QUEENSLAND UNIVERSITY OF TECHNOLOGY

DISCIPLINE OF CHEMISTRY

Sohair G. Elbagir

A thesis submitted in partial fulfilment of the requirements for the degree of Master of Applied Science by Research

Approved by __________________________________________________ Chairperson of Supervisory Committee

Characterisation and Source Identification of Selected Pollutants in House Dust

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STATEMENT OF ORIGINAL AUTHORSHIP

The work contained in this thesis has not been previously submitted for a degree or

diploma at any other tertiary educational institution. To the best of my knowledge

and belief, the thesis contains no material previously published or written by another

person except where due reference is made. All photos contained in this thesis were

taken by the author.

Signed------------------------------------------

(Sohair G. Elbagir)

Date: April 2011

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ACKNOWLEDGMENTS

I would like to express my sincere gratitude and warmest appreciation to my

principal supervisor, Associate Professor Godwin A. Ayoko, for providing a wealth

of knowledge, advice, initial and ongoing enthusiasm. He has been a constant source

of help and encouragement. I appreciate his valuable suggestions, insightful

comments, major contributions, helpful advice and adept leadership that made this

challenging study truly successful. I doubt that I will ever be able to convey my

appreciation fully, but I owe him my eternal gratitude.

I wish to express my appreciation to the other members of my committee, Associate

Professor Serge Kokot for the excellent guidance and the assistance he provided at

all levels of the research study, especially in the area of chemometrics.

Many people have been very helpful in supplying information to me without

hesitation. I would like to acknowledge their assistance, and in particular, those

occupants of South-East Queensland for responding to my questionnaires and

providing the house dust samples for analysis.

I would like to acknowledge and thank Patrick Stevens, Leonora Newby, and Chris

Carvalho and the other laboratory technicians of the training and providing technical

advice in the laboratory and instrumental works.

I would also like to thank my family for the patient support, encouragement

throughout my study. Huge thanks go to my kids (Nehal, Mohamed and Waddah),

for being incredibly understanding, supportive, and most of all, passionate patient.

Many thanks to my niece Lemis Yousif for her valuable help. I also gratefully

acknowledge and recognise the support and sincere encouragement of my sisters

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Shadia and Amahl for supporting me, and to my husband Farouk Eltahir and

extended family in Sudan.

Finally, I would like to acknowledge, express my gratitude and sincere thanks to my

past and present colleagues Adrian Friend and Marietjie Mostert at the Faculty of

Science and Technology, Chemistry department for their friendships, exchanges of

knowledge, skills, and vent of frustration during my graduate program, which helped

enrich the experience, and special thanks to Suen Tin Ong for using some of her

house dust samples for pesticides analyses.

Further I would like to thank Dr Anjana Singh and Dr Dana Morgan for helping in

proof writing the thesis.

Without the help, contribution and support of all those mentioned, this study would

not have been produced.

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ABSTRACT

House dust is a heterogeneous matrix, which contains a number of biological

materials and particulate matter gathered from several sources. It is the accumulation

of a number of semi-volatile and non-volatile contaminants. The contaminants are

trapped and preserved. Therefore, house dust can be viewed as an archive of both the

indoor and outdoor air pollution. There is evidence to show that on average, people

tend to stay indoors most of the time and this increases exposure to house dust.

The aims of this investigation were to:

assess the levels of Polycyclic Aromatic Hydrocarbons (PAHs), elements and

pesticides in the indoor environment of the Brisbane area;

identify and characterise the possible sources of elemental constituents

(inorganic elements), PAHs and pesticides by means of Positive Matrix

Factorisation (PMF); and

establish the correlations between the levels of indoor air pollutants (PAHs,

elements and pesticides) with the external and internal characteristics or

attributes of the buildings and indoor activities by means of multivariate data

analysis techniques.

The dust samples were collected during the period of 2005–2007 from homes located

in different suburbs of Brisbane, Ipswich and Toowoomba, in South East

Queensland, Australia. A vacuum cleaner fitted with a paper bag was used as a

sampler for collecting the house dust. A survey questionnaire was filled by the house

residents which contained information about the indoor and outdoor characteristics

of their residences.

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House dust samples were analysed for three different pollutants: Pesticides, Elements

and PAHs. The analyses were carried-out for samples of particle size less than 250

µm. The chemical analyses for both pesticides and PAHs were performed using a

Gas Chromatography Mass Spectrometry (GC-MS), while elemental analysis was

carried-out by using Inductively-Coupled Plasma-Mass Spectroscopy (ICP-MS). The

data was subjected to multivariate data analysis techniques such as multi-criteria

decision-making procedures, Preference Ranking Organisation Method for

Enrichment Evaluations (PROMETHEE), coupled with Geometrical Analysis for

Interactive Aid (GAIA) in order to rank the samples and to examine data display.

This study showed that compared to the results from previous works, which were

carried-out in Australia and overseas, the concentrations of pollutants in house dusts

in Brisbane and the surrounding areas were relatively very high. The results of this

work also showed significant correlations between some of the physical parameters

(types of building material, floor level, distance from industrial areas and major road,

and smoking) and the concentrations of pollutants.

Types of building materials and the age of houses were found to be two of the

primary factors that affect the concentrations of pesticides and elements in house

dust. The concentrations of these two types of pollutant appear to be higher in old

houses (timber houses) than in the brick ones. In contrast, the concentrations of

PAHs were noticed to be higher in brick houses than in the timber ones. Other

factors such as floor level, and distance from the main street and industrial area, also

affected the concentrations of pollutants in the house dust samples.

To apportion the sources and to understand mechanisms of pollutants, Positive

Matrix Factorisation (PMF) receptor model was applied. The results showed that

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there were significant correlations between the degree of concentration of

contaminants in house dust and the physical characteristics of houses, such as the age

and the type of the house, the distance from the main road and industrial areas, and

smoking. Sources of pollutants were identified. For PAHs, the sources were cooking

activities, vehicle emissions, smoking, oil fumes, natural gas combustion and traces

of diesel exhaust emissions; for pesticides the sources were application of pesticides

for controlling termites in buildings and fences, treating indoor furniture and in

gardens for controlling pests attacking horticultural and ornamental plants; for

elements the sources were soil, cooking, smoking, paints, pesticides, combustion of

motor fuels, residual fuel oil, motor vehicle emissions, wearing down of brake

linings and industrial activities.

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TABLE OF CONTENTS

STATEMENT OF ORIGINAL AUTHORSHIP I 

ACKNOWLEDGMENTS II 

ABSTRACT IV 

TABLE OF CONTENTS VII 

LIST OF FIGURES X 

LIST OF TABLES XII 

LIST OF APPENDICES XIV 

ABBREVIATIONS XV 

CHAPTER 1: INTRODUCTION 1 

1.1  Background 1 

1.2  Indoor Air Pollutants 2 

1.3  House Dust 4 

1.4  Elemental Composition of House Dust 5 

1.5  Polycyclic Aromatic Hydrocarbons (PAHs) Composition of House Dust 6 

1.6  Pesticides 7 

1.7  Chemometrics 8 

1.8  Project Aims and Objectives 9 

CHAPTER 2: LITERATURE REVIEW 10 

2.1  Indoor Air Quality (IAQ) 10 2.1.1  Sources of Indoor Air Pollutants 10 2.1.2  Indoor Air Quality Concerns 12 

2.2  House Dust 13 2.2.1  Environmental Pollutants in House Dust 13 2.2.2  Health Hazards 15 

2.3  Elemental Composition of House Dust 16 2.3.1  Elemental Sources of House Dust 16 2.3.2  Environmental Hazards of Elements 19 

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2.4  Polycyclic Aromatic Hydrocarbons (PAHs) 21 2.4.1  PAHs Sources: 21 2.4.2  Health Effects 24 

2.5  House Dust Pesticides 25 2.5.1  Definition, Classification and Sources 25 2.5.2  Environmental Hazards of Pesticides 26 

2.6  Chemometrics and its Application in Environmental Studies 29 2.6.1  Applications of Chemometrics Methods in Environmental Studies 31 

CHAPTER 3: MATERIALS AND METHODS 34 

3.1  Sampling Sites 34 3.1.1  Area (Site Description) 34 3.1.2  Sample Site Profile and Parameters 36 

3.2  Pre-treatment of House Dust Samples 47 3.2.1  Sample Collection 47 3.2.2  Sample Preparation 47 

3.2.2.1  Preparation of Sub-samples 47 3.2.2.2  Digestion and Extraction Procedures 48 

3.2.2.2.1  Microwave-assisted extraction (MAE) versus Soxhlet extraction 48 

3.3  Elemental Composition Analysis 49 3.3.1  Extraction Procedure 49 

3.3.1.1  QA/QC: 50 3.3.2  Chemical Analysis (Elemental Analysis) 50 

3.3.2.1  ICP-MS 50 3.3.2.2  Limits of detection for ICP-MS of Elements 52 

3.4  Polycyclic Aromatic Hydrocarbon (PAH) Composition Analysis 55 3.4.1  Extraction Procedure 55 

3.4.1.1  Clean-up of the extracts: 56 3.4.1.2  Quality Assurance (QA)/Quality Control (QC) 56 

3.4.2  Chemical Analysis 57 3.4.2.1  Gas Chromatography Mass Spectrometry (GC-MS) 57 3.4.2.2  Limits of Detection for GC-MS of PAHs 59 

3.5  Pesticides Composition Analysis 61 3.5.1  Extraction Procedure 61 

3.5.1.1  Clean-up of the extracts: 61 3.5.1.2  QA/QC 61 

3.5.2  Chemical Analysis 62 3.5.2.1  GC-MS 62 3.5.2.2  Limits of Detection for GC-MS of Pesticides 64 

3.6  Multivariate Data Analyses 65 3.6.1  Data Pre-treatment: 66 3.6.2  Principal Component Analysis (PCA) 67 3.6.3  Multi-Criteria Decision Making Method (MCDM) 68 3.6.4  Source apportionment - Positive Matrix Factorisation (PMF) 71 

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CHAPTER 4: RESULTS AND DISCUSSION 73 

4.1  PAHs Pollutants 73 4.1.1  Concentrations 73 4.1.2  Comparison of PAHs detected in house dust samples from this study and

other countries 76 4.1.3  Principal Component Analysis (PCA) 76 4.1.4  PROMETHEE and GAIA Analyses of PAHs 80 4.1.5  Positive Matrix Factorisation (PMF) 85 

4.2  Pesticide Pollutants 88 4.2.1  Soxhlet extraction vs. Microwave digestion extraction 88 4.2.2  Concentrations 89 4.2.3  Comparison of pesticides detected in house dust from different places in

the world 91 4.2.4  Principal Components Analysis (PCA) 93 4.2.5  MCDM analysis of the pesticides data 96 4.2.6  Positive Matrix Factorisation (PMF) 100 

4.3  Elements Pollutants: 103 4.3.1  Concentrations 103 4.3.2  Comparison of elements detected in house dust samples collected from

different countries 104 4.3.3  Principal Components Analysis (PCA) 106 4.3.4  Multi-Criteria Decision Making (MCDM) Analyses of Elemental

Composition 110 4.3.5  Positive Matrix Factorisation (PMF) 114 

CHAPTER 5: SUMMARY AND CONCLUSIONS 118 

5.1  Building Materials 119 

5.2  Floor level 119 

5.3  Age of the house 119 

5.4  Distance from main street activity and industrial area 119 

5.5  Smoking and cooking activities 120 

CHAPTER 6: FUTURE WORK 121 

CHAPTER 7: REFERENCES: 122 

CHAPTER 8: APPENDICES 134 

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LIST OF FIGURES

Figure 1: A map of the Brisbane suburbs where dust samples were taken ................ 37 

Figure 2: A map of the Brisbane suburbs where dust samples were taken ................ 38 

Figure 3: Retsch mechanical shaker with stacked sieves ........................................... 48 

Figure 4: Chemical structures of the PAHs and 2-bromonaphthelene ....................... 58 

Figure 5: Names and chemical structures of compounds in the EPA Pesticide Mix Standard ............................................................................................ 63 

Figure 6: Concentrations of PAHs (µg/kg) in the house dust samples ...................... 74 

Figure 7: PCA scores plot showing correlations between the parameters ................. 77 

Figure 8: PCA loadings plot of PC1 vs. PC2 showing correlations between the PAHs and the physical characteristics of the houses ................................ 78 

Figure 9: GAIA analysis of house dust samples showing correlations between the objects (houses, ▲) and the pi decision axis (●) ................................ 83 

Figure 10: GAIA analysis showing correlations between the PAHs (■), the

house characteristics (●) and the pi decision axis (●) .............................. 83 

Figure 11: Observed vs. calculated PAHs concentrations in the house dust samples ..................................................................................................... 85 

Figure 12: Source profile for PAH factors ................................................................. 87 

Figure 13: Concentrations of pesticides (µg/kg) in the house dust samples .............. 90 

Figure 14: PCA scores plot showing correlations between the parameters ............... 94 

Figure 15: PCA loadings plot of PC1vs PC2 showing correlations between the pesticides and the physical characters of houses ...................................... 94 

Figure 16: GAIA analysis of pesticides in house dust samples showing correlations between the objects (houses) and the pi decision axis (●) ............................................................................................................. 98 

Figure 17: GAIA loading plot showing the correlations between the pesticides (■), the house characteristics (■) and the pi decision axis (●) ................. 98 

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Figure 18: Observed vs. calculated values of pesticides in the house dust samples ................................................................................................... 100 

Figure 19: Source profile for the factors .................................................................. 102 

Figure 20: Concentrations of the elements (µg/kg) in house dust samples ............. 104 

Figure 21: PCA loadings plot of elements showing correlations between parameters ............................................................................................... 107 

Figure 22: PCA loadings plot of PC1vs PC2 showing correlations between the elements and the physical parameters of the houses .............................. 108 

Figure 23: GAIA analysis of elements in house dust samples showing correlations between the objects (houses) (▲) and the pi decision axis (●) .................................................................................................... 111 

Figure 24: GAIA loadings plot showing the correlations between the elements (■), the house characteristics (▼) and the pi decision axis (●) .............. 113 

Figure 25: Observed vs. calculated values of elements in the house dust samples ................................................................................................... 115 

Figure 26: Source profile for the factors for elements ............................................. 117 

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LIST OF TABLES

Table 1: Summary of the locations of the sampling sites .......................................... 40 

Table 2: Information collected via questionnaires on house characteristics .............. 42 

Table 3: Summary of the anthropogenic characteristics at the sampling sites .......... 44 

Table 4: Operation parameters used for the microwave digestion of the house dust samples for the determination of their elemental compositions ....... 49 

Table 5: ICP-MS Instrumental parameters and conditions ........................................ 51 

Table 6: Limits of detection and quantification of elements analysed by ICP-MS ............................................................................................................ 53 

Table 7: Percent recovery of the elements by using laboratory reference standards ................................................................................................... 54 

Table 8: Percent recovery by using NIST Certified Reference Standards ................. 55 

Table 9: Microwave digestion operating parameters for the analysis of PAHs ......... 56 

Table 10: Limits of detection and quantification of the PAHs analysed by GC-MS ...................................................................................................... 59 

Table 11: Percent recovery for the PAHs by using Reference standards .................. 60 

Table 12: Percent recovery with the use of NIST Certified Reference Standards ..... 60 

Table 13: Limits of detection for GC-MS analysis of the pesticides ......................... 64 

Table 14: Percent recovery of the pesticides by using Reference standards .............. 65 

Table 15: Percent recovery by using certified reference materials ............................ 65 

Table 16: Concentrations of PAHs (µg/g) in house dust samples ............................. 74 

Table 17: Comparison of PAHs (µg/g) detected in house dust samples from this study and other countries ................................................................... 75 

Table 18: The PROMITHEE ranking of objects (houses) based on the concentrations of PAHs ............................................................................ 81 

Table 19: Concentrations of pesticides (µg/g) in house dust samples extracted by Soxhlet extraction procedure ............................................................... 88 

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Table 20: Concentrations of pesticides (µg/g) in house dust samples extracted by Microwave assisted extraction procedure ............................................ 89 

Table 21: Concentrations of pesticides (µg/kg) in the house dust samples ............... 90 

Table 22: Comparison of PAHs (µg/g) detected in house dust samples from this study and other countries ................................................................... 92 

Table 23: The PROMETHEE ranking of objects (houses) based on the concentrations of pesticides ...................................................................... 97 

Table 24: Concentrations of elements (µg/g) in the house dust samples ................. 103 

Table 25: Comparison of elements (µg/g) detected in house dust from this study and other countries ........................................................................ 105 

Table 26: The PROMETHEE ranking of objects (houses) based on the concentrations of elements ..................................................................... 114 

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LIST OF APPENDICES

Appendix 1: Survey questionnaire (information on the outdoor and indoor characteristics of residences.) ................................................................. 134 

Appendix 2: Results of PAHs (all results in ng/kg) ................................................. 137 

Appendix 3 Results of pesticides (all results in ng/kg) ............................................ 139 

Appendix 4: Results of elemental analysis (all results in ng/kg) ............................. 142 

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ABBREVIATIONS

In this document the following abbreviations are used to signify the respective terms:

α-BHC: alpha-Lindane

ACE: Acenaphthene

ACY: Acenaphthylene

ANT: Anthracene

AQI: Air Quality Index

AQS: Air Quality System

β-BHC: beta-Lindane

BAA: Benzo (a) anthracene

BAP: Benzo (a) pyrene

BBF: Benzo (b) fluoranethene

BGP: Benzo (g, h, i) perylene

CFCs: Chlorofluorocarbons

CHR: Chrysene

CPF: Conditional Probability Function

δ-BHC: delta-Lindane

DDD: Dichlorodiphenyldichloroethane

DDE: Dicholrodiphenyldichloroethylene

DDT: Dichlorodiphenyltrichloroethane

DIE: Dieldrin

End: Endosulfan

Ends: Endosulfan sulphate

End I: Endosulfan I (ALPHA)

End II: Endosulfan II (BETA)

EN: Endrin

EPA: Environmental Protection Agency

FLT: Fluoranethene

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FLU: Fluorene

HC: Heptachlor

HCE: Heptachlor epoxide

IAQ: Indoor Air Quality

IAP: Indoor Air Pollution

IEQ: Indoor Environmental Quality

IND: Indeno (1, 2, 3-cd) pyrene

HAPs: Hazardous Air Pollutants

MAE: Microwave-assisted Extraction

MCDM: Multi-Criteria Decision Making

MET: Methoxychlor

NAAQS: National Ambient Air Quality Standards

NAMS: National Air Monitoring Stations

NAP: Naphthalene

PACs: Polycyclic Aromatic Compounds

PAHs: Polycyclic Aromatic Hydrocarbons

PCA Principal Component Analysis

PHE: Phenanthrene

PM: Particulate Matter

PM10: Particulate Matter of 10 µm in diameter or less aerodynamic diameter

PM2.5: Particulate Matter of 2.5 µm in diameter or less aerodynamic diameter

PMF: Positive Matrix Factorisation

PYR: Pyrene

QDERM: Queensland Department of Environment and Resource Management

SBS: Sick Building Syndromes

SHD: Settled House Dust

VOCs: Volatile Organic Compounds

TSP: Total Suspended Particles

1

CHAPTER 1: INTRODUCTION

1.1 BACKGROUND

In Australia today, people highly value clean air. They are deeply concerned about

air pollution, which may drastically affect their health, development and lifestyle. If

one examines the area of South-East Queensland (SEQ), where the number of

residents is not as high as in other areas, and where there are not as many industrial

sites, pollution tends to be low—this has been the case for many years. However, the

situation could change as more people migrate to the region, thus creating

commercial and economic activities. This, in addition to the abundance of sunshine

and prevailing wind patterns, could pose significant danger of toxic air pollution such

as photochemical smog in the future. The need could also arise for environmental

strategies to be organised in order to deal with other potentially dangerous sources of

air pollution such as bushfires and large-scale burning off events in agricultural

areas [1-3].

Indoor air quality is of particular concern as Australians spend over 80% of their

time inside and the quality of air in their homes is of particular worry for them [4].

Yet, it is not easy to carry out assessment of these problems as there is insufficient

information about the scale and origin of indoor pollutants and their effects on

people’s health. Currently, little consideration is given to the quality of indoor air as

part of a full building appraisal by the community at large. Indoor air pollution

whether it is in offices or residential spaces potentially has a major impact on

people’s health [5]. Several studies [5-7] have demonstrated that for individuals and

the community as a whole, the environment inside homes can be a real source of

passive or active exposure to toxins; the elderly, sick and children are particularly at

2

risk. Many types of toxins can infiltrate buildings and reside for varying periods of

time in places where they are easily inhaled by anyone inside these structures.

Pollutants can come from multiple sources. Exposure to these toxins may manifest as

serious issues of health concern depending on the level and duration of exposure [8].

1.2 INDOOR AIR POLLUTANTS

Indoor Air Quality (IAQ) refers to the quality of the air inside buildings, which

affects the health or wellbeing of the residents. Agencies that deal with issues of

health and the environment such as Environment Protection Agencies (EPA) [5] are

concerned about air pollution in homes and associated health problems.

Indoor Air Pollution (IAP) is considered to be the primary cause of many diseases

around the world and a major environmental hazard [9]. Pollution occurs as a result

of the introduction of pollutants in various forms and ways. For example, toxic

contamination can occur in the form of: air pollution (e.g. carbon monoxide, sulphur

dioxide, chlorofluorocarbons (CFCs), nitrogen oxides, ozone, smog and

hydrocarbons); water pollution; soil contamination (e.g. hydrocarbons, heavy metals,

herbicides, pesticides and chlorinated hydrocarbons); radioactive contamination;

noise pollution; light pollution; visual pollution; and thermal pollution [10]. Air

pollutants in particular are classified into two major categories: hazardous air

pollutants (air toxics) and common contaminants (indicator or criteria air

pollutants) [2, 11]. Hazardous air pollutants include gases, aerosols, heavy metals,

volatile and semi-volatile organic compounds, aldehydes and polycyclic aromatic

hydrocarbons (PAHs). The origins of hazardous air pollutants are mainly related to

activities carried out by the human population such as manufacturing, motor vehicles

and wood heaters. On the other hand, criteria air pollutants are regulated by EPA

3

because of their effect on human health and environment; they include carbon

monoxide, lead, nitrogen dioxide, ozone, particulates and sulphur dioxide.

Biological pollutants are another class of pollutants. They emanate from

microbiological sources such as moulds, skins and remains of animals and humans,

and droppings of pests such as cockroaches. These pollutants can be airborne and can

have a conspicuous effect on the quality of air in buildings.

Pollutants which occur in buildings can include radiation (e.g. radon gas), biological

contaminants (e.g. pesticides, moulds, viruses and dust mites), chemical

contaminants (e.g. pesticides, metals and flame retardants), combustion products

(e.g. environmental tobacco, smoke, carbon monoxide and nitrogen dioxide) and

others [12]. Many of the contaminants are absorbed by particulate matter and are

later incorporated into settled house dust. Studies which looked into exposure to

toxic contaminants in settled house dust (SHD) have often focused on lead [13-17]

and pesticides [18-23]; however, combustion products such as PAHs have also been

found in house dust, and these substances may pose added health concerns [8, 24-

34]. In addition, several of these compounds have been classified as mutagens and

possible or probable human carcinogens [35].

Research has shown that the concentration of pollutants is significantly higher in the

indoor environment than the outdoor environment [10]. Significant pollutants in the

indoor environment such as formaldehyde and other Volatile Organic Compounds

(VOCs) are repeatedly found to be higher indoors than outdoors, while in some

areas, indoor particulate concentration can be twice that measured outdoors [10, 36].

The possible health risks that are linked to the exposure of air toxics are well

researched and documented [2]. Thus, there is a growing recognition of the need to

4

reduce those risks. In an attempt to identify and document the possible health

problems arising from exposure to air toxins, Australia has set benchmarks for 5

prioritised types of air toxins in ambient air: benzene, toluene, xylenes, formaldehyde

and PAHs [37].

Several factors are found to influence the level of air pollution build up in houses.

These include the presence of indoor sources of pollutants, the quality of air outside

the houses which affect the indoor air quality through the air exchange. The air

exchange rate varies with climatic conditions, lifestyle and how buildings are

designed. The effect of the outdoor air on indoor air quality could probably be very

significant in subtropical cities such as Brisbane where windows were opened widely

during most months of the year [38].

As mentioned earlier, Australians spend most of their time in different indoor

environments [2, 39]; however, relatively limited research has been performed on the

quality of indoor air in Australian residences [1, 2]. Therefore, a study such as the

one described in this thesis is highly desirable, particularly because of the links

between indoor quality of air and the emotional and mental wellbeing of

residents [40].

1.3 HOUSE DUST

The advantage of using house dust in measuring the degree of contamination of

indoor air is that we can measure the past and the present pollutants in contrast to the

air sample, which gives only the current pollutants. House dust is a receptor of and

repository for chemicals in the home. Thus, it is a potentially significant source of

exposure especially for small children and babies who spend many hours of their

5

time on floors and engage in regular activities, which involve touching and tasting

things that lie on the floor [24].

House dust comprises a mixture of minerals, metals and heavy metals, particles from

combustion processes (including tobacco smoke and PAHs), organic material from

various biological sources (e.g. human skin scales, animal allergens, microbial

growth and pollen) and many chemical substances which have been established as

dangerous to health (e.g. PAHs and pesticides). After pollutants are absorbed onto

house dust particles, they either do not degrade or degrade at slower relative rates

than their ambient counterparts. House dusts are therefore useful reservoirs for

chronic exposure to pollutants which reside indoors [41-44]. In addition, house dust

is a source of particles, which may be suspended or cause dermal exposure [45].

Inhalation, dermal absorption and ingestion of house dust have been recognised as

significant exposure pathways for organic contaminants—especially in the case of

crawling children. Therefore, analysis of organic contaminants in house dust should

be performed in an effort to characterise human exposure in the domestic

environment [46].

In the present study, experiments have been conducted to investigate the levels of

chemical contaminants present in house dust samples taken from urban and suburban

residential houses in and around Brisbane.

1.4 ELEMENTAL COMPOSITION OF HOUSE DUST

In industrial societies around the world, heavy metal contamination of the

environment is everywhere. Most such cases are anthropogenic in origin except

where natural and bedrock sources are involved [47].

6

Many heavy metals have chronic effects on humans; therefore, they are potential

environmental health hazards particularly to young children, who spend the majority

of their time playing in their homes, and accidentally ingest metals through their

hand-to-mouth action. A great deal of attention has been paid to the study of metal

pollution in city air, roadside dusts and soils; however, there is a lack of concern for

the presence of trace metals in house dust. Therefore, in order to reduce overall

exposure to metals it is important to determine the concentration of heavy metals in

floor dusts in the home environment.

1.5 POLYCYCLIC AROMATIC HYDROCARBONS (PAHS)

COMPOSITION OF HOUSE DUST

PAHs are usually introduced into the environment by both naturally-occurring

combustion processes, such as forest fires, and manmade combustion processes. In

the past three decades, studies have been carried out to profile and identify the levels

of PAHs in various environmental matrices. Such studies are driven in part by the

suspected carcinogenic, mutagenic and other adverse health effects of PAHs [44].

Incomplete combustion of organic matter releases PAHs into the atmosphere as a

complex mixture of compounds. Wood-burning heaters, agricultural waste burning,

motor vehicle exhaust, cigarette smoke, asphalt road and roofing operations are all

sources of PAHs. Although hundreds of PAHs have been identified in atmospheric

particles [48], toxicological endpoint and/or exposure data are available for only

33 PAHs.

PAHs in the indoor environments originate from tobacco smoke, smoke from heating

(burning wood or oil), char grilled food and cooking foods (grilled, barbecued,

smoked or charbroiled meat), roasted coffee and peanuts, refined vegetable oils and

7

any food grown in PAH-contaminated soil (such as near a hazardous waste site),

candle burning, and incense burning [5].

1.6 PESTICIDES

Pesticides and herbicides are substances used in indoor and outdoor environments to

control and eradicate pests such as insects, rats and weeds. They are used indoors

primarily to control cockroaches, flies, rodents and fleas in pets, and outdoors mainly

to control weeds and to prevent the entry of insects. Apart from common pesticides,

there are a wide range of pesticides that are specifically used to control termites.

Such pesticides are used indoors and outdoors at residences, office buildings,

schools, hospitals, nursing homes and other public buildings to protect the buildings

and the residents [40]. The repeated use of large quantities of pesticides increase the

level of contamination of indoor air [40, 49].

This study is focused on organochlorine pesticides (OCs) because they were

commonly used in Australia’s past to protect plants, farm animals, buildings and

households from the damaging effect of insects. Many of them have subsequently

been removed from the market because of their effect on human health and the

environment. However, these compounds are very persistent in the environment and

are thus still agricultural contaminants. OCs and pyrethroids are the two most widely

used pesticides and have been recognised as carcinogenic compounds—they are also

endocrine disrupters in a number of organisms because of their toxicity [50].

Pesticides are very dangerous and threatening to public health because they are

environmentally persistent and do not degrade readily. Thus, they can be continually

released and leaked into the air from either a treated or storage area, or from the soil

and sediments [51].

8

1.7 CHEMOMETRICS

Chemometrics can be defined as the application of statistical and mathematical

methods to chemical data to permit maximal collection and extraction of useful

information [52]. Massart et al. [53] defined chemometrics as “a chemical discipline

that uses mathematics, statistics, and formal logic: (a) to design or select optimal

experimental procedures; (b) to provide maximum relevant chemical information by

analysing chemical data; and (c) to obtain knowledge about chemical systems.”

Chemometrics has been evolving as a sub-discipline of chemistry for over 30 years

as the need for advanced statistical and mathematical methods has increased with the

increasing sophistication of chemical instrumentation and processes [52]. There has

continued to be the concurrent improvement of new analytical instruments that have

produced data that demanded new and more useful data analysis methods while the

increasing facility of personal computers has permitted more computationally

intensive calculations to be performed without the need for access to

supercomputers. This combination of developments has opened more options to

improve analytical methods. Thus, over the intervening years, chemometrics has

developed a significant role within analytical chemistry including the incorporation

into the operating systems of a number of commercial analytical instruments [52].

Chemometric methods are used for the classification and comparison of different

samples to show the interrelationships between different variables, and help in the

identification of the sources of pollutants in the environment. Chemometric methods

are also useful where large volumes of information can be processed in order to

generate and recognise relationships between variables [54].

9

In relation to this work, the data collected is multivariate and therefore, difficult and

cumbersome to present and interpret by conventional means. However,

chemometrics methods such as Principle Component Analysis (PCA), Multi-

Decision Criteria Making MCDM (e.g. PROMETHEE and GAIA) and the Positive

Matrix Factorisation (PMF) procedure will allow data display correlation, ranking of

objects and source apportionment. This will enable detailed analysis and

interpretation of the PAHs, pesticides and elements found in the house dust to be

performed.

1.8 PROJECT AIMS AND OBJECTIVES

Since settled dust is a sink and repository of indoor air pollution, the primary aim of

this study is to examine the chemical composition of settled house dust in Brisbane

and its environments.

In short, the objectives of this study were to:

assess the levels of polycyclic aromatic hydrocarbons (PAHs), elements and

pesticides in the indoor environment of the Brisbane area;

identify and characterise the possible sources of elemental constituents

(inorganic elements), PAHs and pesticides by means of Positive Matrix

Factorisation (PMF); and

establish the correlations between the levels of indoor air pollutants (PAHs,

elements and pesticides) with the external and internal characteristics or

attributes of the buildings and indoor activities by means of multivariate data

analysis techniques.

10

CHAPTER 2: LITERATURE REVIEW

2.1 INDOOR AIR QUALITY (IAQ)

2.1.1 Sources of Indoor Air Pollutants

The atmosphere is a layer of gas that envelops the earth, it is maintained by

gravitational force and is essential in the sustenance of earth’s ecological

system [55]. Pollution occurs in the atmosphere by the excessive ejection of

substances from one or more sources into the natural environment. Such

contamination can occur both inside and outside buildings and has been an

increasing source of concern because of their real and potential health and

environmental effects [2].

Pollution can occur on the land, air and water. Air pollution in particular is a broad

term that describes the presence of contaminants including biological agents,

chemical agents and particulate matter in the natural ecosystem. Several pollutants

present in the air are categorised as criteria air pollutants, air toxics and biological

pollutants, where the term “criteria air pollutants” describes such pollutants as carbon

monoxide, lead, nitrogen oxide, ozone, particulates and sulphur dioxide [2]. These

pollutants are described as “criteria air pollutants” not only because their presence is

associated with adverse environmental and health effects but also because their

presence in the environment is regulated by established limits [2].

Air toxics can be emitted from natural sources such as bush fires, volcanoes and dust,

or anthropogenic sources. They may arise from point sources (for example, large

emitters such as factories and industrial sites) and non-point sources (such as motor

vehicles and solid fuel combustion) [2]. Their presence in the atmosphere can have

detrimental effects on biological processes and organisms.

11

Air pollutants can also be categorised as primary and secondary pollutants. Primary

pollutants are chemicals, which are directly emitted into the atmosphere whereas

secondary pollutants are produced in the environment and may take some time

before they are detected. Examples of primary pollutants include biological

substances, or chemical substances produced by fossil fuel combustors such as motor

vehicles and industrial processes. Photochemical smog on the other hand, is a well-

known secondary pollutant, which is formed by the interaction of foreign substances

in the natural environment [11].

Another approach is to categorise pollutants as: physical, chemical (organic and

inorganic) and biological [56], or according to sources as: anthropogenic (if they

arise from human activities) or natural (if they arise from natural processes). There

are a number of biologically important pollutants such as dust mites, houseplants,

mould, pest droppings and fine dust. These pollutants tend to be present at higher

concentrations in the indoor rather than outdoor environment because they have

significant indoor sources or do not degrade rapidly in the indoor environment. For

example, Ando et al. [57] found suspended particle matter and PAHs to be present at

higher concentrations in indoor than in outdoor environments in super cities studies

in Japan and China. In some areas indoor particulate concentrations can in fact be

twice those measured outdoors [58]. Similarly, many other studies have noted that

the concentrations of many pollutants are generally higher indoors than outdoors [2,

5, 59].

The concentration of the pollutants in indoor environments are greatly influenced by

factors such as rates of ventilation, location of the indoor environment, nature of the

pollutants present, and the surrounding climate. Indoor air quality is also largely

modified by the amount of outdoor air entering the indoor environment, and the

12

quality of the outdoor air. In the urban area, indoor air quality is strongly influenced

by emissions from motor vehicles and industrial facilities, which may occur close to

or even far away from a particular indoor location. Additionally, indoor air quality is

significantly influenced by the rate of outdoor/indoor air exchange which varies

according to prevailing climate, lifestyle of occupants and building design.

2.1.2 Indoor Air Quality Concerns

According to Bijlsma [60], 15–24 year olds spend 68% of their time indoors while

over 65s spend 90% of their time indoors. Although the adverse effects of long term

exposure to inadequate air quality are not completely understood because of

physiological and behavioural reasons, children are thought to be the most

susceptible group to health problems associated with poor indoor air quality. Little is

known about many of the chemicals present in indoor air. However, it is known that

many people exhibit a wide range of reactions to the chemicals they are exposed to.

Neurotoxin symptoms, hypersensitivity and skin irritations are amongst the common

problems that could occur after prolonged exposure to harmful pollutants [61]. It was

also evident that indoor air pollution was a factor in cases of lead poisoning,

leukaemia and allergies found in children [42]. VOCs contribute to the formation of

ozone and photochemical oxidants, which can lead to health complications such as

asthma [62]. Additionally, a number of organophosphate compounds have been

found to be neuro- and geno-toxic [5]. Long periodic exposure to chemicals such as

formaldehyde and asbestos indoors can cause health complications such as cancer

and respiratory problems [5].

A number of chemicals can be found in settled house dust (SHD). However, the

factors responsible for the toxicity and mutagenicity of SHD are uncertain [44]. Of

13

the chemicals present in SHD, PAHs and polycyclic aromatic compounds (PACs) are

thought to be the cause of some of the mutagenicity of SHD [44].

In general, air pollution is a growing source of public concern in Australia because of

the increase of public awareness about the link between airborne pollutants and

health complications. Certain pollutants existing in the atmosphere have adverse

effects on human health and locally have been a major source of concern for the

Queensland Department of Environment and Resource Management (QDERM).

2.2 HOUSE DUST

2.2.1 Environmental Pollutants in House Dust

House dust is the accumulation of a number of semi-volatile and non-volatile

contaminants. These are trapped and preserved in house dust and it can be viewed as

an archive of both the indoor and outdoor air pollution [63, 64]. House dust is also a

heterogeneous matrix which contains a number of biological materials and

particulate matter gathered from several sources. There is evidence to show that on

average, people predominately stay in one indoor environment or another, which

increases exposure to house dust [5, 24, 34, 65, 66].

The cocktail of materials that has been observed in house dust samples include:

flame retardants, heavy metals, pesticides, smoke residues, PAHs, plasticisers and

asbestos [67]. These materials are usually sourced from domestic and commercial

product use, combustion fugitive emissions, automobiles and biological

decomposition. Some of the materials accumulated in house dust can persist in the

indoor environment due to the lack of ventilation and sunlight that is experienced in

outdoor settings. These particles which are present in the house dust can then lay

dormant with little to no degradation because of the lack of disruption [40, 66].

14

House dust is therefore an ideal collection of pollutants from both outside and inside

environments. Thus, studies of house dusts can help in understanding the effects of

certain particles that humans are exposed to [24, 64, 68].

Factors such as the construction, furniture, flooring, heating and location of a

building will influence the type of house dust that is accumulated [69]. The size and

composition of the house dust also influences the adsorption of chemical

contaminants to the particulate materials. The particulate composition of house dust

can also vary depending on the building itself or the season of the year. House dust

can contain a number of toxicants that provide information about the indoor and

outdoor sources of the pollutants as well as loadings of the indoor and outdoor

environments [8].

Lewis et al. [34] collected house dust samples from residential homes which were

obtained by a commercial cleaning service in North Carolina and were tested for 28

pesticides and 10 PAHs. The results concluded that 14 pesticides and 10 PAHs were

detected, while the highest pesticide residue found was the synthetic pyrethroids, cis-

and trans-permethrin.

While atmospheric dust and attic dust can contribute to house dust, it must be noted

that the term house dust used in this study refers to settled house dust. Atmospheric

dust, on the other hand, can be sourced from a number of natural processes such as

forest fires, wind erosion, combustion, agricultural activities, construction activities

and volcanic residues. Atmospheric dust comprises of molecular particles which

contain a number of heavy metals, organic and inorganic compounds, biogenic

materials and acids, which contribute to house dust contamination as it infiltrates into

buildings and residences and settles indoors. Attic dust usually remains undisturbed

15

for long periods which increases the amount of dust accumulated as well as the

contaminants contained in the dust.

2.2.2 Health Hazards

Household dust has been shown to be a significant danger to human health by the US

EPA [44]. Indoor toxicant and pollutant exposure may be largely attributed to contact

with house dust as a number of pollutants found in house dust, including banned

compounds, pose significant risks to public health [24]. Exposure to house pollutants

may be contacted by inhalation of house dust re-suspended into the indoor air, direct

skin interaction or ingestion. These forms of exposure have been linked to

occurrences of developmental abnormalities, childhood leukaemia and attention

disorders in children and infants [8]. In addition, there are significant links between

human exposure to heavy metals and contact with house dust, which contains metals

such as cadmium and lead. High levels of exposure to such pollutants have been

found to lead to adverse health effects. Exposure to lead, in particular, has been

linked to occurrences of developmental disorders as well as disturbances to the

hematopoietic, renal and neurological systems [13].

House dust gathers most considerably in carpets, which can leave children and

toddlers especially susceptible of high levels of exposure to the pollutants [34].

Contact with house dust in the carpet either directly or through re-suspension in air,

is largely dependent on the particle size. The distribution of the pesticides and PAHs

present in house dust is also dependent on the particle size.

Total lead exposure to infants and children may be greatly attributed to contact with

house dust, as opposed to exposure to soil in walkways and other outdoor

environments. Studies conducted using US EPA measures have established that

16

PAHs, heavy metals and other pollutants are also carried on clothes. In one study,

transfer of dust from outdoors has been posited as a major source of PAHs in house

dust [6].

Consumer products containing chemical additives also make a significant

contribution to indoor pollution and are suggested to be a serious environmental

risk [24, 70]. Potentially harmful chemical additives like thymol and quarternary

ammonium compounds are present in cleaning products [71] and this may heighten

the risk of dangerous levels of exposure to such chemicals and contamination of

indoor environments [68].

2.3 ELEMENTAL COMPOSITION OF HOUSE DUST

2.3.1 Elemental Sources of House Dust

House dust has been found to contain considerably higher levels of pollutants such as

lead, cadmium, mercury and antimony than samples collected from garden soil and

street dust. However, the concentration of elements varied greatly depending on the

location and the characteristics of the residences. Construction of the buildings and

the frequency of the use of each room also impacted on the amount of dust found as

well as the toxicants detected [47].

Studies on the sources of metals in indoor environments have shown that there are

direct correlations between the levels of indoor and outdoor environments [72, 73].

Urban air, street dust, motor vehicles, soil re-suspension and attic dust are amongst

the many sources of the metals found in the indoor environments. House dust has

been shown to contain elements such as As, Au, C, Cd, Co, Cu, Pb, Sb and Zn [5]

that originate from different sources inside residences. The fact that the levels of

17

heavy metals varied from Cd > Zn, Pb > Mn, Cu > Fe is indicative of the differences

in the availability and mobility of the detected elements [6].

Several studies on the elemental composition of house dust have been reported in the

literature. Akhter and Ismail [74] collected samples from streets and residential

homes in Bahrain; the samples were analysed for the elements Cd, Cr, Ni, Pb and Zn.

High levels of metals were detected in the samples and it was determined that motor

vehicles were the likely reason for those results.

Cizdziel and Hodge [63] gathered samples of attic dust and soil from underneath

rocks nearby the houses selected in their southern Nevada and Utah (USA) based

study. They investigated the quantities of substances and elements including As, Ba,

Cd, Co, Cu, Cs, Ga, Li, Mn, Pb, Rb, Sb, Sn, V and Zn. It was determined that the

attic dust contained elements such as Cd, Pb, Sb, Sn and Zn, which were otherwise

found in higher concentration in roads and house dust. Pb was found to be the

element with the highest concentration in the soil samples; it was reasoned that the

age of the house affected the concentration of Pb. Therefore, the study concluded that

attic dust was associated with the dust found in the outdoor and indoor environments

and could be retaining airborne particles.

Davis and Gulson [75] collected ceiling dust samples from several houses in Sydney,

Australia, that were analysed for metal constituents from houses located in industrial,

semi-industrial and non-industrial environments. It was concluded that the particles

collected were primarily of anthropogenic origin with contributions from crustal soil

and plant matter. Cd, Cu, Pb, Sb and Zn were detected in higher concentrations at

industrial locations, relative to those from semi- and non-industrial locations.

18

Samples of house dust in Christchurch, New Zealand were collected by Kim and

Fergusson [76] to investigate the elements Cd, Cu, Pb and Zn. The amounts of the

elements detected were directly linked to the quantity of dust in each house and the

wear of carpets. Pb was primarily found to originate from lead in petrol and paints

whilst the source of Zn was found to be contributed by the materials used for carpet

underlay and galvanised iron roofs. It was also determined that the volume of road

traffic was linked to the amounts of Cu in the samples.

Meyer et al. [13] conducted an investigation in the eastern part of Germany on the

amounts of metals children were exposed to indoors. House dust samples were

analysed for As, Cd and Pb metals. It was concluded that the loading for As was

0.023, Cd was 1.14 and Pb was 0.034 µg/m2 per day. Location was the most notable

influence that determined the elemental content of the samples.

Jabeen et al. [77] studied the quantities of elements Cd, Cu, Pb and Zn in samples of

house dust from selected locations in Jalil Town, Gujranwala, Pakistan. The elements

were assessed and it was determined that most elements detected were sourced from

outdoor environments, while indoor environments also contributed to the

contamination of houses, which were not well ventilated.

Indoor house dust, garden soil and street dust samples from Ottawa, Canada were

gathered by Rasmussen et al. [47] to assess the relationship between the sample

sources. It was found that house dust had higher concentrations of many of the

detected elements, whilst garden soil had higher amounts of Al, Ba and Tl.

House dust samples were collected and analysed by Chattopadhyay et al. [78] at

several locations in Sydney, Australia. It was concluded that factors such as

household income, house type and the number of residences did not significantly

19

alter the amounts of most of the metals found in the samples. However, Zn and Fe

were found in higher concentrations in houses with a maximum of 2 occupants while

higher quantities of Mn were linked to households with low-incomes.

It was established by Hogervorst et al. [79] that the exposure of adults to heavy

metals was potentially due to their contact with house dust in areas with a high

concentration of Cd in soil.

Meyer et al. [13] investigated the levels of Pb and Cd in house dust samples taken

from the Hettstedt, Bitterfeld and Zerbst areas of eastern Germany. Factors such as

location, urban environments, ventilation, and types of heating and building

characteristics were found to be significant for the amount of Pb and Cd detected.

Outdoor environments played a major role in locations where metal concentration

was high; whilst in locations where the contamination was low, the indoor

environments were significant contributors to the levels of heavy metals.

2.3.2 Environmental Hazards of Elements

Dust is a pollutant and can contain unexpected metals at unexpectedly high levels.

Thus, house dust can be a useful indicator of the amount of metals in indoor and

outdoor environments. It has been well established that house dust can demonstrate

links between the quantities of certain pollutants in the dust and the quality of the

surrounding environment. The amount of heavy metals in samples from houses were

assessed by Tong and Lam [80] to determine the effect the surroundings had on the

residential environment. It was concluded that location, time of construction and

characteristics of the outdoor environments seemed to be more significant to the

quality of the indoor environment than the wealth of the residents. The main source

20

of the elements was found to be anthropogenic activities, as opposed to the small

amounts released naturally.

Much research has been conducted to determine how harmful metals are to human

beings if ingested orally or inhaled. Although it is well established that metals can

cause complications to the human body, it is still uncertain what the exact health

implications are of long exposure to high amounts of trace elements [80]. Metal

pollutants are harmful to young children and infants [80] and can cause health

complications for adults as house dust is a major repository of such elements.

Heavy metals are always found in house dust and can remain for a long time because

they do not degrade easily. They can be hazardous to animals and vegetation [80].

Metals may also be harmful to humans by being accumulated in the fatty tissue or

circulatory system, and this can cause serious medical complications [81]. Cadmium

and lead are highly toxic and exposure to these metals can cause them to build up in

the human body.

Dust particles are contaminants that accumulate within the urban environment [82,

83]. It has been found that house dust contains trace metals that can affect young

children; the effects of exposure to Pb have been found to increase the blood Pb

concentration of children [14, 84-86].

Wind-borne dust, which often resides on ceilings, can also affect indoor

environments [63, 87-89]. In Australia, houses are frequently built with terracotta

clay tiles which can cause an increase in harmful pollutants in ceiling dust [75].

21

2.4 POLYCYCLIC AROMATIC HYDROCARBONS (PAHS)

2.4.1 PAHs Sources:

PAHs are environmental pollutants, which contain two or more fused benzene rings

connected linearly, angularly or in a cluster arrangement. Typically PAHs are found

in water, air, soil, food and other areas of the environment. Naphthalene, which can

be found as a vapour, contains only two rings and is categorised as the first PAH.

PAHs with more than five rings occur predominantly as solids, whereas PAHs with

fewer than 5 rings occur in the vapour and particulate phases [67, 90, 91].

PAHs are ubiquitous in the environment and can originate from anthropogenic and

natural combustion processes such as volcanoes, motor vehicle emissions and forest

fires. During incineration of wastes, and burning of organic materials and fuels,

ample amounts of PAHs are usually released into the environment as gases and

particles. The presence of PAHs in indoor environments is partly due to indoor

combustion activities such as cigarette smoking, cooking, candle burning and coffee

roasting. However, indoor PAHs levels are influenced by other factors such as the

quality of the outdoor environment and the use of fossil fuels in the surrounding area.

Additionally, certain commercial and household products, cosmetics, shampoos,

pesticides and dyes that utilise coal tar, creosote treated wood and mothballs in their

process of production can contain PAHs [92-96].

In the outdoor environment, PAHs usually originate from anthropogenic sources

including motor vehicles, crude oil, asphalt roads, creosote, natural combustion,

industrial zones, burning of waste and production facilities. In addition, PAHs also

originate from biological sources such as plants, algae and micro-organisms [97].

However, commercial activities emit only a small number of PAHs such as

22

anthracene, fluoranethene, fluorene, naphthalene, phenanthrene and pyrene [98]. In

Australia, operations such as coal and petroleum processing, metal manufacturing

and productions using fossil fuels are the major sources of PAHs [48].

Energy production is a vital part of the modern society. However, it is also a major

source of PAH emission into the indoor and outdoor environments. Therefore, the

increased use of combustive processes to generate energy has a direct effect on

indoor and ambient air quality. But it is important to know that the PAHs produced

by one combustive process are not the same as those produced from another. Thus,

vehicles and bush fires are known to produce different sets of PAHs [91], and the

knowledge of the types of PAHs produced by specific processes can prove to be very

useful in source identification exercises.

During the last decade of the twentieth century, numerous PAHs, including

acenaphthene, acenaphthylene, anthracene, 2-methyl-benzo[a]anthracene,

benzo[a]pyrene, benzo[b]fluoranthene, benzo[e]pyrene, benzo[g,h,i]perylene,

benzo[k]fluoranthene, chrysene, coronene, dibenzo[a,h]anthracene, fluoranthene,

fluorene, indeno[1,2,3-c,d]pyrene, naphthalene, 2-methylperylene, phenanthrene, and

pyrene were detected in Australian cities [48]. Of these PAHs, benzo[a]pyrene

(BAP) and naphthalene appear to be the most frequently studied, and probably are

the best understood. This may partly be due to the fact that BAP is the most

hazardous PAH, while naphthalene is commonly found in sources such as sanitisers

and mothballs [48, 99].

Lim et al. [90] studied the PAH emission from 12 in service buses that used low-

sulphur diesel (LSD) and ultra-low-sulphur diesel (ULSD) in order to compare the

effect of fuel sulphur content on the levels of PAHs emitted by diesel vehicles. They

23

concluded that the buses emitted the same types of PAHs (acenaphthene,

acenaphthylene anthracene, fluorene, fluoranethene, naphthalene, phenanthrene, and

pyrene) regardless of the type of fuel used.

Many of these PAHs are in the particulate phase. However, incomplete combustion

also produces PAHs in the form of vapour [98, 100]. Both forms of PAHs can then

be absorbed into soil or settled house dust, and remain there because of their octanol-

air partition coefficient. Although PAHs are ubiquitous because of their tendency to

travel with air currents, urban and inner-city areas experience high concentrations of

PAHs, because of the increased anthropogenic activities in urban areas. Inhalation is

the most common means of exposure to PAHs but exposure also occurs via contact

with or ingestion of contaminated soil, dust, water, food and commercial products.

PAHs found in the open environments can endure chemical and photochemical

reactions but sunlight can accelerate their degradation and transformation. Due to

their reduced exposure of sunlight, PAHs in house dust do not degrade easily and this

contributes to the accumulations of PAHs in house dust.

PAHs can get attached to dust and travel through the air, or be found in settled house

dust, which could also harbour other pollutants [97]. In order to assess the origin of

PAHs in settled house dusts, Ong et al. [67] collected dust samples from homes in

Brisbane, Australia. They found that the commonest PAHs sources were: cooking,

motor vehicle emissions, natural gas and a number of unidentified combustion

processes.

Samples of settled house dust were also analysed by Maertens et al. [46] and it was

found that the levels of PAHs varied between 1.5 and 325 µg/g. In addition, the

correlation of the levels of PAHs in the house dust samples with the information on

24

the physical characteristics of the houses and indoor activities of the households

showed that the frequencies of activities such as vacuum cleaning were found to

increase the levels of PAHs found in the house dust samples. Vacuum cleaners are

prone to dispersing the fine dust particles through leakage in bag or mechanical

action of the beater bars thus resulting in re-suspension of the contaminants [34].

2.4.2 Health Effects

Due to the considerable amount of time that humans, particularly the elderly,

children and the infirm, spend indoors, the health risks associated with the exposure

to PAHs are of vital importance.

There is an undeniable link between the presence of PAHs in the environment and

the health of humans, animals and vegetation [6]. Sustained exposure to PAHs can

result in health complications for human beings [6]. Therefore, the US

Environmental Protection Agency (EPA) selected 16 PAHs as the most harmful

which must be monitored as a matter of priority in the indoor and outdoor

environments. Of these PAHs, benzo(a)anthracene, benzo(a)pyrene,

benzo(b)fluoranthene, benzo(k)fluoranthene, chrysene, dibenzo(a)anthracene, and

indeno(1,2,3-cd) and pyrene are classified as probable carcinogens [5]. In addition,

PAHs such as benzo[a]anthracene, benzo[a]pyrene, benzo[b]fluoranthene,

benzo[j]fluoranthene, benzo[k]fluoranthene, chrysene, dibenzo[a,h]anthracene, and

indeno [1,2,3-c,d]pyrene have caused adverse health effects [18, 101]. Incidents of

eye irritation, digestive disorders, confusion and skin aggravation in animals and

humans due to the exposure to high levels of a number of pollutants that included

PAHs have been reported [62]. Anthracene and benzo[a]pyrene are skin sensitisers

and capable of causing skin irritation and allergic responses [102]. The potency of

25

PAHs, the form of contact, the length of exposure are important factors when

determining the health effects that they are capable of manifesting in humans.

It is known that PAHs contribute to approximately 25% of the mutagenic potency in

settled house dust, and can be significantly dangerous to susceptible members of the

population, especially babies and toddlers who spend an appreciable amount of time

on the floor and in contact with objects, which contain house dust [44]. Organic

complications, cataracts and jaundice have also been observed in people that have

experienced prolonged exposure to PAHs [99].

Settled house dust has been analysed by Maertens et al. [44] for the presence of

PAHs as well as their carcinogenic and mutagenic properties. It was concluded that

cigarettes smoking contributed significantly to the level of PAHs in house dust [102].

2.5 HOUSE DUST PESTICIDES

2.5.1 Definition, Classification and Sources

‘Pesticides’ is a general term that covers several types of specific substances. These

include: insecticides used to control insects and pests; herbicides used against

unwanted plants or weeds; fungicides and fumigants used to control fungi and

moulds; and germicides used for disinfection [103]. Pesticides can be chemical or

biological substances, which are used both indoors and outdoors to control, contain

and deter unwanted pests.

US EPA [40] classified pesticides according to their chemical composition. For

example, they may be:

Organometallic compounds such as (zinc bis(dimethyldithiocarbamate));

26

Inorganic substances such as (chlorine oxide, potassium bromide arsine,

copper arsenate);

Semi-volatile organic compounds (SVOCs) such as propoxur, diazinon,

pentachlorophenol and chlorpyrifos;

VOCs such as carbon tetrachloride, naphthalene, dichlorobenzene and

methyl-bromide;

Non-volatile organic compounds (NVOCs) such as permethrin,

2,4-dichlorophenoxyacetic acid and 2,4-D dimethylamine salt;

Organochlorine pesticides (OCs), such as chlordane, dieldrin,

dichlorodiphenyltrichloroethane(DDT), methoxychlor, lindane and

heptachlor;

Organophosphorous compounds (OPs) such as diazinon, dichlorvos,

chlorpurifos, isofenfos and malathion;

Pyrethroids, such as cyfluthrin, permethrin and cypermethrin;

Carbamates which include carbaryl, propoxur and bendiocarb;

Phenols, such as pentachlorophenol(PCP), o-phenylphenol and chlorocresol.

2.5.2 Environmental Hazards of Pesticides

Organochlorine pesticides (OCs) are often used in farming operations to protect

plants, animals and houses from pests. They are extremely toxic organic chemicals

and are persistent. Therefore, they can have detrimental effects on human health and

27

the environment. Australia has largely discontinued the use of these chemicals.

However, large traces can still be found in the environment [104].

Indoor pesticide products, which come in a variety of formats, can be purchased

readily by individuals for use inside and outside residential buildings. There are a

number of products that are used to control and repel pests, which may infest their

homes and gardens. Many inert chemicals in household pesticides have no effect on

pests but can be toxic to humans and the environment [40].

Pyrethroids are manmade substances which are used on farms and can often be found

in household pesticides [105]. They are harmful to the human body if directly

contacted and can cause a number of complications. In a study by Lewis et al. [34], it

was found that cis-permethrin and trans-permethrin were the most common

pesticides found in households which were located in North Carolina, USA.

Pyrethroids, which are often found predominantly in house dust samples, are also

harmful to humans and have been found to cause health complications [106].

Cyclodiene has similarly been found to cause a number of health problems [24] and

like all pesticides, is often harmful on dermal contact, ingestion or inhalation by

humans and animals.

Marlund et al. [70] reported that house dust samples from 15 indoor environments

were found to hold as many as twelve kinds of organophosphorus compounds (OPs).

The most common OPs detected in the samples were tris(butocyethyl)phosphate,

tris(chloroethyl)phosphate, tris(chloropropyl)phosphate and

tris(dichloropropyl)phosphate. Chlorpyrifos and diazinon were found to be prevalent

in urban and farming residences in Washington State, USA [107], although diazinon,

chlorpyrifos and azinphosmethyl were found readily in the farming residences.

28

Diazinon was only found in the urban homes during the summer months. On the

other hand, in Singapore, OCs and polychlorinated biphenyls (PCBs) were common

in house dust samples. In a fairly recent study, Tan et al. [51] tested for

organochlorine pesticides, such as hexachlorocyclohexanes (HCHs), chlordanes ,

DDTs and 41 PCBs in the house dust. Although Singapore has discontinued the use

of DDTs in the 1980s, they were still prevalent in the indoor environment of several

buildings and as many as 28 types of PCBs were quantified in the samples.

SVOCs are more likely to be found in the home because they have a high degree of

volatility. Therefore, traces of the pesticides can be found long after they are first

used. For example, SVOC pesticides can often be found in the indoor environment

regardless of the area of their application, while pesticides applied in the garden can

often contaminate the indoor environment. Usually this effect results when the home

is sheltered from harsh weather and other outside conditions [20].

Similarly, chloropyrifos are readily detected in residences of agricultural

communities during the summer months while insecticides such as resmethrin,

tetramethrin and mecoprop are detected in the winter season [108].

Generally, more pesticides were found to be present in residential environments

during the winter months and especially in carpets because of the lack of ventilation

and exposure to the natural environment, and also because the indoor environment is

sheltered away from the harsh weather conditions that are present outdoors [20].

Pyrethroids such as cyfluthrin, cypermethrin, permethrin and deltamethrin persist for

a long time in the indoor environment. For example, they were found in detectable

amounts in house dust and indoor airborne particles a year after pest control

operations were administered to households [106].

29

Although pesticides offer huge benefits to Australian agricultural and veterinary

communities, Agriculture and Veterinary chemicals (AgVets), which are most

commonly used in the agricultural and veterinary fields, can leave hazardous

residues that may contaminate humans and animals [109]. For this reason, it is

important to characterise the pesticide composition of residential house dust in order

to assess the risk of pesticide exposure to residents. This is particularly so in

Queensland where warm summer weather is conducive to rapid reproduction of

insects, and large gardens are regular features of most residential buildings.

2.6 CHEMOMETRICS AND ITS APPLICATION IN

ENVIRONMENTAL STUDIES

As mentioned before in Chapter one (1.7) “Chemometrics is the chemical discipline

that utilises statistical methods, mathematics and formal logic to determine and

outline chemical data” [110]. Since the formation of the International Chemometrics

Society in 1974 [53], chemometrics has been extremely useful in the field of

analytical chemistry for data display ,classification and prediction as well as for data

interpretation [52].

Many chemometrics methods have been used in the literature. Some of these include

Principal Component Analysis (PCA), Multivariate Calibration, Partial Least

Square (PLS), Principal Component Regression (PCR), and Preference Ranking

Organisation Method for Enrichment Evaluation (PROMETHEE) and Geometrical

Analysis for Interactive Aid (GAIA). PCA facilitates the reduction, transformation

and interpretation of data. It transforms and compresses data by employing a method,

which uses principal components to produce scores and loadings [111, 112]. A

“scores” plot describes the relationship among the objects while a “loadings” plot

describes the relationship of the original variables to one another. When “scores” and

30

“loadings” values are displayed together, a biplot, which provides information on the

association between the objects and variables, is obtained. In general, a biplot

provides information on the variables that contribute most to the positioning of the

objects in the scores plot.

The PROMETHEE and GAIA methods are examples of Multi-Criteria Decision

Making methods (MCDM), which are used to assist the simplification of evaluations,

optimisations and selections of alternatives by decision makers [113]. PROMETHEE

ranks the data and associates each object with a relative value, Φ index [113]. The

PROMETHEE method allows the user to compare and assess each alternative by

defining a scale and associating preference functions to each variable, PROMETHEE

procedures are usually used as data pre-treatment methods for the GAIA method,

which reveals the convergences, conflicts, unwanted information and other essential

aspects of the data [65, 114-117].

Other chemometrics techniques such as Positive Matrix Factorisation (PMF) [118]

are very useful for the analysis of data in order to identify and apportion the sources

of the pollutants. When it is combined with Conditional Probability Function

(CPF) [118], an understanding of the direction of transportation and process of

transformation of the pollutants is facilitated. The application of PMF depends on the

estimated uncertainties for each of the data values while the uncertainty estimation

provides a useful tool for the reduction of the weighing of the missing and below

detection limit data on the source profiles and source contributions obtained. PMF in

combination with CPF has been used to identify the locations of sources of air

pollutants in many air quality studies; however, little or limited application of these

methods to house dust sample are reported [67, 119, 120].

31

2.6.1 Applications of Chemometrics Methods in Environmental

Studies

Literature suggests that although house dust is an important topic, chemometrics has

not been frequently applied to house dust analysis [45, 72, 80, 104, 114-127]. It was

noted that most of the studies used univariate data analysis processes such as

correlation analysis graphs, charts and summary statistics. However, chemometrics is

used in other areas of environmental science.

River water was analysed by Brodnjak-Vončina et al. [121] with the use of methods

such as PCA; it was determined that there was a link between the sensory and

chemical data that was collected. The application of the PROMETHEE and GAIA

methods to determine the course of development of the Saint Charles River alluvial

plain was reported by Martin et al. [122]; political and scientific factors were

considered to produce the final outcome. PROMETHEE and GAIA methods have

been found to be very practical to assess data and to determine vital environmental

factors affecting pollutant concentrations [65]. In 2004, residential homes in

Brisbane, Australia were investigated using these methods to determine the air

quality of indoor environments. It was found that the air quality of the indoor and

outdoor environments were highly dependent on the houses' construction

characteristics and attributes [65].

An investigation of the chemical characteristics of aerosols in Brisbane, Australia

[123] found that elements such as Al, Cd, Co, Cr, Cu, Fe, Mn, Mo, Si, Sn, Sr and Zn

abound in the total suspended particles (TSP) and PM2.5 fractions, but the PM2.5

fraction had a higher concentration of Al, Mn, Mo, Se, Sr, V and Zn. One of the

city’s sports centres, the ANZ stadium, had a lower concentration of the elements

32

because it has bushland surroundings, while the Woolloongabba area, near the CBD

has many busy roads and exhibited higher concentrations of elements such as Co,

Cu, Mg, Sn and Zn resulting from vehicular emissions.

Using the MCDM, PROMETHEE and GAIA methods, Herngren et al. [124] studied

the amount of metals deposited on the roads at industrial, commercial and residential

areas in Queensland, Australia. It was found that samples with higher amounts of

metal concentrations were detected at the industrial area, while the commercial site

exhibited higher quantities of heavy metals as well as a higher deposit load.

PCA, PROMETHEE and GAIA methods were adopted to study elements produced

by Ford Falcon Forte cars powered by Unleaded Petrol (ULP) or Liquefied

Petroleum Gas (LPG). It was concluded that the cars powered by LPG surpassed

those that were powered by ULP in terms of the quality of their exhaust emissions;

chemometrics analysis also suggested that other parameters such as mileage and

engine speed influenced the emission levels [112].

Tokalioğlu and Kartal [125] studied the amounts of Cd, Co, Cr, Cu, Mn, Ni, Pb and

Zn in outdoor samples of dust collected from the Organised Industrial District (OID)

in Turkey. PCA, Cluster Analysis (CA) and correlation analysis were used to

interpret the data collected and to determine the sources of the metals found in the

samples.

Water samples were studied by Ayoko et al. [114] to determine the physicochemical

qualities of surface water and groundwater and to assess the quality of the water

samples collected. They employed PROMETHEE and GAIA methods in the analysis

of the data and the water sources were ranked according to the measured water

quality parameters.

33

In 2007, air quality data collected by the QDERM between 1993 to 2003 was

assessed by chemometrics techniques on the basis of the 21 chemical elements

present in the airborne fine particles [126]. PROMETHEE and PCA methods were

employed to examine the data and it was concluded that the quality of the particulate

constituents decreased gradually throughout the years. The adopted MCDM methods

helped the assessment of the data and the identification of the pollutant sources

which included human combustion activities, secondary aerosols, sea salt, airborne

soil and other pollutants.

34

CHAPTER 3: MATERIALS AND METHODS

3.1 SAMPLING SITES

3.1.1 Area (Site Description)

South-East Queensland (SEQ) is a flourishing expanse of land that is one of the most

industrialised and fastest growing regions in Australia [127]. This area includes

Brisbane, the state capital, with the Gold Coast to the south, the Sunshine Coast in

the north, and to Toowoomba in the west. Rain is most frequent in summer and

autumn in this region. The landscape is bordered by hills and ranges and this reduces

the amount of pollution occurring in the region. Wind directions are north and north-

east, and the average speed is 1.9 m/sec, which carries most pollution from the urban

district towards the sea. However, this is contrasted by the steady breeze that builds

up during the day and carries pollutants back inland. This occurrence is supported

when, later in the day and at night, land breeze moves the pollutants into the urban

regions [56].

SEQ is a rapidly growing region of 2.32 million as of 2005 [127]. This growth is

expected to amplify the development in this region as well as the pollution produced.

One possible reason for this is that the population growth will occur primarily in the

outlying suburbs, thus leading to a hike in the use of automotive vehicles to commute

in and out of the Central Business Districts (CBDs) of the major cities of the region.

The increased population [4] and the use of motor vehicles, which already contribute

57% of air emissions in SEQ [128], are likely to also cause a corresponding increase

in the overall air pollutants in SEQ in the next few years leading to a definite decline

in air quality. According to Queensland Environmental Protection Agency [9], the

amount of air emissions produced by vehicles is already high; they produce 70% of

35

nitrogen oxides and 83% of carbon monoxide as well as an 18% increase in overall

airborne particles by mass [9]. But apart from contribution from vehicular emissions,

air pollution contains a number of other pollutants which are directly the result of

human activities. Along with these anthropogenic sources, there are also a number of

natural occurrences that contribute to the pollution in the air. These include dust

storms and bush fires, which are frequent in SEQ because of the dry, hot

environment. Bush fires are especially noxious because they can produce a great

amount of airborne chemical and physical pollutants, which may be carried by the

wind over long distances. Incorporated in this air are substances such as: aldehydes,

carbon dioxide, carbon monoxide, free radicals, inorganic material, particulate

matter, methane, nitrogen, non-methane hydrocarbons, PAHs, sulphur oxides and

water [129, 130]. Dust storms can also carry other organic compounds and small

particles that are shifted from their original source by the high speed winds, which

may cause respiratory and ocular complications. Combined with these natural

disruptions, there can also be a great deal of matter derived from vegetation that can

contribute to air pollution. All these contaminants are then carried throughout SEQ

by wind and water in the rainy periods of the year.

Brisbane is one of the fastest growing capital cities in Australia [4]. It is a subtropical

city located on the eastern coastal edge of SEQ and is situated in a unique location

where it is surrounded by mountainous ranges that form the SEQ basin. This basin

shuts in the polluted particles from the air and stores them until wind and rain carry

them to the wide surrounding areas. In SEQ, the period between November and April

usually displays a high amount of rain and storms, and so it is the period when

pollutants are typically transferred from the basin. It is also the period when there is

an increase in the number of bushfires; thus producing more airborne pollutants and

36

particles [131]. However, the period between May and October, is characterised by

insufficient rainfall and low wind speeds resulting in the removal of limited amounts

of pollutants from the basin.

Further inland is the city of Ipswich, a prosperous subtropical area located to the west

of Brisbane. This town is conveniently located between metropolitan area and the

agricultural regions of the state, and has a population of 145,000 [132].

Toowoomba, further to the west of Brisbane has a population of 90,199 [132] and is

located approximately 700 metres above sea level at the top of the Great Diving

Range. This city has an average annual rainfall of 928 millimetres with a daily

maximum temperate of 27 °C in summer and 16 °C in winter.

3.1.2 Sample Site Profile and Parameters

House dust samples were collected from a total of 62 residential houses, based on

random selection, which were selected from different suburbs of three cities: namely,

Brisbane, Ipswich and Toowoomba located in SEQ, Australia. The locations of those

sites were 55 sites in Brisbane suburbs, 2 in Ipswich, and 5 in Toowoomba. Table 1

summarises the locations and suburbs of the residential sample sites which were

sampled. Figure 1 and Figure 2, page 38, are maps indicating the suburbs of the

sample sites. All houses surveyed in this study were single family homes. The survey

was conducted during the period of 2005 to 2007, where 26 samples were collected

during 2005, 17 during 2006 and 19 in 2007.

37

Each participating householder was asked to fill out a survey questionnaire, which

set out to find detailed information on the indoor and outdoor characteristics of the

residences. The first part of the questionnaire was based on the external features of

the residential buildings (house characteristics), and the second part consisted of

questions about the interior of the buildings (anthropogenic activities in the site). A

number of characteristics were noted for each sample site, which may have a bearing

on the levels of the measured pollutants. For example, such characteristics as the type

of residential building, the approximate age of the building, materials used for

1 mm = 1km

Figure 1: A map of the Brisbane suburbs where dust samples were taken

38

building, number of floors and bedrooms, flooring type, renovation, and the distance

of the house from the major roadway, industrial areas, and commercial centres may

significantly affect pollutant levels in a house. The exact age of some buildings

assessed was not known by the occupants.

Table 2, page 42, shows the summary of the sampling site profiles (house

characteristics). In addition to the general sample site characteristics, a number of

anthropogenic activities which may contribute to the levels of pollutants in and

around a building, were also recorded, e.g. cooking and cleaning habits, heating (gas,

electricity and wood), ventilation system (use of air-conditioning, exhaust fans and

Figure 2: A map of the Brisbane suburbs where dust samples were taken

39

percentage of open windows), number of actual residents on site, resident smokers,

number of cars and their parking areas, pest control performed, presence of garden

and its maintenance.

Table 3, page 44, summarises the anthropogenic characteristics of the sampling sites.

All the required information in the survey questionnaires was completed by the

occupant during the collection of the house dust samples. The survey questionnaire is

presented in Appendix 1.

The majority of the houses sampled in the study were significantly different in age

and design attributes, with some houses being elevated above the ground on timber

or brick stumps and some set at ground level. The construction materials for most of

the houses were brick with concrete-flooring and carpet. Kitchens and bathrooms

were generally tiled. The sampling sites also included older houses, which were

primarily built of timber. Two of the houses sampled were panelled with fibro

weather board.

40

Table 1: Summary of the locations of the sampling sites

Sample site # City Suburb

1 Brisbane Mt Gravatt

2 Brisbane Carindale

3 Brisbane Mt Gravatt

4 Brisbane Upper Mt Gravatt

5 Ipswich Ipswich

6 Ipswich Ipswich

7 Brisbane Morningside

8 Brisbane Carindale

9 Brisbane Darra

10 Brisbane Annerley

11 Brisbane Annerley

12 Brisbane Moorooka

13 Brisbane Tarragindi

14 Brisbane Woolloongabba

15 Brisbane Woolloongabba

16 Brisbane Mt Gravatt

17 Brisbane Victoria Point

18 Brisbane Carindale

19 Brisbane Aspley

20 Brisbane Flinders View

21 Toowoomba Toowoomba

22 Toowoomba Toowoomba

23 Brisbane Sandgate

24 Brisbane Carindale

25 Brisbane Clontarf

26 Brisbane Woolloongabba

27 Brisbane Robertson

28 Brisbane Forest Lake

29 Brisbane Tarragindi

30 Brisbane Darra

31 Brisbane Eaton Hill

32 Brisbane Wishart

33 Brisbane Deception Bay

41

Sample site # City Suburb

34 Toowoomba Toowoomba

35 Toowoomba Toowoomba

36 Toowoomba Toowoomba

37 Brisbane Sunnybank

38 Brisbane Runcorn

40 Brisbane Kuraby

41 Brisbane Highgate Hill

42 Brisbane Sunnybank

43 Brisbane Boondall

44 Brisbane Highgate Hill

45 Brisbane Middle Park

46 Brisbane Brisbane

47 Brisbane Chermside West

48 Brisbane Brisbane

49 Brisbane Brisbane

50 Brisbane Brisbane

51 Caboolture Caboolture

52 Brisbane Brisbane

53 Brisbane Boondall

54 Brisbane Brisbane

55 Brisbane Highgate Hill

56 Brisbane Calamvale

57 Brisbane Wishart

58 Brisbane Highgate Hill

59 Brisbane Brisbane

60 Brisbane Brisbane

61 Brisbane Brisbane

62 Brisbane Brisbane

42

Table 2: Information collected via questionnaires on house characteristics

Sample Site

Age (yrs)

House Type Building Materials

Proximity (m) Floor Level

Floor Type Sampling

Date Road Ind.*

1 >50 Separate house Timber 200 5000 1 Carpet May-06

2 17 Separate house Brick 250 4500 1 Carpet Sep-06

3 >50 Separate house Timber 200 5000 1 Wood Sep-06

4 25 Separate house Brick 0 5000 1 Carpet Sep-06

5 >50 Separate house Timber 250 500 1 Wood/Tiles Sep-06

6 >50 Separate house Timber 200 2000 1 Carpet Sep-06

7 20 Separate house Brick 300 4000 1 Tiles Sep-06

8 17 Separate house Brick 250 4500 1 Carpet Sep-06

9 new Separate house Brick 260 1000 1 Carpet Sep-06

10 20 Separate house Brick 200 500 2 Wood/Tiles Sep-06

11 25 Flat Brick 75 600 2 Carpet Sep-06

12 20 Flat Brick 200 600 1 Carpet Sep-06

13 35 Flat Brick 300 1000 1 Carpet Sep-06

14 30 Flat Brick 50 500 2 Carpet Sep-06

15 30 Separate house Brick 50 500 1 Carpet Sep-06

16 40 Separate house Fibro 200 5000 1 Carpet Sep-06

17 21 Separate house Brick 500 5000 1 Carpet Sep-06

18 22 Separate house Brick 400 2000 1 Carpet Feb-07

19 18 Separate house Brick 500 1000 2 Wood/Carpet Feb-07

20 30 Separate house Brick 20 500 2 Carpet Feb-07

21 27 Separate house Brick 400 1500 2 Wood Mar-07

22 30 Separate house Brick 400 1500 1 Carpet Mar-07

23 90 Separate house Timber 300 2000 1 Wood/Tiles Mar-07

24 15 Separate house Brick 400 2000 1 Wood/Carpet Mar-07

25 40 Separate house Fibro 0 2000 1 Wood/Carpet Mar-07

26 14 Separate house Brick 400 3000 1 Carpet Mar-07

27 30 Separate house Brick 450 1000 2 Wood/Tiles Mar-07

28 10 Separate house Brick 200 5000 2 Wood/Tiles Mar-07

29 70 Separate house Brick/temper 400 2000 2 Wood/Tiles Mar-07

30 7 Separate house Brick 0 1000 1 Tiles Mar-07

31 7 Separate house Brick 600 2000 2 Carpet/Tiles Mar-07

32 30 Separate house Brick 250 5000 2 Wood/Tiles Mar-07

33 25 Separate house Brick 500 -5000 1 Carpet/Tiles Jul-07

34 >50 Separate house Timber 500 4000 1 Carpet Jul-07

35 16 Separate house Brick 800 4000 2 Carpet Jul-07

36 2 Flat Brick 100 5000 1 Wood Jul-07

37 17 Flat Brick 200 2000 2 Carpet Mar-05

38 13 Flat Brick 300 3500 2 Carpet Mar-05

39 15 Flat Brick 50 1000 2 Carpet Mar-05

40 13 Flat Brick 400 3000 3 Carpet Mar-05

41 25 Flat Brick 50 1000 2 Carpet Mar-05

42 17 Flat Brick 400 1000 2 Carpet Mar-05

43 5 Flat Brick 150 1000 1 Carpet/Tiles Mar-05

43

Sample Site

Age (yrs)

House Type Building Materials

Proximity (m) Floor Level

Floor Type Sampling

Date Road Ind.*

44 14 Flat Brick 50 1000 2 Carpet Mar-05

45 15 Flat Brick 450 5000 3 Carpet Mar-05

46 14 Flat Brick 400 4000 2 Carpet Mar-05

47 13 Flat Brick 250 3000 2 Carpet Mar-05

48 17 Flat Brick 200 4000 2 Carpet Mar-05

49 17 Flat Brick 250 1000 2 Carpet Mar-05

50 13 Flat Brick 200 1000 2 Carpet Mar-05

51 13 Separate house Brick 50 2000 1 Carpet Mar-05

52 15 Flat Brick 350 2500 2 Carpet Mar-05

53 5 Separate house Brick 400 1000 1 Carpet/Tiles Mar-05

54 17 Flat Brick 300 3000 2 Carpet Mar-05

55 13 Flat Brick 50 1000 3 Carpet Mar-05

56 12 Flat Brick 400 5000 2 Carpet Mar-05

57 17 Flat Brick 200 4000 2 Carpet Mar-05

58 25 Flat Brick 50 1000 2 Carpet Mar-05

59 16 Flat Brick 200 5000 2 Carpet Mar-05

60 17 Flat Brick 250 4000 1 Carpet Mar-05

61 16 Flat Brick 350 3500 2 Carpet Mar-05

62 14 Flat Brick 400 4000 2 Carpet Mar-05

*Ind. = Industrial area

44

Table 3: Summary of the anthropogenic characteristics at the sampling sites

H No.

% windows opened daily

Cooking Air-con Number

of residents

Residents smokers

Cooking Frequency

Extract fan

Sweep/vacuum frequency

Pets Pest

Control Garden

Use of chemical

spray

1 75 Electric No 4 No Daily No Twice a week No Yes Yes No

2 25 Electric Yes 5 No Daily Yes Weekly No Yes Yes No

3 25 Electric Yes 2 No Daily Yes Occasionally No Yes Yes No

4 75 Electric No 5 No Daily Yes Occasionally No No Yes No

5 25 Gas No 4 Yes Daily Yes Occasionally No No Yes No

6 75 Electric No 4 Yes Daily Yes Occasionally No No Yes No

7 75 Electric No 3 No Daily Yes Occasionally No No No No

8 25 Electric Yes 5 No Daily Yes Occasionally No Yes Yes No

9 75 Electric No 3 No Daily No Occasionally No No No No

10 75 Electric No 3 No Daily No Occasionally No No No No

11 100 Electric No 4 No Daily No Weekly No Yes No No

12 75 Electric No 4 Yes Daily No Twice a week No Yes No No

13 75 Electric No 1 No 3×/week No Weekly No No No No

14 50 Electric No 2 No 3×/week No Weekly No No No No

15 50 Electric No 2 No Daily Yes Occasionally No No No No

16 75 Electric No 3 Yes Daily No Occasionally Yes No Yes No

17 75 Electric No 4 No Daily Yes Occasionally No No Yes No

18 75 Electric No 4 No Daily Yes Occasionally No Yes Yes No

19 50 Electric Yes 2 No Daily Yes Twice a week No Yes Yes No

20 25 Electric No 2 No Daily No Occasionally Yes Yes No

21 25 Electric No 2 No Daily Yes Occasionally No No Yes No

45

H No.

% windows opened daily

Cooking Air-con Number

of residents

Residents smokers

Cooking Frequency

Extract fan

Sweep/vacuum frequency

Pets Pest

Control Garden

Use of chemical

spray

22 50 Electric No 1 No Daily Yes Occasionally No Yes Yes No

23 75 Electric No 6 No Daily Yes Twice a week Yes Yes Yes Yes

24 100 Electric No 4 No Daily Yes Twice a week No Yes Yes

25 50 Electric Yes 2 No Daily No Occasionally Yes Yes Yes Yes

26 50 Electric Yes 4 No Daily Yes Occasionally No Yes Yes Yes

27 50 Electric Yes 3 No Daily Yes Twice a week No No Yes Yes

28 25 Electric Yes 4 No Daily Yes Occasionally No Yes Yes Yes

29 50 Electric Yes 2 No Daily Yes Occasionally Yes Yes Yes Yes

30 75 Electric No 6 Yes Daily Yes Occasionally No No Yes No

31 75 Electric No 3 No Daily No Every day Yes No Yes No

32 50 Electric No 4 No Daily Yes Occasionally Yes Yes Yes No

33 25 Electric Yes 2 No 3×/week Yes Occasionally No Yes Yes No

34 50 Electric No 4 No 2×/week No Occasionally No Yes Yes No

35 25 Electric No 4 No Daily Yes Occasionally Yes Yes Yes Yes

36 25 Electric Yes 5 No 1×/week Yes Weekly Yes No No No

37 75 Electric No 4 No Daily Yes Occasionally No No No No

38 50 Electric No 3 No Daily Yes Occasionally No No No No

39 50 Electric No 3 No Daily Yes Occasionally No No No No

40 75 Electric No 2 No Daily Yes Occasionally No No No No

41 75 Electric No 3 Yes 4×/week Yes Weekly No No No No

42 75 Electric No 3 No Daily No Occasionally No No No No

43 100 Electric No 4 No Daily No Occasionally No Yes No No

44 75 Electric No 3 Yes 4×/week Yes Weekly No No No No

45 75 Electric No 3 No 1×/week Yes Weekly No No No No

46

H No.

% windows opened daily

Cooking Air-con Number

of residents

Residents smokers

Cooking Frequency

Extract fan

Sweep/vacuum frequency

Pets Pest

Control Garden

Use of chemical

spray

46 75 Electric No 4 No Daily No Weekly No No No No

47 75 Electric No 3 No 1×/week Yes Weekly No No No No

48 25 Electric Yes 3 No 3×/week Yes Weekly Yes No No No

49 25 Electric Yes 4 No Daily Yes Weekly Yes No Yes Yes

50 25 Electric No 4 Yes Daily Yes Weekly Yes No No No

51 100 Electric Yes 4 No Daily Yes Occasionally No Yes Yes Yes

52 25 Electric No 3 No Daily Yes Weekly No No No No

53 100 Electric No 4 No Daily No Weekly No Yes No No

54 50 Electric No 4 No Daily Yes Weekly No No No No

55 100 Electric No 4 No Daily Yes Weekly Yes No Yes Yes

56 75 Electric No 4 No Daily Yes Weekly No No Yes Yes

57 25 Electric Yes 4 No Daily Yes Weekly No No No No

58 25 Electric No 3 No Daily Yes Weekly Yes No No No

59 75 Electric No 2 Yes 3×/week Yes Weekly Yes No Yes Yes

60 50 Electric No 3 Yes Daily Yes Weekly Yes No No No

61 50 Electric No 4 Yes Daily Yes Weekly No No No No

62 75 Electric No 3 No Daily Yes Weekly No No No No

47

3.2 PRE-TREATMENT OF HOUSE DUST SAMPLES

3.2.1 Sample Collection

House dust samples were collected in a single visit from the floors of the living

rooms of the residential buildings by vacuum (3 to 4 sweeps over the area). About

60% of the area was vacuumed from the edges towards the centre of the rooms using

a vacuum cleaner for 10 minutes (Model: Volta Sprite Series II, 1400 watt) fitted

with a paper bag. To avoid cross-sample contamination, each dust sample was

collected in a clean paper bag. In the laboratory, the content of each bag was sieved

prior to extraction by means of a mechanical shaker (Model: Retsch AS 200,

Figure 3) which was fitted with a set of stainless steel sieves, namely, 1 mm, 500 μm,

250 μm, 125 μm, 63 μm, 45 μm and a pan. Samples were sieved for 15 minutes. The

fractions were weighed, labelled and transferred into glass vials with polyethylene

lids, then stored at room temperature until further analysis.

Quality Control (QC)/Quality Assurance (QA): to avoid cross-contamination of

samples, the sieves were cleaned before and after use by washing them with

detergent and then alcohol.

3.2.2 Sample Preparation

3.2.2.1 Preparation of Sub-samples

The house dust was sifted by using a mechanical sieve shaker as described above. A

composite sample of equal amounts of the fractions with particle sizes under 250 μm

(125 μm, 63 μm, 45 μm and less than 45 μm) was employed for further chemical

analysis. Prior to further processing of the samples, the fractionated samples were

stored in glass vials at room temperature until required for further analysis as above.

48

Figure 3: Retsch mechanical shaker with stacked sieves

3.2.2.2 Digestion and Extraction Procedures

3.2.2.2.1 Microwave-assisted extraction (MAE) versus Soxhlet extraction

The most widely used liquid/solid extraction technique is still Soxhlet

extraction [133], which requires 6–48 hours consuming large volumes of organic

solvents and time. Microwave extraction has been reported as an alternative sample

preparation technique for various solid samples [133-135]. This technique, which

was developed to reduce the volume of solvents required, improve the precision of

analytical recoveries, reduce extraction time and decrease the costs of the analysis, is

becoming a technique of choice for many types of extraction [134]. As described

below, microwave digestion was used for the extraction of the elemental and PAHs

contents of the house dust samples, and the traditional Soxhlet extraction was

employed for the extraction of the pesticides. Although the microwave digestion

method is appropriate to use, in this work the traditional Soxhlet extraction was

49

employed for the extraction of pesticides to ensure comparability with previous

studies (e.g. [136-138]).

3.3 ELEMENTAL COMPOSITION ANALYSIS

3.3.1 Extraction Procedure

Microwave-assisted acid digestion was used to extract the elemental components of

the samples. Digestion of the house dust samples was carried out in accordance with

the method of Kiln dust extraction [139] which is found to be the most suitable

method for house dust extraction. CEM Microwave Digester (MDS-2000) was used

for the extraction. For each sample, 0.5 g was accurately weighed and transferred to

the Teflon-lined digestion vessels. Ten millilitres of ultra pure nitric acid (70%) of

Analytical Reagent grade was added to each vessel, and the apparatus was

completely assembled and placed in the carousel. The samples were then digested

using the program summarised in Table 4.

Table 4: Operation parameters used for the microwave digestion of the house

dust samples for the determination of their elemental compositions

Program Variables

Stage

1 2 3 4 5

Power (%) 70 0 0 0 0

PSI* 70 0 0 0 0

Time (min) 20:00 0:00 0:00 0:00 0:00

TAP** 10:00 0:00 0:00 0:00 0:00

Fan Speed 100 100 100 100 100

*PSI = Pounds per square inch **TAP = Time at pressure

Upon completion of the digestion, the carousel containing the vessels was removed

from the microwave and placed in a fume hood in the clean room to cool for a period

50

of no less than twenty minutes. Following cooling, the sample vessels were

disassembled and the sample extract was filtered using a 45 μm glass micro-fibre

filter and a sintered glass funnel, diluted to 100 mL with ultra-pure water in a

standard 100 mL volumetric flask. The diluted samples were then placed in plastic

containers and stored at room temperature.

3.3.1.1 QA/QC:

All glassware and Teflon digestion vessels were cleaned in 10% nitric acid overnight

and then rinsed in ultra-pure water prior to extraction procedures. The Teflon rupture

membranes in the caps of the digestion vessels and the pressure monitoring vessels

were replaced with new ones prior to each sample preparation. Procedure blanks

were used for quality control. All dilutions were prepared using calibrated automated

pipettes and A-grade volumetric flasks; all solvents were of Analytical Reagent

grade. The glass wool used for filtration was extracted twice by sonification with

dichloromethane (DCM).

3.3.2 Chemical Analysis (Elemental Analysis)

3.3.2.1 ICP-MS

The elemental analysis of the house dust samples was carried-out by using

Inductively Coupled Plasma-Mass Spectroscopy (ICP-MS) (Model: Agilent

7500CE). This instrument combines the benefits of the ICP as an ion source with the

power of mass spectrometer for detection and quantitative analysis. It has a linear

range covering concentration measurement from 1 ppt to 1000 ppm with fast analysis

time and relatively simple spectra. Another advantage of using ICP-MS is the

possibility of isotope ratio and isotope dilution techniques.

51

Tab

le 5

: IC

P-M

S I

nst

rum

enta

l par

amet

ers

and

con

dit

ion

s

Ion

Len

ses

Ext

ract

1:

0.5

V

Ext

ract

2:

–126

V

Om

ega

bias

- C

e:

–18

V

Om

ega

Len

s -

Ce:

1.

2 V

Cel

l ent

ranc

e:

–26

V

QP

Foc

us:

1 V

Cel

l Exi

t:

–48

V

Det

ecti

on

Par

amet

ers

Dis

crim

inat

or:

8 m

V

Ana

log

HV

: 16

70 V

Pul

se H

V:

1140

V

Det

ecti

on

Oxi

de r

atio

(1

56/1

40):

1.

7%

Dou

ble

char

ge

(70/

140)

: 2.

57%

Sen

sitiv

ity:

L

i: 57

209

Y:8

209

Tl:

4655

Rea

ctio

n C

ell

H2

flow

rat

e:

5 m

L/m

in

He

flow

rat

e:

1.5

mL

/min

Qu

adro

pol

e P

aram

eter

s

AM

U G

ain:

12

7

AM

U O

ffse

t: 12

8

Axi

s G

ain:

0.

9996

QP

Bia

s:

–16

V

Oct

opol

P

aram

eter

s

Oct

p R

F:

180

V

Oct

p B

ias:

–8

V

Pla

sma

Con

dit

ion

RF

Pow

er:

1550

W

RF

Mat

chin

g:

1.8

V

Sam

ple

Dep

th:

5 m

m

Car

rier

gas

flo

w r

ate:

0.

9 L

/min

Mak

e up

gas

flo

w r

ate:

0.

25 L

/min

Neb

uliz

er P

ump:

0.

1 rp

s

S/C

Tem

p:

2 °C

52

The ICP-MS was fitted with an octopole reaction-collision cell, operated in collision

mode, which minimises isobaric interferences from polyatomic ions such as oxides.

Before the analysis, the instrument was tuned by using the tuning parameters

(Table 5). Two different sets of calibration standards were prepared from

AccuTraceTM Reference Standard solutions (AcuStandard Inc, New Haven, and

USA). The first set prepared from the following elements: Ag, Al, Ca, Cd, Co, Cr,

Cu, Fe, Ga, Li, Mg, Mn, Ni, Pb, Sr, Tl, and Zn, while the second set prepared from

the following three elements: As, Mo, and V. Each set was prepared at the following

concentrations: 0.2, 0.5, 1, 2, 5, 10, 20, 50 and 100 ppm.

3.3.2.2 Limits of detection for ICP-MS of Elements

The limits of detection (LOD) and limits of quantification (LOQ) were obtained by

preparing seven individual blanks. The mean and standard deviation of the blanks

were calculated using Microsoft Excel 2003 while the detection limit and

quantification limit were determined using the following equation [140] (Table 6):

LOD: Ylod = Yblank + 3S

LOQ: Yloq = Yblank + 10S

LOD = Limits of detection

LOQ = Limits of quantification

Blank = Average blank concentration

S = Standard deviation

Yblank =Mean

53

The percentages of recovery for the elements were measured in two different ways:

first by using 3 sets of laboratory controlled reference standards with different

concentration: 0.0 µg, 0.5 µg, 1.0 µg, 2.0 µg, 5.0 µg, 10 µg and 20 µg. These

samples were extracted and analysed with the same procedures as for the samples.

As seen in Table 7, the percent recoveries for all the elements were satisfactory.

Table 6: Limits of detection and quantification of elements analysed by ICP-MS

Element Atomic Mass

Limit of Detection

(ppm)

Limit of Quantification

(ppm)

Aluminium Al 27 0.039 0.114

Silver Ag 107 0.041 0.126

Arsenic As 75 0.000 0.000

Cadmium Cd 106 0.051 0.142

Calcium Ca 43 0.092 0.245

Chromium Cr 53 0.038 0.114

Cobalt Co 59 0.037 0.113

Copper Cu 63 0.045 0.138

Gallium Ga 69 0.042 0.127

Iron Fe 56 0.016 0.047

Lead Pb 208 0.045 0.137

Lithium Li 7 0.051 0.140

Magnesium Mg 24 0.039 0.117

Manganese Mn 55 0.041 0.124

Molybdenum Mo 95 0.000 0.000

Nickel Ni 60 0.046 0.140

Thallium Tl 205 0.041 0.124

Strontium Sr 88 0.037 0.113

Vanadium V 51 0.000 0.000

Zinc Zn 66 0.049 0.150

54

The second approach was by validation against NIST Certified Reference

Standard1649a [141]. Table 8 showed that the percentage recoveries for most of the

elements were also satisfactory except for Mg, Cr, Fe, and Ag. This may be because

the material used was old.

Table 7: Percent recovery of the elements by using laboratory reference

standards

Elements Atomic Mass % Recovery

Aluminium Al 27 99.9

Silver Ag 107 105

Arsenic As 75 105

Cadmium Cd 106 101

Calcium Ca 43 92.8

Chromium Cr 53 94.7

Cobalt Co 59 100

Copper Cu 63 105

Iron Fe 56 96.6

Gallium Ga 69 98.4

Lead Pb 208 105

Lithium Li 7 100

Magnesium Mg 24 98.3

Manganese Mn 55 97.1

Molybdenum Mo 95 102

Nickel Ni 60 101

Thallium Tl 205 104

Strontium Sr 88 97.5

Vanadium V 51 101

Zinc Zn 66 101

55

Table 8: Percent recovery by using NIST Certified Reference Standards

Element Experimental value

(ppm) Certified value

(ppm) % Recovery

Mg 6261 9200 ± 6 68.0

Cr 97.0 211 ± 6 46.0

Mn 218 237 ± 8 92.0

Fe 17288 29800 ± 0.27 58.0

Co 17.0 16.4 ± 0.4 104

Ni 130 166 ± 7 78.2

Cu 206 223 ± 7 92.5

Zn 1703 1680 ± 0.03 101

Cd 24.0 22 ± 6 109

Ag 2.2 3.5 ± 0.2 62.5

Pb 13322 12400 ± 0.01 107

Mo 12.4 13.5 ± 0.9 91.9

As 58.4 67 ± 2 87.2

V 280 345 ± 13 81.2

3.4 POLYCYCLIC AROMATIC HYDROCARBON (PAH)

COMPOSITION ANALYSIS

3.4.1 Extraction Procedure

PAH contents of dust samples were extracted with the use of microwave-assisted

extraction (MAE) method, and a CEM Microwave Digester (MDS-2000). A sample

of house dust (0.5 g) was transferred accurately to each of the Teflon-lined digestion

vessels, spiked with recovery standards (5 μL of 1000 ppm of anthracene D-10

surrogate) to monitor recovery rates, and then extracted using 30 mL of solvent

mixture, n-hexane-acetone (1:1, v/v). Digestion of the samples was carried out in

accordance with the method described by Lopez-Avila et al. [142], and the samples

were then digested using a microwave digestion program (Table 9).

56

Table 9: Microwave digestion operating parameters for the analysis of PAHs

Program Variables

Stage

1 2 3 4 5

Power (%) 70 0 0 0 0

PSI* 70 0 0 0 0

Time (min) 20:00 0:00 0:00 0:00 0:00

TAP** 10:00 0:00 0:00 0:00 0:00

Fan Speed 100 100 100 100 100

*PSI = Pounds per square inch **TAP = Time at pressure

Upon completion of the digestion, the carousel containing the vessels was removed

from the microwave and placed in a fume hood in the clean room to cool for 20 min.

3.4.1.1 Clean-up of the extracts:

Following the cooling after extraction, the sample vessels were disassembled. The

extracts obtained were cleaned using a Pasteur pipette packed with glass wool and

topped with 1 cm anhydrous sodium sulphate, and eluted with acetone. This gave

clear solutions that were free of solid debris. After the filtration, the extracts were

reduced in a rotary evaporator to 5 mL, centrifuged twice (Eppendorf 5424

centrifuge) at a speed of 2300 rpm for 10 min, and then reduced to 1 mL under a

gentle stream of dry ultra-pure nitrogen. The final concentrates were dried by the

addition of a small quantity of sodium sulphate and then transferred to micro-vials.

The prepared micro-vials were stored in a refrigerator until analysis. Field blank

samples were extracted in the same manner.

3.4.1.2 Quality Assurance (QA)/Quality Control (QC)

Prior to the extraction procedure, all glassware and Teflon digestion vessels were

cleaned with detergent and then rinsed in water followed by acetone. The Teflon

rupture membranes in the caps of the digestion vessels and the pressure monitoring

57

vessel were replaced with new ones prior to each sample preparation. Procedure

blanks were used for quality control. All solvents were of analytical reagent quality

and the glass wool used for filtration was extracted twice by sonification with DCM.

3.4.2 Chemical Analysis

3.4.2.1 Gas Chromatography Mass Spectrometry (GC-MS)

Prior to chromatographic analysis, 0.5 μg of semi-volatile internal standard (Supleco

Analytical) containing naphthalene-D8, acenaphthene-D10, phenanthrene-D14,

chrysene-D12 and perylene-D12 was added to all extracts for quantification

purposes. The US EPA priority PAH standard used for the analyses was purchased

from Supleco Analytical and contained 2-bromo-naphthalene (BNAP) and the 15

PAHs in dichloromethane [48] (Figure 4).

PAHs analysis was performed on an Agilent HP 6890GC with HP5973 MSD Gas

Chromatography Mass Spectrometry (GC-MS) with an autosampler; it was fitted

with an HP-5MS column (30.0 m x 0.32 mm x 0.25 mm). Helium with a purity of

99.99% was used as the carrier gas at a constant flow of 1 mL/min. The following

oven temperature program was used: firstly the initial temperature was set to 100 °C

for 2 min, and then increased to 170 °C at a rate of 10 °C/min; thereafter, it was

raised to 320 °C at a rate of 10 °C/min and kept at 320 °C for 18 min.

The MS was operated in the single ion monitoring (SIM) mode. Identification was

based on the GC retention times relative to those of related internal standards and the

relative abundance of the ions monitored. The quoted concentration value for each

analyte was obtained after subtraction of the field blank value [124].

58

Acenaphthene (ACE)

Acenaphthylene (ACY)

Anthracene (ANT)

Benzo(a)pyrene

(BAP) Benzo(b)fluoranthene

(BBF) Benzo(g, h, i)perylene

(BGP)

Chrysene (CHR)

Dibenzo(a, h)anthracene (DBA)

Fluoranthene (FLT)

Fluorine (FLU)

Indeno(1, 2, 3-c d)pyrene (IND)

Naphthalene (NAP)

Phenanthrene (PHE)

Pyrene (PYR)

2-Bromonaphthelene (BNAP)

Figure 4: Chemical structures of the PAHs and 2-bromonaphthelene

59

3.4.2.2 Limits of Detection for GC-MS of PAHs

The limits of detection and quantification for the GC-MS chemical analysis

techniques were calculated in the same way as the LOD and LOQ of the elements as

outlined in section 3.3.2.2 above. Table 10 contains a summary of the LODs and

LOQs of the PAHs analysed by GC-MS. The percent recovery for the PAHs was

calculated in the same way as for the elements. Most of the PAHs showed acceptable

recovery from the certified reference standards Table 11). The results of the recovery

by using certified NIST 1649a showed that most of the PAHs were recovered well,

except BBF and FLU (Table 12).

Table 10: Limits of detection and quantification of the PAHs analysed by

GC-MS

Poly-aromatic Hydrocarbon

Abbreviated Name

Limit of Detection

(ppm)

Limit of Quantification

(ppm)

Acenaphthene ACE 0.079 0.234

Acenaphthylene ACY 0.020 0.058

Anthracene ANT 0.050 0.120

Benzo(a)anthracene BAA 0.020 0.058

Benzo(a)pyrene BAP 0.120 0.312

Benzo(b)fluoranethene BBF 0.152 0.423

Benzo(g,h,i)perylene BGP 0.000 0.000

Chrysene CHR 0.020 0.058

Fluoranethene FLT 0.052 0.146

Fluorene FLU 0.036 0.102

Indeno(1,2,3-cd)pyrene IND 0.000 0.000

Naphthalene NAP 0.060 0.165

Phenanthrene PHE 0.052 0.151

Pyrene PYR 0.065 0.178

2-Bromonaphthalene BNAP 0.122 0.318

60

Table 11: Percent recovery for the PAHs by using Reference standards

Poly-aromatic Hydrocarbon

Abbreviated Name

% Recovery

Acenaphthene ACE 90.0

Acenaphthylene ACY 89.8

Anthracene ANT 87.7

Benzo(a)anthracene BAA 89.2

Benzo(a)pyrene BAP 118

Benzo(b)fluoranthene BBF 115

Benzo(g,h,i)perylene BGP 101

Chrysene CHR 88.9

Fluoranthene FLT 89.3

Fluorene FLU 90.6

Indeno(1,2,3-cd)pyrene IND 101

Naphthalene NAP 103

Phenanthrene PHE 100

Pyrene PYR 87.1

2-Bromonaphthalene BNAP 120

Table 12: Percent recovery with the use of NIST Certified Reference Standards

PAH Certified Value

(ppm) Experimental Value

(ppm) % Recovery

FLU 0.23 ± 0.2 0.3 130

PHE 4.14 ± 0.20 3.2 77.3

ANT 0.43 ± 0.10 0.4 92.6

FLT 6.54 ± 0.20 5.2 79.5

PYR 5.29 ± 0.20 4.6 87.0

BAA 2.1 ± 0.30 1.9 86.1

CHR 3.0 ± 0.30 2.9 95.1

BAP 2.5 ± 0.30 2.6 104

BBF 6.45 ± 0.30 4.42 68.2

IND 2.9 ± 0.20 2.9 91.2

61

3.5 PESTICIDES COMPOSITION ANALYSIS

3.5.1 Extraction Procedure

The pesticide contents of most of the dust house samples were extracted by using the

Soxhlet extraction procedure. For comparison, some of the samples were extracted

with the MAE procedure using methods that are similar to those described for the

PAHs in Section 3.4.1 above.

For the Soxhlet extraction method, 2 g of the sieved house dust samples were

accurately weighed from the 125 μm, 63 μm and 45 μm fractions. Each sample was

spiked with 5 μg of pesticides surrogate mixture to monitor recovery rates, and then

extracted by Soxhlet for 18 hours using a 1:1 dichloromethane:acetone mixture.

Following the extraction, the extracts were reduced in a rotary evaporator to 5 mL.

3.5.1.1 Clean-up of the extracts:

After cooling, the sample vessels were disassembled. The extracts obtained using the

MAE and Soxhlet procedures were cleaned up using a Pasteur pipette packed with

glass wool and topped with 1 cm of anhydrous sodium sulphate, and eluted with

acetone. This gave clear solutions that were free of solid debris. The extracts were

centrifuged twice (Eppendorf centrifuge 5424) at a speed of 2300 rpm for 10 min and

reduced to 1 mL under a gentle stream of dry ultra-pure nitrogen. The final

concentrates were dried by the addition of a small quantity of sodium sulphate, and

then transferred to micro-vials. The prepared micro-vials were stored in a refrigerator

until analysis.

3.5.1.2 QA/QC

All solvents were of Analytical Reagent quality. All glassware were washed with

deionised water, ethanol and acetone and rinsed two times with DCM before use.

62

Glass wool for filtration was extracted twice by sonification with DCM. All

glassware for the Soxhlet extraction were washed with deionised water, ethanol and

acetone and rinsed two times with DCM. Before use, Soxhlet thimbles were

extracted twice by sonification with DCM and dried in an air oven. Glass wool used

for filtration was extracted twice by sonification with DCM. For the MAE, all

glassware and Teflon digestion vessels were cleaned with detergent and then rinsed

in water followed by acetone prior to extraction procedure. The Teflon rupture

membranes in the caps of the digestion vessels and the pressure monitoring vessel

were replaced with new ones prior to each sample preparation.

3.5.2 Chemical Analysis

3.5.2.1 GC-MS

Before the chromatographic analysis, 0.5 μg of semi-volatile internal standard

(Supleco Analytical) containing naphthalene-D8, acenaphthene-D10, phenanthrene-

D14, chrysene-D12 and perylene-D12 was added to all extracts for quantification

purposes. The EPA Pesticide Mix standard (Supleco Analytical) contained 16

pesticides in methanol is as follows [143, 144] (Figure 5).

Pesticide analysis was carried out by using the Agilent HP 6890GC with HP5973

MSD Gas Chromatography Mass Spectrometry (GC-MS) with an autosampler; it

was fitted with a HP-5MS column (30.0 m x 0.32 mm x 0.25 mm). Helium with a

purity of 99.99% was used as the carrier gas at a constant flow of 1 mL/min. The

following oven temperature program was used: firstly, the initial temperature was set

to 100 °C for 2 min, increased to 280 °C at the rate of 12 °C/min and then kept at

280 °C for 20 min. The injector and the interface (between GC and MS) were kept at

280 °C, and the injection mode was pulsed splitless. The MS was operated in the

single ion monitoring (SIM) mode. Identification was based on the GC retention

times relative to those of related internal standards, and the relative abundance of the

ions was monitored. The concentration of each analyte was obtained after subtraction

of the field blank value.

Dichlorodiphenyl

trichloroethane (DDT) Dicholrodiphenyl

dichloroethylene (DDE) Dichlorodiphenyl

dichloroethane (DDD)

Methoxychlor (MET)

Endrin (EN)

Dieldrin (DIE)

Heptachlor (HC)

Heptachlor epoxide (HCE)

Endosulfan I Alpha (EndI)

Endosulfan sulphate (Ends)

Endosulfan (End)

Alpha-Lindane (α-BHC)

Beta-Lindane (β-BHC)

Delta-Lindane (δ-BHC)

Gama-Lindane (γ-BHC)

Figure 5: Names and chemical structures of compounds in the EPA Pesticide

Mix Standard

64

3.5.2.2 Limits of Detection for GC-MS of Pesticides

The LOD and LOQ for the GC-MS chemical analysis techniques were calculated

similarly to the LOD and LOQ for the elements (3.3.2.2). The percent recovery for

the pesticides was calculated in the same way as for the elements (3.3.2.2). A

summary of the pesticides analysed by GC-MS is listed in Table 13. The results

showed an acceptable recovery from the controlled reference standards (Table 14).

The results of the recovery by using certified NIST 1649a showed that most of the

pesticides recovered well (Table 15).

Table 13: Limits of detection for GC-MS analysis of the pesticides

Pesticide Abbreviation Limit of

Detection (ppm)

Limit of Quantification

(ppm)

alpha-Lindane α-BHC 0.063 0.089

beta-Lindane β-BHC 0.106 0.226

delta-Lindane δ-BHC 0.086 0.138

Heptachlor HC 0.104 0.218

Heptachlor epoxide HCE 0.068 0.102

Endosulfan I (ALPHA) End I 0.089 0.179

Endosulfan II (BETA) End II 0.079 0.134

Dichlorodiphenyldichloroethane DDD 0.073 0.099

Dicholrodiphenyldichloroethylene DDE 0.073 0.099

Dieldrin DIE 0.073 0.099

Endrin EN 0.079 0.134

Endosulfan sulphate Ends 0.079 0.128

Dichlorodiphenyltrichloroethane DDT 0.079 0.128

Methoxychlor MET 0.078 0.133

65

Table 14: Percent recovery of the pesticides by using Reference standards

Pesticides Name % Recovery

α-BHC 86.5

β-BHC 86.9

δ-BHC 97.5

Heptachlor 90.0

Heptachlor epoxide 92.3

Endosulfan I 90.4

Endosulfan II 91.9

DDD 80.4

DDE 95.4

Dieldrin 94.5

Endrin 92.0

Endosulfan sulphate 92.3

DDT 94.5

Methoxychlor 101

Table 15: Percent recovery by using certified reference materials

Pesticides Certified value

(ppm) Experimental values (ppm)

% Recovery

4,4`DDE 40.4 ± 1.7 36 89.1

4,4`DDD 34.01 ± 0.48 30 88.2

4,4`DDT 212 ± 15 188 88.7

Heptachlor 18.9 ± 0.5 16.2 85.7

3.6 MULTIVARIATE DATA ANALYSES

Considering the fact that many variables were investigated in this study, multivariate

data analysis techniques were employed in order to unveil the structure of the data.

Thus an auto scaled data matrix was analysed. Part of the aims of this study was to

rank the sites and identify the sources of the pollutants in indoor air by analysing the

house dust samples collected from the sites. To achieve these, the multivariate data

66

analysis techniques exploratory PCA and MCDM procedures, PROMETHEE and

GAIA were applied in order to unveil and analyse the structure of the many variables

obtained in this study. In addition to these techniques, PMF was also applied in order

to find the number of pollutant sources present in each house dust sample and the

relative contributions of the sources present in each house dust sample as well as the

relative contributions of the sources to the pollutant concentrations at the sites.

3.6.1 Data Pre-treatment:

Data pre-treatment involves mathematical or statistical treatment of the data matrix

before submission to a chemometric method for processing. The main reason for this

is to prepare the data matrix so that the chemometrics methods are able to extract

information as meaningfully as possible from the measurements in the matrix.

There are many methods of data pre-treatment such as:

I. Y-mean scaling: the mean of a column is subtracted from each cell value

in that column.

II. Standardisation: each value in a column is divided by its standard

deviation

III. Normalisation: each row entry is divided by the sum of the row.

IV. X-and y-mean scaling: removes the common characteristics from the data

matrix.

V. Auto scaling: was used throughout this work.

67

3.6.2 Principal Component Analysis (PCA)

PCA is one of the most useful exploratory techniques that can be used to display the

data structure [111]. This method is designed to compress and reduce the original set

of data matrix into a set of new variables, the principal components (PCs), which are

orthogonal, linear combinations of the original variables. To apply this method, the

data must be arranged into a data matrix which consists of the variables (such as the

pollutants) displayed in the columns and the objects (such as the house dust samples)

in the rows. Then, the raw data matrix should be pre-treated using one of the pre-

treatment methods outlined above.

The first PC explains most of the data variance while the second PC accounts for the

next highest amount of variance and so on until as many PCs are produced as there

are original variables. To aid the visual display of the PCA results, they were

presented either as scores plots which show the relationships between the objects, or

loadings plots which explain the relationships of the variables with one another. Each

object has a score value on each PC, and every variable is also linked with a loading

on each PC. Biplots combine both the loadings and the scores in the one plot. This is

more useful than either the scores or loadings plot because it shows the relation

between the objects and the variables in the same plot.

PCA can also be used to examine the importance of particular variables for each PC.

If the variables have high positive values or high negative ones, it means that these

variables are important. On the other hand low loadings show that the variables are

less important. Also the objects can be clustered together or widely dispersed. If they

lie in the same direction and the angles between their vectors are less than 90°, then

the vectors are to a greater or lesser extent correlated. However, if the angles

68

between the variables vectors are equal to 90°, the vectors are independent, while

they are opposite each other if the angles between the vectors are about 180°. The

PCA analyses in this study were carried out by using SIMCA-P-10 software from

Umetrics. This software has the advantage of displaying Hotelling-T2 95%

confidence limit ellipse on the score plot to show the presence of outliers or atypical

objects. So that any object outside the Hotelling is regarded as an outlier.

3.6.3 Multi-Criteria Decision Making Method (MCDM)

MCDM method is very powerful and useful tool in the real world context; it provides

a pathway and means for bringing together many different variables and

stakeholders. It also provides an experimental procedure visually effective modelling

and testing of competitive scenarios, in order to achieve final acceptable choice or

compromise solutions for decision makers [113]. There are many MCDM methods

that one can choose from such as: ELECTRE (Elimination Et Choix Traduisant

Realite), HDT (Hasse Diagram Technique), and PROMETHEE . In this study, the

combination of PROMETHEE-GAIA methodology was used for the multi-criteria

problems. This method is known as one of the most efficient and popular MCDM

methods [53]; moreover it is becoming the preferred method for analysing data in

environmental studies [65, 114, 145].

PROMETHEE is a nonparametric ranking method which was developed by Brans in

1982, and then completed by Brans and Vincke in 1985 [146-148]. The method is

very useful in ranking or ordering of objects or actions (in this study, the house dust

samples) across several criteria according to the preference and pre-selected

weighting conditions applied to the variables (concentrations of elements, PAHs or

pesticides). Each variable in the data matrix was optimised separately either by

69

applying top-down (maximise) or bottom-up (minimise), preference to it. That is

higher or lower variables values can be preferred.

GAIA is a special PCA biplot obtained from a matrix, which has been derived from a

decomposition of the PROMETHEE net outranking flow indices [115]. It produces a

PCA biplot of PC1 versus PC2 by reducing the variables into two principal

components, in order to evaluate and display PROMETHEE results visually. Thus,

GAIA helps the analysis of the significance of the different variables. In the biplot,

there is also a decision axis (π) showing the quality of the preferred decision which

points in the direction of the most preferred objects.

In this study, the objects which are located in the direction of the decision axis, π,

have the lowest levels of the pollutants, while the objects that are located in the

opposite direction of the decision axis or are pointed away from decision axis are

objects (houses) with the highest levels of the pollutant.

To apply PROMETHEE method, we need to choose a preference function for each

variable. The preference function can be chosen from the list of six different

preference functions such as usual shape, U-shape, V-shape, linear shape and

Gaussian shape.

PROMETHEE calculates both the positive out ranking flow (Φ+) and negative out

ranking flow (Φ–) for each alternative. The positive flow states how much an object

outranks all other objects and the negative flow shows how much it is outranked by

the other objects. The higher the value of Φ+ for an object, the more it is preferred.

The positive and negative scores may be combined into a net flow score (Φ+) + (Φ–)

= net flow Φ, so that the closer the values of Φ for two objects the more similar the

70

objects, while the larger the difference in the values of Φ for two objects, more

different the objects.

There are two ways of displaying PROMETHEE: PROMETHEE I give partial

ranking of the objects and PROMETHEE II provides complete ranking of

alternatives from the best to the worst ones. In this work, PROMETHEE II was used

for ranking the objects. The PROMETHEE and GAIA analysis used in this work was

performed by using Decision Lab 2000 Executive Edition Software for ranking and

ordering a number of objects according to preference and weighing, and applied to

the variables, and the results obtained were interpreted according to the guidelines

described by Espinasse et al. [117] and Ayoko et al. [65] as follows:

I. The longer the projected vector for a variable, the more variance in the

data matrix the variable accounts for;

II. Variables that have orthogonal or almost orthogonal vectors are

independent of each other;

III. Variables with vectors that are oriented in the same direction are

correlated while those with vectors that are oriented in opposite directions

have opposing effects on the objects;

IV. If an object is projected in the direction of a particular variable, the object

is strongly related to that variable;

V. Objects that are dissimilar have significantly different PC1 or PC2

coordinates while objects that are similar tend to cluster together; and

71

VI. The decision axis, π, aids the decision making process; if it is long, the

best objects are oriented in its direction and vice versa.

3.6.4 Source apportionment - Positive Matrix Factorisation (PMF)

There are some advanced chemometrics techniques such as receptor modelling that

provide information on the source of the pollution, source profile of each source, and

source contribution. The most commonly used receptor models are: Principal

Component Analysis/Absolute Principal Component Scores (PCA/APCS), PMF

Chemical Mass Balance (CMB) and UNMIX [149-151].

PMF has non-negative constraints which mean that the results always lead to positive

source contributions. In addition, it uses point-to-point weighing of errors in the data

matrix and can extract more sources than CMB and PCA/APCS. PMF method is

very useful to analyse data in order to apportion the pollutant sources and to

understand how the pollutants are transferred from place to place [118, 119, 152-

155]. The user provides two files; one file contains the sample species concentrations

and the other one the uncertainties which the model uses to calculate the number of

sources, source profiles, relative source contributions, and a time series of

contributions.

The application of PMF depends on the estimated uncertainties for each of the data

values; the uncertainty estimation provides a useful tool to decrease the weight of

missing and below detection limit data in the results, as well as to account for the

variability in the source profiles.

To solve the PMF problem, a least squares method is used by introducing an object

function (Q) to be minimised [156]:

72

⁄

Where is an uncertainty estimate of a data point with the jth element measured in

the ith sample.

is the amount of measured mass that was not explained by the model [157].

Determination of the number of factors is the first step in the analysis then followed

by the examination of the Q value by trial and error. The value of Q should be equal

to the number of data points, this means if there are 10 variables and 50 samples, Q

should be around 500 [158]. In this study, the United States Environmental

Protection Agency's (US EPA) software (EPA PMF 1.1 software program) was used.

Although this method is slow because it involves several iterative procedures before

the final outcome is produced, it helps in dealing with missing or below detection

limits data, and also has an evaluation tool for the results [158].

73

CHAPTER 4: RESULTS AND DISCUSSION

This study has been carried out for samples collected from 62 houses. Although 62

houses were sampled for dust, the size of some samples was not enough for the

analysis of the three types of pollutants. Therefore, for such samples priority was

given to pesticides analysis because very few studies have been carried out to

investigate the levels of pesticides in house dust in Australia. Thus, 59 samples were

analysed for pesticides and 46 for the elements. Only 43 samples were analysed for

both the elements and the PAHs.

4.1 PAHS POLLUTANTS

4.1.1 Concentrations

The concentrations of 2-bromonaphthalene (BNAP) and fifteen of the US EPA

priority PAHs are presented in Table 16 and Figure 6. The concentration of BNAP

was higher than that of the PAHs. The compositional analysis of PAHs samples

showed that BBF was the highest component followed by BAP, ACY, PHE and then

ANT for the majority of the samples. BAP is the most hazardous PAHs [48].

74

Table 16: Concentrations of PAHs (µg/g) in house dust samples

PAHs Mean Max Min SD

NAP BDL 0.15 BDL 0.02

BNAP 1.46 12.13 BDL 2.77

ACY 0.12 1.31 BDL 0.25

ACE 0.01 0.07 BDL 0.02

FLU BDL 0.05 BDL 0.01

PHE 0.02 0.14 BDL 0.03

ANT 0.02 0.22 BDL 0.04

FLT BDL 0.00 BDL BDL

PYR 0.01 0.06 BDL 0.01

BAA BDL 0.01 BDL BDL

CHR BDL 0.01 BDL BDL

BBF 0.30 2.03 BDL 0.46

BAP 0.23 1.67 BDL 0.39

IND BDL BDL BDL BDL

BGP BDL BDL BDL BDL

DBA BDL BDL BDL BDL

BDL: Below detection limit

Figure 6: Concentrations of PAHs (µg/kg) in the house dust samples

75

Tab

le 1

7: C

omp

aris

on o

f P

AH

s (µ

g/g)

det

ecte

d in

hou

se d

ust

sam

ple

s fr

om t

his

stu

dy

and

oth

er c

ount

ries

Au

stra

lia

On

g et

al.

[67]

Med

ian

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

***B

DL

= B

elow

det

ecti

on li

mit

95%

CI

128

nd

5.49

134

9.04

7.8

2.67

50

72

5.05

3.6

5.8

2.2

1.15

1.44

15.2

Aus

tral

ia

Rob

erts

on e

t al

. [15

9]

Med

ian

0.19

nd

0.01

BD

L

0.01

4.33

BD

L

0.13

1.64

0

0.04

0.17

0.08

0.04

0.06

nd

95%

CI

0.4

nd

0.03

0.03

0.14

8.67

0.02

1.82

5.09

0.06

0.19

0.26

0.16

0.08

0.19

nd

Uni

ted

Stat

es

Cam

ann

et a

l. [2

9]

Med

ian

nd

nd

nd

nd

nd

nd

nd

nd

nd

0.13

6

0.27

0.31

0.15

4

0.16

1

nd

0.03

6

**nd

= n

ot d

etec

ted

95%

CI

nd

nd

nd

nd

nd

nd

nd

nd

nd

1.79

2.84

3.95

2.39

2.39

nd

0.5

Ger

man

y

Sim

rock

, [16

0]

Med

ian

nd

nd

nd

nd

nd

nd

nd

nd

nd

<0.

2

<0.

2

<0.

2

<0.

2

<0.

3

nd

<0.

3

95%

CI

**nd

nd

nd

nd

nd

nd

nd

nd

nd

0.7

0.7

0.6

0.2

0.3

nd

<0.

3

Res

ult

s fr

om

this

stu

dy M

edia

n

0.29

2.02

1.05

3.87

0.29

10.6

3.91

0.06

0.62

0.85

0.72

4.92

4.41

BD

L

BD

L

BD

L

CI

= C

onfi

denc

e in

terv

al

*95%

CI

6.83

8.29

7.43

5.57

2.9

10

12.5

0.03

3.53

0.91

0.92

13.6

11.5

***B

DL

0.16

0.13

PA

Hs

NA

P

BN

AP

AC

Y

AC

E

FLU

PHE

AN

T

FL

T

PY

R

BA

A

CH

R

BB

F

BA

P

IND

BG

P

DB

A

76

4.1.2 Comparison of PAHs detected in house dust samples from

this study and other countries

As can be seen from Table 17, page 75, the 95% Confidence Interval (CI) of BBF,

BAP, ACY, PHE and ANT were higher than the results obtained in Australia by

Robertson et al. [159] and Ong et al. [67], and the concentrations of NAP, ACE and

FLU were higher than Robertson et al. results and lower than the results of Ong et al.

On the other hand, the concentrations of FLT, PYR and BGP were lower in this

study than the other two Australian ones. The concentrations of BAA and CHR were

higher in this work than the results reported by Simrock [160] in Germany and the

Robertson et al., but lower than the results obtained by Camann et al. in the United

States [29] and Ong et al. [67] in Australia.

4.1.3 Principal Component Analysis (PCA)

PCA was performed by SIMCA-P 10.0 (Umetrics AB). The data matrix consisted of

43 objects (houses) and 30 variables (PAHs and physical parameters). The score

plots of the objects were grouped together into 7 clusters (Figure 7). Most of the

objects were inside the Hotelling-t ellipse (p = 0.05) except for house 30 which was

atypical with a relatively high positive value on PC1, because it contained the highest

concentrations of PAHs. The high concentrations of these substances are most

probably due to a combination of two important parameters known to make high

contributions to PAH concentrations in the houses [161, 162]: these houses face a

major road and are inhabited by smokers. The house objects in clusters 5, 6 and 7

have positive values on PC1, and those in clusters 1, 2 and 3 have negative values on

this PC, while houses in cluster 4 have very low PC scores. Houses in clusters 1 and

7 have relatively high positive values on PC2, and those in clusters 3 and 5 have

77

negative values, while houses in clusters 2 and 4 have moderate to low scores on

PC2. House number 41 shows a relatively high negative score on PC2.

When a biplot was constructed from the scores and loadings information, it was

found to be too crowded. Hence, the loadings and the scores plots were presented

separately. But the patterns are interpreted relative to each other.

In the loading plot, 64% data variance is described (Figure 8). PAH vector groups*

(BBF, BNAP, BAA, CHR, ACE, BAP, PYR), (IND, NAP, ANT, FLU), and (FLT)

have high, moderate and low positive loadings values on PC1; physical parameter

vectors (DIS1, DIS2, B), (S, AC), and (W) have high, moderate and low positive

loadings values on the same PC.

PAHs vector ACY, DBA, PHE, and BGP have moderate to low negative loadings on

PC1; the physical parameter vectors (F), (A), (C1 and P) have high, moderate and

* Brackets indicate grouping of vectors or variables.

Figure 7: PCA scores plot showing correlations between the parameters

78

low negative loadings values. PAH vectors BBF, BNAP, BAA, CHR and ANT have

moderate to low positive loadings on PC2 as do the physical parameters A, DIS1,

DIS2, AC, and P. PAH vectors (NAP, IND, FLT), (PYR, BAP, PHE, ACY, DBA,

BGP), and (FLU) have high, moderate and low negative loadings values on PC2 and

the physical parameters (F, W), (B, C1), and (S) have high, moderate and low

negative loadings values on this PC.

Comparing qualitatively the relationships of the score clusters and the loading vector

distributions, it would appear that:

I. In QUAD1, BBF, BNAP, BAA, CHR, and ACE PAHs correlated with

each other, and DIS1, DIS2 and AC parameter vectors also correlated

Legend: QUAD = Quadrant A: age of building C2: chemical used in garden F: floor level

AC: air conditioner DIS1: distance from main street P: pets

B: types of building materials DIS2: distance from industrial and commercial area

S: smoking

C1: cooking frequency W: window opening

Figure 8: PCA loadings plot of PC1 vs. PC2 showing correlations between

the PAHs and the physical characteristics of the houses

79

with cluster 7. In cluster 7, houses 26 and 28 were air-conditioned; while

houses 21 and 22 were located at higher topography. These conditions

were likely making them less polluted due to adequate ventilation.

II. In QUAD 2, cluster 1 was correlated with vector A (age of the house);

this vector was in the opposite direction to vectors NAP and IND which

means that the concentration of these PAHs decreased with the increase

of the age of the houses.

III. In QUAD 3, cluster 2 correlated with DBA, ACY and vector F (floor

level); cluster 3 was correlated with BGP, PHE and C1; these PAHs may

originate from cooking. On the other hand, vector F (floor level) was in

the opposite direction to the following PAH vectors: BBF, BNAP, BAA,

CHR, and ANT, which suggests that there is less pollution from these

variables on the lower floor.

IV. In QUAD 4, cluster 5 showed correlations with the FLT and W vectors,

this means that the concentrations of this PAH increase with the increase

in open windows, because the percentage of time the windows were left

open daily was higher in these houses. Cluster 6 was correlated with BAP

and PYR. The long BAP vector and FLU and ACE vectors were strongly

correlated with the vectors S (smoking) and B (types of building

materials). The above PAHs may have the same source in the house

dust [2, 161-163] and this could be the cigarette smoke. In cluster 6, the

houses were located near the city centre and faced a major road (traffic

emissions). House 41 was affected by IND and NAP, and this correlation

might be due to its location near major road.

80

4.1.4 PROMETHEE and GAIA Analyses of PAHs

PCA gave useful information about the effect of variables such as age of the

buildings and other building characteristics on the outcomes. In order to gain a

deeper understanding of the results, the data was further subjected to PROMETHEE

and GAIA analysis so as to obtain a ranking of the houses based on their PAH

concentrations and the factors that affected the distributions of the objects in the

GAIA plot.

The data matrix with 43 objects (houses) and 16 variables (PAHs) was submitted for

analysis using the Decision Lab Software. The V-shaped preference function was

chosen for each variable because this function is best suited for quantitative criteria.

The variables were minimised so that we aimed to see the lower concentration

objects and the total variance accounted for by the first two PCs was 60.84%.

The results of the PROMETHEE II ranking of the objects based on simultaneous

assessment of the 16 chemical species are presented in Table 18.

On a relative basis, the differences or similarities between the objects (houses) can be

demonstrated by the differences in the values of the net ranking index (Φ). That is

the closer the values, the more similar the objects; the larger the difference in the Φ

net ranking index, the more different the objects. The highest ranking houses (with

least pollution) 6, 36, 45 and 47 have similar values of the net ranking index (Φ), and

the twelve lowest ranking houses were objects 28, 46, 44, 31, 26, 14, 39, 41, 22, 42,

20 and 30. As seen from the rankings, only few samples define the highest ranking

positions, while twelve samples define the lowest ranking positions. Generally, the

range (0.11 to –0.15) is quite narrow; however object 43 of relatively low Φ value

(-0.38) and is apparently quite different.

81

Table 18: The PROMITHEE ranking of objects (houses) based on the

concentrations of PAHs

Rank Houses Φ Rank Houses Φ

1 6 0.11 23 11 0.01

2 36 0.11 24 19 0.01

3 45 0.11 25 38 –0.01

4 47 0.10 26 13 –0.01

5 1 0.10 27 33 –0.02

6 24 0.10 28 27 –0.02

7 17 0.10 29 21 –0.02

8 5 0.10 30 4 –0.04

9 25 0.09 31 12 –0.04

10 37 0.09 32 28 –0.05

11 34 0.08 33 46 –0.07

12 2 0.07 34 44 –0.08

13 10 0.07 35 31 –0.09

14 32 0.07 36 26 –0.09

15 23 0.07 37 14 –0.09

16 15 0.06 38 39 –0.11

17 35 0.05 39 41 –0.12

18 16 0.05 40 22 –0.13

19 9 0.04 41 42 –0.15

20 29 0.04 42 20 –0.15

21 43 0.03 43 30 –0.38

22 3 0.02

Φ indicates the net performance flow used the ranking

In order to understand the variables that gave the ranking of the objects, GAIA

analysis was performed; it showed three separate clusters of objects on PC1

(Figure 9, page 83).

Generally, clusters 1 and 2 consisted of objects that had positive scores on PC1. The

objects in cluster 2 were located in the direction of the decision axis (π) indicating

82

that they were preferred, i.e. they had the lowest concentrations of PAHs. In

cluster 3, the objects had negative PC1 scores and were grouped away from the

π-axis, i.e. they contained the highest concentrations of PAHs. By reviewing the

physical parameters which were collected in the questionnaire during sample

collection, houses in clusters 1 and 2 were located away from the main streets and

industrial areas, and timber houses were distributed among these clusters. On the

other hand, houses in cluster 3 were located near the main roads, with some facing

major roads (houses number 20 and 30) and some located near the city centre

(houses number 14, 39, 41, and 42). They have some of the lowest negative scores on

PC2. These observations about the relationships between the high levels of PAHs

and house characteristics are in agreement with the work of Ong et al. [67]. In

cluster 2, the houses were grouped together showing that the common attribute

among them was that the residents did not cook every day [67]. House number 30,

which was made of bricks, ranked last (rank 43) because it faced a major road and

the residents smoked inside. It was previously reported that cigarette smoking

contributed significantly to the level of PAHs in indoor house dust [67, 102, 164,

165]. Therefore, indoor cigarette smoking appears to be a significant contributor to

PAH concentrations in the house dust samples studied in the current work.

The results of GAIA analysis supported the PROMETHEE ranking observations by

showing that clusters 1 and 2 were ranked as the highest. The decision axis was

pointed towards clusters 1 and 2, while cluster 3, which was ranked the lowest, was

in the opposite direction to the vector (π) [159].

83

Figure 9: GAIA analysis of house dust samples showing correlations

between the objects (houses, ▲) and the pi decision axis (●)

Legend: QUAD = Quadrant

A: age of building C2: chemical used in garden F: floor level

AC: air conditioner DIS1: distance from main street P: pets

B: types of building materials DIS2: distance from industrial and commercial area

S: smoking

C1: cooking frequency W: window opening

Figure 10: GAIA analysis showing correlations between the PAHs (■), the

house characteristics (●) and the pi decision axis (●)

84

In the loadings plot (Figure 10), PAHs vector groups (BBF, FLU ANT, PYR, BAP,

ACE, BAA, CHR, BNAP), (FLT) and (IND) have high, moderate and low positive

loadings values on PC1; physical parameter vectors (DIS1, DIS2), (W), (B, AC and

P), have high, moderate and low positive loadings values on the same PC. PAHs

vectors (ACY, BGP), (NAP), (PHE and DBA) have moderate to low negative

loadings on PC1; the physical parameters vectors (F), (A), (C1 and S) have moderate

and low negative loadings values on this PC.

PAHs vectors BBF, ANT and PYR have relatively high positive loadings on PC2,

and the physical parameters (W), (A and P) have moderate to low positive loadings

on this PC. Also, PAHs vectors (FLU), (ACY, BGP, ACE, ANT, BNAP, BAA),

CHR, NAP, PHE, BAP, DBA, and IND) have relatively high moderate to low

negative loadings values on this PC, and the physical parameters (DIS1, DIS2), (AC,

F), (S, B, and C1), have high; moderate and low negative loadings values on the

same PC.

The vector of W was pointed in the opposite direction to vectors S and C1; this

means that increasing the percentage of windows opened decreased the effect of

pollution coming from smoking and cooking indoor. Also, the vectors of C1 and S

were associated with the vectors of the following PAHs: DBA, PHE, BGP and ACY,

suggesting that these PAHs resulted from cooking and smoking indoors [161, 163,

166]. Vector F was in the opposite direction of BBF, ANT, and PYR, and this

suggests that these PAHs increase with a decrease in floor levels, i.e. the upper levels

are further away from motor vehicle emissions, which is a major source of these

pollutants.

4.1.5 Positive Matrix Factorisation (PMF)

As mentioned before in Chapter 3 (3.6.3), PMF method is very useful to analyse data

in order to identify and apportion the source of pollutants, and to find out how the

pollutants are transported from place to place. The user provides two files; one file of

sample species concentrations and the other of the uncertainties which the model

uses to calculate the number of sources, the source profiles, relative contributions,

and time series of source contributions.

A comparison of the calculated mass for all sources with the observed mass is shown

in Figure 11.

Figure 11: Observed vs. calculated PAHs concentrations in the house dust

samples

The correlation coefficient for the observed versus calculated values was 0.703. This

correlation coefficient is relatively low and reflects the scatter of the points on the

plot. This could be due to the fact that the house dust samples were obtained from

widely different indoor environments.

86

Nevertheless the results showed that there were four factors which can be used to

explain the results of this work as presented in Figure 12. Some PAHs were common

to all the four factors. BNAP, BAP and BBF are the major PAHs in the four factors.

However, each factor has other PAHs as follows:

Factor 1: The percentage contribution of this source to the PAHs is 39%. Beside the

three major PAHs, the other PAHs in this factor are ACE, ANT, ACY and FLU

suggesting that the source of pollutants in this factor are mostly smoking and oil

fumes because the presence of ACE and ACY have previously been related to oil

fumes [167-169].

Factor 2: This factor contributes only 2% of PAHs in house dust samples; the minor

criteria are PHE, ANT, PYR, BAA and CHR. It shows a mixture of sources, as the

presence of CHR and ANT seems to be associated with traces of diesel exhaust

emissions [67], while the presence of BAA probably originates from natural gas

combustion [170].

Factor 3: This factor accounts for 48% of the PAHs in the house dust samples and

beside the major three PAHs; the minor PAHs were BAA, CHR, NAP, FLU and

DBA. Therefore, this source is probably attributable to low temperature cooking

activities [169]. As expected from residential buildings where cooking occurs, this

source is the most important contributor to the PAHs in the studied house dust

samples. Thus, cooking is a significant source of PAHs in the indoor environment.

Factor 4: The percentage contribution of this source is 11%. Beside the three major

PAHs, there are minor ones such as ACY, PHE and ANT. These PAHs may occur as

a result of traces of diesel exhaust emissions or vehicle emissions [171].

87

Factor 1 (39%) Smoking and Oil fumes

NA

P

BN

AP

AC

Y

AC

E

FL

U

PH

E

AN

T

FL

T

PY

R

BA

A

CH

R

BB

F

BA

P

IND

BG

P

DB

A

Co

ncen

trat

ion

s (u

g/kg

)

0.0001

0.001

0.01

0.1

1

Factor 2 (2%) mixture of sources

NA

P

BN

AP

AC

Y

AC

E

FL

U

PH

E

AN

T

FL

T

PY

R

BA

A

CH

R

BB

F

BA

P

IND

BG

P

DB

A

Co

nce

ntra

tions

(ug

/kg

)

0.001

0.01

0.1

1

10

Factor 3 (48%) cooking activities

NA

P

BN

AP

AC

Y

AC

E

FL

U

PH

E

AN

T

FL

T

PY

R

BA

A

CH

R

BB

F

BA

P

IND

BG

P

DB

A

conc

ent

ratio

ns(u

g/k

g)

0.0001

0.001

0.01

0.1

1

Factor 4 (11%)Vehicle emissions

NA

P

BN

AP

AC

Y

AC

E

FL

U

PH

E

AN

T

FL

T

PY

R

BA

A

CH

R

BB

F

BA

P

IND

BG

P

DB

A

Co

nce

ntra

tions

(ug

/kg

)

0.0001

0.001

0.01

0.1

1

Figure 12: Source profile for PAH factors

88

4.2 PESTICIDE POLLUTANTS

4.2.1 Soxhlet extraction vs. Microwave digestion extraction

As mentioned before in Chapter 3 (3.2.2.2), 20 samples of house dust were extracted

by two different methods, namely, Microwave-Assisted Extraction (MAE) procedure

and Soxhlet procedure. Table 19 and Table 20 summarise the mean concentrations of

the results obtained from this work. After the extraction, each of the obtained extract

was analysed for pesticides as described in the experimental Section 3.5.3. The two

sets of results from the different microwave digestion method can be used for

pesticides extraction. This method has been reported as an alternative sample

preparation technique for many solid samples [133-135].

Table 19: Concentrations of pesticides (µg/g) in house dust samples extracted by

Soxhlet extraction procedure

Pesticides Mean Max Min STDEV

α-BHC 0.50 3.10 0.10 0.70

β-BHC 0.80 4.70 0.10 1.00

δ-BHC 2.40 25.1 0.30 5.20

Heptachlor 0.50 3.10 0.10 0.80

Heptachlor epoxide 0.70 4.20 0.10 1.00

Endosulfan I 0.60 3.80 0.10 1.00

Endosulfan II 1.30 5.80 0.10 1.80

DDD 2.00 19.9 0.20 4.00

DDE 1.20 9.00 0.20 2.00

Dieldrin 22.0 293 0.20 59.0

Endrin 2.70 31.3 0.10 6.50

Endosulfan 0.70 3.70 0.10 0.90

Endosulfan sulphate 4.30 30.3 0.20 7.40

DDT 4.30 30.3 0.20 7.40

Methoxychlor 0.80 4.70 0 1.20

89

Table 20: Concentrations of pesticides (µg/g) in house dust samples extracted by

Microwave assisted extraction procedure

Pesticides Mean Max Min STDEV

α-BHC 0.50 1.20 0.10 0.30

β-BHC 1.00 3.40 0.20 0.80

δ-BHC 2.80 13.6 0.40 3.20

Heptachlor 0.60 1.80 0.10 0.50

Heptachlor epoxide 0.70 1.60 0.10 0.50

Endosulfan I 0.80 2.10 0.20 0.50

Endosulfan II 1.00 3.10 0.10 0.80

DDD 2.30 9.40 0.20 2.40

DDE 1.60 4.50 0.20 1.40

Dieldrin 22.3 131 0.30 35.3

Endrin 5.90 50.0 0.20 11.3

Endosulfan 5.40 58.9 0.20 11.7

Endosulfan sulphate 13.8 114 0.30 30.1

DDT 13.8 114 0.30 30.1

Methoxychlor 1.10 3.20 0.20 0.80

4.2.2 Concentrations

Compositional analysis showed that Dieldrin (DIE) was the major pesticide

component for the majority of the samples, followed by ENDS and then DDT

(Figure 13 and Table 21). This result is noteworthy on one hand because the use of

DIE has been banned in Australia since 1985 [172], but on the other hand, the result

shows that DIE is very stable with respect to degradation in indoor environments.

90

Figure 13: Concentrations of pesticides (µg/kg) in the house dust samples

Table 21: Concentrations of pesticides (µg/kg) in the house dust samples

Chemical Name Abbrev. Mean Max Min SD LOD

α-Lindane α-BHC 0.50 3.10 0.07 0.60 0.06

β-Lindane β-BHC 0.90 4.70 0.10 1.00 0.11

δ-Lindane δ-BHC 2.30 25.10 0.30 3.50 0.09

Heptachlor HC 0.60 3.10 BDL 0.70 0.10

Heptachlor epoxide HCE 0.80 4.20 0.10 0.90 0.07

Endosulfan END 1.40 38.50 0.09 5.00 0.09

Endosulfan sulfate END S 1.00 5.60 0.08 1.20 0.08

Endosulfan I (ALPHA) End I 2.20 19.90 0.10 3.60 0.07

Endosulfan II (BETA) End II 1.40 9.00 0.10 1.80 0.07

Dichlorodiphenyldichloroethane DDD 22.30 293.20 0.10 50.30 0.07

Dicholrodiphenyldichloroethylene DDE 2.30 31.30 0.10 4.60 0.08

Dieldrin DIE 1.40 13.00 0.09 2.10 0.08

Endrin EN 6.20 44.60 0.20 8.80 0.08

Dichlorodiphenyltrichloroethane DDT 6.20 44.60 0.20 8.80 0.08

Methoxychlor MET 0.90 4.70 0.10 1.00 0.08

BDL=below detection limit

91

4.2.3 Comparison of pesticides detected in house dust from

different places in the world

Compared to the previous studies conducted on the content of pesticides of house

dust samples, the present work is very comprehensive because it measured a full

range of pesticides. Table 22 compares values found in this study to the published

results.

The median concentrations of BHC was higher in this study compared to the results

obtained by Tan et al. [173], Becker et al. [174], Walker et al. [175] and

Rudel et al. [26, 40], while the concentrations of DDT and its metabolites DDD and

DDE were higher in this study than the results obtained in Germany by

Becker et al. [174], Walker et al. [175] as well as in the United States by

Camann et al. [29] and Rudel et al. [26, 40]. Lower results were obtained from the

Singapore work by Tan et al. [51].

92

Tab

le 2

2: C

omp

aris

on o

f P

AH

s (µ

g/g)

det

ecte

d in

hou

se d

ust

sam

ple

s fr

om t

his

stu

dy

and

oth

er c

ount

ries

Un

ited

Sta

tes

Ru

del e

t al

. [26

, 40]

Med

ian

BD

L

BD

L

BD

L

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

***C

I =

Con

fide

nce

inte

rval

95%

CI

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

Ger

man

y

Wal

ker

et a

l. [

175]

Med

ian

nd

nd

<0.

1

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

0.31

0.92

95%

CI

nd

nd

0.83

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

4.2

27

Uni

ted

Stat

es

Cam

ann

et a

l. [2

9]

Med

ian

nd

nd

nd

nd

nd

nd

nd

nd

0.02

3

nd

nd

nd

nd

0.09

0.07

4

**B

DL

= B

elow

det

ecti

on li

mit

95%

CI

nd

nd

nd

nd

nd

nd

nd

nd

0.2

nd

nd

nd

nd

1.6

2.07

Ger

man

y

Bec

ker

et

al. [

174]

Med

ian

nd

nd

<0.

05

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

<0.

05

<0.

05

95%

CI

nd

nd

0.75

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

1.2

5.8

Sin

gapo

re

Tan

et

al. [

51]

Med

ian

BD

L

BD

L

BD

L

*nd

nd

nd

nd

1.3

3.3

nd

nd

nd

nd

11

nd

*nd

= n

ot d

etec

ted

95%

CI

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

Res

ults

fro

m

this

stu

dy Med

ian

0.16

0.25

0.91

0.18

0.22

1.27

0.3

0.91

0.45

12.8

3

1.17

0.53

2.24

2.24

0.25

*95%

CI

0.32

0.52

1.16

0.38

0.44

0.39

0.58

0.8

0.7

3.17

0.74

0.71

2.14

2.14

0.58

Pes

tici

des

α-B

HC

β-B

HC

δ-B

HC

Hep

tach

lor

Hep

tach

lor

epox

ide

End

osul

fan

I

End

osul

fan

II

DD

D

DD

E

Die

ldri

n

End

rin

End

osul

fan

End

osul

fan

sulp

hate

DD

T

Met

hoxy

chlo

r

93

4.2.4 Principal Components Analysis (PCA)

PCA was performed on the data matrix which consisted of 59 objects (houses) and

24 variables (pesticides and physical parameters). The data was pre-treated by

autoscaling. The objects on the score plots were grouped together into 4 clusters

(Figure 14). Most objects were inside the Hotelling-t ellipse (p = 0.05) [104]. The

house objects in clusters 3 and 4 have positive values on PC1, and those in clusters1

and 2 have low negative values on this PC. Houses in clusters 1 and 4 have moderate

to low positive scores on this PC2. Houses in clusters 3 and 4 have moderate to low

negative scores on PC2.

In the loading plot, 51 percent data variance is described Figure 15. All pesticides

have positive loading values on PC1. Pesticide vector groups (d-BHC, B-BHC, a-

BHCHCE, HC, DDD, DDE, MET, DIE, EN, ENDII, IND), (END, END1) and

(DDT, ENDS) have high, moderate and low positive loadings values on PC1,

respectively; physical parameter vectors (A), (S) and (DIS1 and C1) have high,

moderate and low positive loadings values on the same PC. The physical parameter

vectors (F, W) and (P, DIS2, and C2) have moderate to low negative loadings values.

Pesticide vectors groups d-BHC, DDD, DDE, EN, ENDII, DIE have high positive

loadings values on PC2; the physical parameter vectors F and C1 have high and low

positive loadings values on the same PC, respectively. Pesticide vectors groups (HC,

HCE, MET, a-BHC, B-BHC, END END1) and (ENDS and DDT) have high to

moderate negative loadings values on PC2; physical parameter vectors W, DIS1,

DIS2, A, P, and C2 have moderate negative loadings values on this PC.

94

Figure 14: PCA scores plot showing correlations between the parameters

Legend: QUAD = Quadrant A: age of building C2: chemical used in garden F: floor level

AC: air conditioner DIS1: distance from main street P: pets

B: types of building materials DIS2: distance from industrial and commercial area

S: smoking

C1: cooking frequency W: window opening

Figure 15: PCA loadings plot of PC1vs PC2 showing correlations between the

pesticides and the physical characters of houses

95

Comparing qualitatively the relationships of the score clusters and the loading vector

distributions, it would appear that:

I. In QUAD1, d-BHC, DDE, DDD, ENDII, DIE, and EN pesticides were

strongly correlated with each other and moderately correlated with

cluster 4 indicating that these pesticides originated from the same

source [176] probably the residues from past termite treatments.

II. In QUAD 2, cluster 1 was correlated with vector F, and in the opposite

direction to vector DIS1, this suggests that the concentrations of

pesticides in these houses decrease with the increase of the distance from

major roads, as most of the houses in this cluster were located away from

major roads.

III. In QUAD 3, cluster 2 was correlated with DIS2, DIS1, P, C2 and W;

most of the houses in this cluster have high percent of windows opening

daily.

IV. In QUAD 4, cluster 3 showed correlations with the END1, END, DDT,

and ENDS and A (age of the house) vectors. This suggested that the

concentrations of pesticides in the house dust increase with the age of the

house possibly because these pesticides were used in the past as

insecticides and wood treatments [104]. This group of pesticides was in

the opposite direction of vector F, this means that the concentrations of

these pesticides increase with the decrease of the floor level.

96

4.2.5 MCDM analysis of the pesticides data

A data matrix with 59 objects (houses) and 15 variables (pesticides) was submitted

for data analysis. The modelling of all the variables included minimised criteria

which indicate that low amounts of pesticides are preferred, V-shaped preference

function was selected as a function which reflects the build up of the pesticides in the

dust samples. This method best suited for quantitative criteria and weighing = 1. The

total variance accounted for by the first two PCs was 85.16%. The PROMETHEE II

complete outranking flow Φ index values (Table 23) showed that on the basis of the

concentrations of the pesticides: the highest ranking houses (with least pollution)

were 51, 54, 58, 55, 53, and 27and the lowest ranking houses were 3, 9, 14 and 6

with highest pesticide content. House 6, which ranked the lowest (Φ = 0.90), is a

timber home that contained the highest concentrations of the following pesticides:

Endosulfan II, DDD, DDE, Dieldrin, δ-BHC and Endrin. In general, the value of the

net ranking index (Φ) ranged from +0.89 to –0.9 which is quite a narrow range and

similar to that for the PAHs; that means the objects were closer in the values.

A close look at the PROMETHEE results presented in Table 23 and the GAIA plot in

Figure 17 showed that the results of both procedures support each other, in that the

objects (houses) could be separated into two clusters. GAIA results showed that two

separate clusters of objects were observed on PC1 score plot (Figure 16, page 98).

The first cluster had positive scores on PC1, while the other cluster had objects with

negative scores on this PC. Based on the concentrations of the pesticides, the

decision axis (π) pointed towards cluster 1. The objects in this cluster had the most

positive net flow (Φ) index values and this referred to the least polluted houses.

Cluster 2 was located away from the decision axis, showing that the houses in this

cluster were the more polluted ones.

97

Table 23: The PROMETHEE ranking of objects (houses) based on the

concentrations of pesticides

Rank Houses Φ Rank Houses Φ

1 51 0.89 31 61 –0.05

2 54 0.85 32 42 –0.06

3 58 0.84 33 35 –0.06

4 55 0.8 34 38 –0.08

5 53 0.78 35 29 –0.1

6 27 0.74 36 31 –0.13

7 48 0.64 37 26 –0.18

8 36 0.62 38 11 –0.23

9 49 0.59 39 19 –0.27

10 30 0.57 40 46 –0.31

11 24 0.56 41 39 –0.35

12 50 0.55 42 15 –0.35

13 59 0.54 43 45 –0.39

14 43 0.5 44 4 –0.4

15 62 0.45 45 18 –0.43

16 21 0.44 46 40 –0.49

17 60 0.42 47 16 –0.52

18 52 0.31 48 23 –0.54

19 32 0.29 49 25 –0.56

20 1 0.29 50 17 –0.6

21 56 0.23 51 8 –0.64

22 34 0.23 52 12 –0.64

23 57 0.19 53 20 –0.68

24 13 0.17 54 5 –0.72

25 44 0.15 55 33 –0.73

26 47 0.11 56 3 –0.77

27 22 0.08 57 9 –0.81

28 41 0.05 58 14 –0.87

29 37 0 59 6 –0.9

30 28 –0.02

Φ indicates the net performance flow used the ranking

98

Figure 16: GAIA analysis of pesticides in house dust samples showing

correlations between the objects (houses) and the pi decision axis (●)

Legend: QUAD = Quadrant

A: age of building C2: chemical used in garden F: floor level

AC: air conditioner DIS1: distance from main street P: pets

B: types of building materials DIS2: distance from industrial and commercial area

S: smoking

C1: cooking frequency W: window opening

Figure 17: GAIA loading plot showing the correlations between the

pesticides (■), the house characteristics (■) and the pi decision axis (●)

99

Based on the concentrations of the pesticides, the decision axis (π) pointed towards

cluster 1. The objects in this cluster had the most positive net flow (Φ) index values

and this referred to the least polluted houses. Cluster 2 was located away from the

decision axis, showing that the houses in this cluster were the more polluted ones.

By reviewing the physical parameters, most of the houses in cluster 1 were found to

be new units, flats, students' accommodation and town houses with no gardens. Most

dwellings were not on the first floor. This agreed with the results obtained by

Tan et al. [51] and supported the view that houses on the upper floors are less

polluted with pesticides than those on the lower floors because they are more

ventilated and farther away from ground sources. The common aspect among the

houses in cluster 2 was that most of them were old with gardens, and most of the

timber houses were located in this cluster.

In the loadings plot (Figure 17), all pesticide vectors have positive loadings values on

PC1; DIE and DDE showed moderate loadings values, while the remaining

pesticides (END11, ENDS, DDE, DDT, HC, MET, HCE, a-BHC, b-BHC and d-

BHC) have low positive loadings values on this PC, respectively. The physical

parameter vectors (DIS2), (S, A) and (C2 and P) have high, moderate and low

positive loadings values on the same PC, respectively. The physical parameters

vectors (DIS1, W), (F) and (C1) have high, moderate and low negative loadings

values on PC1.

Pesticide vectors (DIE, DDD) and (END11, ENDS, and DDE) have moderate to low

positive loadings on PC2, and the physical parameters (W), (F) and (C1) have high

moderate and low positive loadings on this PC. Also, pesticides vectors (END), (a-

BHC, b-BHC, d-BHC, HC, HCE, and MET) have moderate to low negative loadings

values on this PC, and the physical parameters (DIS1, DIS2), (S, A), (C2 and P) have

high, moderate and low negative loadings values on the same PC.

4.2.6 Positive Matrix Factorisation (PMF)

In the present work, the source profile showed good correlation between observed

and calculated values (Figure 18). The squared correlation coefficient (R2) of 0.997

obtained for this linear relationship indicating that the modelled mass effectively

accounted for most of the observed pesticides mass concentrations in the house dust

samples.

Figure 18: Observed vs. calculated values of pesticides in the house dust samples

As shown in Figure 19, page 102, five factors were required to explain the modelled

pesticide concentrations:

Factor 1: This factor contributes 17% of pesticides found in the house dust samples.

The main constituents of pesticides were Endrin and its stereoisomer and Dieldrin.

These two pesticides were used in the past to control termites in buildings and

fences [104]. The minor pesticides were Endosulfan (END), Endosulfan sulphate

101

(ENDS) (which are still being used in Australia) and DDT, followed by β-BHC and

δ-BHC. This source may probably be from termite control.

Factor 2: This source accounts for 15% of pesticides in the house dust samples and

contains ENDS and DDT, which were used in the past for treating indoor furniture;

this suggests that the source of these pesticides is from treating indoor furniture [40].

Factor 3: This source contributes 20% of the pesticides in the house dust samples,

which is dominated with Dieldrin, DDD and DDE. Both DDD and DDE are more

stable than DDT.

Factor 4: The percentage contribution of this factor is 18%, which is dominated with

END and its metabolites, ENDS along with DDT. The minor pesticides detected are

Dieldrin, EN and δ-BHC. This factor is assigned to END and ENDS which are still

used in horticultural and ornamental plants to fight pests [176].

Factor 5: This source accounts for 30% of the pesticides in the house dust samples.

Most of the pesticides studied in this work are detected in this factor. It is a mix

source of pesticides, some of them comes from application of indoor pesticides such

as δ-BHC, β-BHC, Methoxychlor, others from applications of pesticides outdoor

such as heptachlor and heptachlor epoxide, or from treated indoor furnishing such as

DDT and Endosulfan.

102

Factor 2 (15%) treating indoor furniturea-

BH

C

B-B

HC

d-B

HC

HC

HC

E

EN

D 1

EN

D 1

1

DD

D

DD

E

DIE EN

EN

D

EN

D S

DD

T

ME

T

Con

cent

ratio

ns(u

g/kg

)

0.0001

0.001

0.01

0.1

1

Factor 3 (20%) unknown

a-B

HC

B-B

HC

d-B

HC

HC

HC

E

EN

D 1

EN

D 1

1

DD

D

DD

E

DIE EN

EN

D

EN

D S

DD

T

ME

T

Con

cent

ratio

ns(u

g/kg

)

0.0001

0.001

0.01

0.1

1

Factor 4 (18%) control of pests

a-B

HC

B-B

HC

d-B

HC

HC

HC

E

EN

D 1

EN

D 1

1

DD

D

DD

E

DIE EN

EN

D

EN

D S

DD

T

ME

TCon

cent

raat

ions

(ug/

kg)

0.0001

0.001

0.01

0.1

1

Factor 5 (30%) mix sources

pesticides

a-B

HC

B-B

HC

d-B

HC

HC

HC

E

EN

D 1

EN

D 1

1

DD

D

DD

E

DIE EN

EN

D

EN

D S

DD

T

ME

T

Con

cent

ratio

ns (

ug/k

g)

0.0001

0.001

0.01

0.1

1

Factor 1 (17%) termite control

a-B

HC

B-B

HC

d-B

HC

HC

HC

E

EN

D 1

EN

D 1

1

DD

D

DD

E

DIE EN

EN

D

EN

D S

DD

T

ME

TCon

cent

raat

ions

(ug/

kg)

0.0001

0.001

0.01

0.1

1

Figure 19: Source profile for the factors

103

4.3 ELEMENTS POLLUTANTS:

4.3.1 Concentrations

Elemental analysis of the samples identified calcium to be the major component for

the majority of the samples, followed by iron, aluminium, magnesium, lead and then

zinc (Table 24 and Figure 20).

Table 24: Concentrations of elements (µg/g) in the house dust samples

Elements Mean Max Min STDEV 95%CI Median LOD

Li 14.9 51.7 BDL 13.2 3.80 10.9 0.04

Mg 2632 7512 578 1453 420 2240 0.04

Al 3384 16176 301 2960 855 2487 0.00

Ca 30760 279126 3011 47049 13596 14616 0.05

Cr 62.6 369 7.00 66.5 19.2 44.2 0.09

Mn 120 541 15.9 90.7 26.2 96.6 0.04

Fe 5425 19018 390 3863 1116 4732 0.04

Co 5.60 21.6 BDL 5.30 1.50 3.60 0.05

Ni 51.6 182 5.50 37.9 11.0 39.7 0.04

Cu 169 774 25.6 148 42.6 119 0.02

Zn 1266 7644 100 1228 355 1013 0.05

Ga 15.1 64.0 1.30 13.2 3.80 10.3 0.05

Sr 98.4 672 8.10 103 29.6 76.6 0.04

Cd 17.2 110 2.60 19.5 5.60 10.6 0.04

Ag 1.60 43.4 BDL 6.40 1.80 0.50 0.00

Tl 1.00 18.2 BDL 3.70 1.1 0.0 0.05

Pb 1357 36356 9.30 5691 1645 130 0.04

Mo 98.8 4247 BDL 626 181 1.70 0.04

As 7.70 85.9 BDL 15.0 4.30 1.70 0.00

V 3.40 36.2 BDL 6.60 1.90 0.50 0.04

104

Figure 20: Concentrations of the elements (µg/kg) in house dust samples

4.3.2 Comparison of elements detected in house dust samples

collected from different countries

There have been many studies conducted on the elemental analysis of house dust

samples. Some of these studies were carried-out in Australia and others were

performed overseas. At first, most of the studies were focused on Pb [14, 83, 177,

178] which later broadened to include Pb, Cd, Zn and Cu [76, 80, 179]. In the current

study 20 elements were analysed in the house dust samples. Table 25 summarises the

concentrations of the elements from this study and those from the literature.

105

Tab

le 2

5: C

omp

aris

on o

f el

emen

ts (

µg/

g) d

etec

ted

in h

ouse

du

st f

rom

th

is s

tud

y an

d o

ther

cou

ntr

ies

Can

ada

Ras

mus

sen

et

al.

[47]

Med

ian

6.1

9,28

5

22.9

45.2

5

69.2

266.

1

13.1

5

8.7

51.5

157.

3

633.

1

nd

249

4.3

1.3

0.13

9,28

5

1.7

4.1

22

95%

CI

8.2

13,3

90

40.1

6

66.7

191.

8

407.

3

21.5

75

15.1

116.

4

488.

9

1460

.8

nd

382

7.32

6.5

0.21

13,3

90

14.2

2

18.5

39.9

Nor

th W

ales

Dav

ies

et a

l. [1

79]

Med

ian

nd

nd

nd

nd

nd

nd

nd

nd

nd

159

937

nd

nd

0.7

nd

nd

346

nd

nd

nd

95%

CI

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

Nd

New

Zea

lan

d

Kim

and

F

ergu

sson

[76

]

Med

ian

nd

nd

nd

nd

nd

nd

nd

nd

nd

165

8980

nd

nd

4.23

nd

nd

573

nd

nd

nd

95%

CI

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

Hon

g K

ong

Ton

g an

d L

am.[

80]

Med

ian

nd

nd

nd

nd

nd

nd

nd

nd

nd

311

1409

nd

nd

4.3

nd

nd

157

nd

nd

nd

95%

CI

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

nd

Nd

Ger

man

y

Sei

fert

et

al. [

180]

Med

ian

nd

2000

nd

900

63

108

3900

nd

nd

76

469

nd

32

0.9

nd

nd

4 nd

2.1

Nd

95%

CI

nd

6400

nd

2780

0

178

nd

1100

0

nd

nd

339

nd

nd

119

nd

nd

nd

nd

nd

6.7

nd

Aus

tral

ia

Rob

erts

on

et a

l. [1

59]

Med

ian

12

1583

6567

.5

1901

5

nd

nd

nd

nd

nd

nd

nd

nd

45

nd

nd

nd

nd

nd

nd

nd

*nd

= n

ot d

etec

ted

95%

CI

14.5

1728

9320

3440

3

*nd

nd

nd

nd

nd

nd

nd

nd

83.1

nd

nd

nd

nd

nd

nd

nd

Res

ult

s fr

om

this

stu

dy

Med

ian

10.9

2240

2487

1461

6

44.2

96.6

4732

3.60

39.7

119

1013

10.3

76.6

10.6

0.50

0 130

1.70

1.70

0.50

*95%

CI

3.8

420

855

1359

6

19.2

26.2

1116

1.50

11.0

42.6

355

3.80

29.6

5.6

1.80

1.10

1645

181

4.30

1.90

Ele

men

ts

Li

Mg

Al

Ca

Cr

Mn

Fe

Co

Ni

Cu

Zn

Ga

Sr

Cd

Ag Tl

Pb

Mo

As V

106

As can be seen from the median concentrations, the concentrations of Mg and Sr

were higher in this study than the results obtained in Australia by

Robertson et al. [159] and in Germany by Seifert et al. [180], but both elements were

lower than the results obtained by Rasmussen et al. [47] in Canada. On the other

hand, the concentrations of Al and Ca in this study were lower than the Canadian and

the Australian studies and more than the German study.

The concentrations of Cu, Zn Cd and Pb in house dust were studied by many authors

such as Kim and Fergusson from New Zealand [76], Seifert et al. in Germany [180],

Tong and Lam in Hong Kong [80] and Davies et al. in North Wales [179]. By

comparing the results for these four elements in this work with those obtained in the

five previously mentioned studies and the Canadian one, it was found that the

concentrations of Cu and Pb were higher in this study than in the German results but

lower than in the Canadian, Hong Kong, New Zealand and North Wales results, On

the other hand, Zn had higher concentrations than that in the German, Canadian and

North Wales and lower than that in the New Zealand and Hong Kong results. The

concentration of Cd in this work was higher than in all other studies. The

concentrations of Cr, Mn and As were found to be lower than those reported in the

Canadian and German studies. In addition, the concentrations of Co, Ni, Ag, Tl and

V were found to be lower than those in the Canadian results.

4.3.3 Principal Components Analysis (PCA)

PCA was performed on the data matrix which consisted of 46 objects (houses) and

30 variables (the elements and physical parameters). The data was pre-treated by

autoscaling.

Figure 21: PCA loadings plot of elements showing correlations between

parameters

The scores plot showed that the objects were grouped into 6 clusters (Figure 21).

Most objects were inside the Hotelling-2 ellipse (p = 0.05) except for houses 5 and 3.

The house objects in clusters 1, 5, 4 and 6 have relatively moderate to high positive

values on PC1, and those in clusters 2 and 3 have moderate to low negative values on

this PC. Houses in cluster 1 have moderate positive values on PC2, while houses in

clusters 2, 3 and 6 have low positive scores on this PC. Houses in cluster 5 have

moderate, negative values on PC2, while houses in cluster 3, 4 and 6 show low

negative scores on this PC. House number 32 shows a moderate negative score on

this PC. Houses 5 and 3 showed relatively high positive values on both PC1 and

PC2. In the loading plot, 69 % data variance is described (Figure 22). Vector groups

of the elements included (Fe, Mg, Mn, Al, Ni, Co, Tl,), (Ag, Cu, Cr, Sr, Cd, Zn), (Ca,

V, and As) have high, moderate and low positive loadings values on PC1; physical

parameter vectors (A), P, S, and C1 have moderate to low positive loadings values on

the same PC.

108

Legend: QUAD = Quadrant

A: age of building C2: chemical used in garden F: floor level

AC: air conditioner DIS1: distance from main street P: pets

B: types of building materials DIS2: distance from industrial and commercial area

S: smoking

C1: cooking frequency W: window opening

Figure 22: PCA loadings plot of PC1vs PC2 showing correlations between

the elements and the physical parameters of the houses

Element vectors Pb and Mo have low negative value on PC1; the physical parameter

vectors (B), (DIS1, DIS2) and (C2), have high, moderate and low negative loadings

values on this PC.

Element vectors (Co, Tl), (Sr, Ag, Li, Ga, Cd, Zn), (As, V, Pb, Mo) have high,

moderate and low positive loadings on PC2, and the physical parameters (A, F), (S,

C1, and AC) have moderate to low values on the same PC .

Element vectors (Fe, Mg, Mn, Ni, Al), (Cd, Cr, and Ca) have high to moderate

negative loadings values on PC2 and the physical parameters (B), (DIS1, DIS2), (C,

W and P) have high, moderate and low negative loadings values on this PC.

109

The long vectors of Mg, Al and Fe in the loading plot were oriented in the same

direction and there are strong correlations among them. This suggested that they are

mostly originated from the same source and that soil is the most reasonable source of

these elements [123]. On the other hand, the vectors of Cd and S (smoking) were

strongly correlated with each other and this suggests that these elements originated

from smoking [181]. The vector of A (age of the houses) is in the same direction

with the vectors of Co, As and V, the concentrations of these elements increased with

the increase of the age of the houses because As is a component of wood

preservatives used in older houses [182].

The vectors of the elements Cd, V, Zn, Ga, Sr, Co, As, and Li were in the opposite

direction to the parameters DIS1 (distance from main road) and the DIS2 (distance

from industrial area), indicating that as the distance decreases the concentrations of

these elements increase, since the source of these elements is mainly from the motor

vehicle emissions as well as industrial activities. This agrees with the previous result

which was obtained by Mayer et al. [13].

Comparing qualitatively the relationships of the score clusters and the loading vector

distributions, it would appear that:

I. In QUAD1, Co, Tl, Ag, Cd, Sr, Li, Ga, Zn, V and As elements correlated

with each other and with A, F, S, C1 and AC parameters vectors, and this

suggests that these elements originated from many sources such as

smoking (S) and cooking (C1). Cluster 1was also correlated with this

group of elements and parameters and so do houses 5 and 3. Most of the

old timber houses were in this QUAD (5, 3, 16, 1 and 6). These houses

110

were correlated with vector A (age of the house) as these elements were

found in the old houses more than in the new ones.

II. In QUAD 2, clusters 2 and 3 were correlated with vectors Pb and Mo.

Some of the houses in this cluster are located were near a major road.

III. In QUAD 3, cluster 3 correlated with B (types of building materials),

DIS1, DIS2, C2 and W.

IV. In QUAD 4, cluster 5 showed relatively high correlations of Mn, Mg, Mn,

Al, and Ni. Cluster 4 showed correlations with Cu, Cr, Ca vectors. Houses

6 and 34 were old timber houses which contained high amounts of many

elements.

4.3.4 Multi-Criteria Decision Making (MCDM) Analyses of

Elemental Composition

A data matrix with 46 objects (houses) and 20 variables (elements) was submitted for

data analysis. The modelling of all the variables included minimised criteria which

indicate that low amounts of elements are preferred; V-shaped preference function

was selected as a function which reflects the build up of the elements in the dust

samples. Weighting was set to 1. The PROMETHEE II complete outranking flow

(Φ index values (Table 26, page 114) showed that the highest ranked houses (with

the least pollution) were houses number 35, 9, 46, 4, 47, 2 and 10. The lowest ranked

houses (with high pollution) were numbers 6, 26, 34, 37, 22, 25 and 13. House 6

which ranked the lowest (Φ = –0.77) is a timber home that contained the highest

concentrations of most of the elements. The rank order is best understood when

compared with the associated GAIA plot (Figure 23). Houses 35, 9, 46, 4, 47 and 2

which are in the top 6 ranks correspond to the six houses with the highest positive

scores on PC1 (least polluted), while houses 6, 26, 34, 37, 22, 25 and 13 have the 7

lowest ranks and correspond to the seven objects with negative scores on this PC.

Houses 27, 21, 41, 38, 33, 39, 18, 42, 40, 31, 5, 16 and 23 have very low scores on

PC1. A fairly large group of houses 32, 27, 21, 41, 38, 20, 33, 39, 42, 40, 18, 31, 15,

5, 16 and 23 had very low scores on PC1, but were well spread out on PC2 with high

and low scores. Interestingly, houses with positive scores on PC1 (however small)

ranked in a group close to the most preferring houses (least polluted), while those

with negative scores ranked closer to the worst preferring houses. The remaining

objects distributed themselves between these three marker groups discussed.

In order to understand the variables that gave the ranking of the objects, GAIA

analysis was performed. Results of PROMETHEE shown in Table 26 and that of

GAIA plot in Figure 23 demonstrated that the results of both methods reinforce each

Figure 23: GAIA analysis of elements in house dust samples showing

correlations between the objects (houses) (▲) and the pi decision axis (●)

112

other, in that the objects (houses) could be grouped into two clusters. Figure 23

showed that the objects in the first cluster were located on the positive side of PC1

and were in the direction of the decision axis (π) indicating that they contained the

lowest concentrations of the elements, while the objects in the second cluster were on

the negative side of PC1 and were in the opposite direction of the decision axis (π)

indicating that they contained the highest concentrations of elements (Co, Zn, Sr, Ni,

Ga, Mn, Tl, Ag Cd, As and V). From the results of the physical parameters (A, B and

S), it was noted that most of the houses in cluster 1 were less than 30 years old, and

those in cluster 2 were more than 30 years old. All the houses in cluster 1 were made

of bricks, and all the timber houses were included in cluster 2 (houses 1, 3, 5, 6, 16,

23, 25 and 34). Houses with smokers were located in cluster 2 (houses 5, 6, 16 and

30) and the highest concentration of Cd was in house 5 followed by house 30 [79,

181]. It was noted that house 30, which was located near a highway junction,

contained the highest concentrations of Cu and Zn [183]. Houses 3, 5 and 6 (old

timber houses) contained the highest concentrations of Tl, which was used in the past

as a component of insecticides, and rat and ant poison [184].

In the loadings plot 60% variance explained (Figure 24) all elements vectors have

positive loadings values on PC1 except for Tl. Elements (Ca, Sr, Al, V, Ni and Zn)

have relatively high values, while (As, Mo, Cr, Cu, Pb, Mg, Mn, Co and Fe) showed

moderate loadings values, and the rest of elements (Ag, Ga ,Li and Cd) have low

positive loadings values on this PC; the physical parameters vectors (DIS1, DIS2),

(W) and (C1 and P) have high, moderate and low positive loadings values on the

same PC; Tl had a low negative loading on this PC, and the physical parameters

vectors (A), (Ac, S and F) have moderate and low negative loadings values on PC1.

113

Legend: QUAD = Quadrant

A: age of building C2: chemical used in garden F: floor level

AC: air conditioner DIS1: distance from main street P: pets

B: types of building materials DIS2: distance from industrial and commercial area

S: smoking

C1: cooking frequency W: window opening

Figure 24: GAIA loadings plot showing the correlations between the

elements (■), the house characteristics (▼) and the pi decision axis (●)

Element vectors (Al, Cu, Cr, Co, Mn, V and Mo), (As, Ga, Li, Mo and Pb ) have

relatively high to moderate positive loadings on PC2, and the physical parameters

vectors (A), (DIS1, DIS2, F, S and AC) have moderate to low positive loadings on

this PC. Also, element vectors (Fe, Mg, Zn, Ni, Sr, Ca), (Ag) and (Tl and Cd) have

relatively high, moderate to low negative loadings values on this PC, and the

physical parameters vectors (W), (P and C1) have moderate to low negative loadings

values on the same PC.

114

Table 26: The PROMETHEE ranking of objects (houses) based on the

concentrations of elements

Ranking House # Φ Ranking House # Φ

1 35 0.68 24 32 –0.03

2 9 0.63 25 39 –0.03

3 46 0.52 26 21 –0.05

4 4 0.49 27 23 –0.05

5 47 0.47 28 38 –0.08

6 2 0.45 29 5 –0.08

7 10 0.41 30 20 –0.09

8 8 0.39 31 12 –0.16

9 28 0.35 32 29 –0.22

10 43 0.27 33 30 –0.24

11 45 0.26 34 1 –0.24

12 17 0.25 35 24 –0.25

13 44 0.24 36 3 –0.27

14 19 0.21 37 14 –0.27

15 42 0.17 38 11 –0.3

16 18 0.14 39 36 –0.36

17 27 0.14 40 13 –0.41

18 15 0.08 41 25 –0.41

19 33 0.05 42 22 –0.43

20 31 0.02 43 37 –0.45

21 40 0.01 44 34 –0.5

22 41 0 45 26 –0.52

23 16 –0.03 46 6 –0.77

Φ indicates the net performance flow used the ranking

4.3.5 Positive Matrix Factorisation (PMF)

The comparison between the calculated mass for all sources of elements with the

observed mass is presented in Figure 25. The squared correlation coefficient (R2) of

0.8979 obtained for this linear relationship indicated that the modelled mass

accounted for most of the observed mass of the elements in the house dust samples.

Figure 25: Observed vs. calculated values of elements in the house dust samples

The analysis produced four factors which accounted for 40, 29, 20 and 11% of the

data variance, respectively (Figure 26, page 117).

Factor 1: This factor contributes 40% of the elements in the house dust samples. The

major elements detected are Ca, Fe, Mg, Al, and the minor ones are Mn, Cu, Zn, Pb

and Sr. This source may probably be from the soil [118, 150], which played a large

role in the elemental composition of the dust samples.

Factor 2: This source accounts for 29% of the elements in the dust samples and

contains the major elements such as Cu, Mn, Sr, Ni, Cr and Li, and with minor

elements such as Pb, Zn, Ga, Cd, Mo and As. Most of these elements are released

from the combustion of motor fuels and then settle in house dust. Therefore, motor

vehicle emissions and oil combustion are likely being the sources of these groups of

elements. It is noteworthy that Ni and V come from oil combustion and residual fuel

116

oil. Element V also occurs in motor oil [112, 150] while Cr comes from motor

vehicle exhaust as the crude oil contains traces of it, and from the wearing down of

brake linings which contains this metal [2]. Elements Mn and Pb, on the other hand,

were used in the past as additives in the fuel [123, 185], and Cd is produced from the

combustion of motor fuels [74, 79, 186]. In addition, the loadings plot (Figure 22,

page 108) indicates that most of these elements were displayed in the opposite

direction to DIS1, as the distance decreases the elemental concentration increases.

Factor 3: This factor contributes 20% of the elements found in the house dust

samples. Based on the source profile, its main constituents are Mn, Cu, Ni, Sr, Zn

and Pb and the minor ones are Cd, Ga, Cr, Mo and Ni. These elements probably

come from industrial activities [75, 77, 187]. Furthermore, the loadings plot

(Figure 24, page 113) indicates that most of these elements are displayed in the

opposite direction to DIS2, as the distance decreases the elemental concentration

increases.

Factor 4: Only 11% of the elements come from this source. The major elements in

this source are Mn, Cr, Cu, Sr, and Ga and the minor ones such as Pb, As, Cd and Co

are also visible. It looks like the elements in this factor come from mixed sources

such as pesticides, paints, smoking and cooking and other indoor sources as lead can

come from the old paints on the walls, and As, Cu and Cr from pesticides used to

treat wood [2, 182]. Elements Cr, Co and Mn are also known to be widely used in

paints and pigments [123], while V is a common component of hard steel alloys.

From the loading plot, it was found that the vector of A (age of the houses) was

correlated with the vector of As, and this may suggest that the concentration of As is

directly proportional to the age of the house. The vector of Cd is strongly correlated

with S (smoking) [181].

117

Factor 2 (29%) motor vehicle emissions and oil combustion

ElementsLi Mg Al Ca Cr Mn Fe Co Ni Cu Zn Ga Sr Cd Ag Tl pb Mo As V

Con

cent

ratio

ns (

ug/k

g)

0.0001

0.001

0.01

0.1

1

Factor 4 (11%) Mixed sources

Elements

Li Mg Al Ca Cr Mn Fe Co Ni Cu Zn Ga Sr Cd Ag Tl pb Mo As V

Con

cent

ratio

ns (

ug/k

g)

0.0001

0.001

0.01

0.1

1

Factor 3 (20%) Industrial activities

Elements

Li Mg Al Ca Cr Mn Fe Co Ni Cu Zn Ga Sr Cd Ag Tl pb Mo As V

Con

cent

ratio

ns (

ug/k

g)

0.0001

0.001

0.01

0.1

1

Factor 1 (40%) Soil

ElementsLi Mg Al Ca Cr Mn Fe Co Ni Cu Zn Ga Sr Cd Ag Tl pb Mo As V

Con

cent

ratio

ns (

ug/k

g)0.0001

0.001

0.01

0.1

1

Figure 26: Source profile for the factors for elements

118

CHAPTER 5: SUMMARY AND CONCLUSIONS

This study was initiated to assess the levels of pollutants (PAHs, elements and

pesticides) in house dust samples collected from residential homes in Brisbane,

Ipswich and Toowoomba and to identify and characterise the possible sources of

these pollutants as well as the correlations between the levels of pollutants and

characteristics of buildings and indoor activities using multivariate data analysis

techniques.

The most findings of this study are as follows:

The compositional analysis of PAHs samples showed that BBF was the

highest component followed by BAP, ACY, PHE and then ANT. For the

elemental composition, Ca was detected in the highest level followed by Fe,

Al, Mg, Pb and then Zn. Dieldrin (DIE) was the major pesticide component

followed by ENDS and then DDT.

There were two routes of introducing contaminants to the indoor

environment. The first route was from the indoor sources such as cooking

activities, smoking, and treating indoor furniture, and the second route was

from outdoor such as vehicle emissions, oil combustion, industrial activities,

soil as well as application of pesticides in the garden.

Application of Principal Component Analysis showed correlations between

the levels of indoor pollutants with the building materials, floor level, age of

house, distance from main street and industrial area as well as the lifestyle of

occupants. The summary of the most important findings in this regard are

presented below.

119

5.1 BUILDING MATERIALS

Timber houses contained lower concentrations of PAHs and relatively higher

concentrations of elements and pesticides than brick houses. This suggests that

pesticides are found more in the timber houses because they are old and pesticides

such as DDT and Lindane were used in the past especially for wood preservation and

timber treatment, and elements such as Cd and As were for controlling termites.

5.2 FLOOR LEVEL

It seems that the level of the house also has an effect on the degree of pollution of the

indoor air. Low set houses were more polluted with elements and pesticides than the

high set houses and flats, because the low set houses are close to the sources of

contamination such as pesticides applied in the garden, heavy metals drifting in from

the street, vehicle emissions and street dust

5.3 AGE OF THE HOUSE

The relationship between the age of the house and the degree of contamination varied

depending on pollutant type . The concentrations of pesticides and elements were

higher and that of PAHs were lower in the old houses than the new ones as most of

the old houses in this study were made out of timber.

5.4 DISTANCE FROM MAIN STREET ACTIVITY AND

INDUSTRIAL AREA

Most of the pollutants showed linear behaviour to the distance from the industrial

area and the main street, which means that industrial and motor vehicle emissions are

the sources of the pollutants (PAHs and elements) in these houses.

120

5.5 SMOKING AND COOKING ACTIVITIES

Strong correlations were noted between the concentrations of some PAHs (such as

BAP, FLU and ACE) and element Cd with smoking.

121

CHAPTER 6: FUTURE WORK

The samples used in this study were collected in one season; it should be

possible to collect samples in different seasons from the same houses to

determine the effects of seasons on concentrations of pollutants.

Samples could be collected from houses which use different cooking types

such as deep firing, steaming and oven.

Samples could be collected from different parts of the house, such as bed

rooms and living room.

Study on sources of secondary pollutants (sulphates, nitrates and ammonium

salts), sea salts (sodium and chloride ions) and sources of lead (bromide ion

together with lead from ICP-MS).

122

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CHAPTER 8: APPENDICES

Appendix 1: Survey questionnaire (information on the outdoor and indoor

characteristics of residences.)

First: Outdoor information (external features of the residential buildings):

Location of the residential building (suburb). Estimated distance of the residential building to the nearest industrial or

commercial areas.

Estimated distance of the residential building from a major roadway. Is the residential building facing a major road? Yes____ No____

Is the residential building facing the back of a major road? Yes ___ No____

Estimated population density of the area.

Second: Indoor information (interior features of the residential buildings) (A) House characteristics

Approximate age of the building (years). Type of the residential building:

Separate house □ Semi-detached house □ Town house □

Terrace □ Flat apartment □ Other (specify) ______________________

Type of the materials used to build the outside wall of the residential building:

Brick □ Timber □ Concrete □ Other (specify) _______________________ How many floor levels are there in the building?

How many bed rooms are present in the residential building? What are the colours of the interior walls of the building?

When was the building last renovated? What was the type of painting used for the renovation?

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Date last painted.

What is the flooring type of the living room?

Wood or tile □ Carpet □ Mat □ Other (specify) __________________________

(B) Indoor activities Cooking habits √ How often do you cook at home (cooking frequencies)? √ Do you use extractor fans while cooking: Yes ______ No _____ Cleaning habits √ How often do you sweep the floor (cleaning frequencies)?

Every day □ Every two days □ Occasionally □ Other_____________________

√ How often do you dust furniture at home?

Every day □

Every two days □

Occasionally □ Other _____________________ √ Do you use vacuum cleaner for cleaning the home? Yes ____ No _____ Ventilation system

√ Percentage of windows in the house that are regularly opened:

All windows open □ 75% open □ 50% open □ 25% open □

No open windows □ √ Windows opened:

Daily □ Occasionally □ √ Use of air-conditioning: Yes _____ No _____ √For how long is the air conditioner on during the day?

On all day □ On two hours a day □ On four hours a day □

On eight hours a day □ Off □

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Heating type:

Electricity □ Gas □ Wood □ Other (specify): ___________________

√ For how long do you use the heater during the day?

Smoking (resident smokers) Smoking: Yes_____ No _____ √How many smokers are in the building?

√How frequent do people smoke at home?

Daily □ Two times/week □ Four times /month □ Other ___________________

√Do people smoke inside the building? Yes______ No ______

Use of chemical sprays (pesticides)

√ Do you perform pest control operations (PCO) at home? Yes____ No ___ √What type of PCO is used? √When the last time PCO was used?

√ Is there any garden at home? Yes_____ No _____

√Do you use pesticides for gardening? Yes_____ No _____ √How often do you use chemical spray for gardening?

Daily □ Weekly □ Monthly □ None □ Number of actual residents at the site √ How many people live in the house? √ Do you have pets in the home? Yes_____ No _____ Cars

√ How many cars are in the home?

√ Where do you park your cars?

Inside the garage □ On the roadside □

Other (any useful information)____________________________________

137

Appendix 2: Results of PAHs (all results in ng/kg)

H No. NAP BNAP ACY ACE FLU PHE ANT FLT PYR BAA CHR BBF BAP IND BGP DBA

1 2.1 0.2 102.7 BDL 0.2 BDL BDL 0.07 BDL BDL BDL BDL BDL BDL 0.05 0.09

2 0.3 0.8 211.7 0.3 BDL 20.3 BDL 0.09 BDL 2.3 BDL BDL BDL BDL 0.1 BDL

3 4.7 0.9 241.7 0.8 BDL 108.6 BDL 0.3 0.07 BDL BDL BDL BDL BDL BDL BDL

4 0.4 298.5 11.1 13.5 33.6 4.5 7.1 0.1 5.8 6.4 6.4 BDL BDL BDL BDL BDL

5 0.3 0.5 203.8 0.2 0.2 27.4 BDL BDL BDL BDL BDL BDL BDL BDL 0.09 BDL

6 0.9 78.4 0.04 0.01 0.01 0.03 0.07 0.02 0.03 BDL BDL BDL 85.2 BDL BDL BDL

9 0.4 0.7 638 0.6 0.4 29.5 BDL 0.2 0.04 BDL BDL BDL BDL BDL 0.2 BDL

10 0.3 0.4 527.7 0.3 0.3 20.2 BDL BDL BDL BDL BDL BDL BDL BDL 0.7 BDL

11 0.06 1477 0.8 1.4 1.8 1.3 2 0.03 0.03 4.4 4.4 382 747.1 BDL BDL BDL

12 1.6 0.6 239.3 BDL 0.5 113.8 BDL 0.2 BDL BDL BDL BDL BDL BDL 3.5 BDL

13 0.09 241.8 3.8 5.8 21 6 8.2 0.04 1 2.5 2.5 618.5 621.2 0.009 0.009 BDL

14 BDL 9693 16.5 33.4 6.9 23 35.5 0.009 0.9 2.4 2.4 814.2 824 BDL BDL BDL

15 0.3 1.5 BDL 0.4 0.3 9.2 BDL 0.1 BDL 0.4 0.6 BDL BDL BDL 0.1 1.1

16 0.5 1.6 244.9 0.5 0.5 17.5 BDL BDL BDL BDL BDL BDL BDL BDL 0.2 1.7

17 0.2 0.3 156.9 0.2 0 25.6 BDL BDL BDL BDL BDL BDL BDL BDL 0.07 0.1

19 0.6 0.9 72.6 0.7 0.6 36.9 BDL BDL BDL BDL BDL BDL BDL BDL 0.3 0.4

20 BDL 1817 21.9 33.1 5.9 11.2 17.4 0.2 20.7 5.1 5.1 2031 671.7 BDL BDL BDL

21 0.05 108.8 2.1 7.5 BDL 7.5 14.4 0.07 12.3 1.5 1.5 633.6 1667 BDL BDL BDL

22 1 BDL 6.1 27.2 BDL 140.3 216.8 0.09 15.3 3.6 3.6 332.9 355.3 BDL BDL BDL

23 0.2 0.3 663.3 BDL 0.2 19.7 BDL 0.07 BDL BDL BDL BDL BDL BDL 0.08 BDL

24 0.2 543 3.9 7.3 0.6 BDL 2.8 0.008 4.7 0.1 0.1 58.7 37.5 BDL BDL BDL

25 0.3 391.1 34.8 8.2 BDL 2.8 4.4 BDL 1.1 0.6 0.6 153 103.2 BDL BDL BDL

26 0.2 1618 5.6 57.1 50.2 10.7 16.6 BDL 8.4 1.6 1.6 384.3 326.3 0.008 0.008 BDL

138

H No. NAP BNAP ACY ACE FLU PHE ANT FLT PYR BAA CHR BBF BAP IND BGP DBA

27 1.5 1320 15.6 24.1 6.1 36.1 56 0.06 4.9 1.9 1.9 520.4 222.4 BDL BDL BDL

28 BDL 1243 BDL BDL BDL 12.7 19.6 0.1 61.7 1.7 1.7 1365 BDL BDL BDL BDL

29 BDL 3729 2.3 3.8 2.2 2.2 3.5 0.006 0.5 2.2 2.2 606 235.1 BDL BDL BDL

30 150.1 6432 24.6 53.2 9.1 10.6 16.3 0.4 43.4 8.1 8.1 0 1533 0.08 0.08 BDL

31 0.03 BDL 5.3 4.8 3.3 9.1 14.6 0.1 11.6 12.3 12.3 872 BDL BDL BDL BDL

32 0.09 705.7 2.2 4 2.2 1.7 15.8 0.1 2.7 0.4 0.4 155.8 109.1 0.004 0.004 BDL

33 14.1 2.3 1313 BDL BDL 116 BDL BDL BDL BDL BDL BDL BDL BDL 0.7 BDL

34 0.5 53.8 0.05 0.01 0.02 0.02 0.1 0.06 0.05 BDL BDL 39.6 20 0.03 0.03 BDL

35 0.4 1064 2.9 12.8 BDL 39.1 60.5 0.003 0.5 1 1 129.5 50.7 BDL BDL BDL

36 0.07 16 0.07 0.02 0.03 0.02 0.2 0.05 0.07 0.009 0.009 4.7 18.8 BDL BDL BDL

37 BDL 500.1 2.7 5.9 1.6 2.9 4.5 BDL 2.3 0.6 0.6 257.7 70 BDL BDL BDL

38 BDL BDL 166.3 0.5 BDL 28.5 BDL 0.2 0.02 0.7 0.8 BDL BDL BDL 0.2 2.1

39 0.8 6442 34.6 67.6 17 9.4 37.9 0.1 10.8 1.5 1.5 718.9 203.4 BDL BDL BDL

41 1.7 12131 10.1 6.6 0 8.3 11.5 0.06 6.8 7.4 7.4 1245 518.1 BDL BDL BDL

42 0.3 1186 10.8 40.6 0 30.5 167.7 0.07 13.8 7.3 7.3 948.7 349.7 BDL BDL BDL

43 0.1 1550 8.8 22 7.1 14.2 24.2 0.01 0.7 1.3 1.3 229.9 272.9 BDL BDL BDL

44 0.7 7156 24.8 58 BDL 9.1 19.5 0.05 5.8 2.9 2.9 562.3 606.8 BDL BDL BDL

45 0.5 161 BDL 0.02 BDL 0.07 0.1 0.05 0.1 BDL BDL BDL 58 BDL BDL BDL

46 1.6 2910 10.3 26 6.4 9.6 18.7 0.07 5 10.5 10.5 BDL BDL BDL BDL BDL

47 0.3 69.7 0.01 0.01 0.01 0.06 0.1 0.1 0.09 BDL BDL 32.7 33.1 BDL BDL BDL

139

Appendix 3 Results of pesticides (all results in ng/kg)

H N

o.

α-B

HC

β-BH

C

δ-BH

C

Hep

tachlor

Hep

tachlor

epoxid

e

En

dosulfan I

En

dosulfan II

DD

D

DD

E

Dield

rin

End

rin

En

dosulfan

En

dosulfan

sulp

hate

DD

T

Meth

oxychlor

1 0.28 0.42 0.93 0.42 0.37 0.32 0.37 0.56 0.51 2.82 0.42 0.32 0.88 0.88 0.42

3 1.53 2.76 5.52 1.84 2.45 2.15 3.37 8.59 4.91 124.23 3.07 2.15 5.83 5.83 3.07

4 0.61 1.1 2.2 0.73 0.98 0.85 0.85 1.22 1.34 5.73 1.1 0.85 11.34 11.34 1.22

5 0.67 1.35 2.69 1.08 1.08 1.08 3.77 4.71 2.83 161.62 6.46 1.75 24.36 24.36 1.61

6 2.37 4.27 25.14 2.85 3.8 3.8 5.22 19.92 9.01 293.17 31.31 3.32 6.64 6.64 4.27

8 0.76 1.38 2.75 0.92 1.22 1.22 1.22 8.86 3.97 90.15 1.83 1.68 17.72 17.72 1.38

9 1.75 3.15 6.29 2.45 2.8 2.45 2.8 13.29 6.64 143.01 3.15 2.8 4.55 4.55 3.15

11 0.33 0.66 2.26 0.47 0.53 0.6 0.53 1 0.86 30.32 5.59 0.66 2.26 2.26 0.8

12 0.91 1.82 3.27 1.63 1.45 1.27 1.45 10.89 5.81 102.56 4.72 1.63 2.18 2.18 1.82

13 0.28 0.5 0.99 0.33 0.44 0.39 0.39 0.55 0.61 0.72 0.72 0.33 1.71 1.71 0.5

14 3.14 4.71 9.41 3.14 4.18 3.66 3.66 6.8 5.75 34.52 6.28 3.66 9.41 9.41 4.71

15 0.53 1.26 1.89 0.63 0.84 0.84 0.84 1.05 1.16 1.58 0.95 4 9.79 9.79 1.05

16 0.62 1.25 4.11 0.75 1 0.87 1.25 2.37 1.37 1.75 1.12 2.24 44.64 44.64 1.12

17 1.05 1.9 3.8 1.27 1.69 1.48 1.48 2.11 2.32 2.11 12.03 1.27 2.95 2.95 1.9

18 0.71 1.29 2.57 0.86 1.14 1 1 1.43 1.57 1.43 1.29 1 12.14 12.14 1.29

19 0.6 1.08 2.15 0.72 0.96 0.84 0.84 1.2 1.31 1.2 1.08 0.72 3.94 3.94 1.08

20 0.81 1.79 2.92 1.14 1.3 38.51 2.11 1.62 1.79 7.47 2.92 13 13.49 13.49 1.62

21 0.14 0.28 0.76 0.14 0.16 0.14 0.16 1.44 0.66 10.75 0.74 0.14 0.6 0.6 0.18

22 0.27 0.48 0.97 0.38 0.43 0.38 0.43 0.8 0.59 4.77 0.64 0.43 2.79 2.79 0.48

23 0.81 1.62 2.92 0.97 1.3 1.46 1.3 3.08 2.27 22.53 1.46 1.13 3.24 3.24 1.46

24 0.1 0.17 0.35 0.12 0.16 0.16 0.19 1.09 0.64 19.28 0.58 0.14 0.29 0.29 0.19

140

H N

o.

α-B

HC

β-BH

C

δ-BH

C

Heptach

lor

Heptach

lor epoxid

e

En

dosulfan

I

En

dosulfan II

DD

D

DD

E

Dield

rin

En

drin

En

dosulfan

En

dosulfan

sulphate

DD

T

Meth

oxychlor

25 2.42 2.34 2.79 2.21 1.58 1.84 1.82 0.71 2.03 2.14 2.51 2.44 2.7 2.7 2.36

26 0.29 0.52 1.16 0.35 0.46 0.46 0.58 1.04 0.64 8.88 1.28 0.58 30.29 30.29 0.7

27 0.11 0.17 0.81 0.11 0.15 0.13 0.13 0.44 0.26 3.17 0.24 0.13 0.28 0.28 0.17

28 0.35 0.44 0.97 0.31 0.35 0.35 5.58 1.84 0.57 20.99 1.45 0.4 1.45 1.45 0.4

29 0.35 0.63 1.39 0.42 0.56 0.56 0.63 0.7 0.77 1.05 0.63 0.56 12.41 12.41 0.63

30 0.14 0.25 0.59 0.2 0.23 0.2 0.23 0.42 0.31 2.23 0.31 0.17 0.37 0.37 0.25

31 0.39 0.71 1.42 0.47 0.63 0.55 0.55 1.26 0.95 5.28 0.71 0.71 1.42 1.42 0.79

32 0.26 0.47 0.88 0.36 0.41 0.31 0.36 0.52 0.57 0.57 0.52 0.31 1.71 1.71 0.47

33 1.28 2.3 5.89 1.54 2.05 1.79 1.79 4.35 2.82 19.71 2.3 1.54 14.85 14.85 2.3

34 0.17 0.43 1.59 0.2 0.27 0.3 0.23 0.37 0.37 0.76 0.4 0.86 7.87 7.87 0.5

35 0.39 0.79 1.42 0.55 0.63 0.55 0.63 0.87 0.87 1.89 0.63 0.79 1.42 1.42 0.71

36 0.14 0.26 0.55 0.17 0.23 0.2 0.2 0.29 0.32 0.72 0.26 0.17 0.4 0.4 0.26

37 0.35 0.64 3.19 0.43 0.57 0.5 0.5 0.71 0.78 0.71 0.64 0.71 0.92 0.92 0.71

38 0.32 0.57 1.14 0.38 0.51 0.57 0.63 0.63 0.7 1.27 0.57 0.44 23.88 23.88 0.63

39 0.48 1.27 7 1.15 0.48 1.63 0.66 0.6 0.78 6.88 10.02 3.26 1.63 1.63 0.6

40 0.94 1.7 3.39 1.13 1.51 1.32 1.32 1.89 2.07 1.89 1.7 1.13 2.64 2.64 1.7

41 0.26 0.51 0.92 0.31 0.41 0.36 0.36 0.57 0.57 0.57 6.42 0.36 18.29 18.29 0.62

42 0.3 0.61 1.06 0.3 0.41 0.35 0.71 3.95 1.27 44.81 6.13 0.46 0.76 0.76 0.51

43 0.18 0.32 0.64 0.21 0.28 0.28 0.25 0.35 0.39 0.85 0.32 0.25 0.49 0.49 0.32

44 0.2 0.36 0.89 0.24 0.32 0.52 0.36 0.48 0.44 0.44 0.57 1.29 18.36 18.36 0.36

45 0.52 0.94 1.87 0.62 0.83 0.83 0.94 1.14 1.14 6.45 1.04 1.77 18.73 18.73 0.94

46 0.45 0.81 2.08 0.63 0.72 0.81 0.81 1 1.72 22.7 0.9 0.81 5.16 5.16 1

141

H N

o.

α-B

HC

β-BH

C

δ-BH

C

Heptach

lor

Heptach

lor epoxid

e

En

dosulfan

I

En

dosulfan II

DD

D

DD

E

Dield

rin

En

drin

En

dosulfan

En

dosulfan

sulphate

DD

T

Meth

oxychlor

47 0.32 0.58 1.16 0.39 0.52 0.39 0.45 0.64 0.71 0.64 0.58 0.45 1.55 1.55 0.58

48 0.12 0.2 0.5 0.12 0.16 0.18 0.14 0.24 0.22 0.24 0.2 2.3 0.76 0.76 0.22

49 0.07 0.13 0.26 0.1 0.11 0.1 0.97 0.51 0.27 5.01 0.16 0.21 2.08 2.08 0.13

50 0.34 0.2 0.41 0.16 0.18 0.38 0.34 0.23 0.25 3.76 0.23 0.16 0.63 0.63 0.2

51 0.07 0.12 0.35 0.07 0.1 0.1 0.08 0.12 0.13 0.13 0.12 0.46 0.24 0.24 0.26

52 0.27 0.44 0.93 0.33 0.44 0.33 0.38 0.55 0.6 0.55 0.49 0.33 0.77 0.77 0.49

53 0.09 0.17 0.33 0.13 0.15 0.13 1.36 0.2 0.2 0.22 0.17 0.11 0.22 0.22 0.17

54 0.09 0.17 0.34 0.1 0.14 0.12 0.14 0.29 0.19 1.89 0.15 0.17 0.21 0.21 0.15

55 0.09 0.15 0.33 0.1 0.14 0.12 0.14 0.45 0.19 2.43 0.15 0.14 0.29 0.29 0.15

56 0.17 0.26 0.58 0.17 0.23 0.26 0.26 0.52 0.32 8.51 1.38 0.23 5.54 5.54 0.52

57 0.15 0.24 0.44 0.15 0.19 0.17 0.19 2.09 0.51 20.43 4.9 3.23 2.14 2.14 0.24

58 0.07 0.12 0.38 0.08 0.1 0.09 0.09 0.35 0.17 3.05 0.13 0.09 0.69 0.69 0.12

59 0.12 0.22 0.49 0.15 0.2 0.17 0.2 0.25 0.27 0.3 0.25 2.51 1.13 1.13 0.27

60 0.13 0.24 0.72 0.16 0.21 0.24 0.19 0.37 0.29 3.96 0.29 8.5 1.1 1.1 0.24

61 0.25 0.42 2 0.29 0.33 0.33 1.75 3.92 1.13 43.29 2 0.5 1.17 1.17 0.42

62 0.15 0.29 0.55 0.17 0.23 0.2 0.23 0.67 0.38 3.55 0.26 0.52 0.41 0.41 0.26

142

Appendix 4: Results of elemental analysis (all results in ng/kg)

H No. Li Mg Al Ca Cr Mn Fe Co Ni Cu Zn Ga Sr Cd Ag Tl Pb Mo As V

1 47.2 2512 1349 109255 28.8 116 6814 7.5 32.9 118.2 679.6 12 119 5 1.4 0.6 80 34.2 11.4 10.5

2 6.3 2034 857 59803 16.6 66 2766 1.6 36.2 39.3 256.6 4.7 87.1 9.6 0.1 BDL 29 0.4 BDL BDL

3 31.1 1867 1061 43411 35.7 79.6 5125 21.6 92.3 36.2 2831 23 672 41.3 3.8 17.9 119 5.2 BDL BDL

4 13.3 577.9 805 3011 7 38.3 1736 2.9 14 25.6 282.5 8.6 50.4 6.5 BDL 2.7 36356 1.1 BDL BDL

5 19.4 2022 1524 6331 92.1 104 2548 19.8 12.9 60.4 749 22 55.1 61.4 43.4 18.2 82 1.7 36.7 BDL

6 48 5448 8391 56841 152 257 11667 13.5 113 341.6 1844 28 160 20.4 1 1.9 1038 6.7 7 6.7

8 11.2 3121 1268 20988 28.5 54 1777 2.1 21.9 100.6 338.5 10 49.9 15.7 BDL BDL 56 BDL BDL BDL

9 5.3 789.3 778 3774 13.1 34.7 946 1.4 5.5 32.1 187.3 40 32.3 9 BDL BDL 36 0.1 BDL BDL

10 6.5 1277 1358 6955 22.1 42 1541 1.1 17.1 54.8 514.9 7.7 49.2 11.1 BDL 0.2 376 0.2 4.6 BDL

11 39.2 3129 7471 12627 60.7 135 7929 7.6 49.3 170.9 1244 34 92.4 6.8 0.3 BDL 219 3.8 0.7 2.9

12 31.8 3218 4504 13608 44.4 116 7768 5.1 34.2 155.3 990 58 72.1 10.1 0.3 BDL 144 2.4 BDL 2

13 30.8 3532 4704 11690 128 145 9926 7.9 54.4 280.6 1809 27 76.4 10.5 0.9 BDL 291 5 3.1 2.8

14 16.2 2930 3448 11144 369 98 4750 8.7 48.1 229.8 1176 20 78.9 9.6 1.5 0.2 244 2.2 1.5 1.7

15 5.7 1290 1486 20283 63.4 82.8 6095 4.3 22.3 97.2 1562 9.7 80.6 10.1 0.1 BDL 189 4247 BDL BDL

16 14.8 1580 3117 16355 15.4 67.6 3708 2.3 23.6 146.3 737 14 60.6 24.7 0.1 BDL 139 106 39.7 36.2

17 6.3 1498 1891 5536 14.8 93.1 2615 1.7 9.3 37.9 338.9 5.8 35.2 16.3 BDL BDL 2203 26 17.9 13.9

18 22.7 2084 2212 10108 73.4 56.3 2918 21.2 58.3 107.5 1157 11 37.5 4.5 0.8 BDL 73 1 BDL BDL

19 12.1 1795 1564 13657 13.5 59.5 2370 1.3 15.5 33.4 255.9 5.4 171 23.8 BDL BDL 114 22.8 23.1 17.2

20 22 2972 5246 24000 50.5 136 7048 5.6 43.1 83 2070 7.4 76.8 10.7 0.2 BDL 75 0.4 BDL 3.7

21 17.1 2710 2566 53663 41.3 134 6380 5.2 54.5 103.3 2038 4.1 111 4.6 1.3 BDL 92 0.2 4.2 BDL

22 26.3 4988 16176 23720 80.2 389 19018 9.4 70.7 166.3 1422 26 97.7 15.7 BDL BDL 117 1.1 6.3 10.2

23 5.6 1484 1316 9043 41.1 92 3203 9.2 19 94.3 7644 64 59.2 17.5 0.2 BDL 14477 3.1 8 7.2

24 22.5 4502 6302 13500 54 126 6996 4 87.1 451.6 1185 14 61.2 69.2 BDL BDL 180 0.7 2.7 2

143

H No. Li Mg Al Ca Cr Mn Fe Co Ni Cu Zn Ga Sr Cd Ag Tl Pb Mo As V

25 25.6 3733 9398 15894 84.3 207 14941 6.6 57.3 203.9 942.4 17 88.3 8.5 0.2 BDL 162 31.8 15.4 14.7

26 26.9 5557 6343 35224 210 190 13449 6.2 86.4 279.6 1267 18 182 8 0.1 BDL 309 5.9 85.9 4.8

27 14.6 3636 2010 148720 57.2 128 6940 2.7 32.4 70.8 573.8 3.8 147 3 0.6 BDL 53 0.1 BDL BDL

28 9.4 987.4 2445 6305 53.6 62.7 2558 2.3 31 93.9 553.8 8.9 37.7 2.6 0.5 BDL 55 2.2 15.7 BDL

29 12.9 2283 7867 7694 139 92.3 5214 9.2 88.8 339 1919 17 90.8 11.3 BDL BDL 255 6 BDL 0.8

30 10.9 2131 3466 11945 44 95.1 5134 6.6 92.9 774.4 3852 9.9 179 34.5 3.2 BDL 152 5.1 BDL BDL

31 9.4 2220 3862 6936 82.5 139 4714 3.1 50.5 144.7 793.1 8.8 41.5 7 1.2 BDL 66 1.5 14.5 0.1

32 2.8 7512 867 279126 18.2 176 8626 2.9 135 153.1 1920 5 275 9.3 0.6 BDL 36 0.1 6.2 BDL

33 BDL 2017 4153 20641 18.2 153 6829 BDL 57.8 408.1 1070 4.4 97.8 16.7 BDL BDL 345 4 BDL BDL

34 4.4 5058 5779 14150 46.5 541 10405 16.2 132 324.9 1798 25 227 11.8 1.6 0.2 805 0.7 3.5 8.1

35 1.7 1006 301 3048 14.8 15.9 390 0.9 8.3 34.1 100.4 1.3 8.1 5.5 3.1 BDL 9 0.7 4.1 0.7

36 5.4 2395 3158 35711 218 149 4505 5.8 182 228.1 2003 14 101 12.5 0.5 0.3 1619 0.8 2 2.4

37 6.9 3626 5275 44902 46.1 192 7229 3.9 95.6 450.6 1189 12 120 110 1 BDL 150 5.2 8.9 4.4

38 3.2 4144 2276 77898 108 112 5254 3 35.1 150 1036 6.3 115 13.8 0.6 BDL 122 2 BDL 0.2

39 4.5 1923 1573 46573 31.6 78.3 4607 2.8 45.7 120.6 2090 20 75.9 13.2 1.9 BDL 530 1.7 0.9 BDL

40 51.7 3038 1970 15082 25.9 67.6 2674 4.1 66.3 112.2 593 11 48.6 23.8 0.7 2.1 63 3.6 BDL BDL

41 5 2637 3836 32486 33.5 141 5276 2.9 70.8 333 879.7 8.7 88.6 10.4 0.7 BDL 114 BDL BDL BDL

42 3.8 2259 2785 10201 47.6 99.3 3715 2.9 25.6 188.5 640.8 5.2 45.8 5.4 0.4 BDL 80 1.4 11.1 2.4

43 3 2195 2808 6270 52.6 85.2 3339 3.2 35.7 124.6 309.2 6 25.7 5.6 0.4 BDL 38 0.5 17.5 1.7

44 2.7 2168 1557 9887 17 93.2 3052 3.1 20.6 93.9 1591 16 49.8 7.2 1.1 BDL 94 1.7 BDL 0.3

45 4.7 1368 2530 15754 40 64.9 2805 2.5 30.9 91.7 540 7 42.5 7.9 0.6 BDL 259 0.8 BDL 0.8

46 4.3 702 1348 3700 21.1 59.4 753 0.4 34.4 43.9 927.7 7 24.3 32.9 BDL BDL 336 BDL BDL BDL

47 10.9 1132 662 17510 24.9 48 1480 2.1 22 48.6 341.6 4.8 32.1 17.6 0.3 0.8 42 BDL BDL BDL