ASSESSMENT OF NATURALLY OCCURRING RADIOACTIVE MATERIALS 
AND TRACE ELEMENTS IN PLAYGROUNDS OF SELECTED BASIC 
SCHOOLS IN THE GA EAST MUNICIPAL DISTRICT, ACCRA, GHANA 
 
 
ERASTUS EIGHT TAAPOPI 
(10444957) 
 
Bachelor of Science in Physics, Geology (University of Namibia, 2012) 
 
This thesis is submitted to the University of Ghana, Legon, in partial fulfillment of the 
requirements of the award of  
 
MPhil Nuclear Science and Technology 
 
 
JULY, 2015  
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DECLARATION 
 
This is to certify that this thesis is the result of research work undertaken by Erastus Eight 
Taapopi towards the Degree of Master of Philosophy in Nuclear Science and Technology 
in the Department of Medical Physics, School of Nuclear and Allied Sciences (SNAS), 
University of Ghana, under the supervision of Prof. S.B. Dampare and Dr. A. Faanu. 
 
………………………     Date: ………………………. 
Erastus Eight Taapopi 
(Student) 
 
 
………………………     Date: ……………………… 
Prof. S.B. Dampare  
(Principal Supervisor) 
 
 
………………………     Date: ……………………... 
Dr. A. Faanu 
(Co-Supervisor) 
 
 
 
 
 
 
 
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ABSTRACT 
 
The 235U, 232Th series and natural 40K are the main source of natural radioactivity in soil 
and have long half-lives up to 1010 years. Therefore their presence in soils and rocks is 
simply considered as permanent. Also due to rapid urbanization, most of Basic School 
playgrounds in Accra are built close to major roads or industrial areas for which they are 
subject to many potential pollution sources, including vehicle exhaust and industrial 
emissions.. A study has been carried out on playgrounds of basic schools in the Ga East 
municipal district in order to determine the exposure of the school children to naturally 
occurring radioactive materials (238U, 232Th and 40K) and trace elements [aluminum (Al), 
cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), potassium (K), 
lanthanum (La), manganese (Mn), sodium (Na), nickel (Ni), lead (Pb), titanium (Ti), 
vanadium (V), zinc (Zn)]. The activity concentrations were determined using high‐purity 
germanium (HPGe) detector. The average  activity  concentrations  of  238U,  232Th and  
40K determined were 19.8 ± 8.7, 29.1 ± 16.3  and 119.4 ± 97.9 Bq.kg-1 respectively. The 
average annual effective dose was 0.039 ± 0.021 mSv and it is below the dose limit of 1 
mSv/year recommended by International Commission on Radiological Protection (ICRP) 
for public exposure control. Radiological hazard assessments arising from the natural 
radionuclides were carried out. The average concentration of 222Rn and exhalation rate 
were estimated to be 32.13 kBq.m-3 and 0.016 Bq.m-2.s-1 respectively, which compared 
well with the world average values [78 kBq.m-3 and 0.033 Bq.m-2.s-1 reported by 
(UNSCEAR, 2000)]. Soil samples were also analyzed for trace elements by Instrumental 
Neutron Activation Analysis and Atomic Absorption Spectrometry in order to assess the 
potential adverse health effects of the exposure of children to trace elements during their 
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games at school. Doses incurred via ingestion and inhalation and the dose absorbed 
through the skin were calculated using the United States Environmental Protection 
Agency’s hourly exposure parameters for children. The toxicity values considered in this 
study were adapted from the Risk Assessment Information System (RAIS) compilation of 
United States Department of Energy (USoDE). The results of the risk assessment showed 
that the highest risk pathway of exposure was associated with ingestion of soil particles. 
It was found that the trace element of concern was As and its estimated cancer risk was 
found to be 3.48E-06. This can be considered insignificant since it is below the recommended 
trivial cancer risk value of 1.00E-05. The total hazard index was found to be 0.17, below 
the threshold value of 1.0. The concentrations of trace elements were also used to 
evaluate the level of contamination. Soil pollution evaluation was carried out by 
enrichment factor (EF), geo-accumulation index (Igeo) and contamination factor. The 
enrichment factor (EF) for the trace elements (As, Cd, Cr, Mn, Ni, Pb, Ti and Zn) show 
that the soils in the study area are uncontaminated to moderately contaminated (Zn). This 
indicates that some of these elements are derived from both natural and anthropogenic 
sources. The overall Igeo values have revealed that the playground soils are practically 
uncontaminated to moderately contaminated, whilst contamination factors indicate that 
the study areas are none to medium polluted. Multivariate Statistical Analyses, Principal 
Component and Cluster Analyses, suggest that Al, K, La, Mn and Ti are derived from 
crustal origin, As, Hg and Ni which are identified as a result of atmospheric pollution, 
Cd, Co and Na which previously identified as a result of particulate matters emitted from 
the geologic media. Cr, Fe and Zn are derived from both natural sources (Cu, Fe) and 
traffic sources (Cr, Zn) since some of the playgrounds were found to be close to the road. 
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Whilst Chromium (Cr) and Zinc (Zn) may have resulted from emissions of chromium-
based automotive catalytic converters, cement and high Zn content in the soils may also 
come from traffic sources, particularly vehicle tyres. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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DEDICATION 
I dedicate my work to the Almighty God for the Gift of Life, his Blessings, and his 
Protection during my studies in Ghana. If it was not for Him, this project could not have 
been possible. I would like to dedicate this work to my parents Madam Monika Taapopi 
and Mr. ulius Aukongo Taapopi, as well as my great grandmothers Hileni Angula and 
Monika Taapopi for their words of encouragements and for keeping me in their daily 
prayers. To my siblings and entire family, I say thank you for inducing moral in me, 
thank you for having faith in me and for always believing in what I do.  
To God be the Glory 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ACKNOWLEDGEMENTS 
 
First and foremost, I would like to gratefully acknowledge the International Atomic 
Energy Agency (IAEA), the Government of the Republic of Namibia (GRN) and 
University of Namibia (UNAM) for the opportunity to pursue this Master of Philosophy 
Degree in Nuclear Science and Technology. Their financial support/sponsorship is very 
much appreciated. I thank the School of Nuclear and Allied Sciences (SNAS), University 
of Ghana (UG) for equipping me with the requisite skills and knowledge, and for general 
assistance and intellectual support. 
My sincere thanks goes to my experienced and trusted supervisors, Prof. S.B. Dampare 
and Dr. A. Faanu as well as Prof. A.W.K. Kyere (Head of Department of Medical 
Physics at SNAS) for all their supports, assistance, guidance and inspiration. 
Thanks to Mr. David Okoh Kpeglo, a Research Scientist of the Radiation Protection 
Institute (RPI) of Ghana Atomic Energy Commission (GAEC), for offering me a helpful 
training on gamma spectrometric analysis. My sincere gratitude goes to the entire staff of 
the Radiation Protection Institute (RPI) Laboratory, and Neutron Activation Analysis and 
Atomic Absoption Spectrometry Laboratories of the National Nuclear Research Institute 
of the Ghana Atomic Energy Commission (GAEC) for their assistance during sample 
analyses. 
I would like to thank Ms. Florence Esi Damali (Municipal Director of Ga East – 
Abokobi) and Mr. Rowland Ayisa (Personel Officer, Ga East – Abokobi) for assisting me 
with necessary information required for my work. I do not want to forget Mr. Emmanuel 
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Kwabena Owusu of SNAS for his assistance during soil sampling and my fellow students 
of SNAS for the knowledge shared among ourselves. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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TABLE OF CONTENTS 
 
DECLARATION ................................................................................................................. i 
ABSTRACT ........................................................................................................................ ii 
DEDICATION .................................................................................................................... v 
ACKNOWLEDGEMENTS ............................................................................................... vi 
LIST OF FIGURES ......................................................................................................... xiii 
LIST OF TABLES ............................................................................................................ xv 
LIST OF ABBREVIATIONS ......................................................................................... xvii 
CHAPTER 1 ...................................................................................................................... 1 
INTRODUCTION .............................................................................................................. 1 
1.1. Background .......................................................................................................... 1 
1.2. Statement of the Problem ..................................................................................... 3 
1.3. Objectives ............................................................................................................. 5 
1.3.1. Primary Objectives........................................................................................ 5 
1.3.2. Specific Objectives ....................................................................................... 5 
1.4. Relevance and Justifications ................................................................................ 5 
1.5. Scope of the study ................................................................................................ 6 
CHAPTER 2 ...................................................................................................................... 7 
LITERATURE REVIEW ................................................................................................... 7 
2.1. Naturally Occurring Radiation and Man-made Radiation ....................................... 7 
2.1.1. Cosmogenic Radiation ....................................................................................... 7 
2.1.2. Primordal Radionuclides ................................................................................... 8 
2.1.3. Radon ............................................................................................................... 13 
2.2. Anthropogenic Radioactivity ................................................................................. 15 
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2.3. External Exposure (outdoors)................................................................................. 17 
2.4. Hazards Associated with NORMs.......................................................................... 19 
2.5. Trace Elements (TEs) ............................................................................................. 20 
2.5.1. Sources of Trace Elements .............................................................................. 21 
2.6. Hazards Associated with Trace Elements .............................................................. 22 
2.7. Review of NORMs and TEs in Soils ..................................................................... 22 
2.8. Gamma Spectrometry Analytical Techniques........................................................ 24 
2.8.1. Instrumental Neutron Activation Analysis (INAA) ........................................ 25 
2.8.2. HPGe Detector ................................................................................................. 27 
2.9. Atomic Absorption Spectroscopy .......................................................................... 31 
2.9.1. Atomic Absorption Instrumentation ................................................................ 32 
2.9.2. Single Beam Optics ......................................................................................... 32 
2.9.3. Double Beam Optics ........................................................................................ 33 
2.9.4. Atomization ..................................................................................................... 34 
2.10. Multivariate Statistical Analysis .......................................................................... 34 
2.10.1. Principal Component Analysis (Factor Analysis) ......................................... 34 
2.10.2. Cluster Analysis (CA) ................................................................................... 35 
CHAPTER 3 .................................................................................................................... 37 
MATERIALS AND METHODS ...................................................................................... 37 
3.1. Description of the study area .................................................................................. 37 
3.1.1. History of Accra .............................................................................................. 37 
3.1.2. Basic Education Statistics ................................................................................ 38 
3.1.3. Climate of Accra .............................................................................................. 38 
3.1.4. Geographical and Geological Description of the Study Area ......................... 39 
3.2. Sample Collections ................................................................................................. 41 
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3.3. Instrumentation and Calibration ............................................................................. 41 
3.3.1. HPGe Detector System Setup .......................................................................... 42 
3.3.2. Energy Calibration ........................................................................................... 43 
3.3.3. Efficiency Calibration...................................................................................... 45 
3.3.4. Detection Limit and Minimum Detectable Activity (MDA) ........................... 47 
3.3.5. INAA system set-up ........................................................................................ 48 
3.3.6. AAS System Set-up ......................................................................................... 50 
3.2.1. Determination of NORMs ............................................................................... 51 
3.2.2. Determination of Trace Elements by NAA ..................................................... 53 
3.2.3. Determination of Trace Elements by AAS ...................................................... 54 
3.4. Determination of Concentrations in samples ......................................................... 54 
3.4.1. Radionuclides Activity Concentrations ........................................................... 55 
3.4.2. Radon Concentration in Soil............................................................................ 56 
3.4.3. Radon Exhalation Rate .................................................................................... 56 
3.4.4. Trace Elements Concentration by NAA .......................................................... 57 
3.4.5. Trace Elements Concentration by AAS ........................................................... 58 
3.5. Dose Assessment .................................................................................................... 58 
3.5.1. Absorbed Dose Rate in Air (D) from Activity Concentration ......................... 59 
3.5.3. Annual Effective Dose Equivalent (AEDE) .................................................... 59 
3.6. Cancer Risk Assessment ........................................................................................ 60 
3.6.1. Radiological cancer risk assessment................................................................ 60 
3.6.2. Elemental Risk-based Assessment .................................................................. 60 
3.7. Quantification of Soil Pollution ............................................................................. 63 
3.7.1. Enrichment Factor (EF) ................................................................................... 63 
3.7.3. Geo-accumulation index (Igeo) ......................................................................... 64 
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3.7.3. Contamination Factor (CF) .............................................................................. 65 
3.8. Computational Analysis (NORMs) ........................................................................ 66 
3.9. Statistical Analysis (Trace Elements) .................................................................... 68 
CHAPTER 4 .................................................................................................................... 69 
RESULTS AND DISCUSSIONS ..................................................................................... 69 
4.1. Assessment of Natural Radioactivity ..................................................................... 69 
4.1.1. Ambient measurements ................................................................................... 70 
4.1.2. Activity Concentrations of NORMs in Soil .................................................... 71 
4.1.3. Estimation of Radon Concentration in Soil ..................................................... 72 
4.1.4. Estimation of Absorbed Dose (D) and Annual effective Dose Equivalent 
(AEDE) ...................................................................................................................... 73 
4.1.5. Natural Radioactivity Model ........................................................................... 76 
4.1.6. Radiological Hazard Assessment .................................................................... 76 
4.2. Assessment of Elemental Concentrations .............................................................. 79 
4.2.1. Validation of Results ....................................................................................... 79 
4.2.2. Analytical Results ............................................................................................ 82 
4.2.2. Cancer Risk-based Assessment ....................................................................... 86 
4.2.3. Pollution Indices .............................................................................................. 88 
4.2.4. Pollution Source Identification ........................................................................ 93 
CHAPTER 5 .................................................................................................................... 99 
CONCLUSIONS AND RECOMENDATIONS ............................................................... 99 
5.1. Conclusions ............................................................................................................ 99 
5.2. Recommendations ................................................................................................ 101 
5.2.1. Researchers .................................................................................................... 101 
5.2.2. Basic School Managements (Ga East district) .............................................. 102 
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5.2.3. Regulators ...................................................................................................... 102 
REFERENCES ............................................................................................................... 103 
APPENDICES ................................................................................................................ 112 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF FIGURES 
 
Figure     Description     Page 
 
Figure 2.1: The process of neutron capture followed by the emission of gamma rays 
during activation analysis (Win, 2004) ............................................................................. 26 
Figure 2.2: Configuration of closed end coaxial n-type and p-type semiconductor 
detectors and Cross sections perpendicular to the cylindrical  axis of the  high-purity  
germanium p or n type  crystal  and corresponding electrode configuration for each type 
(Knoll, 2010) ..................................................................................................................... 28 
Figure 2.3:  Block diagram of a particular gamma spectroscopic system (Shimboyo, 
2012). ................................................................................................................................ 29 
Figure 2.4: Diagram showing absorption of atomic radiation .......................................... 31 
Figure 2.5: Block diagram showing single-beam atomic absorption spectrometer .......... 33 
Figure 2.6: Block diagram showing double-beam atomic absorption spectrometer ........ 34 
Figure 3.1: Layout of Ga East showing sampling points .................................................. 40 
Figure 3.2: Gamma spectrometry set-up at Radiation Protection Institute (GAEC) ........ 43 
Figure 3.3: Energy calibration curve for standard ............................................................ 45 
Figure 3.4: Absolute full-energy peak efficiency as function of energy for the HPGe 
detector .............................................................................................................................. 46 
Figure 3.5: Set up of AAS equipment at Nuclear Chemistry and Environmental Research 
Center (GAEC) ................................................................................................................. 50 
Figure 3.6: A typical bare playground of Taifa Community Basic School in Ga East 
District............................................................................................................................... 52 
Figure 3.7: Sealed Marinelli beakers containing soil samples. ......................................... 53 
Figure 4.1: Activity concentrations of 238U, 232Th and 40K in soil samples ...................... 72 
Figure 4.2: Comparison of Absorbed dose rate in different playing grounds .................. 74 
Figure 4.3: Comparison of annual effective dose in different playing grounds ............... 75 
Figure 4.4: Comparison of annual effective doses from different exposure pathways of 
radiation ............................................................................................................................ 75 
Figure 4.5: Predicted annual effective dose in study areas ............................................... 77 
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Figure 4.6: Elemental concentrations of As, Cd, Cr, Cu, Hg, Pb and Zn in soil samples 83 
Figure 4.7: Dendogram acquired by hierarchical clustering analysis for parameters ....... 95 
Figure 4.8: Tree diagram acquired by clustering of sampling sites .................................. 97 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF TABLES 
 
Table     Description     Page 
 
Table 2.1: Long-lived cosmogenic radionuclides appearing in meteorites and rain water 
(Choppin et al., 2002) ......................................................................................................... 8 
Table 2.2: Thorium series (4n) (Cember & Johnson, 2009) ............................................. 10 
Table 2.3: Neptunium series (4n +1) (Herman Cember & Johnson, 2009) ...................... 11 
Table 2.4: Uranium series (4n+2) (Cember & Johnson, 2009) ........................................ 12 
Table 2.5: Actinium series (4n +3) (Cember & Johnson, 2009) ....................................... 13 
Table 2.6: Events leading to large injection of radionuclides into the atmosphere 
(Choppin et al., 2002) ....................................................................................................... 17 
Table 2.7: Abundance of major radionuclides in different rock types and soils (IAEA, 
2003) ................................................................................................................................. 18 
Table 3.1: Minimum detectable activities of 238U, 232Th and 40K ..................................... 48 
Table 3.2: Detriment adjusted nominal risk coefficients for stochastic effects after 
exposure to radiation at low dose rate (ICRP, 2007). ....................................................... 60 
Table 3.3: Six classes of the Geo-accumulation Index ..................................................... 65 
Table 4.1: Sample locations and coordinates .................................................................... 69 
Table 4.2: Average absorbed dose rate in air at 1 m above the sampling points in the 
study area and calculated annual effective dose ............................................................... 70 
Table 4.3: Estimated concentration of 222Rn and their corresponding exhalation rate ..... 73 
Table 4.4: Estimated cancer risk components for external irradiation of 238U, 232Th and 
40K in soil .......................................................................................................................... 78 
Table 4.5: Comparison of measured values of SAMPLE4 (114ISE4) as analyzed by 
INAA with its reference values ......................................................................................... 80 
Table 4.6: Comparison of measured values of SAMPLE 4 (131ISE4) as analyzed by 
INAA with its reference values ......................................................................................... 80 
Table 4.7: Comparison of measured values of SAMPLE 2 (114ISE2) as analyzed by 
INAA with its reference values ......................................................................................... 81 
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Table 4.8: Comparison of measured values of two reference materials with their 
respective certified/reference values (mg.kg-1) ................................................................. 82 
Table 4.9: Comparison of elemental concentrations of the As, Cd, Cr, Cu, Hg, Ni, Pb and 
Zn from this study with published data............................................................................. 84 
Table 4.10: Summary statistic of the analytical results (mg.kg-1) .................................... 85 
Table 4.11:  Exposure point concentration term (C, mg/kg), reference dose (mg/kg per 
day) and slope factor (mg/kg per day)-1 (from RAIS as of 2015 except Pb, from WHO), 
and Hazard Quotient and Cancer Risk for each element and exposure route. .................. 87 
Table 4.12: Descriptive statistics for enrichment factors and geo-accumulation indices 
(Igeo) of heavy metals for playground soil ......................................................................... 90 
Table 4.13: Descriptive statistics for enrichment factors and geo-accumulation indices 
(Igeo) of heavy metals for playground soil (continued) ..................................................... 91 
Table 4.14: Metal contamination factors (CFs) for playground soils ............................... 92 
Table 4.15: Rotated component matrix of six-factor model with moderate to strong 
loadings in bold typeface .................................................................................................. 93 
Table 4.16: Scores for the six-factor model for sampling sites relatively high scores in 
bold typeface ..................................................................................................................... 96 
Table 4.17: Elemental characteristics of the analyzed soils from playgrounds as depicted 
by and R-mode principal component ................................................................................ 97 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF ABBREVIATIONS 
 
AAS   Atomic Absorption Spectroscopy 
ABS   Dermal Absorption Factor 
AEDE   Annual Effective Dose Equivalent 
AG   Agbogba Anglican 
AH   Atomic Hills 
AM   Akporman Model 
AMA   Accra Metropolitan Assembly 
AP   Abokobi Presby 
AS   Ashongman M/A 
AT   Average Time 
BW   Average Body Weight 
CA   Cluster Analysis 
CF   Contamination Factor 
CRM   Certified Reference Material 
D   Absorbed dose rate in air 
DA   Dome Anglican 
DDREF  Dose and dose rate effectiveness factor 
DNA   Deoxyribonucleic Acid 
EC   Electron capture 
ED   Exposure Duration 
EF   Exposure Frequency also Enrichment Factor 
FA   Factor Analysis  
FWHM  Full Width at Half Maximum 
GA   Greater Accra 
GAEC   Ghana Atomic Energy Commission 
GHRR-1  Ghana Research Reactor 
GP   GAEC playground 
GPS   Global Positioning Satellite  
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GRN   Government Republic of Namibia 
GS   GAEC basic school 
HC   Haatso calvary presby 
HCA   Hierarchical agglomerative clustering 
HM   Hillview Montessori 
HPGe   High Purity Germanium 
IAEA   International Atomic Energy Agency 
ICP-AAS  Inductively Coupled Plasma Atomic Absorption Spectroscopy 
ICP-MS  Inductively Coupled Plasma-Mass Spectrometry 
ICRP   International Commission on Radiological Protection 
Igeo   Geo-accumulation index 
INAA   Instrumental Neutron Activation Analysis 
IngR   Ingestion Rate 
InhR   Inhalation Rate 
IPCS   International Programme on Chemical Safety  
KA   Kwabenya Atomic 
KW   Kwabenya M/A 
LEPS   Low-Energy Photon Spectrometers 
LET   Linear Energy Transfer 
LN2   Liquid Nitrogen 
LNT   Linear-Non-Threshold 
MATLAB  Mathematics laboratory 
MCA   Multi-Channel Analyzer 
NORMs   Naturally Occurring Radioactive Materials 
PCA    Principal Component analysis  
PEF   Particle Emission Factor 
RAIS   Risk Assessment Information System 
RM   Reference Material 
RPI   Radiation Protection Institute 
SA   Exposured skin area 
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SL   Skin adherence factor 
SNAS   School of Nuclear and Allied Science 
SPSS   Scientific package for the social sciences 
TC   Taifa Community 
TD   Taifa St Dominic  
TEs   Trace Elements 
UG   University of Ghana 
UNAM  University of Namibia 
UNSCEAR United Nations Scientific Committee on the Effect of Atomic 
Radiation 
USDoE  United States Department of Energy 
USEPA  United States Environmental Protection Agency 
VF   Volatilization Factor 
VGA   Vapor Generation Accessory  
WHO   World Health Organization 
WNA   World Nuclear Association 
 
 
 
 
 
 
 
 
 
 
 
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CHAPTER 1 
INTRODUCTION 
This chapter gives an overview of exposure of natural ionizing radiation, and briefly 
discusses the statement of the problem, objectives of the study, justification/relevance as 
well as scope and limitation of the study. 
1.1. Background 
Radionuclides are present everywhere  in  the  natural environment (Aguko, 2013).  The  
main  natural contributors  to  external  exposure  from  gamma-radiation  are  the  
uranium  and  thorium  series, together  with potassium-40 (40K) which may be  present  
in  small  quantities  on the surface of  the  earth  (Aguko, 2013; Faanu et al., 2012). 
Long-lived radioactive elements such as uranium, thorium and potassium and their  decay  
products,  such  as  radium  and  radon  are  examples  of  Naturally  Occurring 
Radioactive  Materials  (NORMs). These elements have always been present in the 
earth’s crust and atmosphere since the beginning of creation. 
The 238U and its daughters rather than 226Ra and its daughter products are responsible for 
the major fraction of the internal dose received by humans from naturally occurring 
radionuclides. Even though the concentrations of these radionuclides are widely 
distributed in nature, they have been found to depend on the local geological conditions 
and as a result vary from place to place (Faanu et al., 2011; Baba et al., 2004). This is 
because the specific levels are related to the type of rocks from which the soil originates. 
Throughout the  history  of  life  on  earth,  organisms  have  been  continuously  exposed  
to radiation mainly from cosmic rays  in  the  atmosphere,  and  from  naturally  occurring  
radionuclides  which  are  ubiquitously distributed in all living and non-living 
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components of the biosphere . A wide  range  of  activity  concentrations  in  a  wide  
variety  of  materials  is  reported (IAEA, 2011). 
Also, trace elements such as As, Cd, Co, Cr, Cu, Hg, Pb and Zn that are found in soil are 
known persistent environmental pollutants. The presence of these elements in the 
environment could be as a result of pollution from different sources in urban areas. It has 
been identified that vehicular and atmospheric pollution is a major contributor to trace 
elements contamination (Baba et al., 2004). Although some trace metals such as Cu and 
Zn at small amounts are harmless, others notably Pb, Hg and Cd could be extremely 
harmful at low concentrations. These toxic elements are potential co-factors, and known 
as initiators or promoters of many diseases including cardiovascular diseases and cancer 
(Meza-Figueroa, 2007). 
Others sources of pollution of environment from these elements are particulate matter 
emitted from the geologic media and may pose threats to human health and the 
environment (Meza-Figueroa, 2007). Anthropogenic particles derived from road 
construction (asphalt, concrete and road paint), automobiles (tire dust, brake dust), 
industrial inputs or atmospheric depositions are potential sources of pollution. All these 
anthropogenic activities tend to have introduced contaminants in topsoil from 
atmospheric deposition by sedimentation, impaction and interception (Baba et al., 2004). 
And as time goes by, dust can be carried by wind into sensitive environments. Lead (Pb) 
and a variety of other metals from automobile exhaust have been found to contaminate 
roadways  (Baba et al., 2004). This is particularly of concern for schools along the roads 
with bare playgrounds since children whilst playing inhale dust which could be polluted 
with significant levels of trace metals including radionuclides. As a result of poor waste 
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management practices and poor observance of environmental regulations, environmental 
pollutants [such as lead (Pb) from paints, buning of plastics and paper, mercury (Hg) 
from electronics, plastic waste, pesticides, pharmaceutical and dental waste, cadmium 
(Cd) from electronics, plastics and batteries waste] could be littered all around. All these 
could eventually find their way into these bare playgrounds for the children in these 
schools and could pose serious health hazards in the long term. In the Ga East Municipal 
of the Greater Accra Region, there are thirty one (31) public Basic Schools in total that 
are registered within their respective five (5) circuits. Preliminary survey of the schools 
shows that there are fourteen (14) schools with bare lands, and children population in 
these schools range from 48 to 560 pipils. 
A large body of knowledge has been developed over the last decades on the exposure of 
children to particulate materials such as soil and street dust. This is driven by the 
realization that children are the most sensitive segment of the population to 
anthropogenic contamination. Also by the strong indication that toxic trace elements may 
reach concentrations of potential concern for human health in the environments (De 
Miguel et al., 2007). Specifically, some researchers have looked at the chemical 
composition of playground soil and dust (De Miguel et al., 2007). According to De 
Miguel et al, (2007), the exposure of children to trace elements is particularly high 
relative to the kind of activities in the surroundings and types of games they play. 
1.2. Statement of the Problem 
Trace elements and naturally occurring radioactive materials (NORMs) are commonly 
found in soil at varying concentrations. Their levels in soil are largely controlled by 
geological factors and potential pollution from different sources. In most developing 
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countries, including Ghana, it is very common to find schools with very large population 
with bare playgrounds with no or very little knowledge of the extent of pollution of the 
soil of these playgrounds. However, because in our society the issues of environmental 
quality is sometimes relegated to the background, impact assessment of these school 
compounds are not carried out to ascertain the radiation levels as well as trace element 
contents by the authorities concerned before school enrollments. This is particularly of 
concern where the ground of the school compound is bare and there is constant inhalation 
of dust.  
Meanwhile, the radiological and trace elements hazards of these playgrounds have not 
been investigated and as a result, the levels of exposure are unknown. Consequently, this 
study is being conducted to assess the levels of [aluminum (Al), cadmium (Cd), cobalt 
(Co), chromium (Cr), copper (Cu), mercury (Hg), potassium (K), lanthanum (La), 
manganese (Mn), sodium (Na), nickel (Ni), lead (Pb), titanium (Ti), vanadium (V), zinc 
(Zn)] and health risk associated with their exposure levels. This is because the exposure 
to children may lead to development of potential health consequences such as cancer. 
Generally, children are more susceptible to air pollutants than adults because children 
breathe higher volumes of air relative to their body weights and their tissue and organs 
are still growing. Small particles are associated with higher pollutant concentrations and 
these are the particles that children are most likely to inhale or ingest while playing 
outdoors. It is a known fact that young children are more likely to ingest significant 
quantities of dust than adults because of the behavior of mouthing non-food objects and 
repetitive hand/finger sucking. Besides, children have a much higher absorption rate of 
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5 
 
heavy metals from digestion system and higher hemoglobin sensitivity to trace elements 
than adults.  
 
1.3. Objectives 
1.3.1. Primary Objectives 
The purpose of this study is to assess naturally occurring radioactive materials (40K and 
radionuclides from the 238U and 232Th decay series) and trace metals in soil from selected 
school playgrounds in the Ga East Municipal of Ghana in order to appraise their potential 
exposure to school children. 
1.3.2. Specific Objectives 
 To assess activity concentrations of natural radionuclides in selected basic schools 
play grounds. 
 Develop a model for prediction of future exposure due to NORMs 
 To evaluate soil pollution using enrichment factor (EF), geo-accumulation index 
(Igeo), contamination factor (CF) 
 To embark on risk-based evaluation of the exposure of school children to trace 
elements in the playgrounds 
1.4. Relevance and Justifications 
The data from this study will serve as baseline research/data for monitoring natural 
radionuclides and trace elements levels in school playgrounds in Ghana and Ga East 
district in particular. The research data can be used for development of control and 
environmental protection strategy by providing information on the degree and spatial 
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6 
 
distribution of trace elements pollution of the school playgrounds. This will serve as 
reference for authorities’ regulation purposes in Ghana, and help develop management 
strategy for protecting children’s health. The study also demonstrates the importance of 
multivariate statistical tools in routine monitoring of trace elements pollution of 
playgrounds of basic schools. 
1.5. Scope of the study 
The work is limited to assessment of NORMs and trace elements in playgrounds of 
selected basic schools. This was achieved by determining the levels and distribution of 
the naturally occurring radionuclides 40K as well as 238U and 232Th decay series 
radionuclides. Soil samples were collected from selected school playing grounds and 
analyzed by gamma spectrometry using a high purity germanium detector (HPGe 
detector) for NORMs. For trace elements, the analysis was done by using instrumental 
neutron activation analysis (INAA) and atomic absorption spectrometry (AAS). A short 
MATLAB script was written for the prediction of future exposures arising from external 
gamma radiation. 
 
 
 
 
 
 
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CHAPTER 2 
LITERATURE REVIEW 
This chapter gives a general review of natural radiation background in the terrestrial 
environment, radionuclides decay series, as well as a review of other research works that 
have been done in the subject area of naturally occurring radionuclides and trace 
elements in soils of basic schools play grounds. The detector system and setup and 
biological effects of ionizing radiation and trace elements are also briefly discussed. 
2.1. Naturally Occurring Radiation and Man-made Radiation 
Natural background radiation exists everywhere and every natural substance contains 
some amount of radioactive material. The natural radiation environment consists of 
cosmic rays and naturally radioactive materials (Gupta et al., 2010). Some of the 
materials are cosmogenic, others are primordial and others exist naturally because of the 
radioactive transformation of substances produced through these processes. The 
radiological significance of naturally occurring radioactive materials and radiation 
sources is closely linked to the physical behaviour of the materials in the source and how 
they change with time (James, 2006). 
2.1.1. Cosmogenic Radiation 
The cosmogenic radiation is the oldest source of radiation, which is believed to have 
originated at the birth of the universe, about 13–14 billion years ago (Cember, 2009). The 
earth is bombarded continuously by radiation originating from the sun, and from sources 
within and beyond the galaxy. This cosmic radiation slams into the earth’s upper 
atmosphere which provides an effective shield for living things below (James, 2006). 
Cosmic irradiation of the atmosphere produces neutrons and protons which react with N2, 
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O2, Ar and other atoms resulting in the production of radioactive nuclides. These nuclides 
are produced at constant rates and brought to the earth surface by rain water. Though they 
are formed in very low concentrations, the global inventory is by no means small 
(Choppin, 2002). Equilibrium is assumed to be well-known between the production rate 
and the mean residence time of these radionuclides in terrestrial basins leading to 
constant specific radioactivities of the elements in each reservoir. If a reservoir is closed 
from the environment, its specific radioactivity declines. This can be used to determine 
exposure times of meteorites to cosmic radiation (Choppin et al., 2002). Table 2.1 
contains some of the cosmogenic radionuclides produced as a result of the cosmic 
radiation interaction with the atmosphere. 
Table 2.1: Long-lived cosmogenic radionuclides appearing in meteorites and rain water 
(Choppin et al., 2002) 
Nuclide Half-life 
(years) 
Decay Mode & 
particle energy 
(MeV) 
Atmospheric 
production rate 
(atoms m-2s-1) 
3H 12.32 β-  0.0186 2500 
10Be 1.52 × 106 β
- 0.555 300 
14C 5715 β- 0.1565 17000 – 25000 
22Na 2.605 β+ 0.545 0.5 
26Al 7.1 × 105 β
+ 1.16 1.2 
32Si 160 β- 0.213 1.6 
35S 0.239 (87.2 d) β- 0.167 14 
36Cl 3.01 × 105 β
- 0.709 60 
36Ar 268 β- 0.565 56 
53Mn 3.7 × 106 EC (0.596)  
81Kr 2.2 × 105 EC (0.28)  
Values within parenthesis after EC are decay energies. 
 
2.1.2. Primordal Radionuclides 
Many of the naturally occurring radioactive elements are participants of one of four long 
chains, or radioactive series, stretching through the last part of the chart of the nuclides. 
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There are four main radioactive decay series, namely the Uranium (4n+2), Thorium (4n), 
Actinium (4n+3), and Neptunium (4n+1) series. Th-232 is the most abundant (about 
100%) of the naturally occurring radioisotopes. The identification numbers are based on 
the divisibility of the mass numbers of each of the series by 4 (Cember, 2009; James, 
2006). With the exception of neptunium, each of the parent radionuclides is primordial in 
origin because they are so long lived that they still exist some 4.5 billion years after the 
solar system was formed (James, 2006). 
Uranium consists of three different isotopes; about 99.3% of naturally occurring uranium 
is 238U, about 0.7% is 235U, and a trace quantity (about 5×10−3 %) is 234U. The 238U and 
234U belong to one family, the uranium series, while the 235U isotope of uranium is the 
first member of another series called the actinium series(Herman Cember & Johnson, 
2009; Choppin et al., 2002). Uranium is ever-present in the natural environment and is 
found in the soil at average concentrations of about 3 ppm (parts per million) by weight, 
which corresponds to∼2 pCi or ∼74 mBq/g soil (Cember, 2009).  
The first series is the thorium decay series, consisting of radionuclides going through the 
decay in which all the mass numbers are divisible by four (the 4n series). It has its natural 
origin in232Th which occurs with 100 % isotopic abundance. Natural thorium has a 
specific activity of 4.06 MBq/kg, as its half-life through a-decay is 1.41 x 1010 y 
(Choppin et al., 2002). The final nuclide in this decay series is the stable species. The 
breakdown from the original parent to the final product requires 6 alpha and 4 beta 
decays. The longest-lived intermediate is 5.76 y 228Ra (Cember & Johnson, 2009; 
Choppin et al., 2002). The uranium decay series consist of nuclides that, when their mass 
number is divided by 4, have a remainder of 2 (the 4n + 2 series). The parent of this 
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series is 238U with a natural abundance of 99.3 % it undergoes a-decay with a half-life of 
4.46 x 109 y. The stable end product of the uranium series is 206Pb which is reached after 
8 alpha and 6 beta decay steps (Choppin et al., 2002). 
Table 1.2: Thorium series (4n) (Cember & Johnson, 2009) 
   ENERGY 
(MEV) 
 
 
NUCLIDE 
 
HALF-LIFE 
 
ALPHAa 
 
BETA 
GAMMA 
(PHOTONS/TRANS.)b 
𝑇ℎ90
232  1.39 x 10
10 years 3.98   
𝑅𝑎88
228  (MsTh1) 6.7 yrs  0.01  
𝐴𝑐89
228  (MsTh2) 6.13 h  Complex decay 
scheme Most 
intense beta 
group is 1.11 
MeV 
1.59 (n.v) 0.966 (0.2) 
0.908 (0.25) 
𝑇ℎ90
228  (RdTh) 1.91 yrs 5.421  0.084 (0.016) 
𝑅𝑎88
224  (ThX) 3.64 yrs 5.681  0.241 (0.038) 
𝑅𝑛86
220  (Tn) 52 s 6.278  0.542 (0.0002) 
𝑃𝑜82
216  (ThA) 0.158 s 6.774   
𝑃𝑏82
212  (ThB) 10.64 h  0.35, 0.59 0.239 (0.40) 
𝐵𝑖83
212  (ThC) 60.5 min 6.086 (33.7%) 2.25 (66.3%)
c 0.04 (0.034 branch) 
𝑃𝑜84
212  (ThC’) 3.04 x 10
-7 s 8.776   
𝑇𝐼81
208  (ThC”) 3.1 min  1.80, 1.29, 1.52 2.615 (0.997) 
𝑃𝑏82
208  (ThD) Stable    
 
 
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Table 2.3: Neptunium series (4n +1) (Herman Cember & Johnson, 2009) 
   ENERGY 
(MEV) 
 
 
NUCLIDE 
 
HALF-LIFE 
 
ALPHAa 
 
BETA 
GAMMA 
(PHOTONS/TRANS.)b 
𝑃𝑢94
241  13.2 yrs  0.02  
𝐴𝑚95
241  462 yrs 5.496  0.060 (0.4) 
𝑁𝑝93
237  2.2 x 10
6 yrs 4.77   
𝑃𝑎91
233  27.4 d  0.26, 0.15, 
0.57 
0.31 (very strong)c 
𝑈92
233  1.62 x 10
5yrs 4.823  0.09 (0.02) 
0.056 (0.02) 
0.042 (0.15) 
𝑇ℎ90
229  7.34 x 10
3 yrs 5.02   
𝑅𝑎88
225  14.8 d  0.32  
𝐴𝑐89
225  10.0 d 5.80   
𝐹𝑟87
221  4.8 min 6.30 
 0.216 (1) 
𝐴𝑡85
217  0.018 s 7.02   
𝐵𝑖83
213  47 min 5.86 (2%)
d 1.39 (98%)d  
𝑃𝑜84
213  4.2 x 10
-6 s 8.336   
𝑇𝐼81
209  2.2 min  2.3 0.12 (weak)
c 
𝑃𝑏82
209  3.32 h  0.635  
𝐵𝑖83
209  Stable    
 
 
 
 
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Table 2.4: Uranium series (4n+2) (Cember & Johnson, 2009) 
   ENERGY 
(MEV) 
 
 
NUCLIDE 
 
HALF-LIFE 
 
ALPHAa 
 
BETA 
GAMMA 
(PHOTONS/TRANS.)b 
𝑈92
238  4.51 x 10
9 yrs 4.18   
𝑇ℎ90
434  (U X1) 24.10 d  0.193, 
0.103 
0.092 (0.040) 
0.063 (0.03) 
𝑃𝑎91
234  (U X2) 1.175 min  2.31 1.0 (0.015) 
0.76 (0.0063), I.T. 
𝑃𝑎91
234  (UZ) 6.66 h  0.5 Many (weak)
 
𝑈92
234  (UII) 2.48 x 10
5 yrs 4.763   
 
𝑇ℎ90
230  (I0) 8.0 x 10
4 yrs 4.685  0.068 (0.0059) 
𝑅𝑎90
230  1.622 yrs 4.777   
𝐸𝑚86
222  (Rn) 3.825 d 5.486  0.51 (very weak) 
𝑃𝑜84
218  (RaA) 3.05 min 5.998 (99.978%)
c Energy not 
known 
(0.022%)c 
0.186 (0.030) 
𝐴𝑡85
218  (RaA’) 2 s 6.63 (99.9%)
c Energy not 
known 
(0.15)c 
 
𝐸𝑚86
218  
(RaA”) 
0.019 s 7.127   
𝑃𝑏82
214  (RaB) 26.8 min  0.65 0.352 (0.036) 
0.295 (0.020) 
0.242 (0.07) 
𝐵𝑖83
214  (RaC) 19.7 min 5.505 (0.04%)
c 1.65, 3.7 
(99.96%)c 
0.609 (0.295) 
1.12 (0.131) 
𝑃𝑜84
214  
(RaC’) 
1.64 x 10-4 s 7.680   
𝑇𝐼81
210  (RaC”) 1.32 min  1.96 2.36 (1) 
0.783 (1) 
0.297 (1) 
𝑃𝑏82
210  (RaD) 19.4 yrs  0.017 0.0467 (0.045) 
𝐵𝑖83
210  (RaE) 5.00 d  1.17  
𝑃𝑜84
210  (RaF) 138.40 d 5.298  0.802 (0.000012) 
𝑃𝑏82
206  (RaG) Stable    
 
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Table 2.4: Actinium series (4n +3) (Cember & Johnson, 2009) 
   ENERGY 
(MEV) 
 
 
NUCLIDE 
 
HALF-LIFE 
 
ALPHAa 
 
BETA 
GAMMA 
(PHOTONS/TRANS.)b 
𝑈92
235  7.13 x 10
8 yrs 4.39  0.18 (0.7) 
𝑇ℎ90
231  (U Y) 25.64 h  0.094, 0.302, 
0.216 
0.022 (0.7) 
0.0085 (0.4) 
0.061 (0.16) 
𝑃𝑎91
231  3.43 x 10
4 yrs 5.049  0.33 (0.05) 
0.027 (0.05) 
0.012 (0.01) 
𝐴𝑐89
227  21.8 yrs 4.94 (1.2%)
a 0.0455 (98.8%)c  
𝑇ℎ90
227  (RdAc) 18.4 d 6.03  0.24 (0.2) 
0.05 (0.015) 
 
𝐹𝑟87
223  (AcK) 21 min  1.15 0.05 (0.40) 
0.08 (0.24) 
𝑅𝑎88
223  (AcX) 11.68 d 5.750  0.270 (0.10) 
0.155 (0.055) 
𝐸𝑚86
219  (An) 3.92 s 6.824  0.267 (0.086) 
0.392 (0.048) 
𝑃𝑜84
215  (AcA) 1.83 x 10
-3 s 7.635   
𝑃𝑏82
211  (AcB) 36.1 min  1.14, 0.5 Complex spectrum, 
0.065-0.829 MeV 
𝐵𝑖83
211  (AcC) 2.16 min 6.619 (99.68)
c Energy not 
known 
(0.32)c 
0.35 (0.14) 
 
𝑃𝑜84
211  (AcC’) 0.52 s 7.434 0.65 0.88 (0.005) 
0.56 (0.005) 
𝑇𝐼81
207  (AcC”) 4.78 min 
 1.47 0.87 (0.005) 
𝑃𝑏82
207  Stable    
 
2.1.3. Radon 
 
Radon  is  a  gas  with  three  natural  isotopes of  the  radioactive  element:  actinon,  
(219Rn) emitted from the 235U decay series, thoron (220Rn) emitted from the 232Th decay 
series, and radon (222Rn) which is emitted from the 238U decay series (UNSCEAR, 1993). 
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Since the activity concentration of 235U is low and the half-life of 219Rn is short (3.96 s), 
the radiation exposure from 219Rn is considered not to be significant for human exposure. 
Whilst 220Rn with a half-life of 55.6 s is of concern only when the concentration of 232Th 
is high, the 222Rn with a half-life of 3.82 days is the isotope of concern in terms of human 
radiation exposure. It is a noble gas with slightly chance to form compounds under 
laboratory conditions. The density of 222Rn is 9.73 g/L  at 0 oC and the solubility in water 
at 0 oC is 510 cm3/L, decreasing to 220 cm3/L at 25 oC and 130 cm3/L at 50 oC (Faanu, 
2011; UNSCEAR, 2000). 
The production of 220Rn and 222Rn in terrestrial materials is based on the activity 
concentrations of 228Ra and 226Ra present and these are predominantly alpha emitters.  
Radon is the most significant element of human irradiation by natural sources through 
inhalation of its short-lived products of 210Pb and 210Po (UNSCEAR, 1993, 2000). 
The  concentrations  of 222Rn  in  surface  air  vary  with  time-average concentrations  in 
the range of 2-30 Bq/m3 (UNSCEAR, 1993). In the soil,  radon  concentrations  may  be  
higher  by  a  factor  of  about  1000  than  in  the  open  air, and the average 
concentration varies widely depending on the composition of the soil and the bedrock 
(UNSCEAR, 1993). For soil with an average 226Ra concentration of 40 Bq.kg-1, the 
average 222Rn concentration in the soil water would be about 60 Bq/m3. The action level 
of radon recommended by the ICRP for which intervention is necessary is 1000 Bq /m3. 
This value is based on an assumed occupancy of 2000 hours per year and this is 
equivalent to an effective dose of 6 mSv per year. This value is also the midpoint of a 
range of 500-1500 Bq/m3 (Faanu, 2011). Radon  generation  and  transport  in  porous  
materials  involves  solid,  liquid  and  gaseous phases through the process of emanation, 
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diffusion, advection, absorption in the liquid phase and adsorption in the solid phase 
(Faanu, 2011). The main mechanism for radon entry into the atmosphere is molecular 
diffusion. 
Some factors that may influence the levels of 222Rn concentration in soil, water and air 
include the following (Faanu, 2011): 
 Grain  or  particle  and  shape  determine  the  emanation  of  radon  from  the  
soil.  The emanation factor is inversely proportional to the grain size. 
 Soil moistures control the emanation of radon and diffusion in soil by capturing 
the radon recoils from the solid matrix. 
 Advection  caused  by  wind  and  changes  in  barometric  pressure  between  the  
building shield and the ground around the foundation. 
 Temperature, the solubility of radon in water decreases with temperature. 
 The geology, which determines the 226Ra concentration and climatic conditions. 
 The distribution and concentrations of the parent radium radionuclides in the 
bedrock and overburden and permeability of the soil. 
 Seasonal  variation  because 222Rn  in soil gas  vary over  many orders of  
magnitude  from place to place and also show a significant time variations at any 
given site. 
2.2. Anthropogenic Radioactivity 
 
As far as the analysis of a sample is concerned for its content of natural radioactivity, it is 
necessary to have a look at the possibility that the sample may be contaminated by non-
natural radioactivities. Radionuclides may be added to the environment by human 
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activities, and these are called anthropogenic sources. The cases of nuclear weapons tests, 
nuclear satellites burnt-up in the atmosphere, and nuclear power accidents have released 
significant amounts of activities into the environment. The nuclear power industry is 
permitted by health authorities to continually release small, controlled amounts of 
specified radionuclides into the atmosphere and into open waters (Choppin et al., 2002). 
Usually, one distinguishes between near field and far field effects of radioactivity 
releases. Near field effects are observed close to the release source, with examples being 
the nuclear power plant or nuclear waste storage facility. The dissolution of nuclear waste 
by rain or ground water is a typical near field problem. Since the source is known, it can 
be controlled and the environment can be monitored. If the radioactivity exceeds 
permitted levels, access to the contaminated area is restricted. Far field effects involve the 
behaviour of radionuclides which have spread out of such a restricted area, caused either 
by nuclear power accidents and weapons tests or by leakage from nuclear power plants 
(Choppin et al., 2002). However, some of these cases are sometimes known and 
contaminated areas are regulated. 
 
 
 
 
 
 
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Table 2.6: Events leading to large injection of radionuclides into the atmosphere 
(Choppin et al., 2002) 
SOURCE COUNTRY TIME RADIOACTIVITY 
(Bq) 
IMPORTANT 
NUCLIDES 
Hiroshima 
&Nagasaki 
Japan 1945 4 x 1016 Fission production. 
Actinides 
Atmospheric 
weapon tests 
USA 
USSR 
-1963 2 x 1020 Fission production. 
Actinides 
Windscale Uk 1957 1 x 1015 131I 
Chelyabinsk 
(Kysthym) 
USSR 1957 8 x 1016 Fission production. 
90Sr, 137Cs 
Harrisburg USA 1979 1 x 1012 137Cs 
Chernobyl USSR 1986 2 x 1018 137Cs 
Fukushima Japan March, 2011 1 x 1018 137Cs, 131I 
 
2.3. External Exposure (outdoors) 
External exposures outdoors arise from terrestrial radionuclides present at trace levels in 
all soils ( Faanu et al., 2012; UNSCEAR, 2000). Higher radiation levels are associated 
with igneous rocks, such as granite, and lower levels with sedimentary rocks. There are 
exceptions, however, as some shales and phosphate rocks have relatively high content of 
radionuclides. There have been many surveys to determine the background levels of 
radionuclides in soils, which can in turn be related to the absorbed dose rates in air 
(UNSCEAR, 2000). The latter can easily be measured directly, and these results provide 
an even more extensive evaluation of the background exposure levels in different 
countries. Table 2.7 provides information regarding the abundance of radionuclides in 
different natural materials (IAEA, 2003), which is a summary of typical concentrations of 
radionuclides in different geological media. The radionuclides in the uranium and 
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thorium decay chains cannot be assumed to be in radioactive equilibrium. The isotopes 
238U and 234U are in approximate equilibrium, as they are separated by two much shorter-
lived nuclides, 234Th and 234Pa. The decay process itself may, however, allow some 
dissociation of the decayed radionuclide from the source material, facilitating subsequent 
environmental transfer. 
Table 2.7: Abundance of major radionuclides in different rock types and soils (IAEA, 
2003) 
 40K  232Th  238U 
Rock type Total K 
(%) 
Bq/kg  ppm Bq/kg  Ppm Bq/kg 
Igneous rocks         
Basalt, crustal average 0.8 300  3 to 4 10 to 15  0.5 to 1 7 to 10 
Mafic 0.3 to 1.1 70 to 400  1.6, 
2.7 
7, 10  0.5, 0.9 7, 10 
Salic 4.5 1100 to 1500  16, 20 60, 83  3.9, 4.7 50, 60 
Granite, crustal average > 4 > 1000  17 70  3 40 
Sediment rocks         
Shale, sandstones 2.7 800  12 50  3.7 40 
Clean quartz < 1 < 300  < 2 < 8  < 1 < 10 
Dirty quartz 2? 400?  3 to 6? 10 to 25?  2 to 3? 40? 
Arkose (unconsolidated) 2 to 3 600 to 900  2? < 8  1 to 2? 10 to 25? 
Beach sands 
(unconsolidated) 
< 1 < 300?  6 25  3 40 
carbon rocks 0.3 70  2 8  2 25 
Continental upper crust 
(average) 
2.8 850  10.7 44  2.8 36 
Soils 1.5 400  9 37  1 to 8 66 
Note: Question marks indicate estimates in the absence 
of measured values. 
      
 
 
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Hence, 234U may be somewhat deficient relative to 238U in soils (UNSCEAR, 2000; 
Vanmarcke, 2002). The radionuclide 226Ra in this chain may have slightly different 
concentrations as compared to 238U, because separation may occur between its parent 
230Th and uranium and because radium has greater mobility in the environment 
(UNSCEAR, 2000). The decay products of 226Ra include the gaseous element radon, and 
it diffuses out of the soil, reducing the exposure rate from the parent radionuclide 238U. 
The radon radionuclide in this series, 222Rn, has a half-life of only a few days, but it has 
two longer-lived decay products, 210Pb and 210Po which are important in dose evaluations. 
2.4. Hazards Associated with NORMs 
The exposure to ionizing radiation depends on the magnitude of dose absorbed, the time 
period, the dose rate as well as the specific organ that is exposed. The inhalation of short-
lived decay products of 222Rn, and the decay products of 220Rn, and their successive 
deposition along the walls of the various airways of the bronchial tree provide the main 
pathway for radiation exposure of the lungs. This exposure is mostly produced by the 
alpha particles emitted by several of these radionuclides, although some beta particles 
and gamma radiation are also emitted. 
Biological effects of ionizing radiation in humans, due to physical and chemical 
processes, occur immediately following the passage of radiation through living matter. 
These processes will involve successive changes at the molecular, cellular, tissue and 
whole organism levels. For acute whole-body exposures above a few gray from radiation 
of low linear energy transfer (LET), damage occurs principally as a result of cell killing. 
This can cause damage to organ and tissue and even death. These effects, termed early or 
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deterministic, occur principally above a threshold dose that must not be exceeded 
(UNSCEAR, 2000). A second type of damage can occur at late times after exposure 
(stochastic effects). This damage consists primarily of damage to the nuclear material in 
the cell, causing radiation-induced cancer to develop in a proportion of exposed persons 
or hereditary disease in their offspring.  
2.5. Trace Elements (TEs) 
The trace elements that have been studied most extensively in soils are those that are 
essential for the nutrition of higher plants such as B, Cu, Fe, Mn, Mo, and Zn (Domy, 
2001). Similarly, those extensively studied in plants and foodstuffs because of their 
essentiality for animal nutrition are such as As, Cu, Co, Fe, Mn, Mo, Zn, Cr, F, Ni, Se, 
Sn, and V. Conversely, this study intends to observe a wide range of all trace metals 
(carcinogenic and non-carcinogenic) in the soils of basic school playgrounds. 
The term trace element used in the literature has different meanings in various scientific 
disciplines. Often it designates a group of elements that occur in natural systems in 
minute concentrations (Adriano et al., 1986 ; Domy, 2001). Also, it is defined as those 
elements used by organisms in small quantities but believed to be essential to their 
nutrition. According to Kabata (2010), trace element concentrations significantly differ 
among both soil groups and geographic regions. This indicates that parent material and 
climatic conditions have predominated impact on the trace elements status of soils. In this 
study, trace elements are referred to elements that occur both in natural and by human 
activities and perturbed environments in small amounts, but when present in adequate 
bioavailable concentrations, they become toxic to living organisms. Other terms such as 
trace metals, heavy metals, micronutrients, microelements, and minor elements are 
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commonly used as synonyms for trace elements. The trace elements  considered in this 
study are: arsenic (As), cadmium (Cd),  cobalt (Co), chromium (Cr), copper (Cu), 
mercury (Hg), manganese (Mn), nickel (Ni), lead (Pb), vanadium (V), lanthanum (La), 
potassium (K), Alminium (Al), Titanium (Ti), sodium (Na), iron (Fe) and zinc (Zn). 
Moreover, the term heavy metals usually refers to elements having densities greater than 
5.0 g.cm-3 and signifies metals and metalloids that are associated with pollution and 
toxicity but also includes elements that are required by organisms at rather low 
concentrations (Domy, 2001). 
2.5.1. Sources of Trace Elements 
Natural sources of trace elements are found in both mineral and in the form of organic 
chelates (Shakeri et al., 2009). Most raw minerals (such as rock phosphate, greensand, 
granite dust and basalt) are rich in trace elements. Organic sources include composts, 
manures and green manures, but the trace mineral content of these sources will vary, 
depending on how much of these elements were in the environment where the sources 
were originally formed.  
Besides, trace metals may also come from many different sources especially in urbanized 
areas (Li, Poon, & Liu, 2001; Shakeri et al., 2009). These anthropogenic inputs include 
waste disposal, waste incineration, urban effluent, traffic emissions (Shakeri et al., 2009). 
Atmospheric pollution is one of the major sources of trace elements contamination and 
can accumulate in top soil. It is believed that Cd, Cu, Pb and Zn are good indicators of 
contamination in soils because they appear in gasoline, car components, oil lubricants, 
industrial and incinerator emissions (Li et al., 2001). 
 
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2.6. Hazards Associated with Trace Elements 
Potential hazards associated with trace elements are related to their accumulation in soils 
(Hagedorn et al., 1999). The elements of primary concern are arsenic, cadmium, copper, 
mercury, nickel, lead and zinc. Acute inhalation exposure to high levels of trace elements 
in humans may have effects on the lung, such as bronchial and pulmonary irritation and 
can even result in long-lasting impairment of lung function (IPCS, 2002; US EPA.). 
Children are known to be more vulnerable to these effects as they undergo cell divisions 
through their growth processes (Almeida et al., 2011; De Miguel et al., 2007; Wong & 
Mak, 1997). 
2.7. Review of NORMs and TEs in Soils 
The average dose received by all human from background radiation is around 2.4 mSv/yr, 
that varies depending on the geology and altitude where people live and it ranges between 
1 and 10 mSv/yr, but can be more than 50 mSv/yr (UNSCEAR, 1993, 2000). The highest 
recorded level of background radiation affecting a substantial population is in Kerala and 
Madras States in India where some 140,000 people received doses which averaged over 
15 millisievert per year from gamma radiation, in addition to a similar dose from radon 
(WNA, 2011). Comparable levels occur in Brazil and Sudan, with average exposures up 
to about 40 mSv/yr to many people. Also , another highest level of natural background 
radiation recorded is on a Brazilian beach: 800 mSv/yr, although people do not live there 
(WNA, 2011). 
 
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Other places in Iran, India and Europe (WNA, 2011) are noted for the natural background 
radiation that gives an annual dose of more than 100 mSv to people and up to 260 mSv 
(at Ramsar in Iran, where some 200,000 people are exposed to more than 10 mSv/yr). 
Lifetime doses from natural radiation range up to several thousand millisievert. 
Nevertheless, there is no evidence of increased cancers or other health risks arising from 
these high natural levels. 
The study performed in Ghana (Faanu et al., 2012) shows that activity concentrations of 
natural radionuclides 226Ra, 232Th and 40K in soil, rock, waste and tailing samples were 
measured by gamma spectrometry using high-purity germanium detector. In addition, 
radiological hazard assessments due to these natural radionuclides were carried out.  The 
average activity concentrations of 226Ra, 232Th and 40K determined were 13.61 ± 5.39 
Bq/kg, 24.22 ± 17.25 Bq/kg and 162.08 ± 63.69 Bq/kg, respectively and the average 
annual effective dose was 0.17 + 0.09 mSv. The activity concentrations of NORMs from 
this study are expected to be lower that obtained by Faanu et al. ( 2012) since he 
conducted his research at the mining site where the existing exposure is likely to be 
elevated. 
A similar study performed in Qatar (Al-Kinani et al., 2012) revealed that the soil activity 
concentrations ranged from 25.01- 40.31 for 226Ra, 12.37- 4.99 for 232Th and 133.8 - 
250.1 for 40K with mean values of 57, 87 and 207 Bq/kg, respectively. The corresponding 
outdoor annual effective doses ranged from 49.5 to 20.146 µSv with  an  average value  
of 136.95 µSv. Whereas the world wide average annual effective dose is approximately 
460 µSv. Al-Kinani et al. (2012) collected soil samples from urban areas of Qatar. 
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Therefore, it is necessary to compare his result with the result of this study since the soil 
samples were also collected from playgrounds within the urban area of Accra, Ghana.  
Moreover, studies on the level of trace metals in samples have been performed worldwide 
(Almeida et al., 2011; Baba et al., 2004; De Miguel et al., 2007, 2007; Kabata et al., 
2010; Wong et al., 1997; Meza-Figueroa et al., 2007; Almeida et al., 2011) . De Miguel 
et al. (2007) carried out a study on children’s playgrounds in Spain. The results of the risk 
assessment indicated that the highest risk was associated with ingestion of soil particles 
and that the trace element of most concern was arsenic, the exposure to which resulted in 
a cancer risk value of 4.19·10 x 10-6 close to the 1·10 x 10-5 probability level deemed 
unacceptable by most regulatory agencies. Regarding non-cancer effects, exposure to 
playground substrate yielded an aggregate Hazard Index of 0.28, below the threshold 
value of 1 (with As, again, as the largest single contributor, followed by Pb, Cr, Al and 
Mn). According to De Miguel et al. (2007), the work was done for two consecutive years 
(2002 – 2003) considering seasonal variations. In this study, trace elements were 
determined using INAA and AAS whereas De Miguel et al. (2007) used inductively 
coupled plasma-mass spectrometry (ICP-MS). 
2.8. Gamma Spectrometry Analytical Techniques 
In this study, two techniques were used for the analysis of gamma emitting naturally 
occurring radionuclides and trace metals in soil samples. These are High Purity 
Germanium (HPGe) detector and the Instrumental Neutron Activation Analysis (INAA). 
In this section, I will briefly discuss these techniques. In addition to INAA, AAS was also 
used for the analysis of some trace elements 
 
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2.8.1. Instrumental Neutron Activation Analysis (INAA) 
Neutron Activation Analysis is a quantitative and qualitative analytical method with high 
high efficiency employed in the determination of trace elements in different kinds of 
samples. NAA involves nuclear reactions between neutrons and target nuclei. It can 
simultaneously determine about 25-30 major, minor, trace and rare elements of 
geological, environmental, biological  samples in ppb-ppm range without or with 
chemical separation (Win, 2004). NAA was discovered in 1936 (Win, 2004). The initial 
step in neutron activation analysis is irradiating a sample with neutrons in a nuclear 
reactor or sometimes in other neutron sources. During irradiation the stable naturally 
occurring isotopes that constitute a sample are converted into radioactive isotopes by 
neutron capture. The activated nucleus is allowed to decay according to its half-life. 
Since nuclides contained in a sample will emit particles or gamma-quanta with specific 
energies, the quantity of radioactive nuclides is determined by measuring the intensity of 
the characteristic gamma-ray lines in the spectra. To perform these measurements, a 
gamma-ray detector is used. The irradiated samples consist of radionuclides of different 
half-lives and different isotopes can be determined at their respective time intervals. 
Nuclear reactors with high fluxes of neutrons produced from uranium fission yield the 
highest sensitivities for most elements contained in a sample compared to other sources 
of neutron. 
A high-resolution gamma-ray spectrometer is used to detect the delayed gamma rays in 
the presence of the artificially induced radioactivity in the sample for both qualitative and 
quantitative analysis. The incident neutron hits the target nucleus, which captures the 
neutron and is converted into a compound nucleus (Figure 2.1). The last immediately 
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emits radiation called prompt gamma radiation and forms the radionuclide, which then 
excites out a beta  particle and emits the delayed gamma radiation (emitted after 
sometime delay), forming the product nucleus (Win, 2004). The qualitative 
characteristics are the energy of the emitted gamma lines and the half-life of the nuclide. 
Whereas the quantitative characteristic is the intensity (the number of gamma quanta of 
energy measured per unit time). 
 
Figure 2.1: The process of neutron capture followed by the emission of gamma rays 
during activation analysis (Win, 2004) 
 
NAA is chosen as an analytical technique in this study as it is competitive in many areas 
and for its advantage that it has high sensitivity and accuracy in respect of some trace 
elements. The method is of a multi element character and it simultaneously determine 
many elements without chemical separation. The samples preparation involves only 
pulverization or homogenization and this decreases the threat of contamination to a least 
and help accelerates the whole analytical process. 
 
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2.8.2. HPGe Detector 
Germanium  detectors  are  semiconductor  diodes  with  a  p-i-n  structure  in  which  the 
intrinsic region  is  sensitive  to  ionizing  radiation,  especially  gamma rays (Knoll, 
2010). When a gamma photon enters a detector, it interacts with the detector material by 
ionization process and generates an electric signal which is converted to a pulse by a pre-
amplifier electronics connected to the detector (Knoll, 2010). The pulse is then amplified 
in amplifier electronics. Further amplification is done to the pulse to shape it and reduces 
electronic noise and sent to the multi-channel analyzer (MCA). The MCA sorts the pulses 
into full energy peaks. The main characteristic of a nuclear detector in activation analysis 
is its resolution which is expressed as full width at half maximum of the peak (FWHM). 
It is believed that, the narrower the peak, the lower the FWHM, the better the ability of 
the detector to resolve and separate close peaks or interference. Hence, the detector is 
said to have the better resolution.  
Since germanium has a low band gap, these detectors are cooled in order to reduce the 
thermal generation of charge carriers.  Also, leakage current produce noise that destroys 
the energy resolution of the detector (Knoll, 2010). Liquid nitrogen, which has a 
temperature of -196.15 ᵒC, is the commonly to cool such detectors.   
The  detector  is  put  on  a  vacuum chamber  which  is  attached  and  inserted  into  a  
LN2 dewar (Knoll, 2010).  The  sensitive  detector surfaces  are  thus  protected  from  
moisture  and  condensable  contaminants. The  energy resolution  of  these  detectors  is  
very  high,  but  because  of  their  small  volume,  their sensitivity  is low and it may take 
several  minutes to record a spectrum.  Arrangement of p-type and n-type semiconductor 
detectors is shown in the Figure 2.2. 
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Figure 1.2: Configuration of closed end coaxial n-type and p-type semiconductor 
detectors and Cross sections perpendicular to the cylindrical  axis of the  high-purity  
germanium p or n type  crystal  and corresponding electrode configuration for each type 
(Knoll, 2010) 
 
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Figure 2.3:  Block diagram of a particular gamma spectroscopic system (Shimboyo, 
2012). 
 
HPGe detectors that are in use come in two geometries. These are the planar and the co-
axial HPGe detectors. Coaxial detectors have large active volume that makes them most 
appropriate for detection of high energy gamma radiation (300 – 2000 KeV). Planar 
detectors are widely used to measure lower energy gamma photons (60 – 300 KeV) and 
X-rays (Khandaker, 2011). Planar detectors are also known as low-energy photon 
spectrometers (LEPS). In this study, the co-axial Ge detector was used. 
Gamma-ray detection is based on the effect of a γ-ray interacting with matter 
(Khandaker, 2011). There are only three important types of interaction of a γ-ray with 
matter. These are namely, the photoelectric effect, the Compton Effect, and the pair-
production. The characteristics of these effects are important in detector design.  
In the photoelectric process, the gamma ray contributes all of its energy to the withdrawn 
electron. The recoil electron ejected from the shell of atoms and hence yields the 
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electron-hole pairs in the detector that produces the output pulse. This output pulse is 
proportional to the energy of the gamma ray that made the interaction. In the spectrum, 
these events will appear as full-energy photo-peaks. The Photo electric effect is 
significant for the incident gamma energy of 0-150 keV. 
The Compton cross section is the dominant one for all energies except the very lowest (E 
 150 keV) and the very highest (E = 8.5 MeV). The Compton Effect too contributes 
strongly to the full energy peak by multiple Compton scattering under the condition that 
the last interaction is a photoelectric one and that all the preceding Compton interactions 
take place in the Ge crystal. In large-volume detectors the probability of multiple 
Compton scattering increases. If the last interaction does not occur by the photoelectric 
effect or if one of the multiple Compton interactions takes place outside the sensitive 
volume of the detector, the pulse will contribute to the Compton continuum 
The pair-production process also provides a total absorption of the γ-ray energy. The 
gamma ray interacts in the detector and creates an electron positron pair. By the law of 
conservation of mass and energy, the initial gamma must have energy of at least 1.02 
MeV since it takes that energy to initiate both the negative and positive electrons. 
The positron will yield a pulse proportional to Ee+ and because these two pulses are 
produced simultaneously, the resulting pulse from the detector would be the totality of 
the two pulses. When the positron enters the detector, the annihilation radiation γ1 and γ2 
are produced. If both γ1 and γ2 goes beyond the boundaries of the detector without 
making additional interactions, the energy of exactly 1.02 MeV also escapes from the 
detector and this is subtracted from the initial total energy that entered the detector. In 
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most cases, only one of the gammas creates a photoelectric interaction in the detector as 
others escapes. In such cases, the total energy absorbed by the detector is 0.511 MeV less 
than the original incident gamma-energy. It is possible that both gammas cause 
photoelectric interactions without escaping and the incident γ-energy will be absorbed by 
the detector. Therefore, in the measured spectrum three peaks can be observed for each 
gamma-energy. These peaks are referred to as full-energy peak, single escape peak, and 
double-escape peak which can be distinguished by 0.511 MeV increments (Khandaker, 
2011; Knoll, 2010). 
2.9. Atomic Absorption Spectroscopy 
Atomic absorption, coupled with atomic emission, was first used by Guystav Kirchhoff 
and Robert Bunsen in 1859 and 1860, for the qualitative identification of atoms. The 
atomic emission made progress and developed as an analytical technique. Atomic 
Absorption Spectroscopy (AAS) is the term used when the radiation absorbed by atoms is 
measured (Ebdon & Evans, 1998). The energy of the radiation absorbed or emitted is 
quantized according to Planck’s equation as shown by equation (2.1).  These quanta are 
known as photons, the energy of which is proportional to the frequency of the radiation. 
 
Figure 2.4: Diagram showing absorption of atomic radiation 
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ℎ = ℎ𝜈 =
ℎ𝑐
𝜆
         (2.1) 
Where h is Plank’s constant (6.62 x 10-34 J.s), v is velocity of light (3.0 x 108 m.s-1) and 𝜆 
is the wave length (m) for the propagation of light. 
The so called ground state atom absorbs light energy of a specific wavelength as it goes 
in the excited state. When the number of atoms in the light path increases, the quantity of 
light absorbed also increases. By considering the amount of light absorbed, a quantitative 
evaluation of the amount of analyte can be made. The use of special light sources and 
careful selection of wavelengths allow the specific determination of individual elements. 
2.9.1. Atomic Absorption Instrumentation 
There are five basic components of the atomic absorption instrument. These are namely, 
the light source that emits the light band of the element of interest, the absorption cell in 
which atoms of the samples are produced, a monochromator light dispersion, a detector 
that measure the light intensity and amplifies the signal and the display that indicated the 
reading after being processed by the instrument electronics. Atomic absorption 
spectrophotometers are designed to use either the single-beam or double-beam optics. 
2.9.2. Single Beam Optics 
In a single beam optics, the light source in the form of hollow cathode lamp or 
electrodeless discharge lamp produces a spectrum specific to the element of which it is 
made of, and which is concentrated through the sample cell into the monochromator 
(Harvey, 2000). The light source has be electronically structured to differentiate between 
the light coming from the source and the production from the sample cell. The 
monochromator separates the light and the definite wavelength of light isolated goes to a 
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photomultiplier tube that acts as detector (Ebdon & Evans, 1998; Harvey, 2000). The 
electrical current is formed based on the light concentration and managed by the 
electronics instrument. The electronics will quantity the light attenuation in the sample 
cell and transform the readings to the real sample concentration. In single-beam systems, 
a short warm up period is required to allow the source lamp to stabilize (Harvey, 2000). 
 
Figure 2.5: Block diagram showing single-beam atomic absorption spectrometer 
 
2.9.3. Double Beam Optics 
The light from the light source is split into a sample beam and is focused through the 
sample cell, and a reference beam, which is absorbed around the sample cell. In a double-
beam arrangement, the display represents the ratio of the sample and reference beams. 
Therefore, an oscillation in source intensity does not influence the oscillations in 
instrument display, and stability is improved. Generally, analyses can be performed 
immediately with no lamp warm-up required (Ebdon & Evans, 1998; Harvey, 2000) 
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Figure 2.6: Block diagram showing double-beam atomic absorption spectrometer 
2.9.4. Atomization 
The most fundamental different between a spectrophotometer for atomic absorption and 
that of molecular absorption is the necessity to transform the analyte into a free atom 
(Harvey, 2000). The process of transforming an analyte in solid, liquid, or solution form 
into atomic state is referred to as atomization. Most of the time, the sample comprising 
the analyte undergoes some stages of sample preparation that causes the analyte in an 
organic or aqueous solution. Generally, two methods of atomization used are the flame 
atomization (used in this study) and electro thermal atomization. Whilst, elements such as 
As and Hg are atomized using Hydrode Generation (HG) and Cold Vapour (CV) 
respectively. 
2.10. Multivariate Statistical Analysis 
2.10.1. Principal Component Analysis (Factor Analysis) 
Principal component analysis (PCA) is designed to convert original variables into, 
uncorrelated variables (axes) which are called principal components. This method 
decreases the dimension of data by a linear combination of original data to produce new 
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underlying variables that are orthogonal and uncorrelated to each other (Jolliffe, 2002). 
The new axes lie alongside the directions of maximum variance. According to (Bhuiyan 
et al., 2011), PCA offers an independent way of discovering indices of this type so that 
the deviation in the figures can be taken into account for as briefly as possible. The 
Principal Components (PC) which are linear combination of observable variables, 
account for information on the most significant parameters, which define the whole data 
set capable of data reduction with the least loss of original information (Bhuiyan et al., 
2011). 
The only difference between factor analysis (FA) and PCA is the preparation of the 
perceived correlation matrix for abstraction and the fundamental theory. FA helps 
reducing the involvement of less significant variables to make it easier for data 
construction from PCA. This is done by rotating the axis defined PCA based on well-
established rules and fabricating new variables called VF (Bhuiyan et al., 2011). It can 
contain unobservable, hypothetical, underlying variables. PCA of the normalized 
variables is presented to extract significant principal components and to decrease the 
involvement of variables with less significance. These PCs are exposed to varimax 
rotation generating VFs (Bhuiyan et al., 2011; Jolliffe, 2002).  
2.10.2. Cluster Analysis (CA) 
Cluster analysis is defined as a group of multivariate systems that serve to accumulate 
objects depending on their characteristics (Bhuiyan et al., 2011). Cluster analysis 
categorizes objects, so that each object is matching to the others in the group based on a 
prearranged selection criterion.  The subsequent clusters of objects are imaginary to show 
high internal homogeneity and high external (between clusters) heterogeneity. Thus, each 
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cluster  defines the data composed, the class of which participants belong and this 
explanation may be preoccupied via use from the specific to the general class (Bhuiyan et 
al., 2011). Hierarchical agglomerative clustering (HCA) is the mutual approach, which 
presents perceptive similarity between a sample and the whole data set, and is ordinarily 
illustrated by a dendrogram or a tree diagram. The dendrogram presents a graphic 
summary of the clustering processes, by showing a picture of the groups and their 
closeness, with a dramatic decrease in the dimension of the initial data. The Euclidean 
space gives the likeness between two samples and a distance can be represented by the 
disparity between the obtained analytical values of the samples (Bhuiyan et al., 2011; 
Boamponsem et al., 2010) 
 
 
 
 
 
 
 
 
 
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CHAPTER 3 
MATERIALS AND METHODS 
 
In this chapter, the history, geographical location and the geology of the study area, the 
materials used as well as the methods used for sampling, sample preparation and 
measurements are discussed. Also, the dose and radiological risk assessments, level of 
trace elements are discussed in this section. 
3.1. Description of the study area 
3.1.1. History of Accra 
Accra is the capital and largest city of Ghana, with an estimated urban population of 
2,269.143 million as of 2012 (“Accra,” 2015). The name Accra is believed to be derived 
from the Akan word nkran, meaning "ants", a reference to the numerous manner in which 
the natives of Accra kept re-appearing like army ants during a war with the Ashantis 
(“Accra,” 2015). It is said to be the capital of the Greater Accra Region and of the Accra 
Metropolitan District. Furthermore, Accra provides stability and confidence for a 
larger metropolitan area, the Greater Accra Metropolitan Area (GAMA), which is 
inhabited by about 4 million people and this makes it the second largest metropolitan 
conglomeration in Ghana by population, and the eleventh-largest metropolitan area in 
Africa (“Accra,” 2015). 
 
 
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3.1.2. Basic Education Statistics 
Pre-school comprises nursery and kindergarten. In 2001, there were 7,923 children (3,893 
girls and 4,030 boys) in pre-schools in Accra. In 2010, the enrollment rate at pre-school 
was 98 %. Pre-schools are regulated by the Ministry of Employment and Social Welfare, 
and are mostly privately owned and operated. In 2001, there were 62 government-owned 
pre-schools in the Accra metropolis. In primary school enrollment of girls is higher than 
that of boys. In 2010, the enrollment rate at primary school level was 95 % (“Accra,” 
2015).. 
The Junior High School is part of Ghana's basic education program. Its nationwide 
implementation began on 29 September 1987. In the 2001/2002 academic year, 61,080 
pupils had enrolled in Accra, representing 57.17 % of the 129,467 school-age 12–to-14-
year-olds. In 2010, the enrollment rate at Junior High School level was 95 %. The ratio of 
girls is also higher at this level (“Accra,” 2015). 
3.1.3. Climate of Accra 
Accra features a tropical savanna climate that borders on a semi-arid climate. The 
average annual rainfall is about 730 mm, which falls primarily during Ghana's two rainy 
seasons. The major rainy season begins in April and ends in mid-July, whilst a minor 
second rainy season occurs in October (“Accra,” 2015). Rain usually falls in short 
intensive storms and causes local flooding in which drainage channels are obstructed. 
There is very little variation in temperature throughout the year. The mean monthly 
temperature ranges from 24.7 °C (76.5 °F) in August (the coolest) to 28 °C (82.4 °F) in 
March (the hottest), with an annual average of 26.8 °C (80.2 °F). It should be noted, 
however, that the "cooler" months tend to be more humid than the warmer months. 
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 As a result, during the warmer months and particularly during the 
windy harmattan season, the city experiences a breezy "dry heat" that feels less warm 
than the "cooler" but more humid rainy season (“Accra,” 2015). As Accra is close to 
the equator, the daylight hours are practically uniform during the year. Relative humidity 
is generally high, varying from 65% in the mid-afternoon to 95% at night. The 
predominant wind direction in Accra is from the WSW to NNE sectors. Wind speeds 
normally range between 8 to 16 km/h. High wind gusts occur with thunderstorms, which 
generally pass in squall along the coast (“Accra,” 2015). 
The maximum wind speed record in Accra is 107.4 km/h (58 knots). Strong winds 
associated with thunderstorm activity often cause damage to property by removing 
roofing material. Several areas of Accra experience micro-climatic effects (“Accra,” 
2015). Low-profile drainage basins with a north-south orientation are not as well 
ventilated as those orientated east-west. Air is often trapped in pockets over the city, and 
an insulation effect can give rise to a local increase in air temperature of several degrees. 
This occurs most notably in the Accra Newtown sports complex areas (“Accra,” 2015). 
3.1.4. Geographical and Geological Description of the Study Area 
Ga East Municipal District, located between 5° 33′ 00″ N and 0° 12′ 00″ W, is bordered on 
the north by the Akuapim South District in the Eastern Region of Ghana (“Accra,” 2015). 
It is bordered on its other three sides by other districts in the Greater Accra Region of 
Ghana. On its west is the Ga West District, south by Adentan municipal is the Accra 
Metropolis and in the east by La-Nkwantanang-Madina municipal is the Tema 
Metropolis (“Accra,” 2015). The geology of the Ga East district consists of Precambrian 
Dahomeyan schists, granodiorites, granites gneiss and amphibolites to late Precambrian 
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Togo Formation [comprising mainly quartzite, phyllites, and quartz breccias] (“Accra,” 
2015). The towns in Ga East District include Abokobi (the capital), Dome, Taifa, 
Ashongman, Haatso, Ayi Mensa, Bansa, Oyarifa, Pantag and Kwabenya. The Ghana 
Atomic Energy Commission (GAEC) is found in Kwabenya. 
 
Figure 3.1: Layout of Ga East showing sampling points 
 
 
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3.2. Sample Collections 
A total of 70 soil samples were collected from selected school playing grounds (Fig 3.1 
and Fig 3.2) within Ga East District of Greater Accra Region during October and 
November 2014. For the soil samples, each playing ground of an area of (64 × 100) m2 
was marked and 5 samples were randomly taken at depth up to 5 cm using a plastic dust 
pan and brush and transferred into a clean polythene bag (Faanu et al., 2012). The 
samples were properly labeled catalogued and brought to the Radiation Laboratory at the 
Radiation Protection Institute (RPI), Ghana Atomic Energy Commission (GAEC).  
At each location, 5 measurements of the ambient gamma dose rates were taken at 1 m 
above the ground using a digital environmental radiation survey meter (RADOS, RDS-
200, manufactured in Finland). The dose rate meter was calibrated at the Secondary 
Standard Dosimetry Laboratory (SSDL) of the RPI. The average value was taken in 
µSv.h-1. At the same time, the geographical coordinates for each sampling location were 
recorded using a Global Positioning Satellite (GPS). 
In the laboratory, each of the 5 soil samples collected from the same playing ground were 
mixed to obtain a composite sample that represents a particular school playing ground. 
The composite samples were air dried on trays for 7 days and then oven dried at a 
temperature of 105 ˚C for between 3 and 4 h, to remove all the moisture contents (Faanu 
et al., 2012). 
3.3. Instrumentation and Calibration 
 
In this study, two analytical techniques namely gamma spectrometry and INAA were 
used. The quantitative determination of NORMs in soil samples was done using HPGe 
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detector while determination of trace element in soil samples was carried out by INAA 
with Ghana Research Reactor – 1 (GHARR–1). The AAS analytical technique was also 
used for some trace elements that INAA could not analyze due to some unexpected 
challenges that occurred during the study. 
3.3.1. HPGe Detector System Setup 
Direct instrumental analysis without pre-treatment (non-destructive) was used for the 
measurement of gamma rays for the soil using a coaxial one open end, closed end facing 
down HPGe detector (detector model GX4020, cryostat model 7500SL and preamplifier 
model 2002CSL). The detector has a diameter of 60.5 mm, length of 61.5 mm and 
distance from window (outside) of 6 mm. The resolution of the detector is 2.0 keV and 
relative efficiency of 40 % for 1.33 MeV gamma energy of 60Co. The output of the 
detector is connected to PC. The identification of individual radionuclides was performed 
using their gamma ray energies, and the quantitative analysis of radionuclides was 
performed using gamma ray spectrum analysis software package, “Gennie 2000”. The 
detector was surrounded by a lead shield (100 m) on all sides to reduce the background 
radiation level of the system, and lined inside with copper, cadmium and plexiglass (3 
mm each) sheets to minimize the X-rays emitted due to interaction of cosmic radiation 
with lead (Faanu et al., 2012). 
The detector is cooled with liquid nitrogen at a temperature of -196 ˚C (77 k). In order to 
determine the background distribution in the environment around the detector, the empty 
Marinelli beakers were thoroughly cleaned and filled with distilled water and counted for 
36,000 s at the same geometry as the samples. The background spectra were used to 
correct the net peak area of gamma rays of measured isotopes. The background spectra 
University of Ghana                              http://ugspace.ug.edu.gh
43 
 
were also used to determine the minimum detectable activities of 238U, 232Th and 40K of 
the detector.  
 
Figure 3.2: Gamma spectrometry set-up at Radiation Protection Institute (GAEC) 
3.3.2. Energy Calibration 
The relationship between the channel numbers corresponding to specific gamma-ray 
energies was determined before sample measurement (Faanu et al., 2012). The 
establishment of this relationship is known as energy calibration and the idea is to 
identify the radionuclides in a sample. The linearity of energy response is an essential 
feature for any γ-ray detector and the direct proportionality between the quality of energy 
Computer screen Printer 
Lead shield 
Cryostat 
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44 
 
deposited in the detector by the incident radiation event and the height of the output pulse 
ensures that the system is working properly (Osvath et al., 2008). This is illustrated by 
fitting a straight line of incident photon energy of a number of known sources against the 
channel number of peak centroid in each spectrum. Accurate calibration involves a 
standard source with gamma ray energies that are not widely different from those to be 
measured in the unknown spectrum (Faanu et al., 2012). 
The energy calibration was done by means of multi peaked and multi nuclide radioactive 
standard sources emitting gamma rays of precisely known energy, and the peak position 
in channels with this energy was identified. In this study, this was carried out by counting 
standard radionuclides (a mixture of 241Am, 109Cd, 139Ce, 57Co, 60Co, 137Cs, 113Sn, 85Sr 
and 88Y) of known activities with well-defined energies in the energy range of 60 to 
~2000 keV. The standard was counted on a detector for 10 hours or 36000 s. A certificate 
of the standard used is shown in Appendix I and the energy calibration curve is shown in 
Figure 3.3. 
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45 
 
 
Figure 3.3: Energy calibration curve for standard 
 
3.3.3. Efficiency Calibration 
 It is known for gamma-ray radiations that they can pass through relatively large 
distances before an interaction begins (Osvath et al., 2008). Therefore, the efficiency is 
said to be less than 100 %. Thus, it is important to accurately determine the counting 
efficiency of the detector in order to quantify the radionuclides in a sample.  The 
efficiency of the detector is defined by the ratio of the pulses recorded under the photo 
peak to the total number of gamma rays emitted by the source (Osvath, 2008). The 
geometrical counting arrangement and detector characteristic influence the detector’s 
efficiency. The standard was also counted on a detector for 10 hours or 36000 s, and the 
net counts for each of the full energy peak in the spectrum was determined and their 
corresponding energies used in the determination of the efficiency. The expression (3.1) 
used to determine efficiency is given below (Faanu et al., 2012). 
y = 0.244x + 0.093 
R² = 1 
0
200
400
600
800
1000
1200
1400
1600
1800
2000
0 1000 2000 3000 4000 5000 6000 7000 8000
En
e
rg
y 
Channel Number 
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46 
 
𝐸𝑓𝑓(𝐸) =
𝑁𝑒𝑡 𝐴𝑟𝑒𝑎
𝐴𝑠𝑡𝑑×𝑃𝛾×𝑇𝑠𝑡𝑑
        (3.1) 
Where, 𝐸𝑓𝑓(𝐸) is the efficiency of the detector,  𝐴𝑠𝑡𝑑  is the activity (Bq) of the 
radionuclide in the calibration standard at the time of calibration, 𝑃𝛾 is gamma emission 
probability for energy (E), and 𝑇𝑠𝑡𝑑 is the counting time of the standard. The efficiency 
calibration curve is shown in Figure 3.4. 
 
Figure 3.4: Absolute full-energy peak efficiency as function of energy for the HPGe 
detector 
 
 
 
y = -0.0347x4 + 0.9495x3 - 9.6821x2 + 42.811x - 72.116 
R² = 0.9985 
-5.5
-5
-4.5
-4
-3.5
-3
4 4.5 5 5.5 6 6.5 7 7.5 8
Ln
(E
ff
) 
LN(E) 
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47 
 
3.3.4. Detection Limit and Minimum Detectable Activity (MDA) 
The MDA (Bq) is defined as the smallest quantity of radioactivity that could be 
distinguished from the blank under specified conditions (Cember & Thomas, 2009). 
Determination of MDA for each nuclide in a sample is required in order that a 
measurement can be expected to correctly imply the presence, and correctly 
quantitatively assess the activity with a predetermined degree of confidence. Therefore, 
the calculation of Minimum Detectable Activity for a given nuclide, at the 95 % 
confidence level, was determined based on Currie’s derivation formula (3.2), given as 
below. 
𝑀𝐷𝐴 = (
2.71+4.65√𝑁𝑇𝐵(
𝑡𝑇
𝑡𝐵
)+𝑁𝑐
𝑡𝑇.𝐼.𝐸
)      (3.2)  
where 𝑁𝑇𝐵 is the background count, 𝑡𝑇 is the sample counting time, 𝑡𝐵 is background 
counting time, 𝑁𝑐 is the integral count or the continuum, 𝐼 is the gamma yield or gamma 
intensity or the emission probability and 𝐸 is the efficiency (Faanu et al., 2012). The 
above formula takes into account both kinds of errors [i.e., false positive (deciding there 
is sample activity when there, in fact, there is none); and false negative (concluding there 
is no sample activity when there is some)]. It yields the smallest level of activity which 
can be detected with 95 % confidence, while also having 95 % confidence that activity is 
not detected falsely from a null sample. In this study, the gamma line energies that were 
used to calculate MDA were 1460 keV for 40K; 609 keV for 214B; 295 keV for 214Pb and 
911 keV for 228Ac. The  MDA values for 238U, 232Th and 40K are tabulated in Table 3.1. 
 
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48 
 
Table 3.1: Minimum detectable activities of 238U, 232Th and 40K 
Radionuclide MDA (Bqkg-1) 
238U 0.13 
232Th 0.40 
40K 4.75 
3.3.5. INAA system set-up 
The reference materials (RM) SAMPLE 4 (114ISE4) of International Soil-analytical 
Exchange 2011 and SAMPLE 4 (131ISE4) of International Soil-analytical Exchange 
2013 were used to validate the short-lived elemental concentration for quality control of 
the facility. Also, SAMPLE 2 (114ISE2) of International Soil-analytical Exchange 2011 
was used in order to validate the medium lived elemental concentration. These were 
prepared by weighing 100 mg onto polyethylene film of about 6 mm by 4 mm in length 
and thickness. Three separate packaging were done; short and medium irradiation. For 
short irradiation, each sample was encapsulated in one container. Certified reference 
materials (CRM) were first irradiated, followed by separate irradiation of the samples and 
finally irradiation of the quality control materials. This allowed for pre-instrumentation 
calibrations and subsequent standardization for sample analysis. The packaging for 
medium irradiation was done in such a way that the samples were packed into one 
capsule with two RM and CRM arranged evenly amongst the samples. All the activations 
were performed at irradiation site B2 (using pneumatic transfer system B) under thermal 
neutron flux of 5.0×1011ncm-2s-1 with the reactor operated at half power of 15 kW.  
An irradiation time of 1 minute were applied for short-lived radionuclides (elements) 
determination and 2 – 6 hours for medium-lived radionuclides determination. After the 
short irradiation, the samples were allowed a delay time of about 4 minutes and measured 
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49 
 
for 300 seconds each within the first 15 minutes and recounted. These were to ensure 
good selectivity and reduction of high background by Al and other short-lived 
radionuclides. The medium irradiations was allowed decay times of few day(s) (between 
1 and 2) and weeks (spectrum acquired after 2 weeks) and their spectra acquired for 600 
seconds and 10800 seconds respectively for identification of medium-lived elements. All 
the measurements were done at a source-detector distance of 50 mm and 10 mm 
respectively for short and medium counting. Gamma spectrometry measurements of 
induced radionuclide(s) were performed by a PC-based -ray spectrometry set-up. It 
consisted of an N-type, High purity Germanium detector (HPGe-coaxial type) coupled to 
a computer based multi-channel analyzer (MCA) via electronic modules. The relative 
efficiency of the detector is 40 %.  Its energy resolution is 1.95 keV at a -ray energy of 
1332 keV for 60Co, Peak-to-Compton Ratio for 60Co is 59:1.  
The detector’s dimensions were 63.0 mm in diameter, 65.0 mm in length with 4 mm End 
Cap to detector distance and an absorbing layers of 0.5 mm for beryllium, 0.3 µm 
inactive Germanium and 0.5 thin foil Aluminum top cap. The data acquisition and 
identification of -rays of product radionuclides were performed via ORTEC 
MAESTRO-32. The peak reduction and interpretation of gamma spectrum and elemental 
quantifications of samples and controls were done using multipurpose γ-ray spectrum 
analysis software; WinSPAN-2010 version 2.10 which works on the basis of relative 
comparator methodology. 
 
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50 
 
3.3.6. AAS System Set-up 
Figure 3.5 shows the Varian AA-240FS First Sequential Atomic Absorption 
Spectrometer. It consists of three major chambers in addition to the monitor and the 
compressor. These components are the lamp, spray and atomizing chambers indicated as 
a, b and c respectively on the diagram. 
 
Figure 3.5: Set up of AAS equipment at Nuclear Chemistry and Environmental Research 
Center (GAEC) 
 
The lamp chamber consists of a multi-cathode chamber that can accommodate four lamps 
at the same time. The spray chamber houses the burner head, spray block as well as the 
nebulizer. The atomizing chamber consists of the monochromator and detector lenses. 
a 
b 
c 
d 
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51 
 
The function of the compressor labelled d in the figure is to supply compressed air that 
aids the flame during the burning process as well as creating pressure in the capillary tube 
that introduces the analyte into the system. Calibration of the instrument was done by 
aspiring into the flame samples of reference standard solutions containing known 
concentration of each element to be determined and then measuring absorption of each 
solution. The calibration curves for each element determined are shown in Appendix VII. 
After each reading, the instrument was reset to read zero % precision by aspiring into the 
flame with distilled water. This was done to completely remove trapped elemental ions 
from the previous reading that could give incorrect readings.  For each element, the 
measured absorbance was plotted against the concentration. In this study, the reference 
standards were from Fluka Analytical Sigma Aldrich Chemie GmbH, product of 
Switzerland. 
3.2.1. Determination of NORMs 
The composite samples were ground into fine powder (pulverized) using a Retsch 
vibratory ball mill to increase the total emission  area (Faweya, Alabi & Adewumi, 
2014); and sieved through a  
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52 
 
 
Figure 3.6: A typical bare playground of Taifa Community Basic School in Ga East 
District 
 
500 µm mesh size pore so that particles may be homogenized and packed into 1 litre 
Marinelli beakers (Figure 3.7). The weights of empty Marinelli beakers and beakers with 
samples were measured using a mass balance (OHAUS Corp, Model: EP2102C, SNR: 
B0390772836, Mass range: 0.01 g – 2100 g, Temperature range: +10 oC – +40 oC, Power 
requirements: 12 V ~ 1 A, made in Switzerland). The Marinelli beakers with the samples 
were then sealed and left for at least a month in order to allow secular equilibrium 
between 226Ra and its decay products before counting by gamma-ray spectrometry (Faanu 
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53 
 
et al., 2012). Each sample was placed on a high purity germanium detector (HPGe) and 
activity of the naturally occurring radionuclides measured for 10 hours. 
 
Figure 3.7: Sealed Marinelli beakers containing soil samples. 
 
3.2.2. Determination of Trace Elements by NAA 
Each composite sample was sieved through 250 µm, 100 µm and below 100 µm mesh 
size pore so that each sample was fractionated into different size particles. For each 
fraction, a mass of 0.1 g was weighed using a weighing balance and wrapped into a 
transparent sheet of polythene and labelled. When samples are packed into “rabbit 
capsules”, they are sent into the reactor for irradiation with thermal neutrons and 
analyzed for trace elements by instrumental neutron activation analysis (INAA). The 
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54 
 
reference materials SAMPLE 4 (114ISE4) of International Soil-analytical Exchange 
2011, SAMPLE 4 (131ISE4) of International Soil-analytical Exchange 2013 and 
SAMPLE 2 (114ISE2) of International Soil-analytical Exchange 2011 were also prepared 
in the same manner for quality control and validation of data. 
3.2.3. Determination of Trace Elements by AAS 
A portion of each sample sieved through a 90 µm mesh screen was placed in air tight 
plastic bag and reserved for elemental analysis by AAS. The acid digestion method was 
used for soil samples according to the GAEC acid digestion protocol. Two grams (2 g) of 
sieved soil was weighed and transferred into a 100 mL borosilicate beaker. 25 mL of  
aqua regia [prepared in the ratio of 1 mL concentrated HCl (37%) to 3 mL of 
concentrated HNO3 (65%)] was added to the beaker and covered with cling film and left 
overnight. The contents were then placed on a hot plate and digested for 3 hours and 
again left to cool at room temperature. The cooled suspension was filtered into a 100 mL 
measuring cylinder and 30 mL of distilled water was added to it. The filtrate was 
transferred to a plastic container ready to be analyzed. The two reference materials 114 
ISE4 of International analytical soil exchange 2011 and NIST Standard Reference 
Material (SRM) 16469a (Estuarine sediment) were prepared and digested in the same 
way as that of the samples for the purpose of validating the results for samples 
measurements. 
3.4. Determination of Concentrations in samples 
In order to obtain the qualitative and quantitative elemental concentrations, the prepared 
samples were counted using the gamma-ray spectrometry system with HPGe for 36000 s. 
The spectra of each sample were analyzed and the identification of unkown radionuclides 
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55 
 
was done by considering their peak centroid energies. The centroid energies of the peaks 
from the spectrum were compared with reference gamma-ray energies obtained from 
other nuclear data (Nichols et al., 2008). The radionuclides contained in the samples were 
identified and the areas under the peaks then activity concentrations of each nuclide was 
calculated (Diab et al., 2008; Hamdy et al., 2008). Typical photopeaks of 228Ac, 214Bi and 
40K analyzed using Genie 2000 are shown in Appendix II. 
3.4.1. Radionuclides Activity Concentrations 
The relation (3.3) was used in determining activity concentration (Bq/kg) of 
radionuclides in samples (Ebaid et al., 2010; Thabayneh et al., 2012; Darko et al., 2005). 
𝐴𝑠𝑝 =
𝑁𝐷.𝑒
𝜆𝑃𝑡𝑑
𝑝.𝑇𝑐.𝜂(𝐸).𝑚
         (3.3) 
where, ND is the net counts of the radionuclide in the samples, td is the delay time 
between sampling and counting, p is the gamma ray emission probability (gamma ray 
yield), η(E) is the absolute counting efficiency of the detector system, Tc is the sample 
counting time, m is the mass of the sample (kg) or volume (l), exp(λptd) is the decay 
correction factor for delay between time of sampling and counting, and λp is the decay 
constant of the parent radionuclide. The specific activities of the measured radionuclides 
are in good agreement with the reference values. The activity concentration of 232Th was 
determined by the mean of the specific activities of 208Tl, 212Pb and 228Ac. The activity 
concentration of 226Ra  was  the  mean  specific  activities  due  to  gamma energies of 
214Pb  and 214Biand 40K  was measured directly using the 1460 KeV photo peak. Each 
sample was counted for 10 hrs in order to  reach ± 5 % of analytical accuracy of 
measurements (Faweya et al., 2014). 
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56 
 
3.4.2. Radon Concentration in Soil 
The estimation of the concentration of radon in soil CRn in the absence of radon transport 
was determined using a proposal in UNSCEAR report from the activity concentrations of 
226Ra (Faanu, 2011; UNSCEAR, 2000). The equation (3.4) was employed in calculation. 
𝐶𝑅𝑛 = 𝐶𝑅𝑎. 𝑓. 𝜌𝑠. 𝜀
−1(1 − 𝜀)(𝑚[𝑘𝑇 − 1] + 1)
−1    (3.4) 
where 𝐶𝑅𝑎 is the activity concentration of 
226Ra in soil (Bq.kg-1), 𝑓 is the radon emanation 
factor (0.2), 𝜌𝑠 is the density of the soil grains (2700 kg.m
-3), 𝜀 is the total porosity (0.25), 
𝑚 is the fraction of the porosity that is water filled (0.95), 𝑚 is zero if the soil is dry and 
𝑘𝑇 is the partition coefficient of radon between the water and air phases (0.23). In this 
study, soil samples were dried before activity concentration measurements; therefore, 𝑚 
is zero and the last term of the expression becomes one.   
3.4.3. Radon Exhalation Rate 
The major mechanism by which radon gets into the atmosphere is molecular diffusion. 
An expression to estimate the diffusive entry rate of radon into the atmosphere was used 
(UNSCEAR, 2000). It is suggested that for a porous mass of homogeneous material 
semi-infinite in extent, the flux density of radon at the surface of dry soil JD (Bq.m
-2.s-1) 
is given by equation (3.5). 
𝐽𝐷 = 𝐶𝑅𝑎. 𝜆𝑅𝑛. 𝑓. 𝜌𝑠(1 − 𝜀)𝐿       (3.5) 
where 𝐶𝑅𝑎 is the activity concentration of 
226Ra in earth material (Bq.kg-1),  𝑓 is the 
emanation fraction for earth material (0.2), 𝜌𝑠 is the soil grain density (2700 kg.m
-3), and 
𝜀 is the porosity. This equation is said to be valid only for dry soil and all the parameters 
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57 
 
were previously discussed in equation (3.4), except 𝜆𝑅𝑛  and 𝐿 which are the respectively 
decay constant of 222Rn (2.1 x 10 s-1) and diffusion length [which 
equals(
𝐷𝑒
𝜆𝑅𝑛
⁄ )
1/2
respectively, where 𝐷𝑒 = 2 × 10
−6𝑚2. 𝑠−1].  
3.4.4. Trace Elements Concentration by NAA 
In order to determine elemental concentration of element of interest, the equation (3.6) 
was used by the comparator method using the same geometry, equal weights of both 
sample and standard, with the same irradiation, decay and counting times (Landsberger, 
1994). 
𝐶𝑠𝑎𝑚 = 𝐶𝑠𝑡𝑑 (
𝐴𝑠𝑎𝑚
𝐴𝑠𝑡𝑑
)        (3.6) 
Where Csam is the unknown concentration of the element in the sample, Cstd is the known 
elemental concentration of the element in the standard, Asam is the activity concentration 
of the sample and Astd is the activity concentration of the standard. By introducing the 
terms D and C and also normalizing the weights between standards and unknowns, the 
overall equation becomes equation (3.7) in ppm or mg.kg-1(Landsberger, 1994). 
𝐶𝑠𝑎𝑚 = 𝐶𝑠𝑡𝑑 (
𝐴𝑠𝑎𝑚
𝐴𝑠𝑡𝑑
) (
𝐷𝑠𝑡𝑑
𝐷𝑠𝑎𝑚
) (
𝐶𝑠𝑡𝑑
𝐶𝑠𝑎𝑚
) (
𝑊𝑠𝑡𝑑
𝑊𝑠𝑎𝑚
)     (3.7) 
Where Wsam  and Wstd are the weights of the sample and the standard respectively. The 
product is required to be radioactive and capable of emitting at least one gamma-ray 
photon. The  gamma  ray  photon  emitted  was  counted  on  a  gamma  ray  detector  
using  HPGE. The  duration  of  irradiation  of  the  sample depends  on  the  
characteristics  of  the  sample and element of interest.  The duration of the irradiation 
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58 
 
also depends on the neutron flux density, mass of the sample and the efficiency of the 
gamma detector. The samples were sealed in capsules and transferred to the reactor core 
and irradiated with high flux neutrons. The activated components were then analyzed to 
identify and determine quantitatively the concentration of each radionuclide applying 
gamma spectrometry technique. 
3.4.5. Trace Elements Concentration by AAS 
A Varian AA-240FS First Sequential Atomic Absorption Spectrometer was used to 
determine the concentration levels of all the elements of interest. Replicate analyses were 
carried out for each determination to ascertain reproducibility and quality assurance. 
After every five real sample measurements for all the elements of interest, both the 
standard and blank were re-read to detect any drift in the instrument as soon as possible. 
Arsenic has short-wavelength and primary resonance lines; therefore, different equipment 
was used for generating its hydride before being atomized in the AAS equipment. The 
equipment is called Vapour Generation Accessory (VGA) that makes use of a gas-liquid 
separator to separate the gaseous hydrides from the liquid reagents prior to introduction 
into the atom cell. The cold vapour generation was used for mercury as it is the only 
analyte that has an appreciable atomic vapour pressure at room temperature (Evans  et al., 
1998) 
3.5. Dose Assessment 
The digital environmental radiation survey meter (RADOS, RDS-200, manufactured in 
Finland) was used to measure five outdoor external gamma dose rates at 1 m above the 
ground from each sampling location. The radiation survey meter was calibrated at 
Secondary Dosimetry Laboratory (SSDL) of RPI at GAEC with a provided calibration 
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59 
 
factor. The absorbed dose rate in air and the annual effective dose were estimated from 
activity concentrations measured in the soil samples. 
3.5.1. Absorbed Dose Rate in Air (D) from Activity Concentration 
A direct relationship between radioactivity concentrations of natural radionuclides and 
their exposure is referred to as absorbed dose rate in air at 1 m above the ground. This is 
calculated from the activity concentrations using the equation (3.8) (Faanu et al., 2012; 
Oyedele, 2006; Oyedele & Shimboyo, 2013; Oyedele, Sitoka, & Davids, 2008). 
𝐷𝛾(𝑛𝐺𝑦ℎ
−1 = 𝐷𝐶𝐹𝐾 × 𝐴𝐾 + 𝐷𝐶𝐹𝑈 × 𝐴𝑈 + 𝐷𝐶𝐹𝑇ℎ × 𝐴𝑇ℎ  (3.8) 
where DCFK= 0.0417, DCFU= 0.462  and DCFTh= 0.604  are the absorbed dose rate 
conversion factors for 40K, 238U and 232Th in nGy.h-1/Bq.kg-1 and AK, AU and ATh are the 
activity concentrations for 40K, 238U and 232Th, respectively.  
3.5.3. Annual Effective Dose Equivalent (AEDE) 
In order to provide the radiological risk to which the public is exposed, the absorbed dose 
is considered in terms of annual effective dose equivalent from terrestrial gamma 
radiation taking into account the conversion coefficients from absorbed dose in air to 
effective dose which is estimated to be 0.7 Sv/Gy and the outdoor occupation factor of 
0.2. Therefore, the outdoor annual effective dose equivalent was estimated by using the 
following equation (3.9)  (Darko et al., 2008; Faanu et al., 2012; Gbadago et al., 2011). 
𝐸𝛾 = 𝐷𝛾 × 0.2 × 8760 × 0.7       (3.9) 
Where Eᵧ is the average annual effective dose and Dr is the absorbed dose rate in air. 
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60 
 
3.6. Cancer Risk Assessment 
3.6.1. Radiological cancer risk assessment 
Table 3.2: Detriment adjusted nominal risk coefficients for stochastic effects after 
exposure to radiation at low dose rate (ICRP, 2007). 
Exposed 
population 
 
Cancer fatality 
  
Heritable effects 
  
Total 
ICRP publication 103 60  103 60  103 60 
Whole 5.5 6  0.2 1.3  5.7 7.3 
Adults 4.1 4.8  0.1 0.8  4.2 5.6 
Note: Detriment-adjusted nominal risk coefficients (1E-02 Sv-1) for stochastic 
effects after exposure to radiation at low dose rate.                                                                                                                                                                                                                                                                                                                                                                                                                                          
 
The radiological fatality cancer risks and the severe hereditable effects due to exposure to 
NORMs were assessed from the playgrounds of Basic Schools. This was done by using 
the ICRP recommended risk assessment technique and the use of appropriate nominal 
probability coefficients for stochastic effects (ICRP, 2007). The ICRP recommended 
nominal risk coefficients for stochastic effects are shown in Table 3.2 (ICRP, 2007). The 
risk to fatality cancer and hereditary effect were estimated as shown below: 
Fatality cancer risk = [Annual effective dose (Sv)] x [cancer nominal risk factor] 
Hereditary effects risk = [Annual effective dose (Sv)] x [hereditary nominal risk factor]  
3.6.2. Elemental Risk-based Assessment 
Exposure of children playing in school playgrounds to trace elements can occur via three 
main pathways: (a) direct ingestion of substrate particles; (b) inhalation of re-suspended 
particles through the mouth and nose; and (c) dermal absorption of trace elements in 
particles adhered to exposed skin (De Miguel et al., 2007). The dose received through 
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61 
 
each of the three pathways considered has been calculated using the following four 
equations: 
𝐷𝑖𝑛𝑔𝑒𝑠𝑡𝑖𝑜𝑛 = 𝐶 ×
𝐼𝑛𝑔𝑅×𝐸𝐹×𝐸𝐷
𝐵𝑊×𝐴𝑇
× 10−6      (3.10)  
𝐷𝑖𝑛ℎ𝑎𝑙𝑎𝑡𝑖𝑜𝑛 = 𝐶 ×
𝐼𝑛ℎ𝑅×𝐸𝐹×𝐸𝐷
𝑃𝐸𝐹×𝐵𝑊×𝐴𝑇
       (3.11)  
𝐷𝑑𝑒𝑟𝑚𝑎𝑙 = 𝐶 ×
𝑆𝐴×𝑆𝐿×𝐴𝐵𝑆×𝐸𝐹×𝐸𝐷
𝐵𝑊×𝐴𝑇
× 10−6     (3.12) 
𝐷𝑣𝑎𝑝𝑜𝑢𝑟 = 𝐶 ×
𝐼𝑛ℎ𝑅×𝐸𝐹×𝐸𝐷
𝑉𝐹×𝐵𝑊×𝐴𝑇
       (3.13)  
where D (mg.kg-1.day-1) is dose contacted through ingestion (𝐷𝑖𝑛𝑔𝑒𝑠𝑡𝑖𝑜𝑛), and inhalation 
(𝐷𝑖𝑛ℎ𝑎𝑙𝑎𝑡𝑖𝑜𝑛) of substrate particles and dermal contact with the substrate particles 
(𝐷𝑑𝑒𝑟𝑚𝑎𝑙), C (g.kg
-1) is the concentration of trace element in substrate (“exposure point 
concentration”) (USEPA, 1997).  
IngR: Ingestion rate; in this study, 20 mg.h-1 (USEPA, 1997)  
InhR: Inhalation rate,; in this study, 1.2 m3.h-1 (EPA, 2002)  
EF: Exposure Frequency (site specific), in this study 990 h year-1 is used 
ED: Exposure Duration (site specific; in this study, 14 years is used 
SA: Exposed skin area; in this study, 2800 cm3 (De Miguel et al., 2007)  
SL: Skin adherence factor; in this study, 0.07 mg.cm-2h-1 (EPA, 2002)  
ABS: Dermal absorption factor (dimensionless); in this study, 0.001 for all elements 
except for arsenic which is 0.03 
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PEF: Particle emission factor; in this study, 6.8 x 108 m3.kg-1  (De Miguel et al., 2007) 
VF: Volatilization factor; in this study, for elemental Hg, 32.376.4 m3kg-1 (De Miguel et 
al., 2007). 
BW: Average body weight; in this study, 15 kg (De Miguel et al., 2007) 
AT: Average time, for non-carcinogens is (ED x 365 days) and carcinogens is (70 x 365 
days) 
The obtained data for particles with diameter below 100 µm were selected in order to 
perform the estimation of this risk-based assessment. This is because they can easily be 
inhaled faster through the nose and mouth. Exposure frequency was estimated based on 
the information obtained from the Municipal Education Office of Ga East Municipal 
District. 
Children spend about 30 minutes on playgrounds during their 2 break times at school (A 
total of 1 hour a day). They are assumed to visit the playground from Monday to Friday 
(5 times a week). There are three school terms per year, and children vacate after every 
three months of school days. The duration of their vacation is four weeks (1 month), so 
they go to school for a period of 9 months in a year. The total exposure frequency is 
therefore estimated to be 990 hours/year (5 ×
1 ℎ𝑜𝑢𝑟
1 𝑑𝑎𝑦
×
22 𝑑𝑎𝑦
1 𝑚𝑜𝑛𝑡ℎ
×
9 𝑚𝑜𝑛𝑡ℎ
1 𝑦𝑒𝑎𝑟
). It should be 
noted that this estimation is strictly based on the children that are enrolled within their 
respective schools, and they graduate from their elementary school after 14 years from 
the day of their enrollment. So, the exposure duration is taken as 14 years. The remaining 
exposure factors used in this study are the USEPA’s default exposure factors for children 
(De Miguel et al., 2007). Inhalation-specific toxicity data are available only for Al, As, 
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63 
 
Cd, Cr, Hg, Mn and Ni. For the rest of elements included in the risk analysis, the toxicity 
values considered for the inhalation path are the corresponding oral reference doses and 
slope factors. And on the postulation that, after inhalation, the absorption of the particle-
bound toxicants will result in similar health effects as if the particles had been ingested 
(De Miguel et al., 2007), especially for this extended particle size range. 
The concentration term C which is the exposure point concentration in equations (3.10) – 
(3.13) combined with their corresponding exposure values shown above to give an 
approximation of the sensible maximum exposure. The concentration term C used is the 
upper limit of 95 % confidence interval for the mean (95 % UCL) which cater for the 
uncertainties associated with estimating the true average concentration at a site (US EPA, 
2002). In this study, the 95 % UCL was calculated with Methods for Specific 
Distributions (UCLs for Normal Distributions) using Microsoft Excel 2013. A step by 
step method of calculating 95% UCL is shown in Appendix VIII (US EPA, 2002). The 
doses calculated with Equations (3.10) – (3.13) for each element and exposure pathway 
were subsequently divided by the corresponding Reference Dose to yield a Hazard 
Quotient, HQ (or non-cancer risk), whereas for carcinogens the dose was multiplied by 
the corresponding slope factor to produce a level of cancer risk 
3.7. Quantification of Soil Pollution 
3.7.1. Enrichment Factor (EF) 
The enrichment factor for each metal was calculated using the relationship in equation 
(3.14). It is the quotient of the ratio of the normalizing element in a sample and the same 
ration established in the chosen baseline (Bhuiyan et al., 2010). 
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64 
 
𝐸𝐹 =
(𝑀𝑒𝑡𝑎𝑙 𝐹𝑒⁄ )𝑠𝑎𝑚𝑝𝑙𝑒
(𝑀𝑒𝑡𝑎𝑙 𝐹𝑒⁄ )𝐵𝑎𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑
        (3.14) 
Bhuiyan et al. (2010) recommended the use of concentration of metal in unaffected soils 
of the study area. Nevertheless, in this study, the world average shales background values 
were used (Ebenezer, 2011; Rubio et al, 2000). This is because shales integrate crustal 
material derived from different earth-surface environments. Therefore, their deposition 
and formation is an averaging process and shale soils are normalized enabling the 
elemental concentrations to be well distributed. The EF values close to unity indicate 
original crustal elemental concentration, those less than 1.0 show a possible mobilization 
or depletion of metals, whereas EF >1.0 is an indication that the element is a result of 
anthropogenic activities. EFs greater than 10 are considered to be non-crusted source 
(Bhuiyan et al., 2010). In this study, iron (Fe) was used as the reference element for 
geochemical normalization. Bhuiyan et al. (2010 justified this with three reasons that Fe 
is associated with fine solid surfaces, its geochemistry is similar to that of many trace 
metals and its natural concentration tends to be uniform. 
3.7.3. Geo-accumulation index (Igeo) 
The geo-accumulation index (Igeo) was originally expressed by Muller (1979) for metal 
concentrations in the < 2 µm fraction as follows (Rubio et al., 2000): 
𝐼𝑔𝑒𝑜 = 𝑙𝑜𝑔2 (
𝐶𝑛
1.5×𝐵𝑛
)        (3.15) 
Where 𝐶𝑛 is the measured concentration in the sediment for the metal n, 𝐵𝑛 the 
background value for the metal n and the factor 1.5 is used to correct for possible 
variations of the background data due to lithological changes.  
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65 
 
The Index of Geo-accumulation consists of seven grades (Table 3.3), with Igeo of 6 
indicating almost a 100-fold enrichment above background values (Rubio et al., 2000; 
Yaqin et al., 2008).  
Table 3.3: Six classes of the Geo-accumulation Index 
Class Value Soil quality (pollution intensity) 
0 Igeo ≤ 0 Practically uncontaminated 
1 0 < Igeo< 1 Uncontaminated to moderately contaminated 
2 1 < Igeo< 2 Moderately contaminated 
3 2 < Igeo< 3 Moderately to heavily contaminated 
4 3 < Igeo< 4 Heavily contaminated 
5 4 < Igeo< 5 Heavily to extremely contaminated 
6 5 < Igeo Extremely contaminated 
 
3.7.3. Contamination Factor (CF) 
The CF is the ratio obtained by dividing the concentration of each metal in the soil by the 
baseline or background value of concentration in unaffected soil (Bhuiyan et al., 2010). 
The expression is as follow: 
𝐶𝐹 =
𝐶𝑚𝑒𝑡𝑎𝑙
𝐶𝑏𝑎𝑐𝑘𝑔𝑟𝑜𝑢𝑛𝑑
         (3.16) 
The contamination levels are classified based on their intensities on a scale ranging from 
1 to 6 [0 = none; 1 = none to medium; 2 = moderate; 3 = moderately to strong; 4 = 
strongly polluted; 5 = strong to very strong; 6 = very strong]. The highest number (6) 
indicates that the metal concentration is 100 times greater than what would be expected in 
the crust (Bhuiyan et al., 2010). 
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66 
 
3.8. Computational Analysis (NORMs) 
The activity concentrations of NORMs were reconstructed by using the Forward 
Different Interpolation Method. This was achieved by expressing the exponential term 
“𝑒−𝜆𝑡” of the radionuclide decay equation “𝐴 = 𝐴0𝑒
−𝜆𝑡” into a 4th order Taylor 
polynomial form (Sey, 2014). The decay factor 𝑒−𝜆𝑡 was approximated to a polynomial 
form by the following analysis. 
𝑃𝑛(𝜆𝑡) = 𝑃𝑛(𝑥) 
Since 𝑃𝑛(𝑥) = 𝑒
−𝜆𝑡 = 𝑒−𝑥 ; this yields the polynomial of 
𝑒−𝑥 = 𝑃𝑛(𝑥) = 𝑎0 + 𝑎1(𝑥 − 𝑥0) + 𝑎2(𝑥 − 𝑥0)(𝑥 − 𝑥1) + 𝑎3(𝑥 − 𝑥0)(𝑥 − 𝑥1)(𝑥 − 𝑥2)
+⋯+ 𝑎𝑛(𝑥 − 𝑥0)(𝑥 − 𝑥1)(𝑥 − 𝑥2)… (𝑥 − 𝑥𝑛−1) 
And the fourth order of this can be written as; 
𝑒−𝑥 = 𝑃𝑛(𝑥) = 𝑎0 + 𝑎1(𝑥 − 𝑥0) + 𝑎2(𝑥 − 𝑥0)(𝑥 − 𝑥1) + 𝑎3(𝑥 − 𝑥0)(𝑥 − 𝑥1)(𝑥 − 𝑥2)
+ 𝑎4(𝑥 − 𝑥0)(𝑥 − 𝑥1)(𝑥 − 𝑥2)(𝑥 − 𝑥3) 
 
Where     𝑎0 = 𝑦0 = 𝑃0(𝑥0) 
𝑎1 =
𝑦1 − 𝑦0
ℎ
=
∆𝑦0
ℎ
 
𝑎2 =
𝑦2 − 2𝑦1 + 𝑦0
2ℎ2
=
∆2𝑦0
2ℎ2
 
𝑎3 =
𝑦3 − 3𝑦2 + 3𝑦1 − 𝑦0
2! ℎ3
 
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67 
 
𝑎4 =
𝑦4 − 4𝑦3 + 6𝑦2 − 4𝑦1 + 𝑦0
4! ℎ4
 
So that, 
𝑃𝑛(𝑥) = 𝑎𝑥
4 + 𝑏𝑥3 + 𝑐𝑥2 + 𝑑𝑥 + 𝑒 
And  
𝑎 = 𝑎4, 
𝑏 = 𝑎3 − 𝑎4(𝑥0 + 𝑥1 + 𝑥2 + 𝑥3) 
𝑐 = 𝑎2 − 𝑎3(𝑥0 + 𝑥1 + 𝑥2) + 𝑎4(𝑥0𝑥1 + 𝑥0𝑥2 + 𝑥0𝑥3 + 𝑥1𝑥2 + 𝑥1𝑥3 + 𝑥2𝑥3) 
𝑑 = 𝑎1 − 𝑎2(𝑥0 + 𝑥1) + 𝑎3(𝑥0𝑥1 + 𝑥0𝑥2 + 𝑥1𝑥2) + 𝑎4(𝑥0𝑥1𝑥2 + 𝑥0𝑥1𝑥3 + 𝑥0𝑥2𝑥3 +
𝑥1𝑥2𝑥3). 
𝑒 = 𝑎0 − 𝑎1(𝑥0) + 𝑎2(𝑥0𝑥1) − 𝑎3(𝑥0𝑥1𝑥2) + 𝑎4(𝑥0𝑥1𝑥2𝑥3) 
Microsoft Excel (2013 version) was used for the computation of these relations to obtain 
the coefficients  a, b, c, d and e, and the polynomial (3.17) equals to 𝑒−𝑥 was obtained 
(Sey, 2014). 
𝑒−𝑥 ≈ 0.0067𝑥4 − 0.0820𝑥3 + 0.3993𝑥2 − 0.9560𝑥 + 1    (3.17) 
Based on the expression and an assumption that radionuclides activity concentrations in 
the soils are uniform, a MATLAB R2011b script (Appendix V) was written to 
approximate the naturally occurring radionuclides (238U, 232Th and 40K) activity 
concentrations for the next 10 years as of 2015. The decay constants of 238U, 232Th and 
40K were calculated using the half-lives of 226Ra, 228Ra and 40K respectively. 
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3.9. Statistical Analysis (Trace Elements) 
The experimental data were treated statistically using SPSS software (version 20.0 for 
Windows). Principal Component Analysis (PCA) was used to deduce the hypothetical 
source of heavy metals whether natural or anthropogenic. Factor Analysis (FA) or the 
components of the PCA was done by Varimax Rotation. Varimax Rotation was used 
since Orthogonal Rotation minimizes the number of variables with a high loading on 
each component and therefore facilitates the interpretation of PCA results (Bhuiyan et al., 
2010, 2011). Cluster Analysis (CA) was applied to classify different geochemical groups, 
clustering the samples with comparable trace metal contents. CA was expressed 
according to the Ward-algorithmic Method, and the squared Euclidean distance was 
employed for quantifying the distance between clusters of identical metal contents. 
 
 
 
 
 
 
 
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CHAPTER 4 
RESULTS AND DISCUSSIONS 
In this chapter, the activity concentrations of 238U, 232Th and 40K measured in soil 
samples collected from selected sampling site in Ga East are presented. The absorbed 
dose rate in air, annual effective dose, cancer risk assessment due to effective dose, and 
the approximation of future doses are also presented. In addition, the elemental 
concentrations of trace metals determined in soil sample are presented as well as their 
risk assessments. The indices of pollution due to elemental concentrations and pollution 
source identification are also discussed. 
4.1. Assessment of Natural Radioactivity 
Table 4.1: Sample locations and coordinates 
Sampling ID School Coordinates 
  Latitude Longitude 
AP Abokobi Presby 5˚43'45"N 0˚12'4"W 
AG Agbogba Anglican 5˚41'31''N 0˚12'3''W 
AM Akporman Model 5˚43'35"N 0˚12'52"W 
AS Ashongman M/A 5˚42'13''N 0˚12'42"W 
AH Atomic Hills 5˚42'0''N 0˚14'8"W 
DA Dome Anglican 5˚39'4''N 0˚14'10''W 
GS GAEC Basic School 5˚39'39''N 0˚14'66''W 
GP GAEC Playing Ground 5˚40'7''N 0˚13'58"W 
HC Haatso Calvary Presby 5˚40'3''N 0˚12'25"W 
HM Hillview Montessori 5˚41'4''N 0˚13'55''W 
KA Kwabenya Atomic M/A 5˚39'54''N 0˚14'28''W 
KW Kwabenya M/A 5˚41'12''N 0˚14'48''W 
TC Taifa Community 5˚39'4''N 0˚14'10''W 
TD Taifa St Dominic 5˚40'26''N 0˚15'8''W 
 
 
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4.1.1. Ambient measurements 
Table 4.2: Average absorbed dose rate in air at 1 m above the sampling points in the 
study area and calculated annual effective dose 
Sampling ID 
 
School 
 
Absorbed dose rate 
(nGyh-1) 
Annual effective 
dose (mSv) 
  
Average ± σ  Range 
 AP Abokobi Presby 86 ± 18 60 – 110 0.105 
AG Agbogba Anglican 94 ± 13 80 – 100 0.115 
AP Akporman Model 74 ± 13 60 – 90 0.091 
AS Ashongman M/A 80 ± 20 60 – 100 0.098 
AH Atomic Hills 74 ± 15 60 – 90 0.091 
DA Dome Anglican 92 ± 30 60 – 140 0.113 
GS GAEC Basic School 76 ± 17 60 – 100 0.093 
GP GAEC Playing Ground 78 ± 18 60 – 100 0.096 
HC Haatso Calvary Presby 80 ± 20 60 – 100 0.098 
HM Hillview Montessori 74 ± 17 60 – 100 0.091 
KA Kwabenya Atomic M/A 96 ± 29 60 – 130 0.118 
KW Kwabenya M/A 90 ± 23 60 – 110 0.110 
TC Taifa Community 88 ± 29 60 – 130 0.108 
TD Taifa St. Dominic 66 ± 09 60 – 80 0.081 
Average ± σ 
 
82.0 ± 6.5  0.101 ± 0.011 
 
Table 4.2 shows the absorbed dose rate measured in air at 1 m above the ground at the 
sampling points. The table indicates the average and range values of the absorbed dose as 
well as the calculated annual effective doses. The measured absorbed dose rates varied in 
a range of 60–140 nGyh-1 with an average value of 82.0 ± 6.5 nGyh-1. The corresponding 
average annual effective dose was calculated to be 101 ± 11 µSv (0.101 ± 0.011 mSv) in 
a range of 81 – 115 µSv (0.081 – 0.115 mSv). The results of the absorbed dose rates in 
this study compare well with the range of dose rates values reported for other countries 
(UNSCEAR, 2000). The highest absorbed dose rate value of 140 nGyh-1 was measured at 
Dome Anglican School Playground. The high absorbed dose rate in this area could be 
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71 
 
attributed to cosmic radiation and natural abundance of radionuclides in the soil of the 
area. 
4.1.2. Activity Concentrations of NORMs in Soil 
Figure 4.1 shows the Activity Concentrations of 238U, 232Th and 40K. The activity 
concentrations of 238U, 232Th and 40K are in the range of 9.7 – 40.3 Bqkg-1, 9.2 – 66.4 
Bqkg-1 and 20.4 – 342.2 Bqkg-1, respectively. The average activity concentrations of 
238U, 232Th and 40K are respectively 19.8 ± 8.7 Bqkg-1, 29.1 ± 16.3 Bqkg-1, and 119.4 ± 
97.9 Bqkg-1. These results are tabulated in Appendix II. The worldwide average activity 
concentrations of 238U, 232Th and 40K in soil samples from similar studies are reported in 
the United Scientific Committee on Effect of Atomic Radiation (UNSCEAR) for 
individual member of public as 35, 30 and 400 Bqkg-1, respectively (UNSCEAR, 2000).  
In comparison to the average activity concentrations observed in this study, 238U is about 
two times lower than the world average; 232Th is nearly similar and compares well to the 
world average; whilst 40K is about three times lower than values in normal continental 
soils (UNSCEAR, 2000). The average values in this study are lower than the worldwide 
average values. The activity concentrations are way far below the exemption values of 
1000 Bqkg-1 for 238U and 232Th, and 10000 Bqkg-1 for 40K in material that will warrant 
regulatory control (IAEA, 2011). 
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72 
 
 
Figure 4.1: Activity concentrations of 238U, 232Th and 40K in soil samples 
 
4.1.3. Estimation of Radon Concentration in Soil 
The activity concentration of 222Radon in the soil matrix was calculated from the activity 
concentration of 226Ra in the soil samples (Table 4.3). The range of activity 
concentrations of 222Ra in the soil was 15.79–65.36 kBq.m-3 with a mean value of 
32.13kBq.m-3. These activity concentrations are below the value of 78 kBq.m-3 
(UNSCEAR, 2000). The mean exhalation rate was 0.016 Bq.m-2.s-1 (in a range of 0.008 – 
0.033 Bq.m-2.s-1), and this compares well with the value of 0.033 Bq.m-2.s-1 (UNSCEAR 
(2000). The observed measurements of exhalation rates of radon from soil indicate the 
unpredictability that reflects the variability of radon concentrations in near-surface pore 
spaces.  
0
50
100
150
200
250
300
350
400
A
ct
iv
it
y 
co
n
ce
n
tr
at
io
n
 (
B
q
/k
g
) 
Playground 
Th-232 K-40 U-238 (Ra-226)
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73 
 
Table 4.3: Estimated concentration of 222Rn and their corresponding exhalation rate 
Sample 
Identity 
School CRa 
(Bq.kg-1) 
CRn 
(kBq.m-3) 
Ex rate 
(Bq.m-2.s-1) 
AP Abokobi Presby 21.89 35.46 0.018 
AG Agbogba Anglican 40.35 65.36 0.033 
AP Akporman Model 16.08 26.04 0.013 
AS Ashongman M/A 26.86 43.52 0.022 
AH Atomic Hills 10.55 17.09 0.009 
DA Dome Anglican 25.52 41.35 0.021 
GS GAEC Basic School 23.15 37.51 0.019 
GP GAEC Playing Ground 17.38 28.16 0.014 
HC Haatso Calvary Presby 23.48 38.03 0.019 
HM Hillview Montessori 11.52 18.67 0.010 
KA Kwabenya Atomic M/A 27.20 44.06 0.023 
KW Kwabenya M/A 9.75 15.79 0.008 
TC Taifa Community 10.18 16.50 0.008 
TD Taifa St Dominic 13.74 22.27 0.011 
Mean  19.83 32.13 0.016 
St.dev  8.74 32.13 0.007 
Min  9.75 15.79 0.008 
Max  40.35 65.36 0.033 
 
This is because the concentrations of 222Rn in soil gas vary over many orders of 
magnitude from place to place and show significant time variations at any given site. 
4.1.4. Estimation of Absorbed Dose (D) and Annual effective Dose Equivalent 
(AEDE) 
The average gamma dose rate and annual effective dose from terrestrial gamma rays 
calculated from soil activity concentrations are shown in Appendix IV. The average 
absorbed dose rate is found to be 31.7 ± 17.4 nGyh-1 in a range of 11.3 – 73.0 nGyh-1, 
which is by a factor of three lower than the dose rate measured in air at 1 m above the 
ground. The average absorbed dose rate due to the soil concentrations is observed to be 
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74 
 
about two times lower than the worldwide average value of 60 nGyh-1 (UNSCEAR, 
2000).  
This difference could be attributed to differences in the geology and geochemical state of 
the sampling sites. The corresponding average annual effective dose estimated from the 
soil concentrations is 0.039 ± 0.021 mSv in a range of 0.014 – 0.090 mSv. 
 
Figure 4.2: Comparison of Absorbed dose rate in different playing grounds 
 
0
10
20
30
40
50
60
70
80
A
b
so
rb
ed
 d
o
se
 r
at
e 
in
 a
ir
 (
n
G
y/
h
) 
Playground 
Absorbed dose
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75 
 
 
Figure 4.3: Comparison of annual effective dose in different playing grounds 
 
 
Figure 4.4: Comparison of annual effective doses from different exposure pathways of 
radiation 
 
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
A
n
n
u
al
 e
ff
ec
ti
v
e 
d
o
se
 (
m
S
v
) 
Playground 
Effective dose
0
0.02
0.04
0.06
0.08
0.1
0.12
Airborne gamma radiation Soil samples
A
n
n
u
al
 e
ff
ec
ti
v
e 
d
o
se
 
Exposure pathway 
Average effective dose
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76 
 
4.1.5. Natural Radioactivity Model 
The naturally occurring radionuclides activity concentration was predicted using the 
Forward Differential Approach and a written MATLAB script in Appendix V, based on 
their current measured concentrations. A difference of 2 years was chosen between 
successive years. From the predicted results in Appendix VI, it is observed that there is 
no great variance in activity concentrations of 238U and 232Th whilst activity concentration 
of 40K is uniform up to the chosen period of time. However, as a result of numerical 
analysis used, there is a decrease in the data. This decrease in exposure is observed from 
estimated annual effective dose in the study areas (Figure 4.5).  
4.1.6. Radiological Hazard Assessment 
 
The ICRP Risk Assessment Technique was employed in the estimation of the 
radiological fatality cancer risks for the whole population as well as severe hereditary 
effects (ICRP, 2007). The practical scheme of radiological risk recommended by ICRP is 
established on the assumption that at doses below 100 mSv, a specified increment in dose 
will yield a directly proportionate in the probability of incurring cancer of hereditary 
effects attributable to ionizing radiation. The model is commonly known as Linear-Non-
Threshold (LNT) dose-response for which any dose greater than zero has an optimistic 
probability of producing an effect (ICRP, 2007). 
 
  
 
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77 
 
 
 
Figure 4.5: Predicted annual effective dose in study areas
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
A
E
D
E
 (
m
S
v
) 
Playground 
2015
2017
2019
2021
2023
2025
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78 
 
The appraisal of risk covered the exposure pathway considered in this study. Table 4.4 
shows the average annual effective dose from soils and the estimated risk components. 
The  risk  of  exposure  of  low doses  and  dose  rates  of  radiation was  estimated  using  
the  2007 recommended  risk  coefficients (ICRP, 2007)  and  an  assumed  70  years  
lifetime  of continuous  exposure  of  the  population  to low  level  radiation. 
Table 4.4: Estimated cancer risk components for external irradiation of 238U, 232Th and 
40K in soil 
Sample ID Annual 
effective 
dose (Sv) 
Fatality 
cancer risk 
to 
population 
per year 
Life time 
fatality 
cancer risk 
to 
population 
Severe 
hereditary 
effect per 
year 
Estimated 
lifetime 
hereditary 
effects 
Abokobi Presby 4.38E-05 2.41E-06 1.68E-04 8.75E-08 6.13E-06 
Agbogba Anglican 8.95E-05 4.92E-06 3.45E-04 1.79E-07 1.25E-05 
Akporman Model 3.07E-05 1.69E-06 1.18E-04 6.13E-08 4.29E-06 
Ashongman M/A 5.98E-05 3.29E-06 2.30E-04 1.20E-07 8.37E-06 
Atomic Hills 1.38E-05 7.62E-07 5.33E-05 2.77E-08 1.94E-06 
Dome Anglican 3.94E-05 2.17E-06 1.52E-04 7.89E-08 5.52E-06 
GAEC Basic School 4.04E-05 2.22E-06 1.55E-04 8.08E-08 5.65E-06 
GAEC Playing Ground 3.65E-05 2.01E-06 1.41E-04 7.30E-08 5.11E-06 
Haatso Calvary Presby 5.48E-05 3.01E-06 2.11E-04 1.10E-07 7.67E-06 
Hillview Montessori 1.87E-05 1.03E-06 7.20E-05 3.74E-08 2.62E-06 
Kwabenya Atomic M/A 5.81E-05 3.19E-06 2.24E-04 1.16E-07 8.13E-06 
Kwabenya M/A 1.50E-05 8.27E-07 5.79E-05 3.01E-08 2.10E-06 
Taifa Community 1.89E-05 1.04E-06 7.26E-05 3.77E-08 2.64E-06 
Taifa St. Dominic 2.49E-05 1.37E-06 9.60E-05 4.99E-08 3.49E-06 
Average 3.89E-05 2.14E-06 1.50E-04 7.78E-08 5.44E-06 
Standard deviation 2.13E-05 1.17E-06 8.22E-05 4.27E-08 2.99E-06 
Min 1.38E-05 7.62E-07 5.33E-05 2.77E-08 1.94E-06 
Max 8.95E-05 4.92E-06 3.45E-04 1.79E-07 1.25E-05 
 
 
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The average fatality cancer risks for all playgrounds were in a range of 7.62E-07 - 4.92E-
06 with the average of 2.14E-06. This suggests that approximately 2 persons out of a 1 
000 000 people are likely to suffer from cancer as a result of external irradiation from soil 
and this is considered to be insignificant. The lifetime fatality cancer risk for all were in a 
range of 5.33E-05 - 3.45E-04 with the average of 1.50E-04 which suggests that 
approximately 1 person out of a 10 000 is likely to suffer from cancer. On the other hand, 
severe hereditary effects per year and estimated lifetime hereditary effects were in a 
range of 2.77E-08 - 1.79E-07 and 1.94E-06 - 1.25E-05 with average of 7.78E-08 and 
5.44E-06 respectively. Similarly, this proposes that approximately none out of 100 000 
000 people is likely to suffer from hereditary diseases per year and approximately 5 
persons out of 100 000 are likely to suffer from hereditary related diseases due to low 
background radiation exposure. The lifetime fatality cancer risk for the population in the 
study area is slightly above the USEPA acceptable range of risks of 1 × 10-6   to 1 × 10-4 
values for the population of the study area (Faanu et al., 2012; USEPA, 1993). However, 
a risk value of 1 × 10-6 (that is, 1 case out of a million people dying from cancer) is 
considered as trivial. 
4.2. Assessment of Elemental Concentrations 
4.2.1. Validation of Results 
4.2.1.1. INAA Results 
For INAA, the reference materials SAMPLE 4 (114ISE4) of International Soil-analytical 
Exchange 2011 and SAMPLE 4 (131ISE4) of International Soil-analytical Exchange 
2013 were used to validate the short-lived elemental concentration for quality control. 
Also, SAMPLE 2 (114ISE2) of International Soil-analytical Exchange 2011 was used in 
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order to validate the INAA method for the determination of medium lived elemental 
concentration. These reference materials were prepared, irradiated in the inner pneumatic 
irradiation sites of the GHARR-1 facility. Irradiation duration ranged from 10 s to 1 h 
depending on the half-life of element of interest and counted in the similar way as that of 
samples. The results measured were compared with the reference values in Tables 4.5, 
4.6 and 4.7. 
Table 4.5: Comparison of measured values of SAMPLE4 (114ISE4) as analyzed by 
INAA with its reference values 
Element Measured value (mgkg-1) Reference values (mgkg-1) 
Al 6.35 x 104 6.05 x 104 – 6.7 x 104 
Ti 3.75 x 103 3.370 x 103 – 4.380 x 103 
V 1.031 x 102 8.40 x 101 – 1.07 x 102 
Mn 1.047 x 103 1.02 x 103 – 1.192 x 103 
 
Table 4.6: Comparison of measured values of SAMPLE 4 (131ISE4) as analyzed by 
INAA with its reference values 
Element Measured value (mgkg-1) Reference value (mgkg-1) 
Al 3.881 x 104 3.74 x 104 – 4.46 x 104 
Ti 4.253 x 103 3.974 x 103 – 4.693 x 103 
V 4.968 x 101 4.6 x 101 – 5.69 x 101 
Mn 6.025 x 102 5.42 x 102 – 6.54 x 102 
 
 
 
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Table 4.7: Comparison of measured values of SAMPLE 2 (114ISE2) as analyzed by 
INAA with its reference values 
Element Measured value (mgkg-1) Reference value (mgkg-1) 
Na 7.076 x 103 6.440 x 103 – 7.675 x 103 
K 1.4750 x 104 1.3900 x 104 – 1.5400 x 104 
La 2.560 x 101 2.27 x 101 – 2.87 x 101 
 
From the observation, all the measured values are within the range of the reference values 
and that indicates the high degree of accuracy and precision of the analytical technique. 
4.2.1.2. AAS Results 
The two reference materials 114 ISE4 of International analytical soil exchange 2011 and 
the NIST Standard Reference Material 16469a (Estuarine sediment) were analyzed by 
means of AAS in order to validate analytical method. The results obtained were 
compared with the NIST certified values (Table 4.8). The measured values are observed 
to compare well with the certified/non-certified values and the calculated percentage 
recoveries show that the experiment was conducted well and there is accuracy in the 
procedures.  
 
 
 
 
 
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Table 4.8: Comparison of measured values of two reference materials with their 
respective certified/reference values (mg.kg-1) 
 NIST Standard Reference Material 16469a 
(Estuarine sediments) 
 114 ISE4 of International analytical soil 
exchange 2011 
 Measured  
values 
Certified  
values 
Percentage  
recovery (%) 
 Measured  
values 
Reference 
 values 
Percentage  
recovery (%) 
Co 4.848 ± 0.24 5* 97.0  21.411 21.7 98.7 
Cr 39.8 ± 2.0 40.9 ± 1.9 97.3  270.968 271.4 99.8 
Fe 19939 ± 996.97 20080 ± 0.039 99.3  40342.742 40000.34 100.9 
Cd 0.151 ± 0.007 0.148 ± 0.007 102.4  8.558 8.567 99.9 
Cu 10.21 ± 0.51 10.01 ± 0.34 102.0  160.131 159.5 100.4 
Zn 4851.5 ± 242.6 48.9 ± 1.6 101.1  1046.371 1045 100.1 
Pb 12.1 ± 0.6 11.7 ± 1.2 103.3  299.395 299.8 99.9 
Ni 22.3 ± 1.12 23* 97.4  60.907 61.31 99.3 
As 6.06 ± 0.30 6.23 ± 0.21 97.3  45.968 46.18 99.5 
Hg 0.0313 ± 0.0015 0.048* 78.3  3.129 3.925 79.7 
        
 *Non-certified values      
 
4.2.2. Analytical Results 
Figure 4.6 shows graphical presentation of the elemental concentrations of the metals of 
environmental interest (As, Cd, Cr, Hg, Ni, Pb and Zn). The elemental concentrations of 
As, Cd, Cr, Cu, Hg, Ni, Pb and Zn are (in ranges) of 0.075 – 1.45 mg.kg-1, 0.06 – 0.225 
mg.kg-1, 3.42 – 15.72 mg.kg-1, 7.44 –30.84 mg.kg-1, 0.01 – 0.05 mg.kg-1, 7.335 – 31.785 
mg.kg-1, 0.15 – 11.85 mg.kg-1 and 58.5 – 81.5 mg.kg-1. The elemental concentration of 
As, Cd, Cr, Cu, Hg, Ni, Pb and  Zn are 0.64 mg.kg-1, 0.12 mg.kg-1, 9.62 mg.kg-1, 14.87 
mg.kg-1, 0.02 mg.kg-1, 16.06 mg.kg-1, 5.60 mg.kg-1 and 233.89 mg.kg-1 respectively. It is 
observed that As has low concentration in Kwabenya M/A and high concentration in 
Ashongman. Cd has low concentration in Taifa community and high concentration in 
Haatso Calvary Presby. Cr has low concentration in Hillview Montessori and high 
concentration in Abokobi Presby. Cu has low concentration in Taifa community and high 
concentration in Dome Anglican. Hg has low concentration in Akporman Model and high 
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in Ashongman. Pb has low concentration in Ashongman and high concentration in 
Abokobi Presby. Zn has low concentration in Hillview Montessori and high 
concentration in Ashongman. 
 
Figure 4.6: Elemental concentrations of As, Cd, Cr, Cu, Hg, Pb and Zn in soil samples 
 
 
 
 
 
0.01
0.1
1
10
100
1000
E
le
m
en
ta
l 
co
n
ce
n
tr
at
io
n
 (
m
g
/k
g
) 
Playground 
As Cd Cr Cu Hg Ni Pb Zn
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The main descriptive statistic of the comprehensive analysis results are shown in Table 
4.10 which is a combination of results obtained by INAA and AAS. Even though straight 
comparisons of the results of other investigations done in other playgrounds are different 
by the disparity of playgrounds construction, sampling protocol and sample preparation 
procedures, the levels of trace elements in the playground soils of basic schools in the Ga 
East Assembly (with the exception of the common natural trace elements such as Al, Fe, 
K, Na, Ni, Zn and Ti) are very much lower than those found by other researchers (e.g., 
De Miguel et al., 2007). This is because in most European developed countries such as 
Spain, there is a general expectation of heavy traffic emissions as well as industrial 
activities compared to African country including Ghana (Table 4.9). In this study, the 
results obtained for all trace elements compare well with the world average concentration 
in shale (Ebenezer, 2011). 
Table 4.9: Comparison of elemental concentrations of the As, Cd, Cr, Cu, Hg, Ni, Pb and 
Zn from this study with published data 
Study    Elemental 
concentration(mg/kg) 
 Reference  
 As Cd Cr Cu Hg Ni Pb Zn    
Ghana 0.64 0.12 9.62 14.87 0.02 16.06 5.60 233.89 This study  
Spain (2002) 7.30 0.19 20 20 0.24 6.9 38 78 (De Miguel et al., 2007) 
Spain (2003) 6.90 0.14 17 14 0.070 5.7 22 50 (De Miguel et al., 2007) 
Word 
average 
(Shale) 
13 0.3 90 45 0.17 68 20 95 (Ebenezer, 2011) 
 
 
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Table 4.10: Summary statistic of the analytical results (mg.kg-1) 
 Sample Identity (playground)    
 AP AG AM AS AH DA GS GP HC HM KA KW TC TD Mean St.Dev 95% UCL 
Ala 28010 31080 21305 30610 17240 25770 12790 9761 29840 10970 27860 18730 22040 41150 23368.29 8915.594 27588.053 
Asb 2.3 1.15 2.27 2.45 1.105 1.375 1.65 1.24 0.225 1.755 2.195 1.075 1.695 1.62 1.578929 0.610117 1.8676982 
Cdb <0.002 <0.002 0.125 <0.002 <0.002 0.14 0.105 0.11 0.225 0.13 0.12 0.075 0.06 0.145 0.1235 0.044724 0.1446682 
Cob 0.75 8.7 3.3 7.5 12.45 12.6 12.3 6.3 13.5 5.7 4.65 6.9 7.05 4.8 7.607143 3.869896 9.4387721 
Crb 15.72 11.52 7.17 14.67 9.57 13.87 4.17 13.17 11.97 3.42 10.02 6.27 8.37 4.77 9.62 4.048979 11.536389 
Cub 16.74 10.14 9.54 22.14 8.79 30.84 10.74 8.79 <0.003 11.49 <0.003 <0.003 7.44 26.94 14.87182 8.157673 18.73286 
Feb 23560.7 21575.4 22080.6 24957.6 22676.0 24653.1 21077.6 23813.6 21512.1 20436.0 23558.9 22340.0 22340.0 21704.7 22591.9 1345.5 23228.7 
Hgb 0.04 0.02 0.01 0.05 0.035 0.012 0.025 0.019 0.025 0.018 0.024 0.019   0.02475 0.011639 0.030259 
Ka 2152 14170 3153 8149 975.3 6773 1054 1381 11290 483.2 7540 1749 4081 1621 4612.25 4302.78 6648.7638 
Laa 18.22 21.47 21.35 22.28 9.89 16.71 4.32 4.73 22.14 3.63 30.27 864 8.4 15.24 75.90357 226.9739 183.33072 
Mna 189.8 254.7 193.6 274.7 107.3 278.1 80.45 117.3 239.5 49.63 111.1 165.2 230.7 183.2 176.8057 74.17251 211.9117 
Naa 636.3 1495 368.4 754.4 417.2 3312 703.4 674.4 1313 343.6 763.2 541.4 1997 373.6 978.0643 826.9683 1369.4699 
Nib 17.235 19.485 16.185 29.985 17.385 31.785 12.735 10.185 23.085 7.485 14.535 8.235 9.135 7.335 16.05643 7.904009 19.797411 
Pbb 11.85 <0.001 <0.001 0.15 7.05 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 4.8 <0.001 5.6 5.890883 8.3881658 
Tia 3010 2829 2475 3409 3116 3089 733.9 2173 4957 978 3008 2554 4044 4851 2944.779 1205.868 3515.518 
Va 75.75 36.21 40.41 34.53 25.66 86.48 66.89 32.45 31.93 20.53 55.01 35.87 33.13 48.98 44.55929 19.59591 53.834067 
Znb 162 64.5 93 781.5 223.5 708 127.5 114 102 58.5 150 202.5 375 112.5 233.8929 231.2168 343.32819 
 
_________________________________________________ 
aElements analyzed by INAA 
bElements analyzed by AAS 
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4.2.2. Cancer Risk-based Assessment 
The risk assessment results of 10 environmental elements of interest are presented in 
Table 4.11. Arsenic appears to be the largest single contributor to the overall risk, with a 
value of carcinogenic risk of 3.48E-06. However, this value is below the 1.00E-05 level 
considered undesirable by most regulatory agencies. Moreover, for non-cancer risk, all 
the trace elements exhibit a Hazard Index below the threshold value of 1.0. Vanadium is 
the second largest contributor after aluminum, followed by chromium, manganese, 
arsenic, lead, nickel, zinc, copper, cadmium and mercury. The hazard indices contributed 
by these elements are nearly five times the magnitude lower than the regulatory level of 1 
(USEPA, 2015). The exposure pathway that scored the highest contribution to the overall 
figure of risk is found to be ingestion of substrate soil particles followed by dermal 
absorption of trace elements in these particles. Similar results were obtained by De 
Miguel et al.,(2007), in a study of risk-based evaluation of the exposure of children in 
Spain to trace elements.  
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Table 4.11:  Exposure point concentration term (C, mg/kg), reference dose (mg/kg per day) and slope factor (mg/kg per day)-1 (from 
RAIS as of 2015 except Pb, from WHO), and Hazard Quotient and Cancer Risk for each element and exposure route. 
Elements C(95% UCL) RfDing RfDinh RfDder HQing HQinh HQder HQvap HI=∑HQi Risk 
  SFing SFinh SFder Risking Riskinh Riskder    
Al 27588.053 1.00E+00 5.00E-03 1.00E-01 0.071265 1.26E-03 6.98E-03  8.00E-02  
As- non cancer 0.876 3.00E-04 1.50E-05 1.23E-04 7.55E-03 1.33E-05 1.80E-04  7.74E-03  
As-cancer 0.876 1.50E+00 1.51E+01 3.66E+00 3.40E-06 3.02E-09 8.12E-08   3.48E-06 
Cd-non cancer 0.145 1.00E-03 1.00E-05 1.00E-05 3.74E-04 3.30E-06 3.66E-04  7.43E-04  
Cd-cancer 0.145  6.30E+00   2.08E-10    2.08E-10 
Cr-non cancer 11.536 3.00E-03 1.00E-04 6.00E-05 9.93E-03 2.63E-05 4.87E-03  1.48E-02  
Cr-cancer 11.536  4.20E+01   1.10E-07    1.10E-07 
Cu 18.733 4.00E-02 4.00E-02 1.20E-02 1.21E-03 1.07E-07 3.95E-05  1.25E-03  
Hg 0.030 1.60E-04 3.00E-05 2.10E-05 4.89E-04 2.30E-07 3.65E-05 1.69E-03 5.25E-04  
Mn 211.912 1.40E-01 5.00E-05 1.84E-03 3.91E-03 9.66E-04 2.92E-03  7.79E-03  
Ni-non cancer 19.797 1.10E-02 1.40E-05 5.40E-03 4.65E-03 3.22E-04 9.28E-05  5.06E-03  
Ni-cancer 19.797  8.40E-01   3.79E-09    3.79E-09 
Pb 8.388 3.50E-03 3.50E-03 5.25E-04 6.19E-03 5.46E-07 4.04E-04  6.60E-03  
V 53.834 5.04E-03 1.17E-01 7.00E-05 2.76E-02 1.04874E-07 1.95E-02  4.71E-02  
Zn 194.114 3.00E-01 3.00E-01 6.00E-02 1.67E-03 1.47E-07 8.19E-05  1.75E-03  
 
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4.2.3. Pollution Indices 
4.2.3.1. Enrichment Factor 
 
The results of the present study show that with the exception of As, Cr, Hg, La, Mn and 
Ni, most of the trace elements are slightly enriched in the playgrounds soils (Table 4.12 
and 4.13). The EF values for Al range from 0.24 to 1.12, Cd from 0.42 to 1.65, Co from 
0.08 to 1.56, Cu from 0.35 to 1.31, K from 0.04 to 1.17, Pb from 0.01 to 1.19, Ti from 
0.36 to 2.36, V from 0.36 to 1.27 and Zn from 1.31 to 8.34. Overall, the average order of 
EF values for the elements is Zn (3.24) > Ti (1.33) > Co (0.84) > V (0.71) > Cd (0.62) > 
Al (0.61) > Cu (0.54) > Ni (0.49) > Mn & Pb (0.43) > K (0.36) > La (0.33) > Cr (0.30) > 
As (0.10). According to Bhuiyan et al. (2010), EF values between 0.05 and 1.5 specify 
that the element is totally from crustal materials or natural processes, whereas EF values 
higher than 1.5 suggest that the sources are likely to be anthropogenic. The authors split 
the contamination into different categories based on EF values, where EF ≤ 2 suggests 
deficiency to minimal metal enrichment, whereas EF > 2 suggests several degrees of 
metal enrichment. 
4.2.3.2. Geo-accumulation Index 
 
The geo-accumulation index (Igeo) was also employed to estimate the degree of metal 
pollution. The Igeo values for the metals of environmental interest are presented in Table 
4.11 and 4.12) as  -8.02 to -3.75 for As, -2.91 to -1.00 for Cd, -5.30 to -1.60 for Cr, -3.18 
to -1.13 for Cu, -5.35 to -3.07 for Hg, -4.68 to -2.20 for Mn, -3.80 to -1.68 for Ni, -3.23 to 
-0.48 for Ti and -1.28 to 1.40 for Zn. Overall, the average order of Igeo values for the 
metals is Cd (-1.95) > Co (-2.17) > Cu (-2.35) > Ni (-2.83) > Mn (-3.00) > Ti (-3.02) > 
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Zn (-3.03) > As (-3.22) > Cr (-3.75) > Pb (-3.88) > Hg (-4.23). From observation, the Igeo 
values indicate practically the soils of the investigated schools playgrounds are 
practically uncontaminated with respect to the measured elements, except Zn which is 
moderately contaminated. Among the environmentally most toxic metals, Zn is 
significantly accumulated in the soils, as indicated by their respective average Igeo values 
and its highest value is found in TC (Taifa Community) playground.  
4.2.3.3. Contamination Factor 
 
The contamination factors (CFs) of the trace metals of environmental concern are in the 
following ranges: As (0.006 – 0.112), Cd (0.200 – 0.750), Cr (0.038 – 0.496), Co (0.039 
– 0.711), Cu (0.165 – 0.685), Hg (0.059 – 0.294) and Ni (0.108 – 0.467) (Table 4.14). 
Overall, the average order of CF values for the elements is Cd (0.412) > Co (0.4) > Cu 
(0.33) > Pb (0.28) > Ni (0.236) > Cr (0.147) > As (0.049). These results indicate that 
there is no significant contamination found in the soils of playgrounds due to these trace 
elements. 
 
 
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Table 4.12: Descriptive statistics for enrichment factors and geo-accumulation indices (Igeo) of heavy metals for playground soil 
Sample ID Al  As  Cd  Co  Cu  Cr  Fe 
 EF Igeo  EF Igeo  EF Igeo  EF Igeo  EF Igeo  EF Igeo  EF Igeo 
AP 0.70 -2.10  0.20 -3.91     0.08 -5.25  0.75 -2.01  0.35 -3.10  1.00 -1.59 
AG 0.85 -1.95  0.03 -7.02     1.00 -1.71  0.49 -2.73  0.28 -3.55  1.00 -1.71 
AM 0.57 -2.49  0.21 -3.94  0.89 -1.85  0.37 -3.11  0.45 -2.82  0.17 -4.23  1.00 -1.68 
AS 0.72 -1.97  0.21 -3.75     0.75 -1.93  0.93 -1.61  0.94 -1.60  1.00 -1.50 
AH 0.45 -2.80  0.02 -7.54     1.36 -1.19  0.41 -2.94  0.22 -3.82  1.00 -1.64 
DA 0.62 -2.22  0.06 -5.70  0.89 -1.68  1.27 -1.18  1.31 -1.13  0.72 -1.99  1.00 -1.52 
GS 0.36 -3.23  0.11 -4.91  0.78 -2.10  1.45 -1.21  0.53 -2.65  0.10 -5.02  1.00 -1.75 
GP 0.24 -3.62  0.04 -6.34  0.73 -2.03  0.66 -2.18  0.39 -2.94  0.29 -3.36  1.00 -1.57 
HC 0.82 -2.01  0.02 -7.29  1.65 -1.00  1.56 -1.08  0.00   0.29 -3.50  1.00 -1.72 
HM 0.32 -3.45  0.13 -4.69  1.00 -1.79  0.69 -2.32  0.59 -2.55  0.09 -5.30  1.00 -1.79 
KA 0.70 -2.11  0.18 -4.03  0.80 -1.91  0.49 -2.62     0.22 -3.75  1.00 -1.59 
KW 0.49 -2.68  0.01 -8.02  0.53 -2.58  0.77 -2.05     0.15 -4.43  1.00 -1.66 
TC 0.58 -2.44  0.11 -4.81  0.42 -2.91  0.78 -2.02  0.35 -3.18  0.20 -4.01  1.00 -1.66 
TD 1.12 -1.54  0.10 -4.98  1.05 -1.63  0.55 -2.57  1.30 -1.33  0.12 -4.82  1.00 -1.71 
Min 0.24 -3.62  0.01 -8.02  0.42 -2.91  0.08 -5.25  0.35 -3.18  0.09 -5.30  1.00 -1.79 
Max 1.12 -1.54  0.21 -3.75  1.65 -1.00  1.56 -1.08  1.31 -1.13  0.94 -1.60  1.00 -1.50 
Mean 0.61 -2.47  0.10 -5.49  0.87 -1.95  0.84 -2.17  0.68 -2.35  0.30 -3.75  1.00 -1.65 
 
 
 
 
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Table 4.13: Descriptive statistics for enrichment factors and geo-accumulation indices (Igeo) of heavy metals for playground soil 
(continued) 
Sample ID K  La  Mn  Ni  Pb  Ti  V  Zn 
 EF Igeo  EF Igeo  EF Igeo  EF Igeo  EF Igeo  EF Igeo  EF Igeo  EF Igeo 
AP 0.16 -4.21  0.40 -2.92  0.45 -2.75  0.51 -2.57  1.19 -1.34  1.31 -1.20  1.17 -1.36  1.31 -1.20 
AG 1.17 -1.49  0.51 -2.68  0.66 -2.32  0.63 -2.39     1.35 -1.29  0.61 -2.43  1.49 -1.14 
AM 0.25 -3.66  0.50 -2.69  0.49 -2.72  0.51 -2.66     1.15 -1.48  0.66 -2.27  2.09 -0.62 
AS 0.58 -2.29  0.46 -2.63  0.61 -2.21  0.83 -1.77  0.01 -7.64  1.40 -1.02  0.50 -2.50  3.61 0.35 
AH 0.08 -5.35  0.22 -3.80  0.26 -3.57  0.53 -2.55     1.41 -1.15  0.41 -2.93  4.90 0.65 
DA 0.49 -2.56  0.35 -3.05  0.63 -2.20  0.89 -1.68     1.29 -1.16  1.27 -1.17  4.19 0.55 
GS 0.09 -5.24  0.11 -5.00  0.21 -3.99  0.42 -3.00     0.36 -3.23  1.15 -1.54  3.01 -0.16 
GP 0.10 -4.85  0.10 -4.87  0.27 -3.44  0.30 -3.32     0.94 -1.67  0.49 -2.59  2.38 -0.32 
HC 0.93 -1.82  0.53 -2.64  0.62 -2.41  0.74 -2.14     2.36 -0.48  0.54 -2.61  2.36 -0.48 
HM 0.04 -6.37  0.09 -5.25  0.13 -4.68  0.25 -3.77     0.49 -2.82  0.36 -3.25  1.42 -1.28 
KA 0.57 -2.40  0.66 -2.19  0.26 -3.52  0.43 -2.81     1.31 -1.20  0.85 -1.83  3.16 0.07 
KW 0.14 -4.51  0.20 -4.00  0.41 -2.95  0.26 -3.63     1.17 -1.43  0.58 -2.44  4.50 0.51 
TC 0.32 -3.29  0.19 -4.04  0.57 -2.47  0.28 -3.48  0.51 -2.64  1.86 -0.77  0.54 -2.56  8.34 1.40 
TD 0.13 -4.62  0.36 -3.18  0.47 -2.80  0.23 -3.80     2.29 -0.51  0.82 -1.99  2.58 -0.34 
Min 0.04 -6.37  0.09 -5.25  0.13 -4.68  0.23 -3.80  0.01 -7.64  0.36 -3.23  0.36 -3.25  1.31 -1.28 
Max 1.17 -1.49  0.66 -2.19  0.66 -2.20  0.89 -1.68  1.19 -1.34  2.36 -0.48  1.27 -1.17  8.34 1.40 
Mean 0.36 -3.76  0.33 -3.50  0.43 -3.00  0.49 -2.83  0.57 -3.88  1.33 -1.39  0.71 -2.25  3.24 -0.14 
 
 
 
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Table 4.14: Metal contamination factors (CFs) for playground soils 
Sample ID Contamination factors (CFs) 
 Al As Cd Co Cu Cr Fe Hg K La Mn Ni Pb Ti V Zn 
AP 0.350 0.100  0.039 0.372 0.175 0.499 0.235 0.081 0.198 0.223 0.253 0.593 0.654 0.583 0.653 
AG 0.389 0.012  0.458 0.225 0.128 0.457 0.118 0.533 0.233 0.300 0.287  0.615 0.279 0.679 
AM 0.266 0.098 0.417 0.174 0.212 0.080 0.468 0.059 0.119 0.232 0.228 0.238  0.538 0.311 0.979 
AS 0.383 0.112  0.395 0.492 0.496 0.529 0.294 0.306 0.242 0.323 0.441 0.008 0.741 0.266 1.911 
AH 0.216 0.008  0.655 0.195 0.106 0.480 0.206 0.037 0.108 0.126 0.256  0.677 0.197 2.353 
DA 0.322 0.029 0.467 0.663 0.685 0.376 0.522 0.071 0.255 0.182 0.327 0.467  0.672 0.665 2.189 
GS 0.160 0.050 0.350 0.647 0.239 0.046 0.447 0.147 0.040 0.047 0.095 0.187  0.160 0.515 1.342 
GP 0.122 0.018 0.367 0.332 0.195 0.146 0.505 0.112 0.052 0.051 0.138 0.150  0.472 0.250 1.200 
HC 0.373 0.010 0.750 0.711  0.133 0.456 0.147 0.424 0.241 0.282 0.339  1.078 0.246 1.074 
HM 0.137 0.058 0.433 0.300 0.255 0.038 0.433 0.106 0.018 0.039 0.058 0.110  0.213 0.158 0.616 
KA 0.348 0.092 0.400 0.245  0.111 0.499 0.141 0.283 0.329 0.131 0.214  0.654 0.423 1.579 
KW 0.234 0.006 0.250 0.363  0.070 0.473 0.112 0.066 0.094 0.194 0.121  0.555 0.276 2.132 
TC 0.276 0.053 0.200 0.371 0.165 0.093 0.473  0.153 0.091 0.271 0.134 0.240 0.879 0.255 3.947 
TD 0.514 0.048 0.483 0.253 0.599 0.053 0.460  0.061 0.166 0.216 0.108  1.055 0.377 1.184 
Min 0.122 0.006 0.200 0.039 0.165 0.038 0.433 0.059 0.018 0.039 0.058 0.108 0.008 0.160 0.158 0.616 
Max 0.514 0.112 0.750 0.711 0.685 0.496 0.529 0.294 0.533 0.329 0.327 0.467 0.593 1.078 0.665 3.947 
Mean 0.292 0.049 0.412 0.400 0.330 0.147 0.479 0.146 0.173 0.161 0.208 0.236 0.280 0.640 0.343 1.560 
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4.2.4. Pollution Source Identification 
Further evaluation of scope of trace element contamination in the study area and source 
identification was performed using the Multivariate Statistical Analysis to allow rigorous 
inspection and evaluation of variability present in voluminous data (Ayivor et al., 2012). 
Principal Component Analysis (PCA) was used following standard procedures reported 
in literature (Bhuiyan et al., 2010, 2011). PCA was performed on the logarithmic form of 
the data. The Varimax Rotation was used to exploit the sum of the variance of the factor 
coefficients. This technique clusters variables into groups, such that variables going to 
one group are greatly correlated with one another. The results of the PCA of trace 
elements contents are shown in Table 4.15.  
Table 4.15: Rotated component matrix of six-factor model with moderate to strong 
loadings in bold typeface 
Parameters PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 Communalities 
Al 0.890 -0.122 0.065 0.123 -0.212 0.111 0.884 
As -0.062 0.860 0.325 0.084 -0.180 0.145 0.91 
Cd -0.390 -0.403 -0.210 0.238 0.402 0.510 0.838 
Co -0.055 0.021 0.037 0.039 0.888 -0.197 0.833 
Cr 0.488 0.467 0.676 -0.112 0.087 -0.001 0.934 
Cu -0.081 -0.090 0.218 -0.884 -0.205 0.007 0.885 
Fe 0.217 0.332 0.851 0.016 -0.119 0.124 0.911 
Hg -0.296 0.874 -0.020 0.240 0.121 -0.021 0.925 
K 0.852 0.300 0.074 0.145 0.277 0.120 0.934 
La 0.245 0.064 0.175 0.856 -0.192 -0.021 0.865 
Mn 0.859 0.030 0.361 0.058 0.038 0.091 0.882 
Na 0.518 0.123 0.270 -0.128 0.592 0.346 0.843 
Ni 0.444 0.783 0.243 -0.138 0.184 0.073 0.927 
Ti 0.797 -0.255 0.308 0.222 -0.066 -0.166 0.876 
V 0.206 0.188 0.213 -0.071 -0.214 0.873 0.936 
Zn 0.194 0.029 0.915 -0.030 0.171 0.075 0.911 
Eigenvalues 4.037 2.843 2.659 1.78 1.684 1.292  
% of variance 25.23 17.769 16.617 11.123 10.528 8.073  
Cumulative % 25.23 42.998 59.616 70.739 81.267 89.34  
 
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Six variation factors (Vfs) or (PCs) with eigenvalues greater than 1 were extracted. PCA 
leads to a reduction of the initial dimension of the dataset to six components which 
explain 89 % of the data variation. Therefore, these six factors play a significant role in 
explaining trace element contamination in the study area. 
Principal component 1 (PC1), which has the highest loadings of Al, K, Mn, Na and Ti 
and accounts for 25 % of variance (Table 4.15) can be a measure of leaching of crustal 
components.  PC 2, which has positive loadings of As, Hg and Ni, accounts for 17 % of 
variance and these elements may have originated from both natural sources through 
weathering of minerals and ores, and anthropogenic sources through industrial processes 
as byproducts of the smelting process. Also, the level of mercury in the environment can 
increase due to incineration of municipal and medical waste as well as from a global 
reservoir of airborne mercury. To the best knowledge of the student or author, there has 
not been any report of mineral deposits in the study area and, therefore, natural sources 
for As, Hg and Ni may be ruled out.  PC 3 has positive loadings of Cr, Fe and Zn and 
account for 16 % of variance. PC 4 is positively loaded with La and negatively loaded 
with Cu and account for 11 % of variance. PC 5 accounts for 10 % of variance and has a 
positive loading of Co and Na whereas PC 6 has a positive loading of Cd and V and 
accounts for 8 % of variance. The concentrations of these elements in soils result from 
natural sources and from vehicular emissions, especially Cr and Zn. Chromium comes 
from emissions of chromium-based automotive catalytic converters and cement dust, and 
high Zn content in the soils may come from gasoline, car components, oil lubricants, 
industrial emissions, and traffic sources particularly vehicle tyres. 
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Based on information evaluated from Principal Component Analysis, hierarchical cluster 
analysis was executed (Ayivor et al., 2012; Bhuiyan et al., 2010, 2011). Cluster analysis 
(CA) was performed on the analyzed parameters with Ward’s method and the squared 
Euclidean distance as a similarity measure. Figure 4.7 shows that four main clusters can 
be distinguished in the dendrogram acquired from the CA. Cluster 1 includes elements 
Al, K, La, Mn and Ti, which in the previous discussion were recognized as natural 
elements from crustal origin. Cluster 2 comprises As, Hg and Ni which are identified as 
result of atmospheric pollution. Cluster 3 involves elements Cd, Co and Na which 
previously identified as a result of particulate matters emitted from the geologic media. 
Cluster 4 consists of elements Cr, Cu, Fe, V and Zn which are said to be derived from 
both natural and traffic sources as most of the playgrounds are close to the road.  
 
Figure 4.7: Dendogram acquired by hierarchical clustering analysis for parameters 
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Similarly, sampling points were also analyzed by clustering methods using factor analysis 
(FA) to determine the structure in the data set and to investigate the relationship between 
the sampling points and their elemental concentration (Table 4.16). This was organized in 
the dendrogram to identify the identical geochemical groups (Figure 4.8). 
The sampling points AG, AH, AM, AP, GP, HC, KA and KW are clustered in Group 1. 
Group 2 contains As and DA. The sampling points GS and HM are included in Group 3, 
whereas Group 4 contains TC and TD. 
Table 4.16: Scores for the six-factor model for sampling sites relatively high scores in 
bold typeface 
Sample ID PC 1 PC 2 PC 3 PC 4 PC 5 PC 6 
AP 0.482 0.650 0.143 -0.570 -2.489 0.744 
AG 1.558 1.004 -1.447 -0.658 0.203 -0.628 
AM 0.010 0.400 -0.669 0.067 -0.790 0.311 
AS 0.608 0.742 1.770 -0.367 -0.126 -1.197 
AH -0.532 0.443 0.510 -0.433 -0.049 -1.957 
DA 0.339 0.422 1.588 -0.446 1.255 1.741 
GS -1.407 0.550 -0.822 -0.799 0.651 1.402 
GP -1.234 0.253 0.606 -0.366 0.290 -0.129 
HC 1.271 0.156 -1.163 0.944 1.494 -0.289 
HM -1.776 -0.603 -1.156 -0.560 0.165 -0.824 
KA 0.129 0.545 -0.234 1.286 -0.150 0.831 
KW -0.702 -0.264 0.516 2.760 -0.323 -0.257 
TC 0.661 -1.893 0.630 -0.653 0.842 -0.126 
TD 0.593 -2.405 -0.273 -0.206 -0.973 0.378 
  
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Figure 4.8: Tree diagram acquired by clustering of sampling sites 
 
Table 4.17: Elemental characteristics of the analyzed soils from playgrounds as depicted 
by and R-mode principal component 
AG AP AS AH DA GS GP HC KA KW TC TD 
Al As Al Cr Cr As Cr Al As Cr Al Al 
K Hg K Fe Fe Hg Fe K Hg Fe K K 
Mn Ni Mn Zn Zn Ni Zn Mn Ni Zn Mn Mn 
Na  Na  Co Co  Na La La Na Na 
Ti  Ti  Na Na  Ti Cd  Ti Ti 
As  As  Cd Cd  La V  Cr  
Hg  Hg  V V  Co   Fe  
Ni  Ni     Na   Zn  
  Cr        Co  
  Fe        Na  
  Zn          
 
 
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The following elemental associations obtained from PCA and CA are Al-K-Mn-Na-Ti, 
which could be linked to geogenic source, appear to be common markers for sampling 
points such as AG, AS, HC, TC and TD (Table 4.17). Also, As-Hg-Ni associations are 
common markers for AG, AP, AS, GS and KA. The association of Cr-Fe-Zn which may 
have resulted from traffic emissions are observed to be common markers for the sampling 
points AS, AH, DA, GP, KW and TC. These observations suggest that most of these 
sampling points (playgrounds) share same characteristics, as it is clearly illustrated in the 
tree diagram (Figure 4.8). 
 
 
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CHAPTER 5 
CONCLUSIONS AND RECOMENDATIONS 
 
In this chapter, the general conclusions are drawn from the results obtained in this study 
and necessary recommendations to the next relevant researchers are made. 
5.1. Conclusions 
The study considered the evaluation of NORMs and trace elements in the playgrounds of 
basic schools in the Ga East District of the Greater Accra Region of Ghana in terms of 
exposure of children and the whole public to NORMs and trace elements. In this study, 
the cancer risk assessments, soil pollution indices, pollution identification, estimation of 
future doses were also carried out.  
The mean activity concentrations of 238U, 232Th and 40K, were estimated to be 19.8 ± 8.7, 
29.1 ± 16.3 and119.4 ± 97.9 Bq.kg-1 respectively. The results in this study compared 
well with the worldwide average activity concentrations [35 Bq.kg-1 for 238U; 30 Bq.kg-1 
for 232Th and 400 Bq.kg-1 for 40K]  (UNSCEAR, 2000). The mean annual effective dose 
estimated from direct external gamma ray exposure from natural radioactivity 
concentrations in soil is estimated to be 0.039 mSv/year.  This value is lower than the 1 
mSv/year dose limit recommended by the ICRP for public radiation exposure control 
(ICRP, 2007) and indicates that there is no significant radiological hazard to the public. 
The decrease in exposure is observed from estimated future annual effective doses in the 
study areas. The results obtained from cancer risk assessment performed according to 
ICRP method shows no significant hazard to the whole public. The activity concentration 
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of 222Rn in soil in the absence of transportation and the exhalation rate were estimated 
and they compared well with the world average values suggested by UNSCEAR (2000). 
The levels of trace elements in the soils from playgrounds are lower than the world 
average concentrations in shales, and are significantly lower than the concentrations 
found in similar studies in other cities of other countries, such as Spain (De Miguel et al., 
2007). The results of a risk assessment indicate that the amount of carcinogenic risk is 
below the level deemed unacceptable by most regulatory agencies and the aggregate of 
non-cancer risk is below the unit 1. Although the figures of risk could be affected by the 
estimation of exposure factors and the toxicity data used, the risk assessment has proven 
to be a very useful means to explain the exact meaning and significance of the 
concentrations of trace elements found in urban media. 
Several useful tools, methods and indices have been used for evaluation of soil pollution 
in the playgrounds soils. The metal enrichment factor (EF) and geo-accumulation index 
(Igeo) of most of the metals (As, Cd, Cr, Mn, Ni, Pb, Ti and Zn) show that the soils in the 
study area are uncontaminated to moderately contaminated (Zn), whereas other (Cr, La, 
Mn and Ni) indicate crustal origin. Contamination factors of all trace elements suggest no 
contamination except Ti and Zn that suggest none to medium contamination. Multivariate 
analysis (PCA, CA) used in this study offered essential tools for better understanding of 
the source identification and dynamics of pollutant. The PCA applied on the investigated 
heavy metals identified six components. Among them, PC1 loaded with Al, K, Mn, Na 
and Ti are derived from crustal origin. The others PC2, PC3, PC4, PC5 and PC6 are 
loaded with metals that are from either crustal origin or anthropogenic activities. Four 
main clusters of elements are acquired by CA. The first cluster is considered to be of 
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geogenic sources, which contains Al, K, La, Mn and Ti. Second, third and fourth clusters 
which are loaded with As, Cd, Cu, Co, Cr, Fe, Hg, Na, Ni, V and Zn  are mainly 
attributed to anthropogenic sources with some contributions from natural sources. 
5.2. Recommendations 
Based on the research findings from this study, the concluding comments underlined 
some aspects in order to improve on future studies. Therefore, the following 
recommendations are made. 
5.2.1. Researchers 
 The annual effective dose in basic school playing ground was found to be below 
the recommended dose limit per year. There is a need to follow up on future 
annual effective dose in order to validate the model used. 
 Children’s background exposures such as dietary intake, inhalation of dust at 
home were not accounted for in this study. Therefore it should be considered to 
enable calculation of overall risk to children. 
 Scientific community should consider sampling at monthly to yearly interval in 
order to observe the seasonal trend of potentially toxic trace elements in the soils 
of schools playgrounds.  
 Researchers should also consider other part of Greater Accra especially Ga West 
which was not covered in this study. 
 
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5.2.2. Basic School Managements (Ga East district) 
 Basic School Managements should consider proper constructions of their play 
grounds. This involves introducing grass playing fields in order to avoid dust 
inhalable by children during their games at schools. 
5.2.3. Regulators 
 Regulatory bodies of Ghana should use results from this study for authorities’ 
regulation purposes.  
 
 
 
 
 
 
 
 
 
 
 
 
 
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Oyedele, J. A., & Shimboyo, S. (2013). Distribution of radionuclides and radiation 
hazard assessment in soils of Southern Namibia, Southern Africa. Radiation 
Protection Dosimetry, 156(3), 343–348. http://doi.org/10.1093/rpd/nct081 
Oyedele, J. A., Sitoka, S., & Davids, I. (2008). Radionuclide concentrations in soils of 
Northern Namibia, Southern Africa. Radiation Protection Dosimetry, 131(4), 
482–486. http://doi.org/10.1093/rpd/ncn194 
Rubio, B., Nombela, M. A., & Vilas, F. (2000a). Geochemistry of major and trace 
elements in sediments of the Ria de Vigo (NW Spain): an assessment of metal 
pollution. Marine Pollution Bulletin, 40(11), 968–980. 
Rubio, B., Nombela, M. A., & Vilas, F. (2000b). Geochemistry of major and trace 
elements in sediments of the Ria de Vigo (NW Spain): an assessment of metal 
pollution. Marine Pollution Bulletin, 40(11), 968–980. 
Sey, M. (2014). Characteristic of Mine Waste and Radiation Dose Reconstruction of a 
Historical Mine of Konongo-Odomase in the Ashanti Region, Ghana. University 
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Shakeri, A., Moore, F., & Modabberi, S. (2009). Heavy metal contamination and 
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Shimboyo, S. (2012). Natural Radioactivity in Soils of The Walvis Bay – Henties Bay 
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UNSCEAR. (2000b). Exposures from natural radiation sources, 2000 Report to General 
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US EPA. (2002). Calculating Upper Confidence  Limits for Exposure Point  
Concentration at Hazardous  Waste Sites. Washington, D.C. 20460: Office of 
Emergency and Remedial Response. 
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Belgische Vereniging Voor Stralingsbescherming, 27(2), 41–65. 
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WNA. (2011). Nuclear Radiation and Health Effects. 
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Wong, J. W. C., & Mak, N. K. (1997). Heavy metal pollution in children playgrounds in 
Hong Kong and its health implications. Environmental Technology, 18(1), 109–
115. 
Yaqin, J. I., Yinchang, F., Jianhui, W. U., Tan, Z. H. U., Zhipeng, B. A. I., & Chiqing, D. 
(2008). Using geoaccumulation index to study source profiles of soil dust in 
China. Journal of Environmental Sciences, 20(5), 571–578. 
Accra. (2015). In Wikipedia. Wikipedia, The Free Encyclopedia. Retrieved from 
https://en.wikipedia.org/wiki/Accra 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDICES 
 
APPENDIX I 
A certificate for a standard source used  
 
 
 
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APPENDIX II 
 
Example of a photo peak of 238U acquired by Gennie 2000 software 
 
 
Example of a photo peak of 232Th acquired by Gennie 2000 software 
 
 
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Example of a photo peak of 40K acquired by Gennie 2000 software 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDIX IV 
 
Activity concentration of 238U, 232Th and 40K in soil samples 
Sample ID School Activity concentration (Bq/kg) 
  238U 232Th 40K  
AP Abokobi Presby 21.9 ± 1.5 37.9 ± 1.4 63.7 ± 6.8 
AG Agbogba Anglican 40.3 ± 3.1 66.4 ± 1.5 342.2 ± 35.6 
AP Akporman Model 16.1 ± 0.5 23.2 ± 1.0 85.2 ± 9.0 
AS Ashongman M/A 26.9 ± 1.5 41.6 ± 3.2 269.1 ± 28.1 
AH Atomic Hills 10.5 ±  0.5 9.2 ± 1.7 20.4 ±  2.3 
DA Dome Anglican 25.5 ± 1.4 23.9 ± 4.1 142.0 ± 14.8 
GS GAEC Basic School 23.2 ± 1.3 32.1 ± 0.3 67.8 ± 7.2 
GP GAEC Playing Ground 17.4 ± 1.3 28.8 ± 0.6 103.5 ± 10.9 
HC Haatso Calvary Presby 23.5 ± 1.1 40.9 ± 2.5 219.5 ± 22.9 
HM Hillview Montessori 11.5 ± 0.6 14.8 ± 1.4 24.4 ± 2.7 
KA Kwabenya Atomic M/A 27.2 ± 1.2 45.8 ± 2.1 170.1 ± 17.8 
KW Kwabenya M/A 9.7 ± 1.5 9.5 ± 1.9 47.9 ± 5.1 
TC Taifa Community 10.2 ± 0.6 12.3 ± 2.4 78.4 ± 8.3 
TD Taifa St Dominic 13.7 ± 0.4 20.6 ± 1.5 37.3 ± 4.0 
Average  19.8 29.1 119.4 
Standard deviation  8.7 16.3 97.9 
Range  9.7 - 40.3 9.2 - 66.4 20.4 - 342.2 
World average  35 30 400 
 
 
 
 
 
 
 
 
 
 
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Absorbed dose rates, radium equivalent activity, external and internal hazard and annual 
effective doses due to 238U, 232Th and 40K in soil samples 
Sample ID School Absorbed Dose rate 
(nGyh-1) 
Annual Effective 
dose (mSv) 
AP Abokobi Presby 35.7 0.044 
AG Agbogba Anglican 73.0 0.090 
AM Akporman Model 25.0 0.031 
AS Ashongman M/A 48.8 0.060 
AH Atomic Hills 11.3 0.014 
DA Dome Anglican 32.2 0.039 
GS GAEC Basic School 32.9 0.040 
GP GAEC Playing Ground 29.8 0.037 
HC Haatso Calvary Presby 44.7 0.055 
HM Hillview Montessori 15.3 0.019 
KA Kwabenya Atomic M/A 47.3 0.058 
KW Kwabenya M/A 12.3 0.015 
TC Taifa Community 15.4 0.019 
TD Taifa St Dominic 20.3 0.025 
Average  31.7 0.039 
Standard deviation  17.4 0.021 
Range  11.3 – 73.0 0.014 – 0.090 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDIX V 
 
MATLAB script for calculating future activity concentrations 
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APPENDIX VI 
 
Predicted activity concentrations (Bq.kg-1) in selected schools playgrounds.  
Sample 
ID 
 2015    2017    2019    2021    2023    2025  
 238U 232Th 40K  238U 232Th 40K  238U 232Th 40K  238U 232Th 40K  238U 232Th 40K  238U 232Th 40K 
AP 21.89 37.94 63.66  21.87 29.97 63.66  21.85 23.54 63.66  21.83 18.40 63.66  21.82 14.35 63.66  21.80 11.18 63.66 
AG 40.35 66.36 342.21  40.31 52.42 342.21  40.28 41.17 342.21  40.25 32.18 342.21  40.21 25.09 342.21  40.18 19.56 342.21 
AM 16.08 23.21 85.22  16.06 18.34 85.22  16.05 14.40 85.22  16.04 11.26 85.22  16.02 8.78 85.22  16.01 6.84 85.22 
AS 26.86 41.61 269.08  26.84 32.87 269.08  26.82 25.81 269.08  26.80 20.18 269.08  26.78 15.73 269.08  26.75 12.26 269.08 
AH 10.55 9.22 20.36  10.54 7.28 20.36  10.53 5.72 20.36  10.52 4.47 20.36  10.51 3.49 20.36  10.50 2.72 20.36 
DA 25.52 23.92 141.97  25.50 18.90 141.97  25.48 14.84 141.97  25.46 11.60 141.97  25.44 9.05 141.97  25.42 7.05 141.97 
GS 23.15 32.12 67.81  23.13 25.37 67.81  23.11 19.92 67.81  23.09 15.58 67.81  23.08 12.14 67.81  23.06 9.47 67.81 
GP 17.38 28.85 103.54  17.37 22.79 103.54  17.35 17.89 103.54  17.34 13.99 103.54  17.33 10.91 103.54  17.31 8.50 103.54 
HC 23.48 40.87 219.55  23.46 32.29 219.55  23.44 25.35 219.55  23.42 19.82 219.55  23.40 15.45 219.55  23.38 12.04 219.55 
HM 11.52 14.76 24.37  11.51 11.66 24.37  11.50 9.16 24.37  11.49 7.16 24.37  11.49 5.58 24.37  11.48 4.35 24.37 
KA 27.20 45.82 170.12  27.17 36.20 170.12  27.15 28.43 170.12  27.13 22.22 170.12  27.11 17.33 170.12  27.08 13.50 170.12 
KW 9.75 9.53 47.92  9.74 7.53 47.92  9.73 5.91 47.92  9.72 4.62 47.92  9.71 3.60 47.92  9.71 2.81 47.92 
TC 10.18 12.27 78.42  10.17 9.69 78.42  10.17 7.61 78.42  10.16 5.95 78.42  10.15 4.64 78.42  10.14 3.61 78.42 
TD 13.74 20.57 37.34  13.73 16.25 37.34  13.72 12.76 37.34  13.71 9.98 37.34  13.70 7.78 37.34  13.69 6.06 37.34 
 
 
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APPENDIX VII 
 
Preparation of calibration standards and calibration curves 
Iron (Fe): Standards for the determination of Fe were prepared to a maximum concentration of 
10.00 mg/L as follows: 
Standard Concentration 
(mg/L) 
Mean Absorbance 
 
0.00 0.0000 
2.00 0.1579 
5.00 0.3975 
10.00 0.7478 
 
 
 
 
 
y = 0.0758x 
R² = 0.9985 
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0 2 4 6 8 10 12
A
b
so
rb
an
ce
 
Concentration (mg/L) 
Fe s tandard cal ibrt ion curve  
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Copper (Cu): Standards for the determination of Cu were prepared to a maximum 
concentration of 8.000 mg/L as follows: 
Standard Concentration 
(mg/L) 
Mean Absorbance 
 
0.000 0.0000 
2.000 0.2597 
5.000 0.7396 
8.000 1.0889 
 
 
 
 
 
 
 
 
 
y = 0.1387x + 0.0019 
R² = 0.996 
0
0.2
0.4
0.6
0.8
1
1.2
0  1  2  3  4  5  6  7  8  9  
A
b
so
rb
an
ce
 
Concentration (mg/L) 
Cu s tandard cal ibrat ion curve  
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Lead (Pb): Standards for the determination of Pb were prepared to a maximum 
concentration of 10.0 mg/L as follows: 
Standard Concentration 
(mg/L) 
Mean Absorbance 
 
0.0 0.0000 
2.0 0.0803 
5.0 0.2005 
10.0 0.4011 
 
 
 
 
 
 
 
 
 
y = 0.0401x + 2E-05 
R² = 1 
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0  2  4  6  8  1 0  1 2  
A
b
so
rb
an
ce
 
Concentration (mg/L) 
Pb s tandard cal ibrat ion curve  
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Cobalt (Co): Standards for the determination of Co were prepared to a maximum 
concentration of 8.000 mg/L as follows: 
Standard Concentration 
(mg/L) 
Mean Absorbance 
 
0.000 0.0000 
2.000 0.1435 
5.000 0.3705 
8.000 0.6019 
 
 
 
 
 
 
 
 
 
y = 0.0754x - 0.0037 
R² = 0.9998 
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0  1  2  3  4  5  6  7  8  9  
A
b
so
rb
an
ce
 
Concentration (mg/L) 
Co s tandard cal ibrat ion curve  
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Zinc (Zn): Standards for the determination of Zn were prepared to a maximum 
concentration of 1.000 mg/L as follows: 
Standard Concentration 
(mg/L) 
Mean Absorbance 
 
0.000 0.0000 
0.250 0.1437 
0.500 0.3213 
1.000 0.6464 
 
 
 
 
 
 
 
 
 
 
y = 0.6523x - 0.0075 
R² = 0.9991 
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0  0 . 2  0 . 4  0 . 6  0 . 8  1  1 . 2  
A
b
so
rb
an
ce
 
Concentration (mg/L) 
Zn s tandard cal ibrat ion curve  
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Chromium (Cr): Standards for the determination of Cr were prepared to a maximum 
concentration of 5.000 mg/L as follows: 
Standard Concentration 
(mg/L) 
Mean Absorbance 
 
0.000 0.0000 
1.000 0.0844 
2.000 0.1679 
5.000 0.4364 
 
 
 
 
 
 
 
 
 
 
y = 0.0875x - 0.0028 
R² = 0.9997 
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0  1  2  3  4  5  6  
A
b
so
rb
an
ce
 
Concentration (mg/L) 
Cr s tandard cal ibrat ion curve  
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Nickel (Ni): Standards for the determination of Ni were prepared to a maximum 
concentration of 10.000 mg/L as follows: 
Standard Concentration 
(mg/L) 
Mean Absorbance 
 
0.000 0.0000 
2.000 0.1860 
5.000 0.4625 
10.000 0.9656 
 
 
 
 
 
 
 
 
 
 
y = 0.0966x - 0.0069 
R² = 0.9995 
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
0  2  4  6  8  1 0  1 2  
A
b
so
rb
an
ce
 
Concentration (mg/L) 
Ni s tandard  ca l ibra t ion  curve  
 
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Mercury (Hg): Standards for the determination of Hg were prepared to a maximum 
concentration of 1.000 mg/L as follows: 
Standard Concentration 
(mg/L) 
Mean Absorbance 
 
0.000 0.000 
0.200 0.057 
0.400 0.114 
0.800 0.229 
1.000 0.288 
 
 
 
 
 
 
 
 
 
y = 0.2877x - 0.0005 
R² = 1 
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0  0 . 2  0 . 4  0 . 6  0 . 8  1  1 . 2  
A
b
so
rb
an
ce
 
Concentration (mg/L) 
Hg standard cal ibrat ion curve  
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Cadmium (Cd): Standards for the determination of Cd were prepared to a maximum 
concentration of 3.000 mg/L as follows: 
Standard Concentration 
(mg/L) 
Mean Absorbance 
 
0.000 0.0000 
0.500 0.1589 
2.000 0.6126 
3.000 1.0344 
 
 
 
 
 
 
 
 
 
 
y = 0.3384x - 0.0138 
R² = 0.9942 
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
0  0 . 5  1  1 . 5  2  2 . 5  3  3 . 5  
A
b
so
rb
an
ce
 
Concentration (mg/L) 
Cd s tandard cal ibrat ion curve  
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Arsenic (As): Standards for the determination of As were prepared to a maximum 
concentration of 0.08 mg/L as follows: 
Standard Concentration 
(mg/l) 
Mean Absorbance 
 
0.00 0.0000 
0.02 0.1070 
0.04 0.2223 
0.08 0.4470 
 
 
 
 
 
 
 
 
 
 
y = 5.6061x - 0.0021 
R² = 0.9999 
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0  0 . 0 1  0 . 0 2  0 . 0 3  0 . 0 4  0 . 0 5  0 . 0 6  0 . 0 7  0 . 0 8  0 . 0 9  
A
b
so
rb
an
ce
 
Concentration (mg/L) 
As s tandard cal ibrat ion curve  
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APPENDIX VIII 
 
Procedure for computing the UCL of the mean when the underlying distribution is 
normal 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDIX IV 
 
Trace elements determined by Instrumental Neutron Activation Analysis (INAA) 
  
Elemental concentration (mg/kg) 
Sample ID School Al K La Mn Na Ti V 
AG Agbbogba 31080±404 14170±481 21.47±1.29 254.7±12.99 1495±17.94 2829±421 36.21±1.71 
AP Abokobi Presby 28010±221 2152±178 18.22±1.08 189.8±6.84 636.3±9.55 3010±415 75.75±1.97 
AS Ashongman M/A 30610±367 8149±415 22.28±1.27 274.7±10.44 754.4±11.32 3409±456 34.53±1.35 
HC Haatso Calvary Presby 29840±358 11290±406 22.14±1.36 239.5±9.11 1313±17.07 4957±634 31.93±1.25 
AH Atomic Hills 17240±206 975.3±73.15 9.89±0.62 107.3±7.84 417.2±5.01 3116±405 25.66±1.01 
AM Akporman Model 21305±136 3153±126 21.35±0.88 193.6±0.12 368.4±4.79 2475±1.71 40.41±0.03 
DA Dome Anglican 25770±309 6773±162 16.71±1.19 278.1±10.57 3312±1.21 3089±441 86.48±1.65 
KA Kwabenya Atomic 27860±334 7540±180 30.27±1.03 111.1±9.89 763.2±7.64 3008±406 55.01±1.44 
KW Kwabenya M/A 18730±243 1749±1.32 864±1.74 165.2±11.9 541.4±0.31 2554±375 35.87±1.8 
TC Taifa Community 22040±264 4081±163.24 8.4±0.81 230.7±9.69 1997±15 4044±525 33.13±1.13 
TD Taifa St Dominic 41150±534 1621±81.05 15.24±0.86 183.2±13.2 373.6±4.49 4851±664.59 48.98±2.21 
GP GAEC Playground 9761±126 1381±111 4.73±0.41 117.3±11.97 674.4±8.1 2173±306 32.45±1.11 
GS GAEC Basic School 12790±166 1054±113 4.32±0.45 80.45±7.81 703.4±8.45 733.9±199.63 66.89±1.68 
HM Hillview Montessori 10970±109 483.2±99.06 3.63±0.33 49.63±0.11 343.6±5.16 978±0.01 20.53±0.02 
 
 
 
 
 
 
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Trace elements determined by Atomic Absorption Spectroscopy (AAS) 
  
Elemental concentration (mg/kg) 
Sample ID As Cd Cr Co Cu Fe Hg Ni Pb Zn 
Abokobi Pressby 3.3 < 0.002* 15.72 0.75 16.74 235606.7 0.9 17.24 11.85 162 
Agbogba Anglican 3.15 < 0.002* 11.52 8.7 10.14 215758.4 0.6 19.49 < 0.001* 64.5 
Akporman Model 3.27 0.225 7.17 3.3 9.54 220804.5 1.2 16.19 < 0.001* 93 
Ashongman M/A 3.45 < 0.002* 44.67 7.5 22.14 249577.6 1.95 29.99 0.15 781.5 
Atomic Hills 3.105 < 0.002* 9.57 12.45 8.79 226761 1.35 17.39 7.05 223.5 
Dome Anglican 3.375 0.24 33.87 12.6 30.84 246530.1 2.55 31.79 < 0.001* 708 
GAEC Playground 3.165 0.255 4.17 12.3 10.74 210771.6 1.65 12.74 < 0.001* 127.5 
GAEC School 3.24 0.27 13.17 6.3 8.79 238139.6 1.95 10.19 < 0.001* 114 
Haatso Calvary Pressby 2.04 0.225 11.97 13.5 < 0.003* 215125.1 2.25 23.09 < 0.001* 102 
Hillview Montessori 1.755 0.3 3.42 5.7 11.49 204360 1.8 7.49 < 0.001* 58.5 
Kwabenya Atomic 3.195 0.12 10.02 4.65 < 0.003* 235586.9 2.4 14.54 < 0.001* 150 
Kwabenya M/A 3.075 0.075 6.27 6.9 < 0.003* 223396.9 1.95 8.24 < 0.001* 202.5 
Taifa Community 1.695 0.06 8.37 7.05 7.44 223396.9 < 0.001* 9.14 4.8 375 
Taifa St Dominic 1.62 0.225 4.77 4.8 26.94 217044.7 < 0.001* 7.34 < 0.001* 112.5 
 
*Detection Limit 
 
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