pollution analysis through gis and rs

7/31/2019 Pollution Analysis Through GIS and RS

1/70

An investigation of Spatial Patterns of Urban Air

Pollution and Source Recognition through GIS and

Remote Sensing in Lahore

Tasawar Iqbal

Dissertation Submitted In part fulfilment

of the requirement for the award of the degree of Masters inApplied Geographical Information Systems and Remote Sensing

School of Environment and Life Sciences

University of Salford

Greater Manchester

November, 2011


2/70

Tasawar Iqbal ID: @00253100 [email protected]

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

2

Declaration

I, Tasawar Iqbal, hereby declare that the following dissertation is entirely my own

work and has not been submitted, in whole or in part, for any award to any other

academic institution.

Signed.....................................

Date.....28/09/2011


3/70


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

3

Acknowledgements

With pleasure, I acknowledge this research work to my supervisor Professor Mark Danson

for his energetic and valuable guidance in the completion of this dissertation. His scientific

passion, ever willing, determined assistance and amiable appreciations of my limitations

enabled me to bring these pages to the light of the day. I will remain grateful to him for his

valuable guidance and intellectual competence. My thanks and appreciations are for Dr.

Richard Armitage who taught me different tools and analysis techniques before this research

during my studies.

I will remain thankful to Miss Isma Younes and Miss Ibtisam Butt, Lecturers in University of

Punjab, for their help and guidance in data collection. They encouraged me for research and

learn problem solving techniques. I extend my indebtednesses for my friends Mr. Nasir

Ashraf and Mr. Abdul Jabbar, for their assistance in data collection and heartfelt cooperation

in this research.

I can never forget my parents and siblings at this occasion for their kind and never ending

assistance in this piece of research work. It was difficult to complete this work without theirfinancial support and moral assistance.


4/70


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

4

Abstract

Air pollution in urban Lahore was significantly increased in last two decades because of

unplanned infrastructure and rapid urbanisation. Pollution data of 22 sites in Lahore was

collected for the year of 2007 from Punjab EPA while satellite images were used to analyse

the spatial distribution of pollutants through GIS and Remote Sensing techniques. ArcGIS

and Erdas Imagine were used for the analysis of pollution source identification, spatial

distribution of pollutants in relation to land-use and epidemiological extent of pollutants from

source. Image classification, proximity, intersection and interpolation techniques, were used

to obtain results. Correlation between land-use density and concentration of NOx, SO2, CO,

O3 and PM10 was calculated. From results, it was concluded that all pollutants except O3 areproduced from traffic because correlation was significant for the concentration of pollutants

with only road density. The values of correlation were highest within 200-250 meters

distance from main roads that indicates high risk of vulnerability within this distance.


5/70


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

5

Chapter 1

INTRODUCTION

1.1. IntroductionAtmosphere is one of the most important elements of nature on the earth that supports life. A

person can live for days without eating and drinking but cannot survive for minutes without

breathing. Significance of atmosphere is from the fact that an average person breaths 14 to 18

kg of air daily (Fenger, 1999). Atmosphere is a mixture of different gases and all of them are

in a specific proportion. Gases are dynamic and any change in the proportion of gases can

disturb the natural environment. A significant increase in the harmful gases and chemicals

was observed after industrial revolution. Increased traffic emission, industrial waste and

urbanisation have added many harmful gases and chemicals in air that contaminated the

urban environment in the form of atmospheric pollution (Omer, 2009).

Longhurst (1989) classified air quality as very good, good, poor and very poor. Poor or

very poor air quality is a result of air pollution. Atmospheric or air pollution is defined as,

the contamination of air through chemicals and other materials resulting in the degradation

of air quality (Gupta, 2010).

European Union 1996 Council Directive on Integrated Pollution Prevention and Control

(IPPC) has defined air pollution as the direct or indirect introduction as a result of human

activity, of substances, vibrations, heat or noise into the air, water or land which may be

harmful to human health or the quality of the environment, result in damage to material

property, or impair or interfere with amenities and other legitimate uses of the environment

(IPPC, 1996).

Potential health and non health issues are related with pollution in all over the world.

Different diseases such as irritation to eyes, nose and throat, respiratory infections, allergy,

asthma and lungs cancer are observed in urban areas because of air pollution (EPA, 2010).

Burning of fossil fuel, industrial discharge, plastic and organic compounds combustion,

incomplete combustion of synthetic material and some others are major sources of air

pollution (Emberson, 2009). Sources of pollution may be point or mobile sources, and both of

them relate to specific geographic location. Point sources are industries, houses, landfills and

incineration sites whereas mobile sources are automobiles (cars, buses, trucks, aircrafts and


6/70


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

6

motorcycles). With the increasing trends in urbanisation, number of industries and transport

increased. As a result, pollution levels suddenly rose but its monitoring and management is

slow. Monitoring of pollution and its sources is necessary for the management of air quality

and implementation of anti pollution laws. The process of monitoring helps to identify

pollution patterns, nature and concentration of pollutants and their harmful effects (Arsalan,

2002). Pollution monitoring and modelling is encouraged on regional, national and global

scales. With the development in technology, ordinary techniques of monitoring are being

replaced by advanced tools. According to Matejicek, et al., (2005) pollution mapping and its

spatial analysis provide a better understanding of pollution patterns in monitoring and

visualization than ordinary surveys and measurements.

In the present times, Geographical Information Systems (GIS) with remote sensing data andground surveys is used for monitoring, mapping and spatial analysis of air pollution

(Matejicek, 2005). Remote sensing and ground surveys provide accurate, timely and spatial

information for the processing and analysis of data in GIS environment.

1.2. The ProblemAir pollution is a threat to life and well being of man. Almost, all major cities of developing

countries are facing this problem. With the development of urban land and increasing of

traffic, concentration of pollution has increased. Lahore, Karachi, Kolkata, Mumbai,

Chinghai, Beijing and many other cities are under great threat of pollution.

In the previous decade, a considerable amount of pollutants has increased in Lahore urban

area because of unplanned urbanization and transportation. Automobiles, industries and

incineration sites are major contributors of toxic gases and particulate matter in the air of

Lahore that contaminate urban atmosphere (Ali and Athar, 2010). According to various

surveys of Punjab EPA, pollution levels in Lahore have either crossed safe limits or have

reached the threshold values (Punjab EPA, 2009). Potential health and non-health issues are

related with pollution and asthma, cancer and irritation are observed in Lahore (MoE, 2009).

Pollution source identification, monitoring, its spatial analysis and visualisation of pollutants

in Lahore, are still under developing stage. People living closer to major roads, industrial

units and in dense settlements are more vulnerable to health issues.

Although some organisations such as, WHO, EPA, PCSIR, SUPARCO and different

researchers (Ali and Athar, 2010; Stone, et al., 2010; Schneidemesser, et al., 2010) have

worked on air pollutions patterns, problems and impacts on local population but some


7/70


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

7

important aspects are not well addressed. Spatial patterns of air pollutants on micro scale,

spatio-temporal trends, spatial based source contribution to pollution, meteorological

parametric relation with pollutants and epidemiological indicators related with pollution on

geographical basis are not clearly defined.

In the situation of poor monitoring and management of air pollution, improvements through

latest technologies such as, GIS and remote sensing are required. Capabilities of these fast

growing technologies should be tested in environmental and urban management studies.

1.3. Aims and Objectives of ResearchThe research is based on the pollution analysis for the city of Lahore. Pollution is produced

from point and mobile sources therefore spatial element is important in the study of source

and exposure modelling. Aims of research can be expressed as,

To perform spatial analysis of pollution concentration for pattern recognition andpredict pollution level at unsampled locations

Identify the source of pollution in urban Lahore on the base of hypothesis thatpollution is related with land use and land cover

Calculate density of settlements, roads and other land cover classes from satelliteimage to construct a relation between pollutants and land cover classes

Analyse the exposure of pollution from source and calculation of area and settlementsunder the threat of maximum pollution

Research is undertaken through GIS and remote sensing techniques and their capabilities are

tested therefore objectives of this research are,

Creation of pollution maps and graphs in GIS and remote sensing environment Analyse capabilities and tools of remote sensing and GIS in environmental and urban

management studies

Usefulness of specific tools of both subjects in classification, area calculation, spatialmapping, interpolation and exposure modelling

Address the strength and command of both technologies for monitoring, managementand problem solving of pollution and urban land

1.4. Research PatternResearch is conducted for the partial fulfilment of the degree of masters in the University ofSalford. It is based on the study of spatial based pollution monitoring, source identification


8/70


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

8

and recognition of its effective extent. Dissertation is based on five chapters, references and

appendices. First chapter is introduction of dissertation; second chapter is literature review

that relates the study with other researches. Third chapter explains the whole analysis

performed during research whereas, fourth chapter presents results gained from methods and

discussions are included in it. The last chapter is conclusion of dissertation that ends with

some recommendations.

1.5. Significance of the StudyAir quality is proving detrimental to human health in many parts of Lahore. These trends are

likely to continue. According to World Health Organisation, air pollution is a threat to the

health and well being of man in the whole world (WHO, 2009).

In the present research, the purpose of pollution monitoring and sampling is not only to

present data, but to provide necessary information to planners, policy makers and scientific

researchers in the process of decision making for the management and improvement of

environment. In the formation of scientific basis, strategy development, setting goals and

objectives, achieving targets and enhancing administration, study of spatial monitoring,

geographical basis of source and spatial based epidemiological analysis play a key role.

Lahore is one of the badly polluted cities of Pakistan. Monitoring is done on government,

private and research based but spatial based analysis and relation of pollutants with

geographical location of source are ignored. Spatial based study is not only helpful in

visualisation but also in source identification, land cover relation with pollution and to build a

relation of concentration with population density.

Use of remote sensing and GIS in the study of urban pollution can help in spatial based

analysis. Tools of these technologies provide spatial based pattern recognition, geographical

based source identification, calculating number of population effected by pollution and

determine spatio-temporal trends. The present research will try to investigate all these topics

through GIS and remote sensing capabilities.


9/70


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

9

Chapter 2

LITERATURE REVIEW

2.1. IntroductionAtmosphere is a dynamic and complex mixture of gases that are essential to support the

ecosystem on the earth. Atmosphere is composed of 78% of Nitrogen (N), 21% of Oxygen

(O2), 0.94% of Argon (Ar), 0.002% of Carbon dioxide (CO2) and little amount of many trace

gases (Prather, 1994). Increasing human population on the earth and reduced plants or forests

have disturbed the natural balance of these gases. With rapid urbanisation and

industrialisation, addition of toxic material, chemicals and trace gases in atmosphere is

contaminating the air quality and degrading the environment with a threat to life. These are

released from the burning of fossil fuel, solid waste, organic compounds and some natural

sources such as volcanism. Pei-Chena, et al., (2011); Smith & Mehta, (2003); Hajat, et al.,

(2002) and many other environmentalists have indicated diseases or deaths of living

organisms and degradation of natural environment from toxic pollutants. Pollution is caused

by the release of toxins from homes or domestic incineration while major sources are

industrial waste and automobile emission (Ostro, 2004).

Pollution is a global issue because it does not have political and geographic boundaries

(Srebotnjak, 2007), but urban areas in all over the world are more affected than rural areas

from this noxious (WHO, 2011). Visible effects of pollution were observed in the incident of

London smog 1952 (Williams, 2004) and today all major cities of developing world are

facing such problems. Great threat of pollution is from unplanned development, automobile

exhaust and burning of fossil fuel.

Monitoring and management of pollution are necessary because of its severe effects for life.

Remote sensing and GIS play an important role in this context. The role of these technologies

is discussed in this chapter and contribution of other researcher in the study of pollution, GIS,

remote sensing and functions of these technologies in pollution analysis is described in detail.

2.2. Pollution SourcesPollution is released from different sources but it varies with geographical location and time.

There are some natural sources of pollution as volcanoes and wind dust but Weng and Yang,

(2006) believe that rapid urbanisation, burning of fossil fuel, indoor and outdoor incineration


10/70


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

10

and automobile exhaust are the major contributors of pollution in metropolitans. Major man

made sources of pollution are following (US EPA, 2011),

a) Fossil Fuel Burning: Petroleum and its products, coal and natural gas are largely usedin industries, automobiles and many other energy production sectors. These are major

contributor of carbon dioxide (CO2), carbon monoxide (CO), Methane (CH4), Nitrogen

Oxides (NOx), Sulphur Oxides (SOx), smoke and particulate matter (PM). Natural gas is less

pollutant than oil and coal.

b) Incineration of solid waste: Urban houses produce solid waste that requires properrecycling and management. Developing countries are still on the way to manage solid waste

where it is burned in open air. Burning of solid waste produces a heavy amount of SO2, NOx,

Soot, Smoke, CO, PM and many other harmful pollutants.c) Indoor pollution: House hold pollution is another major contributor of pollutants.House hold smoke, pollution from burning of fossil fuel in homes, use of synthetic chemicals,

smoke from cigarettes and such other sources cause of domestic or indoor pollution.

d) Dust: Developing countries have a major problem of dust around roads in urban area.Hot and dry climate helps in the production of heavy amount of dust and improper

management along road sides causes a huge amount of dust and particles in air.

2.3. Major Pollutants and their EffectsSeverity of pollution depends on the nature of pollutants and degree of their emission.

Pollutants are such chemicals that are at wrong place with wrong concentration in atmosphere

that adversely affect the physical or biological system (Emberson, 2009). According to

USEPA, (1985) NO2, O3, SO2, Lead (Pb) and Particulate Matter (PM) are criteria air

pollutants, while Acrylic Acid, Benzyl Chloride, Carbon Tetrachloride, Chloroform,

Hydrogen Sulphide, Phosphorus and Nickel compounds etc. are hazardous pollutants

(USEPA, 2002). Criteria pollutants have more concentration in air. As pollutants are found in

all stats of matter therefore World Health Organisation (WHO) has classified them as,

Suspended Particulate Matter, gaseous pollutants (SOx, NOx, CO, O3, organic compounds

etc.), odour and heat (WHO, 2000). Major pollutants analysed in present study are discussed

below,

2.3.1. Particulate Matter (PM)Particulate Matter (PM) or suspended particulate matter (SPM) is a general term for solidparticles and liquid droplets as a combination in atmosphere (Vassilakos, et al., 2005). It is a


11/70


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

11

mixture of different particles such as, total suspended particles (TSP), PM10, PM2.5, coal fly

ash, diesel exhaust, compounds of nitrate, sulphate and ammonium, mineral and metal dust,

carbon black, oil smoke and such other particulates (Roosli, et al., 2000; Hueglin, et al.,

2005). PM10 is coarse particulate matter with diameter 2.5-10m (WHO, 2000). Asthma and

some respiratory issues are related with PM10 in Pakistan, especially Karachi and Lahore are

more polluted cities where motor vehicles, industry, power plants, roadside dust and wind-

blown dust cause of MP10 (Yousufzai, et al., 2001; Stone, et al., 2010). Zhang, et al., (2008)

showed that 24 hour springtime average concentration of PM10 in Lahore is 460g/m3.

2.3.2. Sulphur Dioxide (SO2)SO2 is a water soluble and reactive gas that may react with other compounds to produce

oxides of sulphur, sulphuric acid and sulphates (Hameed, 1991). Combustion of poor quality

fuel in industry and other sectors, excess use of fossil fuel, poor mining methods and forest

fire are major contributors of SO2 (Cohn, et al., 2004). In Pakistan, thermal power stations,

industries and transport are major sources of SO2 but agriculture and domestic sectors are

also playing a little role (Arsalan, 2002). Wellburn, (1994) explained that SO2 causes

destruction to plants, Porter, et al., (2002) gave identification of asthma, irritation to eyes,

acidification in air and discussed the respiratory problems, Wong, et al., (2002) gave

evidence of chronic diseases and Busech & Posfai, (1999) indicated eye problems, vomiting,abdominal pain and sore of throat from SO2. The region of Pakistan, India and China is

highly polluted with high concentration of SO2 (Emberson, 2009).

2.3.3. Ozone (O3)Ozone (O3) is a compound of oxygen which densely lies in the stratosphere (15-45 km above

earth surface) where it absorbs the ultraviolet radiations of sun and protects life. In the

modern industrialised world the concentration of ozone is increasing near the earth surface

because of excess burning of fossil fuels, plastic and synthetic material, incineration of

organic compounds, solid waste and combustion of diesel at very high temperature (Ali &

Athar, 2010). Its concentration near the earth surface not only traps the reflected heat from

the earth but also causes of different health issues when inhaled. Health effects of ozone on

humans are relating with eye, nose and throat irritation, throat dryness and pain, cough, chest

tightness, malaise and nausea (Arsalan, 2002). According to Valacchi and Bocci, (2000), long

term daily exposure to O3 is more than its 1-hour peak concentration in atmosphere because

long term effects relate to respiratory, eye and skin infection. Lawson, et al., (2001) claims


12/70


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

12

that plants plasma or cell membrane is injured and chlorophyll in leaves decreases by surplus

of O3 while sensitive cells in leaves and roots are also damaged (Wellburn, 1994). A study on

rice crops around Lahore in 1996 shows that 27 percent yield of rice crop was reduced

because of high concentration of O3 in surface atmosphere (Naim, 1996). Different researches

in Los Angeles (Kilburn, et al., 1992), Denmark (Emberson, 2009), Karachi (Arsalan, 2002)

and other parts of the world have given the indication of global warming by surface O3.

2.3.4. Oxides of Nitrogen (NOx)Major oxides of Nitrogen are Nitrogen dioxide (NO2) and Nitrous Oxide (NO) that

contaminate the quality of air. These oxides are emitted from transport and industrial

emission and play a major role in the formation of photochemical smog while coal is

considered as major contributor of NOx in atmosphere (USEPA, 1996). Interaction of NO2,

SO2 and water vapour forms acid rain while long term and short term human health effects of

NOx are observed in some cities (Al Koas, 2010).

According to USEPA (2000), respiratory disease, asthma and allergy are cause by NOx while

plant tissues are damaged.

2.3.5. Carbon Monoxide (CO)CO is a colourless, odourless, non-irritating and sneaky without poison which is formed whencarbon in fuel is not completely burned (Arsalan, 2002). In other words, it is produced when

quantity of fuel is high but temperature of combustion is relatively low and oxidation process

becomes slow (Schwela & Zali, 1999). It is a by product of vehicle exhaust. CO affects the

blood cells of body. The threat of heart failure and even mortality is observed from CO

effects on body (Wong, 2002). Wellburn, (1994) indicated headache, irritation, vomiting,

nausea, weakness and even death from the severe effects of CO. Some studies show that

Karachi and Lahore have high levels of CO where most of the CO is emitted from vehicular

exhaust (Qureshi, 1996).

2.4. Pollution EffectsPollution has many adverse effects on life and infrastructure because it results as smog, acid

rain, greenhouse effect and ozone depletion. NASA, (2010) has reported many health and

non-health effects of pollution. Short-term health effects are irritation to the eyes, nose and

throat, upper respiratory infections such as bronchitis and pneumonia, headaches, nausea,

allergic reactions, asthma and emphysema aggravation while long-term health effects are


13/70


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

13

chronic respiratory disease, lung and other cancers, heart disease, damage to the brain,

nerves, liver or kidneys and lungs damage of children. Non-health effects are nuisance-

smoke, odour, grit and dust, eutrophication, haze and smog, effects on wildlife, ozone

depletion, crop and forest damage, global climate change, building damage and

discolouration and economic costs such as fuel inefficiency. Zhang, et al., (2008) claims thatannually 1.2 million people die because of urban air pollution whereas, heart ailments, cancer

and diabetes become common.

According to some reports of World Health Organisation, many people die because of direct

effects of air pollution in the world and different diseases are produced from this noxious.,

WHO (2010) published a map of deaths caused by air pollution in 2004 as shown in figure-

2.1. Eastern Europe, UK, Russia, China, USA and Canada have most deaths whereasPakistan also observed 150 to 250 deaths per million people in a year (WHO, 2010).

Figure-2.1: Map showing deaths from air pollution in 2004 in shaded colours for each country

(WHO, 2010)

There are some standards for clean air and threshold limits of pollutants. Guidelines given byWHO (2009) and USEPA (2009) for ambient air quality are shown in table-2.1.


14/70


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

14

Pollutants USEPA standards WHO guidelines

SO2 (ppb) 140 (24 h) 7.518 (24 h)

NO2 (ppb) 53 (annual mean) 20.94 (annual mean)

CO (ppm) 9 (8 h) -PM10 (g/m) 150 (24 h) 50 (24 h)

O3 (ppb) 80 (8 h) 50 (8 h)

Table-2.1: Guidelines for ambient air quality (Ali & Athar, 2010)

2.4.1. Spatial DimensionsPollution is a global issue but its concentration is more in industrial states, urban areas of

developing countries and unmanaged transportation zones. According to Srebotnjak (2007)

and Wood & Dow (2011) developing countries of Asia are observing comparatively more

pollution in cities and industrial zones than developed nations. Cohen, et al., (2004) has

explained the amount of PM10 in major cities of the world as shown in figure-2.2. Western

Europe and North America lies between concentrations of 30-60g/m3, while East Asia and

South Asia bear highest level of PM10 between 100-254g/m3. The amount of NOx, SO2, O3,

CH4 and other pollutants is also highest in urban regions of East and South Asia because of

dry climate, rapid industrialisation, urbanisation, unplanned transportation and burning of low

quality coal, diesel and other fuels (Emberson, 2009).

Figure-2.2: World map showing estimated annual average concentration of PM10 in different urban

regions, East Asia and South Asia have highest concentration (Cohen, et al., 2004)


15/70


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

15

There is rapid urbanisation, industrialisation and development in China, India, Pakistan and

other countries of the region. Sometimes cities are covered with a layer of pollutants. People

of Beijing, Chinghai, Mumbai, Chandigarh, Kolkata, Karachi and Lahore experience huge

concentration of pollutants and are at more vulnerability. Asthma, nausea and diseases of

lungs, heart, skin, throat, eyes and ear are observed in these cities (Gupta, 2010; Weng &

Yang, 2006; Arsalan, 2002). Pakistan is a developing country that lies in South Asia and its

major urban centers have high concentration of PM10 and other pollutants with arid or semi

arid climate. Automobile and industrial exhaust, crop residual burning, wood burning,

household pollution and incineration of solid waste have become common because of less

effective environmental laws and lake of public awareness (Arsalan, 2002). Karachi, Lahore,

Rawalpindi and Faisalabad are most polluted cities with high concentration of PM10, CO,

SO2, NO2, NOx, O3, CH4, Pb, smoke, soot and roadside dust (Zhang, et al., 2008).

2.5. Pollution in LahoreLahore is located in the industrial hub of Pakistan at boarder, near the Indian industrial zone

(Rattigan, et al., 2002). Small and heavy industries are located within and around the city

while and industrial estate named as Sundar Industrial Estate has been established close to

urban area. There is a huge transport network with heavy and light traffic in the city of about

10 million people. Moreover, the urban area produces a huge amount of solid waste that is

allowed for incineration. In the presence of all these pollution sources, environmental

management and anti pollution laws are not applied for citizens and stockholders. Therefore,

Lahore is considered as the second polluted city of Pakistan after Karachi (Ali & Athar,

2010).

Monitoring system for pollution and public awareness are weak that leads to environmental

and health issues. Major contributors of pollution are heavy traffic and two stroke rickshaws.

In previous five years some steps from government and environment department were taken

to combat air pollution, in which introduction of CNG engines for all traffic and Green

Scheme Rickshaws are most important. In 2011, Pakistan is at the top in having highest

number of CNG transport and CNG stations (MoE, 2011). But major development in this

sector is because of increasing oil prices.

Although the development is rapid in Pakistan countries but still there are deficiencies in

monitoring pollution and vulnerability data. In Pakistan, Pak EPA, Ministry of Environment,

educational departments and some individual collect pollution data but there is a running


16/70


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

16

debate in establishing pollution guidelines (Ali & Athar, 2010). Punjab EPA collects

pollution data and publishes its report every year but monitoring sites are not many that can

cover the whole city. From 2008, only 3 major stations are permanently fixed that are not

enough to observe the pollution level for the whole city (Punjab EPA, 2011). Sometimes

these stations become inefficient because of energy shortage. On the other hand, health data

regarding pollution attacks, asthma and other pollution related diseases is also not available

for the whole urban area (MoE, 2011). In this situation it is not easy to analyse spatial

patterns of pollution, exposure modelling and vulnerability assessment in Lahore. With

pollution monitoring problems, some challenges of air pollution management in Pakistan are

(Punjab EPA, 2011),

Less research on pollution

Lack of emission inventory and database Lack of spatial based pattern analysis of pollutants Lake of up-to-date emission standards Problems in the enforcement of environmental standards Lack of incentives to check the rate of pollution Poverty, illiteracy or less public awareness Insecurity Inadequate technical expertise

2.6. Pollution Monitoring and ModellingMonitoring of pollution is important to calculate its concentration for spatial distribution and

vulnerability analysis. Monitoring also helps to identify sources of pollution and rate of

change in the concentration level. Mostly, concentration of pollutants is measured by

different chemicals and instruments but analysis is performed through statistical and

graphical methods. These do not explain spatial distribution for the whole urban area and

construction of relationship with land use is difficult. GIS and remote sensing are intelligent

and smart technologies to perform spatial analysis. They can define the relation of pollution

with land use and identify source.

2.6.1. Remote SensingRemote sensing is the sensing of the earth surface from space by making use of the properties

of the electromagnetic waves emitted, reflected or diffracted by sensed objects, for the

purpose of improving natural resource management, land use and the protection of the


17/70


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

17

environment (UN, 1986). It is the science of acquiring information about any object on the

earth or in atmosphere without any physical contact with the object under observation

(Elachi, 1987; Skidmore, 2002).

Remote sensing instruments provide the information of the earth in the form of images,

graphs and other formats that are processed in remote sensing software. Organisations use

remote sensing data for not only defence purpose but also for meteorological, communication

and environmental purposes. Meteorological data such as, humidity, temperature, wind speed

and direction, atmospheric pressure and clouds state, give the condition and prediction of

weather (Voogt and Oke, 2003), whereas, environmental data provides the amount of

pollutants in atmosphere, surface temperature, cyclonic movement, flood condition and

hazard prediction and consequences (Zhang and Guindon, 2006). Images of satellites givetemporal changes on the earth and one can investigate land-use changes, trends and their

relation with environment.

Remote sensing devices provide information for different applications of mapping, spatial

distribution and concentration of air pollutants. Optical satellites and radar such as, Landsat,

SPOT, Radarsat, TerrSAR-X and Quick Bird provide mapping of earth features to relate

them with pollution levels and spatial distribution of pollutants (Mather, 2004). However,

ground surveys or in-situ data are compulsory to verify and improve accuracy of satellite data(Guindon, et al., 2004).

Remote sensing data and ground surveys are analysed in GIS for management, decision

making and solving problems of the real world (Fagbeja, 2008). Landsat MSS (Multi-

Spectral scanner), TM (Thematic Mapper) and TM+ (Thematic Mapper plus) and Quickbird

images are commonly used for land-use/land-cover classification to observe the change

detection and related analysis in GIS (Bhatta, 2009). Image classification can help to

calculate the area of land-use/land-cover for the purpose of pollution sampling and source

identification (Ahmad, et al., 2010). Moreover, density of settlements, roads and and land

covers help to calculate the proportion of sources for urban pollution.

2.6.1.1.Image Classification

Classification is a useful technique that identifies land-use and land-cover classes on the

image. Training areas from each class are selected and software divides the whole image

according to information stored in these areas (Li & Yeh (2004)). Information is based on

spectral, spatial and radiometric properties of land classes (Mather, 2004).


18/70


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

18

Image classification technique is important for the identification of land cover classes to

relate them with pollution concentration. Mostly it is used to calculate urban growth and rate

of land cover change (Bhatta, 2009). Spatial and temporal based classification is helpful to

analyse the change in land cover and its relation with changing concentration of pollutants.

Two types of classification techniques are important, supervised and unsupervised.

Unsupervised technique is used when land cover classes are not known but supervised

classification is used if classes are known (Mather, 2004). Before classification, pre-

processing steps such as geometric correction, atmospheric correction and enhancement, are

used to reduce flaws and deficiencies in image (Wang, el al., 2008).

Classification identifies the density of settlements, vegetation, water, free land and other

classes that helps to relate concentration of pollution with the densities of classes. This typeof analysis is helpful to identify the source of pollution and exposure modelling.

2.6.2. Geographical Information Systems (GIS)Geographic Information Systems (GIS) is concerned with the description, explanation and

prediction of patterns and processes of spatial scales (Longley, et al., 2005). It is considered

as a computer based tool or technique (Stevens, et al., 2006), science, as well as disciplines

(Goodchild, 2010) and applied problem solving technology (Longley, et al., 2005).

Department of Environment (DoE) London, (1987) and Environmental Systems Research

Institute (ESRI), (2011) has defined it as GIS is a computer based system for capturing,

storing, checking, integrating, manipulating, analysing and displaying of data which is

spatially referenced to the Earth. Mapping is an old art but accurate geographic mapping in

digital form, spatial analysis and problem solving techniques are introduced by GIS

(Goodchild, 2010).

GIS has provided the answer of what, where and when (Goodchild, 1997), while it can

investigate basic questions of location, condition, trend, routing, pattern and modelling

(Rhind, 1990). GIS is used in different fields for decision making and problem solving

because of its spatial analysis techniques. It is not only used for natural resource management

but also for urban planning, environmental and transportation modelling and disaster

management whereas, meteorological data analysis and pollution mapping require spatial

analysis techniques of GIS (Weng & Yang, 2006).

In environmental studies, pollution is a focussing research while modelling and prediction of

air quality is important in the management of environment (Fenger, 1999). Urban areas


19/70


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

19

remain more polluted because of transportation, industrialisation and urbanisation. GIS

provides the structure of urban environment, air quality information and spatial distribution

of pollutants (Kang, et al., 2009). Mapping techniques, spatial analysis, prediction models

and trends in pollution through graphs, charts and diagrams, transport network, traffic

emissions and air quality models in GIS environment provide better understanding and

analysis than ordinary surveys (Fedra et al., 1999).

2.6.3. Integration of GIS and Remote SensingStudy of atmosphere and pollution is widely carried out by ground surveys or in-situ

measurements. Dispersion models of pollutants, assessment of air quality, analysing

pollutants, tropospheric ozone increment and stratospheric ozone depletion are also carried

out by in-situ measurements and mathematical models (Fagbeja, 2008). But the use of GIS

and remote sensing in this field has revolutionised it through different analysis and mapping

techniques (Zhang and Guindon, 2006). Remote sensing provides temporal information about

atmospheric particles and pollutants, weather and heat data, land-use, vegetation,

infrastructure, pollution sources and demographic data (Dadvand, 2011; Weng & Yang,

2006). GIS uses remote sensing data as database and performs different analysis, modelling,

mapping and visualisation to represent spatial variations, trends and spatio-temporal

modelling (Sohrabinia & khorshiddoust, 2007).

GIS helps to locate emission sources and predict pollutant concentration on geographic scales

(Gupta, 2010). Nichol, et al., (2010), used MODIS satellite images to produce three

dimension (3d) air quality data for urban area and created a model for the prediction of

Aerosol Optical Thickness (AOT) in GIS environment. Arsalan, (2002) used GIS techniques

for the sampling of pollution data with land-use type. He explained that in GIS, concentration

of pollutants can be related with the density of roads, settlements, industries, vegetation and

open space measured by remote sensing techniques. Tavoosi, et al., (2009) used satellite

images of Tehran to classify land-covers, analysed their relation with pollution sources and

calculated concentration of pollutants by relating with land classes in GIS. Spatial and

temporal variations in pollution with land-use change were also analysed.

2.6.4. Spatial Analysis and ModellingAlmost 80% of the worlds data is spatially based and GIS is a sophisticated tool to handle

spatial analysis of environmental, demographic and political data (Worrall, 1991). Analysis is

possible in many software and tools but GIS adds location or spatial character to the analysis.


20/70


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

20

GIS is a supporting tool for wide range of spatial problems that require coupling of

simulation models with GIS (Goodchild, 1993), spatial data visualisation (Dadvand, 2011),

spatial decision support system (Demers, 2009) and policy or decision making programmes

(Goodchild, 1997).

Matejicek (2005) explained that spatial interpolation, raster algebra and case oriented analysis

in GIS are useful analysis for pollution study. Moreover, he presented a spatial model through

GIS to present pollutants of flat urban area in Czech Republic.

2.6.5. GIS Techniques and ToolsMapping, analysis and visualisation of data on spatial or geographic scale make GIS better

than ordinary surveys and statistical measurements (Arsalan, 2002). A variety of statistical

methods, mathematical techniques and graphical representation of data are available in GIS

software packages. Interpolation, point pattern analysis, overlay, proximity and 3d analysis

are valuable tools of GIS used by many researchers in the study of pollution (Banja, et al.,

2010; Nichol, et al., 2010; Arsalan, 2002; Horalek, et al., 2005).

Jerrett, (2001) applied spatial statistical tools of GIS to relate the particulate air pollution with

socioeconomic status of people in Canada. Exposure modelling showed that people of lower

socioeconomic status are more likely to be at risk than people of higher socioeconomic status.

Spatial analytical techniques of GIS help to study health issues related to pollution and it is

extensively used in disease mapping, epidemiological inquiries, health services analyses and

planning, environmental health and justice analyses, exposure modelling, risk assessments,

disease diffusion and clustering studies (Bowman, 2000; Maantay, 2007).

2.6.5.1. Interpolation

Interpolation is a technique that withholds the data of particular point and predicts condition

at neighbouring area of the point on spatial scale (Horalek, et al., 2005). It holds the idea thatspatially distributed objects are spatially correlated and is mostly used when limited data sets

are available for a large area as values of neighbouring cells in a raster grid are predicted

(ArcGIS Help, 2011). This method is commonly used to model geographic point data relating

to pollution, rainfall, chemical concentration, elevation and noise level.

Zhang, (2006) calculated the concentration of pollutants in urban soil of Ireland on spatial

scale by using interpolation, Horalek, et al., (2005) applied it to predict concentration of PM10

and O3 over Europe and Arsalan, (2002) used interpolation in Karachi, Pakistan to investigate

the concentration of criteria pollutants.


21/70


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

21

Issue with interpolation is that neighbouring area would contain much high or very low

concentration but interpolation predicts on the base of values given for the point. Common

methods for interpolation used in GIS are, inverse distance weighted (IDW), kriging, spline,

natural neighbour and trend (ArcGIS Help, 2011).

Kriging is an advanced geostatistical technique that can generate estimated surface from a set

of points. The z-value (concentration of pollutant) gives estimation of surface on spatial scale

and resultant low value indicates high degree of confidence (ArcGIS Help, 2011). A circular,

spherical or semivariogram shape for ordinary or universal model are used in kriging

(ArcGIS Help, 2011). If points are coincident then kriging may produce surface of

unexpected values therefore points should be well separated and sampling is necessary. It is

mostly used in pollution sampling and health science (www.esri.com). Li & Revesz, (2004)have explained kriging, IDW and other types of interpolation to produce the surface of

unsampled points.

Inverse Distance Weighting (IDW) produces an estimated surface by averaging the values of

scattered point data for the neighbour cell of each point (ArcGIS Help, 2011). The point

represents as a center for neighbouring values. IDW is a weighted distance average that

cannot become higher than highest value and lower than lowest value and does not well

locate two consecutive extreme vales if extreme values are not sampled (Watson and Philip,1985). Moreover, it only produces best results when points are densely located. According to

Li and Revesz, (2004) IDW and kriging are useful tools for spatial as well as temporal

modelling of pollution and other geographic point data.

2.6.5.2. Proximity Analysis

Proximity gives the nearness of pollution concentration to land classes. It is not only helpful

to identify the source but also for effects of pollution on nearest population (Arsalan, 2002).

Proximity is based on the idea that whats near what (ArcGIS Help, 2011) and defines a

relation of closeness between different features. Different techniques of proximity are used in

ArcGIS but buffering is most advanced and widely used method.

Buffering is a useful technique for proximity analysis used to delineate protected zones

around features or to show areas of influence (Jerret, et al., 2005). Buffer area around the

point, line or polygon is defined by the user and value of object or theme is considered as

same for the whole buffer area. It is helpful to make relationship between pollutants and land

features within the buffer zone.


22/70


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

22

2.7. HypothesisAir pollution and its spatial patterns are related to geographical distribution of land covers

and closer population to sources of pollution is at more risk.


23/70


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

23

Chapter 3

METHODS AND ANALYSIS

3.1. IntroductionThe study of pollution monitoring, source recognition and risk analysis is of great importance

for environmentalists, urban planners and health agencies. Monitoring and spatial analysis are

helpful to measure severity of pollution to establish pollution standards and manage its

sources. Spatial analysis is valuable that helps to identify source and high concentration of

pollutants on geographical basis. This chapter explains the analysis of pollution through GIS

and remote sensing tools in urban Lahore. Punjab EPA is playing its role in the collection of

pollution data in Lahore that helps for baseline studies. Traffic is not fully planned in Lahore

that is a major contributor of pollution but indoor and industrial pollution is also a great threat

for the environment of the city.

Remote sensing provides timely updated land-use and land-cover data to observe the density

of settlements, roads, vegetation, free land, water bodies and other features. Whereas, GIS

can analyse the relation of pollution with these land features to identify sources of pollution.

It also indicates the area under more threat of pollution by spatial analysis techniques. Image

classification, proximity analysis and interpolation are applied in this chapter. Statistical

techniques are also applied to create a relation between concentration of pollutants and type

of land covers.

3.2. The Study AreaThe study area was conducted as urban Lahore, the capital city of Punjab province in

Pakistan as shown in figure-3.1. Total area of urban Lahore is about 480 km2

having

approximately 10 million population (Schneidemesser, et al., 2010). It is the 2nd largest city

of Pakistan situated at the height of 702 feet from sea level. With hot and arid climate, the

annual rainfall of Lahore during last decade remained in the range of 333 to 1232 mm (PMD,

2010). Mean monthly temperature of the city varies from 10.9 C to 34.2 C while annual

average temperature remains within 23 C to 26 C; similarly relative humidity varies from

17% to 70% which increases during July to September with a range of 60-70% (PMD, 2010).


24/70


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

24

Figure-3.1: The study area, Lahore showing major roads and points of pollution observation (P&D

Department, 2011; Punjab EPA, 2011)

Lahore is located in the northeast of Pakistan near Indian boarder which is the industrial hub

of Pakistan and India having high concentration of pollution (Rattigan, et al., 2002; Ghauri, et

al., 2007). Winds from all sides bring some concentration of pollutants in the city but the

major sources are local industry and transport (Waheed, et al., 2006). Transport accounts for

more than 65% of pollution in Lahore, as the length of major roads is 365 km and the

registered vehicles were 1.4 million in 2009 (Ali & Athar, 2010). Buses, heavy vehicles and

two stroke rickshaws are major contributors of traffic pollution because of their poor engines

using low quality fuel. Road infrastructure of Lahore is shown in figure-3.2. Motorway,

major roads, secondary roads, streets and footway is given in different colours. There is Ravi

River in the west and a canal passes from the middle of the city.

A number of environmental agencies such as, Environmental Protection Agency (EPA),

Ministry of Environment and Worldwide Fund (WWF) are working in the city to prevent the

degradation of environment whereas, some laws and rules have been implemented for heavytraffic and rickshaws but pollution is yet not controlled because of rapid urbanisation and


25/70


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

25

poor traffic management (Stone, et al., 2010). Like other cities of developing countries,

population, road length and vehicles are increasing in Lahore urban area. According to

Punjab EPA (2007), pollution level in the city was 63.6 i.e. higher than international and EPA

standards. Pollution models and spatial analysis are helpful for the study of pollution sources,

spatio-temporal variations in pollutants over the city and their epidemiological exposure.

Spatial patterns of pollution, its source and exposure analysis in Lahore city are analysed by

using data collected in May 2007.

Figure-3.2: Road infrastructure of urban Lahore (P&D Department, 2011)

Detailed methodological framework is given in figure-3.3 that shows the whole procedures

used in current research.


26/70


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

26

Figure-3.3: Thematic diagram of methods used in the research

3.3. Data SetsData is most important because all type of analysis and research depend on it. Data collection

techniques and database is not fully standardized in Pakistan like many other developing

countries. There is a need to formulate pollution standards as well. Data collected in this

research is also not according to the measures of developed countries but still satisfactory for

spatial and landscape analysis because of EPA data collection techniques. Most of the data in

this research was used from secondary sources. Different sets of secondary data were


27/70


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

27

collected from government and semi-government sources. Following data sets are used in the

research

i. Pollution data of Lahore for May 2007 with meteorological parameters and observedconcentration of pollutants at most busy and affected sites was collected from Punjab

EPA Lahore. 22 busy and most popular sites of Lahore were observed by placing the

instruments on road sides near crossings and 2-3 meters away from the road corner.

Data of 7 points out of 22 were taken from Department of Environment, Islamia

University, Bahawalpur collected by Ali and Athar (2010) as EPA did not cover the

whole urban area. The observations of Ali and Athar were according to EPA

standards and their paper about pollution is published in Springer. Most of the points

such as, Railway Station, Town Hall, Charring Cross, Chuburgi Chowk, Chowk

Yateem Khana and Kalma chowk are in highly dense area with heavy transport on

major roads. Thokar Niaz Beg and Yadgar Chowk bear heavy burden of transport

emission and industrial waste whereas, Chungi Amarsadhu, Harbanspura and GT

Road have heavy traffic but some small industries are near them. Some points such as

park near airport, Wapda Town and Shah di Khoi are in less dense and vegetative or

green area.

Instruments and methods used by Punjab EPA and Ali & Athar to calculate pollutant

concentration are discussed below (Punjab EPA, 2011).

The concentration of sulphur dioxide (SO2) was calculated in glass impingers throughsodium tetrachloro mercurate absorption solution using APM 410 and 415 Sampler

(2001 Model, Manufactured by VBU Ltd., India). The samples for 24 hour

observations were transferred in laboratory in a cold box having eutectic cold packs

with a maintained temperature of 5C. Colorimetric method was used for the analysis

of samples. Samples and analysis was according to USEPA Method: 40 CFR 50.

Ozone (O3) was collected through ozone analyser based on USEPA DesignatedMethod: EQOA-0880-047 in Thermo Environmental Instrument, Model 48-C, USA.

The samples were collected on the base of 24 hours observations.

Oxides of Nitrogen (NOx) were collected in glass impingers using triethanolamineabsorption solution through APM 410 and 415 Sampler (Model 2001 of VBU Ltd.,

India). Transportation of samples and analysis were same as for SO2 but related with

NIOSH Method CAS 10102-43-9.


28/70


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

28

The concentration of PM10 was calculated on a fiber glass filter by using high volumesampler with size-selective inlet PM10 sampler (Model TE 6070, 2004 Trish

Instruments USA). The samples were stored in vacuum desiccators to transfer in

laboratory. 24 hours observed samples were analysed according to USEPA Method:

40 CFR 50.

The concentration of CO was collected through Automated Analyzer based on gasfilter correlation technique with data logging facility i.e. Thermo environmental

Instrument, Model 48-C USA. 24 hours observations were calculated on the base of

USEPA designated method: RFCA-0981-054.

Meteorological parameters such as, temperature, relative humidity, wind speed and wind

direction were calculated by Davis Vantage Pro-2, USA. Instruments were installed at 2 to 3meters distance form road corner and 2 meters above ground level.

ii. Lahore road network data was taken from Urban Unit, Planning and DevelopmentDepartment, Lahore. Roads are classified as, motorway, primary or major roads,

secondary roads, streets and footways. Classified road network data is important

because motorway and primary roads bear a heavy burden of major transport,

rickshaws and heavy vehicles. These are considered as major contributors of

pollutants in the urban atmosphere.

iii. Landsat ETM+ image of 2003 was used for land-cover classification to calculatedensity of settlements, water, green areas and open spaces in proximity to observation

points. Landsat ETM+ image has 15m resolution in panchromatic band that was

downloaded from USGS website (earthexplore.usgs.gov). Landsat images are

commonly used for land cover classification to calculate the distribution of land

covers and changes over time (Bhatta, 2005). All Landsat images after 2003 have

missing scan lines because of instrumental errors therefore image of 2007 requires

high techniques and complex methods of image processing to remove gaps (Dadvand,

2011). To avoid these issues and complex methods, image of 2003 was used.

QuickBird image of 2008 was used for digitising major roads in vector format to

calculate the density of roads in proximity with observation sites. QuickBird image

was taken from SUPARCO Lahore as it has high resolution of 0.6m that is helpful for

the digitization of road area.


29/70


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

29

iv. Weather data for May, 2007 was collected from Pakistan Meteorological Department(PMD), Lahore to compare with observed meteorological parameters by EPA and Ali

& Athar (2010).

3.4.

Analysis of Data RelationshipArcGIS 10 and Erdas Imagine 9.2 were used for the whole analysis in this research.

Microsoft Excel was used to compare the relationship between different data sets and weather

parameters. Data for pollution and weather, was arranged in excel according to geographic

location in Universal Transverse Mercator (UTM) projection zone 43 and WGS 1984 datum.

ArcGIS can perform analysis on dataset which is geographically referenced to a projection

system. The excel file was converted into shape file (.shp) in ArcView to perform analysis in

GIS environment. Arranged data of excel or shape file for May 2007 is given in table-1. It

shows concentration of 5 pollutants, NOx, CO, SO2, O3 and PM10 and wind speed, wind

direction, temperature and relative humidity (RH) at 22 different points for 24 hours mean

observations. Concentration of NOx, SO2 and O3 is given in parts per billion (ppb) while CO

is in parts per million (ppm) and PM10 in micro gram per cubic meters (g/m) whereas, wind

speed is measured in meter per seconds (m/s), temperature in degree Celsius (C) and relative

humidity in percentage (%).

The issue with data is that each point was observed on different day and date. In this case, itis possible that pollution level at one location can transfer to the other location because of

weather parameters. To observe these issues analysis of weather and pollutants is important.

An analysis and comparison of data relationship is helpful to test the validity of data. Weather

data collected from meteorology department is compared with the climatic parameters used in

this report. Relation between pollutants and meteorological parameters is useful because wind

speed, wind direction, temperature and relative humidity affect the pollution level observed at

different time periods. Wind direction can disperse pollutants and cause of reduction at one

place but increase at another. PM10 and some other pollutants increase with the increase in

temperature, temperature at night is supportable for pollutants to become stable, whereas

increased amount of humidity and rainfall helps to decrease the concentration of pollutants

(Matejicek, 2005). To observe the effects of meteorological parameters on pollutants

correlation was tested. No relation between weather parameters and pollutants suggests that

pollution level is not affected by temperature, relative humidity, and wind speed and

direction. Moreover, GIS techniques such as, interpolation of temperature, can determine the

variations. Wind direction map can help to investigate the movement of pollutants in a


30/70


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

30

particular direction. Yanosky, et al., (2008) believes that at normal atmospheric pressure less

than 3m/s wind speed is considered as stagnant air that does not affect the pollution level of

particular point within 24 hours.

No. ID (Points) Date X Y WindSpeed

(m/s)

WindDirection Temp(C) RH(%) NOx(ppb) CO(ppm) SO2(ppb) O

3(ppb) PM10(g/m)

1 Town Hall 02/05/2007 434045.5 3492935.8 1.5 NW 32 35.26 63.6 2.3 18.7 16 462

2 Yadgar Chowk 03/05/2007 434266.6 3495099.4 2.6 SW 33.3 37.2 78.7 7.1 48.4 32 1019.4

3 Railway station 04/05/2007 437321 3493294 2.1 NW 33 41.3 76.9 8.2 52.3 36.2 908

4 Shad Bagh 05/05/2007 436769.9 3496560 2 NW 32.2 40.3 56.7 5.1 29.3 21.3 572

5 Ravi Road

Interchange

06/05/2007 433384.7 3496944 2.3 NE 29.8 40 76.5 6.2 39.6 28.2 914

6 Bund Road

Mahmood

07/05/2007 443702.3 3495002 1.8 SW 33 44.5 42.5 4.7 32.5 17.9 926.2

7 Mughalpura

Flyover

08/05/2007 441228.4 3492091 2.1 SW 32.6 39.6 56.4 3.5 22.7 17.1 569

8 Harbanspura 09/05/2007 446315.5 3493224 2.5 NE 32 36.4 32 3.4 15.3 17 620.7

9 Chungi Amar

Sadhu

12/05/2007 438591.3 3479516.6 1.6 NW 32 47.2 54.6 5.7 23.1 20.5 867.3

10 Kalma Chowk 13/05/2007 436471 3485676 1.4 NW 32.7 44.7 48.6 7.2 24.8 21.7 641

11 Muslim Chowk 14/05/2007 435940.5 3487430.5 1.5 NW 33 47.3 34.2 5.6 20.6 18.5 519

12 Mochipura

Township

15/05/2007 434660.6 3481396.2 2.2 NW 31.8 51 20.3 2.1 38.2 44.7 502

13 Multan Chungi 19/05/2007 430126.9 3484775.6 2.7 SW 32.3 46.1 27.9 3.1 19.7 51.8 804.6

14 Charring Cross 22/05/2007 435897.3 3491797.9 1.9 NW 31.9 39.7 46.2 2.3 23.9 20 434.6

15 Chuburji Chowk 21/05/2007 434023.7 3491211 1.1 NW 32.5 45.4 53 6.6 22 19.3 766

16 Chowk Yateem

Khana

20/05/2007 432339 3488798.6 1.9 SW 33.1 43 68.5 6.2 39.7 48.2 831

17 Shah Di Khoi 18/05/2007 432849.1 3483807 2.4 NW 31.6 38.6 16.3 2.7 11 14.3 281.6

18 Mall Road

Canal

10/05/2007 438220.3 3490107.2 1.8 SW 30.7 39 47 3.2 17.4 21 306

19 Park View Near

Airport

24/05/2007 441548 3484731 2.4 SW 33 38.4 38.1 2 10 18.7 268.7

20 Airport Access 23/05/2007 443113.3 3489904.9 2 SW 32.4 41.3 43.2 2.1 10.4 20.2 578

21 Thokar Niaz

Beg

17/05/2007 427978 3482008 2.3 NW 32 39.6 81 7.9 50.2 64.5 974.8

22 Abu Bakar

Chowk

16/05/2007 430094.4 3477614 2.1 SE 33.2 38.7 34.3 1.8 24.3 38 307

Table-3.1: Observation sites (Points) and observed pollutants with meteorological parameters for 22 sites

Correlation was analysed between pollutants as it shows the relationship between pollutants

whether they are directly related, inversely or not making any relation. Relation between

pollutants also gives indication of similar source. A positive relation between pollutants can

help to formulate the hypothesis that the source of these pollutants is the same.

3.5. Image Processing and ClassificationErdas Imagine is useful software to study the properties of images and apply different

operations on image. Images have some flaws and errors in their raw form that require some


31/70


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

31

pre-processing steps (Mather, 2004). Landsat ETM+ image of 2003 was downloaded from

Earth Explorer website to observe the land use and land cover in Lahore urban area because

Landsat data is freely available and is commonly used for land use classification (Bhatta,

2005). There are some missing scan lines in the images of Landsat after 2003 that cause in

the missing of data and require complex image processing techniques. To avoid these

complex issues the image of 2003 was used for classification purpose. Enhancement

techniques are used for noise reduction and diminish atmospheric influence on image.

Histogram match and equalization increase the radiometric and visual properties of image

that helps to select algorithms for classification of land uses (Foody, 2002). Information

about the image is given in table-3.2.

Table-3.2: Metadata for Landsat ETM+ image of 2003 used for classification

(earthexplorer.usgs.gov)

Supervised classification with maximum likelihood parametric rule using probability surface

method was applied (Bhatta, 2005). Supervised classification is performed on the basis of

known land use features, which automatically separate the clusters of same class using

statistical or radiometric, spectral and spatial properties (Bay, 2011). Different training areas

from each category were selected containing more than 10 pixels in each training area. For

the first time 10 classes were created then 11 but both were not satisfactory because accuracy

was low. Then image was classified in 9 classes from which 4 classes were assigned to

settlements as, dense settlement, less dense settlement, sparse settlement and new

constructions. Classes are given in figure-3 where name of class, its colour and other

properties are given. Settlements are more important because proximity of settlements to

observation points gives the idea of pollution sources and their exposures. Moreover, a

comparison of settlement and road density at different points can help to investigate the

Data Set Attribute Attribute Value

File Format GeoTIFF

Platform Landsat

Sensor ETM+

Datum WGS84

Units Meters

Acquisition Date 2003/01/28

UTM Zone 43

Pixel Resolution 15 m


32/70


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

32

causes of difference in pollution level, such as density of roads and settlements is directly

proportional to the concentration of pollution.

3.5.1. Accuracy AssessmentClassification is more useful if its accuracy is high and Kappa Coefficient is commonly used

for this purpose (Foody, 2002). To calculate Kappa, an error matrix is constructed on the base

of observations from classified image reference to the original image. Settlement classes were

merged in 1 class, 2nd

class was of free land and all other classes were kept in 3rd

class and 30

points from each class were selected to compare with classes of original image. Points were

well separated and most of them were selected from the edges of classes. Points from actual

and observed classes were plotted in matrix and Kappa was measured by the following

formula,

Where

n = number of observed points

k = number of cells in matrix diagonal

q = last cell in the diagonal

Good agreement of accuracy is considered when the value of Kappa lies between 0.60 and

0.80 while very good agreement is for values between 0.80 and 1.00 (Foody, 2011).

Issue with classification is that it produces salt and pepper effect with isolated pixels because

of local variations. Classification is affected by sensors low sensing efficiency and

sometimes by choosing very large image area (Foody, 2002). Moreover, 100% accuracy of

classification is not possible because some pixels at the boundaries of land features are

misclassified having common properties of surrounding features.

3.6. Spatial AnalysisArcView has become useful software for data handling, spatial analysis, pattern study and

exposure modelling and visualisation of all types of geographical data. Spatial statistics,interpolation, overlay and proximity are commonly used in the study of pollution patterns and


33/70


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

33

exposure modelling (www.esri.com). Some analysis techniques were used in the study of

pollution as discussed below,

3.6.1. Proximity and Correlation AnalysisBuffering tool creates buffer polygons of specified distance around the input data feature

(ArGIS help, 2011). Offsets produced by buffers around the specific point, line or polygon

can help in proximity analysis. As traffic is major contributor of pollution in Lahore therefore

analysis of roads and their proximity to settlements are important. To analyse the proximity

and exposure from road pollution, 200 meters buffers of major roads were constructed. 150-

200 m from a main road is the distance within which concentrations of primary vehicle

traffic pollutants are raised above ambient background levels (Venn et al., 2001; Maantay,

2007). Most of studies explain that respiratory exposures to traffic related emission are within

150-200 m distance (Nitta et al., 1993; Wilkinson et al., 1999).

All observation points are along major roads and most of them are close to the dense

settlements therefore buffering around observation points can help to determine the proximity

of roads and settlements from observation points. Buffers around the observation points were

created as 100m, 150m, 200m, 250m, 300m, 350m, 400m, 450m and 500m buffers are more

important in the study of relationship between pollution and roads or settlements (Jerret, et

al., 2005; Zhu, et al., 2002). The density of roads and settlements within these buffers were

calculated from the classified image of Lahore (Rose, et al., 2009). Classified image can be

converted into raster and vector form in ArcGIS to calculate the area of settlements. Figure-

3.3 shows the process of buffer analysis and area calculation for settlements and other

features from classified Landsat ETM+ image.

Density of roads was measured from digitized road area on QuickBird image. Before the

calculation of settlement density, road area was subtracted from settlement area of classified

image because during classification roads were included in settlements class. High density of

roads and settlements within the buffer area indicate more concentration of pollutants and the

type of their source. Density for settlements and roads is defined as the proportion of class

area from a unit area and mathematically can be expressed as (Rose, et al., 2009),

Density = Class Area/ Total Area of Buffer X 1000

The process of measuring road density in each buffer is shown in figure-3.4. Major roads are

digitized on high resolution image as given in figure-3.5 and their area is calculated from

attribute table using selection tool as shown in Table-3.3.


34/70


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

34

Figure-3.4: Buffer analysis and calculation of settlement, vegetation and other land feature area

Figure-3.5: Digitization of road area and calculation of road density from high resolution image


35/70


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

35

Table-3.3: Calculation of Major Road area from attribute table

Relation between concentration of pollutants and density of land classes was calculated

through regression and correlation analysis. These relations investigate that whether a change

in one variable is related to the change of other variable. Correlation is mostly used to check

the statistical significance of relation between two variables while regression describes the

relation precisely by means of an equation that have predictive value (Price, 2011). In the

present analysis the value of regression is represented by R2

whereas, r is used to show the

coefficient of correlation. Regression plots a straight line along the distribution of points

showing relation between concentration of pollutants and density of classes (roads andsettlements). It is important to test the significance of relation and is mostly used as

coefficient of significance for statistical significance at 95% confidence level (Deacon, 2011).

In each buffer area regression between road density and concentration of pollutants, and

settlement density and concentration of pollutants is determined and their statistical

significance is also tested through coefficient of correlation.

3.6.2. InterpolationSpatial interpolation is a statistical technique in GIS that helps to estimate the value of

variables at unsampled places (Li & Revesz, 2004). Kriging and IDW are common

interpolation methods used in ArcView to model the unpredicted values of pollutants over an

urban area. Ordinary method of kriging with spherical semivariogram model is used in the

research to interpolate the raster surface of pollutants (ArcGIS Help, 2011). Spherical

semivariogram model interpolates the surface in a spherical shape but the shape is changed

automatically if the concentration of pollutants is greatly different at well separated points.

For values of small difference, kriging does not produce best surface and IDW suites best to


36/70


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

36

interpolate such narrow valued variable (Watson and Philip, 1985). IDW produces the raster

surface on the base of extent of similarity or degree of smoothing in the values of

observations (www.esri.com).

In the present study, interpolation only gives the visual representation of pollutants over the

surface of Lahore. Interpolated surface is formed according the mean values or weightage of

each point and degree of smoothing therefore it does not predict the exact concentration. But

it is a better technique than ordinary statistical measurements and much quicker than surveys

to save time and money. Moreover, it is difficult to measure each crossing and every point in

a large area, and interpolation is useful technique for this purpose. IDW and kriging are

limited by methodological restrictions that require assumptions regarding the form of the

distance weighting function and by the often sparse spatial distribution of air monitoringsites, which allows interpolation to capture only large scale spatial gradients over most of the

domain (Yanosky, et al., 2008). But analysis in present research provided satisfactory results.


37/70


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

37

Chapter 4

Results and Discussion

4.1. IntroductionThe present study has investigated pollution sources, its spatial patterns and exposure

modelling in urban Lahore. Although the research was timely short with miner limitations in

data but analysis has proved that highly dense roads with heavy transport have more

concentration of pollution than less road density. Major roads are most polluted spots and

settlements along these busy roads are at more vulnerable. According to results, concentration

level of pollutants is not making good relation with settlement density but it decreases ingreen spaces. Most of dense settlements are also closer to the denser roads such as Town Hall

and Yadgar Chowk. Remote sensing and GIS techniques have provided methodology of

spatial analysis and interpolated the urban environment to predict pollution concentration in

the whole city. From 22 observation sites, the concentration of NOx, SO2, CO, O3 and PM10 is

predicted for the whole urban Lahore through ArcGIS and Erdas Imagine. Old Lahore region

near Yadgar Chowk, Railway Station, Ravi Road Interchange and Town Hall, is at more risk

of pollution whereas, Thokar Niaz Beg is more polluted because of traffic junction and

industrial area. Airport area, Wapda Town, Mall Road Canal Crossing and Shah di Khoi have

less concentration of pollution because of sparse settlements, presence of vegetation and less

traffic. Proximity and regression analysis have proved that major cause of pollution in urban

land is heavy traffic because points with high road density are more polluted than lower road

density. Proximity analysis model is meaningful for all pollutants except ozone (O3). But it is

possible that the sources of O3 are not traffic and indoor because during 2007 and 2008 most

of cars and buses were converted on CNG (compressed natural gas) (Punjab EPA, 2008).

4.2. Relation between PollutantsAnalysis of temperature, wind speed and direction and humidity can help to understand that

whether pollutants are disturbed by these parameters or not. Main source of pollution in

Lahore is urban transport. Heavy transport and rickshaws produce a considerable

concentration of pollutants in the city. Settlements are also responsible for indoor pollution

therefore highly dense settlements with roads are observing more pollution. Pollutants

observed at different places make a positive relation with each other and indicate that their


38/70


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

38

source is same. Oxides of Nitrogen and CO are linearly correlated with each other with the

value of R2=0.58 as shown in Figure-4.1. Correlation is a statistical technique that can show

whether and how strongly pairs of variables are related (Briggs, et al., 1997). Correlation

between PM10 and CO, SO2 and NOx and PM10 and SO2 is given in Figure-4.2, 4.3 and 4.4

respectively. The value of r is significant at 95% confidence level and all relations show

strong and positive relationship and indicate towards the same source of pollution.

Figure-4.1: A positive linear relation between CO and NOx

Figure-4.2: Linear relationship between PM10 and CO


39/70


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

39

Figure-4.3: Strong positive relation between SO2 and NOx

Figure-4.4: Linear relationship between PM10 and SO2

4.3. Weather Parametric AnalysisPollution data was collected in different days that may be affected by weather. To check the

effects of weather on the concentration of pollutants, analysis of weather parameters is useful.

Observation points with North West wind direction are compared with points of south

western wind and there is not any significance difference in pollution concentration produced

by wind. In figure-4.5 points with North western wind are selected whereas points with south

western wind are highlighted in figure-4.6. Comparison of these two figures shows that wind

direction is not affecting the concentration of pollutants as no specific wind direction has

highest or lowest pollution concentration at observation points.


40/70


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

40

Figure-4.5: Observation points with north western wind direction and concentration of pollutants

Figure-4.6: Observation points with south western wind direction and concentration of pollutants


41/70


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

41

Statistical analysis of wind direction for different points is also analysed to observe its effects

on concentration level. Graphical comparison helps to understand difference in pollution

concentration at observation points having different wind directions. Concentration of NOx,

SO2 and O3 is compared at points of

pollution analysis through gis and rs

Documents