, intermittent exposures can produce greater effects

than continuous exposure regimes Ihat result in a higher cumulative exposure. These

results suggest that disease pathogenesis depends on the elTec!s produced by

defensive responses to the direct selves (Lippmann, 1989a).

Few studies Southern California suggest that chronic oxidant exposures affect baseline

respiratory function. Comparing two communities from this area, Detels el al. (1987)

found thaI baseline lung functions were lower and that here was a greater rate of decline

ltl function in the high oxidant community over 5 years. However. this study has

been criticised for reasons (poor exposure measurements, of adjustment for

potential confounding factors such as indoor air pollution, and occupational exposure)

Kilburn el al. (1992), comparing the lung functions of school children from Los Angeles

and Houston. observed that children from Los Angeles had 6 percent lower baseline \'aluc

for FEY I and 15 percent lower values for FEF2l,n than children from Houston Aerosol

administration of metaproterenol to Los Angeles (USA) children improved FEY 1 by I

percent and FEF2l,71 by 6.6 percent, but expiratory flows were still below the values of

Houston children, suggesting that impairment was not reversible . Repeated measurements

of lung functions among 106 Mexican-American children from Los Angeles showed that

FEY I and FEF2l,71 were respectively 2 percent and 7 percent lo\\'er than the predicted

value in 1987 compared with 1984. The FYC remained unchanged. The authors

concluded that the worsening of airway obstruction in these children was probably due (0

air pollution (Kilburn el at.. 1992) Schwartz. (1989) indicated that ambient OJ

concentrations were associated with a loss of lung function when the annual a\'erage

concentration was above 80 /lg rtt'.l (0.04 ppm).

Euler el al. (1988) assessed the risk of chronic obslnlctive pulmonary disease due to long

term exposure to ambient levels of total oxidants and N02 in a cohort of 7,445 Se\'enth

Day Adventists non-smokers. who had resided in California for at least II year and were

also aged at least 25 years. The results suggested a significant association between

chronic symptoms and total oxidants exceeding 200 ~tg m,J (0 10 ppm). However. when

cumulative exposure to SPM was considered as confounding variable in the model. only

SPM was considered as confounding variable in the model, only SPM exposure above

200 ~Ig m,3 showed statistical significance. Other studies conducted among Seventh Day

Adventists suggested that OJ exposure could lead to an increase in respiratory cancer

(Abbey e/ al .. 1993). This result should be interpreted with caution because of small

number of cases studied. Sherwin and Richter (1990) conducted an autopsy study among

107 young non-smoker adults aged between 14 - 25 years. and who died of non

respiratory traumatic causes in Los Angeles County. They found that 29 of them had

lungs with severe respiratory bronchiolitis of the kind first described in young smokers.

Moderate changes were present in a further 5 I cases.

In summary. effects which have been associated with hourly average oxidant

concentrations beginning at about 200 /lg m,J (0 . 10 ppm), include eye, nose and throat

irritation, cough, throat dryness. thoracic pain, increased mucous production, rales. chest

tightness, substernal pain, lassitude, malaise and nausea. The transient effects of 0, seem

(,J

to be more closely related to cumulative daily exposure than to I-hour peak

concentrations (Henderson el al., 1993; Ueno el aI., 1998; Gong el al., 1998; Bermudez el

al., 1999; Yalacchi and Bocci, 2000) Several studies provide sufficient information to

allow a quantitative evaluation of the potential impact of short-term exposure to 0) on

population subgroups (WHO, 1995a). Kleinman el al. (1989a). summarising data from

different studies, derived a dose-response relationship between change in FEY I and

effective dose of 0) (product of concentration of OJ x time x ventilation rate). These

authors also calculated dose-response nlnctions to determine the change in the percentage

of the population affected by specific symptoms according to 0) ambient level. The

model fitted well data derived from clinical studies. Finally, the work of Ostro (1989) can

be used to determine the change in restricted activity days associated with changes in

ambient OJ Many health outcoines associated with the change in peak daily ambient 0)

concentrations observed in epidemiological studies (WHO, 1995a). However, these

models can only provide an approximation to the health outcomes because the effects of

OJ may be enhanced by the presence of other environmental variables such as acid

aerosols. The effects of long-term chronic exposure to 0) remain poorly defl11ed , but

recent epidemiological and animal inhalation studies suggest that current ambient levels

(240 Ilg m') or a 12 ppm) are sufficient to cause premature lung ageing (Lippmann,

1989b, 1993).

A study of the Punjab University, Lahore, showed the rice yield that found to be reduced

by more than 27 percent due to high ozone level near Lahore (Nairn, 1996). Surface

Ozone is ooe of the pollutants, which is leading to respiratory, eye and skin diseases and

also affecting the crop outputs. Average surface ozone level in Karachi is 50 ppb and in

Lahore 40 ppb which is very .high from permissible limit of30 ppb (Rashid, 1996)

2.1.2 .5 Suspended Particulate Matters (SPM)

Particulate matter suspended in air includes total suspended particles (TSP), PM IO , PM21,

fine and ultra-fine particles, diesel exhaust, coal fly-ash, mineral dusts, metal dusts and

fumes, acid mists, fluoride particles, paint pigments, pesticide mists, carbon black, oil

smoke and many others (Roosli el al., 2000). Suspended particulate pollutants provoke

respiratory diseases, and can cause cancers, corrosion, deslruclion 10 plant life, elC. They

(,.)

can also constitute a nuisance, interfere with sunlight and also act as catalytic surfaces for

reaction of adsorbed chemicals (WHO, 2000)

Several studies have used time-series analysis to look at the relative change in the rate of

monality associated with changes in air quality variables (e,g. USEPA, 1982b; Abbey el

aI., 1993, 1995; Broja-Abuno el af" 1997; Goldberg e/ aI., 2001a, 2001b), In these

studies weather conditions and other potential confounding factors were accounted for

Although various measurements of paniculate pollution were used, the result of most

studies suggested that a 10 ~lg m'l increase in PM IO was associated with an increase in

daily monality equal to 05 - l.S percent (Pope el af" 1995) and the absence of a

threshold level.

Many endeavours provide a split of monality by causes of death. A review estimate of

these studies suggested that an increase of I O-~g m') in PM IO (24-hours average) was

related to a 3.4 percent increase in respiratory monality (range 2 - 8 percent) and a 1.4

percent increase in cardiovascular monality (range 0.8 - 1.8 percent) (Dockery and Pope,

1994), A detailed examination of cause of deaths showed the relative increase was higher

for chronic obstructive lung diseases and pneumonia. Also, almost all-cardiovascular

deaths on days with high paniculate air pollination have a respiratory cause as a

contributing factor (Schwanz, 1994),

Comparable results have been described in several European countries (Katsouyanni d

af" 1995; TouIoumi and Katsouyanni, 1995; Zmirou e/ af" 1995; Nowak el af" 1996;

Schindler e/ af" 200 I; Ebelt e/ af" 200 I) as well as in Latin American countries (Romieu

and Borja-Abuno, 1997). Peaks of acid aerosols observed in Canada during the sUlllmer

could have been responsible for the association observed between sulphate concentrations

and hospital admissions, to the .extent that sulphate constitutes a surrogate variable for

sulphuric acid aerosols (Bates and Sizto, 1987) Similar results have been observed in

USA. (Dockery and Pope, 1994). Taken together, these studies found an increase of

hospital admissions for all respiratory diagnoses ranging from 0.8 percent to 3.4 percent

(weighted mean, 0.8 percent) for each 10 ~g m') in PM IO has been reponed; (Samet e/ aI.,

1981; Schwanz e/ aI., 1993; Sunyer el af" 1993).

Asthmatic subjects seem to be more susceptible to particle pollution, and several studies

have examined the impact of PM IO on the occurrence of asthma attacks and on the

frequency of use of bronchodilators. The incidence of asthma attacks increased for I. I to

11.5 percent (weighted mean, 3 percent) with an increase of 10 ~g m') in PM", daily

average, and bronchodilator use increased from 2.3 percent to 12 percent (weighted mean,

2.9 percent) (Dockery and Pope, 1994). Ostro (1989) calculated that an increase in the

annual mean of 1 fig m') in fine particulate mater (2.5 flm) could be associated with a 3.2

percent increase in acute respiratory diseases in adults aged 18 - 65 years . In some

studies, observed deviations in the lung function of children hale been associated with

short-term fluctuations in particulate concentration (WHO, 1987c). From data collected

by Dockery el aI, (1982) during air pollution episodes it can be calculated that in the most

sensitive children (approximately 25 percent of the study population), a deficit in lung

function at least four times greater than in hose of average sensitivity was observed,

corresponding to a decrease in FEV 1 of 0.39 ml per each increase of I ~lg m'J of exposure

to SPM . However, more recent panel studies have suggested the absence of a threshold

value in the adverse health effect of particulate matter (WHO, 1995a). In a study about

the population of Utah Valley (USA), PMIO concentrations were associated with

reductions in PEFR and increased symptoms and medication use (Pope el al., 1991).

Aerosol acidity was the below detection limit. Decreases in lung function parameters

related to particle exposure have also been observed in studies conducted in the

Netherlands (Hoek and Brunekreef, 1993; Roemer el al., 1993). Germany (Peters e/ "I.,

1995) and Mexico (Romieu el at:, 1996).

Some studies showed a decrease in lung function associated with aerosol acidity as well

as with PM 10 (Neas el al., 1992, (995). Raizenne el al., (1989) reported a 3.5 - 7 percent

decrease for FEV 1 and PEFR associated with air pollution episodes with maximum OJ

concentrations of 286 fig m') (0. 143 ppm) and 47.7 fig m') ofH,S04. Most of the studies

suggested that a 10 fig m') increase in reparable particles resulted in less than 1 percent

decline in lung function (Pope el aI., (995). In addition to declines in lung function, many

of these studies observed increases in respiratory symptoms. A 10 fig m') increase in

PM 10 was typically associated with a 1 - 10 percent increase in respiratory symptoms

such as cough, combined lower. respiratory symptoms and asthma attacks. These etl'ects

66

were also observed at comparable PMlo concentrations near, or even below, 150 ilg Ill'"

(Pope el aI" 1995)

Dockery and Pope (1994) and Pope el al. (1995) have determined cross-sectional

differences in mortality among cohorts of adults in the USA The results from the study of

Dockery and Pope (1994) suggested that a \ 0 ilg m') increase in average PM II) exposure

was associated with an increase in chronic mortality equal to 9 percent (odds ratio of 1.09

with 95 percent conftdence interval between 1.03 and 1.15). This estimate reached 13

percent for fine particles (PM2l), with a 95 percent confidence interval between 1.04 and

1.23, and 36 percent for sulphates with a 95 percent confidence interval between 1.10 and

1.62. In the second study (Pope el al., 1995) an increase of 7 percent was observed for

total mortality for each 10 ilg m') increase in PM2,S , The strongest association was

observed with cardiopulmonary disease and lung cancer deaths (Pope el al., 1995),

In a prospective study conducted in the Netherlands (Van de Lande el aI., 1981) in two

communities over a 12-year period, residents of the community with higher pollution

levels had higher rates of lung function decline, implying that exposure to high ambient

pollution during adulthood may be a risk for COPD. In a study conducted among a

preadolescent population aged 6 - 9 years living in six USA cities (Ware el aI., 1986).

frequency of chronic cough was significantly associated with the annual average

concentration of these air pollutants (SPM, total sulphates (TSO.) and S02) during the

year preceding the examination, The maximum concentrations observed were 114 ilg 01' )

for SPM, 68 ilg m') for S02 and 18 ilg m') for TSO •. The rates of bronchitis and

composite measure of respiratory illness were significantly associated with average

particulate concentration. Similar results have been confirmed in a second cross-sectional

survey of the same population (Dockery e/ aI" \989) A subject with asthma and

permanent wheeze experienced a higher rate of pulmonary symptoms in relation to

increased pollutants. There was no evidence of impaired lung function associated with

pollutant levels,

There is some growing evidence that chronic exposure to smoke may play an important

role in the genesis of chronic lung disease . In developing countries, prevalence rates of

chronic bronchitis often appear to be much higher than in industrialized countries, and

with sex ratio tending to \, which cannot be explained solely by cigarette smoking

(Bumgartner and Speizer, 199 J). Although exposure to multiple risk in developing

countries may much higher than in developed countries, these results thaI

exposure to indoor smoke pollution which is much more common among women, may

largely accounl for the differences.

Non-neoplastic and neoplastic effects on the lung from exposure 10 diesel engine exhaust

have recently been reviewed by a VolHO Task Group on Environmental Health Criteria for

diesel fuel and exhaust emissions (WHO, 1996a). Non-neoplastic effects of diesel exhaust

include mucous membrane irritation, headache and light-headedness. Diesel-bus garage

workers reported signiftcantly more incidents of cough, itchy or burning headache

and wheezing. Several cases of persistent asthma and asthma attacks have also been

reported after acute exposure. Many studies of occupationally exposed individuals with

long, well-defmed exposure and follow-up (> 20 years) investigated the prevalence of

lung cancer (Grashick el aI., 1987, 1988; Gustavsson e/ al., 1990; Emmelin el aI., 1993).

Most of studies showed an increased risk of lung cancer with exposure to diesel

exhaust. The relative risks reported ranged between 1.3 and The point estimates were,

however, imprecise and had wide confidence intervals including a relative risk of I. 0 as a

lower limit of the 90 percent confidence intervals. Other studies supported these results

with relative risks in the range 1.2 - I but did not always achieve statistical significance

(WHO, 1 996b).

Some important fmdings suggest that there is no threshold for the adverse health effects

of SPM Ihat may occur when ambient levels are lower than the older WHO guideline for

respirable particles (WHO, 1987c) or the current USEPA standard for PM JO (USEPA

1982b) It seems that the role of secondary products as acid sulphate is strongly

involved in the effects of the Sh - particulate matter complex (Spengler el "I"

1990) Findings of several studies have been for quantitative evaluations of the

health impact of particulate matter and have also been llsed for risk assessment (Kleinman

e/ aI., \989a; Ostro, 1990; Romieu e/ al., 1990) Fmdings prQ\'ide more insight into the

amplitude of the effect and have suggested that at low concentrations of 24-hours

exposure (defmed as 0 - 200 jlg m') PM w), the exposure-response curve probably fits a

straight lille reasonably well. However, some studies conducted ill an area with a higher

level of air pollution (several hundreds of jlg m'} PM IO) suggest a curvilinear relationship

with the slope becoming shallower at higher ambient concentrations. Estimates of the

magnitude of effect occurring at low levels of exposure should, therefore not be used to

extrapolate to higher concentrations outside the range of exposures that existed in most of

the recent acute studies (WHO, 1995a). Particle composition and size distribution within

PM IU fraction, as well as synergy with other pollutants, are important factors to produce

variety of disorders (e.g. Perry e/ aI., 1983; Xu e/ al., 1995; Peters e/ (II., 1997; Ormstad

e/ (II., 1998; Neukirch e/ aI., 1998; Peters e/ al., 1999; Rogers e/ aI., 2000; Ibald-Mulli el

aI., 2001; Hameed and Khodr, 2001; Borai and Soliman, 2001: Muramoto e/ ai., 2001;

Ng and Lam, 200 I; Fan e/ aI., 2002; Triantafyllou e/ al., 2002)

In many developing countries, people are exposed to PMIO levels that are greatly in

excess of USEPA standards and there is great concern for the impact of these levels on

the health of population exposed (Naeher e/ aI., 2000; Chhabra e/ lIl., 2001, Kumar e/ ai.,

2001; Aneja e/ al., 2001; Ravindra e/ aI., 2001) . Ten of Asia's II mega cities exceed

WHO guidelines for particulate matter by a factor of at least three (WHO and UNEP,

1992) Levels of smoke and dust, a major cause of respiratory diseases, are generally

twice the world average and more than five times as high as in industrial countries and

Latin America (ADB, 1997). Recent forest fires in Indonesia are further, notorious source

of air particulates.

Some studies show that smoke and dust particles can significantly damage human health.

According to WHO estimates, Bangladesh, India, Nepal and Indonesia together account

for about 40 percent of the global mortality in young children caused by pneumonia

(WHO 1993). In China, smoke and small particles from burning coal cause more than

50,000 premature deaths and 400,000 new cases of chronic bronchitis a year in II of its

large cities (WB, I 997b). The negative impacts of domestic burning of solid fuels are not

confined to developing countries. Winter air pollution, mostly from coal and wood

burning fires in private homes, is a persistent problem in New Zealand (New Zealand

Ministry for the Environment, 1997).

In Karachi where a large number of people are crammed, noxious gases and dust in cities

air are at choking levels (Awan, 1997a) Surveys conducted as early as 1988 found that

SITE and Saddar had levels of dust and suspended particles 0.000459 and 0.000397 g/m'

respectively, between two and three times higher than the WHO guideline of 0000 15 to

0.00023 g m'] Levels in the SITE area at 0.000788 g m·3 were the highest and were

sometimes five times higher than the recommended WHO guidelines (Qureshi, 1996)

Y ousufzai el al. (2001), monitored major ambient aIr pollutants in Sindh Industrial

Trading Estate (SITE), Karachi and revealed that the time weighted average

concentrations of PMIO were 2 'I, times higher than WHO guidelines.

2.1.2.6 Lead (Pb)

Lead may cause both acute and chronic effects, which usually result from the

accumulation of. lead in the body over a certain period of time. Aboul 10 % of the

ingested lead is absorbed in the gastrointestinal tract. This proportion may be higher for

infant and children. The degree of pollution differs from country to country, depending on

motor vehicle density and efficiency of efforts to reduce the Pb content of petrol (WHO,

1987e; Mortada el aI., 2001). Concentrations oftetraalkyl Pb amounting to more than 10

percent of the total Pb content of ambient air have been measured in the immediate

vicinity of service station (Petrol pumps) (NSIEM, 1983).

Most of the Pb in ambient air occurs as fme particles (10 ~m). For adults, the retention

rate of airborne particulate matter ranges from 20 - 60 percent. Young children inhale a

proportionately higher daily air volume per unit measure (weight , body area) than adults

(Barltrop, 1972). It was estimated that children have a lung deposition rate of Pb that can

be up to 2 .7 times higher than that of adults.

The proportion of Pb absorbed from the gastrointestinal tract is about 0-15 percent

(Rabinowitz el aI. , 1980). Absorption is influenced by dietary intake; fasting and diets

with low levels of Ca, vitamin D, Fe and Zn have been shown to increase Pb absorption

(Mahaffey, 1990). The un-excreted fraction of absorbed Pb is distributed among three

bodily components: blood, soft .tissues and mineralised tissues (bones, teeth). About 9S

percent of the body burden of Pb in adults is located in the bones, compared with about

70 percent in children. Non-absorbed Pb is excreted in the faeces. Of the absorbed

fraction, 50 - 60 percent is removed by renal and bilary excretion (Sanchez-Fructuoso el

al., 2002; Olivi el al., 2002). The concentration of Pb in "milk" (baby) teeth provides a

lIsefullong-term record of Pb exposure in growing children.

7(J

the gondal and reproductive systems (Rohn el a/., 1982), It also depresses thyroid

function (Tuppurainen el a/., 1988) and impairs hepatic metabolism of cortisol (Saenger

el a/., 1984), In young children, Pb exposure is also associated with a decrease in the

biosynthesis of 1.2S-dihydroxyviamin D, an important metabolite of vitamin D (Angle

and McIntire, 1979; Taskinen el aI" 1981 ; Mahaffey el a/., 1982; Gan el aI" 1982;

Williams el aI" 1983).

The central nervous system is the primary target organ for Pb toxicity In children.

Exposure to high concentrations of Pb can result in an encephalopathy, which is more

frequent in childhood Pb poisoning than in adult poisoning, The reason may be due to the

ease with which Pb crosses the blood-brain barrier in children. Encephlopathy has

occurred in children with blood Pb concentrations in excess of 800 - 1,000 ~g 1'1 (NAS.

1972) , The brain seems more sensitive to alkyl Pb exposure (NSlEM, 1983), Exposure of

children to lower concentrations of Pb may produce neurophysiological disorders,

including impairment of learning ability, behaviour, intelligence and fine motor

coordination, Evidence for such disorders has been reviewed recently by (Davis and

Svendsgaard, 1987; ATSDR, 1988; Grant and Davis, 1990), Needleman el a/. (1979), in a

community-based study of children in Boston in whom previous exposure to Pb was

estimated by examination of shed teeth. reported evidence of Pb-related

neuropsychological deficits, This negative association between Pb concentration in teeth

and mental development was also reported in subsequent studies (Winneke el al,. 1983.

1985), However, in other studies (Smith el a/., 1983 ; Harvey el aI" 1984). correcting for

social environment greatly attenuated this inverse association.

Some researchers (Hawk el a/.. 1986; Fulton el al., 1987) have reported a significant

inverse linear association between cognitive ability and concentration of the group with

the highest concentration in the study by Fulton el a/. (1987) was 221 ~g 1'1, suggesting

that lQ (intelligence quotient) deficits are related to Pb exposure below 250 ~g 1"1 In

agreement with these findings, a recent study conducted in Mexico City among school

children. from low to medium social status and aged 9 - 12 years, showed a strong

negative correlation between blood Pb concentration and intellectual coefficients and

71

------_._-- -

teacher grading, without evidence of threshold concentration (Monz el al., 1993).

Although above studies cannot provide definite evidence that low-level Pb exposure is

linked to reduced cognitive performance in children, the overall pattern of fmdings

supports the conclusion that low-level Pb exposure is related to neurobehavioral

dysfunction in children. In a meta analysis using data from 12 cross sectional

epidemiological studies, Needleman and Gastsonis (1990) concluded that blood Pb

concentrations as low as 100 - ISO Ilg r' are associated with IQ impairment in children.

Based on this body of data the lowest-observed adverse effect level has been defined as

possibly < 100 Ilg r' (ATSDR, 1990).

Lead (Pb) is transported to the foetus across the placenta because there is no metabolic

barrier to foetal Pb uptake Furthermore, the amount of Pb available for foetal uptake may

actually be increased because part of the Pb stored in bone is released into the blood

during pregnancy. Prenatal exposure to Pb produces toxic effects on the human foetus

including reductions in gestational age, birth weight and mental development. These

effects occur at relatively low blood Pb concentrations. An inverse association between

maternal (or cord) blood Pb concentrations and gestational age has been reported by

different authors (Okamoto and Kawai, 1984; Wilkins and Sinks, 1984; Alfano and Petit,

1985; McMichael el al., 1986; Dietrich ef aI., 1986, 1987b). Based on risk estimates made

by McMichael ef al. (1986), the risk of premature delivery increases approximately four

fold as cord or maternal blood Pb concentration increase from 80 Ilg r' to greater than

140 ~lg ,.'. Data from a Cincinnati study suggest an inverse relationship between prenatal

maternal blood Pb concentration and birth weight and postnatal growth rates (Dietrich el

aI., 1987a; Shulka el al., 1987). Other studies also support this inverse association

(Bellinger el aI., 1984; Ward el dl., 1987; Audrey el al., 2002).

A senes of long-term studies have investigated the effect of early Pb exposure and

developmental effects. Bellinger el al. (J 987, 1989) in Boston, USA studied the

relationship between umbilical cord blood Pb and early cognitive development over 6 and

24 months of age. Lead (Pb) concentrations were measured in 249 umbilical cord blood

samples of infants born to middle and upper middle class parents. Cord blood Pb

concentrations were categorised as low (mean = 18 Ilg r'), mid (mean = 65 Ilg r') and

high (mean = 146 Ilg r') After accounting for factors related to infant development, such

as mother's age, race, IQ, education, care-giving environment, social class, and infant 's

72

sex, birth weight, birth order and gestational age, there was a significant inverse relation

between performance on the Baylet Mental Development Index (MDl) at 6, 12 and 24

months and cord blood Pb concentrations (McMichael el al., (988).

The MDI is a composite scale to assess sensory-perceptual acuity, memory, learning

ability, verbal communication and other cognitive functions (Shy, 1990) The difference

in MDI between the low and high exposure groups was between 4 and 7 points. Postnatal

blood Pb concentration showed no association with MDI deficit Children in the lower

socio-economic stratum were adversely affected at lower levels of prenatal exposure

(Needleman, (989). These findings are supported by other studies (Petit and Alfano,

1979; Dietrich I!I aI., 1987a; Emhart el al., 1987; Haraguchi el al., 2001; Morgan III aI.,

200 I; Counter and Buchanan, 2002).

Another prospective study was conducted in Port-Pirie, South Australia among a cohort

of 537 children born during 1979 to 1982 to women living near a Pb smelter (McMichael

el al., (988). Blood samples were collected from the mother, before and at delivery from

the umbilical cord, and at ages 6, 15, 24 months and every year thereafter from the

infants. At the age of two, the mean blood Lead (Pb) concentration was 212 J,lg rl with a

range of 49 - 566 J,lg rl. The developmental status of each child was tested using the

McCarthy Scale of Children's Abilities (MSCA). Maternal intelligence and care-giving

environment were also evaluated. The blood Pb concentration at each age, particularly at

2 and 3 years and integrated postnatal average concentration were inversely related to

development at the age of 4 years. independently from other factors that may affect child

development, subjects with an average postnatal blood Pb concentration of309 J,lg rl had

a cognitive score seven points lower than those with an average concentration of I 03 ~lg r I (McMichael el aI. , 1988, (994) .

Although the cognitive and neurosensory efiects of low-level blood Pb are particularly

difficult to study mainly because of the variety of tests used and the number of different

factors that may affect child development, there is an impressive convergence of animal

and human studies (Shy, 1990; Bunn el al., 2001; Dietrich el aI., 2001). Grant and Davis

(1990) concluded that neurobehavioral deflcits and reduction in gestational age and birth

weight are associated with prenatal internal exposure levels, indexed by maternal or cord

73

--------

blood Pb concentration of 100-150 Ilg rl and possibly lower. In addition a follow-up

investigation of the children whose dentine concentrations of Pb had been measured in

primary school showed that, II years later, children with high Pb level (dentine

concentrations ~ 20 ppm) were significantly more likely to drop out of high school and to

have a reading disability (Needleman el aI., 1992). This study has been criticised because

of potential bias (Good, 1991); nevertheless the results suggest that early Pb exposure

may result in long-term neurobehavioral impairment.

In addition to the assessments discussed above, other aspects of Pb-associated

neurotoxicity have also been examined. Hearing thresholds in children appear to be

adversely affected by Pb. Schwartz and Otto (1987) reported the probability of Pb

induced hearing loss increased with increasing blood levels across the entire range of

concentrations studied (between about 40 and 500 Ilg 1'1). Exposure to high

concentrations of Pb may lead to functional disorders of the gastrointestianl tract; a

common sigh of acute poisoning is colic. Lead may also produce damage in the kidneys,

which leads to increased urinary excretion of amino acids, glucose and enter a chronic

stage with fibrosis and arteriosclerotic changes in the kidney (Choie and Richter, 1980;

L10bet til a/., 2002).

Epidemiological information indicates that Pb increases blood pressure. In a study

conducted in the USA, systolic and diastolic blood . pressure were significantly related to

blood Pb in white males aged 20 - 47 years, after adjusting for potential confounding

variables (Pirkle el al., 1985). These findings have been confirmed by another study

(Pocock e/ al., 1988). In a Danish follow-up study, Moller and Kristensen (1992) reported

a decrease in blood pressure associated with a decrease in blood Pb concentrations over a

five year period. However, the causal relationship between blood Pb concentrations and

blood pressure is still unclear. The mechanism of toxicity could involve the Ca-mediated

control of vascular smooth muscle contraction (Chai and Webb, 1988), and the renin

angiotensine system (Vander, 1988).

In summary, the adverse effects 'of Pb exposure in early neurobehavioral development are

of primary concern. It occurs at levels well below those considered "safe" in recent years

There can be little doubt that exposure to Pb, even at blood concentrations as low as 100-

7~

150 >Ig rl and possibly lower, is linked to undesirable developmental outcomes in human

foetuses and children (Davis and Svendsgaard, 1987). The most clearly identified effect

has been lower scores on the MDI of the Baylet Scale of Infant Development, poor school

attainment and lower intellectual coefficients, reduced gestational age, and lower binh

weight. In different studies, a dose-response curve for blood Pb concentration and

neurobehavioral impact has been derived (McMichael el al., 1986, 1988; Bellinger el al.,

1987), and can be used to estimate the health impact of Pb exposure at the population

leveL In terms of implications for public health, an overall four point downward shift in a

normal distribution of Baylet MDI scores would result in 50 percent more children

scoring below 80 in this test.

It is also imponant to mention that lead (Pb) absorption may be modulated by nutritional

status. Nutrient deficiencies such as Ca, Fe and Zn have been associated with high blood

Pb concentrations (Mahaffey and Michelson, 1980; Mahaffey, 1990; Wang el al., 2001;

Domingo el al., 2001) and, in some studies, Ca intake was inversely related 10 blood Pb

concentrations (Mahaffey, 1990; Hernandez-Avil el aI., 1996). Some epidemiological

studies in Pb exposed populations are in progress to determine the impact of Ca

supplementation on Pb absorption and mobilisation.

The use of Pb in petrol has been declining in various countries in the world and this has

been responsible for a substantial decrease in blood Pb concentrations in the general

population (Annest el al., 1983) such as alopecia, diarrhoea, headache, irritability,

Jaundice, Sleeplessness, Fume fever, Reproductive problems elc. (Yokoyama el al., 1985;

Behringer el aI., 1986; Wagnerova el al., 1986; Wibow el aI., 1986). A similar impact has

been observed in Mexico among residents of Mexico City (Hernandez-Avila el al., 1996).

Pakistan in mid 90s was nearing the top of the list for lead content in both premium and

regular grades of gasoline available in the market. (Editorial daily Dawn, 1996). Over 12

million tons of leaded petrol used to be burnt in Pakistan every year, emitting

approximately 550 tons of lead into the atmosphere. Because of the small traces in

furnace oil, oil-fired thermal power stations add another 190 tons of lead to the air . That

lead damages the brain, blood-forming organs and the kidneys (Naveed, 2000). A study

conducted by the Quaid-e-Azam University, ISlamabad, where milk from many dairy

75

forms along the Grand Trunk road, Rawalpindi, had two to three times more lead than the

'safe' level recommended by WHO. Another study depicted high level of lead in the

blood of school children. It was, in fact, double the WHO limits (I\aim, 1995).

According to UNEPfWHO (1992), Awan (l997b) and Shams (I 997b) Karachi ranked as

one of the worst third world city even among top ten with very high air born lead levels.

A study was conducted to investigate lead level around the city. Data collected from three

spots in Karachi indicated prevalence of an average 5.8 milligrams of lead per cubic

meter. In that study at Tibet Centre on M A Jinnah road in commercial district of Karachi

the lead level was as high as 9.60 mg per cubic meter. Interestingly more than 90 percent

of about 0.86 million vehicles playing on the Karachi roads do not correspond to fitness

standards, thus emit lead in raw shape (Qureshi, 2000). A survey repol1 reveal that in

Karachi, lead content was up to 10 Mg 1m3. That is very high as compared to the WHO

recommended maximum range of 0.5 to 1.0 Mg 1m3 (Ikram. 1996) Traffic police in

Karachi had levels of 46 mg/dl and school children in Saddar, Karachi had 36 mgldl

against the permissible lead limit of 20 mg/dl (Husain, 1997b).

7(,

2.2 DEVELOPMENT OF TECHNOLOGIES

2.2.1 Remote Sensing (RS)

The launching of the first artificial satellite. Sputnik I, on 04 October 1957 ushered in an

era of unprecedented technological development and scientific progress. The

development of space technology in 'the past four decades has indeed been an

unparalleled event in human experience in terms of technological innovations. which

characterized it. and in its impact on the human life. It provided a remarkable boost to

increased efficiencies and miniaturization III Imaglllg devices. (Drury. 1990)

microcomputers. data storage/transmission systems, conversion of solar energy into

electrical energy elc. These advances in space technology, besides making it possible to

carry out research in terrestrial. (Jensen. 1986; Foody, 2002) interplanetary and deep

space. permit applications . in a variety of areas such as meteorology (Hubert and

Timchalk. 1969; Battan, 1973; Spalding. 1974; Witter and Chelton. 1991). natural

resource monitoring & mapping such as agriculture, soils, Landuse/land-cover, ecology.

forestry, geology/geophysics(mineral exploration),water resources (Stancioff el al.. 1986;

Niemi. 1975; McRoberts el al .. 2002; Chang and Islam. 2000). environment (Komjathy el

al .. 2000; Miller and Yool, 2002; Friedl. 2002), communication (Wilke. 1990). search

(Slavecki . 1964; Alpers el aI., 1981; Prakash el al .. 1995; Corbley. 1997; Zhang el al..

1997) and rescue (Vekerdy and Genderen, 1999; Williamson and Baker. 2002) elc. The

advances in space technology have amply demonstrated their potential both for developed

as well as developing countries (Verbyla, 1995). In fact, space activities have become one

of the major generators and stimulators of scientific, technical, industrial. economic and

all-round progress of societies and the lack of such activity leads to a widening of the gap

between the developed and developing countries (Colwell, 1967; Meyer and Werth.

1990).

Over the years capabilities for remote sensing have been much extended by the invention

and steady improvements of a variety of specialized instruments. This field has grown up

with right from pigeon photography to the Satellite Remote Sensing (SRS). Aerial

photography has long been employed as a tool in urban analysis (Jensen 1983). Indeed.

this form of remote sensing is still extensively used today and can now benefit from

77

digital image-processing techniques, provided of course that the photographs are digitized

first (Futz 1996), Aerial photography has been used in various applications of earth

resources monitoring, management and exploration (Collins, 1978; Kupfer el al. 1987,

Long el aI., 1989; Lo and Noble, 1990; Fent el aI., 1995; Baker el al., 1995; Mack el al.,

1995; Tomer el aI. , 1997; Ramsey el al., 2002)

Satellite Remote Sensing (SRS) has accommodated vanous technologies such as

photographic camera, Vidicon camera, Infrared radiometers, Microwave radiometers,

micrometer radar, Laser radar (Lidar) elc. (Barrett and Curtis, 1976; Curran, 1988;

Lillesand & Keifer, 1994; Rango, 2000; Blackburn, 2002; Drake, 2002) , Remote Sensing

promises to have several advantages over the conventional methods (Chouhan, 1992),

The major socio-economic and developmental factors like population, production and

pollution exert strain on earth's resources due to which depletion or degradation of

environment results and the world starts facing crisis (Nadakavukaren, 1990), In a

scenario of dynamic changes taking place on land, water and atmospheric environment,

the resources have to be periodically mapped and monitored (Nielsen el al., 1998; Elmore

el aI., 2000; Rogan el al., 2002) To help in this stupendous task, the comparatively new

technique of remote sensing provides an effective tool to the present day scientists and

environmentalists (Faruqi, 1992). Comprehensive approach has great importance to study

the interactions among biotic and abiotic creatures (Gates, 1967; Greegor, 1986) where

remote evaluation empowers the environmentalist and decision-makers for better

governance at affordable cos't

The trend of urban studies via remotely sensed data, was initiated with the advent of 'fIrst

generation' satellite sensors, notably the Landsat MSS, and was given further impetus by

a number of second-generation devices, such as SPOT HRV. Data from the former were

initially used to analyse regional urban systems and for exploratory investigations of

some of the larger cities in North America (Forster 1983; Jensen 1983; Stow and Chen,

2002), The availability of still higher spatial resolution images from the latter enabled

more detailed studies of the older, more compact urban areas characteristic (Forster,

1983) The advent of a third generation of very high spatial resolution satellite sensors is

likely to stimulate the development of urban remote sensing still further (Aplin el al..

1997; Fritz, 1999). The data they produce facilitates improved discrimination of the dense

and heterogeneous milieu of the old urban cores (Ridley el al., 1997), and will also help

78

- --------_ . - - --

to disentangle the urban fabric in the rapidly expanding agglomerations and 'edge cities'

of many developing countries (Ganas e/ al., 2002).

Silva (1996) accounted for Remote Sensing as a powerful technique for surveying,

mapping and monitoring earth resources and environment. This techn ique has become

indispensable and increasingly more meaningful because of the synoptic coverage of

satellites over large areas rendering its cost and times effectiveness. Furthermore, in areas

that are difftcult to access, this technique is perhaps the only method of obtaining the

required data more effectively and speedily. Shukla and Suchandra (1996) considered

satellite remote sensing technology that provide an effective system of temporal

monitoring of urbanization with consequent depletion of other natural resources in the

immediate environs of big cities and metropolis. These easily available sources of

information provide inputs in the urban Landuse planning of large cities and metropolis

with a futuristic trend based on the past dynamic observation. Sutanto (1991) suggested

remotely sensed data for urban problem evaluation and planning studies owing to fast

growth of urban areas whereas ·the continuous provision of updated huge city maps on

such a large scale is difftcult for city officials. Conversely, acquisition of updated and

highly detailed (with higher resolution) information is easy and cheaper.

Several studies have been conducted with the use of moderate resolution satellite data

such as NOV AA, Landsat and SPOT both panchromatic and multi spectral analysis. Gao

and Skilleorn (1998) described the capability of SPOT -XS data in producing land cover

maps at the urban-rural periphery. On similar lines Toll (1984): Gupta and Munshi

(1985); Misra (1989); Doi (1990); Forghani (1994); Shukla and Suchandra (1996);

Coppin and Bauer (1996); Kwarteng and Chavez (1998); and Gao and Skilleorn (1998)

have attempted to study urban environment and land cover by using remotely sensed

imageries. Brouwer (1990) used remote sensing techniques to rapid assessment of urban

growth. Quattrochi and Luvall (1999) studied the growth of Atlanta (USA) city vis-a-vis

meteorology and air quality using remote sensing. Anthony and Xia (1996) studied urban

growth management in the Pearl River delta (USA) by using remotely sensed

information.

Haack e/ al. (1987) found that in some cases Landsat TM data might not always lead to

required results than Landsat MSS in mapping urban and near-urban land covers if they

are less homogenous. Landsat TM data allowed many classes such as densely built-up

79

(settlements) . sparsely buill-up (settlements). Rangeland. irrigated crops. and irrigated

pasture in suburban region of Denver (USA). However. the improvement was minimal for

spectrally heterogeneous classes such as urban residential areas. From Landsat TM

imagery. Harris and Ventura (1995) performed a five-category (residential. commercial.

industrial. open spaces and freeways) classification for the small urban areas of Beaver

Dam. Wisconsin (USA). The accuracy was improved after such ancillary spatial

information as zoning and housing density was incorporated in the classification.

Therefore, in spite of its being a primary data source, TM imagery functions better in a

complementary role in mapping land covers at the urban periphery (Milazzo, 1980).

With the growing technology, several new techniques have been introduced to monitor

changes in the land cover I lana use. Some of these include image difference, ratioing.

principal component analysis (PCA), and selective principal component analysis (e.g

Chavez el al., 1977; Jensen and Toll, 1982; Gunther, 1982; Singh 1989; Chavez and

Kwarteng, 1989; Chavez and MacKinnon, 1994).

Remote Sensing techniques has also been used to monitor some constituents of

atmospheric pollution (Barrett and Curtis. 1976; Deeter el al .. 200 I ;) . However, in an

indirect approach air pollution could be estimated by way of the monitoring of the relative

parameters such as landuse, traffic count and speed elc. (Ulshafer and Rosner , 200 I)

Ulshafer and Rosner (2001) used Lichens on apple trees as bio-indicators of air pollution

in the region of Reutlingen (Germany). Remote Sensing could help to map the road

network, geometry of choking regions, expansion of plume including its concentration,

under threat popUlation elc. (Gonzalez and Wintz., 1987, Lillesand and Keifer. 1987) .

These parameters have strong relationship with the generation. distribution, effects and

control of air pollution (Hayes, 1979; Stern el a1., I 984;Collins ~I al., 1995; Weide el al.,

1999). Beside these, there are some direct pollutant-monitoring techniques also being

used, through remote sensing (Hayes, 1979). Some adaptive instruments I devices use the

same concept of remote sensing but their platforms are on ground rather satellites (Bilbro

el al., 1986; McCaul el al., 1987; Rothermel el al., 1997).

The integration of GIS and remote sensing has justifiably received widespread and

extensive mention in recent literature (e.g. Aronoff, 1989; Korte, 1992; Denegre, 1994;

Verbyla, 2000; Steede-Terry, 2000;) . According to Fedra (1993); and Lillesand and

8U

Keifer (1994), the interface between GIS and remote senlsing can be envisaged in one of

different ways

a ) Remote sensing can as a tool 10 gather sets for use in GIS;

b.) GIS data sets can be used as ancillary information with which to improve the

products derived from remote sensing and;

c.) Remote sensing data and GIS data can be used together tor modelling and

analysis.

Some important satellites' payloads been sent into orbit to monitor atmospheric

parameters like carbon methane (C~). A the Measurement

Pollutants In The Troposphere (MOPITT) was launched on the Terra platform

and tropospheric carbon monoxide (CO) profiles and methane (CH4) columns

(Drummond e/ at, 200 I). Gas filter correlation radiometry with pressure modulation and

length modulation techniques are used in MOPITT The same methodology has been IIsed

for MA TR but with the different objectives. MATR has two fUllctions. The first function

is to collect data to test ability to <I"I,,,p,,,,, correct atmospheric CO or

from MOPITT-like filter radiometer data. The second function is to

MOPITT data (McKernan el al., 2001).

amounts

validate

The MOPITT Airborne Test Radiometer (MATR) uses gas filter correlation radiometry to

measure troposphere carbon monoxide (CO) with three optical channels or methane

(CH.) with one channel. MATR data serves to test retrieval techniques for converting

infrared radiometric data into atmospheric CO or CH4, amounts. MATR is also applied to

MOPITT data validation (Tohon el aI., 1999; Gillea et aI., 2001)

In Pakistan, Space and Upper Atmosphere Commission, Pakistan (SUPARCO), is the

governmental organization involved in space technology development, claims to its credit

a host of studies addressing diverse resource, mapping and environmental problems

covering various disciplines such as hydrology, agriculture, forestry, geology, geography,

oceanography, etc. (Mehmud, 19&8; SUP ARCO, 2000). Out of these projects, chief

studies includes LanduselLandcover Studies of Karachi, Lahore, Islamabad,

Peshawar e/c. with integration of GIS; Monitoring of Cholistan al

Dingarh, Fort Abbas and lslamgarh; Land Degradation Monitoring of Hyderabad District:

81

Mapping Biomass and other Terrain Units around Chotiari in Sanghar District; Pakistan

Coastal Mangrove Degradation: a temporal variation from 1973 - 1998; Land Suitability

around Moen jo Daro Area; Water logging and Salinity Mapping -WAPDA (SCARP VI

Project); 1 Km Landcover Database ' of Asia; Mapping of Forests in Northern

Mountainous Regions of Pakistan; Landuse!Landcover Estimation of Karachi Region and

Urban Sprawl Studies of Karachi and other major cities of Pakistan (SUP ARCO, 2000) .

Zareen e/ al. (1995) performed statistical analysis of wind speed and direction obtained

through satellite based TCP network in Pakistan. Baig e/ al. (1995) processed satellite

based data of Bannu basin, Pakistan to enhance the geological features. Siddiqui (1995a),

elaborated the diversifIed applications of satellite imageries in exploration / development

of oil and gas fIeld in Pakistan. Rangoonwala and Ahmed (1995a), wrote a review article

on integrated use of optical and Radar sensors for resource development. Khan and

Siddiqui (1986) used Landsat data for urban growth monitoring in Karachi metropolitan

area. Bertaud, Marie-Agnes (1989) used aerial photograph at scale I :40,000, SPOT PAN

image at scale I :40,000 and SPOT XS image at scale I :24,000 to identification of

different patterns of development and with the help of AutoCAD and GIS software PC

ARC/INFO produced the land use and road network maps of Karachi . Siddiqui (1991)

merged high-resolution SPOT-pAN data with SPOT XS and Landsat TM data for urban

land use application. Kazmi (1991) applied SPOT HRV-2 data to identiftcation and

mapping of types of land use classes and with the help of ground truth information

Landuse classes were checked. It was suggested that SPOT images could effectively be

used for the identification and interpretation of land use.

Rangoonwala e/ at. (1991) used satellite remote sensing data for identifying residential

units for change detection in Karachi. A follow-up study by Rangoonwala and Ahmed

(1995b) described the urban growth monitoring and development planning using remote

sensing and GIS technologies. Siddiqui e/ at. (1995) have undertaken an urban land

use/land cover study through satellite remote sensing techniques by classifying Landsat

and SPOT imageries. It was concluded that the systematic monitoring of urban growth,

proper management of the city and planned future expansion would lead to improvement

in the living standards and environmental conditions of the city as a whole. Siddiqui e/ al.

(1996) used satellite remote sensing data for mapping and monitoring temporal changes

in Larkana district and presented the application of satellite remote sensing data to urban

land use c1assiftcation studies. Siddiqui (1993 and 1995b) looked at the possibility of SRS

technology in monitoring deforestation and land degradation in Pakistan. Malik and

Majeed (1995) threw light on RS application in water resources research in Pakistan.

Nasir and Raouf (1995) wrote a review article on remote sensing for coastal resources and

marine pollution.

Kazmi (1995, and 2001b) utilized SPOT HRV and Landsat 5, imageries to monitor land

degradation and crop assessment in the rural vicinity of Karachi . Afsar (2001) and Mehdi

el al. (200 I) contributed their work related to the signifIcance of satellite remote sensing

in the fIeld of urban studies of Karachi metropolis. Afsar (200 I) has estimated 500

square kilometres, built-up urban area with rapid growing rate. Mehdi el al. (200 I)

indicated that the major portion of urban built-up in 1998 was covered by medium density

of selliements. But the major concentration of population and selliement was in core

Karachi .

2.2.2 Geographic Information Systems (GIS)

GIS technology provides an excellent platform upon which different types of spatially

referenced data can be united for analysis and display purposes. Prior to the advent of GIS

technology, many operations that involved the concerted utilization of datasets derived

from different sources and in different formats used to be carried out using a " push-pin"

approach in which hard copy. maps were generated and overlaid upon one another. The

approach was both costly and time-consuming and often yielded substandard results

(Dent el al .• 1998)

It has been estimated that over eighty percent of the world's data have a spatial

component (Worrall, 1991; MIC, 1999). These spatial components could include an

address in a database or coordinates in sampling data. GIS has been used to manage

natural resources, utility networks, government activities such as revenue management,

crime analysis, crime monitoring and environmental degradaiion monitoring elc. GIS has

evolved to provide solutions in three areas: GIS as an information database, GIS as an

analytical tool, and GIS as a decision support system (Bellman and Zadeh, 1970; Bodily,

1985; Banai-Kashani, ) 989; Armstrong, el al., ) 991; Huizing and Bronsveld, 1994;

Eastman, ) 995). A trend affecting the GIS application is the result of recent advances of

Xl

computer technology and graphic user interface operating systems. In particular, desktop

GIS have emerged, capitalizing on the advancement of direct manipulation (Theobald.

1998; MIC, 1999; PCI, 2002).

There are many prominent trends in the GIS-industry (Theobald, \ 998) . More

sophisticated GIS are being used to address a broader range of problems and in a wider

variety of situations. For example, GIS is being applied to problems that require coupling

of simulation models with GIS (Goodchild, 1993); the visualization of spatial data

(MacEachren and Taylor 1994; Buttenfleld 1996) the development of spatial decision

support systems (e.g. Faber e/ al. 1998); and to support public policy and decision

making (e.g., Hobbs e/ al. 1997).

Environmental management, decision-making. and planning require a large and diverse

ensemble of data that includes time series of monitored environmental variables and

geographical datasets. Most environmental phenomena show five different dimensions of

data: the location in space, (set. of 3 coordinates - latitude, longitude and altitude), the

position in time and the particular phenomenon (variable/theme being analyzed)

(Langram, 1993; Waters, 1995; Hibbard e/ aI. , 1995; Oracle, 1995). The goals of an

environmental monitoring and information system is thus to collect data in this five

dimensional world , store it and promote its use in the most efficient possible way (Raper

and Livingstone, 1995; Oliveira and Ribeiro da Costa, 2000; McConnell e/ al. 2000)

Some of the pnor environmental cartographic visualizations (Pape, 1980), where

analytical and composite maps of urban air pollution are proposed, together with temporal

(static) displays of changes. Hayes (1979) has given an example of more specific

demands for environmental visualizations. Since the ICA cartographic conference ill

Tokyo in 1980, which was dedicated to environmental cartography, environmental

mapping and visualization work has increased (lCA, 1980; Pape, 1980; Grot jan, 1982;

Kadmon, \983; Lavin and CelVeny, 1987; EuroCarto 7, 1988). By the late 1980s

environmental cartography was also concerned with environmental data integration, the

spatial characteristics of the data to be mapped and environmental databases (Ormeling,

1989). Computer capabilities have, in the meantime, promoted the development of

systems for efficient environmental monitoring and impact studies and more realistic

visualizations (IEEE 1984, Papathomas e/ ((I. 1988; Wolfe and Liu 1988; DiBiase e/ a l.

1991; Abel el al. 1 992a, 1 992b; Emmell 1992; Gantz 1992; Lang. 1992; Lang and Speed

1992; Kruse el at. 1993; Stenberg 1993). However, apart from the visualization need, the

various thematic variables related to the air pollution phenomenon introduce more

possibilities and demands for data analysis and mapping (Koussoulakou, 1994) .

2 .2.2.1 . Air Pollution Dispersion Modelling

Air dispersion modelling is a well-established discipline that can produce results in a

spatial context. Integration of this discipline \Vith GIS technology is optimal because it

leverages the predictive capacity of modelling with the data management, analysis, and

display capabilities of GIS. The utilization of air emission data and air dispersion

modelling with GIS enable public health .professionals to identify and define a potentially

exposed population, estimate the health risk burden of that population, and determine

correlations between point-based health outcome results and estimated health risk (Stern

el al. 1984; Dent el al., 1998). Geographic Information Systems (GIS) make the

atmospheric quality measures more comprehendible with the help of inherent digital

Cartographic Visualization Functions.

Models of Air dispersion estimate the concentrations of pollutants in the atmosphere

resulting from point or non-point atmospheric emissions. They take into account variolls

factors that can affect a substance's concentration in a plume as it migrates through the

air . These influential factors include gravity, meteorological conditions, and chemical

reactions (Fedra, 1994; Folgert, 1997).

Geographic Information System and air dispersion modelling algorithms are separately

designed . Three well known methods exist, which could be adopted to empower GIS with

dispersion modelling abilities. Strong comparisons of procedures for linking of GIS with

models are provided by Haining el al. (1992) Able (1994), and Fedra (1994) . Table 2 .1 I,

reveals possible methods of integration.

Table 2.11: Methods for Integration of Air Pollution Dispersion Models with GIS

'~ '. .. . -,~,:s.'~.:~-i~ ... :. .~( .'

r.1elb:o.di~lnic:gtjdii5jl . .. . EXllinple Application , . .." . .. ' : '. . ..

Full Inte.l(ration Bishoo and Robev (1994) Loose Couolin" Collins (1995) Tight Couoling Buckley (1993)

Source: Folgen . 1997

In the filII ill/egrfl/ioll approach, a programming language such as Maplnfo's MapBasic

or Arclnfo® Arc Macro Language (AML) is used to create and implement the model and

the model becomes one of the analytical functions of GIS . This method is adequate for

modelling simple processes where computational requirements are not extremely great,

and may be utilized if a phenomenon to be modelled is very specialized (Foigert, 1997).

Bishop and Robey (1994) created a model to run within the GIS, written as a series of

A.J\1L commands, that used the GRID module' s capacity to undertake visibility, proximity

and overlay analysis. They suggested work is made easier if models use data from or are

implement within a GIS. assessed and compared the accuracy and efficiency of a

Arclnfo® GIS-only model with the coupling of a model with Arc lnfo® GIS . In her study,

she quantified the advantages and disadvantages of these two approaches (Chou and

Ding, 1992). As suggested by Goodchild (1992) although GIS supports a broad range of

data models, the ability to wdte the environmental model directly in the command

language of the GIS is being researched (Steyaert and Goodchild, 1994; Parks, 1993).

Nyerges (1992) and Fedra (1993) and others argue that the best approach is to link GIS

and other systems, resulting in more efiicient modelling tools.

Zack and Minnich (1991) applied a diagnostic wind field model for forest fire

management. In this loose coupling approach, a GIS was used to help prepare model

input data, and display model analysis . Collins e/ aI., (\995) estimated pollutant

concentrations using a stand-alone dispersion model then used the GIS to visualize the

model's numerical output, without directly linking the two systems. The advantage of this

method is that each system complements each other; the model provides potentially

valuable information to the GIS for further spatial analysis and the GIS can be used to

prepare input files and allows model output to be visualized in map form.

Tight coupling involves full integration of an existing model with GIS, uSing one

common graphical user interface (GUI) to facilitate the modelling process. The GUI

provides a veneer, which assists and guides the user through the modelling process while

hiding the processing intricacies. Such systems offer a virtual environment within which

decision-makers and scientists can explore theory and evaluate competing management

strategies (Bennett, 1997). One advantage of this method over the full integration

approach is that the speed of model calculations is a function of the computer ' s

processing speed, not the speed in which the GIS interpret command-macros.

Buckley (1993) integrated atmospheric dispersion modelling with GIS technology to

produce the Computer-Assisted Protective Action Recommendation System (CAP ARS),

which provides plume and health impact predictions from toxic chemical emergencie$. In

this integration, a sophisticated and fault tolerant system was designed to produce plume

paths and predict plume spread over complex terrain. Novak and Dennis (1993) provide a

synopsis of a regional air quality and 'acid deposition model. and provide a plan to

develop an integrated modelling and analysis framework to ensure that GIS functionality

is directly accessible by the researchers. Lathrop el al. (1994), have applied a tight

coupling approach in modelling the impact of atmospheric deposition and climate change

on northeastern USA forest ecosystems.

As stated by Goodchild (1993), the computational power of computers and GIS make it

easier to integrate models, to disaggl'egale them to greater levels of spatial detail and to

reaggregale results over relevant areas. The GIS function of visualization can be used to

make powerful products that carry far more weight than tables of numbers . The

interactive nature of many current GIS allows the decision-makers to work with complex

models in a comfortable, reassuring environment. Table 2.12, shows the dispersion

models integrated with GIS.

87

Table 2.12: GIS integrated Air Pollution Dispersion Models

'. M;o~~t ::.: . ~Si~Pl~D~~i.iptiOif" ''',", .. . ;/,> .;,:,.' Ex~niple

ISCST3 Induslrlal Source Complex Shorl Term. Folgen. 1997, Delli <I al .. 1 ~~X Version 3

MATDOR Long~Range transport and deposition 0/ Ball nnd Rodgers. t ~% ,,"Iohur and nilro~en emilled up 10 10 km

UKADMS Atmospheric dispersion oJ gaseous releases Ball and Rodgers. t ~% 10 Ihe almosohere liP 10 50.km

V1SPACT A rmospheric dispersion Gnd vis;bility of Ball nnd Rodgers. 1%(, water vapour and droplels from cooling towers up 10 5 km

TRAC Terrain Responsive A lmosoheric Code Hodgin <I al.. 19~7 CDM2 Climalolagiwl Di.'persian Aladel 2.0 Zalluclli . 1990: Comrie el 01.

1997: Weide el {Ii. 1~9~

MSGEIS GIS Application 10 Generale Mobile Source Canwriglll <I al .. 1997 Emission inventories

SYMOS 97 A group oj Sialisl/cal Models, Jor po/Julallls Jancik. 21100 disoersion mode/linF

UAt"l Urban A irslred Model Comrie (!{ al. t~97

2.2.2.2 Multi Criteria Evaluation

Traditional approaches for suitability analysis in GIS are overlay and the more

complicated multi criteria evaluation. Despite being widely used, these methods have al

least three problems:

I. Difficulties in handling spatial data processlllg inaccuracy, multiple measurement

scales, and factor interdependency; .

2. Requirements of prior knowledge III identifying criteria, assigning scores,

determining criteria preference and selecting aggregation functions; and

3. Typically, 'unfriendly' user interface.

Suitability analysis usually requires making decisions among multiple factors . There are

two methods commonly practiced to accomplish this task in GIS , a simple overlay and

the more complicated multi criteria evaluation (MCE).

Overlay can only combine deterministic digital map information to define areas

simultaneously satisfying two or more specifiC criteria (Carver, 1991). The integration of

MCE into GIS has attracted much attention (Carver, 1991; Pereira and Ducktein, 1993;

Heywood el at., 1995; Jankowski, 1995). It is well known that the spatial data in GIS

usually have properties that are difficult to handle by traditional methods, such as

inaccuracy, multiple measurc;ment scales, and interdependency among factors . Traditional

methods require prior knowledge to identifY all relevant criteria, assign scores, determine

the criterion preference, and select the aggregation function. Methodological uncertainty

and error may appear in these procedures. For example, a user may be asked to provide a

set of values, such as criteria weights. These questions are usually cognitively demanding

and far beyond his or her intuition.

The application of digital map overlay for the purpose of identifying suitable areas is a

classic application of GIS. In raster GIS, for example in IDRISI or in ArcYiew (Eastman,

1995; Lee and Wang, 2001), a suitability map is produced from a series of Boolean

images, where each image represents all areas meeting the criterion being depicted. These

images are then combined using the overlay combination procedure to yield a final map

that shows the sites meeting all the specified criteria. However, overlays have limitations

when dealing with information of a non-deterministic nature (Carver, 1991). Heywood

(1995) considered four factors - school location, roads, urban areas and insurance. After

the identification of relevant criteria, the scores, which indicate the impacts of alternatives

on each criterion, were determined. There existed many methods for this task (Hepner,

1984; Pereira and Ducktein, 1993).

The methods for criterion importance determination and aggregation can be classified

into compensatory and non-compensatory (Hwang and Yoon, 1981; Minch and Sanders,

1986; Jankowski, 1995). In a compensatory method, the high performance of an

alternative achieved on one or more criteria can compensate for the weak performance of

the same alternative on other criteria. Weighted summation is such a method. Other

compensatory methods include concordance analysis, analytical hierarchy process, and

ideal point. Non-compensatory techniques are the stepwise reduction of the set of

alternatives without trading off their deficiencies along some evaluation criteria for their

strengths along other criteria. Another promising method for suitability analysis and

mapping is based on the Dempster-Schaefer's theory of evidence and fuzzy logic

(Boham-Carter, 1994).

The ultimate rum of GIS is to support spatial decision-making (Malczewski, 1999).

Today, available GI-Systems offer unique opportunities to tackle mOre efficiently and

effectively problems traditionally associated with data handling and analysis (Belton and

89

Gear, 1997). They playa vital role of spatial decision-making by sloring and managing a

large amount of spatial data and information.

The hypothesis formulated earlier in this study falls within the broad class of MAD\1

(multi-attribute decision making). Conventional multi-criteria technique largely assumes

90

2.3 LITERATURE SYNOPSIS

The global air pollution problem was explored and studied from a number of different

perspectives with emphasis on developing countries. The literature on this subject was

largely for the developed parts of the world . However. there is now a more refmed

understanding on the number of issues such as land use / land cover, population,

epidemiology etc., as compared to fifteen years ago . There has emerged a literature

consensus that the locus classicus ideas and solutions suitable for developed countries are

not viable in the indigenous environment for the developing countries. The areas, which

need further attention, are around understanding the diverse' perceptions on

environment', and utilization of modern tools in an integrated approach.

The basic literature searched was based on the nature, sources, and effects of criteria air

poI/II/allis. It is felt that the whole set of literature developed worldwide in this field was

so enormous in magnitude that the author had to go for precise abridgments. A major

concern experienced during this review was the dearth of indigenous literature.

The later part of this chapter dealt with the evolution of remote sensing and geographic

information systems. These notes have convinced the author of the tremendous potential,

which these technologies could put forth in the analyses of geographic inquiries, with

special reference to environmental simulation and management. In the upcoming sections

of this thesis, the conceptual framework is developed for monitoring spatial patterns of air

pollution in Karachi metropolis through the '3-S' technologies .

~I

---.---------

3. METHODOLOGICAL CONSTITUTION

Most of the urban areas in the world have high concentrations of air pollution sources

resulting from human activities; sources such as motor vehicle traffic, power generation,

residential heating and industry. Urban air pollution not only represents a threat to human

health and the urban environment, but it can also contribute to serious regional and global

atmospheric pollution problems. Air pollution is experienced in most urban areas and is

therefore a worldwide problem and an issue of global concern, it has been estimated that

globally about 500,000 people die prematurely each year as a consequence of exposure to

ambient pollution of suspended particulate matter. Increases in morbidity from respiratory

diseases due to air pollution are estimated to occur in about 40 million people; several

million infants die each year from acute respiratory infections exacerbated by air

pollutants (Murray and Lopez, 1996; Schwela, 1996; WHO 1997).

Pakistan covers 0.7 percent of the world's land area, but accounts for a little over 2

percent of the world's population (McDevitt, 1999). With a per capita annual income of

hardly US$ 450 (WB, 1998), a fast depleting natural resource base, inadequate social

service, and an arid or semi-arid climate where health issues remain a predominant

concern. The countIy is particularly vulnerable to the negative impacts of environmental

degradation. Inefficient use of existing resources, particularly energ\' resources, has added

to the country's susceptibility.

Air pollution is considered to be primarily, an urban problem in Pakistan. As the rate of

urbanization increases air pollution levels are expected to increase significantly. Karachi

is the biggest and the highly polluted city of Pakistan. Population growth rate according

to population census 1998 is more than 3.0 that depict the annual growth of population at

risk while pollution growth is also considerable. There has been no seriolls study of the

impacts of air pollution in Karachi (UNEP, 1999), although its most obvious expected

effects are those related to human health (Pipe, 1995; WHO, 2000) Air pollutants are a

major cause of respiratory and ocular diseases (Ware el aI., 1986; Schwartz, 1989;

Dockery el al., 1989; Pope and Dockery, 1992; Hoek el al., 1992; Donaldson el aI.,

2002), which are highly prevalent in almost all over the urban area . Although some

individuals and some organizations like SUPARCO, PCSIR and AKU have worked on

93

the problem of air pollution and its impacts on dwellers of Karachi Metropolis (e.g.

Manser el al., 1990; Yousufzai, 1991; Yousufzai el al., 1994; Ghauri el al., 1999;)

However, certain important aspects could not be addressed by them. For example, spatial

patterns within a city on micro scale, epidemiological indicators, socio-economical

aspects of residents, perception and awareness about pollution among the community elC.

This study strives to overcome these shortcomings and to present a cause-effect

relationship based on logical and factual spatial dimensions of burgeoning human induced

environmental hazard . These aims reveal that there is a need for carefully designed

research on spatial pattern of air pollution and impacts within Karachi metropolis, In

order to provide deep insight . The study is important for environmental protection of

urban areas of developing countries. Conceptually a systematic approach is required to

cope this manner that not only accompanies the discipline but also encompasses all

aspects of study Figure 3. 1.

Ii

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j 1 B ] ~j ~ ~ j ~ 1 OJ

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a-iL:

3.1 REMOTE SENSING TECHNIQUES

Remote Sensing is the technology to acquire and measure the facts of some property of an

object by a recording device that is not in physical or intimate contact with that under

study phenomenon. These facts can be processed to make particular organized

information. Diversity in the applications of the remotely sensed information now ranges

from the detection of very tiny objects in geo-spheres (i. e. lithosphere, hydrosphere,

atmosphere and biosphere), to the precise quantification of huge surface and subsurface

resources through its direct, .indirect and multi·dimensional integrated observation

methods.

Remote sensing has become an organized tool to provide from raw to processed

information for various environmental applications. This technology greatly enhances

abilities to analyse landscape level relationship of environmental factors and impacts . As

a result, nowadays in many places scientist have been monitoring various environmental

impacts by focusing on their factors with the help of Remote Sensing and GIS tools

(Kazmi, 2001a)

3.1.1 Data Acquisition

The use of satellite imageries for mapping appears to be one of the most straightforward

applications of remote sensing. Multi-spectral images are being used for land cover

elassijicaliolls. Landsat spacecraft provide images of global coverage every 16 days for

down·to·earth projects such as land-cover inventories, natural resource mapping, water

quality assessment, and flood control.

In this study Landsat TM (1998), SPOT Pan (1992) and SPOT XS (1992) multispectral

data has been used to classity Karachi's Land cover and its distribution Table J. I. The

density of land utilization with respect to individual elI/sIers of the images has been

determined. The quality of the' images was found satisfactory however, reclijicalioll,

correeliolJs and euhaneemem was conducted as per protocols.

Table 3.1: Satellite Imageries: Sensors, Resolution and Acquisition

SPOT Pan 10 x 10 m March. 1992 SPOTXS 20 x 20 m March. 1992 Landsat TM :\0 x 30 III Febmar\'. 1998 KVR 2x2m 1998

Source: cQUnes) KaZllll . J.H .. UnI\Crsl~' afKarachl

For targeting the congestion locations due to heavy traffic and dense land use (resulting in

unbearable air pollution). use of high-resolution imagery i.e. KVR (2m resolution) of

Sovinform Sputnik. Russia has been employed. Unfortunately it was not containing the

whole of the metropolitan Karachi but had the coverage of about two larger districts . The

author utilized this high-resolution imagery and his life time exposure of the city in

deciding the list of more than 300 locations for the ground data collection.

3.1.2 Satellite Image Processing

The remotely sensed image is processed in various steps according with the needs and

objectives. Broadly speaking, the major steps in satellite images processing are image

Rectiftcation, Enhancement, Stitching/Sub-setting and Classification. These procedures

are applied through high-speed computers using pertinent software

3.1.2.1 Environmental Attenuation Corrections

Atmospheric error creeps into the data acquisition process and may degrade the quality.

This in turn may have an impact on the accuracy of subsequent human or machine

assisted image analysis (Meyer ef at.. 1993). Therefore, it is usually necessary to pre

process the remotely sensed data prior to actually analysing it (Teillet, 1986).

Rectification. a pre-processing step, involves application of some correction factors on

the remotely sensed imagery acquired from the source.

3.1.2 .1.1 Compensation for seasonal Differences

Angle of incidence imparts great influence on radiant flux from the surface objects. It

changes digital numbers (DNs) during variations especially in temporal studies involving

time spans of less than a year. It is important to compensate for the difference in sun

elevation angle throughout the year (Figure 3.2).

;;I- Sate ll ite

Zenith

Sun Position

~S:::::------ Summer

Spring/Fa ll

Wi nter

Tangen t Plane

Figure 3.2: Variations ill Solar Elevation Angle

The above-mentioned correction was accomplished uSlDg program available In

Geomatica 8.x Algorithm library:

This MODEL algorithm nonnalizes the sun's elevation angle to that of its zenith position.

This is achieved by dividing each image by the sine of the solar elevation angle for the

scene (PCI, 2002). Solar elevation angles were provided as ancillary data in the header of

the acquired imagery.

3.1.2.1.2 Haze Compensation

Particulate matter in the atmosphere is responsible for the scatter of electromagnet ic

energy. This scattered component (known as "haze") increases the overall radiance

thereby reducing tbe contrast in an image. A quick and simple method to compensate for

haze is to estimate the additive haze factor independently for each channel. These values

were obtained by observing the Grey level for scene features for which the reflectance

should be known.

9S

Having determined a quantitative measure for haze, compensation for this effect by

subtracting the haze factor value · from every pixel in the image has been adopted .

Subtraction of an image by a constant grey level of above-mentioned additive haze was

achieved using Image Database Arithmetic algorithm:

3.1 .2.2 Instnlmental Error Correction

Noise is the result of sensor malfunctions during the recording or transmission of

information. Noise in a digital image can manifest itself as either inaccurate grey level

readings or missing data altogether. Unlike geometric distortions and other radiometric

degradations, Noise is readily identiflable, even to those unfamiliar with the scene of the

image. Satellite Image Processing software offers correction algorithms for the most

commonly encountered types of Noise such as Line Drop, Regular Stripping elc.

3.1.2.2.1 Line Drop Noise Correction

To make proper image data sei Line Drop Replacement algorithm has been applied on

imperfect images, which were used in this study. This function fdled the several adjacent

pixels or entire missing lines or noticeably different to each other.

3,1.3 Image Geometric Correction

Raw digital images contain geometric distortions, which make them not viable as maps.

Map is a flat representation ofa part of the earth's spheroidal surface. To be useful, a map

should conform to an internationally accepted type of cartographic projection, so that any

measurements made on the map will be accurate with those made on the ground.

The steps to correct the satellite images as well as scanned maps geometrically, are shown

in the flow chart as follows Figure 3.3 :

99

Ma.ter Data-sc:t Georefo~"ce(/ Jmag~ I map GPS C oominote. Pub/i .• lled RlUP Grid

Figure 3.3

~"'iwOCh Col1 .. -ctiOQ \\;lh nuntnmm R.\IS Thr~old

1n1Ol~ l'CWI1IpIiA. wiD JIC'N til.:

p.-ratiIM.

Slave Data-set Satellite Images Scanned }',fap3

In above-mentioned model master data set was projected on the Geometric Model !

Projection described in Table 3.5, which is one of the most appropriate projections tor

this region of the world:

100

3.1.4 Image Enhancement

Image Enhancement techniques are used to remotely sensed data to improve the

appearance of an image for human visual analysis or occasionally for subsequent machine

analysis. There is no such thing as the ideal or best image enhancement because the

results are ultimately evaluated by humans, who make subjective judgments as to whether

a given image enhancement is useful (Jensen, 1996). In this study, various enhancement

algorithms have been used to improve the quality of images through world-renowned

software: ERDAS Imagine version S.3 .1 and PCI Geomatica S.x.

The algorithms employed are: Contrast Enhancement (Linear, Root, Frequency,

Adaptive), Band Ratioing, Spatial Filtering (Low Pass filters including, Average (mean),

Median, Mode, Gamma) and High Pass Filters (Gaussian Filter, Laplacian, Sobel, Prewitt

Edge Detector and Edge Sharpening etc.) (PCI, 2002).

Procedure for saving the implemented enhancement is as follows:

3.1.4.1 Direct Application

for machine analysis, enhancement algorithm was permanently applied on the Image

bands and saved afterward in the same file.

3.1.4.2 Indirect Application

The enhanced values have been saved as Look Up Tables (LUT) files separately. These

LUTs were applied at the time of Visual Interpretation over loaded image bands in RGB

channels.

3.1.5 Study Area Development (Mosaicking/Subsetting)

Mosaicking is the blending together of several arbitrarily shaped images, to form one

large radiometrically balanced image such that the boundaries between the original

images are not easily seen. This allows creating a complete study area as a single image,

which could be consisted of several images before. At times, area under study is not

III 1

wider than the image. Instead of mosaicking of several images, sub selling of image is

done .

For the convenience of processing, it was necessary to study the metropolitan Karachi

only and image of adjoining areas was overlooked. This process of focusing the study

area and clipping the image of irrelevant locations is known as Subselling. Here the

Karachi metropolis in Landsat synoptic coverage is extracted from the image through

subselling. Landsat TM image covers 185 x 185 km (34225 Sq. km) synoptic view .

Karachi Division occupies 10.2 percent of the total TM scene. In this study, Area of

Interest (AOI) has been developed in form of georeferenced vector data. Subset has been

taken out from image on the basis of AOI. The total Karachi Division is covered in the

same image illustrated in Figure 3.4.

3.1.6 Land-cover Classification

Remotely sensed data of the earth surface may be analysed to extract useful thematic

information. This raw data are then transformed into information. Multispectral

classification is one of the most· practicing methods for the information extraction. This

information is the best source of available, updated and synoptic Land-cover extraction.

The scale of details and accuracy depend upon the image resolution, accuracy of

algorithms along with their limits and expertise of users.

The land cover classification procedures used are illustrated In the form of a Flow

diagram Figure 3.5:

For the achievement of maxImum land cover accuracy several supervised and

unsupervised classifIcation algorithms has been applied with different classification

schemes on exercise level. This exercise gave very profound observation on the study

area's land cover.

102

Figllrc 3.4

11

.~ . -

KARACHI Landsat 5 - TM

Subset

, W{¢}E

S 0 II ZI

Xilflwwtrr

{

"

(

103

Obtaining initial grollnd referenced Selection of Appropriate Remotely database a priori Sensed Application D<lla

I I

I Illlage Rectification

r I Tntilling Siles I

sUpcl'\'is.:d C'1:lssilil',llil/lt ,\I;uric]I"1 l ' lI':;u ,WI'" J.~~.I CI:I:<osilil';&(il1l1 ,\I!.I.U !'ithm j'(lf fT ltelr;ptpcd ISOl.klT.-l ,\111111'111 111 dl~rU/ln: ,:'; ·t!I~lIIl \

,\hIXlm,'tm I!l.:idlhood FIt=>

I

I A.: .. ·ur.u.:y [yahlitlilln

Figure 1,5 ,- --..

SourCfJ: After Jenscn. 1996 Digital ThcJn"uic

Map' ~

3.1.0.1 Land cover Classification Scheme

With the detailed spectral examination of images, the following scheme of land cover

classes has been designed, ·each of which has it s own significance for this study Tlt e

classes studied were basically live and flll1her classified depending upon the sparseness

and density of that particular land cover illustrated as under Figure 3.6 :

Iii.!

Land Coyer Classification Scheme

Urban

Densely BlIilt-up

:'vIedium Built-up

LolV Built-up

Mixed Landcoyer

Urban-Vegetatiol/

Vegetation

Dense

Sparse

Mangroves

Unused Land

-.J Open Plain -.J Sandstune Reflectiun -=.J Rock Shaduw

"'ate.·

/-Vater Bodies

F~lln.; 3.6

105

3.1.6.2 Supervised Classification Method

After going through in details of spectral distribution with individual bands and several

combinations, criteria land-covers have been explored. After detection of proper land

cover sites classification has been performed under the supervision of training areas.

3.1.6.2.1 Mining a/Training Sites

An analyst may select training sites within the image that are representative of the classes

of his/her interest. For each class 3 to 5 sample training sites has been marked.

Complexity and overlapping was avoided and the sites were kept straightforward . Class

homogeneity has been given the highest priority in the overall mining of training sites.

3.1.6.2.2 Classification Algorithm

Various available supervised classification algorithms have been tested. Eventually,

Minimum distance classifier (MDC) was used with explicit training areas over the

images. This classifier assigns each unknown pixel to the closest category mean Figure

3.7. This algorithm is computationally simple and commonly used . When used properly it

can result in classification accuracy comparable to other more computationally intensive

techniques. The author was satisfied with the analysis results of this exercise.

** *~* *1' ... ... , ... ...

i .*'1 • ! ... ,),

. . . . ,. ~ ••• • ,,,,,.,A , " t';~'. '~e ...• ~.

. e' • • •• Channel A

Figur\! 3.7, ConceptWlI diagram of MDC algorilrun: A simpk MDC shows tho Class of Pixel, -A" which is on lh~ ba:;is of iB distanCt! from the! nt!ighbourlng. c1a~s: .

1U6

3.1,63 Classification Result Editing and Aggregation

In lilly interactive (supervised or unsupervised) classification result, it is very rare that

classification results are lOO% accurate. One of the post-classification processes, "elms

Ediling and Aggregalion" is one of the essential steps to enhance the accuracy. When

classification results are analysed, it might be realized that the classifier had difliculty

distinguishing two classes. These classes may be relatively similar land-covers, In this

regard merging of two same land-cover classes completely or under the selected mask

covered area is II common one, Similarly, during the class editing results can be stored in

II new class as welL In this study these facilities of today's powerful available toolboxes,

has been used almost after all classification schemes,

3.1,6.4 Accuracy Assessment

The purpose of the accuracy assessment is to create a report, which indicates the accuracy

of classification results compared to the raw image data. It compares what is assumed to

correct with an image classification based on pixel groupings, The results of the

accuracy assessment are shown in the form of Error (confusion) matrix and as an

accuracy statistic. The Error (Confusion) Matrix is II method for displaying the results of

the accuracy assessment process. Reference data are listed in the columns of the matrix

represents the number of correctly classified samples,

3.2 GROUND REALITIES

In all empirical studies, most important considerations are that whether the data collected

is on suftlcient spatial scale or not Another consideration is regarding the coltection

procedure and the presentation of data< Lastly the credibility of the source is

important.

The urban variables that play an important role with respect to pollution were

identified and the scope of work was extended beyond the approved title of the project It

is often very difficult that all types of information would be provided by the remote

sensing techniques. Remote sensing is one of the premium technologies to acquire

107

information but within its limitations. For air pollution shaping, there are many dynamic

urban variables, information on which had to be acquired, in situ via non-image sources.

The types of information collected and analysed for this study could be broadly outlined

as under :

• Air Pollution Information

• Traffic Flow Information

• Land use Information

• Demographic Information

• Epidemiological Information

• Perception Information

3.2.1 Traffic Flow Information

Being a non-funded research work, it was initially decided that the source of traffic data

would be secondary i.e. the Traffic Engineering Bureau (TEB) of Karachi Development

Authority (KDA), which was the department responsible to design, plan and implement

traffic improvements in the Karachi metropolis.

It was found that the data collection efforts by the TEB were carried out way back in

1982 and 1994.These counts were available as hard copies. These counts were

categorized as technical papers having discrete numbers and are termed as Classified

Turning Movements of a particular intersection or a mid block location on a major

roadway. These surveys by the TEB were conducted normally for sixteen hours . Table

3.2, presents the flgures of daily traffic (I6-hours) for the locations surveyed by TEB,

KDA. These were obviously, high volume locations in 1982 and 1994. Updated figures

were direly required for this study but unfortunately were not available from the

concerned bureau, TEB.

IUS

Table 3.2: Daily Traffic of Sixteen 'Hours at Major Intersections of Karachi

S. NO: '~., '. ". . ",' . in~netticiru

Gurumandir 2 Nazimabad Chowranei No. 2 3 Old Exhibition 4 Gora Oabrislan 5 Civic Centre SQuare 6 Liaauatabad No. JO 7 Nazimabad Chowrari2i No, I 8 Shaheed-E-MillatJUniversilV Rd 9 A. Haraon Rd.lMcre Weather Rd. 10 Musical Fountain II Mansfield Street / M,A. Jinnah Rd 12 NewTown 13 Board Officc 14 Club Road / Abdullah Haroon Rd 15 Dakhana (Liaauatabadl 16 Dr. Dawood Pola Rd / MA Jinnah Rd 17 Karimabad 18 Aisha Manzil 19 Nishter Rdl Love Lane Rd 20 Tin Halti 21 Nazimabad No. 7 22 Shaheen Complex, 23 Sharea Faisal / Rashid Minhas Rd 24 Abdullah Haraon Rd / Hoshin2 Rd 25 Britto Rd / Nishter Rd 26 Kda Chowrari2i / North Karachi 27 Water Purno (North Nazimabadl 28 Lea Market 29 Mere Weather Tower 30 Pidc House 31 Fatima Jinnah Rd / Sharea Faisal

'." ... .. Daily Trame '. '(16 bours)

H1317 318438 302318 270133 2560411 252624 243765 220831 216498 210741 205510 197007 191522 191184 189804 189083 181593 174093 163685 162082 156132 145340 144144 143598 142677 131583 130015 125185 118653 111 689 105384

Source: Traffic Engmccnng Bureau, Karachi Development Aulhonty (KDA), 1991

At this juncture, it was realized that efforts should be made to collect current statistics.

The reason of this decision was the obsoleteness of sufficient traffic data available at the

traffic-engineering bureau. Hence, hourly traffiC volumes at selected 90 locations of

Karachi were manually recorded, Annexure H presents the traffic volumes for 90

locations of old city (core). This special case study is being presented separately as

predictive modelling.

The said case study was limited to 90 locations, whereas, the sample size for current

carbon monoxide concentration level in metropolitan Karachi was greater than 300 . For a

meaningful appraisal of air pollution vis-a-vis traffic volume, the researcher had no other

J09

option than to rely on his exposure during the survey for categorizing the locations

qualitatively at different times of the day.

Table 3.3 from the secondary (TEB) and Annexure C & H from the pnmary data

collection exercise helped in conceptualising the traffic volumes and formulation of a

denomination criterion. The criteria formulated for the traffic volumes at study spots is as

under:

Table 3.3: Categorization of Observed Average Hourly Traffic at Monitoring Stations

..

brieliolY "):'." ".< .' "'. " :e,tt*ilrj>" " " " 1;/,; Aver~:,e~~q~~:~ !r~fic "

<>f ,.':: "+:~' Not.Hill!' ,; :,..~. ~ " , .' ", ,' ) f "" . ... '7' : ," } AHT·: : ... . VeryLow VL Less than 200 Low L 200 - 400 Moderate M 400 - 2,000 High H 2000 - 10,000 Verv High YH 10,0011 and above • All modes and Ihelr turfllng movements. al Ihe locallon, summed up

I

Annexure C tabulates the six temporal variations of traffic at 308 locations all across

Karachi. Based on the above stated traffic criterion, qualitative thematic maps illustrate

the state of traffic affairs for the metropolis, Karachi.

3.2.2 Land use Information

Urban land use studies are designed to provide basic data on land characteristics and the

various activities that occupy land in the urban area. These data are used in analysing the

current patterns of urban land use to ascertain the character and quality of environment.

Land use surveys furnish information on the use, misuse and non-use of urban land. In

planning and zoning studies, it i.s essential to know the amounl of land use for different

purposes.

The available records from the Master Plan and Environmental control department of

Karachi Development Authority were studied and considered as the only basis of land use

information regarding the metropolis. These documents include:

l. Land use analysis (MP- RP/37) March 1972, KDA

110

2, The Karachi Development Plan 1974

I. Land use Map 1972

II, Land use Map 1985

3, The Karachi Development Plan 2000

I. Land use Map 1987

II. Land use Map 2000

The land use map 2000 was scanned from the published document into a digital form and

later geo-referenced, This ' maps was then digitised (vectorised) and finally these

vectorised maps were converted into grid form for further analysis , It was found that the

land use zoning maps developed by the Karachi Development Authority (KDA) were

more towards aggregation and understanding on a macro scale , A geographic inquiry

searching the micro patterns and the changes thereof, within the metropolitan city, needs

an intensive and extensive information gathering effort requiring human and financial

resources,

Due to the unavailability of land use data on micro-geographic scale in Karachi, the study

region was chosen as old Karachi (core) (Figure 3,8), thus Primary data generated on land

use for zonal appraisals and predictive modelling has a unique distribution of various

combinations of land use mixes. spread over the entire area In old Karachi (core) it has

been very difficult to find areas, which have a single land use category because

businesses are usually found linked with residential areas, Being unplanned

neighbourhoods, complex situations exist, which presents the old city area as a case to

investigate, To proceed further into the study, a comprehensive data set was needed,

Annexure H shows the list of selected locations for the collection of land use and air

pollution data , A sample size of 90 locations was considered to be large enough for

statistical analysis, Attempts were made to maintain uniform data acquisition procedures ,

It was initially decided to acquire the details of land use for each location within a vicinity

of 100-meter diameter. Physical measurements and distribution of floor space were

carried out. Site-specific maps (condition diagrams) were drawn for each location, They

contained land use information for the vicinity (all sides) of the location,

III

OLD CITY AREA OF

KARACHI

o i .

N

1

Kilometer

Sample Localionl

Figutc 3.8

112

A radius of 50 meter was superimposed on that vectorised maps. Site-wise, computation

of areas was done through Arc View and MS Excel.

3.2.3 Air Pollution Concentration

Karachi is the biggest metropolis in Pakistan and one of the worst effected cities of the

world due to unchecked air pollution. A few organizations have been 'working on this

issue but spatial dimensions within metropolis have been largely ignored mainly due to

less comprehension and underestimation of spatial techniques as well as the difficulty in

collecting. processing. and analysing the data at micro geographic scales. Some studies

have evaluated Karachi with a little geographical sample size; however. their studies are

milestones in the field of investigation of hazardous air pollution. Significant among these

is a study conducted by SUPARCO (Ghauri el al.. 1999). This study is discussed here. as

it was recent and monitored criteria pollutants. Moreover. the author on the data

furnished. which is discussed elsewhere in the thesis. does some analyses .

3.2.3.1 S02, NO" 0 3, and Particulate Matters

The publication "assessmeni of air quality in the metropolitan Karachi" (Ghauri el al.

1999) was collaborative project of SUP ARCO and KESC. The collaborative program

included air pollution monitoring on round the clock basis using the KESC Mobile

Laboratory. The air quality measurements were taken at eleven different sites of the city

(Table 3.4).

Total suspended particulate were collected through the high volume air sampler. The

appraisal of the most harmful inhalable particulate was conducted through PM IO sampler.

It separates particles smaller than 10 microns (aerodynamic diameter) from the larger

particles (Ghauri el al., 1999). Nitrogen Oxides (NOx), Sulphur dioxide (S02) and Ozone

(03) were conducted through their specifically designed analysers.

113

Table 3.4: Air Pollution Assessment in the Karachi Metropolis (1999)

~~(DDb\ . . ~PM. " • t~.~liivm Site · •. ~ .Ma.i ' ;MiiX: """""n.· '00 .>010..:', .

Civic Centre L7 U 36 17 13 10.6 233 195

Garden Road 1.3 L3 25 16 14 12 254 201l

Elendcr Road L5 U 37 22.5 17 13.6 263 193

West Wharf 1.4 U 14 10.7 IS 12.5 23S 21(,

SITE 2.6 1.6 37 26.5 14 12.3 222 205

Korangi 1.5 U 44 22 .6 15 13.4 31S 238 Industrial Area

lbrahim Hadri L3 L2 12 9.5 16 12.S 261 238 v_ .. : Creek

Gizri L2 1:2 19 14 15 12.4 214 179

F. B. Area L2 1.1 IS 14.5 16.5 13 290 248

Bin Qasim S.O 5.0 23 15 -- -- 307 --

~ H 1.0 1.0 12 S 15 12.4 225 1911

Source:Gi\llilri el ai, 1999

3.2.3.2 Carbon Monoxide Levels

Carbon monoxide is the one pollutant. which is mostly produced by mankind. who

generates about 70 percent of the total (MacEachern. 1990; Seitz. 1995). In a city. almost

100 percent of CO is anthropogenic. the CO being produced by fossil-fuel consumption

and motor vehicles (Coburn. 1970; Calabrese. 1991; Schwela and Zali. 1999). CO is

produced when the fuel to oxygen ratio is too high or the temperature of combustion is

too low. slowing down the oxidation process. Motor vehicles are normally operated at a

slight excess of petrol. which gives maximum power and performance. and produces high

CO levels .

114

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However, CO concentrations are highly variable in cities, with maximum occurring at the

peak traffic times. When measurements are made immediately down wind of a road, it is

possible to obseIVe plume from individual vehicles. Effects of Carbon monoxide vary

from discomfort to death within a short period. Whereas intensity of effects deviate

according to the dose level (i.e. CO concentration and exposure time). CO is a definite

pOison for the people travelling in open-air vehicles or pedestrians walking along the

road .

The author used his scarce resources to monitor this pollutant (which is harmful human

induced and prominent in urban environment) on micro geographic scale. The monitoring

criterion selected was based on the quest of spatial and vertical diffusion patterns of CO

concentration.

I. Average values of ten minutes at 1 - 2 meter from the source about 3 - 3Y, feet

above the surface

2. Average values of ten minutes at 2 - 3 meter from the source about 4Y, - 5 feet

above the surface

Most of the population travelling through Cars, Rickshaws, and Motorbikes are at the

highest dosage level in Karachi metropolis. The pedestrian especially young children

owing to their lower height are at much vulnerable risk . Nevertheless, average CO

concentration from the source about 4Y, - 5 feet above the surface depicts the CO levels

in ambient air, harming the neighbouring population Annexure B.

3.2.3 .2.1 Monitoring Sites

Settlements of Karachi metropolis occupy more than 500 sq. km area . (Mehdi el 01. ,

200 I) Coverage of Karachi on micro scale was a difficult task especially owing to

number of financial and human resource constraints. Nevertheless sample-size of more

than 300 sites was targeted and successfully covered, Figure 3.9, Annexure A . Sampling

was conducted on logical and practical criteria :

116

• Carbon monoxide (CO) potential

• Spatial coverage

• Target neighbouring parameter such as near source, not in open grounds etc.

3.2.4 Demographic Information

The source for obtaining the demographic data of Karachi metropolis is the Bureau of

Census, Statistics Division, Government of Pakistan. Fortunately the data on population

and its attributes have. been managed comprehensively in the form of District Census

Reports (DCRs) for the five districts (Central, East, South, West and Malir) of Karachi

along with the maps that shows the boundaries of circles and charges. These circles and

charges are the subdivisions in various localities. Charge being the larger entity while

circle smaller. The Census maps are to be read in conjunction with the District Census

Reports (DCRs) to extract the information on micro geographic scale. These reports

portray detailed account on the circles and charges regarding the age and gender

distribution of their respective populations.

Since various inquiries of this study are done taking each of the' Analysis Zones' defined

by the KDA, as an entity. Hence, it was mandatory to reconstruct the population

contained by 'Charges' and 'Circles' into the respective zones. It was a painstaking and

laborious task .

The transformed information was converted into a digital database compatible for

geographic information system. GIS data sets are finally developed for the whole of the

metropolitan city incorporating the demographic information as fields and their records .

3.2.5 Epidemiological Information

Although relating disease causes with environment is dated back to Greek period, but it

took a proper shape in the last century when disciplines like Medical Geography, Disease

Ecology and Epidemiology developed. Epidemiology is concerned with the distribution

and determinants of health and disease, morbidity, injury, disability and mortality in

populations.

117

Epidemiological analyses determine why certain diseases concentrate among particular

population group (Learmonth, 1972). The discipline of Environmental Epidemiology

focuses on the environmental health, monitoring the levels of diseases (in the dwellers)

and looks for causal relationships between exposure and a subsequent disease

(McGlashan, 1972; Barker, 1987; Bentham, 1994). In the environmental framework,

epidemiological studies with geographical dimensions are at large used to assess the

impacts on human health .

In terms of prevalence and incidence of diseases, there is an extensive variation in

developed and developing countries. Pakistan being a developing nation is no exception

as incessant environmental degradation especially in urban areas, inadequate health-care

services and its inaccessibility are making it an ideal breeding ground for environmental

diseases. Unfortunately, there is a very little information available on such environmental

diseases. As a result, the prevalence and frequency of "air pollution based diseases" and

their variations on landscape in Karachi, is an area yet to be discovered . To study these

phenomena, two sources were identified as primary and secondary.

The secondary source was the records of the major public sector's hospitals, while the

quest to explore from primary sources resulted in the formulation of a questionnaire for

the citizens and the physicians practicing in the different neighbourhoods of Karachi .

Annexure E (E. I & E.2), which provides the recent disease data, acquired from two major

hospitals of Karachi. The absence of recording and compiling the patient's detailed

address has created several deficiencies in the indigenous data provided by the hospitals.

Elsewhere in world , both in developing and developing countries, linking of postal I zip

codes (Banta el al., 200 I; Stone, 2001) with disease occurrence has provided the basis for

spatial analysis .

To generate primary disease data a rigorous literature search was conducted to identify

diseases, whose relationship vis-a-vis air pollution has been established (Waldbott,

1978; Wellburn, 1994; Peters el aI. , 1997, 1999; Rogers el aI., 2000; Schindler el al.,

2001; Chhabra el al., 2001, Ibald-Mulli el al., 2001; Goldberg el al., 2001a, 2001b,

Ravindra el al., 2001). To make this search more meaningful, a critique by physicians

was obtained . The instrument employed was a questionnaire. QualifIed Physicians were

decisively asked some questions such as: .

118

• What do you think about air pollution as a contributing factor among the listed

diseases?

• Mark the diseases frequent'in your practicing area from the given list.

Basing on the physicians experience at various localities of Karachi. diseases having

significant relationship with air pollution were ranked. The names of diseases are highly

technical and could not be asked to a common man. It was necessary to rephrase the

diseases with their symptoms to make the list understandable. Thus a list of air-borne

diseases was incorporated in the questiomiaires Annexure F (F.Q I & F. Q2) presented to:

a. Dwellers of air pollution risk zones (core city area)

b. General Public across the. metropolis

The questionnaires provided the data on the occurrence of each of these symptoms /

diseases on a micro geographic scale i.e. for various areas in Karachi. Later, the data on

the frequency of air pollution based diseases in various regions of Karachi was integrated

with the GIS database to discover the prevalence in each zone.

3.3 GEOGRAPHIC INFORMATION SYSTEMS

Geographic Information Systems (GIS) is automated set of functions that provides

professionals with advanced capabilities for the storage, retrieval, manipulation and

display of geographically located multidimensional data . It is a decision support system

involving the integration of spatially referenced data in a problem-solving environment.

GIS technology provides an excellent platform upon which different types of spatially

referenced data can be united for analysis and display purposes.

119

Geographic Information Systems was the main investigative tool for this 'iudy. Remote

Sensing and In situ (Ground Realities Collection) techniques to provide and process the

inputs for GIS. Here GIS has been implemented according to elemental requirements. A

general conceived GIS model for this study is shown below:

11 ...... 1)' s..-4 ... _ Carh)gJ:aphic Ptodud I.I!. o..wcf~ SubocII M ... ickod and da:~ilicd digital-r pril1ll:d iRJOl~$ , .. 'c.

I

Elements or GIS

\ ,

l 07 '

~ \

¥un~tion.. or GJ

bI.hu ..... On'nlidon

Such IS co concenlntioR. ()"'1I"Vaph)', Land Uk, Tr.lffic. cliscur:Ilnfonnatiod etc.

I

Data Storage I Data Retrie-ing I

_________ D_a_ta_Eru __ ·_ting I Data Manipulating .J

_____ D_a_ta Analysing J Infonnation Publishing I

Figure 3.10: G!S Organizationa! Diagram

120

3.3.1 Cartographic Techniques

Cartography is the making and study of maps in all their aspects (Robinson, 1990). Just as

spoken and written languages allow us to express ourselves without having to point to

everything, a map extends out ·normal range of vision. A map lets us see the broader

spatial relations that exist over large areas or the details of microscopic particles. It is

carefully designed instrument for recording, calculating, displaying, analysing, and

understanding the interrelation of things. Nevertheless, its most fundamental function is

to bring things into view.

The increased flexibility and capabilities bf the new technology has enabled from manual

cartography to Computer Assisted Cartography (CAC). Now the jargon of the electronic

age has become part of the cartographer's language. Numerous techniques, which were

only dream in past without digital cartography now has become practical and advances

are coming more instantly. Cartography is one of the fundamental elements of

Geographic Information System that provide spatial canvas on which ancillary

infornlation are analysed. For the purpose of study following maps were developed by

using digital cartographic techniques.

3.3.1.1 Base Map Development

Remote Sensing quickly replaced laborious and inexact data collection by plane table

surveying and field sketching (Robinson, 1990). PlanimetricaJly corrected ortho-rectified

digital images of high spatial resolution are being used all over the world for cartographic

map preparation. Applying image-processing techniques has developed base map, which

has been described earlier Figure·3.1!.

Base map for every GIS based research is essential because it is the only thing that directs

the data to the clear spatial dimension. Survey of Pakistan is the sole authority of mapping

and surveying in Pakistan but beside that different other governmental organizations

(KDA, municipal corporations) also develop their own maps.

121

DETAILED BASE MAP

of KARACHI METROPOLIS

'i .... .. . .

..... - . .... ~ ' .. ~ ... . ;-~ . , -~

. ~~, .... ....,.r--. +

• • ---

600

,.. ., ....

[B]

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Meters

5bopplaIC e rnl.n

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1101."

IIppilllh

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G"'\~ .. rd ..

'Ii"lin), Ro.6.\

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~Ir«'"

figure 3. 11

122

l Published maps OJ I Satellite Image J I

I Enhancement 1

I Geo·Referencing I Orthorectificahon ~

I I Digilizalion !

Base Map (

J, 12: Base Map Development Procedure Diagram

Source: After Kazmi, 1999

To develop any GIS model it is essential to make a suitable base map in vector format so

that there can be attached attribute data to the geographical entities such as Polygon

(Regional Feature). Polyline (Linear feature) or Point (Exact object location),

Taking

elsewhere

Karachi metropolis guide map (on appropriate scale) and discussed

the enclosed map for Karachi has been produced, This map is

cOllsiliting of the major infrastructure of the city urban area such as main roads, lU~'UI\I""'.

railway-line, administrative divisional boundaries and Sample points, The base

map has the Metadata (Table 3.5):

123

Table 3.5: Metadata of Base Map

,- ' '; "'. . ~ -,' . . '

Name ' -~:jI{ •• , · f.;a~d. ·~s~ Category . Topology -.. Districts Administrative Polygon Hotels Commercial Point Shopping Centres -do- Point Hospitals Health Point Industrial Estates Industrial Polygon Grounds Recreational Point Parks -do- Point Mosques Religious Point Bridges Transportation Line Major Roads -do- Line Primary Roads -do- Line Roundabouts -do- Polygon Secondary Roads -do- Line Streets -do- Line Features Miscellaneous Point Grdveyard -do- Point

Projection Zone Unit Ellipsoid

Universal Transverse Mercator (UTM) 42 Meter Everest (Pakistan)

3.3.1.2 Mapping of Monitoring Stations

During the Carbon Monoxide (CO) sample collection throughout Karachi metropolis for

more than 300 locations, latitudeJJongitude coordinates has been taken for each sampling

site. Magellan GPS tracker, USA has been used which is hand held and very easy to use

in the field during sampling, That tracker provided x, y coordinates on preset Projection

system Table 3.5. That tracker has ilO-meter planimetric accuracy. After that the

collected x, y coordinates were plotted in GIS software through add event theme in

ArcView GIS environment.

Owing to the maxImum available accuracy error points may deviate from its original

locations on map, But all these more than 300 samples were located near by or exactly on

the same location within the same described error. To overcome that problem interactive

geocoding has been used to locate those sample point exact and accurately on the map.

With the help of same geocoding method previously surveyed sample points were

mapped as accurate as possible within available information.

124

3.3 .1.3 Analysis Zones' Map

Intra-metropolitan analysis shows the spatial variation, similarities, differentiation,

distributional pattern, intensive risk zones etc. within city on a micro geographic scale.

Karachi Development Authority (KDA) had divided the whole metropolis into 58

analysis zones, covering the urban area and adjacent rural settlements Table 3.6 and

Figure 3. \3 . Since various inquiries of this study are done taking each of the • Analysis

Zones' defined by.the KDA, as an entity. The developed map shows the analysis zones as

polygonic entitical set. In this study KDA map has been converted into vector based

digital format. Later, the analytical data has been integrated with this map.

Table 3.6: Analysis Zones

Zone Zooe Naine . ' Zone

ZooeNamc No. ·No.

I. JlUU1 Milfkc:t. Old Town ar~a 30. North Nnimabad 2. Ranchon: Lin~ &. Ramsawami 31. North Karal:hi

3. S.dd.r & Artillery Maidan 32 . Qasba. Manghopir An:u

4. Civil Lines Area 33. Orangi. Metroyille·1 .-S. I.L Chundrigar Road & New Queens Road 34 . Baldi.

6. Port Area 35. Masroor (Mauripur) 7. Nawabad, Baghdadi Lane, Khanular 36. Hawk«bay and Adjoining Ar""

8. AS'a Taj , Bihar Colony 37. Doh Mooch, Nay.1 Depot

9. U:a Market, Gul Mohammad Lane 38. Doh Lal Bhakhar & Hawke.bay $oheme

ID. Chakiwara, Kalakot 39. Korangi (Part)

II. Garden, Soldier Bazaar, lamshcd Quartc:rs 40. Landhi Colony

12. Lilies Area & Khudadad Colony 41. Landhi Indu,t rial , Schome J &. 4

13. NaYal Hospital, JPMC and Liaquat Barracks 42. Shah Latil: Doh Khanto

14 . Bath Island, Fro« Town, Defens< Society (part) 43. Mood and Malic Colonies 15. Gmi Area, Ddhi Colony 44 . ; Kamchi AiIjXlrt

16. Clifton 45. : Drigh Colony & Malir

17. Baba Bhit Islands 46. Korangi Induslrial Area - East 18. Shcr.shah, S.I.T.E . (part) 47. Korangi Industrial Area - Wc:S( 19. S .I.T.E. (Sindh Industrial Trading Estate) 48. Korangi Creek and Rcfml!f'j 20. Asil: Pak Colony & T.P.1. 49. Steel Mill and Port Qa<im

2 1. Rizvia, Firdous Colony. Golimar 50. Doh in the East

22 Liaquatabad 51. Malir Cantorunt!tlt 23. Gulshan...,·lqbal (part), P.I.B. Colony 52. $oheme JJ

24 . Gulshan-e-Iqbal, Chandni Chow!:, Society Area 53. lkfenC4! Socil!ty 2;. Akhtar & Baloch Colony, Chane<ar Goth 54. Swjani Town

26. Drigh Canlonment, 9th Mile 55. T aisar T o\\-n 27. Gulshan.e.lqbal, National Cement Factory 56. Hall.:ani StheIne! 28. F. C. Area and Mansoora 57. Dehs in the West a100g Hub River

29. NszUn.bad,Paposhnagar 58 . Deh. along Supor Highway

Source. Karachi Development Authonty (KDA), 199 I

125

rv 0\

KARACHI METROPOLIS Analysis Zones

55

56

57 37

38

42

+ 41

7 ~ 1 I~

Kilomc,"h:t

Source: Karachi Development Authority (KDA). 1991 Figure 3.13

.IS

49.

3.3.1.4 Cartographic Layouts Development

Without applying advance digital cartographic techniques it may not be possible to

comprehend the digital thematic results . Presentation and interpretation of maps are

impractical without the following ingredients :

Projection, Symbols, Scale,Colour Scheme, Legend, Title, North Arrow, Grid, Border,

Neat Line e/c.

After facilitating these requirements maps are aesthetically and logically legible for users.

To fmalize layouts almost all GIS, Remote Sensing and Cartographic software have their

toolboxes and editors. But advance graphics packages such as Adobe® lIIlIs/ra/o,.,

Adobe;g· Ph%Shop, macromedia:g1 FREEHAND, Map Pllblisher, PCI -Advallce

Car/ographic Ellvirollmell/ e/c. have very special tools such as graphic filters, effects e/c.

During the study some of the tools have been used to produce better results.

3.3.2 Database Integration

GIS data comes from many sources, such as maps, remote-sensing imagery, and Ground

information collections. These diverse se.ts of data are not always easily integrated The

central data integrator for GIS is the database that accepts and merges diverse data sets

and different types of data, giving the user flexible and powerful sets of data for analyses .

Another major strength of GIS is the interactive link between the database and the map.

Using parameters has been set out in form of tables normally separately but some time

within map-linked tables. The following Table 3.7 is showing the inputs of data tables

and relational map objects.

127

Table 3.7: Input Data Tables Integrated with Map Objects

bi~, l~r~iiii~tlQ~Qiatat~l~i ", .. , :';i,j,;. ". li\lg"i:leci widl " , , "

) y, " ' •.• " iMiu',Obil :si ' . ~ > " '.', 1. ' . .' $~·~;~ . , .. < •••. ,., •• ;.1- ' -; ." '. a , edJ . ""' . " Air oollutant Data Monitored sites' Doint Air pollutant Data AnalYsis Zones Traffic Data Monitored sites' paint Demowiohic Data AnalYsis Zones Disease Data AnalVsis Zones Imaee classified Land cover AnalVsis Zones KDA Land use Data Analysis Zones

3.3.3 Analyses

The analytical processes of this research composed of three kinds of works, which were

carried out simultaneously, The first was related to the creation of themes of data and its

integration, generation of surfaces and interpretations, Secondly statistical analysis for

forecasting of air pollution was carried out. Thirdly all of these outcomes had to be

qualitatively examined based on the knowledge of the metropolitan Karachi.

3 ,3,3 , I Change Detection: Growth of Settlements

Advantage of image-based analysis could be extended to monitor the urban growth of the

metropolis over a period of time, This co'uld be done using two or more classified images

of different dates of the study area to demarcate the new settlements, This is particularly

useful for highlighting changes that have occurred over time such as in urban analysis,

Change detection technique has been applied to see the changes in human settlement

patterns across the metropolitan from 1986 to 1998.

SPOT -xs October 1986 and March 1998 had been processed and classified by Afsar

(2001) , The digital versions . of both the classified images were acquired and then change

detection was conducted in SPANS, The change detection procedure overlays two grids

and determines the areas of change from the first grid to the second grid , An overlay of

two identical Land-cover classes indicates an area of no change and it is therefore

assigned a class value of zero (0),

128

On the other hand, where there are changes, a new grid is created using the new classes.

The cell size of the new grid is same as the lower of the maximum cell size of two input

grids . The results of the overlay are organized into a table as well as map result (Stevens,

1999). The results would be pres·ented in the later chapters.

3.3.3.2 Air Pollution Spatial Variation

According to an estimate more than eighty- percent organizational and non-organizational

datasets have some sort of geographical matters (MIC, 1999). Ajr pollution is amongst

them containing rigorous relationship with geo-spheres. Spatial analyses of this kind of

variables do not only show the distribution, pattern and variation but also assert to find

out the reasons of these variations. Every phenomenon that have relation with earth, are

spatially organized and be full of interaction with other spatially distributed objects I

factors, Some times the study , of those factors provide clues about the main studied

objective owing to their spatial associations,

3,3,3,2,1 Development of Continuous Spatial Pal/ems

In GIS analysis, 'Interpolation' is the procedure of predicting the value of attributes at

unsampled sites from measurements made at point locations within the same area or

region (Journel and Huijbregts, 1978; Clark, 1979; Mousset-Jones, 1980; Brooker, 1991;

Geron e/ aI" I 994), Interpolation is used to convert data from point observations to

continuous fields so that the spatial patterns sampled by these measurements can be

compared with the patterns of other spatial entities, Spatially monitored data do not cover

the domain of interest continuously (i,e, they are samples). Interpolation can be

performed through miscellaneous statistical techniques in which Inverse distance

interpolation is commonly used in environmental GIS modelling (Isaaks and Srivastava,

1989; Deutsch and Journel, 1992) to create raster I grid overlays from point data.

Grid themes have been constructed for each set of samples in ftrst step by completing

appropriate values of required parameters under the ArcView Spatial Analyst

Environment. In second step interpolated grid has been reclassified according to definite

criterion,

119

3.3.3.3 Air Pollution Temporal Variations

Time is one of the controlling factors of every dynamic phenomenon in this world . Air

pollution emitted and diffused with variety of speed depending upon the nature of the

pollutant . That speed reveals the rate of dynamism with respect to time. On micro scale

CO concentration may change with in millisecond with the availability of favourable

reaction environment. It is difficult to monitor continuously to some extent especially

when spatial canvas is huge and with quite enough details. On such, scale time-to-time

variation may be recommended to scrutinize with respect to morning, afternoon, evening

or low to high peak times. GIS may provide spatio-temporal analysis functions that depict

temporal variations as well as spatial variations at a time through different modes. One of

the methods have been utilised to analyse collected data temporally is elaborated here

under.

3.3.3.3 .1 Statistical Deviations

One of the themes, which have been developed for temporal variation, is the use of

algebraic grid. Map algebra is a high-level computational language for performing

cartographic spatial analysis using girded (raster) data. It provides a way to create

mathematical and statistical operations that compare grid themes. In this regard collected

data has been converted into interpolated grids for mornings, afternoons and evenings of

working and off days. Finally standard deviation has been calculated for each grid to

identify the variation of air pollution during these times' sessions and days.

3.3 .3.4 Road Density Index: massiveness measure

Congested road network is assumed to be a fundamental source of Carbon monoxide

concentration. It was observed that road intersections are more vulnerable as compared to

smooth traffic streets due to choking in congested areas during peak hours. The postulate

further revealed a relationship between choking point's density and broadness of pollution

concentration zones (a continuous area having similar hazardous pollution concentration) .

Nevertheless, demarcation of precise pollution concentration zones on micro geographic

scale is a tedious task.

130

After generation of equidistance points along the roads, these have been categorised

according to the road classification of the Traffic Engineering Bureau (TEB). Weights

were assigned to the points with respect to the functional importance of associated origin

roads . Converted into the point geometry and have been given high value of pollution

concentration weights. That is also a fact that traffic on straight smooth running roads

also carries pollution, therefore, sample points has been extracted with low weight.

The density function generated a surface by applying a sampling radius to each point in

the data layer. No output values were calculated for areas lying outside of any sampling

radius. This technique generated output in the grid data format. The value for each cell in

the output grid is calculated based on the value of each point whose sampling radius

overlaps the centre of that quad cell. Therefore,

Where:

Point Density = " ~ L..." d-

z ~ Pollution weighl

d ~ dislance from Ihe cenlre of the quad cell 10 Ule dala poinl

i r cHeh point ollhe centre of the sampling radii that overlap the grid cell

3.3.3 .5 Population Distribution

Human activity produced polluti.on and human population is the ultimate victim itself. An

envirorunental study has to quantify the impacts of specific pollution on population.

Therefore, the initially formulated hypothesis of this research was revolving around the

concept of risks and the population under risk . The assumption of the analysis is that

higher the population concentration at a location would generate more vehicular travel

yielding higher air pollution and obviously affecting larger segments of population.

Popllialion density is considered to be a better measure as compared to population

numbers merely. Densities reflect the concentration and distribution patterns much better

than the absolute figures. At large they are obtained arithmetically by dividing the

131

numbers to the entity areas (Rehman, 1983). As the entities for this study are the fifty

eight analysis zones of Karachi metropolis, areas of these zones had to be computed

through GIS.

Dot maps are often used to represent the distribution of spatially distributed phenomenon.

The result of differences in dot density is a visual impression of the distribution pattern of

actual population (Campbell, 1984). The location of these dots on the map was governed

by the sources that finally yielded the base map; classified satellite imagery, population

census maps and knowledge about study area .

GIS can present the population density with its gradient trends through population density

gradient map. The density function generated a surface by applying a sampling radius to

each point in the data layer. That function generated output in the grid data format. The

value for each cell in the output grid is calculated based on the value of each point whose

sampling radius overlaps the centre of that quad cell (PCI, 2000).

3.3.3.6 Epidemiological Investigations

Qualified and unqualified medical practitioners, Homeopaths and Hakims treat the vast

majority of Karachiites. There is also a strong tradition of self-medication in this part of

the world , where every medicine is available over the counter, without any prescription.

Patienis are carelessly handled in private and public sector hospitals of populous Karachi .

Lack of proper record keeping of these service providers convinced the author that most

of these sources, could not furnish statistics on scientific premise. Therefore some

quantitative and qualitative epidemiological analysis has been performed on the data

generated primarily by one thousand and one hundred (1100) questionnaires.

3.3.3 .6.1 Morbidity treated by Physicians

To inquire more about the frequency of air pollution based diseases, the seventy five (75)

practicing physicians were explicitly asked to mark the diseases frequent in their

practicing areas. These public health statistics can be invaluable in understanding local

patterns of diseases in Karachi .

132

practicing areas. These public health statistics can be invaluable in understanding local

patterns of diseases in Karachi.

3.3 .3.6.2 Disease Grading by Professionals

After establishing the contribution of air pollution in the occurrence and prevalence of

some diseases in Karachi through scientific studies and morbidity data of hospitals and

practitioners, there appears an obligation to find what do the professionals think about it.

There was a specific question:

• What do you think about air pollution as a contributing factor among the listed

diseases?

To seek not only their perception but also empirically deduce a link between spread of

disease and air pollution in Karachi. Assigning of weights to predetermined responses is a

familiar approach in such inquiries (Manheim, 1979). The arithmetic summation ranks

the diseases (symptoms) as Highly associated, Fairly associated or Less associated with

air pollution in Karachi.

3.3 .3.6.3 Morbidity and Mor/aliiy: Vital Indices

The number of Yearly Indoor Patients (YIP) treated at the two teaching hospitals

(Disease-wise and Institution-wise was annexed as Annexure E (E. I & E.2). This data

gives an overall picture of various diseases prevalent in the metropolis. Some of the

diseases have a significant linkage with air pollution. The share of patients affected by

air-induced diseases from the total number of patients is an interesting dimension to be

looked at.

Annexure E (E. I ) presents the indoor morbidity and mortality statistics for 200 I at the

Civil Hospital Karachi. Similarly Annexure E (E.2) gives figures for the Jinnah Post

graduate Medical Centre (JPMC). Viewing the morbidity, moreover the mortality (if any)

due to the diseases having linkage with air pollution was considered worthwhile for this

study.

133

3.3.3.6.4 Airborne Diseases Prevalence

The prevalence of air pollution induced diseases indicates the effected population during

a certain time period. This measure provides weight to the trends of environmental

impacts (Woodward and Francis, 1988). The following relation calculates prevalence

Prevalence of air-borne diseases =

(Moon el al., 2()OO)

l\(1 ofoormlalion fltte..100 b\ iiir wl!ulion

A'!'~.: ,IlOPUIShOf(

Available hospital records were nol able to relate the disease information with location.

Elsewhere in world. both in developing and developing countries. linking of postal I zip

codes (Banta el al., 2001; Stone. 2001) with disease occurrence has provided the basis for

spatial analysis.

To the results of diseases prevalence on micro geographic scale, the instrument of

questionnaire has been used. With the combination of three queries the relevant

information has been extracted to achieve the objective:

L Address

2. Household size

3. Diseases

Address helped to geocode the extracted results 011 zonal basis; sample zone wise

population was obtained through aggregate zonal households whereas numbers of

diseases events were the representatives of affected population cases.

3.3.3.7 Public Perception Evaluation

Air pollution could be found everywhere especially in the urban environment both in the

form of indoor and outdoor pollutants. As a result, these pollutants being inhaled by the

people from the direct sources and ambient air as well. Urban dwellers effected mostly by

industrial and traffic induced poJlulants while rural inhabitant harmed by agronomic

contaminants, However, in a developing country like Pakistan, acceptance of a

134

constituent as pollutant and to perceive its harmful effects is very difficult for public.

Nevertheless, perception of the people is a key-player around the world to control the air

pollution. Perception is based on the knowledge and experience of a person (Bernstein e/

al., 1988). Knowledge creates awareness whereas experience strengthens the learning

(Dember and Warm, 1979). Missing either vague the depth of reality and make the things

fantasy.

Most of the research processes assume that some form of basic knowledge is already

available on the subject. Such basic data is normally available in most of the established

research topics (Ali, 1997). However, in unexplored research in developing countries,

official records and other basic research are usually not available (Bulmer and Warwick,

1983). In a developing nation where the literacy rate (and its definition) is awful, carrying

out a meaningful perception survey becomes more difficult. In Pakistan, the socio

economic information on micro scale may not be always available. Public health data is

not spatially managed. Living conditions of urban (and rural) localities are not

scientifically documented. All these factors contribute in awareness building of

individuals.

In the wake of scarcity and paucity of published data on environmental awareness,

questionnaire survey is the best alternate to gauge the extent of pollution (Ali, 1997; De

Souza, \999) . Therefore, it was indispensable to conduct the awareness/perception

analysis through interviews and questionnaires. Three following focus groups were

identified to achieve this task:

1. General Public of metropolitan Karachi

2. Competent Physicians practising in different localities

3. Inhabitants of Air pollution Risk zones

Subject receptive questionnaires were developed to accomplish the following targeted

objectives:

1. Understanding the disease patterns on micro scale

2. Perception analysis of Health professionals

3. Awareness appraisal of the society

135

To overcome the local data acquisition problems and to collect relevant information in

depth, special questionnaires were designed. These questionnaire surveys were carried out

around the metropolitan city (referred Table 3.8).

Table 3.8: Target Groups, Criteria and Description of Questionnaires

. : . . ' ... " . :~ } ... ~. ~

';"

. ,~~~',~~~;~~~ .. i~<~~.:-:' . ' ,' ' : ' :~'dpti,~ .. ..

. . ,Targe! ,G,r~\I:p "'~> .. . ' ~et~:reDce : ' , ' , .. ~~ ,', , ' . " ..... .: .. , .. : .. '; .... , . , '.'

Health Professionals Spatial co,'eragc of the Linkage belween pollulion Annexure F (F-melropolis and disease Prevalence QI)

General Public Spalial coycrage of Ihe Socio-economic and Annexure F > 18 years Age melropolis environment relaled (F-Q2)

queslions

The results of these surveys would be a handful of significant information, which will be

helpful in developing a cognition scenario of the problem and its intensity.

3.4 ZONAL APPRAISALS

The air pollution concentration samples were discretely distributed all over the Karachi

metropolis according to sampling criterion. Geographic Information System has the

strong topological intelligence such as adjacent I neighbouring, containing, and

intersecting features information. That topological intelligence has been used to select

spatial objects (i.e. underling grid cells) within each individual KDA zone. The territorial

air pollution surfaces (i.e. portion of underlying surface) were aggregated for that

particular territory (KDA zone). This analysis would provide the data behaviour in terms

of minimum, maximum, mean, range and standard deviation (ESRI, 1998; MIC, 1999)

The zonal analysis map was overlaid on the classified land cover and year- 2000 land use

map for the quantification of each category per zone. SPANS PCI, Canada was used for

this analysis. This software terms such analysis as area cross tabulation analysis. The

information collected on population and diseases in terms of different parameters was

transformed to the grass root level i.e, the 58 analysis zones of this study. Tabulations in

Microsoft Excel, and data integration into a GIS in ArcView facilitated to map these

phenomena. Classed Choro'pleth maps (Campbell, 200 \) of disease occurrence, point

prevalence and population densities are used to show the zonal variation in

epidemiological and demographical studies (Aral el al., 1994; Jacquez el al., \996;

136

----------------------. . -- - -

Goodchild, 1998; Jacquez, 1998; Haining, 1998; Kulldorff, 1998) Similar methodology

has been adopted here in this study.

3.5 MULTI CRITERIA EV AL UATION OF RISK Il"

NEIGHBOURHOODS

GIS furnishes advance statistical and mathematical modelling techniques. It supports a

wide range of advanced functions, such as auto-correlation, spatial interactions, location

allocation, simulation, and decision analysis (Cliff and Ord, 1973 ; Amrhein, 1985;

Anselin and Getis, 1992; Fotheringham and Rogers, 1994; Altman, 1994). Multi criteria

analysis is one of those advance and complex spatial statistical models . It is a type of

analysis performed through maps when analysing data; some factors may weigh more

heavily in the decision-making process than others. For risk evaluation the factors, which

could be causes or effects, were analysed under this study has been taken as overlays with

their precedence. Multi criteria analysis is a modelling technique that allows discovering

patterns and associations in data . The understanding of these relationships increases the

interpretability of spatial information and provides the basis for making accurate

predictions. The input overlays (maps) are listed below:

• Land Cover Classification Grid (LCCG)

• Population Density Grid (PDG)

• Averages of CO a t3 Feet Grid (AC3FG)

• Averages of co at 4 Feet Grid (A C4FG)

• Averages of CO Total Grid (ACTG)

• Disease Prevalence Grid (DPG)

• Disease Occurrence Grid (DOG)

• Roads Buffers Grid (RBG)

• Road Density Index Grid (RI;JIG)

• Standard Deviation of co at 3 Feet Grid (SDC3FG)

• Standard Deviation of CO at4 Feet Grid (SDC4FG)

• Standard Deviation of co at Total Grid (SDCTG)

137

. . -- _._----_. ---

There were some other maps (overlays) that could have been also used as input maps for

this multi-criteria analysis (such as the overlays of other pollutants from secondary

sources) . The author restricted his selection of input maps based on the sources and

quality of information. 11 was tried utmost that maps generated from primary sources be

selected. Secondly, this study had already been confined to Carbon monoxide (CO)

pollutant and since other pollutants have different sources of generation so their overlays

were avoided.

The land cover classification gird had been generated through the imagery; population

density overlay was produced by incorporating the census data into a GIS. Grids

penaining to CO were basically interpolated surfaces of the primary pollutant data

collected in the field, both disease maps were obtained through the instrument of

'questionnaire' filled by city dwellers while the grids related to road network were in fact

the products of GIS based analytical techniques.

The modus operandi of multi criteria analysis can be described as under:

.. A blank overlay template is created which incorporates all of the grids, which are used in

this study as components. This template is edited to assign weights and scores. Weights

were assigned to each grid (layer) as an indication of the intended contribution of that

grid . The scores were assigned to each class in every grid (layer) to define its suitability"

(Stevens, 1999).

3.5.1 Weights Reckoning

Principal component analysis is a transformation procedure used as a preliminary search

procedure to identify variables for funher monitoring and analysis (Daultrey, 1976) In

this study, Principal Component Analysis (PCA) using SPSS 10.0 was applied to check

the significance of the twelve above-mentioned parameters (Refer Section of Results and

Discussions) and thereof weights for each layer were calculated through Weight

Extraction Method . The flow of weight extraction is illustrated as follows Figure 3.14.

The results of above described weight extraction method are presented in the fonh

coming section (Refer the section in Result and discussion) .

138

Map Composition (All Layers)

Random sample points

o.~t.&.d La~ 1 "Illue o.uWd ~ 2 vdw Ox:rl.ud l...IIy'cI" ) Y.l.l\Jel OverlAid u~ -4 \o',due O\.aJ.lId 1Ay!::I" S \IlUut, ~1Md Uy!:t 6 v.iliJ.e: Ovdt.ud I.Ayu 7 vlI.hll:

Figure 3. 14

Weight Edradloll Model

1 2 3

•

21 17 80 2i

15

" 31 7\

o

,. 56 14 00

Random Point Generation

Correlation Matrix j (by SPSS)

Weight for each

variable

L X; ·Su"~~LC~ot.~~ .. iUa 1II04bcf

......... L x, -Sumo/Sum alaJfT'd.lir.:Jasaflill \..,...

139

3.6 PREDICTIVE MODELLING: ASSESSMENT OF CARBO:"l MONOXIDE (CO)

One of the most fundamental problems of science and engineering is to predict and

forecast future phenomena. Using existing theories and then extending such theories by

means of experimental results frequently accomplish this. The basic problem is to

develop models analytic form so those future real world situations can be predicted

with a reliable degree of accuracy. Likewise in transportation and environmental

engineering. analytical models are needed to forecast urban traffic pollution that is

reliable. It is the one of the objectives of this research to develop II prediction

methodology for use in land use planning, which is II major component of urban

transponation planning.

Methodology has been developed to estimate CO pollution at intersections by generating

estimates from various types of adjacent land uses, traffic volumes, road geometry etc. in

order to provide traffic engineers and urban planners a less expensive and fairly accurate

means 10 facilitate critical decision making regarding pollution. The methodology could

also be adopted elsewhere as well by incorporating indigenous patterns.

After a thorough investigation, the of Predictive Modelling was limited to core

areas (Old City) in Karachi. Since this study is unfunded research and constrained by the

limited time and manpower available, the magnitude was kept I(} a reasonable level.

The old city (core) areas of Karachi are quite unique in nature as far as land use is

concerned. Simultaneous mixed activities at different floors of a single building in core

Karachi calls for a model study that could provide insight on these unique situations.

Photographs revealing these chaotic circumstances are illustrated as Annexure r

The existing secondary data from the Master Plan and Environmental Control Depanment

of the Karachi Development Authority (KDA) is although helpful but does not go far into

looking at the micro geographic patterns of land use in various regions of Karachi. The

appraisal of land use patterns for an environmental study on metropolitan Karachi

required more effort. Hence, a field study was conducted to address this issue. Owing to

14!l

the financial, human resource and time constraints, a total of 90 locations were identified

in old Karachi for understanding the ground realities.

Stepwise Regression Analysis is a statistical technique, which is considered to be very

effective in identifying relations between various independem and dependent variables

(McCuen 1985; Gujarali, 1995). The objective of stepwise regression is to develop a

prediction equation relating a criterion (dependent) variable to one or more predictor

variables. Stepwise regression,' in addition to calibrating a prediction equation, uses

statistical criteria for selecting which of the available predictor variables will be included

in the final regression equation; the multiple regression technique includes all available

variables in the equation and is often plagued by irrational regression coefficients.

Stepwise regression usually avoids the irrational coefficients because tbe statistical

criteria of F-statistics that are used in selecting the predictor variables usually eliminate

predictor variables that have high inter-cotrelation (Theil, 1978; Gujarati, 1995).

The multivariate linear model structure with p independent explanatory variables, the

regression equation is (Fomby, 1984; McCuen \985; Rogerson, 2001):

(2.1 )

Hameed (1990) and Ali (2000) have utilized Stepwise linear multiple regression for rural

and urban transportation research. Ali (2000) in attempting to model rural travel

behaviour, developed probabilistic models for individual activity choice and the resulting

travel Hameed (1990) produced three pedestrian volume prediction models using

adjacent land use data at intersection through stepwise regression analysis. Both had used

SPSS for their statistical calculations.

Due to the inherent characteristics of land use variables, they were aggregated into major

categories to keep the classification of land use GIS and Statistical modelling to a

reasonable number. The variables is shown in list and a brief description of each variable

is given as follows:

141

I. Residential Space (XI) : indicates all space utilized for residential purposes, which

includes, single family, multifamily, flats, hostels etc.

2 . Commercial Space (Xz): refers to the space used for bazaar, petrol pumps, retail

stores, shopping areas, warehouses, cottage industries, restaurants, governmental and

private offices, banks, courts, etc.

3. Special Purpose Space (X.J): this category refers to religious institutions, medical

facilities, educational instiiutions, social, cultural Institutions, Parks, Cinemas,

playgrounds, zoo, picnic resorts etc.

4. Transportation Right of Way (X.) : consist of all space used for Roadway pavement,

sidewalks, all sorts of parking space, median etc.

5. Vacant space (X5): designated to show all space allocated for some activity that is not

utilised presently. This includes vacant lots found in different study locations.

6. Hourly traffic count (X6) •

7. Roadway width (X7)

8 . Built-up Space (Xs): consist of all space utilized for whether residential, commercial

or special purpose summed together. This clearly excludes the space for Vacant and

Transportation right of way.

142

4. RESULTS & DISCUSSION

- - ---~------~~- -

4.1 RECTIFIED SATELLITE PRODUCT

The section on methodological framework has thrown light on the pre-processing techniques

often referred to as Rectification. adapted in this research. The most comprehensible

approach towards the presentation of results on remote sensing products is to provide the

visual comparisons, "Before and After" each step of processing. The software applied was

Geomatica of PCI Geomatics (Canada). Figure 4.1 is the raw image acquired for analysis.

Figure 4.2 is the georeferenced version of the raw image. This exercise of georeferencing has

made the image of Karachi more geometrically familiar for the users in the form of having

geographical coordinates for 'manoeuvring' and 'environmental attenuation' and ' noise

correction' .

In Figure 4.3, 'a', 'c', 'd ' are the set of selected pre-enhanced subsets whereas 'b', 'd ', and

or are the enhanced outputs through Linear distribution, Root distriblJtion alld Adaptive

distriblJtion of spectral values respectively. These enhanced images would help in the

upcoming classification process for the assessment of land cover clusters for metropolitan

Karachi.

Figure 4.1, and Figure 3.3 are the before and after renditions of the image 'Subsetting'

technique. As already discussed in the methodology, it is evident here that the subsetting

techniques have successfully narrowed down the depiction of the developed study area .

However, the land beyond inhibited areas was included in the picture as the administrative

boundaries of the then districts that had to be accounted for. Secondly, the author wished to

render a pictorial overview of the physiographic features surrounding the study area.

144

Figure 4.1

KARACHI AND ITS ENVIRONS FROM SPACE

(Landsat TM FCC)

Satellite

Sensor Bands

: Landsat 5

:TM : 2,3, 4

Date of Acquisition : Feb 1998

Resolution : 30 x 30 m

KARACHI AND ITS ENVIRONS GEOMETRICALLY CORRECTED IMAGE

( ( :,

Projection UTM Zone UTM ROW Ellipsoid UTM 42 R Everes'

t'igurc 4.2

145

SELECTED SUBSETS OF PRE AND POST ENHANCED

Imageries (Pre - Enhanced) (Post-Enhanced)

(a)

c

(e)

~~.....,..

, ,

(i • . 1,

" ,

(Linear Distribution o.fSpectral Values)

(Root Distriblltion

(Adaptive Distribution of Spectral Vallles) l·i.",[I: 4.3

(0

4.2 LAND COVER

The land cover classification scheme adopted in this study has been presented in the chapter

of Methodology (Figure 3.4). Image classification of Landsat-5 1998, produced a value

added digital thematic map (Figure 4.4) that contains itemized account of eleven land cover

classes as illustrated in Figures 4.5. This itemized account was for the subsetted version

(Figure 3.3) of acquired imagery (Figure 4.1).

To make the query more meaningful, the area occupied by the 'landmass' was detached for

further scrutiny. The appraised land cover of Karachi division indicated that the major share

by area is comprised Mountains I barren land (8/.5%), Urban Land use (15 .3%) and

Vegetation canopy (3.2%) however, the urban land cover is sprawling day by day.

The classified thematic map could be interpreted on the basis of physiography and historical

expansion of urban land, which is mainly due to the human activities (as dwelling, transport

and business).

Classified results indicate the existence of three distinct widely spread physiographic

features . Karachi has plain land, coastal zone and hilly ranges. The processed image has

yielded the acreage of plain land of Karachi division to be around 2000 square kilometre.

Out of which about 25 percent is covered by urban settlement, whereas the rest is either too

far from the settlements or isolated pockets surrounded by hills. The planned development of

the metropolitan has been mostly on the plain land. The likelihood of future expansion of

urban cover lies around the plains near Hawks bay, Pipri, and Gadap. The coastline shown on

the image has occupied more than 75 percent by the built environment. It has expanded due

to reclamation of shore around Clifton and Defence Housing Authority (DHA). over the

period of time. This reclamation process is on going ever since the date of image acquisition.

The southern part of the city is cOnnected with the Arabian Sea. On the south and southeast

corridors lie a widespread extent of mud flats, sandbanks and mangrove swamps, crossed by

a complex system of branching creeks and inlets.

148

~ -,~. =-~ --50.

Figur~ 4 A

CLASSJFIED LAND COVER OF

KARACHI METROPOLIS

------• l_ ......

-~, --.-------~_'--

" •. ---."""""-~ . -'- . --- .

149

The Kirthar Range can be seen on the northwestern portion of the classified image near the

prominent Hub dam. The Pabb Range, south of Kirthar is manifested in the cluster of rock

shadow and sand stone reflection. In the extreme Southwest to the centre of Karachi

Division, Jhil Range is dominating, a series of hills and ridges, extending from Cap Monze to

the Manghopir area. These features are quite obvious to pinpoint on the classified image.

The human settlements are portrayed in four clusters on the classified image. The most

prominent cluster on the image is of densely built up land cover. The areas inscribed in this

class are Nazimabad, Liaqatabad, Lasbela, old city areas (from south of Layari river to Club

road), Shreen Jinnah Colony ·and Shah Faisal Colony. The localities falling under densely

built-up class are congested because the residential and economic activities that take place

around them, have expended over time. The dense road network and high rises have occupied

all open spaces and amenities. Population densities at some of these neighbourhoods are as

high as 1,800 persons per acre (URC, 2002). In these areas concentration of commercial

activities are very high resulting in mixed land uses all over.

As evident on the classifled thematic map, the class of low built-up area shows actually the

suburban areas, urban outskirts and peripheries. This class includes Surjani Town, Gulshan

e-Maymar, Gulshan-e-Hadeed, Northwest of Orangi Town, Phase VI and VII of Defence

Housing Authority (DHA).

A very interesting phenomenon is observed while examining the medium built up cluster of

the classified output. The author observed that vegetation is almost non-existent in the

densely built up class since most of it is historically pre-independence Karachi .. Whereas,

greenery can be seen in the medium built up class on the image and these are mostly the post

independence planned localities developed by KDA and Cooperative Societies. The medium

built up class encompasses the vicinities of North Nazimabad, PECHS, Co-operative

Societies, Cantonment areas around Shahrah-e-Faisal, Clifton and Defence Housing

Authority.

150

Appraisals of Land Cover

Figure 4.5

Me. ". ' .... -t~ - .. ... B~II up Land covel

If.''''''' . '-1501. 14 Km' J Open Land I

943.11 Km' I Sand Stone Reflection I Unused Land

/ IJ 220.78 Km' I Rock Sbadow

2%

22.1 %

40 60 so

151

4.3 CHANGE DETECTION: SETTLEMENTS GROWTH

It has been found that pinpointing of growth corridors is quite difficult. During the analysis

period of twelve years (1986 - 1998) the city has grown literally in all land available

directions as shown in Figure 1.4 and Figure 4.6. It was also deduced that the human

settlements around Karachi during the analysis period, were the by·products of infrastructure

development. Major growth has been observed along National highway, Super highway,

University road and RCD highway. Similar results were obtained by Afsar (2001), when

Karachi's urban sprawl was studied. KDA (1991) had identified future growth corridor

directions. Figure 4.6 presents the updated reality.

There are three major highway projects in the pipeline viz. the Northern bypass, the Layari

Expressway and the Southern bypass (Correspondent daily Dawn, 2002). It is envisaged that

the Northern bypass has acquired a priority among these projects. Its construction (Super

highway to Karachi port) is expected to provide another corridor for future settlements.

Decision-makers should start visualizing the future scenario of the metropolitan growth when

all these highway projects would be materialized.

Concluding the findings of classification exercise, it is suggested that the problem be viewed

ill a broader perspective. In Karachi metropolis, enormous traRic and land use problems are

degrading the environment as a whole. Analogous ·to the formulated hypothesis, the

functional agglomerations of landuse attract the population and traffic that give rise to air

pollution. Therefore, after land cover, of the city's land use was scrutinised in depth.

4.4 LAND USE

Rapid urban growth generally means that little land is reserved for public space and amenity.

Lack of recreational space is yet another cause of deteriorating urban quality of life in DCs

and LDCs and contributes to increasing social instability (Barrow, 1995). Allied to low

quality housing, poverty, disruption of community cohesion, and other declining urban

conditions are public health threats.

152

-, -

GROWTH OF SETTLEMENTS IN

KARACHI METROPOLIS (1986 - 98)

,

"

' . .... ~ . .: ...

.; .. "r ' -

- . ; ", ,~

. "l' "

, " ';r '~ .:"", ' .

Arabian Sea

Sourc.: SPOT xs, Ocloher 1986 and March 1998

,

+ ., ______ O~----~6 ____ ~12

Kilomdl'r

Figun: 4.6

· - :~:··fr " :\!W*!.~'f. !fI"'lI-

t ' ';'' • :: . ,.- J

, ,

153

Drawing the analysis towards land use from land cover, it could be well said that Karachi is a

cosmopolitan city and having multi-faceted urban functions . As described in the section 3.2.2

on land use / Ground Realities; year 2000 land use plan of Karachi Development Authority

(KDA, 1991) was the source of information whose further analysis yielded the following

categorized information illustrated in Figure 4 .7 and Table 4.1. The categories spelled out in

the document were not altered due to logical reasons. Primarily it was to maintain the

relevance of analysis with the 'original document and to facilitate in comprehension.

However, it should be noted that the boundaries of the metropolis are not the ones as the

author had demarcated in the previous analysis of land cover. In fact this land use map of

KDA had considered only the urban limits of Karachi. Another limitation of the source map

was that the KDA had marked the classes / areas based on dominant land use of localities.

These tabular and geographically mapped outcome can well be considered as value added

products benefiting the planners and administrators of the city.

Table 4.1: KDA Land Use 2000

:.·;~·:·;~i~\1:~f:!~~~~~'~~1~~t~i~~~~'1 P~~~"a' :7~~; 4li~rceiiF f~)(§qJliiii)f ;;;,s.bare\ , ~; . ' . ' '~ "

Planned Residential 163.7 18.12 Military Arel!s 121.3 13.43 Schemes 10 infill 98.8 10.94 Low Income Senlements 82.7 9.15 Unplanned Residential 70.1 7.76 Industrial 67 7.42 Agriculture 50.9 5.63 New InduslfV 48 5.31 Densification Areas 47.8 5.29 Flood Plain 47.1 5.21 Vacanl Undeveloped 16.7 1.85 Buffer Areas 14.3 1.58 Recreational 14 1.55 Transport Facilities 13.5 1.49 Urban Renewal 11.2 1.24 Commercial 10.7 1.18 Utilities 8 0.89 Education 7.7 0.85 New Commercial Cenlres 4.9 0.54 Burial Ground 3.2 0.35 Vacanl Developed 1.9 0.21

Total 903.~ 99.99

154

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DnbiIiati.n .\~

B .... . \ne

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\'IQIII On:c-........

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KUOIath'r

--

P f{OJECTED L AND U SE 2000 OF

KARACHI METROPOLIS

I

,'" ~

•

~ • ,

I ~: • • • ~ ,

•

Source: Ka rachi Development Authority 1 ... 1P & ECD, 1991 Fi,gurc 4.7

I

There are actually the first ever development of a statistical and georeferenced database on

actual land use and functions for the metropolitan city of Karachi . This digital database has

opened avenues for looking at the land use scenario. For instance, if these twenty-one land

use classes outlined by the KDA are grouped together broadly on the basis of their functions,

interesting figures are derived, which are tabulated as under:

Table 4.2: Land .use Groups of KDA Defined Categories

. ~~:~ .. ~·~ff~~.~~ ~$trl~)~:i1,J:~i{~~}~tri~i¥ti~~~ p~~~:ml~!ljH~<~~i ~ ;' :: :':~t;~~~: .. ":' -'1ucenV " .,' .:~ -:~::-:;~?

ndustrial 61 7.41

Agriculture 50.S 5.63 Economic New Indusm 41 5.31

ommercial 10.7 LIS

New Commercial Centres 4.9 0.54

Total 181.5 20.08 ;Recreational 14 L5 II'ransport Facilities 13.5 1.49

Infrastructure ~lilities S O.S'

~ducation 7 . 0 .S5

iBurial Grounds 3.2 0.35

Total 46.4 5.1 Planned Residential 163. IS.I

~ehemes to infill 9S.S 10.94

Housing "",,w Income Settlements 82.7 9." Unplanned Residential 70.1 7.71

iDensification Areas 47.8 5.2\

Urban Renewal 11.2 1.24

rrotal 474.3 52.S:

MilitaI)' Areas 121.3 13 .43

Vacant Undeveloped 16. 1.8 Special Purpose Buffer Areas 14.3 l.51

Vacant Developed U 0.21

Flood Plain 47.1 5.21

[rotal 201.3 22.28

These appraisals of land use in metropolitan Karachi provide legitimate ideas of urban and

environmental inquiries. Amongst them, some are presented here, when the whole city is

looked at macro level.

I . It would be very unfair with the metropolis if it were not denominated according to

conventional functional classification theories (e.g. Harris, 1943). In fact it has

156

----------------_. -_ ._--

emerged as a "diversified city" with multi-faceted activities. Karachi cannot be

viewed on the basis of functional specialization only.

2. In the context of Pakistan, Karachi metropolis may qualify to be placed under the

"Central Place Theory" (Christaller, 1933) The groups of economic and

infrastructure land use themselves indicate that the centrality that is crucial to the

development of urban phices, does exist in the light of a very large hinterland and

sufficient potential for basic and non-basic goods (e.g. Myrdal, 1967; Friedmann and

Weaver, 1979)

While introducing Karachi earlier, the two Seaporls, an Inlemalional Ai/porI and

rail/highway linkages were earlier discussed. Being a trall.lporlaliol/ Hllb comprising

of around 47 sq. kms. of infrastructure land use (Table 4.2), Karachi's hinterland

goes beyond Afghanistan up to Central Asia (MS Encarta, 2002; Correspondent

Dawn, 2002). Therefore, the city has a multiplier effect due to the economic

expansion of its own kind (e.g. Friedmann and Weaver, 1979).

3. Looking at urban morphology, it is observed that the metropolis understudy is

different than the theoretical 'Concentric zone' model (Burgess, 1925), Sector model

(Hoyt, 1939) and Multiple nuclei model (Harris and Ulllman, 1945). Reasons for this

are deep rooted in its history and evolution, as touched upon in the first chapter.

Unprecedented huge migration is partially responsible for the mixed and chaotic

land use. Secondly, there has been no institutional control of land use due to weak

governance. Moreover, the socio-economic eccentricity of the "urban poor" of

Karachi has contributed to the emergence of many squatter settlements kalchi

abadies.

Over crowded and poor quality housing may aid the transmission of diseases and

pollution (Barrow, 1995). In Karachi city, at prima facia, for the housing sector,

(Table 4.2 and Figure 4 .8) the area occupied jointly by Low Income Selliemenfs and

Unplanned Residenlial comes out to be about 150 sq. km (17% of total urban land

157

use). If these two poor residential categories are summed up and their share out of

the total land for the housing group is assessed, it is 32 percent. A7ad eloJ. (200 I) has

discussed the continually changing urban structure, peculiar to Karachi through 8 case

study of residential neighbourhoods being transformed slowly into commercial

markets.

4. It is to be emphasized that the functional aggIomecation has put tremendous stress on

housing and settlement. The western concept of 8 classical CBO is nonexistent due

to mixed Ianduse all around the metropolis. This has obviously resulted into a host of

manifestations of urban environmental degradation: air quality being one of them

Housing Distribution In Karachi

• Planned Residenlal IaSchemes to 1""11 o lDw lnoome SeIIIeI i oelts o lJI1lIanned ResIcIentIaI I!I DenslfIcaIIon ~

1:1 UrbIn Renewal

Figun:4.8

4.5 POPULATION DISTRIBUTION

The source of the population data has been descnDed in the previous chapter; the pertinent

analysis done on this data has also been discussed. The results of demographic analyses are

presented in Table 4.3 and Figures 4.9, 4.10 and 4.11. Deliberations on the spatial

demographic patterns of present-day Karachi could be given by taking into BCCOWlt the

historical. cuhural and socio-economic filctors. Karachi city has gone through phenomenal

158

changes since the creation of Pakistan (Rehman, 1983). This is also reflected in the intra

urban distribution of its population.

The pre-independence population of Karachi i.e. 386655 in 1941 (GOP, 1985) was attributed

to its port and strategic activities limiting its boundaries up to Soldier Bazaar. The population

around 1068459 in 1951 was because of the first migration of Muslim refugees from India .

This influx was accommodated in the Jhuggies around Numaish to Jamshed Road and via

vertical expansion in the form of flats / apartments all across the old city. The population of

3515402 in 1971 could be explained by factors as natural growth, migration from up country,

development of industry (SITE, KITE etc.) and trade (wholesale and distribution) across the

then metropolis. The city had grown to include F.B. Area, Nazimabad, North Nazimabad,

Liaquatabad, Landhi, Korangi, Malir Extension, PECHS and other societies. The population

of 5437984 in 1981 (GOP, 1985) includes the second migration from eastern parts of the

Indian sub-continent, perpetual migration from up country (especially rural to urban) and

natural increase. The city frontiers had been extended to Orangi Town, Gulshan-e-Iqba1.

North Karachi, New Karachi, Kehkshan, and Defence Housing Authority. The latest

published figures of exceeding 9 millions in 1998 (GOP, 2000b; MS Encarta, 2002) are for

the times when Karachi has broadened to the present limits Figure I. 4.

The above discussion could · be enhanced by indicating the need for a comprehensive

demographic / spatial distribution study for Karachi in the light of relevant variables . Jafri

(1973) and Husain (1992) worked on these lines but time has outpaced their endeavours since

Karachi is now considered to be one of the top fifteen populous cities on the globe (PRB,

2001).

Remaining within the constraints of this study, the author has contributed towards the micro

geographic understanding of population distribution within the metropolis by presenting the

following Figures 4.9,4.10 and 4.11 and Table 4.3.

159

em

•• +.~q'~f~------~--------------------------~'J--~~~'~'~'~" ~~~~----------------------------I\----~~~.~ ••• ' ~ I ~-.~_ i • 5 .,

----0-.

~ Anlbian Sea ~ ~

,~~------------------------------------~~~---------------------------------------+~ .,..or! t I' rf!¥DKf.

• • •• -

Table 4.3: Zone-wise Population Distribution of Karachi MetropoliS

I 4

218041 59726 86247 31771

194399

86790.25

4 094.4' !45.6'

20196.63 1.1999

~--~---+------~111971 1'6.01

10 II

I. !819 :096

2, )48 1,

12

!l08.28 Il88. !l

.4 23 13

~ __ ~5~ __ ~ ____ ~~~~~:~ ________ ~~:~~~~~:

17 10008 5234.33

20 119256 18l84.10

!l 26

132688 6l 10 1.)4 267477 7l968.01

78720 30<1 .79 210752 Il497.09

71833 10909.4l 201984 8l84.26 236319 l3620 239l0l 291078 819981

r-~1~2 __ ~ __ ~~~~02ll~)1~2 ________ ~'6~9.~i2~ 33 67s99O 1414 .. 34 371343269U. 3l 1792l 19J2.' 36 19043 Il1.4' 37 2099l 286.89

6010 68.28 ,9 580115 10 227837 128l9.02

284710 10201.69 16040 3716.93

43 398289 32 ' .46 44 8601 .l2 4l 3272 21 . 11 46 383, 47 624' 39l2.77 48 : 14l 914.:

96036 41l.1O 9OIl8 466.79 44464 1111.16

160278 1868.21 6471. 12.61 74126

5l 8628 '08 l6 12420 78: l7 . 6711 l8 11833 81.8'

161

54

POPULATION DENSITIES IN

ANALYSIS ZONES

S4. 55

Persons ! Km2 56

143588

57 .14 t. 52

37 19 ~ U •

~6

~ ~ II.FII:IIIItr -.,.

+' _,. rrl- ~-.i' 47 4. ~ 42 \ 4g

\JILl- I~'-' ct> ,..li.... 15 r ,~~ 411 41

tv I 7 0 11 411 r 7 U D

i ----,

Kilometer

Figure 4_10

58

The lowest density of population (about 54 persons per square kilometre) occurred at zone 57

(Dehs in the west along Hub River), which is essentially a rural fringe of Karachi. While the

highest population density (143588 persons per square kilometre) was found in zone 10

(Chakiwara, Kalakot etc.), which is an old neighbourhood of Karachi often referred as a

portion of 'core of the city' . Out of fifty-eight, nineteen (19) zones exceed the popUlation

density of 25,000 persons per square kilometre. There are only five (05) zones in Karachi

that are having a population density of less than 100 persons per square kilometre and

incidentally these are all rural-urban fringe or rural areas. The following statistics (Table 4.4)

are self-explanatory.

Table 4.4: Population Density: Descriptive Statistics

:·m~~i~:- C· : :1'd~S'bid;:~,~}~'t:}: ::~ • :;;Y<YlIiiia:;4· .",. Mean 23217.94 Slandard Error 3861.08 Median 12349 Slandard Devialion 29405. )(l Sample Variance 864660300 Kurtosis 4.69 Skewness 2.03 Range 143534 Minimum 54 Maximum 143588 Sum 1346641

COUll I 58 Largesl(2) 103108 Smallesl(2) 68 Confidence Level(95.0%) 7731.68

The population density map (Figure 4.10) for Karachi illustrates noticeable distribution

patterns. An elongated pattern of population concentration is clearly visible encompassing

analysis Zones # 1,2,7,8,9, 10, II, 12,21,22,23,28,29,30 and 31. The author is of the

opinion that cheaper land vallie is the chief explanation for this elongated spatial pattern.

Rehman (1983) had partially attributed the population agglomeration (at that time) to 'land

value'. Zones # I, 2, 7, 8, 9, 10, II and 12 are parts of Old City inhibited by initial settlers.

Zones # 21, 22, 23, 28, 29, 30 and 31 comprise 'several subsidised schemes' furnished to the

public by the government for settling the upper and

163

KARACHI Popul tion Density Gradient

;W~. _~Q='~E~ ____________________________________ ~~~W~t~'~ ____________________________________ ~~~W~"t .. , ~1I.a)-n-.. ..

I

......

( )

i Arabian Sea ~ • ,~~--~~------------------~--------------,-----~==~====~~==~====~==~--~-------+~

". ~r( tp"jIS"[ .1' " ' !Jr! _ ..... ,u.

: " :t • •

lower middle social classes. For instance the scheme of North Nazimabad was advertised at

the rate of Rs. 07 per sq. yards (to be paid in easy instalments) in 1960s by Karachi

Development Authority. The two exceptions, zone 33 (Orangi Town) and zone 43 (Malir and

Model colonies) can also be credited to the author's raison d'etre of cheap rather almost free

land. Orangi the largest 'Katchi Ahadi' of Asia was founded for the refugees from East

Pakistan in early 1970s. Malir and Model colonies were established in 1950s for socially

lower segment of population.

Another presentation of population distribution is portrayed in the form of 'Dot Map' Figure

4.9 in which each dot represented 200 persons. This gives a better picture of population

distribution and its concentrations within study area. Through this map analysis of affected

popUlation (under risk) has been facilitated. Further, cartographic output in the form of map

Figure 4 . 11 depicting population density gradient was derived. Density gradient map shows

the ascending and descending gradual changes across the metropolis (Rehman, 1983).

As discussed in the context of land use, there are several factors responsible for the spatial

concentrations of this kind in Karachi . Keeping the historical factors aside, the economic

conditions of the urban inhabitant are also the cause of densely crowded neighbourhoods

Like many third world countries, in urban areas of Pakistan, 25 percent of the population

lives below poverty line (UNCHS, 1996; Drakakis-Smith, 2000). Karachi's urban poor

does not have the commercial worthiness to move home from where he/she was born. In the

olden parts of Karachi, third generation of initial post independence dwellers is being

witnessed residing.

4.6 ROAD NETWORK

The base map had the road network of Karachi updated until 1998 Figures 3.1 I and 1.8 .

There was a need to examine its relevance within the framework of this study. Therefore,

widespread traffic assessment was performed at a later stage. Momentarily the graphical

presentation of this densely distributed aspect is illustrated in Figure 4 .12.

165

-

ROADD INDEX .·t'W~a~7'~ __________________________________________ -=~~W~'~I~ __________________________________________ ~V~W~"=t., !. o.w.-l ia)-.r I ~

~TCM'II h

f

"

''''''-

I ~ Arabian Sea ~

~±-=----------------=----=~--~-------------------------+~ w.,. ~t ..,."t! '·l

.. , 41l

• J • •

ROAD PROXIMITY BUFFERS ':t~~C~."~~ ______________________________________ ~~~~~r~'~ __ ~ ____________________________________ ~v~·~·t,'

• - - $ ,. ~

Arabian

PI'''''''

Sel

,. "

~ " ~ ~t.~,OK~'----------------------------------~~~---W~.~~--------------------------------------------~~

r 1 [ ~ I • " ,,-1 . " .. ~r l

•

1 • + 1 • •

~ II - 511

50 - 100 100 - SOH '> !'ilK) lIIeler

167

This map was produced through GIS technology and the task IS commonly referred as

density mapping in the texts (Demers, 1999).

The core city areas (Old Karachi) again emerged as a case to study at its own merit. Some

other neighbourhoods developed afterwards by the cooperative housing societies. These also

have some dense and narrow gaped roads such as PECHS, KCHS efC. The city developed by

the Karachi Development Authority (KDA) can be appreciated as a spacious, aesthetically

pleasant and well-planned. The neighbourhoods of Clifton, North Nazimabad, Federal 'B'

Area, Gulshan-e-Iqbal and Gulistan-e-lohar can be seen on this map as good examples of

fairly apart roads. Noticeably, North Nazimabad can be taken as a model neighbourhood

where planning has contributed in reducing the future environmental degradations.

Figure 4.13 is another endeavour usmg modern cartography where roads' proximity IS

magnified through a technique known as 'linear buffering' (Lang, 1999; Audet and Ludwing,

2000; Steede-Terry, 2000). This map could be considered complementing the (previoLls)

Figure 4.10 of density mapping. The legend shows the lateral distances marked on both sides

of the roads. Although, a different exercise in approach, it produced quite identical results to

understand this scenario of road density variations from place to place.

4.7 WIDESPREAD ASSESSMENT OF TRAFFIC PATTERNS

Traffic congestion is a common occurrence and fairly widespread over the metropolitan

Karachi. In this section, it has been attempted to give a general outlook of traffic conditions

for the three daily variations and two-week wise variations. Figures 414, 4.15, 4.16, 4.17,

4.18 and 4.19, were produced as GIS themes indicating these six variations respectively.

These maps represented the development of gravitational centres (Active Zone) in Karachi

from time to time and place to place.

Figure 4. 14 shows the 'Morning' session on Friday I Saturday I Sunday. It is observed that

high traffic at this juncture is along the major arterial road (TEB classification). The localities

do not encounter congestion very often during this segment of time. The neighbourhoods of

168

-

KARACHI Tr c Concentr tions

WAr

.~t~~O~'~'~ ______________________________________________ ~~~'~'~ __________ ~ __________________________________ -=O~-~D" ~ ~~ S L ~T""," II: ~ ~

. Arabian

•

... • •

Sea

•

• •

• •

K,,,,," Unr.a.ty

. .....

L

'" I~ ~

~ . ~.~~----:-----------..:::~----;;;;;:;---------;::=---=~~. 1IIf'f'l.,r1 Q'O 'II'I'1I.n ., r f

,

+ ~~ .. ~'~' .... ~J ...... '~ .... ~.~ .. ~.

Jr.rr, (ODe' Ir IIOll

• I 'l Js ~I'l

• 1/{V.1r

'f'JJt ~11/1 10"

I . , f L 'It

" <..

,

KARACHI Traffic Concentrations

rn a !tI' " 12(t'i f7' Qi' q ". .. n!11 .~~~--------------------------------------,--~~~----------------------------------------~~~, ~ ~_ :a . >;

• "

•

• ••

~ Arabian Sea ~ ~ ~

l~~~~~---=~------------------------------~;;~--------------------------~::~::::~:::-~~~ 111' ." f"[ur "''' '' .$'~ ~

If.nil (nn(l.fr linb

'J • •

• r In J( 1):1t

• II 'Xi, \I, .. , I

I .

• •

•

Arabian

KARACHI Traffic Concentrations

W~kt d ,v I.

•

-. I •. •

• • • • • • • • • • •

• t..noh

i • "

z

" ~ • • %,+-;;~-:=------------;;;;;;:;---------;::::-::::::::::::::::::::::=~~' Il"' .. JlY[ w'{ur l " I" t

l~ I " h.ffir 'nncrnlr nn

0 :t • • --el'''lti· • 1/, ,I:

"('I I t , . ,r , ..

7 •

Orangi Town, Clifton, DHA and Landhi posses low traffic volume while the radial roads

from Lasbela / Teenhatti via Grumandir up to Tibet centre remain the two busiest arterials of

Karachi.

Slight changes occur in the patterns of traffic concentration in the' Afternoons' owing to the

citywide activities, Figure 4.15. Major roads remain same in their business but the arterial,

which passes through industrial and residential zones in the city, shows comparative calm.

'Evenings' in Karachi, either during the business or during holidays, are the peak hours of

traffic activity across the whole metropolis. However, congestion is often experienced around

Karachi District Central and the core city area. The roads that contain several choking points

are Shahrah-e-Pakistan, Shahrah-e-Faisal, M . A Jinnah Road, S M. Taufiq Road, Nawab

Siddique Ali Khan Road, Rashid Minhas Road, and Abdullah Haroon Road . These roadways

on the evenings of Friday / Saturday / Sunday carry high volume of vehicular traffic (Figure

4. 16)

The morning scenario of traffic iri Karachi metropolitan (Figure 4.17) during working days

exhibits fairly widespread moderate to very high volumes. With the exception of reclaimed

DHA, nearly all-residential and business localities of districts South, Central and East remain

occupied.

Afternoons of Monday / Tuesday / Wednesday / Thursday become less busy than other two

time-segments of these days (Figure 4. 18) . . Slight low trends are found and the major arteries

are engaged with low, moderate and high volumes of traffic. The activity surrounds the

business centres and other trip generators in the city.

The thematic map Figure 4.19 portrays the appraisal of evening traffIC volume in metro

Karachi. This session happens to be the most congested of the six sessions studied. The

volumes remain in the range of high to very high at more than 75 % of308 intersections.

172

-

KAR CHI Tr c Co centratioD

WOrklDC

WJfQ.IiH :+-------------------------------------------.~ ., '" ..

. ~

• ~ •

AJabian Sea

•

e. PECHS

•

I •• •

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<1'WO , ~

~ ~

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• •

• •

•• •

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~.+---------------------------... WU'f' l

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173

KARACHI Trame Concentrations

Work! 10 Y rraooas

Z~+~~~~7~' ______________________________________ ~~~~"~ ________________________________________ ~~~~~Ut.' ~ ~~~ ~ • ~ToWn ~

,.

•

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KARACHI Tramc Concentrations

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Nishtar road, M. A Jinnah Road, Shahrah-e-Faisal, Rashid Minhas Road, S. M. Tauflq,

Nawwab Siddique Ali Khan Road and Tariq Road have several choked up intersections.

Whereas, Shahid-e-Millat Road, Shahrah-e-Quaideen, and Abdullah Haroon Road give way

to thousands of vehicles during this time segment similar to the roads, previously mentioned .

The industrial areas of SITE, Landhi and Federal B. Area are also involved in traffic

congestion due to work to home trips.

4.8 SPATIAL PATTERNS OF AIR POLLUTANTS

4.8.1 Suspended Particulate Matter (SPM)

The aerosol contents of the atmosphere are sensitive to the location of local sources and

meteorological conditions. Human-induced particle emissions result from sources such as

cars. trucks, steel mills, cement plants, ceramic industries, construction dust and waste

incineration. The fme fraction of the suspended particles PM IO in an urban area is combustion

product of I-C engines (vehicles) .

1. Days: Time: Measure: Figure :

7'h .fuly 10 n"d .filly 1999 24 hours Suspended Particulate Matters (SPM) 4.20

Spa/irt! Patterns:

The interpolated surface shows total suspended particulate concentrations and their

spatial variations all over the sampling area. Regions had SPM concentration of the

order of 214 - 3 18 I-lg / m3. Higher values of SPM have spatial boundings with open

plain nearby industrial activity that attract the heavy traffic for transporting raw

materials and manufactured products.

176

-

SPATI L. PAlTERNS OF SPM EMI SION

•• t' ~~"~.'~------------------------------------~--~=c~----------------------------------------~~~W~"+ ' ~ i

KarM:hl A upon.

PECHli

AIabian Sea

, "

• "

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+ ! • • •

SP\l onctntr n

, ...

•

SPATIAL PAlTERNS OF PMIO EMISSION

~"'UIH Q'~'lDyt ~ il lJtH

E~~------------------------------------~~~--------------------------------------~' .. ~~-n. ..:; ii ~TCIIm 5 ~ ~

:: =

--~ Arabian Sea :.: ~ ~

+-----------------------------~--------~--------------------------------------~: i7l., ".r [ tJ"'16'~ ... ,

I' 110 Conceolntlon

+ ~ . ..

• • .. -

2.

Ho/spots:

Landhi. Korangi industrial area. Sohrab goth. AI-Asif square

Days: Time: lv/easllre: Figure:

1h Jllly 10 22nd .lilly 1999 24 hours Inhalable Particles (PM 10) 4.21

Spatial Pal/ems:

On the average 10 to 20 % of the total suspended particulate were found to be PM to

in the sampling areas. High concentrations of PM JO are indicative of high density of

vehicular traffic . Throughout the sampling area. PM IO values were found exceeding

the prescribed USEP A standard of 150 ~g/m]

Ho/spo/s:

North Karachi, New Karachi, Buffer zone, North Nazimabad, Sohrab goth, Landhi,

Korangi, Harbour area

4.8.2 Sulphur Dioxide (S02)

Sulphur dioxide is mainly generated from the industrial sources , Considering the furnace oil

consumption in oil fired power plants at Port Qasim. an estimated amount of 40,000 ton per

annum of Sulphur dioxide is produced from power plants in Karachi , 80% of city's total

consumption is being used in power generation at Port Qasim, which is therefore, the main

source of S02 (Ghauri el at.. 1988)

I. Days: Time: Measure: Figure:

1h July to 22"d Jllly 1999 24 hours Sulphur dioxide (Sal), Maximums 4,22

179

-

SPATIAL PATTERNS OF S02 E J\IISSI 0,

Maximums

.~+.~'"'~'~ __________________________________ ~~~~~~'~'~ ______________________________________ ~~2W2"tf' ~ -- ~ :. ~ToJIIIIm ., ~ J I

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+ I.' .. • • • .. .- -

SPATI l PATTERNS Of S02 EMISSION

Averages

0" bn , r-tayvw _T~

t7' W ~ E

£ " ••

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+ I • , • •

SOz ('nol' nlr&llon

.. '" -

2

Spatial Patterns:

Map illustrating Sulphur dioxide (S02) maXImums of the location variation of

concentrations in ambient air with in the sampling area. Spatial patterns of S02 are

seem to be nucleated due to the industrial activity at Bin Qasim that has emerged out

as the prime contributor . of sulphur dioxide emissions in the metropolis . Sindh

Industrial Trading Estate (SITE) is arising as the second major risk zone in the

metropolis

Hotspots:

SITE. Bin Qasim Thermal Power Plant Area

Days: Time : Measure: Figure:

1h July 10 22"d July 1999 24 hours Sulphur dioxide (S02), Averages 4 .23

Spatial Patterns:

Since this pollutant is induced by the location of power plants. Spatial patterns in this

map once again are nucleated around Bin Qasim that has surfaced out as the major

contributor of sulphur dioxide emissions in the region.

Hotspots:

Bin Qasim Thermal Power Plant Area

4.8.3 Nitrogen Oxides (NO.)

Beside natural process NOx formed when combustion occurs at very high temperature. It

enters the atmosphere in approximately equal amounts from auto-emissions and power plants

(Nadakavukaren, 1990). These oxides of nitrogen are not only important as they effect

human respiratory system but they also have their significant role in promoting

photochemical pollution such 'as surface ozone (OJ). Nonetheless, in Karachi the prevalent

bronchitis and eye ailments are the manifestations of the presence of NO x in the atmosphere.

182

I.

2.

Days: Time: Measure: Figure:

1h July 1o 22nd July 1999 24 hours Nitrogen Oxides (NO.). Maximums 4.24

Spa/illT PilI/ems:

It is evident that regions where risk is higher are the vicinities of Korangi. SITE. and

Elender/l. I. Chundrigar Road. The reasons for higher concentration are industrial

activity especially the thermal power stations and the heavy traffic volume. The

residents and commuters of these regions are vulnerable to eye diseases. and

respiratory disorders.

Hotspots:

Landhi. Korangi


1h July 1o 22nd July 1999 24 hours Nitrogen Oxides (NO.). Averages 4.25

Spatilll PilI/ems:

Similar spatial patterns of NOx (averages) presence in the atmosphere of

Karachi metropolis are portrayed. It is obvious to observe spatial interaction

between emission of NOx and human activities. NOx existed with higher

concentration where sources of emissions are present with their higher level of

intensity. SITE and Korangi industrial areas are rich in heavy cargo vehicle

movement. where as M. A. Jinnah road is one of the busiest roads of Karachi

due to offices and commercial neighbourhoods.

183

,. -.. ~ ...

I\T1AL PATTERNS OF NOx EMISSION

Maximums

-" ......

'EC~

• ArabiaJi Sea t 5 , . ,+-----------------------------------------,------------------------------------------+~ 0:#..- \U' I "''' .IW ~

NO (oDe Dtratlo

I + • • •

.. .. ..

ilU'W UI1 !

i ~ .. "' \j,· ~t ,

SPATIAL PATIERNS Of NOX EMISSIO

Aver ges

I

PECKS

Ambian

fi1'~.J-l

NO

• -+ ,. JO

• , • .. .- -

a • "

~

'" • .. .7'II'-:::JII'I

Concentration

~.dl.Z Ihl.

4.8.4 Surface Ozone (OJ)

In the turbulent troposphere below the stratosphere. the major source of 0) is the photolysis

of N01, which produces monatomic oxygen (0), which then forms 0) by reacting with

molecular oxygen. Hydrocarbons, aldehydes and CO accelerate this initial photolysis by

increasing the rate of (NO) oxidation. Surface ozone concentration relates directly to nitrogen

oxides and hydrocarbon, which means that it has some correlation with vehicular traffic,

refuse burning, sunshine and wind direction. In Karachi, high-risk groups of adverse affects

of 0 .. , include children, the aged, individuals with pre-existing respiratory of immune-related

diseases and those with coronary hear diseases.

I.

2.


jh July /0 22"d July 1999 24 hours Ozone (0) , Maximums 4.26

Spatial Patterns:

This analysis is providing a new GIS based risk zone concept of interpolated ozone

surfaces. Spatial patterns of 0) are more or less on the same trends as the patterns of

NOx. The effected areas comprise of the core city centre. industrial areas and

residential areas . However, the cantonment areas are well safe of ozone. The regional

effects on the inhabitant population could in future be monitored if spatially

referenced epidemiological data on eye irritation and asthma cases be compared

simultaneously.

Ho/spots:

Korangi. Ibrahim hyderi, Sohrab goth, Kaemari


jh July /0 22"d July 1999 24 hours Ozone (0),. Averages 4.27

Spa/ial Patterns:

Spatial dimensions of concentrations of average ozone (0) were indicating the same

diffusion and extensions of risk that was depicted by the maximum values of surface

ozone (0) .

186

------------------ --- -

SPA I l PATTERNS Of 03 E 11

Ma 1m ms

ION

!:'~+W~~='~! ________________________________ ~~~~~~~<~V'~~;;;-____________________________ ~~~.~"~ .. ~ _t ,_ 77 .... _ ....

if; ..... Tun ii

" ::/

-' ........ ---- .. ~ /'

(/ " .... ..... 1M......., r_ .... T_ . /4> ~

/..,. /.; , .......... , ~/ ............... --• ~

~ .. b "' .... --"""'"

~

"- ~. ~

" "",'" ~,

'"""" " .",v-> -. .,'" #" .,,~ .. ./

• 1: Arabian Sea ~

~--------------------------------~~-----.------------------------------------------+7.

'" " ,"' ... 'll .. ,

0, (onct'nlr.llon

•

+ • ] , •

..

.. k't'D JaIKtsJ..

" -- ~-' ... ,

.~ ,

• Arabian Sea. 1

t ~ • • !+~~<7~, ----------------------------------~~--~-~~.-r-t-----------------------------------------~-~+~~

.. ..

4.8.5 Carbon Monoxide (CO)

Carbon monoxide is a vehicular traffic induced air pollutant, which reacts with the oxygen in

the atmosphere yielding oxides of carbon. Before discussing the spatial patterns of this

contaminant, it would be quite appropriate to take a look at the existing roadway network of

the metropolis. In Figure 1.7 the extension and agglomeration of the roads is shown and quite

expectedly the carbon monoxide (CO) readings are higher along the road corridors.

Air pollution in Karachi metropolitan deserves research with several aspects of analyses.

Previous endeavours never provided a detailed coverage as compared to the city size of

Karachi. The foregoing endeavours on Karachi had generally been restricted to point based

observation. The reader is advised to refer the sections on Air pollulion cOllcenlratioll levels

(3.2.3.1) and Carbon Monoxide Levels (3.2.3.2) in the chapter of conceptual framework.

Being the major pollutant in urban environment, carbon monoxide has been selected to study,

the spatial variation and weekly changes in the patterns of air pollution. Primary field

observations are tabulated in Allne.xure A. The patterns developed and analysed through GIS

techniques provide entirely new understanding of the environmental scenario of the mega

city, Karachi .

'Interpolation' is used to convert data from point observations to continuous fields so that the

spatial patterns sampled by these measurements can be compared with the patterns of other

spatial entities. Spatially monitored data do not cover the domain of interest continuously

(i .e. they are samples). 'Interpolation' can be performed through miscellaneous statistical

techniques in which Inverse distance interpolation is commonly used (Arsalan, 2000; Mehdl

el aI., 2002) in environmental GIS modelling to create raster I grid overlays from point data

as discussed in section 3.3.3.2.

Grid themes have been constructed for each set of samples in first step by completing

appropriate values of required parameters under the ArcView Spatial Analyst environment

189

with cartographic limitations l. In the second step interpolated grid has been reclassified

according to definite criterion. For the purpose of demarcating the spatial patterns of

pollution, safe to very high-risk zones were spelled out for Karachi. WHO (1987) and WHO

(2000) guidelines have been used to fonnulate the following criterion:

CO Risk CriterioD ,---. · • · ~ • · » · · •

It • · • ... -

Sale Moderate Risk HigbRisk Very Higb Risk

The maps produced depicting the locational patterns are listed as under:

\. Days:

Time: Measure: Figure:

Weekend

Mornings 3 feet 4.28

Spatial Patients:

The pattern shows that some important business areas in the old city are under

tmderate risk. The scenario is influenced by the high-risk patterns, which covers a

large part of the city. Administratively speaking, the district Karachi West, Ma1ir and

a large portion of district Karachi East are well out of risk.

I II is worth mentioning al this poinl thai while Jld filIming the surface interpolalion in An:View, the boundaries of the maximum diffusion were inadvertenlly not taken care of:

190

J

_:'-cr l 11 ~ :J, ( ~~~~ ____________________________________________ ~=c~ ______________________________________________ ~~,

" CI

~ ~

; ;; "

..... -

~ Ar.hian Sea t ~ • • .+---------------------------------------~=------.------------------------------------------------+~ .... WU! --=."C""..::=-:.'t..--==-- 11' ~"J"J. "-.-:.11' (

f ,

19 1

. .. · U l

Sp TI L PA ER S OF CO E 11 SION

Weekend Mornin s (411]')

_ .....

..... .......

~ .. ,' [

;; Arabaao Sea i: ~ ~

• • .;+-------------------------------------~------------------------------------+~ w SClr I --=e-~..::..~ .. -- U-f'I'lU'! "'''!1I1

.. - , --

CO Ri k Crilerlon

~url:'

lodl!r Ie Ri k

High RI k

Vel) Hi h Risk

2.

3.

Hotspots:

Isolated crowded roundabouts / intersection such as Lasbela. Golimar. Empress Market and Tibet Centre


Weekend Mornings 4 y, feet 4.29

Spatial Patterns:

The spatial patterns seem to have direct relationship with vehicular traffic and the

concentration of urban activities . . The planned neighbourhoods of Defence and

societies are under safe zone. While the older parts of the city are under moderate

risk. The inadequate roadway width has contributed for the sparse clusters of high

risk zones around Malir. Grumandir and Empress Market.

Days: Time : Measure: Figure:

Weekend Afternoons 3 feet 4.30

Spatial Patterns:

Apart from the cartographic limitations. this figure shows a spatial expansion in the

patterns formed as compared to the morning situation. The DHA and P AF Masroor

are the safe regions. while the major arterial roads in district Karachi-central are

under high risk of Carbon monoxide. The old city areas in district Karachi-south

show spatial similarity. The remaining metropolis falls under moderate risk

temporally

Hotspots:

Nagan Chowrangi. Scihrab Goth, Water Pump (F. B. Area) Karimabad, Empress

Market. Tibet Centre and Nigar Cinema.

193

- -- ------ - - - -- ._- ----- - --

~ Arabi.. Sea ~ k ~ . ~~w~o .. ,,-~, ~,~~~'~~r.==;i;:~;.~--------~~--~~~w~.;r.'---------------------------------;~;tw~~

CO HI k Criterion

.. -~ --

~art:

'\Ioder te RI k

High Ri\k

\ en High HI k

1'11.

Sp TI L P TIER \\' kend A

OF CO EMI

r oon (4'h') 10

":'- <O'!: irlllJ r " t-------------------:----"==-=---------------------"-:..:j-y ~ I .......... · .~ ~ If s.,.n. T__ IJ ~ .

Arobian ~

;+-----------------~~---r---------------------+;:. I*-,.-Ul I--.. =-~-~~~ "' ''''' f" ~ "')f.v"f

-- -

CO RI { rltenon

fc

lodcrate Ri k

High Ri k

en Uigh RI

1 'I"

4.

s.


Weekend Afternoons 4 '12 feet 4.31

Spatial Palterns;

This figure could be discussed considering the diffusion property of carbon monoxide

gas and by comparing it with the spatial patterns for equal monitoring height in the

morning. The spatial elongation of clusters can be seen around the locations as

already discussed .

Hotspots;

Empress Market

Days: Time: MeaSllre: Figure:

Weekend Evenings 3 feet 4.32

Sp(l(ial Palterns;

Only the · undeveloped KDA scheme-33, is found safe and the rest of the metropolis

portrays a horrific picture. The metropolitan Karachi has enormous social activity

during the evenings owing to demographic characteristics. The congestion at the

roads is due to different trip purposes i. e. recreation and business. It can be noticed

here that the DHA has also fallen under the moderate risk zone and the entertainment

centres of Clifton and sea view are clearly under high risk .

Hotspots;

Aladdin and Sindbad . amusement parks, Garden east , 4-legged Intersections of

districts Karachi- central and Karachi-south

196

-- "

... ·Ul

P. TI L P F~R' OF CO EMI 10 W k nd Eveni g (3')

S Arabia. Sea ~

.~~--~~~~~~~~----------~=-----~--------------------------------------+~ !'vc..rt -::.~-=-_":..~ ~"III' S" ! ~WYI

-~ -

(0 Ri k ( rilerlon

<Ire

loder Ie Risk

High Ri k

"en fll" Ri ...

1 ~7

Sp 11 l. P ER Of CO E IS 10 \\" kend E\ enin (4 1h')

tII"Cj'~ ... ,." YI fI .. ' [ ,~~----------------------------------~~~--------------------------------~~~> }j r~~ a ~ -- ~ ,.

• .,-/

• >

~ ~

~,+---------------------------------------,,---------------------------------------+~ WWCFt -'-=--e::.='_':...,.-_"~ ~·.,. ... r~ ' ''!!':>IE

-- -

( 0 Ri.,k (rlter! 0

fl'

\Ioder Ie Ri k

High RI k

er IIlgh Ri k

6.

7.


Weekend Evenings 4 \I, feet 4.33

Spatial Patterns:

The spatial contraction is quite visible but the affected localities are more or less the

same. This could be explained again due to the diffusion characteristics of the

pollutant itself The F. B. Area / North Nazimabad joining arterial, "Shahrah-e

lahangir" has emerged as a major carrier for late evening, social and shopping trips.

Hotspots:

Aisha Manzil to Babul Ilm, Sindbad to Aladdin park. Nagan Chowrangi and the

stretch of M. A. linnah Road from Civil hospital to Boultan Market


Working Day Mornings 3 feet 4.34

Spatial Patterns:

This figure should be viewed with the already discussed cartographic limitations.

Morning trips in Karachi are mostly school and work related. DHA falls in safe zone;

Societies SITE and Korangi Industrial areas, Orangi Town and Gulistan-e-lohar are

in moderate risk zone; rest of the city is within the high risk , whereas the core old city

and major intersections of district Karachi -central are under very high risk . The

patterns formed in this figure would help the reader in discovering the spatial

variations and interactions for the upcoming themes.

199

-

I L P TIER' OF CO EMI Workin D y Mornin s ( 3')

10.

.~t.~" ~' r~' ______________________________ ~ __ ~~~~~,,~. ___________________________________ ~~"'~l ~£ Iil ~..".... ~ !I ~T__ l;: ~ J;J

.. on

- , --

( 0 RI~1o. (rll rtOI

10, rt lodtr Ie RI k

Jti"h RI .. k

\ er Iflgh RI

SPATIAL PATIERNS OF CO EM SSION Working 0 v Mornings (4 Ih')

II'M' Qr . P ' I ; ~' ( J1' lrDI'[ ~~--------------------------------~~~~-~----------------------------~~i

~ ~ r__ II; ~ b

."'''''

' .....

~ Arabian Sea ~

• • ~~~====~----~~----~~~~~,~ w~rllrt.· """""-=--=W:--"-- 1.1":IU. ~ '''Inl' (

.

+ , , • -- .-...

• • .. • --

CO Ri k (ruerlon

Safe !\Ioderate Risk

High Ri k Very High Risk

8.

9.

Hotspots:

Major wholesale and commercial area i.e. core of Karachi city, Lasbela, Grumandir,

NIPA, Hassan Square, Stadium, Qaidabd Malir, Liaquat market Malir, intersections

of Shahrah-e-Pakistan and Nagan Chowrangi.

Days: Time: Mecmlre: Figllre:

Working Day Mornings 4 'Iz feet 4.35

Spatial Pat/ems:

Unlike some western mega-city dwellers, the Karachiites are not early birds, which is

manifested in this figure . The undeveloped schemes and DHA is safe, most of the city

is under moderate risk , the core old city and vicinities of M. A. Jinnah road are under

high risk with a couple of hotspots in the very high risk zones.

Ho/spo/s:

Nagan Chowrangi, and Robson Road

Days: Time: Measlll'e: Figure:

Working Day Afternoons 3 feet 4.36

Spa/ial Palterns:

Monday to Thursday afternoons are occupied with work and other activity trips. The

traffic and narrow roadway width produce congestion . In this figure the hotspots are

surrounded by very high-risk zone, the city is divided into moderate and high-risk

zones. Only the DHA is shown in the safe zone.

202

SPATIAL PATTERNS OF CO EMISSION Working Day Afternoons (3')

:~'"~"'~" __________________________ ~~'~>'~"~ ____________________________ ~~~~U~" ~ ~~ S --

,-v .u-l!i .......

• "

~ Arabian Sea ~

• • ' .. :+:: .• ;:-~::--====:=-:==c==----------------~---------------------------------+" ... ...,-, .'-..,.......==-- ~~ ~ •• n n"tnJ, 1

CO RI k Criterion

• •

• • I • -- --'are Moderate Ri k

High Risk

Very Hi b Ri k

2 j

~ ~ ~

SPATI L PATIERNS OF CO EMISSION Workin D y Afternoons (4Ihl)

Pt",,,

'''"'"'

, :-" • • ~.~----,----------------.,--------------------+,..

'I oIM" -i-o-='~..t.. "- t1" •• ~·f

,.

• • • , ..

--

CO Risk Criterion

S fe

Moderate Ri k Hi h RI k

WW!l' t

Very High Ri k

~r~ f ....

10.

II .

r-,

The patterns formed help in understanding the traffic and trip trends of the city and

answer the inquiry at micro geographic scale. Once again the reader is reminded of

the cartographic limitations in the map.

Hotspots:

Major wholesale and commercial areas i. e. core of Karachi city; Lasbela, Grumandir.

NIP A, Hassan Square, Stadium, Qaidabd Malir, Liaquat market Malir, intersections

of Shahrah-e-Pakistan and Nagan Chowrangi .


Working Day Afternoons 4 V, feet 4.37

Spatial Pattems:

This figure has to be understood vis-a-vis its counter part for mornings. There are

clear spatial contractions and the high risk zone has nucleated around the core old city

and stretched out to localities where traffic manoeuvring is difficult due to geometric

and space limitations. Overall the city remains under moderate risk.

Hotspots:

Robson Road, Empress market, 10har Morre (Rashid Minhas Road)

Days: 71me: Measure: Figure:

Working Day Evenings 3 feet 4.38

205

-

Spatial Patterns of CO Emission Worldn Oa Evenings ( 3')

.C'ft 1.1" , I"I"II!I" , '': 11'wa t .~~------------------------------~~------------------------------~~. II ~~ -a

~T___ ~ ~ r

..... ~

,--

I

Arabian Sea • }: ~

:+.---~~~~--~~------------------~----------------------------------+~ M'+4IIIr ~=:'-=--'--_':...~ .. - t-:--l'o'~ n .1"W Ifl

..

.. • J • • • - _ ..

CO Ri k CrIterion

Safe Moderate Risk

nigh RI k Very Hi h Ri k

-

Spati I Patterns of CO Emission Working Da Evenln s (4 1/2')

W'.!f'l!ir[ , :''' If r~ ,"I lU'I <+-~------------------------------~~------------------------------~~. ~ ~~ ~ ~ i'iIIQM T,*", " · ~

• • o Arabian s. ...

,-P£CHS

..... u • ....,

. ~ t.

"

~ · ~ ~~~~~~,~~.~~~ __ =¢~~~:;:~;;~------------~~~w~.;,,--------------~==-=~~~--~--;~;.n!!'" co HI k Criterion

. , • ~ • -- ..........

..

.. • ..

--

Suft:

Moderate Ri k

IIIgh Ri k

Very Hlgb Ri k

2C7

12.

13 .

Spatial Patterns:

There is no part of the city, which is safe at this time. The scenario is overwhelmed

by the high and very high-risk zones comparable to its counter parts for weekends. As

the activities on Monday to Thursday are more than the three other days, the spatial

patterns show agglomeration of high and very high-risk clusters. The Landhi /

Korangi and Defence areas are under moderate risk. This figure and the upcoming

figure confirms that tbe peak hours of activities and traffic in Karachi Metropolis are

between 6:00 p.m. and 11.00 p.m.

Hotspots:

Shahrah-e-Pakistan (from Suhrab Goth to Liaquatabad 10), from Nagan Chowrangi to

Grumandir, Core of Karachi, Malir and Shahrah-e-Faisal, Core city of Karachi


Working Day Evenings 4 y, feet 4.39

Spatial Patterns:

The trend of high-risk zone seems to be shifting in this figure from old city towards

district Karachi Central. The districts West, Malir, East and partially South are in the

moderate risk zone. The most populous district of Pakistan i.e. Karachi Central,

although a planned and relatively younger locality is under high risk due to high rate

of vehicle ownership and urbanization. Even at 4 y, feet, CO seems to be affecting the

population at this juncture.

Hotspots:

Intersections of district Karachi Central, lohar morre, National Stadium

Day & Time: Measure: Figwe:

Whole Week Diurnal Averages J feet 4.40

208

14.

Spatial Patterns:

This map is illustrative of the overall situation of carbon monoxide (CO)

concentration at 3 feet vertical distance from the fmished road surface on the arteries

of Karachi metropolis. There is hardly a single locality. which falls under the safe

zone. The majority of the city is under the high risk while about twenty-five major

intersections are constantly under extreme risk. Rest of the city is nevertheless under

moderate risk, A worthwhile outcome of this map is the formation of an "extremely

risk corridor" in the vicinity of Saddar and M, A Jinnah Road.

Hotspots:

Nagin Chowrangi, Sakhi Hasan, Teen Haiti, Lasbela, Sohrab Goth. Water Pump.

Aisha Manzil. NIPA Chowrangi. Johar Morre. Hasan Square. Empress Market. M. A

Jinnah Road and Nishtar Road,

Day & Time: Measure: Figure:

Whole Week Diurnal Averages 4 Y, feet

4.41

Spatial Plll/erns:

This figure portrays the cumulative carbon monoxide (CO) enrichment at 4 112 feet

high from the surface, on the roads of Karachi, There is only a single neighbourhood

i, e, DHA, which remains safe otherwise major regions of the city, comes under the

moderate risk,

209

SPATIAL PAlTERNS OF CO EMISSION Diurn I Aver e (3')

•~~W~"~.,~ ______ ==~ __ ~ ____________ ~~.,~_~,~.,. ______________________________ ~~ I ... \l"' ln , i

c G~.,....,.. } ~ s.t-. T~ to ~ .

P'lEC K3I

'. ,

• ..

1 " • • -- _ ..

CO RI k Crit ri

fe

loderate Risk High RI k

Very High Ri k

21

> ~ .. b

SPATIAL PATTERNS OF CO EMISSION Dluro lAver e (4 Ih')

!I'Wflrl .,. .. '" roll' . ~

_ .... ..-• ~ --

·~HI.~, s ... ~

~~~lI:~

(.",4 , ,./

?' "," "-" M'" u~ C_

",- T_ " ~ , ,..,,- < ~ ,.. ~

,"" ~ ... .. ,. ~tu. .o\up:rI

M.~

• . - ~ "" ~ PKII' :ar.IIf~ C~1Mf

"'-.. ...., .....

\ ",-fI,'

" " ~

.;"" - ...... .,.6 ~

_.I"',. ,,+'" ,; '" ~

~ AIllbian Sea • ~ t ~+¥-~~,--~ __ ~ __ ~~~~-=~.-=~.-=-----------------~-.~.-"-, ------------------------------------W-_4"~.,

I • - + , • • • • --

CO RI k Criterio

re :\Ioderate Risk High RI k

Very HI h Risk

SPATI L PA ERNS OF CO EMISSIO • Diurnal Aver ge

~t·~~"~"~ __________________________ ~ __ ~~~w~r~'~ ______________________________ ~~~".~" ~ ~.,.,... t

= -- =

"'~"'c.u.,

• ......bian Sea ~ ~ • • ~;t~::;"';;;\ __ ;:''""T"~.=-==~,,";:::;::,::.=::.....-=---------------: ... ~.r",-:-, ,------------------------------.,.-"-+;'f

.. + •

1 • • • • --

co R k Crit ri

ofe

Moderate Risk High RI k Vel") HI h RI k

212

15.

Although there are no extremely high-risk locations across the city due to the fast

dispersion property of Ihis unstable gas, however, a distinct boundary of "high risk

corridor" emerged prominently. This deserves serious contemplation of all the

stakeholders.

Hotspots:

Old city area, J\azimabad, B. Area and Liaquatabad


Whole Week Diurnal Averages (Total) 4.42

Spntill/ Patterns:

This thematic map has been produced through the superimposition of twelve thematic

layers of field observation of CO concentration across the metropolis; at the two

specified monitoring heights, three predetermined times the day, and two week

wise \llIrlalions. The author regrets existence of unreal high-risk corridor in the

eastern portion of the map owing to extrapolated estimation. The other patterns

formed are in line with the real life observations. Here a relationship could be

explored between the road density, traffic volume and enrichment of the pollutant.

Hotspots:

Nagao Chowraogi, Aisha Manzil, Water pump, Empress market and area near the

zoological garden

213

16.

17.

IS.

Day & Time: Measure: Figllre:

Whole Week 3 feet Diurnal Deviations 4.43

This fIgure is the ancillary statistical analysis performed by GIS indicating the data

variations. It has to be studied along with Figure 4.38 to appreciate the spatial

proximity and the severity of risk itself. For instance, the locations under extremely

high-risk corridors are also depicting minimal standard deviation; manager and

stakeholders need to look into the spatial phenomenon with care.

Day & 71 me: Measure: Figure:

Whole Week 4 y, feet Diurnal Deviations 4.44

This map illustrates the deviations among the monitored values at 4 y, feet height

from the surface. It is a supponing theme of the Figure 4.39. Wherever the average

value is higher and the standard variation is low, the risk severity is enhanced.


Whole Week 3 and 4 Y2 feet Diurnal Deviations 4.45

This figure qualifIes the pattern of risk overall in Karachi metropolis and suppons the

geographical distribution ofCa emissions across the city Figure 4.40.

214

SPATIAL PATTERNS OF CO EMISSIO Diurnal Devi lion ( 3')

.-

+ • •

.......

•

, .. '" ~

( )

~

J-d ~ - 16 16 - 24 24 - 32 32·40

"--~

SPATIAL PATTERNS OF CO EMISSION Diurnal Devi tion (41/2')

--

"<1"

.....

:; Arabian Sea ~ ~

• • -+---------------------------------~------------------------------~~~ WlfUlt ._-=;:- ~- t."":/t" Y ( "'tftl'l

Staedanl Don

-+ --:.' __ ~.--~::~'--~.--~.

21C

Sp IAL PAITERNS OF CO EMISSION Dlurn I Deviation

"·W.lrl :~~ ______________________________ ,-__ ~"~w~,. ,~ ________________________________ ~~~,,~"., : ~ 5 h ...- r".. :

"""'K_

.

• + 1 , • • .... _-'

u-tS Ii - 1G 16 - 24 24 - 32 32 - 40

217

4.9 TEMPORAL VARIATIONS IN CARBON MONOXIDE (CO)

ENRICHMENT PATTERNS

Carbon monoxide (CO), a criteria-pollutant has rapid diffusion property due to its light

density. The patterns shown in Figures 4.28 to 4.39 throughout the metropolis at the heights

of 3 feet (approx. 1 m) and 4 Y, feet (approx. \.5 m) illustrate this phenomenon. The

passengers inside the automobiles (i.e. Cars, Taxies etc.), low height pedestrian (mostly

children), and motorcycle riders are more vulnerable than the riders of large size vehicles

(Buses, Minibuses, Trucks, Coaches) and air-conditioned vehicles.

Temporally, on weekday mornings, carbon monoxide enrichment remains on lower sides as

compared to weekday afternoons and evenings. As the city activities increase during the later

parts of the day, the spread of carbon monoxide becomes broaden at large. Variations have

been found in this fashion due to the neighbourhood characteristics. Nevertheless, weekday

evenings have been discovered as busier than afternoons in the residential localities of

district Karachi Central and district Karachi East vis-a-vis the old city (core) areas of district

Karachi South.

Understandably, for the weekend mornings the concentrations of carbon monoxide (CO) is

less than any other time during the week refer Figures 4.28 and 4.39. The inference is

obviously found in the connection between the carbon monoxide concentration and the

vehicular tramc. Weekend afternoons are more polluted than the weekend mornings whereas

evenings are the most polluted times of the weekend. The temporal trends have been

observed as similar among the concentrations recorded at both monitoring heights.

High-risk (Figure 4.42) zones are invariably having lesser diurnal deviations (Figure 4.45)

indicating consistency in carbon monoxide enrichment throughout the week. Looking at

Figures 4.40, 4.41 and 4.42 along with Figure 4.43, 4.44 and 4.45 simultaneously, large parts

of the Karachi metropolis have been unearthed as under perpetual high risk with little

temporal relief. These include old city (core) area and almost all of the localities district

Karachi Central. However, some locations indicated the occasional very high-risk zone

21S

owing to higher temporal variations such as Rashid Minhas Road (near Aladdin Park) and

Nazimabad Chowrangi.

4.10 AIRBORNE EPIDEMICS

In many health applications, a crucial issue is data quality: the use of complete and reliable

data based on standard disease definition is prerequisite. In Pakistan, the quality of most

routine health data is often questionable (Kazmi and Pandit, 200 I) therefore, instead of

relying on the data from hospitals, most of the analysis presented hereunder is from the data

primarily generated through questionnaires. This practice data based on the Physicians'

experience has much higher quality than the conventional hospital statistics due to the

educational background of the health professionals.

Most of the respondents were belonging to the age group of 25 to 40 years. They were fresh

and recently graduated male and female doctors. They have been practicing their profession

in governmental and private hospitals and clinics. However about 30 percent, doctors who

were in the age group of fifties were well-experienced. Approximately 50 percent of the

respondents were MBSS and remaining had some additional qualification. ' Majority of

respondent ' s (65%) had more than five years experience. Whereas, 39 percent doctors had

more than IS years experience. About 37 percent had started their career recently and had

less than five years experience. Thirty five percent of doctors had diversified, spatially

distributed information of air-based epidemics because they practice more than one location

with in the city.

4.10.1 Disease Gradin~ by Professionals

To assess how much air pollution was considered as a contributing factor for the incidence of

specified diseases . The diseases that have known and established relationship with air

pollution were listed and the focus group (Physicians practicing in Karachi) was requested to

rank their perceived contribution for each of them, quantitatively. The epilogue of that

grading has been narrated under the titles of:

219

• Highly associated

• Fairly associated

• Less associated

The diseases that emerged as highly associated with air pollution were, in descending order

Asthma and Allergies (#7)2, Air way and Lung irritation (#1), Bronchitis (#10), Nasal

irritation (#30), Headache (#21), Eye irritation (#18), Lung silicosis (#27), Emphysema

(# 17), Asphyxiation (#6), Irritability (#22), Sleeplessness (#36), Fume fevers (1119) and

Gastroenteritis (#20).

The diseases perceived as fairly associated with air pollution, were Lung fibrosis (#26),

Diarrhea (#16), Dermatitis (#15), Sarcoidosis (#35), Brain impairments (#9), Visual

impairments (#38), Dental discoloration (#14), Anaemia (#3), Cancer (#11), Cyanosis (#12)

and Premature Aging (#33).

The diseases perceived as less associated by physicians with air pollution, in descending

order, were: Dental caries (#13), Melanosis (#28), Reproductive Problem (#34), Alopecia

(#2), Arteriosclerosis and coronary heart disease (#4), Nerve impairments and ataxia (#31),

Para thyroid disturbances (#32), Muscular impairments (#29), Jaundice (#23), Liver

malfunctions (#25), Thyroid disturbances (#37), Kidney malfunctions (#24), Arthritis (#5)

and Bone diseases (#8) .

4.10.2 Morbidity treated by Physicians

Through the same questionnaire, Physicians practicing across Karachi were asked to provide

information on the morbidity and frequency of air pollution induced diseases. Figure 4.46,

ranks symptoms and diseases frequent in Karachi metropolis. This review has consequently

categorised 38 Symptoms and diseases into three broader distinctions.

Frequent

Fairly Frequent

, Numbers in brackets arc serial numbers Allnexure 'F (F. Q I)

220

Puatllyroid dishllbw'll:'" Epidemics of Airborne Diseases (2001)

Nttl'C' iDIpairmtrf. and II ... (inq'" ..,,,, __ .)

Alapcdl Oon of_) ".=J Rep(od1X1~ ploblcml

...... -a Bone Diac .. e.

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o ,. .. .. .. 50 .. Figure 4.46

221

The critique of Physicians has marked "Airway and Lung Irritation"(#I) as the most

frequent in their respective practising neighbourhoods. This is very much expected as the

pollutants such as CO, C, FeOl, SOl, N01, 0), Ch, NH), As, Pb, Asbestos, quartz and silica

dusts (Coburn, 1970; Waldbott, 1978) are considered as the causing factors for airway and

lung irritation. In fact this symptom could be the initial stage of few diseases such as

Tonsillitis, Influenza, Respiratory tract infections etc. The airway of people with Asthma (#7)

IS extra sensitive to irritating things m the air (called irritants)

(http ·lIwww.familydoctoLorglhealthfactslO 14D

Interestingly, the health professionals have marked Asthma and allergies (#7) as the second

most frequent disease in Karachi. Ca, Pollen, Fungi, Insect scales, House dust, isocyanates,

PVC, SOl, epoxy resins, 03, N01, HCHHO are the pollutants responsible for Asthma and

allergies (Waldbott, 1978; Calabrese, 1991). Basically Asthma is a disease of the Lungs.

Patients are extra sensitive to things that they are allergic too (called allergens)

(http://www.familydoctor.orglhealthfactslOI4D. Ansari (1998) declared uncontrollable and

growing pollution in metropolitan cities of Pakistan as a main radix of burgeoning Asthma.

Bronchitis (# I 0) ranked as the third highly morbid disease according to the appraisal

provided by Physicians. It is the inflammation of one or more bronchi

(IlIlp:llaliserv.rug.ac.beI- rvdslich/euglossIDIC/dictioI3.hlmlN0259). Girt (1972), attempted to model the

number of Bronchitics and air pollution variables mathematically . Mass Miniature

Radiography Committee of the Pakistan Anti-Tuberculoses Association presented some

study results. 121 children out of 851 were found suffering from different diseases; acute

bronchitis amongst one of them. Their investigation revealed that pollution was the leading

cause for chest diseases (The News, 1998). Literature on Epidemiology has widely reported

SOl, 0) and NOl being the pollutants in air causing Bronchitis (NAS, 1977; OECD, 1984).

Eye Irritation (# 18) was rated as the fourth highly frequent symptom across Karachi by the

professionals. SOl, NH3, H2S, HCHO, V, Peroxyacetyl nitrates (PAt "Is), Benzyl chloride and

Acrolein cause Eye irritation (Waldbott, 1978; Guderian, 1983). Here, it would be appealing

to look at the figures provided by the Civil hospital, Karachi for 200 I (Annexure E. I) Two

222

major eye ailments viz. Glaucoma and Cataract had indoor patient cumulative morbidity of

549 persons. Although eye irritation is merely a symptom but prolonged exposure to S02,

P AJ'\Is and other pollutants could lead to chronic eye diseases. Orakzai (1998) remarked that

the environmental pollution had taken a serious dimension in Pakistan and thus causing eye

diseases, which were affecting almost every second citizen of the country.

The account made through the Physicians' experiences rests 'Sleeplessness' (# 36) as on of

the major complaints by the patients in Karachi. Lead (Pb) causes sleeplessness (WHO,

1980, 1989). A study (Yousufzai, 1991) found a definite correlation between ambient mean

Lead (Pb) level and daily average traffic. In spite of the fact relatively very pure sea breeze

blow across the city, traffic jams, poor conditions of the engines and heavy traffic are the

major cause of high Lead pollution (2989 ppm) in the city centre of Karachi (Yousufzai,

1991). A World Bank news publication had included Karachi along with Cairo, Algiers,

Jakarta, Cape Town, Jeddah, Lim,!, Mexico City and Nairobi as the cities where people were

susceptible to develop certain diseases owing to increased lead levels in the environment

(APP, 1996). These flDdings are academically encouraging as they are in synthesis with the

assumptions of this study. Fortunately when these lines are written, Lead in Gasoline has

been eradicated in Pakistan by control measures.

Another health problem associated with Lead (Pb) and Fluoride (F.) (WHO, 1970, 1980,

1989), "Headache" (#21) was confirmed as frequent by the questionnaires' result. In a

separate inquiry of questionnaire responded by general public, Headache was the highest

complaint of Karachiites Annexure E. 3. This symptom is inherent with the urban life style.

The ache across the forehead or within the head could be induced due to tension, another

urban epidemic. Nairn (1995) reported an IUCN and Baqai University's study of 100 traffic

police personnel in Karachi indicated that about one·third of the healthy young men recruited

rrom the rural areas develop central nervous system problems, including vertigo, loss of

concentration and headaches, within two years of serving on the city's streets.

Diarrhea (# 16) is defined as abnormal frequency and liquidity of faecal discharges

(http://allser,,.mg.ac.beI-rvdstich/euglossIDIC/dictio25.htmlIlO487). Binder and Hohenegger (1982)

223

studied the human metabolism and held F- and HF responsible for this very common

disorder. Other studies (Walbott, 1978; Bruaux and Friberg 1985) have exposed the

contribution of As and Pb for causing Diarrhea. Pb was prevalent in Gasoline until recently

and much has been published OIi this episode. A study conducted by the University of

Karachi (Asadullah, 2000) authenticated significant correlation between the number of

passing petrol driven vehicles and the amount of lead in the pal1iculate deposits collected

from the plants growing along the road at different designated sites in Karachi (Asadullah,

2000)

Gastroenteritis (#20) was appraised as the next frequent disease in Karachi metropolis . It is

an acute inflammation of the lining of the stomach and intestine, characterized by Nausea

and Diarrhea, which has several causes including air pollution. This disease is also called as

, Enterogastritis' (hup:llallserv'. mg aC.beJ-rvdstich/euglossfDIC/dictio36. hllnl#()704 ). The biological

impacts of CO, HF, Pb, F-, ln, Hg, As and Se present in the air are supposed to be causing

this physiological disorder (Coburn, 1970, Tomatis, 1990). Interestingly, a study conducted

by University of Karachi examined Lead (Pb) content in numerous food items sold in

Karachi . They had considerable amount of lead, much greater than WHO's standards. The

researcher believed that the food items had absorbed Lead (Pb) from smoke exhausted by

heavy vehicles using low-grade petrol then (Husain, I 997a).

Nasal irritation (#30), a symptom leading the Physicians to many complicated diseases has

its roots in air pollution due to S02, HF, Ch, F-, As, Ni, Cr, Se, and V (Stern, 1977). The

pollutants that accumulate in the nose can cause problems locally, or be absorbed in the nasal

mucosa, resulting in a number of deleterious effects on the body. The air pollutants that are

absorbed also affect the nose in an immunologic way - allergic rhinosinusitis. Patients with

existing allergies become more allergic and less responsive to therapy. In Pakistan, according

to a news report (Correspondent the Muslim, 1998; Correspondent Frontier Post, 1998) about

60 percent of respiratory diseases are caused by unhygienic conditions and environmental

pollution.

224

Anaemia (#3) is deficiency in the amount or quality of red blood corpuscles or of

haemoglobin in the blood (Wagman, 1985). Pb, Mo, Se and V contribute towards Anaemia

(Waldbott, 1978; Wellburn 1994). Air pollution is the source of a wide variety of materials

that may enter the blood. Chemicals with known adverse effects, often or usually airborne,

include benzene. lead and other heavy metals, carbon monoxide. volatile nitrites, and

pesticides and herbicides. All of these have been found to lead to deleterious effects on the

blood and the hematopoietic system (http://www.napenet.org/apslidesi22blood.html). The

assessment of Physicians' ranked Anaemia at lOth position for Karachi. Yousufzai (1994) did

some work on Blood Pb levels in sample of Population of Karachi and reported Sixty-two

percent (62%) had blood lead level between 100 and 200 ~g 1'1. If it is compared to the

critical value of I OO~gIL (WHO, 2000), the risks are obvious. Another pertinent work done

at the University of Karachi studied the level of lead in fruits, vegetables, grains and pulses at

various locations in Karachi. Most of the analysed samples contained higher levels than the

maximum permissible limit of 0 I parts per million.

Jaundice (#23) essentially a liver disease is reported to be originated from the heavy metals

in the air such as Pb, Ti, Se and Nitrogen Oxides (NAS, 1977; Wellburn, 1994). Exploring

the relationship between Lead (Pb) in the ambient air and blood lead concentration, in the

general population of Karachi, Taqvi (1993) showed higher blood lead level in those person

e.g. policemen and drivers, who spend a good deal of their working hours outdoor.

Dental Carries (# 13) is the gradual decay of the white crown (hard tissues) of teeth , due to

the actions of micro organisms or chemicals such as Se (Waldbott, 1978; Wellburn 1994).

General Physicians (respondents). categorized it as a frequent anomaly in Karachi. It is

suggested that the frequency of this anomaly be explored further through a critique of Dental

Surgeons.

Dermatitis (# 15) i.e. inflammation of skin

(http://allserv.rugac.be!-rvdstichieuglossIDIC/dicti024.html#00471 ) IS also among the

frequent diseases in Karachi, according to the doctors. It is also one of the clinical symptoms

induced by large quantities of air-borne pollutants such as As, Cr, Ni, Se, V,

225

Organophosphate and HCHO. The skin is the body's external interface with the environment.

It is a target organ for pollution and also the site of significant absorption of environmental

pollutants. The atmospheric pollution of ozone-depleting chemicals such as

chlorofluorocarbons (CFCs) is a major concern because of its suspected relationship to the

development of skin cancer (http://www.napenet.orglapslidesl24skin.html).

The breadth of this study prevents from going into the further particulars of diseases related

to air pollution and their frequency in Karachi . However, it would be worthwhile to

enumerate the symptoms and diseases that were evaluated fairly freqllelll than the ones

discussed as highly frequent. In descending order they were : Irritability (#22), Thyroid

disturbances (#37), Dental discoloration (#14), Arthritis (#5), Kidney malfunction (#24),

Visual impairments (#38), Lung fibrosis (#26) , Arteriosclerosis and coronary Heart Disease

(#4) and Liver malfunctions (#25)

This revIew would remain incomplete if the diseases marked as leas/ frequent in the

practicing localities of the respondents are not mentioned. In descending order, they were:

Cancer (#11), Lung silicosis (#27), Premature ageing (#33), Asphyxiation (#6), Bone

diseases (#8), Emphysema (# 17), Brain impairments (#9), Reproductive problems (#34),

Alopecia (#2), Nerve impairments and ataxia (#31), Fume fevers (#19), Sarcoidosis (#35),

Cyanosis (# 12), Melanosis (#28) and Parathyroid disturbances (#32). Probably, these med ical

terminologies were intensive enough with respect to the limited and precious time available

to the respondents. Another learning from this task was that focusing on symptoms rather

than specific disease was more convenient for the respondents .

To discover the spatial distributions of air pollution induced diseases within Karachi, new

questions (Annexure F Q. 2) were developed for distribution among the general public

(residing across the metropolis) and amongst the people working in the old city core of

Karachi . Necessary modifications were made according to the experience from the

Physicians appraisal. The narne of diseases was mostly replaced with either symptoms or

easy to understand synonyms. The diseases, which were ranked Least Freqllelll by the

doctors' critiques, were either not included in these new questionnaires or the ailment was

226

clearly explained. For instance. Lung and Throat Cancers were specifically spelled out. Even

some of the diseases characterized as Fairly Freqllel1/ through the Physicians assessment

were also dropped from the list. The main reason of this short listing was the low awareness

and literacy level of the man out in the street.

4.10.3 Morbidity and Mortality: Vital Indices

On the basis of literature reviewed on clinical discoveries. the diseases that could be

categorized as • airborne' were extracted. Prominent among those were Bronchitis.

Emphysema. Asthma, Acute Bronchitis, Pulmonary & Respiratory Tuberculosis, disorders of

Peripheral Nervous System, Whooping cough, disorders of Thyroid Glands. Anaemia. Eye

ailments. Influenza and Skin diseases. The leading public sector hospitals records (where

reputed medical colleges are being run) were approached to find out the spread of pollution

based diseases over the metropolis.

Civil Hospital, Karachi located in the old city (core) Karachi caters thousands of patients

daily on the Out Patient and Emergency departments. There has been no disseminated data

for this huge influx of Out Patients at the busiest public sector hospital of Karachi .

Fortunately. the statistics for Indoor Morbidity and Mortality has been managed according to

the disease code number and detailed list number. Table 4.5, manifests the findings of twelve

months statistics of the largest hospital in Karachi . Analyses reveal Air-induced diseases

capture about one fifth of the morbidity . One in every four deaths in Civil Hospital Karachi

was on account of air pollution based diseases. This limited data exhibit that had there been

daily Out Patient figures publicly available, surprising findings could have been possible to

derive.

Table 4.5: Indoor Morbidity and Mortality Statistics 2001

227

The second major hospital managed by the government in Karachi is the Jinnah Post

Graduate Medical Centre, located near old city (core) of Karachi . Here too, thousands of

patients are daily catered at the Out Patient and Emergency depanments. The hospital has

several Indoor treatment sections. More than two thousands Indoor patients have been found

affected by air-induced disorders in last two years . Table 4.6, shows the Monality and

Morbidity statistics revealing very high ratio between both parameters. Again, the statistics

on Out Patient was not maintained at this imponant hospital. The numbers presented in Table

4.6, calls for the dire need of managing this vital public health data so that surprising results

could be explored .

Table 4.6: Indoor Morbidity and Mortality Statistics of Airborne Diseases 2000 and 2001

Air pollution contributes to monality and morbidity. Kunzli el at. (2000) estimated the

impact of outdoor (total) and Traffic-related on public health in Austria, France and

Switzerland. Attributable cases of morbidity and monality were estimated in this European

assessment. Air pollution caused 6% of total mortality or more than 40,000 attributable cases

per year. About half of this (all mortality caused by air pollution) was attributed to motorized

traffic, accounting also for more than 25,000 new cases of chronic Bronchitis (adults >= 25

years); more than 290,000 episodes of Bronchitis (children less than 15 years); more than 0.5

million asthma attacks; and more than 16 million person-days of restricted activities.

4.10.4 Spatial Distribution of Airborne Diseases

A comparison between the prevailing diseases among general public and population at risk

(working near the congested areas) is presented in Figure 4.47. The comparison appeared to

be pretty consistent within the framework of both of these focused groups. Figure 4.47, again

has pointed out that the public could identifY the common disorders more easily than the

228

once which were difficult to comprehend such as high cholesterol , efficiency loss, types of

cancers etc. The family members of the respondents were largely suffering from Headache,

Hypenension, Eye ailments, Chronic Influenza, Stress and Tonsillitis. While Nausea,

Chronic Cough, Hearing Loss, Learning Loss and Palpitation were prevalent disorders to a

lesser extent found in the respondents.

An interesting convergence could' be deduced if Figure 4.47 is studied along with Figure

4.46. Keeping the differences in technical and popular terminologies in mind, the highly

prevalent diseases (from the questionnaires) are also those categorized frequent by the

Physicians critique. The symptoms of Headache, Hypenension, Eye ailments, Chronic

Influenza, Stress and Tonsillitis were highly prevalent, according to the outcome of this

analysis. Moreover, the frequent diseases, according to the Physicians critique, were Airway

and lung irritation, Bronchitis, Eye Irritations, Sleeplessness and Headaches. This

coincidence was although unexpected but is much relevant from epidemiological viewpoint .

The disease data collected covered 55 out of 58 analysis zones. This data was penaining to

the occurrence and prevalence refers to Annexure E. 3. The characteristics of data are given in

Table 4.7 and Table 4.8.

Table 4,7: Disease Occurrence: Table 4.8: Disease Prevalence: Descriptive Statistics Descriptive Statistics

. ;' :: :.:MeilSlir'e. - '2;. -c. 'f'~7' v..aii.~"'-:;' .-,'j ' ~":2 MtaSlirer --; : , .,: ;::. _"Value . Mean 8.67 Mean I3 . 8~

Slandard Error 1.23 Slandard Error 1.96 Median 5 Median 9.33 Mode 4 Mode 9.09 Slandard Deviation 9.17 Slandard Deviation IU~

Samole Variance 84 .15 Samole Variance 211.42 Kurtosis 5.53 Kurtosis 5AS

Skewness 2. 19 Skewness 2.2(, Ran~e 46 Range (.6. 12

Minimum I Minimum 1.02 Maximum 47 Maximum 67 . 1~

Sum 477 Sum 763 .79 Counl 55 Count 55 Lar.est(2) 32 Largcst(2) 66.66 Smallesl(2) I Smallesl(2) 1.80

Confidence Level(95 .0%) 2.47 Confidence Lcvel(95 .0% 3_9.1

229

tv uJ 0

Headache

High Blood Pressure

Eye ailments

Chronic Infiuenza

Stress

Tonsillitis

High Pulse R<>I.

Leaming Loss

Travel Sickness

(Nausea)

Hearing Loss

Chronic Cough

H;gh Cholesterol

Efficiency Loss

Ulcer

Asthma

Birth Defects

Throat Cancer

Lung Cancer

(% ) ~ ~ ~ ~

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The analysis zones determined by the then Karachi Development Authority serves as the

precinct of metropolitan Karachi for the purpose of this study. Figure 4.48 maps the disease

occurrence for Karachi. The higher occurrence of air pollution based diseases in the study

area was in the district Karachi Central, specifically, Nonh Karachi (Zone # 31), F. C Area

and Mansoora I F B Area (Zone #28) The affected zones of district Karachi East were

Garden, Soldier Bazaar, lamshed Quarters (Zone #11), Korangi (Zone#39), Landhi Colony

(Zone#40) and Akhtar and Baloch Colony, Chanesar Goth (Zone #25) . In district Karachi

South, which comprises of mostly the old city (core) localities of Karachi, Saddar and

Artillery maidan (Zone # 3) had the highest occurrence of airborne diseases. Orangi,

Metroville-I (Zone#30) of district Karachi West posses a significant disease problem.

Figure 4.49 map the normalised disease point prevalence for the metropolis of Karachi . The

wide coverage of metropolis was made possible via extensive questionnaire survey. The

outskins and relatively rural zones of Karachi having scattered population were fortunately

involved. The spatial patterns of disease point prevalence are diffused from the city centre

towards the peripheries. These results are apparent outcome of the existing demographic and

socio-economic behaviour of zonal regions, which may create the urban functional

agglomerations and accessibility to the health facilities

This assessment estimated the public-health impacts of current patterns of air pollution in

Karachi metropolis with the limited data available. Although, individual health risks of air

pollution are relatively small, the public-health consequences are considerable. Trafiic

related air pollution remains a key target for public-health action in European countries

(Kunzli, It! aI., 2000). The results presented in this chapter have attempted to provide the

decision makers, quantitative assessment of environmental health scenario for the

metropolitan city of Karachi.

231

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Figure 4.48

57

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OCCURRENCE OF AIRBORNE DISEASES IN

Analysis Zones

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POINT PREVALENCE OF AIRBORNE DISEASES IN

Analysis Zones

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4.11 PERCEPTION CRITIQUE

Focus group questionnaires supplemented the quantitative data and provided insights into the

perception dynamics of air pollution among various demographic and income groups. The

participants were selected and organised by age, gender, education, occupation and income

levels. The participants were also selected to ensure racial and ethnic diversity associated to

the cosmopolitan city of Karachi for ascertaining their attitudes and perception of the urban

air pollution problems.

The people working across the old city (core) area of Karachi were pre-dominantly (69%) .

• Patharay walas' vendors and small businessmen; the rest were office workers. The author

could reach 20% female respondents, which is a high break up in a pre-dominantly male

chauvinistic society. Having known the international debate about education and income (De

Souza, \999), the focus was to target urban poor (10%), working (lower middle) class (52%)

and middle class (24%). The definitions of literacy and social classes were according to

Pakistani standards. About 8% of participants were illiterate, 37% had gone up to secondary

school and the rest were higher (2J % higher secondary, 27 % University graduates and 7 %

Postgraduates).

The perceptions regarding the linkage among environmental problems and urban /

illfrastructure variables were uncovered through various queries. Most of the Karachi's

workers live in the peripheries (Surjani Town, New Karachi, Landhi, Korangi, Orangi, Malir

etc.) so they have to commute long distances everyday. About 3/4'h of the respondents have

daily work trip time exceeding 2 hours. The inhabitants at risk were asked to identify the

mode of transportation for their work-related trips. Most of the participants (51 %), owing to

their social status were compelled to use the cheep fared, low quality, over crowded and

diesel propelled transport modes. Motorcycle (20%), a two stroke engine vehicle, has

emerged as a symbol of middle and lower middle working class means of transport,

according to the findings of this survey. Some (12%) respondents appeared to be the

residents of adjacent neighbourhoods as they walk for their work trips, thus their

susceptibility to disease is higher and perpetual.

234

Drawing conclusions towards the environs. pertinent respondents were inquired about the

nearest road to their workplace/residence, its traffic conditions, operating speeds etc. The

participants perceived their proximity to the nearest main road within 100 feet (42%), within

300 feet (47%), and beyond (10"/1,). It appeared that habitants were too close to the source of

pollution i.e. Traffic. Traffic jams were perceived as a chronic problem throughout (85%) the

study area. The AM rush hours discovered were 07:00 - 09:00 (80"/1,) and 9:00 - 11:00

(92%). The PM peak hours were opined as 5:00 - 7:00 (93%) and 7:00 to 9:00 (81%) refer to

Figure 4.50. Three-fourth of the participants linked the congestion during rush hours with

medium and slow traffic operating speed, which is because of the narrow roadway width.

They were requested to furnish the reason for the slow operating speed at the nearest road.

Unmanaged high volumes (47%), roadway conditions (35%) and on-street parking and

encroadunents (18%) were seen as the chief reasons for congestion. Participants indicated

that the reckless attitude of drivers of heavy vehicles (Trucks, Mirubus and Buses) and lack

of enforcement of traffic rules made it difficult to expect any change. The roads in the old

city (Core) region of Karachi could not be widened due to the space restriction and built

environment.

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Figun:4.S0 I

235

Understanding of problem, its insight, effects, personal impact and controlling measures are

all dependent upon the collective perception of the pertinent population. The most important

source of air pollution in Karachi City is the urban transport system. Opinions were gathered

about the adverse affects of the traffic present nearby. It was mainly an attempt to examilW

the quality of societal perception and to see if the population at risk could establish the link

between cause and effect themselves. The focus group was probed on the affects of the

prevailing conditions. The responses varied again, according to their levels of understanding.

77 % of the respondents could only perceive up to pollution, whereas 14 % of the responded

had the ability to think beyond. Some people (11%) had the approach to correlate these

conditions with health problems in an open-ended question. Similarly very few (3 %)

perceived that the traffic on road was the main problem of weathering and erosion of their

buildings. It cost a lot because they had to spend money again and again on to repairs their

buildings. Few (9 %) respondents were unable to perceive any problem whatsoever related to

nearest road and traffic.

In Karachi, another polluting object in the vicinity is often a Kaehl'll Kundi. One third of the

sample population was suffering from nearby Kachra Kllndi. Though, it is apparently an

issue of Solid Waste Management but it is linked with air pollution closely. The practice of

burning the waste at these locations is considered to be part of daily life in Karachi . 82% of

the participants reside / work within 100 feet of its location, which must be hurting their

health in a subtle manner. Majority (55%) believed location of a garbage dumping places are

adversely affecting human health and conditions of building. As the participants could be

denominated as 'layman' so about (38%) could not behold beyond ugly and obvious

pollution.

Industry, a source of pollution in the environment, was in the neighbourhood of some (23%)

partakers. Nearly half of them (49%) attributed air and noise pollution to the positioning of

nearby industry and a good number (39%) linked its impacts on their health and houses.

Overflowing of sewerage manholes is a great nuisance in the urban life of Karachi. Although

(311%) respondents reported this problem, however, only (13%) out of them complained

236

about bad smell (H2S gas odour), a large majority (78%) linked it with other harmful health

effects besides Malaria, few (5%) were unable to imagine it as a problem

Perception regarding air pollution around the neighbourhood varied according to the level of

education and economic status of the respondents. Inhabitants of the study area felt highly

concentrated air pollution (75%) in their neighbourhoods and this might be effecting their

quality of life. Nearly all participants (98",1,) clearly considered that air pollution was having

adverse effects on their health. However, when asked to rate the extent at which they

perceive air pollution effecting human health, the perceptions varied according to the

educational level of the respondents. A high correlation was found among educated

participants and problem intensity ratings Figure 4.51.

CompMtlon 8etVJeen ...... ~ EducZIDn and PerceIved Ef'Fecbi ~ Air Pollution 011 Human 1_1th

There bas arisen a dilemma of comprehending the complete dimensions of air pollution

problem among the Kara.chiites. After having an almost cent per cent affirmation on the

adverse affects of air pollution, the same percentage of people literally do not know about

any remedial measures. It is a typical third world citizen's pessimistic psychology and

irresponsible individual behaviour. Such perceptions about personal impact on air pollution

intensifY risk at community levels.

237

It is interesting to note that in a country of 140 million with an annual population growth rate

of 2.8 (PRB, 2001), (90%) of the urbanites thought that growing population was the primary

contributor to environmental degradation. Nearly all the governments in Pakistan have been

generous in advertising the slogans of family planning. Large-scale media campaigns have

influenced the citizens to start perceiving population as a problem than resource.

To explore the notion of 'safe haven', a deliberate question was coined . Substantial (41%)

number of people believed the Places of worship, approximately one third considered their

honies and some (23%) perceived recreational places as 'away from environmental stresses'.

Concluding the chapter on Perception analysis, the author would like to offer his own

perceptions about the issue. If the detrimental effects of air pollution on health were

widespread and more publicised in the Pakistani society, the public would have been more

willing to change their mind-set. In the efforts to reduce air pollution, initiatives and

incentives are needed. Legislation and enforcement could only produce results partially. The

bottom line lies in the poverty and socio-economic conditions of the citizens and the country.

The living conditions of the public go together with the financial health of the state. Owing to

uncertainty, dissension, regionalism, class disparities and lack of national interest; politics

and governance in developing nations as Pakistan manifest negative impressions on the

public psychology. Pessimism could be seen in the qualitative inferences of this Perception

Analysis.

238

4.12 ZONAL APPRAISAL

4.12.1 Zone # 1: Juna Market, Old Town area

Land cover (Figure ~v. I)

Land use (Figure Gr. I )

Populalioll Densily (Figure -1. 1 OJ

Disease Occurrence (jigure 4.48)

Frequenc airborne disease

Disease Prevalence (Figure -1.49)

Referred Figures of Zonal Aggregales

CO concentrations across the zone

.;-,/ '2';;< ';::'-:.\ ,.

Densely buih up (87%)

Urban RenewallRedeveloplllenl (7M%).

Commercial (22%)

46,610 persons per square km

Low

Hypenension. high choleslerol

High

Series of Figures from 4.52104.74

"" Z" " .:~., I <~'; . " .

. ,: :.' :' ~".:. ' .• ',(C .

. <;:: .' ,. ,>/ j] ., i'( ~ '. ': . Meal!' ..

3 "" 17 8 46

W.,k;n< Dav , Heel 17 49 W.,k;ng Day Ewn;n"" 3 "" 12 8 H Won.;ng Day ,4;''''' 12 6 26 IVork;n" D-av ,4b"" 26 IV«kday Even;n .. 4 Y, fee' 10 7 32 . ; , fccl 10 43 21

3 f," 10 7S 29 I Evenin .. 3 1.<1 10 104 47

,4 Y, ,,,., .10 36 14 ,4 Y, feet 10 42 19

, Even;n., 4 'h I,,, 10 90 30 ,3,«< 17 66 41

W«lJv Am.« 4 'f, ",' 40 25 Wukl, IS SJ 33

Tnhlr- 4.9 : CO cOllcrnlnlllo .. s across Ihe .Jw)a l\.liukrL. Old TOll'll ;Ire"

4.12.2 Zone # 2: Ranchore Line & Ramsawami

Land cover (Figure Gy.2)

Lllnd use (Figure Gr. 2):

PopulaIion Densit)· (Figure 4.10)

Disease OccuJfcncc (FIgure -1.48)

Frequenc airborne disease

Disease Prevalence (Figure 4.49)

Referred Figures of Zonal AggJegales

Densely buih'up (74%)

Urban fCnewal/redeveloplllenl (63%).

Commercial and planned Residencial

86790 persons per square kilomelre

Considerable

Headache. Chronic Cough. Chronic

Influenza

High

Series of Figures fro1114.5210 4.74

239

CO concentrations across the zone:

W"!!''''!l Dav ' 3 10., 24 87 l4 Workin, Day Evenin., 3 1«1 2l 88 S8 Work',," Day ,4 V, r,,, ~ S6 28 \\,<>!! ill" Dav ,4 V, I;'" 67 JU

, Ev,"in~' 4 Yd;''' 69 3S, ,If<<' 14 4S 2S

,If<<' 14 47 29 W"k,nd Evenin", 31'", 19 129 SU " '"k",,1 ,4 V, 1«1 '0 IS IS

,4 1', 1;''' 10 32 16 \\'"k",d Ev<nin~' 4 V, ,«I III ~3

\\'"klv.!\,,,.&<: 1«' 29 69 4S

Tublt, ",10: CO cunct'nlnallons u.l."roSlllh~ R: .... cllort' Lb,~ & R.unsaw:ulU

4.12.3 Zone # 3: Saddar & Artillery Maidan

Land cOl'cr (rlgure Gy,3) Densely Built-up (76%)

Land use (Figure Gx,3)

PopulllJion Densil)' (Figure -1,10)

Disease Occurrence (Figure -I, -18)

Frequenl airborne disease

Birth Defecls

DisC<lse Prevalence (Figure 4.49)


CO conccnlrations across the zone

Commercial (71 %), Urban Renewal (28'Y.)


Ver), High

Headache, Hypertension, Chronic Flue,

Very High

Series of Figures from 4,52 10 4,74

240

4.12.4 ZONE # 4: CIVIL LINES AREA

Land cover (Figure Gy. 4)

Land use (Figure Gx.4)

Population Density (Figure 4. /0)

Disease Occurrence (Figure -1.48)

: Densely Built-up (42%). Vegetation (3-1%)

: Commercial (58%). Planned Residential (40%)

: 41094 persons per square kilometre

: Low

Frequent airbome disease :Headache. Nausea

Disease Prevalence (Figure -1.49) : Considerable

Referred Figures of Zonal Aggregates :Series of Figures from 4.52 to 4.7-1


T~ble -',12: co cOlleenlruUons aeruss the ChU Lim,'s Are-.1

4.12.5 Zone # 5: 1.1. Chunddgar Road & New Queens Road



Population Density (Figure -I. /0)

Disease Occurrence (Figure 4.-18)

Frequent airborne disease


Referred Figures of Zonal Aggregates

Densely Built-up (58%). Medium Built-up (2()'Y")

Planned Residential (45%). Tmnsport

Facilities (39%)

7245 persons per square kilometre

Low

Stress. Chronic Flue

High

Series of Figures from 4.52 to -1.74

.------ -

241

CO conccntrations across the lonc :

, . . :':" AUri: *;:e·Coiltelitriltioo.(ppm) .

," l>,~l'lIc~t~t . ; .. ... , . .

. ;': . i'Minbil.'uiil· ')janm\iJij . · '·'Mea" ,': .. Workinl! D.lV Momin.ltI) feet II 74 40 Working Da .. · Mernoons 3 feet II 64 36 Working Day Evenings 3 Ic.et II 8S 41 \VOrKin~ Day ~fomjn!l;S 4 v, ftel 10 42 22 Working Dav Aft~moons 4 J" feet 10 3S 20 Weekday Evc:nings. 4 %. fed to S3 2S W e~k¢f\d Momin~ 3 feet 10 40 17 W~~kl!nd Aft~moon<; J fe.e1. II 4S 23 Weekend Ewnin~ 3 ft¢t 10 62 27 W.:do;end ~{oming.~ 4 II I fed 10 26 13 W.:c:k.:nd Attc::moons 4 \ti reet \0 31 IS Weekend E\'cnin2S 4 ~/l f.ed 10 43 17 Wc::eklv Average 3 feci II S8 31 Wc::eklv Average 4 VI f~et 10 33 19 Wec::kl'l AvC'ra~c:: Total '1\ 46 21

Table -1.13, CO COIIC'cneraliun.s actuu eht' 1.1. ChWldricar Roud &. New Queen:'!! Road

4.12.6 Zone # 6:

Land cover (Figure Gv.6)

L,md usc (Figure Gr. 6)

Port Area

Population Density (Figure 4. 10)

Disease Occurrencc (jigure 4.48)

Freqnent airborne disease




T:lblt' ........ : CO ('onceneraUons IICruSS lit" Port Ana

Densely Built-up (76%). Medium Buill-up (11 %)

Transport Facilities (42%). Industrial (29%). Urball

Renewal (13%)


Low

Headache. Chronic Flue

Considerable

Series of Figures from 4.52 to 4.74

242

4.12.7 Zone # 7: Nawabad, Baghdadi Lane, Kharadar

Land cover (Figllre Gy.7)


Population Density (Figure 4.10)

Disease Occurrence (Figure 4.48)

Frequent airborne diSCllse




Densely Buill up (84%). Medium Built up (12%)

Urban RenewaVRedevelopmem (100%)

72396 persons pcr square kilometre

Low

Headache. Hypenension

Considemble

Series of Figures from ~ . 52 to ~ . 74

Tabl~ 4.15: CO conct'nlnltions across tht' NRwablld, BOlhdlldi L:mt',

4.12.8 Zone.# 8: Agra Taj, Bihar Colony

Land cover (Figllre Gy.8)

Land nse (Figure Gx.8)

Population Density (Flgllre 4.10)





Densely Built-up (77%). Vegetation (11%)

Urban RenewaVRedevelopment (100%)


Low

Stress. Headache. Hypenension

Considerable

Series of Figures from 4.52 to ~.74

243

CO conccrorations across the zone

4.12.9 Zone # 9: Lea Market, Gul Mohammad Lane



Popul",ion Density (Figure 4.10)

Disease Occurrence (Figure 4 . ./8)


Disease Prevalence (Figure 0/.49)


CO concentralions across the zone

Densely Built-up (82%)

Uroan RencwallRedevelopment (99%)


Low

Stress, Headache and High cholesterol

Considcrable


Tablr 4.17: (oncrntrations ",cross .he Lt1I Markr~ G'" Moh.:uIUlI'3d Lanr

. :, .

244

4.10.10 Zone # 10: Cbakiwara, Kalakot

Land cover (Figure Gy.IO)

Land use (Figure Gx. 10)

Population Density (Figure -I. 10)

Disease Occurrence (Figure .J.48)


Disease Prevalence (Figure -1.-19)


CO concentrations across Ihe zone

across

Densely Built-up (78%). Medium Built up (10%)

Urban Renewal (75%), Recreational (33%)

143588 persons per square Idlometre

Low

Tonsillitis

Low

Series of Figures from ~.52 to ~.74

4.12.11 Zone # 11: Garden, Soldier Bazaar, Jamsbed Quarters

Land cover (Figure Gy. I I)

Land use (Figure Gr. II)




Disease Prevalence (Figure -1.49)


Densely Built up (6 I %). Urban Vegetation Mi,

(17%), Medium Built up (13%)

Planned Residential (95%). Unplanned Residential

(3 %)


High

Headache, Hypertension. Chronic flue, Eye ailmenls

High

Series ofFigurcs from 4.52104.74

245


TAble 4.19: CO COlleentnations across tile Jamshed Qu;u1en;

4.12.12 Zone # 12: Lines Area & Khudadad Colony

Land cover (Figure G y. /2)

Land use (Figure G x. /2)



Frequent airbo(ne disease




Table . CO COllcentrutions

Densely Buill-Up (56%). Medium Built up (16%).

Urban Vegetation Mix (13%)

Planned Residential (82%), Recreational (8%)


Low

Headache, Stress

Considerable


246

4.12.13 Zone # 13: Naval Hospital, JPMC and Liaquat Barracks

Land cover (Figure Gy./3)

Land use (FIgure G.r. /3)






CO concentrations across the 20ne

Tnble 4.21: CO

Densely Built-up (38%). Medium Built up (23%) .

Urban Vegetation Mix (20%)

Planned Residential (60%), Education (18%).

Unplanned Residential (14%)


Low

Tonsillitis

Low

Series of Figures from 4.52 to 4,74

4.12.14 Zone # 14: Bath Island, Frere Town, Defense Society (part)

Land cover (Figure Gy. /4)

Land use (Figure G x, /4)

Population Density (Figure 4, /0)





Urban Vegetation (36%), Densely Built-up (H%),

Mediwn Built-up (20%)

PlaMed Residential (78%), Densificalion Areas (9%)


Low

Headache

Considerable

Series of Figures from 4.52 to 4,74

247


4.22: acruss

4.12.15 Zone # 15: Gizri Area, Delhi Colony

Land cover (Figure Gy. J 5)

Land use (Figure Gx. J 5)

Population Density (Figure 4. J 0)






across

Urban Vegetation (33%). Densely Built·up (25%).

Medium Built·up (22%)

Planned Residential (56%). Densifieation "reaS

(28%)


High

Headache, Hypertension

. High


248

4.12.16 Zone # 16: Clifton


Land use (Figur. G x. J 6)







'., ,

4.24: CO conccntrations across the CUftun

Densely Built·up (51%), Medium Built·up (16%).

Water (16%)

Densification Areas (83%). Commercial (11%)


High

Headache, Chronic Cough

High


4.12.17 Zone # 17: Baba Bhit Islands


Land usc (Figure Gx. J 7)





Open Land (31%), Sparsely Built-up (24%), Medium

(21%)

Military areas (98%), Recreational (2%)


High

Nausea. Birth defects

High

249

4.12.18 Zone # 18: Shershah, S.I.T.E. (part)









Tahir 4.25: CO

Densely Built·up (41%). Medium Built·up (20%).

Sparsely Built·up (19%)

Unplanned Residential (32%), Industrial (30%).

Planned Residential (22%)

1200 I persons per square kilometre

High

Hypenension. Nausea

High


4.12.19 Zone # 19: S.1.T.E. (Sindh Industrial Trading Estate)





Frequent airborne diseases



Sparsely Built·up (33%). Medium Built·up (24%).

Densely Built·up (18%)

Industrial (62%), Unplanned Residential (31 %)


High

Headachc. Chronic Cough

Vcry High


250


,.

4.12.20 Zone # 20: Asif, Pak Colony & T.P.I.


L~nd lise (Figure Gx.20)

Population Density (Figure -1.10)

Disc"se Occurrence (Figure -1../8)

Frequent airborne discase

Disease Prevalence (Figure 4../9)



Densely Built-up (35%), Vegetation (27%), Urb~n

Vegetation (14%)

Unplanned Residential (33%), Agriculture (31 %),

Industrial (I 1%)


Low

Learning Loss

. Considerable


251

4.12.21 Zone # 21: Rizvia, Firdous Colony, Golimar


Land usc (Figure Gr. 21)






CO conccntrations across the zone

Densely Built·up (73%), Medium Built·up (13%),

Urban Vegetation (9%)

UnplalUled Residential (76%), Planned Residential

(15%), COfIUIlercial (9%)


Low

Headache, Stress

Considerable


Tablr 4.28: CO concentNllon. ~cross the R!J.,'I:.. Firdous Colony, Golimar

4.12.22 Zone # 22: Liaquatabad

L<lIld cover (Figure Gy.22)

Land use (Figure Gr. 22)






Densely Built-up (79%), Medium Built-up (7%).


Planned Residential (61%). Unplanned Residential

(23%), Commercial (16%)


Considerable

Eye ailments, Chronic Flue

Considerable


252

CO concenlrations across the zone

4.12.23 Zone # 23: Gulshan-e-Iqbal (part), P.lB.Colony

Land cover (Figure Ov.23)

Land usc (Figure Gr. 23)

Population Density (Figure ~. J 0)

Disease Occurrence (Figure ~. ~8)


Disease Preyalenee (Figure ~A9)



Densely Built-up (54%), Medium Built-up (24%),


Planned Residential (49%), Unplanned Residential

(23%), Commercial (20%)


Low


High

Series of Figurcs from 4.52 to 4.74

253

4.12.24 Zone # 24: Gulshan-e-Iqbal, Chandni Chowk, Society Area


Land usc (Figure Gx.24)


Disease Occurrence (Figure 4,48)


Disease Prevalence (Figure 4,49)



('onC'C'nfrutions

Urban Vegetation (38%), Densely Built-up (26%),

Medium Built-up (17%)

Planned Residential (85%), Education (5%),

Conunercial (5%)


Considerable

Headache, Eye ailments, Hypertension

High

Series of Figures fro11l4.52 to 4.74

4.12.25 Zone # 25: Akhtar & Baloch Colony, Chanesar Goth








Mediulll Built-up (52%), Densely Built-up (18%).

Sparsely Built-up (18%)

Unplanned Residential (58%), Densification areas

(17%), Planned Residential (15%)


High

. Headache, Hypertension, Slrcss

Very High

Series of Figures fro1ll4.52 to 4.74

254

CO concentralions across lhe zone

conc~ntrallon' across th~ Aldtbr"

4.12.26 Zone # 26: Drigh Cantonment, 9th Mile


Land use (FIgure Gx.26)

Population Density (Figure 4. ) 0)






Open Land (31%). Sparsely Built-up (24%). Urban

Vegetation (15%)

Military (71%). Recreational (6%) Planned

Residential (7%)


Low


Considerable.


~<TI~;;~~,'~2~,''B; : ; ,<,; t}\ .o.'!> ':; .. . · .... i,,:,~ ~" . . . ~ ,"

rut Working J).y , Ir..,

1 v, r«\ 1 v,r«.

E"," ng> 4 , ; ru' , H,"

11«1 I EHnillg> 3 ru' ~ !oming> 4.\iI ~

54' r.d E","in", 4 V, I ,..

w .. Jy A"".g. 3 rcc. W .. lly Am.go.' 'f, r.et W .. I.lv

12 13 13

.8

12 10

12 I 1

12 II 12

49 81 79 21 69 '1 2' 3' 119 J3 24 84 11 36 46

44 48 16 26 29

" 21 34 10 14

26

255

4.12.27 Zone # 27: Gulshan-e-Iqbal, National Cement Factory




Disease Occurrence (Figure 4.-/S)





.:', ",

Medium Built·up (32%), Sparsely Built-up (22%),


Planned Residential (76%), Dcnsification areas (7%)


Low

Chronic Flue, Binh defects

Low


TllbI~ ~ .J~ : CO ('un('fnlr:allons across the GuIshan-f-lqlYoll, NaUon:ll CmwlIl Factory

4.12.28 Zone # 28: . F.e. Area and Mansoora

Land cover (Figure Gy.2S)


. Population Density (Figure 4.10)





Medium Built-up (37%), Densely Built-up (19%),



(12%)


High

Headache, Chronic Flue, Hypertension

Considerable


256

CO concenlrll1ions across Ihe zone

Table·US: acron

4.12.29 Zone # 29: Nazimabad, Paposhnagar

Land cover (Figuu Gy.29)


Populalion Density (Figure.J. 10)


FrcqueIll airborne disease



CO concentrations across Ihe zone

con(enlnlJull$ across

Densely Buill-up (40%). Medium Buill-up (26%).

Urban Vegelalion (22%)

Planned Residential (62%), Conunercial (18%),

Unplanned Residential (13%)


Considerable

Chronic Cough and Chronic Cough

Considerable

Series of Figures from 4.5210 4.74

257

4.12.30 Zone # 30: North Nazimabad


Land use (Figure Gr. 30)



Frequent ahOOrne disease



CO concentrations across the 20ne

Table 4.37: across

Medium Built·up (31 %). Urban Vegetation (24%),

Sparsely Built·up (22%)


(23%)


Considerable

Headache, Hypertension, Stress

Considerable.

Series of Figures from 4.52 to ~.74

: "

4.12.31 Zone # 31: North Karachi

Land cover (Figure Gy.3J)

LlIld use (Figure Gr. 3 J)

Populalion Density (Figure 4. JO)

Disease Occurrence (Figure 4..18)




Sparsely Built·up (37%), Medium Built·up (34%).

Open Land (14%)

Planned Residential (82%). New Indust')' (7%)


High

Headache, Hypertension, Chronic Flue

Considcrdblc

. Series of Figures from 4.52 to 4.74

258

CO concenU3tions across the zone

TwblE' 4.38: co aero ..

4.12.32 Zone # 32: Qasba, Manghopir Area



Population Density (Figure -I. J 0)



Disease Prevalence (FIgure 4.49)


. Open Land (70%), Sparsely Built-up (10%), Medium

Built-up (7%)

Low Income Settlements (47%), Unplanned

Residential (45%)

9569 persolls per square kilometre

Low

Tonsillitis, Eye ailments, Chronic Flue, Chronic

Cough

Considerable

4.12.33 Zone # 33: Orangi, Metroville-I



Population Densit)' (Figure -I. J 0)





Sparsely Built-up (35%), Medium Built-up (35%).

Open Land (15%)

Unplanned Residential (62%), Planned Residential

(28%)


Considerable

Chronic Flue, Chronic Cough, Stress

Considerable


259

CO concenlrations across the zone

n~ro5S

4.12.34 Zone # 34: Baldia


Uilld use (Figure Gx.34)


Disease Occurrence (Figure ,/.48)


Disease Prevalence (Figure ./ . ./9)

Open Land (53%). Sparsely Built-up (23%). Medium

Built-up (16%)

Unplanned Residential (53%). Low Income

Settlements (44%)


Low

High Cholesterol

Low

4.12.35 Zone # 35: Masroor (Mauripur)



Population Density (Figure ,/. 10)

Disease Occurrence (Figure ./ . ./8)


Disease Pre,·alence (Figure 4 . ./9)

. Open Land (50%). Sparsely Built-up (24%)

Military areas (87%). Unplanned Residential (7%)


Low

Headache

Considerable

4.12.36 Zone # 36: Hawkesbay and Adjoining Area


Land Use (Figure G:r.36)

Opcn Land (42%). Sparsely Built-up (20%). Medium

Built-up (11%). Urban Vegetation (10%)

Military (28%). Recreational (25%). Schemes to

infill

260

Population Density (Figure 4,10)



Disease Pre\'alence (Figure 4,49)


Low

Headache

Considerable

4.12.37 Zone # 37: Deh Moach, Naval Depot


Land use (Figure Gx, 3 7)





Open Land (95%)

Military (74%), Vacant Undeveloped (14%)


Low

Headache

, Considerable

4.12.38 Zone # 38: Deh Lal Bhakhar & Hawkesbay Scheme






Disease Prevalence (Figure 4..19)

Open Land (99%)

Schemes to infill (50%), Recreational (28%)


Low

Chronic Flue, Chronic Cough, Eye ailments

Considerable

4.12.39 Zone # 39: Korangi (Part)

Land cowr (Figure Gy.39)



Disease Occurrence (Figure 4..18)




Medium Built-up (36%), Sparsely Built-up (23%),

Open Land (16%), Urban Vegetation (16%)

Planned Residential (68%), Vacant Undeveloped

(10%), Vacant Developed (7%)


Considerable


Considerable

Series of Figures from 4,52 to 4,74

261


T .. ble~AO: across

4.12.40 Zone # 40: Landhi Colony









T:Jble ~.~1: .erou dae LandW

Open Land (28%), Mediulll Built-up (19%), Urban

Vegetation (19%), Sparsely Built-up (17%)

Vacant Undeveloped (43%), PlarUled Residential

(40%), Military (13%)


Considerable

Headache, Eye ailments

Considerable


262

4.12.41 Zone # 41: Landhi Industrial, Scheme 3 & 4

Land cover (Figure Gy.4I)

Land use (Figure GxAI)







CO con('ntrations auoss Ihe Landhl

Open Land (42%), Sparsely Buill-up (19%), Medium

Built-up (16%), Urban Vegetation (13%)

Low Income Settlements (63%), Industry (11%),

Vacant Undeveloped (11%)

1020 I persons per square kilometre

Low

Nausea, Eye ailments, Throat Cancer and Lung

Cancer

Considerable


4.12.42 Zone # 42: Shah Latif, Deh Khanto




Disease Occurrence (Figure 4A8)




Open Land (71%), Sparsely Built-up (11%), Urban

Vegetalion (7%)

New Industrial (74%). Agricullure (14%)


Low

Nausea, Eye ailments, Learning Loss, Efficiency

Loss

Considerable

. Series of Figures from 4.52 to 4.74

263


4.12.43 Zone # 43: Model and Malir Colonies

Land cover (Figure Gy. ~3)

Land use (Figure Gr . .J3)

Population Densit)' (Figure ~. J 0)

Disease Occurrence (Figure ~A8)


Disease Prevalence (Figure ~A9)



across

Medium Built-up (34%), Sparsely Built-up (23%),

Urban Vegetlltion (17%)

Planned Residential (93%), Military (7%)


Low

Stress, Eye ailments

Considerable


264

4.12.44 Zone # 44: Karacbi Airport


Land use (Figure G:r.44)

Population Density (Figure ./. /0)


Frequent airbome disease

Disease Prevalence (Figure ./../9)



Open Land (50%). Sparsely Built·up (17%). Urban

Vegetation (12%)

Military (70%), Planned Residential (12%), Utilities

(7%)


Low

Headache, Nausea, Chronic Cough, Hypertension

High


4.12.45 Zone # 45: Drigb ColollY & Malir

Land coyer (Figure Gy../5)

Land lise (Figure G:r . ./5)

Population Densit)' (Figure ./. /0)



Disease Prevalence (Figure ,/.49)


Vegetation (20%), Urban Vegetation (20%), Medium

Built-up (18%), Sparsely Built-up (16%)

Flood Plain (43%), Planned Residential (36%), New

Industrial (8%)


Low

Headache, Chronic Flue, Hearing Loss

Low


265


neron

4.12.46 Zone # 46: Korangi Industrial Area - East


Land use (Figure Gx. -16)


Disease Occurrence (Figure -I. -18)





across

Urban Vegetation (24%), Vegetation (22%), Open

Land (14%), Sparsely Built·up (17%)

New Industrial (61%), Industrial (21%)


Low

Headache, Stress, Hypenension

Considerable


266

4.12.47 Zone # 47: Korangi Industrial Area - West


Land use (Figure G:r:.47)







pen Land (30%), Sparsely Built-up (24%),

Vegetation (15%)

New Industrial (46%), Flood Plain (25%), Industrial

(22%)


Low

Stress, Nausea, Eye ailments, Palpitation

Considerable


4.12.48 Zone # 48: Korangi Creek and Refinery

Land cover (Figure ((y.48)

Land lise (Figure Gx.48)




Disease Prevalence (Figure 4..19)

Open Land (29%), Sparsely Built-up (29%), Medium

Built·up (17%), Urban Vegetation (12%)

New Industrial (48%), Military (20%), Recreational

(4%)


Low

Nausea

Considerable

267

4.12.49 Zone # 49: Steel Mill and Port Qasim


Land use (Figure Gx.'9)


Disease Occurrence (Figure 4../8)



Open Land (77%), Vegetalion (7%), Urban

Vegetation (7%)

Industrial (43%), Densifieation (17%), Buffer Areas

(14%). Planned Residential (10%), Transport Facilities

(8%)


Low

Nausea

Considerable

4.12.50 Zone # 50: Deh in the East


Lnnd use (Figure Gx . .J2)





Open Land (80%), Vegetation (8%)

Agriculture (48%), Flood Plain (38%)


Low

Throat Cancer

Considerable

4.12.51 Zone # 51: Malir Cantonment


Land use (Figure Gx. 5 I)


Disease Occurrence (Figure 4 .• 9)


Disease Prevalence (Figure • .• 9)


. Open Land (67%), Sparsely Built·up (17%). Urban

Vegetation (9%)

Military (74%), Schemes to Infill (25%)


Low

Hyperteusion

Considerable


268


concrnllOltioM ucross Ihe

4.12.52 Zone # 52: Scheme 33




Disease Occurrence (Figure .J../8)


Disease Prevalence (Figure .J.49)



Open Land (70%), Sparsely Built-up (15%)

Schemes to Infill (70%), New Indumial (11%), New

Commercial (6%)


Low

Headache, Nausea

Considerable


269

4.12.53 Zone # 53: Defence Society




Disease Occurrence (Figure 4A8)





. i

actuss

Open Land (52%), Sparsely Built·up (22%), Medium

Built·up (12%)

Densifieation Areas (64%), Recreational (13%),

Planned Residential (12%), Military (9%)


Low


Considerable


4.12.54 Zone # 54: Surjani Town

Lmld cover (Figllre Gy.54)


Population Densit)' (Figure 4.10)


Frequent ~irbome disease

Disease Prevalence (Figure 4A9)

Open Land (85%), Sparsely Built·up (10%)

Low Income Senlement (76%), New Industrial

(II %), Planned Residential (7%)


Low

Nausea

Considerable

270

4.12.55 Zone # 55: Taisar Town

Land co\'er (Figure Gy.55)

Land usc (Figure GJd5)





Opcn Land (97%)

Low Income Settlements (62%), Flood Plain (38%)

94 pcrsons pcr square kilometre

Low

Hypcnension

Considerable

4.12.56 Zone # 56: Halkani Scheme


Land usc (FIgure Gx.56)


Open Land (91%), Vegelation (5%)

Low Income Settlement (48%), Agriculture (41 %)


4.12.57 Zone # 57: . Dehs in the West along Hub River

Land cover (Figure Gy. 5 7)




4.12.58 Zone # 58: Dehs along Super Highway





271

IV

" IV

~?""1--

ZoNAL AGGREGATES OF CO EMISSION

Weekend Mornings 3 feet

, ~ ,

CORl.liCi"k.~ s.r. ~ u Mod~nlt' R~k

,

,-. " ~v--~ .~ Rb" HIgb Rbk

h , '~v--~

(Mi nimum) (Maximum)

.-

------,

~R.ng.' __ -1 I 0

..... ~ ~ "',~,

Sraad.rd Ol'l"I. lIo"

o

"~v--~ (Average) 75

POIrt:. f'~T ~Iillio"

Ippm) I 'i~ L - ! f rr ...... n_ ... "'J

, -- -. ----'. " ~ ,

N

W~E S ~?""}r-..

. "~~"::---~ 6 0 ti 12

Figure 4.52 ........... '\r-~

(Range) (Standud Deviation)

IV ....., w

~>

ZONAL AGGREGATES OF CO EMISSION

Weekend Afternoons 3 feet

./

, .-' .. -_...--

CO Rillk Cri.erloD

I:' s.rC'

~: ,

~~ Ii'- (' ~ ,----~ '~../',...r- .. -=- Mode.,,,. RbI> , r~HI.' Rbk ....... ~ V.I')' High Risk L _

,,~~~

(M inimum)

-' ~" ,

Ralnj.!l'

(I

I'mb P~'r .\1il lion (ppm)

~~;r'~\.r-~--...,

l)() '~I..,.t"n"'r (Average)

~ , N

:..-~ . . '. IT"-! ,~r\ ~

Figure 4.53 \..; \.,--- "'-~ '"""""""

W-\¢tE S

6 0 6 12

(Maximu m)

~r-

(Range) (Standard Deviation )

Studlird OnlaU1 oR

o

____ 1_4 ,~J...-rt .. II,

-'

~ ,

i~


Weekend Evenings 3 feet

-' r---"

/-----r~~v__~ I ,CORliiCCiiterloa

s.r~ I~ Mod.ro'. Risk HIl:b RIsk \lory H I,b Rhk

(Minimum)

'" --------'---

C --Rong<- I

o Parts Pt , MlIIion t_, • 120 114/'fI>II'-rn~

~ - ........F r/ I ~

_.-' .. ,,~ \..r - ~ (Averag~)

-' _---r-'"

, N

~-r--> .J \.'" I , ;;---,

.,~ ('~~ Figure 4.54 V ~

W-r¢tE S • 0 • 12

Kl~IOI1

(Maximum)

(Range) (Standard Deviation)

•

r-"'--~

~

Slnd.U1t Dn-i.rio.

o

___ 17"..,-I .... f~

-' ~--

,

'" '-' (J1

----. ~ ~ :rr-.


Weekend Mornings 4lh feet

" _/

co RilfCrii"ioa ,: ___ Slff' _-r-.~>

_ r--...t -...r " ,.... ,

"" ,--------,

'~~~,~~-.. _ _ Mod ...... Rlok

Hlgb Risk Very Hlgb Risk '~\,~~--

~n~l'~ o

(Minimum)

_"' __ r-~~ _____ ~ r , IT'-

(Maximum)

.--------'

!oi1udllf"d On"i_lio,.]

o

P.trt:. r(r "Iillilln I (ppml

, ,~\,,--.~--5 ,~ ___ J J I

I~/"",""

, ~--

,

, - ~~> ' ,~ r /f'r

r'igurc 4,55 "~\...r--.-.t--"'~ " •

(Range)

(Average)

N

W~E o S. 12

........... - ~~)

~--...r ,£ /.

" '.

.-/ ,

"~~~ (Staodard D~\iatioD)

'" ...., en

~-../i


Weekend Afternoons 41 /: feet

" ~.,

.-

CO Rilk Criteriou I

,it-Safe' , Mod~ro!. Risk

.. ~~> .. ... --' . /.

~. r-,

:-. .'~~~. r High Ri,k Vory "Ilb Rl,k '~~~----'

(Miaiml!m) (Maximum)

-- ~'

Fo R,nJ< ----=.:l

Par1s t'.:r Mill ion I IPpm)

.~~~ "~'V-............... ~~.-'"'-.

66 ~"". (i\nrage)

" -------,

N

s ~~f\, ~ ~~ W{¢}E

Figur" 4.56 ~~

a a e 12

"'-(Range) (Standard Deviation )

Sn""dlml D",-i ll riun

o

1$ Ir""'l~'

~. ~

IV -., -.,

Zonal Aggregates of CO Emission Weekend Evenings 4th feet

;' -~. .-- ,

~~ .. -,

"....,~

~ J" };--.,

'~\.r-~

CO Riol< Cril~r;".

I~ __ s.re . Mod.n,. Risk

Higb Rlik Vtry Higb Risl< I

~,

~ I . r: ......t-'~,,_. '-~- '~\r=

o

'l'I

RllnJ:.t." __

I'(1) rH Milliun (ppm)

1~1JIo,".1

(Min imum)

;'

... ~ ,

;~~;.,...., .

"~v--~~ (Average)

, ~

N

(Maximu m)

~.rd - [)n-i.tlvR

o

15 U f/aIf *,"fII,

'. ;' ,------'

,

" -~~"v-~ Figure 4.)7 (Range)

• W~E

o S a

"""""""' " ~p-"}-

,~,-,--.~ (Slandard Devi.'ion)

...., '-' CD


Working Day Mornings 3 feet

.. , -~'

co RIsk Criteriom

.=--- Sarr .',-,,~j

~~ /i'-t

, ,~~~~ ! Modeno •• Risk

Hlgb Rilk ..,; _ V.ry High Rl,k

~.n~<_~

J - l':Jrt!i j1cr Million (ppm)

75 lbiN( J/wTT..iI ;

(Mioimum)

~~~

, _ ..... r-

,~~)r...,

"~'.r-~. (A"frage)

~' -_.---' N

W-t¢1-E s

figure 4.58 ,~\,,....~

t! (I 6 12

"""""""

(Maximum)

"

,----' ,

St.lldlilrd Dn-lilfH:. ft

o

_ IJ II..,.." ... -.roJl

~ - ~

,.....--' ,

~~ (R~nge) (Slandard Devialion)

'N

'" '-D


Working Day Afternoons 3 feet

, " ~'

~)

___ ,~ .r '~\..-_~~

o

"'

Rliln:~<~ __

l~illt S Per ~1il!ion (rrml

(/~_IttI.Y"t .. j

(Minimum)

, ___ '".,-""----1

../ -_/'

,

.. ,-.-,~>.., - ..",- """' ,

Figure 4,59 .. !;: (' \./-~ ... .' ~ ,.r-~

. to Riok Criterion i

l~sart' . Mod.", te Ri'"

HI~b RIsk " el")' HIlh Rl>k

,,~

./T""I

-' ~ ,

~\...--. --~ .. ,~-,.-~ .. -. (AYcragc)

~.

N

W{¢tE S , 0 6 12 -""""""'"

---~

'----' ,

(Maximum)

S';.d.,. 1m''', ~-

o

1 51/~""""',m,

" .-.-'

"

(\.;-~

(Range) (Standard Deyiation)

'" CD o

- '-:-'~},..., : _..--"''--' . ~


Working Day Evenings 3 feet

.' ._./-'

,

(1 -r---~ . ~ V-~

~co Risk Criler~

s.r. Mod.no,. Rbk Higb Rhk Vcry H leh Rbk

(Minimum)

-' ...---,-' -

FoRlnJ:,C ~ P<lru I' c:r ~' ill i on

(ppm ) I - 113 Ir~"""'IJ

~ f\ ~-.. " V"-'"'- ~

(Ayuage)

~"'-" ,

N

Figure 4.60

W1.¢tE S , o ~ 12

K11Cmt~

~ '~~v--~

(Maximu m)

./

--,...---,

r---

Standli rd Dni.lioa

o

J 8 "--twl """"~

~ ,

f'\..,_~

(Range) (Standard Dnialioo)

N Q:l

/""'I~~ . "....,

ZoNAL AGGREGATES OF CO EMISSION

Working Day Mornings 4111 feet

'" --,. -' i CORkk Criteria. I

i ,~ ____ s.rt

. '~v--~ ____ , Mode"'l< RI.~

High RIsk W ry High R15k

~

Fo Ran9 Purl:> Per Million

(r"Pnt l

., ____ .:;.j..,-mn~

(Minimum)

'~ i

,

,. ,----r--'

.. ~~ ,,,",,, (' 'v~....I'--"~ ~,

\..; V-

(Average)

----_.; N

W~E s

---,-('\..r-~ _ ..... -

(Msximum)

Stli. lllhn'd D~i.fio.

o

, - 8 If"L~j",~

, ,------'

,

Figure 4.61 ,~,,---~

6 0 6 12

"","' ..... '~'v--~~~ (Range) (Standard Deviation)

N (l)

'"

~,


Working Day Afternoons 41h feet

-' -----' ;

CO-RlJ"-C" f';ri<>n s.r.

~ Mod .... '. Risk

~-'

-

r.r 'tr-- ~ I -""--- . ~ - ~ -...--- - ~ '..r- , Rbk

,~ __ • HIR~ Risk

-"'"'~ ...... ~ ,. ~ . " (\\J'- ~~.

.r-, (Minimum) (Maximum)

'" I

~

. ~~ ..-.~ ". "I , '

I~St'O d il ni D~' l.lio.1 RUI~",-< -----I o

P;m s PC-I :'I.lil liofl (ppm)

"~v--~-,._ ~ .. ~J " _____ ---'",~ IlIHfl"l

_ rC'.~> ~ --........J.,- ., 11"-

.- -./~-...

, ...... ~ f' '-'~~ , fig ure 4.62 \..; ~

(Range)

,

(Average)

N

W1¢tE S

0 , 12

"""""'"

~~ .!1'.

;' ~-_0''''-

...... ~'v-~ .. _ (SlndBrd Devialion)

...., CD w


Working Day Evenings 4th feet

;' .----... __ .. .-

co RlIkCri.irlOn

.~~ .'~v-~~

S.rr Mode"'lo RI.~

Hi,~ Risk ~rry Hlgb Rid, J

--Rlloec =--l o

P"rh I' N Millioo ~

(ppm, J 6' • _ _ d<l"4IA j,*_~"1

~-'~-

figure 4.63

(Minimum)

--~ _ _ I.

,

~~> . /'!'-t

--

h

, ,/--"

-~

'~ rv-~ _____

'~ '--~~-.r-v (AYe rage)

N

W1¢tE S • o • Il -Ki~ItIr!>

(Maxi mum)

--

---~

~ ,~

,

---

StudamI D,.\ i.,io.

o

11"..,.....1 ........ ,

------~

(1v-~


N Q) &-

ZONAL AGGREGATES OF CO EMISSION 3 feet Averages

(

, ~ ..... ----

:~~~~v-~ CO Risk Cri,erio. ;

I~Sar~ +=---, Mod ...... Ri ... i. "l1tb Risk

,"':_ V.ry "11h Risk ,

(Minimum)

IoR.n~. ~ ,-~ ,I,

/ .------

P!ln:'i Per Million (ppm) '~~~-~

bI j>t"" '_~ .t:o

, .,...., . . " _.__-------r - ;,:'--> . ft

~ ..... ---

Figure 4,64 ,~ n~ ~ V ,/,*~

(Average)

N

W-r¢tE • o S ~ 12

KiIomt""

r~

(M8Iimum)

r--"'---'

-

v-~

Mu.d";n'-Dn.l-;rlOft o

~ '2 ., --,

,~ -

f\..r-~~ (Range) (Standard Deviation)

N

en ()'l

.....---""---. > ,7


41/2 feet Averages

;' ~-"

----,

CO-Risk Cri •• rioa

Sare ~..-.~ .."....,

, ~ ,

' ~ r, ...-t-""'..-.,. " "v 'v-- ~.

Mod.n". Rio .. H1Xb Risk Vt ry Hilb Risk

,~r~~~ "v v-

I----R-;nJ!~ o

..,

P ~ln li P~T .\1illion ({lflml

)1 '/ .... _n.

(Min imu m)

.~~~ " '~~'v

(Average)

;'

----~-, N

rr'~ ' .""-- ' ~I', ~~ '-""' - " \.,> .... ~ v--

Figure 4.65 (Rollge)

W~E s

fi 0 6 12

>Oicmo""

(Maximum)

;'

,.- -----"'-' ,

Sludllrd Dn'i.'IOII'

o

......I-"'~.~. - " /1 '1._ ... .. ..".;

" ,.---.--' ,

r\...-...-t-""'~

(Standard DC"ialioo )

N (J) (])

. .-, ....... -./)


Total Averages

-- -_/

CO RBI< Cri.erion ,

I~ __ s.r.

,-.....,.,.~-..

-

~.<' . . ~n.. ~~ .- " "v'\./'- v-I ~Mod~f1I le IUs" ~Higb RUk ~_V.ry High Ri," "~~~~---.

.-----R.n~c ~o

r~Ir1 S P~r :\'1il!ion (;.'1('11'11)

_ -1 7 1~/""m'"

(Minimum)

. ~~> -.- ~

.,.

-~.~ -

--.~>

,"'_ .. ,j .. K r, ~ '_,",-' --- "'\..r 'v"- ,r-

(Average)

~ . .......-" .,.

N

W~E S

"~\.r-~ , 0 6 12 .

K"we"'" Figure 4.66

(Maximum)

.----.---~

Stnd.rd Dn"j.tto.

o 1 9.,."".J

". .---.,.

8 ::!!I!!O

'~'v--~ (Range) (Standard Deviation)

'" CD .....,

ZONAL AGGREGATES OF SPM EMISSION

" -~ ~

" ~SPM CO.U.ll1Iliol1' ~

, - "'""'"""'""""'-""'> ~ --..r r ,/l"'-o ", '" "~'v---~ t:"

Lo .. Mod.ral. High VtJ)' HI~b

(Min imum)

" ,

~ ,)

~R.ne< , J ).t J; 1 m

70 IL<I'ttIIltrt.:TT

~P"o-/} '. ' ~ f\ '--""'--'"

~ '\., \..r= ~J--"'-' (Avcr~gc)

, ~

, N

(Maximum)

~}r-~-- "~'v-~

W~E S • 0 6 12

r-~-,) ".. 11"-> ~ ~

~

"'-Figure 4.67 (Range)

(Standud Deviatioo)

,-/--_. ,

~nd D(,"· I. tio" o

• I& '~ I" '~!

, --

... ---"'

~

tv m lb

ZONAL AGGREGATES OF PMJO EMISSION

#_...----.. --/-'

PMIU CODt'alr.lio •• 1 ,

~~>. .. Low

:~ U Mod.",«

'~----HIKh .- Vtry Hlg~

~.

(Minimum) (M.ximum)

-----_ . •

C-LR'.~< ~ Jl I m'

.. 61 /J~mJ J ... , .... ~ (Average)

Srudllrd Pn·i.fio.

r 0

-I611 ... ~/"'"Tr""

-' I~.-/ -' ~ , • N

~

Figure 4_68 ~

I ~..,.,.........> fl'-.

'~~\..r- ~

W-r¢tE S , o • 12 -, ~~'\.r-~

(Range) (Standard On-iafion)

tv CD <D

ZONAL AGGREGATES OF S02 EMISSION

Averages

.-' -.-.-/ ~

~ SOl COIIC. DIr-IIIo ..

,~~~ ~ ~~'. ~"""""""' - l:

thnl!c

o

'-' - _.-. -----.. . v I,..'

(Minimum)

~,r-~}r-

Low Modcra'c

Hlab ••.. I v.~

.-'

-.-_r

.-

- ...-<-~} ~'-V • /i'-

'~".-r- .::---.---. (Maximum)

--'" ~ ,

Patb P&:r Millilin (ppm)

. '~'v--------~ ·S!OOd;rd p;;j'til

1.0,,,,,,, .. ,,~.1 ,; rF..,MoII I'I/r ,..-uI, ! (Average)

.-'

~ .-' •

.-/-, ,

N

Figure 4.69

W~E S

""""""" ~?'-""'> • " /i'- I

'~ (Standard Deviation) ~ ~

\ ...-...._ ;-~-'"o,J...-/,,,, . '-- ~ .... 'i'r-

'~'v--(Ra nge)

6 0 5 '2

'" o.D o

ZONAL AGGREGATES OF S02 EMISSION

Maximums

" -' .-./

SQ.iTOD.enlrationl

". ~ ,

r~j,..., r -1'-'"'.-....... ~ ',~\/- ~ + Lo ...

.l. W Modenle Hilh Very HI,h _I

~-~

~v----,,r--r-...-

R.n~ l o

ParI) I~t:r Million <rpm )

- 5 rr..,Nl.llhtknv/1

~.

Figure 4.70

(Minimum) (Maximum)

" ...---.--J ,

~ r<-'~ ) ~~ ..r If"->

Mud .. ", D~i.tir~

o

" ~\-~~ C::.....:I.::.3'.!'" .. .., ..... .J (Average)

, --' , N

~ r\-~ __ ' \..... ,~

W{¢}E • oS , "

Kl_

(Raoge)

" ,

,--' ,

~ .~ >

""".m D~~V--~

N <D

ZONAL AGGREGATES OF NOx EMISSION

Averages

r" ,~ ,

NO;-CODcenfrartaDI

r" ,.-r-'

~~-../Jr-, . "~\..-~~

,. ~

Lo,,· Mod .... I. H1Xb

~~}r-.,

"~~~

~o RMn

2C

II Parh Pt"r Million

fppm )

~~. __ ! .. FI((,.A IIIl~r" lIh

Figure 4.71

(Minjm~m)

(Range)

'" ,-, Very Hixh

(Maximum)

---~' ,

(Average)

-.----. --, St..dud DevillclClo. I 0

1h '~IJ --~> ' .'

~ J: /f"-'> f\ ~~--..., ~-- ,,~ '\.r- V-

I

~ .-'

---" ,....---, /

N

W{¢}E ,r-,~,_ A. . ' I ~ :(--;,...;,. .

'''~~~ ~

80S

6 12

K,_

(Standard Deviation)

'" <D N

~~)" , I7'"-:l

ZONAL AGGREGATES OF NOx EMISSION

Maximums

,/

~

NO! Cooeenru.ittDi

Sore .. '~'v~~_~_, " ..

,-,

Moderal. High Very H igh

~o RUJl:c

r;ln~ r tf" I\t'illion (pprll)

J I r£"INUi JI11.'Tlulj

(Min imum)

~ ~ " ~\J Figure 4.72

./ ,...-/,..-/

,

. ~ .---~ . ..... Ii

' ''~I\.r-~~ (Average)

~-' , N

W-t¢tE S ~

'~ 6 0 6 " ~ --

(Maximum)


,/ ~---.-;

,

v-~

~t.lld.lilrd Dr'illllim,

o

9 11,,"'( I-.TTtiJ

". ,~ ,

~

'" cD w

_ ~ Ill!t.'_ o

Pmh ['cr [J illioo I)'!pbl

t) t f.f(ta.lI /,Jtj,' nu!1

~ Figure 4.73

ZONAL AGGREGATES OF 03 EMISSION

Averages

~.-J ,

0, COD«nrr.riolll I ' Low : Modcn.rC' I ,) I I High I

,,;., III Vrry H I~

(Mi nimum) (M aximum)

.-/.r-

-

(Awrage)

~ , N

'~v-~ W~E

'L-? S fi 12

"- ,.,,

, _ ,...r-~) v-r '-". "......

'~ (Range) (Standud De~jation)

"

....J

-

LStlindalnJ ~~.

o

3 " <twII~

... ----~-.. ,

.,;-----

N -0 ...

ZONAL AGGREGATES OF 03 EMISSION

Maximums

1--0 RlIIl1j!l:.'_ ---I

P.:Jrh Pl." Uilli Drt Ipph)

L.....:_I ~ . f.'1Hi.ll/rrltnvl/

(Minimum)

----)

~ ,;, I~l

11";'1

~ ~

~-/> ' , .. 1'- ~.

Figure 4. 74 ~~ r\ '\.,>_r- v-~

(!tang~)

Conct"JIII"'I..ion.1

Low Modl'nte

Hil~ . I V<ryH IK~

(Average)

N

W{¢}E s

L 0 I) 12

-......

(Maximu m)

,...../""".,

- ~---i . ~ '-V -r /T'-<

"'~/~ (Slandard Devialion)

"

-..",.,-'/

,.

v----

St.ndud l>~

o

-==-';):..!.I~I ~tJJ.:

~

,I

.,....---"-

,_.

4.13 MUL Tl CRITERIA RISK

The analyses explained until now were conducted by taking up one variable at a time. The

success of GIS analysis is inherent in its capabilities of handling multivariate data (Zhou and

Civco, \996) and the same has been done in this study. The ultimate risk due to air pollution

in its present form is because of all other variables explained previously. In the chapter of

conceptual framework, the decision analysis tool of "Multi-criteria Analysis" has been

described .

Many studies of complex geographic phenomena begin with a set of data and notions of

hypotheses and theories that are vague at best. Factor analysis may be used as a data

reduction method, to reduce a dataset containing a large number of variables down to one of

more manageable size. When many of the original variables are highly correlated, it is

possible to reduce the original data from a large number of original variables to a small

number of underlying factors (Foster, 2000). Miles (I99S) annotated an example of factor

analysis using SPSS for a study in Human Psychology. Kazmi (i 996) performed factor

analysis (PCA) for investigation of the resurgence of Malaria in Pakistan.

Exploration of the significance of variables is done through Principal component analysis

(PCA). Tables 4.52, 4.53 and 4.54 are outcomes of PCA. Commonalities indicate the

amount of variance in each variable that is accounted for. Initial communalities are estimates

of the variance in each variable accounted for by all components or factors . For principal

components analysis, this is always equal to 1.0 or the variance of the variable. Extraction

communalities are estimates of the variance in each variable accounted for by the factors (or

components) in the factor solution. Very small values indicate variables that do not fit well

with the factor solution, and are usually dropped from the analysis. The variables based on

monitored pollutant data Table 4.52 shows highest extraction communalities (for averages

and standard deviations of CO). Factor analysis communalities indicate the amount of

variance in each variable that is accounted for by the factors in the factor solution (Foster,

2000). It was reasonable to get the low~st extraction communality for the grid of road

proximity since it was added as a layer in the multi criteria analysis purposely to account for

295

rapid chemical conversion (Seinfeld, 1986) of studied pollutant and to acquire justified

cartographic results. Further it could be observed from Table 4.53 that component one

explains greater than 55 % variance and one and two cumulatively explains greater than 68

% variance. An eigenvalue (sometimes called an eigenvactor) is a measure of how important

the factor is in accounting for the variance (Miles, 1998).

Table 4.52: Factor Analysis Communalities

l"~'l:: ':i:;Y:~~J~~}l~~-:~,~ i%'):; :\Y'6r~

:~i1t:~~~~~~~~r; :,f' Jj)mj~i¥A ,~_.'\i.?%'';'. '

I , RBG 1.000 0.299 , LCCG 1.000 0.424

I PDG 1.000 0.429 DOG 1.000 0.438 DPG 1.000 0.484 RDIG 1.000 0.574

I SDC3FG 1.000 0.849

I SDC4FG 1.000 0.879 AC3FG 1.000 0.939

I AC4FG 1.000 0 .943 I ACTG 1.000 0.950 , SDCTG 1.000 I Extraction Method: Principal Component Analysis.

0.955

Table 4.53: Factor Analysis Total Variance Explained

296

Table 4. S4 shows the component matrix canying correlations, which could help to formulate

an interpretation of the factors or components. Ten out of twelve variables load highly on this

component to include most of the health, environmental and demographic indicators.

Table 4.54: Factor Analysis Component Matrix

297

4.13.1 Weight Extraction

Table 4.55: Correlation Matrix HIJIG I'J>G 00C; LCCG Acre AC'.JI'C AC .... C socrc SIJCJFG SOC~FC OPC KBG

~IG I 0.39790 0 .344366 .0 .2380323 0.4~0791 0.479199 0.477\04 0,461002 0.415813 0.402162 0.3888~8 .0.2004

DC 0.39790 I 0 .4~2'"6 .0.2736311 0.575405 0.569296 0.l79466 0.l17l65 0.45540 0.5011013 .0.019421 .0.02613

DOG 0344366 0 .4~2786 1 .0 .2609571 0.4444 0.433322 0 .46059~ 0.38167 0.33064 0.3769794 0.3408649 .0.0622,

.£CC ..o.2.lK03 .0.27363 .0.26096 1 .o.246~ .0.25141 ..0.23454 .0.2542 .0.237 ·0.223)011 .0.1816015 .0.00452

~CTG O.4N0791 0.l75405 O.4444~ .(J.2469891 1 0.997646 0.99039. 0.941 O.t«44275 O.86503K4 0.184082 0.014501

~C3t'C 0.479199 0.l69296 0.433322 -0 .25140~2 0.997646 1 0.978588 0 .9434l2 0.8418l 0.M96669 O.I89000l 0.DI77l1

!,C4FC 0.47710 0,)79466 0.460598 .0.1345407 0.990398 0.978588 I 0.9243l 0.8370 0.883619 0.171l174 0.00773'

~ocrc 0.461002 0.517l65 0.3816' .o.2l41993 0.9416 0.9434l2 0.9243l7 I 0.9G8l41 0.9407419 0.2182993 0.00491

DC3FG 0.415813 . OAl540 0.330648 .0.237397 0.844275 . 0 .84185 D,RJ704 0.968541 1 0.93ll985 0.2269l76 .0.0027

'DC4FC 0.402163 0.lO1101 0.376979 .o.223l011 0.865038 0.849667 0.883619 0.940741 0.93ll99 I 0 . 14746~ .o.006?

PPC 0.388881 .0.01942 0.H086l .0.18 16015 0.184083 0. \89001 0.171527 0.118295 0.2269l 0.1474646 I .0.1163

~ .0.20049 .0.02613 .o.0622? .o.OO<ll73 0.0 14501 0.0177l2 0.007737 0.00491 .0 .002 .0.0066954 .0.1163408 I

.:. lfabsolute (modulus) values are not accounted for, the sum would not reflect the strength of variable hence, Table 4.56 are the absolute values of Table 4.55 .

RDIC POG DOC LCCG AcrC ACJFC AC4FC SDcrC SDOFC SOC4FC OPC RBC

~IG I 0.39790 0.344366 0.23110322 0 .48079) 0.479199 0.0477104 0.461002 0.41l813 0.402162 0.388888 0.200481

Poe 0.39790 I OA827~6 0.2736310 0.57l405 0.569296 0.l794Q1 0 .l17l65 0.4ll40 0.5011013 0.019421 0.02612. poe 0.344366 0.48278 I 0.2609l713 0.44448 0.433322 0.460598 0.nI6' 0.33064 0.376979 0.3408~ 0.06226.

~G 0.23803 0.273631 0 .260957 I 0.246989 0.251406 0.234l41 0.2l4199 0.23739 0.1235011 0.1816015 0.00451.

~~"G O.4tc079I 11.575405 0 .44441.: O.2JG9K9101 I U.9976016 0.99039X 0.9416 O.M4275 OJe6503t( 0 . 18408~21 0.014l01

'OPG 0.47919 0.lG929( 0 .433312 0.251406: 0.99164 I 0.978588 O .9434~2 0.8418l' 0.849666! 0.1890ool O .Oln~~

I-C4FC 0.4771<l< 0.l7946( 0.460l9" 0.234540: 0.99039 0 .97858' I 0.92435: 0.8370< 0.88361! 0.171527 O.OO713j

"Derc: 0.46100 0.!'11!i6!i 0.3"167 0.2l419933 0 .941( 0.943452 0.924357 I o 968S41 0.9407411 0.2181993 0.004911

ISDClFG O.oll5KIJ 0.4l540: O.J30641C 0.2373977' O.~44275 O.lC41K51 o.gno.; 0.968541 I 0.9355989 0 .2269l76 O.DOli

' OC4FC 0.402163 O.lOIIOI 0.376979 0 .223l0113 0.86l038 O .~49667 0.8836 1 0.94074 0.935591 I 0. 147464 0.00669l

pPG O.3888X8 0.019422 0.340865 0. 18160151 0.184083 0.189001 0.17\52 0.218299 0 .2269~ O. 1474~ I 0.116341

~BC 0.200481 (J.026 12 0.062267 0.004l l725 0.014501 0.0177l2 0 .007737 0 .004918 0.0021 0.006695' 0.1\6340 I

Table 4.56: Correlation Matrix (Modulus)

298

Following Table 4.57 provided the extracted 'weights' for each variable for multi criteria

GIS analysis.

Table 4.57: Extracted Weights for Variables

Weights Variables L:x, (L:lfx, , 100)

1 /?oads Buffers (REG) 1.464 2.15

2 Disease Prevalence (DPG) 3.184 4.67

3 and Cover Classification (LCCG) 3.406 5.01

4 pisease Occurrence (DOG) 4.918 7.22

5 '{load Density Index (RDIG) 5.285 7.76

6 'l'opulalion Density (PDG) 5.398 7.92 7 'Ptandard Deviation o/CO at3 Feet (SDC3FG 7.096 1M2 8 'Ptandard Deviation o/CO at4 (SDC4FG) 7.132 10.47

9 4verages a/CO at4 Feet (AC4FG) 7.544 11.08

10 4verages a/CO at 3 Feet (AC3FG) 7.551 11.08

11 ~Iandard Deviation a/CO at Total (SDCTG) 7.556 11.09

12 4verages a/CO Totaf (ACTG) 7.585 11.13

The disease data was not large enough but needed to be incorporated due to its environmental

and public health relevance. 'Similarly, The land-cover overlay got lower 'weight', as a

classified imagery it was quite different in nature than the other grids. Interestingly, the

highest weight statistically extracted happened to be for the overlay of CO overall average.

After elucidating the importance of 'weights' of the overlays, it must be remembered that

each overlay has multiple classes of data; therefore, the classes are also assigned 'scores'

depending upon their technical relevance and the author's professional judgement. • Scores'

are assigned on the scale of 0 to 10 where ' 0' qualitatively represents safe, and '10'

represents very high risk.

The following template depicts the assigned 'scores' and 'weights' of every overlay Table

4.58 :

299

Table 4.58: Multi~Criteria Overlay Template

New MaplD & Tille' MulliCri Air Rlsk Multi Criteria

No oflnput Maps: 12 Input Maps (Id Max Color)

LandCoverCiassijicalionl998 7 PopulalionDensi/y 10 AverrageCOa/3Fee/ 4 AverrageCOal4Feei 3 AverageCOToral .J Dis.asePrevalence 5 DiseaseOcclJrrence 5 RoadsBuJJers 5 RoadDensilyl",Jex 10 SldDevialianCOa13Feel 4 SrdDevialionCOal4Feei 4 SldDevialiollCOTola/4

Format ~ Weight Map ID

5,01 LnndCoverClassljicalian 1998 (LCCG)

Null • 0: (l

I Water • I: (l

2 Vegetation • 2: 01 3 Urban Veg. (Mix) • 4: 05 4 Medium Built up • 5: 08 5 Densely Built up • 6: 10 (, Low Bml! up • 7: 05 7 umd • 8: 0

7,92 Papu/alionDenslly (PDG)

Null · 0: 0 1 54 467 · L 01 2 467 liB · 2: 02 3 1113 2359 3: 03 4 2359 7246 • 4: 04 5 7246 12859 • 5: 05 6 12859 19205 • 6: 06 7 19205 22724 · 7: 07 II 22724 46610 • 8: 08 9 46610 W3I08 • 9: 09 10 HlJHlS 143588 . 10 10

BOS

Null · 0: 0 I 10 · I: 01 II· 20 · 2: 02 21- 50 · 3: 08

>50 • 4: \Q

300

.........• -, ......... --............. -~

1.08 AverrageCOal4Feel (AC4FG)

Null I 10 11 20 21 50

- 0: - I: - 2: - 3:

o 01 02 08

11.13 AverrageCOTolal (.4CTG)

Null - 0: 0 10 - I: 01

II 20 - 2: 02 21 50 - 3: 08 51 200 - 4: 10

~ . 67 DiseasePrevalence (DPG)

Null 1 - 14 14 - 27 27 - 41 ~1 54 5~ - 67

- 0: - 1: - 2: - 3: - ~ : - 5:

o 01 02 05 08 10

7.22 DiseaseOccurrence (DOG)

Null - 0: 3 - 1:

~ 8 - 2: 9 1~ - 3: 15 23 - ~ : 2~ 47 - 5:

2. 15 RoadsBufJers (RBG)

Null - 0: 50 - 1:

51 100 - 2: 101 500 - 3: 501 1000 - 4:

> 1001 - 5:

o 01 02 05 08 10

0 10 08 05 02 01

301

7.76 RoadDensitylnder (ROIG)

Null - 0: 0 1 86 - 1: 01 86 173 - 2: 02 173 259 - 3: 03 259 346 - 4: 04 346 ~32 - 5: OS ~32 519 - 6: 06 519 60S - 7: 07 60S 691 - 8: 08 691 778 - 9: 09 778 864 - 10: 10

10.42 SrdDel'iationCOar3Feer (SDC3FG)

Null 10

\0 20 20 30 30 40

- 0: - 1: - 2: - 3: - 4:

o 05 OS 03 03

10.47 SrdDeviarionCOar~Feer (SDC~FG)

Null 10

10 20 20 30 30 40

- 0: - 1: - 2: - 3: - ~ :

o OS 05 03 03

11 .09 SrdDel'iarronCOToral (SDCTG)

Null - 0: 0 1 10 - 1: OS 10 20 - 2: OS 20 30 - 3: 03 30 40 - ~ : 03

302

Figure 4.75 illustrates the final results of the whole endeavour in the form of thematic map. It

has been established that the exploration of spatial patterns of air pollution and its risk

evaluation over the whole metropolis was conducted through the integrated approach of Field

and Remote Sensing techniques along with Geographic Information System. This map is able

to clearly identify and demarcate the five classes of risk due to air pollution and the

popUlation at risk, under each zone. It is worth mentioning here that even the class of

. moderate risk' be not taken lightly since the outcome is involving twelve variables of

diverse nature.

4.13.2 Very High Risk

Area: 0.866 Km2

Population: 28,000 persons

On the digital format, there are two zones of very high-risk identified as heart of the Karachi

(i.e. Saddar / Empress Market) and section of M. A. Jinnah Road (from the intersection of

Garden road to Robson road known as Eidgah). These have emerged as very high-risk zone

based on the applied criteria counting on various parameters and not only the concentration

of CO. These parameters included high population density, dense road network, concentrated

habitation, higher disease prevalence, and hazardous observations of carbon monoxide with

low temporal variations.

The Saddar- Empress Market area comprises of several bus terminals/transfer station, dense

network of roads, crowded retail markets, thousands of commuters, a lot of pedestrians, long

queued vehicles, mixed land use and high-rise buildings all around. The commercial activity

of this old part of the city involves unavoidable trips, therefore, the actual human population

at risk is difficult to quantify. Appraisal of population at risk yields the figure of 18600

persons for this smaller area of 0.566 Krn2. There is no pedestrian data available and this

study possesses the census figures of the resident population only. Environmentalists are

called to recommend intervention measures for this peculiar very high-risk zone of Karachi .

303

The section of M. A. Jinnah road from the intersection of Garden Road up to Robson Road

known as Eidgah is a two way traffic, section with commercial vehicles such as buses,

minibuses, coaches, rickshaws etc., plying with countless number of two stroke motorcycles

and loading vehicles. This road is also a part of old Karachi with many factors common as in

the Saddar zone. The GIS analysis has figured out the population of this very high-risk zone

as 9,400 persons and the area as 0.30 Km2.

4.13.3 High Risk

Area: 16.941 Km2

Population: 792,600 persons

It is reiterated that the risk zones have been derived due to the effect of a combination of

urban and environmental factors endangering human potential. Reader would observe three

major high-risk zones with some hazardous islands. The first high risk zone covers the old

city areas, adjoining neighbourhoods of Nishtar road, the Katchi abadies besides Layari river

and borders the Karachi cantonment area. The popUlation at risk is computed to be 436,800

persons and the area is 9.28 KmZ, The open green space of Karachi zoological garden is

clearly out of high risk proving the ground reality.

Second high risk zone has the jurisdiction across Liaquatabad and Federal'S' Area. The

critical arterial of Shahrah-e-Pakistan, which later becomes S. M. Taufiq road, bears

thousands of vehicles and people daily. The software has calculated the area of this risk zone

as 4.945 Km2 and population as 250,200 persons. Six major junctions (earlier Roundabouts

now turned as signa Ii sed intersections) are the choking traffic points viz. Oak khana,

Liaquatabad No. 10, Karimabad, Aisha Manzil, Water Pump and Sohrab Goth. The transfer

of Subzi Mandi to its new location on Karachi-Hyderabad Super Highway has worsened the

traffic and environmental conditions of this risk zone.

304

KARACHI ulti Cr· eri Risk

.~+~~Q~'~'~ __________________________________ ~~~~'~' ______________________________________ ~~~w~~~~

:! .-.. .... ,..., ~ • ,'j

Alabian Sea ~ , . .. +-------~~--------------------------~----~---------------------------------------------+~ lI" !fnl r n t1"WtW E ,)I' In~r t

I • • I

t'ntt'

Low Sa

3('

North Karachi risk zone although relatively smaller area i.e. Km2 carries 98,800

persons. The effect traffic and pollution at Nagan Chowrangi largely expanded the

boundaries this risk zone. The. population density here is quite high, which might have

contributed in the formation this multi criteria based risk zone.

On the map, there are two. distinct spots of high risk. One of them is Chowrangi of

Nazimabad at Nawab Siddique Ali Khan road. This high-risk spot covers an area of 0.06

Km! and has the population around 3000 persons. It is II location coming in the way for II

large number of commuter trips during the day. The second significant high-risk spot is seen

on the intersection of Shahied-e-Millat road and the University road (Jail Chowrangi)

Analogous to Nazimabad Chawrangi, this high-risk spot witnesses thousands of vehicles

and passengers daily due to its mainstream location. This spot shown on the map wrap an

area of 0.1 Km!, bears a population of3800 persons.

4.13.4 Moderate Risk

Area: 103 Km!

Population: 2,186,200 Persons

Administratively speaking, the two populous districts (Karachi Central and Karachi South)

are lying under moderate risk, Through GIS techniques, it has been possible to micro

geographic details of this risk zone as the count of polygons was thirty-five. For the sake of

brevity. the moderate risk zone could be discussed in two folds. The agglomerated moderate

risk zone largely spread out over the map and shows an elongated pattern towards the

northeast direction. This agglomerated zone encompasses an area of 50.96 Km! and a

population of 2,122,600 persons at risk. This particular risk zone covers a large portion of

urban land and imbibes several planned, unplanned neighbourhoods and squatter selllements.

The figure of greater than two million people under moderate risk, by itself are a legitimate

theme of environmental inquiry.

306

The scattered moderate risk spots spread on an area of 4.15 Km2 and have a population of

63,600 persons. The locations on map are identified as Liaquat Market Malir and Saudabad

No. 15 Malir Extension, which are densely, populated commercial areas. These locations

have narrow roadway widths and traffic congestion is reported during the off peak timings as

well. Other spots are the intersection of NIP A, the road section between Hassan Square and

National Stadium, which lies 'on route for many public transport trips. The last hotspot is

recognized as the intersection of Shahrah-e-Faisal and Shahrah-e-Quaideen.

4,13.5 Low Risk

Area: 56.36 Km2

Population: 1126400 Persons

As the Figure 4.75 and its legend shows that lastly but not the least, there exist some low risk

zones. Which are useful for spatial decision makers. The above described moderate risk zone

has adjoining regions which are under low risk . The pattern of this region is similar to the

agglomerated moderate risk zone. This particular risk zone travels similarly to its adjoining

moderate risk zone in elongated fashion from southwest to northeast. The GIS give the area

of this zone as 26.134 Km2 and covers poptllation of 646,800 persons.

The Rashid Minhas Road has a low risk zone corridor on its both sides, which further

stretches to main Shahrah-e-Faisal and Shah Faisal Colony. The T -intersection of lohar

Morre at this road is the root cause in the creation of this risk zone. Conflicting turning

movements at the lohar Morre are made without any traffic-control device (TCD) or even

without a policeman. This location has to be either grade separated through an Overpass or

Underpass or at least be signalised. The existence of recreational parks (Aladdin and

Sindbad) on left and right sides of Rashid Minhas road generates congestion. This low risk

zone comprises of an area of 19.044 Km2 and population of 23 6, 000 persons.

Malir extensions, Quaidabd area, and parts of Shah Faisal colony make the low risk zone on

the eastern outskirts of the metropolis. Population of 243,600 happens to be under low risk

307

and the area it surrounds is 8,145 Km2, The grids of population density and disease

prevalence might have played an important role in the formation of this low risk zone,

4.13.6 Safe Zone

The enclosed map illustrates rest of Karachi metropolis under the safe category, It is only a

pictorial representation of the present day situation, The rising population, in migration

(Khan e/ ai, 1998) registration of motor vehicles (especially two stroke engine), formation of

new Ka/chi A badies, decrease in vegetation canopy suggest that sitting idle without seeking

alternative solutions would turn this safe haven into risk zones in future,

308

4.14 CARBON MONOXIDE (CO) PREDICTION

In the section, the results of an intensive study of Old City (core) Karachi are presented. In

this study, primary data on land use (floor space), traffic and air pollution (CO concentration)

has been originally collected.

Carbon monoxide (CO) enrichment during specific time period was the dependent variable

whereas roadway traffic and landuse related parameters were independent variables

determined for the purpose of predicting air pollution in the old city centre of Karachi.

Prediction models for CO-concentration for the following time periods have been developed.

I. Working Day Mornings

2. Working Day Afternoons

3. Working Day Evenings

4. Weekend Mornings

5. Weekend Afternoons

6. Weekend Evenings

7. Working Day Averages

8. Weekend Averages

9. Weekly Averages

This was possible usmg the backward elimination procedure for estimation of linear

regression models in statistical analysis software (SPSS). This procedure eliminates all non

significant coefficients such that the final model contains statistically significant coefficients

only (McCuen 1985; Gujarati, 1995).

Model parameters were estimated for equation (2.1). Table 4.59, summarizes the model

selected on the basis of following statistical parameters:

I. The low value of intercept (constant) is statistically desirable (which is the case here)

because theoretically when there will be no traffic and land use (i. e. Xl, X2 .... ..... Xs all

309

tend towards zero) then CO concentration should also tend to zero. Therefore,

intercept value should be smaller (Hameed, 1990).

2. These models were found to be statistically significant on the basis of the F-statistic

(Gujarati, 1995).

3. The coefficient of multiple detenninations R 2 is the proportion of variation in the

dependent variable explained by the regression model (Ali, 2000). The values of R

squared range from 0 to 1. Small Vlllues indicate that the model does not fit the data

well. The sample R squared tends to optimistically estimate how well the models fit

the population (Christ, 1966). The values of R2 greater than 0.5 are considered to be

acceptable for engineering data (McCuen, 1985).

4. If the significance value of the F statistic is small (smaller than say 0.05) then the

independent variables do' a good job explaining the variation in the dependent

variable (Fomby et al., 1984).

4.14.1 Prediction Models

The best model developed for predicting the carbon monoxide (CO) concentrations in the

study area (under the experimental conditions) during the selected times of week are given as

follows:

Working Day Mornings

Y"" ....... __ = 2.161838 + 0.004801 X6 + 0.138413 X 7 - 0.00019 XI - 0.00013 XJ + 0.000177 X,

Working Day Afternoons

Y (W ..... D."'~~I= 6.138887 + 0.005743 X6 - 0.00016 XI - 0.00014 XJ+ 0.00019 X,

Working Day Evenings

Y""""D~'_I= 17.65964 + 0.005868X6-· 0.0002 XI - 0.00012XJ - 0.00014X4+ 0.000187 X,

Weekend Mornings

1.749067 + 0.004467 X 6- 0.0000591 XI + 6.03E-05 X,

310

Weekend Afternoons

4.169156 + 0.005001 X6 + 0.000114 Xa

Weekend Evenings

4.729716 + 0.007588 - 0.00011 X1 + Xa

Working Day Averages

3674709 + 0.017228 X 6 - 0.00054 X1 - 0.00036 Xj - 0.00022 Xs+ 0.00054 X,

Weekend Averages

Weekly Averages

9.957327 + 0.016575 X6 - 0,00024 X1 + 0.000264Xs

8.299302 + 00,,0, X 6 - 0.00013 Xr 0.00000656 Xr 0.0000513 0.000137 Xs

311

Tablc 4.59: Constllllis and Cocfficicnts of P"cdictivc Modcls

• Monday. Tu~y. W~y;and 11lU~y, Avcrasc v ... luc; oft.cn minulcs .1.1 1 - 2 meier Irom the $OU(~e about 3 - 3~ feet above !.he sutf~

1 Friday. SabJtday and Sunday. Average values often minutes "I I - 2 nwter from the 5OW\:C about) -)~ fect above the s.unitce

312

4.14.2 Evaluation of Models

Some important aspects of the formulated models are:

• The negative coefftcients for residential (XI) and special purpose (X) suggest their

cancelling affect on the dependent variable. In other words, greater the space utilized for

residential and special purposes, lower would be the concentration of CO in the vicinity

of that particular location. It happens to be a rational aspect.

• All coefftcients for traffic volume (~) bear positive sign. Higher the traffic volume,

higher would be the pollution (CO enrichment). These coefficients are higher in

magnitude as compared to all coefftcients of other variables. Surprisingly, these

coefftcients complement the hypothesis that high level of air pollution in an urban

environment is the by-product of vehicular traffic.

• The regression analysis for almost all the nine models eliminated the variable of

Commercial space (X2) in the first step.

• It was realized that the results could be improved to a good extent if all the built up

spaces added (Xs) as follows.

The relative accuracy of the models improved statistically after this modification.

• Vacant space (Xs) emerged as completely insignificant variable for all prediction models

developed. This is quite understandable, as zero activity on land would translate into no

pollution.

• Since transportation ROW (X4) consisis of Roadway pavement, sidewalks, aU sorts of

parking space, medians etc. , its impact on the dependent variable would be of cancelling

out. The negative coefftcients for this category of land use in all the models developed

supplement to this postulate.

313

4.14.3 Applications of Models

The air pol/ulanl dispersion models (e.g. Hatano el al., 1989; Zannetti, 1990; USEPA, 1995;

Folgert, 1997; Hodgin el al., 1997; Comrie el at., 1997; Cartwright el at., 1997; Coe el aI.,

1998; Dent el at., 1998) deal with the phenomenon of pollutant dispersion rather than its

generation. The predictive models developed in this study provide the basic input needed for

the various air pol/ulanl dispersion models found in the literature.

The models developed for predicting air pollution (CO) in this study head to tremendous

potential. These models have the capability of forecasting carbon monoxide (CO)

concentrations for different temporal (diurnal, weekly) phases in Karachi. These models have

practical applications in urban and transportation planning, land use control, traffic and

environmental engineering. The practicality of the prediction models lies as being tool in

saving time and effort compared to the cumbersome and expensive field sampling methods.

These models have another interesting application, when transportation engineers and

regional planners deal with future planning and design problems. Whenever a change in land

use might be anticipated, it would be desirable to study its impact on air pollution at nearby

intersections in order to implement modifications and improvements. These models could be

used for this purpose very efficiently.

4.14.4 Constraints of Models

Models developed in this study give a reasonable estimation of carbon monoxide

enrichments from traffic and land use variables. The limitations should also be clearly

recognized.

• The data for this study were obtained from Old City (core) of Karachi. The land use

patterns in the study area exempted an important class of • Industrial', which may be

characteristic of other cities. Therefore, keeping such indigenous factors, cities may

314

develop models for their use. Frankly, the models develop here should not be applied to

other cities.

• These models have been developed for primarily a dense network of roads . When

applying to sparse network of roads, caution should be exercised.

• The study area characterise a significant fraction of land use for 'Warehouses' (Hasan e/

al., 1999). The results may not be consistent at location where there is a drastic change in

land use concentration e.g. ' special purpose' is predominant.

• Lastly, the temporal nature of regression models should be understood. The variables

used in the development of these models may be changed periodically by urban renewal,

change in allocation of space for various activities, future changes in transit services of

Karachi such as Karachi Mass Transit Program (KMTP) and up-gradatipn of Karachi

Circular Railway (KCR). Catastrophic events changing landuse patterns can disturb the

predictability of the derived models.

• Therefore, the predictive modelling exerCise should be validated periodically and

modified in order to maintain stability in the process.

315

5. CONCLUSIONS AND RECOMMENDATIONS

This chapter summarizes the research and gtves some conclusions and suggestion for

possible improvements and extensions of the research.

5.1 CONCLUSIONS

It is really a thrilling experience to study air pollution in one of the badly affected cities

of the world with meagre field resources we have. Nevertheless, by any means it is

equally comparable with all such studies being conducted elsewhere in the world.

This study establishes that RS and GIS integrated techniques are the most reliable and

expeditious technologies in monitoring air pollution. Although RS/G1S seem expensive

in initial financial outlay, nevertheless, in the longer run, these are cost effective, lasting

aDd dynamically adaptable in any part of the world.

It is revealed through this study that human behaviour is the key in creating air pollution

and that it should not be combated only through technological solutions. Rather a

balanced modification in human perceptions would come up with more concrete remedial

measures. It is with this basic intention that parallel physical and perception studies have

been initiated to achieve the main objective of the study, which has been outlined in the

hypothesis:

"The higher concentrations and spatial patterns of air pollution are in conformity with the

geographical distributions of land cover I land use, traffic and population, which resulted

in high incidence of airborne diseases and the human resource near those areas, are on

vulnerable risk. "

The developed GIS evaluation combined the data sets, various analyses and the resultant

maps with the capability to integrate further parameters for future risk assessments.

317

Multi-criteria decision analysis in the GIS environment was successfully employed first

time in Pakistan.

Micro-geographic appraisals of the metropolis were performed by considering 58 zones

outlined by the local development authority. Each zonal assessment included area,

population density, distribution of land cover classes, split of land use categories, and

frequency of airborne diseases, their prevalence scenario and temporal variations in CO

concentrations within the zone,

Multiple regression models for predicting carbon monoxide (CO) enrichment at the olden

region of Karachi metropolis have been formulated in which traffic and land use

parameters act as independent variables.

Further conclusions and suggestions concerning the general flow of the study within

RS/GIS framework and the specific aspects of epidemiology and public perception in

Karachi are as follows:

• The research attempted to. explore the advanced capabilities of Remote Sensing (RS)

and Geographic Information Systems (GIS) for simulating the parameters and

variables that happened to be the causes and effects of air pollution generation

respectively.

• The satellite image processing has yielded the classified product that is able to give,

in quantitative terms, the geographical spread of major land covers throughout the

metropolis.

• Another RS/GIS integrated technique of change detection has illustrated the historical

growth of human settlements and the future expansion potentials available.

• The study investigated the land use scenario of this mega-city and has furnished the

first ever development of a statistical and geocoded database on explicit land use

318

-------------------------------

types and functions for Karachi, which has also raised some issues requiring further

studies.

patterns of population have been monitored on micro geographic scale

analysis zones. Computer Assisted Cartography provided a combination of

demographic themes on which aUlhor has briefly tried to reflect with a socio

economic viewpoint

.. II has been firmly established that GIS and GPS enabled the researcher to render

temporal assessment of traftie and spatial variations of the road network across the

city.

.. Criteria pollutants (as defined by USEPA) have been ventured for spatial patterns

monitoring. Recent published have been used for interpolation and

transformation of po in I observation into continuous sUrface.

However, Ground tmlhing was endeavoured for hazardous carbon monoxide (CO).

Out of 308 stations monitored, more Ihan 32 percent sites exceeded the WHO

guidelines (I-hour) Annexure B.

Techniques such as Map Algebra, Density computation, and Surface development

produced the outcomes of the previous chapter.

.. Effects of air pollution on the human health within the study area have been

investigated by means of conventional tools. The study has gone further to measure

the epidemiological indicators of dlsea5:e occurrence and point prevalence at the

micro scale of Analysis Zone.

line public health information pertaining to Morbidity, Morbidity-Mortality

Ratios, Airborne disease freq1lency and Intensity of Frequency has been computed.

319

The linkage between rur pollution and subsequent diseases has

through II critique of experiences of the physicians practising in Karachi.

evaluated

• The research focused on the understanding and awareness asp'''''' of the population at

risk. Pathetic community attitude towards an important societal problem was

concluded. Findings of perception survey on air pollution indicate jeopardy at

community level.

5.2 FURTHER RESEARCH AVENUES

The products of this study have created many possibilities for further research:

• Due to many constraints, this research monitored enrichments; predicted pollution

levels; assessed risks; and appraised regions on the basis of a single criteria pollutant

Carbon Monoxide (CO). It is recommended that further studies should be done by

taking into consideration the criteria pollu/anls.

• A number of air dispersion models, have been developed in western countries. There

exist a need for suitable Air Pollution Dispersion Models within GIS framework for

the state of affairs in the developing countries.

• Further comprehensive simulated models embedding meteorological parameters

(vertical and horizontal temperatures; moisture; Field, direction and velocity of wind),

which could evaluate I animate the changes in air quality seasonally and their effects

henceforth.

• Land:sat 5 image, which was processed in this research for the extraction of land

cover themes. had a spatial resolution of 30 meters that suits beller to large-scale

investigations. Today's world offers a host of high·resolution commercial satellite

imageries like Quick Bird, IKONOS, DKI and KVR, whose spatial resolution are in

the range of to 0.60 meters. Urban investigations require detailed topography for

modelling etc in fonn of Digital Elevation Model (OEM), Further research.

applications in Pakistan can be conducted on similar lines provided the availability of

stereo pairs of low altitude aerial photographs and/or high-resolution digital

imageries,

.. Satellite Remote Sensing has been employed worldwide for time-lapse monitoring of

land covers, Developing countries such as Pakistan could watch for specifics e,g,

vegetation, environmental quality, settlement patterns and desertification, This work

has provided a footing for building on these inquiries,

.. In the developing countries, urban parameters are drastically different from that of

developed countries that' has propounded environmental implications,

Interrelationships Ilmong categories of land use ill the metropolitan cities

developing countries in Asia are interesting research areas. Karachi in depth

autopsy in this regard,

Studies' identilYing gradual land use changes at the neighbourhood level and its

consequent impacts on environmental deterioration are an avenue of future

investigation for Karachi metropolis,

• Risk assessment studies incorporating gender information of the affected

population be carried out as outlined in this thesis,

.. Pakistan lacks in vivo as well in vitro epidemiological studies establishing indigenous

linkage between environmental pollution and subsequent effects on human health,

Correlation of diseases incidence and enrichment of a particular pollutant has to be

inferred through recurrent community based studies,

The suggested clinical studies should spatially widespread and imbibe socio-

economic and ethnic clues of the focus population,

321

• Cross-sectional studies should be taken to evaluate the perceptions about the

understanding of such an important environmental problem and psychological desires

to abate them. These studies may provide the perception variations among the various

socio-economic and ethnic target groups.

5.3 RECOMMENDATIONS

• The quality of data on land use in the study area had not been excellent as it consisted

many approximations. There is a dire need of developing a comprehensive

cadastral database in the GIS framework for the metropolitan city of Karachi. The

beneficiaries of such MAGIS (Metropolitan Area Geographic Information Systems)

would not only be planners and researchers but also utility and services providers;

law enforcement agencies and revenue departments of Federal. Provincial and Local

governments.

• It is suggested that a metropolis-wide traffic volume count program be launched to

ascertain the current magnitudes. The storing and computing power of GIS and its

analysis tools provide potential for· more detailed descriptions and analysis of

roadway and traffic data. There is no doubt that traffic management in Karachi could

greatly be enhanced by GIS technology due to its visuaJising. analyses, simulating

and modelling capacities.

• Although complete population data are spatially managed at the level of districts, it is

not to deliver goods at the micro levels. Detailed demographic and socio

economic households' information is required to be maintained on GIS at smaller

local entities.

• This study has exposed another requirement of this society. Vilal statistics are largely

managed on GIS worldwide. Steps towards this direction need initiation in Pakistan.

:122

• Satellite Remote Sensing (SRS) can demarcate the large Plume of air pollution

particularly, from industrial units (cement factory, thermal power plant, and smelter).

Two real life examples for Karachi are illustrated in Annexure J. It is highly

recommended that further work be carried out to quantifY the affected regions,

property and population due to cloud cover concentration of such disturbances.

• Air Quality Index (AQI), a dynamic phenomenon could only be investigated subject

to the availability of continuous, multi-variate monitoring. In a LDC e.g. Pakistan, no

local resources are yet available to examine it. It is hoped that time would come when

AQIs on spatial canvas be explored.

• Technological innovation, institutional development, and co-operation by all levels of

. government and industry have proven to be successful strategy for abating air

pollution in the Developed nations. Keeping in view the monetary constraints for

developing countries like· Pakistan, concurrent endeavours by all the stakeholders

incorporating educational (ethical), engineering and enforcement (legislation)

measures are recommended.

323

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Annexure A

Monitoring Sample Locations around Karachi Metropolis A.l

m

Annexure B

Carbon Monoxide Concentrations at Monitoring Sample iAlcations around Karachi Metropolis

B.l

379

Annexure C

Criterion of Traffic Flow Observation C.I

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9 M M H H -"H YH 10 M M _H H VH VH

II M M H H VH VH 12 ~1 ~I H H VH ..\'H

13 M M H H VH vH I. M ~1 H H VH VH IS M M H H VH VH 16 M M H H VH VH 17 H H _VH \'H VH VH

I8 _M L H H M YH 19 H H VH VH VH VH 20 M L H H M VH 21 L M M ) I H H 22 L M M ¥ H H 2) M M " M H ~1 H 2. M M M H )1 H

2S H H VH VH VH VH

26 M H H M H H 27 M H H M H H 28 ~f H H M H H 29 M H VH VH VH VH )0 L M VH M H VH

386

32 I! I! VH VI! I! VI!

~ I! VH VI! I! VI!

I! ~ VI! VH I! 3~ L I! I! ~\ I!

36 I! VL 1 \'[ L

VL L VL L 39 VL VL M M ~\ I!

40 L ~ M M ~\ I!

4\ ~ ~ ~ VI! I! VI!

~ ~ ~I M M M I!

~ VI! VH VI! VI! 44 ~ I! VI! VI! VI! VI!

~ --.!i I! M VL VL M

48 -"l. ~ M VL \'1 M

s\ VL VL L YL L VL

~2 L 'I VL I! I! ~I n ~ 1 VI I! I! M

~ VL L L ~I ~ ~l I! I! VH VI! VI! VI!

~7 VL VL VL M L M

2! VL VL VL I! .~ t.I. ~ M I! I! ~I I! I!

60 M I! I! VH VI! VI!

! L ~I L I! \I I!

64 VL VL VL L VI 1

6S ~l I! I! M I! VI!

L L -,'.I ~ _" --.!i 66 M I! VI! VI! VI! --"-I!

-; M II VI! VI! VI! VI!

M 'I! VI! VI! vI! VI!

69 M I! -"1:1. VH VI! VH

70 M I! I! VI! I! '.III

..2l M II VH VH VI! VI!

72 ~l I! VI! VH VI! VI!

73 M I! VI! VH VH VII.

:: M I! VI! VI! \'I! VI!

387

~t~lft.m'~ ., " . ' 76 ~I M ~ H M VH

77 M H M VH

7H VL VL M M L M 79 VL VL VL L L L

SO VL VL VL l L L

SI VL VL , VL L L L

&2 VL VL M L H

~3 H H VH VH VH

S4 H VH VH VH

SS VL VL M L l H 86 VL VL L L VL L

87 VL VL M M L H

8S VL L VL L

&9 VL VL L L VL L

90 M M H H H VH

,91 M M H H

92 VL L L L ~I

93 VL VL VL L VL L

94 VL L L M H 9S VL VL VL M H 96 VL VL VL M H 97 VL VL L L L M 98 VL VL VL ~I H

99 VL VL VL M ~I H

100 VL VL VL M M H 101 VL VL VL L L L

102 _\lL VI. ..YL M ~I H

103 VL VL VI. M M H

104 VL VL VL M ~I H

lOS _\,L L VL VL

106 VL VL VL M M H 107 VL VL VL VL

lOS VI. VL L M ~I H

109 VL VL L M ~I H

110 VL VL L ~t H

III L VL ~I VL VL M

112 L VL M VL VL M

113 VL VL • VL L L L

114 VL VL VL L L J,.

liS VL VL VL L L I.

116 VL VL VI. VI. VL ' VI.

117 L M H VI.

H VH I. I. M 119 M H ~ I. M H 120 VL VI. M I. VL !I.

388

,~- , - --- ,--

,\;9 ~~:';'~ " ' " ' 1M ' ,' ,', : " '. ' , .... ,', , " ',", : ' 'if":, ," ,' ,' , " , ' ," ' " , ' :, , .. 121 _VL Vl ~ J. Vl M

122 VL VL M l Vl M

III VL VL M l \'L M

124 Vl VL VL VL \'L _VL 121 VL VL VL l \'l l

126 . "'L 11). VL l -''L L

127 VL Vl ' VL l \'l l

12' Vl l M l \'l H

129 VL L M l \'l H

130 VL l M l -"l. H

ill l l _VH M \1 VH

132 l l H \1 \1 H

133 Vl Vl l Vl L !-134 VL _VL V~ ..\Il. \'L VL

131 M H VH l \1 H

136 L l VH M \1 VH

137 l l H \I \1 H

138 l L H \! .\1 H

139 l. l H M \1 H

140 Vl VL VL Vl Yl L

141 VL Vl VL VL YL l

142 Vl VL l l J.. M

.!.Q M ]. , .M ~ \1 M

144 M L M \I \1 M

141 l l H H \1 YH

146. J.. L H H \1 VH

147 L l H H \1 VH

14' VL l l M l \1

149 VL Vl VL Vl -,:l VL

llO Vl ..1 L M L M

III VL L H H \1 VH

III l l M M l H

113 \I H H VH \'H VH

114 \1 H .1i Vii \ 'H VH

III M H H YH YH VH

116 L ' M M H \'H ,'IlL 117 l L L M \1 M

11. l M -'-- M \1 H

119 M H ' VH H H VH

160 M H VH H H VH

~1 Vl J.. M J.. \1 H

161 Vl M H \1 H VH

163 Vl \1 H \1 11 YH

164 \1 H -".I! .H H VH

161 L \I. L M \1 M

)89

-----~ ... " ,-", "

·~':'i}l'· .

166 VL VL VL VL L L

167 L M H H VH

168 H H H VH VH VH

M H VH H \ 'H VH 170 L

171 L 101 H H VI!

172 L M I! H VH

17) L M H M H

174 L H

17l M L H VH VI!

176 VL VL VL VL VL VL



VL VL VL VL _VL VL


IKI VL L M L

. 182 M M

18) VL VL L ' VL L

184 M M H H VH VH

H VH VH VI! VH VH

186 M H H H VH VH 187 L L M M L ~I

188 L ,L M M L M

189 L L L

190 L L M L

191 L L L

192 L L M L H

19) L ' L M L

194_ L L M L H

19l VL VL M L M

196 VL VL M M L

197 VL VL M M L

198 -"L L L

199 VL L L M

200 VL L L

201 VL L L

202 VI, L M L

20) VL VL L L L M

204 VL VL L L VL L

20l VL VL L L L

206 VL VL L L

207 VL VL L M L M

208 VL VL L L

209 M M H H VH VH 210 M M H H VH VH

390

211 M M H H VH VH 212 M M H H VH VH 213 L VL L M L H 214 l l L ~! ~I H 211 L ~! M M ~I H 216 L M M M ~I H 217 L M M M ~I H 21~ VL_ M ~ M J, lot 219 L L M M L ~I

220 L L M M L M

221 VL L ~I M L M

222 VL L M ~I L M

223 VL VL VL Vj. \'L VL 224 VI. VL Vl L L L 22S VL VL VL L VL L 226 VL VL L M L L 227 VL VL VL L L L

,~ VL VL "'yL ~ L L 229 VL VL VL L L L 230 VL VL L L VL L 231 VL , VL VL VL VL VL

232 VL L H L L ~I

233 M M VH H ~I VH

23' VL L M VL VL L 23S, L M ..M. M ~I H 23G vl VL VL L VL L 237 M H VH VH H VH 238 VL VL VL L \'L VL

239 "'yL _VL VL .L VL VL 240 H H VH VH "H VH 241 L ~I M M ~I H

242 VL L .M. L L M

243 VL L M l l M

24. VL L VL L L M

24S VL L VL L L M

~ VL 1- --"k L l M

247 VL VL VL Vl Vl VL 24~ L L M L ~I M

249 L L M M L ~I

2S0 L , L M M L M

2S1 L VL ~I M L M

212 L VL ~! M L ~!

253 L VL M M L M

2S' L VL M M L M

2SS L L M M L ~I

391

~:~; ,.:~:~ '~, .. ' .. .•. .. .. ........ ..... . ......... ... ' .. .. .. ......... .

256 .L L M M L M

2S7 H M H H ~I H

258 H H VH VH VH VH

259 L L L ~f M M

260 VL VL VL M L M

261 . VL VL VL M L ~I

262 VL VL VL L L L

263 VL VL VL L L L

264 VL VL VL L L L

265 L L L M M ~I

266 L L L M ~I M

267 L L M M ~I H

268 L L M M \1 H

269 L . L M M L ~I

270 L L M M \1 H

271 ~I ~I H H ~I VH

272 VL L L L L M

273 L L M M \1 H

.~ M M H H ~l VII

275 II H VH VII VH VH

276 VL VL VL VL VI. VL

277 VL .VL VL VL VL VL

278 M M M M \1 ~I

279 L ~I H M H VH

..lli L M II M H VH

2H1 L L .1, ~I L \1

2~2 L L L M l. M

210 L L L M L M

2~4 L L L M L M

285 L . L L M L ~l

2.6 L L L M L ~I

2.7 M H VH H H VH

2"" M H VH H H VH

2M9 M. .li 'II! H H VH

290 M H . VH H H VH

291 L . \1 M H YH VH_

292 VL L L M !, \1

293 VL L L M L \1

294 VL L L \l L ~I

295 L M M \l H H

296 L M M M H H

297 L L L M L \1

298 M H VH H H VH

299 M H H .\1 H H

300 VL VL L VL VL L

392

AnnexureD

Specifications of ADalyser

Model GasAlm (CO) Pollutant Detector Carbon Monoxide

Units Parts Per Million (ppm) and Percent (%)

Part No. GA-M

Manufucturer BW Technologies

Origin Canada Power 3 V lithium CR2- series battery

Detection Range o ppm - 999 ppm Detection Increments I ppm SeosorType Plug-in electrochemical cells Operating Temperature _20°C to +50 °C

Operating Humidity 5 % to 95 % Relative Humidity

Figure D.I, CO analyser is also filcilitated with Audible alarm of 90 dB (A) at I ft (O.3m)

variable pulsed beeper and Visual aIann, which indicates the aIarming conditions, by Red

light -emitting diode (LED).

FIgure D.l

394

-----. -- ---

Annexure E

Health Statistics

Indoor Morbidity and Mortality Statistics 2001

Medical Records and Statistical Office, Civil Hospital, Karachi E.l

". . ~&~l ":~~~~~ 7. 'H· :;~Q. .< . . • • . . i i'i'':~5. ' ~ ;; •. , · .:;/:ir<~<~'"<'. ' . '. '. .' .' " . . ~.' . "'.' ...• '.~ .~ :W~t# ,; ' .. ' it!; r ~ :>. 'j , . , >~~. ?

. " . . .. ',. . ,. ' "'.";";'" )16 i . , .nd other lung di ...... due 10 «lorna! .......

l59 Other injuri ... urly, , oflr.um 381 3991 .7801 ill 113 28(

l29 Dums . 25, 61~ 861 .5 83 13 '

29. r di .. """ 339 164 503 63 21 9(

499 "1 mju ... In"'", 442 lSI 623 41 29 T

~23 "' 161 III 28l 21 2: 44

J5. Othudi ...... , 232 15E 381 3( 12 42

270 A""., . I in .... cti .. 63 41 IIC 2( _2.1 46 Vi .. 1 Hepa,itis 22 143 36< I: I· 2~

191 149 101 251 I' " 2(

J6 . I i'.'etien 9] .1' 161 I I: 2(

350 " .i·i ,n.plvo'i. \31 41 172 I I 2l

419 \3IC 88t 219t I I: 24

320 Acute ,,.d 8l 1\ 161 I 2(

'"' D,abe, .. mellnu. 211 151 422 I' I'

II M,I,,,i. 141 161 .}21 II I

.'" 131 114 3\2 " I,

.1.11 ilL"''''''' onh. jaw 163 104 261 I I,

300 I i, ot'pro",,', 191 12 319 6 I

.17 Tetanus II 59 173 1 I:

27. Otht.~ : heart disc"", 11 47 111 1 I:

47 Rabi .. 3' 2C 54 1 I:

20 59 52 II 13

101 Mali"" .. ,. , .~. lIun. 91 6' 15: 12

209 Iron ' anpmi", 161 112 33l 6 I

91 MIi"",n': 33 11 5e -~ 93 i lof,olon 49 31 8C

341 Ulcer, .. nd 739 21' 951

~21

~iC'· .etc. ) 81 31 II'

~2.

. pulmonary dis ...... and allied, 31 31 71

22' Other, , =",,1 nomou. ,,-.tern " 21 61 I other diorden od the " ..... 1 n''''ous

229.1 ,,,'«. 5' 41 9J

1K9, Simpleand I.w, 31 2' l! 251 elvoni, : heart dis""" 81 4\ 131

••• ; .p .. ins and ofio'" and M~I .. ) 61 4( 102

239. 1 Com"" op"ity and Olh<r di.order> of ""'" 3\ 31 T

395

-r:;' I ~';';,<" ....... ' " .. '~~Jji~_ . .... .... .. ' .. " y . .. .•. _ ".';'''.'''c ..•.• ' .. _ •.. ~\1\~

. N~? I ;: :,~:l~, .: ' ., .! .' .,.' . • . • ..• . ~ . . . ~ f~t 94 ' I iunction and anus n 21 44 1

IS9 Othe, benj"" , 18 61 7~

II T\1,oid r,ver 168 S! 211

230 74 l! III

21 ,orintut nes. ,d ' : <lands l2 31 8J

141 I. 20 I 31

229 .2 i , orth' I n""ous svstom 31 21 l5

239.2 i 31 21 l~

., ~I<asles 11 8 21

419 No=.1 d.liv.ry 409a 409' - -3~9 Abortion 198! 1981 - - · 429 Di"' .... ofskin , ti",,, 811 31 1121 · · · 3J9 001", D~"'" or 331 m 471 · · · 231 C,,,,,,, 291 m 43~ · 21 Othe" 221 161 3~ · ·

31S Chronic d''''''''' oft .... il ..... adenoids 161 181 HO -'- · ·

4'9 20' 141 341 · · · 239.3 O\h" di ...... of ey. 17: Il! 310 · · · }99 Di,,,,,' cbs,,,,ic <a""" · 301 301 · · -

.169 Othe, di .. ue' of mal. Rmital 0",'" 281 281 · · · 14 140 81 225 · · ·

269 : di'eAses m 41 171 · · 11.1 : bro'" 161 161 ..:. · -

2)0 Acul" ; I,,,, li 4' 101 · · · )74 I p,ol.",. · 91 91 · · ·

329.1 "''''om , • pleu,isv, t elc . l' 2! 8, · · · 12 45 11 81 · · 149 Oth", I II 2, Sf · · Il I in '<-1ions due 10 other 31 39 71 -'- · · 13 Food I I 41 28 65 -'- · -,4 ,orbo""!a,,d joints 31 2: 6' · · · 311 "nd, " · 6J 6: · · ·

192 O\h" p,ol. in cl.ori. 2: 2e 4: · · -

4. 2' Ie 3' · 7l .. nd 2' Ie 3' ·

2092 : and othe, II Ie 2t · 40 A,,,. II I 21 '- · ·

1~9 . ' I<i",d 13 1, 21 '- · ·

79 : inf""ion 18 26 · · · 219 ~1 .. v.1 d~,dc .. 9 11 · · · 220 r th,n' 8 11 · · JJ 9 II · · 249 . • of the ear .nd """,'oid P'OC'" I .. · · · 79.2 Oth", ; D~, 5 1: · · · JJO Dise ..... or the l<eth and: 1 · · J4 . ,oou"" · ·

396

Indoor Morbidity and Mortality Statistics of Airborne Diseases Medical Records and Statistical Office

Jinnah Postgraduate Medical Centre, Karachi: 2000 and 200t

E.2

397

Zone Wise Epidemiological Statistics (Questionnaire Based)

Dl : HrlMbrh .. DOl : Hurine Lou DIS: Throat ClIJ1crr E.3 Dl: SlrrA D09 : Jlith Blood Preuurr DI6 : [..un, CJmur DJ : Tnnl Slrknrsa (NaUSC'a) DI0 : HJ&h C'1Iollrllrrol 017 : Birth Ddt"'" D'" : Eye a1lJmhta 011 : Hlp Puls, Rllit DI8 : Alttuna

D~ : TonalWI'- DIl: Ltamlnl Lou D19: Other 06 : alrON" AUf' Oil: Emdruc)' lAn D7 : ebron!" COUlh 01-1 : tJlur

. Sam~ " " . iDs ' .' . , . :",' . j. :\ ::

~l' .. '

"1114' '-: ' ~: Wj Q), ~~r~ ;rrnaH Zon. ·111 . D1 : 113 Dol ..

", . »1 :~< .,~ : DIO ;Il.1~ .. J)J~ .~ :b~' 0.11 . 'op. ';.; . ~ : . . ~ ... ;: ~ ;1

1 21 0 0 0 0 0 0 0 1 1 0 0 0 0 C 0 1 ., IZ.O<

2 " 3 1 0 4 1 1 2 1 0 1 0 1 1 0 0 0 • 1 17 20.91:

3 7. 11 1 1 3 I , I 4 , 1 2 0 0 I 0 I • 0 0 47 61.1'

• '0 1 0 1 C 0 0 0 0 1 1 0 0 0 0 0 0 0 4 ' .0( , 3S " 3 0 0 2 3 0 0 1 C 1 0 0 0 0 0 1 11 31,4;

• '" 3 " 0 " 0 1 " " 0 0 0 0 0 0 0 • 0 4 4.4t

7 •• 2 0 0 0 0 0 " 0 1 0 0 0 " 0 0 0 0 3 4 .3~

• 4' 1 2 0 0 0 0 0 0 1 0 0 0 0 " 0 0 I I 1O.2{

• 6. I 2 • • " 0 • 0 • 1 G 0 " " 0 " 1 I 1. Sf

10 " 0 C 2 • 0 0 0 0 0 0 0 0 • 0 • 2 J .lt

11 "' 1 1 3 1 , 0 ' 1 • 0 2 1 1 " 0 0 I 0 19 2U~

12 GO 1 " 1 0 0 0 1 • 0 0 • " " 0 2 7 I O. I~

13 3G " " " 0 I • 0 0 0 0 • 0 0 0 " " 1 2.1.

14 107 2 " " • " I 1 0 I 0 1 " • " 0 " • 5.61

Il 19 , 0 1 0 1 • 1 0 , " 0 0 0 " " 0 " 0 7 36.R.t.

1G 4G 4 1 1 0 0 0 J 1 0 • 0 0 " " " , 0 " '" 21.11

17 3l 1 0 , 1 " 0 " 0 " 0 0 I 1 2 " 0 , 22.1((

" J7 0 1 2 I " 0 0 1 , 1 • 0 " " 0 " 0 0 1 • 24 .]~

I. • 2 " 0 0 I 2 0 I " 0 0 0 " " 0 0 0 • GG .6~

20 II 0 I 1 " " 0 0 0 0 0 0 2 1 0 0 0 0 0 " l 9.~

21 " 1 1 0 " " 1 I 0 0 " 0 " " 0 " " " 0 4 6.4~

12 12. 2 0 4 ." J , I , 0 " 0 0 " " 0 0 2 IG 12.9<

23 37 J " " 1 2 0 0 0 J 0 0 0 0 0 " 0 0 J 12 )2.4:

24 .. • 0 1 2 I 1 " 1 2 0 0 0 " 0 0 0 " 2 .. un: 2l 41 • , 0 1 0 1 2 1 3 I 1 2 0 C " 1 1 I 23 !lUI

2. 91 3 0 0 1 1 1 0 0 2 C 1 0 0 " " " 0 • 0 • 9.4~

17 111 0 0 0 • 0 I 0 • " , 0 0 " " 0 1 0 " 2 !.fl(

" 249 11 I " " 0 7 0 1 • 2 0 1 " " 0 " 2 1 .11 11 . 4~

19 111 1 1 I 0 U 4 3 1 1 " 0 0 0 1 " " 0 1 1l \) . .51

3" IJ!I J J 0 2 " " 1 1 3 I " 0 , 0 " 0 U 1 2 17 IU~

.\1 372 • 1 " I 0 " , • , 1 I " 0 " " 1 " 3 12 W.6(

.n 14' " " I 1 1 I 1 " 0 0 1 0 " " " 0 0 0 • 4.0:

JJ JOG 1 , 1 1 " . J 2 0 0 I 1 0 2 " 0 " , 14 4 . .5f

H OK 0 " 0 " " " 0 0 0 1 0 0 " u 0 0 0 0 0 1 1.0:

II II 1 U 0 U " " 0 0 . 0 0 0 " " 0 " u 0 1 ·U!

398

:':':,"',: I ,~"; .. I '~'l :bi' . . ' ~I :~~ '~' ~f~;! ,!,#._ ." ,~~~~~~

" • • 0 '

l6 20 -" -" • • 0 • 0 • 0 0 0 0 C • 0 ..s .• l1 SS 0 0 • 0 0 C • -" -" 0 0 0 C -' 0 S 9 .•

II 44 • 0 I 0 0 0 0 0 0 C 0 0 C 0 C S Il.l

l\ 20S 4 0 0 C 0 C 2 C C C 0 0 ( 0 ( Il 6.2

4C 106 0 0 l 0 C 0 C C 0 C I C 12 ~ 41 III 0 C 0 ( 0 C ( C 0 0 C 0 l.O

42 54 0 ( 0 C 0 . 0 0 U C 0 U 4 7.4

4l ISl 0 I -' ( 0 0 C 0 C C 0 ( 0 C ( 0 U .2.,-

44 11 I C 0 ( 0 0 C 0 -" 0 0 0 ll .l

45 151 0 0 0 0 C C 0 0 0 C 0 1.9

46 \8 I 0 C 0 0 ( ( 0 0 0 ( 0 16.6

41 _29 ( 0, 0 C 0 0 0 0 0 0 0 0 -, l .7

41 Sl 0 0 0 ( -" -" U -" C 0 -" 0 0 5.6

4\ 7S U 6 ( 0 0 ( • 0 0 0 0 0 0 0 0 7 9.l

5C 62 U 0 0 0 0 I a a 0 0 0 a 7 11.2

51 21 0 a 0 0 U C ( a a 0 0 0 C ( 0 C 0 0 I 4.7

Sl 75 a 0 u c 0 a 0 0 0 0 0 0 0 C 0 0 1 ~ SJ 30 C C 0 0 0 _0 0 -" 0 0 C -" C 0 0 l 6.6

5' l4 0 ( I a 0 c 0 .0 C 0 a 0 0 C 0 0 C 0 ( 2.9

S! IC 0 C a a 0 0 0 a a ( ( 0 0 u C 10.0

399

Annexure F

Questionnaires (English Translation) F -Q I. Filled by Health Professionals F -Q2. Filled by General Poblic

Name:

Specialization:

.1 Age: L..I __ ...J

....... __ ......11 years

Practi~ing Lotality: 1...1 ______ ...J1 . IL-___ ....J

• Mark the diseases frequent la·.your practitiJ!& area (Air pollDtion bued):

2 3 4 5 6 7 8 9 10 II

12

13 14 15

16

19

• Lhf three mO!lt prevaleDt diacue. (nun tile ahove-meatioaed iA yow- praitice area (Air pollution based):

L 2. 3.

F-QI

. slin> _ .

400

• What do you think about air pollution as a contrlbuline factor In the given listed diseases:

Rallking Legend 100% Just only because of Air Pollution,' 50% Moderately Significant factor 75% Air Pollution is a Significant Factor 25% Less Significant Factor

o % Not Significant

D D D Irritability

D D D D D Arteriosclerosis and coronary D D D D Jaundice heart disease

D D 0 D D Arthritis D D D D 0 Kidney malfunctions

0 0 0 D D Asphyxiation D D D D 0 Liver malfunctions

0 0 0 D D Asthma and allergies D D D D D Lung fibrosis

D 0 0 0 0 Bone Diseases D D D D D Lung silicosis

D D 0 0 D Brain impainnents D D D D D Melanosis (darkened skin)

D D D D D Bronchitis D 0 D D D Muscular impairments D D 0 D 0 Cancer D 0 D D D Nasal irritalion

D D 0 D 0 Cyanosis (blue skin tint) D D D D 0 Nerve impairments and ataxia (irregular movements)

D D D D 0 Dental caries D D D D 0 Parathyroid disturbances

D D D D D Dental discoloration D D D D D Premature ageing D D D D D Dermatitis D D D D 0 Reproductive problems

D 0 D 0 D Diarrhea D D D D D Sarcoidosis (granuloma formation in lungs)

D 0 D 0 0 Emphysema D D D D 0 Sleeplessness

D D 0 0 0 Eye irritation D D D D 0 Thyroid disturbances

D D 0 D D Fume fevers D D D D D Visual impairment

D D 0 D 0 (Other)", ."", .. . , .. ". , .. , .. D D D D 0 (Other), .. , .. ",." . .. ,., ...

401

Questionnalrell: . I..., __ ...,......1; Name: ...,1 --,-_~I Age: I ... ~.,.;...;. ... ~~; 1 .... __ --11 F.Q2

Present Addresi:1 L. ___ ... 1 Working Place Address: rl..., -_"'-;,. -_-:-'0-_--'--_-_-_ -_ ~_ -_ -_ -_-_ -_ -_-_ -_ ' ... 1·

Duration of work (at present address): 1...1 -"-__ ... 1 Occupation: Education: 1...1 __ oJ 1 .

House Hold: < 02 yrs old 0 , 2 - 12 yrs old 0, 12 - 27 yrs old 0,27 - 40 yrs old 0, 40 - 60 yrs old 0 > 60 yrsoldO

Social Status: < 2000 Rs. D 2000 - 5000 Rs. r,.{onlhly Income) 10000 - 15000 Rs. D > 15000 Rs.

o 5000 - 10000 Rs. D D

How much time do you spent outside (in hOUrS): During Working days 0 W~kends! off days 0 Tra"ei Time (Daily average): 0 Most Frequently Used Transportation Mode:

Bus! Minibus D Coach Taxi! School Van! Contract Bus D Cycle or Motor Bike Metro Bus (Non AC) D Car (AC) Animal Drawu D By foot

D Rickshaw D Metro BUs (AC) D Car (Non A C) D Other

During what hourS, the traffic 'remains higb at the nearest street to your work place?

B D D

From D hours to D hours. From D hours to D hours ,From D hoUrs to D hours

What is the operating speed at tbe nearest road at the bigb traffic times?

Ever High D High al congestion D Medium D times only

Slow al congestion DEver Solw times only

If traffic speed is slow nearby you: Reasons for slow operaling speed.

High traffic volume D· Roadway condition D Encroachments! Parking in Ule vicinity D

Does this road have traffic jams? Yes D No D Only temporarily D

In your o)linion, wbat are the adverse affects of tbe Traffic resent at the nearb street to ~ourself llnd your work )llace? '--_________________________ --'

Is tbere any location n .... by. where garbage is burnt and the fumes reach your work placc/bou .. ?

Yes D NoD If )'es how far? .... 1 ______ --'

In yOllr opinion. wbat are the ad,'erse affects of tbis smoke to you?

Is Ibere any factory or industrial unit near your work place/house? Yes D No D If yes. bow far? 0 In your ollinion. what adyerse affect does it have on you? .... 1 ________ ---,. ____ --'

Do you b.,'e a wastewater overflow problem near your work place! home? Yes D No D

402

D

Despite Mosquitoes. What other problems do you haye due to this wastewater? 11... ______ -'

Is there another source of noise and smoke nearby your work place I house?

Yes 0 No 0 If yes how far? 1'---______ -'

Do you think Growing population is the main cause of enyironmental degradation? Yes 0 No 0

Do you think surrounding air is yulnerable? Yes 0 No

Has anyone from your ramily members. been affected by any of the following disorders:

How would you rate air pollution in your neighborhood?

Very High 0 High 0 Moderate 0 Low 0 Very Low 0

Do you consider that air pollution arfects human health? Yes 0 No 0 If yes. at what extent?

Very High 0 High 0 Moderate 0 Low 0 Very Low 0 Do you haye any remedial measures for the preYention or air pollution?

I. ,-I __ -,i.2. ,-I __ -,I . 3.1 '-__ ~I . 4.1 L-__ ..... 1 .5. L-I __ -'

Which place do you perceiYe to be clear of air pollution?

Home 0 Office 0 Educational Place 0 Place of worship 0 Recreational Place 0

403

Annexure G

Zone-wise Land Use and Land Cover appraisals

l"i~ul"C: Gx. I

AMlpsZ-Z LiHldU • .--~~

Anolysl. Zone 3 LandUoe ,% ,

c Plarnd RtSlderTiII

QC oofl'leJoal

DUI'b..n Renewai

DP~R~tm..11

IiiJUrb., A.cn:wtI

OCCfI"JlleJc.IIiI

tl%

404

AnoIp_ Zone 1 L_C_

PM. J"It ( q&qib

3% l "tb

Analysis Zone 3 Lande _

'''''

D O. 8 u;::t ~

oM . 0"* 141

OS. BLlt!: Lfl

DUrb.-, VCQ.

D '~ l!Qo!i.lIln

o~

F4;urt G)I.1

Q D. Bt&ltl.4l

OM .B~ ap

os. 8 ti!t up

O Urb., v eg.

DV!gd.1oo

0 0 ........

gO. eiJIt ~

C~. 8taklfl'

os.s .... 1.C)

OUtb.., Veg .

Fi~urt' Gy.3

Figure Gx4

Anal"';s Zclne 5 Land Use

0%

rigllrt Gx.5

Analysis Zclne II LWldU.

DU",1Mwed Rewdtt'IWl

o U rban Jl.crft'4111 tJ Der'I5lf1cmonAR115

gC_

."',.,rW tlT~rt F-=*1IiI!5

l-'igurt G:c.6

8%

45%

D P\IMed RsdstiIJ

Q Ul"fI\lmed R~ert. CU tbClRetlCWW

"Ccmneroll ICTf'lraport fdlle$

2"""

• %

." '"

405

gO.Bult~

oM . B.~

OS. Bult 14'

CUrb..,"~ .

CV~ct~

c Opttt.ard

12%

Ftl!ur~ Gy.4

'10 . 8!J!t 1.4)

c!r4 .8 1..1111,4)

as. 81J1t up

O Uri>Ir1 Veg .

O VtgdMlCiI

cOpert..-d

fo·i~u~Gy.5

00. Bldte>

C M. B tAll.4>

C S.8lJ.t\4>

OU TtlanVtJil .

Hgurt Gy.6

- Urb~R~

. 'i l!urt' Gx..7

Analpo Zone 7 LandU ...

""''It

, 00'lb

Anolps Zane 9

. UrtNnR~

L_Use ... ,%

• U III &IrrcId RewdAal

. BIS1I11GJotn!

O lll!il:ltOltJOnaI

Q Urb~lt~

406

AnalvolsZane 7 Land C...-

ANlpo Zone 9 LandC __

DO.Bqll""

QH .alllt~

as. Sult I.p

IJ UrbM VaJ·

t!I'VcgCIII-.Jcn

o Opo<Lwl

Fig u no Gy. 7

OO. BI.iltlC)

c ,lo4. 8UiltIC)

C S.B .... It~

FIRure Gy.&

C O. BlJllLP

OM . BI.IIII4I

OS.BlfIlt41

O Urblrlveg.

OV egel.-.on

c C......-

,,%

A .... y ... Z-U L_u.e

.,,.

.~~IIC"t' Gx./ I

'''''

1%

.. PIIrn!d RlllidtrUll C U If! IIrn:d R sidst'-l . 9 lIS. Grot.rd

Ii U "11\ RfnIWII OC~ o E\ecraLlonlf

IiJP1ncdR~

CtJ~ Ru8'tllll

. Ed\DbOn c RClCnlllJoniI

Analysis Zone 12 L .. du.e

oPWrlned Rewde1Jll

oC CltffT'aW'daI

.Ed~lC"

[J R«.rClltkl~1

407

A .... ,..da .... U ...... C_

Analysl. Zane 12 LandC _

".

D O. 81.tJt L4J

OM . BUlIt"",

os. Sult.up

1:10. 61.10-'(",

I:J M.BIAIt !4)

oS .6tw.t ~ CU f'bIl'l VfQ .

gO.e"" LfI

aM . BIAk~

O S, BWI. ~

QUrb"Ve:J .

D Ve;I!!I.ClO rl

F~tll"~ Gy.11

a PWn'ed RI!ildet1: '-I

c Uf!l1«Y'o1 RtsidO'f1111

C C 0ITY'IWdI1

. EdlGll!on

FiJ llrr Gx.11

DI'1iInd ReliUMY6

D UflIUIYIOJ Rl$idbf).JI

OC~I

O P'«rMlCM"li&

a l>tir6rfC:lhon A rt:<J$

A ..... y.I. Zone 15 LiUldU ..

a Ul1'i.m'If!d RCI&MicmQ4

O RecrM:lOlliJj

'D Oe1ilf~on Areil!li

Ficurc Gx. IS

, ...

408

...

"'.

Analysis Zone 14 LandC.,....

Analysl. Zone 15 LandC~

",

" ..

gO. BIJIt IC)

OM.Sli!;( I4J'

05. 8ult up

Q Urb.1n V t!IJ .

D '/tQtl.jgn

Fi~urt Gy.13

II!ID.S"Itf.4)

O M .8 Lo1l ...

os O-..ltup

ClU Jb .. Vcg .

ClV et;let .• IO~

0 0-.....

F.gul,t Gy./J

DO. e l.l Jt 1fI

eM . Buitt qI

oS. B "It~

[]UroanV~ .

Ii v eo«~hOfl 0 """'"",

F~urt G)'. IS

Analysis Zone 16 Land Use

"

flcurc Gx.16

Analysi. Zone lJ L_ u...

I'~u("c Gx.J 7

Fi~urr Gx.IS

."

C RfCf'lilltlONi

a DetlificMJQn Af t.:lS

o M IMtary A ratS

D'I..-..dll.~

IiiiI Vr'CII...-..<IR~1:iaI

. 11n.<a-1IU'ld Q uttltlu -......... • rduttllon

DLowh;~5erllll'lldt

409

Analysis Zone 16 Land C.--

Analysis Zone lJ LandC..-

Analysl. Zone 18 LandCCM!I"

''''

, ....

DO.8 tJ!J: If)

0"' . BlAt".,

D S. BtMUp

OU rblnveg.

C VCQf!I.1OIt

00_ . WATE R

51'"

F~un~ ~'y.16

,g O. 81.1ItLP

eM .BUI: I..CI

DS. B"'It~

CU rb., vcg

IIVegt(.akln

0 --....

"'.

ILIO. aLiI! "" OH . B ... 'P

a s. BIJIl4)

cU,*VCIIjI.

DVt:r.ld:.lon

ao_

Fi~ ul'"c G)'.18

A"oIy,,;s Z ... e 1O Lind Use

0%

11l1'.:. 6%

10'10

F\:urt Gx.20

9%

FilUl"~ Gx.JJ

aP~ R~""

iJ UlfOl'aatnrrd R ~lderc~

O~Aelids&l

QU~ Resldet&l

. B 111111 GJotnl

C UtlliUes

s Agrlailwe

. h::liRr1a!

DP~ R~

&l Uf1)lttnlld RI5Jdetlill

o C """'"""

410

Analyois Zone 19 LandC.-,.

2%1%

~ D . 8 ~1t lCI

[JM . 8 1.ill~

[]S . 8 ~JtLC)

C Urb" v l!g.

Il Vegct"K30

cO........,

.·i~urt Gy.19

gD. B~""

OM . 8LA ~

OS_8 L11 ~

a urb4n Vcv.

DVeg t!lation

QOPM~

Fi~ure Gy.l{J

QO.Bult '41

OM . 8wt./.4I

OS.B IJJtI.4)

a Urb""'ea. CJ Veg,e(aflOtl

0 """"",,

t1t;Uf't Cy,lJ

O P\ll'r1ed RO$)(tm;14I

IilU flI'-nd RCAfcrWI

c C """""""

16%

Aznlysi. Zone 23 LandlJ5e

aP'-"'d R_~1aI iii U ",Mw:! RI!5id 8XIiIi

c c--"" • EdUCltlon

FiRllrr Gx.2J

AlYlpi. Zone 14 LandU..,

DPWn!d Rstdtmal 15:1 U",1Amed Reslamial 1 o C "'"'""'" . EdlOtion CM MKafyAr-. O RecrMlo,.t

Fi~tHT Gx.24

6 ' %

411

1% 1%

Analysis Zane 23 LandC __

c n.B uiitl..Jl

DM . 8 1Al ~

OS . [hi .• l.op

OUIb.,. Veg .

Fig ... G)'.12

0 0. BlIlt I.P

cM .a.L4I

OS. 8 U111: L4I

C Urb .. Veg.

II \I e;«:ilUIKI

Figure Gy,]]

QO. a .. l l4J

0;101 , Built ""

as . B"It~

OUrtlClVI!I9.

D PLnled A,ajdcrtW IQ Ul'l'lamed R~ild~ oUMtirs o M lilt.., Areu . Flood PIIm gDtnallt.tltlOf'l AIUI

Flr,unGx.26

CJ PIn'cd Raldftial IiiJUflItimed Res.dertl.)j

(] u tlt.ties • hll&tnli

• Education

OReaeUlMl

. SCh!IrntfI to HIli

o ee.llbtlon A/Or.>

s ...

Analysis Zone 215 L..sU.

''II>

a PI:rnd. Rcu::tertMI IiIUfJI'-mcd R-.id.ut c Utll.ie5 . h!IA1:rW DMawyAttu C Recl'elDONl . flOod...., OV IttrCD~ed DD~nA.~_

76%

412

, ....

,,..,

QO. 8 1J1k 14>

CM .SlAlt LP

CS. BI.I"l1.tJ

O UI'bIifl Veg .

C VI!9I!!.4_km

c~

Q Veg ___ lOn

OUfb., V'$.

OH ,e.LP

&l O. BIoi. I.P

0 5. 0 IJft up

Fi~uR Gy.26

DVeg •• )Orr

C Urbln\l fIJ •

OM . 9 1d: 141

go. 8111tf Lp

C S. BUlIt ~

[]~

to'i~urt: Gy.27

DP~R .. dcrtW

C COI'ITI'1!t'CIoIIJ

.h:lwtnal

. Edf.Dl1on

• Flood Pl4 0

.~t~ C(ll"rlref(:laI Cert~

CI~R~

ElUI1Iiath!d R.e:sldotJtI

O UblCJ!5

DC OII'ITWaM

C Va:at'I Un:lCYdoped

Fraurc G:c.l'

Anllysls Zone 3D ~_Use

CI B -.nil GrolJld

DC OfTm!tdil

OV4ICM Urdflll'dopcd

o Low tlrom SctiIrrwc..

F~urr Gx.JO

111%

2% 0%

1% S~ 1%

413

Analysis Zone 1II LandC.-.

.. --~)""'

Analysis Zone 19 LandCCMr

2%

11%

aVegdltlOn

O Urb",VIg.

I:H'LS I.i"If 14)

QO. Buft L4l

OS.S uitup

00_

a VQgef .. ll:ln

o U cb., V!!g.

OM .8.""

0 0 .81"14)

a s. S\lJt '41

c O ........

Fi~urt Gy.19

DVeg_OJI:;o.,

CUrbanveg .

a;'o1. sl.llh~

QO.8~ 14)

o s. BtJI L4I

C"""'-'''''

a P\b.t R ellidt:r1 'III o Uflll.i11"Cld R.c::WtrOII 13 9 \6111 GrolRf Dublrbel: ...... "" . FIood "'n DNtlHhl.LIIi~ry Qlow ftomSdllcm!m1

A"".,.. Zone 32 l_Use

Fla . ... G.dJ

6200:-_._" F~Urt Gr.))

8200

o P'W#'IDd A. ~cnMI DU l'1)btnd Realdertll.l IJ B II'W Gro.t.ni oUtJln5 DA~ o Real!lllicll1l! .NM h;lustTv aLowkDmSettI~

OU'll~ RellidftJII . NCllfrial Dl.nwkoms~t:

414

2%

Analysis Zone 32 l.ndCooer

7%

&:I I/ eo_titian

DU J!I."Vcg .

ClM .Stilt""

QO. Bu.k ~

Ds. e ~lt~

DOo«t-nI

f"i~urt GJ'.J I

~~" .. J I"""

7%

o 1/ eg elilllioo

C Urban Vc:g .

EJ M. 9li1t lCl

aO.au. t.p

O S.8ult1.P OOpcUnd

a VfIg«illOn

OUrbInVflIJ.

a M. BIMt t.p

QO.fh . lt lC)

OS.SUlk '-'

D OpoUd

Fij[urt Gy.JJ

0 ...

87%:-..... _ .. ~,...

Fijturt Gx.J.f

2 ....

lo1gyrt Gx.J6

QU~ Resld8tilll

.B";.G~

o lOw k-com 5a1l8lWts

, ...

IiI U~ R5.tt!l'tNtl

.Pf~q1

. £du3tlo n

OY &IIOI urwS~

o H tAJY MIllIS

o Low h:Dm SdtBn'!fts

o Plim!d Reslderfial I5 Uflllllrn:!d ~~enr:\aL .b1 __ 1'1aI

[] M _t1ry Af~

OR ecrutlOnll . Sdltr'nes; 10 Irfli

415

J%

...... ytlsZone 3S L_C.--

.""

2-<1%

, ... 10%

-_.""

eVegct..lltion

CU rt>IfIVI!Q.

DH . 8 Ui11~

8D. 8Li1up

OS.9 L1..~

0 0priAnl

Figurt: G'y.3.J

.VegetatiOn

O IJrbIl'l Yt.ll ·

DM . a ~14l

a D. oiMt IC)

as. alA I4J

o OpetUrd

Io'i"ur~ G)'.J5

VegtllUon

CUrb~ Veg.

II M. BuitlC!

iii D. fhJII: lC)

O S. fhd LCJ

00_ . wAl1:.R

FiRurt Gx.J7

FiRur~ G.f.)8

DPYmect ReMdcttla IJBllraGroilld OUultSel

ClA~no.iIUfI

O V Ie.n U rclClYdcped

O M llicary Areu . Sct.nes to llii. OlDw h:Qm Stttkmrts

22%

. Sdwr& to IrtlJ

alDw h:cmSett~

416

A nalp • Zone 37 LandC.--

16%

)6%

)%

O lJllgtbbOn

O UfbMl Vf!g ,

O~ , 8 IiJ1:LP

QO. 81Mt~

O S, SII" L4'

c CocltR

~UI"C G. ... ,J7

a Vegetatlon

DU!b~V~ ,

a M,611ktl)

DO,BLlItL4)

oS, Bille Up

C O......,

I!I veo«aiOn

C Urb.,Veg.

~M ,BLAtI:~

.,0,8 lilt 141

C S.BI.lItI4l

00_

"'igul"c Gy.J9

[] p~ Reitdettlal

IJUIllIamad RoC$idatll;l 0% OUtiilles . N lA'lrIIIIi o Y en: U I"IIh:vd:I peel

OM~""NS

F~ure G:c..40

Analysis Zone 41 L_U.

a Pin1ed Resldl5fill I:I U~Wn!d R,.;dcrcial CU tiJhs • ftI."iQI'\AI aVaQI'Cund~

OT~f1Fdlt". 13 ScNn. 10 I rfil II NM" h:l1&try

Olcw kolflSl!ttm..tl[J.

.... a" Gx..fI

Analysi. Zcne 42 ~u.

a Pllr"I1!d R..r'lt~MII trUilly/ftld Rald~

l % CUUIUt:Ii DAgoaMtil:

1% .h1uarill • Edtatbn . Flood PlaIn WNcwhfusfry a Low h;otq Scttlern:1tJ. a8t.1f •. :ueM;

FiR""' G.t41

417

a V.gd.lDn

a Urb.,Veg.

aM . BLI/t 14>

IiIO. Bult lJI

oS. Bul'l: ""

17%

a Yeg-«on

aU rbVlveg •

oM . eutL4)

IiiID. fhd"P

o S. 8111" '4J

c"-

OUtb,-,V!9.

a M. 8\i1t I.4l

QD.SUllt I.P

oS. ew!t up

C O .......

Analysis Zcne 4D LondC.--

2 ...

19%

Analysi. Zone 41 L_C.--

1 ... 6%

Analysis Zone 42 ~_C_

1 ...

19%

FiKUrt Gy.JO

'I'll>

13%

Fi~urt Gy.41

4%

,%

2%

r;lRurf Gj'.-I1

Anal"';' Zone 00 LandUlle

Analyois ZDne 44 UndU..,

o PI«'w'cd R~drrI.O U~llkIeI

O M IlltllY ~roas o'r~ort faalrtid .~~o,fIlll

t1gun: Gx,U

FiJ!ure Gx..l5

AMp' ZontI4Ii LandUlle

D PIImed R.eMdedWIII

o MIII\alYArMS

.F~d PWn

S'lb

D PI ... dR ...... I ... au~ ....... I .. .1!I!81"~ O Ulillbti a l'lOfllll'1IiIn a,._ hUCrY

418

o VegctoHon

cU rtl., v~.

oll! . 8iJ1t ~ go.a..- up DS. B~ 4)

D Op.uro

DVeg_lIIion

Qu r1:lanVeg .

CM .SIiI",

QD.8I.1ll4l'

os. Dtill IQ

DOpcrhrd

.'/egSAlIOn

QUrtl,IVeg,

a foll .BIiIt\,p

n O. 91.t1t up

o S. BIAIt up

0 0_

15%

17%

3 .....

17'1b

FiJturc 0)<..15

(I i>IIrnd R elldertW gV11'1M\'Ied Ae£i:I'wtW .hd,*n.M a M \fit.., Arms CA~loNl

. FlQodPIIiII

.~to .rtlll

. NI&.III"G'lAt1V

aOffU~AI~"

Fla .... Gx.46

II P&nta::l A. nenlill QUl'lIiIm!d RI5ldeft)itJ

• h:lushi. O "I'IItwyA~

• Flood plArfl

.K~WLtl"" D OCin5f1"c:alO nAre.s

J.·if.!ure Gx.4 7

". ~ ~

: ' ,

Anal";. Zone 445 Landlloe

61%

'''' 8'"

An~";s Zone 47 Land Use

2%

IIPWed Relii:lf4t11 au!J:'~ AeJ):::Inlai .BwialGm~ ...... riaI o V.:" Und~pe:j O M ;ilrtl:lIyArmI

QRflI:~IO"'" • Flood P'-in o Va:n DewWoped • New Ir'I!h.lltJY

.. .. ~~,: I'. -' .. ~~ •• ,'

" '.

FiRure Gx.4S

419

aV~ttmon

c Urttn Veg.

0"' . 8 lilt...p

aO, 8 1jft~

os. 8Uft up

" ",..,.....,

DVfJget.-..on

CUrb¥t VC'fJ .

OM . 8 ~.,.

g O. Bult IC)

OS.BLm up

DOpcrtn

a l/tga.1Oft

OUrtl-lnV(Ig.

OM .S ~ t.IJ

gO 81il",

Analysis Zone 46 LandC.--

22%

F~urt' Gy.46

Analysis Zone 47 LandC""",

Il'IOo

13%

Fi~lIr(' Gy.47

.'"

F~l"t' Gx.<l9

I'~un' Gx.SO

m: P\irftd R..adeIIIII

OM IIit.ryArDIIS

IIFIood P~

OS~ to Irfift

10% 0 ...

II Piwftd RKh~eth" Cumlt~

. hlLMri4l

. Ftood PWI! O T1If'II4I olt Facials . Sdlemei to >rtlb o OcNfIatlon Arc. C Low b:omt'l lfmtru Q et/f8" Areal

C PIIINd RsJdD1l141 C 9r.6W Gro.....t D Agrci.lt:LI1I! o H IIby II reM.

• flood ~ 8 SdwNsto irftII . Ncw¥dU5try ODer&ifbtklnA remo

420

mV a;lo!I:&lon

C Urb.nVl'g.

O M .Bwil4J

oO.9 rJ. ""

av~~

OUrh nV CI!il •

ClM . B·Lfl

IiIO. BUlIt loCI

05. 8\111: ~

CO........,

a VevdCio\'l

D UrbM Veg .

DM .BUlt Io4)

"O. Buft~

OS. elJl~

D~""

•

t' iJ,!ul"t Gy.51

m"~.-d1! ... tt..cl1ll

Dlk1JI~R"""UIll a,~ .. ~o:x.nd QUla l)" • ....,1. . c...r.1orI [J"'Ji t.-., Ar ISM

O ll!«rwUJOMIi . floodPltIn

. Sd!ene to trJiU ..... -a \pw"'IIII'I.5~(I..,.., Ct

....... COllWllCrC'-fCcnc ....

Fi2ure Gx.S2

.....

mp~R~~~ ~ .......... ... a~tt.~o.,u., 64% C tolll t.-y Ar_

O R«NIIIItl

C o..nc.l(loroNMC

Fl&ure Gx..U

O'J...cd1l_~I '"

• f'tood PI.,

.It~hlatr(

Olowh:QII('I.$.~a

Ii'~urt Gx.S4

7% .%

70'1b

421

CJ V t.OeQll lO n

C UlhnVcv.

OM. BIiI!: \4l

Q O.B ~

5

ove;;«ltlOn

o Vrb-.Vcg.

ClM . 8 "'~

DO. BtMt'4'

os.S,,"'J.C:l o Opcrtand

OVegetoilion

OU fb .... Veg.

0"' . 9 111114)

OO. Bttlt L4'

Analysis Zone 52 LandC,,-

2 ....

AnMyois Z .... 54 L.r1dC .....

.%

=--::!4% _ 1%

2%

Figurr Gy.H

••• II1II "'%

ficure G}'. S4

AnoIYS;6 Zone 55 L:ondUoe

. floodPloilin

.H_~IY

O LON'k0l'!l5 effieDlftt

Filturr Gx.J5

Analysis Zone 56 Land Use

O~"":lR __ "o."l(l.

a AWi'IMw-t . fJGudPIAn .H~~ry O (.QII't"N0III5tttl4OMnlt

J.l gurr Gx.56

.""

-

• %

422

D lJl!qs.lcm

[JU rb"VflIJ·

OM . 8lllt l4l

gD.e~~

OS.Bt.t.~

0 0 ..........

aVegd.1l.1OO

OU rbWlVeoj .

DM .8~~

&1D. B'" ~

os. Bul lC) D Opert.ond

Q 'YegtlctQn

o Ufban 'lolt\1.

00""""'"

Analysis Zone 56 LandCCM!I"

.. 'It

FiRLlre Gy,5S

F~Llrr c.;:v. S6

fi2arr G},.J7

Annexure H

Land Use find Traffic Statistics arollnd Monitoring Stations in Old City Area within IOO-rneter Oi;mleter

HOUl'l)' Ro:.d,,":lY Conuncrdal Rtshl('nli;'l) S(l<'dill

V'h."~UlI Trans['I0I1:1

1"ame- of LoC:ltiuR 1~",ft1c w'dlh· SpACC·· Sp:u:~"·

Purp(JSC _pace· .. lion Riehl ('omlt !oiP;'Itt" • Qr"'a)'· •

}.; ild'llf C cntre: 288 G4.S-l 7J7X~ .O I .521:1 6. 00 1-19.00 16900 4914,00

),;1,11 1 e .1~1t )' Bridbe 10974 5S.S' 11 8224.87 0.00 0 00 II GI6.0U ' 3)18.00

Cll\' SI~\iol.l lhhJb B;U\k f'!Uo\ 3724 42.56 5287152 0.00 43-16 .00 000 13962.00

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• l"nU: r .. d •• \jult: Sqlmrc (1'('1

Annexure I

Pictorial mustrations of the Problem

Figure 1.1:

Traffic Congl.'Stiol1 fomllng Very High Ri:sk

Figure 1.2: Visibility Problem ncarby Emprc .. Market (Very High Risk Zone)

426

Figure I.J: Typical Old City llru:lin;J (Very Hi\1l Risk Zone)

Figure 1. 4: Slack SlK>lre e.u..s.Lor> in C'Dn:)ested Streets o f very High Risk Zone

427

Annexure J

Plume Dispenion from Industry, Karachi: Monitoring through OptiCllI Imageries

Fi gure J . l : Landsa t 5 - TM, 1992, Bands 2 , 5, 7

Figure J. 2: Landsat 5 - TN, 199B, Bands 2 , 3, 4

428

Figure J. 3: KVR, 1998, Very High Spatial Resolution (2 meter)

42~

Monitoring Spatial Patterns of Air Pollution in Karachi Metropolis

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