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University of Balochistan Quetta

Ph.D. THESIS R A T E O F D U S T F A L L A N D P A R T I C U L A T E S A N A L Y S I S I N Q U E T T A :

MUHAMMAD SAMI

October 29th, 2009

Department of Chemistry

Dedication

The more I know, the more I come to know that I don’t know…

Dedicated to all those, who have been toiling to eradicate chaos, fear & uncertainty by sustaining the delicate divinely set natural intra & inter balance among diverse lingual, cultural, political, economical & ecological systems of this universe in order to bring peace, tolerance & harmony in both sensual and eternal worlds by following the path of all chosen ones/Messengers/Prophets of Allah, particularly the very last one MUHAMMAD(PBUH)…

University of BalochistanQuetta

Ph.D. THESISRATE OF DUST FALL AND

PARTICULATES ANALYSIS IN QUETTA:MUHAMMAD SAMI

October 29th, 2009


University of Balochistan

Quetta

I HEREBY RECOMMEND THAT THE THESIS/DISSERTATION PREPARED UNDER MY SUPERVISION

BY Muhammad Sami

“Rate of Dust Fall and Particulates Analysis in Quetta.” SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR

OF PHILOSOPHY IN CHEMISTRY

Dissertation Supervisor: Prof. Dr. Sher Akbar


i

PREFACE This thesis is submitted to the Faculty of Basic Sciences at the

University of Balochistan, Quetta, Pakistan in order to meet the requirements

for obtaining the Ph.D. degree. The research work was carried out at the

Department of Chemistry, Geological Survey of Pakistan Quetta, Central Hi

Tech. Lab. of University of Balochistan, Quetta, and PCSIR (Pakistan Council

of Scientific and Industrial Research, Quetta). Above all I would like to

express my gratitude to my very honorable supervisor Prof. Dr. Sher Akbar for

his tremendous supervision during my whole studies and for his enthusiasm

and daily guidance.

I would like to say thanks to the very respectable (Ex-Dean Quality

Assurance Prof. Dr. Abul Nabi), Prof. Dr. Yaqoob, Dr. Muzaffar Khan and Dr.

Amir Waseem (Chemistry Department, University of Balochistan, Quetta) for

always boosting my confidence. I owe a debt of gratitude to Prof. Dr. Yasmin

Zahra Jafri (Chairperson, Department of Statistics) and Prof. Dr. S. Mohsin

Raza (Meritorious Professor, Department of Physics) for their priceless

guidance in developing the statistical ARIMA modeling in order to make

predictions.

I am also indebted for the cooperation purely on volunteer basis of my

buddies/Assistant Professors Abdur Rab Kakar, Waja Basheer Baloch and

Saddaqat of education department (Colleges), for voluntarily helping me in

collecting dust samples and boosting my morale through encouragement and

healthy criticism. Here I must say thanks to all those private/public site/station

owners, who permitted us to keep dust fall collectors on the roofs of their

property. I am also gratified to the Education Department (Colleges Section)

ii

of Balochistan Provincial Government for granting me study leave to embark

upon this Ph.D. research programme.

Last but not least, I would like to thank my whole family for being there

for me or not being there but with their invocations from the beginning to the

final stage of this thesis, and a very special thanks to my (late) father, whose

sweet memories are the assets of mine and would always haunt me till my last

breath.

Finally so thanks to Almighty Allah for paving way for me to

accomplish this task.

Quetta 29th, October 2009

MUHAMMAD SAMI

iii

S U M M A R Y This thesis presents an ample research work conducted at the end of a

severe drought spell from 1997 to 2002 (6 years) in Balochistan, Quetta. This

by and large caused irreparable damage to the whole region, and the arid

region of Balochistan including its fast developing capital 'Quetta' in

particular. Till the inception of my research work, no major study had been

conducted whatsoever apropos of Quetta by focusing specifically on the

chosen topic of mine "rate of dust fall and particulates analysis in Quetta".

Though there are sophisticated equipments available in order to monitor the

burden of particulates in ambient air, yet what matters is the rate of settlement

of those particulates per square area per unit time, including their sizes, shape,

chemical nature, and quantitative presence of toxic metals in them in relation

to the meteorological conditions. In addition to all that the geographical

location and geological nature of the region play a pivotal role in this aspect as

well.

Keeping in view all the above mentioned conditions and the bowl

shape of Quetta valley at an altitude of 5550 feet above sea level, an area of

2653 Km2 (narrow between the mountains of 'MURDAR' and 'CHILTAN')

between east and west and a bit wider between the hills of ‘TAKTOO and

ZARGHOON' in north and north west, a conventional but laborious method

was adopted to monitor the rate of dust fall for the crucial year of 2004 on

daily basis.

For the next four years (2005-09) the dust fall samples were collected

on monthly basis, because the severe drought situation was almost disappeared

and pragmatically it was impossible for me alone to collect the samples on

iv

daily basis as well. However, on weekly basis or randomly I had to keep a

strict check on my dust collectors.

Simultaneously with the help of all collected samples and

meteorological data the rate of dust fall per square area per unit time, the

amount of heavy/toxic metals Pb, Zn, Mn, Ni, Cr, Co present in the collected

dust fall samples was detected with the help of atomic absorption

spectrophotometer (AAS) and the quantity of Na and K was calculated with

flame photometer. The particle size determination on wt. % basis for nine

fractions (PM<1.0, PM1.0-2.5, PM2.5-5, PM5-10, PM10-15, PM15-25, PM25-50, PM50-100

and PM>100) was carried out by using ASTM (American Standard Test

Method). Moreover, the typical chemical composition of the dust fall was

determined for loss on ignition, silica and oxides of aluminum, iron, calcium,

magnesium, sodium and potassium to match the samples with the chemical

composition of the soil of Quetta, 'DASHT-E-LUT' (Iran) and 'Dalbindin'

desert in Pakistan.

Initially ARMA modeling was tried, but due to the random non

stationary data, it was not found to be suitable for our results. Therefore,

ARIMA (Auto regressive integrated moving average) and SARIMA (Seasonal

Auto regressive integrated moving average) modeling were selected, which

were found properly applicable for our data/results. Three sites out of ten

sampling sites were selected in this regard, keeping in view the optimum

(Maximum and Minimum) and moderate levels of dust fall at those locations.

In a nut shell Quetta was found to be one of the very few top most dust fall hit,

toxic and heavy elements particularly Pb (lead) contaminated atmosphere

cities of the globe.

v

TA B L E O F CO N T E N T S

Page No. Preface i Summary iii Table of Contents v List of Tables viii List of Figures xi CHAPTER 1: INTRODUCTION 1 1.1 Environmental science 1 1.2 Pollution 1 1.3 Types of pollution 1 1.4 Composition of atmosphere 1 1.4.1 Uniform gases 1 1.4.2 Variable gases 2 1.5 Air pollution 2 1.5.1 Definition 2 1.5.2 Types of air pollution 2 1.6 Particulates 3 1.6.1 Definition 3 1.6.2 Comparison of PM2.5 and PM10 5 1.7 Chemical types of particulates 8 CHAPTER 2: REVIEW OF LITERATURE/BACKGROUND 10 2.1 Origin of dust/particulates 10 2.2 Trace/heavy and toxic elements 13 2.3 Effect of particulates on humans’ life 18 2.4 Effect of particulates on plants 24 2.5 Effect of particulates on materials 25 2.6 Effect of particulates on climate 26 2.7 Air quality standard for dust fall 26 2.8 Measurement of rate of dust fall 27 2.9 Thermal inversion 39

vi

2.10 A study of different methods used for the collection of

settling dust particulates collection 44

2.11 Chemical analysis of settled/deposited dust particulates for heavy and toxic metals

59

2.12 A study of the size of the dust particulates 72 CHAPTER 3: HYPOTHESIS AND AIMS AND OBJECTIVES 75 3.1 History of Quetta 75 3.2 Geographical Location of Quetta 76 3.2.1 The People 79 3.2.2 The Museum 79 3.2.3 Askari Park 79 3.2.4 Hazarganji Chiltan National Park 80 3.2.5 Fauna 80 3.2.6 Excursions from Quetta 80 3.3 Current picture of Quetta city 82 3.4 Dust fall collection sites 97 3.4.1 Army Recruitments Centre 97 3.4.2 Ashraf/ Sariab Road 97 3.4.3 C.G.S Colony, Satellite Town 98 3.4.4 Civil Hospital 99 3.4.5 Gawalmandi Chowk 99 3.4.6 Qadoosi Store/Quick Marketing Services 100 3.4.7 Railway Station 102 3.4.8 Sadda Bahar Sweets, New Adda 103 3.4.9 Sirki Road 103 3.4.10 T.B. Sanatorium 104 CHAPTER 4: METHODOLOGY/MATERIALS AND METHODS 106 4.1 Preparation of collected samples for digestion 107 4.2 Determination of rate of deposition/settlement of dust fall 108 4.3 Chemical analysis of dust fall samples 109

vii

4.4 Tests for the particulates size distribution 109 4.4.1 Analysis for Na and K 110 4.4.2 Digestion method of dust samples for the analysis of

toxic/heavy elements by atomic absorption spectrophotometer

110

CHAPTER 5: RESULTS AND DISCUSSION 115 5.1 Rate of dust fall/settlement/deposition 116 5.2 (Desert) Dsht-E-Lut 154 5.3 Chemical analysis of dust fall 165 5.4 Detection of heavy and toxic metals in dust samples 171 5.5 Average size distribution of settled and air dust particulates 186 CHAPTER 6: APPLICATION OF STATISTICAL (ARIMA AND

SARIMA) MODELING FOR FUTURE PREDICTIONS

190

6.1 Literature survey 190 6.2 Stochastic time series modeling, simulation and prediction 193 6.3 Model sketch 199 6.4 Autoregressive moving average (ARMA) models 200 6.4.1 Autoregressive integrated moving average (ARIMA) non

seasonal and seasonal models 202

6.5 Simulation of wind speed and forecasting 204 6.5.1 Reason of non-selecting of ARMA and selecting of ARIMA 204 6.6 Results and discussion 205 CHAPTER 7: CONCLUSIONS AND RECOMMENDATIONS FOR

FUTURE RESEARCH 230

7.1 Conclusion with suggested precautionary measures 230 7.2 Recommendations for future research work 236 REFERENCES

APPENDIX 239 257

viii

LIST OF TABLES

S. No. Page No. 2.1 National estimates of particulate emission (106 metric tons/year). 12 2.2 Number of deaths attributed to silicosis in specific industry 19 2.3 Comparative rate of dust fall of different countries 35 2.4 Karachi(mg/sq.m/day) 1980-1985 (6 years) 37 2.5 Peshawar(mg/sq.m/day) 1992-1998 (7 years) 38 2.6 Metal concentration in dust samples in various countries 61 2.7 Concentration of cadmium, lead and copper in dust particulates,

collected from road side at distance of 5 and 20 meters (µg/g) 61

2.8 Concentration of heavy and toxic metals in dust and aerosol in different cities and countries.

65

3.1 Severe drought spell in Balochistan and particularly Quetta from 1997-2002 (6 years).

91

3.2 Level of suspended particulate matters, major cities. 93 4.1 Instrumental conditions for elements 114 5.1 Balochistan and particularly Quetta faced a severe drought spell

from 1997-2002 (06 years) 117

5.2 Dust fall for the year 2004 at Army recruitment centre. 118 5.3 Dust fall for the year 2004 at Ashraf, Sariab Road. 119 5.4 Dust fall for the year 2004 at CGS colony. 120 5.5 Dust fall for the year 2004 at Civil Hospital. 121 5.6 Dust fall for the year 2004 at Gawalmandi Chowk. 122 5.7 Dust fall for the year 2004 at Qadoosi Store. 123 5.8 Dust fall for the year 2004 at the Railway Station. 124 5.9 Dust fall for the year 2004 at T.B. Sanatorium 125 5.10 Dust fall for the year 2004 at Sada Bahar Sweets, New Adda. 126 5.11 Dust fall for the year 2004 at Sirki road. 127 5.12 The fall-out dust standards from standards south Africa (SANS) 20

are shown as below 128

5.13 Classification – American standard test method ASTM D1739 129

ix

5.14 Average monthly rate of dust fall for the year 2004 (mg/m2/day) 130

5.15 Average monthly rate of dust fall for the year 2005 (mg/m2/day) 134

5.16 Average monthly Rate of dust fall for the year 2006 (mg/m2/day) 136



5.19 Average monthly Rate of dust fall from the year 2004-2008

(mg/m2/day) 144

5.20

5.21a

5.22

Monthly average rate of dust fall at Karachi (1980-1985)

Monthly average rate of dust fall at Peshawar (1992-1998)

Monthly average rate of dust fall at Quetta (2004-2008)

157 157

158

5.21b Rate of dust fall of different countries (mg/m2/day) 161 5.34 Typical natural trace element concentrations of surface soils 168 5.35a 5.35b

Typical chemical compositions of dust fall at Quetta for the year

2004-2008.

Average typical chemical compositions of dust fall at Quetta for the

year 2004-2008 during the thermal inversion spells.

169

169

5.36 Average typical chemical composition of dust fall at Karachi for the

year 1980-1985. 170

5.37 Average typical chemical composition of dust fall at Peshawar for

the year 1992-1998. 170

5.38 CALA directory laboratory. 174 5.39 Concentration of heavy and toxic metals in the dust fall at Quetta

during 2004 (µg/g (ppm) 175

5.40 Concentration of heavy and toxic metals in the dust fall at Quetta






x



5.44 Average concentration of heavy and toxic metals in the dust fall at

Quetta detected during 2004-08 µg/g (ppm) 178

5.45 Average concentration of heavy and toxic metals of Quetta from

2004-08 179

5.46 Concentration of heavy and toxic metals in dust and aerosol in

different cities and countries. 184

5.47a 5.47b

Average size distribution of dust fall 2004-08 at Quetta. fraction

% age by weight

Average size distribution of dust fall during thermal inversion period

(days) 2004-08 at Quetta, fraction % age by weight.

188

189

6.1 ARIMA & SARIMA Tables of 3 selected sites 206 5.22a 5.22b

Mean daily temperature (2004)

Mean monthly temperature 257 258

5.23 Daily precipitation (2004) 258 5.24 Daily precipitation (2005) 259 5.25 Daily precipitation (2006) 259 5.26 Daily precipitation (2007) 260 5.27 Daily precipitation (2008) 260 5.28 Wind speed (2004) 261 5.29 Wind speed (2005) 262 5.30 Wind speed (2006) 263 5.31 Wind speed (2007) 264 5.32 Wind speed (2008) 265 5.33 Daily visibility of Quetta 2004-08 272

xi

LIST OF FIGURES AND GRAPHS

S. No. Page No. 1.1 A typical benzene-extractable fraction an organic particulate

respirable in 1µ range 9

2.1 Soot particle from the combustion of fossil fuel 21 2.2 Satellite pictures 28 2.3 Asian dust rises to ~2km (1km above terrain) 28 2.4 Plume rises from the surface (at about 300 m) 29 2.5a 2.5b

Satellite picture of dust plume Heavy dust plume

30 31

2.6 Wind speed 40 2.7 Inversion layers 41 2.8 Depiction of thermal inversion layers. 41 2.9 London UK, 1952 42 2.10 Graph showing massive deaths due to the Thermal Inversion of

London in 1952

42

2.11 (a-e)

Donora PA—1948 43

2.12 Sample collector 45 2.13 Photograph of a typical dust trap 49 2.14 (a-c)

Dust watch standard single bucket collector 51

2.15 Dust watch standard four buckets collector 52 2.16 Position of bird strike preventer and supporting

struts 54

2.17 Cross section through the collecting bowl of the Frisbee type of dust deposit gauge (from Hall, Upton and Marsland, 1993)

54

3.1 Normal annual precipitation rate of Quetta city 82 3.2 Normal annual wind pattern of Quetta city 82 3.3 Normal annual temperature of Quetta city 83 3.4 Bruce Street, Quetta, before the earthquake 84 3.5 Another view of the devastation in Bruce Road 84

3.6 Depletion of ground water in Quetta city 86

xii

3.7 Improper disposal of solid waste/hospital waste at Quetta city 87

3.8 (a-d)

Haphazard ‘Quetta city’ growth, pathetic public transport etc 88

3.9 (a-b)

Los Angeles CA, inversion layers 89

3.10 Smog US global 90

3.11 (a-e)

Photos of Quetta while dust wrapped the city 93

3.12 Map of Balochistan and Quetta 95

3.13 Ten Selected Samples Collection sites of Quetta City 96

4.1 4.2 (a-f)

Flame photometer

Photographs while getting AAS & other instruments training at

Central Hi-Tech. Lab; U.O.B & working at PCSIR Labs. Quetta

110 113

5.1 Graph showing monthly rate of dust fall at Quetta (mg/m2/day)

(2004) 128

5.2 Graph showing average monthly rate of dust fall at Quetta

(mg/m2/day) (2004) 131


(mg/m2/day) (2005) 134


(mg/m2/day) (2005) 135


(mg/m2/day) (2006) 137


(mg/m2/day) (2006) 138


(mg/m2/day) (2007) 139


(mg/m2/day) (2007) 140

xiii


(mg/m2/day) (2008) 142


(mg/m2/day) (2008) 143


(mg/m2/day) from 2004 to 2008 145


(mg/m2/day) from 2004 to 2008 151

5.13 Graph showing rate of dust fall at Karachi (mg/m2/day) 158

5.14 Graph showing rate of dust fall at Karachi (mg/m2/day) 159

5.15 Graph showing rate of dust fall at Peshawar (mg/m2/day) 159

5.16 Graph showing rate of dust fall at Peshawar (mg/m2/day) 160

5.17 Graph showing rate of dust fall at Quetta (mg/m2/day) 160

5.18 Graph showing rate of dust fall in different countries. 161

5.19 Graph showing rate of dust fall at Quetta (mg/m2/day) 162

5.20 Graph showing comparative rate of dust fall at Karachi (1980-1985),

Peshawar (1992-1998) and Quetta (2004-2008)(mg/m2/day) 162

1

CHAPTER 1

INTRODUCTION

1.1 ENVIRONMENTAL SCIENCES:

It is an inter subject range of study that defines problems instigated by

anthropological use of natural world and pursues solutions for those problems.

1.2 POLLUTION:

The disturbance in the balance of naturally harmonized systems or

cycles by increasing or decreasing any one of the constituents

anthropologically is called Pollution.

1.3 TYPES OF POLLUTION:

Air Pollution

Water Pollution

Soil Pollution

Noise Pollution

Light Pollution

Aesthetic Pollution, etc.

1.4 COMPOSITION OF ATMOSPHERE:

1.4.1 Uniform gases:

Nitrogen (N2) ~ 78%, (O2) ~ 21%, Argon (Ar), trace gases (Neon,

Helium, Methane (CH4), etc.) ~ 1%.

2

1.4.2 Variable gases:

Water vapor (H2Ov), O3, CO2.

1.5 AIR POLLUTION:

1.5.1 Definition:

There is a divine set inter and intra equilibrium between the

different hydrological, oxygen, nitrogen, phosphate and sulphur cycles

of eco-system. So is in the case of our atmosphere (The disturbance in

the said set divine dynamic equilibrium of the atmosphere by injecting

certain pollutants Naturally or Anthroprogenically is called Air

Pollution.

1.5.2 TYPES OF AIR POLUTION:

Air pollutants are divided into two categories.

(1) Primary Pollutants

(2) Secondary Pollutants

(1) Primary Pollutants:

Primary pollutants include carbon mono-oxide (CO), hydrocarbons,

particulates, sulphur dioxide (SO2) and nitrogen compounds [1].

• Particulates, part

• Carbon monoxide, CO

• Sulphur oxides, SOx

• Nitrogen oxides, NOx

3

• Hydrocarbons, HC

(2) Secondary Pollutants:

Whereas ozone (O3), peroxyacetyl nitrates (PAN), lead and toxic

chemicals are considered as the secondary pollutants. So far numerous

compounds have been known in polluted cities air, but their collaboration, for

instance, soot chemistry, is very multifaceted. Photochemical pollution is

nowadays more communal than was initially assumed. It happens so

extensively that is significant to converse as well. Nitrogen existing in the air

and as a contamination in fossils fuels changes to nitric oxide in emitted gases.

Similarly, other trace contaminations can provide increase to a diversity of

contaminant gases in release. The occurrence of chlorine and sulfur in fuels

outcomes in the discharge of gaseous chlorine and sulfur compounds [1].

In USA, about 140 to 150 million tons of pollutants are given to the air

every year. Industries account for 20 to 30 million tons, space heating 10 to 15

million tons, refuse disposal 5 to 10 million tons and motor vehicles 90 million

tons or more [1].

• Ozone, O3 O ║ • Peroxyacetylnitrates (PAN) (CH3-C-OONO2)

• Lead and toxic chemicals.

1.6 PARTICULATES:

1.6.1 Definition:

Any material, except uncombined water, that exists in the solid or

liquid state in the atmosphere or gas stream at standard condition or “Small

4

solid particles including lead from gasoline additives and liquid droplets (or

aerosols) such as dust, ash, soot, lint, pollen, smoke, spores, algal cells and

other suspended materials; originally applied only to solid particles but now

extended to droplets of liquid are collectively termed as particulates” [2].

It should be kept in mind that the total particulate matter burden of

ambient air is less important than the chemical nature, size and rate of

deposition/settlement/fall of the particulates. The particulates possess large

areas in general and hence present good sites for sorption of various inorganic

and organic matters [2].

The most important physical property is size. Particulates range in size

from a diameter of 0.0002 µ (about the size of small molecule) to a diameter

of 500 µ (1µ= 10-6 meter) having lifetimes varying from a few seconds to

several months. This lifetime, however depends on the settling rate, which

again depends upon the size and density of the particles and turbulence of air

[2]. Particulates matter in the air is generally divided into further two

categories depending upon their size and diameter.

(i) Particles whose effective diameter is less than 5 microns are

classified as suspended materials because their falling rate under

gravity is so low that due to air movement they remain suspended

in air for a long time.

(ii) Particulates with diameter greater than 5 microns are identified

as settleable materials. It is the material, which is found settled on trees,

buildings and is noticed even by naked eyes until the rain washes it away. The

material thus collected is commonly called dust fall.

5

Particle (also called particulate matter or PM) pollution is the term

used aiming at a blend of solid and liquid drops exist in the atmosphere. Few

particulates for instance smoke; dirt, soot or dust could be visualized without

any equipment as they are having pretty big sizes. While some are this much

small that could only be spotted with the devices like electron microscope.

Particulates comprise of inhalable ‘coarse’ (rough) particulates containing the

diameter within 2.5-10 µm, ‘fine’ particulates having sizes ≤ 2.5 µm and the

particulates having the sizes ≤ 0.1µ (0.0000001 m) are termed as ultra-fine

particles.

1.6.2 COMPARISON OF PM2.5 AND PM10:

• PM2.5 and PM10 refers to size of particles in microns (µ)

• Recall size of a micron:

– 1 µ = 1 millionth of a meter = 0.000001 m

– 70 µ = thickness of human hair = 0.00007 m

– 10 µ = Respirable PM = 0.00001 m

– 2.5 µ = Fine PM = 0.0000025 m

– 0.1 µ = Ultra-fine PM = 0.0000001 m

The number of particles in the atmosphere varies from several hundred

per cm3 in clean air to more than 100,000 per cm3 in highly polluted air, in

urban areas like Karachi, Peshawar and Quetta the particulates mass level may

range from 60 µg to 20000 µg per m3.

Sporadic plumes of particulates could wrap the city in winters, which

cause a severe nuisance among the residents (as experienced by Quettaites

6

during our research period 2004-08) vis-à-vis their daily life and health. The

presence of any heavy industry in city causes the production of aerosols,

which are an amalgamation of primary and secondary particulates in the

atmosphere. Primary particulates (e.g. ultra fines having the size below 1µ in

diameter) along with trace metals e.g. Fe, Na, Zn, K etc. might emerge directly

from diverse sources mainly from soil originated dust [3], whereas secondary

particulates appearance in the atmosphere from gaseous release of sulfur

dioxide, oxides of nitrogen, ammonia, and heavy organic gases. Resultant

aerosol development may take place under dull air circumstances, following

old mixed gaseous emanations from diverse basis, and when contaminants

produced on earlier days build up or are recycled by winds and are stocked up

suddenly in surface-based inversions. Severe and chronic bronchio-pulmonar

diseases are the reasons of dust and particulates bound discharge in the

environment. They are usually linked with PAH (poly-aromatic

hydrocarbons), PCP (penta-chlor-phenol) and furans / dioxins, as they gamely

stuck on non-volatile aerosols. These particulates have a tendency to

concentrate in the bronchio-pulmonar region where they are simply immersed

by the tissue. Besides that, since these particles mostly hold heavy metals, PM

characterize a considerable origin of the toxic load accumulated by humans,

which frankly cause cardio-vascular diseases. Unluckily, current PM-detectors

record merely particulates matter. Nevertheless, research works have found

that particulates number is far more pertinent than their cluster. Therefore,

usual finding apparatus spotlights on cluster merely, therefore sense only a

part of the particulates record. Diesel vapors are particularly troublesome since

they hold nitro-aromates; a set of chemicals which are used to speed up the

7

incineration course of diesel fuel. Nitro-aromatic sort of compounds are

notorious for their tendency of causing mutagenic effect within the GIT

(gastro-intestinal tract). At the start they become the reason of diarrhea.

A massive segment of the globe’s population settled in the huge cities

developed approximately 5% to 50% for the last two centuries.

Anthropologists assess that till the year 2030 about two third of the global

population would dwell in large cities and towns. The high rise of urbanization

has produced many environmental nuisances for instance scarcity of water

supply and sewerage system, over congestion, inadequate transport, slums,

haphazard and unplanned development, particularly for the metropolitan areas

like, Karachi, Lahore, Quetta etc. The main environmental problems of

Karachi are water pollution, marine pollution, disposal of solid waste and air

pollution. Among this environmental degradation, key worry is air pollution

that is upsetting the settled regions. Traffic and industries emit pollutants into

the environment as chief supplier. A few decades ago traffic did not play an

important role in air pollution. Today it is the major supply of pollutant in the

urbanized and industrialized countries. With an improved standard of living

and increased demand on the transport sector, automobile re1ated pollution is

fast growing into a problem of serious dimension in our cities. This is caused

not only by rapid rise in number of automobiles but also due to narrow roads,

slow moving traffic, unfavorable driving cycles, poor enforcement of the laws

relating to vehicles road worthiness and poor emission control measures etc.

[4].

8

1.7 CHEMICAL TYPES OF PARTICULATES:

A. INORGANIC PARTICULATES:

1. Metal oxides on burning of fossil fuels containing metals.

3FeS2 + 8O2 → Fe3O4 + 6SO2

2. V2O5 is produced from residual fuel.

3. CaO is emitted through stack on burning of coal containing CaCO3

Heat

CaCO3 →CaO +CO2

4. (Lead halides) PbBrCl, PbBr2 and PbCl2are produced on

combustion of leaded gasoline containing tetraethyl lead (anti

knocking agent)

Pb(C2H5)4 + O2 + C2H4Cl2 + C2H4Br2 →CO2 + H2O + PbCl2 + PbBr2 +

PbBrCl

5. Aerosol MISTS of H2SO4 droplets appear, produce acid rain

2SO2 + O2 + 2H2O → 2H2SO4

6. (NH2)2SO4 or CaSO4 salts are produced in the presence of basic air

pollutants.

H2SO4 + 2NH3 → (NH2)2SO4

H2SO4 + CaO → CaSO4 + H2O

7. Fly ash is produced through stack in the absence of collector devices

on coal combustion.

9

B. ORGANIC PARTICULATES:

1. C32.4H48O3.8S0.083 (Halogen)0.065 (Alkoxy)0.12

Figure 1.1: A typical benzene-extractable fraction an organic particulate respirable in 1µ range

2. Polycyclic aromatic hydrocarbons (PAH) occur in urban

atmospheres at level of about 20 µg/m3 and sorbed on soot particles.

3. Soot particle (another product of PAH) consists several thousand

inter connected crystallites made up of graphitic platelets which

consists of roughly hundred condensed aromatic rings.

Soot consists 1-3 % Hydrogen, 5-10 % Oxygen, trace metals like (Be,

Cd, Cr, Mn, Ni and V) and toxic organic such as Benzo (α-) pyrene absorbed

on its surface.

10

CHAPTER 2

LITERATURE REVIEW/BACKGROUND

2.1 ORIGIN OF DUST/PARTICULATES:

Our atmosphere contains between one and three billion tons of dust

and other particles at any given time. Wind assists in keeping this dust

airborne, but gravity wins most of the time, forcing the dust particles

earthward, proving the old adage: “what goes up, must come down due to

gravitational force 'g'.” Dust comes from many different sources. Some, like

the by-product of the combustion of fossil fuels, are man-made. Others come

from natural sources – like sea-spray blowing off the ocean, or dust blowing in

from the desert. Dust comprises inorganic matter, such as sand particles, as

well as a large amount of organic matter, including pollen, spores, moulds, and

viruses. These minute particles, ranging in size from around 100 micro meters

(µm) to a few nano metres (nm), invade our airspace every day, a part of life

that we aren’t even aware of, except when we dust the furniture [5].

Through a natural and as well anthropogenically dust enters into

atmosphere. There are numerous natural processes injecting particulate matter

into the atmosphere (800-2000 million tons each year) [2]. The natural

operations which inject huge amount of particulate matter into the air are

volcano eruptions, oceanic spray, dust storm, gusting of dust and dirt or soil by

the storm. It has been reported that up to 15% of the total settleable dust and

an estimated 25% of suspended particulate matter is of natural origin. It has

been estimated that over the United States about 43 million tons dust settled

per year. Of this, 31 million tons was from natural resources including one

11

million tons of pollen, the remainder of 12 million tons was caused by human

activities. Oceanic spray brings annually about 2000 million tons of salt dust

into the air [6-8].

The aerodynamic stress primarily causes dislodging of dust particles

due to the strong winds upon exposed grains. The larger particles fall

obliquely after attaining considerable horizontal speed, bombarding other

particles on the surface which in turn become dislodged and further the

process. Soil condition and meteorological factors are the most decisive

criterion for the development of dust storm. This depends on the vegetative

cover upon binding of soil by moisture. In semiarid regions which includes

cities like Quetta, the earth is least covered by vegetation. Loosening of soil

owing to over human plowing and over grazing of grasslands are prime

contributors to setting up soil conditions favorable for dust storm. Surface

wind speed varies according to soil characteristic. Wind of 35-45 Km/hour

may cause extensive dust storm. In fact dust particles can travel appreciable

distance e.g. the great dust storm of November 12-13, 1933 in the plain states

of United States caused discoloration of snow in New England where 25 tons

of dust per square mile was deposited and dust collected in Europe had their

origin in Sahara [8].

Anthropogenic behaviors also contribute almost equally (one-third

each) the total particulate emission (200-450 million tons per year) to the

phenomena of atmospheric dust which includes transportation (1.2 million

tons per year) [2], fuel burning in still supplies e.g. wood, coal, fuel oil, natural

gas (8.9 million tons per year), solid waste disposal (1.1 million tons per year),

12

miscellaneous processes i.e. forest fire, structural fire, coal refuse burning,

agricultural burning (9.6 million tons per year) and industrial processes (7.5

million tons per year) [9a]. In developed countries like USA the annual

particulate emission is about 20×106 tones, including 5×106 tons of fine

particles (<3µ) [2],. Historical trends in emissions of particulate matter from

1940-1978 is shown in the Table 2.1 [9b]

TABLE 2.1: National Estimates of Particulate Emission

(106 metric tons/year)

Source category 1940 1950 1960 1970 1975 1978

Stationary fuel combustion

8.7 8.1 6.7 7.2 5.1 3.8

Industrial processes 9.9 12.6 14.1 12.8 7.4 6.2

Solid waste disposal 0.5 0.7 0.9 1.1 0.5 .5

Transportation 0.5 1.1 0.6 1.1 1.0 1.3

Miscellaneous 5.2 3.7 3.3 1.0 0.6 0.7

Total 24.8 26.2 25.6 23.2 14.6 12.5

Table 2.1 shows that industrial processes are the main source of

particulate pollution. It is evident that the total 40-55 %, particulate matter is

emitted by industrial processes. Mining and quarrying crushing and sorting of

coal and mineral ores; stone cutting and hew in metal crushing and polishing;

lime and cement; textile industries; saw mills and wood working, glass

works; leather work and certain chemical processes contribute significantly to

dust production during manufacturing [15].

13

2.2 TRACE/HEAVY AND TOXIC ELEMENTS:

The scattering of trace metals in air particulates has been detected to be

reliant on meteorological states. It has been stated that metal substances

display an activist relationship with temperature and an opposite association

with rainfall. Wind rate and track have also been affecting the trace metal

division in fine and coarse particulates parts. Furthermore, in the dearth of

further atmospheric pollutants trace metal quantities are taken as a valuable

guide of air quality of the local environment. Many statistical models have

been recommended for enhanced classification of atmospheric particulates.

The multivariate statistical techniques, principal component analysis (PCA)

and cluster analysis (CA) are deemed a sturdy mean to recognize the causes

and to comprehend the sharing of trace metals in the ambiance [10].

Another study on division of heavy metals in the deposits of Lagos

Lagoon was conducted by Nwajei and Gagophien. The concentrations of

cadmium, lead, nickel, chromium, copper, zinc, iron, manganese, cobalt and

mercury in the sediments of the Lagos Lagoon were determined by atomic

absorption spectrophotometry in the year 1998. The respective limits of the

quantities of the metals were Cd: 0.13-8.60, Pb: 4.10-295.70; Ni: 11.60-

149.40, Cr: 23.30-167.20, Cu: 4.80-102.70, Zn: 27.30-323.70, Fe: 10579.80-

85548.00, Mn: 276.00-748.00, Co: 6.40-41.50 and Hg: 0.04-0.53 mg/kg-1 dry

weight. It highlighted the impact of domestic and industrial discharge of waste

on the levels of cadmium, lead, nickel, chromium, copper, zinc, iron,

manganese, cobalt and mercury metals in the sediments of Lagos Lagoon and

compared the distribution of metals in top and bottom sediments [11].

14

Air pollution intensity in Pakistan’s most populated cities are amongst

the uppermost in the globe and mountaineering, originating grave health

problems. The height of ambient particulates smolder particulates and dust,

that become responsible of respiratory illness, are usually double the global

mean and above than five times as elevated as in industrial countries and Latin

America [12a] as was investigated while a study of atmospheric pollution due

to vehicular exhaust at the hectic roads in Peshawar by Khan et al., [12c].

For contact evaluation, it is essential to calculate particulate release

intensities, and as well to establish particulate trend after releases, because

they are moved away from the release source. Trace elements in the ambiance

are linked with dust particulates, which are included mostly of dust, and fly

ash particulates and some trace elements might be in the gaseous state. Even

though dust particulates are generally more than 5 µm in thickness, there is

always the possibility that some dust will consist of windblown clay

particulates which are by description smaller than 2 µm in width. There are

several field studies in which soils were analyzed for various trace elements.

For example, Bradford et al analyzed soil and plant samples taken from

several location around a 1500 MW power station in Nevada and found

decrease in concentration for Ca, Mg, Sr, Ba and B in saturation extracts of

surface soils and similar effects for Ca, Sr and B in plant samples [13a].

Pinto stated that vehicle donations occurred from exhaust releases

enhanced in Pb; from corrosion as Fe; tire wear particulates developed in Zn;

brake coatings augmented in Cr, Ba and Mn; and cement particles resulting

from roadways by scrape. The major constituents releases from diesel and

15

gasoline fueled automobiles are organic carbon (O.C) and elemental carbon

(EC). It is reported that most of the PM emitted by motor vehicles is in the PM

size range [13b].

Ahmed et al., [13c] observed heavy metals concentration in free fall

dust along a main road. They analyzed free fall dust for determining the

contents of heavy metal elements such as Pb, Cd, Zn, Ni and Cu. A decrease

in heavy metal concentrations by moving away from the road was

significantly apparent at Muredkey, Ferozwala and Shahdra, whereas at Kala

Shah Kaku heavy metals concentrations were not significant by moving away

from the road. Relatively higher concentrations of Pb, Zn, Cu, and Cd were

observed at Shahdra near the road, which may be attributed to traffic density at

the respective site.

They also elaborated about heavy metals concentration in Roadside

dust. At Murdkey, concentrations of Pb, Zn and Ni decreased with moving

away from GT-road. Whereas Cd and V showed almost same concentrations

at all distances. At Kala Shah Kaku, Pb, Cd, Ni and Zn concentrations

followed the decreasing trend by moving away from the highway, however the

amount of V was same at all distances. Concentrations of whole metals in the

highway shoulders dust of Ferozwala decreased with moving away from GT-

road. At Shahdra, concentrations of Zn, Ni and Pb followed the same

decreasing trend while Cd and V concentrations did not follow the above

trend. Maximum concentration of Pb in roadside dust was found at Shahdra

(14 mg/Kg) near to the road. This may be due to greater traffic density at the

respective sites. Relatively greater concentration of Cd in roadside dust was

16

found at Ferozwala (0.96 mg/Kg) and Kala Shah Kaku (0.95 mg/Kg). This

might be owing to the occurrence of zinc – cadmium smelting industries in the

Kala Shah Kaku industrial estates. Concentration of Zn in roadside dust was

found to be greater at Muredkey (33.6 mg/Kg). Relatively greater

concentration of Ni in roadside dust was observed at Kala Shah Kaku (40.7

mg/Kg) close to the road. This may be because of the existence of industrial

estate there. Most of these industries used oil and coal for combustion purpose,

which are the primary sources of emission of Ni [13c].

A recent study was conducted in Islamabad regarding classification of

chosen metals in ambient air hovering particulate stuff regarding

meteorological circumstances [14].

There has been an increasing apprehension on the atmospheric

pollution trouble arising from industrialization, transportation: urbanization

and additional anthropogenic actions. The problem has got additional severe

notice because of the existence of heavy toxic trace metals in ambient air

floating particulate material in the air. Numerous studies have paid attention

on elemental composition of environmental aerosol particulates'. The character

of climate circumstances on the way to "explain the sharing of aerosols in the

environment”, have been reported by several workers. These researches have

provided evidence for a correlation between metal concentrations in aerosols

and weather limitations such as humidity, temperature, wind speed and

rainfall. Divergence in metal stuffing, with diurnal limits, was as well

documented for diverse regions of the globe. These data exhibited a positive

relationship among metal substance and temperature, whilst an opposite

17

correlation was recorded with some changeable as moisture and precipitation.

The said researches held the truth that weather aspects have an imperative

function to the allocation and elemental amount of floating particulate stuff in

the environment [14].

Similar to the majority other under-developed countries, the

progressions of industrial growth and urbanization in Pakistan have not moved

at the speed essential for ecological protection, consequential in many troubles

occurring from unfettered environmental pollution. For many years, in the

capital city, Islamabad, the transportation change has mounted up enormously,

and additionally, an industrialized zone set up at the core of the city to fulfill

the necessities of industrial merchandise for a large inhabitants section. As a

result, the neighboring city inhabitants are nowadays confronting distinctive

unsympathetic fitness consequences of air contaminants that are produced

from industrial releases and enlarged automobiles thickness [14].

An earlier research held in the city proved that the local environment

was overloaded with ambient air hovering particulate stuff loaded with heavy

toxic trace metals, with intensities in extreme surplus to those in the

surroundings environment. Consequently, the city environment might now be

assumed analogous with any disgustingly contaminated city of the globe. The

research pointed out considerable humans enrichment of trace metal

absorption in road region dumped earth crust, water and air linked to the city

region [14].

18

2.3 EFFECT OF PARTICULATES ON HUMANS’ LIFE:

Flying particles, including dust, soot, fumes and mist are very

harmful. The problems caused by dust fall and grit effect the lives of dwellers

of urban and arid areas. The toxic symptoms caused by particulate matter in

human body are extensive pulmonary fibrosis, minimal fibrosis, chemical

irritation, systemic poisoning, allergic manifestation, febrile reaction etc. [7a].

The dust of coal, residual oil, auto exhaust, detergents, steel, nonferrous

alloys, paints, tobacco smoke may cause cancer, lung cancer, dental carries,

brain damage, convulsions, behavioral disorder and even death [7b]. The

most common effect of dust is silicosis which is debilitating chronic lung

disease caused by dust containing high percentage of free acid insoluble

crystalline silica arising during the drilling, crushing, cutting and polishing of

minerals. Some of the common pneumoconiosis is as follow [7c]:

State Dust

Nodular silicosis Free crystalline SiO2

Non-nodular silicosis Ultramicroscopic crystalline SiO2

Calcined diatomite crystalline SiO2

Asbestosis Silicate-3MgO 2SiO2.2H2O

Talcosis Silicate-3MgO4 SiO2.H2O

Coal miners pneumoconiosis Coal dust carbon

Over a period of years death may occur due to dust. The number of

deaths accredited to silicosis in specific industry in Great Britain is given in

the Table 2.2 [7d]

19

Table 2.2: Number of deaths attributed to silicosis in specific industry

Industry No. of deaths attributed to Silicosis

Mining 915

Pottery manufacture 376

Sandstone mason work 374

Stone quarrying and dressing 230

Metal grinding 194

Refractory manufacturing 72

Sand blasting 69

Steel foundry work 13

Stone, pebble, flint and sand crushing

10

Abrasive manufacturing 10

4000 persons died as a result of exposure to dust in London during December

4-9, 1952 because of smog disaster [7e].

Hashmi et al., [4] measured the major ambient air pollution

components such as O3, SO2, NO and NOx in order to obtain baseline data for

some selected areas in Karachi. The areas were categorized on the basis of

traffic congestion. The main contributors of pollutants in these areas were

vehicular traffic and industries. A survey of local hospitals was also conducted

to correlate the prevailing diseases with air pollution levels. The survey

showed that 70% of the patients were suffering from air pollution related

diseases, like chronic bronchitis, pulmonary edema and pulmonary

emphysema. The ratio 2:1 of male to female patients was discovered.

20

Obuekwe et al., [16a] studied the effects of contacts to cement dust and

powder on workers in cement delivery/trade shops in Benin city, Nigeria in

fifteen cement distribution/retail outlets in Benin City, Edo State, South-West

Nigeria. Forty workers from these retail outlets were initially surveyed by

using detailed and open-ended questionnaires as well as oral interview.

Twenty of them were finally subjected to microbiological tests and medical

examinations after series of oral interviews and depending on the physical

effects of the cement dusts on their skins, nose and eye swabs as well as

sputum samples of the subjects were collected and cultured using various

growth media. The results of this study have shown that depending on the

length and level of exposure to cement dust and powder, effects may range

from chest infections, immediate or delayed irritation of eyes, contact

dermatitis, as well as skin rash.

Dust fall contains high concentration of heavy and toxic metals i.e.

lead, cadmium, inc, manganese, nickel, chromium, cobalt, copper etc. [16b].

The symptoms caused by these metals present in dust fall are; anemia,

headache, irritability, vomiting, diarrhea, muscular aching, gastrointestinal

disorder, skin and mucosal changes, dizziness etc. [16c]. Air borne asbestos

and toxic metals, e.g. Be, have reasoned a lot distress because of its

carcinogenic nature. Asbestos employees in building occupations for

apartments and offices endure from lungs pertaining diseases problem.

Asbestos is a fibrous silicate mineral which could keep it up for lengthy

phases of time in the atmosphere.

The fine particulates (<3 µ) are the most terrible reasons of lung harm

21

owing to their capacity to enter into the cavernous air ways. Bigger

particulates (>3 µ) are ensnared in the snout and esophagus where they are

effortlessly removed from, but finer particulates could settle together for

years in the deepest areas of the lungs, where there is no valuable system for

particulate elimination.

The stuck particulates in the lungs could create rigorous inhalation

problem by material obstruction and frustration of the lung, vessels, coal

miner’s black-lung infection, asbestos worker’s pulmonary fibrosis, and

emphysema or metropolitan inhabitants are all connected with the buildup of

such tiny particulates [2].

Air contamination, and particularly particulate material, coagulates

the blood and increases swelling, established by experimental research in

Occupational and Environmental Medicine. Ultra-fine particulates of

breathed in particulate stuff could penetrate into the bloodstream, increasing

22

the risk that their "coagulating" effects on macrophages may have an effect

on the plaques discovered on blood vessels walls. Macrophages are a main

constituent of arterial plaques. This could help to elucidate why air

contamination is connected with an amplified danger of heart attacks, stroke,

and deteriorating respiratory troubles [17].

Kaplan and his colleagues [18] found that there could be a connection

among elevated intensity of air contamination and the danger of appendicitis.

Fresh investigation findings revealed at the 73rd Annual Scientific Meeting of

the American College of Gastroenterology in Orlando, suggests a novel

connection. Dr. Kaplan et al discovered more than 5,000 adults who were

admitted at hospitals for appendicitis in Calgary between 1999 and 2006

having used data from Environment Canada's National Air Pollution

Surveillance (NAPS) monitors that gather hourly intensities of particulate

substance of diverse sizes as well other air contaminants.

Baccarelli [19] supported by grants from the Environmental Protection

Agency Particulate Matter Center; National Institute of Environmental Health

Sciences; MIUR Internationalization Program; and from the CARIPLO

Foundation and Lombardy region reviewed contact to particulate substances

smaller than 10 micrometers in thickness between 870 patients who had been

identified with deep vein thrombosis in Lombardy, Italy, between 1995 and

2005. Long-standing contact to air contamination shows to be linked with a

greater than before risk of deep vein thrombosis, blood coagulates in the thigh

or Deep Leg Veins, as said by a fresh editorial. Contact to particulates air

contamination, very little particulates of solid and liquid chemicals which

23

appear from flaming fossil fuels and supplementary supplies; have been

coupled to the amplified menace of increasing or dying from heart ailment and

stroke. It is worth mentioning here that I myself have got the varicose vein

problem for a long time therefore this research finding tempted me more to

pay more heeds on my research work.

Blood coagulation danger might boost estimations of Death numbers

by contamination [20]. Air contamination "has become so much worldwide

above the last century as to be normally professed as a usual natural thing, 'the

lazy, hazy days of summer," writes Brook of the University of Michigan, Ann

Arbor, in a supplementary editorial." At the same time since we have found

out to exist inside this smog with no regret, air contamination is neither normal

nor tender," he carries on. "Although the utter cardiovascular danger caused to

one person at any particular time position is little, owing to the world over and

continuous type of contact, particulate material positions as the 13th topmost

reason of worldwide deaths (approximately 800,000 deaths annually)" [21].

Baccarelli et al., [19] showed proof of a fresh sort of fitness threats

linked with pollution, he writes. "If future studies maintain their conclusions

and tackle some of the limits, it might be established that the real sum of the

health trouble created by air pollution, previously identified to be terrific,

might be yet superior than ever predicted,".

Schwartz [22], that study was published in the March 15, 2006 issue of

the American Thoracic Society journal, The American Journal of Respiratory

and Critical Care Medicine) — SAN DIEGO, public having diabetes, heart

failure, chronic obstructive pulmonary disease and inflammatory diseases such

24

as rheumatoid arthritis are at greater risk of death when they are contacted to

particulate air contamination, or soot, for one or more years, as said by a

research shared at the American Thoracic Society International Conference on

May 22nd.

Lisabeth [23] in a fresh study probed the link amid short-range contact

to ambient fine particulates material and the threat of stroke and established

that yet small contaminant intensities can boost that danger.

Lanone and colleagues [24] report states that Paris tubes create flying

dust particulates that can harm the lungs of travelers, scientists in France are

covering in a research of the Paris tube system.

Lisabeth [23] and Lisabeth et al., [25] in a research discovered the

connection among temporary contact to ambient fine particulate stuff and the

danger of stroke and established that still small contaminant intensities could

boost that menace.

Air Pollution condenses the blood [26], study shows.

From mainly particulate material, coagulates the blood and increases

swelling [27].

2.4 EFFECT OF PARTICULATES ON PLANTS:

Comparatively minute research has been carried out on the effects of

particulates on vegetation. Dust deposited on the leaves, when combined with

a mist or light rain, forms a thick crust on upper leaf surface. The entrusted

dust interfere with the gaseous exchange and affect photosynthesis in the

plant by shielding out needed sunlight and upsetting the process of CO2

25

exchange with the atmosphere. Low rate of photosynthesis reduces the total

sugar and reducing sugar contents in the leaves and also decreases

carbohydrates [18]. The degradation of chlorophyll contents of the lichen

physcia adscendens has been observed [19].

2.5 EFFECT OF PARTICULATES ON MATERIALS:

Airborne particles including soot, dust, fumes and mist are potentially

harmful for a variety of materials. The extent and type of damage depend upon

the chemical composition and physical state of the pollutant. Extensive

chemical damage occurs when the particulates themselves are corrosive or

when they carry toxic substances along with them particularly in urban and

industrial atmospheres [2]. Particulates with sulphur containing compounds

accelerate corrosion. Painted surfaces are very susceptible to particulate

damage before the paint is dry. Some particulate fumes and mist react directly

with dry painted surfaces and cause considerable damage. Paint damage is

common on automobile frequently parked near industrial plants [20]. Soiling

due to air born particles from manmade sources results in increased cleaning

costs for buildings and other materials and frequent cleaning reduces the

useful life of fabrics. Dust fall carrying acid and soluble salts also contribute to

the chemical decay of marble, sculptures, lime stone, dolomite stone work and

concrete structure if it [21].

On 3rd December, 1984 in Bhophal (India) the Union Carbide factory,

which manufactured Carbaryl (Carbamate pesticide) by using methyl

isocynate (MIC), a disaster happened. Due to the sudden leakage of MIC more

than 10,000 people died, 1,000 people became blind while more than 1 lakh

26

people continue to suffer from various disorders. Further the soil within a 16

Km radius was coated with thick dust as a result of MIC leakage, and its

fertility lost for the next ten years [2].

2.6 EFFECT OF PARTICULATES ON CLIMATE:

Particulates in the ambiance decrease visibility by spreading and

assimilation of astral rays. They manipulate the environment in the course of

the development of smoke, precipitation and snowfall, by performing as

nucleus on which water condensation may happen. Atmospheric particulates

intensities could be linked with the level of rainfall over cities and their

peripheries [2].

2.7 AIR QUALITY STANDARD FOR DUST FALL:

Nevertheless research standards have been laid down for the

permissible concentrations of various pollutants in air but no such standard is

available for the rate of dust fall that could be considered safe for human in

particular and other living beings in general. Perhaps it is because of the fact

that dust fall alone is not an indicator of hazard to human or animal health. As

a rough estimate dust fall should not exceed 5 tons/Km2 per month [14].

Settled dust intensities are showed in units of mass settled larger than time

mg/m2/day. Although no Statutory (legislative) range or rate for deposited

dust in the UK or Europe is given, a frequently used principle assessment is

200mg/m2/day.

27

CLASSIFICATION – AMERICAN STANDARD TEST METHOD ASTM D1739

Dust = Milligrams/day/square meter

Classification - ASTM S.A. German Din Air

Department of Environmental Affairs and Tourism

Equivalent Quality Monthly Limit

Slight <250 650 – Non-industrial

Moderate 251 – 500 limit

Heavy 501 – 1200 1300 – Industrial limit

Very heavy >1200

Units are normally monitored weekly and particulate collected

fortnightly or monthly if uninterrupted monitoring is carried out or shorter

periods if limited to a small area evaluation wants to be measured. To help in

building the masses (weight) indicate somewhat we note the mass of some

daily objects:

A. – Paracetamol tablet=608.83 mg

B. – After handling the Paracetamol tablet=608.63 mg

C. – Pinch of salt=140.31 mg

D. – A single drop of homeopathic medicine=75.32 mg (as the drop

evaporated, the mass dropped by about 1.5 mg per second).

2.8 MEASUREMENT OF RATE OF DUST FALL:

There are numerous sophisticated devices, with which the total burden

of particulates in ambient atmosphere could easily be determined. For

instance a satellite Terra equipped with five gadgets (two Moderate-

Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging

28

Spectroradiometer (MISR) particularly for observing the

particulates/aerosols) was launched by NASA in December 1999 in order to

monitor clouds and aerosols with (MODIS) and to distinguish among

different sorts of plumes, particulates, and planes allowing scientists to

establish worldwide aerosol quantities with exceptional accurateness by

means of (MISR).

Figure 2.2: Satellite pictures

With the help of (MODIS and MISR) famous dust storms (originated

from SAHARA and even traveled up to Europe) in March 2003, March 30,

2007(Asian Dust Plume Juyan Lake Basin in Mongolia)

MISR stereo heights (MINX), Asian Dust Plume, Juyan Lake Basin in

Mongolia, March 30, 2007

Figure 2.3: Asian dust rises to ~2km (1km above terrain [28]

29

and Feb 2008 (Plume raised from the surface ‘at about 300 m’ to 1000 - 1100

m at a distance of 200 km, dust was injected near-surface and rises to 1km)

were monitored.

MISR stereo heights (MINX) Saharan Dust Source Plume Bodele

Depression Chad 20 February 2008 around 0930 UT

Figure 2.4: Plume rises from the surface (at about 300 m) up to

1000-1100 m at a distance of 200 km. Dust is injected near-surface and

rises to 1km [28]

30

Since the storm swept over Earth’s gigantic arid regions, it pulled out

scores of sand and dust particulates and takes them alongside. These were

larger particulates that would drop out of the environment after a little time

where they were removed to elevated heights (3,650 meters [12,000 feet] and

higher) for the period of powerful dust storms. At elevated heights, the winds

were sturdy, transport the particulates greater than extended aloofness.

Freshly on Tuesday 23rd Sep 2009one more intense dust storm was observed,

which was originated from Queensland (Australia) and traveled even a

distance up to Newzeland. The storm, which black out the mining town of

Broken Hill on Tuesday 23rd Sep 2009 prior to far-reaching east, was

originated by a main cold front thrashing up the dust from the drought-hit

hinterland.

Figure 2.5a: Satellite picture of dust plume

The strong winds force- measured in surplus of 60mph - also triggered

bush fires in the state. Till noon on Wednesday the storm, carrying a probable

5 million tons of dust, had extended to the southern division of Australia's

tropical state of Queensland. The dust storms uncovered precious mud from

31

farmlands. At one stage up to 75,000 tons of dust per hour was gusted across

Sydney and dumped in the Pacific Ocean. 'We've got a mixture of dynamics

which have been erecting for ten months already - floods, droughts and strong

winds,' said Craig Strong from Dust Watch at Griffith University in

Queensland. 'Add to these factors the existing drought circumstances that

diminish the plants cover and the earth surface is at its most susceptible to

storm erosion.'

Figure 2.5b: Heavy dust Plume

Health officials, in the meantime, have insisted citizens having asthma

or inhalation troubles to wait indoors. The authorized air quality index for

New South Wales recorded pollutant levels as high as 4,164 in Sydney. A

level above 200 is measured dangerous.

But having kept in mind that the total particulate matter burden of

ambient air is less important than the chemical nature, size and rate of

deposition/settlement/fall of the particulates” the particulates possess large

32

areas in general and hence present good sites for sorption of various inorganic

and organic matters [2]. Scientists/researchers have to use different simple

conventional tiring painstaking methods in order to calculate the amount of

dust fall/settled per square area per time.

The concentration of atmospheric dust is measured by calculating the

amount of dust settled per square area, size of particles and their chemical

composition. Samplers used to identify fine particle fractions typically are

designed to have inlet and sub stage cut points that are as sharp as possible.

Miller et al., [29] proposed a sampler cut point of 15 microns related to

respiratory system deposition but did not recommend desirable cut point

sharpness. Some of the commonly used particulate matter samplers

employing direct mass measurement techniques include the Total high

volume sampler, the dichotomous sampler, cyclone sampler, high volume

sampler with size selective inlet, cascade impactors etc.

Total suspended particulate high volume sampler is the US

Environmental protection agency [30] reference method for total suspended

particulate. The heap of particulates collected on the filter is measured from

the difference between weights before and after exposure. Quartz fiber and

glass fiber are used as filter medium [31]. The principle of particle separation

in dichotomous sampler collects two particle size fractions, 0 to 2.5 micron

and 2.5 to about 15 microns. Teflon membrane filters with porosities as large

as 2 microns can be used in the sampler and have been shown to have

essentially 100% collection efficiency [31]. Lippman and Chan [32]

summarized the available cyclone samplers for ambient particle sampling

33

below 10 microns and noted that the separation effectiveness of cyclones can

be designed to match respiratory deposition curves. Wedding et al., [33]

demonstrated that the cyclone separation principle can be applied to larger

particle, 15 micron sampler inlet. Glass fiber filter was used as a filter

medium in the cyclone sampler used in the Community Health

Environmental Surveillance Studies (CHESS) [34]. Lippman [35] discussed

the effect of sample flow rate on the performance of cyclone sampler. Knight

and Lichti [36] compared the performance of the 10 mm cyclone sampler to

that of horizontal elutriators and noted that the results were comparable if

appropriate flow rates were used. To meet the monitoring requirements for

inhalable particles (LP) as proposed by Miller et al., [29] Environmental

Protection Agency (EPA) commissioned the design of a size selective inlet

for existing total suspended particulate high volume samplers to provide a

single 0 to 15 micron particle size fraction and it has been tested by

McFarland and Ortiz [37]. Cascade impactor samplers have 2 to 10 stages

and are commercially available. Lee and Goranson [38] modified a

commercially available impactor sampler to obtain larger mass collection on

each stage. These have also been designed to mount on a high-volume

sampler. Cascade impactors are not normally operated in routine monitoring

networks because of the manual labor required for sampling and analysis.

Although sampling systems are not extremely complex, careful operation is

required to obtain reliable data; especially if coated collection surfaces are

[39] examined the inlet of the cascade sampler and determined that particles

larger than 10 microns were unlikely to reach the collection stages because of

substantial wall losses. Larger particles in atmosphere have appreciable

34

settling velocities. They are collected by deposition in a dust fall container

and this standard method is considered as the best procedure [40, 41].

Significant work on the measurement of rate of dust fall has been

undertaken in developed as well as in developing countries. In mid-1950’s,

monthly dust fall (tons per square mile per month) for a number of cities in

North American and Great Britain has been determined and given below:

Detroit, 72.0 tons; New York City, 67.5 tons; Chicago, 61.2 tons;

Cincinnati, 34.0 tons; Los Angeles, 33.3 tons; Pittsburgh, 45.7 tons;

Rochester, 26.4 tons [42]. Birmingham, 27.8 tons; Glasgow (east), 26.6 tons;

Leeds (park square), 35.9 tons; Manchester, 42.9 tons. Radermacher et al.,

[43] conducted a thorough research on the dust fall and heavy metal

deposition in the state of North Rhine-Westphalia, Germany. The average

annual dust fall at the city for years (1980, 1981, 1984, 1985, 1986 and 1988)

was (0.18, 0.19, 0.16, 0.15, 0.14, and 0.13) grams per square meter per day

respectively [44-46]. They stated that significant changes have happened in

the past five years and during 1986 (0.14 g/m2.day), the total dust fall was the

lowest of the last 23 years. The study of Okubo et al., [47] revealed that the

mean value of dust fall at Kodatsuno-Spot Kanazawa City, Japan was 5.77

tons per square kilometer per month during 1974-1986. In Japan, there are

more than twelve hundred dust fall collecting stations, whose results are

reported regularly every year by Air Observation Board, Japan [48]. The rate

of dust fall in some other countries in different years has also been summed

up in the following Table No. 2.3

35

Table 2.3

Comparative Rate of dust fall of Different Countries

Rate of dust fall of different countries (mg/m2/day)

S.No. Country Rate

1. USA (1951) 516.66

2. USA (1951-52) 1513.33

3. USA (1954)a 1870

4. USA (1954)b 2056.66

5. USA (1955)a 1630

6. USA (1955)b 1013.33

7. Saudi Arabia (1990) 1725

8. India (1996-97) 1163.98

The average rate of dust fall in developing countries like India

(Mumbai), was 21.92-28.5 tons per square kilometer per month in 1966 [49].

Vora et al., [50] conducted a relative study of dust fall on the leaves in huge

pollution and little pollution areas of Ahmadabad, India. His results showed

that dust fall was very high in high pollution areas. A comprehensive study for

the measurement of monthly dust fall was undertaken by Salam et al., [51] in

the city of Cairo, Egypt and comparison was made with the year 1960. Due to

the increase in dust storms there was increase in the average value of dust fall

in residential area. Another assessment of arsenic contamination in Raipur city

(21◦14N, 18◦38E) of Chhattisgarh in the central part of India is reported here

on the above mentioned table No. 2.3 for a monitoring period between

November 1996 to June 1997, in airborne dust particulates. The month wise

collection and analysis of dust fall out rate between 3.0(±0.10)–91.3(±1.4) mt

(metric tons) km−2 month−1 or 1163.98 mg/m2/day were observed at all 6

36

sampling sites. Anthropogenic and environmental factors play important roles

in the contribution of arsenic in airborne particulate matters [52]. Similarly an

assessment was carried to measure the dust fall rates at eight localities in

Riyadh city during the period 21 March-June 21, 1990. High rates of dust fall

were recorded in all districts with an average of 24.48 tons/km2/month and a

range of 9.87-51.76 ton/km2/month at an average of 1725 mg/m2/day. The

collected dust samples were analyzed for the following contents: Sulphate,

nitrate, chloride, calcium, sodium, potassium, lead and tar. The results are

discussed and compared with other findings [53]. In USA the rate of dust fall

has consistently been recorded / monitored for a long time and in 1954 the

average rate of dust fall recorded in 2056.66 mg/m2/day, which was beyond

the extremely high set limits.

Very little heed has been paid to the atmospheric pollutants in general

and to the dust fall in particular. Minor data is available for some big cities of

Pakistan. Beg et al., [54] carried out six (06) years work from 1980-1985 for

the rate, composition and quantity of dust fall in Karachi at two (02) locations.

The dust fall was measured by exposing dust fall containers of standardized

shape and size at the said two sites for a period of one calendar month

corrected to 30 ±2 days. The monthly average dust fall obtained between 13.0

to 15.7 tons per square kilometer per month (157.13 to 177.17 mg/m2/day). It

was concluded by Beg et al., [54] that dust fall caused by the construction

activities, automobile exhaust and industrial emission of cement factories.

37

Table 2.4

Karachi (mg/sq.m/day) 1980-1985 (6 years) [54]

S.No. Months 1980 1981 1982 1983 1984 1985 Averages 1 January 87.9 90.64 82.74 102.41 70.32 94.83 88.14 2 February 187.24 129.13 100.86 185 137.41 135 145.77 3 March 189.19 171.77 165.8 222.74 200.96 250.64 200.18 4 April 209 203.16 244.16 243.83 229 227 226.02 5 May 223.38 221.61 196.61 200.96 250.64 198.54 215.29 6 June 218.66 257.83 269.16 175.83 247 295.16 243.94 7 July 202.09 242.9 220.8 166.29 249.51 207.41 214.83 8 August 235.64 191.45 166.77 218.22 183.38 184.83 196.71 9 September 252.16 167.83 148.83 158 125.5 232 180.72 10 October 142.9 133.7 134.19 104.67 105 140.64 126.85 11 November 51.8 84.16 82.5 75.5 81.83 78 75.63 12 December 83.54 71.29 73.22 46.93 84.35 82.09 73.57 Averages: 173.62 163.78 157.13 158.36 163.74 177.17 165.63

Similarly, in Islamabad an effort was successfully made by National

Physical and Standard Laboratory (N.P.S.L), Islamabad. Khan et al., [55]

installed four dust fall collecting stations at different places in Islamabad and

collected dust fall samples each month from 1985-1988. The average rate of

dust fall was 8.5 tons/Km2.month and during 1989-90 it raised to about 10.0 ±

0.2 tons/Km2.month (3). He suggested that the significant increase in the rate

of dust fall might be attributed to desertification, weathering of rocks, increase

in industry and vehicular emission in and around Islamabad.

In Lahore about 1390 tons/mile2.month dust fall was calculated in

June, which was rather higher [56].

Another fabulous research work was conducted by Khan et al., [57] for

the marathon period of seven (07) years from 1992-98 in order to calculate the

rate of dust fall by using the recommended standard method [58]. Dust fall

containers/collectors of standardized shape, i-e., 22-24 cm mouth diameter, 20

cm base diameter and 25 cm height were used and installed at four (04)

38

different locations. The selection of the sites for the study was done with

respect to the number of motor vehicles, which are the only main source of

transportation in Peshawar. After a period of one calendar month corrected to

30 ±2 days, the collectors were taken off, covered with plastic lid and brought

to the laboratory. The samples were analyzed by standard chemical and

physical method [59]. Table 2.5

Peshawar (mg/sq.m/day) 1992-1998 (7 years) [57]

The average rate of dust fall was generally increased from 1992 to

1998 and was found to be 730.62 mg/m2/day to 976.92 mg/m2/day. A

variation was found in the dust fall from place to place and month to month.

Meteorological conditions have striking effect on the rate of dust fall

pollution. Chemical analysis of the dust fall showed by Farid U Khan et al.,

that it has contribution from particulate emission from automobile exhausts,

construction activities, soil and sand particles of the proximities [57].

In Quetta the amount of dust fall, smoke particles and lead (Pb) was

determined on daily (24 hours) basis from 10 different sites by Sher Akbar et

Months 1992 1993 1994 1995 1996 1997 1998 Averages January 530.64 553.87 620.96 631.93 661.29 622.25 678.71 614.23 February 587.24 671.03 603.1 713.79 549.65 780.34 846.55 678.81 March 629.67 732.9 795.8 780.32 845.48 833.54 785.16 771.83 April 716 889 870.66 931 996 1015.33 1008 917.99 May 693.54 992.25 1077.74 1098.38 1155.16 1272.9 1191.93 1068.84 June 861 1179.33 1268.66 1287.66 1339.33 1427 1392.66 1250.8 July 1021.93 1039.67 1002.58 1091.93 1160.96 1141.29 1230.32 1098.38 August 905.48 1085.8 944.51 1006.45 1080.32 1090.32 1091.61 1029.21 September 834.66 909.33 869.33 949.66 1001 1057.66 1077.33 956.99 October 740.64 775.16 736.77 583.54 871.29 842.9 871.29 774.51 November 698.66 710.66 690.33 761.33 809.33 780.33 844.66 756.47 December 548.06 610.64 597.41 630.32 748.38 671.93 704.83 644.51 Averages 730.62 845.8 839.82 872.19 934.84 961.31 976.92 880.21

39

al., on daily basis the dust fall was found between 0.3844-0.5291 grams and

on monthly basis between 11.5321-15.8721 grams [194]. Another study was

conducted by Sami et al on the same pattern in order to ascertain the

concentration of Pb and smoke particles emitted from vehicles and the rate of

dust fall on daily (24 hours) basis, which was found between 1.5-4.3 grams

from five different sites by using deposit gauge method, while between 1.1-2.4

grams on 10 different sites by using Petri dish method [195].

2.9 THERMAL INVERSION:

As has been quoted earlier that “It should be kept in mind that the total

particulate matter burden of air is less important than the chemical nature, size

and rate of deposition/settlement/fall of the particulates”. The particulates

possess large areas in general and hence present good sites for sorption of

various inorganic and organic matters [2].

The rate of deposition/settlement/fall of the particulates depends upon

following two factors.

(1). Particulates Size

(2). Weather

Rate of settlement/deposition of Particulates is inversely proportional

to their size. Larger the size of the particulates would be, shorter the time they

would take to settle and vice versa.

Air pollution and weather are linked in two ways.

• Positive way concerns the influence that weather conditions have

on the dilution and dispersal of air pollutants.

40

• The Negative way is the reverse and deals with the effect that air

pollution has on weather and climate.

Air is never absolutely dirt free. Examples of “natural” and

anthropogenic air pollution comprise: pollen, Ash, and spores, smoke and

windblown dust, Industrial, vehicular emission and salt particles etc. The

straight effect of wind velocity is to manipulate the amount of contaminants.

Figure 2.6: Effect of different wind speed on air pollutants

Stability of atmosphere decides the area up to where perpendicular

movements would combine the pollution with cleaner air beyond the exterior

levels. The perpendicular space amid Earth's plane and the elevation to which

convectional activities expand is called the mixing depth. Usually, the larger

the mixing depth, improved the air quality would be and vice versa. That’s

why EPA has strongly recommended the height of stacks at a maximum level

particularly for the cities settled in valleys. So that pollutants might not get

trapped in the inversion layers.

41

Figure 2.7: Inversion layers.

Inversion layers trap cold air, allowing pollutants to build up in concentrations,

including the compounds needed for photochemical smog

Cold air

Warm air

Figure 2.8: Depiction of thermal inversion layers

Numerous unfortunate incidents have been occurred time to time in the

different industrial cities of the world due to the above mentioned

phenomenon. For instance on December 1952 in London in the result of huge

42

amounts of coal burning such conditions developed, which caused thermal

inversion and more than 4000 people died just in few days followed by

additional 8000 deaths in the following months.

Donora PA—1948 [60]

While Thermal Inversion layer Air near ground is denser than the air

higher up no convection currents to lift pollutants.

London, UK 1952

Central London:

48 hours with < 50 m visibility

For one week, visibility did not exceed 500 m

Figure 2.9: Worst smog caused due to Thermal Inversion at London in

1952

Figure 2.10: Graph showing massive deaths due to the Thermal Inversion

of London in 1952

In Another Air Pollution Episode at Donora, Pennsylvania (USA) in

1948 [60], the whole valley was wrapped in the pollutants from zinc and steel

mills became trapped by a temperature inversion. Over a period of 5 days, 17

people died, 5910 people became ill and turned/proved worse for people with

existing problems like asthma, elderly, very young etc.

43

(a)

(b)

(c)

(d)

(e)

Figure 2.11a,b,c: Episode at Donora, Pennsylvania (USA) in 1948 during 5 days it caused, 17 deaths and 5910 people became ill [60] Figure 2.11d,e: The same modern

Donora, Pennsylvania (USA) showing difference of clear and polluted air

44

2.10 A STUDY OF DIFFERENT METHODS USED FOR THE

COLLECTION OF SETTLING DUST PARTICULATES:

Mucha et al., [61] measured the Pb dust fall by the APHA technique

502 as modified by Farfel et al., [62]. The technique consisted of plastic

containers with a distinct surface area of 506.71 cm2 filled with 1 Lt. of de-

ionized water, hanged to inhalation region elevation and opened to the

ambiance for a calculated phase of time. A sampling team of two persons was

deputed in two cars which normally sampled single destruction per day.

Sampling started once the adjoining area (about a two building block radius)

was observed to have no dynamic destruction or wreckage elimination.

Sampling took place around once a week, every other week from March to

October 2006, climate allowing. Sampling was either stopped or not carried

out when rainfall happened. Backdrop sampling in general occurred on days

during which a demolition site was not recognized and in parts in which no

dynamic destruction was happening and where destruction sampling was

about to happen or had previously been completed. One time a destruction site

was recognized, samplers were arranged using previously set equipment. The

equipment was positioned on govt. belongings at the structure border

surrounding the locality of attention. Samplers were hanged to accessible light

poles, utility poles and trees. The space from the locations diverse but was

more or less 5 m from destruction activities. On one occasion the device was

held at about 2 m (inhalation level) on top of earth height, 1l of de ionized

water was transferred into the bucket and the collection time was commenced.

A supposed minimum of 4 samples were used for each destruction site, one at

every turn of the site. As shown in Fig.2.12.

45

Figure 2.12: Sample collector

A label was stuck on the buckets having the names and a phone number to call

to ask questions. It was noted that putting sticker minimized the amount of

tampering with the samplers or removing the samplers by passersby. Sampling

generally was done until the destruction happened for the day. For 2/3 of the

demolition sites sampled, the collection period was around 6.42 h or greater.

The mean quantity of time at every destruction location was 6.42 h and varied

from 3.2 to 8.52 h. Writings on destruction risks were given to attracted

passersby with a contact phone number for the Chicago Department of Public

Health lead poisoning prevention program. In any case one area worker was

there throughout sampling to check the buckets to look at for people or

animals spoiling the samples, in addition to other actions that might have

doubted the reliability of the sample.

In the Premier city of Chhattisgarh region of central India, Raipur

having an urban population of approximately 0.6 million, the rate of airborne

46

dust particulates fall was found out by Deb et al., [52] having followed the

procedure recommended in the literature [63] for a monitoring period between

November 1996 to June 1997. The dust collection glass jars used, were

cylindrical in figure having a diameter of 15 cm, and a height of 45 cm. In all,

six sampling sites were selected in the study area. In each sampling site, four

separate samplers in different directions at a radial space of 50 m were placed

for the most precise sampling. All values obtained at a particular sampling site

were, thus, the average of 4 samplings. Distilled water was kept in every

collector to prevent sample loss by gusting air. The collectors were kept in

guard-frames at heights of 5–15 m above the earth altitude, depending on the

obstructions in the individual site [64]. The jars were examined each week and

changed by fresh collection jars after duration of 30 days. The prevailing

weather periods in this part of India are July–October, spring (southwest

monsoon); March–June, summer; November–February, winter. The pre and

post monsoon month measurements were made of the dust fall rate

concentration, and flux of arsenic for a whole hydrological period for every

sampling location. The dust fall rate was calculated for every location with the

following equation [65].

R = 1.273 (W/D2) × (30/N) ×104 where

R = dust fall rate, in mt km−2 month−1;

W = the total weight of dust fall-out in the collecting of samples;

N = number of sampling days.

The dried particulate fall-out sample (0.1–0.5 g) was taken in a 50-mL

beaker and leached with cold and concentrated HCl-HNO3 (3:1) acid (2–4 ml).

47

The residue after filtration was digested for 1 hr with hot (50◦C) and diluted

(1:10) HNO3 acid (3 ml) and the digested residue was filtered and the filtrate

was combined with the leachate and diluted to a known volume (25 ml) in a

volumetric flask [66].

Crabtree [67], of Texas, USA researched to measure the quantity of

dust that was being settled over the area and to find out whether there is a

relationship among dust settlement and various meteorological limits for

example wind rate, wind track, temperature and rainfall. Dust settlement was

found out by investigating the mass of dust that was deposited per unit of area.

With the intention to ascertain the rate at which the dust was being settled the

weight of the dust settled was divided by the time above which the dust was

collected. An analogous investigation plan was carried out by Singer et al.,

[68] in order to assess the rate of dust fall happened over Dead Sea for a

period of three years. Dust Samples were collected with the interval of each

two months having the settlement rate 6.7-15.2 g/m2/annum1 and 11.4-

24.7g/m2/annum1 in winter and summer respectively. Besides that average

particulates size was detected 10µm varying between 8-20µm. A very few

particles were having the size more than 100µm. An increase in the annual

dust settlement was recorded during the three years period as well as

particulates spreading was recorded from medium to long range movement of

the dust.

Samara and Tsitouridou [69] conducted a same sort of experiment by

collecting dust samples with the interval of every one month. Particulate size

and rates of settlement of particular ionic substances were determined as well.

48

They deduced that 68% of the total settled particulates were having a size

<10µm (PM10) almost closer to the set standards.

Lui et al., [70] conducted a study regarding dust fall in area of China

where repeatedly hit by dust storms. The rate of dust fall of 2 hour period

while, dust storm, happened. The research was extended for two years on bi

monthly bases to calculate the total quantity of dust fall. Their findings

show1.33*104µg/cm2/year1 or equal to 133g/m2/year1. The results clearly

reflect the more values than obtained by Single et al., [68].

Reheis and Kihl [71] conducted another research apropos of rate of

dust fall in Nevada and California for a marathon period of five years. 55

samplers were in the area to collect samples for a total of one year. Dust

collectors were having angel food cake pan shape contained marbles to stop

trapped dust going out of pan with blowing wind. Samples were obtained by

soaking the inert marbles, collector etc. with distilled water. At 35°C, the

water was evaporated and dried samples were analyzed for soluble salts,

organic matter, gypsum and to determine sizes of the particulates etc. The

average dust fluctuation was found between 4.3-15.7/gm2/yr. in the area

Nevada and southeastern California and it was as high as 30/gm2/yr in

southwestern California. The research scholar attempted to correlate

metrological parameters with the rate of dust fall in the region having a view

of no such research work done earlier in these cities. The correlative results

were not found good enough as the dust collection time interval was one year

contrary to the shorter intervals of collection, which is supposed to be the

suitable one for making such correlations. It was also surprising by comparing

49

the rate of dust fall findings that cultivation didn’t affect very much on overall

results and 15-20 % increase in the dust fall was recorded compare to the other

sites. However, cultivation left a decisive effect in dust fall increase.

Figure 2.13: Photograph of a typical dust trap

The above snap in Fig. 2.13 used by Reheis and Kihl demonstrates a

distinctive dust catch. The inactive marbles didn’t let settled dust go out of

collector (layered with a sticky matter to deter birds sit on) by wind blow.

Loans [5a] carried out fall-out dust levels around two enterprises in the

Western Cape Town South Africa from 2001 to 2005. The present method to

establish precipitant dust levels is the ASTM (American Standard Test

Method) D-1739 of 1998 “Standard Method for Collection and Analysis for

Dust Fall (Settleable particulates)” [5b]. There are many measurements that

can be used to quantify fugitive dust concentrations. The use of precipitant

dust level measurements is suitable to the South African economy where

finances for instruments that measure continuously from the atmosphere are

50

not usually available. While single open buckets partly-filled with a capture

medium will accumulate all precipitating dust, this does not establish

precipitant dust emanating from a given direction unless the bucket is closed

to any dust from other directions [5c]. Such open buckets are also subject to

inaccuracies due to wind turbulence within the buckets, lower air densities

over the bucket and other factors [5c]. The single bucket precipitant dust

collection method [5b] “is a crude and nonspecific test method, but it is useful

in the study of long term trends” [5b]. There are many different types of

equipment that are used to monitor precipitant dust levels. Gerry Kuhn

Environmental and Hygiene Engineering have used a method [5b, d] based on

the American Standard Test Method [5b]. The equipment used to measure the

dust is called the “Dust Watch” unit [5d]. The unit is wind operated and

different buckets open under different wind conditions. The unit has four

buckets that are used for directional identification of localized dust sources as

well as to identify ambient precipitant dust concentrations [5d]. The Dust

Watch unit has four buckets that face north, south, east, and west respectively.

The export bucket is defined as the bucket that is in line with the industry or

factory being monitored. The precipitant dust from the dust source is

predominantly collected in the export bucket. The export bucket is also the

bucket that is used for legislative compliance purposes, as this is the bucket

that collects the dust being exported from the source towards the sensitive

area. The Dust Watch units prevent the ground level dust from contaminating

the precipitant dust sample by the specially-designed lid that covers the

buckets. The lid prevents wind-blown dust from being collected when the

wind speed is greater than about 3 m/s. The only way that ground level dust

51

can be deposited into the bucket is if the wind is gusting at regular intervals,

thus lifting dust into the air and into the buckets. Ground level wind-blown

dust that is larger than 100 micron is not normally lifted to a height higher

than two meters and a particle size analysis will be able to confirm if there is

contamination from ground level dust. The water in the buckets collects the

dust [5e].

(a)

(b)

(c)

Figure 2.14a,b,c: Dust watch standard single bucket collectors

52

Figure 2.15: Dust watch standard four buckets collector

Stockholm Environment Institute in collaboration with Mr. Ian Hanby,

[72] developed a simple but fabulous device "dry Frisbee (with foam insert)

dust deposit gauge". A Frisbee-shaped bowl type of collector of anodized,

spun aluminum having the opening 1.7m elevated up the earth surface with a

muddy drain pipe fixed with the stem below to a precipitation collector of at

least 5 dm3 on the earth surface for the continuous collection of one calendar

per month. The whole collecting unit was assembled to trap maximum dust

fall and escape minimum one. In order to collect the samples usually after one

month Frisbee shaped bowl was washed with distilled or de-ionized water to

retrieve the whole dust out of it along with precipitation collector (5 liter

bottle) brought in the lab; the major part of dust with water in the 5 liter bottle

was also obtained by finally washing with de-ionized water and mixed with

the contents of samples collected from Frisbee. The soluble and insoluble parts

of dust were calculated separately with by using watch glass (or Petri dish)

and evaporating the whole water from rest of the samples. The rate of dust fall

53

was calculated by using the said simple but extremely painstaking procedure

by having used the following formula.

(W2-W1) x 24.7 mg m-2 day-1

T

where W1 = initial dry weight of filter (in mg)

W2 = final dry weight of filter plus dust (in mg)

and T = length of exposure period (in days)

Seiy [73] has been involved in the development of an improved design

of airborne dust deposit gauge (in collaboration with Warren Spring

Laboratory, Stevenage, UK and Selby District Council, North Yorkshire, UK.)

since 1987. The accumulating sink of this measurement was having the shape

of an inverted Frisbee and a number of diverse editions of it been assessed in

the ground throughout dust watching plans close to coal-fired power stations

in North Yorkshire, UK [74-76]. The description of the Frisbee gauge

explained in this modus operandi carried out with competence about 36%

bigger than that of the current British Standard deposit gauge [76]. Instruction

principles for dust fall founded on ‘likelihood of complaint’, suitable for

readings from the Frisbee (with foam insert) dust deposits gauges, are

recommended by Vallack and Shillito [77]. In discussion with Seiy [73], the

Frisbee (with foam insert) dust deposit gauge has been manufactured

commercially and is accessible around half the price of the British Standard

dust deposit gauge.

54

Figure 2.16 Position of bird strike preventor and supporting

struts

Figure 2.17 Cross section through the collecting bowl of the Frisbee type of

dust deposit gauge (from Hall, Upton & Marsland, 1993)

Keeping in view the qualitative nature of the hazard from airborne and

settled particles Lieberman et al., [78] established the fact that in many cases

involving chronic exposures, quantitative information is not available

concerning the tolerable dose before damage to health or property occurs.

Atmospheric particulate materials, which are often generated by industrial

processes, may contain or be composed of toxic, corrosive, and erosive

compounds. Their presence in the atmosphere or on sensitive surfaces may not

only reduce visibility but also cause damage to health, to appearance, and to

plant life, In London, a man was executed in 1306 for burning coal while

55

Parliament was in session. Present monitoring systems are designed either to

meet legal limit at ions or to obtain sufficient information, about the nature of

the particulate air pollution to permit remedial action. Thus, the present

systems are used to observe the particulate debris that is deposited on

horizontal or vertical surfaces, to observe the particles suspended in the

ambient atmosphere, or to monitor the vents and stacks associated with a

process that may cause emission of particulate debris. It should be emphasized

that scientifically justified limitations on tolerable levels of particulate air

pollution are not quantitatively known. The complicating factors such as

environmental conditions, contributing pollutants, synergistic effects,

collection site status, and health deterioration or damage cannot be specifically

stated for any single air pollutant or for a simple combination of air pollutants.

Limitations have been set by industrial hygienists concerned with such aspects

as control of radioactive hazards. Maximum-acceptable concentration (MAC)

limits have been set for 8-hour exposures of healthy working male individuals

in reasonably controlled environments. These limits, however, are not useful

for generalized air pollution control. Air pollution results in exposure of the

entire cross section of population from strong, young men to newborn infants

under environmental conditions that are widely variable and uncontrolled. The

best MAC limits are only one part of the input for finding the tolerable air

pollutant levels. Combinations of materials may act in a synergistic manner

not completely defined. Even more important, long exposure times at low

levels have not been adequately studied. Esthetic considerations involved in

seeing a clear view as compared to a smoky view are also of real, but

immeasurable, importance in setting air-pollutant-concentration limitations.

56

When a qualitative analysis of particulate contamination is required, a quantity

of particles is usually collected and analyzed by procedures similar to those

used in any analytical laboratory. Physical measurements range from direct

observation of the particles in the atmosphere and of their effects as they

deposit or interact with various substrates to indirect observation of

interactions between the particles and the means of measurement. The

techniques ranging from routine to conceptual are in varied stakes of

development. Present instrumental techniques are capable of very high

sensitivity for analysis of particulate contamination. An excellent sensitivity is

required because of the lack of quantitative information for tolerable

contamination levels. In general, the analysis of particulate contamination is

based on physical measurements. In categorizing the instruments used for

monitoring particulate air pollutants, the methods in terms of instrument types,

of monitored pollutants, of effects of monitored pollutants, or of instrumental

applications may be considered. The authors have chosen instrumental

applications as the most useful way to categorize the instruments used the

examination of settled particles, of airborne particles, and of the emitting

sources considered. The settled dust particles from the atmosphere may have

irritating and corrosive effects and are usually very obviously present. They

are usually measured and reported in terms of mass of material of several

types per unit area. Sizes of airborne particles permit inhalation and retention

in the respiratory system; thus, health hazards occur. Air borne particles also

cause reduced visibility in the atmosphere. These particles are measured in

terms of mass per unit gas volume, number of particles per unit gas volume,

or, indirectly, as a visibility through the atmosphere. Emissions are of interest

57

because they are normally the source of both settled and airborne particles and

are a direct indication of the processes that cause air pollution. They are

usually measured in terms of mass per unit volume of emission, visibility

through the emission, or as mass of materials emitted per specific process or

process subdivision. Instruments used [78] in one monitoring application can

often be used in another. However, correlations from one application to

another are usually difficult and often impossible. For example, settled dust

must come from the atmosphere. In a fixed volume covering a given area, a

direct correlation should be found. However, the differences in settling rates

for particles of different Stokes’ diameters and the presence of random eddy

currents through the atmosphere make such a direct correlation impossible.

Some of the instrumental techniques, methods, and devices used to

analyze particulate air pollution will be discussed. Particle-collection

techniques are applicable to both particle analysis and particle control

(collection of particles removes ‘them from the environment). In situ

measurement techniques are applicable to analysis only.

Instruments for settled-particle analysis are perhaps the simplest

devices. Interpretation of results may be complex because of the diverse nature

of the deposition of particulate material in the settled particle collectors, the

so-called “dust jars.” The dust jar is usually a glass, metal, or plastic container,

6 inches in diameter and 8 inches in height. It is placed in a stand at a level

where restrained dust from the normal traffic is not lifted to its interior. A

layer of liquid is placed in the bottom of the jar. During winter or inclement

weather, antifreeze may be added. A fungicide or algaecide should be included

58

to prevent growth of cultures that could change the reported results. Bird

guards are usually used to prevent birds from perching on the edge of the jar

and adding deposits to the fluid in the jar. Jars are usually left out for a period

of 1 month is taken after the settled material is thoroughly dispersed. The

liquid is evaporated, and the settled material is analyzed in terms of weight per

unit area in the jar; the result then is extrapolated to unit weight per square

meter or, in some cases, per square mile. It is also possible to extract, with

suitable solvents, the organic-soluble and water soluble components to

determine combustible materials, and to report each component separately

[50]. Another device used for determination of settled particulate material is

the tacky or adhesive sheet. The adhesive-coated sheet is uncovered to the

environment for a set phase of time, ranging from 1 hour to 1 wk. The sheet is

then examined visually for specific particulate contamination. In some cases,

magnification with f~ low-powered microscope may be necessary [51]. A

variation on the above technique allows the use of nutrient plates for analysis

of bacteriological debris, especially in the interiors of hospital rooms. The

nutrient plates are exposed for a suitable time period, and viable colonies are

visually counted to indicate the level of bacteriological contamination in the

atmosphere. In general, measurement of settled particulate material is helpful

in determining trends in air pollution. Trends in airborne particles often follow

trends in settled particles and, therefore, long-term changes in air pollution can

usually be followed by observing the levels of settled debris. However,

quantitative correlations between ambient air-pollution levels in terms of mass

per unit volume in the atmosphere and the total number or mass of settled

particles are difficult, if not impossible, to obtain.

59

2.11 CHEMICAL ANALYSIS OF SETTLED/DEPOSITED DUST

PARTICULATES FOR HEAVY AND TOXIC METALS:

Special focus has been given to heavy and toxic metals in the dust fall

in the last few decades. It is a very multifarious substance, the composition of

which is hardly ever invariable. It is as well the stuff that is now known as an

important source of heavy metals in the city atmosphere. It has been

recommended that dust could be a significant source of metal ingestion for

juvenile kids owing to unintentional intake of the dust [79]. To stop

unnecessary lead, cadmium, titanium ingestion from intake of dust, standards

were established in Federal Republic of Germany in 1983 [80]. Their

restrictions were PbD= 250; CdD= 5; TiD= 10 µg/m2/days as yearly average.

Another famous renowned Laboratory at Canada CALA (Canadian

Association for Lab. Accreditation) has also set some ranges of heavy and

toxic elements in suspended dust fall particulates as given below.

CALA Directory Laboratories Canadian Association for Lab. Accreditation Inc.

Email: [email protected] Scope of Accreditation

Dust fall Range/Limit

Total Suspended particulates/Insoluble

dust fall-dust fall (020)

RDL Range

Lead 10 – 50 ppm Manganese 10 – 50 ppm Nickel 10 – 50 ppm Chromium RDL Range Cobalt RDL Range Zinc 10 – 50 ppm Sodium 10 – 50 ppm Potassium 10 – 50 ppm

60

Akhter and Madany [81] sampled street and household dusts

throughout Bahrain and analyzed for Pb, Zn, Cd, Ni and Cr using the atomic

absorption spectrophtometric method. They suggested that motor vehicles

form a major source of these metals in dust samples. Chakvaborti and

Raeymaekers [82] collected dust samples from street, houses, restaurants and

top of leaves in the city of Calcutta, India. The concentration of eight heavy

metals Pb, Cd, Zn, Ni, Cr, Co and Cu were measured by Atomic Absorption

Spectrophotometer (AAS) and inductively Coupled Plasma Atomic Emission

Spectrometry (ICP-AES). The concentrations of these heavy metals in the dust

were higher when compared to the soil of the same region. Hopke [83] et al.,

found that deposited dusts in urban areas are substantially enriched in many

potentially toxic trace elements. He determined lead and cadmium in urban

roadways dust by atomic absorption spectrophotometer and thirty three other

elements were determined by instrumental neutron activation analysis. Klein

[84] analyzed urban soil (industrial, agricultural and residential) samples for

Hg, Ag, Ca, Cd, Co, Cr, Cu, Fe, Ni, Pb, and Zn. His results showed that all

these metals were more concentrated around the airport. Numerous

investigators have determined the concentration of toxic metals in dust fall and

their results are presented in the Table 2.6 given below.

61

Table 2.6 Metal concentration in dust samples in various countries

Heavy metal concentration (µg/g) Location Pb Cd Zn Ni Cr Mn Ref. Bahrain: Street dust 697.2 72.0 158.8 125.6 144.4 - [190] House hold dust 36.0 37.0 64.4 110.2 144.7 - India: Street dust 2011.1 12.0 890.0 50.0 130.0 380.0 [191] House hold dust 915.0 10.0 954.0 50.0 110.0 542.0 Poland 13.7 5.6 46.4 1.6 3.1 13.2 [188] Saudi Arabia Industrial 208.0 2.8 - - - - [189] Rural areas 106.0 1.6 - - - - Germany 69.0 1.1 - - - - [193] Pakistan 19.0 9.9 - - - 6.2 [192]

Road side dust particles on Jamrud Road, Peshawar at a distance of 5

and 20 meter cadmium, lead and copper by ion selective electrode

(potentiometric) method were analyzed by Liaquat [85] and the results are

shown in the Table 2.7 given below.

Table 2.7 Concentration of Cadmium, Lead and Copper in dust particulates,

collected from road side at distance of 5 and 20 meters (µg/g) S.No. Location Element 5 meters

from 20 meters from

road side road side 1. Stadium Chowk Cd 3.22 1.35 Peshawar Cant. Pb 80.42 55.33 Cu 49.60 8.51 2. Hayat Avenue Cd 3.32 3.36 Peshawar Cant. Pb 72.45 50.00 Cu 36.22 18.90 3. University town Cd N.D. 0.84 Chowk Peshawar Pb 84.23 60.62 Cu 10.23 10.20 4. KhyberHospital Stop Cd 3.92 2.36 Peshawar Pb 49.02 19.02 Cu 20.57 39.48 5. Secondary Board Cd 3.42 5.78 Chowk Peshawar Pb 70.30 62.76 Cu 43.64 24.77

62

Samples of road side dust were collected in the months of April, May,

and June 1990 by Yousufzai [86] from forty one locations along the

intersection of major roads where traffic density was found to be high. Heavy

metals such as Pb, Zn, Mn, Cu, and Cd were estimated. The range of average

concentration of Pb was around 810 to 4527, Zn 112 to 2215, Cu 46 to 315,

Mn 72 to 481 and Cd 0.2 to 4.5 ppm. The daily average traffic was also

recorded. A definite correlation was found between mean Pb level and daily

average traffic. It was concluded that the major source of Pb in road side dust

of Karachi city was mostly contributed by leaded gasoline from vehicular

traffic. Raising children in high leaded environment will definitely have long

term effect on mental and physical behavior in future.

The analysis of Pb, Zn, Ni, Mn, Cr, and Co in dust fall samples were

collected from various deposition sites in urban Saeed et al., [87] during the

period January 1993 to December 1994. Dust fall samples were collected in

accordance with the standard method [88]. A plastic bucket of about 22-24 cm

mouth diameter, 20 cm diameter and 25 cm height was secured in a bucket

shape cage mounted on a metallic pole. After a period of one calendar month,

the bucket was taken off, covered with plastic lid and brought to the

laboratory. Samples were sieved (30 mesh) to exclude materials like leaves,

insects, twigs, stones if any and a portion was dried at 105°C for 24 hours and

weighed. The samples were digested and analyzed on atomic absorption

spectrophotometer (Pye Unicam Model CB 2PX England) in Zeeman flame

mode. Results show that dust fall samples contained significant levels of

metals studied. The average concentrations (µg/g) of Pb, Zn, Mn, Ni, Cr and

Co were found to be 425, 763, 358, 637, 83, and 54 respectively. It was

63

suggested that vehicles form a major source of these metals in dust fall

samples.

Yoursufzai et al., [89] conducted a research work for the measurement

of major ambient air pollution components O3, SO2, CO, NO, NOx,PM10,

methane, non-methane along with the metrological parameters at sub-urban

area of Karachi in a mobile laboratory. The average concentration of O3 was

found to be between 4.62 and 20.36 ppb, SO2 0.73 and 4.69 ppb, CO 0.14 and

0.77 ppm, NO 0.92 and 2.73 ppb, NOx 3.1 and 7.5 ppb, PM10 142 and 251 µg

m-3, methane 1.09 and 2.7 ppm, non-methane hydrocarbon 0.41 and 0.96 ppm.

Krolak [90] carried out a research work for the detection of the

concentration of heavy metals Cu, Zn, Mn, Cr, Ni, Pb and Cd in falling dust in

Eastern Mazowieckie Province (Poland) in 1995-1998. Neither dust fall

crossed the allowed limits nor Pb and Cd. It was also found that the elements

(Pb and Zn) having low melting points were present larger in settled dust

particulates, specifically in the scorching heat vis-à-vis summer. Ni was found

the most stable among all the other metals. Thermal and electric power

industries were discovered the major sources of these metals.

Nwajei et al., [11] worked on the distribution of heavy metals in the

sediments of Lagos Lagoon (Nigeria) in order to find out the concentrations of

Cd, Pb, Ni, Cr, Cu, Zn, Fe, Mn, Co and Hg with the help of AAS in the year

1998. The respective ranges of metals were Cd: 0.13-8.60, Pb: 4.10-295.70,

Ni: 11.60-149.40, Cr: 23.30-167.20, Cu: 4.80-102.70, Zn: 27.30-323.70, Fe:

10579.80-85548.00, Mn: 276.00-748.00, Co: 6.40-41.50 and Hg: 0.04-0.53

mg kg-1 dry weight. The data showed considerable variation in the values from

64

one sampling station to the other. Sampling station 3 (Iddo) showed the

highest values of metals.

An investigation was carried out by Khan et al., [57] in Peshawar for

the detection of Pb, Zn, Mn, Ni, Co and Cr in insoluble dust fall (1993-98) by

using atomic absorption spectrophotometric technique. Elemental

concentrations of the studied elements did not vary significantly at different

sample location. A comparison of the elemental contents with the local soil

was also made. Soil, road dust, vehicle exhaust, metallic corrosion, tire wear,

zinc compounds in rubber material, galvanized material, weathering and

corrosion of building material were some of the possible sources of heavy

metals pollution in Peshawar. The concentration of Pb, Zn, Mn, Ni, Cr and Co

in the dust given below in the table 2.6 was compared with the concentration

of these metals in soil of Peshawar. Imdadullah et al., [91] reported the

concentration (mg kg-1) of Pb, Zn, Mn, Ni, Cr and Co in the soil of Peshawar

to be 1.19, 17.39, 20.61, 5.71, 2.06 and 2.40 respectively. That suggested that

heavy metals ultimately settle down on the earth as soil acts as a recipient of

all types of wet and dry depositions from the atmosphere. Substantial amount

of heavy metals is added to the soil through air.

65

Table 2.8

S.No. Country Location/City Sample Unit Pb Zn Ni Mn Co Cr1 Poland Lecz-Wlodawa Dustfall G/m2 m 13.7 46.4 1.6 13.2 - 3.12 USA ILLIONOIS Street dust µg/g 1000 32 250 35 6.8 2103 Saudi Arabia Riyadh Outdoor dust µg/g 1762 443 44 - - 35.14 Pakistan Abbotabad Dustfall mg/kg 446 931 - 533 - -5 Pakistan Islamabad Dustfall µg/g 22.7 8.3 5.6 - - -6 Pakistan Peshawar Dustfall µg/g 525 763 358 637 54 837 Pakistan Karachi Street dust mg/kg 810-4527 112-2215 72-481 - - -8 Hong Kong Hong Kong Surface dust mg/g 302 1517 - - - -9 Jamaica Kingston Dust µg/g 909 0.8 - - - -

10 Egypt Various sites Dust µg/g 126 - - - - -11 Mexico Chihuahua Dust µg/g 277 - - - - -12 Mexico Monterrey Dust µg/g 467 - - - - -13 Mexico Torreon Dust µg/g 2448 - - - - -14 W. Germany W. Berlin Dust µg/g 8-2943.01 - - - - -15 Saudi Arabia Jeddah Street dust ppm 745 - - - - -16 U.K. Birmingham Street dust ppm 1630 - - - - -17 U.K. Manchester Street dust ppm 970 - - - - -18 Belgium Belgium Street dust ppm 2255 - - - - -19 Malta Malta Street dust ppm 1825 - - - - -20 USA Av. Of 77 cities Street dust ppm 240-1500 - - - - -21 Saudi Arabia Riyadh Falling dust ppm 66.8 141.8 26 319 20.6 -22 Bahrain Various sites ppm 697 151 125 - - 14423 U.K. Lancaster ppm 1880 534 35 - 9.1 2924 Greece Various sites ppm 65-259 75-241 52 - - 1325 Nigeria Various sites ppm 40-243 12-48.01 1-3.3 - - 23-2626 Netherlands Near Smelter ppm 761 1.5 - - - -27 Hong Kong Various sites ppm 1080 1517 - - - -28 New Zealand Christ church ppm 887-1070 - - - - -29 Malaysia Kualalumper ppm 2466 344 - - - -30 Kenya Various sites ppm 23-950 - - - - -31 Taiwan Taipei ppm 196 - - - - -32 England London ppm 345 - - - - -33 Canada Halifax ppm 674-1919 - - - - -34 Equador Various sites ppm 108 218 - - - -35 Kuwait Salmich ppm 136 - - - - -36 USA Various sites ppm 900 - - - -37 Scotland Glagow ppm 308 - - - - -38 Jeddah ppm 745 - - - - -39 Hong Kong ppm 1627 - - - - -40 Brimingham ppm 1630 - - - - -41 London ppm 1200 - - - - -42 Glasgow ppm 960 - - - - -43 Manchester ppm 970 - - - - -44 Urbana III, USA ppm 3600 - - - - -

AAS Atomic Absorption Spectrophotometery SV Striping Voltametry FAAS Flame Atomic Absorption Spectrophotometry SV Striping Voltametry ICP Inductively Coupled Plasma AES Atomic Emission Spectrophotometry ES Emission Spectrograph

Concentration of Heavey and Toxic Metals in Dustfall and Aerosol in Different cities and Countries

Khan et al., [92] evaluated the river Jhelum water for heavy metals Zn,

Cu, Fe, Mn, Ni, Cd, Pb and Cr to determine its aptness for irrigation and

drinking reasons at district Muzaffarabad (A.K) from diverse locations and at

dissimilar occasions. Except Fe rest of the heavy metals were found having

different concentrations at different sites and periods. Though Fe remained

almost having same concentrations in all locations, yet its concentration

considerably varied at higher and lower flows. Rest of the heavy metals (Cr,

Ni, Cd and Pb) crossed the WHO drinking water limits. However Cd and Pb

were found to be in the set standards of USEPA. Mn and Zn were detected

66

within the WHO and USEPA set limits for irrigating and drinking needs.

Nevertheless Fe was found to be dangerous for human’s usage contrary to its

allowable range for irrigation. But Cu was detected even more than the

standards set for irrigation at elevated stream.

Khan et al., [93] conducted another excellent study to detect the toxic

and trace metals Cd, Pb, Zn, Cu, Mn, Ni, Cr and Co in dust, dust fall/soil at

Peshawar. It was found that the said elements emitted into the urban air from

different sources like coal and petroleum burning, municipal incinerators,

automobile exhaust, refuse burning, pesticide use in agricultural, diverse

industrial manufacturing process etc. and being an ultimate sink for all types

of pollutants. To assess health hazard and other problems posed by metal

component, information was needed on their concentrations, particle size and

chemical forms. Therefore a review was done in the Table No.2.6 for these

toxic and traces metals concentrations in dust/dust fall particulates and soil.

Ahmad et al., [13] studied the dispersion gradient of free fall dust and

heavy metal elements concentration in dust along a main road at Lahore

(Pakistan). Gradient of mass flux of free fall-fall dust was measured at

distances 50, 100 and 200 m away from grand trunk road at four different

locations in addition to detect the metal concentrations in free fall dust and soil

samples too. Average monthly free-fall dust values were found to be decreased

as the distance increased. Monthly free fall dust ranged between 24-96

tons/km2/month at 50 m, 15-90 tons/km2/month at 100 m and 9-27

tons/km2/month at 200 m distance, which implied that maximum reduction in

the range of 62-71 % had occurred at 200 m distance. Free- fall dust values

67

were found to be alarmingly higher than permissible limit (5tons/km2/month).

Samples of soil up to 3 inches depth from different locations on analysis

showed the accumulation of these metals decreased with depth. Higher values

of mass flux of free fall particles and metal elements loadings Dispersion

gradient of metal elements, measured at three distances (50, 100 and 200 m),

showed decrease as distance increased road indicated that vehicle exhaust

emissions could be the major cause of particles and heavy metals.

Shah et al., [14] characterized Na, K, Fe, Pb, Zn, Mn, Cd, Ni and Co in

airborne particulate matter in relation to meteorological conditions in

Islamabad, Pakistan on glass fiber filters using high volume air-sampler. The

quantification of the metals was done by the flame atomic absorption method

using HNO3 as a sample digestion medium. Among all the analyzed metals

maximum average concentration was found of Na (1.632 µg/m3), followed by

K (0.932 µg/m3), Pb, Fe and Zn showed mean concentrations of 0.267 µg/m3,

0.574 µg/m3 and 0.645 µg/m3, respectively. The metal-to-metal and metals-to-

meteorological parametric correlations were investigated. Strong positive

correlations were observed between K-Fe (r=0.704), K-Zn (r=0.679) and Fe-

Mn (r=0.561), while Cd and Co showed some significant negative correlation

with other metals. Significantly, positive correlation was observed for the Cr

and Ni concentrations with temperature, while the relative humidity was

mostly negatively correlated with selected trace metals except Na. The

sunshine parameters also indicated a negative correlation with metal

concentration, as was the case with wind speed. The pan evaporation showed

significant negative correlation with Na, K, Fe and Zn, while positive with Cr

68

and Ni. Against other studied parts of the world by and large the metal

pollution in the focused area was reviewed.

In the urban area of Islamabad on 24 hour basis, during June 2004-

May 2005 TSP (Total Suspended Particulates) collected for a period of one

year by using high volume sampling method. The HNO3-HCLO4was used to

detect metals by Atomic Absorption Spectrophotometer. The highest mean

concentration was found for Ca at 4.531 µg/m3, followed by Na (3.905

µg/m3), Fe (2.464 µg/m3), Zn (2.311 µg/m3), K (2.311 µg/m3), Mg (0.962

µg/m3), Cu (0.306 µg/m3), Sb (0.157 µg/m3), Pb (0.144 µg/m3) and Sr (0.101

µg/m3). On an average basis, the decreasing metal concentration trend was:

Ca> Na> Fe> Zn> K> Mg> Cu> Sb> Pb> Sr> Mn> Co> Ni> Li> Cd≈Ag. The

TSP levels varied from a minimum of 41.8 to a maximum of 977 µg/m3, with

a mean value of 164 µg/m3, which was found to be higher than WHO primary

and secondary standards. The correlation study revealed very strong

correlations (r>0.71) between Fe-Mn, Sb-Co, Na-K, Mn-Mg, Pb-Cd and Sb-

Sr. Along with the weather factors, temperature, wind rate and collector

volatility were discovered to be certainly connected with Mn, Mg, TSP, Ca,

Ag, K and Fe. However they showed negative correlation with slight moisture.

Conversely Li, Pb, Cd, Sb, Zn and Co showed pretty associations

(correlations) with rather moist and negative with wind rate, collector

volatility and temperature. Vehicular and industrial discharges were found to

be responsible for the main suppliers of ambient toxic metals detected by

principle component analysis and cluster analysis, re-suspended soil dust and

earth crust. The Ca, Fe, Mg and Mn were recorded highest during the spring

69

while TSP and selected metals showed that Na, K, Zn, Cu, Pb, Sb, Sr, Co and

Cd peaked during the winter and remained lowest during the summer.

Mucha et al., [61] carried out a study to develop a sampling

methodology in order to determine the Pb dust fall from demolition of

scattered site family housing in Chicago, USA. Background: 2 More than 3

thousand old homes are bulldozed every year in Chicago having Pb based

paints. Past researchers’ findings detected excessive amount of Pb in dust fall

originated from the destruction of the multifamily houses. Scattered single-

family homes destruction pertaining research was not conducted by this

research work. Until this research no set limits of Pb in dust fall of demolished

houses were present. 10 houses which were supposed to be demolished in

Chicago were selected to conduct the research work from March to October

2006 in order to find out the rate of dust fall while bulldozing and rubbles

clearance and matched the values with 5 standing houses. Rate of dust fall was

calculated by APHA procedure 502; plastic sampling collectors having de-

ionized water hanged at inhaling level and kept in the vicinity of bulldozing

location region. Later on, the samples were retrieved, filtered, digested and

analyzed by ICP/MS. While bulldozing the arithmetical average Pb dust fall

(n=43 at 10 locations) was 64.1 mg Pb/ m2/h (range: 1.3–3902.5), while the

geometric mean lead dust fall for areas with no demolition (n=18 at 6

locations) was 12.9 mg Pb/m2/h (range: 1.8–54.5). This difference was highly

statistically significant (p=0.0004). While using dust minimizing steps, dust

fall Pb concentrations were lesser even though the deficit/margin was not

statistically considerable. The arithmetic mean having controlled (lesser) dust

(n =25 at five locations) and without (n=22 at six locations) was 48 Pb

70

mg/m2/h and 74.6 mg Pb/m2/h, correspondingly. Easy dust subduing

techniques are probably to decrease the pollution significantly. Bulldozed dust

fall Pb levels are greatly more than backdrop limits of Pb throughout

bulldozing of single-family accommodations and might comprise a so far

undistinguished but significant basis of Pb contact to close by inhabitants.

Deb et al., [52] worked on an assessment of arsenic contamination in

Raipur city (21◦14'N, 18◦38'E) of Chhattisgarh in the central part of India is

reported here, for a monitoring period between November 1996 to June 1997,

in airborne dust particulates. The concentration level of As were higher in the

industrial site, followed by heavy traffic as compared to other sites. The

monthly atmospheric arsenic deposition, in µg As per g of dust fall, of 6 sites

are in the range of 0.100(±0.020)–4.00(±0.020); site no. 1 industrial area,

0.100(±0.020) –0.320 (±0.020); site no. 2 residential area, 0.044 (±0.070) –

0.337 (±0.030); site no. 3 commercial area, 0.093 (±0.068) –1.870 (±0.020);

site no. 4 residential area, 0.111 (±0.020) 1.912 (±0.010); site no. 5 residential

area and 0.068 (±0.040) –3.037 (±0.060); site no. 6 heavy traffic area. The

month wise collection and analysis of dust fall out rate between 3.0 (±0.10)–

91.3 (±1.4) mt (metric tons) km−2 month−1 were observed at all 6 sampling

sites. Anthropogenic and environmental factors play important roles in the

contribution of arsenic in airborne particulate matters. The total annual flux of

as in the fall-out at different zones is in the range 0.033–1.12 kg km−2 yr−1.

Crabtree [67] conducted a research work on the southern high plains of

Texas, America keeping in view the dust and dust storms, which are a

regularly occur over there. In line with earlier investigations, the Plains were

71

shaped by a “slow and gradual process of Aeolian dump on lowland flora”

[94]. In the beginning of 20th century the Southern High Plains was changed

into farming grounds which were extremely vulnerable to storm attrition. Till

the middle 1980s a great deal of the extremely erodible harvest soil was

detached from production by the Conservation Reserve Program (CRP) in an

attempt to decrease the quantity of top soil on hand for storm wearing away:

Bernier (1995) [95] explained the dilapidated figure of gusting dusty time

following the beginning of the CRP program. This investigation scheme

assessed statistics from indirect dust fall collectors placed in Lubbock and/or

Big Spring, Texas ever since the late 1990s. The apparatus were examined on

a periodical basis and the collection and some of the physical and chemical

distinctiveness of the dust were documented. Weather and atmosphere feature

information related to the phases of dust fall samplings were also taken.

Weather variables were obtained on an hourly mean basis and air class

information as daily means. An aim was to endeavor and compare the quantity

of dust fall with weather factors for instance wind rate, temperature and

rainfall. In general, among weather factors and dust fall no arithmetically

important correlation was established.

Momani et al., [96] conducted research on environmental settlement of

Cu, Zn, Cd and Pb in Amman, Jordan. Environmental samples were collected

using a low-volume air sampler and dust fall collectors in the summer of 1995

at diverse locations in the city of Amman, Jordan. The heavy metal

concentrations in settle able particulates (dust fall) and particulates

(suspended) in atmosphere were examined by graphite furnace atomic

absorption spectrophotometer. The atmospheric concentrations of Cu, Zn, Cd

72

and Pb were 170, 344, 3.8 and 291 ng/m3, respectively. The intensity of these

elements in the dust fall settlement was 505, 94, 74 and 3.1 µg/g, respectively.

The fluctuations and dehydrated settling rates of these heavy metals were

detected and judged against the results of other international researchers. The

enhancement coefficients of the heavy metals in the dust fall were 12.1, 6.1,

11.7, and 1.1 for Zn, Cu, Pb, and Cd, respectively and were detected to be

considerable.

The chemical composition of the Black Sea aerosol was calculated by

Kubilay et al., [97] in July 1992 CoMSBLACK 92 cruise on board R/V

BilinL. It was found that the chemical composition of the Black Sea aerosols

could vary extremely swiftly in line with alteration in the wind rule. The

power of the Saharan desert particulates could change the elemental quantities

of trace metals. The dispatch has been summed up on enhancement feature

figures to allow relationship with earlier and upcoming researches. Such heaps

could play a significant function in the current condition of the ocean. As the

environmental participation of oxidized nitrogen (NO3+NO2-N) could attain

13% of the total inorganic nitrogen contribution of the Danube, Lead (Pb)

contribution arrived at 39% of this riverine involvement.

2.12 A STUDY OF THE SIZE OF THE DUST PARTICULATES:

Khan [98] found the particulates size distribution % by weight for ten

(10) different sizes (211, 150, 125, 105, 76, 65, 53, 44, 42 and < 42µm) for the

period of three years 1992-1994 at three different sites. Spatial changes in

whole poised particulate matter (TSP) were examined by Shah et al., [99] for

the sharing of metals and particulates volume portions in the town and rural

73

ambiance of Islamabad, Pakistan. The metals Fe, Mn, Pb, Cd, Cr, Zn, Co, Ni,

Na and K, and the particulates divisions of four (04) diverse mass classes (<

2.5, 2.5-10, 10-100 and > 100 µm) were integrated in the research. TSP

samples were collected on glass filament filters by means of high volume

samplers and amount of metals was ascertained by using Atomic Absorption

Spectrometry using HNO3 based wet digestion. At the city site, Na was

leading at 2.384 µg/m3 followed by K, Fe and Zn with 0.778, 0.667 and 0.567

µg/m3 as average amounts, correspondingly. The metal levels for the rural

locations varied from 0.002 µg/m3 for Cd to 1.077 µg/m3 for Na. Nevertheless,

evaluated with the city site, average Pb amounts proved an approximately dual

improvement, i.e. 0.163 Vs. 0.327 µg/m3. Metals and particulates dimension

foundation recognition was completed by means of Principal Component

Analysis and Cluster Analysis. Five bases were pointed out for the city

location: soil, industrial, metallurgical industries, excavation activities and

automobile emissions. For the countryside site, four causes were witnessed in

terms of excavation activities automotive emissions, metallurgical industries

and agricultural. For the countryside location, four bases were traced in terms

of excavation activities, agricultural, metallurgical units and automotive

emissions. Jointly, each city and rural sites, PM10-100 emerged as a major

contributor to TSP, subsequently PM2.5-10, PM<2.5 and PM>100 correspondingly.

The metals proved generally optimistic association with fine particulates

portions (PM10-100, PM>100). The level of Ni, Mn, Co, Na, K and Fe was

determined to be poorer than those of the different polluted Asian cities of the

world.

74

Shah and Shaheen [10] for nine different segments (PM<1.0, PM1.0-2.5,

PM2.5-5.0, PM5.0-10, PM10-15, PM15-25, PM25-50, PM50-100 and PM>100) studied the

ambient air particulates material by gathering it on glass fiber filters in city

ambiance of Islamabad, Pakistan, using high volume sampler. The particulates

samples were investigated for 10 selected metals (Na, Zn, Fe, K, Pb, Mn, Cr,

Cd, Co and Ni) by FAAS technique. Utmost average involvement was

observed for Fe (1.761 µg/m3), afterward Na (1.661 µg/m3), Zn (1.021 µg/m3),

K (0.488 µg/m3), and Pb (0.128 µg/m3). The particulates dimension

determination on vol. % basis for seven fractions (PM<1.0, PM1.0-2.5, PM2.5-5,

PM5-10, PM15-25, PM50-100 and PM>100 µm) was done by means of Mastersizer.

Shah et al., [100], PM5.0-10 were detected to be most plentiful in the

local ambiance subsequently PM2.5-5 and PM15-25 whilst coarse/giant

particulates (PM50-100 and PM>100) proved lesser part. The trace metals were

detected to be mostly linked with lesser intensities whereas comparative

moisture proved negative relationship. The origin detection was done by main

constituent examination and bunch study. Five metal bases were known:

vehicular emissions, industrial, metallurgical operations, soil derived dust and

garbage burning. In general, 181 particulates samples were collected,

throughout September 2003-March 2004, a phase obvious by a common 'dry

spell' (no precipitation) with approximately 50% or with a reduction of

comparative moisture and dull ambiance circumstances existing throughout.

75

CHAPTER 3

HYPOTHESIS/AIMS AND OBJECTIVES

3.1 HISTORY OF QUETTA:

In the beginning a loose tribal confederation, Balochistan was later on

divided into four principalities that were sometimes under Persian, sometimes

Afghan suzerainty. In the 19th century British troops tried to subdue the

inhabitants until a treaty in 1876 gave them autonomy in exchange for British

army outposts along the Afghan border and strategic roads, on the exchange

for British army outposts along the Afghan border and strategic roads. On the

division of India in 1947 the Khan of Kalat declared Balochistan independent;

the insurrection was crushed by the new Pakistani army after eight months.

Three rebellions followed the last being from 1973 to 1977, when 3,300

Pakistani soldiers lost their lives and some 6,000 Balochs embraced

martyrdom as well. Quetta more commonly called the fruit basket of Pakistan

is the capital of Balochistan and used to be one of the most beautiful cities due

to its small population and well planned infrastructure. Plums, peaches,

pomegranates, apricots, apples, some unique varieties of melon like "Garma"

and cherries, pistachios and almonds are all grown in abundance. Some

pistachios also grow in Qila Saif ullah. Saffron grows very well on mountains

around 5000 ft (1524 meters) high. It is being cultivated on a commercial scale

here. The yellow and red varieties of tulip grow wild around Quetta.

Quetta is an important trade centre; other industries include fruit

canning and chromite mining. In 1876 the British acquired Quetta by treaty

with the Khan of Kalat. The city was capital of the British province of

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Balochistan until that province became part of Pakistan in 1947. Pop. (1981

prelim.) 285,000. Quetta is also widely known as the summer resort of

Pakistan. It has rail links with Afghanistan and Iran, and in 1982 a gas pipeline

to Shikarpur in Sindh was built. Quetta is a centre for fruit growing, trading in

wood, carpets and leather. There is a military staff college and now number of

Universities. Quetta was first mentioned in the 11th century when it was

captured by Mahmud of Ghazni on one of his invasions of the subcontinent. In

1543 the Moghul emperor Humayun rested here on his retreat to Persia,

leaving his one year old son Akbar until he returned two years later. The

Moghuls ruled Quetta until 1556, when it was taken by the Persians, only to be

retaken by Akbar in 1595.

The powerful Khans of Kalat held the fort from 1730. In 1828 the first

westerner to visit Quetta described it as mud-walled fort surrounded by 300

mud houses. Although occupied briefly by the British during the First Afghan

War in 1839, it was not until 1876.

3.2 GEOGRAPHICAL LOCATION OF QUETTA:

The name Quetta is derived from the word "Kuwatta" which means a

fort and, no doubt, it is a natural fort surrounded as it is by imposing hills on

all sides. The encircling hills have the resounding names of Chiltan, Takatoo,

Murdar and Zarghun that seem to brood upon this pleasant town. There are

other mountains that form a ring around it. Their copper red and russet rocks

and crests that are powdered with snow in winters add immense charm to the

town.

77

Quetta is located on the world map at Latitude 29o48' to 30o25' North

and Longitude 66o13' to 67o17' East. The city is situated at an Altitude of 1692

meters (5550 feet) above sea level. The total geographic area of Quetta is

about 2653 Km2. The area experiences cold gusty winds during winter. Heavy

snowfalls occur during December, January and February frequently on high

mountains and occasionally in the valley. The humidity is low. The

uncultivated part supports a sparse cover of natural vegetation, which

comprises: Fagonia Arabica (kandero), Peganum harmala (harmal) and

Haloxylon sp. (lana). The climate is sub-tropical having all four seasons spring

(March and April), summer (June, July and August), autumn (September and

October) and winter (December, January and February). The series occupies

level to nearly piedmont plains. The parent material is derived from

sedimentary rocks comprising mainly limestone. The series occurs in a mean

annual rainfall of 200 mm, most of which falls during the winter the mean

annual temperature is about 64 °F and the mean summer temperature is about

78°F and the mean winter temperature is about 40°F. July is the hottest month

with a mean maximum temperature of about 96°F and January is the coldest

month with a mean minimum temperature of about 27°F.

Quetta, is the legendary stronghold of the western frontier lies at

35Km/20 mi northwest of the Bolan Pass had a population 350,000 (1991).

Geographically Quetta also holds a vital and strategic position, and is

one of the most important military stations of the country. Boundaries of Iran

and Afghanistan meet here and the Bolan Pass controls important lines of

communications.

78

Balochistan, a mountainous desert area, comprising a province of

Pakistan, was earlier a part of the Iranian province of Sistan and Balochistan,

and a small area of Afghanistan. The Pakistani administered province has an

area of 347,200 sq km.134, 019 sq mi and a population (1993 est.) of

6,520,000. Sistan/Balochistan covers an area of 181, 6000 sq km/70,098 sq mi

and has a population (1986) of 1,197,000; its capital is Zahedan. The Quetta

region has become important for fruit growing, coal, natural gas, chrome and

other minerals have also been discovered and exploited. The 1,600 km.1, 000

mi rail network has strategic as well as economic significance. Although

Quetta is on the western edge of Pakistan but still it is connected with the

country through a wide network of roads, railways and airways.

The port of Gawadar in Balochistan (Pakistan) is strategically

important, situated close to the Indian Ocean and the Strait of Hormuz. The

1,600 km/1,000 mi rail network has strategic as well as economic significance.

Quetta is connected to Lahore by 727 mile long railway line. Similarly it is

also connected through railways with Peshawar (986 miles away) and Karachi,

which is 536 miles away. It is also connected by roads to the rest of the

country. A road was built to connect Karachi through Mastung, Kalat,

Khuzdar and Las Bela. Another road connecting Quetta to Karachi follows the

Sibi, Jacobabad, Sukkur and Hyderabad route.

Quetta and Lahore are also linked through two routes. The older is the

Sibi, Sukkur, Rahim Yar Khan, Bahawalpur and Multan route. Another route

is via Loralai (265 kms away), Dera Ghazi Khan and Multan.

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Quetta is also connected with Afghanistan through Chaman; and to Iran

through the Mastung, Nushki, Dalbindin and Taftan route.

3.2.1 The People:

The inhabitants are mainly Baluch Brahui and Pathan; you can also

find Uzbeks, Tajiks and Turkamen rubbing shoulders with the other

inhabitants. Nomadic tribesmen pass through Quetta Valley during spring and

autumn with their herds of sheep and camels and their assorted wares for sale.

This seasonal nomadic pastoralist movement from the drier area, when it

becomes too arid, adds color to the life of the city. The rugged terrain has

made the people of the area hardy and resilient. They are known for their

friendly and hospitable nature. Many of them are settled agriculturalists,

growing wheat, barley, millet, maize, and potatoes. To make a visitor

comfortable is part of their tradition, like the rest of the people of Pakistan.

3.2.2 The Museum:

The archaeological Museum at Fifa road has a collection of rare

antique guns, swords and manuscripts. Geological Survey Department on

Sariab road (6 Kms) has a collection of rocks and fossils. Only six kms from

the city is the campus of the University of Balochistan.

3.2.3 Askari Park:

Askari Park at the airport road offers amusement and recreational

facilities.

80

3.2.4 Hazarganji Chiltan National Park:

In the Hazarganji Chiltan National Park, 20 kms. south-west of Quetta,

‘Markhors’ have been given protection. The park is spread over 32500 acres,

altitude ranging from 2021 to 3264 meters. Hazarganji literally means "Of a

thousand treasures". In the folds of these mountains, legend has it, there are

over a thousand treasures buried, reminders of the passage of great armies

down the corridors of history. The Bactrains, Scything, Mongols and then the

great migrating hordes of Bloch, all passed this way.

3.2.5 Fauna:

Markhor of which there are five distinct kinds, is the national animal of

Pakistan. The kind that is photographed the most often is the Chiltan Markhor

which, because of its long horns looks very conspicuous. Ever since the

markhor has been given protection its number has multiplied. Other animals in

the park are straight horned markhors, "Gad" wild sheep) and leopards which

occasionally migrate to the park from other areas, wolves, striped hyena,

hares, wild cats and porcupines. Many birds like partridge, warblers, shikras,

blue rock pigeon, rock nuthatch, red gilled choughs, golden eagle, sparrow,

hawks, falcons and bearded vultures are either found here or visit the park in

different seasons. Reptiles like monitor and other wild lizards, eckos, Afghan

tortoise, python, cobra, horned viper and Levantine may also be seen in the

park.

3.2.6 Excursions from Quetta:

Karkhasa is a recreation Park situated at distance of 10 kms to the west

of Quetta. It is a 16 kms long narrow valley having a variety of flora like

81

Ephedrine, Artimisia and Sophora. The Urak valley is 21 kms from Quetta

City. The road is lined on either side with wild roses and fruit orchards.

Peaches, plums, apricot and apples of many varieties are grown in this valley.

The waterfall at the end of the Urak valley, which is full of apple and apricot

orchards, makes an interesting picnic spot.

A little short of the place where the Urak valley begins and 10 kms

from Quetta is the Hanna Lake, where benches and pavilions on terraces have

been provided. Golden fish in the lake, comes swimming right up to the edge

of the lake. A little distance away, the waters of the lake take on a greenish

blue tint. Right where the water ends, pine trees have been planted on the grass

filled slopes. The greenish-blue waters of the lake provide a rich contrast to

the sandy brown of the hills in the background. One can promenade on the

terraces. Wagon service operates form city bus station at Circular road.

Some 50 kms from Quetta is the valley of Pishin with its numerous

fruit orchards, which are irrigated by "Karaz", a kind of artificial spring made

by boring holes into rocks to bring to the surface the subterranean water.

Sixteen kms from Pishin is the man-made lake Bund Khushdil Khan. Its cool

gentle waters attract many visitors for duck shooting in early winter.

At a distance of 70 kms from Quetta on Sibi road is situated a popular

picnic spot known as Pir Ghaib. Here a waterfall cascades down rocky

mountain side making its way through many streams and ponds among the

shady palm trees. You need a 4-wheel-drive vehicle to reach the spot from the

main road.

82

3.3 CURRENT PICTURE OF THE QUETTA CITY:

In normal weather conditions of Quetta precipitation occurs around 58

mm mostly in winter season due to the wind patterns originate clouds from the

Turkey and Greece seas, black sea near Jordan and Persian Gulf while in

summer the average precipitations happen up to 13 mm as the track originates

from Gulf of Bangal and causes heavy downpour in the most parts of India

and Pakistan, doesn’t pass through the 90% area of Balochistan and results

mostly dry spells or very less rain.

Normal annual precipitation rate ofQuetta city

Weather

Clouds and Precipitation

0102030405060

Janua ryMarch

May July

September

Novembe r

Month

Pre

cipi

tatio

n (m

m)

Series1

Normal annual wind pattern ofQuetta city

Wind Speed Quetta

0

10

20

30

40

50

60

January

March MayJu

ly

September

November

Months

Kts

Mean Wind SpeedMax Wind Speed

Weather (Contd…)

Figure 3.1: Figure 3.2:

Normally the maximum Temperature of Quetta reaches up to almost

35°C in summer and during it drops below to −6 °C.

83

Normal annual Temperature Conditions of Quetta City

Min/Max Tempreture

-100

10203040

JanuaryMarch May

July

September

November

Month

o C MaxMin

Weather (Contd…)

Figure 3.3:

25 years back in the said region of northern Balochistan and Quetta

averagely 5 to 6 times snow fall would happen between November to March

and snow used to keep the tops of surrounding mountains covered even till

July to August. There were no ice factories in the city 30 years ago and the

traders associated with ice cream business used to use the same natural snow

having collected it from the tops of surrounding mountains. Neither people

used to use fans nor flies nor mosquitoes were there.

The main thoroughfare of Quetta is Zarghoon road. It is a long

boulevard used to be lined with trees and called 'THANDI SARAK' 'cold road'

due its chilled surroundings even in the peaks of summer. But as soon as the

population grew, the road was widened and dense trees on its shoulders were

cut ruthlessly having no further plantation. Many important buildings like the

Civil Secretariat, Provincial Assembly, Balochistan High Court, Army

Recruitment Centre, Governor House, Chief Minister House, Post and

Telecommunication Offices etc are located along Zarghoon road.

84

Quetta used to be called 'small London' due to its very thin population,

superb sanitation, dense vegetation, fabulously well planned architects and

wide roads even after the devastating earthquake at 3.03 am on 31st May

1935, which perished 30,000 to 40,000 souls within few minutes and

completely turned the whole city into rubbles by a severe earthquake lasting

about 30 Seconds having an intensity of 7.5 recorded on Richter scale

followed by many aftershocks during the twilight period of British rule.

Figure 3.4: Bruce Street (now Jinnah road), Quetta, before the

earthquake

Figure 3.5: Another view of the devastation in Bruce Road (now

Jinnah road)

85

After the great disaster, Quetta houses were generally rebuilt as single

level dwellings. In what became the first building codes for earthquake zones

the houses were built with bricks and reinforced concrete. The structures are

generally of lighter materials than those that were destroyed in the great

earthquake.

Even after the end of British rule for a long time it retained its status to

some extent after the division of sub-continent and emergence of Pakistan. But

gradual steady urbanization, lack of planning, corruption and above all the

massive influx of Afghan refugees soon after the start of Soviet invasion in

Afghanistan in 1979, the city turned into a thickly populated city having

sudden huge traffic increase mainly because of the Afghan transit trade. At the

same time natives from the rural remote areas of across the Balochistan,

adjacent regions of Sindh, Punjab and NWFP/PASHTOONKHWA settled in

Quetta in order to seek education and search job opportunities. This caused the

appearance of numerous problems regarding sudden fall in already scarce

water table, choked sewerages, improper disposal of garbage having even

hospital waste, unplanned haphazard slums in the suburbs of Quetta city.

According to the official figures nowadays total generation of waste in the city

is 500-600 tons per day. But unofficial sources claim that out of two towns of

city (ZarghoonTown and ChiltanTown) only Chiltan town produces more than

300 tons garbage per day. Hardly 200 tons of it is disposed of. Zarghoon town,

which is far larger than Chiltan town in area, produces far more amount of

waste daily and just around 200 tons is disposed of. The whole garbage of the

city is disposed in an area of two square kilometers in the foot/base of

mountain 'MURDAR' on eastern side of the city, where huge slums of

86

downtrodden people are growing up. Thousands of tons garbage is left in the

city and can't be disposed of properly due to the lack of resources, corruption

and other multiple factors. On the eastern by-pass side, where garbage is

dumped, in its proximity around 20 flour mills operate, a huge Govt. slaughter

house is also situated there, and number of housing schemes like 'SASTEE

BASTEE' have been launched for poor peoples’ residence purpose. It is a

growing menace for the population living around it. 14 Stone crushing Units

are operating in Quetta District besides iron smelting units and some other

industrial units of marble industry, flour mills, ghee mills etc. including small

industries of edible items, furniture making etc. have been operating in the

centre of the city. 3000 animals (Small/Large) animals are slaughtered per

day. Though 89 brick kilns within the valley have been relocated by the EPA

Balochistan with the support of corps of volunteer, PAF and civil

administration, still pretty numbers of operating within the proximity of huge

clusters of population.

Depletion of Ground Water in Quetta City

Figure 3.6: Statistics of Water Table of Quetta

87

Improper disposal of Solid Waste/Hospital Waste at Quetta city

Waste dumps are breeding ground for diseases

Figure 3.7: Scattered Garbage lying openly on ground

Further the villages (KILLIS) in the proximities of Quetta absorbed in

the city jurisdictions. Officially though it is now claimed that the population of

Quetta is almost equal to 1.5 Million, yet unofficially it is acknowledged that

it has even crossed the figures of 2.5 Million [101]. So is the case of public

transport, where Rickshaws are (took-took) ≥ 5000, the Stone Age local buses

are ≥ 100 not to speak of two wheeler traffic, infinite donkey carts, pushing

carts in addition to enormous private transport. Vast majority of which are run

on diesel, smuggled Iranian petrol, diesel and other lubricants, in which

adulteration is done in order to gain/earn maximum profit and most of the

licensed owner petrol pumps/gas filling station sell the same smuggled fuel.

Eventually the said traffic, when comes on the road after dawn till the late

dusk one can witness choked roundabouts and traffic jam on every corner in

the whole city.

88

On new Adda (Bus and goods vehicular) and old fruit market people

usually sell and eat fruits, on the edges on nullha, and garbage dump of stale

fruit etc; which cause disgusting smell in the area along with wind storms it

remained in the atmosphere permanently and spread in its proximity.

(a)

(b)

Pathetic Public Transport

Vehicular emission is one of the reasons behind respiratory illnesses (Circular road Quetta)

(c)

Haphazard Quetta City Growth

Congested bazaar in Quetta City (Suraj Ganj Bazaar) (d)

Figure 3.8a, b, c, d: Haphazard Quetta city growth, pathetic public transport etc.

So a dire need was felt to conduct a research work on the rate of dust

fall and its particulates analysis in Quetta, keeping in mind the above

described irrefutable grim situation of Quetta city in addition to its somewhat

similar geography/topography to some of the other cities of the world like

London, Donora, Los Angeles, Denver, Houston, Tokyo and Beijing, the

89

inhabitants of which had to experience the worst thermal inversion smog time

to time and consequently suffered with heavy casualties.

Los Angeles, CA

(a)

Inversion Layers

Inversion layer:Air near ground is more dense thanair higher up; no convectioncurrents to lift pollutants.

(b)

Figure 3.9a, b: Los Angeles CA, Inversion layers

90

Smog – US, global

Denver Houston

BeijingTokyo

Figure 3.10: Smog US and Global

Above all a severe 6 years spell of drought from 1997-2002 hit

Balochistan including Quetta [101], during that either precipitation didn’t

happen whatsoever or very rare light showers were reported in a few scattered

parts of Balochistan. Its adverse impacts were vividly noticed in Northern

Balochistan including Quetta, where scorching heat intensity mounted and the

temperature increased between 38-40°C from May and June to August and

September (Table 3.1).

91

Table 3.1

Severe Drought spell in Balochistan and particularly Quetta from 1997-2002 (06 years)

RAINFALL/PRECIPITATION DATA OF BALOCHISTAN FROM 1998-2002

S.No. Year & Session Normal/expected Rainfall/Precipitation

Actual Rainfall/ Precipitation occurred

Difference %age of Precipitation Occurred

Deficit of Precipitation/ Rainfall

1 1998 Summer1998-99Winter

59.05 mm74.01 mm

26.72 mm65.98 mm

32.33 mm8.03 mm

45.2 %89.2 %

54.8 %10.8 %


59.01 mm74.01 mm

29.11 mm19.90 mm

29.94 mm54.21 mm

49.3 %26.8 %

50.7 %73.2 %


59.01 mm74.01 mm

30.54 mm27.54 mm

28.51 mm46.47 mm

51.7 %37.2 %

48.3 %62.8 %


59.01 mm74.01 mm

35.50 mm29.60 mm

23.54 mm44.41 mm

60.1 %40.0 %

39.9 %60.0 %

Overall Situation 532.4 mm 264.80 mm 267.44 mm 49.8 % 50.2 %

During which Quettaites (the inhabitants of Quetta city) had to

brave/experience the similar sort of situation in terms of 'thermal inversion' as

the residents of London, Donora, Los Angeles, Denver, Houston, Tokyo and

Beijing faced sporadically in past.

92

(a)

(b) (c)

(d) (e)

Figures 3.11a, b, c, d, e : Photos of Quetta while dust wrapped the city

93

(f) (g)

Figure 3.11f, g: Photos of Quetta while dust wrapped the city

Moreover, no focused/solid research work whatsoever had been done

on the crucial subject though with the help of some equipments the amount of

particulates was determined in the ambient air sporadically by the EPA Pak

from 1993-2003 except Quetta not to speak of "measuring the rate of dust fall

of Quetta" (Table 3.2).

Table 3.2

Level of Suspended Particulate MattersMajor Cities

µg/m3 Microgram per Cubic Meter

Multan 1030

Faisalabad 870

Lahore 895

Karachi 230

Rawalpindi 709

Islamabad 520

Peshawar 834Source: EPD/SUPARCO/NWFP EPA/PAK-EPAStudies carried out in 1993-2003

WHO Guidelines: 120 µg/m3

Japanese Standards: 200 µg/m3

94

However numbers of researches have been conducted in the different

famous cities across the globe and in the different cities of Pakistan (like

Karachi, Islamabad and Peshawar) as well on the rate of fall/settlement of dust

particulates [102].

The above mentioned extremely important factors tempted my

honorable supervisor and me to carry out profound research work in this

regard. So that we could owe the debt of our beloved Quetta city upon us by

suggesting some solid measures in order to rehabilitate its beauty, cleanliness

and calmness.

Therefore following objectives/goals were set to conduct our research

work.

1. To find the rate of dust fall in Quetta.

2. To ascertain the origin of dust plumes hit Quetta particularly

during very rare thermal inversion episodes in the end of

drought spell.

3. To detect the quantity of toxic/heavy elements Pb, Zn, Mn, Ni,

Cr, Co, Na and K present in dust fall samples.

4. To discover the percentage of particles having different sizes in

the dust fall.

5. To make a prediction of dust fall and amount of toxic/heavy

elements for the coming period by using statistical ARIMA and

SARIMA modeling.

95

In this regard having done an extensive literature survey vis-à-vis set

goals; ten (10) different sites of Quetta were selected keeping in view their

locations, population, traffic density, public places, surveillance approach, the

distance between each collection site, industries etc.

Figure 3.12: Map of Balochistan and Quetta

96

Figure 3.13: Ten Selected Samples Collection sites of Quetta City

97

3.4 DUST FALL COLLECTION SITES:

3.4.1 Army Recruitments Centre:

The dust collector was installed on the roof of one of its

garages/abandoned buildings at the height of 20 feet within the premises of the

said office. This collection Centre is located in the cantonment area of the city

right opposite to the roundabout near Serena hotel, Provincial Assembly and

Balochistan High Court. That’s why there is not much hustle bustle compare

to the rest of the city being located in an area of high security zone. Population

is not much dense and plantation is pretty better than other parts of the city.

3.4.2 Ashraf / Sariab Road:

This center is located on Sariab road having huge traffic as it directly

leads to the other cities like Karachi and Lahore. Further it is near some of the

public institutions like University of Balochistan and the Head Office of

Geological Survey of Pakistan. On its southern side there is a new well

planned housing scheme "GREEN HOUSE". On the northern side there are

98

thickly populated shops, heavy Vehicles repairing garages and small untidy

hotels and motels for the drivers and mechanics. The collector was kept on the

roof of the stores of Ashraf's business property at a height of almost 18 feet,

where he has been doing the whole sale business of bricks, girders, tiles, iron

etc. for construction purpose.

3.4.3 C.G.S. Colony, Satellite Town:

This colony was constructed in 1979 and pretty population of Central

Government Servants resides in it in double storey flats. I have been living in

this colony along with my family as well. It is located on the eastern back side

of University Colony. On the southern side of it there are business shops,

small vehicles' workshops and block three of Satellite town Housing Scheme

are located. There is a Government Girls Middle School in the boundary of the

colony, where on its roof at a height of 22 feet the dust collector was installed.

Between the C.G.S colony (near the collection site) and University colony

there is a single road and on the northern side similar sort of road is there too,

99

both roads remain pretty busy for the whole day but at night remain almost

deserted.

3.4.4 Civil Hospital:

The dust fall collector was placed on the roof 'MOLANA UMER

FOUNDATION' at a height of 17feet adjacent to the civil hospital located in

the heart of city right on one the main busiest roads 'JINNAH ROAD'. Day

and night the road remain busy. Particularly in day time mostly the traffic jam

is witnessed due to the congested road and the massive burden of patients on

the single oldest public hospital located inside the city. On the opposite of road

there is cluster of Pharmaceutical/chemist/Medical stores, some hotels, other

business shops and even pushing cart hawkers etc., who encroach the road and

even footpaths on its both sides in order to earn their bread and butter.

3.4.5 Gawalmandi Chowk:

It is the roundabout of four roads and for the whole day traffic remains

either jam or moves at the snail pace due to the all five roads, which joins at

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this location. 'Sirki' road joins it with 'NEW ADDA' known as new bus stand

on southern side, 'KAWARI' road leads towards northern side to the main city,

'MACKONGY' road leads to the old city, 'KACHRA' (Garbage) road on

eastern side leads to a chaotic slum of mostly muddy type of houses known as

'PASHTOON-ABAD' and another road towards east-north side guides to the

densely crowded old city. On the bottle neck of this junction of 5 roads the

dust collector was installed on the roof of an old 'Union Council' office (which

has not been demolished in order to widen the road) at the height of 13 feet.

This is the junction, where two roads ('SIRKI' road & 'KACHRA' road) out of

only four routes link the old Quetta city with the newly settled Quetta city.

3.4.6 Qadoosi Store/Quick Marketing Services:

These are in fact two sites, which were alternatively used as one of the

ten sites used to collect the dust fall samples. Both are located from one

another almost 200-300 meters away from each other. 'QADOOSI STORE', a

famous mutli-departmental grocery store is located right in the heart of Quetta

city in the east-north corner of 'MAYZAN CHOWK'. Old store has been

101

demolished and a new store has just been constructed there at the same place.

From here the junction of 'MAYZAN CHOWK', a road ‘SHARA-E-

LIAQUAT’ links to 'LIAQAT BAZAR' on southern direction, other leads to

the western side through the famous 'KANDHARI BAZAR' up to 'MANNAN

CHOWK' and further links with the 'ZARGHOON ROAD', another

‘MISSION’ road on northern side links cantonment, fourth ‘TOGI’ road

through north-eastern side of this dust collection site links to an ethnic

(Persian speaking) population settled on the eastern side of the city in a

massive cluster of houses called 'MAREE-A-ABAD, fifth road on eastern side

leads to the 'LANDA BAZAR' old used items market and further to the old

city. For almost two and half years, the dust collector was fixed on its roof at a

height of almost 20 feet. This site remains busy day and night being situated in

the middle of hub of business on all sides of it. Similarly, its alternate dust fall

samples collection station 'QUICK MARKETING SERVICES', is located on

Art School road and is one of the pioneers of computers marketing related

business in the city. On art school road there are old city residences, which are

gradually being sold to the business men due to its location in the middle of

city and adjacent to the most famous 'LIAQUAT BAZAR'. The road remains

busy for the whole day and night. The dust collector was kept on its roof as

well at a height of 19 feet for the rest of the two and half years initially due to

the law and order situation, when some firing was done on a religious

procession near 'QADOOSI STORE' and later on due to the re-construction of

the 'QADOOSI STORE'.

102

3.4.7 Railway Station:

It is located near 'ZARGHOON ROAD' right on its west. There is

mosque near it. For three years the dust collector was kept right on the roof of

the building of railway station at the height of 22 feet. But due to some

renovation work, it was to be kept on the mosque right in the premises of

railway station on its roof at the height of 21 feet. On the left southern side is

Civil Secretariat, on back western side railway colony is located and on

southern side again there are the residences of railway officers.

103

3.4.8 SADDA BAHAR Sweets New Adda:

This site (Sweets Shop) is located near satellite town, where there were

bus and truck stands. A round about is close to it on southeastern side. On its

back southern side a huge graveyard is located and on eastern side road leads

to satellite town, on western side the road link it with Sariab road, and the road

on southern side is 'Sirki road", which links it with 'GWALMANDI CHOWK'

and ultimately the old city. This area also remains busy particularly in day

time. The dust collector was placed on its roof at the height of 25 feet.

3.4.9 Sirki Road:

This road links old city and new city (satellite town etc.). It used to be

the only industrial area of the city. Though most of the new industry is now set

up on the western and southern side out of the city, yet old industries like

'CHILTAN GHEE MILL', 'DITTU & SONS, furniture industry, flour mills

etc. are still working in the area situated on Sirki road. The dust collector was

placed on the roof, initially on the roof of NATIONAL BANK for almost

three years at a height of 18 feet & later on the roof of 'DITTU & SONS' at the

height of 20 feet just to check the variance in the results on the same place.

This dust collection site is located in the described industrial area on the

western side of the Sirki road, businesses shops across the road having thick

population of 'KACCHI-ABADI' entirely muddy with some muddy-brick

houses in a slummy disorder are situated on its eastern side.

104

3.4.10 T.B. Sanatorium:

This is the only second well planned site of the city, where a

sanatorium was built up in far past keeping in view its rather clean

air/atmosphere. Though there are new well planned housing schemes (Railway

Hosing Scheme etc.) have been laid down on the southern side of this samples

collection station, on the western side Meteorological office besides some

other offices are located right in the foot of western walled mountain

(CHILTAN) of the city, on its southern side there is scattered population of

muddy and partially muddy houses, on its eastern side a recently constructed

women university (SARDAR BAHADUR KHAN WOMEN UNIVERSITY)

and the second largest public hospital of the Quetta city 'BOLAN MEDICAL

COMPLEX' are located, yet due to rather better planning, its elevated position

in the bowl shape valley of Quetta, and less traffic population, it has far better

atmosphere/air than rest of the major part of Quetta city has got except

cantonment area. The collector was kept on the roof of the 'DANGGE VIRUS'

(a fatal disease believed to be spread through goats in far flung northern

regions of Balochistan adjacent to Afghanistan) patients’ ward at a height of

105

13feet, where the patients of that peculiar dangerous virus have been kept in

quarantine.

106

CHAPTER 4

METHODOLOGY/MATERIALS AND METHODS

An extremely simple apparatus in terms of a "dust jar" or "dust

collector" was used to collect the settled dust particles. However the

interpretation of the deposition of particulate material might be complex

because of the diverse nature of the material. The dust jar was an imported

plastic container, 9"inches or 22-24 cm in diameter and 13" inches or 33 cm in

height. It was placed at a level where re-entrained dust from the normal traffic

was not lifted to its interior. A layer of liquid (de-ionized H2O) was

consistently maintained in the bottom of the jar so that settled dust might not

have been swirled out by the wind and water could deter it to escape out of the

dust collector. During winter or inclement weather, inert anti-freeze was

added. Keeping in view the dry climate of Quetta there was no need to add any

fungicide or algaecide, which is usually recommended [103] to prevent growth

of cultures that could change the reported results. Bird guards, which are

usually suggested to prevent birds from perching on the edge of the jar and

adding deposits to the fluid in the jar were not used as well as there is not

enough population of birds in the city and particularly those places/sites,

where the dust collectors were installed. It was made sure that the collectors

were placed in a horizontal surface, where there were no obstructions such as

buildings, trees and overhead wires within 5 meters of the dust collectors.

Settled particles were analyzed by weight initially daily after 24 hours for the

period of one year 2004 and then for the period of four year 2005-2008 with

the intervals of every1 (one) month. Samples were collected in this manner

107

that an aliquot of the liquid was taken after the settled material was thoroughly

dispersed. The liquid would evaporate, and the settled material was analyzed

in terms of weight per unit area in the jar; the result then was extrapolated to

unit weight per square area in terms of in some cases, in grams per square

meter per month, or in tons per square km per month or in most recommended

cases in mgs per square meter per day or month.

It is also possible to extract the settled dust samples, with suitable

solvents, the organic-soluble and water soluble components to determine

combustible materials, and to report each component separately [104]. Before

installing the dust collectors, these were cleaned with detergents, washed

thoroughly with tap water and then rinsed with distilled and finally with de-

ionized water. The mouth was covered with sterilized lid, and was taken to the

sites in order to install on the selected place. For the first year 2004 after every

24 hours (one day), the samples were collected from all the ten different sites

by using de-ionized water in the plastic bottles pre-washed properly with de-

ionized water. While collecting the samples from 2005-2008 (04) years with

the gap of every one month of calendar year corrected to 30± days, dust

collectors were regularly monitored and de-ionized water level was not let dry

by keeping up its level around 500-600 milliliters.

4.1 PREPARATION OF COLLECTED SAMPLES FOR

DIGESTION:

Insoluble matter for instance stones were either removed manually from

the dust samples depending upon their size, while collecting samples from the

spots/sites. After that collected samples in plastic bottles having de-ionized

108

H2O were manually shacked well in order to dissolve the soluble remaining

part of dust fall before filtering it by normal sieve (25-30 lattices) so as to

separate the remaining dead insects, small stones or any insoluble material

other than dust. Then filtrate was again filtered through Whattman No. 41 or

42 papers directly into a one liter volumetric flask in such a way that it was

inclined in an inverted position over the filtering funnel and the jet of de-

ionized bottle was used to wash down all the dust left in the beaker. Finally the

level of volumetric flask was filled up to its mark of one liter.

The filter paper having insoluble residual dust was dried in an oven at

100-105°C up to a constant weight to remove the water. It was cooled for 25

minutes in a dedicator. A blank Whatmann No. 41 or 42 filter paper was first

moistened with de-ionized H2O and dried up to 100-105°C, cooled and

weighed in the similar fashion finally to subtract it from that containing dust.

In this way merely the insoluble part of the dust was obtained. For soluble dust

fall, two aliquots of 50 ml each were taken from the filtrate in pre-weighed

china dishes, evaporated to dryness on steam bath, dried at 100-105°C in an

oven, cooled and weighed. The results of these duplicate evaporations were

averaged and calculated for one liter. The sum of the insoluble and soluble

dust gave the total weight of the dust.

4.2 DETERMINATION OF RATE OF

DEPOSITION/SETTLEMENT OF DUST FALL:

The average rate of dust fall was calculated by the following formula:

W= w (dissolved + un-dissolved) × 30 mg m2day1 Ac× Nd Where

109

W = Total wt. of dust fall (dissolved + un-dissolved) in a month (30 days)

'w' = Weight of total dust fall actually collected over Nd (No. of days)

Ac = Area of the mouth of dust collector (m2)

Nd = Actual number of days.

4.3 CHEMICAL ANALYSIS OF DUST FALL SAMPLES:

It was carried out for the loss on ignition, for the quantitative analysis

of Silica as (SiO2), Aluminum as (Al2O3), Iron as (Fe2O3), Calcium as (CaO),

Magnesium as (MgO), Sodium as (Na2O) and Potassium as (K2O) by standard

physical and chemical methods [105].

4.4 TESTS FOR THE PARTICULATES SIZE DISTRIBUTION:

Mesh size distribution was used to determine the fractionation on wt.

% basis for nine size categories: PM<1.0, PM1.0-2.5, PM2.5-5.0, PM5.0-10, PM10-15,

PM15-25, PM25-50, PM50-100 and PM>100, as it is yet supposed to be one of the

suitable methods using ASTM (American Standard Test Method) [106].

Though another method to determine the particle size determination using

Mastersizer 2000 (Malvern, Ver. 3.01, UK) by Shah et al., [100],Shah et al.,

[99] and Shah and Shaheen [14] on vol. % basis for seven, four to nine

fractions (PM<1.0, PM1.0-2.5, PM2.5-5, PM5-10, PM15-25, PM50-100and PM>100µm),

(Pm<2.5, PM2.5-10, PM10-100 and PM>100 µm) and (PM<1.0, PM1.0-2.5, PM2.5-5.0,

PM5.0-10, PM10-15, PM15-25, PM25-50, PM50-100and PM>100 µm) was supposed to

be one of the best methods as well, ironically in spite of having had a keen

desire to use it, it couldn’t be used due to its non-availability in any

institutions of even the capital of our province (Balochistan), Quetta not to

110

speak of rest of the deprived underdeveloped province. The climatic data was

obtained from Pakistan Meteorological Department on regular daily basis. All

the climatic parameters were recorded for the particulate sampling matched

duration using standard procedure [107,108].

4.4.1 Analysis for Na and K:

Sodium and Potassium were detected by using Flame Photometer

(Model JENWAY PFPZ) at PCSIR Labs; and Model Corning 400 (England)

at chemistry department Lab; University of Balochistan.

Figure 4.1: Flame photometer Model Corning 400 (England)

4.4.2 Digestion Method of dust Samples for the Analysis of Toxic/heavy

Elements by Atomic Absorption Spectrophotometer:

It was well known that there is no universal dissolution method for all

type of samples. The most needful characteristics of selecting the most

suitable procedure were based upon the following criteria

111

(1) the tendency to dissolve the sample completely having no

insoluble resides

(2) pragmatically prompt and safe & sound

(3) no possible dangers of sample loss by volatility, adsorption on

the walls of the apparatus and

(4) removal of sample contamination from the reagents used while

dissolution process

The majority of dissolution methods involve dry ashing or wet

digestion using one or a combination of concentrated mineral acids [197]. So,

numerous acid combinations for instance HNO3+HCl, HNO3+HCLO4 and

HF+HCLO4 were attempted for the digestion. Eventually the combination of

4:1 (v/v) HNO3+HCLO4[109] was found most suitable for the substances like

dust fall samples, soil samples and clay minerals etc contrary to the

combination HF+HCLO4 used by Khan et al., [110] used.

Approximately 0.5 grams of the dust fall sample was taken in a

platinum crucible by adding few drops of de-ionized H2O merely to moisten it.

Then 5 cm3 of 6M HCLO4 and 6 cm3 of HNO3 were put into the crucible. The

crucible was kept on a sand bath in order to destroy the organic matter till the

mixture was evaporated leaving behind HCLO4. Then the crucible was kept on

a heating plate until HCLO4 was totally evaporated. The inner sides of the

crucible were washed with a stream of de-ionized H2O and dried again. The

method was repeated twice again. After that 10 drops of 6M HClO4 and 20 ml

of de-ionized H2O were added to the residue. Finally 6 cm3 of 30% H2O2 was

added to oxidize any resistant organic matter if presented. The whole mixture

112

was transferred to a 100 cm3 volumetric flask and by adding de-ionized H2O

the volume was made up to the mark.

The digested samples were analyzed for the analysis of Pb, Zn, Mn, Ni,

Cr, and Co by using Atomic Absorption Spectrophotometer [PERKIN-

ELMER 2380 and SOLAAR] in zeeman flame mode. Standard burner was

used for acetylene-air flame as mentioned in the Table 4.1.

Multivariate statistical methods based on standard procedure were used

for metal source identification [111]. All reagents used were of AAS grade

(certified purity > 99.99 %) purchased from E-Merck. Standard metal stock

solutions (1000ppm) were used to prepare working standards. De-ionized

water was used throughout the present investigation. Standard Reference

Material (NIST SRM, 1573a, TL) was routinely employed to ensure reliability

of the finished metal data. Regular inter-laboratory relationship of the data was

done at the PCSIR (Pakistan Council for Scientific and Industrial Research,

Quetta), GSP (Geological Survey of Pakistan, Quetta) and Central Hi Tech

Lab. of University of Balochistan.

113

(a) (b)

Figure 4.2a: Ex-Vice Chancellor University of Balochistan from right (presently VC of Quaid-e-Azam University Islamabad) Prof. Dr. Masoom Yasinzai with Prof. Dr. Sher Akbar and Muhammad Sami (Ph.D. Research Scholar) are attending a workshop held at Central Hi Tech Lab. at U.O.B. to train different faculties in using AAS (b) Ph.D. Research Scholar Muhammad Sami while working and Assisting Senior Scientific Officer (Late) Zahoor Ahmed on AAS at PCSIR labs, Quetta

(c) (d)

(c) (Late) Senior Scientific Officer of PCSIR labs Zahoor Ahmed working on the project of Ph.D. Scholar (Muhammad Sami) (d) from right Prof. Dr. Sher Akbar, Chief Scientific Officer Mujeeb, (Late) Scientific Officer Zahoor Ahmed and Muhammad Sami

114

(e) (f)

(e, f) (Late) Senior Scientific Officer of PCSIR Zahoor Ahmed working on the project of Ph.D. Scholar (Sami)

Table 4.1

Instrumental conditions for elements Condition Pb Cd Zn Mn Ni Cr Co Fe

Wavelength (nm) 217.0 228.8 213.9 279.5 232.0 357.9 240.7 248.9 Lamp current (mA) 5.5 5.3 13.5 11.0 13.5 9.0 13.0 13.0 Bandpass (nm) 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 Flame Air/

Acet. Air/ Acet. N2O/

Acet. Air/ H2O

N2O/ Acet.

Air/ Acet.

Air/ Acet.

Air/ Acet.

Oxidant Pressure (1/min) 5.0 5.0 5.0 5.0 5.0 5.0 5.0 5.0 Fuel pressure (1/min) 1.0 1.0 1.2 1.2 1.0 1.4 1.0 1.2 Burner height (mm) 8.0 8.0 8.0 8.0 8.0 8.0 8.0 8.0 Sensitivity (µg/ml) 0.08 0.30 0.009 0.02 0.05 0.04 0.06 0.05 Detection limit (µg/ml) 0.01 0.024 0.001 0.003 0.005 0.004 0.006 0.008

115

CHAPTER 5

RE S U L T S A N D D I S C U S S I O N

Results pertaining to prime goal of determining the "rate of dust fall in

Quetta 2004-2008" have been given in a sequence manner in the tables on

daily basis for the year 2004 and on monthly basis for the years 2005, 2006,

2007 & 2008 for the all ten selected sites of Quetta. Tables 5.2 to 5.11 show

the rate of dust fall for all the months of year 2004 on daily basis for all ten

selected sites (Army Recruitment Center, Ashraf Sariab Road, C.G.S colony,

Civil Hospital, Gawalmandi Chowk, Qadoosi Store / Quick Marketing

Services, Railway Station, Sada Bahar Sweets, Sirki Road& T.B. Sanatorium).

Tables 5.14 to 5.18 represent the average rate of dust fall for the year 2004,

2005, 2006, 2007 & 2008 on monthly basis for all the ten selected sites.

Meteorological data showing mean daily temperature of 2004, mean monthly

temperature for the years 2005, 2006, 2007 & 2008, daily precipitation of

2004, 2005, 2006, 2007 & 2008, daily wind speed and daily wind direction

from the 2004-2008 are given in their corresponding tables. Daily visibility

data in terms of UTC (Universal Coordinated Time) & PST (Pakistani

Standard Time) twice per day (at 0000 UTC & 1200 UTC) or (at 8:00 a.m.

&5:00 p.m.) is given in Table 5.37. Typical average annual chemical

composition of dust fall is mentioned in Tables 5.35. Average annual particle

size distribution of dust fall is shown in Table 5.47. Finally the average annual

concentrations of heavy/toxic metals are given in Tables 5.44 & 5.45.

116

5.1 RATE OF DUST FALL/SETTLEMENT/DEPOSITION:

It is vividly evident from the data given in Table 5.1 from the year

1997-2002 that whole Balochistan and particularly Quetta faced a severe

drought spell [101]. A drought is a period of abnormally dry weather which

persists long enough to produce a serious hydrologic imbalance. It is a slow

onset, “creeping phenomenon”. It emerges, when in an area there is a 50% less

precipitation occurs than the normal rainfall happens in that region. Drought

gradually emerged in the beginning of 90s and its intensity reached on its

acme/culmination from 1997-2002. Though it was abating in the year 2004,

when we commenced collecting dust samples on daily basis from the 10

different locations all along Quetta, yet it caused massive dust fall compare to

the normal conditions.

117

Table 5.1

Balochistan and particularly Quetta faced a severe DROUGHT spell from

1997-2002 (06 years)

RAINFALL/PRECIPITATION DATA OF BALOCHISTAN FROM 1998-2002 S. Year & Normal/expected Actual Rainfall/ Difference %age Deficit

No.

Session Rainfall/ precipitation

precipitation occurred

precipitation occurred

precipitation/ rainfall

1. 1998

Summer

1998-99

Winter

59.05 mm 74.01 mm

26.72 mm 65.98 mm

32.33 mm 8.03 mm

45.2 % 89.2 %

54.8 % 10.8 %

2. 1998

Summer

1998-99

Winter

59.01 mm 74.01 mm

29.11 mm 19.90 mm

29.94 mm 54.21 mm

49.3 % 26.8 %

50.7 % 73.2 %

3. 1998

Summer

1998-99

Winter

59.01 mm 74.01 mm

30.54 mm 27.54 mm

28.51 mm 46.47 mm

51.7 % 37.2 %

48.3 % 62.8 %

4. 1998

Summer

1998-99

Winter

59.01 mm 74.01 mm

35.50 mm 29.60 mm

23.54 mm 44.41 mm

60.1 % 40.0 %

39.9 % 60.0 %

5. Overall

Situation 532.4 mm 264.80 mm 267.44 mm 49.8 % 50.2 %

118

Table 5.2

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC332.76 305.82 305.07 302.66 2477.2 628.12 632.87 757.11 690.16 886.27 710.19 581.34324.64 303.3 307.51 306.66 1019.22 631.88 630.77 710.21 683.84 878.73 703.13 574.78329.61 303.05 312.16 302.19 1004.19 635.21 626.26 709.03 687.61 866.16 708.66 580.3327.79 301.07 310.42 427.13 1002.25 634.79 609.38 755.13 686.39 863.84 704.66 575.82330.89 254.73 314.25 1031.32 1021.29 636.73 61.81 757.31 689.78 860.93 1407.4 580.07326.51 249.39 308.33 1048 2337.4 633.27 608.83 754.93 684.22 910.12 1403.9 576.05332.46 253.91 312.98 1032.03 2342.1 637.37 612.83 759.01 688.87 1406.7 1408.7 578.09324.94 200.21 309.6 1027.29 2320.2 632.63 607.81 1653.23 685.13 1407.4 1402.6 578.03332.73 202.64 315.04 1030.23 2342.4 638.1 610.47 1657.05 590.14 1407.1 708.83 580.3324.67 201.48 307.54 1029.09 103.74 631.9 610.17 775.19 583.86 908.77 704.49 575.82329.37 204.94 321.86 1029.73 1002.7 645.07 612.18 850.1 587.41 912.6 700.03 581.31328.03 201.29 310.72 1029.59 1002.7 634.93 608.46 652.14 586.59 839.07 696.29 574.81351.18 250.65 314.02 1002.65 1004.47 636.98 612.8 630.88 589.83 814.65 608.35 581.03326.22 248.47 308.56 1006.67 801.97 633.02 607.84 667.36 584.17 808.68 504.97 575.09332.54 202.06 312.66 1000.49 806.03 637.85 601.16 556.79 588.38 709.88 509.09 579.99324.56 250.11 311.29 1038.83 823.22 640.11 775.32 555.45 586.62 812.88 504.23 578.06328.7 204.55 315 1031.15 825.01 635.09 787.11 556.12 690.12 707.58 510.18 579.25

327.49 249.57 307.58 1028.17 820.04 631.91 755.53 554.6 683.88 712.35 503.14 576.87352.7 253 311.46 1032.19 807.23 635.9 532.62 558.63 687.21 707.58 508.85 578.02324.7 251.12 311.12 1027.13 919.21 634.1 507.95 553.61 686.79 907.4 504.47 578.04

329.29 255.67 313.71 1030.57 1003.61 636.47 510.63 556.85 689.92 911.3 545.67 578.37328.11 248.45 308.87 1028.75 2338.8 633.53 610.01 555.39 684.08 706.88 585.65 577.75332.65 254.22 312.53 1030.63 2341.8 637.74 611.45 557.49 688.29 818.12 507.63 579.05324.75 249.9 310.05 1028.69 2342.4 632.26 609.19 604.75 685.71 908.7 695.69 577.07329.88 253.3 314.94 1031.29 1020.35 638.06 612.5 558.46 690.1 1406.2 600.66 581.26327.52 250.75 307.64 1028.03 806.57 631.94 608.14 553.78 783.9 1404.3 597.66 574.86331.51 300.85 311.68 1032.59 800.69 636.6 611.13 681.55 787.01 911.57 561.59 581.16325.89 301.27 310.9 1026.73 609.87 633.4 609.51 683.69 886.99 708.43 562.73 574.96332.02 304.01 313.47 2412.7 623.36 638.11 711.5 689.23 889.44 712.19 546.67 580.86305.38 309.11 2476.7 623.08 631.89 709.14 1286.01 884.56 707.81 584.65 575.26309.9 309.92 620.41 709.48 1288.64 710 576.13

Average: 328.7 252.06 311.29 1029.66 1223.22 635 610.32 756.12 687 910 706.66 578.06

DUST FALL FOR THE YEAR 2004 AT ARMY RECRUITMENT CENTRE

119

Table 5.3

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC909.12 895 857.09 816.9 3858.4 1267.5 1228.88 1321.11 1382.4 1663.4 1623.98 1154.96903.12 887.74 857.6 817.1 1706.78 1255.82 1218.86 1323.71 1374.26 1654 1614.02 1146.96909.11 895.03 857.81 825.23 1706.16 1266.79 1226.89 1328.4 1379.28 1648.98 1654.1 1154.53903.13 897.71 856.88 812.77 1707.21 1253.53 1220.85 1376.42 1377.38 1648.42 1663.9 1147.39907.14 893.72 858.92 1816.27 1707.94 1265.68 1224.96 1379.28 1279.92 1695.35 2004 1151.54905.1 899.02 855.77 1820.73 4069 1257.64 1122.78 1375.54 1276.74 1693.97 2094 1150.38

906.23 892.6 856.8 1820.1 4037 1264.86 1127.08 1380.19 1281.19 2042.1 2002.9 1153.79906.01 890.14 857.89 1815.9 4073.6 1258.46 1120.66 3374.6 1275.47 2043.4 2095.1 1148.13909.13 891.52 858.49 1817.93 4036.7 1266.76 1124.86 3381.1 1282.01 2043.4 1063.33 1152.53903.11 891.22 892.2 1815.07 1731.55 1286.56 1122.88 1373.73 1274.65 1600.19 1054.67 1149.39908.81 893.1 905.8 1825.23 1702.71 1304.16 1125.76 1378.27 1282.38 1602.9 1041.75 1155.16910.43 899.64 895.89 1818.77 1702.45 1289.16 1021.98 1376.55 1074.28 1594 1036.25 1146.76923.1 893.12 845.48 1822.2 1028.48 1267.49 1028.52 1078.98 1178.83 1562.68 1039.37 1155.19

919.14 899.62 897.21 1816.8 1706.7 1255.83 1019.22 975.84 1177.83 1554.72 938.63 1150.96906.21 894.89 895.93 1815.19 1734.7 1261.85 1026.85 1040.16 1080.07 1599.37 962.33 1151.03906.12 897.85 856.76 1821.81 1132.58 1261.47 1020.89 1024.66 1076.59 1598.03 955.67 1150.89907.66 891.37 858.89 1823 1032.45 1262.59 1023.87 1031.04 1081.8 1599.63 963 1154.95904.58 898.24 855.8 1815 1027.21 1260.73 1020.42 973.78 1074.86 1598.7 955 1146.97908.61 894.58 861.88 1824.23 1734.71 1262.79 1028.87 977.41 1080.52 1600.23 960.41 1151.86903.63 898.16 855.9 1819.87 1733.58 1260.53 1018.87 977.05 1076.14 1607.17 1057.59 1150.06906.33 895.04 860.03 1822.19 1731.58 1262.68 1027.97 979.34 1179.7 1681.58 1059.85 1151.94905.91 897.7 857.75 1816.81 3168.5 1260.64 1019.77 975.48 1176.96 1695.82 1058.15 1149.98907.42 892.3 861.39 1822.97 3173.5 1263.75 1025.56 979.8 1182.35 1697.21 1062.19 1152.73904.82 891.07 856.39 1814.51 3078 1259.57 1022.18 975.02 1274.31 2089 1055.81 1149.19908.39 891.59 861.94 1816.03 1230.45 1264.78 1124.63 980.93 1279.53 2076 1059.93 1153.21903.85 891.15 819.84 1819.49 1233.81 1258.54 1123.11 1073.89 1377.13 1699.69 1058.07 1148.71906.51 893.08 810.87 1819.87 1211.62 1267.36 1226.71 1377.66 1481.84 1607.77 1159.99 1154.99905.73 849.66 816.91 1824.13 1231.35 1225.69 1221.03 1377.16 1674.82 1607.71 1158.01 1146.93892.46 854.5 811.78 3866.9 1262.71 1224.6 1324.66 1378.55 1678.48 1602.68 1163.95 1152.96889.78 816.3 3857.1 1258.19 1228.46 1322.75 2076.27 1678.18 1604.72 1154.05 1148.96899.03 818.8 1260.37 1327.32 2077.77 1626.39 1146.73

Average: 906.12 891.37 855.8 1822 2032.58 1261.66 1123.87 1377.41 1278.33 1698.7 1259 1150.96

DUSTFALL FOR THE YEAR 2004 AT ASHRAF SARIAB ROAD

120

Table 5.4

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC787.95 609.37 557.31 581.49 2980.8 830.58 716.09 712.61 857.67 853 928.91 1008.72781.03 613.37 561.07 587.17 1529.49 831.42 714.23 712.87 849.65 854.18 928.13 1000.94

787.2 610.05 564.32 503.42 1522.76 825.71 716.37 700.53 857.35 855.82 924.41 1007.31781.82 612.69 584.06 893.24 1487.68 862.29 713.95 794.95 849.97 857 935.19 1002.35785.99 617.86 584.77 901.16 1485.86 866.78 718.96 798.32 855.8 1078.57 2002.22 1006.82783.03 604.88 582.45 1395.5 2264.1 861.22 711.36 897.16 851.52 1077.23 2001.1 1002.84

885.5 666.97 584.29 1399.2 2230.8 864.96 717.19 900.7 855.3 2001.48 2001.94 1004.94883.52 605.77 584.09 1397.7 2229.9 864.04 713.13 2893.8 852.02 2002.77 2002.38 1004.72886.05 616.77 585.14 1394.3 2268.7 967.51 715.32 2939.4 853.67 2000.08 900.67 1007.52862.97 606.57 583.24 1393.3 1524.88 960.49 715 936.05 853.65 1283.36 938.13 1002.14866.96 714.76 585.77 1302.8 1510.79 915.77 718.17 901.98 857.66 1282.32 934.35 1008.33832.06 607.98 582.61 1393.9 1192.75 962.23 712.15 417.5 849.66 1263.14 828.97 1004.83838.11 613.61 588.21 1393.9 1527.6 917.63 716.69 404.11 857.14 1261.86 500.2 1005.31884.51 611.37 610.17 1390.8 1025.94 990.37 713.63 777.74 850.18 1277.68 500.29 1004.35884.61 612.92 608.22 1390.4 1229.34 916.03 717.39 489.15 856.41 1283.57 542.47 1006.06884.41 622.82 600.16 1390.2 1324.2 961.97 712.93 496.33 850.91 1216.43 540.85 1003.6

886.4 661.52 587.92 1398.9 1328.36 921.81 715.54 463.47 857.57 1280 533.59 1007.62782.62 611.22 584.19 1397.8 1325.18 863.19 715.16 449.04 849.75 1275.85 639.73 1002.04786.77 611.37 585.31 1392.1 1426.85 887.5 716.39 489.02 856.22 1278.34 643.92 1008.22782.25 613.38 582.07 1391.6 1526.77 860.5 713.93 497.46 851.1 1276.64 739.9 1001.44787.84 617.73 586.44 1301.7 1526.69 865.33 716.66 402.15 855.06 1282.74 745.53 1005.56781.18 605.01 581.94 1394.9 2262.8 862.67 713.65 493.33 852.26 1277.26 737.79 1004.1784.66 616.91 585.71 1403.4 2269.2 866.74 717.89 400.66 853.88 1281.66 742.65 1006.02784.36 615.83 582.61 1393.2 2230.1 861.26 712.43 494.82 853.44 2004.92 744.64 1003.64685.76 566.02 588.19 1393.4 990.38 864.49 718.97 898.47 855.12 2002.99 738.68 1006.9683.26 596.72 580.19 1395.5 983.16 763.51 711.35 797.01 852.22 1283.44 738.53 1002.76687.16 565.28 587.39 1701.2 824.36 765.12 716.26 700.89 854.01 1177.01 904.19 1008.64631.86 557.46 580.99 1803.3 823.63 712.88 714.06 894.59 853.31 976.56 903.03 1001.02617.95 559.13 587.31 2934.1 828.5 716.22 716.02 859.07 854.18 984.04 1002.12 1005.76611.07 581.07 2940.6 825.04 711.78 714.3 2106.4 853.14 925.96 1005.79 1003.9610.91 580.46 823.4 714.78 2105.4 924.15 1001.33

Average: 784.51 611.37 584.19 1398.3 1526.77 864 715.16 897.74 853.66 1280 941.66 1004.83

DUSTFALL FOR THE YEAR 2004 AT C.G.S. COLONY

121

Table 5.5

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC790.58 710.12 704.08 1616.33 2046.1 970.89 965.92 1006.6 1055 1032.18 1057.87 1075.92781.02 701.6 703 1608.33 1707.79 968.43 971.68 1005.16 1060 1025.88 1051.45 1067.94790.51 709.55 714.75 1614.29 1705.14 969.78 964.87 1005.49 1094.99 1029.82 1035.73 1072.78781.09 702.17 702.33 1610.37 1898.72 969.54 972.73 1006.27 1087.01 1028.24 1038.59 1071.08788.93 708.34 705.85 1624.55 1824.15 973.14 995.93 1059.39 1092.76 1347.76 2004.5 1073.09792.67 703.38 701.23 1620.11 2739.7 966.18 991.67 1057.37 1089.24 1446.2 2099.8 1070.77797.01 707.5 706.58 1622.51 2742.6 970.78 997.02 1058.9 1091.88 2170.3 2000.3 1074.66784.59 704.22 735.5 1622.15 2724.5 968.54 990.58 2557.86 1090.12 2171.9 2000.1 1069.2786.87 706.85 741.65 1620.51 2642.3 971.79 996.91 2611.7 1093.68 2172.1 1166.74 1072.72834.73 704.87 770.43 1614.15 1821.57 967.53 994.1 1105.06 1088.32 1451.27 1062.58 1071.14856.93 707.43 690.74 1622.79 1803.04 971.66 991.68 1110.07 1092.13 1450.41 1016.2 1073.73854.67 704.29 590.34 1621.87 1850.82 967.66 993.5 556.69 1089.81 1438.42 953.12 1070.13786.73 708.71 596.43 1616.32 1724.17 969.83 995.92 590.64 1094.79 1339.64 965.45 1074.04784.87 705.86 690.65 1408.34 1719.69 969.49 994.72 586.12 1127.21 1347.65 963.87 1068.02789.15 709.29 596.62 1626.34 1720.26 972.78 1003.9 561.69 1166.29 1351.57 967.15 1074.47

785.8 702.43 690.46 1518.32 1721.93 966.54 1003.7 555.07 1149.71 1349.03 962.17 1069.39786.93 709.86 593.54 1524.2 1719.95 971.37 1003.8 558.38 1140.07 1449.03 967.89 1072.52784.67 701.86 590.63 1420.46 1786.89 967.95 1002.88 558.34 1120.93 1445.94 961.53 1071.93789.87 706.64 693.62 1419.84 1823.91 970.29 1004.82 560.27 1093.85 1449.55 966.08 1073.37781.73 705.08 693.46 1614.82 1818.16 969.03 992.78 556.49 1088.15 1448.51 1063.24 1070.49788.86 707.1 695.43 1625.95 1823.51 970.64 994.71 561.02 1094.45 1447.32 1065.23 1075.8782.74 704.62 791.65 1618.71 2739.4 968.68 992.89 1055.74 1087.55 1446.79 1064.09 1068.06788.11 706.22 794.54 1623.27 2726 971.79 995.01 1061.58 1092.31 1450.74 1067.03 1074.28783.87 705.5 792.54 1621.39 2725.7 967.53 992.59 1055.18 1089.69 2165.9 1062.29 1069.58785.91 707.84 784.43 1620.76 1020.35 973.03 995.17 1058.59 1091.71 2172.1 1067.86 1072.26785.69 703.88 892.65 1620.18 1023.27 966.29 992.43 1058.17 1090.29 1449.22 1061.46 1071.6786.77 708.17 994.75 1623.9 991.25 972.07 1000.69 1059.71 1093.35 1346.01 1065.92 1073.07784.83 703.55 992.33 1624.48 970.59 967.25 1001.78 1057.05 1038.65 1348.84 1063.4 1070.79716.93 703.01 1014.53 2065.6 964.37 971.03 1004.8 1060.82 1034.56 1051.36 1044.84 1075.42714.67 1612.55 2059 969.49 968.29 1005.82 2055.94 1037.44 1046.7 1073.48 1068.44712.45 1616.45 963.6 1002.8 2058.42 1052.12 1071.34

Average: 785.8 705.86 793.54 1622.33 1821.93 969.66 993.8 1058.38 1091 1449.03 1164.66 1071.93

DUSTFALL FOR THE YEAR 2004 AT CIVIL HOSPITAL

122

Table 5.6

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC2676.36 2667.42 2696.31 3390.78 6648.3 2999.67 2330.45 2765.21 2520.8 2525.98 2603.71 2397.692665.56 2695.68 2690.13 3394.54 4044.05 2991.65 2327.31 2758.99 2519.2 2527.88 2621.29 2391.332675.45 2697.86 2695 3391.39 3045.37 2999.66 2329.31 2764.78 2524.13 2519.16 2624.01 2395.242666.47 2695.24 2592.44 3392.93 3044.95 2991.66 2327.45 2759.42 2515.87 2524.7 2625.99 2393.782674.58 2699.25 2596.11 3396.36 4066.05 2999.27 2330.69 2763.59 2524 2522.19 5096.9 2397.422667.34 2693.85 2590.33 3388.96 6083.2 2992.05 2326.07 2760.61 2516 2520.1 5198.1 2391.62673.22 2699.7 2594.2 3095.66 6085.8 2997.66 2330.05 2762.18 2523.85 5295.7 5199.7 2396.212668.7 2693.4 2492.24 3091.66 6066.1 2993.66 2326.68 6762.02 2516.15 5292.8 5097.3 2392.812672.5 2696.72 2495.18 2917.86 6087.1 3296.87 2228.5 6763.99 2520.25 5293.9 2600.13 2397.62

2669.42 2696.38 2491.26 2915.46 5065.66 3294.45 2228.26 2791.21 2519.75 3175.93 2400.91 2391.42671.85 2709.66 2095.83 2910.53 3063.27 3295.79 2032.4 1795.34 2523.89 3164.25 2041.87 2395.162670.07 2703.44 2090.6 2914.53 3064.27 3295.53 2024.36 1790.86 2516.11 3147.91 2043.48 2393.862672.9 2708.11 2096.33 2917.07 3045.18 3139.43 2030.87 1705.11 2522.54 3155.95 2045.88 2397.36

2669.02 2694.99 2590.11 2912.29 3045.14 2993.89 2028.38 1749.09 2517.46 3159.61 2044.12 2391.662670.96 2699.29 2593.74 3300.39 3065.17 2996.65 2128.83 1864.38 2524.09 3141.38 1947.9 2394.512668.62 2696.55 2592.7 2749.53 3065.16 2994.67 2127.93 1862.1 2515.91 3038.48 1942.1 23932674.16 2699.6 2593.22 2500.39 3065.25 3346.96 2329.51 1862.69 2521.11 2201.93 1948.51 2397.582667.76 2693.5 2191.01 2752.93 3065.07 3294.36 2327.25 1861.55 2518.89 2168.77 2041.49 2391.442675.71 2697.28 2194.31 2756.89 3064.27 2999.67 2332.48 1864.08 2520.58 3073.14 2046.37 2394.942666.21 2695.82 2292.13 3390.43 3065.49 2993.63 2324.28 1860.12 2519.42 3070.72 2043.63 2394.082676.13 2697.75 2295.11 3393.64 4064.66 2997.69 2332.75 1882.84 2520.91 2667.93 2145.45 2397.282665.79 2695.35 2291.33 3391.68 6284.8 2993.63 2325.01 1761.36 2519.09 2669.28 2044.55 2391.742671.05 2698.89 2495.71 3398.39 6367.2 2996.78 2330.27 2743.87 2524.04 3174.58 2347.29 2395.892670.87 2694.21 2590.73 3394.93 6267.2 2994.54 2326.49 2760.33 2515.98 5290 2342.71 2393.132672.07 2699.54 2593.44 3390.78 4063.14 2998.66 2332.31 2264.41 2523.71 5295.9 2346.18 2394.782669.85 2693.56 2593 3387.54 3066.02 2992.66 2524.45 2259.79 2516.29 2625.09 2363.82 2394.242674.95 2700.4 2994.33 3397.66 3064.57 2998.99 2532.47 2265.3 2521.35 2623.42 2365.09 2397.152666.97 2692.7 3092.11 3395.66 2999.3 2330.33 2624.29 2258.9 2518.65 2620.44 2396.69 2391.872676.39 2693.81 3395.96 6639.7 2999.58 2327.43 2632.07 2522.31 2524.1 2622.31 2391.31 2397.542665.53 3390.48 6633.7 2999.74 2333.89 2724.69 5521.9 2515.9 2611.59 2393.52 2391.482673.3 3395.43 2999.15 2725.89 5519.8 2608.36 2396.02

Average: 2670.96 2696.55 2593.22 3396.66 4065.16 2995.66 2328.38 2762.1 2520 3171.93 2645 2394.51

DUSTFALL FOR THE YEAR 2004 AT GAWALMANDI CHOWK

123

Table 5.7

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC1370.91 1067.53 1208.06 2114.76 2829.6 1650.51 1447.62 1668.56 1634.63 1637.43 1770.46 1543.421358.75 1060.73 1200.32 2108.56 2503.33 1643.49 1441.4 1661.76 1633.37 1621.59 1779.2 1535.921370.46 1064.84 1205.27 2132.55 2489.45 1647.96 1447.26 1668.12 1638.13 1633.7 1784.77 1540.621359.2 1063.42 1203.11 2130.77 2483.45 1646.04 1461.76 1662.2 1629.87 1645.32 1799.89 1538.72

1369.14 1037.06 1206.69 2113.39 2407.77 1649.69 1466.5 1666.64 1638.01 1621.78 3184.3 1541.041360.52 1031.2 1201.69 2009.93 3325.1 1644.31 1462.52 1663.6 1629.99 1622.89 3180.4 1538.831366.46 1036.95 1206.58 2034.03 3327.8 1648.81 1464.6 1665.29 1637.37 2837.24 3187.8 1542.51363.2 1031.31 1201.8 2029.29 3308.6 1645.19 1464.42 3665.07 1630.63 2836.13 3186.9 1536.84

1367.79 1005.53 604.56 2032.45 3328.5 1650.49 1465.77 3666.72 1636.41 2855.3 1678.66 1540.381461.87 1002.73 903.82 2030.87 2404.45 1663.51 1463.25 1663.6 1631.59 1916.74 1689.75 1538.961570.15 1007.42 907.27 2034.73 2489.38 1658.75 1286.73 1167.42 1634.57 1917 1694.91 1540.381559.51 830.84 901.11 2028.59 2483.52 1646.25 1282.29 1162.9 1633.43 1612.02 1698.36 1538.961668.32 835.3 905.59 2032.09 2408.68 1649.57 1287.65 1168.49 1635.11 1615.9 1666.59 1542.261661.34 864.13 602.79 2031.23 2403.44 1844.43 1261.37 1161.83 1632.89 1613.12 1666.3 1537.081666.93 867.32 606.29 2033.85 2396.56 1850.44 1265.28 1196.01 1636.58 1715.18 1668 1539.671664.83 830.94 602.09 2029.47 2496.34 1843.56 1263.74 1194.31 1631.42 1713.84 1640.33 1538.261668.89 936.87 604.19 2033.18 2406.45 1847.63 1366.42 1196.6 1638.1 1615.92 1630.43 1540.211360.77 931.39 902.2 2030.14 2404.22 1846.37 1363.32 1183.42 1629.9 1606.06 1594.23 1539.131366.34 1034.55 907.36 2033.91 2505.39 1749.36 1364.51 1187.84 1637 1802.96 1599.55 1543.371363.32 1033.71 901.02 2129.41 2508.36 1744.64 1462.51 1162.48 1631 1812.28 1595.11 1535.971368.22 1035.61 906.17 2134.7 2506.56 1648.15 1467.54 1165.16 1634.56 1813.1 159.03 1542.031361.44 1032.65 1202.21 2128.62 3324.5 1645.85 1461.48 1161.75 1633.44 1914.51 1596 1537.311370.38 1134.52 1204.53 2128.65 3359.5 1650.39 1565.07 1165.53 1634.96 1914.68 1597.44 1539.861259.28 1133.74 1503.85 2129.5 3309.5 1543.61 1563.95 1164.79 1633.04 2833.72 1597.22 1539.481170.86 1237.18 1506.85 2132.56 2355.34 1543.23 1566.25 1666.4 1636.83 2836.59 1500.36 1540.91158.8 1231.08 1501.53 2130.76 1656.34 1446.77 1662.77 1663.92 1631.17 1912.43 1594.3 1538.44

1067.55 1206.57 2107.63 2133.45 1657.56 1449.11 1666.72 1647.27 1638.12 1764.96 1550.06 1543.191062.11 1201.69 2100.75 2134.67 1654.26 1444.89 1662.3 1643.05 1629.88 1764.06 1544.6 1536.151069.63 1202.96 2106.07 2873.89 1657.56 1450 1667.71 1638.16 1635 1785.73 1538.63 1541.841060.07 2102.31 2869.43 1653.36 1444 1661.31 3132.16 1633 1783.29 1544.33 1537.351062.73 2105.18 1655.14 1666.51 3138.57 1774.34 1541.08

Average: 1364.83 1034.13 1204.19 2131.66 2506.45 1647 1464.51 1665.16 1634 1914.51 1797.33 1539.67

DUSTFALL FOR THE YEAR 2004 AT QADOOSI STORE

124

Table 5.8

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC1205.31 991.06 1089.65 1050.02 3171 1565.79 1587.16 1584.93 1555.09 1812.46 1510.15 1525.281197.27 986.86 1083.25 1042.64 2345.16 1562.87 1583.16 1578.93 1546.91 1804.94 1512.43 1537.31203.78 989.78 1088.93 1553.55 2329.92 1567.8 1586.39 1583.74 1551.99 1807.77 1515.25 1543.031298.8 988.14 1083.97 1559.11 2326.2 1560.86 1583.93 1580.12 1550.01 1809.63 1516.51 1539.5

1206.46 990.93 1088.14 1518.26 2348.75 1565.2 1585.37 1582.83 1453.73 1828.7 3054.3 1542.151396.12 987.99 1084.76 2014.4 3167.5 1563.46 1484.95 1581.03 1448.27 1727 3052.4 1440.431205.16 991.01 1087.03 2057.32 3165.6 1568.72 1486.37 1584.57 1452.85 2751.3 3066.1 1443.881297.42 886.91 1085.87 2055.34 3149.8 1560.94 1383.95 3579.29 1379.15 2750.4 3060.6 1438.71203.07 889.74 1088.29 2059.98 3170.9 1565.51 1288.17 3583.28 1354.66 2752.2 1125.71 1445.11310.51 888.18 1074.61 2052.68 2327.57 1563.15 1282.15 1580.58 1347.34 1631.27 1030.95 1437.481305.74 890.73 1079.41 2558.7 2326.69 1564.74 1188.06 1584.66 1355.03 1632 1034.98 1442.721296.84 887.13 1073.49 2553.96 2337.16 1543.92 1182.26 1589.2 1246.97 1624.81 1031.68 1339.861203.45 891.02 1079.66 2558.17 2326.34 1546.33 1185.16 1082.79 1251.81 1622.59 1033.92 1341.291320.13 988.96 1073.24 2054.49 2329.78 1542.33 1185 1081.07 1250.19 1726.09 1032.74 13611306.3 989.59 1077.44 2057.04 2328.11 1547.76 1187.32 1081.93 1253.41 1725.97 1033.5 1346.99

1301.29 988.33 1075.46 2055.62 1748.01 1540.9 1183 1089.59 1248.59 1718.22 1032.86 1338.441204.62 990.39 1076.45 2059.92 1749.19 1564.34 1185.19 1083.37 1252.53 1719.18 1034.58 1444.991197.96 987.53 1075.19 2052.74 1946.93 1564.32 1185.13 1080.49 1249.47 1725.4 1032.08 1437.591202.57 990.97 1078.75 2057.78 2330.28 1565.4 1187.09 1082.45 1254.31 1729.67 1136.42 1442.471200.01 986.95 1074.15 2054.88 2328.06 1563.26 1183.23 1081.41 1347.69 1527.73 1130.24 1440.111205.89 989.75 1079.9 2059.22 2348.55 1566.84 1185.37 1084.19 1355.05 1526.13 1235.51 1441.621196.69 988.17 1073 2053.44 3170.4 1561.82 1284.95 1089.67 1386.95 1527.66 1531.15 1440.961203.59 990.62 1077.33 2056.46 3159.4 1567.8 1386.13 1083.29 1381.22 1529.74 1533.4 1443.411197.99 1087.3 1075.57 2056.2 3150.5 1560.86 1484.19 1080.57 1450.45 2765.2 1533.26 1439.171206.35 1091.04 1078.04 2052.85 2339.05 1565.56 1487.53 1081.89 1452.16 2769.5 1534.46 1444.361196.23 1086.88 1074.86 2055.02 1546.73 1583.1 1482.65 1080.97 1849.84 1527.88 1532.2 1438.221004.41 1089.42 1079.22 2057.64 1565.84 1587.29 1587.97 1583.47 1854.11 1532.13 1536.48 1442.84998.17 1088.5 1073.68 2059.81 1566.34 1580.37 1582.35 1580.39 1847.89 1525.27 1530.18 1439.74980.57 1086.9 1049.63 3108.4 1567.15 1585.2 1588.07 1584.32 1815 1529.7 1526.45 1444.06990.01 1043.27 3104.3 1568.97 1583.46 1582.25 3070.54 1817 1517.7 1520.21 1438.52996.28 1047.71 1565.23 1585.32 3054.27 1511.43 1441.58

1201.29 988.96 1076.45 2056.33 2348.06 1564.33 1385.16 1581.93 1451 1828.7 1533.33 1441.29

DUSTFALL FOR THE YEAR 2004 AT THE RAILWAY STATION

125

Table 5.9

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC763.6 532.73 528.04 1256.44 1512.4 752.33 624.5 627.27 715.59 711.52 777.46 820.22

755.42 534.51 530.98 1250.22 1353.11 750.33 628.08 623.05 711.07 717.47 772.25 812.02763.2 542.4 525.85 1254.19 1354.73 749.59 633.75 626.24 714.64 719.76 780.41 818.94

755.82 534.84 523.17 1252.47 1358.97 714.07 628.83 804.08 712.02 723.81 775.2 813.3761.75 540.53 501.37 1274.43 1360.75 725.68 632.53 806.09 714.32 743.75 1513.06 816.3757.53 536.71 500.65 1272.23 1576.5 712.98 661.05 904.23 712.34 737.85 1519.6 815.94761.49 541.6 300.88 1275.63 1582.3 722.85 631.31 907.23 715.44 2527.3 1523.59 820.03757.53 535.64 322.14 1271.03 1562.4 715.81 631.27 2503.09 711.22 2523.4 1509.07 812.21760.37 539.41 320.54 1173.33 1579 726.84 632.83 2506.41 715.01 2525.8 591.68 817.83758.65 547.83 320.48 1173.14 1356.51 711.82 629.75 803.91 711.65 890.52 500.98 814.41786.03 542.7 326.35 1176.41 1337.87 800.34 633.55 607.18 714.22 780.85 592.71 820.18782.99 534.54 322.67 1170.25 1337.61 811.32 629.03 543.14 712.44 780.43 574.86 827.06686.53 541.52 328.6 1276.27 1232.97 800.91 634.29 505.86 714.83 781.03 577.8 834.63682.49 535.72 320.42 1270.39 1256.51 800.75 628.29 504.46 711.83 788.25 589.95 813.61687.8 542.28 327.42 1274.12 1249.71 800.71 632.28 507.21 715.33 782.57 581.9 816.43

681.22 534.96 321.6 1272.54 1245.77 777.95 630.3 503.11 711.33 788.71 590.76 815.81688.6 539.19 425.19 1276.52 1353.25 727.3 633.27 507.04 714.77 890.76 593.72 816.12

680.42 538.62 423.83 1270.14 1352.23 711.33 629.31 501.16 711.89 868.86 588.94 812.72684.62 539.49 627.91 1274.19 1357.74 727.13 634.51 506.45 713.69 890.64 594.75 817.47684.4 537.75 624.51 1272.47 1356.82 711.53 631.29 505.27 712.97 887.53 587.87 814.77

686.35 542.68 626.24 1275.04 1330.45 724.85 631.38 507.58 715.49 892.89 592.01 802.15682.67 534.56 622.78 1270.24 2386 713.81 631.2 504.14 711.17 888.39 690.65 812.09684.51 541.45 827.23 1273.88 2385.8 719.46 633.06 507.34 714.78 887.42 692.76 818.38681.52 535.79 821.79 1272.78 2384.7 719.2 629.52 504.38 711.88 2527.5 789.9 813.86588.11 539.43 928.16 1276.37 1235.21 620.35 633.91 507.31 713.71 2528.6 794.08 819.49580.91 537.81 920.86 1270.24 928.66 628.31 628.67 553.01 712.95 989.7 788.58 812.75538.52 542.05 1228.58 1272.62 757.98 625.26 634.48 716.55 715.45 893.74 795.13 817.77530.5 535.19 1220.44 1275.62 756.51 628.4 628.1 713.77 711.21 787.54 817.53 814.47536.7 537.05 1235.62 1513.7 740.27 623.53 631.84 715.93 714.57 793.06 822.83 816.21

532.32 1247.4 1513 753.01 625.13 630.74 1714.39 712.09 788.22 819.83 816.03537.5 1254.11 753.17 628.07 1713.28 773.95 819.52

684.51 538.62 624.51 1273.33 1357.74 719.33 631.29 805.16 713.33 1090.64 791.33 816.12

DUSTFALL FOR THE YEAR 2004 AT T.B. SANATORIUM

126

Table 5.10

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC1651.65 1397.37 1398.79 1390.67 3765.52 1772.41 1563.17 1874.13 1873.13 2028.12 1950.3 1766.631641.3 1383.99 1390.23 1394.65 2623.98 1773.86 1556.17 1865.87 1870.53 2030.74 1947.02 1768.531651.6 1395.16 1398.38 1396.77 2624.4 1771.33 1561.6 1872.96 1874.66 2032.48 1915.98 1773.331641.3 1386.2 1390.64 1391.55 2642.86 1774.67 1557.74 1867.04 1888 2035.1 1921.34 1771.83

1651.46 1393.55 1395.82 1397.17 2645.48 1770.62 1560.11 1871.36 1893.84 1948.63 3110.3 1775.441641.44 1387.81 1393.26 2232.15 3762.9 1855.38 1559.23 1868.64 1888.82 1949.89 3270.1 1769.721649.46 1396.15 1395.39 2237.57 3765.1 1900.74 1561.84 1873.53 1892.97 3075 3248.8 1774.491643.44 1385.21 1393.63 2233.75 3751.4 1865.26 1527.5 3866.47 1889.69 3073 3238.6 1770.671648.77 1394.54 1396.6 2237.67 3770.4 1868.01 1260.99 3874.1 1892.32 3075 1622.17 1775.751644.13 1386.84 1392.42 2233.65 2644.3 1857.99 1258.35 1865.9 1890.34 1954.43 1615.15 1779.411647.82 1391.22 1396.06 2236.66 2648.51 1969 1260.17 1872.85 1845.47 1646.78 1610.92 1776.611645.13 1390.14 1392.96 2234.66 2639.87 1967 1259.17 1867.15 1837.19 1646.44 1606.4 1768.551646.82 1392.21 1394.51 2235.76 2617.98 1970.29 1362.98 1370.73 1845.1 1640.5 1619.96 1773.511646.08 1389.15 1391.24 2235.56 2638.64 1965.71 1359.67 1369.27 1837.56 1648.79 1617.36 1771.651646.45 1397.09 1397 2236.83 2644.19 1871.37 1362.46 1371.84 1843.59 1653.3 1601.84 1772.581545.46 1384.27 1392.02 2234.49 2542.09 1864.23 1456.88 1368.16 1839.07 1751.61 1695.48 1769.961547.45 1390.68 1398.58 2236.77 2146.18 1772.14 1459.74 1370 1842.19 1754.94 1621.52 1773.911545.45 1386.3 1390.44 2234.55 2042.22 1763.86 1459.6 1366.31 1889.47 1748.28 1615.8 1771.251548.18 1396.04 1396.8 2239.18 2144.5 1771.68 1560.99 1372.72 1893.91 1752 1650.08 1774.791544.72 1385.32 1392.22 2232.14 2043.88 1764.32 1558.35 1367.28 1888.75 1751.22 1717.24 1770.371548.58 1395.95 1396.12 2270.78 2031.5 1671.89 1562.88 1374.05 1891.59 1853.86 1722.38 1776.591444.32 1385.41 1392.9 2331.54 3767 1664.11 1556.46 1365.95 1891.12 1849.36 1714.94 1768.571448.24 1392.99 1398.45 2339.66 3750.3 1669.98 1560.6 1370.81 1895.77 1842.72 1721.14 1775.971444.66 1388.37 1390.57 2331.66 3749.7 1666.11 1658.74 1369.19 1886.89 3078 1716.18 1769.191346.78 1391.5 1395.5 2433.93 2016.88 1668.99 1763.34 1371.47 1894.5 3075 1719.65 1773.071346.12 1389.86 1393.52 2434.73 1741.4 1567.01 1856 1368.53 1888.16 1954.18 1717.67 1772.091348.35 1394.04 1396.42 2436.05 1738.06 1570.13 1860.67 1873.58 1892.79 1952.38 1752.43 1774.761394.55 1387.32 1392.6 2437.39 1747.98 1565.87 1858.67 1866.42 2029.87 1950.84 1754.89 1770.41398.75 1395.06 1398.18 3779.8 1773.26 1572.32 1861.77 1872.41 2023.41 1948.32 1769.67 1776.481394.15 1390.84 3771.6 1776.29 1563.68 1867.57 3367.59 2029.25 1949.04 1769.65 1768.681397.44 1397.78 1774.08 1876.36 3373.69 1949.92 1775.2

e: 1546.45 1390.68 1394.51 2235.66 2644.19 1768 1559.67 1870 1891.33 2051.61 1918.66 1772.58

DUSTFALL FOR THE YEAR 2004 AT SADA BHAR SWEEETS, NEW ADDA

127

Table 5.11

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC2173.7 2142.5 2286.6 2288.1 5316.25 2228.3 2227.4 2240 2237.4 2436.16 2115.57 1908.22165.7 2134.8 2282.4 2287.1 2355.99 2227.1 2219.7 2231.6 2227.2 2438.02 2118.75 1902.22171.7 2140.1 2286 2281.2 2337.22 2229.1 2215.3 2236.6 2236.1 2452.54 2130.85 1907.62167.6 2137.2 2283 2280.8 2335.02 2226.3 2221.8 2235 2228.6 2461.64 2138.47 1902.72172.8 2139.3 2285.2 2253.9 2342.33 2227.9 2234.5 2238.2 2109.2 2455.21 3596.38 1906.22166.6 2137.9 2283.8 2274.9 6369.91 2227.5 2082.6 2233.4 2105.4 2453.96 3597.94 1904.22171.7 2141.6 2286.5 2265.5 6362.33 2229 1986.5 2239.1 2009.9 3078.97 3597.13 1906.92167.7 2135.7 2282.5 2276.5 6377.09 2226.3 1980.6 6232.5 2004.7 3080.22 3599.19 1903.52173.7 2030.4 2286 2268 6376.45 2228.4 1887 6237.1 2007.9 3079.71 2058.45 1908.12165.7 1134.9 2283 2254 2837.1 2227 1680.1 2234.5 1806.7 2461.52 2050.87 1902.22171.8 2000 2286.6 2782.1 2835.79 2228.8 1684.8 2236.4 1812.4 2456.59 1855.31 1905.22167.6 2137.2 2284.5 2779.9 2800.99 2226.5 1682.3 2235.2 1802.2 2347.59 1854.01 1905.22173.6 2139.1 2285.4 2783 2839.1 2228.5 1684.2 2235.8 1810.9 2357.74 1856.18 1906.52165.8 2138.6 2283.6 2779 2853.01 2226.8 1683.5 1231.8 1903.7 2357.09 1853.14 1903.82172.9 2141.3 2284.6 2781 2856.12 2228.9 1715.7 1239.3 1912.1 2360.6 1852 1905.22191.7 2136 2284.4 2781 2853.82 2226.4 1711.4 1232.3 1902.6 2353.58 1852.32 1902.6

2273 2142.2 2284.6 2781.9 2858.11 2228.7 1714.8 1236.7 1908.7 2358.46 1858.37 1905.92166.3 2135.1 2284.4 2780.1 2854.13 2226.7 1782.3 1234 2005.9 2355.72 1850.95 1904.42173.6 2139.7 2286.6 2782.2 2857.09 2229 1787.4 1238.6 2110.7 2337.02 1855.89 1906.32165.8 2137.4 2282.4 2779.8 2855.15 2226.3 1979.7 1233 2104 2337.16 1853.43 19042171.8 2228.9 2285.8 2787 2862.32 2227.8 1984.5 1240.7 2108.3 2352.66 1854.84 1907.62167.6 2238.4 2283.2 2775 4379.92 2227.6 1982.6 1230.9 2106.3 2359.43 1854.48 1902.72172.6 2290.9 2285.5 2786.7 5379.2 2228.8 1986.3 1239.3 2109.5 2434.75 1956.93 1905.82166.7 2286.4 2283.5 2775.4 4407.3 2226.5 1980.8 1232.3 2105.2 3084.47 1952.39 1904.62170.8 2292 2286.6 2787.1 2205.12 2228.1 1986.9 1237.5 2408.6 3080.22 1907.79 1908.22168.6 2284.3 2282.4 2774.9 2225.15 2227.2 2230.2 2233.1 2406.1 2223.96 1911.53 1902.22151.7 2290.6 2286.5 2778 2230.67 2229 2234.7 2236.5 2427.3 2128.86 1916.97 1907.52147.7 2286.7 2282.5 2784 2216.57 2226.4 2232.4 2235.1 2432.3 2125.32 1912.35 1902.82142.1 2288.2 2286 5328.1 2217.12 2228.4 2235.8 2238.2 2436.8 2126.44 1917.03 1906.72141.3 2283 5313.9 2224.91 2226.9 2231.3 4233.4 2432.9 2119.31 1905.29 1903.62140.5 2282.4 2233.42 2232.9 4239.8 2114.87 1907.8

: 2169.7 2138.6 2284.5 2781 3356.12 2227.7 1983.5 2235.8 2107.3 2457.09 2154.66 1905.2

DUSTFALL FOR THE YEAR 2004 AT SIRKI ROAD

128

Graph 5.1

Monthly rate of dust fall at Quetta (mg/sq.m/day) 2004

0

500

1000

1500

2000

2500

3000

3500

4000

4500

Janu

ary

Febr

uary

March

April

May

June Ju

ly

Augus

t

Septem

ber

Octobe

r

Novem

ber

Decem

ber

Months

mg/

sq.m

Army Recruitment Centre

Ashraf SariabRoad

C.G.S Colony

Civil Hospital

Gaw almandiChow k

QadoosiStore/QuickMarketing servicesRailw ay Station

Sada Bhar Sw eets

Sirki Road

Table 5.12

The fall-out dust standards from STANDARDS SOUTH AFRICA

(SANS) 20 are shown as below

Dust fall standards SANS (2005)

Classification Dust fall

(mg m-2 d-1)

Permitted frequency of

exceeding the levels

Target – long term average

300 Long-term average (annual)

Action-residential 600 Three within any year, no two sequential months

Action-industrial 1200 Three within any year, no two sequential months

Alert threshold 2400 None. First time exceeded, triggers remediation and reporting to authorities

129

Table 5.13

Classification – American Standard Test Method ASTM D1739

Dust = Milligrams/day/square meter

Classification

Department of

Environmental Affairs &

Tourism

ASTM equivalent S.A. German Din Air

Quality Monthly

Limit

SLIGHT <250 650 non industrial

limit MODERATE 251-500

HEAVY 501 – 1200 1300 ≥ industrial limit

VERY HEAVY > 1200

Units are normally monitored weekly and particulate collected

fortnightly or monthly if continuous monitoring is undertaken or shorter

periods if localized assessment needs to be considered. To assist in making the

masses (weight) mean something we note the mass of some everyday items:

A. – Paracetamol tablet=608.83 mg

B. – After handling the Paracetamol tablet=608.63 mg

C. – Pinch of salt=140.31 mg

D. – A single drop of homeopathic medicine=75.32 mg (as the drop

evaporated, the mass dropped by about 1.5 mg per second).

Keeping in view the above mentioned set limits of dust fall given in

Table 5.8-5.9, the tables 5.2-5.11 clearly indicate that, there has almost been

heavy dust fall i-e 501-1200 mg/m2/day or even >1200 mg/m2/day recorded

throughout the year 2004 at most of the sites except 'Army Recruitment

130

Centre' & T.B. Sanatorium, where only in the thermal inversion period dust

fall was recorded >1200 mg/m2/day. The Maximum dust fall for 2004 at all

ten sites Gawalmandi Chowk, Sirki road, Ashraf Sariab road, New Adda,

Railway Station, Qadoosi General Store, CGS Colony, Civil Hospital, T.B.

Sanatorium & Army Recruitment Center was recorded 6763.99, 6377.09,

4073.6, 3874.1, 3583.28, 3366.72, 2980.8, 2742.6, 2586.2 & 2477.2

mg/m2/day respectively. Simultaneously minimum rate of dust fall at all 10

sites Army Recruitment Center, T.B. Sanatorium, CGS Colony, Civil

Hospital, Qadoosi General Store, Ashraf Sariab road, Railway Station, Sirki

road, New Adda & Gawalmandi Chowk was observed 200.21, 300.88, 500.2,

590.34, 602.09, 810.87, 866.91, 1134.9, 1258.35 & 1942.1 mg/m2/day

respectively.

Table 5.14 Average Monthly Rate of Dust Fall for the Year 2004 (mg/m2/day)

S.No. Month Army Recruitment Centre

Ashraf Sariab Road

C.G.S. Colony Satellite Town Quetta

Civil Hospital

Gawalmandi Chowk

Qadoosi Store/Quick Marketing Services

Railway STATION

Sada Bhar

Sweets New Adda

Sirki Road

T.B Sanatorium

Average

1 JANUARY 328.70 906.12 784.51 785.8 2670.96 1364.83 1201.29 1546.45 2169.67 684.51 1244.28 2 FEBRUARY 252.06 891.37 611.37 705.86 2696.55 1034.13 988.96 1390.68 2138.62 538.62 1124.82 3 MARCH 311.29 855.8 584.19 793.54 2593.22 1204.19 1076.45 1394.51 2284.51 624.51 1172.22 4 APRIL 1029.66 1822 1398.33 1622.33 3396.66 2131.66 2056.33 2235.66 2781 1273.33 1974.69 5 MAY 1223.22 2032.58 1526.77 1821.93 4065.16 2506.45 2348.06 2644.19 3356.12 1357.74 2288.22 6 JUNE 635 1261.66 864 969.66 2995.66 1647 1564.33 1768 2227.66 719.33 1465.23 7 JULY 610.32 1123.87 715.16 993.8 2328.38 1464.51 1385.16 1559.67 1983.54 631.29 1279.57 8 AUGUST 756.12 1377.41 897.74 1058.38 2762.1 1665.16 1581.93 1870 2235.8 805.16 1500.97 9 SEPTEMBER 687 1278.33 853.66 1091 2520 1634 1451 1891.33 2107.33 713.33 1422.69 10 OCTOBER 910 1698.7 1280 1449.03 3171.93 1914.51 1828.7 2051.61 2457.09 1090.64 1785.22 11 NOVEMBER 706.66 1259 941.66 1164.66 2645 1797.33 1533.33 1918.66 2154.66 791.33 1491.23 12 DECEMBER 578.06 1150.96 1004.83 1071.93 2394.51 1539.67 1441.29 1772.58 1905.16 816.12 1367.51 Average 669.01 1304.81 955.18 1127.32 2853.34 1658.62 1538.06 1836.94 2316.76 837.15 1509.72

131

Graph 5.2

Average Monthly Rate of Dust Fall for the Year 2004 (mg/sq.m/day)

0

500

1000

1500

2000

2500

3000

Army R

ecrui

tmen

t Cen

ter

Ashraf

Sariab R

oad

C.G.S. C

olony

Sate

llite To

wn Que

tta

Civil H

ospita

l

Gawalm

andi C

howk

Qadoo

si Store/

Quick M

arketi

ng Servi

ces

Railway

Station

Sada B

har S

weets

New A

dda

Sirki R

oad

T.B. San

atoriu

m

Center

mg/

sq.m

Average Monthly Rate of Dust Fall for theYear 2004 (mg/m2/day)

Similarly the average rate of dust fall (Table 5.14) for the year 2004 at

all ten selected sites Army Recruitment Center, Ashraf Sariab road, CGS

Colony, Civil Hospital, Gawalmandi Chowk, Qadoosi General Store, Railway

Station, Sada-Bahar Sweets New Adda, Sirki road & T.B. Sanatorium & was

recorded 669.01, 1304.81, 955.18, 1127.32, 2853.34, 1658.62, 1538.06,

1836.94, 2316.76 & 837.15 mg/m2/day respectively. The overall average rate

of dust fall for the year 2004 at Quetta was observed 1509.72 mg/m2/day. In

spite of the subduing spell of drought four sites (Army Recruitment Center,

T.B. Sanatorium, CGS Colony & Civil Hospital) faced pretty dust fall (within

heavy range of 501- 1200 mg/m2/day) though their location in terms of height,

vegetation, less traffic & population etc. is far better than other six sites. The

other six sites (Qadoosi General Store, Ashraf Sariab road, Railway Station,

Sirki road, New Adda & Gawalmandi Chowk); experienced the "very heavy

132

dust fall" (> 1200 mg/m2/day) or even more than set Industrial limit (> 1300

mg/m2/day). Two sites out of these six sites even touched or crossed the "alert

threshold limit" (2400 mg/m2/day). We the residents of Quetta bewilderedly

experienced the phenomenon of thermal inversion in our life span for the very

first time. During the periods/days of thermal inversion in 2004 (on 29-30

April, 01May, 06-09 May, 22-24 May, 07-09 October, 25-30 October & 05-08

November) heavy dust cloud wrapped the whole Quetta valley. The dust fall

data collected in those particular days even at the minimum dust receiving

stations in normal conditions (Army Recruitment Center & T.B. Sanatorium)

was significantly heavy or even beyond the alert threshold limits not to speak

of rest of the sites. The distinctiveness of my research work was that

painstaking collection of samples on daily basis for the year 2004 (though

some days collection for some days in the said year 2004 couldn’t be done due

to law and order and other reasons, the values were assessed with the

contemporary value of other sides on the same missing days with the mean

average of the value of the same site), when in the days of thermal inversion

on daily basis dust fall was recorded. Those days (the readings of which have

been highlighted in red color) were 29th, 30th April, 1st, 6th-9th May, 22nd May

to 24th May, 8th-9th August, 30th, 31st August, 7th-9th October, 24th, 25th October

& 5th-8th November 2004. The dust fall recorded in those was exceptionally

high. Having been trapped under the warm lid the fine and ultra-fine

particulates of dust remained suspended in the atmosphere for a long time and

wrapped the city in those continuous days. Luckily from the point of view of

environmentalists though Quetta doesn’t have heavy industry, in spite of the

absence of photochemical smog, the dust particulates triggered the intensity of

133

diseases of the patients already had been suffering with asthma, tuberculosis,

angina, depression, anxiety, blood pressure etc. Further it harmed and

aggravated the already suffering growth of vegetation. Flights were cancelled

and markets, public places & streets turned deserted caused the business

activities bearish as well. The absence of heavy industry resulted in the

absence of photochemical smog and proved to be a blessing in disguise for

Quettaites as, inhabitants of the city didn’t face the worst situations as the

population of London, Donora, Los Angeles etc. had to face, which caused

massive causalities within few days. Because of the bowl shape of the Quetta

valley, the said dust during the thermal inversion periods took normally 4-5 or

sometimes even 7 days to completely settle down/sink in a deadly calm wind

or got diluted/cleared by drifting away with the wind as soon as the lid of

warm inversion layer gradually removed by the changing wind pattern,

atmospheric pressure and temperature. From Table A-J it can also simply be

deduced that dust fall varies from place to place and from time to time having

great importance as it could give some idea about the local factors, which

could contribute to the atmospheric dust fall and would be summed up in the

end of discussion.

134



Ashraf Sariab Road


Civil Hospital

Gawalmandi Chowk


Railway STATION

Sada Bhar

Sweets New Adda

Sirki Road

T.B Sanatorium

Average

1 JANUARY 326.29 870.8 599.19 808.54 2608.22 1219.19 1091.45 1409.51 2299.51 639.51 1187.22 2 FEBRUARY 240.06 929.37 649.37 743.86 2734.55 1072.13 1026.96 1428.68 2176.62 576.62 1162.82 3 MARCH 354.60 932.11 810.50 811.70 2696.95 1390.82 1227.28 1572.44 2195.66 710.50 1270.25 4 APRIL 303.7 881.12 759.51 760.8 2645.96 1339.83 1176.29 1521.45 2144.67 659.51 1219.28 5 MAY 313.29 857.8 586.19 795.54 2595.22 1206.19 1078.45 1396.51 2286.51 626.51 1174.22 6 JUNE 334.7 912.12 790.51 791.8 2676.96 1370.83 1207.29 1552.45 2175.67 690.51 1250.28 7 JULY 595 1221.66 824 929.66 2955.66 1607 1524.33 1728 2187.66 679.33 1425.23 8 AUGUST 354.60 932.11 810.50 811.70 2696.95 1390.82 1227.28 1572.44 2195.66 710.50 1270.25 9 SEPTEMBER 319.60 897.11 775.50 776.70 2661.95 1355.82 1192.28 1537.44 2160.66 675.50 1235.26 10 OCTOBER 308.7 886.12 764.51 765.8 2650.96 1344.83 1181.29 1526.45 2149.67 664.51 1224.28 11 NOVEMBER 535.06 1107.96 961.83 1028.93 2351.51 1496.67 1398.29 1730.58 1862.16 773.12 1324.61 12 DECEMBER 608.06 1180.96 1034.83 1101.93 2424.51 1569.67 1471.29 1802.58 1935.16 846.12 1397.51 Average 405.75 1007.98 796.32 873.69 2657.86 1383.95 1260.32 1581.47 2145.92 696.03 1281.34

Graph 5.3


0

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Army R

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T.B. San

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mg/

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135

Graph 5.4

Average Monthly Rate of Dust fall for the year 2005 (mg/sq.m/day)

0

500

1000

1500

2000

2500

3000

3500

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Months

mg/

sq.m

Army Recruitment Center

Ashraf Sariab Road

C.G.S. Colony Satellite Tow nQuettaCivil Hospital

Gaw almandi Chow k

Qadoosi Store/Quick MarketingServicesRailw ay Station

Sada Bhar Sw eets New Adda

Sirki Road

T.B. Sanatorium

After a marathon dry spell at last in December 2004 Quetta received

40.2mm rainfall and in the beginning of 2005 town was hit by heavy down

pour in January, February & March having 33.7, 129.2 & 63.3mm of rainfall.

Keeping in view the drastic change in weather, the samples were started to be

collected with the interval of every one calendar month from all the same 10

sites. Further it was humanly impossible as well to continue the collection on

daily basis. The average rate of dust fall observed on all ten selected sites

Army Recruitment Center, Ashraf Sariab road, CGS Colony, Civil Hospital,

Gawalmandi Chowk, Qadoosi General Store, Railway Station, Sada-Bahar

Sweets New Adda, Sirki road & T.B. Sanatorium & was recorded 405.75,

1007.98, 796.32, 873.69, 2657.88, 1383.95, 1260.32, 1581.47, 2145.47 &

696.03 mg/m2/day respectively. The overall average rate of dust fall for the

year 2005 was recorded 1281.34 mg/m2/day, which is significantly less

(228.38 mg/m2/day) than the overall average of the year 2004. It is evident

136

from the data that from January to April there was not very heavy dust fall due

to the continuous downpour in these months. But again as it normally happens

that Quetta doesn’t receive much rainfall in summer, except May (36.9mm)

the months of April (0.4), June (4.8mm) & August (3.4mm), July, September,

October, November & December absolutely dry spell was experienced having

no rainfall whatsoever. So again a sharp increase in dust fall was recorded in

April and then a consistent trend was observed.



Ashraf Sariab Road


Civil Hospital

Gawalmandi Chowk


Railway STATION

Sada Bhar

Sweets New Adda

Sirki Road

T.B Sanatorium

Average

1 JANUARY 585.06 1157.96 1011.83 1078.93 2401.51 1546.67 1448.29 1780.58 1912.16 823.12 1374.612 FEBRUARY 610.39 1126.36 720.51 991.8 2330.96 1458.83 1379.29 1556.45 2110.67 624.51 12903 MARCH 580.06 1152.96 1006.83 1073.93 2396.51 1541.67 1443.29 1775.58 1907.16 818.12 1369.614 APRIL 612.32 1125.87 717.16 995.8 2330.38 1466.51 1387.16 1561.67 1985.54 633.29 1281.575 MAY 578.06 1150.96 1004.83 1071.93 2394.51 1539.67 1441.29 1772.58 1905.16 816.12 1367.516 JUNE 606.66 1159 841.66 1064.66 2545 1697.33 1433.33 1818.66 2054.66 691.33 1391.237 JULY 588.06 1160.96 1014.83 1081.93 2404.51 1549.67 1451.29 1782.58 1915.16 826.12 1377.518 AUGUST 319.29 863.8 592.19 801.54 2601.22 1212.19 1084.45 1402.51 2292.51 632.51 1180.229 SEPTEMBER 705.66 1258 940.66 1163.66 2644 1796.33 1532.33 1917.66 2153.66 790.33 1490.2310 OCTOBER 615 1241.66 844 949.66 2975.66 1627 1544.33 1748 2207.66 699.33 1445.2311 NOVEMBER 289.6 867.11 745.5 746.7 2631.95 1325.82 1162.28 1507.44 2130.66 645.5 1205.2612 DECEMBER 298.7 876.12 754.51 755.8 2640.96 1334.83 1171.29 1516.45 2139.67 654.51 1214.28 Average 532.56 1095.02 849.26 981.69 2524.71 1508.68 1373.87 1678.78 2059.18 721.96 1331.56

137

Graph 5.5


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Army R

ecrui

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T.B. San

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m

Center

mg/

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Average Monthly Rate of Dust Fall for the Year 2006 (mg/m2/day)

Graph 5.6


0

500

1000

1500

2000

2500

3000

3500


mg/

sq.m


Ashraf Sariab Road

C.G.S. Colony SatelliteTow n QuettaCivil Hospital

Gaw almandi Chow k

Qadoosi Store/QuickMarketing ServicesRailw ay Station

Sada Bhar Sw eets NewAddaSirki Road

T.B. Sanatorium

The average rate of dust fall for the year 2005 for all ten sites Army

Recruitment Center, Ashraf Sariab road, CGS Colony, Civil Hospital,


Sweets New Adda, Sirki road & T.B. Sanatorium & was recorded 532.56,

138

1095.02, 849.26, 2524.71, 1508.68, 1373.87, 1678.78, 2059.18 & 721.96

mg/m2/day respectively. Similarly the overall average of the year 2005 was

1331.56 mg/m2/day, which is slightly more (50.22 mg/m2/day) than the

previous year 2005. It was again because of the change in weather as the

precipitation occurred during March, August, November & December 2006

was 26.1, 54.9, 46.9 & 43.8mm respectively, while rest of the 08 months

received meager drizzling. The maximum dust fall during the year 2006 was

recorded in the mid of year due to the earlier described one peculiar

metrological conditions of low atmospheric pressure in the mid of summer

from May up to September, high winds & geographical location of Quetta.

139

Table 5.17Average Monthly Rate of Dust Fall for the Year 2007 (mg/m2/day)


Ashraf Sariab Road


Civil Hospital

Gawalmandi Chowk


Railway STATION

Sada Bhar

Sweets New Adda

Sirki Road

T.B Sanatorium

Average

1 JANUARY 421.29 965.8 694.19 903.54 2703.22 1314.19 1186.45 1504.51 2394.51 734.51 1282.22 2 FEBRUARY 240.06 879.37 599.37 693.86 2684.55 1022.13 976.96 1378.68 2126.62 526.62 1112.82 3 MARCH 338.7 916.12 794.51 795.80 2680.96 1374.83 1211.29 1556.45 2179.67 694.51 1254.28 4 APRIL 290.7 868.12 746.51 747.8 2632.96 1326.83 1163.29 1508.45 2131.67 646.51 1206.28 5 MAY 324.60 902.11 780.50 781.70 2666.95 1360.82 1197.28 1542.44 2165.66 680.50 1240.26 6 JUNE 333.29 877.8 606.19 815.54 2615.22 1226.19 1098.45 1416.51 2306.51 646.51 1194.22 7 JULY 915 1703.7 1285 1454.03 3176.93 1919.51 1833.7 2056.61 2462.09 1095.64 1790.22 8 AUGUST 1225.22 2034.58 1528.77 1823.93 4067.16 2508.45 2350.06 2646.19 3358.12 1359.74 2290.22 9 SEPTEMBER 611.32 1124.87 716.16 994.8 2329.38 1465.51 1386.16 1560.67 1984.54 632.29 1280.57 10 OCTOBER 830 1618.7 1200 1369.03 3091.93 1834.51 1748.7 1971.61 2377.09 1010.64 1705.22 11 NOVEMBER 251.29 795.8 524.19 733.54 2533.22 1144.19 1016.45 1334.51 2224.51 564.51 1112.22 12 DECEMBER 614.32 1127.87 719.16 997.8 2332.38 1468.51 1389.16 1563.67 1987.54 635.29 1283.57 Average 532.98 1151.23 849.54 1009.28 2792.90 1497.14 1379.83 1670.02 2308.21 768.94 1396.01

Graph 5.7

Average Monthly Rate of Dust Fall for the Year 2007

(mg/sq.m/day)

0

500

1000

1500

2000

2500

3000

Army R

ecrui

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Railway

Station

Sada B

har S

weets

New A

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Sirki R

oad

T.B. San

atoriu

m

mg/

sq.m

Average MonthlyRate of Dust Fall forthe Year 2007(mg/m2/day)

140

Graph 5.8

Average Monthly Rate of Dust fall for the year 2007 (mg/sq.m/day)

0

500

1000

1500

2000

2500

3000

3500

4000

4500


Months

mg/

sq.m

Army RecruitmentCenter

Ashraf Sariab Road

C.G.S. Colony SatelliteTow n Quetta

Civil Hospital

Gaw almandi Chow k

Qadoosi Store/QuickMarketing Services

Railw ay Station

Sada Bhar Sw eetsNew Adda

Sirki Road

T.B. Sanatorium

As has been stated earlier that Quetta experiences normally 58mm rain

fall in winter due to the wind patterns originate clouds from the Turkey and

Greece seas, black sea near Jordan and Persian Gulf (contrary to the other

parts of the country, where normally precipitation occurs in summer due to the

monsoon patter originates from gulf of Bangal). While in summer the average

precipitations happen in Quetta (and the major other parts of Balochistan) up

to 13 mm as the track originates from Gulf of Bangal and causes heavy

downpour in the most parts of India and Pakistan, doesn’t pass through the

90% area of Balochistan and results mostly dry spells or very less rain.

Therefore, Quetta experienced 46.9 & 43.8mm rainfall at the end of 2006 (in

the beginning of winter), which lasted to 17.2 & 77.3 mm in the January &

February of 2007. The average rate of dust fall at all 10 stations Army

Recruitment Center, Ashraf Sariab road, CGS Colony, Civil Hospital,


141

Sweets New Adda, Sirki road & T.B. Sanatorium for the year 2007was

recorded 532.98, 1151.23, 849.54, 1009.28, 2792.90, 1497.14, 1379.83,

1670.02, 2308.21 & 768.94 mg/m2/day respectively. So a descending trend

was observed till February and a consistent horizontal movement (Graph 5.8)

was witnessed from February to June due to the 20.1, 8.1 & 34.4 mm rainfall.

But from May and particularly soon after June a sharp increase in dust fall was

observed again due to very low atmospheric pressure and very little or no

precipitation in the successive months. Then again there was a sudden

decrease happened up to the mid of November and then finally little increase

took place in the dust fall till December due to the rather high atmospheric

pressure and calm weather and bearish activity in the markets of Quetta

though there was extremely low (5.0mm) rainfall recorded in December. The

reason of the lesser population of inhabitants and traffic is because of the

severe cold weather of the city as most of the population shifts to the hotter

places of the province and other parts of the country at the beginning of winter

as soon as the educational institutions are closed for winter vacations. The

overall rate of dust fall for the year 2007 detected 1396.01 mg/m2/day, was

again (64.45 mg/m2/day) more than the previous year 2006.

142



Ashraf Sariab Road


Civil Hospital

Gawalmandi Chowk


Railway STATION

Sada Bhar

Sweets New Adda

Sirki Road

T.B Sanatorium

Average

1 JANUARY 281.29 825.8 554.19 763.54 2563.22 1174.19 1046.45 864.51 2254.51 594.51 1092.222 FEBRUARY 1275.22 2084.58 1578.77 1873.93 4117.16 2558.45 2400.06 2696.19 3408.12 1409.74 2340.223 MARCH 308.7 886.12 764.51 765.8 2650.96 1344.83 1181.29 1526.45 2149.67 664.51 1224.284 APRIL 285.6 863.11 741.5 742.7 2627.95 1321.82 1158.28 1003.44 2126.66 641.5 1151.265 MAY 520.06 1092.96 946.83 1013.93 2336.51 1481.67 1383.29 1715.58 1847.16 758.12 1309.616 JUNE 269.29 813.8 542.19 751.54 2551.22 1162.19 1034.45 852.51 2242.51 582.51 1080.227 JULY 1185.22 1994.58 1488.77 1783.93 4027.16 2468.45 2310.06 2606.19 3318.12 1319.74 2250.228 AUGUST 1213.22 2022.58 1516.77 1811.93 4055.16 2496.45 2338.06 2634.19 3346.12 1347.74 2278.229 SEPTEMBER 806.66 1359 1041.66 1264.66 2745 1897.33 1633.33 2018.66 2254.66 891.33 1591.2310 OCTOBER 338.7 916.12 794.51 795.8 2680.96 1374.83 1211.29 1556.45 2179.67 694.51 1254.2811 NOVEMBER 1009.66 1802 1378.33 1602.33 3376.66 2111.66 2036.33 2215.66 2761 1253.33 1954.6912 DECEMBER 568.06 1140.96 994.83 1061.93 2384.51 1529.67 1431.29 1762.58 1895.16 806.12 1357.51 Average 671.8 1316.8 1028.57 1186 3009.7 1743.61 1597.01 1787.41 2481.94 913.63 1573.16

Graph 5.9


0

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2000

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3000

3500

Army R

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uetta

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Sirki R

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T.B. S

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mg/

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143

Graph 5.10

Average rate of dust fall for the year 2008 (mg/sq.m/day)

0

500

1000

1500

2000

2500

3000

3500

4000

4500


Months

mg/

sq.m


Ashraf Sariab Road

C.G.S. Colony Satellite Tow nQuetta

Civil Hospital

Gaw almandi Chow k

Qadoosi Store/QuickMarketing Services

Railw ay Station

Sada Bhar Sw eets NewAdda

Sirki Road

T.B. Sanatorium

The average rate of dust for the all 10 sites Army Recruitment Center,

Ashraf Sariab road, CGS Colony, Civil Hospital, Gawalmandi Chowk,

Qadoosi General Store, Railway Station, Sada-Bahar Sweets New Adda, Sirki

road & T.B. Sanatorium for the year 2008 was recorded 671.8, 1316.8,

1028.57, 1186, 3009.7, 1743.61, 1597.01, 1787.41, 2481.94 & 913.63

mg/m2/day respectively. The overall average rate of dust fall for the year 2008

was recorded 1573.16 mg/m2/day, which is significantly high (204.15

mg/m2/day) than the previous 2007 year. It was even high (63.44 mg/m2/day)

then the overall average rate of dust fall for the year 2004 (1509.72

mg/m2/day). This clearly reflects the overall impact of global warming, which

has vanished the gradual change in the weathers. In January 2008 55.8mm

rainfall suppressed the dust fall but it sharply increased in February up to

March due to the zero precipitation and high winds of SW & NE. In April

heavy downpour occurred (91.4mm) which subdued the dust fall. But again in

144

May and from July to November there was absolutely no rainfall occurred. In

addition to that average high temperature and low pressure caused very heavy

dust fall from June to September (dust fall conditions witnessed somewhat

similar to 2004 year). Eventually from the mid of October the amount of dust

fall recorded going towards low trend because of the high atmospheric

pressure and some light showers in December 2008.

Table 5.19 Average Monthly Rate of Dust Fall from the Year 2004-2008 (mg/m2/day)


Ashraf Sariab Road


Civil Hospital

Gawalmandi Chowk


Railway STATION

Sada Bhar

Sweets New Adda

Sirki Road

T.B Sanatorium

Average

1 JANUARY 523.01 1145.04 873.9 984.392 2735.65 1504.44 1392.81 1420.23 2186.21 757.42 1352.312 FEBRUARY 616.19 1232.11 909.66 1098.61 2836.76 1570.99 1469.18 1773.52 2354.79 814.85 1467.663 MARCH 320.57 891.45 739.84 782.7 2650.8 1328.09 1171.71 1511.45 2188.03 667.9 1225.254 APRIL 500.92 1169.39 866.27 1005.89 2840.46 1464.46 1364.94 1615.38 2307.77 776.92 1391.845 MAY 637.6 1258.71 1045.55 1145.08 2764.32 1678.45 1555.04 1882.67 2228.65 878.52 1507.466 JUNE 595.14 1220.38 944.002 1055.05 2865.24 1650.53 1500.71 1792.44 2296.93 820 1473.347 JULY 726.84 1389.98 1019.48 1189.44 3031.81 1764.86 1650.9 1899.38 2424.21 882.1 1597.98 AUGUST 824.16 1498.13 1112.08 1307.12 3218.27 1944.64 1779.13 2101.29 2631.07 975.89 1739.189 SEPTEMBER 541.47 1103.88 825.1 973.41 2576.26 1534.3 1363.61 1701.71 2126.17 710.19 1345.6110 OCTOBER 514.86 1120.46 874.54 1005.78 2711.76 1472.47 1351.83 1658.93 2247.18 795.05 1375.2911 NOVEMBER 411.07 991.78 734.44 895.1 2573.7 1345.1 1212.49 1563.38 2142.69 668.21 1253.812 DECEMBER 537.21 1080.69 804.43 984.6 2407.4 1442.11 1345.47 1611.4 2011.98 703.46 1292.87 Average 562.42 1175.16 895.77 1035.59 2767.7 1558.4 1429.82 1710.92 2262.4 787.54 1418.77

145

Graph 5.11

Average Monthly Rate of Dust Fall of Quetta from the Year 2004-2008 (mg/sq.m/day)

0

500

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2000

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3000

Army R

ecru

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atellit

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uetta

Civil H

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Sada B

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New A

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Sirki R

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T.B. S

anato

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Center

mg/

sq.m Average Monthly Rate of Dust fall of Quetta

from the year 2004-2008 (mg/sq.m/day)

The overall average rate of dust fall of five years 2004-2008 of all ten

selected sites Army Recruitment Center, Ashraf Sariab road, CGS Colony,

Civil Hospital, Gawalmandi Chowk, Qadoosi General Store, Railway Station,

Sada-Bahar Sweets New Adda, Sirki road & T.B. Sanatorium was recorded

562.42, 1175.16, 895.77, 1035.59, 2767.7, 1558.4, 1429.82, 1710.92, 2262.4

& 787.54 mg/m2/day respectively. Finally the overall average of rate of dust

fall of all five years 2004-2008 was recorded 1418.77 mg/m2/day. This is an

ample proof of the gravity of the dust fall situation in Quetta. That Quetta is

one of those cities of the world, which experiences the very heavy dust fall

due to its geographical and to some extent anthropological reasons.

As was described earlier that there was great divergence found in the

dust fall from site to site and time to time, which has immense value hence it

could provide us some notion apropos of local factors playing pivotal role to

the atmospheric dust fall. If one goes thoroughly through the whole data of

146

dust fall collected for five years and compare them with each other, it could

easily be figured out that out of ten selected site, the two sites (Army

Recruitment Center and T.B. Sanatorium) got the least amount while other

two sites (Gawalmandi Chowk and Sirki road) received the very high level of

dust fall. Three other sites (Sada Bahar Sweets new Adda, Qadoosi Store &

Railway Station were nothing short of heavy dust fall receiving stations. As

heavy amount of dust fall observed on these three sites as well. While Ashraf

Sariab road, CGS Colony & Civil Hospital could be termed as comparatively

medium dust fall receiving centers. Otherwise, yet if among these two so-

called medium dust fall receiving sites (Ashraf Sariab road & Civil Hospital)

are compared with the international standards, they are ranked among very

heavy dust fall receiving centers (having > 1200 mg/m2/day average rate of

dust fall in 05 years).

Army Recruitment Center is located in Cantonment area and due to

regular maintenance and cleanliness, less traffic and humans population and

being located in the proximity of high-profile Government offices like

Balochistan High Court, Serena Hotel and above all properly well-developed

area of cantonment having pretty vegetation contrary to the rest of the city, it

received the minimum amount of dust fall. Further it is situated almost in the

center of the whole city/valley. So the dust plumes striking Quetta become less

dense or dilute (till they hit this station) due to the presence of dense

population, buildings, trees etc. all around from the edges to the core of the

city (this site) in a circumference of around 2653 Km2. Consequently the least

amount in terms of average rate of dust fall from 2004-08 (562.42 mg/m2/day)

was detected on this site. Later on the results of size of dust particulates prove

147

that merely the major amount of fine and ultra-fine particulates, which remain

present in atmosphere for a long time at a high altitude, were detected rather

more at this site.

T.B. Sanatorium was built in English period at a site, where the

tuberculosis and asthmatic patients prone to the air pollutants might have an

environment free of particulates and other air pollutants. Its location on the

extreme west of the city near the foot of the 'Chiltan' Mountain at the highest

point of the bowled shape valley makes it having a clean environmental site

than rest of the city. The ridge of the 'Chiltan' deters the dust blowing from the

western or south western side of the city, to strike on the site. Even though the

mounting population and increasing traffic has badly effected the air

environment of the whole city by and large, nevertheless the unique natural

location of this site doesn't let fall prey to the air pollutants particularly dust

particulates. That is why the least amount of overall rate of average dust fall

for the 5 years 2004-08 was recorded (787.54 mg/m2/day) at this site as well

besides Army Recruitment Center.

The location of C.G.S Colony at well planned satellite town on the

southern part of the city in recent decades developed area though having huge

but properly settled population was also not extremely dust polluted (received

average dust fall for the said 5 years areas 895.77 mg/m2/day) compare to

most of the other areas of Quetta.

Another important site Civil Hospital is located in the heart of city and

is surrounded by all around the congested cluster of residential, Govt. offices,

trading centers etc. The roads on its all four sides remain busy twenty four

148

hours. On chemical analysis it was found that the amount of average dust fall

particulates for the 5 years 2004-08 (1035.59 mg/m2/day) contained the smoke

particles as well in pretty amount due to the massive traffic particularly moves

at the snail pace on its eastern, southern & northern sides. The 'Jinnah' road on

eastern side mostly remains blocked for traffic because of its narrowness and

indiscriminately allowed public transport in terms of large bunker type old

'local' stone age buses, which not only emit huge amount of un-burnt/semi-

burnt and other carcinogenic particulates of diesel fuel in already suffocated

atmosphere but also cause traffic jam triggering the emission of more Pb

contained vehicular exhaust.

'Ashraf ' iron merchant located at one of the main entrances in the city

on southern part, is surrounded by muddy constructed shops, workshops and a

small newly developed residential scheme 'green house' on its western,

northern & southern sides simultaneously. On eastern side across the Sariab

road, the Government buildings like Geological Survey of Pakistan,

University of Balochistan etc. are having green belts. Therefore it was only hit

from three sides by the dust particulates having an average 5 years dust fall

1175.16 mg/m2/day, while the front eastern part didn’t affect it much.

Quetta 'Railway Station' is also one of those few public places/sites set

up with the establishment of city. Being the sole railway station of the city on

its eastern front mostly there is a jam packed rush could be witnessed. The

northern, western and southern sides of it are mostly populated with the

thickly extremely small cabin type quarters of railway employees, encroached

streets & roads by the food vendors, grocery shops, the continuous shunting

149

activities of railway locomotives and the busy narrow roads having thick

traffic are enough to increase the air pollutants in addition to the average dust

particulates settled & collected there for the said 5 years 1429.82 mg/m2/day.

'Qadoosi store' is also one of few sites located right in the middle of the

city (center of the bowl receiving dust particulates adhered with lead halides

PbBrCl, PbBr2& PbCl2 are produced on combustion of leaded gasoline

containing tetraethyl lead, an anti-knocking agent), where all around of it wild

business activities have been carried out since the foundation of the city.

Massive vehicular emission was additional reason along with dust particulates

(average rate of dust fall recorded for the said 5 years1558.4 mg/m2/day) due

to it pathetic atmospheric pollution. There if someone stands for a while, could

easily feel suffocation, eye burn, nuisance and sore throat.

'Sada Bahar' sweets shop is located adjacent to "new Adda (bus &

goods vehicle stop on its northern & eastern sides)" in the southern part of the

city near Satellite town. On its western side there is a lane of thickly populated

business shops, slummy restaurants, on northern side again the same sort of

business shops and fruit and vegetables markets remained hectic in day time.

The junction (roundabout) and dilapidated roads always running with choked

sewage right in front of the site and randomly populated huge graveyard right

on its southern side along with an old muddy village having poor sanitations

etc. are enough to trigger the particulates amount in addition to the dust

plumes, which strike this area due to its naked position in the absence of any

high rise buildings in its vicinities, which might have prevent it from the dust

plumes mostly hit it from southern and western sides. The average rate of dust

150

fall recorded for the set 5 years on this site was 1710.92 mg/m2/day

comparatively higher than the earlier described 7 sites.

'Sirki road' was the second most dust particulates receiving site having

the average rate of dust fall 2262.4 mg/m2/day for the said 5 years. As it is

located in the middle of old and new city linking both with the route called

'Sirki road', having diverse industries adjacent to all its four sides. On the

eastern side across the road there are huge populations of old classical muddy

houses built in haphazard manner with extremely narrow passages/streets. It

received the very huge dust fall hitting it particularly from the open northern,

eastern, western and to some extent northern parts of it. It also included the

industrial emitted waste as well in terms of Metal oxides, V2O5, CaO, Aerosol

MISTS of H2SO4 droplets, (NH2)2SO4 or CaSO4 salts, Polycyclic aromatic

hydrocarbons (PAH) sorbed on soot particles & Fly ash emitting from the

stacks of few remaining brick kilns were located on the eastern part of the

muddy houses. However EPA (Environmental Protection Agency of

Balochistan) strongly recommended, which enforced the local and provincial

governments to relocate the brick kilns out of the city.

The 10th site found to be the most vulnerable for all sort of air pollution

and specifically against dust particulates pollution, was the 'GAWALMANDI

CHOWK'. Five narrow roads having huge traffic moving to all corners of city

join on this bottle neck spot, where day and night traffic remains jam small

infinite shops on both shoulder of each road along with encroachments by the

every shopkeeper and extremely contracted each single roads for both side

traffic are enough to jam the traffic on this site. Though later on the local govt.

151

building, on the roof of which the dust collector was kept, was demolished

later on in order to widen the junction, yet it went futile. Because, it wasn’t

enough for massive population of donkey carts, pushing carts, all sort of

public and goods vehicles moving on both sides of every road. Interestingly,

the road adjoining the site building is called "KACHRA road' means garbage

road. As the filth containing trucks of local govt. pass through this road taking

the trash of whole city on the eastern by-pass. The thick population of muddy

and few newly but poorly built concrete brick houses and unplanned private

residential schemes on east-north side of the site are nothing short of slums.

The choked sewage mostly flow on the front 'QAWARI road' reflects a

miserable state of the area. This site is directly hit by all sorts of air pollutants

including dust particulates due to its bareness in the absence of high rise

building in its proximities. The average of rate of dust fall collected for the

said period of 5 years (2004-08) was 2767.7 mg/m2/day, which was literally

alarmingly high and should be a gravely serious matter for environmentalists

and the concerned official authorities.

Graph 5.12

Average Monthly Rate of Dust Fall from the year 2004-2008 (mg/sq.m/day)

0

500

1000

1500

2000

2500

3000

3500

Janu

ary

Febru

ary

March

April

MayJu

ne July

Augu

st

Septem

ber

Octobe

r

Novem

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Decem

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Months

mg/

sq.m


Ashraf Sariab Road

C.G.S. Colony Satellite Tow n Quetta

Civil Hospital

Gaw almandi Chow k


Railw ay Station

Sada Bhar Sw eets New Adda

Sirki Road

T.B. Sanatorium

152

The monthly trend of the rate of dust fall of 10 selected sites for all 5

years 2004-08 shows that on the whole in January to April all sites showed a

linear trend having around minimum dust fall at the respective centers and a

bit fluctuation in February upward, in march downward and in April again

upward up to the same level of January. But in April it started to increase in a

consistent mounting trend till August due to the change in weather of summer

having high temperatures, low atmospheric pressure and high dusty winds

particularly in the marathon dry spells. After August it again showed a

downward trend because of the dropping in the mercury level with a minor

increased variation in October and decrease in November up to the minimum

level of dust fall. Finally it again showed a mild increase in December again

due to the metrological conditions. The overall seasonal trend shows that in

winter (except some days in drought period), the rate of dust fall remains at

minimum level due to the high atmospheric pressure and precipitations, while

in summer it increases up to the maximum level in the mid of summer due to

the high temperature, low atmospheric pressure, high winds and minor rare

rainfall.

As meteorological conditions is also one of the major factors among

other geographical and geological factors. Therefore meteorological data was

also obtained from the Pakistan Meteorological Department containing daily

temperatures, daily wind speed & direction, daily precipitation, daily visibility

etc. in order to make a solid deduction. The whole data given in their

respective tables strongly supports and validates this research work. The data

shows that there was absolutely no precipitation occurred in the months of

January, March, April, June, July, August & October in the first year of

153

samples collection 2004. While in February, May, September & November

extremely little rainfall 4.8, 4.1, 9 & 2mm was recorded. Only in the month of

December after a lengthy spell of drought 40.2mm rainfall happened.

Similarly, the visibility recorded twice a day every day at 8:00 a.m. & 5:00

p.m. Table 5.33 dropped in between 92-94 (200-500m & 1000-2000m) in

January, February, March, April, May, July, August, September, October,

November & December for 2, 1, 2, 2, 6, 2, 6, 5, 3, 1 & 2 days respectively. It

also includes those days of inversion period, when dust plume originated from

the deserts of DALBANDIN (Balochistan), Pakistan and DASHT-E-LUT

(Desert of 'LUT') Iran, and wrapped the city in those particular days.

Figure: (Desert) DSHT-E-LUT

154

5.2 (Desert) DSHT-E-LUT:

The desert 'LUT' by & large and southern 'LUT' in particular one of the

extremely parts on the globe; some sectors are less than 1,000 feet above sea

level. In 'LUT' and in the surrounding areas of SEISTAN (a province of Iran),

the hot desiccating gusts of the wind 'wind of 120 days' reach up to 70

miles/hour, generating a mayhem of noise, sand & dust. Dead misery controls

highest for hundreds of miles in the southern 'LUT'. Greenland Ranch,

California (178 feet below sea level), having the landscape somewhat similar

to Balochistan, in Death Valley, might be made a resemblance with the

'Khurasan' Desert Basin region, particularly for the southern 'LUT'. At

Greenland Ranch, the average January Temperature is 51 °C and average daily

is 65° & 37° respectively. The subsequent measures for July are 102°, 116° &

(Desert) DSHT-E-LUT

155

88°. The extraordinary highest from 85° in January to 127° or even up to 134°

in July has been noted. Intensities in southern 'LUT' might cross these values

keeping in view its geography. Imagine the average rainfall at Greenland

Ranch is 1.5", ranging from 4.5" in 1912 to none for a decade till 1929, what

would be the state of 'LUT', which is having far severe conditions than at

Greenland Ranch. The 'LUT' Desert consists of several large basins separated

by shabby mountains and ridges, covering an area of about 200 by 100 miles.

The west desert contains wind-swept corridors separating high ridges. The east

is a sea of sand. Winds pile the sand into dunes up to 500 feet high, as tall as

Washington's monument. The 'LUT' is so menacing that not even bacteria can

exist. Research factions numerously brought sterilized milk into the 'LUT' and

then stored it uncovered in temperatures that could beat 160 degrees

Fahrenheit even in the shadow. The milk remained sterile.

A profound cognizance apropos of the gravity of very heavy rate of

dust fall could be figured out by comparing my findings with some of the

cities in our federation (Pakistan) and international ones.

156

Though very little heed has been paid to the atmospheric pollutants in

general and to the dust fall in particular, Beg et al., [54] carried out six (06)

years work from 1980-1985 for the rate, composition and quantity of dust fall

in Karachi at two (02) locations. The dust fall was measured by exposing dust

fall containers of standardized shape and size at the said two sites for a period

of one calendar month corrected to 30 ±2 days. The monthly average dust fall

obtained between 13.0 to 15.7 tons per square kilometer per month (157.13 to

177.17 mg/m2/day).

Another magnificent research work was conducted by Farid U Khan et

al., [57] for a lengthy period of seven (07) years from 1992-98 in order to

calculate the rate of dust fall by using the recommended standard method

(Robert 1986) [58]. Dust fall containers/collectors of standardized shape, i-e.,

22-24 cm mouth diameter, 20 cm base diameter and 25 cm height were used

and installed at four (04) different locations. The selection of the sites for the

study was done with respect to the number of motor vehicles, which are the

only main source of transportation in Peshawar. After a period of one calendar

month corrected to 30 ±2 days, the collectors were taken off, covered with

plastic lid and brought to the laboratory. The samples were analyzed by

standard chemical and physical method (Scott, 1956) [59].

In the following Table 5.20, 5.21 & 5.22 respectively all the results of

Beg et al., [54] from 1980-1985, Farid-U-Khan et al., [57] for seven (07) years

from 1992-98 and my results for the five (05) years 2004-08 are given for

comparison.

157

Table 5.20 Monthly average rate of dust fall at Karachi (1980-1985), Peshawar

(1992-1998) and Quetta (2004-2008)

Karachi (mg/sq.m/day) 1980-1985 (6 years) [54]

S.No. Months 1980 1981 1982 1983 1984 1985 Averages 1 January 87.9 90.64 82.74 102.41 70.32 94.83 88.14 2 February 187.24 129.13 100.86 185 137.41 135 145.77 3 March 189.19 171.77 165.8 222.74 200.96 250.64 200.18 4 April 209 203.16 244.16 243.83 229 227 226.02 5 May 223.38 221.61 196.61 200.96 250.64 198.54 215.29 6 June 218.66 257.83 269.16 175.83 247 295.16 243.94 7 July 202.09 242.9 220.8 166.29 249.51 207.41 214.83 8 August 235.64 191.45 166.77 218.22 183.38 184.83 196.71 9 September 252.16 167.83 148.83 158 125.5 232 180.72 10 October 142.9 133.7 134.19 104.67 105 140.64 126.85 11 November 51.8 84.16 82.5 75.5 81.83 78 75.63 12 December 83.54 71.29 73.22 46.93 84.35 82.09 73.57 Averages: 173.62 163.78 157.13 158.36 163.74 177.17 165.63

Table 5.21a Peshawar (mg/sq.m/day) 1992-1998 (7 years) [57]

Months 1992 1993 1994 1995 1996 1997 1998 Averages January 530.64 553.87 620.96 631.93 661.29 622.25 678.71 614.23 February 587.24 671.03 603.1 713.79 549.65 780.34 846.55 678.81 March 629.67 732.9 795.8 780.32 845.48 833.54 785.16 771.83 April 716 889 870.66 931 996 1015.33 1008 917.99 May 693.54 992.25 1077.74 1098.38 1155.16 1272.9 1191.93 1068.84 June 861 1179.33 1268.66 1287.66 1339.33 1427 1392.66 1250.8 July 1021.93 1039.67 1002.58 1091.93 1160.96 1141.29 1230.32 1098.38 August 905.48 1085.8 944.51 1006.45 1080.32 1090.32 1091.61 1029.21 September 834.66 909.33 869.33 949.66 1001 1057.66 1077.33 956.99 October 740.64 775.16 736.77 583.54 871.29 842.9 871.29 774.51 November 698.66 710.66 690.33 761.33 809.33 780.33 844.66 756.47 December 548.06 610.64 597.41 630.32 748.38 671.93 704.83 644.51 Average: 730.62 845.8 839.82 872.19 934.84 961.31 976.92 880.21

158

Table 5.22 (continued) Quetta (mg/m2/day) 2004-2008 (5 years)

Graph 5.13

Karachi(gms/sq.m/year)

4500

4600

4700

4800

4900

5000

5100

5200

5300

5400

1980 1981 1982 1983 1984 1985

Years

gms/

sq.m

Karachi(gms/sq.m/year)

Months 2004 2005 2006 2007 2008 Averages January 1224.28 1187.23 1374.01 1282.22 1092.22 1236.546 February 1124.82 1162.51 1290.01 1112.22 2340.22 1406.24 March 1172.22 1270.25 1369.01 1254.28 1224.28 1257.548 April 1974.69 1505.22 1281.02 1206.82 1151.26 1423.65 May 2288.22 1174.61 1367.01 1240.26 1309.61 1475.195 June 1465.23 1250.28 1391.03 1194.28 1080.69 1276.142 July 1279.57 1425.28 1377.03 1790.22 2250.22 1624.037 August 1500.97 1219.26 1180.01 2290.22 2278.22 1693.736 September 1422.69 1235.28 1490.01 1280.57 1591.23 1403.965 October 1785.22 1224.22 1445.01 1705.22 1254.28 1482.79 November 1491.23 1324.82 1205.01 1112.22 1954.22 1417.50 December 1367.51 1397.22 1214.01 1283.57 1357.51 1323.10 Average 1509.72 1281.34 1331.56 1396.01 1573.16 1418.78

159

Graph 5.14

Karachi (mg/sq.m/day)

0

50

100

150

200

250

300

350

Janu

ary

Februa

ryMarc

hApri

lMay Ju

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Augus

t

Septem

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Octobe

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Novembe

r

Decembe

r

Months

mg/

sq.m

1980

1981

1982

1983

1984

1985

Graph 5.15

Peshawar(tons/sq.km/year)

0

5

10

15

20

25

30

35

1992 1993 1994 1995 1996 1997 1998

Years

Tons

/sq.

km

Peshaw ar(tons/sq.km/year)

160

Graph 5.16

Peshawar (mg/sq.m/day)

0

200

400

600

800

1000

1200

1400

1600

January

FebruaryMarch

April MayJune

July

August

Sep tember

Octobe r

November

December

Months

mg/

sq.m

1992

1993

1994

1995

1996

1997

1998

Graph 5.17

Quetta (mg/sq.m/day)

0

200

400

600

800

1000

1200

1400

1600

1800

2004 2005 2006 2007 2008

Years

mg/

sq.m


161

Table 5.21b

Graph 5.18


0

500

1000

1500

2000

2500

USA (1951

)

USA (1951

-52)

USA (1954

)a

USA (195

4)b

USA(1955

)

USA (1955

)

S.Arabia

(199

0)

India

(1996

-97)

Country

mg/

sq.m

Rate of dust fall of dif ferent countries(mg/m2/day)


S.No. Country

1 USA (1951) 816.66

2 USA (1951-52) 1513.33

3 USA (1954)a 1870

4 USA (1954)b 2056.66

5 USA(1955) 1630

6 USA (1955) 1013.33

7 S.Arabia (1990) 1725

8 India (1996-97) 1163.98

162

Graph 5.19


0

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2500

Janu

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Februa

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hApri

lMay

June Ju

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Augus

t

Septem

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Octobe

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Novem

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Decem

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Months

mg/

sq.m

2004

2005

2006

2007

2008

Graph 5.20

Comparative Monthly Rate of Dust Fall of Karachi (1980-1985), Peshawar(1992-1998) & Quetta(2004-2008)

0

500

1000

1500

2000

2500

Janu

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Februa

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Months

mg/

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1980

1981

1982

1983

1984

1985

1992

1993

1994

1995

1996

1997

1998

2004

2005

2006

2007

2008

163

The above mentioned tables 5.20 and 5.21 and Graph 5.13-5.20 and

particularly the Graph 5.20 are self-explanatory enough to describe the

situation of dust fall at Quetta in comparison with the international and two

major cities Karachi & Peshawar in Pakistan. International data clearly shows

that even on four (04) different past occasions (1951-52, 1954a, 1954b, 1955

& 1990) 4 cities of US & one city of Saudi Arabia were having more average

annual rate of dust fall (1513.33, 1870, 2056.66, 1725 mg/m2/day) than the

average annual rate of dust fall at Quetta (1418.76 mg/m2/day) not to speak of

nowadays, when the air pollution has reached beyond the alarming rate. That

has caused global warming, severity of weather, uncertainty in weather

changes, drought spells in most parts of the world, excessive floods in other

parts of the world, melting of polar ice caps etc.

Similarly the average annual rate of dust fall in Karachi & Peshawar

has been compared with average annual rate of dust fall at Quetta found by me

in Table 5.18& Graph No.5.20. It is vivid that the Quetta has got far more

average rate of dust fall than the both cities Karachi & Peshawar. Though both

cities are far larger than Quetta in size/area and population, yet they

experienced far lesser amount of dust fall than Quetta. Particularly Karachi,

where the dust fall is within the international set range <250 for slighter limits

and has been marked with green values. The primary reason for that is the

presence of excessive humidity in the atmosphere of the Karachi city as it is a

coastal city. Further plantation in Karachi is also far more than Quetta.

On the other hand Peshawar has got more average annual rate of dust

fall than Karachi because of its comparatively lesser humidity and topography.

164

But it had even far lesser average annual rate of dust fall than Quetta had.

Besides other multiple reasons described earlier, the primary reason of Quetta

having more average rate of dust fall than other cities of Pakistan is the semi-

arid zone sort of geography and climate of Quetta city. The sole unanimous

phenomena between all three cities (Karachi, Peshawar & Quetta) of Pakistan

is that all had faced heavy annual rate of dust fall in the mid of summer (Graph

5.20) due to high temperatures and resultant low atmospheric pressures.

In the beginning of year 2005, January, February, March & April

Quetta received heavy downpours 33.7, 129.2, 63.3 & 36.9mm besides in

April, June & August very light scattered showers 0.4, 4.8 & 3.4mm

respectively. However, again July, September, October, November &

December complete dry spells were observed having 0 (zero) rainfall. Due to

heavy rainfall only January, March & April experienced the poor visibility in

between 92-94 (200-500m & 1000-2000m) for 2, 2 & 2 days respectively.

During the year 2006, January, February, March, May, July, August,

November & December experienced the 9.5, 6.5, 26.1, 10.7, 5.6, 2.5, 54.9,

46.9 & 43.8mm rainfall. Nevertheless June, September & October again didn’t

have any rainfall whatsoever. The low visibility in between 92-94 (200-500m

& 1000-2000m) was recorded for the January, March, July, August,

September, October & November having 1, 1, 4, 1, 11, 2 & 2 days

respectively.

In 2007, January, February, March, April, June, July, November &

December received 17.2, 77.3, 20.0, 8.1, 34.4, 8.0, 3.5 & 5.0mm rainfall,

while May, August, September & October, received absolutely no (zero)

165

rainfall having dry spells. The visibility in between 92-94 (200-500m & 1000-

2000m) was recorded for the January, February, July, August, September,

October & November containing 2, 1, 7, 3, 4, 5, 2 and extremely low visibility

on 11th December for one day even up to 91 (50-200m 'objects visible at 50m

but not at 200m') was recorded.

In 2008, January, April, June & December received the 55.8, 91.4, 9.1,

& 7.4mm precipitation. While a long dry spell was also experienced along

with very heavy (even a bit more dust fall than the year 2004) in February,

March, May, July, August, September, October & November having zero (0)

rainfall. The visibility recorded in between 92-94 (200-500m & 1000-2000m)

for the months of January, February, March, May, June, July, August,

September, October & December for 3, 4, 2, 2, 7, 6, 12, 9, 5 & 1 days

respectively. Once again for one each day of 21st February & 19th December

2008 had the visibility 91(50-200m 'objects visible at 50m but not at 200m').

However other than those above mentioned days having low visibility, there

were numerous occasions during the thermal inversion periods in 2004 and

2008, when visibility sometimes even reduced up to zero.

5.3 CHEMICAL ANALYSIS OF DUST FALL:

In order to find the origin of dust particulates, chemical analyses of the

samples were carried out sporadically. The loss on ignition, silica and oxides

aluminum, iron, calcium, magnesium, sodium and potassium was found out. It

could be observed from the results given in Table 5.35a; that the chemical

composition collected at different sites is by and large same but varies with

thermal inversion periods. It is important to describe the average annual

166

chemical composition of the dust fall collected in normal usual conditions

mostly consists of (loss on ignition: 20.62%, SiO2: 44.29%, Al2O3: 13.20%,

Fe2O3: 4.56%, CaO: 14.86%, MgO: 1.81%, Na2O: 1.16% and K2O: 0.79%)

Table 5.35a. This obviously points out that under normal conditions (other

than extremely rare period of thermal inversion) dust fall is from the local

earth crust strongly calcareous, moderately fine textured soils developed in

mixed Pleistocene piedmont alluvium derived from CHILTAN, MURDAR,

MASLAKH & TAKATO ranges having a structural (cambic) B horizon of the

series occurs in an arid subtropical continental highland climate and occupies

level to nearly piedmont plains of CHILTAN, MURDAR, MASLAKH &

TAKATO ranges of loam horizon a (substratum) rock layer consists of a

buried profile [112]. It has a brown/dark brown, friable, massive, strongly

calcareous silty clay loam topsoil underlain by dark yellowish brown, friable,

weak coarse sub-angular blocky, strongly calcareous silty clay, which

originates from the local areas like unpaved dusty shoulders of roads,

undeveloped dusty soil land within and in the suburbs of city, neglected

sanitation, and muddy houses mainly of those villages, which have been

merged in the city with its expansion and last but not least due to deadly dry

atmospheric conditions prevail mostly for the whole year. The results were

matched with the already soil pertaining research of the city and were found

exactly accurate [113].

In addition to that the dust fall samples particularly collected in the

thermal inversion spells, were having a fair ribbon sort of texture had almost

no gritty particles reflected its silty clay loam nature. When chemically

analyzed in order to confirm the origin of dust plume, chemical composition

167

of the samples verified the satellite images of different meteorological

agencies of the world, claiming that the dust plumes origin was the deserts of

DALBINDIN (Pakistan) &'DASHT-E-LUT' (Iran). Those specific dust

samples were having (loss on ignition: 24.64, SiO2: 39.19, Al2O3: 9.15, Fe2O3:

3.36, CaO: 20.92, MgO: 10.91, Na2O: 13.66and K2O: 20.99) Table 5.35b. The

results on matching with already done research [114] of the said areas' [115]

soil were found exactly to be the similar chemical composition having similar

percentages of all constituents present in them. It confirmed the source of the

dust particulates plumes hit Quetta city for the very first time in the life span

of even elders. The Brown to light olive brown color of dust showed that it

was a mixture of silt loam & silty clay coarse loam to very fine sandy loam

occasionally loam (soil) of extreme aridic region dust contained soot, smoke,

un-burnt heavily adulterate Iranian smuggled fuel, vehicular emission,

aerosols, dried residual sewage etc. Other important minerals detected in

pretty concentration, were the oxides and hydroxides of Na, K, Mg, Ca.

However, Fe & Mn, were found in lesser concentration, which determine the

color of many soils and have a high sorption capacity for trace elements; again

the concentration of carbonates were comparatively high than silicates had

lesser concentration, which has a major influence on the pH of soils; and in

some cases, phosphates, sulphides, sulphates and chlorides [116]. In

comparison with Peshawar & Karachi the chemical composition of all samples

of dust fall particulates under normal windy conditions deduced that the color

from yellowish brown to dark yellowish brown when moist in uncultivated

areas; or it may range from brown/dark brown to very dark grayish brown

when moist in cultivated areas; and light olive brown dry; in texture very fine

168

silt clay loam approaching silty clay loam to silty clay loam; in structure from

massive breaking into weak fine granular and medium sub-angular blocky;

sticky, plastic, firm moist and hard dry; common very fine interstitial and

common fine and few medium tubular pores; strongly calcareous; common

earth worm casts; common fine and few medium roots; clear smooth boundary

having pH 8.0-8.4 in normal phase. At some places where the soil has been

subject to a past high water-table, the pH of the substratum is as high as 9.0.

The substratum usually consists of a massive to very weakly structured very

fine sandy loam to silty clay loam buried soil. Few fine gypsum crystals and

lime nodules may occur in the lower part of the substratum.

Table 5.34

Typical natural trace element concentrations of surface soils [116]

S.No. Trace element Concentration(µg/g) dry weight

Mean Range

1 Pb 20 1.50-80

2 Zn 60 17-125

3 Mn 450 7-2000

4 Ni 20 1-120

5 Cr 60 5-1100

6 Co 8 0.2-50

169

Table 5.35a

Typical Chemical Composition of dust fall at Quetta for

the Year 2004-2008

S.No. Constituents (% by weight)

Army Recruitment Centre

Ashraf Sariab Road


Civil Hospital

Gawalmandi Chowk


Railway STATION

Sada Bhar

Sweets New Adda

Sirki Road

T.B Sanatorium

Averages

1 Loss on ignition

20.72 16.84 22.88 17.04 24.3 21.21 22.08 19.78 19.19 22.17 20.62

2 Silica as (SiO2)

46.99 46.70 34.32 46.95 40.08 47.16 46.94 45.76 45.52 42.55 44.29

3 Aluminum as (Al2O3)

14.65 11.39 21.43 14.02 8.15 8.93 9.88 12.82 14.84 15.97 13.20

4 Iron as (Fe2O3)

1.93 4.73 4.89 5.3 4.69 4.03 5.07 4.84 4.82 5.35 4.56

5 Calcium as (CaO)

12.74 17.28 13.52 14.83 19.53 15.32 14.16 15.05 13.30 12.95 14.86

6 Magnesium as (MgO)

1.30 2.14 1.42 1.83 1.71 2.46 1.66 1.71 2.2 1.7 1.81

7 Sodium as (Na20)

0.92 1.73 0.71 1.03 1.24 1.37 1.49 1.21 1.14 0.83 1.16

8 Potassium as (K20)

0.72 1.52 0.53 0.68 0.74 0.76 0.76 0.72 0.86 0.68 0.79

Table 5.35b

Average typical chemical composition of dust fall at Quetta for the year 2004-08 during the thermal inversion spells

S.No. Constituents

(% by weight)

Army Recruitment

Centre

Ashraf/ SariabRoad

C.G.S. ColonySatellite

Town Quetta

Civil Hospital

GawalmandiChowk

QadoosiStore/ Quick

Marketiing

Services

Railway Station

SadaBhar

Sweets New Adda

SirkiRoad

T.B.Sanatorium

Averages

1 Loss on ignition

24.74 20.86 26.90 21.06 28.32 25.23 26.10 23.80 23.21 26.19 24.64

2 Silica as (SiO

2 )41.89 41.60 29.22 41.85 34.98 42.06 41.84 40.66 40.42 37.45 39.19

3 Aluminum as (Al

2 O3 ) 10.60 7.34 17.38 9.97 4.10 4.88 5.83 7.77 10.79 11.92 9.15

4 Iron as (Fe2 O3 )

0.73 3.53 3.69 4.10 3.49 2.83 3.87 3.64 3.62 4.15 3.36

5 Calcium as (CaO)

18.80 23.34 19.58 20.89 25.59 21.38 20.22 21.11 19.36 19.01 20.92

6 Magnesium as (MgO)

10.40 11.24 10.52 10.93 10.81 11.56 10.76 10.81 11.30 10.80 10.91

7 Sodium as (Na

2 0)13.42 14.23 13.21 13.53 13.74 13.87 13.99 13.71 13.64 13.33 13.66

8 Potassium as (K 2 0)

20.92 21.72 20.73 20.88 20.94 20.96 20.96 20.92 21.06 20.88 20.99

170

Table 5.36

Average Typical Chemical Composition of dust fall at

Karachi from 1980-1985

S.No. Constituents (% by weight) Quaid's Mazar (QM)

Karachi Laboratories

National Cement factory

Jaredan Cement Factory

Averages

1 Loss on Ignition 26.24 21.56 23.32 18.27 22.34 2 Silica as (SiO2) 39.06 42.81 20.30 17.35 29.88 3 Combined oxides as

(Al2O3 + Fe2O3) 12.01 11.04 - - 11.52

4 Calcium as (CaO) 17.74 18.43 47.01 46.00 32.29 5 Magnesium as (MgO) 1.99 2.26 2.23 2.30 2.19 6 Sodium as (Na2O) 1.27 1.12 - - 1.19 7 Potassium as (K2O) 0.56 0.34 - - 0.45 8 Sulphur as (SO3) 1.03 2.52 - 2.33 1.96 9 Aluminum as (Al2O3) - - 3.66 7.60 5.63 10 Iron as (Fe2O3) - - 2.64 2.55 2.59

Table 5.37

Average Typical Chemical Composition of dust fall at Peshawar from 1992-1998

S.No. Constituents Main G.T. road (city area)

Jamrud road (speen jamat)

Sunchri Masjid road (cant area)

Near new bus stand area

Averages

1 Loss on Ignition % 22.36 17.63 22.58 18.71 20.32 2 Silica as (SiO2) 43.55 45.50 41.16 46.29 44.12 3 Alumina as (Al2O3) 11.12 11.38 13.83 12.89 12.30 4 Iron as (Fe2O3) 3.12 3.69 4.38 5.30 4.12 5 Calcium as (CaO) 15.59 17.14 13.33 13.59 14.91 6 Magnesium as (MgO) 1.33 2.01 1.60 1.10 1.51 7 Sodium as (Na2O) 0.76 1.04 0.84 0.90 0.88 8 Potassium as (K2O) 0.41 0.78 0.35 0.38 0.48

171

5.4 DETECTION OF HEAVY & TOXIC METALS IN DUST

SAMPLES:

The trace elements composition of soil may significantly influence the

elemental composition of the vegetation, eventually which effects the animal

and human tissues or fluids through the food chain. In order to assess the blow

of trace element pollution, it is mandatory to have the knowledge of natural

elemental levels and chemical compositions of the earth's environment. The

earth's crust contains extremely few trace elements. Majority of them are

present as soluble simple salts (Na, K, Rb), cationic constituents in

aluminosilicates (Li, Be, Cs), insoluble carbonate or sulphates (Mg, Ca, Sr,

Ba), oxides, (B, Al, Si, Sc, Ti, V, Cr, Mn, Lanthanides), or sulphides (Fe, Co,

Ni, Cu, Zn, Ga, Ge, As, Se, Mo, Cd, Sn, Sb, Hg, Tl, Pb, Bi). Soils are assumed

as sinks for trace elements, and that is why an important role plays in the

environmental cycling of elements. The mineral constituents of soils are

normally directly related to the parent rock and type of weathering processes.

The principal components of soil are inorganic materials: sand, silt and clay.

Clay minerals may contain low levels of trace elements as structural

components but their surface properties (area and electrical charge) play a

pivotal role in regulating the buffer and sink properties of soils. Results

pertaining to the concentration of heavy & toxic metals (µg/g) in dust fall

samples collected during the 5 years period 2004-08 are given in Table 5.39-

5.43. The concentrations of Pb, Zn, Mn, Ni, Cr & Co were determined with

the help of AAS (Atomic Absorption Spectrophotometer) and the

concentrations of Na & K were found with the help of Flame Photometer. The

concentrations of all Pb, Zn, Mn, Ni, Cr, Co Na & K for the year 2004 were

172

found to lie in the range 981-4430, 49-830, 30-712, 73-412, 15-127, 07-73,

25-73 & 39-88 respectively are given in Table 5.39. Table 5.40shows the

concentration of the said elements for the year 2005 found to lie in the range

973-4425, 47-828, 24-703, 68-411, 96-126, 02-70, 08-40 & 06-43

respectively. The levels of the all said metals for the year 2006 was found to

lie in the range 971-4411, 37-827, 21-711, 68-407, 15-113, 09-69, 05-38 &

07-41 respectively are showed in Table 5.41. Similarly the amounts of all

described metals for the year 2007 was found to lie in the range 988-4438, 51-

838, 28-725, 77-410, 27-127, 11-74, 01-32 & 01-34 respectively are displayed

in Table 5.42 respectively. Finally the Table 5.43 shows the concentrations of

all metals for the year 2008 found to lie in the range 986-4437, 55-835, 44-

718, 77-421, 23-139, 11-80, 36-77 & 32-83 respectively.

There are 90 naturally occurring elements on the earth crust. These

metals are put in the atmosphere from the soil driven [3], automobiles &

industrial origins [117,118]. The amount and chemical composition vastly

vary between and within environmental, geological, biological or marine

systems. The elemental composition of earth crust is mainly O, Si, Al, Fe, Ca,

Na, K, and Mg, while the human body is H, O, C, N, Ca, P, K and Cl. From a

biological point of view, trace elements are most easily be divided in three

categories: essential, non-essential and toxic. The trace elements essential for

all living organisms like plants, animals and humans are those, which play

vital biochemical roles in terms of metabolic cycle in plants by having direct

impact on the organisms so that they could not develop and the reduction of

which consistently results in a deficiency syndrome and reduction reverses the

abnormalities. The trace elements fulfilling these needs are As, Co, Cr, Cu, F,

173

Fe, I, Mn, Mo, Ni, Se, Si, Sn, V and Zn. Besides that there are many non-

essential elements e.g. Li, B, Ge, Rb and Sr are found in the body tissues and

fluid but for which inevitability no evidence has been established. However,

some elements for instance Cd, Hg, and Pb are distinctively categorized as

toxic elements because of their detrimental effects even at low levels.

Nevertheless, all trace elements are supposed to be the toxic elements when

their levels cross the unanimously set limits of safe exposure [116].

It has generally been found out throughout the densely populated cities

of the developed, developing & underdeveloped world, that the environment

of those cities in terms of plants, soil, water & air carry alarming amount of

toxic and heavy metals such as Pb, Zn, Mn, Ni, Cr, Co, Na & K besides

numerous other pollutants [119]. Multiple factors for instance automobiles,

industrial emission and weathered materials are the reason of the rise in the

concentrations of trace elements in massively settled areas. The elements Pb,

Zn, Mn, Ni, Cr, Co, Cu & Cd have been spotted out entering & increasing

from the weather source [120,121]. In addition to adding Pb in the

environment particularly in air automobiles also increase Cd, Cu, Zn, Fe, Cr

and Ni [122,123]. The elevating concentration of Zn, Ni, and Cr might be due

to the erosion of vehicles tyres. It has been described that vehicles tires and

wear and tears are one the major sources of the said elements [124]. Another

factor has been found out to be the kind of road surface as the concentration of

elements Pb, Zn and Cu are supposed to increase from the pavements, to the

choked sewerage and flowing sewage on the middle of roads most of the time

in underdeveloped and corrupt countries like ours (Pakistan) [125]. More

factors need to be measured / determined in order to understand the trend of

174

the concentration of the said elements in the dust fall. Trace elements or heavy

metal contamination can result primarily through atmospheric particles or

particulates.

Table 5.38

CALA Directory LaboratoriesCanadian Association for Lab. Accreditation Inc.

Email: [email protected]

Scope of AccreditationDust fallTotal Suspended particulates/Insoluble RDL Rangedust fall-dust fall (020) Lead 10 – 50 ppmZinc 10 – 50 ppmManganese 10 – 50 ppmNickel 10 – 50 ppmChromium RDL RangeCobalt RDL RangeSodium 10 – 50 ppmPotassium 10 – 50 ppm

175

Table 5.39

Concentration of heavy and toxic metals in the dust fall at Quetta detected during 2004 µg/g (ppm)

61.65035.671.1301.7525.6604.92668.3Average:

39250715733049981T.B. Sanatorium10

8368691123967087903018Sirki Road9

644229623336557012389Sada Bhar Sweets, New Adda8

766537793476707602401Railway Station7

8873731274127128304430Qadoosi Store/Quick Marketing Service6

705852993816987874423Gawalmandi Chowk5

585341833626857843417Civil Hospital4

52471132861706121824C.G.S. Colony Satellite Town, Quetta3

463720583196246201870Ashraf Sariab Road2

403217443083041161930Army Recruitment Center1

KNaCoCrNiMnZnPbLocationS.No.

Graph 5.21

Trend showing the amount of heavy and toxic elements in the dust fall at Quetta in 2004

0200400600800

10001200140016001800200022002400260028003000320034003600380040004200440046004800

Army R

ecrui

tmen

t Cen

ter

Ashraf

Sariab R

oad

C.G.S. C

olony

Sate

llite To

wn, Que

tta

Civil H

ospita

l

Gawalm

andi C

howk

Qadoo

si Store/

Quick M

arketi

ng Servi

ce

Railway

Station

Sada B

har S

weets,

New

Add

a

Sirki R

oad

T.B. San

atoriu

m

Centers

µg/g

(ppm

)

Pb

Zn

Mn

Ni

Cr

Co

Na

K

176

Table 5.40


2424.531.169.4262.9521600.82663.9Average:

06080216682447973T.B. Sanatorium10

3836601083927027833017Sirki Road9


353235773406677592397Railway Station7



252837813576837813415Civil Hospital4





Table 5.41


24.119.831.266.4297.3518.4596.72658.2Average:

07050915682137971T.B. Sanatorium10

3833631073917017903010Sirki Road9


322936713456657592389Railway Station7



242132813626847803411Civil Hospital4





177

Table 5.42


17.314.537.876.5304.5529.4600.52672Average:

01011127772851988T.B. Sanatorium10

3026701174037157873017Sirki Road9


252138853466717642405Railway Station7



181144873756897053418Civil Hospital4





Table 5.43


57.456.439.577.2306.7530.66112672.6Average:

32361123774455986T.B. Sanatorium10

7972701323977147993020Sirki Road9


716942813586787662408Railway Station7



605846873626817893420Civil Hospital4





178

Table 5.44

Average Concentration of heavy and toxic metals in the dust fall at Quetta detected during 2004-08 µg/g (ppm)

S.No. Location Pb Zn Mn Ni Cr Co Na K 1 Army

Recruitment Center

1929.8 116.2 302.4 303 45.2 16.2 19 20.4

2 Ashraf Sariab Road 1867.8 620.2 623.8 316.6 58 15.4 23 19.2

3 C.G.S. Colony Satellite Town, Quetta

1824 612.2 167 85.2 33.4 11 28.6 32

4 Civil Hospital

3416.2 767.8 684.4 363.6 83.8 40 34.2 37

5 Gawalmandi Chowk 4420.8 783.4 697 316 99.8 51.8 38.8 43

6 Qadoosi Store/Quick Marketing Service

4428.2 831.6 713.8 412.2 126.4 73.2 52 57.8

7 Railway Station 2400 761.6 670.2 347.2 78.6 37.6 43.2 47.8

8 Sada Bhar Sweets, New Adda

2387 697.2 654 334 61.6 26.8 29.8 35

9 Sirki Road 3016.4 789.8 708 395.8 115.2 66.4 47 53.6

10 T.B. Sanatorium 979.8 47.8 29.4 72.6 19.2 08 15 17

Average: 2667 602.78 525 294.62 72.12 35.04 33.06 36.28

179

Table 5.45

Average concentration of heavy & toxic metals of Quetta from 2004-08 µg/g (ppm)

Years Pb Zn Mn Ni Cr Co Na K

2004 2668.3 604.9 525.6 301.7 71.1 35.6 50 61.6

2005 2663.9 600.8 521 262.9 69.4 31.1 24.5 24

2006 2658.2 596.7 518.4 297.3 66.4 31.2 19.8 24.1

2007 2672 600.5 529.4 304.5 76.5 37.8 14.5 17.3

2008 2672.6 611 530.6 306.7 77.2 39.5 56.4 57.4

Average 2667 602.78 525 294.62 72.12 35.04 33.04 36.88

Graph 5.22

180

Graph 5.23

From the results given in Tables 5.39-5.43, it is quite obvious that the

concentration of the said toxic and heavy metals varies from each other, site to

site, month to month and particularly season to season. The average

concentration of Pb was observed at a higher level in the summer, in the rush

hours and dusty days for all years and all sites. In addition to that a remarkable

difference could be observed between the concentrations of alone Pb and other

metals. T.B. Sanitarium and to some extent Army Recruitment Centre are the

two sites received least amount of all toxic elements. Specifically T.B.

Sanitarium being located at a high altitude, having almost no industry in its

vicinity, close to the ridge foot of Chiltan Mountain, thin & properly planned

population compare to the rest of the city and above all the less vehicular

population and almost no traffic jam received the minimum amount of all

toxic elements including Pb 971 µg/g (even far beyond the set limit) in the

dust fall. Contrary to it Gawalmandi Chowk, Qadoosi Store, Sirki road, Civil

hospital, Railway Station, Sada Bahar Sweets new Adda are the sites received

181

the maximum amount of all toxic elements particularly Pb e.g. 4438 µg/g at

Qadoosi Store. The major source is obviously the automobile emission due to

heavily contaminated fuel and lengthy traffic jams on these sites. Pb is added

to fuel as an anti-knocking agent to prevent the accumulation of large

quantities of PbO within the combustion engine and thus prevent engine

knocking, tetraethyl lead, mixed with alkylethylmethyl lead, diethymethyl lead

and ethyltrimethyl lead additives. The addition of Pb scavengers, ethylene

dibromide or dichloride, results in the emission of volatile lead halides PbBr2,

PbBrCl, Pb(OH)Br, (PbO)2PbBr2 into the environment through the exhaust

gas. The European Community maximum allowable Lead concentration in

ambient air is 2µgm−3, whereas the concentration of >8 µgm−3 (motorways)

and ~2-3 µgm−3 (urban roads) are common. Around 70-75% is emitted from

the exhaust as inorganic salt of lead and about 1% is evolved unchanged as

tetra alkyl lead [126]. Evaporation loss of fuel from fuel tanks and carburetors

becomes the reason as well tetra alkyl compounds to the atmospheric dust.

The organic lead compounds are volatile and remain in air however the

inorganic salts are liberated as particles. The concentration of Pb in different

gasoline was detected to be (g/dm3) PSO= 0.64, CALTEX=0.55 and

PBS=0.54 [127]. Lead contained paints have also left extensive effect on Pb

concentration in the dust [128]. Other sources of Pb plumbing, glazed pottery

solder used in tin cans, old pewter etc. it is found in rocks as galena (PbS) at

concentration of 0.1-10mg/kg. During weathering Pb2+ can form carbonates

and has the tendency to be integrated in clay minerals, in Fe and Mn oxides

and in organic matter. Lead contamination of soils is a primary problem for

humans and animals as well. Normal surface soil levels are typically less than

182

40mg/kg, but with because of metal mining it sometimes increased up to

450mg/kg and sewage slugged farmland as in the suburbs of Quetta might

increase up to (80-300mg/kg). Plants instead of mainly taking up Pb from soil

receive it with the deposition of lead particulates onto the foliar surface of

plants, from where it turns a dietary source for animals and humans. Normal

limits of Pb on food stuff are <2mg/kg (dry weight); while improved levels

alongside motorways (20-950mg/kg), batter works (34-600mg/kg), and metal

processing industrial sites (45-2714mg/kg). The UK standard for Pb in food is

1mg/kg, with the exception of baby food, which is 0.2mg/kg. The WHO

(World Health Organization) has recommended that a tolerable intake of Pb

per day for an adult is 430µg [116].

Zn is widely used in the production of non-corrosive ally, brass in

galvanizing steel and iron and it is also used in some lubricating oil as an

important component and in many Zn-containing additives for instance as

antioxidant Zn [129]. It showed the same trend as Pb showed on all sites. The

sites like Qadoosi General Store 838 µg/g, Gawalmandi Chowk, Sada Bahar

Sweets, Ashraf Sariab Road, Railway Station, CGS Colony etc. having larger

population and more vehicular jam, so these sites gave more Zn keeping in

view its extensive usage in lubricants. While again the sites like T.B.

Sanitarium 37 µg/g, Army Recruitment Center showed lower values of Zn due

to thin traffic.

The most probable source of Cr could be abrasion of chrome-plating

and alloys in motor vehicles. Additional sources are leather tanning, textile

dying, electroplating, laundry chemicals and wear of wear of metal plating,

183

which contribute Cr to atmosphere dust fall. It was found to be present in the

samples from 15-132 ppm.

Ni is mostly used either as the metal or its alloy i.e. Ni coating on Cu

or Fe and Ni plating articles. Ni was detected in the dust fall between the

optimum ranges of 68-411ppm on the similar trend of minimum at T.B.

Sanitarium and maximum at Qadoosi Store. Residual oil, coal, tobacco,

chemical catalyst and nonferrous alloys are some of the sources of

atmospheric nickel pollution. The variation in the concentration was due to

smoke. It has been reported that the smoke of single cigarette release about

2.2-2.3 µg/g of Ni to the atmosphere [130].

The concentration of Mn was detected in the dust fall at T.B.

Sanatorium and Qadoosi Store on optimum levels of 21-725 µg/g. Its high

concentration might be due to its use is metal alloys, dry cell batteries, feed

additives, fertilizers, pigments, dryers, wood preservatives, coating welding

rods, paints and chemical detergents. Being an important constituent of

explosive, Mn is liberated in the air through fire display and several types of

crackers used at different occasions.

Even though an exact source of Co in the environment is difficult to

find out, yet most probably the use of Co in high speed diesel, steel, cemented

carbide, high temperature alloy in industry and as a catalyst in different

industrial processes contribute significantly to atmosphere Cobalt pollution.

The concentration of Co was detected minimum at T.B. Sanitarium (2 µg/g)

and maximum at Qadoosi Store (80 µg/g).

184

Na and K were present in the in their oxides forms of Na2O & K2O as

it was proved in chemical analysis of the dust fall and is well known and that

they are found in the nature in uncombined state. Their optimum concentration

was found on different locations for instance minimum at T.B. Sanitarium 1

µg/g each and 77 & 88µg/g respectively. Their amount exceeded the normal

set range in the years 2004 & 2008, when there were sporadic heavy dusty

storms in the city originated mainly from the deserts of LUT (Iran) and

DALBINDIN (Pakistan). Otherwise in 2005, 2006 & 2007 their

concentrations remained within the normal limits.

Table 5.46

S.No. Country Location/City Sample Unit Pb Zn Ni Mn Co Cr1 Poland Lecz-Wlodawa Dustfall G/m2 m 13.7 46.4 1.6 13.2 - 3.12 USA ILLIONOIS Street dust µg/g 1000 32 250 35 6.8 2103 Saudi Arabia Riyadh Outdoor dust µg/g 1762 443 44 - - 35.14 Pakistan Abbotabad Dustfall mg/kg 446 931 - 533 - -5 Pakistan Islamabad Dustfall µg/g 22.7 8.3 5.6 - - -6 Pakistan Peshawar Dustfall µg/g 525 763 358 637 54 837 Pakistan Karachi Street dust mg/kg 810-4527 112-2215 72-481 - - -8 Hong Kong Hong Kong Surface dust mg/g 302 1517 - - - -9 Jamaica Kingston Dust µg/g 909 0.8 - - - -

10 Egypt Various sites Dust µg/g 126 - - - - -11 Mexico Chihuahua Dust µg/g 277 - - - - -12 Mexico Monterrey Dust µg/g 467 - - - - -13 Mexico Torreon Dust µg/g 2448 - - - - -14 W. Germany W. Berlin Dust µg/g 8-2943.01 - - - - -15 Saudi Arabia Jeddah Street dust ppm 745 - - - - -16 U.K. Birmingham Street dust ppm 1630 - - - - -17 U.K. Manchester Street dust ppm 970 - - - - -18 Belgium Belgium Street dust ppm 2255 - - - - -19 Malta Malta Street dust ppm 1825 - - - - -20 USA Av. Of 77 cities Street dust ppm 240-1500 - - - - -21 Saudi Arabia Riyadh Falling dust ppm 66.8 141.8 26 319 20.6 -22 Bahrain Various sites ppm 697 151 125 - - 14423 U.K. Lancaster ppm 1880 534 35 - 9.1 2924 Greece Various sites ppm 65-259 75-241 52 - - 1325 Nigeria Various sites ppm 40-243 12-48.01 1-3.3 - - 23-2626 Netherlands Near Smelter ppm 761 1.5 - - - -27 Hong Kong Various sites ppm 1080 1517 - - - -28 New Zealand Christ church ppm 887-1070 - - - - -29 Malaysia Kualalumper ppm 2466 344 - - - -30 Kenya Various sites ppm 23-950 - - - - -31 Taiwan Taipei ppm 196 - - - - -32 England London ppm 345 - - - - -33 Canada Halifax ppm 674-1919 - - - - -34 Equador Various sites ppm 108 218 - - - -35 Kuwait Salmich ppm 136 - - - - -36 USA Various sites ppm 900 - - - -37 Scotland Glagow ppm 308 - - - - -38 Jeddah ppm 745 - - - - -39 Hong Kong ppm 1627 - - - - -40 Brimingham ppm 1630 - - - - -41 London ppm 1200 - - - - -42 Glasgow ppm 960 - - - - -43 Manchester ppm 970 - - - - -44 Urbana III, USA ppm 3600 - - - - -

AAS Atomic Absorption Spectrophotometery SV Striping Voltametry FAAS Flame Atomic Absorption Spectrophotometry SV Striping Voltametry ICP Inductively Coupled Plasma AES Atomic Emission Spectrophotometry ES Emission Spectrograph

Concentration of Heavey and Toxic Metals in Dustfall and Aerosol in Different cities and Countries

185

While comparing the concentrations of all toxic and heavy metals in

dust fall given in Table 5.39-5.45 with the international data in Table 5.46, it

could be observed that the average annual concentration of Pb at Quetta was

detected (2667 µg/g) is less than the average annual concentration only a few

more cities across the globe like Karachi (29889 µg/g [131] and USA, Urbana

III (3600 µg/g) [132]. However, some the cities (former) W.Germany,

W.Berlin (8-2943 µg/g) [133], Malaysia, Kaulampur (2466 µg/g) [134],

Mexico, Torreon (2448 µg/g) [135] and Belgium (2255 µg/g) [136] were

having the lesser average annual concentration of Pb than at Quetta was

detected by me. The average concentration of Zn detected by me for the said 5

years was (602.78 µg/g), which is far more than the normal set limit. It is

anyhow less than Peshawar (763 µg/g) [87,137], and more than some of the

cities like UK, Lancaster (534 µg/g) [138,139], (547 µg/g) Saudi Arabia,

Riyadh and (112-2215 µg/g) Pakistan, Karachi [140] etc. Similarly the

concentrations of Mn, Ni, Cr, Co, Na & K given in Tables W, X & Y were

though having the concentrations of said toxic and heavy elements mostly

within the set limits by the Canadian Association for Lab. Accreditation Inc.

given in Table 5.38 in 2005, 2006 &2007, yet the said elements show higher

concentrations in the year 2004 & 2008 due to the unusual extreme dusty

thermal inversion conditions. On the whole almost the same trend somewhat

similar to Pb & Zn have been showed by Mn, Ni, Cr, Co, Na & K compare to

some cities of the world, where their concentration was less, and with some it

was more. It infers that dust particulates are extremely multifarious substance,

the composition of which is seldom constant. All the urban densely populated

cities are enriched of heavy metals. Masses residing in urban parts are more

186

vulnerable to the people living in rural and far flung areas. By and large

Quetta is almost on the top of the few extremely toxic (elements particularly

Pb) polluted cities in the world and the major reason of it is its bowl shape

geography, small area (space), huge humans and vehicular population during

the previous decades [141,142] and the massive toxic emission of traffic

mostly running on.

5.5 AVERAGE SIZE DISTRIBUTION OF SETTLED & AIR DUST

PARTICULATES:

In most cases the maximum pollution levels are within a few

kilometers of the emission source, but small particulate and aerosol pollutants

can contaminate all areas of a city or even a region. Several studies have

shown a slow accumulation of Pb in both the Arctic and Antarctic regions

since the introduction of lead alkyl additives to petrol in the early [116].

Keeping in view this established fact settled/fallen particles size contrary to

the airborne suspended detection, settled/deposited dust particulates [100,

99,10],fractionation on wt. % basis was got for nine size categories: PM<1.0,

PM1.0-2.5, PM2.5-5.0, PM5.0-10, PM10-15, PM15-25, PM25-50, PM50-100 and PM>100 by

using standard methods for sieve analysis [106] in contrast with the

particulates size determination on vol. % basis by using Mastersizer 2000

(Malvern, Ver. 3.01, UK [10]due to its non-availability in any institutions of

even Quetta city not to speak of Balochistan province. The data on particulates

size categorization showed in Table 5.47a, depicts the average annual wt. %

segments detected in Quetta for the said period of 2004-08 from the 10

selected sites. The data clearly shows that on the average, the PM10-15 portion

187

is the largest, at 16.37 wt. %, followed by PM5.0-10& PM15-25 at 15.48 & 13.00

wt. % respectively. The particulates parts PM2.5-5.0, PM25-50 & PM50-100

showed significant concentrations at 12.09, 11.11 & 10.64 wt. % as well, the

giant particulates PM>100 were having the concentration of 9.78 wt. % and the

fine and ultra fine particulates PM1.0-2.5 & PM<1.0 were found to be present at

8.68 & 5.16 wt. % respectively. In Quetta the southern winds are dominant

mostly in the day times of summer and in winter the northern winds usually hit

the city. The southern regional and local winds of summer are mostly

responsible for the heavy dust fall in the city carrying sub-urban, rural and

rarely during the lengthy dry spells, in the thermal inversion period (as was

witnessed drop regional dust into the urban settled parts of the bowled shape

city. The wind direction and extremely low humidity (having almost dry

atmosphere) therefore play an important role in mixing the air masses of these

different atmospheric fragments.

During the thermal inversion spells (days) the average % age of each

particulate present in the dust fall is given in Table 5.47 b; which clearly

shows that the amount of particulates having sizes PM15-25, PM10-15, PM5.0-10,

PM2.5-5.0, PM1.0-2.5& PM<1.0 were ≥12 % by weight. However, the PM25-50,

PM50-100, & PM>100 were found between the % age weight concentrations of

5.86-7.67. The reason is obvious that the particles having lesser sizes traveled

a greater distance between the lengthy severe arid and deserted belt of Iran and

Pakistan.

188

Table 5.47a

Average size distribution of dust fall 2004‐08 at Quetta. Fraction % age by weight

Particulates size

Army Recruitment

Centre

Ashraf SariabRoad

C.G.S . Colony

Satellite Town

Quetta

Civil Hospital

Gawalmandi Chowk

QadoosiStore/Quick Marketing

Services

Railway Station

SadaBhar

Sweets New Adda

Sirki Road T.B Sanatorium

MEAN

PM < 1.0 7.4 3.01 3.31 5.6 6.4 4.10 3.39 6.20 5.10 7.09 5.16

PM1.0-2.5 10.01 9.11 7.01 9.5 6.1 8.10 9.91 6.42 7.11 13.60 8.68

PM2.5-5.0 15.13 12.11 13.11 9.74 13.14 11.14 13.13 13.31 10.14 10.01 12.09

PM5.0-10 17.01 14.220 17.80 13.51 13.01 15.01 17.82 13.37 16.01 17.06 15.48

PM10-15 10.01 16.20 18.01 16.92 17.32 15.32 19.32 17.23 11.23 22.11 16.37

PM15-25 10.02 13.02 12.01 12.72 10.23 12.32 10.23 12.22 15.22 22.04 13.00

PM25-50 4.01 14.71 11.011 15.11 12.01 13.11 10.01 15.01 12.01 4.01 11.11

PM50-100 11.13 10.81 10.09 13.01 12.12 11.01 12.21 9.87 12.22 4.00 10.64

PM > 100 11.88 11.81 12.13 10.01 11.12 10.11 9.01 9.90 10.90 1.00 9.78

Graph 5.24 a

Trend showing the percentage of particulates of different sizes in dust fall 2004-08 Quetta

0

5

10

15

20

25

ArmyRecruitment

Centre

AshrafSariabRoad

C.G.S.ColonySatellitetow n

CivilHospital

Gaw almandiChow k

QadoosiStore/QuickMarketingServices

Railw aySTATION

Sada BharSw eets

New Adda

Sirki Road T.B Sanatorium

Centers

Con

cent

ratio

n (%

age

by w

t.) PM < 1.0

PM1.0-2.5

PM2.5-5.0

PM5.0-1.0

PM10-15

PM15-25

PM25-50

PM50-100

PM > 100

189

Table 5.47 b

Average size distribution of dust fall during thermal inversion period (days) 2004‐08 at Quetta, fraction % age by weight

Particulates size

Army Recruitment

Centre

AshrafSariabRoad


Civil Hospital

GawalmandiChowk

QadoosiStore/Quick Marketing Services

Railway Station

SadaBharSweets New Adda

SirkiRoad

T.B Sanatorium

MEAN

PM < 1.0 16.4 12.01 12.31 14.6 15.4 13.1 12.39 15.2 10.1 10.09 13.16

PM1.0-2.5 17.01 16.11 14.01 16.5 13.1 15.1 16.91 10.42 10.11 10.6 13.68

PM2.5-5.0 15.13 22.11 13.11 10.74 13.14 11.14 13.13 13.31 10.14 10.01 13.09

PM5.0-10 15.01 12.2 15.8 11.51 11.01 13.01 15.82 11.37 14.01 15.06 13.48

PM10-15 6.06 12.22 14.06 12.97 13.37 11.37 15.37 13.28 7.28 18.16 12.43

PM15-25 19.03 21.03 21.02 11.73 9.24 11.33 9.24 11.23 14.23 21.05 14.91

PM25-50 6.03 6.73 3.03 7.13 4.03 11.13 6.03 7.03 4.03 6.03 6.13

PM50-100 4.16 13.84 13.12 6.04 5.15 4.04 5.24 12.9 5.25 7.03 7.67

PM > 100 12.96 12.89 3.21 1.09 2.2 1.19 10.09 10.98 1.98 2.08 5.86

Graph 5.24 b

Trend showing the % age of particulates of different sizes in dust fall 2004‐08 Quetta during thermal inversion spells

0

5

10

15

20

25

Concen

tratio

n (% age b

y weight)

Location

PM < 1.0

PM1.0‐2.5

PM2.5‐5.0

PM5.0‐1.0

PM10‐15

PM15‐25

PM25‐50

PM50‐100

PM > 100

190

CHAPTER 6

APPLICATION OF STATISTICAL (ARIMA & SARIMA)

MODELING FOR FUTURE PREDICTIONS OF DUST FALL

RATE

6.1 LITERATURE SURVEY:

Stochastic time series model such as ARMA (p,q), non- seasonal

ARIMA and seasonal ARIMA (SARIMA) models were developed to simulate

and forecast hourly averaged wind speed and average annual and monthly rate

of dust fall sequences on five (05) year data ,.i.e., 2004-08 of Quetta, Pakistan.

Stochastic Time Series Models take into consideration numerous fundamental

features of wind rate including autocorrelation, non-Gaussian distribution and

non-stationarity. The positive correlation between consecutive wind speed

observations is taken into account by fitting ARMA process to wind speed

data. The data are normalized to make their distributions approximately

Gaussian and standardized to remove scattering of transformed data

(stationary, i.e., without chaos).Diurnal variations has been taken into account

to observe forecasts and its reliance on lead times. Though the ARMA (p,q)

model is suitable for prediction interval and probability forecasts, nevertheless

this model is only suitable for both long ranges (1-6 hours) and short range (1-

2 hours) indicates that forecast values are the deciding components for an

appropriate wind energy conversion system, WECS. ARMA processes cannot

be applied for non-stationary (chaotic) and random data. Non-seasonal

ARIMA models and the prediction equations for each month and indeed for

each season of five (05) meteorological years 2004-08, rate of dust fall data is

191

predicted. The seasonal ARIMA (SARIMA) and its prediction equations for

each month of five (05) years data were also studied. With non- stationarity or

chaos in data, stochastic simulator in the ARIMA processes even though its

prediction equations do not effectively work, yet ARIMA is good enough to

forecast relatively short range reliable values. Various statistical techniques

are used on five (05) years, .i.e., 2004-08 data of dust fall, average humidity,

rainfall, maximum and minimum temperatures, respectively. The relationships

to regression analysis time series (RATS) are developed for determining the

overall trend of these climate parameters on the basis of which forecast models

can be corrected and modified. Badescu [143] made use of ARIMA models to

forecast daily average surface pressures. His study is of much relevance to us

for reasons that the surface pressure would certainly effect the dust fall rate

and indeed the concentration of pollutants at various locations. We shall,

however, give due considerations of this study in later stages while looking

into a more generalized ARIMA model. However, our SARIMA model will

take into account such considerations indirectly, Badescu et al., [144]

performed the statistical of ambient air pollution in Delhi. A state space model

was developed by using Kalmin filter formulation for the prediction of various

pollutants and repairable suspended particulate matter. The approach was

found quiet pertinent. They used the ARX model (Auto-Regressive) with

exogenous input, which to our analysis are not adequate. We discarded

ARMA modeling for reasons that the dust fall rate and the concentration of

various pollutants follow random nature or non-stationarity. The ARMA

modeling could be used only if the data is standardized. This unfortunately

had not been done by Chelani et al Instead we developed ARIMA & SARIMA

192

models for dust fall rate and indeed their predictions are provided with

prediction equations. A tremendous scattering of predicted data was noted in

the ARX modeling of [145] urban air pollution was studied by them with time

series analysis by using ANN (Artificial Neural Network) and ARIMA

models. ANN was found relatively better than ARIMA on the basis of root

mean square estimation and other several statistical tests. It was established by

us that the ARIMA and SARIMA models could be considered relatively better

than ANN models due to diurnal (seasonal) variations. It was taken into

account in our studies the diurnal variations by considering the diurnal

variations by considering interrelationship between ARIMA and SARIMA.

Bruce et al., [146] followed dual cost function approaches as an alternative to

time series but this analysis is premature for our study unless we find forecast

estimates in conformity with our future experimental data. Los Angeles

performed time series analysis of particulate matter in air by using ACF (Auto

Correlative Function) [147], ARIMA and regression analysis. To our surprise

ARMA model didn’t work for our data as a consequence of which the ACF

wouldn’t be considered. The regression analysis is, of course important too,

but we avoided it because there are diverse statistical tests needed to support

the analysis. Kolehanainen et al., [148] coined a new technique by developing

hybrid neural network modeling for air quality forecasting. They followed

self-organized map algorithm (SOM), Sammon’s mapping and fuzzy distance

metrics. They categorized the clusters of data by overlapping multilayer

perceptron (MLP) models. Needless to mention their work was logically

pertinent and could be used for our data, too. We shall look into such kind of

models in near future but we are handicap due to non-availability of diverse

193

algorithms. Moshrik et al., [149] developed crop evapotranspiration time

series simulation model by using ARIMA. This reflects the strength and

validity of ARIMA model in most of the reported literature.

6.2 STOCHASTIC TIME SERIES MODELING, SIMULATION

AND PREDICTION:

A technique of predicting dust fall yield a few hours before, from a

dust fall collector with which suspended/settled dust fall under ‘g’

(gravitational force), is required to ensure efficient measures, which might be

taken in order to avoid its maximum calamity. Time series modeling of dust

fall has been the subject of many discussions because of the interest in its rate

of deposition, which proves mostly to be catastrophic. When the records of

dust fall are incomplete or of too short a duration or the handling and storage

of large values of the data are not desirable, then a time series model is needed

.Since dust fall is a reason of wind velocity, atmospheric pressure, geography

and topography of the area etc, generally the simulation are derived from

simulations of wind speed. Dust fall simulations can be done with Monto

Carlo techniques that depend upon exclusively on the anticipated factors of the

trivial distribution of wind speeds.

Craggs et al., [150], Aguiar and Pereira [151] and Mora-Lopez and

Sidrarch-de-Cardona [152] made some important contributions from modeling

and simulation point of view, having used stochastic simulation by ARIMA

(autoregressive integrated moving average) modeling of solar irradiation, a

time dependent autoregressive Gaussian model (TAG) for generating synthetic

194

hourly radiation and the multiplicative ARMA (autoregressive moving

average) models simultaneously to generate hourly series of global radiation.

An ARMA process on hourly global radiation data was used by

Lalarukh and Jafri [153]. Stochasting modeling through MTM (Markov

Transition Matrix) was performed by them and synthetic sequences of hourly

global solar irradiation for Quetta, Pakistan were produced as well. They

found MTM [154] approach relatively better as a simulator compared to

ARMA modeling. But, their analysis for ARMA process to simulate and

forecast hourly averaged wind speed for Quetta, Pakistan produced good

results as well.

Numerous non-Gaussian distributions have been recommended as

suitable models for dust fall and wind velocity. These models include the

inverse [155], the log normal distribution [156], the gamma distribution [157],

the Weibull distribution [158-161]; and the squared normal distribution [162].

It has been observed from earlier studies of apropos of Nasir et al., [163]; Raza

and Jafri [164] and Brown [165] that the Weibull distribution fits the actual

wind velocity, and indeed dust fall frequencies were relatively appropriate.

However, the use of inverse Gaussian distribution on wind data [155]

overlooked the encouraging connection between successive observations of

wind speed. Failure to take this autocorrelation into account guides for

miscalculation, of the discrepancies of the time averages of wind speeds and

dust fall. Furthermore, the long runs of high and low dust fall and wind

speeds that are feature of such data do not occur regularly enough in replicated

data when wind speeds are certainly to be uncorrelated in due course.

195

Another attempt was made to incorporate autocorrelation into wind

speed models in order to solve the said problem by Chou and Corotis [166]

and Goh and Nathan [167] without deeming the Gaussian shape of malformed

wind velocity distributions and its related statistics. Some of the studies have

ignored the non-Gaussian shape of the wind velocity division. Brown et al.,

[168] recommended ways to keep in mind the auto interrelated character of

wind velocity, the diurnal non-stationarity and non-Gaussian shape of wind

velocity division so that forecasting of hourly averaged wind rate could be

done. Brown et al., [169] in their earlier study have also pointed out the

requirement for standardization to eliminate diurnal non-stationarity. Diurnal

variations in wind speed happen as a natural phenomenon [170] and as stated

in a paper by Kamal and Jafri [171] standardization relate to even of a profile,

such as of a Gaussian distribution that is achieved after converting a non-

Gaussian form to an about Gaussian shape i.e., by taking speckled data points

close to the sketch. They achieved this standardization method in their study,

for hourly averaged wind data for a period of twenty years,i.e. ., 1985-2004, of

Quetta, Pakistan before using ARMA process.

Jafri [172] found that the hierarchical unsystematic procedure is a

Markovian random process, which can be portrayed by a scaling probability

division. A breeding function for such a procedure was acquired. These

observations can be fruitfully applied to muddle time series [173] to surmount

the non-stationarity in ARMA method but it would need practical stochastic

simulation techniques. Jafri [173] recommended that the chaotic time series

both in Bayesian and non Bayesion statistics is deterministic. Jafri [174] built

up a first order Markov transition matrix (MTM) for non-Gaussian character

196

of wind velocity of Quetta for 1985 and suggested a Gaussian form of MTM

order to produce HAWS (hourly averaged wind speed) series. The similar

effort was extended more on wind and rate of dust fall data for a period of five

years, .i.e. 2004-08. Needless to state, the simulation of wind data using MTM

[175] is rather hard contrast to simulation on solar radiation data [153]. The

number of iterations went beyond a specific boundary therefore causing for

HAWS and DAWS (daily average wind speed) series to become awkward and

entwined. Jafri [174,175] also established autocorrelation coefficient for wind

data, which shows stages of determination in wind velocity frequencies and of

wind velocity enormities when compared with diurnal variations over daily

averaged wind speed (DAWS) orders.

A class of parametric time series models called autoregressive moving

average processes (ARMA) was engaged [176,168] of Box and Jenkins [177].

Such procedures have been in use to form many meteorological time series

[178]. The form of Blanchard and Desrochers [176] takes into consideration

elevated autocorrelation and permits a time series to be produced which

deduces all the main distinctiveness of the statistics; and it does not require

any hypothesis about the wind velocity division. Actually, a larger class of

seasonal models contains ARIMA models [176]. Sfetos [179] studied the

linear ARIMA models and feed forward artificial neural networks (FFANN).

He discovered that the model arrangement is chosen from the minimization of

the assessment set error in the ARIMA process. He proposed the multi-step

forecasting and the consequent averaging to produce mean hourly prediction

of wind statistics. The ARIMA models have been significantly examined by

Jain and Lungu [180]. They considered equally non- seasonal and seasonal

197

ARIMA models by using stochastic parts. The perseverance patterns if any, of

the stochastic components were also calculated to decide by them.

The model of Chou and Corotis [166] is based upon Weibull

distribution and does not need stationarity in the statistics. McWilliams and

Sprevak [181] explained a new description of an existing time series modeling

method [177] from which the distribution of wind velocities and wind

directions are obtained [182,183]. Their model incorporated diurnal variations

observed in wind speed in such a manner that the time series of wind speed

component remain stationary; the sample autocorrelation functions for the

series have identical stochastic behavior as far as the second order statistics are

concerned, consequently plummeting the problem to modeling single

Gaussian series. This model is accurate for autocorrelation functions, to

account for diurnal variations. There is one point which is clear:

transformation of hourly averaged wind speed was not used by them. In its

place, they measured annual deterministic variation µ (t) and σ2 (t) which is

modeled by harmonic series representation to justify diurnal variation of wind

velocity. Diurnal variation [169] ought to be engaged in model development in

a way analogous to McWilliams and Sprevak [183] with reference to our

inference.

The approach of Daniel and Chen [184] was adopted by us which

consists of first fitting ARMA processes of various orders to hourly averaged

wind speed (HAWS) data which have been transformed to make their

distribution approximately Gaussian and standardize to remove the so called

diurnal stationarity . The main benefit of including more than one year of data

198

in the model development is the increased trustworthiness of the estimates of

the model parameter. The methods of changing and standardization were not

likened but favored this approach for the grounds that the model had the

tendency of using wind data of more than one year.

MINITAB (version 11) for ARMA, non-seasonal ARIMA and

seasonal ARIMA modeling and simulation was used by us. ARIMA models

are used to model a special class of non- stationary series. Seasonal ARIMA

(SARIMA) models are used to incorporate cyclic components in models. In

other words, ARIMA models are, in theory the most general class of models

(Parsimonious) for forecasting a time series which can be stationarized by

transformations such as differencing and logging. SARIMA has the same

structure as ARIMA. Both non seasonal and seasonal models on monthly and

annually averaged rate of dust fall data for 2004-08 were used. For non-

seasonal ARIMA modeling and simulation, the six options i.e., random walk

ARIMA (0,1,0), differenced first order autoregressive model ARIMA (1,1,0),

constant ARIMA (0,1,1), linear exponential smoothing (LES) without constant

ARIMA (0,2,1) or (0,2,2) and mixed ARIMA (1,1,1) are tried for each month

and on four seasons. Non seasonal ARIMA (0, 1, 1) which deals with

exponential growth and constant incorporates simple exponential smoothing

(SES) model. MA (1) coefficients correspond to 1-α in the SES formula. The

term α is called training parameter. For LES without constant, MA (1)

coefficient corresponds to 2α. For seasonal ARIMA (SARIMA) modeling and

simulation, the seven options, i.e., SARIMA(0,1,1)x(0,1,1)12,

SARIMA(0,0,0)x(0,1,0)12 with constant, SARIMA(0,1,0)x(0,1,0)12

SARIMA(1,0,1)x(0,1,1)12 with constant, SARIMA following SES with

199

α=0.4772 and Brown’s SARIMA(LES) with α = 0.2106 are tried for each

month only. The most often used model of ARIMA is SARIMA (0, 1, 1) x (0,

1, 1)12 which strictly follows seasonal exponential smoothing. SARIMA (0, 1,

0) x (0, 1, 0)12 is also known as seasonal random trend (SRT) model. The

alternate to SRT model is seasonal random walk model, .i.e., SARIMA (1, 0,

0) x (0, 1, 0)12. There is, of course, a difference between seasonal and simple

exponential models. The values of θ = 1- α is used in exponential smoothing

formulas. The greatest choice is chosen by bearing in mind the mainly

minimum chi- squared value at 5% confidence gap.

6.3 MODELING SKETCH:

• Having used various time series modeling, simulation and prediction,

we could disentangle the unfocused parts of researches plus the areas

which were overstressed. It is already established that statistical

techniques like ARMA, ARIMA, non-seasonal ARIMA and seasonal

ARIMA have capabilities to simplify statistical techniques and achieve

modeling of time series wind and dust fall data.

• The ARMA was initially applied to forecast the average monthly and

annual rate of dust fall.

• Finally ARIMA and SARIMA modeling was found extremely

appropriate for our dust fall data in addition to the meteorological

conditions prevailed while our samples collection period of 2004-08.

200

6.4 AUTOREGRESSIVE MOVING AVERAGE (ARMA)

MODELS:

ARMA models integrate prediction not only past values of the data but

past values of the prediction residuals as well. It was assumed that the

generating mechanism is probabilistic and that the observed series with

equally spaced time interval {x1, x2, xt,} is a realization of a stochastic process

{x1, x2,…,xt,}. Typically, the process was supposed to be stationary and

described by a class of linear models. We were anxious with an idea of

population which evaluates the properties of the probability model used to

generate the observed series. The first order autoregressive (AR) model is

given by

Xt =Φ (Xt-1- µ ) + Z(t) --------------------------(1)

Eq.1 is a simple example of stochastic process. The uncertainty derives

from the variable Zt which is purely a random disturbance term with a mean of

zero and a variance of σt2.. Zt is purely random in the sense that the correlation

between any two of its values at different points in the time is zero. Remaining

features of the model are determined by the parameters µ and Φ, if Φ <I. The

observations fluctuate around µ, which is then called the mean of the process.

Further lagged values Xt-2, Xt-3, and so on as well as the lagged of the

disturbances term could be added enabling a more complicated pattern

dependence to be modeled. A general ARMA (p,q) model can be written as

Xt - µ = Φ1 ( Xt-1 - µ ) +Φ2( Xt-2 - µ ) +…+Φp ( Xt-p - µ )+ Zt + θ1 ( Zt-1) + θ2

(Zt-2)+…+ θq (Zt-q) ---------------- (2)

201

where { Φp } and { θq } are the coefficients of the autoregressive (AR) and the

moving average (MA) parts, respectively, and {Z } is white noise with mean

zero and variance σ2 . We assume Zt is normally distributed, that is, Zt~ N (0,

σ2). Using the backward shift operator B defined by BjXt = X t-j ,the ARMA

(p,q) model can be written as

Φ(B) Xt = θ (B) Zt-q -------------- (3)

where

Φ (B) = 1- Φ1 B – Φ2 B2 - ,…,- Φp Bp ------------ (4)

and

θ (B) = 1- θ1B - θ2 B2 - ,…,- θq Bq --------------(5)

We generally assume that the polynomials Φ(xt) and θ(xt) have no common

zeros. When Xt is a vector, we have a multivariate or vector ARMA model.

Since we are using non stationary data, .i.e., hourly averaged wind data,

therefore, the standardization is imperative for ARMA modeling Kamal and

Jafri (1997). We translate eq (2) into Φ1 B,…,- Φp Bp)U*n,y = (1- θ1 B-,…-

θqBq) an,y --------------(6)

where U*n,y = [ Un,y – µ(t)] / σ(t) ,.i.e., the standardized hourly averaged wind

speed data (after removing diurnal non stationarity from the wind speed data

Un,y), Un,y is hourly averaged wind speed for the yth year, y = 1,2,Y is the

number of years of observation and n = 1,2, N is the number of observations

of a given month of the year. B is the backward shift operator such that BU*n,y

= U*n-1,y ; Φ1,Φ2,,…, Φp are the autoregressive parameter ; θ1, θ2, ,…, θq are the

moving average parameters; and an,y is the white noise process equivalent to

202

Zt-q Bq (uncorrelated random variable with mean zero and variance σ2). It is

obvious that the moving average parameters will be equal to zero if the model

is a pure autoregressive process. Since ARMA processes were not used in our

studies as it would require three main steps, .i.e., identification, estimation and

diagnostic checking. ARMA modeling did not work on our dust fall data. It

yielded very poor ACF (autocorrelation function) and PACF (partial

autocorrelation function), respectively.

6.4.1 Autoregressive Integrated Moving Average (ARIMA) Non Seasonal

and Seasonal Models:

ARIMA models are used to model a special class of non-stationary

series. Seasonal ARIMA (SARIMA) model are used to incorporate cyclic

components in models. We can split the time series into deterministic and

stochastic components. The proportion of variance for each component can be

modeled through Monto Carlo simulations. The stochastic component can be

analyzed for persistence in time series using Box and Jenkins [177].

The general non seasonal ARIMA model is autoregressive to order p

and moving average to order q , and operator on the dth differences of Zt,

where {Zt} are time series values for

t = 1,2,…, N and N is number of observations. Defining

Bs Zt= Zt-s , ∇ s = (1-Bs), d

s∇ = (1-Bs)d ------------------(7)

where d = 0,1,…, B is the backward shift operator, s is the period of the

season (s = 12 in our present case for each month) and∇ is the difference

operator. The general non seasonal ARIMA model can be written as:

203

Φp (B) Zt= θq (B) at -----------------(8)

where { at} are residuals, and

Φp (B) =1 - Φ1 B – Φ2 B2 - ,…,- Φp Bp -------------------(9)

θq (B) = 1- θ1B- θ2 B2 - ,…,- θq Bq -------------------(10)

are the polynomials of order p and q, respectively. Eq (8) can be modified Box

and Jenkins[177] to account for the seasonal dependence. These yields

Φ(Bs) d

s∇ Zt =θQ (Bs)et ------------------(11)

where {et} are normal random deviates,

ΦP (Bs) =1 - Φ 1 Bs – Φ2 B2s- ,…,- ΦPBPs ------------------(12)

And

θQ(B) = 1 - θ1Bs - θ2 B2s - ,…,- θQ BQs- -------------------(13)

are the seasonal autoregressive and moving average operators of order P and

Q, respectively. As et is not necessarily independent of et-j , j=1,2,…. we

propose the following relation for the e-values:

Φp (B) d∇ et = θq (B)at - -----------------(14)

where at is white noise (uncorrelated random variable with mean zero and

variance σ2), combining eqs (11) and (14) for SARIMA model,. i.e.,

SARIMA (p,d,q) (P,D,Q)s, we get a multiplicative SARIMA model of order

(p,d,q)x(P,D,Q)s of the form :

ΦP (Bs) ΦP(B) D

s∇ d∇ Zt = θQ (Bs) θq (B) at ------------------(15)

204

Time series prediction with harmonic analysis can also be accomplished [180].

Theories on regression analysis time series have long been established [185-

187].

6.5 SIMULATION OF WIND SPEED AND FORECASTING:

Brown et al., [168] obtained the model by the steps which would not

be enlisted here. Autocorrelation functions for the observed and simulated data

were found in agreements. All these models can be used to forecast dust fall

few hours in advance. The process of forecasting of dust fall is quite similar to

the process of simulation. In fact a simulated value of dust fall can be regarded

as a one hour ahead forecast to which a random error has been added.

We found sporadic changes in autocorrelation function (ACF) and

indeed in partial autocorrelation function (PACF), as a consequence of which,

the ARMA model did not work. We find that ARMA processes when dealt

with transformed standardized data (hourly dust fall rate) give way to

relatively better precision in forecasts. But the aperiodic variation in ACF &

PACF resulted in larger values of error terms. ARIMA & SARIMA models

are useful only for diurnal dust fall rate, i.e., cyclic stochastic components in

the dust fall.

6.5.1 Reason of Non-Selecting of ARMA & Selecting of ARIMA:

The average daily data of dust fall for the five years (2004-2008) was

taken. ARMA model didn’t work for reasons that our data followed non-

stationary in dust fall, which is random. ARMA model cannot be applied for

non-stationarity and random data. This is why ARMA model in our case failed

badly. We wanted to establish with ARMA modeling the ACF (Auto

205

Correlation Function) and PACF (Partial Auto Correlation Function) but none

of these worked. Therefore, we shifted towards ARIMA modeling, which is

applicable to non-stationary and random data. ARMA modeling can be used if

we translate the non-staionarity into stationarity by some standardized

procedures, i.e., by considering mean and standard deviations. This is a

cumbersome process and evolves a lot of statistical calculations.

6.6 RESULTS AND DISCUSSION:

We considered locations such as GAWALMANDI & T.B. Sanatorium

on the basis of optimum dust fall rate, i.e., the most maximum in

GAWALMANDI & the second most minimum in the T.B. Sanatorium. To

reflect the statistical variations in between the optimum values we considered

a third location C.G.S. Colony, which will provide statistical variations with

respect to mean values of the optimum dust fall rate. Table 6.1 for ARIMA &

SARIMA are shown on the basis of categorization for seasons such as the

spring of Quetta comprises of February, March and April, and so is the case

for other seasons, for GAWALMANDI, T.B. Sanatorium & C.G.S. Colony,

respectively. Each table for different locations for each month of the season

both for ARIMA & SARIMA provides prediction equations obtained for each

month of the season both from ARIMA & SARIMA models, which are

beneficial to predict the dust fall rate for larger as well as shorter lead times.

206

Table 6.1

ARIMA

Gawalmandi (spring) Months ARIMA (p.d.q.) χ2

0.05 d.f AR (1) Φ

MA (1) Θ

Constant (a)

February (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

11.4

112.5

9.5

-

11

11

10

-

-.8939 -

-.7304

-

-

.9360

.9082

-

-.2319

-.0376

-.0459

Prediction equation for ARIMA (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1) March (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

14.8

167.5

5.8

-

11

11

10

-

-.9959 -

-.9283

-

-

.9408

.9629

-

-.0180

-.00698

-.00668

Prediction equation for ARIMA (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1) April (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

4.4

6.1

3.3

-

11

11

10

-

-

-.3236

-1.0054

-

-

.2552

-.8638

-

2.016

1.439

3.324

Prediction equation for ARIMA (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1)

207

SARIMA

Gawalmandi (spring)

Months Seasonal SARIMA (p.d.q.)

χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA (12) Θ

Constant (a)

February (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

10.8

-

-

5.3

5.6

10

-

-

11

9

-

-

-

.3136

-.6958

.2454

-

-

-

-1.175

-1.9938

-

-

-

.2871

.4132

-

-

-.3112

-.1754

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 refers to twelve months as seasonal duration over the year. SAR (1,0,0)× (0,1,0)12 is a seasonal random walk model. The forecasting equation is: x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12)). The seasonal random walk(SRW) is an alternate to seasonal random trend (SRT) model March (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

19.6

-

-

10.9

6.6

10

-

-

11

9

-

-

-

-.3918

-.2276

9.182

-

-

-

1.1200

-

-

-

-

-

.04549

-

-

-.00698

-.00668

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 is an important version of seasonal exponential smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is: x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13) April (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

18.9

-

-

3.5

6.8

10

-

-

11

9

-

-

-

.8548

.7796

.2025

-

-

-

-.0034

-2.2682

-

-

-

1.2420

-3.723

-

-

1.789

3.2443

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 refers to twelve months as seasonal duration over the year. SAR (1,0,0)× (0,1,0)12 is a seasonal random walk model. the forecasting equation is: x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12)). The seasonal random walk(SRW) is an alternate to seasonal random trend (SRT) model

208

ARIMA

Gawalmandi (summer)

Months ARIMA (p.d.q.) χ20.05 d.f AR (1)

Φ MA (1) Θ

Constant (a)

May (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

9.7

18.2

9.8

-

11

11

10

-

-.2623 -

.2470

-

-

.9852

1.0576

-

.531

.1252

-.0870

Prediction equation for ARIMA (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}

June (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

17.1

127.2 -

-

11

11 -

-

-.9977 -

-

-

-

.9549 -

-

-.001

.00405 -

Prediction equation for ARIMA (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}

July (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

28.2

129.7

11.4

-

11

11

10

-

-.9232 -

-.8417

-

-

.9446

.9095

-

-.2403

-.0413

-.0503


209

SARIMA Gawalmandi (summer)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA(12) Θ

Constant (a)

May (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

18.6

-

-

10.4

7.7

10

-

-

11

9

-

-

-

.2004

-.4419

.8398

-

-

-

-.8440

.4631

-

-

-

.6553

.857

-

-

1.263

1.677

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 is an important version of seasonal exponential smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is: x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13) June (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

19.8

-

-

9.4

8.0

10

-

-

11

9

-

-

-

-.8134

-.8353

.8237

-

-

-

.2339

-

-

-

-

-

.0784

-

-

.3387

.3037

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 is an important version of seasonal exponential smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is: x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13) July (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

17.1

-

-

11.8

12.6

10

-

-

11

9

-

-

-

-.5068

.5165

.8698

-

-

-

.7712

2.5203

-

-

-

2.7157

-.12155

-

-

-.3687

.0126

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 refers to twelve months as seasonal duration over the year. SAR (1,0,0)× (0,1,0)12 is a seasonal random walk model. the forecasting equation is: x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12)). The seasonal random walk(SRW) is an alternate to seasonal random trend (SRT) model.

210

ARIMA

Gawalmandi (autumn)


Φ MA (1) Θ

Constant (a)

August (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

0.4

1.4

0.1

-

11

11

10

-

-1.000

-1.009 -

-

-

.8981

-.0325

-

-3.237

-.5870

-3.223

Prediction equation for ARIMA (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1) September (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

22.7

146.1

16.6

-

11

11

10

-

-.9579 -

-.9430

-

-

.9373

1.0574

-

-.0075

-.0141

.0072

Prediction equation for ARIMA (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1) October (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

10.1

10.0

6.8

-

11

11

10

-

-.4231 -

-.9981

-

-

.4519

-.8879

-

.762

.437

1.049


211

SARIMA

Gawalmandi (autumn)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA(12) Θ

Constant (a)

August (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

4.4

-

-

0.5

1.0

10

-

-

11

9

-

-

-

-.0085

.2833

.8966

-

-

-

.4049

-3.2500

-

-

-

2.953

-1.21154

-

-

-5.004

--1.685

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 refers to twelve months as seasonal duration over the year. SAR (1,0,0)× (0,1,0)12 is a seasonal random walk model. The forecasting equation is: x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12)). The seasonal random walk (SRW) is an alternate to seasonal random trend (SRT) model. September (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

11.7

-

-

10.2

7.4

10

-

-

11

9

-

-

-

.5324

-.0759

.3228

-

-

-

.7647

.5225

-

-

-

.4928

.0740

-

-

.0402

.0193

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 is an important version of seasonal exponential smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is: x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13) October (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

9.5

-

-

8.1

9.0

10

-

-

11

9

-

-

-

.4889

.4305

.2418

-

-

-

-.0671

-.0331

-

-

-

-.4557

-.200

-

-

.232

-.275

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 is an important version of seasonal exponential smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13)

212

ARIMA

Gawalmandi (winter)


Φ MA (1) Θ

Constant (a)

November (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

6.0

6.1

6.5

-

11

11

10

-

.0695 -

.2787

-

-

.0663

.2068

-

.316

.333

.253

Prediction equation for ARIMA (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)} December (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

55.1

158.0

45.1

-

11

11

10

-

-.9413 -

-.8374

-

-

.9409

.9419

-

-.5028

-.0067

.0027

Prediction equation for ARIMA (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1) January (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

20.0

116.9

16.1

-

11

11

10

-

-.9988 -

-.9595

-

-

.9332

1.0206

-

-.001

-.0193

.245


213

SARIMA

Gawalmandi (winter)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA (12) Θ

Constant (a)

November (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

5.8

-----

-----

11.7

5.3

10

-

-

11

9

-

-

-

.7654

.6937

-.1544

-

-

-

-.2689

-.2792

-

-

-

-.6521

-2.984

-

-

-1.410

-3.050

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 is an important version of seasonal exponential smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is: x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13) December (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

-

-

-

9.8

7.2

-

-

-

11

9

-

-

-

-.1115

.2931

-

-

-

-

1.000

-

-

-

-

.7300

-

-

-

.0594

.0150

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 is an important version of seasonal exponential smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is: x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13) January (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

30.3

-

-

18,5

19.7

10

-

-

11

9

-

-

-

-.7642

-.7419

1.1041

-

-

-

.0671

.3439

-

-

-

.6639

.12212

-

-

-.2537

.3037

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 refers to twelve months as seasonal duration over the year. SAR (1,0,0)× (0,1,0)12 is a seasonal random walk model. the forecasting equation is: x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12)). The seasonal random walk (SRW) is an alternate to seasonal random trend (SRT) model.

214

ARIMA

T.B. Sanatorium (spring)

Months ARIMA(p.d.q.) χ20.05 d.f AR (1)

Φ MA (1) Θ

Constant (a)

February (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

10.8

145.6

9.3

-

11

11

10

-

-.9589 -

-.8623

-

-

.9457

.9169

-

-.1864

-.0361

-.2331

Prediction equation for ARIMA(1,1,1) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1) March (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

8.1

29.8

9.2

-

11

11

10

-

-.7167 -

-.3349

-

-

.9819

.9662

-

-.227

-.1933

-.2152

Prediction equation for ARIMA(1,1,0) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)}April (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

10.1

12.2

3.2

-

11

11

10

-

-.2068 -

-1.0009

-

-

.1736

-.9839

-

2,431

1.967

4.954

Prediction equation for ARIMA(1,1,1) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1)

215

SARIMA

T.B. Sanatorium (spring)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA (12) Θ

Constant (a)

February (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

6.2

-

-

14.2

-

10

-

-

11

-

-

-

-

-.4476

-

1.1313

-

-

-

-

-1.6798

-

-

-

-

-.0312

-

-

-.0472

-

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a seasonal experimental smoothing (SES) model. The forecasting equation for this model is: x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+ Θθe(t-13) where little θ is the MA(1) coefficient and big Θ is the SMA(1) coefficient. March (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

2.2

-

-

2.6

8.8

11

-

-

11

9

-

-

-

-.0091

.4008

.9699

-

-

-

.4534

.1464

-

-

-

.7559

-.0330

-

-.1613

-

-.8964

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a seasonal experimental smoothing (SES) model. The forecasting equation for this model is: x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+ Θθe(t-13) where little θ is the MA(1) coefficient and big Θ is the SMA(1) coefficient. April (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

10

-

-

14.3

19.1

11

-

-

11

9

-

-

-

.8913

.2722

-.1550

-

-

-

-.6751

1.8732

-

-

-

.8740

-.2763

-

-

2.131

8.1530

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a seasonal experimental smoothing (SES) model. The forecasting equation for this model is: x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+ Θθe(t-13) where little θ is the MA(1) coefficient and big Θ is the SMA(1) coefficient.

216

ARIMA

T.B. Sanatorium (summer)


Φ MA (1) Θ

Constant (a)

May (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

22.4

14

13.3

-

11

11

10

-

-.3259 -

.0547

-

-

.9467

.9682

-

.991

.0329

.0179

Prediction equation for ARIMA(1,1,1) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1) June (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

15.7

277.6

6.6

-

11

11

9

-

.9978 -

-.9774

-

-

.9525

1.0138

-

-.0002

-.0212

-.2010

Prediction equation for ARIMA(1,1,1) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1) July (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

15.7

227.6

6.6

-

11

11

10

-

-.9978 -

-.9774

-

-

.9525

1.0138

-

-.0002

-.0212-

-.0106

Prediction equation for ARIMA(1,1,1) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1)

217

SARIMA

T.B. Sanatorium (summer)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA(12) Θ

Constant (a)

May (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

7.7

-

-

14.8

9.1

10

-

-

11

9

.7570

-

-

-.5065

.5601

.7570

-

-

-

.3936

.3831

-

-

-

.6222

1.099

-

-

1.010

1.614

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a seasonal experimental smoothing (SES) model. The forecasting equation for this model is: x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+ Θθe(t-13) where little θ is the MA(1) coefficient and big Θ is the SMA(1) coefficient. June (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

35.5

-

-

5.4

18.8

10

-

-

11

9

-

-

-

-.8746

-

.9668

-

-

-

-

.3226

-

-

-

-.0971

-.0977

-

-

-.1220

-.2010

Prediction equation follows SARIMA (1,0,0) x (0,1,0)12 where the number 12 refers to twelve months as seasonal duration over the year. SAR (1,0,0)× (0,1,0)12 is a seasonal random walk model. the forecasting equation is: x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12)). The seasonal random walk (SRW) is an alternate to seasonal random trend (SRT) model. July (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

35.5

-

-

5.4

3.8

10

-

-

11

9

-

-

-

-.8746

-.8687

.9668

-

-

-

.0869

.3226

-

-

-

-.6061

-.0977

-

-

-.1220

-.3301

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a very important version of seasonal experimental smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is: x(t)=a+x(t-12)+ Ф{x(t-1)-x(t-12)} –θe(t-1)- Θe(t-R)+θΘe(t-13) where the little θ is MA(1) coefficient and the big Θ is the SMA(1) coefficient.

218

ARIMA

T.B. Sanatorium (autumn)


Φ MA (1) Θ

Constant (a)

August (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

14.9

152.0

11.3

-

11

11

10

-

-.9737 -

-.9745

-

-

.9672

.5331

-

-.0927

-.01507

-.0414

Prediction equation for ARIMA(1,1,1) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1) September (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

39.4

198.2 -

-

11

11 -

-

-.9958 -

-

-

-

.9505 -

-

-.003

-.0071 -

Prediction equation for ARIMA(1,1,0) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)}October (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

6.9

14.6

8.5

-

11

11

10

-

-.6200 -

-.4879

-

-

.6243

.2264

-

.87

1.89

.82

Prediction equation for ARIMA(1,1,0) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)}

219

SARIMA

T.B. Sanatorium (autumn)


χ20.05 d.f AR(1)

Φ

MA(1)

Θ

SMA(12) Θ

Constant (a)

August (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

6.4

-

-

8.4

7.5

10

-

-

11

9

-

-

-

.0783

.7843

.9228

-

-

-

.5747

.4267

-

-

-

.5323

-.1214

-

-

.0308

-.0569

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a seasonal experimental smoothing (SES) model. The forecasting equation for this model is x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+ Θθe(t-13) where little θ is the MA(1) coefficient and big Θ is the SMA(1) coefficient.September (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

14.2

-

-

25.9

12.8

10

-

-

11

9

-

-

-

-.3257

-.0504

.9354

-

-

-

.8861

-.6338

-

-

-

-.7879

-.00072

-

-

.0104

-.0035

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a very important version of seasonal experimental smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is x(t)=a+x(t-12)+ Ф{x(t-1)-x(t-12)} –θe(t-1)- Θe(t-R)+θΘe(t-13) where the little θ is MA(1) coefficient and the big Θ is the SMA(1) coefficient. October (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

12.8

-

-

14.2

13.0

10

-

-

11

9

-

-

-

.1771

.6527

.9257

-

-

-

.4474

-.7875

-

-

-

-.0742

6.937

-

-

-14.44

-6.11

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a very important version of seasonal experimental smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is x(t)=a+x(t-12)+ Ф{x(t-1)-x(t-12)} –θe(t-1)- Θe(t-R)+θΘe(t-13) where the little θ is MA(1) coefficient and the big Θ is the SMA(1) coefficient.

220

ARIMA

T.B. Sanatorium (winter)

Months ARIMA(p.d.q.) χ20.05 d.f AR (1)

Φ MA (1) Θ

Constant (a)

November (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

4.5

4.6

4.5

-

11

11

10

-

.1147 -

.1351

-

-

-.1049

.0206

-

.684

.771

.668

Prediction equation for ARIMA(1,1,1) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1) December (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

9.4

10.5

7.0

-

11

11

10

-

-.6817 -

0.2424

-

-

.9975

.9605

-

.0697

.01124

-0.0247

Prediction equation for ARIMA(1,1,1) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1) January (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

23.6

28.3

164.4

274.5

26.8

31.2

-

11

23

11

23

10

22

-.9966

-

-

-

-.9589 -

-

-

-

-.9950

-

.9513

-

-

.0278

-

-.0144

-

-.00411

- Prediction equation for ARIMA(1,1,0) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)}

221

SARIMA

T.B. Sanatorium (winter)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA (12) Θ

Constant (a)

November (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

6.9

-

-

10.7

8.5

10

-

-

11

9

-

-

-

.6850

.5787

-.2058

-

-

-

-.3400

.1953

-

-

-

.2301

-1.074

-

-

.144

-.042

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a seasonal experimental smoothing (SES) model. The forecasting equation for this model is: x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+ Θθe(t-13) where little θ is the MA(1) coefficient and big Θ is the SMA(1) coefficient.December (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

3.9

-

-

4.1

3.3

10

-

-

11

9

-

-

-

-.0792

-.5896

.9388

-

-

-

.4893

.2434

-

-

-

.6349

.2074

-

-

-.9149

-1.1108

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a very important version of seasonal experimental smoothing (SES) model, i.e., with a constant. The forecasting equation for this model is: x(t)=a+x(t-12)+ Ф{x(t-1)-x(t-12)} –θe(t-1)- Θe(t-R)+θΘe(t-13) where the little θ is MA(1) coefficient and the big Θ is the SMA(1) coefficient. January (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

9.9

-

-

-

18.2

10.8

11

-

-

-

11

9

-

-

-

-

-.4245

.1932

.8075

-

-

-

-

.6495

.9024

-

-

-

-

1.0253

.06362

-

-

-

.2803

-.05185

Prediction equation for SARIMA (0,1,1) x (0,1,1)12 is a seasonal experimental smoothing (SES) model. The forecasting equation for this model is: x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+ Θθe(t-13) where little θ is the MA(1) coefficient and big Θ is the SMA(1) coefficient.

222

ARIMA

CGS Colony (spring) Months ARIMA (p.d.q.) χ2

0.05 d.f AR (1) Φ

MA (1) Θ

Constant (a)

February (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

3.9

111.9

2.1

-

11

11

10

-

-.5163

-

-.0952

-

-

.9490

.9451

-

-.298

.4492

.5138

The prediction equation for non-seasonal ARIMA (1,1,1) yields x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-

θe(t-1) where a is constant, e is the error at period (t-1), Ф=AR(1) and θ=MA(1)

March (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

8.7

121.1

2.8

-

11

11

10

-

-.9355

-

-.8315

-

-

.9491

.8979

-

-.2360

-.0375

-.02664

The prediction equation for non-seasonal ARIMA (1,1,1) yields x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1) where a is constant, e is the error at period (t-1), Ф=AR(1) and θ=MA(1) April (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

6.0

9.0

3.9

-

11

11

10

-

-.3438

-

-.10005

-

-

.2812

-1.0180

-

2.078

1.436

5.143

The prediction equation for non-seasonal ARIMA (1,1,1) yields x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1) where a is constant, e is the error at period (t-1), Ф=AR(1) and θ=MA(1)

223

SARIMA

CGS Colony (spring)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA (12) Θ

Constant (a)

February (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

2.6

-

-

2.3

2.9

10

-

-

11

9

-

-

-

-.0727

-.1502

.8644

-

-

-

.2555

-3.3264

-

-

-

-3.6500

2.424

-

-

6.333

-6.012

SARIMA (1,0,0)×(0,1,0) is a seasonal random walk model. The forecasting equation is: x(t)=a+x(t-12)+Ф(x(t-1)-x(t-12)). The seasonal random walk (SRW) is an alternate to seasonal random trend (SRT) model. March (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

11.2

-

-

6.2

2.6

10

-

-

11

9

-

-

-

-.2383

-.8039

.8182

-

-

-

-.5711

.3090

-

-

-

.7022

-.22211

-

-

-.2654

-.4881

SARIMA (1,0,1)×(0,1,1)12 is an important version of SES model, i.e., with a constant. The forecasting equation for this model is: x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13) where a is a constant, Θ=SMA(12) and e is the error term. April (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

6.3

-

-

4.2

7.7

10

-

-

11

9

-

-

-

.8869

.7880

.2220

-

-

-

-.0783

.2434

-

-

-

.8262

1.989

-

-

1.319

2.201

SARIMA (1,0,0)×(0,1,0) is a seasonal random walk model. The forecasting equation is: x(t)=a+x(t12)+Ф(x(t-1)-x(t-12)). The seasonal random walk (SRW) is an alternate to seasonal random trend (SRT) model.

224

ARIMA

CGS Colony (summer)


Φ MA (1) Θ

Constant (a)

May (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

17.9

9.6

11.6

-

11

11

10

-

-.3328

-

-.1476

-

-

.9817

.9721

-

1.504

.2541

.4379 The prediction equation for non-seasonal ARIMA (0,1,1) yields x(t)=a+x(t-1)-θe(t-1) where a is constant, e is the error at period (t-1)and θ=MA(1) June (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

48.5

185.0

not

working

-

11

11

-

-

-.9601

-

-

-

-

.9549

-

-

-.0427

-.195

-

The prediction equation for non-seasonal ARIMA(1,1,0) yields x(t) =a+x(t-1)+Ф(x(t-1)-x(t-2) where a is constant and Ф=AR(1)July (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

13.3

138.3

7.2

-

11

11

10

-

-.9187

-

-.8464

-

-

.9844

.9538

-

-.0429

-.02995

-.02241 The prediction equation for non-seasonal ARIMA (1,1,1) yields x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-

θe(t-1) where a is constant, e is the error at period (t-1), Ф=AR(1) and θ=MA(1)

225

SARIMA

CGS Colony (summer)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA (12) Θ

Constant (a)

May (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

4.4

-

-

13.9

7.4

10

-

-

11

9

-

-

-

-.2075

.2069

.8150

-

-

-

.4678

.2976

-

-

-

.6396

.532

-

-

10.804

7.491

SARIMA (1,0,1)×(0,1,1)12 is an important version of SES model, i.e., with a constant. The forecasting equation for this model is: x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13) where a is a constant, Θ=SMA(12) and e is the error term.June (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

22.4

-

-

15.5

1.8

10

-

-

11

9

-

-

-

-.2370

-.0907

.8637

-

-

-

.8196

1.0756

-

-

-

1.2202

.1396

-

-

.1221

.08225

SARIMA (1,0,1)×(0,1,1)12 is an important version of SES model, i.e., with a constant. The forecasting equation for this model is: x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13) where a is a constant, Θ=SMA(12) and e is the error term.July (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

11.4

-

-

10.1

7.2

10

-

-

11

9

-

-

-

-.3670

-.0230

.8080

-

-

-

.9913

2.5203

-

-

-

.1243

.1239

-

-

-.0999

-.01776

SARIMA (1,0,1)×(0,1,1)12 is an important version of SES model, i.e., with a constant. The forecasting equation for this model is: x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13) where a is a constant, Θ=SMA(12) and e is the error term.

226

ARIMA

CGS Colony (autumn)

Months ARIMA (p.d.q.) χ2

0.05 d.f AR (1) Φ

MA (1) Θ

Constant (a)

August (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

47.7

129.6

20.5

-

11

11

10

-

-.9345

-

-.8327

-

-

.9852

.9324

-

-.1356

-.05185

-.0185 The prediction equation for non-seasonal ARIMA (1,1,1) yields x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1) where a is constant, e is the error at period (t-1), Ф=AR(1) and θ=MA(1) September (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

10.7

167.2 not

working

-

11

11

-

-

-.9961

-

-

-

-

.0692

-

-

-.0002

.0492

-

The prediction equation for non-seasonal ARIMA(1,1,0) yields x(t) =a+x(t-1)+Ф(x(t-1)-x(t-2) where a is constant and Ф=AR(1)October (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

11.1

8.0

9.3

-

11

11

10

-

-.4207

-

.3272

-

-

.5835

-.9827

-

.548

.3576

.0341 The prediction equation for non-seasonal ARIMA (0,1,1) yields x(t)=a+x(t-1)-θe(t-1) where a is constant, e is the error at period (t-1)and θ=MA(1)

227

SARIMA

CGS Colony (autumn)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA (12) Θ

Constant (a)

August (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

22.6

-

-

19.4

11.3

10

-

-

11

9

-

-

-

-.3402

.2052

.8563

-

-

-

.8079

.2284

-

-

-

.2181

-.0489

-

-

-.0248

-.0420

SARIMA (1,0,1)×(0,1,1)12 is an important version of SES model, i.e., with a constant. The forecasting equation for this model is: x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13) where a is a constant, Θ=SMA(12) and e is the error term.September (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

23.6

-

-

5.7

5.9

10

-

-

11

9

-

-

-

-.8179

-.0759

.8872

-

-

-

.5090

.4041

-

-

-

.7369

-.0404

-

-

.0402

-.07995

SARIMA (1,0,0)×(0,1,0) is a seasonal random walk model. The forecasting equation is: x(t)=a+x(t-12)+Ф(x(t-1)-x(t-12)). The seasonal random walk (SRW) is an alternate to seasonal random trend (SRT) model. October (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

14.0

-

-

12.2

14.5

10

-

-

11

9

-

-

-

.3736

.3506

.4106

-

-

-

-.1227

.4892

-

-

-

.5196

-.1522

-

-

.066

-.241

SARIMA (1,0,0)×(0,1,0) is a seasonal random walk model. The forecasting equation is: x(t)=a+x(t-12)+Ф(x(t-1)-x(t-12)). The seasonal random walk (SRW) is an alternate to seasonal random trend (SRT) model.

228

ARIMA

CGS Colony (winter) Months ARIMA (p.d.q.) χ2

0.05 d.f AR (1) Φ

MA (1) Θ

Constant (a)

November (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

5.7

5.7

5.8

-

11

11

10

-

.0074

-

-.9358

-

-

-.0074

-1.0248

-

-7.63

-7.69

-.2979 The prediction equation for non-seasonal ARIMA(1,1,0) yields x(t) =a+x(t-1)+Ф(x(t-1)-x(t-2) where a is constant and Ф=AR(1) December (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

40.0

94.2

11.5

-

11

11

10

-

-.9600

-

-.8360

-

-

1.055

.9141

-

-.1570

-.0818

.0386 The prediction equation for non-seasonal ARIMA (1,1,1) yields x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1) where a is constant, e is the error at period (t-1), Ф=AR(1) and θ=MA(1) January (0,1,0)

(1,1,0)

(0,1,1)

(1,1,1)

-

21.7

99.8

8.5

-

11

11

10

-

-.9336

-

-.7906

-

-

.9427

-.9047

-

-.2413

-.0421

-.03788 The prediction equation for non-seasonal ARIMA (1,1,1) yields x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1) where a is constant, e is the error at period (t-1), Ф=AR(1) and θ=MA(1)

229

SARIMA

CGS Colony (winter)


χ20.05 d.f AR (1)

Φ

MA (1)

Θ

SMA (12) Θ

Constant (a)

November (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

10.2

-

-

16.2

17.6

10

-

-

11

9

-

-

-

.6714

.5396

.0341

-

-

-

-.1756

.1643

-

-

-

-.7984

-17.96

-

-

-.3534

-46.63

SARIMA (0,1,1)×(0,1,1)12 is a seasonal exponential smoothing (SES) model, The forecasting equation for this model is: x(t)=x(t-12)=(x(t-1)-x(t-13))-θe(t-1)-Θe(t-12)+Θθe(t-13) where θ=MA(1), Θ is SMA(1) coefficient and e is error term. December (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

-

-

-

9.8

7.2

-

-

-

11

9

-

-

-

.7308

-.2385

.9574

-

-

-

.4199

.9732

-

-

-

.1238

.0056

-

-

-.5759

-.1634

SARIMA (1,0,1)×(0,1,1)12 is an important version of SES model, i.e., with a constant. The forecasting equation for this model is: x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13) where a is a constant, Θ=SMA(12) and e is the error term.January (0,1,1)x(0,1,1)12

(0,0,0)x(0,1,0)12

(0,1,0)x(0,1,0)12

(1,0,0)x(0,1,0)12

(1,0,1)x(0,1,1)12

22.0

-

-

13.1

5.3

10

-

-

11

9

-

-

-

-.4589

-.1836

.9532

-

-

-

.3513

.7415

-

-

-

.9308

-.1068

-

-

-.3198

-.0628

SARIMA (1,0,1)×(0,1,1)12 is an important version of SES model, i.e., with a constant. The forecasting equation for this model is: x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13) where a is a constant, Θ=SMA(12) and e is the error term.

230

CHAPTER 7

CONCLUSIONS AND RECOMMENDATIONS FOR

FUTURE RESEARCH

7.1 Conclusions with suggested precautionary measures:

• Quetta is the one of the top most dust fall hit/effected cities across the

world due to its geographical location, severe arid zonal topographic

nature, south western gust and dusty wind pattern sporadically bringing

heavy dust plumes from the regional deserts of Dalbandin (Pakistan) &

DASHT-E-LUT (Iran), extremely less plantation, collapsed

infrastructure, poor sanitation, marathon drought spells, decreasing

water table, dry sub-tropical climate having extremely low humidity, ,

deteriorating poor planning, corruption etc.

• Unfortunately, Quetta is again on the top most cities of globe having

very high level of Pb (lead) in its suffocating atmosphere. The major

contributor of pollutants are automobiles running on Pb contaminated

fuel/gas, a large part of which is smuggled from Iran and adulterated

before its distribution in order to gain more and more profit.

• Due to scarcity of industries, luckily the concentrations of other heavy

and toxic elements in air were not on an alarming level. That is why the

phenomenon of photochemical smog has not been experienced so far.

Though the occurrence of thermal inversion spells, Quetta has been

completed wrapped/blanketed in dust cloud time and again continuously

for three to four days which, of course, triggered the particulates

associated diseases (for instance, asthma, bronchitis, blood pressure,

231

nuisance causing depression & anxiety etc), yet it couldn’t have caused

those much deaths as were reported in the cases of Donora and London

(UK).

• We inferred from the statistical modeling ARIMA equations of our

maximum dust fall receiving site ‘Gawalmandi’ for spring [February

(1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1), March (1,1,1)

yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1) & April (1,1,1) yields

x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1)], summer [May (1,1,1) yields

x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}, June (1,1,1) yields x(t)=a+ x(t-1)+

Ф{x(t-1)- x(t-2)} & July (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}-

θe(t-1)], & autumn [August (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-

2)}- θe(t-1), September (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}-

θe(t-1) & October (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-

1))] & winter are [November (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)-

x(t-2)}, December (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-

1) & January (1,1,1) yields x(t)=a+ x(t-1)+ Ф{x(t-1)- x(t-2)}- θe(t-1)]

and SARIMA equations of the same ‘Gawalmandi’ site for spring

[February x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12)), March x(t)=2+x(t-12) +

Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13) & April

x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12))], summer [May x(t)=2+x(t-12) +

Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13), June x(t)=2+x(t-

12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13) & July

x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12))], autumn [August x(t)=a+x(t-12)+

Ф(x(t-1)-x(t-12)), September x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ

e(t-1) – Θ e(t-12) + θ Θe(t-13) & October x(t)=2+x(t-12) + Ф{x(t-1) –

232

x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13)] & winter are [November

x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) + θ Θe(t-13),

December x(t)=2+x(t-12) + Ф{x(t-1) – x(t-12)} – θ e(t-1) – Θ e(t-12) +

θ Θe(t-13) & January x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12))], respectively.

• Similarly the statistical modeling ARIMA equations of our second

minimum dust fall receiving site ‘T.B. Sanatorium’ for spring

[February (1,1,1) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1),

March (1,1,0) yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} & April (1,1,1)

yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1)], summer [May (1,1,1)

yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1), June (1,1,1) yields

x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1) & July (1,1,1) yields

x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1)], & autumn [August (1,1,1)

yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1), September (1,1,0)

yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} & October (1,1,0) yields

x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)}] & winter are [November (1,1,1)

yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1), December (1,1,1)

yields x(t)=a+x(t-1)+Ф{x(t-1) – x(t-2)} –θe(t-1) & January x(t)=a+x(t-

1)+Ф{x(t-1) – x(t-2)}] and SARIMA equations of the same ‘T.B.

Sanatorium’ site for spring [February x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-

1)-Θe(t-12)+ Θθe(t-13), March x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-

12)+ Θθe(t-13) & April x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+

Θθe(t-13)], summer [May x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+

Θθe(t-13), June x(t)=a+x(t-12)+ Ф(x(t-1)-x(t-12)) & July x(t)=a+x(t-

12)+ Ф{x(t-1)-x(t-12)} –θe(t-1)- Θe(t-R)+θΘe(t-13)], autumn [August

x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+ Θθe(t-13), September

233

x(t)=a+x(t-12)+ Ф{x(t-1)-x(t-12)} –θe(t-1)- Θe(t-R)+θΘe(t-13) &

October x(t)=a+x(t-12)+ Ф{x(t-1)-x(t-12)} –θe(t-1)- Θe(t-R)+θΘe(t-13)]

& winter are [November x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+

Θθe(t-13), December x(t)=a+x(t-12)+ Ф{x(t-1)-x(t-12)} –θe(t-1)- Θe(t-

R)+θΘe(t-13) & January x(t)=x(t-12)+x(t-1)-x(t-13)-θe(t-1)-Θe(t-12)+

Θθe(t-13)], correspondingly.

• Finally the statistical modeling ARIMA equations of our moderate (in a

comparative with other sites of the Quetta city though it received very

huge amount of average rate of dust fall vis-a-vis most of cities of the

world) dust fall receiving site ‘C.G.S. Colony’ for spring [February

x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1), March x(t)=a+x(t-1)+Ф(x(t-1)-

x(t-2))-θe(t-1)& April x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1)], summer

[May x(t)=a+x(t-1)-θe(t-1), June x(t) =a+x(t-1)+Ф(x(t-1)-x(t-2) & July

x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1)], & autumn [August x(t)=a+x(t-

1)+Ф(x(t-1)-x(t-2))-θe(t-1), September x(t) =a+x(t-1)+Ф(x(t-1)-x(t-2) &

October x(t)=a+x(t-1)-θe(t-1)] & winter are [November x(t) =a+x(t-

1)+Ф(x(t-1)-x(t-2), December x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1) &

January x(t)=a+x(t-1)+Ф(x(t-1)-x(t-2))-θe(t-1)] and SARIMA equations

of the same ‘C.G.S. Colony’ site for spring [February x(t)=a+x(t-

12)+Ф(x(t-1)-x(t-12)), March x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-

Θe(t-12)+θΘe(t-13) & April x(t)=a+x(t-12)+Ф(x(t-1)-x(t-12))], summer

[May x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13),

June x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13) &

July x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13)],

autumn [August x(t)=a+x(t-12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-

234

12)+θΘe(t-13), September x(t)=a+x(t-12)+Ф(x(t-1)-x(t-12)) & October

x(t)=a+x(t-12)+Ф(x(t-1)-x(t-12))] & winter are [November x(t)=x(t-

12)=(x(t-1)-x(t-13))-θe(t-1)-Θe(t-12)+Θθe(t-13), December x(t)=a+x(t-

12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13) & January x(t)=a+x(t-

12)+Ф(x(t-2)-x(t-12))-θe(t-1)-Θe(t-12)+θΘe(t-13)], in that order.

• Statistical ARIMA modeling reflects that our ARIMA & SARIMA and

the prediction equations, which we developed, are beneficial to look into

design and engineering consideration to make environment of Quetta

clean from dust fall rate. With these predictions equations, we could

suggest remedial solutions to minimize the dust fall and indeed to make

our environment clean by evolving natural eco system.

• The prediction equations for dust fall rate for each month categorized

with respect to seasons, both for ARIMA and SARIMA are given in

their corresponding tables.

• Shoulders of the city roads should be brick lined in order to avoid the

dust derives into the atmosphere by the vehicles & local blustery winds.

• The construction materials should strictly be deterred to dump on the

road.

• Solid extremely contaminated sewage should be avoided to pile up on

the shoulders of the drains.

• Co-friendly tree plantation should be done on scientific grounds, for

instance, in terms of loose plantation rather thick plantation, etc.

235

• Smuggled Iranian petrol should be regulated in such a manner that the

people associated with this trade should not suffer, as in the absence of

any other jobs opportunities, it is the sole bread and butter wining source

of theirs. This would stop adulteration in the smuggled Iranian fuel as

well, which causes huge Pb pollution.

• All the brick kilns and iron smelting units should be shifted at a distant

place from the settled areas.

• Old Stone Aged buses should be banned. Instead green public transport

should be launched by government, itself. It would certainly break the

ice and tempt/induce private (public) transport owners to include new

vehicles in their crew by replacing with old ones and their monopoly,

services; fares could also be reduced in a competitive environment.

• More and more public parks, family parks, grassy playing grounds be

built and old ones like NAWAB AKBAR BUGTI (SHAHEED) stadium

be properly maintained and renovated. In particular the only but unique

‘HAZARGANJI’ national park having rare species like MARKHOR

(which are on the verge of extinction) could be rescued by planting more

and more trees in it, which would extremely be beneficial for abating the

intensity of dust plumes passing through this track with the north

western winds and strike at Quetta.

• More and more overhead/flyovers and under passes should be built to

minimize the numerous traffic jams, which could be witnessed daily on

so many bottle neck sort of crossings in every part of the Quetta city.

236

• A proper awareness campaign should also be commenced to educate

masses regarding this creeping menace.

• Above all a sense of ownership should be cultivated in the hearts and

minds of the sons of the soil by emancipating them politically,

economically and, last but not least, linguistically & culturally. So that

they may get on board and ultimately could counter the corruption

eventually to make the environment of their city peaceful, clean and

tranquilizing.

7.2 RECOMMENDATIONS FOR FUTURE RESEARCH WORK:

• Keeping in view the geographical nature, meteorological conditions of

the area (Quetta and Balochistan) and present findings, a consistent

monitoring mechanism should be planned in order to find out the rate of

dust fall out door as well as indoor, their sizes (which matter a lot on

humans health due to their adsorbing tendency of toxic elements,

carcinogenic organic compounds etc. on them because of having larger

surface area and complex structures) by using the modern equipments

(e.g. Mastersizer 2000, Malvern, Ver. 3.01, U.K.) more precisely and

accurately.

• As it is an established scientific fact that dust usually contains pretty

concentration of radioactive elements (e.g. Rn) in it, therefore an

extensive research work should be conducted to find their nature and

concentration in it.

• Zeolites, which are widely used in the modern period [196] as

scavengers in different industries in order to gain maximum product by

237

avoiding the loss of a major chunk of the costly raw material (for

instance in the fractional distillation of petroleum), could also effectively

be used on the similar pattern to get clean environment particularly air

by finding the effective techniques of their usages for the said vital

purpose.

• The prediction equations of ARIMA & SARIMA, if correlated

analytically, would also provide a rationale such as diurnal variations for

shorter lead times. For this a different statistical analysis is needed may

be a logistic regression analysis or logit. We can also accomplish

multivariate analysis of parameters of both ARIMA & SARIMA.

Therefore, there is a dire need to evolve hybrid models such as mixing

of ARIMA & SARIMA with any expert system (Intelligent System) to

inculcate minor diurnal variations, the noise effect & the corresponding

sporadic variations.

• Therefore diurnal variations (cyclic stochastic components) should be

incorporated in model development such as in ARIMA & SARIMA.

• Modeling & simulation of asymptotic departure (optimum variations)

from randomness are needed to be developed. This would help resolving

the optimum dust fall rate for each location in Quetta.

• The division of trace elements cannot be detached as well from that of

particulates size, specifically PM2.5-5.0 and PM<1.0, scattered in the

atmosphere. Therefore a correlative & multivariate statistics having all

data could be applied to find out the correlation between toxic elements

connected with peculiar sizes and shapes of dust particulates and to

238

explore the local atmosphere in more detail, which is undergoing

remarkable anthropogenic translocations.

239

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257

APPENDIX

METEROLOGICAL DATA OF QUETTA FOR 2004-2008

PERIOD

Table 5.22a

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC1 - -1.3 3.55 13.85 14.3 22.95 23.65 24.75 18.6 17.15 4.2 0.352 - -2.4 6.3 15.6 13.5 20.7 24.5 162.55 19.95 10.85 5.8 2.453 - 1 6.75 14.45 16.75 20.05 25.25 23.3 19.75 10.45 5.7 6.44 - 2 7.6 15.4 15.35 19.05 25.55 21.75 15.9 14.35 3.35 7.55 - 4.15 8.55 15.45 16 22.25 25.15 23.05 16.75 11.45 5.95 -1.26 - 2.2 9.05 15.75 19.9 21.9 25.05 25.1 20.2 10.45 6.15 -0.657 - 3.65 11.7 17.1 19.9 23.3 25.2 24.75 20.8 12.4 7.25 1.658 - 6.85 13.2 13.95 18.05 22 24.1 25.45 21.95 13.55 7 2.79 - 1.75 14.9 15.95 19.1 22.75 25.2 24.1 21.55 8.85 7.85 5.1510 - 1.65 11.5 14.95 17.3 22.65 22.65 23.8 21.6 6.4 8.05 5.8511 - 3.35 10.55 13.95 16.4 23.9 22.15 23.8 22.7 6.05 9.7 6.2512 - 5.05 12.35 15.35 17.7 25 23.75 23.8 20.7 8.15 8.65 8.2513 - 4.4 14.45 16.65 20 25.3 26.05 24.45 22 11.95 9.15 8.6514 - 4.4 14.8 16.65 22.1 25.3 24.25 25.15 19.95 9.5 8.8 7.1515 - 7.05 15.45 17.5 20.2 25.45 25.75 24.55 20 10 8.25 7.316 - 8.65 14.45 17.3 20.8 24.15 21.4 24.85 18.4 10.35 8.85 8.617 - 8.95 16.25 16.75 19.15 24.4 21.3 25.1 18.55 10.2 8.7 9.318 - 7.25 13.55 16.2 19.3 23.25 21.05 25.15 17.55 9.8 9.05 7.5519 - 5.4 13.05 16.6 23.35 22.3 23.4 23.1 19.1 12.25 8.7 3.1520 - 7.45 13.6 16.45 22.3 23.05 20.3 20.85 20.1 11.25 9.7 0.1521 - 9 14.05 17.15 24.2 22.45 22.7 22.45 18.85 7.6 9.65 1.622 - 6.6 11.45 15.5 25.35 24.45 21.55 22.4 16.35 9.15 10.55 1.423 - 5.9 8.05 16.3 20.5 23.15 22.2 22.35 13.35 10.2 10 0.1524 - 7.8 7.95 13.35 21.9 21.7 23.85 22.05 12.1 11 10.6 -1.625 - 10.15 6.75 13.3 20.8 24.25 23.65 21.7 13.15 12.1 10.45 -1.2526 - 10.55 7.35 19.9 20.35 99.15 24.15 21.65 258.15 7.5 11.55 027 - 8.85 10.8 19.05 20.15 21.7 25.25 20.05 --- 6.3 10.7 -0.3528 - 5.05 9.35 16.45 19.65 21.4 29.2 18.25 15.85 7.7 10.6 4.229 - 6.55 10.65 15.8 17.8 23.25 26.75 19 14.95 5.65 10.25 1.6530 - - 11 12.3 19.5 23.8 25.7 20.15 18.9 7.6 5 1.131 - - 13.45 - 23.15 - 25.45 18.05 - 5.45 - 2.3

MEAN DAILY TEMPRATURE2004

258

Table 5.23

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC- 0 0 0 0 0 0 0 9 0 0 0- 0 0 - 0 0 0 0 0 0 0 0- 0 0 - 0 0 - 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 2.5- 0 0 0 0 0 0 0 0 0 0 0- 0 0 - 0 0 0 0 0 0 0 0- 0 0 - 0 0 0 0 0 0 0 0- - 0 0 0 0 0 0 0 0 0 0- 4.8 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 4.1 0 0 0 0 0 0 0- 0 0 0 - 0 0 0 0 0 0 0- 0 0 - - 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 -- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 0 0 0 0- 0 0 0 0 0 0 0 - 0 2 0- 0 0 0 0 0 0 0 - 0 - 5- 0 0 0 0 0 0 0 0 0 - 9- 0 0 - 0 0 0 0 0 0 - 7.3- - 0 0 0 0 0 0 0 0 0 4- - 0 - 0 - 0 0 - 0 - 12.4

4.8 0 0 4.1 0 0 0 9 0 2 40.2

DAILY PRECIPITATION, 2004

Table 5.22b

MEAN MONTHLY TEMPERATURE MONTH 2005 2006 2007 2008 2009

JAN 3.4 5.5 6.0 1.6 3.9 FEB 5.2 13.3 8.0 5.4 8.9 MAR 12.2 13.6 11.4 15.4 14.6 APR 15.7 19.7 20.4 18.5 16.6 MAY 20.0 20.4 22.9 24.3 24.9 JUN 25.2 26.7 26.0 28.6 25.5 JUL 27.8 28.5 28.1 28.3 29.1 AUG 25.7 26.8 26.8 24.7 28.8 SEP 26.1 23.5 23.0 21.5 OCT 18.2 20.7 15.2 17.1 NOV 10.7 12.4 14.0 9.1 DEC 5.2 5.3 5.9 6.3

259

Table 5.24

DATE JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC1 0 0 9.6 0 TRACE 0 0 0 0 0 0 02 0 0 13.2 0 3.1 0 0 0 0 0 0 03 TRACE TRACE TRACE 0 12.1 0 0 0 0 0 0 04 0 0.3 0.5 0 7.3 0 0 0 0 0 0 05 6.4 7.8 TRACE 0 0 0 0 0 0 0 0 06 0 9.9 TRACE 0 0 0 0 0 0 0 0 07 0 8 8 0 0 0 0 0 0 0 0 08 0.8 4.3 7.2 0 0 0 0 0 0 0 0 09 0 13 0.2 0 0 0 0 2.8 0 0 0 0

10 0 8 0.5 0 0 0 0 0.6 0 0 TRACE 011 0 0 0 0 0 0 0 0 0 0 0 012 1.2 0 0 0 0 0 0 0 0 0 0 013 3.1 4.1 0 TRACE 0 3.6 0 0 0 0 0 014 0 13.1 0 TRACE 0 0 0 0 TRACE 0 0 015 0 11.5 0 0 0 0 0 0 0 0 0 016 0 TRACE 2.8 0 0 0 0 0 TRACE 0 0 017 0 0 1.4 0 0 0 0 0 0 0 0 018 0 0 0 0 0 0 0 0 0 0 0 019 0 0 0.6 0 0 0 0 0 0 0 0 020 0 0 1.7 TRACE 0 0 0 0 0 0 0 021 17.4 0 8 TRACE TRACE 1.2 0 0 0 0 TRACE 022 0 0 0 0.4 TRACE TRACE 0 0 0 0 0 023 1.4 TRACE 0 0 0 0 0 0 0 0 0 024 2.2 10.2 0 0 0 0 0 0 0 0 0 025 TRACE 8.2 9.6 0 0 0 0 0 0 0 0 026 0.3 4 0 0 0 0 0 0 0 0 0 027 0.9 26.8 0 0 0 0 0 0 0 0 0 028 TRACE TRACE 0 TRACE 14.4 0 0 0 0 0 0 TRACE29 0 --- 0 0 0 0 0 0 0 0 0 030 0 --- 0 0 0 0 0 0 0 0 0 031 0 --- 0 --- 0 --- 0 0 --- 0 --- 0

TOTAL 33.7 129.2 63.3 0.4 36.9 4.8 0 3.4 0 0 0 0


Table 5.25

DATE JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC1 0.4 0.0 0.0 0.0 0.0 0.0 0.0 18.3 0.0 0.0 0.0 0.02 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 TR3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.54 0.0 0.0 0.0 0.0 0.0 0.0 0.0 12.3 0.0 0.0 0.0 18.55 0.0 0.0 1.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.06 0.0 0.0 0.0 0.0 0.0 0.0 0.0 6.2 0.0 0.0 0.0 TR7 0.0 0.0 TR 0.0 0.0 0.0 0.0 1.3 0.0 0.0 0.0 0.08 0.0 0.0 2.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.09 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 TR11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.012 0.0 0.0 1.2 0.0 0.0 0.0 0.0 TR 0.0 0.0 2.4 0.013 0.0 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.014 0.0 0.0 0.0 1.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.015 4.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 10.6 0.016 3.2 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 1.8 0.017 0.0 0.0 0.0 0.0 5.6 0.0 0.0 0.0 0.0 0.0 8.2 0.018 1.0 0.0 TR 0.0 0.0 0.0 0.0 3.6 0.0 0.0 7.3 0.019 0.0 0.0 2.6 0.0 0.0 0.0 0.0 10.8 0.0 0.0 11.6 0.020 0.0 0.0 1.0 8.4 0.0 0.0 0.0 TR 0.0 0.0 TR 0.021 0.0 0.0 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 2.2 0.022 0.0 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.8 0.023 0.0 0.6 0.0 0.0 0.0 0.0 0.0 0.2 0.0 0.0 0.0 0.024 0.0 4.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025 0.0 1.0 16.5 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.226 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 7.627 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.028 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.029 0.0 *** 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.030 0.6 *** 0.0 TR 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.031 0.0 *** 0.0 *** 0.0 *** 2.4 0.0 *** 0.0 *** 0.0

TOTAL 9.5 6.5 26.1 10.7 5.6 0.0 2.5 54.9 0.0 0.0 46.9 43.8


260

Table 5.26

DATE JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC1 0.0 0.0 0.0 0.3 TR TR 4.7 0.0 0.0 0.0 0.0 2.02 0.0 0.0 0.0 0.0 0.0 0.0 3.3 0.0 0.0 0.0 0.0 0.03 0.0 TR TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.04 0.0 0.0 0.0 2.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.05 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.06 0.0 6.2 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.07 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.08 0.0 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.09 0.0 7.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 TR

10 0.0 17.8 0.0 0.0 0.0 0.0 0.0 TR 0.0 0.0 0.0 3.011 0.0 1.4 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.012 0.0 0.0 9.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.013 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.015 0.0 2.2 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.016 0.0 2.6 3.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.017 0.0 0.0 0.0 5.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018 11.4 0.0 0.0 0.0 0.0 0.0 TR 0.0 0.0 0.0 0.0 0.019 0.0 0.0 2.2 0.0 TR 0.0 TR 0.0 0.0 0.0 0.0 TR20 0.0 5.2 4.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.021 0.0 22.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.022 0.0 0.0 0.0 0.0 0.0 6.8 0.0 0.0 0.0 0.0 0.0 0.023 0.0 0.0 0.0 0.0 0.0 TR. 0.0 0.0 0.0 0.0 0.0 0.024 3.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025 2.8 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.026 0.0 2.9 0.0 0.0 0.0 4.3 0.0 0.0 0.0 0.0 0.0 0.027 0.0 2.6 0.0 0.0 0.0 10.4 0.0 0.0 0.0 0.0 0.0 0.028 0.0 1.6 0.0 0.0 0.0 6.6 0.0 0.0 0.0 0.0 TR 0.029 0.0 *** 0.0 0.0 0.0 6.3 0.0 0.0 0.0 0.0 3.5 0.030 0.0 *** TR TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.031 0.0 *** TR *** 0.0 *** 0.0 0.0 *** 0.0 *** 0.0

TOTAL 17.2 77.3 20.0 8.1 0.0 34.4 8.0 0.0 0.0 0.0 3.5 5.0


Table 5.27

DATE JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.02 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03 0.0 TR 0.0 15.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.04 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.05 3.0 TR 0.0 56.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.06 5.0 TR 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.07 3.0 TR 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.08 1.0 0.0 0.0 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.09 TR 0.0 0.0 0.0 0.0 1.3 0.0 0.0 0.0 0.0 0.0 0.0

10 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.011 0.0 0.0 0.0 0.0 0.0 6.6 TR 0.0 0.0 0.0 0.0 0.012 4.6 0.0 0.0 1.0 0.0 0.0 TR 0.0 0.0 0.0 0.0 0.013 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.0 0.0 0.014 TR 0.0 0.0 0.8 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.015 4.4 0.0 0.0 8.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.016 15.9 0.0 0.0 9.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.017 7.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 5.019 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.420 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.021 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.022 0.0 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.023 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.024 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025 0.0 0.0 0.0 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.026 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.027 TR 0.0 TR 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.028 11.9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.029 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.030 0.0 *** 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.031 0.0 *** 0.0 *** 0.0 *** 0.0 0.0 *** 0.0 *** 0.0

TOTAL 55.8 0.0 0.0 91.4 0.0 9.1 0.0 0.0 0.0 0.0 0.0 7.4


261

Table 5.28

20040300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC

1 0 4 7 0 7 11 0 11 7 11 4 7 7 7 42 0 4 0 0 7 11 0 11 4 11 4 4 0 7 73 0 0 0 0 7 7 0 7 7 4 11 15 0 7 74 0 11 15 0 22 7 0 0 0 4 11 11 0 11 75 7 7 7 0 7 7 0 4 0 4 7 0 0 7 76 0 7 4 7 7 7 0 11 7 7 7 15 7 15 117 7 11 11 0 7 7 7 11 11 4 0 7 7 4 118 0 19 30 7 7 7 0 11 7 7 7 4 0 4 79 11 11 7 11 0 0 7 18 11 0 7 11 0 7 11

10 0 4 7 0 7 7 7 7 7 7 7 7 7 4 411 0 0 7 0 0 4 0 7 7 0 7 7 0 7 712 0 0 0 7 4 11 11 15 19 4 11 7 0 4 013 0 4 7 7 0 7 11 22 11 4 11 0 4 4 714 0 7 7 7 4 7 11 7 11 0 0 26 7 7 715 0 0 4 0 7 15 0 11 0 4 4 15 7 0 716 4 7 7 0 4 7 7 11 15 0 0 15 7 7 417 0 7 11 0 7 15 11 0 11 4 4 11 7 11 718 0 15 7 0 4 4 11 7 7 0 11 7 0 7 719 4 11 7 0 0 4 0 4 0 11 11 11 0 7 1520 7 15 7 0 4 7 0 7 7 4 7 11 11 7 1121 0 19 4 0 19 7 4 15 4 0 7 7 7 11 722 0 7 7 4 7 11 0 22 11 7 15 19 7 11 1123 0 7 11 4 7 11 7 4 15 7 7 11 7 7 724 0 0 7 0 7 7 0 11 4 7 7 7 4 11 1125 7 11 15 0 4 4 7 7 7 7 19 11 7 7 1126 0 15 15 7 4 4 11 7 7 0 7 7 0 4 727 0 4 15 4 11 4 7 7 11 0 7 7 0 7 028 0 15 18 4 4 7 0 11 15 0 7 7 0 4 029 0 7 7 0 11 7 7 19 19 0 7 7 7 11 1130 - - - 0 7 11 7 0 15 0 7 4 4 11 1131 - - - 7 0 15 - - - 0 11 7 - - -

WIND SPEEDAPRMARJAN FEB JUNMAY

Table 5.28 (Continued)

0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC0 0 7 0 7 7 0 0 15 4 4 14 0 7 7 0 4 40 11 7 0 11 11 0 4 0 0 0 11 0 7 0 4 4 70 0 7 0 7 15 0 4 7 4 7 7 0 11 4 7 4 00 7 0 0 4 0 0 0 0 4 7 4 0 7 4 7 7 00 7 0 0 4 0 0 0 0 0 4 7 0 7 0 0 4 07 7 7 4 0 7 0 0 7 0 4 4 0 7 3 0 0 00 7 7 0 7 11 0 0 0 0 7 4 0 7 11 0 4 07 7 7 0 4 7 0 0 0 0 7 14 0 7 0 4 0 47 7 11 0 0 0 0 0 0 14 14 11 0 - 11 0 7 70 4 11 0 0 0 0 0 0 7 7 7 0 0 7 7 4 110 7 4 0 0 4 0 0 0 0 4 4 0 0 7 7 11 44 11 7 0 4 11 0 0 7 0 7 7 4 11 7 11 11 70 7 11 0 4 4 0 4 4 7 14 11 7 7 7 7 7 70 11 4 0 4 11 0 4 4 0 7 7 4 4 7 7 4 70 11 11 4 4 15 0 0 0 0 7 7 4 4 0 7 7 70 11 7 0 4 15 0 0 0 0 7 11 0 0 0 7 11 40 0 7 11 4 15 0 0 0 0 7 11 0 0 0 7 11 70 7 7 7 4 11 0 4 0 0 11 11 7 0 4 11 14 140 4 11 0 7 11 0 0 0 0 7 7 0 4 4 14 17 110 0 19 0 0 4 0 0 0 0 14 7 4 4 4 0 7 40 11 15 0 0 0 0 15 0 7 7 4 7 0 0 0 7 110 7 0 0 0 7 0 4 7 0 7 7 0 7 4 4 7 74 4 11 0 4 4 0 4 0 0 7 7 4 7 4 0 4 74 0 0 0 7 11 0 0 0 0 7 4 7 7 4 0 11 70 7 4 7 15 4 0 0 0 0 14 7 0 7 11 0 7 70 4 0 0 11 15 0 - - 0 7 7 4 4 0 0 4 00 0 0 0 4 11 - - - 0 4 0 0 4 4 0 11 144 4 7 0 0 0 0 7 7 0 4 4 4 4 4 11 11 110 4 0 0 7 4 0 0 7 4 0 0 7 7 7 11 4 44 7 11 0 0 0 0 0 4 4 0 4 7 4 7 11 4 7

15 4 11 0 0 7 - - - 4 0 4 - - - 0 7 7

DECNOVOCTSEPAUGJUL

262

Table 5.29


1 7 4 7 0 4 0 0 14 7 0 0 0 14 7 7 0 0 02 0 4 0 0 11 14 0 7 11 0 0 0 7 0 11 0 0 03 0 7 4 14 14 18 4 7 7 4 4 4 11 0 4 0 0 04 7 11 4 0 11 7 0 11 0 0 0 4 0 7 0 0 0 05 0 0 4 4 11 14 0 4 0 7 7 11 0 0 0 0 0 06 0 0 0 4 0 4 7 14 18 7 7 28 0 0 4 0 4 47 7 0 7 0 0 4 0 4 7 28 28 28 11 7 4 7 0 08 0 4 4 4 14 7 7 14 18 14 11 14 0 --- 18 0 0 09 4 4 4 0 11 14 0 7 4 14 11 11 7 14 4 0 0 0

10 4 7 7 7 14 14 0 14 14 4 11 14 0 7 0 0 0 011 7 14 11 7 11 11 0 4 11 4 0 0 0 4 7 0 7 412 0 11 4 4 4 11 0 7 7 7 4 0 7 7 7 7 14 1413 4 0 0 0 0 0 0 0 0 0 7 14 7 11 --- 0 7 414 4 4 7 0 0 0 0 4 0 18 7 11 0 0 --- 0 7 1115 0 0 0 4 14 15 4 0 14 11 0 7 --- --- --- 7 7 716 0 0 4 7 4 4 0 0 7 0 4 0 --- --- --- 0 0 717 0 4 4 7 4 11 4 0 18 4 4 0 --- --- --- 0 4 718 0 4 4 0 7 11 0 7 14 0 0 0 --- --- --- 0 7 1119 0 0 0 4 4 4 11 18 7 7 7 4 --- --- --- 11 4 2520 7 0 7 0 0 0 14 18 21 0 7 14 --- --- --- 0 28 1421 0 19 18 0 4 0 4 4 11 0 0 15 --- --- --- 7 1 1122 11 21 11 7 11 11 11 11 14 0 0 0 --- --- --- 0 7 1123 0 7 14 4 11 7 0 14 11 11 14 4 --- --- --- 0 4 1124 11 0 4 0 0 0 0 4 21 0 0 7 --- --- --- 0 4 1125 11 14 14 0 0 0 11 14 7 0 11 11 --- --- --- 0 11 2126 11 14 11 0 11 11 7 11 7 0 19 22 --- --- --- 0 7 727 0 7 7 0 0 0 7 4 11 0 7 15 --- --- --- 7 4 728 0 4 7 0 11 7 4 0 7 4 7 4 --- --- --- 11 4 429 0 7 7 --- --- --- 0 11 11 0 7 7 --- --- --- 4 4 1430 0 0 7 --- --- --- 4 7 4 0 0 4 --- --- --- 1 7 731 0 7 7 --- 0 --- 0 7 11 --- --- --- --- --- --- --- --- ---

MAY JUNJAN FEB MAR APRWIND SPEED


0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC0 4 14 4 4 0 7 7 7 4 4 4 0 0 11 0 4 07 4 4 4 4 4 0 4 7 4 7 11 7 4 7 0 4 0

11 0 14 0 0 4 0 4 0 4 4 0 0 4 4 0 4 711 4 11 4 7 7 7 4 4 0 7 7 4 4 4 0 0 414 4 4 4 4 14 4 0 4 4 4 0 0 4 0 4 4 418 7 21 7 7 18 4 4 11 4 11 11 0 7 7 0 4 011 8 0 4 11 11 7 4 0 4 4 0 7 7 7 11 7 4

7 17 14 14 11 14 11 7 11 0 4 7 4 7 4 0 4 40 0 0 7 7 11 4 7 7 7 4 7 7 7 14 11 4 00 7 11 0 11 0 7 7 4 0 11 7 7 7 4 0 0 74 7 7 4 4 14 7 11 7 7 4 4 0 7 7 0 7 74 4 4 14 7 18 4 14 0 4 0 7 0 7 4 4 4 07 0 4 7 7 7 4 0 4 0 0 11 0 4 0 0 7 00 7 7 7 0 14 11 7 11 4 0 7 7 0 4 0 7 44 7 18 0 7 14 11 7 11 0 7 7 0 7 7 0 4 07 4 0 7 4 11 7 4 7 0 4 7 7 11 0 0 7 74 7 7 4 7 7 0 4 11 4 4 0 0 7 11 4 7 74 7 0 0 11 11 4 0 4 4 4 7 0 0 4 4 4 74 4 4 0 25 7 4 0 0 4 4 11 4 7 11 4 7 70 4 7 4 4 14 4 4 7 4 4 7 7 7 11 0 4 00 7 7 7 4 0 0 7 7 7 0 4 4 7 4 0 4 44 7 0 7 4 11 4 11 7 0 7 0 0 4 4 0 4 07 4 11 0 11 7 4 7 14 0 4 4 4 7 4 0 4 47 7 11 7 4 11 0 0 7 0 7 0 0 4 4 0 0 44 4 7 4 14 7 4 7 4 0 0 0 0 4 4 0 0 04 4 4 7 0 11 0 11 7 0 4 4 0 14 7 7 4 04 7 4 4 4 11 4 7 0 4 0 0 0 7 11 0 7 00 4 4 4 0 4 0 0 0 0 0 4 0 0 0 0 4 04 0 7 4 14 14 0 4 0 0 7 7 0 11 7 0 7 77 4 0 4 7 7 0 4 7 4 7 11 7 7 4 0 14 44 7 7 11 4 7 --- --- --- 0 7 0 --- --- --- 0 7 7

JUL AUG SEP OCT NOV DEC

263

Table 5.30


1 2.0 0.0 4.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.02 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.03 4.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.04 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 2.05 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 4.0 0.06 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.07 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.08 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.09 0.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.011 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.012 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.013 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.014 2.0 2.0 2.0 0.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.015 2.0 0.0 2.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.016 0.0 0.0 2.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.017 0.0 2.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.018 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 2.019 0.0 2.0 2.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.020 0.0 0.0 0.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.021 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.022 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.023 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.024 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.025 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.026 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.027 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.028 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 2.0 2.0 2.0 0.0 0.029 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.030 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 2.031 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0

JAN FEB MAR APR MAY JUNWIND SPEED


0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC 0300 UTC 0900 UTC 1200 UTC0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 2.0 2.0 0.0 4.0 2.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 4.0 0.0 4.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 4.00.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 4.0 0.00.0 2.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.00.0 0.0 0.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 2.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 4.0 0.0 0.00.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.00.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 2.02.0 2.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 2.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 2.0 4.0 0.0 4.0 0.00.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 2.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.00.0 2.0 0.0 0.0 0.0 2.0 0.0 0.0 4.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.00.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 4.0 0.0 0.0 0.0 2.0 0.0 0.0 0.00.0 2.0 0.0 2.0 2.0 2.0 0.0 0.0 0.0 4.0 2.0 2.0 0.0 0.0 2.0 0.0 0.0 0.00.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 2.0 2.0 0.0 4.0 2.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.00.0 2.0 2.0 0.0 0.0 2.0 0.0 2.0 2.0 0.0 2.0 2.0 0.0 2.0 0.0 0.0 0.0 0.00.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 2.0 2.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 2.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 4.00.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.0 2.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.00.0 2.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 2.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.02.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.00.0 2.0 4.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.02.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


264

Table 5.31


1 0.0 0.0 2.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 2.0 4.0 0.0 0.0 0.0 0.0 2.0 0.02 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 2.03 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 4.0 2.0 0.0 0.0 0.0 0.0 0.0 0.04 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.05 0.0 0.0 0.0 2.0 2.0 2.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 2.0 0.0 2.06 0.0 2.0 0.0 2.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.07 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.08 0.0 0.0 2.0 2.0 2.0 2.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 2.0 4.0 0.0 0.0 0.09 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

10 0.0 0.0 0.0 2.0 2.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.011 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.012 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.013 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.014 0.0 0.0 2.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.015 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.016 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.017 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.018 0.0 0.0 0.0 0.0 0.0 0.0 8.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.019 2.0 0.0 0.0 0.0 2.0 0.0 5.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.020 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 4.0 0.0 0.0 0.0 0.021 0.0 0.0 0.0 0.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 0.0 0.0 0.022 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 2.023 0.0 0.0 0.0 2.0 0.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 2.0 2.024 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 2.0 4.025 4.0 0.0 0.0 0.0 2.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 2.0 2.0 4.026 0.0 2.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 2.0 2.027 0.0 0.0 0.0 0.0 2.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 2.0 2.028 0.0 0.0 0.0 0.0 2.0 2.0 0.0 2.0 0.0 0.0 0.0 0.0 2.0 2.0 2.0 0.0 0.0 0.029 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 0.0 0.030 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 2.0 0.0 2.0 0.0 0.0 0.031 0.0 0.0 0.0 0.0 2.0 2.0 0.0 2.0 2.0

MAY JUNFEB MAR APRWIND SPEED

JAN



OCT NOV DECJUL AUG SEP

265

Table 5.32


1 0.0 0.0 0.0 0.0 2.0 6.0 4 4 0 0.0 0.0 0.0 0 0 0 0.0 2.0 4.02 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0.0 2.0 0.0 0 0 0 0.0 8.0 8.03 0.0 2.0 0.0 0.0 0.0 0.0 0 2 2 0.0 2.0 0.0 0 0 0 0.0 2.0 6.04 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 0.0 2.0 2.0 0 0 0 0.0 4.0 4.05 0.0 0.0 0.0 0.0 6.0 2.0 0 2 2 0.0 0.0 0.0 0 0 0 0.0 2.0 0.06 0.0 0.0 0.0 0.0 0.0 0.0 2 0 4 0.0 4.0 4.0 0 0 0 0.0 2.0 0.07 2.0 4.0 4.0 0.0 0.0 0.0 0 0 0 0.0 0.0 2.0 0 0 0 0.0 0.0 0.08 0.0 0.0 0.0 0.0 2.0 0.0 0 2 2 0.0 2.0 0.0 0 0 0 0.0 0.0 2.09 0.0 0.0 0.0 0.0 4.0 0.0 0 0 0 0.0 0.0 2.0 0 0 0 0.0 0.0 0.0

10 0.0 0.0 0.0 0.0 0.0 2.0 0 2 0 2.0 2.0 2.0 0 0 0 0.0 0.0 2.011 0.0 0.0 0.0 0.0 4.0 0.0 0 0 0 0.0 2.0 2.0 0 0 0 0.0 0.0 0.012 0.0 0.0 0.0 0.0 2.0 2.0 0 0 0 0.0 0.0 0.0 0 2 0 0.0 0.0 0.013 0.0 0.0 0.0 0.0 0.0 2.0 0 0 0 0.0 0.0 2.0 0 0 0 0.0 0.0 0.014 0.0 0.0 0.0 0.0 2.0 4.0 0 0 0 2.0 4.0 0.0 0 0 0 0.0 0.0 0.015 0.0 0.0 0.0 0.0 0.0 0.0 0 0 0 2.0 8.0 2.0 0 0 0 0.0 0.0 0.016 0.0 0.0 0.0 0.0 4.0 6.0 0 0 0 0.0 0.0 0.0 0 0 0 0.0 0.0 0.017 0.0 0.0 0.0 2.0 4.0 6.0 0 0 2 0.0 0.0 0.0 0 2 0 0.0 0.0 0.018 0.0 0.0 0.0 0.0 4.0 2.0 0 0 0 0.0 0.0 0.0 0 0 0 0.0 2.0 0.019 0.0 0.0 0.0 0.0 2.0 2.0 0 0 0 0.0 0.0 0.0 0 0 0 0.0 0.0 0.020 0.0 4.0 2.0 0.0 4.0 2.0 0 0 0 0.0 0.0 0.0 0 0 0 0.0 0.0 0.021 0.0 0.0 0.0 0.0 6.0 8.0 0 0 0 0.0 0.0 0.0 0 0 2 0.0 0.0 2.022 0.0 2.0 0.0 0.0 0.0 2.0 0 0 0 0.0 0.0 0.0 0 4 0 0.0 0.0 2.023 0.0 2.0 0.0 0.0 0.0 0.0 0 0 0 0.0 0.0 2.0 0 0 4 0.0 0.0 0.024 0.0 0.0 0.0 0.0 2.0 2.0 0 0 0 0.0 0.0 0.0 2 2 2 0.0 0.0 0.025 0.0 0.0 0.0 0.0 4.0 0.0 0 0 0 0.0 0.0 0.0 2 0 2 0.0 0.0 0.026 0.0 0.0 0.0 0.0 6.0 4.0 0 2 0 0.0 0.0 0.0 0 6 4 0.0 0.0 0.027 0.0 4.0 4.0 0.0 2.0 0.0 0 0 0 0.0 0.0 0.0 2 2 2 0.0 4.0 0.028 0.0 0.0 0.0 0.0 2.0 0.0 0 2 0 0.0 0.0 0.0 0 0 0 0.0 0.0 0.029 0.0 4.0 0.0 0.0 0.0 2.0 0 0 0 0.0 0.0 0.0 0 0 0 0.0 0.0 2.030 0.0 0.0 2.0 0 0 0.0 0.0 0.0 0 2 2 0.0 0.0 0.031 0.0 4.0 0.0 0 0 0 0 0 0

FEB MAR APR MAY JUNWIND SPEED

JAN




266

CODE OF HORIZONTAL VISIBILITY AT SURFACE

90 Objects not visible at 50 meters

91 Objects visible at 50m but not at 200 m

92 Objects visible at 200 m but not at 500 m







99 Objects visible at 50000 m or more

// Night visibility not possible.

267

VISIBILITY at 0000 UTC FOR THE YEAR 2004 Month 90 91 92 93 94 95 96 97 98 99 Jan = , 0 0 0 0 0 0 0 0 0 31 Feb = , 0 0 0 0 0 0 0 0 0 29 Mar = , 0 0 0 0 0 0 0 0 0 31 Apr = , 0 0 0 0 0 0 0 0 0 30 May = , 0 0 0 0 0 0 0 0 0 31 Jun = , 0 0 0 0 0 0 0 0 0 30 Jul = , 0 0 0 0 0 0 0 0 0 31 Aug = , 0 0 0 0 0 0 0 0 0 31 Sep = , 0 0 0 0 0 0 0 0 0 30 Oct = , 0 0 0 0 0 0 0 0 0 31 Nov = , 0 0 0 0 0 0 0 0 0 30 Dec = , 0 0 0 0 0 0 0 0 0 31

VISIBILITY at 1200 UTC FOR THE YEAR 2004

Month 90 91 92 93 94 95 96 97 98 99 Jan = , 0 0 0 0 0 0 6 25 0 0 Feb = , 0 0 0 0 1 1 4 23 0 0 Mar = , 0 0 0 0 0 3 8 20 0 0 Apr = , 0 0 0 2 2 3 11 12 0 0 May = , 0 0 1 0 2 6 9 13 0 0 Jun = , 0 0 0 0 3 4 10 13 0 0 Jul = , 0 0 0 0 2 4 14 11 0 0 Aug = , 0 0 0 1 3 4 14 9 0 0 Sep = , 0 0 0 0 0 1 4 25 0 0 Oct = , 0 0 0 0 2 1 3 25 0 0 Nov = , 0 0 0 1 0 1 3 25 0 0 Dec = , 0 0 0 0 0 6 25 0 0 0

268





269





270





271


Month 90 91 92 93 94 95 96 97 98 // Jan = , 0 0 0 0 0 0 0 0 0 31 Feb = , 0 0 0 0 0 0 0 0 0 29 Mar = , 0 0 0 0 0 0 0 0 0 31 Apr = , 0 0 0 0 0 0 0 0 0 30 May = , 0 0 0 0 0 0 0 0 0 31 Jun = , 0 0 0 0 0 0 0 0 0 30 Jul = , 0 0 0 0 0 0 0 0 0 31 Aug = , 0 0 0 0 0 0 0 0 0 31 Sep = , 0 0 0 0 0 0 0 0 0 30 Oct = , 0 0 0 0 0 0 0 0 0 31 Nov = , 0 0 0 0 0 0 0 0 0 30 Dec = , 0 0 0 0 0 0 0 0 0 31



272

Table 5.33 Daily Visibility of Quetta 2004-08

Station Year Month Date 08:00 A.M 05:00 P.M

Quetta 2004 1 1 96 97

Quetta 2004 1 2 95 97

Quetta 2004 1 3 95 97

Quetta 2004 1 4 95 97

Quetta 2004 1 5 95 97

Quetta 2004 1 6 95 96

Quetta 2004 1 7 95 97

Quetta 2004 1 8 95 97

Quetta 2004 1 9 95 97

Quetta 2004 1 10 95 97

Quetta 2004 1 11 95 97

Quetta 2004 1 12 95 97

Quetta 2004 1 13 96 97

Quetta 2004 1 14 95 97

Quetta 2004 1 15 95 96

Quetta 2004 1 16 96 96

Quetta 2004 1 17 96 97

Quetta 2004 1 18 95 97

Quetta 2004 1 19 95 97

Quetta 2004 1 20 95 96

Quetta 2004 1 21 96 97

Quetta 2004 1 22 96 97

Quetta 2004 1 23 96 97

Quetta 2004 1 24 95 97

Quetta 2004 1 25 95 97

Quetta 2004 1 26 96 97

Quetta 2004 1 27 95 97

Quetta 2004 1 28 94 96

Quetta 2004 1 29 93 97

Quetta 2004 1 30 96 96

Quetta 2004 1 31 96 97

Quetta 2004 2 1 96 97

Quetta 2004 2 2 95 95

Quetta 2004 2 3 95 96

Quetta 2004 2 4 96 96

Quetta 2004 2 5 96 97

Quetta 2004 2 6 95 97

Quetta 2004 2 7 96 96

Quetta 2004 2 8 96 94

Quetta 2004 2 9 96 97

Quetta 2004 2 10 96 97

Quetta 2004 2 11 95 97

Quetta 2004 2 12 95 96

Quetta 2004 2 13 95 97

Quetta 2004 2 14 95 97

Quetta 2004 2 15 96 97

273

Quetta 2004 2 16 95 97

Quetta 2004 2 17 95 96

Quetta 2004 2 18 95 97

Quetta 2004 2 19 95 97

Quetta 2004 2 20 95 95

Quetta 2004 2 21 95 97

Quetta 2004 2 22 95 97

Quetta 2004 2 23 95 97

Quetta 2004 2 24 95 97

Quetta 2004 2 25 95 97

Quetta 2004 2 26 95 96

Quetta 2004 2 27 95 97

Quetta 2004 2 28 95 97

Quetta 2004 2 29 95 97

Quetta 2004 3 1 95 96

Quetta 2004 3 2 96 97

Quetta 2004 3 3 95 97

Quetta 2004 3 4 95 96

Quetta 2004 3 5 95 96

Quetta 2004 3 6 95 96

Quetta 2004 3 7 95 96

Quetta 2004 3 8 95 96

Quetta 2004 3 9 95 96

Quetta 2004 3 10 94 97

Quetta 2004 3 11 94 96

Quetta 2004 3 12 95 96

Quetta 2004 3 13 95 96

Quetta 2004 3 14 95 96

Quetta 2004 3 15 95 95

Quetta 2004 3 16 95 97

Quetta 2004 3 17 95 95

Quetta 2004 3 18 95 96

Quetta 2004 3 19 95 97

Quetta 2004 3 20 95 96

Quetta 2004 3 21 95 95

Quetta 2004 3 22 96 97

Quetta 2004 3 23 96 97

Quetta 2004 3 24 96 97

Quetta 2004 3 25 96 97

Quetta 2004 3 26 95 97

Quetta 2004 3 27 95 96

Quetta 2004 3 28 96 97

Quetta 2004 3 29 96 96

Quetta 2004 3 30 95 97

Quetta 2004 3 31 96 97

Quetta 2004 4 1 969 96

Quetta 2004 4 2 96 96

Quetta 2004 4 3 95 96

Quetta 2004 4 4 95 96

274

Quetta 2004 4 5 95 97

Quetta 2004 4 6 96 97

Quetta 2004 4 7 96 97

Quetta 2004 4 8 95 96

Quetta 2004 4 9 96 94

Quetta 2004 4 10 95 97

Quetta 2004 4 11 95 97

Quetta 2004 4 12 96 96

Quetta 2004 4 13 96 96

Quetta 2004 4 14 96 97

Quetta 2004 4 15 96 97

Quetta 2004 4 16 96 94

Quetta 2004 4 17 95 96

Quetta 2004 4 18 95 96

Quetta 2004 4 19 95 95

Quetta 2004 4 20 95 95

Quetta 2004 4 21 95 96

Quetta 2004 4 22 96 95

Quetta 2004 4 23 96 97

Quetta 2004 4 24 96 97

Quetta 2004 4 25 95 97

Quetta 2004 4 26 95 96

Quetta 2004 4 27 96 95

Quetta 2004 4 28 96 97

Quetta 2004 4 29 95 93

Quetta 2004 4 30 93 93

Quetta 2004 5 1 95 97

Quetta 2004 5 2 95 96

Quetta 2004 5 3 95 94

Quetta 2004 5 4 95 97

Quetta 2004 5 5 95 97

Quetta 2004 5 6 96 95

Quetta 2004 5 7 94 95

Quetta 2004 5 8 94 96

Quetta 2004 5 9 96 97

Quetta 2004 5 10 95 97

Quetta 2004 5 11 95 97

Quetta 2004 5 12 96 97

Quetta 2004 5 13 96 97

Quetta 2004 5 14 96 96

Quetta 2004 5 15 96 96

Quetta 2004 5 16 94 95

Quetta 2004 5 17 95 96

Quetta 2004 5 18 96 96

Quetta 2004 5 19 96 95

Quetta 2004 5 20 96 94

Quetta 2004 5 21 96 96

Quetta 2004 5 22 95 92

Quetta 2004 5 23 94 95

275

Quetta 2004 5 24 95 97

Quetta 2004 5 25 95 96

Quetta 2004 5 26 96 97

Quetta 2004 5 27 95 95

Quetta 2004 5 28 95 97

Quetta 2004 5 29 95 97

Quetta 2004 5 30 95 97

Quetta 2004 5 31 95 96

Quetta 2004 6 1 96 96

Quetta 2004 6 2 95 96

Quetta 2004 6 3 96 97

Quetta 2004 6 4 95 96

Quetta 2004 6 5 95 96

Quetta 2004 6 6 96 95

Quetta 2004 6 7 94 94

Quetta 2004 6 8 94 96

Quetta 2004 6 9 95 95

Quetta 2004 6 10 95 97

Quetta 2004 6 11 96 97

Quetta 2004 6 12 96 96

Quetta 2004 6 13 96 97

Quetta 2004 6 14 96 94

Quetta 2004 6 15 96 94

Quetta 2004 6 16 94 97

Quetta 2004 6 17 95 95

Quetta 2004 6 18 95 95

Quetta 2004 6 19 95 95

Quetta 2004 6 20 96 96

Quetta 2004 6 21 96 96

Quetta 2004 6 22 95 96

Quetta 2004 6 23 95 96

Quetta 2004 6 24 95 97

Quetta 2004 6 25 95 97

Quetta 2004 6 26 95 96

Quetta 2004 6 27 95 96

Quetta 2004 6 28 95 96

Quetta 2004 6 29 95 97

Quetta 2004 6 30 95 97

Quetta 2004 7 1 95 97

Quetta 2004 7 2 95 97

Quetta 2004 7 3 95 96

Quetta 2004 7 4 95 97

Quetta 2004 7 5 95 96

Quetta 2004 7 6 95 96

Quetta 2004 7 7 93 96

Quetta 2004 7 8 95 94

Quetta 2004 7 9 94 95

Quetta 2004 7 10 94 95

Quetta 2004 7 11 95 97

276

Quetta 2004 7 12 96 97

Quetta 2004 7 13 94 96

Quetta 2004 7 14 96 96

Quetta 2004 7 15 96 96

Quetta 2004 7 16 96 96

Quetta 2004 7 17 95 97

Quetta 2004 7 18 95 96

Quetta 2004 7 19 96 94

Quetta 2004 7 20 95 95

Quetta 2004 7 21 95 96

Quetta 2004 7 22 95 96

Quetta 2004 7 23 95 96

Quetta 2004 7 24 96 97

Quetta 2004 7 25 96 97

Quetta 2004 7 26 95 97

Quetta 2004 7 27 95 97

Quetta 2004 7 28 96 96

Quetta 2004 7 29 96 96

Quetta 2004 7 30 96 95

Quetta 2004 7 31 96 95

Quetta 2004 8 1 96 94

Quetta 2004 8 2 95 95

Quetta 2004 8 3 94 96

Quetta 2004 8 4 95 96

Quetta 2004 8 5 95 96

Quetta 2004 8 6 95 97

Quetta 2004 8 7 96 96

Quetta 2004 8 8 93 94

Quetta 2004 8 9 93 94

Quetta 2004 8 10 95 95

Quetta 2004 8 11 95 96

Quetta 2004 8 12 95 96

Quetta 2004 8 13 95 97

Quetta 2004 8 14 96 97

Quetta 2004 8 15 96 96

Quetta 2004 8 16 95 96

Quetta 2004 8 17 95 95

Quetta 2004 8 18 95 93

Quetta 2004 8 19 95 95

Quetta 2004 8 20 95 96

Quetta 2004 8 21 95 96

Quetta 2004 8 22 96 97

Quetta 2004 8 23 95 97

Quetta 2004 8 24 95 96

Quetta 2004 8 25 95 97

Quetta 2004 8 26 95 97

Quetta 2004 8 27 95 97

Quetta 2004 8 28 95 97

Quetta 2004 8 29 95 96

277

Quetta 2004 8 30 94 96

Quetta 2004 8 31 94 96

Quetta 2004 9 1 95 97

Quetta 2004 9 2 95 96

Quetta 2004 9 3 95 96

Quetta 2004 9 4 95 97

Quetta 2004 9 5 95 96

Quetta 2004 9 6 95 97

Quetta 2004 9 7 94 97

Quetta 2004 9 8 95 97

Quetta 2004 9 9 95 97

Quetta 2004 9 10 95 97

Quetta 2004 9 11 95 97

Quetta 2004 9 12 95 97

Quetta 2004 9 12 95 97

Quetta 2004 9 14 95 97

Quetta 2004 9 15 95 97

Quetta 2004 9 16 94 95

Quetta 2004 9 17 94 96

Quetta 2004 9 18 95 97

Quetta 2004 9 19 95 96

Quetta 2004 9 20 96 97

Quetta 2004 9 21 95 96

Quetta 2004 9 22 95 96

Quetta 2004 9 23 95 96

Quetta 2004 9 24 95 97

Quetta 2004 9 25 95 97

Quetta 2004 9 26 94 97

Quetta 2004 9 27 96 97

Quetta 2004 9 28 95 97

Quetta 2004 9 29 95 97

Quetta 2004 9 30 96 97

Quetta 2004 10 1 95 97

Quetta 2004 10 2 95 97

Quetta 2004 10 3 95 97

Quetta 2004 10 4 95 97

Quetta 2004 10 5 95 97

Quetta 2004 10 6 95 97

Quetta 2004 10 7 95 95

Quetta 2004 10 8 93 94

Quetta 2004 10 9 93 94

Quetta 2004 10 10 93 96

Quetta 2004 10 11 96 97

Quetta 2004 10 12 96 97

Quetta 2004 10 13 96 97

Quetta 2004 10 14 95 97

Quetta 2004 10 15 95 97

Quetta 2004 10 16 95 96

Quetta 2004 10 17 95 97

278

Quetta 2004 10 18 97 97

Quetta 2004 10 19 96 97

Quetta 2004 10 20 96 97

Quetta 2004 10 21 96 97

Quetta 2004 10 22 95 97

Quetta 2004 10 23 95 97

Quetta 2004 10 24 95 97

Quetta 2004 10 25 95 96

Quetta 2004 10 26 95 97

Quetta 2004 10 27 95 97

Quetta 2004 10 28 96 97

Quetta 2004 10 29 95 97

Quetta 2004 10 30 95 97

Quetta 2004 10 31 93 97

Quetta 2004 11 1 95 97

Quetta 2004 11 2 95 97

Quetta 2004 11 3 95 97

Quetta 2004 11 4 95 97

Quetta 2004 11 5 95 97

Quetta 2004 11 6 94 97

Quetta 2004 11 7 95 97

Quetta 2004 11 8 95 97

Quetta 2004 11 9 95 97

Quetta 2004 11 10 95 97

Quetta 2004 11 11 95 97

Quetta 2004 11 12 95 97

Quetta 2004 11 13 95 97

Quetta 2004 11 14 95 97

Quetta 2004 11 15 95 97

Quetta 2004 11 16 95 97

Quetta 2004 11 17 95 97

Quetta 2004 11 18 95 97

Quetta 2004 11 19 95 97

Quetta 2004 11 20 95 97

Quetta 2004 11 21 95 97

Quetta 2004 11 22 95 97

Quetta 2004 11 23 95 97

Quetta 2004 11 24 95 97

Quetta 2004 11 25 95 95

Quetta 2004 11 26 96 96

Quetta 2004 11 27 95 96

Quetta 2004 11 28 95 96

Quetta 2004 11 29 95 93

Quetta 2004 11 30 95 97

Quetta 2004 12 1 96 97

Quetta 2004 12 2 96 97

Quetta 2004 12 3 95 96

Quetta 2004 12 4 95 97

Quetta 2004 12 5 95 97

279

Quetta 2004 12 6 95 96

Quetta 2004 12 7 94 97

Quetta 2004 12 8 94 97

Quetta 2004 12 9 95 97

Quetta 2004 12 10 95 97

Quetta 2004 12 11 96 97

Quetta 2004 12 12 95 97

Quetta 2004 12 13 96 97

Quetta 2004 12 14 95 97

Quetta 2004 12 15 95 97

Quetta 2004 12 16 96 97

Quetta 2004 12 17 96 97

Quetta 2004 12 18 95 97

Quetta 2004 12 19 96 97

Quetta 2004 12 20 95 97

Quetta 2004 12 21 95 97

Quetta 2004 12 22 95 97

Quetta 2004 12 23 95 97

Quetta 2004 12 24 95 97

Quetta 2004 12 25 96 97

Quetta 2004 12 26 95 97

Quetta 2004 12 27 95 96

Quetta 2004 12 28 95 96

Quetta 2004 12 29 95 96

Quetta 2004 12 30 95 97

Quetta 2004 12 31 93 96

Quetta 2005 1 1 96 97

Quetta 2005 1 2 95 97

Quetta 2005 1 3 96 97

Quetta 2005 1 4 96 97

Quetta 2005 1 5 96 97

Quetta 2005 1 6 94 96

Quetta 2005 1 7 95 97

Quetta 2005 1 8 96 97

Quetta 2005 1 9 95 97

Quetta 2005 1 10 96 97

Quetta 2005 1 11 95 97

Quetta 2005 1 12 96 96

Quetta 2005 1 13 95 96

Quetta 2005 1 14 95 97

Quetta 2005 1 15 95 97

Quetta 2005 1 16 94 97

Quetta 2005 1 17 96 97

Quetta 2005 1 18 95 97

Quetta 2005 1 19 95 97

Quetta 2005 1 20 96 96

Quetta 2005 1 21 95 96

Quetta 2005 1 22 96 97

Quetta 2005 1 23 96 96

280

Quetta 2005 1 24 96 97

Quetta 2005 1 25 95 96

Quetta 2005 1 26 95 93

Quetta 2005 1 27 96 97

Quetta 2005 1 28 95 97

Quetta 2005 1 29 95 97

Quetta 2005 1 30 95 97

Quetta 2005 1 31 95 97

Quetta 2005 2 1 94 97

Quetta 2005 2 2 95 97

Quetta 2005 2 3 95 97

Quetta 2005 2 4 96 96

Quetta 2005 2 5 95 96

Quetta 2005 2 6 95 96

Quetta 2005 2 7 95 96

Quetta 2005 2 8 96 97

Quetta 2005 2 9 96 97

Quetta 2005 2 10 96 97

Quetta 2005 2 11 96 97

Quetta 2005 2 12 96 96

Quetta 2005 2 13 95 96

Quetta 2005 2 14 96 96

Quetta 2005 2 15 96 96

Quetta 2005 2 16 95 97

Quetta 2005 2 17 95 96

Quetta 2005 2 18 95 94

Quetta 2005 2 19 95 97

Quetta 2005 2 20 95 97

Quetta 2005 2 21 95 97

Quetta 2005 2 22 95 97

Quetta 2005 2 23 96 96

Quetta 2005 2 24 96 96

Quetta 2005 2 25 95 95

Quetta 2005 2 26 94 95

Quetta 2005 2 27 96 96

Quetta 2005 2 28 95 97

Quetta 2005 3 1 95 97

Quetta 2005 3 2 95 96

Quetta 2005 3 3 96 97

Quetta 2005 3 4 95 97

Quetta 2005 3 5 94 97

Quetta 2005 3 6 96 97

Quetta 2005 3 7 96 97

Quetta 2005 3 8 96 96

Quetta 2005 3 9 96 97

Quetta 2005 3 10 95 97

Quetta 2005 3 11 96 97

Quetta 2005 3 12 95 97

Quetta 2005 3 13 94 97

281

Quetta 2005 3 14 95 97

Quetta 2005 3 15 95 97

Quetta 2005 3 16 96 97

Quetta 2005 3 17 95 97

Quetta 2005 3 18 96 97

Quetta 2005 3 19 95 96

Quetta 2005 3 20 96 97

Quetta 2005 3 21 96 97

Quetta 2005 3 22 96 97

Quetta 2005 3 23 96 96

Quetta 2005 3 24 95 97

Quetta 2005 3 25 96 95

Quetta 2005 3 26 95 95

Quetta 2005 3 27 96 97

Quetta 2005 3 28 95 97

Quetta 2005 3 29 95 97

Quetta 2005 3 30 96 97

Quetta 2005 3 31 95 97

Quetta 2005 4 1 96 97

Quetta 2005 4 2 94 97

Quetta 2005 4 3 96 97

Quetta 2005 4 4 96 97

Quetta 2005 4 5 96 96

Quetta 2005 4 6 95 93

Quetta 2005 4 7 94 94

Quetta 2005 4 8 95 96

Quetta 2005 4 9 95 96

Quetta 2005 4 10 95 97

Quetta 2005 4 11 95 97

Quetta 2005 4 12 95 97

Quetta 2005 4 13 96 96

Quetta 2005 4 14 96 97

Quetta 2005 4 15 96 97

Quetta 2005 4 16 96 97

Quetta 2005 4 17 95 97

Quetta 2005 4 18 96 97

Quetta 2005 4 19 96 97

Quetta 2005 4 20 96 96

Quetta 2005 4 21 96 96

Quetta 2005 4 22 95 97

Quetta 2005 4 23 96 97

Quetta 2005 4 24 95 96

Quetta 2005 4 25 95 96

Quetta 2005 4 26 96 97

Quetta 2005 4 27 95 97

Quetta 2005 4 28 96 96

Quetta 2005 4 29 96 97

Quetta 2005 4 30 96 ‐99

Quetta 2005 5 1 96 97

282

Quetta 2005 5 2 95 96

Quetta 2005 5 3 96 96

Quetta 2005 5 4 96 97

Quetta 2005 5 5 96 96

Quetta 2005 5 6 95 96

Quetta 2005 5 7 95 97

Quetta 2005 5 8 95 97

Quetta 2005 5 9 96 97

Quetta 2005 5 10 96 97

Quetta 2005 5 11 96 96

Quetta 2005 5 12 96 94

Quetta 2005 5 13 97 97

Quetta 2005 5 14 96 96

Quetta 2005 5 15 96 97

Quetta 2005 5 16 95 96

Quetta 2005 5 17 96 97

Quetta 2005 5 18 96 96

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Quetta 2005 6 1 96 97

Quetta 2005 6 2 96 97

Quetta 2005 6 3 96 97

Quetta 2005 6 4 96 96

Quetta 2005 6 5 96 97

Quetta 2005 6 6 96 97

Quetta 2005 6 7 96 96

Quetta 2005 6 8 96 97

Quetta 2005 6 9 95 96

Quetta 2005 6 10 96 96

Quetta 2005 6 11 96 97

Quetta 2005 6 12 96 97

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Quetta 2005 6 27 95 97

Quetta 2005 6 28 96 97

Quetta 2005 6 29 96 97

Quetta 2005 6 30 96 96

Quetta 2005 7 1 96 97

Quetta 2005 7 2 96 97

Quetta 2005 7 3 96 97

Quetta 2005 7 4 96 97

Quetta 2005 7 5 96 97

Quetta 2005 7 6 96 96

Quetta 2005 7 7 96 96

Quetta 2005 7 8 96 97

Quetta 2005 7 9 96 97

Quetta 2005 7 10 96 97

Quetta 2005 7 11 96 96

Quetta 2005 7 12 96 97

Quetta 2005 7 13 96 97

Quetta 2005 7 14 96 97

Quetta 2005 7 15 96 97

Quetta 2005 7 16 96 97

Quetta 2005 7 17 96 97

Quetta 2005 7 18 96 97

Quetta 2005 7 19 96 97

Quetta 2005 7 20 96 97

Quetta 2005 7 21 96 97

Quetta 2005 7 22 96 97

Quetta 2005 7 23 95 97

Quetta 2005 7 24 96 97

Quetta 2005 7 25 95 97

Quetta 2005 7 26 96 97

Quetta 2005 7 27 96 97

Quetta 2005 7 28 96 97

Quetta 2005 7 29 96 97

Quetta 2005 7 30 95 97

Quetta 2005 7 31 96 97

Quetta 2005 8 1 96 97

Quetta 2005 8 2 96 97

Quetta 2005 8 3 96 97

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Quetta 2005 8 5 96 97

Quetta 2005 8 6 96 97

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284

Quetta 2005 8 8 96 96

Quetta 2005 8 9 96 96

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Quetta 2005 8 11 96 97

Quetta 2005 8 12 95 97

Quetta 2005 8 13 95 96

Quetta 2005 8 14 95 97

Quetta 2005 8 15 96 97

Quetta 2005 8 16 95 97

Quetta 2005 8 17 95 97

Quetta 2005 8 18 95 97

Quetta 2005 8 19 95 97

Quetta 2005 8 20 95 97

Quetta 2005 8 21 95 97

Quetta 2005 8 22 95 97

Quetta 2005 8 23 96 97

Quetta 2005 8 24 96 97

Quetta 2005 8 25 96 97

Quetta 2005 8 26 95 97

Quetta 2005 8 27 96 97

Quetta 2005 8 28 96 96

Quetta 2005 8 29 95 96

Quetta 2005 8 30 95 97

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Quetta 2005 9 1 96 97

Quetta 2005 9 2 96 97

Quetta 2005 9 3 95 97

Quetta 2005 9 4 95 97

Quetta 2005 9 5 95 97

Quetta 2005 9 6 95 97

Quetta 2005 9 7 95 97

Quetta 2005 9 8 96 97

Quetta 2005 9 9 95 97

Quetta 2005 9 10 96 96

Quetta 2005 9 11 95 96

Quetta 2005 9 12 96 96

Quetta 2005 9 13 95 97

Quetta 2005 9 14 96 96

Quetta 2005 9 15 96 97

Quetta 2005 9 16 96 96

Quetta 2005 9 17 95 97

Quetta 2005 9 18 95 97

Quetta 2005 9 19 96 97

Quetta 2005 9 20 96 97

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285

Quetta 2005 9 26 95 97

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Quetta 2005 10 1 96 97

Quetta 2005 10 2 95 97

Quetta 2005 10 3 95 97

Quetta 2005 10 4 95 97

Quetta 2005 10 5 95 97

Quetta 2005 10 6 95 97

Quetta 2005 10 7 95 97

Quetta 2005 10 8 95 97

Quetta 2005 10 9 95 97

Quetta 2005 10 10 95 97

Quetta 2005 10 11 95 97

Quetta 2005 10 12 95 96

Quetta 2005 10 13 95 96

Quetta 2005 10 14 95 97

Quetta 2005 10 15 95 97

Quetta 2005 10 16 95 97

Quetta 2005 10 17 95 97

Quetta 2005 10 18 95 97

Quetta 2005 10 19 95 97

Quetta 2005 10 20 95 97

Quetta 2005 10 21 96 97

Quetta 2005 10 22 95 97

Quetta 2005 10 23 96 97

Quetta 2005 10 24 95 97

Quetta 2005 10 25 95 97

Quetta 2005 10 26 96 97

Quetta 2005 10 27 95 97

Quetta 2005 10 28 96 97

Quetta 2005 10 29 95 96

Quetta 2005 10 30 96 97

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Quetta 2005 11 1 96 97

Quetta 2005 11 2 95 97

Quetta 2005 11 3 96 97

Quetta 2005 11 4 95 97

Quetta 2005 11 5 96 97

Quetta 2005 11 6 95 97

Quetta 2005 11 7 95 97

Quetta 2005 11 8 96 97

Quetta 2005 11 9 96 97

Quetta 2005 11 10 96 96

Quetta 2005 11 11 96 97

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Quetta 2005 11 13 96 97

286

Quetta 2005 11 14 96 97

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Quetta 2005 11 17 96 97

Quetta 2005 11 18 96 97

Quetta 2005 11 19 96 97

Quetta 2005 11 20 96 97

Quetta 2005 11 21 96 97

Quetta 2005 11 22 95 97

Quetta 2005 11 23 96 97

Quetta 2005 11 24 96 97

Quetta 2005 11 25 96 97

Quetta 2005 11 26 96 97

Quetta 2005 11 27 96 97

Quetta 2005 11 28 96 97

Quetta 2005 11 29 96 97

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Quetta 2005 12 1 95 97

Quetta 2005 12 2 95 97

Quetta 2005 12 3 96 97

Quetta 2005 12 4 96 97

Quetta 2005 12 5 95 97

Quetta 2005 12 6 95 97

Quetta 2005 12 7 96 97

Quetta 2005 12 8 96 96

Quetta 2005 12 9 96 97

Quetta 2005 12 10 95 97

Quetta 2005 12 11 95 97

Quetta 2005 12 12 95 97

Quetta 2005 12 13 95 97

Quetta 2005 12 14 96 97

Quetta 2005 12 15 96 97

Quetta 2005 12 16 95 97

Quetta 2005 12 17 95 97

Quetta 2005 12 18 96 97

Quetta 2005 12 19 95 96

Quetta 2005 12 20 95 97

Quetta 2005 12 21 95 97

Quetta 2005 12 22 95 97

Quetta 2005 12 23 95 97

Quetta 2005 12 24 95 97

Quetta 2005 12 25 95 97

Quetta 2005 12 26 95 96

Quetta 2005 12 27 96 97

Quetta 2005 12 28 96 97

Quetta 2005 12 29 96 97

Quetta 2005 12 30 96 97

Quetta 2005 12 31 96 96

Quetta 2006 1 1 96 95

287

Quetta 2006 1 2 96 97

Quetta 2006 1 3 96 97

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Quetta 2006 1 5 96 97

Quetta 2006 1 6 96 96

Quetta 2006 1 7 96 97

Quetta 2006 1 8 96 97

Quetta 2006 1 9 96 97

Quetta 2006 1 10 96 97

Quetta 2006 1 11 96 97

Quetta 2006 1 12 96 97

Quetta 2006 1 13 96 97

Quetta 2006 1 14 96 96

Quetta 2006 1 15 96 96

Quetta 2006 1 16 96 97

Quetta 2006 1 17 96 97

Quetta 2006 1 18 96 97

Quetta 2006 1 19 96 97

Quetta 2006 1 20 92 97

Quetta 2006 1 21 96 97

Quetta 2006 1 22 96 97

Quetta 2006 1 23 96 97

Quetta 2006 1 24 96 97

Quetta 2006 1 25 96 97

Quetta 2006 1 26 96 97

Quetta 2006 1 27 96 97

Quetta 2006 1 28 96 96

Quetta 2006 1 29 96 96

Quetta 2006 1 30 96 96

Quetta 2006 1 31 96 97

Quetta 2006 2 1 96 97

Quetta 2006 2 2 96 97

Quetta 2006 2 3 96 97

Quetta 2006 2 4 96 97

Quetta 2006 2 5 96 97

Quetta 2006 2 6 96 97

Quetta 2006 2 7 96 97

Quetta 2006 2 8 96 97

Quetta 2006 2 9 96 97

Quetta 2006 2 10 96 97

Quetta 2006 2 11 96 97

Quetta 2006 2 12 96 97

Quetta 2006 2 13 96 96

Quetta 2006 2 14 96 97

Quetta 2006 2 15 96 97

Quetta 2006 2 16 96 97

Quetta 2006 2 17 95 97

Quetta 2006 2 18 96 97

Quetta 2006 2 19 96 97

288

Quetta 2006 2 20 96 97

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Quetta 2006 2 24 96 97

Quetta 2006 2 25 96 97

Quetta 2006 2 26 96 97

Quetta 2006 2 27 96 97

Quetta 2006 2 28 96 97

Quetta 2006 3 1 96 97

Quetta 2006 3 2 96 96

Quetta 2006 3 3 96 96

Quetta 2006 3 4 96 97

Quetta 2006 3 5 96 96

Quetta 2006 3 6 96 97

Quetta 2006 3 7 96 96

Quetta 2006 3 8 96 97

Quetta 2006 3 9 96 97

Quetta 2006 3 10 96 97

Quetta 2006 3 11 96 97

Quetta 2006 3 12 96 96

Quetta 2006 3 13 95 97

Quetta 2006 3 14 96 97

Quetta 2006 3 15 96 97

Quetta 2006 3 16 96 97

Quetta 2006 3 17 96 97

Quetta 2006 3 18 96 96

Quetta 2006 3 19 96 96

Quetta 2006 3 20 96 97

Quetta 2006 3 21 96 97

Quetta 2006 3 22 96 97

Quetta 2006 3 23 96 96

Quetta 2006 3 24 96 97

Quetta 2006 3 25 94 96

Quetta 2006 3 26 96 97

Quetta 2006 3 27 96 97

Quetta 2006 3 28 96 97

Quetta 2006 3 29 96 97

Quetta 2006 3 30 96 97

Quetta 2006 3 31 96 96

Quetta 2006 4 1 96 97

Quetta 2006 4 2 96 97

Quetta 2006 4 3 96 97

Quetta 2006 4 4 96 97

Quetta 2006 4 5 96 97

Quetta 2006 4 6 96 97

Quetta 2006 4 7 96 97

Quetta 2006 4 8 96 96

Quetta 2006 4 9 96 96

289

Quetta 2006 4 10 96 96

Quetta 2006 4 11 96 97

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Quetta 2006 4 13 96 96

Quetta 2006 4 14 96 97

Quetta 2006 4 15 96 97

Quetta 2006 4 16 96 97

Quetta 2006 4 17 96 97

Quetta 2006 4 18 96 97

Quetta 2006 4 19 96 97

Quetta 2006 4 20 96 96

Quetta 2006 4 21 96 97

Quetta 2006 4 22 96 96

Quetta 2006 4 23 96 97

Quetta 2006 4 24 96 97

Quetta 2006 4 25 96 97

Quetta 2006 4 26 96 97

Quetta 2006 4 27 96 97

Quetta 2006 4 28 96 96

Quetta 2006 4 29 96 96

Quetta 2006 4 30 96 96

Quetta 2006 5 1 96 96

Quetta 2006 5 2 96 96

Quetta 2006 5 3 96 97

Quetta 2006 5 4 96 97

Quetta 2006 5 5 96 97

Quetta 2006 5 6 96 97

Quetta 2006 5 7 96 97

Quetta 2006 5 8 96 97

Quetta 2006 5 9 96 97

Quetta 2006 5 10 95 97

Quetta 2006 5 11 96 97

Quetta 2006 5 12 96 97

Quetta 2006 5 13 95 96

Quetta 2006 5 14 95 95

Quetta 2006 5 15 96 96

Quetta 2006 5 16 96 95

Quetta 2006 5 17 96 96

Quetta 2006 5 18 95 95

Quetta 2006 5 19 95 95

Quetta 2006 5 20 96 96

Quetta 2006 5 21 96 96

Quetta 2006 5 22 96 97

Quetta 2006 5 23 96 97

Quetta 2006 5 24 96 97

Quetta 2006 5 25 95 97

Quetta 2006 5 26 96 97

Quetta 2006 5 27 96 97

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290

Quetta 2006 5 29 96 96

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Quetta 2006 6 1 95 97

Quetta 2006 6 2 95 97

Quetta 2006 6 3 95 96

Quetta 2006 6 4 95 96

Quetta 2006 6 5 95 97

Quetta 2006 6 6 95 97

Quetta 2006 6 7 95 97

Quetta 2006 6 8 95 97

Quetta 2006 6 9 95 97

Quetta 2006 6 10 96 97

Quetta 2006 6 11 96 97

Quetta 2006 6 12 96 97

Quetta 2006 6 13 96 97

Quetta 2006 6 14 96 97

Quetta 2006 6 15 96 97

Quetta 2006 6 16 95 97

Quetta 2006 6 17 95 97

Quetta 2006 6 18 95 97

Quetta 2006 6 19 95 97

Quetta 2006 6 20 95 97

Quetta 2006 6 21 96 96

Quetta 2006 6 22 95 97

Quetta 2006 6 23 96 97

Quetta 2006 6 24 95 97

Quetta 2006 6 25 96 97

Quetta 2006 6 26 96 93

Quetta 2006 6 27 96 97

Quetta 2006 6 28 96 96

Quetta 2006 6 29 95 97

Quetta 2006 6 30 96 97

Quetta 2006 7 1 96 97

Quetta 2006 7 2 96 96

Quetta 2006 7 3 96 97

Quetta 2006 7 4 96 97

Quetta 2006 7 5 95 96

Quetta 2006 7 6 96 96

Quetta 2006 7 7 95 96

Quetta 2006 7 8 95 96

Quetta 2006 7 9 94 96

Quetta 2006 7 10 95 96

Quetta 2006 7 11 95 95

Quetta 2006 7 12 94 95

Quetta 2006 7 13 94 94

Quetta 2006 7 14 94 95

Quetta 2006 7 15 95 97

Quetta 2006 7 16 95 97

291

Quetta 2006 7 17 96 97

Quetta 2006 7 18 96 97

Quetta 2006 7 19 96 97

Quetta 2006 7 20 96 97

Quetta 2006 7 21 96 97

Quetta 2006 7 22 96 96

Quetta 2006 7 23 96 97

Quetta 2006 7 24 96 96

Quetta 2006 7 25 96 96

Quetta 2006 7 26 95 96

Quetta 2006 7 27 95 96

Quetta 2006 7 28 95 95

Quetta 2006 7 29 96 96

Quetta 2006 7 30 96 96

Quetta 2006 7 31 96 97

Quetta 2006 8 1 96 96

Quetta 2006 8 2 96 97

Quetta 2006 8 3 96 96

Quetta 2006 8 4 96 96

Quetta 2006 8 5 97 97

Quetta 2006 8 6 96 96

Quetta 2006 8 7 96 97

Quetta 2006 8 8 96 95

Quetta 2006 8 9 96 96

Quetta 2006 8 10 96 97

Quetta 2006 8 11 96 97

Quetta 2006 8 12 94 97

Quetta 2006 8 13 96 97

Quetta 2006 8 14 96 97

Quetta 2006 8 15 96 96

Quetta 2006 8 16 96 96

Quetta 2006 8 17 96 96

Quetta 2006 8 18 96 96

Quetta 2006 8 19 95 96

Quetta 2006 8 20 96 97

Quetta 2006 8 21 95 96

Quetta 2006 8 22 96 97

Quetta 2006 8 23 96 96

Quetta 2006 8 24 96 97

Quetta 2006 8 25 95 97

Quetta 2006 8 26 96 97

Quetta 2006 8 27 95 96

Quetta 2006 8 28 95 96

Quetta 2006 8 29 95 96

Quetta 2006 8 30 96 96

Quetta 2006 8 31 96 95

Quetta 2006 9 1 96 95

Quetta 2006 9 2 96 96

Quetta 2006 9 3 96 96

292

Quetta 2006 9 4 96 97

Quetta 2006 9 5 96 97

Quetta 2006 9 6 95 97

Quetta 2006 9 7 94 97

Quetta 2006 9 8 94 97

Quetta 2006 9 9 94 97

Quetta 2006 9 10 95 97

Quetta 2006 9 11 94 96

Quetta 2006 9 12 94 96

Quetta 2006 9 13 94 96

Quetta 2006 9 14 95 97

Quetta 2006 9 15 95 97

Quetta 2006 9 16 95 97

Quetta 2006 9 17 95 97

Quetta 2006 9 18 95 97

Quetta 2006 9 19 95 96

Quetta 2006 9 20 94 96

Quetta 2006 9 21 95 97

Quetta 2006 9 22 96 97

Quetta 2006 9 23 94 97

Quetta 2006 9 24 94 97

Quetta 2006 9 25 95 97

Quetta 2006 9 26 94 97

Quetta 2006 9 27 94 97

Quetta 2006 9 28 94 97

Quetta 2006 9 29 95 97

Quetta 2006 9 30 95 97

Quetta 2006 10 1 94 97

Quetta 2006 10 2 96 95

Quetta 2006 10 3 95 97

Quetta 2006 10 4 95 97

Quetta 2006 10 5 95 97

Quetta 2006 10 6 96 97

Quetta 2006 10 7 95 97

Quetta 2006 10 8 94 97

Quetta 2006 10 9 95 97

Quetta 2006 10 10 95 97

Quetta 2006 10 11 95 97

Quetta 2006 10 12 95 97

Quetta 2006 10 13 95 97

Quetta 2006 10 14 96 97

Quetta 2006 10 15 95 97

Quetta 2006 10 16 95 97

Quetta 2006 10 17 95 97

Quetta 2006 10 18 96 95

Quetta 2006 10 19 95 97

Quetta 2006 10 20 96 97

Quetta 2006 10 21 95 97

Quetta 2006 10 22 96 97

293

Quetta 2006 10 23 95 97

Quetta 2006 10 24 95 97

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Quetta 2006 10 26 96 97

Quetta 2006 10 27 96 97

Quetta 2006 10 28 96 97

Quetta 2006 10 29 96 97

Quetta 2006 10 30 95 97

Quetta 2006 10 31 96 97

Quetta 2006 11 1 96 97

Quetta 2006 11 2 95 97

Quetta 2006 11 3 95 97

Quetta 2006 11 4 94 97

Quetta 2006 11 5 95 97

Quetta 2006 11 6 95 97

Quetta 2006 11 7 95 97

Quetta 2006 11 8 95 97

Quetta 2006 11 9 95 96

Quetta 2006 11 10 95 96

Quetta 2006 11 11 95 96

Quetta 2006 11 12 96 97

Quetta 2006 11 13 95 97

Quetta 2006 11 14 96 96

Quetta 2006 11 15 96 96

Quetta 2006 11 16 95 96

Quetta 2006 11 17 96 96

Quetta 2006 11 18 94 96

Quetta 2006 11 19 96 97

Quetta 2006 11 20 95 97

Quetta 2006 11 21 95 97

Quetta 2006 11 22 96 97

Quetta 2006 11 23 96 97

Quetta 2006 11 24 96 97

Quetta 2006 11 25 95 97

Quetta 2006 11 26 96 97

Quetta 2006 11 27 96 96

Quetta 2006 11 28 95 97

Quetta 2006 11 29 95 97

Quetta 2006 11 30 95 97

Quetta 2006 12 1 95 97

Quetta 2006 12 2 96 95

Quetta 2006 12 3 96 97

Quetta 2006 12 4 96 97

Quetta 2006 12 5 96 97

Quetta 2006 12 6 95 97

Quetta 2006 12 7 95 97

Quetta 2006 12 8 95 96

Quetta 2006 12 9 96 97

Quetta 2006 12 10 96 97

294

Quetta 2006 12 11 96 97

Quetta 2006 12 12 95 97

Quetta 2006 12 13 96 97

Quetta 2006 12 14 96 97

Quetta 2006 12 15 96 97

Quetta 2006 12 16 95 97

Quetta 2006 12 17 95 97

Quetta 2006 12 18 96 97

Quetta 2006 12 19 95 97

Quetta 2006 12 20 96 97

Quetta 2006 12 21 95 97

Quetta 2006 12 22 96 97

Quetta 2006 12 23 96 96

Quetta 2006 12 24 96 97

Quetta 2006 12 25 96 96

Quetta 2006 12 26 96 97

Quetta 2006 12 27 95 97

Quetta 2006 12 28 95 97

Quetta 2006 12 29 95 97

Quetta 2006 12 30 95 96

Quetta 2006 12 31 95 97

Quetta 2007 1 1 96 97

Quetta 2007 1 2 96 97

Quetta 2007 1 3 96 97

Quetta 2007 1 4 96 97

Quetta 2007 1 5 96 97

Quetta 2007 1 6 96 97

Quetta 2007 1 7 96 97

Quetta 2007 1 8 96 97

Quetta 2007 1 9 96 96

Quetta 2007 1 10 95 97

Quetta 2007 1 11 96 97

Quetta 2007 1 12 96 97

Quetta 2007 1 13 96 97

Quetta 2007 1 14 96 97

Quetta 2007 1 15 95 97

Quetta 2007 1 16 96 97

Quetta 2007 1 17 97 96

Quetta 2007 1 18 96 94

Quetta 2007 1 19 95 97

Quetta 2007 1 20 96 97

Quetta 2007 1 21 95 97

Quetta 2007 1 22 96 97

Quetta 2007 1 23 96 97

Quetta 2007 1 24 95 96

Quetta 2007 1 25 96 97

Quetta 2007 1 26 96 97

Quetta 2007 1 27 92 97

Quetta 2007 1 28 95 97

295

Quetta 2007 1 29 95 97

Quetta 2007 1 30 96 97

Quetta 2007 1 31 96 97

Quetta 2007 2 1 95 97

Quetta 2007 2 2 96 97

Quetta 2007 2 3 96 96

Quetta 2007 2 4 95 97

Quetta 2007 2 5 96 97

Quetta 2007 2 6 96 97

Quetta 2007 2 7 96 97

Quetta 2007 2 8 97 96

Quetta 2007 2 9 95 96

Quetta 2007 2 10 96 96

Quetta 2007 2 11 96 97

Quetta 2007 2 12 95 97

Quetta 2007 2 13 96 97

Quetta 2007 2 14 96 97

Quetta 2007 2 15 96 96

Quetta 2007 2 16 95 97

Quetta 2007 2 17 96 97

Quetta 2007 2 18 95 97

Quetta 2007 2 19 96 97

Quetta 2007 2 20 96 97

Quetta 2007 2 21 96 97

Quetta 2007 2 22 96 97

Quetta 2007 2 23 96 97

Quetta 2007 2 24 96 96

Quetta 2007 2 25 94 97

Quetta 2007 2 26 95 96

Quetta 2007 2 27 96 97

Quetta 2007 2 28 96 97

Quetta 2007 3 1 96 97

Quetta 2007 3 2 96 97

Quetta 2007 3 3 96 96

Quetta 2007 3 4 95 96

Quetta 2007 3 5 96 97

Quetta 2007 3 6 96 96

Quetta 2007 3 7 95 97

Quetta 2007 3 8 96 95

Quetta 2007 3 9 96 97

Quetta 2007 3 10 96 97

Quetta 2007 3 11 96 97

Quetta 2007 3 12 96 97

Quetta 2007 3 13 96 97

Quetta 2007 3 14 95 97

Quetta 2007 3 15 95 98

Quetta 2007 3 16 96 97

Quetta 2007 3 17 96 96

Quetta 2007 3 18 95 96

296

Quetta 2007 3 19 96 96

Quetta 2007 3 20 96 97

Quetta 2007 3 21 96 97

Quetta 2007 3 22 96 97

Quetta 2007 3 23 95 97

Quetta 2007 3 24 96 97

Quetta 2007 3 25 96 97

Quetta 2007 3 26 96 97

Quetta 2007 3 27 96 97

Quetta 2007 3 28 96 97

Quetta 2007 3 29 96 97

Quetta 2007 3 30 96 97

Quetta 2007 3 31 96 97

Quetta 2007 4 1 97 97

Quetta 2007 4 2 ‐96 97

Quetta 2007 4 3 96 96

Quetta 2007 4 4 96 97

Quetta 2007 4 5 96 97

Quetta 2007 4 6 96 97

Quetta 2007 4 7 96 97

Quetta 2007 4 8 96 97

Quetta 2007 4 9 95 97

Quetta 2007 4 10 96 97

Quetta 2007 4 11 95 97

Quetta 2007 4 12 96 97

Quetta 2007 4 13 96 97

Quetta 2007 4 14 96 96

Quetta 2007 4 15 96 96

Quetta 2007 4 16 96 97

Quetta 2007 4 17 96 97

Quetta 2007 4 18 96 97

Quetta 2007 4 19 96 96

Quetta 2007 4 20 96 97

Quetta 2007 4 21 96 97

Quetta 2007 4 22 96 97

Quetta 2007 4 23 96 97

Quetta 2007 4 24 96 97

Quetta 2007 4 25 96 97

Quetta 2007 4 26 96 97

Quetta 2007 4 27 96 97

Quetta 2007 4 28 96 97

Quetta 2007 4 29 96 97

Quetta 2007 4 30 96 96

Quetta 2007 5 1 96 97

Quetta 2007 5 2 96 97

Quetta 2007 5 3 96 97

Quetta 2007 5 4 96 96

Quetta 2007 5 5 96 97

Quetta 2007 5 6 96 97

297

Quetta 2007 5 7 96 97

Quetta 2007 5 8 96 95

Quetta 2007 5 9 95 97

Quetta 2007 5 10 96 97

Quetta 2007 5 11 96 97

Quetta 2007 5 12 96 97

Quetta 2007 5 13 96 97

Quetta 2007 5 14 96 97

Quetta 2007 5 15 96 97

Quetta 2007 5 16 95 97

Quetta 2007 5 17 96 96

Quetta 2007 5 18 96 97

Quetta 2007 5 19 96 97

Quetta 2007 5 20 96 97

Quetta 2007 5 21 96 97

Quetta 2007 5 22 96 97

Quetta 2007 5 23 96 96

Quetta 2007 5 24 96 96

Quetta 2007 5 25 96 97

Quetta 2007 5 26 96 97

Quetta 2007 5 27 96 97

Quetta 2007 5 28 96 96

Quetta 2007 5 29 96 97

Quetta 2007 5 30 96 97

Quetta 2007 5 31 95 97

Quetta 2007 6 1 96 96

Quetta 2007 6 2 96 96

Quetta 2007 6 3 96 97

Quetta 2007 6 4 96 96

Quetta 2007 6 5 96 97

Quetta 2007 6 6 96 97

Quetta 2007 6 7 96 97

Quetta 2007 6 8 96 97

Quetta 2007 6 9 96 97

Quetta 2007 6 10 96 97

Quetta 2007 6 11 96 97

Quetta 2007 6 12 96 97

Quetta 2007 6 13 96 97

Quetta 2007 6 14 96 97

Quetta 2007 6 15 96 97

Quetta 2007 6 16 96 96

Quetta 2007 6 17 96 96

Quetta 2007 6 18 96 96

Quetta 2007 6 19 96 97

Quetta 2007 6 20 96 96

Quetta 2007 6 21 96 96

Quetta 2007 6 22 96 96

Quetta 2007 6 23 96 97

Quetta 2007 4 24 96 97

298

Quetta 2007 4 25 96 97

Quetta 2007 4 26 96 97

Quetta 2007 4 27 96 97

Quetta 2007 4 28 96 97

Quetta 2007 4 29 96 97

Quetta 2007 4 30 96 96

Quetta 2007 5 1 96 97

Quetta 2007 5 2 96 97

Quetta 2007 5 3 96 97

Quetta 2007 5 4 96 96

Quetta 2007 5 5 96 97

Quetta 2007 5 6 96 97

Quetta 2007 5 7 96 97

Quetta 2007 5 8 96 95

Quetta 2007 5 9 95 97

Quetta 2007 5 10 96 97

Quetta 2007 5 11 96 97

Quetta 2007 5 12 96 97

Quetta 2007 5 13 96 97

Quetta 2007 5 14 96 97

Quetta 2007 5 15 96 97

Quetta 2007 5 16 95 97

Quetta 2007 5 17 96 96

Quetta 2007 5 18 96 97

Quetta 2007 5 19 96 97

Quetta 2007 5 20 96 97

Quetta 2007 5 21 96 97

Quetta 2007 5 22 96 97

Quetta 2007 5 23 96 96

Quetta 2007 5 24 96 96

Quetta 2007 5 25 96 97

Quetta 2007 5 26 96 97

Quetta 2007 5 27 96 97

Quetta 2007 5 28 96 96

Quetta 2007 5 29 96 97

Quetta 2007 5 30 96 97

Quetta 2007 5 31 95 97

Quetta 2007 6 1 96 96

Quetta 2007 6 2 96 96

Quetta 2007 6 3 96 97

Quetta 2007 6 4 96 96

Quetta 2007 6 5 96 97

Quetta 2007 6 6 96 97

Quetta 2007 6 7 96 97

Quetta 2007 6 8 96 97

Quetta 2007 6 9 96 97

Quetta 2007 6 10 96 97

Quetta 2007 6 11 96 97

Quetta 2007 6 12 96 97

299

Quetta 2007 6 13 96 97

Quetta 2007 6 14 96 97

Quetta 2007 6 15 96 97

Quetta 2007 6 16 96 96

Quetta 2007 6 17 96 96

Quetta 2007 6 18 96 96

Quetta 2007 6 19 96 97

Quetta 2007 6 20 96 96

Quetta 2007 6 21 96 96

Quetta 2007 6 22 96 96

Quetta 2007 6 23 96 97

Quetta 2007 6 24 96 97

Quetta 2007 6 25 96 96

Quetta 2007 6 26 96 97

Quetta 2007 6 27 97 97

Quetta 2007 6 28 96 96

Quetta 2007 6 29 96 96

Quetta 2007 6 30 96 97

Quetta 2007 7 1 96 97

Quetta 2007 7 2 96 96

Quetta 2007 7 3 94 95

Quetta 2007 7 4 93 96

Quetta 2007 7 5 95 97

Quetta 2007 7 6 96 97

Quetta 2007 7 7 96 97

Quetta 2007 7 8 96 97

Quetta 2007 7 9 96 96

Quetta 2007 7 10 95 97

Quetta 2007 7 11 96 97

Quetta 2007 7 12 95 96

Quetta 2007 7 13 95 96

Quetta 2007 7 14 95 96

Quetta 2007 7 15 94 94

Quetta 2007 7 16 95 96

Quetta 2007 7 17 96 96

Quetta 2007 7 18 95 96

Quetta 2007 7 19 95 97

Quetta 2007 7 20 96 94

Quetta 2007 7 21 93 94

Quetta 2007 7 22 94 94

Quetta 2007 7 23 95 96

Quetta 2007 7 24 95 96

Quetta 2007 7 25 95 96

Quetta 2007 7 26 95 97

Quetta 2007 7 27 95 96

Quetta 2007 7 28 95 96

Quetta 2007 7 29 95 96

Quetta 2007 7 30 96 96

Quetta 2007 7 31 96 97

300

Quetta 2007 8 1 95 97

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Quetta 2007 8 3 95 96

Quetta 2007 8 4 96 97

Quetta 2007 8 5 96 97

Quetta 2007 8 6 96 96

Quetta 2007 8 7 96 96

Quetta 2007 8 8 96 96

Quetta 2007 8 9 95 97

Quetta 2007 8 10 96 96

Quetta 2007 8 11 96 96

Quetta 2007 8 12 96 92

Quetta 2007 8 13 93 94

Quetta 2007 8 14 94 96

Quetta 2007 8 15 95 96

Quetta 2007 8 16 95 96

Quetta 2007 8 17 95 97

Quetta 2007 8 18 95 97

Quetta 2007 8 19 95 97

Quetta 2007 8 20 95 97

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Quetta 2007 8 23 95 96

Quetta 2007 8 24 95 97

Quetta 2007 8 25 95 97

Quetta 2007 8 26 95 96

Quetta 2007 8 27 95 96

Quetta 2007 8 28 95 96

Quetta 2007 8 29 95 97

Quetta 2007 8 30 95 97

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Quetta 2007 9 1 96 97

Quetta 2007 9 2 96 97

Quetta 2007 9 3 96 97

Quetta 2007 9 4 96 97

Quetta 2007 9 5 95 96

Quetta 2007 9 6 95 97

Quetta 2007 9 7 95 96

Quetta 2007 9 8 96 97

Quetta 2007 9 9 95 97

Quetta 2007 9 10 96 97

Quetta 2007 9 11 95 97

Quetta 2007 9 12 95 97

Quetta 2007 9 13 95 97

Quetta 2007 9 14 95 97

Quetta 2007 9 15 95 97

Quetta 2007 9 16 95 97

Quetta 2007 9 17 95 97

Quetta 2007 9 18 95 97

301

Quetta 2007 9 19 95 97

Quetta 2007 9 20 95 96

Quetta 2007 9 21 95 97

Quetta 2007 9 22 95 96

Quetta 2007 9 23 94 95

Quetta 2007 9 24 94 95

Quetta 2007 9 25 95 96

Quetta 2007 9 26 96 97

Quetta 2007 9 27 95 97

Quetta 2007 9 28 95 97

Quetta 2007 9 29 94 96

Quetta 2007 9 30 94 96

Quetta 2007 10 1 95 97

Quetta 2007 10 2 96 95

Quetta 2007 10 3 94 96

Quetta 2007 10 4 95 96

Quetta 2007 10 5 95 95

Quetta 2007 10 6 95 97

Quetta 2007 10 7 95 97

Quetta 2007 10 8 95 97

Quetta 2007 10 9 96 97

Quetta 2007 10 10 95 96

Quetta 2007 10 11 95 97

Quetta 2007 10 12 95 97

Quetta 2007 10 13 95 97

Quetta 2007 10 14 95 97

Quetta 2007 10 15 95 97

Quetta 2007 10 16 95 97

Quetta 2007 10 17 95 97

Quetta 2007 10 18 95 97

Quetta 2007 10 19 95 97

Quetta 2007 10 20 95 97

Quetta 2007 10 21 96 97

Quetta 2007 10 22 95 97

Quetta 2007 10 23 94 97

Quetta 2007 10 24 95 96

Quetta 2007 10 25 95 97

Quetta 2007 10 26 94 97

Quetta 2007 10 27 95 97

Quetta 2007 10 28 96 97

Quetta 2007 10 29 95 97

Quetta 2007 10 30 94 97

Quetta 1907 10 31 94 97

Quetta 2007 11 1 95 97

Quetta 2007 11 2 95 97

Quetta 2007 11 3 95 97

Quetta 2007 11 4 95 96

Quetta 2007 11 5 95 97

Quetta 2007 11 6 95 97

302

Quetta 2007 11 7 95 97

Quetta 2007 11 8 95 97

Quetta 2007 11 9 95 97

Quetta 2007 11 10 95 97

Quetta 2007 11 11 95 97

Quetta 2007 11 12 95 97

Quetta 2007 11 13 95 97

Quetta 2007 11 14 95 97

Quetta 2007 11 15 95 97

Quetta 2007 11 16 95 97

Quetta 2007 11 17 95 97

Quetta 2007 11 18 94 97

Quetta 2007 11 19 94 97

Quetta 2007 11 20 95 97

Quetta 2007 11 21 95 97

Quetta 2007 11 22 95 97

Quetta 2007 11 23 95 97

Quetta 2007 11 24 97 97

Quetta 2007 11 25 95 97

Quetta 2007 11 26 95 97

Quetta 2007 11 27 95 97

Quetta 2007 11 28 95 96

Quetta 2007 11 29 96 97

Quetta 2007 11 30 95 97

Quetta 2007 12 1 96 96

Quetta 2007 12 2 95 96

Quetta 2007 12 3 95 96

Quetta 2007 12 4 95 97

Quetta 2007 12 5 95 97

Quetta 2007 12 6 95 97

Quetta 2007 12 7 95 97

Quetta 2007 12 8 95 97

Quetta 2007 12 9 96 96

Quetta 2007 12 10 96 95

Quetta 2007 12 11 91 97

Quetta 2007 12 12 95 97

Quetta 2007 12 13 95 97

Quetta 2007 12 14 95 96

Quetta 2007 12 15 95 97

Quetta 2007 12 16 95 97

Quetta 2007 12 17 95 97

Quetta 2007 12 18 96 96

Quetta 2007 12 19 96 96

Quetta 2007 12 20 96 97

Quetta 2007 12 21 96 96

Quetta 2007 12 22 96 97

Quetta 2007 12 23 96 96

Quetta 2007 12 24 96 97

Quetta 2007 12 25 96 97

303

Quetta 2007 12 26 96 97

Quetta 2007 12 27 96 97

Quetta 2007 12 28 96 97

Quetta 2007 12 29 96 97

Quetta 2007 12 30 96 97

Quetta 2007 12 31 96 97

Quetta 2008 1 1 95 97

Quetta 2008 1 2 95 97

Quetta 2008 1 3 96 97

Quetta 2008 1 4 96 96

Quetta 2008 1 5 95 96

Quetta 2008 1 6 95 96

Quetta 2008 1 7 96 96

Quetta 2008 1 8 96 97

Quetta 2008 1 9 96 97

Quetta 2008 1 10 96 96

Quetta 2008 1 11 96 97

Quetta 2008 1 12 96 96

Quetta 2008 1 13 92 97

Quetta 2008 1 14 96 95

Quetta 2008 1 15 95 96

Quetta 2008 1 16 96 96

Quetta 2008 1 17 96 96

Quetta 2008 1 18 96 97

Quetta 2008 1 19 96 97

Quetta 2008 1 20 96 97

Quetta 2008 1 21 96 97

Quetta 2008 1 22 96 97

Quetta 2008 1 23 96 97

Quetta 2008 1 24 96 97

Quetta 2008 1 25 96 97

Quetta 2008 1 26 96 97

Quetta 2008 1 27 96 96

Quetta 2008 1 28 94 96

Quetta 2008 1 29 92 97

Quetta 2008 1 30 96 97

Quetta 2008 1 31 96 97

Quetta 2008 2 1 96 96

Quetta 2008 2 2 92 96

Quetta 2008 2 3 96 96

Quetta 2008 2 4 96 97

Quetta 2008 2 5 94 96

Quetta 2008 2 6 96 96

Quetta 2008 2 7 96 97

Quetta 2008 2 8 96 97

Quetta 2008 2 9 96 97

Quetta 2008 2 10 96 97

Quetta 2008 2 11 96 97

Quetta 2008 2 12 96 97

304

Quetta 2008 2 13 96 97

Quetta 2008 2 14 94 97

Quetta 2008 2 15 95 97

Quetta 2008 2 16 96 96

Quetta 2008 2 17 96 97

Quetta 2008 2 18 96 97

Quetta 2008 2 19 96 97

Quetta 2008 2 20 96 97

Quetta 2008 2 21 96 91

Quetta 2008 2 22 96 97

Quetta 2008 2 23 96 96

Quetta 2008 2 24 96 97

Quetta 2008 2 25 96 97

Quetta 2008 2 26 96 97

Quetta 2008 2 27 95 97

Quetta 2008 2 28 95 97

Quetta 2008 2 29 95 96

Quetta 2008 3 1 96 97

Quetta 2008 3 2 96 97

Quetta 2008 3 3 96 96

Quetta 2008 3 4 95 97

Quetta 2008 3 5 95 96

Quetta 2008 3 6 96 95

Quetta 2008 3 7 95 97

Quetta 2008 3 8 95 96

Quetta 2008 3 9 96 96

Quetta 2008 3 10 95 96

Quetta 2008 3 11 95 97

Quetta 2008 3 12 95 97

Quetta 2008 3 13 95 97

Quetta 2008 3 14 95 97

Quetta 2008 3 15 95 96

Quetta 2008 3 16 95 96

Quetta 2008 3 17 95 97

Quetta 2008 3 18 95 96

Quetta 2008 3 19 95 97

Quetta 2008 3 20 95 97

Quetta 2008 3 21 96 96

Quetta 2008 3 22 95 96

Quetta 2008 3 23 95 96

Quetta 2008 3 24 95 97

Quetta 2008 3 25 95 96

Quetta 2008 3 26 95 96

Quetta 2008 3 27 95 96

Quetta 2008 3 28 96 96

Quetta 2008 3 29 95 96

Quetta 2008 3 30 94 96

Quetta 2008 3 31 94 96

Quetta 2008 4 1 95 97

305

Quetta 2008 4 2 95 95

Quetta 2008 4 3 96 96

Quetta 2008 4 4 95 97

Quetta 2008 4 5 95 97

Quetta 2008 4 6 96 96

Quetta 2008 4 7 95 97

Quetta 2008 4 8 95 96

Quetta 2008 4 9 96 96

Quetta 2008 4 10 96 96

Quetta 2008 4 11 95 96

Quetta 2008 4 12 96 97

Quetta 2008 4 13 95 97

Quetta 2008 4 14 96 96

Quetta 2008 4 15 96 96

Quetta 2008 4 16 96 97

Quetta 2008 4 17 95 97

Quetta 2008 4 18 95 97

Quetta 2008 4 19 96 96

Quetta 2008 4 20 95 96

Quetta 2008 4 21 96 97

Quetta 2008 4 22 96 96

Quetta 2008 4 23 95 97

Quetta 2008 4 24 95 97

Quetta 2008 4 25 95 97

Quetta 2008 4 26 95 97

Quetta 2008 4 27 95 97

Quetta 2008 4 28 95 97

Quetta 2008 4 29 95 97

Quetta 2008 4 30 95 97

Quetta 2008 5 1 96 97

Quetta 2008 5 2 95 97

Quetta 2008 5 3 95 96

Quetta 2008 5 4 95 96

Quetta 2008 5 5 96 97

Quetta 2008 5 6 95 97

Quetta 2008 5 7 96 96

Quetta 2008 5 8 95 95

Quetta 2008 5 9 96 96

Quetta 2008 5 10 95 97

Quetta 2008 5 11 95 97

Quetta 2008 5 12 95 96

Quetta 2008 5 13 95 96

Quetta 2008 5 14 95 97

Quetta 2008 5 15 95 97

Quetta 2008 5 16 95 97

Quetta 2008 5 17 96 94

Quetta 2008 5 18 96 97

Quetta 2008 5 19 96 97

Quetta 2008 5 20 95 97

306

Quetta 2008 5 21 95 97

Quetta 2008 5 22 95 95

Quetta 2008 5 23 95 96

Quetta 2008 5 24 95 96

Quetta 2008 5 25 96 96

Quetta 2008 5 26 95 97

Quetta 2008 5 27 96 96

Quetta 2008 5 28 96 97

Quetta 2008 5 29 95 97

Quetta 2008 5 30 95 96

Quetta 2008 5 31 95 96

Quetta 2008 6 1 94 97

Quetta 2008 6 2 95 96

Quetta 2008 6 3 95 95

Quetta 2008 6 4 95 96

Quetta 2008 6 5 95 97

Quetta 2008 6 6 95 96

Quetta 2008 6 7 95 97

Quetta 2008 6 8 96 95

Quetta 2008 6 9 96 93

Quetta 2008 6 10 96 96

Quetta 2008 6 11 95 96

Quetta 2008 6 12 95 95

Quetta 2008 6 13 95 96

Quetta 2008 6 14 95 96

Quetta 2008 6 15 95 95

Quetta 2008 6 16 95 95

Quetta 2008 6 17 94 96

Quetta 2008 6 18 95 95

Quetta 2008 6 19 95 96

Quetta 2008 6 20 95 97

Quetta 2008 6 21 95 96

Quetta 2008 6 22 95 97

Quetta 2008 6 23 95 97

Quetta 2008 6 24 95 97

Quetta 2008 6 25 95 97

Quetta 2008 6 26 95 96

Quetta 2008 6 27 95 94

Quetta 2008 6 28 ‐99 94

Quetta 2008 6 29 95 94

Quetta 2008 6 30 94 95

Quetta 2008 7 1 95 96

Quetta 2008 7 2 95 96

Quetta 2008 7 3 95 96

Quetta 2008 7 4 95 96

Quetta 2008 7 5 95 96

Quetta 2008 7 6 95 96

Quetta 2008 7 7 95 96

Quetta 2008 7 8 95 96

307

Quetta 2008 7 9 95 95

Quetta 2008 7 10 95 96

Quetta 2008 7 11 95 96

Quetta 2008 7 12 95 94

Quetta 2008 7 13 95 95

Quetta 2008 7 14 95 95

Quetta 2008 7 15 95 96

Quetta 2008 7 16 95 97

Quetta 2008 7 17 95 96

Quetta 2008 7 18 95 96

Quetta 2008 7 19 95 97

Quetta 2008 7 20 95 95

Quetta 2008 7 21 94 94

Quetta 2008 7 22 95 95

Quetta 2008 7 23 95 96

Quetta 2008 7 24 95 97

Quetta 2008 7 25 95 96

Quetta 2008 7 26 94 95

Quetta 2008 7 27 95 92

Quetta 2008 7 28 95 94

Quetta 2008 7 29 95 95

Quetta 2008 7 30 95 96

Quetta 2008 7 31 95 96

Quetta 2008 8 1 95 95

Quetta 2008 8 2 96 96

Quetta 2008 8 3 96 96

Quetta 2008 8 4 96 96

Quetta 2008 8 5 95 95

Quetta 2008 8 6 95 96

Quetta 2008 8 7 95 97

Quetta 2008 8 8 95 96

Quetta 2008 8 9 94 95

Quetta 2008 8 10 95 94

Quetta 2008 8 11 92 92

Quetta 2008 8 12 92 94

Quetta 2008 8 13 94 94

Quetta 2008 8 14 94 95

Quetta 2008 8 15 94 96

Quetta 2008 8 16 94 96

Quetta 2008 8 17 95 97

Quetta 2008 8 18 95 96

Quetta 2008 8 19 95 96

Quetta 2008 8 20 94 97

Quetta 2008 8 21 95 97

Quetta 2008 8 22 95 96

Quetta 2008 8 23 95 96

Quetta 2008 8 24 94 96

Quetta 2008 8 25 94 97

Quetta 2008 8 26 95 97

308

Quetta 2008 8 27 95 97

Quetta 2008 8 28 95 97

Quetta 2008 8 29 95 97

Quetta 2008 8 30 95 96

Quetta 2008 8 31 95 95

Quetta 2008 9 1 96 96

Quetta 2008 9 2 95 96

Quetta 2008 9 3 95 96 Quetta 2008 9 4 95 96

Quetta 2008 9 5 94 96

Quetta 2008 9 6 92 93

Quetta 2008 9 7 93 93

Quetta 2008 9 8 93 95

Quetta 2008 9 9 95 97

Quetta 2008 9 10 95 97

Quetta 2008 9 11 95 97

Quetta 2008 9 12 94 97

Quetta 2008 9 13 95 97

Quetta 2008 9 14 95 97

Quetta 2008 9 15 95 97

Quetta 2008 9 16 95 96

Quetta 2008 9 17 95 96

Quetta 2008 9 18 95 96

Quetta 2008 9 19 95 96

Quetta 2008 9 20 94 96

Quetta 2008 9 21 94 96

Quetta 2008 9 22 94 96

Quetta 2008 9 23 95 96

Quetta 2008 9 24 95 97

Quetta 2008 9 25 94 97

Quetta 2008 9 26 96 96

Quetta 2008 9 27 95 97

Quetta 2008 9 28 95 97

Quetta 2008 9 29 95 97

Quetta 2008 9 30 95 97

Quetta 2008 10 1 95 97

Quetta 2008 10 2 95 97

Quetta 2008 10 3 95 97

Quetta 2008 10 4 95 97

Quetta 2008 10 5 95 97

Quetta 2008 10 6 95 97

Quetta 2008 10 7 95 96

Quetta 2008 10 8 95 96

Quetta 2008 10 9 95 97

Quetta 2008 10 10 95 97

Quetta 2008 10 11 95 97

Quetta 2008 10 12 95 97

Quetta 2008 10 13 95 96

Quetta 2008 10 14 95 96

309

Quetta 2008 10 15 95 95

Quetta 2008 10 16 95 96

Quetta 2008 10 17 94 96

Quetta 2008 10 18 95 97

Quetta 2008 10 19 94 97

Quetta 2008 10 20 94 97

Quetta 2008 10 21 94 97

Quetta 2008 10 22 95 97

Quetta 2008 10 23 95 97

Quetta 2008 10 24 95 97

Quetta 2008 10 25 95 97

Quetta 2008 10 26 95 96

Quetta 2008 10 27 95 96

Quetta 2008 10 28 94 97

Quetta 2008 10 29 95 97

Quetta 2008 10 30 95 97

Quetta 2008 10 31 95 97

Quetta 2008 11 1 95 97

Quetta 2008 11 2 95 96

Quetta 2008 11 3 95 97

Quetta 2008 11 4 95 97

Quetta 2008 11 5 95 97

Quetta 2008 11 6 95 97

Quetta 2008 11 7 95 97

Quetta 2008 11 8 95 97

Quetta 2008 11 9 94 97

Quetta 2008 11 10 95 97

Quetta 2008 11 11 95 96

Quetta 2008 11 12 95 96

Quetta 2008 11 13 95 97

Quetta 2008 11 14 95 97

Quetta 2008 11 15 95 97

Quetta 2008 11 16 95 97

Quetta 2008 11 17 95 97

Quetta 2008 11 18 95 97

Quetta 2008 11 19 96 97

Quetta 2008 11 20 96 97

Quetta 2008 11 21 95 97

Quetta 2008 11 22 95 97

Quetta 2008 11 23 95 97

Quetta 2008 11 24 95 97

Quetta 2008 11 25 95 97

Quetta 2008 11 26 96 96

Quetta 2008 11 27 95 97

Quetta 2008 11 28 95 97

Quetta 2008 11 29 95 97

Quetta 2008 11 30 95 96

Quetta 2008 12 1 95 97

Quetta 2008 12 2 95 97

310

Quetta 2008 12 3 95 97

Quetta 2008 12 4 95 97

Quetta 2008 12 5 95 97

Quetta 2008 12 6 95 97

Quetta 2008 12 7 95 97

Quetta 2008 12 8 95 97

Quetta 2008 12 9 95 97

Quetta 2008 12 10 95 97

Quetta 2008 12 11 95 97

Quetta 2008 12 12 95 97

Quetta 2008 12 13 96 97

Quetta 2008 12 14 96 96

Quetta 2008 12 15 95 97

Quetta 2008 12 16 95 96

Quetta 2008 12 17 96 96

Quetta 2008 12 18 95 96

Quetta 2008 12 19 91 95

Quetta 2008 12 20 96 97

Quetta 2008 12 21 95 97

Quetta 2008 12 22 95 97

Quetta 2008 12 23 95 97

Quetta 2008 12 24 96 97

Quetta 2008 12 25 96 97

Quetta 2008 12 26 95 97

Quetta 2008 12 27 96 97

Quetta 2008 12 28 95 97

Quetta 2008 12 29 95 97

Quetta 2008 12 30 95 97

Quetta 2008 12 31 95 97

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