assessment of air-borne particulate matter (pm ) and...

ASSESSMENT OF AIR-BORNE PARTICULATE

MATTER (PM2.5) AND BIO-AEROSOLS IN DIFFERENT

RESIDENTIAL BUILT MICRO-ENVIRONMENTS OF

LAHORE, PAKISTAN

THESIS SUBMITTED FOR THE PARTIAL FULFILLMENT OF THE PhD DEGREE IN

ZOOLOGY

By

Sidra Safdar

Roll # ZP11-16

Session 2011-onwards

Under the Supervision of

Dr Zulfiqar Ali

ENVIRONMENTAL HEALTH AND WILDLIFE

DEPARTMENT OF ZOOLOGY

UNIVERSITY OF THE PUNJAB

QUAID-E-AZAM CAMPUS, LAHORE

In the name of Allah, the Most Merciful, the Most Beneficent

CERTIFICATE OF APPROVAL

This is to certify that the experimental work described in this thesis submitted by Sidra Safdar

has been carried out under my direct supervision. I have personally gone through the raw data

and certify the correctness/authenticity of all results reported herein. I further certify that this

data has previously not been submitted as a partial or complete requirement for the fulfillment

of award of any other degree from any other institution at home or abroad. I endorse its

evaluation for the award of PhD degree through the official procedures of the University.

.

________________________

Supervisor

Dr Zulfiqar Ali

Associate Professor

Department of Zoology

University of the Punjab

Quaid-e-Azam Campus

DEDICATION

To my beloved Father (Late) for whom it was a dream come true; I

wish he would have lived a little longer to see me complete my

doctorate

&

To my loving Mother who encouraged me at every step and has

been a constant source of hope and motivation for me

i

ABSTRACT

The situation of air quality is worse in the developing countries where annually million

lives are lost as a result of impaired air quality which stems from poor socio-economic

conditions and lack of awareness. Although a few studies have been conducted regarding air

pollution monitoring in Pakistan, no baseline data has been generated to gather information

about the indoor air quality. Besides this, there is yet no practical implication to reduce and/or

remove the load of pollutants present in the air. Moreover, the studies conducted so far have

limited their focus on aerosol emissions from biomass burning and the associated health

outcomes in rural areas. So far any detailed study on the indoor air quality of urban centers in

Pakistan has not yet been reported.

Particulate matter and bioaerosols are two of the most important components of the air

we breathe as both of these are ubiquitous in the air. Many studies have reported a number of

negative health outcomes owing to a prolonged exposure to these two pollutants and their

synergistic effect is also documented to be detrimental for human health.

Keeping in view the insufficient data regarding the concentration of fine particulate

matter and bioaerosols in the indoor air of urban centers in Pakistan, the current study was

designed to monitor the air quality of indoor micro-environments of residential houses (n = 30)

of Lahore, Pakistan. The parameters monitored were fine particulate matter and bio-aerosols.

PM2.5 was monitored using DustTrak aerosol monitor (model 8520, TSI Inc.) while Koch

sedimentation method was employed for microbial sampling. The kitchens and living rooms

were identified as two major micro-environments of any residential household and thus were

marked to be monitored at each of the selected sites. The ventilation rates were also measured

using the tracer gas method with carbon dioxide as the tracer gas. PM2.5 monitoring was carried

ii

out for 72 hours each with both micro-environments being monitored in parallel while agar

coated Petri plates were exposed for twenty minutes at each location to collect the bacteria and

fungi suspended in the air settling by gravity. Temperature and relative humidity were also

noted during bio-aerosol sampling.

Our results were indicative of poor air quality in the residential indoor environments of

Lahore. The 24-h average PM2.5 levels at any of the monitored site were manifolds higher than

the WHO recommended limits of 25µg/m³. Overall, the mean levels of fine particulate matter

exceeded 13 times the WHO limits. It was observed that cooking, cleaning, movement of

people, space heating (during winters) and smoking (in some houses) were the principal indoor

sources of particulate pollution. Maximum and minimum air change rate per hour (ACH) was

determined for each micro-environment to observe the influence of ventilation on the indoor

air quality and was observed to have a significant impact upon PM levels. Low ventilation

rates during winter season as well as meteorological factors resulted in elevated PM levels

indoors during the colder months. The exposure risk of the inhabitants, most particularly

women and small children, was greatly increased as they spent maximum time indoors.

The micro-biota of the sampled sites was comprised of common genera which were

also identified as opportunistic pathogens. The bacterial composition was consisting of seven

species including Micrococcus spp., Staphylococcus spp., and Bacillus spp., with occasional

record of Serratia spp. Among the eleven fungal species identified, the dominant ones were

Alternaria alternata and Aspergillus spp., with Trichoderma, Mucor, Fusarium and Rhizopus

also detected in less numbers. The colony forming units per cubic meter for bacteria ranged

from 472 to 9,829 in the kitchens and from 275 to 14,469 in the living rooms. Likewise, the

fungal cfu/m3 ranged between 234 and 1887 in the kitchen and from 314 to 1887 in the living

iii

room. A seasonal variation in bioaerosols was evident in the kitchens while being not so

pronounced in the living rooms. Linear regression model exhibited a direct association of

temperature with bacteria and fine particulate matter but not with fungi. Out of thirty monitored

households, sixteen contained at least one individual with allergic reactions from dust or during

wheat harvesting season. These findings highlight the enhanced risk of exposure to fine

particulate matter as well as bioaerosols in the urban residential built environment in Pakistan.

The study holds its significance in being the first of its kind as previously no data

focusing on simultaneously measured PM and bioaerosol levels in the urban centres of Pakistan

has been reported. With the lack of any definite policies, the area of indoor air quality has been

ignored at large. It is recommended that more detailed studies must be conducted to monitor

air quality in the built micro-environments and guidelines should be formulated to keep a check

on the contaminant levels indoors.

iv

ACKNOWLEDGEMENTS

First and foremost, glory and praise to ALLAH Almighty who created life and blessed

mankind by bestowing him the power of thinking and wisdom. All and every exaltation is for

the HOLY PROPHET (P.B.U.H) who guided man and enabled us to recognize our Deity.

Though only my name appears on the cover of this dissertation, a great many people

have contributed to its production. I owe my gratitude to all those people who have made this

dissertation possible and because of whom my doctorate experience has been one that I will

cherish forever.

My heartiest and warmest felicitations and obligatory gratitude is to Prof Dr Javed

Iqbal Qazi, Professor and Chairman, Department of Zoology, University of the Punjab

for his kind cooperation. My thanks are also extended to Prof Dr. Muhammad Akhtar, ex-

Chairman, Dept. of Zoology who was always helpful and co-operative.

My deepest gratitude is to my respected supervisor, Dr. Zulfiqar Ali. I have been

amazingly fortunate to have a supervisor who gave me the freedom to explore things on my

own and at the same time the guidance to recover when my steps faltered. Dr. Zulfiqar Ali

taught me how to question thoughts and express ideas. His patience and support helped me

overcome many critical situations and finish this dissertation.

Before I acknowledge anyone else, I am immensely indebted to my previous research

supervisor, Dr Asif Mehmood Qureshi, Principal (Retd.) Govt. Islamia College, Civil

Lines. He was the one who motivated me and pushed me forward in the field of research and

truly taught me what a researcher is. Sir, my endless gratitude towards you cannot pay for what

I have gained from you.

I pay my special regards to Dr Zaheer Ahmad Nasir, University of Cranfield, UK

for imparting devotion, professional guidance and constructive suggestions whenever I was

stuck at anything. Thank you sir for bearing up with me and for your unconditional support

and guidance whenever required.

I am also greatly indebted to Dr Shakil Ahmed, Department of Botany-University

of the Punjab for identification of the fungal species which would have been impossible

otherwise. The guidance and support of Dr Sikander Sultan, Department of Microbiology

and Molecular Genetics-University of the Punjab is also duly acknowledged in

v

identification of bacterial species. In fact the guidance provided by these two teachers cannot

be forgotten which made it possible for me to pass through all hardships during this research.

Next to them I would love to acknowledge full time cooperation and spirit lifting

encouragement of the occupants of the sampling sites where this research was conducted. It

would be unfair not to mention their names and I am greatly indebted to Sana Islam, Sana

Bashir, Hadia Chughtai, Shakil Ahmed, Bushra Nisar Khan, Bushra Ansari, Mubashir

Ahmad, Sadia Razzaq, Sadia, Awais Liaquat, Rafia Kamal Butt, Khadija Qasim Butt,

Kamran, Arslan, Abdullah Baig, Uzma, Noreen, Shaista Kanwal, Muhammad Ijaz,

Muhammad Sajjad, Muhammad Abbas, Zulfiqar Ali, Roohi Ejaz, Maryam, Ambreen,

Ansa Shahzadi, Mehwish, Sehrish Ramzan and Aqsa Qayuum. As a matter of fact it was

heart-warming to experience such a positive response from them, many of them totally

strangers, to allow me to run my instruments (which were somewhat noisy) for seventy two

hours at each house. The people were anxious to learn more about my work and how could

they minimize the exposure risks around them. It was truly an over-whelming and

unforgettable experience for me.

I am obliged by the support and co-operation of Dr Waseem Ahmad Khan who

encouraged me a lot while writing this dissertation. Another person whose thanks is due on me

is Mr. Hassan Ali whose valuable assistance is acknowledged in providing me with the GIS

maps of Lahore and the study locations. Muhammad Nouman, Research officer,

Environment Protection Department, Punjab provided with data on ambient air and I am

greatly thankful to him as well.

Most importantly, none of this would have been possible without the love and patience

of my family. My family, to whom this dissertation is dedicated, has been a constant source of

love, concern, support and strength all these years. I have to give a special mention for the

support given by my brother Muhammad Abubakar without whose help this work would

never have been completed as he acted like my driver throughout this research. My uncles were

also a constant source of inspiration and their unending support to fulfill their late brother’s

dream (my father) was always motivating for me. The support of my husband Qazi

Muhammad Imran is also unforgettable and helped me through tough times.

My friends Sumaira, Rafia, Sana and Aqsa have helped me stay sane through these

difficult years. Their support and care helped me to overcome setbacks and stay focused on my

vi

doctorate study. I greatly value their friendship and I deeply appreciate their belief in me. My

lab fellows, Nimra Afzal, Anam Zakir, Khadija Aziz and Ahsan Ashraf also have a

noteworthy share in assisting me whenever I needed help and I am grateful to all of them.

Bushra Nisar Khan, Mubashir Ahmad, Zona Zaidi, Zainab Irfan, Syed Turab Raza and

many others were also a motivating force for me whenever I felt down during my work and I

owe them a bundles of thanks for their support.

The research work was funded by HEC Indigenous Ph.D. 5000 Fellowship (vide letter

No.17-5(2Bm1-478)/HEC/Sch-Ind/2012 dated 01.04.2014 and is highly acknowledged.

Sidra Safdar

PIN # 112-23380-2Bm1-478

vii

ABBREVIATIONS

IAQ Indoor Air Quality

US-EPA United States Environmental Protection Agency

Pak-EPA Pakistan Environmental Protection Agency

PM Particulate Matter

VOC’s Volatile Organic Compounds

WHO World Health Organization

CO Carbon Monoxide

CO2 Carbon dioxide

NOx Nitrogen Oxide

Sox Sulphur Oxide

HVAC system Heating, Ventilation and Air Conditioning system

SPM Suspended Particulate Matter

RSPM Respirable Particulate Matter

ETS Environmental Tobacco Smoke

ACGIH American Conference of Government Industrial Hygienists

ASHRAE American Society for Heating, Refrigeration and Air

Conditioning Engineers

NEQS National Environmental Quality Standards

ACH Air Change rate per Hour

Cfu/m3 Colony forming units per cubic meter of air

viii

COPD Chronic Obstructive Pulmonary Disease

RH Relative Humidity

ALRI Acute Lower Respiratory Infections

GDP Gross Domestic Product

OSHA Occupational Safety and Health Administration

AIHA American Industrialist Hygiene Association

ix

TABLE OF CONTENTS

Abstract i

Acknowledgements iv

Abbreviations vii

List of tables x

List of figures xii

Chapter # Title Page #

Chapter One Introduction 1

Chapter Two Literature review 23

Chapter Three Materials and methods 65

Chapter Four Results 76

Chapter Five Discussion 179

References 202

Annexure-I Questionnaire 235

Annexure-II Maximum and minimum air change rate 241

at the sampling sites

Annexure-III Annual Trend of Ambient Air Quality of Lahore 298

Annexure-IV Installation of instruments at sampling sites 299

Annexure-V Seasonal variation of fine particulate matter in 314

residential micro–environments of Lahore,

Pakistan

Annexure-VI Assessment of Airborne Microflora in the Indoor 322

Micro-Environments of Residential Houses of

Lahore, Pakistan

x

LIST OF TABLES

Table 1 Indoor air pollutants and their sources 3

Table 2 Health effects of exposure to bio-aerosols 15

Table 3 Administrative towns of City District Lahore and their population

(Source: GOP, 2014)

67

Table 4 Profile of Category-A sampling sites 83

Table 5 Representative 24-h, hourly maximum and hourly minimum

averages of PM2.5 recorded in the kitchens and living rooms of

category-A sites

84

Table 6 Profile of Category-B sampling sites 107



category-B sites

108

Table 8 Profile of Category-C sampling sites 131



category-C sites

132

Table 10 Correlation between PM2.5 levels in kitchens and living rooms of

sampling sites (strong correlations shown in bold)

154

Table 11 Overall PM generation observed during different activities in the

kitchens

158

Table 12a One-way ANOVA for seasonal variation in PM2.5 levels in kitchens 162

Table 12b One-way ANOVA for seasonal variation in PM2.5 levels in living

rooms

162

Table 13 ACH and Air flow rate (liter per second per person) in the kitchens

and living rooms of the sampling sites

165

Table 14 Regression modeling: ACH versus PM2.5 (α = 0.05) 166

Table 15 Temperature, Relative humidity and Total bacterial colony forming

units per meter cube (cfu/m3) present in the kitchen and living room

of each sampling site

168

Table 16 Temperature, Relative humidity and Total fungal colony forming

units per meter cube (cfu/m3) present in the kitchen and living room

of each sampling site

169

Table 17 Colony forming units of each bacterial species identified in the

kitchens and living rooms of the sampling sites

170

Table 18 Colony forming units of each fungal species identified in the kitchens

(K) and living rooms (LR) of the sampling sites

171

Table 19 Regression modeling of different parameters in kitchen (α = 0.05).

Significant results are marked in bold text.

174

xi

Table 20 Regression modeling of different parameters in living room (α =

0.05). Significant results are marked in bold text.

175

Table 21 One-way ANOVA for seasonal variation in bioaerosol levels in the

kitchens and living rooms

177

Table 22 One-way ANOVA for association between bioaerosol levels and

PM2.5 in the kitchens and living rooms

178

Table 23 Sources and health hazards posed by the observed bacterial and

fungal species (Source: Kowalski, 2006).

191

xii

LIST OF FIGURES

Figure 1 Flow of pollutants in an indoor environment 4

Figure 2 Categories of particulate matter according to size 7

Figure 3 Deposition of particulate matter in the various regions of respiratory

tract according to their size

9

Figure 4 Map marking the boundaries of City District Lahore 65

Figure 5 Location of sampling sites in Lahore city 76

Figure 6 Location of sampling sites according to number of occupants [up to

5 occupants (red circles); 6 to 10 occupants (blue circles); 11 and

above (green circles)]

77

Figure 7 Proportion of male and female occupants belonging to different age

groups

78

Figure 8a Number of hours spent by male occupants in the house 79

Figure 8b Number of hours spent by female occupants in the house 79

Figure 9 Time spent by females in the kitchen 80

Figure 10 Floor plan of sampling site A1 85

Figure 11a 24-h representative mean values of PM2.5 in kitchen of sampling site

A1

85

Figure 11b 24-h representative mean values of PM2.5 in living room of sampling

site A1

86



A2

87


site A2

88



A3

89


site A3

90



A4

91


site A4

92



A5

93

xiii


site A5

94



A6 (monitored during Ramadan)

95


site A6

96



A7

97


site A7

98



A8

99


site A8

100



A9

101


site A9

102



A10

103


site A10

104

Figure 30 Floor plan of sampling site B1 109


B1

109


site B1

110



B2

111


site B2

112



B3

113

xiv


site B3

114



B4

115


site B4

116



B5

117


site B5

118



B6

119


site B6

120



B7

121


site B7

122



B8

123


site B8

124



B9

125


site B9

126



B10

127


site B10

128

Figure 50 Floor plan of sampling site C1 133


C1

133

xv


site C1

134



C2

135


site C2

136



C3

137


site C3

138



C4

139


site C4

140



C5

141


site C5

142



C6

143


site C6

144



C7

145


site C7

146



C8

147


site C8

148



C9

149

xvi


site C9

150



C10

151

Figure 69b 24-h representative mean values of PM2.5 in kitchen of sampling site

C10

152

Figure 70 Mean values of PM2.5 observed in the kitchens and living rooms of

the sampling sites

153

Figure 71a Average PM2.5 levels generated from different activities in kitchen

of category-A sampling sites

155

Figure 71b Average PM2.5 levels generated from different activities in kitchen

of category-B sampling sites

155

Figure 71c Average PM2.5 levels generated from different activities in kitchen

of category-C sampling sites

156

Figure 72a Average PM2.5 levels generated from different activities in living

room of category-A sampling sites

156

Figure 72b Average PM2.5 levels generated from different activities in living

room of category-B sampling sites

157

Figure 72c Average PM2.5 levels generated from different activities in living

room of category-C sampling sites

157

Figure 73a Comparison of 24 hour average PM2.5 in houses with kitchens and

living rooms connected

159

Figure 73b Comparison of 24 hour average PM2.5 in houses with kitchens and

living rooms partially connected

159

Figure 73c Comparison of 24 hour average PM2.5 in houses with kitchens and

living rooms not connected

160

Figure 74 Mean levels of PM2.5 obtained during different seasons 161

Figure 75a Maximum and Minimum Air exchange rate in the kitchens of

sampling sites

164

Figure 75b Maximum and Minimum Air exchange rate in the living rooms of

sampling sites

164

Figure 76a Proportion of bacterial species present in the kitchens of the

sampling sites

172

Figure 76b Proportion of bacterial species present in the living rooms of the

sampling sites

172

Figure 77a Proportion of fungal species present in the kitchens of the sampling

sites

173

Figure 77b Proportion of fungal species present in the living rooms of the

sampling sites

173

Chapter One Introduction

1

CHAPTER ONE

INTRODUCTION

Air is an essential component of our lives as it provides us with oxygen to breathe. It

is a medium to sustain life. Air pollution is an issue of major concern for the health since an

increased amount of pollutants emitted into the air means the more amounts of pollutants we

breathe. While there are a variety of sources that pollute the ambient air, the built indoor

environment may also not be as safe as it seems to be. Man has been constructing buildings

since long so as to protect him from the hazards present outdoors such as the harshness of

weather, wild animals etc. Consequently, people in many areas tend to spend 90 % of their

time indoors- either it be at workplaces or at homes (Hoppe and Martinac, 1998). Buildings

can therefore be viewed as an indoor ecosystem or a habitat with varying factors such as the

occupants and their activities, building design affecting the ventilation and air pathways,

material used for construction and the environmental conditions. In fact the interaction of man

with the indoor environment is as complex as the outdoor environment (Goyal and Khare,

2010). Although the indoor air may seem safe from pollutants and other hazards, that is not the

case. As a matter of fact the indoor air may be more polluted than the ambient air. According

to WHO (2002), 2.7 % of the global disease burden can be attributed to indoor air pollution.

The issues of air pollution are not recent ones and date back to prehistoric times when

the cave men started burning fire in their caves. The soot deposited on the walls and ceilings

of these caves provides a sufficient insight into the high levels of pollutants that accumulated

within these dwellings. This points out to the low ventilation present at that time but how much

it must have affected the inhabitants is yet unknown (Spengler and Sexton, 1983). The late

twelfth century saw the use of chimneys in some European houses but it was not until the


2

sixteenth century that chimney stacks were commonly used. However little attention was paid

to the hazards posed by accumulation of pollutants in the indoor environments (Brimblecombe,

1987; and Burr, 1997). The first related studies were conducted in the 1920’s and 1930’s. Later

on the energy crisis during the 1960’s and early 1970’s boosted the IAQ problems that persist

till date. In order to prevent the outdoor pollutants from entering the indoor environment,

various steps were taken such as insulation of buildings and making then air tight. Although

these steps proved useful in preventing infiltration from the ambient air, it also posed new

problems. The concentration of pollutants indoors was found to be higher than the outdoor

levels. There are a variety of sources in the indoor environment indoors which may lead to a

higher pollutant level than the ambient air (D’Amato et al., 1994; and Teichman, 1995). It is

therefore necessary to study these sources also in order to ensure a healthy indoor environment.

Indoor air quality is closely defined by the outdoor air quality. However there are a variety of

factors in the indoor environment whose interaction can strongly determine the IAQ. These

factors include:

The movement of occupants resulting in re-suspension of already deposited dust and

contaminants

Activities of the occupants thereby generating varying amounts of pollutants

Sources and sinks of pollutants, and

The movement of air within the different parts of the building and from the outdoors

affecting the removal as well as dispersion of pollutants

There are a variety of sources for the indoor pollutants that can contribute to indoor air

quality (IAQ) as summarized in table 1 below (Source: Jhang and Smith, 2003). These sources


3

vary in nature such as external, internal, biological or chemical sources (Goyal and Khare,

2010).

Table 1: Indoor air pollutants and their sources

Pollutant Major indoor sources

Particulate Matter (PM) Combustion such as fuel burning, second hand smoke,

cleaning, cooking

Carbon monoxide (CO) Combustion such as fuel burning, second hand smoke

Nitrogen oxides (NOx) Combustion such as fuel burning, second hand smoke

Sulphur oxides (SOx),

Arsenic and Fluorine Burning of coal

VOC’s

Combustion such as fuel burning, second hand smoke,

paints, cleaning solvents, furnishing, cooking activities,

materials used for construction

Aldehydes Furnishing, cooking activities, materials used for

construction

Pesticides Cleaning solvents, outdoor sources such as dust from

outside

Asbestos Renovation and/or demolition of construction materials

Lead Remodeling and/or demolition of painted surfaces

Biological pollutants such dust

mites, pollen, animal dander,

air-borne bacteria and fungi

Moist or water-damaged walls, floors, ceilings, carpets,

bedding and from poorly maintained HVAC systems

Radon Soil and construction materials

Apart from these sources, “sinks” also play an important role in defining the IAQ.

“Sinks” are high surface area sites which may be porous in nature. Odours and other gaseous

pollutants can deposit on these surfaces which eventually turn into secondary sources for these

pollutants. In addition to deposition, sinks can include dispersion processes as well as chemical

reactions. Air movement is also an integral part of the IAQ. Air movement in a building may

be natural or forced by a HVAC system. Similarly the ventilation system is responsible for the

infiltration and ex-filtration of air in and out of the building. As a result the pollutants can move


4

in and out of the indoor micro-environments. The flow of pollutants in the indoor environment

is summarized in figure 1.

Figure 1: Flow of pollutants in an indoor environment

INDOOR LIVING ENVIRONMENT

The indoor living environment is integral to humans as in most cases, people tend to

spend more than 90% of time indoors. There are a number of factors that characterise the built

environments and play a substantial role in defining the air quality. There is a complex

relationship between the indoor environment and the wellbeing of the occupants (Bluyssen et

al., 2013). The presence of a multitude of stressor in the indoor environment such as moisture,

mold, noise, light, thermal comfort or discomfort, particulate can significantly affect the life

of people. The synergistic effect of these stressors can produce short term and long term effects

on the human health such as the sick building syndrome (Bluyssen, 2009).

Pollutants enter the house through infiltration

Indoor sources (such as building material, occupant's activities, furnishing, consumer products)

also contribute towards pollutant loads

Are removed or diluted by ventilation

Re-enter the building

Within the building, the pollutants

Are inhaled by the occupants

Are exhaled by the occupants


5

The life style of people generally describes the IAQ and Bluyssen et al. (2011) has

given a checklist to identify the various descriptors in a built environment which include the

following components:

1. Characteristics of the built environment: building location and surroundings

2. Characteristics of building, systems and rooms: building material, furnishing, HVAC

system, lighting system etc.

3. Maintenance and operation of the building and activities: cleaning, renovation,

maintenance of HVAC system etc.

Among the psychosocial environments the living environment includes sub-

components such as number of people, social background etc. These descriptors have been

found to be useful in studying the well-being of an individual in association with the built

environment.

The research undertaken explored two major micro-environments of residential

buildings i.e. kitchens and living rooms. Their air quality was assessed and the presence of

environmental stressors was also investigated via direct questioning. Although the indoor air

hosts a multitude of pollutants, the present study is concerned with concentration of fine

particulate matter and bio-aerosols in the indoor air of residential buildings and so we limit our

focus to these two major indoor pollutants.

PARTICULATE MATTER

Particulate matter (PM) is one of the six criterion air pollutants and the most harmful

one (Pope and Dockery, 2006). Particles are generated into the atmosphere through a variety

of sources and may be natural or anthropogenic; primary or secondary in their origin.


6

Particulate matter includes a variety of chemical and physical pollutants dispersed in the air

and are generally defined as complex mixtures of solid and liquid particles from organic and

inorganic sources in the air (Tiwary and Colls, 2010; and WHO, 2011). Particulate matter is

categorized broadly into:

Suspended particulate matter (SPM)

Respirable particulate matter (RSPM)

The total suspended particulate matter includes refers to larger particles with no specific

size limit and the upper limit is dependent upon wind speed and the sampler orientation. The

respirable PM includes particles with an aerodynamic diameter of 10 µm and below as defined

by the USEPA while the American Conference of Government Industrial Hygienists (ACGIH)

considers the respirable PM to be having an aerodynamic diameter of 2 or less than 2 µm in

size (Goyal and Khare, 2010; Parsia et al., 2010; and Tiwary and Colls, 2010). The

aerodynamic diameter of a particle is defined as follows:

“The aerodynamic diameter of any particle is the diameter of a sphere of unit density (water

density) that would have the same settling rate in still air as the actual particle.”

Particulate matter is defined on the basis of the size or diameter of the particles since it

is a determinant of many properties of the particles such as residence time in the air, distance

travelled before deposition, and deposition in the respiratory system. The deposition efficiency

of various particles in the respiratory system is an important factor since it enables us to

understand the risk we stand at. The most commonly studied PM fractions include particulate

matter with an aerodynamic diameter of 10 µm (PM10) and below. PM10 and PM2.5 are of

greater significance in air quality policies and regulations for particle emissions since they are


7

easily inhaled and affect the human health (Wiseman and Zereini, 2010). The particulate matter

can be classified according to their deposition in the respiratory tract as given in figure 2:

Figure 2: Categories of particulate matter according to size

Particulate matter enters the human body through inhalation. After inhalation, there are

three possibilities (Gentry, 2005):

The particles may be removed through exhalation before they deposit even in the nasal

passage

They deposit in the body and reach deeper in the lungs, or

After deposition, they may be removed by mucociliary transport

Once inside the nasal passage they tend to deposit in the respiratory tract depending on

their aerodynamic diameter, the speed with which air is being inhaled and the residence time.

Larger particles tend to settle by gravity and have higher impaction efficiencies in high

airspeeds while the smaller particles employ sedimentation in addition to the Brownian

diffusion velocities in low airspeeds and longer residence times. Apart from these primary

mechanisms of particles deposition (settling by gravity, Impaction and Brownian diffusion),

there are two secondary mechanisms involved as well namely electrostatic attraction and

INHALABLE < 50 µm

THORACIC < 10 µm ALVEOLAR < 4 µm


8

interception. However these secondary processes are of least significance as major role is

played by the primary mechanisms.

There is a direct relation between the particle size and their settling rate in the

respiratory tract (Fierro, 2000) and the construction of the human respiratory system is

responsible for the way in which particles of varying size deposit in different regions of the

tract. The particles inhaled by us are less than 100 μm in size among whom those with a size

of above 50 μm are deposited immediately at the start of nasal passage before entering the

trachea. Particles with an aerodynamic diameter greater than 10 μm deposit on hair in the nasal

passage, and on the walls of nose and throat through inertial impaction while the particles

having a size of 2 or less than 2 µm tend to deposit in the lower respiratory tract. These particles

reach the alveolar region and can interfere with the gas exchange. Larger particles (size ranging

from 2 to 5 µm) deposit in the conducting air ways of lungs while particles with a size between

5 to 10 µm are trapped in the upper respiratory tract with few particles reaching the lower

respiratory tract. Particles larger than 10 µm are easily removable as they do not reach further

than the nasopharyngeal region and can be removed during coughing and/or sneezing (figure

3).


9

Figure 3: Deposition of particulate matter in the various regions of respiratory tract according

to their size

The balance between the deposition modes of particles is influenced significantly by

the breathing rate. During the resting phase, airspeed is low with the result that not much

settling or diffusion of particles can occur. On the other hand, during heavy exercise, the total

volume of the air entering the lungs through nose is increased. Moreover oral breathing also

contributes a significant volume of air to the lungs. Consequently the penetration of coarse

particles is increased since the nose is unable to filter the particles inhaled through the mouth

(Tiwari and Colls, 2010).

Apart from the mechanism of lung clearance through coughing (mucociliary system),

there is another process i.e. phagocytosis at work that is responsible for protection of the

respiratory system and the human body on the whole from harmful foreign objects that we


10

inhale daily. The smaller particles including not only the particulate matter but also the bio-

aerosols can penetrate deeper into the alveoli and diffuse easily through the thin walled blood

capillaries. However the macrophages (type of white blood cells found in the alveoli) typically

engulf the micro-organisms and the PM that attempts to enter the blood stream in the alveolar

capillaries. As a result, these two mechanisms provide a natural defense against the inhaled

particles provided they do not exceed the normal limits (Kowalski, 2006).

FINE PARTICULATE MATTER (PM2.5)

COMPOSITION & SOURCES

Fine particulate matter refers to the suspended particles with an aerodynamic diameter

of 2.5µm or less and their origin and chemical composition differs greatly from PM10. Fine

particles are obtained by the combustion of oil, coal, gasoline, diesel and wood, Gas to particles

conversions. They are formed by a number of processes such as chemical reactions,

condensation, nucleation, coagulation, cloud and fog processing. They are hygroscopic in

nature and consist of sulphates, nitrates, ammonium, elemental carbon, organic compounds,

water and metals such as lead (Pb), Cadmium, (Cd), Vanadium (V), Nickel (Ni), Copper (Cu),

Zinc (Zn), Manganese, (Mn), and Iron (F). Being smaller in size, existence of fine particles

extends from days to weeks and they travel from hundreds to thousands of kilometers away

from their point of origin (Fierro, 2000). Fine particles have high surface area and the ability

to absorb a number of organic compounds which may be more harmful to human health (Bates,

1995).

Fine particulate matter is generated from a variety of sources as mentioned earlier. Both

the indoor and outdoor environments are associated with aerosol generation. However the

identification of these sources as well as their contribution toward air quality is a relatively


11

complicated process. The source and source strength contributing towards the pollutant level

should be identified in order to have a clear understanding of the source and sink of the

particles. Particulate matter may have a direct and/or indirect (secondary) source. Although

particulate matter is generated from a variety of natural and man-made sources in the ambient

air, the indoor air is mostly described by anthropogenic sources. Some of these sources include

heating, cooking, cleaning, walking around, smoking cigarettes, paints, etc. and also the

building material and furnishing (Ferro et al., 2004; Mitchell et al., 2007). The composition of

PM is also as variable as the sources it comes from. The major components in both indoor and

outdoor PM have been identified to be water along with NaCl, sulfates, nitrates, ammonia,

carbon, and mineral dust (WHO, 2011). The seasons and region under study influence the

composition of the particulate matter. Many studies have been carried out to observe the

components of particulate matter so as to have a deeper understanding of the pollutants we

inhale and the risk they present to us. In a study in USA, 79-85% of the fine particulate matter

was observed to be composed of ammonium, organic carbon, elemental carbon, nitrate,

sulfates, and sodium (Parsia et al., 2010). Apart from these, transition metals, ions such as

those of sulphates and nitrates, minerals, reactive gases, and particles having a biological origin

also constitute the PM2.5 fraction. The presence of these components may be from local and in

some cases, regional sources as well.

HEALTH IMPACTS OF PM2.5

According to the International Agency for Research on Cancer (2013), outdoor air

pollution is a leading cause of lung cancer. In the developing countries, indoor air pollution is

the cause of approximately 2 million premature deaths. Pneumonia causes approximately half

of these deaths in children below 5 years of age. Among the major air pollutants, particulate


12

matter is known to affect more people than other pollutants. Moreover, inhalation of PM2.5 is

estimated to be the cause of about 2.4 million premature deaths per year according to WHO

figures (Tiwari and Colls, 2010). More recently, WHO reports the number of premature deaths

due to household air pollution (HAP) in 2012 to be approximately 4 million which makes about

7.7% of the global mortality (Bruce et al., 2015).

According to Brauer et al. (2012), 99% of people in South and East Asia reside in areas

with poor air quality where PM2.5 levels greatly exceed the WHO limits of 25 µg/m³. Daily

exposure to particulate matter causes an increase in the deaths due to respiratory and

cardiopulmonary diseases (Samet et al., 2000; US-EPA, 2003). Adults are more vulnerable to

pneumonia, asthma, chronic obstructive pulmonary disease (COPD), cough, phlegm and other

respiratory diseases along with the cardiac cases as compared to children (Kappos et al., 2004).

Lung cancer, cardiovascular and respiratory problems are a result of long term exposure to

PM. The situation is worse in developing countries where biomass fuel is still in use in rural

areas which can lead to wheezing, exacerbation of asthma, chronic bronchitis, respiratory

infections, Acute Lower Respiratory Infections (ALRI), Chronic Obstructive Pulmonary

Disease (COPD) and also lung cancer (Bruce et al., 2002). Prolonged exposure can lead to

increased mortality resulting from cardiovascular diseases as well. This excessive exposure

can cause acute bronchial irritation, inflammation and increased reactivity. As a result there is

reduced mucociliary clearance and reduced macrophage response towards foreign elements,

thereby reducing local immunity. This PM induced pulmonary inflammation can also cause

oxidative stress and affect the cardiovascular system by triggering the production of

procoagulant factors in the lungs. The inflammatory mediators may also promote myocardial


13

infarction by acting to increase the levels of procoagulants in the lungs (Fierro, 2000; Bruce et

al., 2002).

In order to measure the health effects related to aerosols, a physical parameter called

dose is employed. It depends upon the amount of particles in the body. However since it is not

possible to measure the dose of inhaled aerosols directly, measurement of the size distribution

of particles in the breathing zone and aerosol deposition, knowledge of the relevant parameters

such as temperature and humidity in the respiratory tract, and a knowledge of the biochemical

processes (translocation, clearance, and absorption) within the lungs is required for reliable

dose determination (Ruzer et al., 2005).

BIO-AEROSOLS

Micro-organisms are present everywhere – in the air, soil, water, plants, human body.

These include bacteria, viruses, fungi, spores, mites, and pollen. Among these, the air borne

micro-organisms and their related products such as their cellular components are termed as

biological aerosols or bio-aerosols. These may be viable (alive) or non-viable (dead) and are

responsible for a variety of health related problems. These microbial particulate matters are

found in both the indoor and the outdoor environment. The sources and sinks of bio-aerosols

vary in different environments. Carpets in an indoor environment can trap these particulates

very easily and firmly and also provide a suitable environment for their survival and re-

distribution into the air (Hospodsky et al., 2012). Similarly water damaged places also provide

the necessary environmental conditions required for the survival and growth of bacteria and

fungi. Most fungi present in the indoor air have their sources in the ambient environments

while in case of bacteria indoor as well as outdoor sources are responsible. The sources of bio-

aerosols may be living such as humans, plants, and pet animals or non-living materials such as


14

building material, damp indoor spaces and many others. Also the bacteria and fungi growing

in damp indoor spaces have a different profile from those generated from human sources

(Damp Indoor Spaces and Health, 2004; Hospodsky et al., 2012; Prussin and Mar, 2015).

The levels of the biological contaminants in the indoor micro-environments are under

the influence of some factors. Among these time of the day, time of the year and also the

geographic location of the concerned place are the most notable. Apart from these, some other

physical parameters are also important such as climate and weather conditions, temperature of

surrounding air, temperature of the depositing surfaces, relative humidity (RH), wind speed,

and turbulence in the air. The external conditions are different for each species and thus are

their responses. The air temperature, surface temperature, relative humidity, and changes in

wind speed are determinants of the variations in bio-aerosols levels as well as different species

composition of the air during the day and night. Similarly the two most important physical

parameters influencing the microbial activity are temperature and RH and are responsible for

the changes in species composition during the shifting seasons. It has been documented that

during the summer season, culturable bacterial and fungal spores are present in higher numbers

than in during winters due to the dry conditions. The location of the study area is also an

important factor since in urban areas higher bioaerosols loads are present in the air than in the

rural areas (Heikkinen et al., 2005).

Human beings are exposed to bio-aerosols through inhaling; the most efficient route of

transmission of infectious agents (Evans, 2000). However it is important to note that not all

the air-borne microbes we inhale are harmful for health. In fact most of them are harmless with

only a small proportion of bio-aerosols being unhealthy (Heikkinen et al., 2005). Exposure to


15

these bioaerosols can cause a variety of diseases which are generally classified under three

categories with the first two being more common (Douwes et al., 2003):

Infectious diseases

Respiratory diseases

Cancer

The response of the human body towards bio-aerosols may result in acute or chronic

health issues. The following table summarizes some of these conditions (Heikkinen et al.,

2005; Walser et al., 2015).

Table 2: Health effects of exposure to bio-aerosols

Acute Chronic

Rhinosinusitis Asthma

Influenza Bronchitis

Pharyngitis Some Pulmonary Infections

Significant decrease in forced vital

capacity of lungs (FVC)

Significant decrease in lung function

parameters

Upper Airway Obstruction

Bronchitis

Alveolitis

Pulmonary Edema

Laryngitis

Bacteria are a diverse group of micro-organisms. They vary in size from 0.2 to 5.0 μm

and in shape from spherical to elongate. They are responsible for a variety of diseases in not

only humans but also animals and even plants. Bacteria may be sporulating or non-sporulating,

existing as individual cells or in groups. Many sporulating bacteria are actinomycetes

(sporoactinomycetes) and are responsible for respiratory infections. These bacterial species

tend to show a similarity in behaviour with fungal spores growing like mycelia in the presence


16

of suitable moisture content and other nutrients. Bacteria originating from human sources

generally include gram-positive Cocci such as Staphylococci spp. and Bacillus spp., while

some other typical bacterial species present in the ambient air include Bacillus anthracis,

Clostridium botulinum, Clostridium perfringens, Corynebacterium, Flavobacterium,

Micrococcus, Pseudomonas, Streptomyces, and other sporoactinomycetes (Damp Indoor

Spaces and Health, 2004; and Kowalski, 2006).

On the other hand, the aerodynamic diameter of most fungal spores is between 2 to 10

µm which tend to readily settle on indoor surfaces due to gravity. They may vary in shape from

round to barrel shaped. Cladosporium, Aspergillus, Penicillium, Alternaria, Fusarium,

Saccharomyces, Trichoderma, Neurospora, Epicoccum, and many other species are most

commonly found in the indoor as well as outdoor air (Mullins, 2001). Fungal spores colonize

frequently in the indoor damp spaces where their levels can exceed those of the ambient air.

These spores have been reported to be responsible for allergic reactions, respiratory infections,

and other related problems (Kowalski, 2006).

IMPACT OF VENTILATION ON INDOOR AIR QUALITY

A constant supply of fresh air in the indoor micro-environments is an important factor

to maintain a healthy environment. Ventilation practices vary around the globe with natural

and/or mechanical sources in use. Ventilation is defined as the supply of sufficient amounts of

fresh air in the indoor air so that the occupants are able to breathe easily and the pollutants

accumulated or generated from indoor sources can be diluted and removed by the influx of air

in and out of the building. A higher rate of ventilation means smaller residence time of the

pollutants indoors. Ventilation may be natural, mechanical (fans and/or HVAC systems) and

hybrid also (using both modes). However while the pollutants are diluted by the influx of


17

ambient air towards indoors, the case can also be opposite when the outside environment is

more polluted causing more pollutants to flow inwards. The HVAC systems can harbor a

variety of micro-organisms which pose a health risk. Still the HVAC systems, if properly

maintained, can be useful in maintaining a good IAQ. Natural ventilation is inexpensive as

compared to mechanical ventilation but it is also uncontrolled as the airflow is unpredictable

under changing climate. Moreover the opening and closing of windows and any other such

opening also affects the infiltration of outdoor air (Awbi, 1991; Allard, 2002).

The location, shape and size of the building along with the wind direction and

topography are some of the numerous factors that affect air flow in naturally ventilated

buildings. The temperature and relative humidity of both the indoor and outdoor air is also

important. The thermal buoyancy and wind pressure are the two driving forces that cause

thermal and pressure gradients in the air causing the airflow from outside to inside environment

(ASHRAE, 1989 and 2001).

The methods employed for measurement of ventilation rates inside a building take into

account either the number of people in a given area (liters/second/person) or use the volume

of the building (air change rate per hour, ACH) (Fischer-Mackey, 2010). For this purpose tracer

gases such as CO2, SF6, and C6F6 are employed. The most commonly used gas is carbon

dioxide due to being readily available and also for being cheap. Three methods are generally

in use namely; constant injection method, concentration decay method and the constant

concentration method (Laussmann and Helm, 2011). The concentration decay method has been

employed in this research work.


18

CONTROLLING AIR POLLUTION

Indoor air pollution is an important issue which needs to be tackled properly. The first

very necessary step in this regard is the identification of the source. US-EPA lays emphasis on

the removal of pollutant source as the most effective strategy in pollution control. Similarly

effective use of adequate ventilation is also a preferred in case of unidentified source, costly

source treatment and/or localized source (Goyal and Khare, 2010). Generally the following

methods are suggested to control air pollution in the indoor environments.

Removal and/or substitution of the pollutant source

Filtration of the pollutants

Dilution of the indoor air with the help of ventilation

Isolation of the source (encapsulation)

Time specific use of the contaminat source if required

Educating the occupants about the IAQ issues

INDOOR AIR QUALITY IN PAKISTAN

Indoor air quality is an issue of prime importance particularly in the developing

countries where biomass fuel is still burnt in rural areas. The status of air quality in the urban

centres is also pitiable as lack of knowledge leads to not realizing the health hazards related to

inhaling polluted air. Like many developing countries, Pakistan also faces serious pollution

issues. The Pakistan Environment Protection Agency (Pak-EPA) has conducted numerous

studies to record ambient levels of particulate matter in various urban centers of the country

with many other related researches at institutional/university level as well (Pak-EPA, 2001,

2002, 2003; Hashmi and Khani, 2003; PEP, 2006, 2007; Ghauri et al., 2007; Lodhi et al., 2009;

Mansha et al., 2012; Zainab et al., 2015; and Zona et al., 2015). The government has setup


19

NEQS for setting a limit for the amount of a particular pollutant to be present in the ambient

air. However sadly enough, indoor air pollution still requires to be recognized as a potential

health hazard at policy level. There is no guideline available for setting the limits for

concentrations of pollutants in the indoor environments. With the exception of some studies

there is no detailed data regarding the monitoring of concentrations of fine particulate matter

or air-borne bacteria and fungi in the residential built micro-environments (Akhtar et al., 2007;

Colbeck et al., 2008, 2010; Siddiqui et al., 2005a, 2005b, 2008, 2009; Nafees et al., 2011;

Janjua et al., 2012; Nasir et al., 2013, 2015; Sidra et al., 2015; Amanat et al., 2015; Ali et al.,

2015a; Saeed et al., 2015; Abbas et al., 2015). Moreover, most of these studies focus on PM

generation from biomass fuel burning and the subsequent health impacts on the exposed

population and the IAQ of the urban areas have not been extensively explored yet.

In many developing countries, including Pakistan, indoor air pollution annually claims

1.2 million lives as reported by WHO (2007). In Pakistan, it is a significant burden on the

economy and costs 1% of the GDP. The World Bank reported an annual figure of 28,000 deaths

with 40 million reported cases of acute respiratory illness in Pakistan (World Bank, 2006).

According to recent figures, these PM2.5 levels are the cause of more than 9,000 premature

deaths per annum which represent 20% of acute lower respiratory infection (ALRI) mortality

among children under five years of age, 24% of cardiopulmonary mortality, and 41% of lung

cancer mortality among adults 30 or more years of age in the major cities of Pakistan. About

12% of the deaths occur in children below five years of age and 88% are among adults. Almost

80% of the death losses are in Karachi. Particulate matter is also estimated to cause 59% cases

of chronic bronchitis in these cities, over 1.6 million cases of ALRI in children, more than 100

million restricted activity days, and over 300 million respiratory symptoms annually. These


20

annual health effects represent 203,000 DALYs, of which 97,000 are from premature mortality

and 106,000 from morbidity (Sánchez-Triana et al., 2014).

Pakistan is one of the world’s highly populated countries (ranking 6th among other

countries) comprising of 2.62% of the world’s population (179 million people estimated in

2012) (United Nations Department of Economic and Social Affairs, 2012). With an increasing

trend in population dynamics, Pakistan Economic Survey (2009 – 2010) estimates the

household size to be 7.2 persons on average. With 64% of its population residing in rural areas,

Pakistan is a predominantly rural community where use of biomass fuels is widespread owing

to its easy availability and low cost. It is estimated that 90% of rural households employ solid

fuels for cooking and other purposes such as space heating etc. while 22% of urban households

also use solid fuels for cooking (Sheraz and Zahir, 2008). However, little are the people aware

of the hazards they are exposed to and at what cost. Nasir et al. (2015a) explored the socio-

economic conditions of Pakistan which play an integral role in fuel selection and highlighted

poverty as the major factor. Colbeck et al. (2010) reviewed the status of indoor air quality in

Pakistan and highlighted the various factors resulting in poor health condition of indoor

environments. Despite the worse conditions of the indoor environments particularly in rural

areas, policy makers are still ignorant of the hazards to which majority of the population is

exposed to. It is a much needed step to recognize indoor air pollution at policy level so that

mitigation measures can be initiated.

Owing to the limited number of studies regarding monitoring of air quality in Pakistan,

we are unable to determine the exposure level of general public towards environmental

pollutants. As mentioned earlier, majority of the studies conducted were concerned with PM

levels in rural settings where biomass fuel burning is a common practice. Urban centres have


21

been ignored largely in these studies. Keeping in mind these facts, the current study was

designed to monitor the levels of fine particulate matter and air-borne microflora in a random

assortment of residential houses of Lahore, Pakistan to gain an insight into the air quality of

these houses and to assess the risk associated with exposures to indoor contaminants for the

occupants.


22

AIMS AND OBJECTIVES OF THE STUDY

As concluded in the above chapter, there is a poor record of indoor air quality in

Pakistan. Consequently, the current study was designed keeping in view the lack of data on

indoor air quality in urban areas of Pakistan. The underlying purpose of this research work was

to determine the state of air quality in the residential micro-environments of urban areas and

to identify the sources responsible for higher levels of particulate matter and micro-organisms

in the indoor environment. It was also perceived to study the relation of various factors

responsible for defining the indoor air quality of residential areas and the exposure risks to the

residents so that reasonable measures may be proposed to overcome the hazards posed by

particulate pollution and bioaerosols.

Based on these aims, the following objectives were defined for this study:

1. Real-time monitoring of mass concentrations of PM2.5 in the kitchens and living rooms

of different houses in Lahore

2. Qualitative assessment of bio-aerosols in kitchens and living rooms of these houses

using settle plates

3. Correlation of particulate matter concentration indoors with different urban

congestions

4. Seasonal variation of fine particulate matter and air-borne bio-aerosols in different

urban residential micro-environments

5. Recommendation for a standard value of permissible levels of PM2.5 concentrations

indoors in Pakistan

Chapter Two Literature Review

23

CHAPTER TWO

LITERATURE REVIEW

Particulate matter is ubiquitous in the environment with a wide variety of sources and

sinks present in the nature apart from the human induced factors. Air quality is an issue of

immense importance owing to the disease burden related to exposure to particulate matter and

subsequent increase in mortality rates all over the world. The indoor environment harbours a

different range of sources than the outdoor environment and many studies have identified and

documented the sources and their emission rates for particulate matter. Although there are a

number of studies regarding indoor air quality in many developed and developing countries,

Pakistan faces a scarcity of data in this context as discussed in the coming paras.

INSTRUMENTS FOR PARTICULATE SAMPLING

There are a number of techniques applied for particulate sampling. The two most

widely used methods for aerosol monitoring are the gravimetric method and the light-scattering

method. Niu et al. (2002) reviewed the efficiencies of these two methods and found out that

gravimetric method is more suitable for use as a reference while the light-scattering

instrumentation should be used for preliminary measurements. The current research work

employed DustTrak aerosol monitor (TSI, Inc. Model 8520) which is a light scattering

instrument for real-time monitoring of indoor PM2.5 levels. As the following comparative

studies concluded, DustTrak gives more precise results when dealing with smaller particle size.

In a study by Yanosky et al. (2002) two direct reading aerosol monitors were collocated

indoors to compare their efficiency. The Aerodynamic Particle Sizer (APS) (TSI, Inc. Model

3320) and DustTrak Aerosol Monitor (DustTrak) (TSI, Inc. Model 8520) were used in this

study and 24-h samples were collected. Paired-t test and regression analysis were applied on


24

the data thus obtained. It was found out that DustTrak gave more precise and accurate

measurements of PM2.5 which were in accordance with a US EPA designated Federal

Reference Method (FRM) PM2.5 sampler, the BGI, Inc. PQ200.

In another similar study by Cheng (2008), DustTrak (TSI Model 8520) and Grimm

Series 1.108 Aerosol Spectrometer were used to measure PM2.5 and PM10 within an iron

foundry. These both are direct reading, real-time aerosol monitors. For comparison of these

two instruments and as a reference gravimetric method the SA Model 241 Dichotomous

Sampler was used. During the study it was found that DustTrak gives more precise results

when particle size decreases.

SOURCES OF PARTICULATE MATTER IN THE INDOOR ENVIRONMENT

There is a wide variety of sources for particulate matter in both the indoor and ambient

air. However, particulate matter is not only generated by the indoor or outdoor sources; the

existing PM can also cause fluctuations in monitored PM levels owing to their deposition rate

and their subsequent re-suspension. Thatcher and Layton (1995) studied the deposition,

penetration and re-suspension of particulate matter in a house in California. The same spot was

selected for studying the three processes. For measuring the deposition rate, the particle

concentration was raised and simultaneously the air infiltration rates were measured. For

particle size ranging from 1-5 µm in diameter, the deposition velocity closely matched the

calculated settling velocity while it was less for particles larger than 5 µm, most probably due

to the non-spherical nature of these particles. The penetration factor was found to be 1 which

was indicative of the non-effectiveness of the building shell in removal of infiltrating particles.

Similarly re-suspension of dust was measured under different conditions and was observed to

be increased up to 100% by walking alone. Presence of four people in the house involved in


25

minor activities generated a re-suspension rate ranging between 1.8 x 105 and 3.8 x 104/hour

for super micron particles.

Recently, Zhou et al. (2011) studied the re-suspension of dust particles deposited in

ventilation ducts. A physical science based model was developed and it was found out that re-

suspension of these previously deposited particles was an important contributor to higher levels

of particulates indoors. While fresh air had little influence on exposure rate, increased

ventilation rate lead to an increase in exposure to this re-suspended dust.

Although the deposition and resuspension of particulate matter are important sources,

the activities carried out in the indoors are also influencing factors. Several studies have been

conducted to confirm the sources and source strengths of everyday activities in relation to

particulate matter.

Monn et al. (1997) investigated the relationship between indoor and outdoor

concentrations of PM10, PM2.5 and NO2 in seventeen different households. Smoking was found

to have the most profound effect on I/O ratios i.e. > 1.8. Houses with no apparent sources had

an I/O ratio of 0.7. Human activities contributed a lot to particulate matter even in those houses

with little or no apparent indoor sources. Gas cooking contributed to higher levels of NO2 in

some homes i.e. > 1.2 while in other homes the I/O ratio was found to be less than 1.

Chao et al. in 1998 observed that a high Respirable Suspended Particles (RSP) to Total

Suspended Particles (TSP) ratio was found indoors. Levels of particulate matter indoors

increased during cooking, smoking and burning of incense. However, in case of heavy rain and

high ventilation rate indoor particulate level fell by 20%.

The source strength of various activities was determined by Abt et al. (2000) in four

houses in Boston as an extension of a previous investigation. A physical model was used to


26

study the source strengths and also the ventilation rates affecting the particulate matter levels

of varying sizes. PM fractions having a size ranging between 0.7 to 10 μm were generated from

activities such as cooking, cleaning and movement of people around the house while cooking

also contributed a significant proportion of particles with a diameter below 0.5 μm. Outdoor

sources were also a contributing factor towards indoor particulate levels. Gravitational settling

caused an increase in the deposition efficiency of larger particles which were thus easily and

readily removed from the surrounding air.

Mass concentrations of PM10, PM2.5 and PM1 were measured inside and outside of

seven urban and two rural houses in UK for a period of twelve months. The study revealed that

the major source of particulates indoors was from outdoors. An activity chart was filled by the

occupants to determine which activity contributed more to particulate matter indoors. Cooking,

cleaning, smoking and general activities contributed highly to PM10 concentrations indoors

while cooking and smoking contributed to PM2.5 and PM1 more than cleaning and other

activities (Jones et al., 2000).

Air samples were analyzed from eight different homes of Hong Kong for source

appointment (Chao and Cheng, 2002). Five sources were identified such as smoking, cooking,

burning incense, human activities indoors, and outdoor sources. The major source of PM2.5 was

identified to be cooking with an average of 61.9% of the total PM2.5 concentrations indoors.

Outdoor sources provided for most of the PM10 concentrations (49.3%) with human activities

on second place (29.9%).

Concentration of particulate matter indoors and outdoors was measured in 34 homes of

Hong Kong. It was found that since windows remained closed during most time of the day due

to fall and winter season, a poor correlation existed between the indoor and outdoor


27

concentrations. The mean indoor PM2.5 and PM10 concentrations were found to be 45.0 and

63.3 μg/m3, respectively while the corresponding mean outdoor levels were 47.0 and 69.5

μg/m3, respectively. Moreover the use of window type air conditioners contributed to low air

change rate (Chao and Wong, 2002).

Riley et al. in 2002 applied a model to study the level of indoor particulate matter due

to sources from outdoors which included the distribution of ambient particles on the basis of

their size, type of building and operational parameters. It was concluded that in order to

determine the exposure to particulates of outdoor origin, the efficiency of removal processes

in different buildings according to size must be considered.

PM2.5 and PM1 were measured in ten homes of urban area of Taipei in winter and

summer season by Li and Lin, (2003). The average concentration of indoor PM1 was measured

to be 25.88 μg /m3 while the outdoor concentration was 25.86 μg /m3. On the other hand the

mean indoor and outdoor concentration for PM2.5 was 37.60 and 37.26 μg /m3 respectively.

Moreover no significant difference was found in the mean concentration of both PM2.5 and

PM1 for the winter and summer seasons. For PM1, the average summer and winter levels were

found to be 25.13 μg /m3 and 26.70 μg /m3 while for PM2.5 their respective levels were

measured to be 36.45 μg /m3 and 38.51 μg /m3.

PM2.5 daily concentrations and 15-min average was measured in three residential areas

of St. Paul (Ramachandran et al., 2003). 9-10 houses were selected from each locality for

indoor measurements while the outdoor monitoring was conducted at a central monitoring site

at each locality. While the outdoor concentrations did not vary greatly, a varying trend in

indoor concentration was noted. Measurements were made in the summer, winter and autumn

of 1999 and a strong seasonal effect on 15-min average PM2.5 concentration was observed. The


28

values were higher in the spring and summer than in fall since the windows were kept open

more often during spring and summer season.

A mathematical model was applied by Ferro et al. (2004) to provide an estimation of

the source strength of various household activities. Different everyday activities such as

folding blankets and clothes, walking around, dancing, sitting on furniture and many more are

responsible for re-suspension of dust particles, forming a “personal dust cloud”. Source

strength for PM2.5 was found to range between 0.03 to 0.5 mg min-1 while that for PM5 was

from 0.1 to 1.4 mg min-1. The source strengths were found to be a function of the number of

persons performing the activity, the vigor of the activity, the type of activity, and the type of

flooring.

Ambient particles are considered to be an important source of particulate matter found

indoors. Hänninen et al. (2004) studied the infiltration of ambient particles into indoor

environments in four European countries. The mean concentration of ambient particles in

indoor air ranged from 7μg/m3 in Helsinki to 21μg/m3 in Athens. However a significant portion

of indoor air could not be explained.

Concentration of elemental carbon and organic carbon present in PM2.5 air sample was

analyzed in a study by Ho et al. (2004). Five roadside buildings i.e. three residencies with

natural ventilation and two buildings (a class room and an office) with mechanical ventilation

were selected for the study. The outdoor concentration of PM2.5 was found to be 78.4 μg/m3

with organic carbon concentration to be 12.6 μg/m3 and elemental carbon concentration to be

6.4 μg/m3. On the other hand the indoor PM2.5 concentration was measured to be 55.4 μg/m3

while the OC and EC concentrations were 11.3 and 4.8 μg/m3. The major source of indoor

PM2.5, elemental carbon and organic carbon was observed to be the penetration of outdoor air.


29

Koistinen et al. (2004) investigated the source contributions to the mass concentrations

of PM2.5 in personal exposures and in indoor and outdoor residential microenvironments, and

also in workplace indoor microenvironments of nonsmoking adult population unexposed to

environmental tobacco smoke in Helsinki, Finland. The major sources were identified to be

inorganic secondary particles, primary combustion, and soil in all the microenvironments and

personal exposures. Resuspension of dust was identified to be a major contributor of fine

particles indoors.

Particulate concentrations were measured for more than 48 hours in fourteen

households of Brisbane, Australia (Gilbert et al., 2005). A Condensation Particle Counter and

a Photometer were used to record the data. The occupants of all the households maintained an

activity diary so that their exposure to particulates could be determined. The highest

concentrations were recorded to be during the cooking time i.e. (47.5´103 particles/cm3) and

PM2.5 concentration (13.4 mg/m3). The highest residential exposure period was the sleeping

period for both particle number exposure (31%) and PM2.5 exposure (45.6%). The percentage

of the average residential particle exposure level in total 24h particle exposure level was

approximating 70% for both particle number and PM2.5 exposure.

Sources of aerosols vary greatly and Meng et al. (2005) studied the relationship

between sources generating aerosols outdoors, indoors and due to personal activities. 212

households were studied in three states of USA to investigate exposure to different pollutants

present in air including VOC’s, PM2.5, carbonyls and more. No smoker lived in any of these

houses and 162 of them were sampled twice. Median indoor, outdoor and personal PM2.5 mass

concentrations for the three sites were 14.4, 15.5 and 31.4 μg/m3, respectively. The

contribution towards PM2.5 concentrations indoors from outdoor sources was estimated to be


30

56% for all study homes i.e. 63% for California, 52% for New Jersey and 33% for Texas study

homes.

Chunram et al. (2007) studied the outdoor and indoor concentrations of PM2.5 in both

residential and workplace buildings. Monthly averages for indoor particulates ranged from

13.6 to 57.9 μg /m3 for residential building, while that for workplace building ranged from 9.9

to 58.5 μg /m3. On the other hand, monthly averages for outdoor PM2.5 ranged from 12.6 to

77.0 μg /m3 for residential building and that for workplace building measured between 15.1 to

70.0μg/m3. Ambient sources were found to contribute more to the PM2.5 concentrations

indoors.

Nature of work, number of occupants and type of ventilation system are responsible for

indoor air quality as found in a study by Helmis et al. in 2007. Indoor air quality in terms of

VOC’s, PM10, PM2.5, CO2, NOx and SO2 of a dental clinic was monitored for three months.

The highest exposure level was found to be during operation hours while non-working hours

showed to have the lowest levels. Moreover the nature of dental procedures and the material

used also affected the air quality. The effect of natural ventilation was also studied. It was

observed that increase in natural ventilation with the help of air renewal and double cross-

ventilation improved the indoor quality a lot.

Kurmi et al. (2008) studied the exposure of residents to particulate matter while doing

domestic work in both urban and rural areas of Nepal. 490 houses in urban and rural areas of

Kathmandu were monitored for respirable dust and PM2.5 over duration of 24 hours. The

average respirable dust proportion was measured to be 1400 µg/m³. On converting this

concentration to an 8-h time weighted average (TWA) it exceeded the UK limit of 4000 µg/m³.


31

Women are more exposed to this high concentration of respirable dust which can result in

respiratory illness.

Exposure to fine particulate matter, polycyclic aromatic hydrocarbons (PAHs) and

black carbon (BC) was studied in a cohort of children of New York for a period of two weeks

after every six months (Jung et al., 2010). Indoor and outdoor levels were monitored between

October, 2005 and April, 2010 to study the impact of seasonal factors on pollutant

concentrations, and to analyse the relationship between ozone and PAH. The results showed a

distinct seasonal factor to be responsible for variations in pollutant levels during the heating

and non-heating seasons with elevated levels of PAHs and BC during the heating season while

PM2.5 levels did not suffer any significant change. The meteorological factors were also

responsible for varying emission rates of pollutants.

Residences are an important source of exposure to particulate matter and Bhangar et

al. (2011) studied seven residences from 2007-2009 to determine the factors and circumstances

responsible for exposure to ultra-fine particles. Cooking caused the greater variation in

exposure to ultra-fine particles.

IMPACT OF SEASONS UPON PARTICULATE MATTER

Seasonal variation along with indoor/outdoor ratios of PM, CO, NOx was observed in

eight households of Delhi, India by Kulshrestha and Khare (2011) with the conclusion that PM

levels were elevated during the colder months. The indoor outdoor ratio was also determined

and regression analysis revealed that indoor environment contributed significantly towards

higher PM and CO levels during the winters. Moreover, PM2.5 was observed to constitute a

higher proportion of RSPM in the indoor environment. The major sources for PM were


32

identified to be the burning of biomass fuel and the cleaning activities carried out within the

houses.

Massey et al. (2012) studied the seasonal variations of PM10, PM5, PM2.5 and PM1 using

Grimm aerosol spectrometer in India. Five urban and five roadside sites were monitored from

October, 2007 till March, 2009. The respective average levels of PM10 indoor and outdoor

were recorded to be 247 μg/m3 and 255 μg/m3 at roadside houses while these levels being

181 μg/m3 and 195 μg/m3 at urban houses. Similarly the indoor and outdoor concentrations of

PM5.0 at roadside houses were 211 μg/m3 and 230 μg/m3 and at urban houses were 145 μg/m3

and 159 μg/m3 respectively. The annual mean concentrations of PM2.5 were measured to be

161 μg/m3 and 160 μg/m3 at roadside houses and 109 μg/m3 and 123 μg/m3 at urban houses.

PM1.0 concentrations at roadside houses were 111 μg/m3 and μg/m3 while at urban houses they

were 99 μg/m3 and 104 μg/m3 respectively. Seasonal variations of all above given particulates

was also studied and it was found that their levels increased prominently during the winter

season. Moreover an increase in pollutant concentration was highly correlated with an increase

in health problems, more prominent in houses with a higher concentration of fine particulates

(PM2.5).

The suburban residential micro-environments of UK were monitored by Nasir and

Colbeck (2013). The particulate matter levels were greatly affected by the activities and

ventilation practices. Cooking contributed to highest PM levels with smoking also playing a

significant part. Indoor smoking during the winter season doubled the PM levels. Cooking

practices were also observed to contribute towards varying PM concentrations as grilling led

to highest PM numbers followed by boiling and frying.


33

Particulate matter levels in the indoor and ambient air along with bioaerosol levels were

investigated across four floors of a building in Seoul, Korea (Oh et al., 2015). The PM levels

were higher during the winter season than the summer season. The bioaerosol level were

highest on the fifth floor where a private academy school was being run. The microbial levels

were noted to vary by the number and activities of the students.

A research was conducted to assess PM2.5 and CO levels during the burning of

mosquito coils. The average PM2.5 levels were noted to be 1031 µg/m3 with mean CO levels to

be 6.50ppm. There was significant reduction in these levels (up to 50%) when the windows

were opened and further decline (90 %) when both the windows and doors were kept open

during the burning of mosquito coils. The average levels were reported to be higher than those

produced during biomass burning and can pose significant health risks. Although the results

were not statically significant, prevalence of respiratory problems was reported to be higher in

residents using mosquito coils (Salvi et al., 2015).

The influence of ambient PM2.5 levels was observed by Zhao et al. (2015) during the

haze-fog episodes in winters of Beijing. Continuous indoor and outdoor levels of PM2.5 were

recorded in a naturally ventilated building. A close relationship was observed between the

indoor and outdoor levels (r2 = 0.9104) when windows were closed and no indoor activity was

being performed. Ambient wind speed and relative humidity also showed a close correlation

with the indoor/outdoor PM2.5 ratios.

Harrison et al. (1997) monitored the concentrations of coarse and fine particles at a site

in Birmingham, U.K. from October, 1994 to 1995. Road traffic was identified as a major source

since it results in increased pollutant concentration not only through vehicular exhaust but also

resuspension of dust. A marked difference was observed during the winter and summer.


34

PM2.5 comprised about 80% of coarse particles (PM10) during winter and was found to be

strongly correlated with NOx. On the other hand, coarse particles (PM10−PM2.5) accounted for

about 50% of PM10 during the summer season. Generation of coarse particles from re-

suspension depended positively on the wind speed while the elemental carbon from traffic was

negatively dependent on the wind speed.

Four sites {control(C), kerb (K), residential (R) and industrial location (I)} were

monitored in Mumbai city for concentration of fine particles in indoor and outdoor air using a

MiniVol PM2.5 sampler. Vehicular emissions were found to affect indoor air at the kerb site

while indoor sources were more contributing for IAQ at all sites. PAH concentrations were

elevated at all outdoor sources. OC percentage in PM2.5 was higher in indoor at control and

residential site, whereas EC percentage in PM2.5 was higher in kerb and control. Strong

correlation was observed between indoors and outdoors, EC and OC at kerb sites which

suggested that indoor concentrations were derived from outdoor environment (Joseph et al.,

2010).

The density of traffic on roads can significantly affect the indoor air quality of nearby

indoor microenvironments through infiltration. El-Batrawy (2011) studied the concentration

of PM10, NOx, and SOx at 22 houses which were located on streets with different traffic

densities. Measurements were made for both indoor and outdoor air quality during winter and

summer. Outdoor sources such as high traffic were found to contribute more to indoor air

quality. Coarse particle concentration increased in winter while NOx and SOx increased in

summer. An increase in I/O ratios was observed during the summer season indicating outdoor

sources to be more predominant during this season.


35

Elemental and ionic composition of PM1 and PM2.5 were conducted after collecting

samples form old age homes in Antwerp. The samples were collected via impaction and

subjected to EDXRF spectrometry and IN for analysis. The ambient levels were higher than

the indoor levels. Zn and Pb were observed in higher concentrations. Moreover a strong

association was concluded between the indoor and outdoor environments. Since no significant

indoor source could be located, ambient sources were considered to be prevailing within the

old age houses (Buczyńska, et al., 2014).

Ambient sources have been concluded by many studies to have a substantial impact

upon IAQ and a recent study by Kearney et al. (2014) used the infiltration factors (Finf) to

better understand the exposure estimation within houses. Fine and ultrafine particles were

monitored in 74 houses of Edmonton during the winter and summer season. A parallel

monitoring of the subsequent outdoor and another ambient location was also carried out. The

Finf for fine particulate matter ranged between 0.10 and 0.92 during the winters while it was

0.31 to 0.99 in summers. The indoor contributors of fine and ultrafine particles were concluded

to be cooking and smoking. However, ambient sources still had a substantial impact in defining

the indoor air quality.

PM2.5 was sampled and collected on polytetrafluoroethylene filter paper from indoor

micro-environments at three sites (urban, rural and roadside). Highest levels were obtained

from the rural site (71.23 µg/m³) as compared to urban (45.33 µg/m³) and roadside location

(36.71 µg/m³). Elemental composition of PM2.5 was determined by inductively coupled plasma

atomic emission spectroscopy and Cadmium and Lead were found to have an association with

cancer risk (Varshney et al., 2015).


36

PARTICULATE MATTER EMISSIONS FROM COOKING

A study on four different types of stoves in use in kitchens of rural Guatemala was

conducted by Naeher et al. (2000). Observations were made for 22 hours in each of the three

houses for PM2.5, PM10, carbon monoxide and total suspended particulates (TSP) in the

kitchen, bedrooms and outdoors. The four different conditions of the kitchens used included

background type with no stove in use, kitchen having traditional open-wood stove, kitchen

with improved woodstove with flue also called plancha, and kitchen using LPG gas stoves. No

smoker lived in any of the test houses and a mother with a baby less than fifteen months old

was present in these houses. Personal measurements of the mothers and their babies were also

made as usually the mother carried her baby on her back during the daily household routine. It

was found that open wood-stoves gave the highest concentrations of all the above listed

pollutants; PM2.5 level was 528 µg/m3, PM10 as 717 µg /m3, TSP level was 836 µg /m3, and

CO had a concentration of 5.9 ppm. On the other hand, background had the lowest

concentration of PM2.5, PM10, TSP and CO i.e. 56 µg /m3, 173 µg /m3, 174 µg /m3, and 0.2 ppm

respectively. The respective levels of these pollutants in kitchens using plancha were 96 µg

/m3, 210 µg /m3, 276 µg /m3 and 1.4 ppm; while those for gas stoves were 57 µg/m3, 186 µg/m3,

218 µg/m3 and 1.2 ppm. Moreover personal measurements of PM2.5 and CO for mothers and

children showed the highest levels while using open wood-stoves and the lowest levels were

observed while using gas stoves.

In a study conducted by Lee et al. (2002) on indoor air quality within flats, it was

observed that the average concentrations of CO2 and PM10 over 8 hours in the kitchens were

14% and 67% higher than those measured in the living rooms. Moreover Liquefied Petroleum

Gas (LPG) stoves were found to be a more potent source of indoor VOC’s than stoves using


37

natural gas as fuel. Also the mean bacterial count in kitchens of most houses was 23 % more

than in the living rooms.

A study was conducted on six households in three different sites (roadside, urban and

rural) of Hong Kong to determine the indoor/outdoor relationship for PM2.5 and carbonaceous

pollutants (Cao et al., 2005). 24h mean concentrations of indoor and outdoor PM2.5 were

measured to be 56.7 and 43.8 μg /m3 respectively. The average concentrations of organic

carbon (OC) and elemental carbon (EC) were measured to be 17.1 and 2.8 μg /m3 respectively.

The contribution of OC towards PM2.5 was 29.5 % while EC contributed 5.2%. It was observed

that while ambient sources contributed more towards PM2.5 indoors, daily activities in houses

also resulted in episodic increase in PM2.5 levels. Also, 2/3rd of carbonaceous pollutants indoors

had their sources outdoors.

He et al. in 2004 studied PM2.5 concentrations and sub micrometer particles in kitchens

of 15 houses of Brisbane. Twenty one types of indoor activities were identified using activity

diaries filled by the occupants. It was found that the level of sub micrometer particles was

elevated as high up to five times during frying, grilling, stove use, cooking, fan heater, candle

vaporizing eucalyptus oil etc. on the other hand smoking, frying and grilling caused an increase

in levels of PM2.5 up to 3, 30 and 90 times higher than background levels.

The impact of improved stoves on particulate concentrations was studied by

Chowdhury et al. (2008) in rural highland Guatemala. PM2.5 and PM1 concentrations were

monitored for 48 hours in the kitchen, bedroom and outdoors of selected houses. In kitchens

with the traditional open fire stoves, PM2.5 concentrations were 1093 ± 906 μg/m3 while this

value decreased to 81 ± 181 μg/m3 in kitchens with improved stoves with chimneys. Similarly

there was 64 % reduction in particulate concentrations in the bedrooms of houses with


38

improved stoves and 69 % reduction in outdoor concentrations. Moreover for PM1, 91 %

reduction was recorded in the kitchens with improved stoves as compared to kitchens with

open fire. However there was not a significant difference of PM1 concentrations in the

bedrooms and outdoor environment of both types of houses. Thus improved chimney stoves

were helpful in reducing particulate pollution indoors.

Improved stoves can reduce the concentration of PM2.5 indoors (Ward and Noonan,

2008). USEPA certified stoves were distributed in a Rocky Mountain valley community to

replace old wood stoves. PM2.5 concentrations were monitored using DustTrak aerosol monitor

in sixteen hoses before and after the intervention. Organic carbon, elemental carbon and other

chemicals from wood smoke were also measured from quartz filters. A significant reduction

(71 %) was observed in the average concentration of fine particulate matter after the

introduction of improved stoves. Resin acids (natural chemicals in bark of wood) were found

to increase while Levoglucosan also decreased by 45 %.

There is a wide variety in how you cook your food. Whichever the cooking method

may be, there is always a significant generation of particulate matter to the surrounding air and

exposure to high levels of PM have been documented to cause adverse health effects.

Buonanno et al. (2009) conducted a study to characterise the PM emissions caused by various

factors during cooking such as the type of food being cooked, type of oil used for cooking, and

also cooking temperature. Increased temperatures led to increased emissions. Moreover

cooking vegetables was observed to contribute less towards general PM loads than the cooking

of fatty foods. Olive oil was found out to be the best for frying in terms of lowest PM emissions

while cooking on a hot plate emitted lower amounts of PM than the gas stove.


39

The type of biomass fuel burned is also an indicator of the amount of PM released into

the surrounding air as concluded by Ansari et al. (2010) in their study. A rural area of Lucknow,

India was selected for the monitoring. Two categories of houses were defined on the basis of

the type of biomass fuel in use. One group burned plant material only while the other group

employed all kinds of biomass fuel to carry on with their work. PM2.5 and PM10 were monitored

along with Polyaromatic Hydrocarbons (PAH) emissions from different fuel types. An obvious

difference was noted during the cooking and non-cooking periods. The respective average

concentrations of PM2.5 and PAHs ranged from 1.19 ± 0.29 to 2.38 ± 0.35 and 6.21 ± 1.54 to

12.43 ± 1.15 μg/m3 during cooking while the respective PM10 and total PAHs mean levels were

in the range of 3.95 ± 1.21 to 8.81 ± 0.78 and 7.75 ± 1.42 to 15.77 ± 1.05 μg/m3.

Huboyo et al. (2011) studied different cooking methods to determine which method

emitted the highest concentrations of PM2.5 and carbon monoxide. Frying caused the highest

levels of PM2.5 indoors while boiling emitted the lowest concentration.

Shimada and Matsuoka (2011) studied the prevalence of PM2.5 in houses using solid

biomass fuels in fifteen Asian countries. Moreover since people stay indoors for different time

durations, their exposure to PM2.5 indoors was also studied. The highest exposure concentration

of 427.5 μg/m3 was observed in China. Nepal came second with an average concentration of

285.2 μg/m3 while Laos and India had an average exposure concentration of 266.3 μg/m3 and

205.7 μg/m3 respectively. Children and women, especially housewives between ages 35-64

were found to be more exposed to PM2.5.

Another study on the impact of improved stoves upon PM levels in Bangladesh was

conducted by Chowdhury et al. (2012). A reduction in levels of indoor particulate matter and

carbon monoxide emissions was observed. This also reduced the exposure risk of the cook.


40

The chemical composition of the PM2.5 fraction generated by burning biomass fuel consisted

of organic matter (59-60%) and elemental carbon (29-30%).

PARTICULATE MATTER AND ENVIRONMENTAL TOBACCO SMOKE

Smoking is also a major source of PM2.5 and PM1 and affects not only the smokers but

also other people around them. While smoking is harmful for the health of the smoker, second

hand smoke poses threats for the non-smokers present around. Laws imposing a ban on

smoking in public places have been found to be useful in reducing concentrations of fine

particles indoors.

Neas et al. (1994) studied the effect of passive environmental tobacco smoke on white

children aged 7-11 years. It was found out that additional cigarette packs smoked per day

within houses increased the incidence of lower respiratory diseases in children. Moreover, the

average PM2.5 concentration in houses with smoking was 48.5 μg/m3; while in houses without

smoking it was 17.3 μg/m3. However PM2.5 was not found to have any direct effect on

children’s pulmonary function. Thus exposure to PM2.5 was weakly associated with decreased

pulmonary function in preadolescent children.

Goodman et al. (2007) studied the effect of ban on smoking in public places on the

health of barmen in Dublin. Second hand smoke affects the health of nonsmokers greatly and

this study focused on the impact of ban on smoking in public places. Forty two bars were

monitored for PM10 and PM2.5 concentrations before the ban was imposed in 2004, and a year

after the ban. Similarly benzene concentrations in 26 bars were also monitored. Eighty one

barmen were questioned and their pulmonary function was examined along with salivary

cotinine. It was revealed that the ban had a positive impact on the health of the workers while

the air quality of the pubs had also improved tremendously in one year. An improvement in


41

the pulmonary function tests was observed with a 79 % decrease in exhaled breath carbon

monoxide and an 81 % decrease in salivary cotinine. Moreover an 83 % reduction in PM2.5 and

80.2% decrease in benzene levels were a clear cut indication of improved air quality of the

bars.

In a similar study, real time monitoring of PM2.5 was done at nine hospitality sites and

a hall of Georgetown, Kentucky before and after the implementation of a ban on smoking in

public places (Lee et al., 2007). Measurements were made before and after the ban and a

significant decrease in PM2.5 levels was observed at all the nine restaurants selected. However,

no difference was observed at the bingo hall due to non-compliance of the law. However after

three months, the PM2.5 levels dropped to 43μg/m3 as the law was enforced then.

Forty public places of Rome were monitored by Valente et al. (2007) for PM2.5 and

PM1 before and after a ban on smoking in public places was imposed in 2005. The

concentration of PM2.5 was 119.3 μg/m3 before the ban which then decreased to a mean value

of 38.2 μg/m3 within three months after the ban. A year later this value was measured to be

43.3 μg/m3. Similarly the mean concentration of ultra-fine particles (PM1) showed a significant

decline from 76,956 particles/cm3 to 38,079 particles/cm3 in three months and then to 51 692

particles/cm3 after a year. Moreover the level of cotinine (a metabolic by-product of nicotine)

in urine of non-smoking workers also decreased from 17.8 ng/ml to 5.5 ng/ml and then to 3.7

ng/ml (p<0.0001). Thus a reduction in mean concentrations of PM2.5 and PM1 was observed

after the imposition of ban on smoking.

Hyland et al. (2008) carried out a study to observe the PM levels generated by

environmental tobacco smoke (ETS) in thirty two countries. It was observed that in countries

where there was a ban on smoking in indoor public areas, particulate concentrations were found


42

to be much lower. Indoor air was sampled at a total of 1822 venues out of which 584 sites were

smoke free and the remaining 1238 venues had no restriction on smoking indoors. The PM2.5

mean concentration was observed to be 21 µg/m³ at smoke-free sites (ranging from 0 to 573

µg/m³) while it was 188 µg/m³ at smoking sites (ranging from 1 to 3764 µg/m³). New Zealand

had the lowest concentration at 8 µg/m³ while highest level was observed in Syria (372 µg/m³).

Fine particulate concentration was observed to be 8.9 times higher in places where smoking

was allowed than the smoke-free areas on the average.

Lee et al. (2010) studied the concentrations of PM2.5 due to second hand smoke in seven

Asian countries. Environmental Tobacco Smoke (ETS) is a major threat to health and second

hand smoke can cause health problems in non-smokers too. In this study, four types of public

places were selected i.e. restaurant, café, bar/club and entertainment sites in China, India,

Japan, Korea, Malaysia, Pakistan and Sri Lanka. Real time analysis of PM2.5 was carried out at

a total of 168 hospitality sites in these seven countries. The average concentration of PM2.5 was

137 μg/m3, with Malaysia having a mean concentration of 46 μg/m3 to India having 207 μg/m3.

In smoking venues, this value was 3.6 times higher (156 μg/m3) than in non-smoking areas (43

μg/m3). There must be some effective legislation for a ban on smoking in Asian countries to

improve the air quality and health of people also.

Tobacco smoke is known to contain a wide assortment of chemicals and elements in it

that are injurious to health. A recent study by Ruggieri et al. (2014) investigated the levels of

heavy metals in indoor PM2.5 levels in 73 houses of South Italy. Gravimetric sampling was

conducted using Teflon filters which were then analyzed for heavy metal content. Higher levels

of Cadmium and Thallium were detected in houses where smokers were present.


43

On the contrary, Bilocca et al. (2014) did not find any significant relation of Cadmium

and Thallium levels with smoking. Particulate matter was sampled in 45 homes to identify the

presence of heavy metals. Moreover, association between exposure to second hand smoke and

asthma in children was also investigated. Greater exposure to second hand smoking was

associated with asthma but sources of Cadmium and Thallium levels in fine particulate matter

were not found to be associated with smoking habits.

IMPACT OF PARTICULATE MATTER UPON WELL-BEING OF PUBLIC

Many studies have documented the health effects of particulate matter on human beings

and it has been concluded that exposure to PM leads to lung cancer, respiratory illness and

cardio-pulmonary disorders. Mishra (2003) conducted a study on the prevalence of Acute

Respiratory Infections (ARI) in children below five years of age in Zimbabwe. The study was

based on 3,559 children included in the 1999 Zimbabwe Demographic and Health Survey

(ZDHS). Among the 66 % children who live in houses using biomass fuel, 16 % suffered with

acute respiratory infections. After adjusting for different factors, it was found that children in

houses with biomass fuel were twice more prone to respiratory infections as compared to

children in those households where natural gas/LPG or electricity was being used as a fuel.

Since ambient particles can increase blood pressure, a study was conducted by

McCracken et al. (2007) in Guatemala on the effect of improved stoves on women. Two groups

were formed: the control group used the traditional open wood-fire stoves, and the intervention

group which used improved stoves with chimneys (also called a plancha). The average

concentration of PM2.5 was measured to be 264 μg/m3 in the control group and 102 μg/m3 in

the intervention group. Moreover blood pressure was also measured and a decrease in the blood

pressure of subjects of intervention group was observed. The systolic blood pressure was 3.7


44

mm Hg lower than the control group while the diastolic blood pressure was 3.0 mm Hg lower

than the control group. Similarly the subjects of intervention group showed a difference in

blood pressure before and after the use of plancha stove.

More recently the effect of PM exposure on mitochondrial activity was studied for the

first time by (Hou et al., 2010). Steel workers in Italy were selected for the study to observe if

incresed exposure to PM lead to incresed mitochondrial DNA copy number which determines

mitochondrial damage. Real time PCR was carried out on day 1 and day 4 in a week to observe

mitochondrial DNA copy number along with measurement of personal exposure to various

fractions of particulate matter. Higher number of mitochondrial DNA copy numbers were

observed on day 4 which lead to the conclusion that oxidative stress is produced as a result of

damaged mitochondria linked with excessive exposure to PM.

MICROFLORA OF INDOOR AIR

Air-borne microorganisms i.e. bacteria and fungi are also a type of indoor air pollutant

and rather a more threatening type as they are responsible for a variety of diseases such as

tuberculosis, fever, nausea, asthma, legionellosis, diphtheria and many more (Di Giorgio et al.,

1996 and Jones, 1999). Indoor micro-flora is reported to be responsible for health problems,

especially among children (Maus et al., 2001). The major factors responsible for micro-

organisms to spread in indoor environment is considered to be the activities of people, air

conditioning systems, animals, plants, material used for construction and particles of dust and

soil (Goddard, 1964).

Air sampling was carried out in Dutch houses, libraries, offices and schools to identify

the airborne myco-flora of these non-industrial indoor environments. Surface sampling was

done by swabs and cello tape preparations while a RCS-Reuter centrifugal air sampler was


45

also used. Among the species identified, the most common ones included Aspergillus

versicolor, Cladosporium spp., Penicillium brevicompactum, Penicillium chrysogenum,

Eurotium spp. and Wallemia sebi with occasional presence of Aspergillus fumigatus,

Scopulariopsis spp. and Stachybotrium spp. it was suggested that both air and surface sampling

should be employed side by side as it ensures the isolation of species left by one method (van

Reenen-Hoekstra et al., 1991).

The concentration of fungi in six apartments of Taipei was measured using Two-stage

Andersen viable impactor (Li and Kuo, 1993). There was a difference in the concentration of

fungi in different rooms of the apartments including kitchens and living rooms also. Moreover,

more than 80 % of fungi present were respirable. Concentrations of Aspergillus, Penicillium

and Cladosporium were found to be more than 500 cfu/m3.

Indoor and outdoor fungal concentrations in Yokohama, Japan were assessed by

(Takahashi, 1997) using a Reuter centrifugal air sampler. Highest ambient averages were

obtained during September and in October in the indoor air. Cladosporium spp. Alternaria spp.

and Penicillium spp. were observed in greater amounts in the ambient air while in the indoor

micro-environments, dominant species were Cladosporium spp., Aspergillus restrictus,

Wallemia sebi, A. glaucus, and Penicillium spp. a significant correlation was revealed between

the fungal composition and physical parameters such as temperature, wind velocity, relative

humidity and precipitation.

A study conducted by Pastuszka et al. (2000) on bio-aerosols in different homes and

offices in Poland revealed that concentration of Penicillium constituted of 90% of the total

fungi in moldy homes while it ranged from 3 to 50 % in healthy homes. Also the concentration

of fungal spores differed with seasons: in winter the concentration was found to be 10 to 102


46

cfu/m3 in healthy homes and 10 to 103 cfu/m3 in moldy homes. On the other hand these levels

rose to 103 cfu/m3 in healthy homes and 103–104 cfu/m3 in houses with molds in summer. The

typical level of air borne bacteria was found to be 103 cfu/m3 in homes and 102 cfu/m3 in

offices. Moreover Micrococcus spp was found in all homes comprising 36 % of the total

bacterial genera while Staphylococcus epidermidis was found in majority of houses being the

second most common species.

Pei-Chih et al. (2000) studied air-borne fungal concentrations in urban and sub-urban

houses in southern Taiwan. Air samples were collected with the help of Burkard sampler and

their concentrations of fungi calculated as cfu/m3. It was observed that the fungal

concentrations were significantly higher in sub-urban houses during the summer season.

Cladosporium and Penicillium were found to be the dominant species resulting in such high

concentrations indoors and outdoors respectively. The mean concentrations of airborne fungi

indoors were 8946 cfu/m3 in winter and 4381 cfu/m3 in summer. The outdoor concentrations

were found to be 11464 cfu/m3 in winter and 4689 cfu/m3 in summer. Moreover in suburban

areas Penicillium was abundant during the winter season while Aspergillus was dominant

during the summer.

Air conditioners (AC) are most commonly used to create a comfortable indoor

environment but they are also a source of microbial contaminants. In a study by Hamada and

Fujita (2002), it was observed that fungal contamination increased about five times more than

that caused by carpets. Inside the filters of AC, Cladosporium and Penicillium were dominating

species. Moreover the air conditioners used on regular basis resulted in a higher number of

fungal species in the air than in rooms where AC was not so frequently used. Interestingly, the

fungal contamination peaked when the AC was switched on but decreased over time.


47

Relationship between air-borne and dust-borne fungi was assessed as fungal samples

are mostly collected from these two sources. Air samples were collected from November 1994

to September 1996 using a Burkard culture plate sampler. The sample size was 496 homes and

sampling was done in the bedrooms. Dust samples were collected on cellulose extraction using

a vacuum cleaner. The dust was sieved and dilution plated onto DG-18 media. There was not

a significant association between the dust-borne and air-borne fungal species except for

Cladosporium and Penicillium. Presence of carpets and also the type of house were indicative

of the dust-borne fungi while infiltration from outdoor air indicated the type of airborne fungi.

Due to a weak relationship between the two types of sampling, it is important to collect samples

from both sources to have a more comprehensive understanding of the exposure risk present

for the inhabitants (Chew et al., 2003).

Hargreaves et al. (2003) studied the association between air-borne fungi and particulate

matter in fourteen houses of Brisbane. The average fungal colony forming units outdoors and

indoors were 1133+759 and 810+389, respectively. Under normal ventilation conditions, the

average outdoor and indoor concentrations of sub micrometer was 23.8 x 103and 21.7 x 103

(particles/cm3). On the other hand, super micrometer average concentration of outdoors and

indoors was 1.78 and 1.74 (particles/cm3), respectively. Moreover there was a direct relation

of concentration of fungal spores with a nearby source i.e. a park. In houses adjacent to the

park, fungal concentrations were found to rise up to about 3100 cfu/m3 and in houses at a

distance of 150 m or more from a park the cfu/m3 was below 1000. There was a lack of

significant association fungal concentrations and PM2.5 while a weak relation was observed

between the fugal levels and supermicrometer particles. However there are not many studies


48

to date which have tried to correlate bioaerosol levels with particulate matter in the indoor

environment and much work needs to be done in this context.

Concentrations of fungal spores and particulate matter in the air were measured during

renovation in a building. Suspended dust was measured to be 6.1 mg/m3 while particulate

sulfate (SO42−), nitrate (NO3

−), chloride (Cl−), ammonium (NH4+) and lead were recorded to be

2960, 28, 1350, 100 and 13.3 µg/m3, respectively. Air borne fungi and fungal spores were

measured to be 1.11 × 106 colony forming unit per gram. The dominant fungal species were

Cladosporium (33%), Aspergillus (25.6%), Alternaria (11.2%) and Penicillium (6.6%).

Renovation activities should therefore be carried out with precautions so that it does not

infiltrate into the occupied area and affect the people residing (Abdel Hameed et al., 2004).

Airborne fungal spores are responsible for a variety of respiratory problems including

asthma. A study was conducted to measure the concentrations of fungi in the ambient and

indoor air of houses of children affected with asthma (n = 414). The indoor fungal levels

correlated significantly with the outdoor levels and were related with the level of dampness in

the house, presence of cat and cockroaches (O’Connor et al., 2004).

A number of health problems have been reported in houses experiencing water and

mold damage. Children are more susceptible to these risk factors present around them and a

study was conducted to investigate the relation existing between the prevalence of lower

respiratory tract infection and allergies with that of visible mold or water damage. The selected

houses (having children at age of 8 months) were visited to check the mold and/or water

damage in the building. The health record of the infants was also investigated. Half of the

houses surveyed had a mold and/or moisture problem with the situation being worse in about

5 % of the houses which twice increased the risk of recurrent wheezing in children. Moreover


49

this risk increased 5 to 6 times in infants with food or aero-allergen sensitization. It was

concluded that recurrent wheezing in infants was significantly associated with the presence of

water and mold damage in houses (Cho et al., 2006).

The levels of microbial species in the air were determined in a Korean high-rise

building. The ambient levels of bacteria were higher in lower floors than in higher floors while

the indoor levels in the lower and higher apartments did not differ much. The seasonal variation

was also distinct with higher concentrations during the summers as compared to the colder

months. Among the fungal fauna, four dominant genera were detected i.e. Cladosporium,

Penicillium, Aspergillus and Alternaria with Cladosporium found in higher levels in the

kitchens as compared to other rooms (Lee and Jo, 2006).

Sampling was carried out in and outside of six mold free homes in Cincinnati area using

a Button Personal Inhalable Aerosol Sampler. Sampling was done for 24 hours during three

seasons. The average colony forming units of fungi in the ambient air were 102 while in the

indoor air, 88 colony forming units were observed. A total of 26 culturable fungal genera were

identified in the indoor and outdoor samples. Indoor environment was found to be more

favourable for the survival of fungal species. There was a significant correlation between the

indoor and outdoor levels of total spores and culturable fungi. Cladosporium, Aspergillus, and

Penicillium were more commonly found with highest culturability observed during the autumn

season. Increased culturability means an increase in release of allergens and is an important

factor to be considered for human health (Lee et al., 2006).

Alternaria alternata is a common fungal species present in the indoor air and exposure

to it may cause asthma. To study the relationship between exposure to Alternaria as a causative

agent of asthma, dust samples were collected from 831 households in 75 locations throughout


50

US. Samples were collected from the kitchens, bedrooms, and living rooms of the study sites

and questionnaires were filled for each site to gain information about the health of the

occupants, building structure and demographics. The results concluded that increase in asthma

symptoms was observed with an increase in exposure to A. alternata in US homes (Salo et al.,

2006).

A study was conducted in Austria to observe the growth of mold in 66 households and

the presence of fungal spores in the air. Among the selected households, 29 had no visible

mold growth while the remaining 37 houses showed signs of mold growth. One-stage MAS-

100® sir sampler was employed to collect samples with Malt Extract Agar and Dichloran

Glycerol Agar as culture media. The number of air-borne fungal spores was much higher in

buildings with a visible mold problem than in buildings with no visible mold growth. Also it

was noted that the air-borne microflora of the houses without a visible mold growth resembled

that of the outdoor air. Penicillium and Aspergillus were found to be the dominant part of the

recorded micro-flora indoors in houses with the mold problem (Haas et al., 2007).

The baseline concentrations of air-borne myco-flora were determined in 100 office

buildings in the US during 1994-1998 as a part of the BASE study. A large number of samples

were taken for different time periods, at different sites, and at different times of the day.

Comparisons were made between fungal species observed indoors and outdoors, during

different seasons and using different sampling methods. More fungal groups were observed

during the summers than in winters (Tsai et al., 2007).

The types and levels of fungal species present in air-conditioned rooms in 18 single

family homes were determined. There was no visible or reported moisture and/or mold

damage. Two samples were collected from the outdoor air while three indoor samples were


51

collected at each site. Indoor levels were observed to be lower than the ambient levels. The

most dominant spore types belonged to Penicillium and Aspergillus genera while Ascospores

and basidiospores were in higher numbers in the outdoor air. Apart from these species,

Chaetomium, Stachybotrys, and Ulocladium species which are indicator of moisture were not

present in significant numbers (Codina et al., 2008).

We are exposed to a variety of microbes present in the indoor air at all times. The

bacterial flora in the dust samples was analyzed from two buildings over a year during the four

seasons. It was observed that gram positive species were the predominant components of the

indoor dust. The bacterial flora varied during the seasons as well as in both buildings. The

occupants of the buildings were identified as the direct source of the dominant phylotypes

(Rintala et al., 2008).

The indoor air of two restaurants in Hong Kong was sampled for bacterial species. A

total of 15 genera were identified using MIDI, Biolog, and Riboprinter. The most common

species were Gram-positive bacteria with Micrococcus and Bacillus species being most

abundant. Majority of the species were opportunistic pathogens but their indoor level was

below the recommended level of Hong Kong Indoor Air Objective (< 500 cfu/m3). The

identified species were representative of species found in the soil, respiratory system of

humans and skin (Chan et al., 2009).

Fungal contamination was examined in 118 buildings in Eastern France. Among the

sample size, 32 buildings had a visible mold problem and self-reported health problems by the

occupants; 27 dwellings were occupied by medically diagnosed allergic patients; and 59

residencies served as the control group. The most abundant species were identified to belong

to Aspergillus, Penicillium, and Cladosporium genera. In the buildings occupied by allergy


52

patients, Penicillium chrysogenum and Penicillium olsoni were found to be in higher numbers

than any other species (Reboux et al., 2009).

The presence of fungal spores in the indoor environment is related to a vast number of

factors among which the home characteristics are also noteworthy. A study was conducted in

Cincinnati, Ohio to investigate the fungal loads in an indoor environment (Cho et al., 2006).

For this purpose, 777 homes were selected for an ongoing birth cohort study. On-site inspection

of the homes was carried out to detect any visible mold problem along with filling of

questionnaires for relevant information. Analysis of cat, house dust mite, and cockroach

allergens was done using monoclonal antibodies while polyclonal antibodies were used for

Alternaria and dog antigens. Water damage and mold problems were present in more than half

the number of homes while above 90% homes were carpeted. However presence of Alternaria

in dust was not associated with visible mold damage but was related to presence of dogs in

homes. Houses with increased humidity were also affected with elevated levels of Alternaria

antigen.

Frankel and colleagues (2012) conducted an investigation to evaluate air-borne content

of microbes during varying seasons with different temperature and humidity levels. Dust

samples were collected from five Danish homes during different seasons. It was observed that

fungal levels were highest during the summer season while bacteria reached the peak during

spring season. Moreover, fungi were higher in concentrations in the outdoor air while the

bacterial levels along with endotoxins were elevated in the indoor air. A direct association

between fungi and temperature and humidity existed while the case was opposite for bacteria.

It was concluded that indoor microbial levels were influenced significantly by temperature,

relative humidity, and ventilation rates as well along with seasonal variation.


53

Fungal diversity and composition in indoor air was assessed during the summer and

winter season (Adams et al., 2013) in a university housing facility. A seasonal variability was

observed in fugal levels being stronger in the ambient air. It was concluded that outdoor

composition of fungal species affected the indoor levels.

Joshi and Srivastava (2013) exposed sterile petri plates coated with nutrient Agar and

Potato Dextrose Agar in residencies to record the micro-flora of indoor environments. The

plates were then incubated at 25oC for 48 hours for bacterial cultures and five days for fungal

cultures to allow growth of colonies. A Polymerase chain reaction (PCR) based method was

employed for the detection of microbes. The common constituent bacterial species in the

indoor air were identified to be Brevibacillus brevis, Arthrobacter spp. and Bacillus cereus

while the fungal biota comprised of Neosartorya fischeri, Aspergillus clavatus and

Trichoderma reesei in the indoor air.

AIR QUALITY IN PAKISTAN

Data on indoor air quality in Pakistan is rather scarce. Indoor air pollution is not given

much importance in the country as it is not considered as a hazard by the policy makers. So far

the Pak-EPA has not yet established any guidelines for PM and bioaerosol levels in the indoor

environment. It is therefore of prime importance that IAQ is studied widely and research on

source appointment conducted. The condition of ambient air quality in Pakistan is also

deteriorating at a fast pace and the policy makers should attend to these issues on a priority

basis as the mortality ratios in Pakistan are alarming. Limited data is available on microbial

composition of air in Pakistan. There are a few studies which have documented microflora of

the indoor air while some other researchers have recorded micro-biota of the ambient air.


54

The air borne micro-flora of Karachi University was studied resulting in identification

of 53 species of fungi in the air. For this purpose glass slides smeared with glycerin were

exposed at varying heights of 1.5 m, 5 m, and 10 m. The exposure time was 24 hours after

which the slides were observed under a microscope. At lower altitude of 1.5 m, 36.22 % fungal

species were observed while the highest proportion of 39.9 % fungal species was found at a

height of 5 m from the ground surface. The remaining 23.86% of myco-flora was obtained

from higher altitude (10 m). Thirteen species belonging to the genus Aspergillus were recorded

in the air making it the most prevalent fungal species present in the air. Apart from Aspergillus,

species of Alternaria, Penicillium, and Cladosporium were also present in large numbers

(Afzal and Mehdi, 2002)

The microbial flora of the ambient air in Karachi was observed during 1998-1999 using

settle plates. The agar coated plates were exposed for five minutes each at different times of

the day. A total of 53 fungal species were identified belonging to 21 genera. A decrease in

number of air-borne fungal colonies was observed during the winter months, particularly

January while the warmer months displayed an increase in fungal levels. It was observed that

the fungal content had a direct relationship with relative humidity and an inverse relationship

with temperature. However the sampling method and agar media used for sampling can result

in variations in results (Afzal et al., 2004).

In another study on airborne microflora of Karachi, similar observations were made in

context with the seasonal variation. A higher concentration of fungal spores was observed

during the summer season as compared to winter. Spore trapper technique was employed along

with exposing of agar coated petri plates in five sampling sites. The most common species

identified belonged to genus Aspergillus such as Aspergillus fumigatus, A. niger, A. flavus, A.


55

candidis, A. terreus, and A. wentii. Apart from them, Alternaria solani, Penicillium notatum,

and Drechslera dematioidea were also notable (Rao et al., 2009).

In an investigation of indoor air quality of rural and urban sites in Pakistan, Nasir et al,

(2012) found that 55 to 99% of the observed micro-organisms were below 4.7mm in size and

therefore capable of entering the lower respiratory tract. The maximum concentration of

culturable bacteria was 14,650 cfu/m3 in the indoor environment in contrast to 16,416 cfu/m3

in the ambient air.

A recent study by Sidra et al. (2015) observed the impact of activities upon bioaerosol

levels. Agar coated petri plates were exposed in the kitchens and living rooms of five

residential houses of Lahore, Pakistan for twenty minutes each. Plates were exposed in the

presence of domestic activities being carried out in the rooms and another set was exposed an

hour after the last activity had been carried out. The results concluded that bioaerosol levels

were higher when there was activity in the rooms while decreased significantly when there was

no work being done in the kitchens or living rooms.

Siddiqui et al. (2005a) conducted an investigation to check the prevalence of ocular

and respiratory maladies in rural women who burned solid fuels for cooking purpose. A strong

association was observed to exist between the two factors. Similarly, in another study, Siddiqui

et al. (2005b) observed that exposure of pregnant females to smoke generated by wood burning

for cooking resulted in low birth weight of the infants.

The use of solid bio-mass fuels results in poor indoor air quality and thus is a serious

health issue in the developing countries. Biomass fuel is widely used as cooking fuel in rural

Pakistan. Colbeck et al. (2008) studied the indoor air quality of some rural and urban areas of

Pakistan in terms of PM10, PM2.5, PM1 and bioaerosols. The concentration of PM10 within


56

kitchens using biomass as fuel was up to 8,555 μg/m3. Smoking and cooking contributed to

high values of particulate matter. Moreover the bioaerosols belonged to the respirable fraction.

Wood is used as a fuel in many rural areas of Pakistan and exposure to the arising pollutants

from wood smoke during the prenatal period can reduce birth weight in babies. Siddiqui et al.

(2008) obtained the birth weight of babies whose mothers used wood as a fuel and also those

using natural gas. A significant association was observed between exposure of pregnant

females to PM2.5 during wood burning and consequent low birth weight in infants.

PM2.5 and CO concentrations were measured by Siddiqui et al. (2009) in a semi-rural

community in Pakistan. 51 kitchens using wood as a fuel and 44 kitchens using natural gas

were selected and monitored for eight hours daily from December, 2005 to April, 2006. Mean

concentration for CO was 29.4 ppm in kitchens using wood while in kitchens using natural

gas, the CO concentration was 7.5 ppm. Similarly in kitchens using wood, PM2.5 concentrations

were again higher i.e. 2.74 mg/m3 than the natural gas users where this value was only 0.38

mg/m3. It was concluded that wood as a source of fuel was hazardous for health in terms of

higher emissions of CO and PM2.5.

Colbeck et al. (2010) conducted a study to understand the variations in indoor/outdoor

ratios of PM10, PM2.5 and PM1 in both rural and urban sites in Pakistan. Since women and

children spend more time indoors, particularly in the kitchen, they are more exposed to

particulate pollution indoors. In rural areas where biomass was used as a major source of fuel,

the respective indoor/outdoor ratios for PM10, PM2.5 and PM1 in the kitchen were 3.80, 4.36

and 4.11. In the living room, these ratios were 1.74, 2.49 and 3.01. At the urban site, these

ratios were 1.71, 2.88 and 3.47. The concentration of particulate matter in rural kitchens was

recorded to be high, in the range of 4000-8555 μg/m3.


57

Nafees et al. (2011) carried out a study at twenty different enclosed public places of

Karachi, Pakistan and observed that second hand smoke was a major contributor of PM2.5

indoors. The mean level of the fine particles was measured to be 138.8 μg/m3 (+ 112.8) with

the highest level being in the snooker/billiards clubs i.e. 264.7 μg/m3 (+ 85.4) and lowest value

at the restaurants (66.4 μg/m3 + 57.6). Moreover the smoking density was highest at the

snooker/billiards clubs.

Seasonal variation in PM levels was studied by Nasir et al. (2013) in rural kitchens. It

is a common practice in rural areas to construct outdoor open and semi-open kitchens for the

summer season. As a result of this practice, PM levels fell considerably in summers than in

winters where indoor kitchen was in use. Moreover, fuel choice also played an important role

in determining air quality in kitchens as natural gas resulted in lower PM levels than biomass

fuel. It was suggested that improvement of ventilation in kitchens can improve the air quality.

Although biomass burning as fuel leads to a poor air quality and associated health outcomes in

the cooks, it is still a major form of fuel used for cooking and heating purposes. Nasir et al.

(2015a) assessed the varying factors which play a detrimental role in fuel choice. Apart from

poverty being indicated as the prime reason for opting solid fuels, location of the household

and access to basic facilities were also observed to be important.

Cooking can be a major source of indoor particulate matter pollution. A study

conducted by Saeed et al. (2015) supports the impression. The levels of PM2.5 in the kitchens

are significantly determined by the location of the stove, ventilation designs and the fuel used

for cooking. The levels of PM2.5 in urban and rural kitchens were monitored by a real time

aerosol monitor for a comparative analysis. The findings of the research declared natural gas

and LPG as cleaner fuels in urban kitchen as compared to the cow dung and wood cast-off in


58

rural kitchen. Highest levels of CO produced during wood, cow dung burning endorses poor

ventilation and ill health of the space.

Burning of solid fuels is a significant contributor towards indoor air pollution. A recent

research by Nasir et al. (2015b) monitored the number concentration of ultrafine particulate

matter in urban and rural houses using different fuels. Sampling was conducted in the kitchens,

living rooms and courtyards of two rural and one urban site using Condensation Particle

Counters. The 24-hour average levels were higher in the ambient air. Generally, the number

concentration was greater in kitchens burning natural gas as a fuel at the urban site than in

kitchens burning solid fuel and natural gas at the rural sites. However, at the two rural sites,

higher numbers were noted in kitchen burning biomass fuel rather than the one using natural

gas. Number concentration in the ambient air was higher at rural sites than the urban location.

The female community in the rural areas of under developing countries such as Pakistan

is highly exposed to PM pollution and thereby to increased health hazards. Amanat et al. (2015)

studied the PM2.5 emission from the different fuels used by the rural community in a country

area of Kasur district, Pakistan. Three rural houses were selected on the base of fuel being

used. One house was consuming wood and while other two were using wood as an energy

source. Smoking was found to be one of vital factor for aggregating PM levels up to 48 times

than the recommended WHO limit of 25 µg/m³.

The building designs, location and ventilation strategy seems to play an important role

while talking about the environmental health of a residential area. Particularly, the role of

ventilation has been discussed various times while defining air quality in the indoor

environments. Abbas et al. (2015) discussed the association of indoor air quality with outdoor

air in context of ventilation. To do so the PM2.5 levels inside a room and outdoor was


59

simultaneously measured by a real time aerosol monitor. It was found that under high

ventilation conditions the indoor air quality significantly correlates with outdoor air quality

while under low ventilation conditions indoor air weakly correlates with outdoor air. The 24-

hour mean value of PM2.5 was logged to be 172.45µg/m3 indoors while it was merely

108.26µg/m3 outdoors. The higher indoor levels might be because of the presence of a smoker

in the room .The results shows the more the ventilation more will be correlation with outdoor

air while low ventilation leads to the definition of indoor air quality majorly by indoor sources

rather than outdoor sources.

The role of these factors was examined by Ali et al. (2015a) during a study in a

residential built environment where the mass concentration of PM1, PM2.5 and PM10 were

measured for 24 hours in the kitchen and living room simultaneously by means of two

DustTrak aerosol monitor (model 8520, TSI Inc. Higher concentration of PM was monitored

in kitchen and living rooms during the winter season than in summers along with high

background concentration during winter. The study results illustrate that increased ventilation

in summer reasons decreases mass concentration of particulate matter. The infiltration from

outdoor sources was found to be one the reason for higher mass concentration of coarse fraction

of particulate matter in the living room as compared to the kitchen.

The entire region of Pakistan is pretentious by air pollution. Recently Zainab et al.

(2015) observed differential behavior of particulate with varying altitude. Using a real time

aerosol monitor, DustTrak DRX (model 8533, TSI Inc.) the PM fractions were monitored at

two sites of China and Pakistan at elevations above 3000m for 24 hours. The average

concentration of PM was higher in Pakistan at elevation above 3000m as compared to China

while both having higher PM mass concentration than the recommended levels of 25 µg/m3 by


60

WHO. The results suggest risk exposure to PM even at places distant from anthropogenic

sources.

Zona et al. (2015) monitored particulate levels at different altitudes of Pakistan.

Various factors affected air quality at each monitored site. High elevation exhibited higher

particulate levels while lowest 24-h average was observed at sea level. Anthropogenic

activities were noted to a contributing factor.

The dispersion of particulate matter is more pronounced at high temperature in

summers as compared to the low temperature in winter. To assess the seasonal impact on the

PM (Ali et al., 2015b) selected a high altitude tourist resort in Pakistan. The PM concentration

was measured for 24 hours during summer and winter season using DustTrak DRX (Model

8533, TSI Inc. while the meteorological properties were stated using Kestrel 4500 Pocket

Weather Tracker (Nielsen- Kellerman). Highest concentration was noted during summers

when the temperature was high along with high wind speed. The study testifies the bulbous

effect of seasonal shift on the average levels of particulate matter.

While discussing health in various imperative sectors, educational sector cannot be

ignored. Pakistan being a developing country is fronting serious health and environmental

issues. The air quality in the educational sector has not been comprehensively considered in

the educational built environment. To provide with the base line data on air quality in this

environment Aziz et al. (2015a) designed a study to assess the correlation of indoor air with

and ambient air and the possible sources of PM determining indoor air quality. Using a

DustTrak Aerosol Monitor (TSI Model 8520) the levels of PM2.5 in the indoor (class rooms)

and ambient air in the University of Punjab were monitored for 24 hours. The class rooms were

selected on the base of occupant density and were alienated as low, medium and high


61

occupancy rooms. The ventilation rate was also measured to find out the correlation of indoor

air with ambient air. According to the results of this study the occupant density and the ambient

air effectively controls the PM levels in the indoor and hence affecting air quality.

Particulate matter has been found as one of the serious threat to the patients and health

care staff in the hospitals. The long term exposure of the patients and the health care staff to

the increase levels of particulate matter in the hospitals can be source of potential damage to

their health. It can, therefore, be used as a tool in determining the air quality of the wards and

operating suites in the hospitals. Nimra et al. (2015) monitored the average levels of particulate

matter for 24 hours in the operation theatres and ambient air of two hospitals of Lahore by

using DustTrak Aerosol Monitor (TSI Model 8520) and DRX Aerosol Monitor (TSI Model

8533). Highest levels of particulate matter were observed in the operation theatres with natural

ventilation while the lowest particulate matter concentration was reported in a theater that was

equipped with vertical Laminar flow system. The major PM contributor was found to be the

human traffic inside the theatre and door opening/closing rate along with the building age.

Similarly, a study was conducted by Gulshan et al. (2015) in the five wards (medical,

pulmonology, surgical, paediatric and nephrology) of Sheikh Zayed hospital of Lahore

Pakistan to assess the indoor and ambient air quality. Two DRX Aerosol Monitors (TSI Model

8533) were installed in parallel to determine the PM levels inside the ward and in the ambient

air for 24 hours. High levels of particulate matter were observed inside the wards excluding

surgical and paediatric ward as compared to outdoor PM levels which shows poor ventilation

and poor health status in Pakistan.

The ever increasing vehicular load on the roads has created a threatening situation for

the survival of human beings on earth. The PM burden created by vehicular exhaust on two


62

busy roads (Campus Bridge-Punjab University and Thokar Niaz Baig) of Lahore Pakistan has

been reported by Ali et al. (2015c). DustTrak DRX (Model 8533, TSI Inc.) was used to monitor

the particulate matter concentration for 24 hours while BW gas probe was used to screen

temperature and humidity levels .According to their finding the meteorological properties and

the vehicular load positively correlates with intensification of PM. It is a need of time to pay

attention on the transport industry to control urban pollution.

With the growth in the transport industry in the developed as well as developing

countries there is a need to evaluate the increase risk pose by the intensification of this sector.

The transport micro environments are of vital importance as these are susceptible to increase

levels of PM, CO, CO2 due to infiltration form road side, traveler’s activities and most owed

vehicular exhaust pollution. To scrutinize the air quality status in these microenvironments

Aziz et al. (2015b) conducted a study in which the mass concentration of PM was measured

along with CO2, CO, temperature and humidity in the diesel power-driven buses in United

Kingdom and Pakistan. The exposure to the PM by the commuters on inter-city journeys was

monitored using DustTrak DRX (Model 8533, TSI Inc.). The observed levels of PM were not

in accordance to the WHO guidelines in both countries. While comparing the vehicular load

and vehicular exhaust emissions in both countries, Pakistan seems to have more intimidating

situation at present by having higher concentration of PM as compared to United Kingdom.

The increasing industrialization, urbanization and subsequent traffic load in the

developing countries is one of the major concerns. Exposure to the particulate matter is of

vigorous importance when the air pollution hazards are being discussed as it reasons

pulmonary obstructive diseases. A study was conducted by Nasir et al. (2015) to assess the

exposure to particulate matter due to automobiles at two different road sites in Lahore,


63

Pakistan. The mass concentration and number concentration of PM was stated by using

GRIMM analyzers (Model 1.101 and Model 1.108) and condensation particle counter (TSI

3781) during week days and were compared with the observed concentration during weekends.

The detected concentration was also compared with a background site. The heavy metals (Al,

Si, Cu, Zn, Mn, Cd, Ni and Pb) were also reported by using Graphite Furnace Atomic

Absorption Spectrophotometer (Unicam atomic absorption, Cambridge, UK). The acquired

results indicated that the mass and number concentrations were higher at road site as compared

to the background site along with higher meditation during week days as compared to

weekends. Among the heavy metals Mn, Ni and Cd were found to be exceeding WHO limits.

The growing urban sector needs to pay special attention on the air pollution scenario in time.

The heavy metal analysis of particulate matter leads to better understanding of the

chemical nature of this pollutant. In Pakistan the heavy metals in the indoor and outdoor

particulate matter and dust at two rural and one urban residential built environment were

analyzed by Nasir et al. (2015d). To collect the air borne PM an eight stage non-viable

impactor (Thermo Fisher Scientific Inc., USA) laden with EMP 2000 glass microfiber filter

papers (Whatman, England) was cast-off. The settled dust from the indoor surfaces was

collected from floors, cupboard in living rooms and kitchens at the rural sites while the

courtyards dust was used as outdoor dust samples. In urban zone the dust samples were also

collected from 27 diverse locations in the outskirts of Lahore along with University of

Veterinary and Animal Sciences as a background site. The cake dungs from a rural site were

also collected because of its usage as a fuel in rural areas. Graphite Furnace Atomic Absorption

Spectrophotometer was used for heavy metal analysis including Si, Al, Zn, Mn, Cu, Ni, Cd,

Pb, Co and As. Higher concentration of heavy metals were observed in the indoor


64

environments except Cu, Si and Pb at rural site 1. Similarly higher heavy metal concentration

were monitored in the outdoor air at rural site 2 except for Ni whose levels were considerably

higher in the indoor than out door. Uppermost heavy metal levels were monitored at urban site

having rich levels in the outdoor air. The heavy metal concentration of Pb was found to be

within the recommendation value of WHO (0.5 μg/m3) but the levels of Ni, Mn and Cd were

greater at all sites than WHO and European commission recommended values emphasizing

health hazards risk by the heavy metals exposure.

As evident from the above mentioned researches, there is a pressing need for more

detailed and repeated measurements of air quality in Pakistan. It is an established fact that

biomass burning is a significant contributor towards higher particulate matter. However, the

sources of particulate matter and the source strength need to be highlighted in urban areas as

well. While rural areas have been monitored for pollutant levels, indoor environments of urban

areas have not yet been monitored so far leading to the formulation of this study.

Chapter Three Materials and Methods

65

CHAPTER THREE

MATERIALS AND METHODS

Study Area

The historical city of Lahore (31°15′—31°45′ N and 74°01′—74°39′ E) is the

provincial capital of Punjab and the second largest city of Pakistan. River Ravi flows along the

north-western side of the city. This city is spread over an area of 1772 km2 at an elevation of

217m above the sea level (Figure 4). In 2001, Lahore was assigned the administrative status of

City District and is divided into nine administrative towns and a cantonment area (under

military administration).

Figure 4: Map marking the boundaries of City District Lahore


66

Being one of the densely populated cities of the world, the population of Lahore is

around 9,086,000 inhabitants (BOS, 2013).

The city district experiences a hot, semi-arid climate with an average temperature of

24.3oC (75.7oF) (Rasheed et al., 2015). During the extremely hot summers, the maximum

average temperature ranges between 33 to 39oC with minimum average temperature falling

between 22 to 28oC. The winters experience an average maximum temperature of 17-22oC and

the minimum temperature ranging between 7 to 12oC (Alam et al., 2012). The city receives an

annual rainfall of between 600 to 800 mm, most of it occurring during the monsoon period

(from Mid-July till September). Annually the city receives an average of 3,094 hours of

sunshine. The day length is also variable with the shortest day being December 21 with 10:05

hours of daylight and 20th June being the longest day with 14:12 hours of daylight (Pakistan

Meteorology Department).

The inhabitants of Lahore enjoy five seasons in a year namely winter, spring, summer,

monsoon, autumn and winter (Köppen climate classification BSh):

Winter season with January being the coolest month (beginning from 15th November

till 15th February)

Spring season which is generally pleasant from16th February to 15th April

Hot summer season starting from mid-April till June which is the hottest month of the

year

Rainy season or Monsoon which begins in July and lasts till September

Dry autumn (16th September-14th November)

http://en.wikipedia.org/wiki/K%C3%B6ppen_climate_classification


67

Selection of sampling sites

The Lahore Cantonment is a legal settlement within the city under military

administration while the rest of the City District Lahore is divided into nine administrative

towns with their details given in table 3 below:

Table 3: Administrative towns of City District Lahore and their population (Source: GOP,

2014)

Administrative town Total number of

Union Councils

Area (km2) Population as

estimated on 31-12-

2014

Aziz Bhatti Town 11 69 610000

Data Ganj Buksh Town 18 33 1048000

Gulberg Town 15 44 841000

Iqbal Town 15 520 835000

Nishtar Town 19 497 1081000

Ravi Town 30 38 1713000

Samanabad Town 19 38 1064000

Shalimar Town 11 24 573000

Wagha Town 12 440 709000

Cantonment - 98 874000

Thirty houses were selected from all over Lahore to serve as sampling sites for

monitoring of indoor air quality in terms of fine particulate matter and bio-aerosols. In order

to ensure a random mix of houses, sampling sites of varying floor area were selected from each

town. Three categories were defined according to the size of the houses with following details.

Small: < 126.5 m2


68

Medium: > 126.5 m2 to 253 m2

Large: > 253 m2

Since the selection of the houses was random, therefore the surroundings of the houses

varied considerably with some houses located in industrial areas, some in semi-urban areas

while some were located in urban areas. This variation was also useful to provide an insight

into the impact of surroundings on the indoor air quality of the sampling sites. Moreover it was

helpful in providing with a more generalized overview of air quality of indoor environments.

All the selected houses were located within 1 km radius from main roads with variable traffic

throughout the day.

Since the number of people and their activities are also an important contributor

towards indoor air quality, three levels of occupancy were also defined as follows:

Low: <5 occupants

Medium: 6-10 occupants

High: >10 occupants

However this classification was not the basis of selection of houses and was used in

describing the results. In order to collect the results following steps were carried out at each

sampling site:

Filling of questionnaire

A questionnaire was filled for each sampling site to gain information about the number

of occupants, their daily activities, occupations, time spent indoors and outdoors by each


69

member of the household, smoking habits, type of cooking fuel, their health status, and other

related factors. The data thus obtained was useful in providing with an insight into the daily

routine of the occupants and the possible exposures to pollutants at home and outside as well.

The questionnaire is attached as Annexure-I.

Sampling for PM2.5 at the selected study sites

There are numerous methods for particulate sampling and two of the most widely used

methods employed for PM monitoring are the light scattering method and gravimetric method.

Although gravimetric method is more suitable as a reference method, light scattering method

is more suitable for preliminary measurents of aerosols (Niu et al., 2002). Among the many

commercially available photometers, DustTrak aerosol monitor (model 8520, TSI Inc.) has

been known to give more precise and accurate readings of PM2.5 (Yanosky et al., 2002; Cheng,

2008) and was employed to monitor PM2.5 concnetrations in this study.

DustTrak aerosol monitor (model 8520, TSI Inc.) is a direct reading real-time

photometer and has a laser diode with 90° light scattering. Its sensitivity ranges between 0.001

to 100 mg/m3 with a particle size range of 0.1 to approximate 10 μm. The monitor is factory

calibrated to the respirable fraction of standard ISO 12103-1, A1 test dust. The aerosol

monitors were factory calibrated before monitoring and the air flow rate was set at 1.7 L/min.

The aerosol monitor has separate inlet nozzles for measurement of PM1, PM2.5, and PM10.

Since fine particulate matter is a major component of the indoor air (Geller et al., 2002),

therefore this size fraction was selected for monitoring in the indoor micro-environments. Data

logging interval was set at 1 minute and the sampling duration was 72 hours in each house.

Before running the instrument at any sampling site, the inlet nozzles were cleaned and

lubricated each time according to the prescribed protocl given in the instuction manual of the


70

equipment. The aerosol monitor is powered by electricity and as an alternative power supply

Cameleon C-size rechargable batteries were used.

Two different micro-environments i.e. kitchen and living room were selected for

sampling within the houses and two DustTrak monitors were run in parallel in the both micro-

environments. The instruments were placed at a height of approximately one meter from the

ground. Care was taken while performing daily routine activties and no major activty was

allowed too near the instrument which could otherwise cause a sudden increase in PM2.5 levels.

The data was later on transferred to a computer using the TrakPro software for further analysis.

The monitoring began in January 2012 and continued till March 2013 with each sampling site

monitored only once. The monitoring was conducted only once at each selected sampling sites

as the instruments were somewhat noisy and many residents were being disturbed by their

presence. This factor was a significant limitation in this study.

PM2.5 generation from different activities

Different household activities were considered to be a cause of variation in

concentrations of particulate matter within a house. The source strengths of particulate matter

arising from different activities being carried out in each sampling site were determined by

identifying the time period during which a specific activity was being performed and

calculating the average levels of PM2.5 during that time. The major activities identified

included cooking, cleaning, material movement such as making bed and shifting of items,

presence of people, space heating during winters and cigarette smoking. In Pakistan, cooking

involves heavy frying paricularly during breakfast preparation so cooking activity was further

divided into two categories: Cooking including extensive frying (during breakfast), and

Cooking with little or no frying (during lunch and dinner). Cleaning activty included both floor


71

sweeping and dusting of surfaces etc. Gas heaters were employed during the winter season for

space heating and PM levels during the usage period were also isolated to determine its impact

on indoor air quality.

Sampling of bio-aerosols at the selected study sites

Gravitational method (Koch sedimentation) was employed for sampling of air-borne

bacteria and fungi in the kitchens and living rooms of the sampling locations. This type of

sampling involves exposure of agar coated surfaces, usually petri plates, for a specific time

period to allow settling of air-borne microorganisms upon the agar medium. The plates are

later incubated to allow growth of microorganisms and the microbial species identified.

Although it is a passive sampling method which does not allow exact quantitative analysis,

data collected by sedimentation method allows the drawing of correct conclusions on types of

microorganisms present in the air and can give a rough approximation of bacterial and fungal

concentration (Stryjakowska-Sekulska et al., 2007). There are relatively few studies which

have employed passive sampling for bioaerosol sampling and the exposure time also varies

between 5 minutes (Afzal et al., 2004), 15 minutes (Stryjakowska-Sekulska et al., 2007) and

30 minutes (Bogomolova and Kirtsideli, 2009).

The medium used for bacterial sampling was Tryptic Soy Agar (TSA) while Malt

Extrose Agar (MEA) was used for fungal sampling. For MEA preparation, the media

containing malt extract and agar having a pH of 6 was sterilized by autoclaving at 121+1°C

for 15 minutes. It was cooled down and antibacterial was added to avoid bacterial

contamination. It was poured in sterilized petri plates in laminar air flow chamber and left for

24 hours at 25+1°C. For preparing TSA medium, 40 g of the medium was suspended in one

liter of purified water. It was heated with frequent agitation and boiled for one minute to


72

completely dissolve the medium. Then it was autoclaved at 121°C for 15 minutes. After

pouring in sterilized petri plates in laminar flow chamber, it was left to solidify overnight at

25+1°C (Cappuccino and Sherman, 2005).

Settle plates containing TSA and MEA agar medium were exposed for twenty minutes

each in both rooms to allow the bio-aerosols (bacteria and fungi) to settle on the agar coated

surface. Temperature and humidity of the two rooms was also noted at the time of exposure.

The Petri plates were then incubated at 27oC for three days to allow growth of settled viable

bioaerosols on the growth medium. The number of colonies formed on the agar medium was

counted and the colony forming units per meter cube was determined using the Omelyansky

formula as followed by (Bogomolova and Kirtsideli, 2009).

N = 5a.104 / (b.t)

Where:

N = colony forming units per m3 (cfu/m3)

a = no. of colonies per Petri dish

b = surface area of dish (cm2)

t = exposure time (minutes)

The plates were observed under a microscope to observe the morphological

characteristics of the colonies such as shape, colour, and margin. Identification was carried out

by following Bergey’s Manual of Systematic Bacteriology and a fungal identification key by

Dugan (2005).


73

Measurement of Air Change rate per Hour (ACH)

All the sampling sites in this study were naturally ventilated. Fans and occasionally air

conditioners were switched on during the warmer months. Air change per hour was measured

to determine the amount of ventilation available at each site. Concentration decay method was

employed using CO2 as the tracer gas. A fire extinguisher cylinder filled with carbon dioxide

gas was the source of CO2 while Gas Probe IAQ (BW technologies) was employed for

measuring the concentrations of gas. Ventilation was measured in both the kitchen and living

room of each sampling site. The procedure was commenced in the absence of people in the

room so that CO2 levels were not affected. The background level of carbon dioxide was noted

prior to releasing the gas into the room. The gas was injected into the room until the levels

were four times the background levels; the levels were then monitored as they decreased over

time. The monitoring continued until background levels were achieved. Ventilation was

determined by plotting the time in hours against the natural log of CO2 concentration where

ACH was the slope of the line of best fit (Fischer-Mackey, 2010). The step wise procedure is

stated below:

The background concentration of CO2 was noted.

CO2 gas was injected into the room and allowed to mix evenly in the room with the

help of fans.

The concentration of gas was noted after every five minutes until the concentration was

within 200 ppm of the baseline value.

Natural log of CO2 (ppm) was plotted over time (hours) where ACH = slope of the line

of best fit.


74

The volume of a room is also an important configuring factor for indoor air quality.

The volume of air entering the specified rooms was also determined by employing the simple

formula given below:

Ventilation rate (L/sec) = ACH x Volume of room (m3)

3.6

Natural ventilation does not ensure a uniform mixing and availability of fresh air at all

times. Therefore, the volume of air present per person in both micro-environments was also

calculated as follows:

Ventilation rate (L/s/person) = L/sec x number of people in the room

Data analysis

The obtained concentrations of PM2.5 for 72 hours were converted into 24-h average

concentrations. Representative PM2.5 averages for 24-hours were plotted against time to

observe the daily trend in PM levels throughout the day. The data were analyzed further to

obtain hourly maximum and hourly minimum statistics to gain an insight into the fluctuations

in levels during the sampling duration and for comparison with the background levels.

Variation of PM levels during the day and night hours also holds a significance in a daily cycle.

Since the monitoring was conducted during different times throughout the year, the length of

day and night for each monitoring period was obtained from the meteorology department.

Subsequently, diurnal variations were also compared for each sampling site. Similarly data

from kitchen and living room was plotted to observe the influence of connection between the

two rooms upon particulate matter levels.

Since a household carries on a variety of activities throughout the day, generation of

PM levels from major activities in the selected sampling sites were also noted. Activities

resulting in highest PM generation were also documented.


75

Seasonal variation was studied by comparing the mean concentrations of particulate

matter in each sampling site during the different seasons. One-Way ANOVA was applied to

observe any significant impact of seasons on particulate matter concentrations (α = 0.05).

Correlation between air exchange rate and PM concentrations was calculated to study the role

of ventilation rates in defining the PM2.5 levels in the indoor air.

Regression analysis was carried out to observe the association of various variables

(temperature, relative humidity, ventilation, PM2.5) with bioaerosol levels. Seasonal variation

was also checked through One-Way ANOVA. SPSS (v.16.0) was employed for statistical

analysis.

Chapter Four Results

76

CHAPTER FOUR

RESULTS

The selected sites (n = 30) were located within a distance of 1 km from heavy traffic

roads with a variety of urban habitat surroundings. Among the ten selected houses located

in industrial areas, two were present in semi urban areas and two were present near railway

lines (Figure 5).

Figure 5: Location of sampling sites in Lahore city

None of the buildings was air tight, rather all were naturally ventilated. Since the

climate of Lahore is warm during most time of the year, windows were generally kept open

except during the winters. Gas heaters were in common use for space heating during the

winters while mostly ceiling fans were used for cooling for the rest part of the year with air

conditioners also used during the hotter months. The number of occupants varied from three

to thirteen at the selected sites (Figure 6).


77

Figure 6: Location of sampling sites according to number of occupants [up to 5 occupants

(red circles); 6 to 10 occupants (blue circles); 11 and above (green circles)]

Natural gas was used as the primary cooking fuel with LPG also used in only two

houses. However it was not used much often. The kitchen and living room were not

connected in nineteen houses, partially connected in six and fully connected in remaining

five houses (Table 4).


78

In most of the houses, majority of occupants were students, house wives and job

workers. The proportion of males and females falling in the various age groups is shown in

figure 7.

Figure 7: Proportion of male and female occupants belonging to different age groups

Time spent by each member in the house varied from less than eight hours to full

day. Mostly females and elderly people spent full day at home while males and students

spending more time outdoors (Figure 8 and 9).

0-10 YEARS 11-20 YEARS 21-30 YEARS 31-40 YEARS 41-50 YEARS 51-60 YEARS61-

ONWARDS

MALES 9 22 26 5 13 10 6

FEMALES 12 31 34 9 15 5 5

0

5

10

15

20

25

30

35

40


79

Figure 8a: Number of hours spent by male occupants in the house

Figure 8b: Number of hours spent by female occupants in the house

7%

73%

20%

0-8 HOURS 9-16 HOURS 17-24 HOURS

0%

38%

62%

0-8 HOURS 9-16 HOURS 17-24 HOURS


80

Figure 9: Time spent by females in the kitchen

0-4 HOURS57%

5-8 HOURS37%

9-12 HOURS6%


81

Trends in PM2.5 levels in microenvironments of Category-A sampling sites

Sampling sites of category-A had a floor area ranging from 75.9 m2 to 126.5 m2. The

number of occupants varied from 4 to 13 occupants among whom two were frequent smokers.

The selected sites were monitored during different seasons with A1 and A10 monitored during

the winter season, A2 and A3 during the spring season, A4 and A5 during the summers, A6

and A7 in monsoon or rainy period and A8 and A9 monitored during the dry autumn. All the

selected sites were located within a radius of 1 Km from main roads with cemented or carpeted

adjacent roads in most cases while the roads outside two sites were unpaved dust roads. The

location of the houses varied considerably with six sites present in industrial areas while one

of them was in semi-urban surroundings. Table 4 provides with an overview of each of the

sampling sites.

Floor plans of each site were prepared to assess the spaces present for air exchange as

well as connection between the two microenvironments. It was noted that no direct connection

existed between the kitchens and living rooms in any of the houses while in three of the houses,

doors of both rooms opened into the same room (partially connected). The dimensions of the

doors and windows along with the rooms where monitoring was conducted are also in the floor

plans (figure 10, 12, 14, 16, 18, 20, 22, 24, 26, and 28).

Since PM2.5 monitoring was carried out for seventy two hours in each house, trends of

PM2.5 were plotted for representative twenty four hours average in both the kitchens and living

rooms. Major activities and the time during which they were performed were identified and

pointed out in a 24-hour period along with PM2.5 levels in each sampling site as shown in figure

11a, 11b, 13a, 13b, 15a, 15b, 17a, 17b, 19a, 19b, 21a, 21b, 23a, 23b, 25a, 25b, 27a, 27b, 29a,


82

and 29b. The 24-hours, hourly maximum and minimum average levels for PM2.5 were

calculated and tabulated in table 5. This data was helpful in comparing the mean and maximum

PM2.5 levels with the background levels (average of hourly minimum) in each house.

The air exchange rate was calculated for both kitchen and living room; once with open

doors and windows to obtain maximum air exchange rate and then with closed doors and

windows to obtain minimum air exchange rates (Annexure-II).


83

Table 4: Profile of Category-A sampling sites

Sampling

site

Size of

house

(m²)

Occupants Location Type of road Distance from

main road

Connection between

kitchen and living

room

Cooking

fuel

No. of

smokers

A 1 126.5 6 Urban, Industrial Tiled 0.2 km Not connected NG 0

A 2 126.5 7 Urban Carpeted 1 km Not connected NG 1

A 3 75.9 7 Urban Cemented 0.2 km Partially connected NG 0

A 4 126.5 8 Urban Cemented 0.5 km Not connected NG 0

A 5 50.6 12 Urban, near railway lines Cemented 0 km Not connected NG 11

A 6 126.5 6 Industrial, near railway lines Carpeted 0.1 km Not connected NG 0

A 7 75.9 7 Semi-urban, Industrial Unpaved 1 km Partially connected NG 0

A 8 63.25 6 Urban, Industrial Cemented 1 km Not connected NG 0

A 9 126.5 13 Urban, Industrial, Main Road under

construction

Cemented 0.1 km Not connected NG 0

A 10 101.2 4 Urban, Industrial Unpaved 0.1 km Partially connected NG 0

1 Smoking carried indoors


84

Table 5: Representative 24-h, hourly maximum and hourly minimum averages of PM2.5 recorded in the kitchens and living rooms

of category-A sites

Sampling

site

PM2.5 IN KITCHEN (µg/m³)

24 HOURS HOURLY MAXIMUM HOURLY MINIMUM

Average Max Min St dev Average Max Min St dev Average Max Min St dev

A1 224.5 236.5 207.0 15.5 529.0 746.7 396.3 190.0 60.2 74.9 51.6 12.8

A2 188.8 200.6 168.2 17.9 336.4 396.1 289.1 54.5 75.4 91.0 63.8 14.0

A3 185.3 206.3 164.9 20.7 1118.5 2355.8 492.2 1071.5 48.9 57.3 36.1 11.3

A4 69.9 92.4 56.1 19.7 206.9 399.1 81.2 169.1 34.0 38.7 24.9 7.9

A5 132.9 169.8 89.1 40.8 369.5 405.0 306.6 54.6 48.6 77.4 26.05 26.2

A6 202.3 296.4 131.8 84.8 1787.4 3666.4 233.8 1739.3 53.4 66.1 29.2 20.9

A7 342.7 437.7 289.4 82.4 1044.2 1443.1 605.0 420.5 135.6 193.4 101.1 50.3

A8 191.8 248.1 136.7 55.7 565.5 749.0 342.2 206.3 84.7 97.6 71.1 13.3

A9 422.7 576.2 259.4 158.7 1256.6 1817.7 847.1 502.5 86.2 129.9 64.1 37.8

A10 456.7 488.8 409.8 41.5 1697.2 2639.4 1208.5 816.1 153.6 212.4 99.7 56.5

PM2.5 IN LIVING ROOM (µg/m³)

A1 139.7 149.7 126.9 11.6 283.1 319.8 245.4 37.2 48.4 55.6 44.8 6.1

A2 149.0 168.4 123.1 23.3 265.3 309.1 239.5 38.2 69.0 89.8 57.9 17.9

A3 168.4 181.4 157.7 12.0 866.3 1774.5 380.1 787.2 46.2 56.6 29.2 14.8

A4 119.9 127.4 113.9 7,0 231.2 281.8 173.3 54.6 66.7 73.1 58.8 7.2

A5 177.2 234.3 125.4 54.7 437.9 591.3 356.1 133.0 69.2 126.7 26.9 51.6

A6 123.4 140.2 109.7 15.5 231.3 295.4 187.0 56.8 54.7 71.4 29.7 22.0

A7 336.3 433.7 287.4 84.3 857.2 1064.9 489.2 319.6 148.2 200.0 114.3 45.6

A8 179.7 253.2 142.7 63.6 398.0 801.4 194.8 349.3 99.7 118.6 81.5 18.6

A9 509.3 660.4 419.5 131.7 1671.5 2626.2 1065.1 836.8 75.4 76.4 74.9 0.8

A10 383.2 433.3 290.6 80.3 874.8 1077.8 745.0 178.1 187.9 235.7 117.9 61.9


85

Sampling site A1

Figure 10: Floor plan of sampling site A12

Figure 11a: 24-h representative mean values of PM2.5 in kitchen of sampling site A1

2 K stands for kitchen, LR for living room, and B1, B2 etc. are the bedrooms. The blue bars represent the windows

while the pink bars represent the doors. The rooms in pink are the sampling sites, blue colour represents the porch

or courtyard while green colour represents grassy lawns.

0

100

200

300

400

500

600

700

800

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Cooking

Cleaning Cooking


86

Figure 11b: 24-h representative mean values of PM2.5 in living room of sampling site A1

0

50

100

150

200

250

300

0 1 2 3 4 5 6 7 8 9

10

11

12

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er…

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Hours

Cleaning

Movement of people


87

Sampling site A2



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Cleaning

Unidentified


88


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(µg/m

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Cleaning

Movement of people

Unidentified


89

Sampling site A3



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2500

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Cleaning


90


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0 1 2 3 4 5 6 7 8 9

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Hours

Infiltration from kitchen

during cooking

Movement of

peopleCleaning


91

Sampling site A4



0

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80

100

120

140

160

0 1 2 3 4 5 6 7 8 9

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conce

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(µg/m

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Hours

Cooking activity at random

times according to varying

occupants’ schedules

Cleaning


92


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0 1 2 3 4 5 6 7 8 9

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Movement of

people

Cleaning


93

Sampling site A5



0

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450

0 1 2 3 4 5 6 7 8 9

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Cleaning


94


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e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Smoking indoors

Cleaning


95

Sampling site A6


Figure 21a: 24-h representative mean values of PM2.5 in kitchen of sampling site A6 (monitored

during Ramadan)

0

500

1000

1500

2000

2500

3000

3500

4000

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Cooking


96


0

20

40

60

80

100

120

140

160

180

200

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Cleaning

Movement

of people

Movement of

people


97

Sampling site A7



0

100

200

300

400

500

600

700

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Cleaning

CookingUnidentified


98


0

100

200

300

400

500

600

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

concn

etra

tio

ns

(µg/m

³)

Hours

Movement of

people

Cleaning

Unidentified

source


99

Sampling site A8



0

100

200

300

400

500

600

700

800

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Cooking

Cleaning


100


0

100

200

300

400

500

600

700

800

900

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Due to no exhaust and window in the

kitchen, activties in the kitchen

influencing the levels in living room

Cleaning


101

Sampling site A9



0

200

400

600

800

1000

1200

1400

1600

1800

2000

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

CookingUnidentified


102


0

200

400

600

800

1000

1200

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Movement of people

Cleaning

Unidentified source


103

Sampling site A10



0

500

1000

1500

2000

2500

3000

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

er…

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Cooking


104


0

200

400

600

800

1000

1200

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Movement of people

+ infiltration from

semi-open kitchen


105

Trends in PM2.5 levels in microenvironments of Category B sampling sites

Sampling sites of category B had a floor area ranging from 177.1 m2 to 253 m2. The

number of occupants varied from 3 to 13 occupants among whom five were frequent smokers.

However smoking was carried out within houses in only two cases. The selected sites were

monitored during different seasons with B1, B2, B8 and B9 monitored during the winter

season, B3, B4 and B10 during the spring season, B5 during the summers, B6 in monsoon or

rainy period and B7 monitored during the dry autumn. All the selected sites were located within

a radius of 1 Km from main roads with cemented or carpeted adjacent roads in most cases

while the road outside one site was unpaved dust road. The location of the houses varied

considerably with two sites present in industrial areas while one of them was in semi-urban

surroundings. Table 6 provides with an overview of each of the sampling sites.


well as connection between the two microenvironments. It was noted that a direct connection

existed between the kitchens and living rooms in three houses while in rest of the cases, both

rooms were located far apart. The dimensions of the doors and windows along with the rooms

where monitoring was conducted are also given in the floor plans (figure 30, 32, 34, 36, 38,

40, 42, 44, 46, and 48).







106






windows to obtain minimum air exchange rates (Annexure-II).


107

Table 6: Profile of Category-B sampling sites

Study

site

Size of

house

(m²)

Occupants Location Type of

road

Distance from

main road

Connection between

kitchen and living

room

Cooking

fuel

No. of

smokers

B 1 253 12 Urban, Industrial Tiled 0.2 km Not connected NG 0

B 2 177.1 4 Urban Carpeted 0.1 km Fully Connected NG 0

B 3 253 6 Urban Carpeted 0.1 km Not connected NG 0


B 5 202.4 7 Industrial, near railway lines Cemented 0.1 km Not connected NG 1


B 7 177.1 4 Urban Carpeted 0.5 km Fully Connected LPG 0

B 8 151.8 6 Semi-urban Unpaved 1 km Fully Connected NG 13

B 9 177.1 13 Urban Carpeted 0.5 km Not connected NG 2

B 10 177.1 6 Urban Carpeted 0.7 km Not connected NG 13



108


of category-B sites

Study

site



Average Max Min St dev Average Max Min St dev Average Max Min St dev

B1 983.0 1259.5 692.4 283.9 5068.1 9597.4 1685.6 4078.6 172.1 200.3 153.0 24.9

B2 236.7 298.5 165.0 67.3 760.9 1333.8 368.7 507.3 107.9 150.4 59.0 46.0

B3 445.6 681.5 237.5 223.3 1588.5 3383.4 542.3 1561.5 126.9 150.6 80.6 40.1

B4 185.3 222.8 143.4 39.9 470.8 516.5 381.5 77.4 58.2 104.8 34.1 40.3

B5 250.5 368.5 162.2 106.3 2013.7 2991.8 775.5 1130.8 51.0 57.1 45.4 5.8

B6 199.6 269.4 143.5 64.1 508.0 823.1 247.7 291.6 92.6 113.0 56.1 31.7

B7 440.3 556.3 289.4 136.8 1073.1 1586.6 582.6 502.4 115.1 165.7 70.4 47.9

B8 736.2 894.9 604.7 147.0 1336.6 1932.2 1020.4 516.1 311.0 361.8 238.1 64.7

B9 383.5 452.1 344.8 59.6 643.0 706.7 600.3 56.2 193.6 217.5 170.4 23.5

B10 743.5 932.4 564.5 184.1 4151.0 6094.1 1962.4 2076.8 107.9 116.8 92.1 13.7


B1 462.7 540.2 421.1 67.2 1065.1 1380.5 853.4 278.4 157.2 208.0 103.8 52.1

B2 203.2 261.9 138.6 61.7 604.3 1020.8 304.7 372.0 98.0 137.1 56.0 40.6

B3 227.4 310.1 165.5 74.5 512.9 681.1 336.6 172.4 91.5 131.6 71.2 34.7

B4 166.5 199.4 129.8 35.0 419.9 467.4 356.1 57.4 56.4 101.8 32.4 39.3

B5 114.6 119.0 106.5 7.0 282.1 348.6 179.4 90.2 53.8 64.1 47.4 8.9

B6 213.6 282.6 154.1 64.8 499.9 697.9 278.1 210.9 102.5 125.9 62.5 34.8

B7 476.0 589.3 306.7 149.4 1192.3 1638.7 577.9 550.0 132.6 179.2 89.9 44.8

B8 894.6 1092.5 727.9 184.3 1740.2 2256.7 1173.8 543.2 394.8 467.5 333.6 67.7

B9 657.2 1068.7 439.2 356.6 4744.1 12291.2 823.0 6537.7 223.7 282.3 188.1 51.2

B10 417.3 497.5 373.2 69.5 1023.9 1477.5 735.9 397.6 122.7 133.5 113.7 10.0


109

Sampling site B1

Figure 30: Floor plan of sampling site B1

’

Figure 31a: 24-h representative mean values of PM2.5 in kitchen of sampling site B1

0

2000

4000

6000

8000

10000

12000

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Cooking

involving frying

for gathering of

30 people


110

Figure 31b: 24-h representative mean values of PM2.5 in living room of sampling site B1

0

200

400

600

800

1000

1200

1400

1600

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

co

nce

ntr

atio

ns

(µg/m

³)

Hours

Gathering of 30 people


111

Sampling site B2



0

100

200

300

400

500

600

700

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Cleaning

Cooking


112


0

200

400

600

800

1000

1200

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Ave

rage

PM

2.5

con

cen

trat

ion

s (µ

g/m

³)

Hours

Movement of people

Cooking in

the adjcaent

kitchen

Cleaning


113

Sampling site B3



0

100

200

300

400

500

600

700

800

900

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Unidentified

Cooking


114


0

100

200

300

400

500

600

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Movement of people

Unidentified


115

Sampling site B4



0

100

200

300

400

500

600

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Unidentified

Cooking

Cleaning


116


0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking activity in

the partially

connected kitchen

Unidentified

Cleaning


117

Sampling site B5



0

100

200

300

400

500

600

700

800

900

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

er…

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning


118


0

50

100

150

200

250

300

350

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cleaning

Movement of people


119

Sampling site B6



0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning


120


0

50

100

150

200

250

300

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cleaning

Cooking in nearby

kitchen and

movement of people


121

Sampling site B7



0

200

400

600

800

1000

1200

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

ns

(µg/m

³)

Hours

Unidentified

Cooking

Cleaning


122


0

200

400

600

800

1000

1200

1400

1600

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Ave

rage

PM

2.5

co

nce

ntr

ati

on

(µ

g/m

³)

Hours

Unidentified

Movement

of people


123

Sampling site B8



0

500

1000

1500

2000

2500

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

CleaningUnidentified


124


0

500

1000

1500

2000

2500

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking in the

adjacent kitchen

Cigarette

smokingCleaningUnidentified


125

Sampling site B9



0

100

200

300

400

500

600

700

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning

Unidentified


126


0

200

400

600

800

1000

1200

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cleaning

Gas heater for

space heating Unidentified


127

Sampling site B10



0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

co

nce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning


128


0

200

400

600

800

1000

1200

1400

1600

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Ave

rage

PM

2.5

co

nce

ntr

ati

on

(µ

g/m

³)

Hours

Movement of people + Cigarette smoking

Cleaning

Cigarette smoking


129

Trends in PM2.5 levels in microenvironments of Category C sampling sites

Sampling sites of category C had a floor area ranging from 278.3 m2 to 506 m2. The

number of occupants varied from 3 to 8 occupants among whom only was a frequent smoker

and carried out smoking indoors. The selected sites were monitored during different seasons

with C1, C9 and C10 monitored during the spring season, C2 during the hot summers, C3

during the rainy season, and C4 monitored during the autumn. The remaining four sites, C5,

C6, C7 and C8 were monitored during the winters. All the selected sites were located within a

radius of 1 Km from main roads with cemented or carpeted adjacent roads in most cases while

the road outside one site was unpaved dust road. The location of the houses was urban with

one house located in industrial area and another in semi-urban and industrial premises. Table

8 provides with an overview of each of the sampling sites.


well as connection between the two microenvironments. It was noted that no direct connection

existed between the kitchens and living rooms in five houses while in three of the houses, doors

of both rooms opened into the same room (partially connected). Kitchen and living room were

directly connected in two houses. The dimensions of the doors and windows along with the

rooms where monitoring was conducted are also given in floor plans (figure 50, 52, 54, 56, 58,

60, 62, 64, 66 and 68).






130







windows to obtain minimum air exchange rates (Annexure-II)


131

Table 8: Profile of Category-C sampling sites

Study

site

Size of

house

(m²)

Occupants Location Type of

road

Distance from

main road

Connection between

kitchen and living

room

Cooking

fuel

No. of

smokers

C 1 506 7 Urban Carpeted 0.5 km Fully connected NG 0

C 2 506 6 Urban Cemented 1 km Not connected NG 0

C 3 455.4 5 Urban Carpeted 0.1 km Not connected NG &

LPG

0

C 4 303.6 5 Urban, Industrial Carpeted 0.1 km Not connected NG 0

C 5 379.5 8 Semi-urban, Industrial Unpaved 1 km Fully Connected NG 14

C 6 379.5 3 Urban Carpeted 0 km Not connected NG 0

C 7 506 7 Urban Carpeted 0.1 km Not connected NG 0

C 8 506 4 Urban Carpeted 0.1 km Partially connected NG 0

C 9 303.6 6 Urban Carpeted 0.1 km Partially connected NG 0

C 10 278.3 6 Urban Carpeted 0.1 km Partially connected NG 0



132


of category-C sites

Study

site



Average Max Min St dev. Average Max Min St dev. Average Max Min St dev.

C1 342.6 441.5 256.7 93.1 1721.7 2365.5 1156.4 608.4 87.1 96.9 71.0 14.1

C2 79.6 107.3 61.7 24.3 193.7 323.2 94.0 117.4 38.5 52.8 29.1 12.6

C3 136.8 165.3 113.8 26.2 319.8 404.0 214.5 96.5 52.3 60.3 43.7 8.3

C4 321.4 355.8 299.4 30.1 1053.3 1290.6 745.7 279.1 75.2 102.8 43.0 30.1

C5 851.8 1089.7 564.4 266.1 1900.8 2760.4 1220.9 785.3 116.2 156.4 77.8 39.3

C6 504.8 536.9 456.5 42.6 916.2 1083 722.7 181.6 218.7 253.7 194.3 31.0

C7 389.0 653.5 166.5 246.2 2474.6 3170.5 1266.2 1050.5 61.3 101.1 37.0 34.8

C8 285.8 337.6 209.0 67.8 554.6 629.2 485.7 71.9 122.2 153.1 78.9 38.6

C9 255.4 333.2 149.8 94.8 453.5 512.1 420.3 50.9 157.0 232.0 34.4 107.0

C10 137.8 217.2 93.4 68.9 1088.4 2495.6 243.3 1226.9 33.1 44.9 25.8 10.3


C1 193.6 228.6 151.6 39.0 502.7 626.5 277.3 195 85.4 93.6 69.1 14.1

C2 68.4 77.3 58.8 9.2 116.1 151.2 86.1 32.8 41.3 50.7 35.0 8.3

C3 137.6 163.4 112.7 25.3 283.4 334.3 195.0 76.9 51.2 66.0 35.2 15.4

C4 388.5 423.7 327.9 52.7 1067.6 1235.0 812.7 224.3 112.9 180.9 53.5 64.1

C5 974.1 1067.3 798.9 151.9 2055.9 2354.9 1697.6 332.6 135.2 183.1 89.4 46.8

C6 729.7 803.1 674.0 66.3 1230.1 1466.1 906.4 290.0 437.7 685.9 278.2 217.8

C7 285.5 443.8 160.3 144.6 797.2 1035.6 477.0 288.1 75.1 123.7 48.9 42.1

C8 386.9 470.2 278.2 98.5 752.7 964.4 604.2 188.2 176.8 233.2 111.5 61.3

C9 449.0 788.5 216.8 300.6 903.2 1352.3 418.7 467.8 258.1 643.6 56.7 333.9

C10 149.0 167.7 122.5 23.6 431.3 461.4 388.3 38.2 49.7 71.7 22.7 24.9


133

Sampling site C1

Figure 50: Floor plan of sampling site C1

Figure 51a: 24-h representative mean values of PM2.5 in kitchen of sampling site C1

0

500

1000

1500

2000

2500

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning


134

Figure 51b: 24-h representative mean values of PM2.5 in living room of sampling site C1

0

100

200

300

400

500

600

700

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cleaning

Movement

of people


135

Sampling site C2



0

20

40

60

80

100

120

140

160

180

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking


136


0

20

40

60

80

100

120

140

160

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cleaning

Movement of

people


137

Sampling site C3



0

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning

Cooking


138


0

50

100

150

200

250

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cleaning Movement of

people


139

Sampling site C4



0

100

200

300

400

500

600

700

800

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Unidentified


140


0

200

400

600

800

1000

1200

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cleaning

Movement

of people

Unidentified


141

Sampling site C5



0

200

400

600

800

1000

1200

1400

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning

Unidentified


142


0

200

400

600

800

1000

1200

1400

1600

1800

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cigarette

smoking

Unidentified

Gas heater

for space

heating


143

Sampling site C6



0

100

200

300

400

500

600

700

800

900

1000

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Unidentified


144


0

200

400

600

800

1000

1200

1400

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

co

nce

ntr

atio

n (

µg/m

³)

Hours

Cleaning

Movement

of people

Gas heater for

space heating

Unidentified


145

Sampling site C7



0

200

400

600

800

1000

1200

1400

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

co

nce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning


146


0

100

200

300

400

500

600

700

800

900

1000

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cleaning

Movement

of people


147

Sampling site C8



0

100

200

300

400

500

600

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning


148


0

100

200

300

400

500

600

700

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Activities in the

adjacent kitchen


149

Sampling site C9



0

50

100

150

200

250

300

350

400

450

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cleaning

Unidentified

Unidentified


150


0

200

400

600

800

1000

1200

1400

1600

0 1 2 3 4 5 6 7 8 9

10

11

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13

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19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Movement

of people

Cleaning

Unidentified


151

Sampling site C10



0

100

200

300

400

500

600

0 1 2 3 4 5 6 7 8 9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cooking

Cooking and

Cleaning


152

Figure 69b: 24-h representative mean values of PM2.5 in kitchen of sampling site C10

0

50

100

150

200

250

300

350

400

450

500

0 1 2 3 4 5 6 7 8 9

10

11

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13

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Av

erag

e

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Hours

Cleaning

Movement

of people


153

Particulate matter levels in sampling sites

The levels of fine particulate matter are governed by a variety of factors as observed in

this study. PM2.5 concentrations varied in different households and in both micro-environments

under observation (Figure 70).

Figure 70: Mean values of PM2.5 observed in the kitchens and living rooms of the sampling

sites

Since both the kitchen and living room of each house were monitored, Pearson’s Chi-

square correlation test was applied with a significance level of 5 % on the measured PM2.5

levels to observe any association between particulate levels in the kitchens and living rooms

of each site. The null and alternate hypothesis were stated as:

Ho = There is no association between the PM concentrations in kitchen and living room

Ha = There is an association between the PM concentrations in kitchen and living room

0

200

400

600

800

1000

1200

B1

B2

A1

C1

A2

B3

B4

A3

A4

C2

B5

A5

A6

B6

C3

A7

A8

A9

C4

B7

C5

B8

C6

B9

A1

0

C7

C8

B10

C9

C10

PM

2.5

conce

ntr

atio

n (

µg/m

³)

PM2.5 in Kitchens PM2.5 in Living rooms


154

A strong positive correlation among PM2.5 levels in kitchen and living rooms was

observed in 17 houses while a negative correlation was observed in only three houses (table

10).

Table 10: Correlation between PM2.5 levels in kitchens and living rooms of sampling sites

(strong correlations shown in bold) (α = 0.05)

House # Category-A Category-B Category-B

1 0.459 0.858 0.854

2 0.187 0.440 0.874

3 0.978 0.814 0.972

4 0.441 0.998 0.369

5 -0.594 0.137 0.367

6 -0.035 0.503 0.969

7 0.847 0.976 -0.050

8 0.953 0.961 0.991

9 0.309 0.714 0.737

10 0.783 0.640 0.893

Generation of fine particulate matter from various household activities

Each household caries out a number of activities throughout the day which result in the

generation of particulate matter. However each particular activity contributes to varying levels

of PM2.5. The major activities identified in the kitchen included cooking and cleaning while in

the living room cleaning and presence of people were the major contributing activities. Space

heating during the winters and cigarette smoking in some houses were also identified to be

contributing factors. Figure 71a, 71b, 71c, 72a, 72b and 72c represent the average PM levels

generated during the performance of different activities in the kitchens and living rooms of


155

sampling sites respectively. The average PM2.5 along with maximum and minimum levels

generated while performing different activities is summarized in table 11.

Figure 71a: Average PM2.5 levels generated from different activities in kitchen of category-A

sampling sites

Figure 71b: Average PM2.5 levels generated from different activities in kitchen of category-B

sampling sites

-500

0

500

1000

1500

2000

2500

3000

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10

PM

2.5

con

cen

trat

ion

s (µ

g/m

³)

Cooking (Breakfast) Cooking (Lunch + Dinner) Cleaning activities

-1000

0

1000

2000

3000

4000

5000

6000

7000

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10

PM

2.5

con

cen

trat

ion

s (µ

g/m

³)



156

Figure 71c: Average PM2.5 levels generated from different activities in kitchen of category-C

sampling sites

Figure 72a: Average PM2.5 levels generated from different activities in living room of category-

A sampling sites

-500

0

500

1000

1500

2000

2500

3000

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10

PM

2.5

co

nce

ntr

atio

ns

(µg/m

³)


0

100

200

300

400

500

600

700

800

A1 A2 A3 A4 A5 A6 A7 A8 A9 A10

PM

2.5

conce

ntr

atio

n (

µg/m

³)

Movement of people Cleaning Smoking Space heating


157

Figure 72b: Average PM2.5 levels generated from different activities in living room of

category-B sampling sites

Figure 72c: Average PM2.5 levels generated from different activities in living room of category-

C sampling sites

0

500

1000

1500

2000

2500

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10

PM

2.5

conce

ntr

atio

n (

µg/m

³)


0

200

400

600

800

1000

1200

1400

1600

C1 C2 C3 C4 C5 C6 C7 C8 C9 C10

PM

2.5

conce

ntr

atio

n (

µg/m

³)



158

Table 11: Overall PM generation observed during different activities in the kitchens and

living rooms

Type of activity Average

(µg/m³)

Maximum

(µg/m³)

Minimum

(µg/m³)

St. Dev

(µg/m³)

Kitchen

Cooking:

Breakfast 884 5779 86 1138

Lunch + Dinner 481 1527 66 390

Cleaning 279 866 61 185

Living room

Movement of people 420 2257 94 340

Cleaning 320 1900 90 348

Smoking 1022 1821 222 513

Space heating 626 1118 354 226

PM2.5 concentrations in connected and not connected kitchens

The connection between kitchens and living rooms was observed to be of significance.

A direct connection between the kitchen and living room existed in only five houses, a partial

connection in six houses while the remaining houses had a variable distance between the both

sites. It was found that in houses where kitchen and living room were connected, the PM levels

of both micro-environments followed almost the same trends while in other two cases (partially

connected and not connected), no direct relation existed. The regression value indicated a

strong relationship between PM levels in both microenvironments where a direct connection

existed (r2 = 0.96) (Figure 73a) while in case of partially connected or not connected rooms,

the value was indicative of a poor relationship among PM values in both microenvironments

(r2 = 0.46 and r2 = 0.43 respectively) (Figure 73b and 73c).


159

Figure 73a: Comparison of 24 hour average PM2.5 in houses with kitchens and living rooms

connected

Figure 73b: Comparison of 24 hour average PM2.5 in houses with kitchens and living rooms

partially connected

y = 1.3918x - 177.54

R² = 0.9658

0

200

400

600

800

1000

1200

0 100 200 300 400 500 600 700 800 900

PM

2.5

in k

itch

ens

PM2.5 in living rooms

y = 0.7384x + 107.39

R² = 0.4584

0

50

100

150

200

250

300

350

400

450

500

0 50 100 150 200 250 300 350 400 450 500

PM

2.5

in k

itch

ens



160

Figure 73c: Comparison of 24 hour average PM2.5 in houses with kitchens and living rooms

not connected

Variations in PM2.5 concentrations during different seasons

Since monitoring of fine particulate matter was conducted in all months of the year, the

obtained results were segregated according to seasons to observe if there was any impact of

seasons upon PM levels or not. As seen in figure 74, highest mean PM2.5 levels were obtained

during the winter season while the summer season exhibited lowest mean concentrations.

y = 0.5526x + 101.11

R² = 0.4354

0

100

200

300

400

500

600

700

800

0 200 400 600 800 1000 1200

PM

2.5

in k

itch

ens



161

Figure 74: Mean levels of PM2.5 obtained during different seasons

In order to test any profound impact of seasons upon particulate matter concentrations

in the sampling sites, the null and alternative hypothesis were formulated as given below:

Ho = Changing seasons have no influence upon fine particulate levels in indoor micro-

environments

Ha = Changing seasons have a strong influence upon fine particulate levels in indoor

micro-environments

The hypothesis were tested using one–way ANOVA. Since the p-values fell in the

critical region, the null hypothesis was rejected and the results indicated a significant influence

0

100

200

300

400

500

600

WINTER SPRING SUMMER MONSOON AUTUMN AVERAGE

PM

2.5

co

nce

ntr

atio

n (

µg/m

³)

PM2.5 in Kitchens PM2.5 in Living rooms


162

of seasons upon PM2.5 levels in both kitchens (p = 0.022) and living rooms (p = 0.005) at a

significance level of 0.05 (table 12a and 12b).

Table 12a: One-way ANOVA for seasonal variation in PM2.5 levels in kitchens

ANOVA

PM.K

Sum of

Squares df Mean Square F Sig.

Between

Groups 537927.140 4 134481.785 3.468 .022

Within Groups 969499.938 25 38779.998

Total 1507427.079 29

Table 12b: One-way ANOVA for seasonal variation in PM2.5 levels in living rooms

ANOVA

PM.LR

Sum of

Squares df Mean Square F Sig.

Between

Groups 707249.308 4 176812.327 4.936 .005

Within Groups 895518.093 25 35820.724

Total 1602767.402 29

Diurnal variations in PM2.5 levels:

Since the major activities in a household are carried out during the day, it was

speculated that there should be a difference in PM concentrations during the day and night

hours. The 24-hour values for particulate matter were segregated according to the length of

day and night. The data for day length was obtained from meteorology department, Lahore.


163

The null and alternative hypothesis were stated as give below and Paired-t test was applied on

PM averages obtained during the day and night hours.

Ho = There is no significant difference in particulate levels during the day and night

hours in the sampling sites

Ha = There is a significant difference in particulate levels during the day and night

hours in the sampling sites

The level of significance was 0.05 and the hypothesis were tested separately for

kitchens and living rooms. The outcome revealed that significant variations existed between

PM levels during the day and night hours in kitchens (t (29) = 0.325, p = 0.747) and the living

rooms (t (29) = -1.496, p = 0.145).

Ventilation rates

Air exchange rate (ACH) was measured under two conditions: once with open doors

and windows, then with closed doors and windows (Annexure-II). Measurements were made

in the absence of people so that CO2 levels may not be affected. The obtained results are given

in figure 75a and 75b below. The flow of air in terms of liter per second per person was also

calculated and is given in table 13.


164

Figure 75a: Maximum and Minimum Air exchange rate in the kitchens of sampling sites

Figure 75b: Maximum and Minimum Air exchange rate in the living rooms of sampling sites

0

2

4

6

8

10

12

14

16

A1

A2

A3

A4

A5

A6

A7

A8

A9

A1

0

B1

B2

B3

B4

B5

B6

B7

B8

B9

B1

0

C1

C2

C3

C4

C5

C6

C7

C8

C9

C1

0

Air

Ch

ange

rate

per

Ho

ur

ACH IN KITCHEN MAX VENTILATION ACH IN KITCHEN MIN VENTILATION

0

1

2

3

4

5

6

7

8

9

10

A1

A2

A3

A4

A5

A6

A7

A8

A9

A1

0

B1

B2

B3

B4

B5

B6

B7

B8

B9

B1

0

C1

C2

C3

C4

C5

C6

C7

C8

C9

C1

0

Air

Ch

nag

e ra

te p

er H

ou

r

ACH IN LIVING ROOM MAX VENTILATION ACH IN LIVING ROOM MIN VENTILATION


165

Table 13: ACH and Air flow rate (liter per second per person) in the kitchens and living

rooms of the sampling sites

Sampling

site

Kitchen Living room

Doors and windows

open

Doors and windows

closed

Doors and windows

open

Doors and windows

closed

ACH L/s/person ACH L/s/person ACH L/s/person ACH L/s/person

A1 7.91 5.60 4.14 0.69 6.78 12.80 3.52 6.65 A2 6.27 7.74 3.19 0.46 3.57 6.74 3.00 5.66

A3 11.07 4.97 11.07 1.58 3.83 6.20 1.97 3.18 A4 4.96 12.65 3.20 0.40 3.51 6.95 1.92 3.80

A5 5.36 4.21 2.79 0.23 7.40 5.82 2.33 1.84 A6 4.82 3.10 3.20 0.53 6.08 14.35 2.49 5.87

A7 11.57 3.25 11.57 1.65 4.75 10.25 2.33 5.02

A8 2.68 1.23 2.67 0.45 5.55 3.64 3.80 2.49 A9 6.26 2.73 2.56 0.20 8.20 7.14 3.75 3.27

A10 11.68 8.04 11.68 2.92 5.34 8.40 2.77 4.36 B1 8.39 4.40 4.99 0.42 6.51 15.35 2.65 6.25

B2 8.32 8.02 4.75 1.19 5.04 12.48 3.44 8.52 B3 5.26 7.44 3.95 0.66 5.66 15.13 2.43 6.50

B4 6.61 14.55 4.24 1.41 5.30 25.00 2.75 12.99

B5 6.60 2.97 3.35 0.48 3.79 7.16 2.32 4.39 B6 7.68 2.64 3.71 0.46 5.42 7.67 3.87 5.48

B7 5.64 7.10 5.64 1.41 5.05 14.28 3.23 9.16 B8 14.97 14.13 14.97 2.50 6.57 6.89 2.04 2.14

B9 4.90 1.19 2.63 0.20 5.50 3.32 2.98 1.81

B10 7.52 2.46 6.69 1.11 2.80 3.53 2.43 3.06 C1 6.01 5.20 3.64 0.52 5.35 14.42 3.78 10.20

C2 5.12 12.08 2.92 0.49 5.48 18.40 4.06 13.63 C3 4.68 3.68 2.48 0.50 6.67 17.62 2.41 6.38

C4 6.74 15.27 2.50 0.50 5.13 15.50 3.20 9.67 C5 10.22 8.68 10.22 1.28 5.11 11.58 2.76 6.25

C6 6.80 11.41 3.91 1.30 8.15 25.63 3.33 10.49

C7 14.51 22.82 2.76 0.39 5.91 11.95 3.40 6.87 C8 11.02 21.66 3.44 0.86 9.12 21.52 3.29 7.75

C9 6.78 3.74 2.95 0.49 4.96 14.06 2.34 6.64 C10 5.40 6.12 3.72 0.62 5.98 9.40 3.46 5.43

Linear regression was applied using SPSS (v 16.0) to observe the impact of ventilation

rates upon the concentrations of fine particulate matter in both micro-environments. The

kitchens exhibited a poor relation between the two parameters while a significant relation

(marked in bold text) was observed in living rooms of Category B and C sites (table 14).


166

Table 14: Regression modeling: ACH versus PM2.5 (α = 0.05)

R2 F P-value

KITCHEN

Category-A 0.277 3.060 0.118

Category-B 0.074 0.637 0.448

Category-C 0.248 2.641 0.143

LIVING ROOM

Category-A 0.109 0.976 0.352

Category-B 0.409 5.528 0.047

Category-C 0.455 6.680 0.032

Levels of bio-aerosols in the sampling sites

The air-borne microflora of the study sites was represented by a total of seven bacterial

species and eleven fungal species. The colony forming units per cubic meter were calculated

for the bacterial and fungal colony counts using the Omelyansky formula. The total bacterial

cfu/m3 ranged from 472 to 9829 in the kitchens and from 275 to 14,469 in the living rooms

(Table 15). Similarly, the total fungal cfu/m3 ranged between 236 and 1887 in the kitchen and

from 314 to 1887 in the living room (Table 16). The average temperature noted during the

monitoring was 27.4oC + 5.6oC in the kitchens and 28oC + 5.6oC in the living rooms while the

average relative humidity levels ranged from 20% to 75% in both the kitchens and living

rooms. The colony forming units of each bacterial and fungal species present in the air of the

monitored sites are given in table 17 and 18. The predominant bacterial species were found to

be Staphylococcus spp. (36.96 % in Kitchens and 35.45 % in Living rooms), Micrococcus spp.

(28.33 % in Kitchens and 29.75 % in Living rooms), and Bacillus spp. (11.75 % in Kitchens

& 14.17 % in Living rooms) along with Serratia spp. and some unidentified Gram negative

and positive rods and Cocci in a few sites (Figure 76a and 76b). Aspergillus fumigatus (25.27


167

% in Kitchens and 22.88 % in Living rooms) and Alternaria alternata (18.86 % in Kitchens

and 30.03 % in Living rooms) were the most abundant fungal species found at all sites along

with some other Aspergillus species, Rhizopus, Fusarium spp. Trichoderma and Mucor (Figure

77a and 77b).


168

Table 15: Temperature, Relative humidity and Total bacterial colony forming units per meter cube (cfu/m3) present in the kitchen and

living room of each sampling site

Sampling site Temperature (°C) Humidity (%) Total number of

colonies in

kitchen

cfu/m3 (Kitchen) Temperature (°C) Humidity (%) Total number of

colonies in living

room

cfu/m3 (Living

room)

A1 18.3 33 49 1927 18 32 37 1455 A2 21 40 13 511 20.5 42 47 1848

A3 27 51 167 6566 26 47 198 7785 A4 36 25 202 7942 35.5 20 164 6448

A5 31.8 59 180 7077 32.1 57 220 8650 A6 30.3 72 88 3460 30.3 72 63 2477

A7 32 65 160 6291 30.4 64 198 7785

A8 33.1 47 168 6605 32 45 190 7470 A9 30.1 48 240 9436 30.1 49 270 10616

A10 32.7 32 151 5937 30.4 33 215 8453 B1 18.1 45 47 1848 18.5 51 11 432

B2 20 55 44 1730 18 55 27 1062 B3 23.4 32 135 5308 23.5 28 10 393

B4 29.9 33 125 4915 29.9 33 115 4522

B5 36.7 36 171 6723 37.9 30 160 6291 B6 32.5 74 164 6448 32.8 74 198 7785

B7 22.8 55 60 2359 22.7 57 74 275 B8 22.6 48 131 5151 22.7 47 170 6684

B9 32.5 20 86 3381 32.8 22 76 2988

B10 20.9 60 20 786 30.3 33 19 747 C1 24.7 46 12 472 26 47 9 354

C2 33.2 31 250 9829 33.2 31 185 7274 C3 30 72 198 7749 30 70 160 6291

C4 29.4 46 210 8257 29.6 45 51 2005 C5 19.7 55 155 6094 19.7 56 38 1494

C6 19.7 56 114 4482 21.5 52 130 5111

C7 26.9 36 83 3263 28 35 229 9004 C8 30.9 37 65 2556 31 38 19 747

C9 25.3 55 240 9436 34.9 29 368 14469 C10 30.9 32 143 5622 32.7 32 181 7116


169

Table 16: Temperature, Relative humidity and Total fungal colony forming units per meter cube (cfu/m3) present in the kitchen and

living room of each sampling site

Sampling site Temperature (°C) Humidity (%) Total number of

colonies in

kitchens

cfu/m3 (kitchen) Temperature (°C) Humidity (%) Total number of

colonies in living

room

cfu/m3 (living

room)

A1 18.3 33 9 354 18 32 12 472 A2 21 40 8 315 20.5 42 10 393 A3 27 51 6 236 26 47 8 315

A4 36 25 19 747 35.5 20 15 590 A5 31.8 59 10 393 32.1 57 11 432

A6 30.3 72 14 550 30.3 72 17 668 A7 32 65 28 1101 30.4 64 25 983

A8 33.1 47 8 315 32 45 28 1101

A9 30.1 48 40 1573 30.1 49 41 1612 A10 32.7 32 48 1887 30.4 33 40 1573

B1 18.1 45 8 315 18.5 51 11 432 B2 20 55 6 236 18 55 9 354

B3 23.4 32 9 354 23.5 28 11 432

B4 29.9 33 13 511 29.9 33 11 432 B5 36.7 36 15 590 37.9 30 32 1258

B6 32.5 74 24 944 32.8 74 39 1533 B7 22.8 55 44 1730 22.7 57 48 1887

B8 22.6 48 20 786 22.7 47 31 1219 B9 32.5 20 28 1101 32.8 22 16 629

B10 20.9 60 10 393 30.3 33 40 1573

C1 24.7 46 7 275 26 47 10 393 C2 33.2 31 28 1100 33.2 31 21 826

C3 30 72 18 708 30 70 27 1062 C4 29.4 46 18 708 29.6 45 30 1180

C5 19.7 55 38 1494 19.7 56 29 1140

C6 19.7 56 34 1337 21.5 52 28 1101 C7 26.9 36 7 275 28 35 9 354

C8 30.9 37 41 1612 31 38 43 1691 C9 25.3 55 11 432 34.9 29 21 826

C10 30.9 32 23 904 32.7 32 24 944


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Table 17: Colony forming units of each bacterial species identified in the kitchens and living rooms of the sampling sites (*GPC = Gram

Positive Cocci, **GNR = Gram negative Rods, ***GPR = Gram Positive Rods) Site Bacillus spp. Staphylococcus spp. Micrococcus spp. GPC* GNR** GPR*** Serratia spp.

Kitchen Living

room

Kitchen Living

room

Kitchen Living

room

Kitchen Living

room

Kitchen Living

room

Kitchen Living

room

Kitchen Living

room

A1 39 118 904 826 315 511 354 0 315 0 0 0 0 0 A2 432 550 0 983 79 315 0 0 0 0 0 0 0 0

A3 590 275 2241 3106 1769 2320 1691 1022 275 1062 0 0 0 0 A4 944 1180 3657 2084 1376 2516 944 668 944 0 0 0 79 0

A5 1927 2005 1809 3735 2713 2045 0 0 511 865 0 0 118 0

A6 393 315 1966 1533 0 197 0 0 1101 432 0 0 0 0 A7 1140 1769 3067 3342 865 1494 865 0 393 1258 0 0 0 0

A8 629 511 3499 3421 865 2162 668 590 944 786 0 0 0 0 A9 786 2359 3303 2280 2359 4089 550 1455 1297 432 1140 0 0 0

A10 511 1415 2516 2792 1494 2831 865 629 550 472 0 315 0 0 B1 275 0 1455 197 118 236 0 0 0 0 0 0 0 0

B2 0 0 629 354 786 432 236 197 79 79 0 0 0 0

B3 236 432 3106 2045 1337 1455 0 0 629 275 0 0 0 0 B4 1022 1180 826 1101 1730 1415 1337 826 0 0 0 0 0 0

B5 2005 2162 2398 1927 1769 1573 0 629 550 0 0 0 0 0 B6 550 786 3263 2516 1573 2084 354 1455 708 944 0 0 0 0

B7 275 354 1022 1022 472 668 0 315 590 472 0 0 0 0

B8 1022 668 1455 2359 1337 2359 275 747 432 550 629 0 0 0 B9 393 236 1415 629 1022 1337 315 590 236 118 0 0 0 79

B10 0 118 236 275 315 236 157 118 0 0 79 0 0 0 C1 0 39 236 197 236 0 0 118 0 0 0 0 0 0

C2 472 1101 1573 2045 6802 2202 983 747 0 1180 0 0 0 0 C3 354 511 3539 2831 1927 747 786 0 1180 1573 0 629 0 0

C4 1062 157 2398 826 2398 668 432 0 1180 354 708 0 79 0

C5 1022 79 1848 590 1101 668 550 0 826 157 747 0 0 0 C6 275 393 1533 1415 983 2162 708 472 354 668 629 0 0 0

C7 354 747 1101 2909 1219 2674 0 1258 354 550 236 826 0 39 C8 315 157 983 236 550 275 393 79 197 0 0 0 118 0

C9 708 1533 2359 4639 3617 4600 1573 1691 590 1140 432 865 157 0

C10 157 747 1927 2556 2005 1691 629 826 393 786 511 511 0 0


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Table 18: Colony forming units of each fungal species identified in the kitchens (K) and living rooms (LR) of the sampling sites

Site. Alternaria

alternata

Aspergillus

fumigatus

Aspergillus

nidulans

Aspergillus

flavus

Aspergillus

niger

Aspergillus

terreus

Trichoderma

Rhizopus

Mucor

Fusarium

K LR K LR K LR K LR K LR K LR K LR K LR K LR K LR

A1 79 79 197 315 0 0 39 0 0 0 0 0 0 79 39 0 0 0 0 0 A2 39 157 0 0 79 118 0 0 118 0 0 0 39 0 0 0 0 0 39 118

A3 118 157 0 0 39 39 0 0 0 0 79 0 0 0 0 0 0 0 0 0 A4 79 275 157 79 0 0 0 0 157 39 0 0 0 0 79 0 39 0 236 118

A5 157 0 39 118 0 118 0 0 0 0 39 79 0 0 79 0 0 118 79 0 A6 79 236 275 79 79 0 0 0 39 39 0 39 0 0 0 0 79 0 0 236

A7 197 0 157 275 39 0 315 0 275 0 0 157 0 0 0 0 0 0 118 118 A8 0 472 79 0 0 79 0 0 0 79 39 0 0 0 39 354 0 0 0 0

A9 511 393 315 275 157 0 0 0 0 354 275 0 0 197 0 0 275 315 0 79

A10 432 511 550 472 157 0 0 0 79 0 118 236 0 0 275 0 0 0 157 315 B1 157 118 0 0 0 0 39 39 39 0 39 79 0 0 0 0 0 0 39 39

B2 79 79 118 157 39 39 0 0 0 79 0 0 0 0 0 0 0 0 0 0 B3 118 79 79 118 0 0 0 39 0 0 39 39 0 0 0 0 79 118 39 0

B4 79 79 197 236 0 0 39 0 0 0 0 0 79 0 0 118 118 0 0 0

B5 197 393 39 275 197 0 0 0 79 275 0 0 0 0 0 79 0 0 79 197 B6 157 668 39 0 0 354 275 275 118 0 0 0 0 0 118 0 79 197 39 0

B7 79 708 0 432 0 0 236 0 0 0 0 118 0 197 79 39 0 0 0 236 B8 157 432 118 0 0 236 79 0 0 0 0 315 275 0 79 0 39 236 39 0

B9 157 118 315 0 275 0 0 0 354 0 0 354 0 0 0 0 0 0 0 118 B10 0 668 236 354 0 118 0 0 0 0 0 275 0 0 79 0 79 157 0 0

C1 0 0 118 79 0 0 39 79 0 0 0 118 0 0 0 39 39 0 0 0

C2 157 432 236 79 0 0 197 118 0 0 157 0 118 197 0 0 157 0 0 0 C3 0 354 118 275 0 0 0 0 0 118 157 0 197 0 0 236 197 0 0 0

C4 39 472 118 197 0 0 236 354 315 0 0 157 0 0 0 0 0 0 0 0 C5 275 354 629 550 79 0 0 0 0 118 39 0 157 0 0 0 0 0 275 0

C6 157 0 472 590 0 0 157 275 0 0 118 0 0 0 79 0 275 0 0 157

C7 0 0 118 197 0 0 118 0 0 118 0 0 0 39 0 0 39 0 0 0 C8 315 79 432 747 354 0 0 157 157 0 157 0 39 393 0 0 0 0 79 118

C9 79 550 236 0 0 0 0 275 39 0 39 0 0 0 39 0 0 0 0 0 C10 275 236 197 275 0 0 0 0 39 79 0 0 0 0 0 0 236 0 0 354


172

Figure 76a: Proportion of bacterial species present in the kitchens of the sampling sites

Figure 76b: Proportion of bacterial species present in the living rooms of the sampling sites

Bacillus spp

Staphylococcus spp

Micrococcus spp

GPC*

GNR**

GPR***

Serratia spp

Bacillus spp

Staphylococcus spp

Micrococcus spp

GPC*

GNR**

GPR***

Serratia spp


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Figure 77a: Proportion of fungal species present in the kitchens of the sampling sites

Figure 77b: Proportion of fungal species present in the living rooms of the sampling sites

Alternaria alternata

Aspergillus fumigatus

Aspergillus nidulans

Aspergillus flavus

Aspergillus niger

Aspergillus terreus

Trichoderma

Rhizopus

Mucor

Fusarium sp

Unidentified

Alternaria alternata

Aspergillus fumigatus

Aspergillus nidulans

Aspergillus flavus

Aspergillus niger

Aspergillus terreus

Trichoderma

Rhizopus

Mucor

Fusarium sp

Unidentified


174

Statistical analysis

The relationship between air-borne microflora and various parameters noted during the

monitoring of bio-aerosols was determined using SPSS (v.16.0.0). The selected independent

parameters were temperature, relative humidity, and air change rate per hour and the dependent

variables were bacterial cfu/m3, fungal cfu/m3 and PM2.5 levels. Linear regression was applied

on single and multiple variables. Temperature was found to have a direct relationship with

bacteria and particulate matter but not fungi. Ventilation also had a significant relation with

particulate matter (Table 19 and 20).

Table 19: Regression modeling of different parameters in kitchen (α = 0.05). Significant results

are marked in bold text.

19a: Bacteria (cfu/m3)

r2 F p-value

Temperature 0.341 14.465 0.001

Relative Humidity 0.002 0.051 0.823

Air change per hour 0.035 1.018 0.322

Temperature & RH 0.361 7.628 0.002

Temp., RH & ACH 0.396 5.676 0.004

19b: Fungi (cfu/m3)

r2 F p-value

Temperature 0.043 1.263 0.271



Temperature & RH 0.043 0.609 0.551

Temp., RH & ACH 0.088 0.841 0.484


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19c: PM2.5 (µg/m³)

r2 F p-value

Temperature 0.146 4.773 0.037



Temperature & RH 0.153 2.443 0.106

Temp., RH & ACH 0.256 2.977 0.050

Table 20: Regression modeling of different parameters in living room (α = 0.05). Significant

results are marked in bold text.

20a: Bacteria (cfu/m3)

r2 F p-value

Temperature 0.324 13.407 0.001



Temperature & RH 0.332 6.711 0.004

Temp., RH & ACH 0.338 4.434 0.012

20b: Fungi (cfu/m3)

r2 F p-value

Temperature 0.064 1.921 0.177



Temperature & RH 0.125 1.933 0.164

Temp., RH & ACH 0.166 1.729 0.186

20c: PM2.5 (µg/m³)

r2 F p-value

Temperature 0.205 7.242 0.012



Temperature & RH 0.206 3.506 0.044

Temp., RH & ACH 0.340 4.472 0.012


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Seasonal variation in bioaerosol levels

Since the sampling was carried out during different seasons, one way ANOVA was

applied to determine the impact of season upon bacterial and fungal levels in indoor

environments. The null and alternate hypothesis were stated as:

Ho = Changing seasons have no significant impact upon bioaerosol levels in the

kitchens and living rooms of the sampling sites

Ha = Changing seasons have a significant impact upon bioaerosol levels in the kitchens

and living rooms of the sampling sites

A significant impact of season was observed upon bacterial and fungal levels in the

kitchens as the p-values did not fall in the critical region (p = 0.035 and p = 0.045 respectively)

while in the living rooms, the effect upon bacterial and fungal levels was not pronounced (p =

0.53 and p = 0.60 respectively) (Table 21).


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Table 21: One-way ANOVA for seasonal variation in bioaerosol levels in the kitchens and

living rooms

ANOVA

Sum of Squares df Mean Square F Sig.

BACTERIA_K Between Groups 7.167E7 4 1.792E7 3.067 .035

Within Groups 1.460E8 25 5841891.821

Total 2.177E8 29

BACTERIA_LR Between Groups 4.613E7 4 1.153E7 .811 .530

Within Groups 3.556E8 25 1.422E7

Total 4.017E8 29

FUNGI_K Between Groups 2215138.228 4 553784.557 2.708 .053

Within Groups 5111668.243 25 204466.730

Total 7326806.471 29

FUNGI_LR Between Groups 1951916.889 4 487979.222 2.606 .060

Within Groups 4681953.138 25 187278.126

Total 6633870.027 29

Association between PM2.5 levels and bio-aerosols

Since bioaerosol sampling was conducted for twenty minutes only, PM2.5 levels were

separated from the 24-hour data for those specific twenty minutes to observe any significance

between the both parameters. The respective highest and lowest mean levels of PM2.5 observed

during twenty minutes of bioaerosol sampling were noted to be 700.4 + 71.8 µg/m³ and 40.8

+ 15.3 µg/m³ in the kitchen and 809.5 + 54.5 µg/m³ and 39.6 + 5.6 µg/m³ in the living room.

Although there are a few limitations such as use of passive sampling for bio-aerosols and real-

time monitoring for particulate matter, this comparison was an attempt to observe if any

association existed or not. The null and alternative hypothesis were devised as:

Ho = There is no significant association between bio-aerosol levels and fine particulate

matter


178

Ha = There is a significant association between bio-aerosol levels and fine particulate

matter

The results revealed no substantial association between the sampling sites and any of

the measured variables. Moreover, no significant correlation was observed to exist between

PM levels and bioaerosols (Table 22).

Table 22: One-way ANOVA for association between bioaerosol levels and PM2.5 in the

kitchens and living rooms

ANOVA

Sum of Squares df Mean Square F Sig.

Bac_kit Between Groups 2.206E7 2 1.103E7 1.522 .236

Within Groups 1.957E8 27 7246599.904

Total 2.177E8 29

Bac_LR Between Groups 5.365E7 2 2.683E7 2.081 .144

Within Groups 3.481E8 27 1.289E7

Total 4.017E8 29

Fungi_kit Between Groups 190417.123 2 95208.561 .360 .701

Within Groups 7136389.348 27 264310.717

Total 7326806.471 29

Fungi_LR Between Groups 151598.785 2 75799.392 .316 .732

Within Groups 6482271.242 27 240084.120

Total 6633870.027 29

PM2.5_kit Between Groups 72534.707 2 36267.354 1.134 .336

Within Groups 863161.973 27 31968.962

Total 935696.680 29

PM2.5_LR Between Groups 211433.585 2 105716.792 2.560 .096

Within Groups 1115024.080 27 41297.188

Total 1326457.665 29

Chapter Five Discussion

179

CHAPTER FIVE

DISCUSSION

Particulate matter and bio-aerosols are two of the most important air-borne pollutants

that have a detrimental impact on human health (Kowalski, 2006; Tiwary and Colls, 2010).

The results obtained in this study revealed that both the PM and bio-aerosol levels were highly

exceeding the recommended limits in both the kitchens and living rooms of the houses under

observation.

The observations of this research explored the air quality of kitchens and living rooms

of the selected sites from various locations of the city. As obvious in figure 7, 8 and 9, most of

the occupants in the selected households belonged to the age group of 21-30 years (29 % males

and 31 % females) with majority of them being students or job holders. Therefore, a large

number of occupants spent variable time at home. It was the children below 5, females

(particularly housewives) and the elderly that spent maximum time in house. According to the

obtained data, only 20% of males spent 17-24 hours in the house whereas 62% of females were

observed to be spending their time i.e. 17-24 hours indoors. However, despite of such a

variation in the time spent by males and females at home this did not mean that the male

occupants were less exposed to pollutants in the indoor environments as compared to females.

Their exposure level may in fact be higher than females as they travel daily to work, are

exposed to a multitude of pollutants at work and on road as well. However, this research

focusses on the pollutant levels in houses and here females were observed to be more exposed

to indoor PM2.5 while performing different activities like cooking and cleaning within the

house.


180

Several internal and external factors affect indoor air quality such as the location and

design of the building, ventilation practices in use, seasonal variation, meteorological factors,

number of people occupying the room, use of room (such as office, waiting room, bedroom,

living room, and kitchen) (Goyal and Khare, 2010; Massey et al., 2012). For instance, an

important factor that influenced PM2.5 levels was the connection between the two monitored

rooms. In five houses, where the door of kitchen opened into the living room, the trends in

particulate levels over the 24-hours period were almost the same (sampling site B2, B7, B8,

C1, and C5). In these houses, activities in the kitchen were found to significantly affect the

particulate levels in the living rooms as well. Similarly, in houses where the doors of kitchen

and living room opened into the same room (sampling sites A3, A7, A10, C8, C9, and C10)

the activities in the kitchens did influence the PM levels in the living rooms but not so strongly

while being absent altogether in houses where both rooms were far apart.

Domestic activities have been observed to significantly affect the indoor air quality by

many researchers (Jhang and Smith, 2003; Ferro et al., 2004; Meng et al., 2005). Chao and

Cheng (2002) identified five different sources of PM2.5 in eight different houses which included

cooking, burning incense, smoking, indoor human activities, and ambient sources. Likewise,

this research also identified major indoor activities that could have a significant contribution

towards elevating the pollutant levels. The kitchens have been documented in many studies to

harbor an elevated level of particulate matter owing to the increased activity levels within its

premises. Cooking is also a major contributor in this regard as the cooking fuel used and the

method employed for cooking causes variations in pollutant levels (Naeher et al., 2000; Lee et

al., 2002; Colbeck et al., 2008; Isaxon et al., 2015). However in the current study, cooking fuel

did not have a strong influence as it was natural gas in all houses which is a comparatively


181

much cleaner and safer fuel than the biomass fuel (Siddiqui et al., 2009; Shimada and

Matsuoka, 2011; Saeed et al., 2015). Nevertheless, cooking practices were a potential factor

that were observed to strongly affect particulate levels. In Pakistan, meals are taken three times

a day with most of the cooking done by female members of the house. Frying of most of the

food items is an essential part of cooking. The general breakfast includes making of ‘Parathas’

which is a traditional fried flat-bread most commonly eaten in breakfast in Pakistan

accompanied by fried eggs or omelets. Similarly the lunch and/or dinner table is incomplete

without food items cooked by frying. Frying has been recognized to lift PM2.5 levels 30 times

more than the background values while grilling was recorded to increase the levels by 90 times

(He et al., 2004). Comparable observations have been described by Huboyo et al. (2011) and

Nasir and Colbeck (2013) and in the current results too, it was observed that in kitchens where

more frying was carried out, PM levels were also higher. The 24-hours representative PM2.5

values were useful in highlighting the effect of cooking upon PM levels. Higher peaks were

observed in majority of households where ‘Parathas’ were made in the breakfast. In fact PM2.5

levels were higher during breakfast preparation in these houses as compared to levels

throughout the day. The best examples in this regard can be given for sampling site A3, A6,

A10, B1, B10, C1, C7, and C8 where pronounced peaks were observed during cooking time.

In A3, the occupants left the house early in the morning as all the children were students

and both parents were schools teachers. The breakfast comprised of bread and tea while no one

was present during the lunch hours. Dinner was the major meal of the family and PM levels as

high as 2355 µg/m³ were observed during that hour as compared to 298 µg/m³ during the

breakfast time.


182

Sampling site A6 was monitored during the holy month of Ramadan5 so there were

only two meals in the day. Only two peaks were prominent throughout the day i.e. during the

“sehri” when “Parathas” were made and during “iftaar” when less frying was done with

respective PM levels to be as high as 3666 µg/m³ and 757 µg/m³.

In house A10, breakfast was the major meal and again ‘Parathas’ were an essential

component. Mean PM2.5 levels were observed to be 2639 µg/m³ during that time. In sampling

site B1 as well, ‘Parathas’ were made in breakfast and in large numbers due to a gathering of

around 30 people in the house. The mean PM2.5 levels were recorded to be 9597 µg/m³ during

breakfast (noted to be the highest levels generated by cooking so far in this study) while

masking the PM levels generated from other sources.

In sampling site B10, three prominent peaks were observed at the time of cooking of

meals. Again the highest peak (4396 µg/m³) was observed during the breakfast. Same was the

case in C1, where particulate levels rose to 2365 µg/m³ during the breakfast. In sampling site

C7, again three peaks were pronounced during the 24-hours PM averages with PM2.5 levels as

high as 1266 µg/m³ during the breakfast time. Similarly in house C8, PM2.5 levels were higher

while breakfast preparation (486 µg/m³) than during the rest of the day.

These examples provide an evidence as how cooking, particularly frying can result in

high amounts of PM2.5 generation in the kitchens. As seen in table 11 as well, highest average

PM2.5 levels were noted during the time of breakfast preparation (884 µg/m³) while during

lunch and dinner preparations these levels were reduced to half (481 µg/m³). Apart from

cooking the second major activity was identified to be cleaning which included floor sweeping,

dusting of surfaces and material movement. The mean PM2.5 levels generated during cleaning

5 The ninth month of Islamic calendar during which the Muslims fast during the day. The first meal or “sehri” is

taken before the dawn and the second meal of “iftaar” that concludes the fast is taken after sunset.


183

activities were 279 µg/m³. PM levels during cleaning were, however, still less than levels noted

during cooking.

Comparatively, the living rooms are an entirely different environment than the kitchens

with its own particular set of activities and sources. The activities carried out in the living

rooms included smoking, floor sweeping, dusting of surfaces, and also movement of people

which had a significant impact upon the PM levels in the rooms. Although smoking was

observed to be carried out within the houses in only four cases, the mean PM2.5 levels generated

were 1022 µg/m³ with levels reaching as high as 1821µg/m³. Smoking is known to increase

PM levels significantly (Monn et al., 1997; He et al., 2004; Colbeck et al., 2008; Nafees et al.,

2011; Nasir and Colbeck, 2013). Tobacco smoke has been identified as a source of heavy

metals by Ruggieri et al. (2014) while a significant association between asthma in children and

exposure to second hand smoke was observed by Bilocca et al. (2014).

Significant contribution from use of gas heaters was also noted, as during space heating,

maximum PM2.5 levels were recorded to be 1118µg/m³. Peaks were noted during the mornings

as the occupants were in a haste while getting ready for work or school etc. and during evenings

as they returned home. Highest levels observed during this time were 2257 µg/m³. Although

movement of people may not generate particulate matter in itself, occupant’s movement in a

room has been associated with the resuspension of already deposited particulate matter this

includes PM deposited on surfaces (Ferro et al., 2004). Personal activities have been associated

with increased PM levels indoors (Van Ryswyk et al., 2014). Rapid movement while getting

ready for work or school should, therefore, be an important contributor towards elevating the

levels of fine particulate matter in indoor micro-environments as perceived in the results of


184

this study as well. The obtained results were similar to those observed in many other studies

such as Chao et al. (1998), Jones et al. (2000), Colbeck et al. (2008), and Bhangar et al. (2011).

A substantial difference in the diurnal patterns of particulate matter was witnessed

during the study. Although there is not much data on diurnal variations of particulate matter in

the indoor micro-environments, a significant difference in day and night averages has been

recently reported in the ambient air of four major cities of Pakistan by Rashed et al. (2015).

Another observation pertaining to levels of PM2.5 levels indicated that in some houses,

particulate levels peaked at or around midnight even in the absence of any obvious source.

Such observations were documented in both the kitchens and living rooms of sampling sites

A2, A7, A9, B3, B, B7, B8, B9, C4, C5, C6, and C9 where a sharp peak was seen at or after

mid-night. Most often, such peaks are observed during the winters when particles are not easily

dispersed due to low kinetic energy. However, interestingly these sites were sampled during

different times of the year and so season could not be the causing reason of such unexpected

behaviour. It is possible that some metrological phenomenon might be the cause of these

elevated levels and to obtain a clearer understanding, this process needs to be reproduced with

simultaneous real time monitoring of indoor/outdoor PM, temperature, relative humidity and

other meteorological parameters. Since these high levels could not be related to ant particular

source, they were labelled as unidentified source in the 24 hour representative graphs of PM2.5

given in the results section.

The 24-h average levels of particulate matter were manifolds higher than the

established standards. Apart from that, the hourly maximum and minimum PM2.5 levels were

also indicative of poor air quality. Even the background levels (hourly minimum) were

observed to be higher than the safety limits. The mean background levels for particulate matter


185

in the kitchen were 103+62 µg/m³ and 124+97 µg/m³ in the living room. These levels were 4

to 5 times higher than the WHO recommended limits. In the absence of any activity, the highest

PM background levels were noted to be 311 µg/m³ in the kitchens and 438 µg/m³ in the living

room while the lowest background concentrations were 33 µg/m³ and 41 µg/m³ in the kitchen

and living room respectively. These results signify an immediate need to implement an air

quality management plan, as prolonged exposure to such high levels can produce substantial

adverse health impacts.

Apart from indoor sources, pollutant levels are greatly affected by outdoor levels.

There are numerous studies that have reported ambient sources to be equally responsible for

defining indoor air quality such as those of Chunram et al. (2007) and Ali et al. (2015a). Since

ambient sampling was not conducted in the current research, it is difficult to say for sure if

outdoor sources played any significant part in defining the obtained results for PM2.5 levels.

Although there are many unexplained peaks observed in the 24-hours representative graphs

of PM2.5 in both the rooms, their source remains unidentified and could have an association

with activities in the surroundings. The trend followed by PM2.5 levels in the ambient air of

Lahore is increasing with annual mean levels to be 123 µg/m³ in 2008, 129 µg/m³ in 2009 and

136 µg/m³ in 2010 as monitored by Environment Protection Department, Government of the

Punjab6 (Annexure-III). Many studies on ambient air quality of Lahore have recognized high

levels of PM2.5 being generated from various sources with vehicular emissions being the prime

factor (Lodhi et al., 2009; Ali et al., 2015c; Nasir et al., 2015c; Rasheed et al., 2015). The

severity of health outcomes from vehicular emissions has led to the recent recommendations

by UK government that PM2.5 is used to judge the influence of combustion sources including

6 Data obtained from the office of Deputy Director Labs, Environmental Protection Department-Government of

the Punjab.


186

road traffic instead of PM10 (Moorcroft and Barrowcliffe et al., 2105). With the ambient levels

exceeding the NEQS manifold, this fact cannot be ignored that indoor air quality may also be

affected by outdoor levels of pollutants.

A persistent source of fresh air in the indoor environments is a central factor to sustain

a healthy environment. Natural ventilation is economical than mechanical ventilation but it is

also uncontrolled as the airflow is unpredictable under changing climate. Moreover the

opening and closing of windows and any other such openings such as cracks and fixtures in

buildings also influence the infiltration of outdoor air (Allard, 2002). Ventilation practices

contribute significantly in defining the air quality of any indoor environment as the infiltration

and exfiltration rates are dependent upon it. Climate also plays an influential role in the form

of a limiting factor when designing and constructing buildings which are naturally ventilated.

Since Lahore is located in the tropical zone and characterized by a semi-arid hot climate,

winters are relatively short with long, hot summers. Therefore it is natural for people to keep

the windows open for most part of the day. Natural ventilation is more suitable in areas with

mild climate (Kowalski, 2006) and all the selected houses for this study were naturally

ventilated as is the common practice in Lahore.

Naturally ventilated buildings tend to be leaky and allow adequate air circulation in

and out of the building, thereby providing the residents with an incessant supply of fresh air.

Still dead spots may be present within the buildings with little or no air exchange at all. This

constant circulation of air in and out of the building envelope has often proved to have a

detrimental effect upon indoor air quality as the ambient air entering the building may be laden

with pollutants. A recent research by Nimra et al. (2015) on indoor air quality of operation

theatres signified the importance of an effective ventilation system as particulate


187

concentration was observed to be minimum in the presence of a laminar flow system but where

natural ventilation was applied, the air quality was questionable. However along with causing

infiltration of pollutants in to the building matrix, constant air change also ensures dilution of

the indoor air and helps in reducing the accumulation of pollutants in indoor micro-

environments (Helmis et al., 2007; Abdel-Salam, 2015). A study by Nasir and Colbeck (2013)

also highlighted the importance of ventilation in reducing pollutant loads in residential

apartments of UK as they noted PM levels to drop to half during the summers in smoking

apartments.

The rate of air change as observed in this research was noted to be lower than the

recommended values. Minimum ACH in any indoor environment should be 4 ACH which

means that air is replaced after every fifteen minutes or four times in an hour. Similarly the

kitchens should also be well ventilated with an air exchange rate of no less than 15 ACH.

However, the air change rate may fluctuate with the building requirements, floor area and the

dwellers living in the building. According to ASHRAE standard 62.2-2013, the minimum level

of ventilation in naturally ventilated buildings should be 3.5 L/s/person or 7.5 cubic feet per

minute/person (CFM/person) to guarantee a healthy setting for the inhabitants. According to

WHO guidelines for indoor air quality (2009), ventilation rates below 10 l/s per person are

associated with significantly advanced occurrence of one or more health consequences or with

poorer perceived air quality in office settings (Seppänen, et al., 1999) whereas ventilation rates

more than 10 l/s per person, nearly 20–25 l/s per person, are related with a substantial reduction

in the frequency of indications of sick-building syndrome or with enhanced perceived air

quality in workplace environments (Seppänen, et al., 1999; Sundell and Levin, 2007).


188

As the results indicate, the ventilation rate was not adequate in both the kitchens and

living rooms in most cases. Only the semi-open kitchens had an ACH of 10-15 while in most

cases the ACH was below 10. In the living rooms, the ACH ranges between 2 and 9 air changes

per hour. One important aspect to be remembered is that these rates are merely representative

values and may not reflect the original air exchange rates which are always a difficult aspect

to be measured due to leaks or small opening from where air can enter the room. The rates

were once measured with the doors and windows open and again with closing all such spaces

while in actual practice it is possible that only one window or only the door was kept open for

most part of the day and not all spaces be utilized for ventilation. Determination of ventilation

according to the number of occupants was also calculated to observe how much air was

available per person. Here it was seen that although in case of maximum ACH i.e. when all

doors and windows were open, there was adequate air for the occupants at most sites, but there

was a tremendous decline in air circulation if all the windows and doors were closed as it was

observed to be a common practice during the winters (Table 13). Besides the maximum ACH

or l/s/person values do not reflect the actual ventilation rates as explained above.

There are a number of factors that may be held responsible for such low levels of

ventilation including poor building design thereby allowing inefficient air exchange with the

outdoor air. Moreover there are some other limitations as well such as determining the actual

ventilation rate in naturally ventilated buildings can prove to be difficult. Meteorological

factors such as local wind speed and ambient temperature are the governing factors while the

number of windows is also an equally contributing factor (Chao and Wong, 2002; Kowalski,

2006; Helmis et al., 2007).


189

Bioaerosols are also an important component of the indoor air. There are many

influencing factors that determine the indoor bio-aerosol levels including temperature, relative

humidity and human beings as well. In fact human beings are considered the biggest source of

indoor air-borne micro-organisms (Mentese, 2009; Mentese et al., 2012; Oh et al., 2015).

Although the method used in this study was passive sampling (Koch sedimentation method),

it is still a useful tool for documenting the species composition of any environment

(Stryjakowska-Sekulska et al., 2007). The bacterial and fungal species identified in the

sampling sites were recognized to be a common constituent of the indoor air and opportunistic

pathogens as well. The prevalence of the identified speices varied from room to room and

house to house.

Abundant bacterial species recorded from the sampling sites included Staphylococcus

spp., Micrococcus spp. and Bacillus spp. The fungal micro-flora, on the other hand, comprised

of Aspergillus fumigatus and Alternaria alternata being the most prevalent with Aspergillus

species, Rhizopus, Fusarium spp. Trichoderma and Mucor were also present in varying

concentrations. Investigations by other researchers have also reported a somewhat similar

micro-biota in the indoor environments. An investigation of Polish homes revealed

Micrococcus spp. to be present at all sites with Staphylococcus epidermidis being second in

number. The fungal composition included species such as Absidia glauca, Alternaria

alternata, Cladosporium cladosporioides and Penicillium aurantiogriseum in most of the

sampled sites (Pastuszka et al., 2000). Likewise Aeromonas, Bacillus, Kocuria, Micrococcus,

Nocardia, Pseudomonas, and Staphylococcus were common bacterial species reported from

indoor residential environments while Aspergillus, Penicillium and yeasts predominated the

fungal fauna (Gorny and Dutkiewicz, 2002). Karwowska (2003), Gorny (2004) and Haas et al.


190

(2007) observed Aspergillus and Penicillium to be the most dominant genera in the indoor

environment while Lee and Jo (2006) documented the presence of Alternaria and

Cladosporium in addition to these genera. Reboux et al. (2009) also reported higher levels of

Aspergillus, Penicillium and Cladosporium in unhealthy houses with visible mold. Recently

the presence of bacterial species Brevibacillus brevis, Arthrobacter and Bacillus cereus and

fungal species of Neosartorya fischeri, Aspergillus clavatus and Trichoderma in indoor air

were reported by Joshi and Srivastava (2013).

Among the common fungal speices, Aspergillus and Penicillium have been reported to

be life threatening pathogens, particularly for immuno-compromised people (Vonberg and

Gastmeier, 2006; Basilico et al., 2007). Similarly, Aspergillus, Alternaria, Fusarium and

Cladosporium are responsible for asthma, sinusitis, rhinitis and many other hypersensitivity

reactions (Hardin et al., 2003; Kalogerakis et al., 2005). Salo et al. (2006) observed an increase

in asthma symptoms with increase in exposure to Alternaria alternata in US homes. Nasir and

Colbeck (2010) also observed wide variation in the levels and size of air-borne microbes in

fifteen residencies reflecting the diversity in behaviour and the exposure to high risk indoors.

A description of the observed microbial species as well as their potential sources and

susceptible sites of entry have been summarized in Table 23.

Though species of Cladosporium and Penicillium were absent at the selected sampling

sites in the present research, other species were present in variable numbers. On questioning

the occupants regarding their health status, it was found out that one or two occupants in sixteen

households suffered from allergic reactions to dust or during wheat harvesting season. One out

of the nineteen reported occupants was an asthma patient with no family history of asthma.

Although previous studies have concluded lack of association between bioaerosols and non-


191

biological particulate matter in residential settings (Pastuszka et al., 2000; Hargreaves et al.,

2003), there is a possibility that the presence of such elevated levels of bioaerosols and

particulate matter and their synergistic effect could have contributed to these adverse health

outcomes as reported in this research.

Table 23: Sources and health hazards posed by the observed bacterial and fungal species

(Source: Kowalski, 2006).

BACTERIA Description Natural Sources Diseases caused Point of

infection

Suggested

indoor limit

Staphylococcus

spp.

Gram positive

bacteria, non-

communicable,

opportunistic

pathogen

Humans, sewage,

nosocomial.

staphylococcal

pneumonia,

opportunistic

infections

Upper

Respiratory

Tract

NA

Micrococcus

spp.

Gram positive

bacteria, non-

communicable,

opportunistic

pathogen

Skin of humans

and other animals

and in soil, marine

and fresh water,

plants, dust, and

air

Pneumonia,

septic arthritis,

endocarditis,

bacteremia and

meningitis

Upper

Respiratory

Tract, skin

NA

Serratia spp. Gram negative

bacteria,

opportunistic

pathogen

Environmental,

indoor growth in

potable water,

nosocomial.

Opportunistic

infections,

bacteremia,

endocarditis,

pneumonia.

Upper

Respiratory

Tract, wounds,

eyes, urinary

tract

NA

FUNGI

Aspergillus spp. Non-

communicable,

causes

Aspergillosis, also

associated with

sick building

syndrome

Environmental,

nosocomial,

indoor growth on

insulation & coils.

Aspergillosis,

alveolitis,

asthma, allergic

fungal sinusitis,

ODTS, toxic

reactions,

pneumonia

possible

Upper

respiratory tract

150-500 cfu/m3

Alternaria

alternata

Non-pathogenic,

non-

communicable,

common indoor

contaminant, can

cause

opportunistic

Environmental,

indoor growth on

paint, dust, filters,

& cooling coils.

Allergic

alveolitis,

rhinitis, sinusitis,

asthma, toxic

reactions

Upper

respiratory tract

150-500 cfu/m3


192

allergic reactions

and contribute to

sick building

syndrome

Mucor Non-

communicable,

opportunistic

pathogen

Environmental,

sewage, dead plant

material, horse

dung, fruits.

Mucormycosis,

rhinitis,

pneumonia

Upper

respiratory tract

150-500 cfu/m3

Trichoderma Non-

communicable,

allergenic

Environmental,

soil, wood,

decaying

vegetation.

Allergic

alveolitis, toxic

reactions,

MVOCs

Upper

respiratory tract

150-500 cfu/m3

Rhizopus Non-

communicable,

opportunistic

pathogen

Environmental,

decaying fruit and

vegetables,

compost.

Zygomycosis,

allergic reactions,

pneumonia,

mucormycosis.

Upper

Respiratory

Tract, sinus,

skin eyes

150-500 cfu/m3

Fusarium spp. Non-

communicable,

allergenic

Environmental,

indoor growth on

floor dust filters, &

in humidifiers.

Allergic

alveolitis, allergic

fungal sinusitis,

toxic reactions,

MVOCs

Upper

respiratory tract,

skin, eyes

150-500 cfu/m3

The temperature measured in the indoor environments during monitoring ranged from

18°C to 37.8°C in the kitchens and living rooms while the average relative humidity levels ranged

from 20% to 75% in both the kitchens and living rooms. Temperature was noticed to be an

influencing factor for bacterial levels but not for fungal levels (table 19 and 20). Bacterial

activity is normally reduced at temperatures higher than 24°C (Tang, 2009) and bacteria also

requires more water activity than the fungi. On the contrary, in the case of fungi, temperature

may not be a limiting factor with most fungi growing at 10–35°C but humidity is still considered

a critical factor affecting fungal growth since dampness facilitates the growth of fungal spores

(Douwes et al., 1999; Nielsen et al., 1999). Relative humidity levels above 90% have been

reported to cause a 30% increase in fungal spore size. This increase in spore size can increase

the risk of deposition of spores in the respiratory tract, particularly bronchi by 20% (Reponen et

al., 1996). Moreover the transport of fungal spores is known to be more under the control of


193

meteorological factors such as temperature, relative humidity and air flow. In fact the most

suitable RH levels for growth of fungi are 70% and above while the optimum temperature range

is between 30 to 40°C (Deguchi and Yoshizawa, 1996). However, variation still exists as similar

bacteria may behave differently at different temperatures and relative humidity levels. The

results of this research were in contrast with the above discussion as although temperature had a

direct but weak relation with bacteria, relative humidity exhibited no significant association

with bacterial and fungal levels.

Pakistan is lacking in baseline data for micro-floral composition of indoor micro-

environments. Previous studies conducted by Colbeck et al. (2008) and Nasir et al. (2012)

found that 55 to 93% of bioaerosols were respirable. They also reported higher levels of

bioaerosols in the indoor micro-environments of Pakistan than the current research except

maximum indoor bacterial levels which were almost similar to levels observed in this study.

A more recent study by Sidra et al. (2015) reported variations in bioaerosol levels in the indoor

micro-environments during activity and non-activity periods. It was noted that during the

performance of daily activities in the kitchens and living rooms, microbial levels were elevated

as compared to levels recorded when there was no work being carried out in both rooms.

Advanced sampling techniques need to be employed for monitoring indoor microflora.

Seasons have been documented by many researchers to significantly influence

pollutant levels in both the indoor and outdoor environments. Particulate matter and bioaerosol

levels were found to be affected by changing seasons in this study too. The current results

indicated highest levels of particulate matter during the winter season and lowest during the

summers as also observed by many other researchers such as Lee et al. (1997), He et al. (2001),

Li and Lin (2003), Ramachandran et al. (2003), Ye et al. (2003), Tiwari et al. (2011), Massey


194

et al. (2012), and Massey et al. (2013). Ahmed (2007) also documented seasons to have a

marked impact upon bioaerosol levels. Likewise, Nasir et al. (2013) reported higher levels of

particulate matter in rural houses of Pakistan during the winter season while Frankel et al.

(2012) noticed elevated bioaerosol levels during spring and summer. Seasonal variability in

fungal levels was recorded by Adams et al. (2013) in both the ambient and indoor air. Recently,

Mentese et al. (2012) and Oh et al. (2015) documented the seasonal variability in levels of

particulate matter and bioaerosols in indoor microenvironments and observed higher levels of

PM during winters while bioaerosols levels were higher during summers. A possible

explanation for this outcome could be the reduced ventilation during the colder months as

windows and doors are generally kept closed throughout the day. Moreover, use of gas heaters

for space heating also generates considerable amounts of particulate matter. People prefer to

spend maximum time indoors and their activities may also influence indoor air quality.

Therefore the accumulation of particulate matter in the indoor environment is possible during

winters. On the other hand, during the summer season, doors and windows are kept open for

maximum part of the day with fans also being used thereby allowing maximum circulation of

air. As a result, the pollutant levels are diluted and easily dispersed. Nasir et al. (2013) also

suggested improved ventilation to reduce pollutant loads in the indoor environments.

Since Pakistan Environmental Protection Agency has not yet set any standards for the

permissible levels of PM2.5 and microbes in the indoor environment, WHO standards were

followed for PM2.5 while microbial levels were compared with standards set by different

countries. According to WHO air quality guidelines (AQG), the permissible levels of PM2.5

should not exceed 25µg/m³ in a 24-hour period while the annual mean should be 10 µg/m³

(WHO, 2006). The average PM2.5 levels documented in this study were 13 times higher than


195

the WHO AQG limits in both the kitchens and living rooms of the selected sites. Meanwhile

the annual average WHO Interim Target-1 (IT-1) value for PM2.5 is 35 µg/m³ which were

exceeded by the documented levels in this study. Long term exposure to such high levels of

fine particulate matter can be detrimental for human health and thus need to be controlled.

It is difficult to set a limit for microbial contaminants in the air owing to the diversity of

microbial species and the health outcomes caused by each of them. The bioaerosol levels

obtained in this study were compared to obtain an insight into the current status of IAQ in the

representative households. The Swedish and Singaporean standards establish the limit to be not

more than 500 cfu/m3 for bacteria and 300 cfu/m3 for fungi in the indoor environments.

According to USA Occupational Safety and Health Administration (OSHA), air is polluted in

the presence of microbial load of 1000 cfu/m3 in the air while the American Industrial Hygiene

Association (AIHA) (2001) recommends the intensities of fungal spores to not surpass 500

cfu/m3 in inhabited structures. Referring to these standard values, it was apparent that the detected

microbial levels in this study were critically exceeding these levels. While the respiratory health

of the inhabitants was assessed through direct questioning and no grave health issue except for

dust allergy was perceived, monitoring of indoor air quality is essential to evaluate the exposure

risk of the occupants.

LIMITATIONS OF THE STUDY

There were certain limitations to this study. The monitoring of each sampling site was

conducted only once while it would have been more useful to monitor each site in all seasons

at different times of the year. This was a major drawback to the current study. Repeated

sampling however was not possible due to the noisy instruments for PM2.5 which were causing

disturbance to the occupants and people were reluctant to allow repeated measurements. Also


196

the excessive load-shedding of electricity was an important constraint as it caused interruption

in continuous sampling of particulate matter. Moreover, passive sampling of bio-aerosols was

conducted as volumetric samplers were not available. Despite these limitations, the study was

an attempt to document the levels of two major pollutants of the indoor environments and to

obtain an understanding of the state of the residential micro-environments of Lahore, Pakistan.


197

CONCLUSION

The presented dissertation focuses on the state of indoor air quality in residential micro-

environments of the metropolis of Lahore. The purpose was to document the intensities of two

major pollutants i.e. particulate matter and bioaerosols and the governing factors along with

their sources and external factors responsible for the obtained levels. Although there were

certain limitations to this study, it can be considered as first attempt to record the IAQ of urban

areas of Pakistan where no previous such data or study exists.

Monitoring of indoor air quality of thirty residences of Lahore revealed an alarming

situation as the observed parameters were greatly exceeding the established standards. Mean

PM2.5 levels were documented to be 13 times higher than the WHO air quality guidelines or

the Interim Target-1 while even the background levels were also 4-5 times higher. Bioaerosol

levels were also above the safety limits. There were a number of factors that described the

indoor air quality in the monitored sites. Seasonal variability had a prominent share as highest

levels were reported during the winter season when air change rate was minimum due to closed

windows and doors. Moreover gas heaters were also being used for space heating thereby

contributing significantly in increasing indoor PM2.5 levels. The mean PM2.5 levels dropped

during the spring season as ventilation practices improved and attained minimum levels in

summers as use of ceiling fans and open windows allowed maximum dilution of air. A slight

increase during the monsoon was observed with further increase during the fall.

Diurnal variations in particulate levels were also noted as throughout the day PM2.5 was

generated and/or re-suspended by a variety of domestic activities while lack of activities during

the night hours led to reduced PM levels. Ventilation was another noteworthy factor as a direct

correlation was observed between air exchange rates and PM levels in the living rooms. Such


198

an association was observed to be lacking in the kitchens most probably due to the nature of

activities within which generated higher levels of particulate matter.

Major activities were identified which could cause substantial fluctuations in pollutant

levels indoors. Cooking (particularly frying) was the highest contributor while cleaning also

had a smaller share in the kitchens. In the living rooms, movement of occupants was

documented to influence indoor levels of fine particulate matter while cigarette smoking and

space heating (carried out at some sites) were also major contributors. The results were in

confirmation with many other studies associating the elevated levels of particulate matter in

residential settings resulting from various routine activities such as cooking, floor sweeping,

presence of people, smoking and space heating. The impact of household activities, ventilation

rates, and changing seasons upon the particulate matter concentrations in the indoor

environments was concluded to be substantial.

Since passive sampling of bioaerosols was conducted in comparison to real time

monitoring of PM2.5, it was challenging to quantify any association between PM levels and

indoor microflora. The micro-biota of the studied sites comprised of common constituent

species which were also reportedly known opportunistic pathogens. The colony forming units

per cubic meter of air were concluded to be higher than any established standards. Statistically

significant seasonal variation was observed for bioaerosol levels as well. Temperature had a

direct impact upon bacterial levels while relative humidity did not indicate any association

with microbial levels.

To sum up, the elevated levels of monitored parameters in the selected sites during the

course of this study pose a crucial situation. Since general public is unaware of the unhealthy

air they are taking in, there are little efforts to improve it. There is a dire need to monitor the


199

indoor environments and to understand their potential sources so that suitable mitigation

measures may be introduced and a healthy environment may be maintained. In the absence of

any policy or guidelines for maintaing indoor air quality in Pakistan, it is absolutely

indispensable to focus on generating a baseline data and formulation of guidelines for

improvement of indoor air quality in both the urban and rural sectors of the country.


200

RECOMMENDATIONS

The research undertaken highlights many aspects which need to be addressed at not

only individual level but most importantly at policy level to improve the living standards of

the general public and their health. The conclusion of the current study lead to proposal of the

following recommendations:

1. The first and most important step that needs to be taken is generation of a baseline data

regarding air quality of not only the residencies but the working indoor micro-

environments as well to assess the levels of various pollutants in the indoor micro-

environments and the potential hazards faced by the occupants.

2. There should be detailed and repeated measurements for background levels as well for

air quality of the rural and urban sites.

3. Since there are no standards set for the permissible limits of various indoor pollutants,

this aspect also needs to be covered. It is necessary to formulate some guidelines for

PM2.5 and bio-aerosols. Even the background levels monitored in this study are much

higher than the WHO limits of 25 µg/m³. It is therefore important to formulate and

implement some permissible standards to ensure a healthy environment.

4. Ventilation plays an important role in defining the air quality and there is a dire need

for the builders and construction authorities to consider maximum air exchange rate in

building designs to dilute the buildup of pollutants indoors.

5. Source appointment and strength of particulate emissions should be determined for

various indoor micro-environments so that a suitable intervention plan can be

implemented.


201

6. Detailed and regular air quality monitoring of urban centres should be conducted as, in

Pakistan, many researchers have limited their focus on assessing the indoor air quality

of rural areas while urbanized areas have largely been ignored so far.

7. In view of the limited data on micro-flora of the indoor residential environments (only

three studies conducted so far) of Pakistan, it is highly recommended to carry detailed

sampling of bioaerosols in both rural and urban sectors. Moreover, sampling via

impaction or other active means should be preferred to obtain a clearer picture of the

bio-hazards we face.

8. To minimize the excessive humidity levels indoors, the exhaust fans in the kitchens,

living rooms and bathrooms venting outside can help to condense the bio aerosols

concentration.

9. Too many plants inside the micro environments can cause increased humidity therefore

increasing bio-flora, so there should be on check on the ornamentals plants to keep the

steadiness.

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Annexure-I

235

QUESTIONNAIRE

Name of respondant: _________________________________________________________

Address:___________________________________________________________________

___________________________________________________________________________

_________________________________________GPS:_____________________________

Contact #: _______________________ Occupation: _______________________________

Members of the family: _______

No. Members of

the family

Age Gender Occupation Time spent by each member

Outdoors In house In kitchen

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

Annexure-I

236

Type of house: Owned Rented

Type of material used for construction: ___________________________________

Size of house: ___________________

Ventilation in:

Living room Kitchen

Area used for

ventilation

Area available

for ventilation

Total area of

the room

Area used for

ventilation

Area

available for

ventilation

Total area of

the room

Outside environment/surroundings:

Type of road:

Carpeted Uncarpeted Unrepaired

Source of aerosols from outside e.g. from drilling, maintenance works, sprays, grounds

(dust), factories etc.: ____________________________________________________

Indoors:

Kitchen: Temperature: ___________ Humidity: ________

Size: length: ________ width: __________ height: _________

Space for ventilation: _______________________________________________

Source/type of fuel used for cooking:

Sui gas LPG Kerosene oil Wood Other?

Maximum time spent in cooking:

Breakfast Lunch Dinner

Annexure-I

237

Eating habits:

Breakfast Lunch Dinner

Exhaust fan present: Yes No

Different items used in the house If Yes, then how often:

Weekly or daily Sometimes, as

required

Air fresheners used: No Yes

Leakage of gas: No Yes

Cleaning solvents

used:

No Yes

Type of floor:

Cemented Bricked tiled

Type of windows:

With wire gauze Without wire gauze

Living room: Temperature: ___________ Humidity: ________

Size: length: ________ width: __________ height: ____________

Space for ventilation: ____________________________________________________



required

Air fresheners: No Yes

Cleaning solvents

used:

No Yes

Annexure-I

238

Type of floor:

Cemented Bricked Tiled

Carpeted Rugs/ floor mats Nothing

Type of windows:


Rest of the house:

Cleaning / dusting of house

Daily Weekly Irregular



required

Air fresheners

perfumes, and body

sprays:

No Yes

Cleaning solvents,

paints, insecticides,

coils, etc.

No Yes

Air conditioners/

heaters used:

No Yes

Use of fertilizers/

pesticides on plants:

No Yes

Computers, printers present: Yes No

Presence of termites: Yes No

Annexure-I

239

Presence of lawn/ potted plants: Yes No

Indoors plants: Yes No

Pet animals: Yes No

Wooden paneling (source of formaldehyde): Yes No

Type of floor:

Cemented Bricked Tiled

Carpeted Rugs/ floor mats Nothing

Type of windows:


Children play indoors or outdoors: _________________________________________

Games played indoors: _________________________________________________

Mode of transport used:

Car Motorcycle Cycle

Health issues:

Daily walk/ exercise: Yes No

No. of smokers (if any) in house: _______

Smoking within house or outside? ________

Any member of the house suffering from any chronic ailment: _____________________

Vaccination of the children: Yes No

Annexure-I

240

Health Problems Details

Respiratory problems

(Asthma, infections, labored

breathing, severe lung

disease etc.)

Allergy (due to dust or

other reasons)

Headaches, vomiting,

nausea, rashes etc.

Irritation of mucous

membranes of eyes

Hypertension

Any other related problem

Annexure-II

241

Maximum and minimum air exchange rate at each sampling site

Figure 1: Maximum ACH in kitchen of A1

Figure 2: Minimum ACH in Kitchen of A1

y = -7.9069x + 8.1408

R² = 0.993

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

y = -4.1361x + 7.9966

R² = 0.9897

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

242

Figure 3: Maximum ACH in living room of A1

Figure 4: Minimum ACH in living room of A1

y = -6.7835x + 8.4088

R² = 0.9824

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

y = -3.5222x + 8.1551

R² = 0.9937

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

243


Figure 6: Minimum ACH in kitchen of A2

y = -6.2765x + 8.2856

R² = 0.9611

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (hours)

ACH

y = -3.1898x + 8.365

R² = 0.9641

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

244



y = -3.5716x + 8.2285

R² = 0.9685

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -3.0016x + 8.3506

R² = 0.9661

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

245

Figure 9: Air Change rate per hour in Kitchen (Semi-open) in A3


y = -11.077x + 8.1112

R² = 0.9891

6.2

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Ln (

CO

2)

Time (Hours)

ACH

y = -3.8306x + 8.3124

R² = 0.9783

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

246



y = -1.9671x + 8.1628

R² = 0.942

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

y = -4.9631x + 8.1958

R² = 0.8885

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

247



y = -3.2017x + 8.0593

R² = 0.9787

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

y = -3.5076x + 8.3994

R² = 0.9584

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

248



y = -1.9193x + 8.3623

R² = 0.9286

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Ln (

CO

2)

Time (Hours)

ACH

y = -5.3551x + 8.2601

R² = 0.9615

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

249



y = -2.7939x + 7.9605

R² = 0.9949

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

8

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -7.4018x + 8.4597

R² = 0.9785

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

250



y = -2.3342x + 8.2098

R² = 0.9415

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

y = -4.8212x + 8.0987

R² = 0.9947

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

251



y = -3.2047x + 8.2588

R² = 0.9841

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

y = -6.0815x + 8.1208

R² = 0.9789

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

252


Figure 24: ACH in kitchen of A7 (Semi-open kitchen)

y = -2.4888x + 8.206

R² = 0.9466

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

y = -11.567x + 8.521

R² = 0.9992

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

253



y = -4.7517x + 8.287

R² = 0.958

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

y = -2.3275x + 8.2805

R² = 0.9568

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

254



y = -2.6797x + 8.0516

R² = 0.9871

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -2.6749x + 8.1059

R² = 0.9667

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

255



y = -5.5495x + 8.0564

R² = 0.9727

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

y = -3.8028x + 8.084

R² = 0.9571

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

256



y = -8.2015x + 8.3994

R² = 0.975

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

y = -3.7501x + 8.1449

R² = 0.9698

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

257



y = -5.4744x + 8.518

R² = 0.9795

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

y = -2.86x + 8.2838

R² = 0.9428

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

258

Figure 35: ACH in semi-open kitchen of A10


y = -11.679x + 8.0647

R² = 0.9996

6.2

6.4

6.6

6.8

7

7.2

7.4

7.6

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Ln (

CO

2)

Time (Hours)

ACH

y = -5.3389x + 8.6804

R² = 0.9618

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

259


Figure 38: Maximum ACH in kitchen of B1

y = -2.7704x + 8.1911

R² = 0.9854

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -8.3944x + 8.1973

R² = 0.9669

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25

Ln (

CO

2)

Time (hours)

ACH

Annexure-II

260

Figure 39: Minimum ACH in kitchen of B1

Figure 40: Maximum ACH in living room of B1

y = -4.9872x + 8.1923

R² = 0.956

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Ln (

CO

2)

Time (Hours)

ACH

y = -6.5084x + 8.2026

R² = 0.9811

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

ln (

CO

2)

Time (Hours)

ACH

Annexure-II

261

Figure 41: Minimum ACH in living room of B1


y = -2.6519x + 8.0153

R² = 0.9812

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -8.3247x + 8.4684

R² = 0.9531

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

262



y = -4.7468x + 8.4521

R² = 0.9665

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Ln (

CO

2)

Time (Hours)

ACH

y = -5.0387x + 8.0359

R² = 0.9842

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln(C

O2)

Time (Hours)

ACH

Annexure-II

263



y = -3.4382x + 8.2513

R² = 0.9717

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -5.255x + 8.3788

R² = 0.9827

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

264



y = -3.9477x + 8.4057

R² = 0.9653

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

y = -5.6596x + 8.0909

R² = 0.9337

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

265



y = -2.43x + 7.6678

R² = 0.9813

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

y = -6.6962x + 8.7511

R² = 0.9909

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

266



y = -4.2373x + 8.3382

R² = 0.9689

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

y = -5.2986x + 7.8673

R² = 0.9803

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

267



y = -2.7536x + 8.2647

R² = 0.915

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -3.353x + 8.2156

R² = 0.9827

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

268



y = -6.6034x + 8.2944

R² = 0.978

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

y = -3.7929x + 8.7279

R² = 0.9127

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

269



y = -2.3245x + 8.5535

R² = 0.9583

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

Ln (

CO

2)

Time (Hours)

ACH

y = -7.6766x + 8.1664

R² = 0.9986

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

8

0 0.05 0.1 0.15 0.2 0.25

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

270



y = -3.0793x + 8.0226

R² = 0.9854

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -5.4196x + 8.422

R² = 0.9758

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

271


Figure 62: ACH in kitchen (semi-open) of B7

y = -3.8696x + 8.2778

R² = 0.9832

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

y = -5.6395x + 8.0652

R² = 0.9974

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

8

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

272



y = -5.045x + 8.2159

R² = 0.9979

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

y = -3.2339x + 8.1495

R² = 0.9514

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

273

Figure 65: ACH in semi-open kitchen of B8


y = -14.973x + 8.1329

R² = 0.8141

0

1

2

3

4

5

6

7

8

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Ln (

CO

2)

Time (Hours)

ACH

y = -6.5732x + 8.596

R² = 0.954

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

274



y = -2.0385x + 8.3153

R² = 0.9767

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Ln (

CO

2)

Time (Hours)

ACH

y = -4.9018x + 8.2489

R² = 0.9736

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

275



y = -2.6257x + 8.2797

R² = 0.9619

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

y = -5.4952x + 8.2582

R² = 0.9684

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

276



y = -2.9839x + 8.2991

R² = 0.977

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

y = -7.5151x + 8.1602

R² = 0.9949

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

277



y = -6.6874x + 8.1857

R² = 0.9931

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

y = -2.8049x + 8.3831

R² = 0.9432

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

278


Figure 76: Maximum ACH in kitchen of C1

y = -2.4325x + 8.1857

R² = 0.9818

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

y = -6.0065x + 8.009

R² = 0.9786

6.2

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

279

Figure 77: Minimum ACH in kitchen of C1

Figure 78: Maximum ACH in living room of C1

y = -3.6443x + 8.1467

R² = 0.9567

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

y = -5.3477x + 8.4504

R² = 0.9593

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

280

Figure 79: Minimum ACH in living room of C1


y = -3.7812x + 8.2261

R² = 0.9884

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

y = -5.1207x + 8.3561

R² = 0.9689

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

281



y = -2.9247x + 8.1209

R² = 0.9903

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

y = -5.4842x + 8.1656

R² = 0.9746

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

ACH

Annexure-II

282



y = -4.062x + 8.2934

R² = 0.9012

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Ln (

CO

2)

Time (Hours)

ACH

y = -4.6801x + 8.3881

R² = 0.9563

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

283



y = -2.4751x + 8.0932

R² = 0.9845

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Times (Hours)

ACH

y = -6.6689x + 8.0059

R² = 0.9571

6.6

6.8

7

7.2

7.4

7.6

7.8

0 0.05 0.1 0.15 0.2 0.25

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

284



y = -2.413x + 8.2684

R² = 0.8955

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

y = -6.7427x + 8.6079

R² = 0.9684

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

285



y = -2.4962x + 8.3356

R² = 0.9369

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

Ln (

CO

2)

Time (Hours)

ACH

y = -5.1335x + 8.392

R² = 0.9227

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

286


Figure 92: ACH in kitchen of C5

y = -3.2015x + 8.2193

R² = 0.9062

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -10.216x + 8.3236

R² = 0.9721

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

287



y = -5.1111x + 8.2085

R² = 0.9638

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

y = -2.7587x + 7.9263

R² = 0.985

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

288



y = -6.8025x + 8.3626

R² = 0.9814

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

y = -3.9065x + 8.4032

R² = 0.9631

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

289



y = -8.1486x + 8.5696

R² = 0.9713

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

y = -3.3347x + 8.1146

R² = 0.9906

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

290



y = -14.507x + 8.6033

R² = 0.9977

0

1

2

3

4

5

6

7

8

9

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

Ln (

CO

2)

Time (Hours)

ACH

y = -2.7614x + 7.2088

R² = 0.8893

6.1

6.2

6.3

6.4

6.5

6.6

6.7

6.8

6.9

7

7.1

7.2

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

291



y = -5.9104x + 8.1498

R² = 0.8414

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

y = -3.3983x + 7.94

R² = 0.9279

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

292



y = -11.019x + 8.8204

R² = 0.9436

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25

Ln (

CO

2)

Time (Hours)

ACH

y = -3.4409x + 8.308

R² = 0.9846

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

293



y = -9.122x + 8.4411

R² = 0.9952

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

y = -3.2858x + 8.2802

R² = 0.9606

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

294



y = -6.783x + 8.1766

R² = 0.9976

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3

Ln (

CO

2)

Time (Hours)

ACH

y = -2.9536x + 8.0779

R² = 0.9672

6.4

6.6

6.8

7

7.2

7.4

7.6

7.8

8

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

295



y = -4.9648x + 8.7169

R² = 0.8735

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45

Ln (

CO

2)

Time (Hours)

ACH

y = -2.3444x + 8.395

R² = 0.9481

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

296



y = -3.7151x + 8.2334

R² = 0.9724

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

y = -5.4048x + 8.1921

R² = 0.9645

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35

Ln (

CO

2)

Time (Hours)

ACH

Annexure-II

297



y = -5.9736x + 8.5281

R² = 0.9387

0

1

2

3

4

5

6

7

8

9

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Ln (

CO

2)

Time (Hours)

ACH

y = -3.4555x + 8.3589

R² = 0.9623

0

1

2

3

4

5

6

7

8

9

0 0.1 0.2 0.3 0.4 0.5 0.6

Ln (

CO

2)

Time (Hours)

ACH

Annexure-III

298

Annual Trend of Ambient Air Quality of Lahore

NO NO2 NOx CO SO2 O3 PM2.5

Wind

Speed Wind Dir Temp RH Radiation

Annual Average µg/m³ µg/m³ µg/m³ mg/m³ µg/m³ µg/m³ µg/m³ m/s degrees degC % W/m2

2008 19.92 35.59 55.51 1.23 52.91 47.04 123.28 1.71 177.61 27.52 64.59 188.56

2009 18.35 37.72 56.06 1.48 67.51 49.49 128.76 1.68 204.03 26.25 60.68 173.33

2010 20.52 39.25 59.77 2.33 69.25 59.28 135.88 1.58 214.25 27.86 59.24 185.69

NEQS

(Annual Average) 40 40 - 5 80 130 25

Remarks: Inhalable (Respirable) Dust PM2.5 was found almost 5 times higher than National Environmental Quality Standard

while oxides of Nitrogen (NO2) is also touching the maximum value.

Muhammad Nadeem

Research Assistant

ENVIRONMENTARIAM

ENVIRONMENT PROTECTION DEPARTMENT

GOVERNMENT OF THE PUNJAB

NATIONAL HOCKEY STADIUM, FEROZPUR

ROAD, LAHORE

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INSTALLATION OF INSTRUMENTS AT THE SAMPLING SITES

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assessment of air-borne particulate matter (pm ) and...

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