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UNIVERSITY OF GHANA 
COLLEGE OF BASIC AND APPLIED SCIENCES 
 
 
ENVIRONMENTAL IMPACT OF THE MOBILE TELECOMMUNICATION 
TECHNOLOGY IN ACCRA, GHANA. 
 
BY 
 
 
WORLANYO KWABENA AGBOSU 
(ID. NO. 10358709) 
 
 
  
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON IN 
PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF PhD 
ENVIRONMENTAL SCIENCE DEGREE. 
 
 
INSTITUTE FOR ENVIRONMENT AND SANITATION STUDIES 
 
DECEMBER, 2015 
 
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DECLARATION 
 
I hereby declare that except for references which have been cited, this work is the result of my 
own research and that it has not been presented in part or whole for any other degree in the 
University of Ghana or elsewhere. 
 
 
………………………………………..                                    ..…………………………………… 
(WORLANYO KWABENA AGBOSU)                              (PROF. DICKSON ADOMAKO) 
                                                                                                       PRINCIPAL SUPERVISOR 
 
 
 ………………………………….. 
(DR. JOSEPH K. AMOAKO) 
                                                                                                                       SUPERVISOR 
 
 
                                                                                                ………………………………….. 
(DR. EMMANUEL M. ATTUA) 
                                                                                                                        SUPERVISOR 
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ABSTRACT 
Currently, there are over 10,000 telecommunication base stations (BSs) in Ghana. Experts insist 
the number of BSs is practically inadequate to ensure quality service nationwide.  However, in 
Accra and other urban centers the installation of BSs to expand coverage has met opposition 
from the public. Accra has a population density of over 15,000 persons per km2 with an annual 
growth rate of 4.4%; hence, the number of people estimated to live closer to BSs is higher in 
Accra than in any other city in Ghana. This study was carried out to assess the environmental 
impact (EI) of the devices of the mobile telecommunication technology (MTT) that depends on 
the frequencies 900MHz and 1800MHz. The study focused on critical perspectives of exposure 
to radiofrequencies, risk assessment, perception and communication as well as waste and noise 
pollution. Individual Based Models (IBMs) are therefore deemed appropriate for predicting the 
level of environmental impact as evaluated at the local level by a well-informed individual. The 
methodology for data collection involved both the quantitative and qualitative approaches. The 
significant activities performed included measurements of some physical and chemical 
environmental pollutants (quantities) and the administration of questionnaire to help understand 
the influence of human behaviour on the MTT. A statistical package (SPSS) was used in the data 
analysis, which included; Analysis of Variance (ANOVA), Pearson correlation analysis, 
Principal component analysis (PCA), Hierarchical cluster analysis (HCA) and Cluster analysis 
(CA), or otherwise combining both quantitative and qualitative evidence. Both descriptive and 
inferential statistical techniques (Chi-square) were used to analyze the data for comparison 
between groups at 0.05 (95%) level of significance. The results of this study indicated reality and 
perception or a combination of both depending critically on the source of information to the 
public.  
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Radiation levels (electric field values) at the vicinity of BSs were below the ICNIRP threshold of 
41.25V/m (SAR of 4.5W/m2) and 58.3V/m (SAR of 9W/m2) for frequencies of 900MHz and 
1800MHz respectively. Noise levels from generator sets at BSs in residential areas were not 
always below the EPA threshold of 48dB within the hours of 22:00pm to 06:00am at a distance 
of 50m from BSs. Heavy metals at specific levels in mg/kg at dumpsites indicated that, Hg and 
Cd levels were not below the US EPA (2011) threshold of 0.2mg/kg and 1.2mg/kg respectively.  
Questionnaire survey was necessary to provide information on what the public considered risky 
but has been proven otherwise scientifically. Information gathered revealed that, what is lacking 
is an effective strategic environmental communication which ought to be supervised by the 
MMDA to eliminate some of the variables that increase the RVI. At the end of the study, it was 
concluded that, Hg and Cd may pose potential health risk to the public whilst noise may equally 
pose potential health risk to inhabitants living nearby BSs. Considering the routes of contact of 
heavy metals to individuals, the recovery and recycling of all WEEE must be encouraged to 
reduce its adverse effect in the future. Again, minimum setback distances must be strictly 
enforced using passive barriers, though the use of active barriers is equally important. Indeed, 
there should therefore be a careful examination of all uncertainties and the transparency of 
assumptions and limitations at this early stage. 
 
 
 
 
 
 
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DEDICATION 
 
 
 
 
This work is dedicated to; my children 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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ACKNOWLEDGEMENT 
 
Glory be to my Creator for guiding me through this challenging research successfully. Again, I 
am grateful for his blessings throughout even the difficult periods in my life. Indeed, His mercies 
endure forever. 
My sincerest thanks and appreciation go to all my supervisors namely; Prof. Dickson Adomako, 
Dr. Joseph K. Amoako and Dr. Emmanuel M. Attua for always being encouraging, patience and 
supportive in diverse ways up to the end of this study. I am also grateful to Dr. Selasie Adanu 
and his team at CERSGIS Legon for the wonderful assistance they provided during the period. I 
also recognize and thank Samuel Osei and Emmanuel Adofo at GAEC and Kobina Annobil 
Forson for their support and open discussions concerning this research. 
I deeply thank my mother, Margaret Forson and madam Mavis Abena Agyeiwaah for their 
financial assistance during my period of study. 
Finally, I honestly acknowledge the generosity of my brother, Akpenyo Kwao Agbosu, my 
sisters, my parents, my entire family, Solomon Odei-Appiah and the numerous individuals who 
assisted in various ways to bring this research to a successful end. Thanks a lot.  
 
 
 
 
 
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TABLE OF CONTENTS 
CONTENT             PAGE 
TITLE PAGE   ………………………………………………………………………                i 
DECLARATION...………………………………………………………………….                 ii 
ABSTRACT …………………………….…………………………………………..                iii 
DEDICATION ……..……………………………………………………………….                 v 
ACKNOWLEDGEMENT …….………………………………………………...….                vi 
TABLE OF CONTENTS…….………………………………………………………               vii 
LIST OF FIGURES ………………………………………………………..……….                 xiii 
LIST OF TABLES ………….……………………………………………………..                   xiv 
LIST OF ABBREVIATIONS ……………………………………………………..                   xvi 
1.0 INTRODUCTION ……………………………………………………………..                   1 
1.1 Background …………………………………………..…………………………                    1 
1.2 Statement of problem…………………………………………………………….                   5 
1.3 Hypotheses………………………………………………………………………..                  7 
1.4 Research questions………………………...………………………………………                 7 
1.5 The main objective of the research………………………………………………..                 8 
1.6 Specific objectives of the study………………….………………………………..                 8 
1.7 Scope of study …………………………..…………………………………………                9 
1.9 Limitation of study ……………………………….………………………………..                9  
 
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CONTENT        PAGE 
2.0 LITERATURE REVIEW ………………………………………………….…..                 10 
2.1 Environmental policy………………………..………………………………….…                10 
2.2 Critical perspectives of exposure to EMF………………………….………………..             12 
2.3 Environmental impact assessment (EIA) ……………………..……………………              14 
2.4 Health impact assessment (HIA) …………………….…………………………….              20 
2.5 Risk assessment……………………………………………………………………               23 
2.6 Risk communication……………………...………………………………………..               29 
2.7 Risk perception…………………………………………………………………….               32 
2.8 Management of mobile telecommunication waste……………………...…………               37 
2.9 Noise pollution……………..………………………………………………………               43 
2.10 Environmental model …………………..…………………………………………              45 
2.10. 1 Purposes of models …………………………………………………………...                45 
2.10.2 Uses of models ………………………………………………………………..                46        
2.10.3 Modeling as a concept ………………………………………………………..                 47 
2.10.3.1The parametric modeling approach ………………………………………...                  49 
2.10.4 Validation of models …………………………………………………………                  51 
2.10.5 Avoiding model uncertainty …………………………………………………                  52 
 
3.0 MATERIALS AND METHODS………………………………………………                  53 
3.1 Description of study area …………………………………………………………                53 
3.1.1 Accra metropolis …..……………………………………………………………                53 
3.1.2 Drainage ………………………….…………………………………………                    54 
3.1.3 Climate …………………………………………………………………………                 55 
3.1.4 Vegetation ………………………………………………………………………                57 
3.1.5 Geology and soil………………………………………………………………..                 57 
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CONTENT                                                                                                                             PAGE 
3.1.6 Seismicity ………………………………………………………………………                 58 
3.1.7 Economy of the study area…………………………………..…………………                  59 
3.1.8 Mobile technology use………………………………………………………….                 59 
3.1.9 Private import …………………………………………………………….…….                 60 
3.1.10 Soil sampling site ……………………...………………………………………                62 
3.2 Data collection……………………………...…………………………………….                 64 
3.2.1 Observation …………………………………………………………………….                 64 
3.2.2 Radiation Measurements …………………………………………………….…                 65 
3.2.3 Noise Measurements …………………………………………………………...                 68 
3.2.4 Soil analysis for heavy metals ………………………………………………….                 69 
3.2.4.1 Digestion protocol for soil sample using milestone acid ………..……………                70 
3.2.4.2 Statistical Analysis ………………..………………………………………….                 73 
3.2.5 The use of questionnaires …………………………………………….………..                  73 
3.2.5.1 Designing of questionnaires …………….…………………………………..                   74 
3.2.5.2 Questionnaires at residential areas ……………………………………………                75 
3.2.5.2.1 Sampling Procedure........................................................................................                75 
3.2.5.2.2 Approach to administering questionnaires …………..…………………….                  76 
3.2.5.3 Government Institutions ……………………..………………………………                  77 
3.2.5.4 MMDA ………………………………………………………………………..                79 
3.3 Ethical considerations………………………………………………………………              79 
3.4 Data analyses ……………………………………………………………………….             81 
3.5 Application of the parametric model to the study…………………………………..              82 
4.0 RESULTS ………………….………………………………………………………             84 
4.1 Observations made at selected BS…………………………….…………………….             84 
4.2 Radiation levels in the vicinity of BSs ………………………………………………            85 
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CONTENT                                               PAGE 
4.3 Mean noise levels at specific distances from BSs ……………………………………          92 
4.4 Levels of heavy metals in soil samples……………….………………………………           93  
4.5 Data from questionnaire responses………………..…………………………………            96 
4.5.1. Responses from residential areas …………….……………………………………           96 
4.5.2. Responses from EPA ………………………………………………………………        104 
4.5.3. Responses from NCA ……………………………………………………………...        105 
4.5.4. Responses from MMDA …………...………………………………………………        106 
4.5.5 Wood (2003) proposed “method and 14 point criteria” ……………………………..       112 
4.6 Contribution to knowledge ……………………………………………………………       113 
5.0 DISCUSSION OF RESULTS ………………………………………………………        114 
5.1 Discussion of observations ……………………………………………………………       114 
5.2 Discussion of Radiation levels around BSs ………………………………………….         114 
5.3. Discussion of mean noise levels recorded around BSs ……………………………..          117 
5.4 Discussion of levels of heavy metals in soil samples ………………………………..         119 
5.4.1 One Way ANOVA …………………………………………………………………         120 
5.4.2 Correlation analysis …………………………………………………………………       121 
5.4.3 Factor Analysis - Principal Component Analysis (PCA) …………………………...       123 
5.4.4 Hierarchical Cluster Analysis (HCA) ………………………………………………..      125 
5.5 Discussion of questionnaire responses ………………………………………………...       126 
5.5.1. Discussion of responses from residential areas ……………………………………..      126 
5.6 Discussion of data from questionnaire responses ………………………………………     127 
 
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CONTENT                                  PAGE 
5.6.1 Participative ………………………………………………………….…………..….       128 
5.6.1.1 Consultation …………………………………………………………………………    128 
5.6.2 Credibility ………………………………….………………………………………….    131 
5.6.2.1 Possible adverse impacts ………………………………….……………………..…      131 
5.6.2.2 Monitoring and evaluation …………………………………………………….……     134 
5.6.3 Focused…………………………………………………………………………….….     137 
5.6.3.1 Environmental requirements ……………………….……………………………….     137 
5.6.3.2 Minimum legal distance …………………….………………………………………     139 
5.6.4 Systematic ……………………………………….……………………………………     140 
5.6.4.1 Waste Management …………………………………………………………………     140 
5.6.5 Adaptive …………………………………………………………………………..….      142 
5.6.5.1 Commitment of stakeholders to EIA principles …………………………………….     142 
5.6.5.2 Achievement of EIA objectives …………………………………..……….……….      143 
5.7 Risk vulnerability index (RVI) …………………………………………………..……       144 
6.0 CONCLUSIONS AND RECOMMENDATIONS …………………………..……          145 
6.1 Conclusions …………………………………………………………………………...        145 
6.2 Recommendations …………………………………………………………………….        147 
REFERENCES …………………………………………………………………………          149 
Appendix A ………………………………………………………………………………..       196 
Appendix B ………………………………………………………………………………..       201 
Appendix C ………………………………………………………………………………..       210 
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CONTENT                                  PAGE 
Appendix D ………………………………………………………………………………..       213 
Appendix E ………………………………………………………………………………..       214 
Appendix F ………………………………………………………………………………..        227 
Appendix G ………………………………………………………………………………..       241 
Appendix H …………………………………………………………………………..……       242 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF FIGURES 
CONTENT       PAGE 
Fig. 3.1: Map showing the study area ……………………………………………………..         54 
Fig. 3.2: Map showing basins in Accra ………………………………………………..….          55 
Fig. 3.3: Location and size of the Agbogbloshie scrap yard ……………………………....         64 
Fig. 3.4: Map showing soil sampling points at “Agbogloshie” …………………………...         70 
Fig. 3.5: BSs at residential areas in Accra where questionnaires were administered ….…          78 
Fig. 4.1: Traces of fuel on the concrete floor of a BS…………………………………….           84 
Fig 4.2: Results of Wood (2003) proposed technique indicating above average of the  
Ghanaian EIA system ……………………………………………………………..       113 
Fig. 5.1: Radiation pattern at Awodome ………………………………….………………        116               
Fig. 5.2: Radiation pattern at Abeka Lapaz ………………………………………………        116 
Fig. 5.3:  Radiation pattern at Kokrobite …………………………………………………        116          
Fig. 5.4: Radiation pattern at Asylum Down …………….……………………………….        116 
Fig. 5.5: Mean noise level patterns at some BSs ………………………………………….       117 
Fig. 5.6: Results compared to US EPA limits …………………………………………….        119     
Fig. 5.7: Results compared to US EPA limits …………………………………………….        119 
Fig. 5.8: Mean levels of heavy metals at sites A, B and C ……………………………….        122 
 
 
 
 
 
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LIST OF TABLES 
CONTENT              PAGE 
Table 3.1(a):   CEPS Import Data in units (2006-2009) ………..……………………..        60 
Table 3.1(b): Estimated Undeclared Private Imports through KIA………………………….      61 
Table 3.2:  Installed Base (from consumer surveys) of some EEE (units and tons, 2009)…        62 
Table 3.3 MMDA in Accra that participated in the research work ……………………………....          80 
Table 4.1: Radiation levels recorded in the vicinity of BSs …………………………………..   85 
Table 4.2: Mean noise levels recorded at specific distances from BSs ………………….…....   92 
Table 4.3: Levels of heavy metals (mg/kg) recorded in soil samples at “Agbogbloshie”….…    93 
Table 4.4: Respondents at selected BSs ……………………………………………………..      96 
Table 4.5: Distance of telecommunication BS from respondents ……………………………    97 
Table 4.6 (a): Cross tabulation indicating the relationship between distance and content……..  97 
Table 4.6 (b): Chi-Square Tests………………………………………………………………..   98 
Table 4.7 (a): Cross tabulation indicating the relationship between distance and health risk in 
children ………………………………………………………………………………………...  98 
Table 4.7 (b): Chi-Square Tests………………………………………………………………     99 
Table 4.8 (a): Cross tabulation indicating the relationship between distance and health risk in 
neighbourhood………………………………………………………………………….……… 99 
Table 4.8 (b): Chi-Square Tests………………………………………………………..…….    100 
Table 4.9 (a): Cross tabulation indicating the relationship between consultation and content..  100 
Table 4.9 (b): Chi-Square Tests……………………………………………………………….  101 
Table 4.10 (a): Cross tabulation indicating the relationship between familiarity and highest level  
of education ……………………………………………………………………………101 
Table 4.10 (b): Chi-Square Tests……………………………………………………………..   102 
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Table 4.11 (a): Cross tabulation indicating the relationship between relocation and landlord or  
tenant……………………………………………………………………………………102 
Table 4.11 (b):Chi-Square Tests…….……………………………………………………….    103 
Table 4.12: Possible risks associated with the MTT and consultation……………………….   106 
Table 4.13: Complaints from residents close to BSs and methods of resolving complaints…   107 
Table 4.14: Risk perception among people living close to BSs and compensation for residents to  
relocate ……………………………………………………………………………….. 108 
Table 4.15: Minimum setback distances and management of material waste products……      111 
Table 5.1: Pearson’s correlation matrix of heavy metals in the soils, ** indicates correlation is  
significant at the 0.01 level (2-tailed) ………………………………………………… 121 
Table 5.2: Principal component loadings (Varimax-normilzed) for heavy metals in the soil  
samples ………………………………………………………………………………..  123 
 
 
 
 
 
 
 
 
 
 
 
 
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LIST OF ABBREVIATIONS 
 
MTT Mobile telecommunication technology 
BS Base station 
RF                        Radiofrequency 
FCC                     Federal Communications Commission 
GSS                     Ghana statistical service 
PHS                     Population and housing unit 
EPA                     Environmental Protection Agency 
WEEE                 Waste electrical and electronic equipment 
EEE                    Electrical and electronic equipment 
UNEP                United Nations environmental program  
IPCS                  International Programme on Chemical Safety 
DNA                 Deoxyribonucleic acid  
EHS                     Electromagnetic hypersensitivity 
WHO                   World Health Organization 
GCAA                 Ghana Civil Aviation Authority 
RPI                     Radiation Protection Institute 
NCA                   National Communication Authority 
EIA                     Environmental Impact Assessment 
US EPA              United States, Environmental Protection Agency 
SEA Strategic Environmental Assessment 
TA                       Technical Assessment 
GIS                     Geographical information systems 
 
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LIST OF ABBREVIATIONS 
HIA                    Health Impact Assessment 
IAIA                  International Association of Impact Assessment 
HHRA              Human health risk assessment 
ITD                    Inter Tropical Discontinuity  
IBM                  Individual Based Models  
 
 
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CHAPTER ONE 
1.0 INTRODUCTION 
1. 1 Background 
Recent years have seen an unprecedented increase in the number and diversity of sources of 
electric and magnetic fields (EMF) used for individual, industrial and commercial purposes 
Nnorom and Osibanjo (2009). All these technologies have made life richer and easier. The 
mobile telecommunication technology (MTT) has greatly enhanced the ability of individuals to 
communicate with each other and have facilitated the dispatch of emergency medical, relief and 
police aid to persons in both urban and rural environments Zain (2005). Mobile phone use has 
enhanced the efficient use of time (WHO fact sheet N181). This dynamics has led to the 
development of meaningful competition by telecommunication service mode, with global mobile 
service exceeding fixed-line subscription by the year 2002, Banerjee and Ros (2004). 
Consequently, the investment in telecommunications infrastructure, with its ability to create 
spillover effects, can impact growth far more significantly than can any other alternative 
infrastructure Nadiri and Nandi (2003) and Roller and Waverman (2001). About half the 
economic growth in industrialized countries is related to some aspect of technology Sachs 
(2000), however, these technologies have brought with them concerns about the life cycle 
assessment (LCA) of devices used and the possible health risks associated with their use. Heart 
(2009) was of the view that, the consequences of LCA are usually ignored or even unknown. In 
specific terms Hilty et al. (2006) emphasized that, “there is the risk that information 
communication technology (ICT) will become counterproductive with regard to especially 
environmental sustainability”. 
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Currently, in Ghana, there are over 10,000 telecommunication base stations (BSs) EPA 
(www.epa.gov.gh, 2014) serving 36, 395, 116 mobile phone subscribers according to the NCA, 
Daily Graphic (12/7/16). Experts insist the number of BSs is woefully inadequate to ensure 
quality service Amoako et al. (2009).  In Ghana and especially in Accra, the installation of BSs 
has met opposition from the public because of concerns that, the radiofrequency (RF) emissions 
from these BSs might have some health consequences. 
The spatial distribution pattern of BSs in Ghana is basically irregular with as many as 2000 BSs 
installed only in Accra. Although Accra has a modest surface area of 200 km2 it has an enormous 
estimated population of over 3,000,000 GSS PHS (2010). In view of this, Accra therefore has a 
population density of over 15,000 persons per km2 and a BS density of 10 BSs per km2. Hence, 
there is an average of 10 BSs on a square kilometer plot in Accra as compared to a nationwide 
average of less than 1 (0.04) BS on a square kilometer plot. Others have reported of population 
densities as high as; 23,802 persons per km2 in Ablekuma and 37,857 persons per km2 in 
Ayawaso World Bank (2010). 
 
York (2004) argued that, technological innovations are environmentally destructive since they 
result in increased pollution. Specifically The Climate Group and GeSI (2008) stated that, the 
ICT sector and its products are responsible for about 2% of the global greenhouse gas emissions 
and that this will increase unless mitigated. Indeed, with the current inconsistent power supply in 
Ghana, generator sets at BSs could possibly if not effectively maintained or monitored be of 
concern to neighbourhoods. Therefore, urban noise and air quality are of concern to most urban 
populations since they have significant consequences on health conditions. In this regard, the 
EPA has established a permissible ambient noise level of 55dB during the day (0600-2200) and 
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48dB during the night (2200-0600). This by law is to be enforced by the Metropolitan, Municipal 
or District Assembly (MMDA), Local Government Act; 462 (1993).  
 
In developing countries, the most popular adopted methods to manage solid waste disposal 
include open burning and dumping at uncontrolled dumpsites. However, there are always 
technical and environmental problems with both the site and the technology Bali Post (2008).  
These methods of disposal cause serious environmental problems including health hazards Ball 
and Denhann (2003).                                                     
 
Waste electrical and electronic equipment (WEEE) has been considered to be one of the most 
important waste streams of the last decade and will continue to be so in the future Mark (2006) 
and Menad et al. (1998). WEEE is also a complex stream, because electrical and electronic 
equipment (EEE) covers a wide variety of products, ranging from mechanical devices to highly 
integrated systems such as computers and mobile phones Crowe et al. (2003).  Furthermore, 
about 75% of the heavy metals found in landfills are from WEEE Leke et al. (2011). 
According to the UNEP (2005), the increase in WEEE worldwide is at approximately 4% per 
annum. Jiang et al. (2008) projected that, more than 50 million tons and 72 million tons of 
WEEE were discarded globally in the years 2009 and 2014 respectfully. 
In Ghana, considering the number of mobile phone subscribers and an estimated 2 million people 
using the internet ITU (2016), there is the need for environmental measures on WEEE. Hischier 
and Eugster (2007) ascertained that, a desktop computer with accessories weighs 22kg, a laptop 
weighs 3.5kg and a mobile phone weighs 0.5kg. With these figures, the number of tons of mobile 
phones, desktop computers and laptops deposited in Ghana is estimated to be not less than 
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43,427 tons per annum. Amoyaw-Osei (2011) however, projected that an amount of 109,000 
tons of WEEE was generated in Ghana in the year 2009. 
 
Ghanaians, notwithstanding the radiation levels consistently below the International Commission 
on Non-Ionizing Radiation Protection (ICNIRP) standards Amoako et al. (2009) are concerned 
about electromagnetic radiation from BS antennae whichare of lower intensity but can be 
continuous Trower (2001). The ICNIRP thresholds used by most countries including Ghana are 
41.25V/m (SAR of 4.5W/m2) and 58.3V/m (SAR of 9W/m2) for frequencies of 900MHz and 
1800MHz respectively. 
Indeed, this concern is based on the “belief” that, radiation could in the long term cause cancer, 
brain tumor, damage to DNA and destroy body cells. However, this public concern could be 
attributed to the fact that, there is no clear and definitive assessment as to whether there exists a 
health risk from long-term exposure to radiofrequency radiation EMF-NET (2009). 
However, there are studies reporting of adverse effects Mann and Röschke (1996), Preece et al. 
(1999), Huber et al. (2000), Koivisto et al. (2000), Krause et al. (2000), D’Costa et al. (2003), 
Cook et al. (2002), Hossmann and Hermann (2003), Sienkiewicz et al. (2005) and also others 
reporting of no effects Besset et al. (2005), Ozturan et al. (2002), Arai et al. (2003) Bak et al. 
(2003), Parazzini et al. (2005),  Uloziene et al. (2005) have been documented on both animals 
and humans worldwide. 
The few studies on residential exposure to RF fields from transmitters had serious limitations, 
however, they are suspected to be the cause of a variety of non-specific self-reported symptoms 
such as headache, fatigue, dizziness and concentration difficulties Ahlbom et al. (2004).  
This emerging development of non-specific medically unexplained health problems attributed to 
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EMFs is described as “electromagnetic hypersensitivity” (EHS). Although symptoms described 
as EHS are real and may be severe and disabling, a relationship between symptoms and RF field 
exposure has not been proven WHO (2005), Koivisto et al. (2001), Seitz et al. (2005) and Rubin 
et al. (2005). 
To properly manage the challenges of the MTT in Ghana, a network operator is mandatory to 
obtain separate permits from the Ghana Civil Aviation Authority (GCAA); Radiation Protection 
Institute (RPI); Environmental Protection Agency (EPA); and the Metropolitan, Municipal or 
District Assembly (MMDA). 
In fact, countries such as the UK, Netherlands, Denmark, Germany, Austria, Spain and the USA 
have regulations and practices that encourage companies to improve upon their environmental 
performance voluntarily Peltonen et al. (2004) and Henriques and Sadorsky (2007).  
 
1.2 Statement of Problem 
There are currently over 4 million BSs worldwide with over 7.4 billion mobile phone subscribers 
GSMA (2016). Radiation emitted by the antennae of BSs could possibly cause health hazards 
Frick et al. (2002), Kristiansen et al. (2009) and WHO (2011).  
In fact, the WHO itself has called for precaution in mounting telecommunication BSs and has 
further emphasized the need for research on the possible effects of BS signals UNCED (1992). In 
definite terms, the Stewart Report (2000) stated that, “Although the balance of evidence does not 
suggest that emissions from BSs put the UK population at risk”, it also concluded that, “the 
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possibility of harm cannot be ruled out with confidence”.   
In Ghana, complaints about the technology continue to increase, though mobile phone operators 
insist the BSs are safe but this sounds unconvincing to the public. 
For example, the EPA (2010) has a list of complaints from individuals and institutions. The 
nature of the complaints includes; 
 BSs erected without neighbourhood consent. 
 BSs close to people's homes and to schools. 
 Masts falling on property. 
 Noise, fumes and vibration from generators at BSs. 
 Health concerns about electromagnetic radiation emitted. 
However, it should be noted that, at “Ashale Botwe” a suburb in Accra, a mast collapsed, killing 
one person and injuring others (Daily Graphic, 25/3/10). Indeed similar complaints have also 
been made in several countries worldwide The Scottish Parliament Information Centre (1999). 
 
In developing countries such as Ghana, research on the MTT has not been done extensively and 
hence the urgent need to conduct studies into all the environmental challenges of the MTT.  
However, possible health risks of radio waves from mobile phone antennae identified in the 
developed world according to Hutter et al. (2006), Frey (1998) and Trower (2001) include but 
not limited to the following; 
 Increased frequency of seizures among children with epilepsy 
 Headaches and nose bleeding 
 Cancer and skin rashes 
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Indeed, research has been conducted to determine the levels of RF exposed to the public Amoako 
et al. (2009) and Deatanyah et al. (2012). However, as the extensive use of the MTT is 
envisaged, much research is needed on the prolonged effects of;  
 Exposure to noise by populations living near BSs. 
 Disposal of devices. 
 Perception on the acceptability of the MTT. 
 
1.3 Hypotheses 
 There is no significant risk associated with living close to telecommunication BSs. 
 There is no significant difference between risk perception and the involvement of 
residents in the selection of sites for mounting BSs. 
 Distance has a significant effect on risk to the public. 
 There is a significant difference between international and local monitoring regulations of 
the MTT.  
 
1.4 Research Questions 
Questions would be framed to cover the following areas; 
 Are telecommunication operators really committed to EIA principles? 
 Do the EIA measures implemented actually attain their expected effects? 
 Do BSs have other significant adverse impacts other than those anticipated during the 
design phase? 
 Is there an issue of risk perception among people living close to BSs? 
 Do the permitting agencies monitor and evaluate measures implemented to mitigate 
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environmental impacts (EI)? 
 What major factors are taken into consideration by regulatory institutions before 
permitting BS proposals? 
 Is there a minimum legal distance for locating BSs from the public? 
 Are neighbourhoods consulted before BSs are installed close to their homes? 
 Are material wastes or products of the telecommunication industry (plastics, metals, 
glasses, batteries, SIM cards and chargers) properly managed? 
 
1.5 The main objective of the research 
To evaluate the environmental impact of the MTT in Accra, Ghana. 
 
1.6 Specific objectives of the study 
The specific objectives of the study are; 
 To assess the level of RF radiations emitted by BSs and compare the results to the 
standards set by ICNIRP.  
 To measure the level of noise from generator sets at BSs and compare the results to the 
standards set by EPA. 
 To assess the effectiveness of the EIA system in addressing the risk factors identified. 
 To evaluate how material wastes or products of the MTT (plastics, metals, chargers, 
glasses, batteries, SIM cards and gases emitted from generators) are managed. 
 
 
 
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1.7 Scope of study 
This research focused on the usage or LCA of EEE (mobile phones, laptops and computers) that 
use the frequencies 900 MHz and 1800 MHz and the infrastructure required for this MTT. 
 
1.8 Limitation of study 
The following limitations were identified; 
 Lack of data to really quantify radiation levels absorbed by individuals or affected 
populations since it is not always reliable to depend only on background scientific 
knowledge. 
 Some major stakeholders such as the mobile telecommunication operators did not 
participate in this research. 
 Financial constraints further limited the soil sampling and the administration of 
questionnaires. 
 
 
 
 
 
 
 
 
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CHAPTER TWO 
2.0 LITERATURE REVIEW 
2.1 Environmental Policy 
Public support plays a major role in decisions especially by politicians on whether or not to 
implement an environmental policy Christoph et al. (2008). Politicians are often reluctant to 
introduce environmental policies that face strong public resistance Banister (2008) and Garling 
and Schuitema (2007). However, the implementation of these policies is often perceived to be 
essential in order to safeguard the global and local environmental quality Steg and Vlek (2009). 
It is therefore important to understand which factors influence the acceptability of environmental 
policies Jakobsson et al. (2000).  
Acceptability has been suggested as one of the main dimensions of attitudes and has been used as 
a tool for assessing attitudes in researchSchuitema et al. (2011), Steg et al. (2005) and Teh et al. 
(2007). Studies on the acceptability of policies have shown that, an important determinant of 
acceptability is the perceived effectiveness of policies Bamberg and Schmidt (2003), Eriksson et 
al. (2008) and Schade and Schlag (2003).  
Research also suggests that individual characteristics are important when evaluating policy 
acceptability De Groot and Steg (2010), Schade and Schlag (2003) and Steg et al. (2005).  
Other researchers have stated policy characteristics as influencing the acceptability of policies. 
Garling and Schuitema (2007) and Poortinga et al. (2003) mentioned the level of coerciveness of 
a policy (pull or push measures) as the foremost characteristic. Pull measures encourage the 
desired behaviour and are generally regarded as non-coercive Eriksson et al. (2006) and 
Loukopoulos et al. (2005). Push measures are more coercive than pull measures and are likely to 
enforce behavioural change Garling and Schuitema (2007) and Steg et al. (2005). Various 
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empirical studies indeed show that push measures are evaluated as less acceptable than pull 
measures Eriksson et al. (2006, 2008) and Schuitema et al. (2011).                                              
Secondly, the behavioural target of a policy (targeting high or low cost environmental 
behaviours) Garling and Schuitema (2007) and Poortinga et al. (2003). 
These are policies aimed at altering various pro-environmental behaviours. The effort it takes to 
change these behaviours varies, and hence referred to as behavioural costs. In this context, the 
definition of costs is not limited to financial costs, but also includes the perceived convenience 
and effort of the specific behaviour that is addressed in a policy. Hence, policies that target high 
cost take much effort to change whilst low cost behaviour takes little effort to change. Policies 
targeting low cost behaviour are more acceptable than policies targeting high cost behaviour, 
Poortinga et al. (2003) and Steg et al. (2005). Adopt 
 
Perceived social norm is an important determinant for explaining policy acceptability Jakobsson 
et al. (2000) and Schade and Schlag (2003). A stronger social norm results in higher 
acceptability levels of a policy and vice versa Schade and Schlag (2003). The importance of 
social norms in explaining the acceptability of environmental policies can be further explained 
by the concept of social dilemmas Biel and Thøgersen (2007) and Von Borgstede et al. (2012). 
In a social dilemma, each selfish decision creates a negative outcome (or cost) for the people 
involved Von Borgstede et al. (2012).                                         
If a large number of people make selfish choices, negative outcomes accumulate, creating a 
situation in which everybody would have been better off if they had not acted in their own 
interest Dawes (1980). Hence, people’s expectation about the behaviour of others (social 
uncertainty) becomes an important factor in this respect Wilke (1991) and Yamagishi (1986). 
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People expect that others do not defect, the perceived effectiveness of policies will increase and 
subsequently, the acceptability of environmental policies will also increase Eriksson et al. (2006, 
2008) and Schuitema et al. (2011). A strong social norm suggests that one can trust that other 
people accept the measure as well (decrease in social uncertainty) Yamagishi (1986).  
 
2.2 Critical perspectives of exposure to EMF  
A causal relationship between EMF exposure and symptoms in provocation studies has never 
been demonstrated. The reason for such symptoms, according to Rongen et al. (2009) is basically 
psychological. To eliminate the psychological factor Hyland (2000) stressed the importance of 
animal studies where there can be no claim that measured symptoms are psychosomatic. 
However, with human health, children’s health status is considered as an important population 
marker of environmental threats. As such Etzel (2003) and Brent et al. (2004), identified children 
as a sensitive subgroup of the population hence, necessitating the need for monitoring sentinel 
health end-points. 
In fact, in relation to environmental agents, both relative risk and absolute risk are larger for 
individuals exposed as children than for those exposed as adults Kodama et al. (2003).   
Proximity to sources, either natural or anthropogenic, is an important determinant for exposure to 
environmental contaminants McGee et al. (2002).   
Exposure is described in terms of the intensity, frequency, duration of contact and also expressed 
as mass per unit time US EPA (1992). When evaluating absorbed doses, the quantitative 
evaluation of the dose–response nature is most preferred IPCS (2004, 2005) and US EPA (2005).  
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Traditionally, a threshold is stated, however, this assumption is based on the known capacity of 
the organism to compensate for or repair damage at various levels of biological complexity 
Kimmel et al. (2006). 
 
Practically, few attempts to study symptom prevalence and symptom severity in relation to 
exposure to RF fields from BSs have been done. Measurements of RF fields by Hutter et al. 
(2006) in the bedrooms of inhabitants living between 20m – 600m away from ten selected BSs 
was performed in Austria. The participants were classified into three exposure groups based on 
calculations of the theoretical maximal power density from the selected BSs (when the BS is 
using 100% of its capacity). The mean power densities were 1.3mW/m2, 0.23mW/m2 and 
0.04mW/m2, in the respective groups. Three out of fourteen self-reported symptoms (headache, 
cold hands or feet and difficulties to concentrate) were significantly reported in the highest 
exposure group.   
Again, a relationship between RF and symptoms in healthy volunteers was investigated in one 
provocation study. No increase in symptoms was observed during RF exposure as compared to 
sham exposure Koivisto et al. (2001). However, Zwamborn et al. (2003) have insisted that, 
people’s well being may be adversely affected by the environmental impact of mobile phone BSs 
sited next to houses, schools or other buildings, as well as by fear of perceived direct effects.  
Therefore, all applications and notifications for permits should be accompanied by a declaration 
that, the equipment and installation is designed to be in full compliance with appropriate ICNIRP 
guidelines on public exposure 
 
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2.3 Environmental Impact Assessment (EIA) 
EIA is a process of identifying, predicting, evaluating and mitigating the biophysical, social and 
other relevant effects of development proposals prior to major decisions being taken and 
commitments made Ridgeway et al. (1996). 
Again, strategic environmental assessment (SEA) is defined as “the formalized, systematic and 
comprehensive process of evaluating the EI of a policy, plan or program and its alternatives, the 
report on the findings, and the use of the findings in public accountable decision making” 
Therivel et al. (1992). SEA has started internationally, and has been used most successfully in 
civil aviation, waste disposal; water and power supply (Wilson, 1993). Earlier, Coates (1976) 
had used the term “Technical Assessment” to describe EIA and further elaborated that, “TA” 
emphasizes those consequences that are unintended, indirect, or delayed. 
 
The methods and techniques applied in SEA vary and may include among others the use of GIS, 
expert judgments Fischer (2007) and Therivel (2004) and public participation Wood (2003). GIS 
facilitate the preparation of maps and thereby, present a SEA support tool to illustrate and 
analyze data Therivel (2004), particularly in land use planning Fischer (2007). Again, GIS is 
increasingly used and widely incorporated in both EIA and SEA practices Vanderhaegen and 
Muro (2005). It is increasingly obvious that the assessment of likely significant environmental 
effects is critically dependent upon the spatial representation and analysis of both environmental 
sensitivities and likely patterns of development Skehan and González (2006). 
Budic (1994) argued that, GIS improves the “operational effectiveness” of SEA as they enhance 
the quality and quantity of environmental or planning information by incorporating the 
geographic dimension. The use of GIS in development plans commonly link land use to location 
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and spatial evidence approaches which significantly benefit decision-making CEC (2001). 
Additionally, GIS can identify the spatial and or temporal variability amongst impacts Patil et al. 
(2002) and have the potential to augment conventional techniques by providing spatial evidence 
as compared to traditional methods González (2010), Vanderhaegen and Muro (2005) and 
González et al. (2008). Presenting baseline data in graphic form improves the delivery of 
information, enhances the understanding of the distribution patterns and linkages between 
relevant environmental factors Vanderhaegen and Muro (2005). As a result, GIS guarantees 
more accurate description, better quantification and identification, improved evaluation of 
spatialand temporal variability of impacts and the prediction of cumulative effects. Therefore, 
GIS has the potential to facilitate a more robust spatial analysis as they enable the integration of 
various datasets and visualize thejuxtaposition or cumulative nature of different impacts Harrison 
and Haklay (2002). 
Nevertheless, a number of constraints affecting the effectiveness of GIS as a tool have been 
reported. This includes data availability, accessibility, costs and data quality in terms of scale, 
completeness and currency Rybaczuk and MacMahon (1995), Vanderhaegen and Muro (2005) 
and van Loenen and Onsrud (2004). Subsequently Therivel (2004) observed that, SEA is subject 
to great levels of uncertainty as a result of the ambiguity about future environmental, economic 
and social conditions. Partidário (2007) reinforces this observation, suggesting that data accuracy 
and quality aspects are overcome with some acceptance that SEA often needs to deal with higher 
levels of uncertainty. Due to such greater complexity and larger geographic context, it is 
generally accepted that environmental aspects in SEA cannot be described in great scientific and 
spatial detail João (2007) and Therivel (2004). 
 
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An effective EIA procedure should among others involve public participation Wood (2003). Its 
adoption has increased the amount of environmental information available to citizens Hokkanen 
(2007) and Pölönen (2007). However, public participation in many situations has often been 
reduced to a procedural exercise.  Analyses by Shepherd and Bowler (1997) and Del Furia and 
Wallace-Jones (2000), confirmed that, public participation is no guarantee for effective public 
involvement in environmental decision making. Public participation also faces many challenges; 
the most important of these seems to be the inconsistent utilization of the public's contribution in 
decision making Hokkanen (2007) and Pölönen (2007). 
From a practical perspective, Bond et al. (2004) argued that, the general public has always not 
been fully involved in EIA processes. In most cases Palerm (1999) observed that, public 
participation has been a mere formality where the government acts as an agency for the people.   
Others have also held to this assertion, for example, in China Cheng et al. (2007) identified four 
participants in the implementation of the EIA system. They include; the government, EIA 
organizations, enterprise entities, and the general public. However, according to Cheng et al. 
(2007) the public usually plays a silent role. 
Another important factor about the public described by Krek (2005) was that, many citizens are 
“rationally ignorant”. According to this theory, citizens decide to be rational, ignoring 
participatory processes, because, participation requires a high investment of time and effort. 
Krek (2005) applied it to public participation in urban planning. The theory of rational ignorance 
has also been presented in public choice theory by Buchanan and Gordon (1962) and Gunning 
(2002). In fact, most people prefer not to take the initiative to be actively involved in the EIA 
evaluation process Cheng et al. (2007).  
 
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EIA's primary purpose is to ensure the minimization of the environmental impacts of projects 
Glasson et al. (2005). Therefore, an effective EIA system should provide comprehensive 
coverage of projects likely to have significant adverse effects on the environment Heinma and 
Põder (2010).  
A core question on the effectiveness of environmental policy tools has been; whether the 
instrument works, or is used as intended and whether it meets the purposes for which it is 
designed Sadler (1996) and Mickwitz (2003). Hilding-Rydevik (2006) and Similä (2007) directly 
linked the “effectiveness” of environmental policy tools to the achievement of policy goals. 
Therefore, the need to integrate EIA within existing governance processes, and in particular the 
development approval and licensing procedures is widely acknowledged Leknes (2001), El-Fadl 
and El-Fadel (2004) and Weston (2002).    
Inter and intra agency collaboration and institutional coordination or integration have been 
identified by Saarikoski (2000) as a key element for the effective implementation of EIA in 
general. If the advantages of an integrated model are obvious, though, yet to be implemented, 
then Clarke (1984) and Hollick (1986) have argued that, it is likely that, the processes lack the 
tools to achieve integration. Recently, Brown and Hill (1995) have proposed that, EIA be 
considered as an input to decision making. 
Though EIA is expected to be made obligatory, others, for example Hokkanen (2007) and 
Sairinen (2000) have argued that, it would result in unreasonable economic burdens. 
Indeed EIA of programs and policies is already carried out routinely in countries, such as New 
Zealand, the U.S., Canada, and Germany. Professional capabilities for this assessment are well 
advanced Buckley (1994b) and Vanclay and Bronstein (1995) however; Buckley (1994b, c) 
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again identified barriers to their adoption and application in most jurisdictions as political, legal, 
and economic rather than technical. 
Ahmad (1996) indicated that, the decision to approve or to reject a project is heavily influenced 
by political considerations. Further complexity is added through the intertwining of political, 
technological, cultural, social and organizational factors Meredith and Mantel (1995) and Cicmil 
(2000). Governments, the overall representative of the general public, are mostly in an 
uncomfortable position of facing the dilemma of economical benefits and social costs. This may 
explain why EIA is nonexistent or lagging behind in some development projects Leknes (2001) 
and Saunders and Bailey (2009). 
 
EIA has some basic constraints, for example Stewart-Oaten et al. (1986) identified scientific 
uncertainty about environmental effects of a specific technology as a major factor. However, 
Stewart-Oaten et al. (1986) further suggested a research tool such as Before-After-Control-
Impact (BACI) design as a remedy to reduce uncertainty. Other researchers have delineated 
statistical constraints as a factor and again nonscientific constraints such as organizational 
constraints, bureaucracy and the duration of operation among others Osenberg et al. (1992). 
Biological considerations according to Jones and Kaly (1996), is again a complex constraint 
affecting the EIA process. 
Qiao Zhiqi (1994) on the other hand, emphasized that, despite the challenges, great progress has 
been made in recent years on EIA in China. This progress Qiao Zhiqi (1994) explained, has been 
marked by; a continual increase in the number of projected EIAs that have been completed, the 
extension of EIA’s field, continued research work on EIA and the diversification of EIA’s 
objectives among others. 
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To satisfy the various activities required in the conduction of EI studies, numerous methods 
(tools) including risk assessment have been utilized. The objectives of the various activities 
differ, as do the usable methods for each Canter (1996). 
There are both major and minor differences in the way EIAs are conducted across a wide range 
of jurisdictions Wood (1995). Two characteristics dominate its universal nature; the production 
of a stand-alone report and its provision of advice to assist in the final decision on the proposal. 
The primacy of the final report in EA leads one to the conclusion that, the requirements of EA 
are such that, it is done rather than anything be done with it Brown and McDonald (1995). 
Secondly, it results in the environmental information becoming available too late in the project 
planning process Wood and Djeddour (1992). 
Graybill (1985) is succinct on this, suggesting that much EIA practice is involved in dispute 
creation and not in dispute resolution. Tripp and Alley (2003) have concluded that, if the 
function of EIA and participation is no more than to legitimate the process and project or control 
the public, EIA easily creates more distrust and new disputes. 
 
Marshall et al. (2005) identified ‘follow-up’ as the last part of an ideal EIA process. They 
explained that, ‘follow-up’ determines the actual outcomes of the project and its goal is to 
prevent and minimize the negative consequences of developments. More specifically, the 
functions of ‘follow-up’ can be divided into three categories; controlling, learning and 
democratic. However, provisions on ‘follow-up’ should leave room for flexible interpretations so 
that ‘follow-up’ could be tailored to each context Marshall et al. (2005). 
 
 
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2.4 Health Impact Assessment (HIA) 
The International Association of Impact Assessment (IAIA) defines HIA as a “combination of 
procedures, methods and tools that systematically judges the potential and sometimes 
unintended, effects of a policy, plan, program or project on the health of a population and the 
distribution of those effects within the population”WHO, European Centre for Health Policy 
(1999).  
HIA is a growing sub-discipline of IA that uses diverse and often novel qualitative or 
quantitative methods to judge the impacts of policies, programs, plans, and projects on 
population health or its determinants Collins and Koplan (2009) and Kemm and Parry (2004). 
HIA further identifies appropriate actions to manage those effects Quigley et al. (2006). Health 
determinants refer to the range of personal, social, economic and environmental factors which 
determine the health status of individuals or populations WHO (1998). Many of these health 
determinants are associated empirically with health status measures, including life-expectancy, 
disease and injury rates, and measures of health care utilization through empirical research 
Marmot and Wilkinson (2006) and Kawachi and Berkman (2003). Diseases can be accentuated 
through beliefs and attitudes that are indigenous to a particular locality or culture Collins et al. 
(2006).  It is of particular importance due to the added exposure impoverished households face in 
relation to multiple disease risks. Farmer (2001) stated that, the poor have no option but to be at 
risk from diseases. Therefore, gaining an understanding of what individuals perceives as risk 
factors, and influences on their reaction to these risk factors, will assist in improving the 
effectiveness of current risk reduction strategies. 
 
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Human health risk assessment (HHRA) is one of the most common tools for quantitative 
estimation in HIA O'Connell and Hurley (2009) and is also used in EIA practice Steinemann 
(2000) and environmental risk management decisions (NRC, 2009). The quality of evidence and 
analytic methods and validity of predictions are prominent concerns for practice quality Mindell 
et al. (2001) and Veerman et al. (2007). 
 
Internationally, cases where projects affect human exposure to a known physical environmental 
hazard, including air, soil and water pollutants, noise, radiation, and biological contaminants, 
currently appear most amenable to quantitative estimation Veerman et al. (2005). 
Efforts to develop standardized quantitative or predictive models broadly applicable in HIA 
practice have also been met with limitations. These limitations include the inability to estimate 
effects on different populations, assumptions of steady-state health, over-complexity, and lack of 
transparency in the causal pathways Lhachimi et al. (2010). 
Despite these limitations, quantitative forecasts have been generated in a number of HIAs 
internationally on a range of policies, including those that affect environmental pollutants, traffic 
hazards, infectious disease risks, housing conditions, and tobacco and alcohol consumption 
Veerman et al. (2005) and O'Connell and Hurley (2009). Making prospective quantitative 
estimates of future health effects of policies or projects first require a logical and plausible model 
linking the decision to health outcomes Mindell et al. (2001). Prior reviews have established the 
essential information requirements needed for quantitative estimation in HIA Hertz-Picciotto 
(1995), Mindell et al. (2001), Veerman et al. (2005) and O'Connell and Hurley (2009).       
Quantification also requires data on affected populations and on exposures and changes to 
exposures as well as valid effect measures, models, or exposure–response functions or 
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relationships to relate policy effects with health effects Hertz-Picciotto (1995), Mindell et al. 
(2001) and O’Connell and Hurley (2009). 
Measures of association among health outcomes and risk factors for disease, illness, or injury 
have been estimated through epidemiological research, but the evidence on how structural 
conditions (for example culture and environment) and policy change influence the risk factors is 
scant Dow et al. (2010). It is critical to consider cumulative impacts from the additive effects of 
new and existing exposures. Particular population vulnerabilities (for example, sensitivity to 
exposures, higher disease prevalence, poverty, unemployment, and poor sanitation) also may 
modify the exposure–response relationships Pope and Dockery (2006) and Makria and 
Stilianakis (2008). 
 
Hübel and Hedin (2003) have mentioned that, interest in HIA is developing in the EU but Kemm 
and Parry (2004) emphasized that, experience of HIA already exists in countries, such as, New 
Zealand, Australia, Germany, Netherlands, UK and Canada.Kemm (2004) stated that, though 
HIA is already being carried out, it is done on voluntary basis. HIA is multidisciplinary and 
participatory British Medical Association (1998) and, therefore, must be integrated into social, 
environmental or strategic IA Bhatia and Wernham (2008). 
To support valid judgments, practitioners recommend a careful examination of uncertainty and 
transparency of assumptions and limitations Quigley et al. (2006). A study by Cave et al. (2005) 
revealed that, only one in every three development planners claimed to have a good 
understanding of HIA. This is of concern because, monitoring is a critical link to enable policies 
to be modified in light of new information and changing environmental conditions Steinemann 
(2000). 
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There is also evidence that those who typically commission the HIAs are health practitioners 
who lack knowledge in environmental policy Davenport et al. (2006). However, the limited 
ability to influence national policy at the local level, regardless of the findings of the assessment 
process, was considered to hinder the value of the process Greig et al. (2004). 
The strength of HIA is the use of a number of analytical approaches (including stakeholder 
interviews and expert consensus) to answer inherently complex questions O'Connell and Hurley 
(2009). Steinemann (2001) provided evidence of the applicability of interviews to offer a richer 
understanding of the circumstances that influence both how and why HIAs are conducted. 
 
2.5 Risk Assessment 
Risk is defined as judgments concerning the likelihood, severity or importance of a threatening 
event or condition (US EPA). However, risk in an environmental context is defined as “the 
probability of the dose–response effect that may cause harm” Calow (1998). 
The many definitions related to risk are interrelated and interchangeable and each of them has 
certain advantages in different applications Plate (2002) and Barredo et al. (2007).  
Gregory and Mendelsohn (1993) identified some characteristics of risk as; Unknown, Uncertain, 
Unfair, Dreaded, Dangerous to children, Catastrophic, Immoral, Uncontrollable, Involuntary and 
Unfamiliar among others. Risk, as evaluated by Covello (2003) should be of great concern as it 
is related to; Health, Safety, Environment, Family, Community, Economic, Trust, Benefits, 
Control, Fairness, Respect and Accountability. 
Controversially, risks are often interrelated. Decreasing the risk of a particular failure scenario 
may increase the risk of other failure scenarios Pickford (2001). The only protection against 
interrelated risks is an integrated risk management approach Pickford (2001). 
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“Risk assessment” is the process whereby the potential adverse consequences (hazards) 
associated with a technology or development is identified, and the probability (risk) of their 
occurrence estimated NRC (1983). Risk assessment, according to Wilson and Crouch (1987) is 
the process of estimating both the probability that an event will occur and the probable 
magnitude of its adverse effects; economic, health, safety related or ecological over a specified 
period. 
Risk assessment is the process that is used to quantitatively or qualitatively estimate and 
characterize risk from exposure of an individual or a population to a chemical, physical or 
biological agent WHO (1998). 
It includes the components of; 
 Hazard identification (Is there an adverse effect?), 
 Dose–response assessment (How severe is it?),                    
 Exposure assessment (What is the level of exposure?), and  
 Risk characterization (What is the risk?) NRC (1983) and IPCS (2000). 
The identification of health hazards and the estimation of its associated risks may be based on 
various sources of information.   
 
Epidemiological studies of people are also important. This involves comparing rates of disease in 
different groups of people according to their exposure to known or suspected hazards. 
Each source of information has advantages and disadvantages to its use. Background scientific 
knowledge can be applied relatively cheaply and quickly, however, experience indicates that it is 
not always reliable. For example, it would have been difficult to predict the hazard of cancer 
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from asbestos on the basis of scientific knowledge at the time the mineral first came into use 
Stewart report (2000).  
Again, by knowing the risks and their consequences, people are capable of creating a safe 
process. A typical characteristic of a safe process is that, risks and risk management actions are 
continuously discussed Dekker (2006). A risk analysis process gives a starting point for these 
continuous risk discussions, and the documentation of this process provides a depiction of an 
organization’s competence to meet inevitable failures in the process. 
 
Therefore Rasmussen et al. (2007) have argued strongly that, since risk assessment has a strong 
science base, the evaluation framework must assign value to state-of-the-art tools and concepts.  
Risk assessment involves a number of well-documented procedures and tools Sundararajan 
(1991). Thus, ‘hazard and operability’ (HAZOP) studies and ‘preliminary hazard analysis’ 
(PHA) are widely employed tools for identifying possible hazards and their effect Sundararajan 
(1991) and Sutton (1992). For example, a large amount of fuel stored in a fuel depot is a major 
safety hazard. Structural thinking, analysis of past failures and lessons learned, going through 
various scenarios, are other useful techniques which are frequently used. 
 
The first step as identified by Olin and Sonawane (2003) and Renwick et al. (2003) in any risk 
assessment is problem formulation. This Olin and Sonawane (2003) and Renwick et al. (2003) 
further included assessments carried out for the purpose of determining the potential risk from 
exposures. Problem formulationis expected to result in a conceptual model, based on the 
qualitative characterization of hazard and exposure Olin and Sonawane (2003), Daston et al. 
(2004) and US EPA (2005). By applying the risk analysis tradition (loss-prevention) in an 
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environmental context Pollard and Guy (2001) and Kletz (1999) stressed on linkages that address 
proactive risk analysis in identifying risks in order to avoid and manage them. Other scientists, 
for example Suter et al. (2003), on justifying the importance of problem formulation observed 
that, all frameworks developed by IPCS and other agencies now incorporate the concept of 
problem formulation. 
 
A challenge of risk assessment identified by Durgin (2009) is the use of insufficient samples for 
exposure assessment. Furthermore Suzanne (2004) highlighted a challenge of risk assessment as 
the use of only a very limited set of data for the risk analysis. Suzanne (2004) further complained 
of how insufficient or incomplete data made flood risk analysis difficult in Australia.                                                                     
On RF exposure Ahlbom et al. (2004) were critically concerned about the quality of assessments 
made in various studies and also the availability of data on the consequences of exposure. 
However, when data are not available or are incomplete, a number of assumptions are applied in 
the characterization US EPA (1991), IPCS (2005) and Kimmel et al. (2006).                                                                           
These assumptions include: 
 It is assumed that, an agent that produces an adverse effect following exposure during 
development in experimental animals will potentially pose a risk to humans following 
sufficient exposure during childhood. 
 It is assumed that, the types of effects seen in animal studies are not necessarily the same 
as those that may be produced in humans. 
 It is assumed that, for health effects other than cancer, a threshold or non-linear dose-
response relationship exists.  
 
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In order not to deal with assumptions, Kimmel et al. (2006) stressed that; human data are 
preferred for determining the potential health effects of exposure.  This confirms why the 
application of human data in risk assessment for especially children has been detailed in a 
number of publications US EPA (1991), Richter-Reichhelm et al. (2002) and IPCS (2005). 
 
Problem formulation should lead to hazard identification IPCS (2000). The IPCS defines hazard 
identification as “the identification of the inherent capability of a substance to cause adverse 
effects when a population is exposed to that substance”.The challenge in life stage risk 
assessment is not only to identify the hazard but to determine, in the later stage of risk 
characterization, whether any adverse effects place a disproportionate risk on potentially 
susceptible subpopulations IPCS (2000). In addition to the potential for harm, the long-term 
consequences of early exposure as precursors for later onset of adult disease must be considered 
IPCS (2001). Therefore it is expected that, exposure assessments identify the pathways, 
magnitude, frequency, and duration of human exposures from various sources Needham et al. 
(2005). The toxicity of a hazard, according to Pelkonen, et al. (1997) depends on numerous 
identified and highly studied factors including; 
 Form and intrinsic chemical activity 
 Dose-response relationship and exposure route 
 Metabolism, excretion and ability to be absorbed by the body 
 Distribution within the body and target organs affected 
 Presence of other chemicals, age, sex and ethnicity. 
The final phase of the risk assessment process is risk characterization. Santos (1987) explained 
that, the risk characterization process estimates the incidence and severity of harm to human 
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health and the environment that may occur as a result of exposure to pollutants. It also describes 
the uncertainties and limitations of the overall process. Risk characterization further involves the 
synthesis of critically evaluated information and data from exposure assessment, hazard 
identification, and dose-response considerations into an overall evaluation of the assessment that 
can be communicated to risk managers and public health officials NRC (1983) and IPCS (2000). 
Risk characterization should be based on the purpose for the risk assessment. Henley and 
Kumamoto (1981), have argued that, generally, the purpose of any risk assessment or analysis is 
to provide support in making correct management decisions. Three types of descriptors of human 
risk are especially useful and important in risk characterization Kimmel et al. (2006).                               
They are; 
 Inter individual variability- the range of variability in population response to an agent and 
the potential for highly susceptible subpopulations.  
 Exposed individuals- individuals who are more highly exposed because of occupation, 
residential location, behaviour, or other factors. 
 Margin of exposure (MOE) - the ratio of the NOAEL (BMDL/BMCL) from the most 
appropriate or sensitive species to the estimated human exposure level from all potential 
sources. This means that the lower the MOE, the greater the risk.  
According to Kimmel et al. (2006), the risk assessor must effectively communicate what is 
known, what is not known, and what is questionable, in order for the risk assessment to be 
appropriately factored into the overall risk management process. 
 
François et al. (2007) identified confidence building as a level in risk assessment. They explained 
that, risk assessment is not an isolated activity; it has no value without risk communication. 
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Communication therefore has an implicit role of building confidence. The importance of the 
interactions of risk assessment with risk management and risk communication has again been 
recognized NRC (1994) and Renwick et al. (2003). 
 
2.6 Risk Communication 
In 1997, the Codex Alimentarius Commission (CAC) defined risk communication as "an 
interactive exchange of information and opinions concerning risk among risk assessors, risk 
managers, consumers and other interested parties" WHO (1998). 
Baker (1990) defined risk communication as “the process of informing people about potential 
hazards to their person, property or community”. Others, for example Lundgren, et al. (1995) 
defined risk communication “as a science-based approach for communicating effectively in 
situations of high stress, high concern or controversy”.  
 
Effective risk communication is crucial to cooperative risk management and the resolution of 
controversial risk-related issues Slovic (1987). The role of emotions was largely ignored in the 
practice of risk communication until researchers started to demonstrate the underlying mental 
structures of risk in the public’s mind Slovic (1999). Risk is not only about coolheaded judgment 
on the magnitudes and probabilities of potential losses, as typically managed by experts, but also 
related to a variety of strong emotions, such as fear and anger Slovic (1999).   
Different causes of hazards can induce specific emotions Brun (1992). Specific emotions, 
according to Lazarus (1991) and BÖhm and Pfister (2000) lead to specific actions. 
As a result, the responsibilities of risk management largely depend on the type of hazard.  For 
example, technological risks usually elicit more anger than natural disasters because people or 
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agencies can be blamed. By contrast, natural catastrophes may elicit more sympathy for the 
suffered victims Brun (1992). Emotions interact with risk judgments as well as actions. Without 
understanding the role of emotions in risk communication, effective risk communication can 
hardly be achieved Loewenstein et al. (2001).  
From the risk manager’s perspective, the purpose of risk communication is to help residents of 
affected communities to understand the processes of risk assessment and management Bier 
(2001). According to Bier (2001), this explains scientifically the likely valid perceptions of the 
hazards, and again makes it obligatory for managers to participate in making decisions about 
how risk should be managed. 
Understanding and communicating risk has clearly been shown by the NRC (1989) to be 
influenced by a host of additional factors; 
 Whether the risk is voluntary or involuntary 
  Whether the distribution of risk and benefit is equitable 
  The transparency of the process 
  The extent to which risk managers are trusted 
  The degree of personal control 
  The individual dread of the adverse effect and  
 The extent to which the risk is unknown.  
Risk communication tools are; written, verbal, or visual statements containing information about 
risk. They should put a particular risk in context, possibly add comparisons with other risks, 
include advice about risk reduction, and encourage a dialogue between the sender and the 
receiver of the message DeRosa (1998). 
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In fact, dialogue is the commonly used tool in externalization. Dialogue triggers the unconscious 
elements of knowing and not knowing, as well as revealing gaps in knowledge compared with 
what the community knows Ayas (1996). Social constructionists for example Burr (1995) regard 
language as a co-ordination of action and therefore a fundamental tool in knowledge creation.  
Risk theorists have proposed four theories of risk communication Covello (2003); 
 Mental Noise Theory  
When people are upset, angry, fearful, outraged, under high stress, involved in conflict, or feel 
high concern, they often have difficulty processing information.  
 Trust Determination Theory  
When people are upset, angry, fearful, outraged, under high stress, involved in conflict, or feel 
high concern, they often become distrustful.  
 Negative Dominance Theory  
When people are upset, angry, fearful, outraged, under high stress, involved in conflict, or feel 
high concern, they often give greater weight to negative information than to positive information.  
 Risk Perception Theory  
Perception equals reality. There is virtually no correlation between public perceptions of risk and 
scientific or technical experts. What matters most in determining risk perceptions and public 
outrage are factors such as trust, benefits, familiarity, voluntariness, control, dread, uncertainty, 
memorability, fairness and accountability. 
The solution then is providing risk information to the public in such a compelling way as to 
result in reductions in the exposures to agents of morbidity, mortality or injury Covello et al. 
(1986).  
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Covello et al. (1991), further outlined Seven Cardinal Rules of Risk 
Communication;  
 Accept and involve the public as a legitimate partner in the decision-making process.  
 Listen to your audience and meet the needs of the media. 
 Be truthful, honest, frank, and open.  
 Coordinate, collaborate and partner with other credible sources. 
 Speak clearly and with compassion.  
 Prepare, plan carefully and evaluate your communication performance. 
 
2.7 Risk Perception 
Risk perception is the influence of human values on risk Sjoberg (1997) and Slovic (1986).                                                                                 
There are various qualitative factors which affect risk perception. 
The basic factors identified by Slovic et al. (1990) and WHO (2002) include; 
 Voluntary or involuntary (imposed).  
 Within one’s control or not within one’s control. 
 Familiar or unfamiliar. 
 Risk well distributed or unevenly distributed. 
 Risk periodic or catastrophic. 
 Natural or man-made. 
 Risks perceived to be generated by a trusted source or by a non-trusted source. 
The diversity of opinions surrounding the concept “risk perception” is also reflected in the range 
of interpretations available. There is considerable debate surrounding the concept of perception 
as objective or subjective, real or constructed, determined by individual psycho cognitive 
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processes or socio-cultural factors Bickerstaff (2004) and Slovic (2000). However, as the concept 
of risk is often linked to decision making, researchers now argue that, the value each perspective 
holds is now converging with increased recognition Taylor-Gooby and Zinn (2005). 
 
Within health risk research, theoretical models of health and risk behaviour Becker (1974), 
Bandura (1994) and Triandis (1995) have been criticized for concentrating too heavily on the 
individual and neglecting the social context Lupton (1999) and Paton and Johnston (2001).  In 
fact, models of individual risk perception and behaviour have been developed in industrialized 
countries where levels of autonomy and access to health information allow greater scope in the 
assessment of health risk and responses WHO (2002). Another perspective is of the role of 
society in shaping risk perceptions including the individualization view of Beck’s (1992) “Risk 
Society”. However, this model has been criticized for only being applicable to societies in which 
individuals have high levels of autonomy Rose (1999). This critique is applicable to the context 
of many developing countries in which levels of agency among the poor can be extremely 
restricted Farmer (2001).  
While debate may continue over the factors influencing risk perceptions, it is generally agreed 
that understanding context-specific factors affecting risk perception is important for public 
health-related behavior change interventions Nettleton (2006). The individual, although 
pessimistic about societal risk factors, is optimistic about their own risk vulnerability Tyler 
(1984). Tyler (1984) therefore suggested that, if people feel being part of a community, they 
apply the same risk assessment to themselves as they do to the wider community. Public 
perceptions of risk can therefore be upheld by the social environment Bishop et al. (2000).   
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For example, Paton and Johnston (2001) explained that, people’s sense of place identity can 
encourage community response, increasing participation and capacity, which in turn reduces 
feelings of hopelessness and improves levels of resilience and coping.  
 
People usually perceive risks as negligible, acceptable, tolerable, or unacceptable Slovic (1987). 
The nature of the risk is then compared to the benefits. Where the benefits greatly exceed the 
risk, then the risk may be considered worth taking. Opinions and decisions will depend on a 
person’s age, sex, education and cultural background WHO (2002).  
The nature of the risk can lead to different perceptions. Surveys have revealed that, the particular 
characteristics of a situation affect a person’s view of the risk of EMF WHO (1998): 
 Voluntary or involuntary exposure. 
People who do not use mobile phones perceive the risk from BSs as high. In contrast, most 
mobile phone users perceive the fields from their phones as low even though they are in fact 
much more intense. 
 Lack of personal control over a situation. 
If people have no input over the installation of BSs, especially near their homes, schools or play 
areas, they will perceive the risk from such installations as being high. 
 Familiar or unfamiliar situation. 
Where people are familiar with a situation or feel they understand the technology, the level of 
perceived risk is lower. The perceived risk increases when the situation or the technology, is new 
or unfamiliar or difficult to understand. However, the perception about the level of risk can be 
significantly increased where there is an incomplete scientific understanding of the potential 
health effects from a particular situation or technology. 
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 Degree of dread. 
Some diseases and health conditions, such as cancer, severe or lingering pain and disability, are 
more feared than others. Thus, even the smallest possibility of cancer, especially in children, 
from EMF exposure receives significant public and media attention. 
 Fairness or unfairness of situation. 
If people are exposed to RF fields from BSs, but do not have a mobile phone, they consider it 
unfair and are less likely to accept any associated risk. 
 
On a broader point of view, it is important, to examine the factors that affect laypeople’s 
perception of risk. This is because; public risk perception influences the acceptance of new 
technologies Siegrist (2000). For laypeople, the most important source of information about 
health issues and risks seems to be the news media Krewski et al. (2006).  Research over a period 
suggests that, the media are more likely to report about studies suggesting that a technology is 
risky than about studies suggesting that a technology is safe Koren and Klein (1991). This 
according to Siegrist and Cvetkovich (2001) is because; people seem to have more confidence in 
studies indicating that a technology is risky compared to studies indicating that a technology is 
not risky. Siegrist and Cvetkovich (2001) further explained that, this negativity bias exhibited by 
laypeoplemay cause unnecessarily or excessive concern about a technology. 
Additionally, Slovic (1993) pointed out that, there is an asymmetry in trust judgments, with trust 
being difficult to establish but easy to destroy. Several possible explanations for this “trust 
asymmetry” have been proposed. For example Taylor (1991) suggested that, negative 
information triggers a stronger reaction than positive information. Slovic (1993) further assigned 
a negativity bias as another explanation for the “trust asymmetry”principle. A negativity bias has 
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been demonstrated in a number of studies Rozinand Royzman (2001). Rozin and Royzman 
(2001) further hypothesized that; there may be innate predispositions to give greater weight to 
negative entities. It has been argued that negative information often has a greater diagnostic 
value than positive information Skowronski and Carlston (1989). 
Other reasons for a negativity bias are associated with the fact that, for most people it is more 
important to avoid losses than to realize gains Kahneman and Tversky (1979).  Experimental 
studies have shown that, people have more confidence in hypothetical scientific results 
suggesting a danger than in results indicating a low level of risk Siegrist and Cvetkovich (2001). 
Similar findings reported by Poortinga and Pidgeon (2004), showed that, negative events had a 
greater impact on trust than positive events. This asymmetry between positive and negative 
information may be one of the reasons why laypeople are concerned about technological risks, 
even when risk assessment studies indicate that there is a low probability of a risk Macri and 
Mullet (2007) and Siegrist et al. (2005). 
Koehler (1993) mentioned confirmatory bias, as another explanation for this “trust asymmetry”. 
According to this view, new information is mostly interpreted in such a way that it 
accommodates already-held convictions. This bias, according to Koehler (1993) has been 
demonstrated in studies suggesting that, research reports that confirmed prior beliefs were judged 
to be of greater quality than reports that did not confirm prior beliefs. Consequently, new 
information is often interpreted so that it meshes with already-held beliefs Plous (1991). 
Therefore, the interpretation of positive or negative information about a hazard may be shaped 
by prior attitudes. For example, prior attitudes were shown to moderate the effect of message 
valence on trust White et al. (2003). People have more confidence in messages that are in line 
with their prior attitudes. Consequently, White et al. (2003) on the contrary concluded that, prior 
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attitudes and not a negativity bias, is the reason why people have greater trust in negative 
messages than in positive messages about hazards.  
Indeed, it has been shown recently that, under some circumstances, positive information and not 
negativity bias can have a stronger impact on trust White and Eiser (2005).  
 
2.8 Management of mobile telecommunication waste 
Berkhout and Hertin (2004) studied the direct, indirect and structural impacts of the ICT sector 
and concluded that, the sector and its impacts are “complex, interdependent, deeply uncertain 
and scale dependent”. Agamuthu (2001) has confirmed that, the proper management of 
municipal solid waste (MSW) is one of the emerging issues in developing countries.  
In reality, rapid urbanization has further exerted heavy pressure on land and water resources in 
especially cities resulting in serious environmental and social problems Simone et al. (2001). 
Most countries, especially in the developing world are struggling to deal with the growing waste 
problems Agamuthu et al. (2009) and Hiramatsu et al. (2009).  
Additionally, the increasing production and consumption of manufactured and packaged goods 
has led to the massive production of solid waste. For example, according to Sutanto (2007) in 
Indonesia, per capita production of solid waste increased ten folds between 1971 and 2000 
though, Indonesia has a poorly-developed infrastructure for waste management Supriadi et al. 
(2000). 
Others for example Huisman et al. (2007) maintained that, in the year 2005, the 27 EU member 
states generated an amount of 8.3-9.1 million tons of WEEE. China and the USA reportedly 
generated 11.1 million tons and 10 million tons of WEEE respectively in only the year 2012 
StEP (2013). Though the developing world does not generate much WEEE, unfortunately Wang 
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et al. (2012) have estimated that 50% to 80% of WEEE from developed countries are exported to 
the developing countries. 
The environmental and human health consequences of severe pollution by WEEE may affect 
surrounding lands and rivers Nnorom and Osibanjo (2008). These environmental concerns have 
prompted the EU to publish Directive 2002/96/EC on WEEE European Parliament and Council 
(2003). The Directive imposes recovery, reuse and recycling targets for 10 WEEE categories. 
However, these targets cannot be reached by metal and glass recovery alone, because WEEE 
contains from 10% to 30% plastics Taurino et al. (2010). Other researchers have estimated the 
amount of plastic found in WEEE to be nearly 34.6% Dimitrakakis et al. (2009) whilst others 
have also estimated values more than 50% Chancerel and Rotter (2009). 
Widmer et al. (2005) also estimated the fraction of Fe, Cu, Al, Au and other metals in WEEE to 
be over 60% whilst plastics account for about 30%, with hazardous pollutants comprising about 
2.7%. For this reason Chancerel and Rotter (2009) further argued that, plastics must 
subsequently be included in the recovery or material recycling streams.  
 
WEEE recovery is challenging because of the presence of a diverse number of materials they 
contain ITU (2003). Electronic devices contain up to 60 different elements, many of which are 
valuable whilst some are hazardous Wang et al. (2012). For example, according to the ITU 
(2003) a mobile phone contains not less than 20 rare metals, and the need to recycle these metals 
is necessary – a ton of Au ore yields just 5g of Au, whereas a ton of used mobile phones yields 
400g of Au. Considering only the plastic content of WEEE, EEE (and the WEEE of the near 
future) can be based on more than 15 different types of engineering plastics, including; 
acrylonitrile–butadiene–styrene (ABS), high-impact polystyrene (HIPS), polypropylene (PP), 
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polystyrene (PS), styrene-acrylonitrile (SAN), polyesters, polyurethane (PU), polyamide (PA), 
blends of polycarbonate (PC)/ABS and blends of HIPS/poly (p-phenylene oxide) (PPO) 
Vilaplana and Karlsson (2008).  
Along with this significant variety of materials, the numerous additives (both organic and 
inorganic) that are also added to plastics, which are often hazardous substances, are capable of 
changing the material properties such as colour, melting point, flammability and density, for 
legal, design and/or cost purposes. These additives may be pigments (TiO2, ZnO, Cr2O3, Fe2O3, 
Cd), flame retardants (often brominated organics combined with Sb2O3 or polychlorinated 
biphenyls (PCBs) and various stabilizers or plasticizers (compounds of Ba, Cd, Pb, Sn and Zn, or 
PCBs) Dimitrakakis et al. (2009), Erickson and Kaley (2011) and Schlummer et al. (2007). 
 
However, a possible solution to promote better WEEE management is the eco-design approach. 
The eco-design approach should be promoted, to make the dismantling step easier. Directive 
2009/125/EC European Parliament and Council (2009), which set the eco-design requirements, 
could promote normalization in EEE. This is intended to make more efficient the dismantling 
and recycling processes when the products reach the end of their life cycle. 
 
Heavy metals are considered as the major indicators of anthropogenic impact from various 
sources such as dry ash deposition, industrial effluents, road runoff and leachate Singh et al. 
(2008). Once dispersed in the environment, heavy metals are slowly removed by geological and 
biological processes Jarup (2003). Therefore, any quantity that can be mobilized and absorbed by 
plants insidiously concentrate along food chains and ultimately in consumers of contaminated 
foods Jarup (2003). Moreover, the metal ions released in water become distributed into the 
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surrounding areas by lateral and vertical movements in the ground Regli et al. (1991). The 
adverse effects according to Regli et al. (1991) are that, metal-contaminated soils often have 
restrictive physical, chemical and biological characteristics that hinder self-regenerating 
mechanisms Regli et al. (1991). 
 
The routes by which humans are affected by heavy metal exposure include breathing in airborne 
dust and dirt, eating contaminated food or nonfood items, and the skin coming into direct contact 
with contaminated dust, soil, and cosmetics Wu et al. (1996). Consequently, monitoring of heavy 
metal concentration is very important in ascertaining the level of impact of dumpsites on 
groundwater and soil quality. Studies indicate that, heavy metals like Cd, Cr, Pb, Zn, Cu, Ni and 
Fe are found in groundwater surrounding dumpsites Barry et al. (1995), Martinez and Motto 
(2000) and Mor et al. (2006). Because of the perceived effects on food quality and safety, which 
are now seen as essential components of public health, the control of potentially hazardous 
elements in soils has become important for sustainable agriculture Howe et al. (2005). 
 
Arsenic pollution, according to Mukherjee and Bhattacharya (2001) has been the most widely 
discussed and researched issue and is often regarded as the biggest mass poisoning in human 
history. However, the presence of natural arsenic, which is carcinogen, is at elevated 
concentrations in groundwater and sedimentary environments Madhavan and Subramanian 
(2000). 
Though heavy metals in the environment have the potential to damage virtually any organ, lead 
is probably the environmental pollutant that has most affected human health across the ages 
Bernard (2004). Lead can cause several unwanted effects, such as disruption of the biosynthesis 
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of haemoglobin and anaemia, brain damage, disruption of the nervous, reproductive and 
circulatory systems in humans Bulut and Bayasal (2006) and Low et al. (2000).  A large amount 
of Pb absorbed in a child may induce anaemia, kidney damage, colic, muscle weakness, and 
brain damage, which ultimately can kill the child Borja-Aburto et al. (1999). A lower-level lead 
absorption may lead to premature newborns, lower birth weight, problems of physical growth 
and mental development, and cause lower intelligence later in childhood Borja-Aburto et al. 
(1999). However, Borja-Aburto et al. (1999) further cautioned that, the risks may increase when 
contaminated cereals are used for the production of baby foods.  
The presence of Fe in water changes the colour of groundwater. Also ingestion of large 
quantities of Fe results in haemochromatosis, a condition in which normal regulatory 
mechanisms do not operate effectively.  
Although Zn has traditionally been regarded as relatively nontoxic, recent studies according to 
Elliott (2001) have increasingly showed that, free Zn2+ is a potent killer of neurons, glia, and 
other cell types. Accidental and intentional ingestion of Zn salts have resulted in deaths but 
debilitating sequelae, such as vomiting, diarrhea, red urine, icterus (yellow mucous membrane), 
liver and kidney derangement and anaemia is often the recorded consequence Fosmire (1990). 
Berry and Ralston (2009) have emphasized that Hg disrupts the function of the pituitary, thyroid, 
and adrenal glands even at low-level exposure. A variety of disturbances primarily in the sensory 
and motor nerves have also been confirmed Ballatori (2002). Hg in both organic and ionic forms 
according to Clarkson (1995) accumulates in the heart and causes hypertension, tachycardia, and 
ventricular arrhythmias. Ballatori (2002) mentioned that, the main effect of chronic absorption is 
irreversible damage to the central nervous system and further described Hg as mutagenic, 
teratogenic and carcinogenic. 
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Cd is also associated with renal and arterial hypertension Lewis (1991). Cd salts cause cramps, 
nausea, vomiting and diarrhoea.  
Ni in groundwater has a potential to cause allergic reactions and also impairs the functions of the 
liver Das et al. (2008). 
 
However, the traditional treatments for metal contamination in soils are expensive and cost 
prohibitive when large areas of soil are contaminated US EPA (1993). Treatments are extremely 
expensive and can be done insitu (on-site), or ex situ (removed and treated off-site).  For example 
Watanabe (1997) estimated that, traditional cleanup in situ may cost between $10.00 and 
$100.00 per m3, whereas removal of contaminated material (ex situ) may cost as high as $30.00 
to $300 per m3. Therefore, Watanabe (1997) in comparison suggested phytoremediation which 
may only cost $0.05 per m3 as a relevant alternative. 
Research performed by Wenzel et al. (1999) and Brady and Weil (1999) has also demonstrated 
that plants are effective in cleaning up contaminated soils. 
Some treatments that are available include US EPA (1993); 
 High temperature treatments (produce a vitrified, granular, non-leachable material). 
 Solidifying agents (produce cement-like material). 
 Washing process (leaches out contaminants). 
 Liming to a neutral pH and dilution by the addition of uncontaminated soil.  
The actual toxicity of a heavy metal will be affected by soil texture, organic matter content and 
soil pH. The health effects of exposure to heavy metals depend on the amount and duration of 
exposure (the volume of contaminated soil or food consumed over time).  
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With the advent of data and intensive cellular standards, power-consumption for each BS can 
increase up to 1,400W and energy cost per BS can reach $3,200 per annum with a carbon 
footprint of 11 tons of CO2 TMT (2010). CO2 gas from automobiles and power plants and Freon 
(refrigerant gas) released into the atmosphere may be involved in deleterious climatic changes 
Schroeder et al. (1996).  
 
2.9 Noise Pollution 
Berkau et al. (1975) defines noise as discordant sound resulting from non- periodic vibrations in 
air or, unwanted sound. Noise is generally defined as unwanted sound and is perceived as a 
pollutant and a type of environmental stressor Smith (2003). 
Moszynski (2011) explained “noise pollution” as sound that is incoherent and irregular, and 
produces an unpleasant sensation that is unwanted or that interferes with the ability to hear. 
Noise is measured in decibels (dB) using a sound level meter. Normal conversation is within the 
range of 50-60dB. However, permissible levels of noise vary from place to place Smith (2003). 
 
The potential risk of adverse health effects associated with exposure to noise is dependent on the 
duration of exposure (acute or chronic), intensity (decibel level) and sound frequency Passchier-
Vermeer and Passchier (2000). For example, noise-induced hearing loss can result from a one-
time exposure to 120dB level of sound or exposure to 85dB level of sound over an extended 
period of time Passchier-Vermeer and Passchier (2000). Chronic exposure to noise is associated 
with increased risk of hearing impairment, hypertension and ischemic heart disease Moszynski 
(2011). For example, in the United States, excessive noise is one of the most pervasive problems, 
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causing some degree of hearing loss directly among an estimated 16 million people US EPA 
(1974). 
Noise, according to Cohen and Weinstein (1981) acts as a general stressor, and environmental 
noise exposure can lead to poor psychological health in several ways. Firstly, acute noise 
exposure directly causes a number of short-term physiological responses such as increased blood 
pressure, enhanced endocrine secretion, and raised heart beat rate. Secondly, these physiological 
outputs may be activated by annoyance. Annoyance may lead to stress responses for some 
individuals that potentially could lead to symptoms and illness. Kempen et al. (2006) identified 
ailments like; indigestion, ulcers, gastrointestinal malfunctions, heart abnormalities, circulatory, 
digestive and nervous disorders as well as vision, emotional upset and irritability as impacts 
associated with noise exposure. 
Terry (1979) further explained that, noise can alter the normal functions of the endocrine, 
cardiovascular and neurological systems. It may affect equilibrium and cause a rise in blood 
pressure, a change in heart rhythm and constriction of blood vessels. Passchier-Vermeer and 
Passchier (2000) again identified; annoyance, sleep disturbance, interference with 
communication, decreased school performance, increased levels of stress, and modification of 
social behaviour as potential impacts associated with noise exposure.  
Since sleep is the body's regenerative process, then, any interference with sleep will affect the 
emotional, psychological and physical health directly Berkau et al. (1975). Uninterrupted sleep is 
a prerequisite for good physiological and mental functioning especially in children Bistrup 
(2003). The primary effects of interrupted sleep identified by Muzet (2007) and Haines et al. 
(2003) are; difficulty in falling asleep, awakenings and alterations of sleep stages, increased 
blood pressure and heart beat rate, vasoconstriction, and changes in respiration and cardiac 
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arrhythmia. Another long-term exposure to noise in children mentioned by Stansfeld et al. 
(2005), WHO (1999) and Moszynski (2011) is learning disability. 
 
2.10 Environmental Model 
2.10.1 Purposes of models 
Without defining the model’s purpose its degree of success cannot be judged and its structural 
complexity cannot be advantageously tuned Beven (2002) and Jakeman et al. (2006). 
The purpose of the model should determine its structure; scope, resolution, and complexity; its 
user interface and output; and how it is evaluated Starfield (1997), Nichols (2001) and 
Kettenring et al. (2006). 
In defining the purpose for a model, certain multiple issues ought to be addressed; 
 Who are the intended end users of the model? What are the technical skill levels of the 
end users? 
 How will the model be used; for evaluating management alternatives, determining high 
priorities for future research or communicating what is known to other stakeholders 
among others? 
 What spatial and temporal context is being explored? For example, is it about breeding 
season patterns only, modeling short-term forecasts or long-term dynamics, a specific 
management area or an ecological province? 
 How will the model be evaluated? 
 Is the model built for long-term use? How will it be updated if the understanding of the 
system improves? 
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Researchers, for example Pielke (2003) have argued that, the prediction for science versus the 
prediction for making policy is an instance where different purposes are frequently confused.  
 
2.10.2 Uses of models  
Any model development process requires the modeler to make a series of simplifying 
assumptions or hypotheses Gupta et al. (2005). In addition to this Heeks and Molla (2009) cited 
the use of scenarios and forecasting to establish impacts in different situations.                                                                                                                       
This is necessary to enable complex natural systems to be described using much simpler 
mathematical models. 
To a large extent all models are aimed at explanation, but models which are good at explaining a 
system’s causal mechanisms, behaviour or patterns are not always designed to predict Jakeman 
et al. (2006). In most disciplines, a “good” model is one that promotes a better decision than 
could be made without it Starfield (1997) and Johnson (2001).  
In general Beven (2002) and Jakeman et al. (2006) argued that, models can be used to; 
 Measure and represent. 
 Describe structure, behaviour and pattern. 
 Reconstruct past or predict future behaviour. 
 Generate and test theories and hypotheses. 
 Display, encode, transfer, evaluate and interpret knowledge. 
 Guide development and assessment of policies. 
 Facilitate collective learning and settlement of disputes Beven (2002) and Jakeman et al. 
(2006).  
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Johnson (2001) has also made known three categories of uses of models; explanatory, predictive 
and decision making. 
 Explanatory models are used to describe or decipher the workings of systems. Such 
models attempt to identify the mechanisms involved in the system. 
 Predictive models are used to forecast future states of systems or results of management 
actions. Prediction is a common use of landscape models and allows the user to 
determine the potential impacts of various proposed management actions Shifley et al. 
(2006). 
 Decision-support models are used to identify management strategies that will produce 
desired results. Optimization technique is a useful example of decision-support model 
used in planning resource management Moore et al. (2000). 
Practical uses of models may be blurred or overlapping, but this does not change the implications 
of the intended purpose for model development Bankes (1993). Bankes (1993) cautioned against 
confusion between the purposes of consolidative and exploratory models. A consolidative model 
according to Bankes (1993) sums up facts known to be correct in a single package, used as a 
surrogate for management interventions.  
Exploratory models, according to Pielke (2003) are models in which not all components of the 
system can be established independently or are known to be “correct”. 
 
2.10.3 Modeling as a concept 
Models are abstract descriptions of systems or processes Starfield and Bleloch (1991) and 
Haefner (1996) and therefore have become pervasive tools in natural resource management, 
large-scale planning and landscape ecology Shenk and Franklin (2001) and Scott et al. (2002). 
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Consequently models help address fundamental questions; for example, models are useful for 
evaluating the potential impacts of management alternatives and assessing economic 
implications of management decisions Morrison et al. (1998), Larson et al. (2004) and Shifley et 
al. (2006). Although many general models are structurally similar (matrix models for 
demographic analyses), specific models are uniquely suited for specific regions and applications 
Caswell (2001). 
Numerous models that have emerged over the last 20 years have used individuals as their basic 
unit Grimm (1999), Judson (1994) and Uchman’ski and Grimm (1996). In contrast with the 
classical analytical models, the so-called Individual Based Models (IBMs), actually made an 
improvement by being developed “bottom-up” rather than “top-down” Grimm (1999). IBMs 
describe systems from the individual’s point of view; hence, the individual’s behaviour and 
characteristics (size, age, sex, physiology, genotype among others) determine the emergent 
properties of the system within which the individual operates Lomnicki (1988) and DeAngelis et 
al. (1994). Competitive interactions are local and usuallystrongly influenced by certain 
characteristics (size,age, physiology, genotype among others) Berger et al. (2008), Herben et al. 
(2000), Murrell et al. (2001) and Purves and Law (2002). 
Landscape models take many forms, including statistical models that quantify relationships and 
patterns among variables Hepinstall et al. (2002) conceptual models that offer a qualitative 
construct of a system and simulation models that project landscape features into the future He et 
al. (1996) and Oliver et al. (1999). 
 
 
 
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2.10.3.1 The parametric modeling approach: 
The parametric approach, introduced by Little and Robin (1983), starts from the perspective of 
limited data and also developed using the “bottom-up” concept Grimm (1999) and consequently 
considered as relevant for this research. The parametric approach aims to estimate the complete 
vulnerability value of a system by using only a few readily available parameters relating to that 
system, though the implementation of the approach is not simple. To reinforce this Ginzburg and 
Jensen (2004) further pointed out that; attempts to include more details than can be justified by 
the quality of the available data may result in decreased predictive power.  
Four types of parametric approaches have been developed by the scientific community;   
 Estimating the complete vulnerability value of a system by using only few parameters 
relating to that system, Little and Robin (1983).                                          
 Estimation of “the imputation of non-observable values”, where the observed parameters 
are used to model the non-observed values, Glynn et al. (1993). 
 The “parametric modelisation via maximum likelihood”, which is not a direct approach 
but is based on a large number of assumptions, Little and Robin (1987). 
 The “semi-parametric approach”, which allows the modeling of only what, is strictly 
necessary, Newey (1990). 
Indeed researchers have identified three significant factors of vulnerability; exposure, 
susceptibility and resilience Bosher et al. (2007). 
Exposure can be understood as the humans who are present at the location where a hazard exists 
Penning-Rowsell et al. (2005).Susceptibility is defined as the extent to which elements at risk 
within the system are exposed, which influences the chance of being harmed Messner and Meyer 
(2006). Resilience describes the ability of a system to preserve its basic roles and structures in a 
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time of distress and disturbance. Therefore, Pelling (2003) and Walker et al. (2004) have argued 
that, resilience can be considered only in places with past events, since the main focus is on the 
experiences encountered.  
For example Balica et al. (2009), used the parametric method to develop a Flood Vulnerability 
Index (FVI). This was based on four components; social, economic, environmental and physical 
dimensions and their interactions. 
The conceptual FVI equation is given by;  
𝐸∗𝑆
FVI =                                                                                                                           (2.1) 
𝑅
where; E = exposure, S = susceptibility and R = resilience. 
The indicators belonging to exposure and susceptibility are directly related to FVI therefore they 
increase with FVI; however the indicators belonging to resilience decrease with FVI as it is 
inversely related to FVI Dinh et al. (2012). 
The vulnerability notion has come from different disciplines; from economics and anthropology 
to psychology and engineering; the notion has been evolving giving brilliant justifications for 
differences in the extent of damage occurred from hazards Adger (2006). Facility location 
models are used in a wide variety of applications, for example, locating hazardous material sites 
to minimize exposure to the public Hale and Moderg (2003). However, studies on environmental 
inequality by researchers, for example Hamilton (1995) have convincingly argued that political 
empowerment is the most important factor in predicting the areas targeted for facility location 
and not facility location models. 
 
 
 
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2.10.4 Validation of models  
The term model uncertainty which is linked to model validation is used to represent lack of 
confidence that the mathematical model is a “correct” formulation of the problem to be solved 
Mayer and Butler (1993). Tsang (1991) explained that, model uncertainty exists if the model 
produces an incorrect result even if the inputs were the exact values for all of the model 
parameters.                                                                                                                                     
The best method for assessing model uncertainties, according to Hoffman and Hammonds (1994) 
is through model validation; a process in which the model predictions are compared to numerous 
independent data sets obtained. Therefore a common definition of validation can be the 
demonstration that a model, within its domain of applicability, possesses satisfactory accuracy 
consistent with the intended application of the model Curry and Deuermeyer (1989). This 
demonstration indicates that the model is acceptable, however, that does not imply that it is the 
best model as Box (1979) firmly concluded that, “all models are wrong, but some are useful”. 
The literature on the definition and the concept of validation of models are abundant and 
sometimes confusing Power (1993) and Rykiel (1996). Power (1993) considered validation as 
sometimes essential whilst Oreskes and Belitz (1994) considered validation as sometimes 
impossible. However, some scientists have strongly indicated that, models can only be 
invalidated McCarl (1984).  In the case of large (complex) models, it is extremely difficult to 
verify that the model is entirely accurate and error free under all circumstances. Therefore, the 
absolute validity of a model can never be determined NRC (1990). If the context changes, the 
model must be re-validated; however, that does not invalidate the model for the context in which 
it was originally validated Rykiel (1996).  
 
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2.10.5 Avoiding model uncertainty 
A key to successful modeling is the avoidance of common missteps that make models unreliable. 
Ineffective or unreliable models maintain the following characteristics and therefore should be 
avoided.  
 Explicit accounting for processes that are not relevant or well understood, Starfield 
(1997). It is possible to consider ways to structure models that rely on well known 
parameters, Nichols (2001).  
 Dependence on parameters that cannot be estimated precisely. With greater uncertainty, it 
becomes even more important to limit the number of input parameters, Mangel et al. 
(2001). 
 Dependence on too many parameters, Burnham and Anderson (2002). 
 Uncritical application of pre-existing models, Mangel et al. (2001). 
Because of uncertainty surrounding our knowledge of a system and limited data, the use of 
complex models may not improve one’s understanding of a system; therefore one should 
construct the simplest model that fulfills one’s purpose adequately Starfield (1997) and Nichols 
(2001). 
 
 
 
 
 
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CHAPTER THREE 
3.0 MATERIALS AND METHODS 
3.1 Description of study area 
3.1.1 Accra metropolis 
Accra is located approximately between latitudes 05°47'30’N and 05°31'0’N, and also between 
longitudes 05°25'30’W and 0°3'30’W as shown (Fig.3.1). Although Accra has an enormous 
estimated population of over 3 million, it has a modest surface area of 200km2 and consequently 
a population density of 15,000 persons per km2 with an annual growth rate of 4.4% GSS PHS 
(2010). Therefore, the number of people estimated to live closer to BSs is higher in Accra than in 
any other city in Ghana.  
Accra, has contributed considerably to the economic development of Ghana by hosting a number 
of industries, oil companies, financial, telecommunication, tourism, education and health 
institutions among others, however, fishing still contributes to household income GSS (2008). 
Economic liberalization and increase in the intensity of engagement with global capital have 
enhanced the growth of the city in the last three decades Grant and Nijman (2002), Grant and 
Yankson (2003) and Owusu (2008). Konadu-Agyemang (1998) has argued that, the rapid growth 
of Accra's population has created the situation in which a wide gap now exists between the needs 
for, and the provision of, housing and related infrastructure. This situation according to Songsore 
(2003) has led to the first stage of urban environmental transition wheremost of the 
environmental problems tend to occur close to homes. 
Practically, considering the size of the study area, time, financial limitations and zoning issues, 
there was the need to narrow down the focus of the research to specific study sites for an in depth 
analysis. 
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Figure 3.1: Map showing the study area. 
 
3.1.2 Drainage 
Several rivers (Volta, Pra, Birim, Ankobra and Tano among others) flow across Ghana into the 
Gulf of Guinea with the Volta River basin dominating the country’s river system. Ghana’s 
coastal area consists of plains and numerous lagoons near the estuaries of rivers CEPA (2000). 
The Densu River Catchment area and the Sakumo Lagoon are the largest of all the coastal basins 
within Accra covering a total drainage area of about 2500km2. The Korle-Chemu catchment area 
is the next coastal basin covering an area of 250 km2 whilst the Kpeshie catchment area covers a 
relatively small catchment area of 110 km2 (Fig 3. 2).  
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Poor drainage is the major problem which affects many parts of Accra. Natural features such as 
the underlying geology, soil conditions and localized topographic features contribute to the 
drainage challenges. Infrastructural development should not have been permitted in some areas; 
however, poorphysical development control has led to urban landencroachment. 
 
 
Figure 3.2: Map showing basins in Accra 
 
3.1.3 Climate 
The climate of Ghana is characterized by dry and wet seasons, a typical tropical monsoonal 
climate. Rainfall in this region is mainly associated with mesoscale convective systems and 
controlled by the advection of moisture from the Gulf of Guinea in the low level atmosphere 
Sultan and Janicot (2003).This system is usually referred to as the West African Monsoon 
(WAM), and its driven by the energy and temperature gradient between the Gulf of Guinea and 
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the Sahara. The maritime tropical air mass, which originates from the Atlantic Ocean, is moisture 
laden and converges with the dry northeast continental tropical air mass, usually along the Inter-
Tropical Discontinuity (ITD) Mounier (2008). Therefore, the spatial pattern of annual rainfall is 
closely related to the Northward and Southward migration of the ITD, resulting in changes in 
therainfall regime from the South to North of the country Manzanas (2014). This gives rise to 
two rainfall regimes: bi-modal in the South, consisting mainly of coastal and the forest zones, 
and uni-modal in the Northern part of the country, consisting of part of the transition and 
savanna zones Manzanas (2014). Annual rainfall ranges from about 1,100mm in the north to 
about 2,100mm in the south-western part of the country Dickson and Benneh (1988). 
 
The study area experiences two rainy seasons with a mean annual total rainfall of below 
1000mm. The first rainy season begins in May and ends in mid-July whilst the second season 
begins in mid-August and endsin October. Rains usually fall in intensive short storms leading to 
flooding which is currently a major disaster risk in the study area. Flooding in Accra is always 
predictable, because of the flat and low-lying terrain, inadequate drainage facilities, haphazard 
location of buildings along water courses and the built up environment which encourages rapid 
run-off Songsore et al. (2005). The flat terrain is estimated to consist of slopes less than 5% and 
many not exceeding 1% Boateng (1998).The mean monthlytemperature ranges from 24.7°C in 
August to 28°C in March withan annual average of 26.8°C. Relative humidity is high varying 
from 65% in the day to 95% at night Muff and Efa (2006). 
The predominant wind direction in Accra is from the WSW whilst wind speeds normallyrange 
between 8-16km/hr with 107.4km/hr as the maximum wind speed ever recorded AMA (2010-
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2013). Indeed, high wind gusts occur with thunderstorm activity which passes insquall line along 
the coast causing damage to property. 
 
3.1.4 Vegetation 
Accra lies in the savannah zone with three broad vegetation zoneswhich comprise; shrub land, 
grassland and coastal lands. The shrub land consists of dense clusters of small trees and shrubs, 
which grow to an average height of 5m with ground herbs at the edge of the shrubs. The 
grassland consists of a mixture of grass speciesfound in the undergrowth of forests. The grasses 
are short, and rarely grow beyond 1m. The coastal zone comprises of two vegetation types 
(wetland and dunes). The coastal wetland zone consists of mangroves and salt tolerant grasses 
whilst the dune lands also consist of several shrubs, grasses, coconuts and palms.  
In addition to the natural vegetation zones, a number of introduced trees and shrubs also thrive in 
Accra.  
 
3.1.5 Geology and Soil 
The geology of southern Ghana is dominated by Middle Precambrian Rock and Birimian 
formation Bekoe et al. (2009) and CEPA (2000). 
The geology of Accra consists of Precambrian Dahomeyan Schists, Granodiorites, Granites, 
Gneiss and Amphibolites to late Precambrian Togo Series comprising mainly Quartzite, Phillites, 
Phylitones and Quartz Breccias. Other formations found are the Palaeozoic Accraian Sediments 
(Sandstone, Shales and Interbedded Sandstone-Shale with Gypsum Lenses). The coastline has a 
series of resistant rocks and sandy beaches; however, it is subjected to severe erosion because of 
the close proximity of the continental shelf and a strong coastal wind action. 
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The soils along the coast of Ghana include the savannah ochrosols and savannah lithosols Bekoe 
et al. (2009) and CEPA (2000).  
The soils in Accra consist of four main groups; 
 Drift materials resulting from deposits by windblown erosion. 
 Alluvial and marine motted clays of recent origin derived from underlying shales. 
 Residual clays and gravels derived from weatheredquartzites, gneiss and schist rocks. 
 Lateritic sandy clay soils derived from weathered Accraian sandstone bedrock 
formations. 
Alluvial ‘black cotton’ soils are found in many low lying poorly drained areas CEPA (2000). 
These soils have a heavy organic content which readily expands and contracts, hence causing 
major problems to foundations and footings. In some areas, lateritic soils are strongly acidic and 
also cause major problems to foundations and footings, however, near the foothills are the large 
areas of alluvial laterite gravels and sands.  
 
3.1.6 Seismicity  
Accra, with a growingnumber of large industrial activities is located in the earthquake-prone 
zone. Bacon and Quaah (1981) stated that, most of the earthquakes experienced in Ghana 
occurred in the western part of Accra at the junction of the two major fault systems (the coastal 
boundary fault and the Akwapim fault zone). 
In 1858 an earthquake was reported to have been felt in Accra Ambraseys and Adams (1986). In 
1862 an earthquake with magnitude 6.5 struck Accra and caused considerable damage to many 
important structures Quaah (1980) and Ambraseys and Adams (1986). Two severe shocks 
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rocked Accra in 1871 and 1872 Ambraseys and Adams (1986) whilst Junner (1941) reported of 
similar shocks in 1883, 1907 and 1911. 
 
The most destructive earthquake in Ghanathat caused a lot of damage to property and loss of life 
occurred in 1939 and was assigned a magnitude of 6.5 Junner (1941). The intensityof the shock 
was greatest in James town (a suburb of Accra), killing 17 people and injuring 133 people Junner 
(1941). In 1964 and 1969 earth tremors of magnitudes 4.5 and 4.7 respectively were felt in Accra 
Quaah (1980). 
The latest tremor, of magnitude 4.8 which was felt in all the regional capitals, occurred in 1997 
Amponsah (2002). Specifically, Ayetey (1988) in zoning Ghana positioned Accra in the highest 
risk area (zone 4) whilst the northern part of Ghana was located in the lowest risk zone (zone 0). 
Ayetey (1988) further listed the seismically active areas in Accra to include; McCarthyHill, 
Weija, Bortianor, Oblogo, James /Ussher Town and all towns along the coast. 
 
3.1.7 Economy of the study area 
Notwithstanding the economic boom, there exist a number of challenges; high unemployment 
levels of about 16% and increasing urban poverty. Indeed, while poverty in Ghana is generally 
reducing (from 39.5% in 1998/99 to 28.5% in 2005/06), that of Accra is increasing (from 4.4% 
in 1998/99 to 10.6% in 2005/06) WDI (2008) and ADI (2008).  
 
3.1.8 Mobile technology use 
According to GSS PHS (2010), only 2.3% of households in Ghana have fixed telephone lines 
whilst ownership of mobile phones by individuals is much higher in Greater Accra region 
(73.5%) followed by Ashanti region (56.1%). Additionally, 7.9% of households in Ghana own 
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either laptops or desktop computers with Greater Accraregion leading by 16.8% followed by 
Ashanti region with 9.3%. Urban dwellers are more likely to own mobile phones (63.4%) and use 
the internet (12.7%) than rural dwellers (29.6% and 2.1% respectively).  
 
3.1.9 Private import  
Private importers (travelers who enter the country with EEE that are not declared at 
customs)importing several tons of EEE have been estimated by Amoyaw-Osei et al. (2011) as 
detailed in Table 3.1 (a). 
Indeed Amoyaw-Osei et al. (2011) emphasized that the statistics from GIS and CEPS are gross 
under-statements and estimated that, about 5.7 million mobile phones become obsolete every 
year in Ghana (see also table 3.2).  
Table 3.1 (a): CEPS Import Data in units (2006-2009)  
EEE 2006 2007 2008 2009 
Mobile phones 61,000 860,000 175,000 34,000 
PCs/Laptops 187,000 125,000 23,000 151,000 
 
Source: Amoyaw-Osei et al. (2011). 
 
 
 
 
 
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Table 3.1 (b): Estimated Undeclared Private Imports through KIA; Private Imports in Units and 
Tons (2009) 
Equipment  Passengers Number of   Tons  
 
 Import Units  
 
Laptop  10%  52'500  184  
  
Mobile Phone  50%  262'500  131  
  
Camera  10%   52'500   37  
  
DVD Player  10%   52'500  263  
  
Game Console  10%  52'500  630  
  
MP3 Player  10%  52'500  11  
  
TOTAL 100% 525,000 1'255  
 
 
Source: Amoyaw-Osei et al. (2011). 
10% means that percentage of all people arriving at Kotoka import one Laptop. The 50% for 
mobile phones implies, that probably 10% bring 5 phones or 5% bring 10 phones at once (not 
that 50% bring one phone). 
 
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Table 3.2:  Installed Base (from consumer surveys) of some EEE (units and tons, 2009) 
Installed Private  Enterprises  Institutions  Total (units)  Total (tons)  
Base (units)       
PC  622'000  471'000  367'000  1'460'000  32'000  
     
Laptop 283'000  70'000  97'000  450'000  1'600  
     
Mobile 17'351'000  N/A  N/A  17'351'000  8'700  
Phone       
 
Source: Amoyaw-Osei et al. (2011). 
 
3.1.10 Soil Sampling Site  
The settlement of Agbogbloshie (Old Fadama) consists of about 6,000 families or 30,000 people, 
situated on the left bank of the Odaw River, and in the upper reaches of the Korle Lagoon in 
Accra Amoyaw-Osei et al. (2011). There are at least four different socio-economic factorsthat 
drive the establishment and growth of AgbogbloshieAmoyaw-Osei et al. (2011).  
They include:  
 Spill-over population associated with the size and growth of the adjacent market;  
 Migration from the north of Ghana, as an outcome of tribal conflict;  
 Social downward movement by those forced out of more expensive areas in Accra, partly 
attributable to the impact of the Structural Adjustment Programme initiated in the early 
1980s; and  
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 Cheaper settlement area free from bureaucratic constraints and high rentals in recognized 
formal areas in Accra.  
The Agbogbloshie site started as a food stuff market for onions and yam. Over the years it has 
grown into a slum with people dealing in all kinds of scrap, and a dumping ground for old 
electrical and electronic products and household waste (fig 3.3). The scrap yard has grown 
steadily into a popular recycling area for WEEE. Hundreds of tons of WEEE end up at 
Agbogbloshie every month, where they are broken apart to salvage Cu, Fe, Pb, Ni and other 
metallic components. 
In order to quantify soil and ash as well as sediment contamination in Agbogbloshie and 
Korforidua, Greenpeace Research Laboratories carried out a sampling campaign Bridgen et al. 
(2008). The two soils and ashes samples with the highest contamination, taken at burning sites in 
Agbogbloshie showed Cu, Pb, Sn and Zn concentrations over 100 times higher than typical 
background levels. Concentrations of Sb and Cd exceeded typical background soil levels by 
around 50 times for Sb and 5 times for Cd. The sample taken from an open burning site in 
Korforidua showed similar metal contents, which could lead to the conclusion that similar 
materials were burnt Bridgen et al. (2008). 
  
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Figure 3.3: Location and size of the Agbogbloshie scrap yard 
 
3.2 Data Collection 
The main feature of any exposure assessment activity is quantitative estimation MacIntosh and 
Spengler (2000). However, current approaches to quantitative estimation have included personal 
measurements or environmental monitoring, modeling and the use of questionnaires MacIntosh 
and Spengler (2000). Questionnaires are more frequently used to give complementary data to the 
actual exposure assessment Zartarian et al. (2005).  
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The techniques employed for data collection in this study are; 
 Observation  
 Radiation Measurements 
 Noise Measurements 
 Soil analysis for heavy metals 
 Administering of questionnaires 
 
3.2.1 Observation 
Visual information assessment (observation) is associated with landscape planning activities and 
is considered very important since it is impossible to be performed with the help of software 
tools Clay and Smidt (2004). On site observations were done to visually assess the use of 
warning signs at BSs and also the compliance to local and international regulations governing the 
mounting of BSs.  
 
3.2.2 Radiation Measurements 
BSs for the study were selected based on their nearness to residential areas. The technique by 
Amoako et al. (2009) and Kuhn (2009) in assessing RFs within the vicinity of BSs was used in 
this study. Eighty (80) BS sites at residential areas in Accra were randomly selected for radiation 
measurements. The locations of some mobile telecommunication BSs within Accra where 
radiation measurements were performed are in Appendix A. The equipment used for the study 
was a stop watch, an Anritsu Spectrum Masterand a log periodic antenna. Data from the Anritsu 
Spectrum Masterwere loaded on to a laptop computer. At each BS, the measurements were done 
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at randomly strategic positions within the vicinity during the peak periods (when most calls are 
done) for 6 minutes.   
According to Amoako et al. (2009) the peak periods during the day are between 10.00a.m. and 
1.00p.m. and in the evenings are between 4.30p.m. and 7.30p.m. 
 
A global position system (GPS), Oregon 200 manufactured by Germin Limited was used to 
record the geographic coordinates of the BSs and the locations where radiation measurements 
were performed (Appendix A). 
The calibrated spectrum analyzer (Anritsu’s Spectrum Master) with serial number MS2721B was 
used to display and/or record the electric field strength versus frequency. The spectrum analyzer 
measures the electric or magnetic field strength from one or more sources in a “narrow” 
frequency band.  
The spectrum master was connected via a coaxial cable to the antenna located at a height of 1.5m 
above the ground so as to maintain a direct line of sight with the RF source Amoako, et al. 
(2009). The log periodic antenna was mounted on a non-conductive tripod stand and connected 
via a lead shielded coax cable to the Anritsu Spectrum Master MS2721B. The antenna cable was 
matched to the receiver input impedance as well as to the antenna load impedance. All other 
properties especially of the antennas (general electrical and mechanical properties) were not 
considered for this study. 
The set up was allowed 5 minutes to warm up; whilst the field assistants from the GAEC 
retreated a distance of at least 4 m from the antenna to prevent perturbation of the field around 
the antenna. RF measurement was made directly at a point in space and at a time where there 
was no interception of radiation. 
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GPS coordinates of locations of and from BSs were taken using the Geo explorer whilst ELF 
measurements were taken from antennae at BSs using the Anritsu’s Spectrum Master and the 
readings recorded. 
The cable loss and the antenna factor were then added using equation 3.1 
F = URX + K+Ak                                                                                         (3.1) 
where,  
F is the field strength level in dBµV/m 
URX is the receiver input voltage across 50Ω in dBµV 
K is the antenna factor in dB/m  
Ak is the cable loss in dB 
The field strength level F in dBµV/m was then converted to E in V/m using the relation in 
equation 3.2 
E (V/m) = 10(F-120)/20                                                                                   (3.2) 
where, 
F is the field strength level, in dBµV/m from equation 3.1 
The power density at each location was calculated using equation 3.3 (FCC, 1997). 
 
E 2
S  = 377H2                                                                             (3.3) 
377
where, 
S = power density in W/m2 
 
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E = electric field strength in V/m 
H = magnetic field strength in A/m 
 
3.2.3 Noise Measurements 
A digital sound level meter with model number AZ8928 which could measure sound levels 
within 4 ranges; 40-70dB, 60-90dB, 80-110dB and 100-130dB was used for the measurement of 
sound levels. Noise levels were measured at 16 BSs in the evenings (22:30pm to 23:30pm) to 
ensure that the only source of noise was the generator set at the BS.Noise levels or intensities 
were measured directly from the digital sound level meter at specific distances from BSs (20m, 
50m, 100m and 150m). A technique ISO (1975) to hold the sound level meter at arm’s length in 
the direction of the sound to minimize the reflection of sound from the body was observed to 
increase consistency. The wind direction (WSW) and average wind speed (8-16 km/hr) AMA 
(2010-2013) which is less than 24km/h therefore made all measurements valid. At any specific 
radius, four (4) readings were recorded at approximately the geographical N, S, E and W 
positions (Appendix B). Measurements were recorded only when a stable minimum and 
maximum noise levels were observed. BSs with initial (at 20m radius) levels less than the 
permissible noise level of 48dB set by the EPA from 22:00pm to 6:00am were not recorded in 
the study. The essence of measuring noise level was to determine the extent to which noise from 
generator sets complied with the EPA standards (compliance coefficient). 
 
3.2.4 Soil analysis for heavy metals 
Other studies have used ashes for this analysis; however, this research considered the claim by 
Lapa et al. (2002) that, the chemical characterization of ash does not necessarily reveal its toxic 
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properties. Therefore, the top soil was deemed as very appropriate as WEEE are deposited at 
backyards and also on farmlands, hence releasing heavy metals into the soil which are 
subsequently absorbed by plants. 
Three sites A, B and C with heavy deposits of used computers/laptops/mobile phones were 
identified and soil samples taken at a depth of 8cm Jarup (2003) for laboratory analysis at the 
chemistry department of the GAEC.  A Grid Cell Sampling Technique Mallarino and Wittry 
(2001) and Rains and Thomas (2001), involving specifically a regular systematic sampling grid 
scheme Franzen (2011) and Mylavarapu and Lee (2011) was used to increase experimental 
consistency. The coordinates of soil sampling points at sites at “Agbogbloshie” can be found in 
Appendix C. The sizes of sites A, B and C as well as the blocks used were as follows; site A- 
total area of site is 25m X 9m whilst each block is 5m X 3m, site B- total area of site is 30m X 
15m whilst each block is 6m X 5m and site C-total area of site is 40m X 21m whilst each block 
is 8m X 7m (Fig 3.4). 
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Figure 3.4: Map showing soil sampling points at “Agbogbloshie” 
 
3.2.4.1 Digestion protocol for soil sample using milestone acid  
(Atomic Absorption Spectrometry) 
The digestion method for heavy metal analysis was used to determine the presence and levels of 
specific heavy metals in soil samples. The digestion involved the following steps; 
 1.5g of each sample was weighed into a Teflon beaker. 
 6ml of HNO3 (65%), 3ml of HCl (35%) and 0.25ml of H2O2 were added to each 
sample, covered tightly in Teflon bombs and loaded onto the rotor using the 
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wrench or torque. The rotor with the Teflon bombs was placed in the ETHOS 900 
Microwave Digester and Digested using the below microwave Program (Report 
Code: 308) 
MICROWAVE PROGRAMME 
STEP TIME (min) POWER (W) PRESSURE TEMP oC 1 TEMP oC 2 
1 00:05:00 250 100 400 500 
2 00:01:00 0 100 400 500 
3 00:10:00 250 100 400 500 
4 00:05:00 450 100 400 500 
 
VENT: 00:05:00  Rotorctrl on             Twist on 
The complete assembly was microwave irradiated for 21 minutes using the Milestone 
Microwave Labstation Ethos 900, MLS-1200 Mega. Ref: Milestone Acid Digestion Cookbook 
update 1st January 1996. 
After digestion, the Teflon bombs mounted on the microwave carousel were cooled in a water 
bath to reduce internal pressure and allow volatilized material to re-stabilize. The digestate was 
diluted up to 20ml (nominal volume) with double distilled water and assayed for the presence of 
Fe, Hg, Ni, Cd, Cr, Cu, Zn, Pb and As.                                                                                
Reference Standards for the elements of interest, blanks and duplicates of the samples were 
digested under the same conditions and procedures as the actual samples were digested. These 
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served as internal positive controls. Reference standards used are from FLUKA ANALYTICAL, 
Sigma-Aldrich Chemie GmbH, a product of Switzerland. 
The following Quality Control and Quality Assurance (QC/QA) measures were employed during 
the analysis; 
 BLANKS: They are to check contaminations during sample preparation. 
 DUPLICATES: To check the reproducibility of the method. 
 STANDARDS: To check the efficiency of the equipment used. 
The Basic Principles of Atomic Absorption spectroscopy can be expressed by three basic 
statements; 
 All atoms can absorb light. The wavelength at which light is absorbed is specific for each 
element. If a sample containing Ni, for example, together with elements such as Pb and 
Cu is exposed to light at the characteristic wavelength for Ni, then only the Ni atoms will 
absorb this light. 
 The amount of light absorbed at this wavelength will increase as the number of atoms of 
the selected element in the light path increases, and is proportional to the concentration of 
absorbing atoms. 
 The relationship between the amount of light absorbed and the concentration of the 
analyte present in known standards can be used to determine unknown concentrations by 
measuring the amount of light they absorb. 
 The elemental concentration was calculated employing the equation below: 
Conc. (mg/kg) = Conc (mg/L)*(Nominal Volume)/Sample weight (g). 
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Sample weight = 1.5g 
Nominal Volume = 20ml 
 
3.2.4.2 Statistical Analysis 
This was done using Analysis of Variance (ANOVA), Pearson correlation analysis, Principal 
component analysis (PCA), Hierarchical cluster analysis (HCA) and Cluster analysis (CA) using 
SPSS (version 20) software package (SPSS Inc., Chicago, IL) for windows. 
 
3.2.5 The use of questionnaires 
The decision to use questionnaire for the study was based on the numerous advantages of using 
questionnaire surveys Zartarian et al. (2005). The advantages include; responses being gathered 
in a standard manner, the speed of collecting information using questionnaires and the 
quantifiable and reliable information obtained that can be generalized for a larger population. 
Zartarian et al. (2005) mentioned some disadvantages of questionnaires; open ended questions 
generate large amounts of data that can take a long time to process and analyze, and the 
superficial answers provided by respondents if questions take a long time to complete.  
The questionnaire survey consisted of both opened and closed ended questions Bond et al. 
(1998) and May (2001). Questions investigated the possibility of a significant difference between 
selected parameters with distance from a BS within the study area.   
 
 
 
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Reliable primary information was gathered from major stakeholders; 
 MMDA in Accra.  
 Residents who have lived within a radius of up to 150m from BSs for at least a period of 
5 years were targeted. At least 8 individuals were selected from each BS site.  
 Government institutions in charge of environmental protection. 
All the above mentioned sources and other nonconventional sources of information were 
collaborated to ensure reliability.In situations where different sources presented contradictory 
views, judgment was based on objectivity and preponderance of evidence as well as technical 
and professional knowledge on EIA.  
Indeed, to unravel the current and local issues in a study area, community participation and 
consultation is identified to be more important Andrew et al. (2009). One of the key 
characteristics of SEA is an emphasis on the use of participatory and consultative processes with 
those who are to be affected by the proposed policy, plan or program Ahmed et al. (2005) and 
Kjorven and Lindhjem (2002). 
Practice in this regard is, however, just emerging in developing countries and currently 
researchers are testing community-based approaches to SEA for achieving more meaningful 
local participation Sinclair et al. (2009). 
 
3.2.5.1 Designing of questionnaires 
Following the evaluation of methods in some countries’ EIA system, Wood (2003) proposed 
“method and 14 point criteria” was adopted (Appendix D). This technique was based on 
effective EIA best guidance practices, with questions directed to the components and 
requirements of a country’s EIA system, as well as the system's capacity to influence decision 
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making. Wood (2003) proposed “method and 14 point criteria” was juxtaposed against the 
Ghanaian procedures, to enable potential weaknesses and areas for improvement to be identified.                                                                                                                    
Based on the environmental concept of community participation or involvement, personal 
opinion was also factored into the designing to seek information from residents living closer to 
BSs.                                                          
 
3.2.5.2 Questionnaires at residential areas 
3.2.5.2.1 Sampling procedure  
The study design is qualitative based, using prospective cohort approach targeting; 
 Parents with children below 15years of age who have lived near BSs for at least 5years. 
 Individuals who were in residence before BSs were mounted. 
 Landlords 
Individuals who were in residence before BSs were mountedwere selected as researchers have 
argued that, any effective EIA procedure should involve public participation Wood (2003) 
though, Palerm (1999) observed that, this has been a mere formality.   
Again, long-term consequences of early exposure as precursors for later onset of adult disease 
IPCS (2001) prompted the involvement of landlords and parents with children below 15 years of 
age who have lived near BSs for at least 5 years.Indeed, Etzel (2003) and Brent et al. (2004) 
have identified children as a sensitive subgroup of the population hence, necessitating the need 
for monitoring sentinel health end-points. 
Proximity to sources, either natural or anthropogenic, is an important determinant for exposure to 
environmental contaminants McGee et al. (2002). Studies by Petts and Eduljee (1994) and 
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Loscher and Kas (1998) stressed on distance as a critical factor in terms of the siting of certain 
sensitive facilities.  
Hence BSs were defined by circles with a radius of 20m, 50m, 100m and 150m (Appendix B). 
The sampling procedure was chosen after a preceding feasibility study to obtain at least 8 
samples in a specific study area (November 2013 to January, 2014; n = 144 parents). The sample 
area was not very extensive because the research objective was to undertake a deep case-oriented 
analysis which some researchers have argued require few samples Sandelowski (1995).   
Data collection at 13 residential areas on household basis was carried out using the purposive 
sampling technique (Thurstone, 1959) which is expected to involve individuals who are 
“information rich” Patton (1990) and Creswell (2002). 
The snowball sampling technique Goodman (1961) was also used as participants’ were 
encouraged to suggest next potential interviewees.  
 
3.2.5.2.2 Approach to administering questionnaires 
Respondents were informed that their responses were only for academic purposes; as such their 
views were anonymous. The questions were then asked systematically and answers given by 
respondents were written on the questionnaire. Leading questions such as asking respondents if 
they knew that radiation causes cancer were avoided.  This was necessary as it could adversely 
affect performance by leading respondents to exceedingly focus on a few facts or ideas to the 
exclusion of other important information Proctor and Van Zandt (2008). Appendix E shows the 
questionnaire used for collecting data at residential areas. 
Interviewing an expected sample size of 200 people “on-site” mainly on Saturday mornings was 
a substantial task. To maximize time efficiency, a total of 6 research assistants were recruited for 
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the data collection. At the end of each working day, all collected questionnaires were reviewed 
(by the Researcher and the Field Assistants) and responses that were not clearly written on the 
questionnaires were discussed with the respective research assistants.  
To obtain information on perception of potential health impacts and scope of assessment, self-
completion questionnaires provide an appropriate method for finding out the current situation in 
a particular field of study Bond et al. (1998) and May (2001). Figure 3.5 shows locations where 
questionnaires were administered in Accra. 
 
3.2.5.3 Government Institutions  
The EPA and the NCA participated in this research work by responding to the same set of 
questions. The Researcher introduced himself to the heads of departments (at the headquarters) 
in charge of issuing permits to the operators and also submitted the questionnaires to them. The 
Researcher and the heads of departments then decided on a convenient date for the collection of 
data and also any possible informal discussions.  Appendix E shows the questionnaire used for 
collecting data at the EPA and the NCA. 
 
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Figure 3.5: BSs at residential areas in Accra where questionnaires were administered 
 
 
 
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3.2.5.4 MMDA 
Table 3.3 indicates all the assemblies in Accra as well as the departments that participated in the 
research work. MMDA that did not participate in this research work directed the Researcher to 
the EPA. Again, the Researcher introduced himself to the heads of the relevant departments 
(town planning and engineering) and also submitted the questionnaires to them. The Researcher 
and the heads of departments then decided on a convenient date for the collection of data and 
also any possible informal discussions. Appendix E shows the questionnaire used for collecting 
data at the MMDA. 
 
3.3 Ethical Considerations 
According to Babbie (2005) a study should be conducted in conformity with ethical standards in 
social research. Therefore, this study was conducted by ensuring voluntary participation, causing 
no harm to the participants, anonymity, confidentiality and compliance with other codes of 
ethics. Additionally, informed consent of interviewees was sought before the interview schedules 
were administered. Other codes of ethics regarding accuracy of research design, data collection 
and processing, as well as acknowledging sources of information have been adhered to in this 
study. 
 
 
 
 
 
 
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Table 3.3: MMDAs in Accra which participated in the research work. 
 
MMDAs CAPITAL TOWN PLANNING WORKS 
 
DEPARTMENT DEPARTMENT 
. 
Ga South Weija X X  
 
Ga Central Sowutuom X √ 
 
 
Ga West Amasaman √ X 
 
 
Ga East Abokobi √ √ 
 
 
La Nkwantang- Madina √ X 
 
Madina 
 
 
Adentan Adentan  √ √  
  
 
 
AMA Accra  √ X 
 
 
La Dade- La X X 
 
Kotopon 
 
 
Ledzokuku- Teshie- X X 
 
Krowor Nungua 
 
Source: Field Survey Data, 2014 
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Where “√” represents assemblies that took part and “X” represents assemblies that did not take part in the 
research 
 
3.4 Data Analysis 
The Researcher and Field Assistants made notes from their observations and interactions with 
the respondents. These notes along with the interviews and discussions with key informants and 
other information gleaned from secondary sources were collated and edited for consistency. 
An overview of the open-ended responses in the questionnaire was critically performed. A 
coding manual was prepared after a careful categorization of responses into thematic areas. Each 
response was assigned a numeric code which was used in coding the questionnaire.  
Data analysis was executed with the aid of a professional software programme -The Statistical 
Product for Service Solutions (SPSS) package. The data analysis consists of categorizing, 
examining, tabulating, testing or otherwise recombining both quantitative and qualitative 
evidence to address the initial proposition of the study. In essence, data analysis involves turning 
a series of recorded observations into quantitative and descriptive statements for practical 
application. Both descriptive and inferential statistical techniques (Chi-square) were used to 
analyze the datafor comparison between groups at 0.05 (95%) level of significance. Descriptive 
statistics such as frequencies with percentages and cross tabulations to examine relationships 
were the basis for the presentation and interpretation of the results. 
 
 
 
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3.5 Application of the parametric model to the study                                                        
The parametric approach is deemed very applicable to this research as it; 
 Starts from the perspective of limited data. 
 Comprises IBMs. 
 Aims to estimate the complete vulnerability value of a system. 
All the significant characteristics mentioned above are perfectly required in the long term to 
entirely provide details to clarify the myth or otherwise surrounding the MTT. 
However, the specific model to be designed is based on risk vulnerability as concluded either on 
perception or on scientific evidence or on both by a well-informed individual. Therefore, 
considered as an IBM developed “bottom-up” Grimm (1999) describing a system (an index) 
from the individual’s point of view Lomnicki (1988) and DeAngelis et al. (1994). 
The design is focused on the FVI equation developed by Balica et al. (2009) as sited earlier, 
equation (2.1); 
𝐸∗𝑆
FVI =                                                                                                       (3.4) 
𝑅
The indicators that increase the FVI are therefore placed in the nominator whilst those that 
decrease the FVI are placed in the denominator Dinh et al. (2012). 
The risk vulnerability index of the MTT will therefore be represented as; 
 
RVI = ∑ (indicators that increase the RVI) duration                        (3.5) 
RVI = ∑ (X1X2X3X4X5 ………….. Xn) y                                          (3.6) 
where; Xn represents the last identified indicator that can increase the RVI and y represents the 
number of years the indicators (variables) have persisted. 
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When y values vary significantly, then equation (4.6) can be expanded as; 
RVI = (X1y1+X2y2+X3y3+X4y4+X5y5…….. Xnyn)                                   (3.7) 
Bertrand (2005) studied movement patterns in Accra and argued that, “as time passes and 
individuals age, stability becomes more of the norm and the tendency to relocate diminishes. 
This therefore suggests that, the duration “y” is likely to increase the cumulative effect. 
Additionally, findings suggest that Xn may also alter as risk perceptions vary over time and are 
interpreted on the basis of visible contamination, cognition and context Collins et al. 
(2006).However, it should be noted that, the construction and use of environmental risk indices 
have been widely documented in literature Alister and Kogan (2006), Mendoza and Izquierdo 
(2009), Xu and Liu (2009) and Senese et al. (2010). 
In reality, the role of risk perception is an important factor in determining the capacity of 
individuals and communities to reduce vulnerability to risk WHO (2002). 
 
 
 
 
 
 
 
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CHAPTER FOUR 
4.0 RESULTS 
4.1 Observations made at selected BSs 
The following remarks can objectively be made after visual assessment at some BSs; 
 Some BSs do not have warning signs. 
 Some BSs do not have aerial lamps. 
 Some BSs have traces of fuel on their concrete platforms. 
 Some BSs are less than 20m from the nearest residential structure. 
 
 
Figure 4.1 Traces of fuel on the concrete floor of a BS.  
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4.2 Radiation levels in the vicinity of BSs 
Table 4.1: Radiation levels recorded in the vicinity of BSs 
                      AWUDOME          N05°34.289´,W000°13.646´ 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC 
POSITIONS BASE STATION (V/m) FIELD (V/m) 
           900MHz        1800MHz 
A    
N05°34.297´,W000°13.654´ 21.25m 5.29E-03 6.97E-04 
B    
N05°34.322´,W000°13.602´ 101.94m 7.76E-03 3.47E-03 
C    
N05°34.297´,W000°13.603´ 81.19m 1.01E-02 1.23E-03 
D    
N05°34.246´,W000°13.655´ 80.73m 2.43E-06 7.77E-04 
E    
N05°34.276´,W000°13.679´ 66.18m 4.43E-03 5.91E-04 
 
 
 
                    ASYLUM DOWN N 5.56327075,  W 0.20241766 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC 
POSITIONS BASE STATION (V/m) FIELD (V/m) 
           900MHz        1800MHz 
A 13.94m 1.06E-03 1.07E-01 
N5o33’47.74”,W0o12’09.14” 
B 12.7m 2.95E-03 4.64E-01 
N5o 33’47.82”,W0o12’09.11” 
C 5.07m 5.20E-03 5.60E-01 
N5o33’ 47.66”,W0o12’08.82” 
D 7.29m 4.64E-02 3.64E-01 
N5o 33’47.82”,W0o12’08.93” 
E 36.07m 6.52E-02 2.50E-02 
N5o 33’48.30”,W0o12’07.65” 
F 17.02m 5.29E-03 4.14E-02 
N5o 33’48.32”,W0o12’08.65” 
G 51.24m 3.11E-01 4.11E-02 
N5o 33’49.32”,W0o12’08.10” 
 
 
 
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                       DANSOMAN               N5°33.774´, W0°16.659´ 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC FIELD 
POSITIONS BASE STATION (V/m) (V/m) 
           900MHz        1800MHz 
A      
N5°33.789´,W0°16.652´ 31.64m 1.63E-02 1.58E-06 
B     
N5°33.825´W0°16.650´ 96.62m 1.78E-02 1.58E-06 
C     
N5°33.796´W0°16.604´ 111.48m 1.78E-02 2.45E-02 
D     
N5°33.791´W0°16.563´ 180.65m 2.33E-02 1.58E-04 
E     
N5°33.742´W0°16.538´ 232.92m 3.44E-02 1.58E-04 
 
 
 
 
 
 
                   GBAWE NORTH                 N5°36.136´, W0°18.121´ 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC 
POSITIONS BASE STATION (V/m) FIELD (V/m) 
           900MHz        1800MHz 
A      
N05°36.131'W000°18.126' 13.47m 3.59E-02 1.58E-04 
B      
N05°36.164'W000°18.103' 62.66m 2.47E-02 1.58E-02 
C      
N05° 36.202'W000°18.190' 177.56m 2.11E-02 1.58E-04 
D      
N05° 36.153'W000° 18.250' 240.96m 1.59E-02 1.58E-04 
E      
N05° 36.115'W000° 18.289' 311.75m 1.68E-02 1.58E-04 
 
 
 
 
 
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                   KOKROBITE                 N05°29.997´,W000°22.104´ 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC FIELD 
POSITIONS BASE STATION (V/m) (V/m) 
           900MHz        1800MHz 
A     
N05°29.999´W000°22.414´ 578.19m 1.65E-03 5.98E-04 
B     
N05°29.047´W000°22.072´ 1023.73m 6.33E-04 7.63E-05 
C     
N05°29.032´W000°22.157´ 434.63m 4.18E-03 5.18E-05 
D     
N05°29.960´W000°22.076´ 91.59m 1.74E-03 1.09E-04 
E     
N05°29.920´W000°22.034´ 193.55m 3.49E-03 7.29E-05 
 
 
 
 
                        AWOSHIE                  N5°35.415´, W0°16.829´ 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC FIELD 
POSITIONS BASE STATION (V/m) (V/m) 
           900MHz        1800MHz 
A      
N5°35.419´W0°16.847´ 34.64m 2.39E-02 1.58E-04 
B      
N5°35.441´W0°16.822´ 49.68m 3.07E-02 1.58E-02 
C      
N5°35.481´W0°16.790´ 141.72m 2.15E-02 1.58E-04 
D      
N5°35.391´W0°16.817´ 51.72m 2.24E-02 1.58E-02 
E      
N5°35.373´W0°16.852´ 88.59m 1.54E-02 1.58E-02 
 
 
 
 
 
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      LATERBIOKORSHIE RADIOGOLD             N5.54680403,  W0.23818963 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC 
POSITIONS BASE STATION (V/m) FIELD (V/m) 
           900MHz        1800MHz 
A      
N5o 32’ 48.94”W0o 14’17.93” 18.82m 3.61E-03 5.66E-04 
B      
N5o 32’ 48.45”W0o14’18.13” 20.28m 7.70E-02 8.70E-04 
C      
N5o32’  48.98”W0o 14’17.68” 16.66m 5.63E-04 9.68E-04 
D      
N5o 32’ 49.62”W0o 14’17.81” 36.43m 6.22E-04 5.32E-04 
E      
N5o 32’ 49.60”W0o 14’21.36” 125.51m 3.54E-04 2.17E-04 
F      
N5o 32’ 46.71”W0o 14’18.75” 67.21m 2.23E-03 2.02E-04 
G      
N5o 32’ 46.36”W0o 14’18.24” 70.29m 4.16E-04 2.16E-04 
 
 
 
 
                   BORTIANOR ALOTEK                        N05°31.327´, W000°20.817´ 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC 
POSITIONS BASE STATION (V/m) FIELD (V/m) 
           900MHz        1800MHz 
A      
N05°31.333´W000°20.808´ 20.83m 2.58E-02 2.14E-02 
B      
N05°31.387´W000°20.806´ 112.29m 5.00E-02 2.79E-02 
C      
N05°31.422´W000°20.885´ 215.98m 4.30E-02 2.75E-02 
D      
N05°31.439´W000°20.796´ 211.67m 1.91E-02 2.02E-02 
E      
N05°31.479´W000°20.918´ 337.24m 1.68E-02 1.78E-02 
 
 
 
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                     GLORYLAND                      N5°34.560´, W0°15.417´ 
MEASUREMENT DISTANCE FROM ELECTRIC ELECTRIC FIELD 
POSITIONS BASE STATION FIELD  (V/m) 
(V/m)        1800MHz 
           900MHz 
A      
N05° 34.597'W000°15.359' 126.22m 8.10E-03 2.63E-02 
B      
N05° 34.566'W000°15.337' 150.13m 3.30E-02 3.46E-02 
C      
N05° 34.521'W000°15.346' 150.43m 1.49E-02 1.58E-04 
D      
N05° 34.572'W000°15.474' 111.47m 4.30E-02 1.58E-04 
E      
N05° 34.572'W000°15.510' 181.17m 3.21E-02 3.46E-02 
 
 
 
 
 
 
 
                         DOME 2                            N05°39.420´, W000°14.346´ 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC FIELD 
POSITIONS BASE STATION (V/m) (V/m) 
           900MHz        1800MHz 
A      
N05°39.419´W000°14.345´ 3.48m 5.20E-03 2.71E-04 
B      
N05°39.360´W000°14.333´ 113.14m 7.91E-03 2.00E-03 
C      
N05°39.364´W000°14.378´ 118.83m 2.11E-02 3.22E-04 
D      
N05°39.410´W000°14.385´ 73.37m 2.10E-03 1.66E-04 
E      
N05°39.450´W000°14.364´ 64.85m 5.71E-03 1.30E-03 
 
 
 
 
 
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           LATERBIOKORSHIE BLACKSMITH              N5.60537000, W0.25021000 
MEASUREMENT DISTANCE ELECTRIC FIELD  ELECTRIC 
POSITIONS FROM BASE (V/m) FIELD (V/m) 
STATION            900MHz        1800MHz 
A      
N5o32’49.49”W0o14’46.45” 135.34m 2.48E-04 2.45E-04 
B      
N5o 32’49.14”W0o14’46.35” 142.16m 3.91E-04 1.99E-04 
C      
N5o32’ 49.13”W0o14’46.20” 194.36m 3.56E-03 2.56E-04 
D      
N5o 32’48.12”W0o14’46.92” 185.84m 1.89E-04 2.00E-04 
E      
N5o 32’48.10”W0o14’44.68” 85.36m 1.72E-04 2.17E-04 
 
 
 
 
                            NEW BORTIANOR                  N5°32.086´, W0°22.512´ 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC FIELD 
POSITIONS BASE STATION (V/m) (V/m) 
           900MHz        1800MHz 
A      
N05°32.083'W000°22.514' 7.25m 1.00E-02 1.58E-02 
B      
N05°32.082'W000°22.540' 53.53m 1.86E-02 1.58E-02 
C      
N05°32.077'W000°22.483' 59.04m 1.95E-02 1.58E-02 
D      
N05°32.065'W000°22.447' 125.82m 5.68E-02 1.58E-04 
E      
N05°32.113'W000°22.454' 117.41m 3.42E-02 1.58E-04 
 
 
 
 
 
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                        ABEKA LAPAZ                           N5.60537, W0.25021 
MEASUREMENT DISTANCE FROM ELECTRIC FIELD  ELECTRIC FIELD 
POSITIONS BASE STATION (V/m) (V/m) 
           900MHz        1800MHz 
A 135.05m 2.57E-01 3.87E-01 
N5o36’21.32”W0o14’56.83” 
B 140.16m 6.81E-04 4.86E-01 
N5o 36’21.50”W0o14’56.73” 
C 194.76m 2.23E-07 4.53E-02 
N5o 36’22.84”W0o14’55.50” 
D 185.46m 5.36E-04 6.86E-02 
N5o 36’21.39”W0o14’55.09” 
E 86.73m 6.18E-02 7.10E-02 
N5o 36’20.59”W0o14’58.28” 
 
Source: Field Survey Data, 2014 
 
 
 
 
 
 
 
 
 
 
 
 
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4.3 Mean noise levels at specific distances from BSs 
Table 4.2: Mean noise levels (dB) recorded at specific distances from BSs, ** indicates values < 
40 
 
BS site 20m 50m 100m 150m 
A 66 52 ** ** 
B 68 50 ** ** 
C 76 60 44 ** 
D 70 58 42 ** 
E 61 45 ** ** 
F 67 47 ** ** 
G 70 60 44 ** 
H 56 46 ** ** 
I 51 48 ** ** 
J 62 50 44 ** 
K 65 48 ** ** 
L 54 45 ** ** 
M 58 50 43 ** 
N 60 48 ** ** 
O 57 41 44 ** 
P 65 58 44 ** 
Mean 62.9dB 51dB 43.6dB        ** 
 
Source: Field Survey Data, 2014. 
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4.4 Levels of heavy metals in soil samples 
Table 4.3: Levels of heavy metals (mg/kg) recorded in soil samples at “Agbogbloshie”, ND 
indicates “not detected”. 
 
SampleID Cr Cd Ni As Hg Pb Fe Zn Cu 
A1 4.68 2.37 8.98 7.80 0.97 15.15 14.47 1.21 14.81 
A2 8.00 1.73 23.79 7.57 0.88 12.31 15.02 1.37 15.55 
A3 8.02 3.07 24.62 7.44 0.83 11.50 14.98 1.36 14.88 
A4 8.78 1.20 9.97 7.40 0.76 10.40 15.02 1.32 14.80 
A5 7.28 1.69 23.69 7.46 1.03 15.15 15.13 1.34 14.73 
A6 5.95 1.57 17.82 6.67 0.56 8.26 15.07 1.31 13.70 
A7 9.17 1.17 15.72 6.81 0.60 8.78 15.21 1.26 13.71 
A8 4.17 0.52 4.17 6.48 0.51 7.22 14.74 1.19 7.36 
A9 7.77 1.57 12.00 6.96 0.53 7.69 15.01 1.35 13.96 
A10 11.86 10.69 27.61 7.09 0.73 10.38 14.82 1.38 15.15 
A11 6.00 0.97 7.16 7.46 0.77 11.88 14.78 1.31 15.35 
A12 6.76 1.39 16.46 6.49 0.57 8.23 14.99 1.39 14.59 
A13 9.78 1.80 20.10 6.00 0.49 6.40 15.12 1.41 13.73 
A14 6.97 1.01 17.04 2.95 0.28 2.06 14.89 1.30 14.47 
A15 3.80 0.45 4.05 2.80 0.21 1.69 14.28 1.44 11.59 
Mean 7.30 2.08 15.55 6.49 0.65 9.14 14.90 1.33 13.89 
 
 
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Sample ID Cr Cd Ni As Hg Pb Fe Zn Cu 
B1 2.93 0.12 2.53 2.72 0.40 2.32 14.19 0.87 11.27 
B2 4.63 0.11 3.92 4.81 0.52 3.93 14.60 1.01 10.99 
B3 4.37 0.33 4.11 4.40 0.47 3.53 14.82 1.06 11.85 
B4 6.43 0.35 8.29 4.51 0.53 4.76 15.09 1.28 6.84 
B5 4.96 ND 2.13 2.81 0.16 1.42 14.31 0.69 3.84 
B6 11.98 0.08 13.45 4.28 0.44 2.50 14.60 1.38 5.95 
B7 6.29 2.13 4.49 4.69 0.39 3.30 14.41 1.37 2.42 
B8 9.22 1.23 7.57 6.56 0.63 5.75 14.91 1.27 11.59 
B9 5.48 0.17 6.09 1.48 0.09 0.69 18.36 1.13 14.76 
B10 3.04 0.04 2.17 8.04 1.17 16.03 13.14 0.34 1.60 
B11 7.52 1.56 35.60 6.01 0.53 7.43 15.17 1.41 15.20 
B12 4.45 0.29 3.48 1.17 0.37 2.11 14.88 1.08 14.79 
B13 3.51 0.32 2.56 1.24 0.40 2.50 16.61 1.02 2.98 
B14 5.91 1.83 31.99 4.88 0.44 3.37 14.37 1.44 6.55 
B15 3.79 0.53 8.10 4.80 0.51 3.26 14.84 1.15 5.98 
Mean 5.63 0.65 9.10 4.16 0.47 4.19 14.95 1.1 8.44 
 
 
 
 
 
 
 
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Sample   ID         Cr         Cd Ni          As         Hg          Pb         Fe         Zn        Cu 
C1 5.68 0.33 8.30 2.77 0.19 1.17 15.15 1.22 6.24 
C2 6.05 ND 2.49 1.67 0.15 1.08 14.55 0.66 2.91 
C3 6.72 0.17 8.65 4.99 0.56 4.25 14.59 1.22 10.15 
C4 3.93 ND 8.38 4.93 0.51 4.12 14.66 1.15 15.00 
C5 6.85 ND 12.42 6.19 0.67 6.94 14.86 1.22 13.55 
C6 6.90 ND 13.41 6.55 0.71 5.63 14.91 1.54 12.53 
C7 22.83 0.91 23.79 7.06 0.67 10.35 15.01 1.39 14.77 
C8 18.24 1.19 22.25 7.73 0.91 14.51 14.97 1.43 15.54 
C9 11.92 1.31 26.94 7.64 0.95 13.69 14.97 1.42 15.11 
C10 3.76 ND 2.99 4.33 0.40 2.61 14.17 1.00 11.74 
C11 2.91 0.13 3.37 4.64 0.29 3.04 13.86 0.90 13.34 
C12 2.85  ND 1.89 2.75 0.19 1.19 18.79 0.44 7.35 
C13 2.85 0.04 2.19 2.68 0.16 0.77 13.87 0.46 3.05 
C14 3.57 0.23 2.35 2.76 0.19 1.49 14.04 0.68 4.50 
C15 9.14 0.91 20.35 6.92 0.67 8.83 15.14 1.33 14.63 
Mean 7.61 0.58 10.65 4.91 0.48 5.31 14.90 1.07 10.69 
  
Source: Field Survey Data, 2014 
 
 
 
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4.5 Data from questionnaire responses 
4.5.1. Responses from residential areas 
Table 4.4: Respondents at selected BSs 
BS site Frequency Percent 
Awudome 13 9.0 
Dansoman 10 6.9 
Gbawe 14 9.7 
Gloryland 13 9.0 
Laterbiokorshie (Radio Gold) 12 8.3 
New Bortianor 6 4.2 
 Bortianor Alotek 11 7.6 
Abeka Lapaz 14 9.7 
Laterbiokoshie (Blacksmith) 10 6.9 
Kokrobite 12 8.3 
Dome2 10 6.9 
Awoshie 12 8.3 
Asylum down 7 4.9 
   
 
Total 144 100.0 
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Table 4.5: Distance of telecommunication BS from respondents 
Distance from BS  Frequency Percent 
20 m radius 40 27.8 
50 m radius 35 24.3 
100 m radius 35 24.3 
150 m radius 34 23.6 
Total 144 100.0 
 
 
Source: Field Survey Data, 2014 
 
 
 
Table 4.6 (a): Cross tabulation indicating the relationship between distance and content 
 Distance of telecommunication BS from respondents Total 
20 m radius 50 m radius 100 m radius 150 m radius 
Count % Count % Count % Count % Count % 
Are you Yes 5 12.5 5 14.3 16 45.7 25 73.5 51 35.4 
content 
living 35 87.5 30 85.7 19 54.3 9 26.5 93 64.6 
closer to No 
a BS? 
Total 40 100.0 35 100.0 35 100.0 34 100.0 144 100.0 
 
Source: Field Survey Data, 2014 
 
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Table 4.6 (b): Chi-Square Tests 
 
 Value Df Asymp. Sig. 
(2-sided) 
Pearson Chi-Square 39.231a 3 .000 
Likelihood Ratio 40.785 3 .000 
Linear-by-Linear 35.424 1 .000 
Association 
N of Valid Cases 144   
a. 0 cells (0.0%) have expected count <5. The minimum 
expected count is 12.04. 
 
 
Table 4.7 (a): Cross tabulation indicating the relationship between distance and health risk in children   
 Distance of telecommunication BS from Total 
respondents 
20 m 50 m 100 m 150 m 
radius radius radius radius 
Co % C % Co % Co % Co % 
un ou unt unt unt 
t nt 
Have you identified any health risk in Yes 2 5.0 0 .0 0 .0 0 .0 2 1.4 
your children that you can link to 
BSs? No 38 95.0 35 100.0 35 100.0 34 100.0 142 98.6 
Total 40 100.0 35 100.0 35 100.0 34 100.0 144 100.0 
 
Source: Field Survey Data, 2014 
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Table 4.7 (b): Chi-Square Tests 
 Value Df Asymp. Sig. 
(2-sided) 
Pearson Chi-Square 5.273a 3 .153 
Likelihood Ratio 5.198 3 .158 
Linear-by-Linear 3.267 1 .071 
Association 
N of Valid Cases 144   
a. 4 cells (50.0%) have expected count <5. The minimum 
expected count is .47. 
 
Table 4.8 (a): Cross tabulation indicating the relationship between distance and health risk in 
neighbourhood  
 Distance of telecommunication BS from respondents Total 
  20 m   5  0 m radius 100 m radius 150 m radius 
radius 
Count %  Count %  Count %  Count %  Count %  
Have you Yes 2 5.0 0 .0 0 .0 0 .0 2 1.4 
identified any 
health risk in 38 95.0 35 100.0 35 100.0 34 100.0 142 98.6 
your 
neighbourhood No 
that you can link 
to BSs? 
Total 40 100.0 35 100.0 35 100.0 34 100.0 144 100.0 
 
Source: Field Survey Data, 2014 
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Table 4.8 (b):Chi-Square Tests 
 
 Value Df Asymp. Sig. 
(2-sided) 
Pearson Chi-Square 5.273a 3 .153 
Likelihood Ratio 5.198 3 .158 
Linear-by-Linear 3.267 1 .071 
Association 
N of Valid Cases 144   
a. 4 cells (50.0%) have expected count < 5. The minimum 
expected count is .47. 
 
 
Table 4.9 (a): Cross tabulation indicating the relationship between consultation and content 
 If yes, were you consulted before the Total 
siting of the BS? 
Yes No 
Count % Count % Cou % 
nt 
Yes 6 23.1 37 35.9 43 33.3 
Are you content living 
closer to a BS? 
No 20 76.9 66 64.1 86 66.7 
Total 26 100.0 103 100.0 129 100.0 
 
Source: Field Survey Data, 2014 
 
 
 
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Table 4.9 (b): Chi-Square Tests 
 Value Df Asymp. Sig. Exact Sig. (2- Exact Sig. 
(2-sided) sided) (1-sided) 
Pearson Chi-Square 1.541a 1 .214   
Continuity Correctionb 1.018 1 .313   
Likelihood Ratio 1.618 1 .203   
Fisher's Exact Test    .251 .156 
Linear-by-Linear 1.529 1 .216   
Association 
N of Valid Cases 129     
a. 0 cells (0.0%) have expected count < 5. The minimum expected count is 8.67. 
b. Computed only for a 2x2 table 
 
Table 4.10 (a): Cross tabulation indicating the relationship between familiarity and highest 
level of education  
 Familiarity with the MTT Total 
Yes No 
N % N % N % 
None 2 2.3 0 .0 2 1.4 
JHS 2 2.3 7 12.5 9 6.3 
SHS 12 13.6 8 14.3 20 13.9 
 
Post- 40 45.5 29 51.8 69 47.9 
secondary 
Tertiary 32 36.4 12 21.4 44 30.6 
Total 88 100.0 56 100.0 144 100.0 
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Table 4.10 (b): Chi-Square Tests 
 Value Df Asymp. Sig. 
(2-sided) 
Pearson Chi-Square 9.795a 4 .044 
Likelihood Ratio 10.543 4 .032 
Linear-by-Linear 3.541 1 .060 
Association 
N of Valid Cases 144   
a. 3 cells (30.0%) have expected count <5. The minimum 
expected count is .78. 
 
 
Table 4.11 (a): Cross tabulation indicating the relationship between relocation 
and landlord or tenant 
 Will you relocate when compensated? Total 
Yes No 
N % N % N % 
Landlord 6 21.4 62 53.4 68 47.2 
 
Tenant 22 78.6 54 46.6 76 52.8 
Total 28 100.0 116 100.0 144 100.0 
 
Source: Field Survey Data, 2014 
 
 
 
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Table4.11 (b):Chi-Square Tests 
 Value Df Asymp. Sig. Exact Sig. (2- Exact Sig. 
(2-sided) sided) (1-sided) 
Pearson Chi-Square 9.279a 1 .002   
Continuity Correctionb 8.038 1 .005   
Likelihood Ratio 9.827 1 .002   
Fisher's Exact Test    .003 .002 
Linear-by-Linear 9.214 1 .002   
Association 
N of Valid Cases 144     
a. 0 cells (.0%) have expected count < 5. The minimum expected count is 13.22. 
b. Computed only for a 2x2 table 
 
 
 
 
 
 
 
 
 
 
 
 
 
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4.5.2. Responses from EPA 
The EPA listed its main duties as; 
 Conducting screening and site verification 
 Preparation of draft schedules and schedules for telecommunication BSs 
The EPA maintained that, residents are consulted or educated before the siting of BSs, but 
suggested that, compensation should be considered for people to relocate as the fear of radiation 
related effects and the possible collapse of towers still exists in communities.                  
The EPA stated the possible risks associated with the MTT as; 
 Potential collapse of towers and noise from generator sets 
 Leakages from fuel tanks and smoke from generator sets 
Additionally, the EPA listed the following as complaints received from residents; 
 Noise from generator sets and radiation emission from antennae  
 No consultation with residents 
According to the EPA, complaints are resolved by; 
 Investigating to determine whether it merits taking an action, inviting the two parties to 
resolve the issue and the imposition of penalty for the operator to pay  
 Requesting the operator to either fix the problem or remove the structure 
 
The EPA stated that, indeed operators comply with local and international regulations and are 
also committed to EIA principles, however, the EIA measures implemented have “not fully” 
attained their expected effects. The plot size and minimum legal distance of BSs from the public 
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and other sensitive installations were mentioned as the major environmental factors considered 
before permitting BS proposals. 
Indeed, the EPA confirmed that, material wastes or products of the MTT are not properly 
managed. 
 
4.5.3. Responses from NCA 
The NCA maintained that, residents are consulted or educated before the siting of BSs and 
further stated that, there are no possible risks associated with the MTT. The NCA stated that 
indeed they receive complaints from residents, but did not list the nature of complaints. However 
the NCA maintained resolving complaints by; 
 Analyzing the complaints and investigating immediately 
 Filing a formal complaint with the service provider and giving feedback to the consumer.  
The NCA stated that, the issue of risk perception among residents living close to BSs still 
existsbut did not list the nature of risks perceived. Furthermore, the NCA added that, there are no 
possible risks associated with the MTT. On the other hand, the minimum legal distance for 
locating BSs from the public was further confirmed by the NCA. Again the NCA stated that, 
operators comply with local and international regulations. All other questions were either not 
answered or simply answered “enquire from EPA”.  
 
 
 
 
 
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4.5.4. Responses from MMDA: 
Table 4.12: Possible risks associated with the MTT and consultation. 
Possible risks associated with the MTT  
 
MMDA Resp Possible risks Are residents 
onse consulted/educated  
 
Ga East (TP) Yes Some of theBSs are mounted Yes- When 
permits  are 
without permits in residential 
acquired 
zoning 
No- When permits  
are not acquired 
Adentan ( TP) Yes Risk of possible collapse of No 
masts in built-up areas eg. A 
mast (Glo) collapsed, killing 
two(2) persons working to 
erect the mast at “Adenta 
Housing” phase four (4)  
Adentan (WD) Yes 1. Normally the risk of Yes 
collapse of the masts 2.  
Electromagnetic radiation 
which   is perceived to cause 
cancer   
AMA Yes The effect of the radiation on No 
the health of residents 
Ga West Yes 1. Collapse of towers  Yes 
2. The perceived emission of 
radiation (harmful)  
 
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Ga Central  Yes 1. Falling of mast towers due Yes 
to poor supervision in its 
construction  
2. The perceived emission of 
radiation (harmful)  
 
Ga East (WD) No - Yes 
 
La Nkwantanang- Yes When BSs are constructed Yes 
Madina without obtaining appropriate 
development and building 
permits 
 
  
Source: Field Survey Data, 2014 
 
 
Table 4.13: Complaints from residents close to BSs and methods of resolving complaints 
Complaints from residents dwelling close to BSs 
 
MMDA Receive If yes, what are the How do you resolve the 
complaints complaints? complaints? 
 
from 
residents? 
Ga East (TP) Yes Landlords are compensated 1. Company is ordered to 
therefore they lose  focus remove structure or to give 
of its future damage necessary safety report to 
 minimize risk.  
2. Applicant pay penalty for 
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mounting without permit   
 
Adentan (TP) Yes 1.Fears of microwave 1. Refer to EPA for 
emissions investigation 
2. Possible collapse of 2. Defer permit, till issues are 
masts investigated and resolved  
3.Noise/vibrations from  
electric generators  
Adentan (WD) Yes 1. Fear of structural Involve stakeholders in 
collapse. evaluating permit application.  
2. Cancerous emissions  
from BSs.  
 
AMA Yes Health issues and security 1. Educate residents on the 
(strength) of the BS need for the BS 
 
2. BSs should be sited outside 
dense populated residential 
areas. 
 
Ga Central Yes The fear of the perceived We educate them that it is not 
emission of harmful scientifically proven so they 
radiations from the should not panic or have any 
antennae. fear concerning the BS.  
  
Ga East (WD) Yes No response No response 
La Yes 1. Siting of BSs close to 1.Conduct neighbourhood 
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Nkwantanang- residential buildings and it consent 
Madina  impacts on the residents.  2. Embark on public 
2. The potential health education to explain permit 
dangers to residents  proceedings.  
  
Ga West Yes 1. No neighbourhood By educating and explaining 
consultation issues to residents 
 
Source: Field Survey Data, 2014 
 
 
Table 4.14: Risk perception among people living close to BSs and compensation for residents to 
relocate 
 
MMDA Is there an issue of risk perception among Will you consider 
people living close to BSs? compensation for 
Response What are the risk perceptions? people to relocate? 
 
Adentan(WD) Yes 1. People living around BSs No 
perceive the notion that they may 
develop cancer in the future. 
La Nkwantanang- Yes 1. The effect of radiation on Yes 
Madina 
residents. 
  
2. The structural integrity of 
towers. 
Ga East (WD)  No No response No 
Ga Central   Yes Emission of harmful rays into the No 
environment 
AMA  Yes 1. Possible collapse of masts. No 
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 2.  Issues relating to health. 
Adentan (TP)   Yes Perceptions of excessive micro No response 
wave effects that cause cancer, 
sexual weakness and all sorts of 
diseases especially in our largely 
ignorant society. 
Ga East (TP) Yes Radiations emitted from antennae Yes 
Ga West  Yes 1. The risk of fear of developing Yes 
cancer over time. 
2. Collapse of masts. 
Source: Field Survey Data, 2014 
 
 
 
 
 
 
 
 
 
 
 
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Table 4.15: Minimum setback distances and management of material waste products.  
MMDA Is there a What major Are material wastes or 
minimum legal environmental factors do products of the MTT properly 
 
distance for you consider before managed? 
locating BSs from permitting BS proposals?  
the public?  
Ga East (WD) No Change of use of the area Yes, This measures is in the 
from residential to mixed environmental permit 
 
used/ commercial 
Adentan (TP) Yes EPA report Don’t know 
  
Adentan (WD) Yes, normal E.P.A report No response 
setback rules as   
applied in the L.I 
1630, 1996 
AMA Yes 1. Safety report  No  
2. Structural details  
 
Ga Central No It should not be sited in a No response 
densely populated area 
Ga East (TP) No 1. Architecture drawings  No idea  
2. Structural details  
   
3. Safety report  
 
4. E.P.A report  
  
 
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La Yes, the minimum 1. E.P.A report No idea  
Nkwantanang- setback required 2. G.C.A.Areport 
 
Madina  is not less 20m  3. G. A. E. C report 
  
Ga West Yes, about 300m B ased on EPA No  
recommendations 
 
 
Source: Field Survey Data, 2014 
 
4.5.5 Wood (2003) proposed “method and 14 point criteria” 
This technique was based on effective EIA best guidance practices, directed towards the 
components and requirements of a country’s EIA system, as well as the system's capacity to 
influence decision making. Institutions that issue permits to operators compared Wood (2003) 
proposed “method and 14 point criteria” to the Ghanaian procedures, to identify potential 
weaknesses and areas for improvement.                                                                                                                                                                              
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LINE INDICATING AVERAGE PERFORMANCE OF 
THE MTT
14
12
10
8
6
4
2
0
EPA & MMDA
 
 
Figure 4.2: Results of Wood (2003) proposed technique indicating above average of the 
Ghanaian EIA system 
 
4.6 Contribution to knowledge 
Finally, this research has made the following contribution to scientific knowledge; 
 Noise is a risk factor to people within a radius of 50m from BSs. 
 All the heavy metals analyzed were detected in the soil samples; however Hg has 
exceeded its threshold whilst Cd has reached its threshold. 
 
 
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CHAPTER FIVE 
5.0 DISCUSSION OF RESULTS 
 
5.1 Discussion of observations 
Visual information assessment made at some BSs indicated that, the complaints from the public 
(Table 4.13) which were also highlighted by the EPA and the MMDA respectively, confirms the 
relevance of observation as argued by Clay and Smidt (2004). This study confirms the claim that 
lack of information, ignorance and poverty in the study area Songsore and McGranahan (1993) 
have contributed greatly to the offering of plots by landlords for the installation of BSs. 
Furthermore, it confirms the claim that, people usually perceive risks as negligible, acceptable, 
tolerable, or unacceptable Slovic (1987). For example, absence of aerial lamps and the traces 
(leakages) of fuel at BSs were not considered by residents as hazardous. Additionally, the 
planning of Accra is sporadic and non-compliant World Bank (2010) and hence forcing 
frustrated land developers to precede with development without the required permits Yeboah and 
Obeng-Odoom (2010). 
 
5.2 Discussion of Radiation levels around BSs  
In this study, the measurement design permitted radiation measurements at different locations 
around specific BSs due to the settlement pattern in the vicinity and accessibility. Radiation 
levels presented a wide range of electric field values (Table 4.1 and Appendix A) at specific 
distances from BSs, yet all the values were below the ICNIRP threshold. The values obtained in 
this study were in accordance with values recorded by Amoako et al. (2009) and Deatanyah et al. 
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(2012) where signals were measured within a radius of about 300m from BSs during peak 
periods. 
The values recorded suggested that, the concern by the public of the health implication of 
radiation from BSs cannot presently be supported by this study. If certainly prolonged exposure 
to low level emissions is harmful Neshev and Kirilova (1996), then possibly arguments raised by 
Chagnaud et al. (1999) and Heikkinen et al. (2001) that, short term effects produce no adverse 
health effects could be supported by this research. The argument that, RFs reduce melatonin in 
humans should be subsequently investigated in neighbourhoods around BSs, since melatonin can 
inhibit the growth of some types of cancer and has been proven to suppress the growth of breast 
cancer Cherry (2000) and Levallois et al. (2001). 
Indeed it should also be highlighted that, though the strength of EMFs decreases as distance 
increases, this study revealed patterns that are on the contrary (Figs 5.1, 5.2, 5.3, 5.4). This can 
occur due to; more power radiated by antennae as the number of users increase Abdel-Rassoul et 
al. (2006), the nearness to antennae and the physical environment WHO (2006). For this reason 
minimum setback distances cannot scientifically be used solely to resolve the suspicion of health 
implications related to radiation. 
These arguments are quite important, because if the number of subscribers increases without a 
corresponding increase in the number of BSs, emission levels arebound to go higher. This 
therefore, implies that, reliable zoning and consistent monitoring of radiation levels should not 
be compromised. Subsequently, this research suggests long term monitoring as it takes into 
consideration the report by Stewart and Kleihues (2003) which estimated the global annual new 
cases of cancer as 10.1 million in 2000 and projected an increase to 15 million by the year 2020. 
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Electric field strength against distance Electric field strength against distance
1.20E-02 6.00E-01
1.00E-02 5.00E-01
8.00E-03 4.00E-01
6.00E-03 3.00E-01
900MHz 900MHz
4.00E-03 2.00E-01
1800MHz 1800MHz
2.00E-03 1.00E-01
0.00E+00 0.00E+00
Distance (m) Distance (m)
Figure 5.1: Radiation pattern at Awodome              Figure 5.2: Radiation pattern at Abeka Lapaz 
 
 
Electric field strength against distance Electric field strength against distance
4.50E-03 6.00E-01
4.00E-03
5.00E-01
3.50E-03
3.00E-03 4.00E-01
2.50E-03
2.00E-03 3.00E-01
1.50E-03 900MHz 900MHz
2.00E-01
1.00E-03 1800MHz 1800MHz
5.00E-04 1.00E-01
0.00E+00
0.00E+00
Distance (m) Distance (m)
Figure 5.3:  Radiation pattern at Kokrobite             Figure 5.4: Radiation pattern at Asylum Down 
 
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Electric field strength Level of radiation (V/m)
91.59 21.25
193.55 66.18
434.63 80.73
578.19 81.19
1023.73 101.94
Level of radiation (V/m) Electric field strength (V/m)
5.07
7.29
12.7
13.94
17.02
36.07
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5.3. Discussion of mean noise levels recorded around BSs  
There are various sources of noise in the communities, however, this study considered noise 
levels from solely generator sets at BSs within a period where residential areas are expected to 
record noise levels ≤ 48dB. 
Though the highest wind speed (5km/h) was recorded at Kokrobite, the sound level at 50m was 
50dB as compared to New Bortianor with a wind speed of 2km/h but recorded 58dB at 50m (Fig 
5.5). Possible reason accounting for compliance co-efficient <1 beyond 50m could be due to the 
reflection of sound by physical structures.  
80
70
60
50
40
30
20
10 20m
0 50m
Location of BS 
 
Figure 5.5: Mean noise level patterns at some BSs 
The compliance coefficients of noise levels calculated were 1.3 and 0.9 at distances of 20m and 
50m radius respectively from BSs (Table 4.2). Though the compliance coefficient at 50m radius 
was <1, there is the need to be concerned as some BSs at that distance recorded noise levels 
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>48dB. With a compliance coefficient >1 at a radius of 20m and in some instances 50m, 
populations are definitely susceptible to potential risk of noise pollution Passchier-Vermeer and 
Passchier (2000), Muzet (2007), Haines et al. (2003) and Stansfeld et al. (2005). 
Indeed, if “Adult Sleep Disturbance” can reduce secretion in melatonin Abelin (1999) which 
suppresses the growth of breast cancer and prevents depression Cherry (2000) and Davis et al. 
(2001) then, this study critically considers the situation in residential areas as disturbing. 
The number of residents that will be affected within a specific radius from a BS can be 
calculated using πr2d. Where all r values (20m, 50m, 100m and 150m) are converted to km and 
d represents population density of Accra (15, 000 persons/km2). Hence, suggesting the number of 
persons within a radius of 20m, 50m, 100m and 150m to be above 19, 118, 150 and 2250 
respectively. The number of persons becomes huge with respect to a specific radius when 
considered across all BSs in residential areas. 
This study confirmed that, at least, the issue of noise as a variable contradicts the concept of 
social dilemma as emphasized by Von Borgstede et al. (2012) and indeed substantiate a strong 
social norm Yamagishi (1986). Information from residents revealed that most generators become 
noisy after a period of 2 years of usage at BSs, hence suggesting the need for barriers. However, 
considering the variety of barriers; physical, procedures, instructions and practices Hollnagel 
(1999), this research suggests the use of zoning in physical planning. 
 
 
 
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5.4 Discussion of levels of heavy metals in soil samples  
According to Hermann et al. (2000), mobile phones/laptops/computers consist mostly of Cu, Pb, 
As, Ni, Hg, Ag, Li, Zn, Cd, Be, Ta, Pd, Sb and Au plating. This study however, tested for the 
presence of Cr, Cd, Ni, As, Hg, Pb, Fe, Zn and Cu at sites A, B and C (Fig 3.4). Though 
permissible mean concentrations vary worldwide Chen et al. (1999) and De Vries and Bakker 
(1998), this study compared the results obtained to the permissible mean concentrations of the 
US EPA (2011) (Figs 5.6, 5.7). 
Results compared to US EPA limits Results compared to US EPA limits
US EPA LIMITS VALUES RECORDED 20 US EPA LIMITS VALUES RECORDED
18
350 18
290
300 16 14.92
14
250
12
200
10
150 120 8
92
100 8270 6
5.19
4
50
6.85 11.01 11.77
0.53
1.67 6.22 2 1.2 1.20.27
0
0
Cr Cu Ni Zn Pb
Hg As Cd Fe
Heavy Metals Heavy Metals
Figure 5.6: Results compared to US EPA limits    Figure 5.7: Results compared to US EPA limits 
All the heavy metals analyzed were detected at low levels; however, Hg has exceeded its 
threshold whist Cd has also reached its threshold (Figs 5.6, 5.7). This therefore suggests that 
timely intervention measures ought to be introduced. Again, since metal ions in water are 
distributed by lateral and vertical movements Regli et al. (1991) and may affect surrounding 
lands and rivers Nnorom and Osibanjo (2008) or even groundwater surrounding dumpsites 
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Mean Concentration (mg/kg)
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Martinez and Motto (2000), the introduction of dumpsites cannot be supported by this research. 
Therefore, this research can confirm that, soils and water bodies at “Agbogbolishie” predispose 
the public to especially the health implications of Hg and Cd.  
 
5.4.1 One Way ANOVA  
One-way ANOVA was conducted to determine whether there is a significant difference (p ≤ 
0.05) in the average concentration of the heavy metal depending on the sampling site. Two 
variables were therefore defined; the concentration of the heavy metal (dependent variable) and 
the sampling sites (grouping variable).  
Firstly, the normality of distribution and equality of variances which are the requirements for the 
application of ANOVA were accordingly tested. The assumption about distribution normality 
was also satisfied, however Levene's test for equality of variances showed that this assumption 
was not justified for four heavy metals (Cr, Ni, Hg and Fe) and were accordingly excluded in the 
ANOVA analysis.  
The result from the one-way between-groups ANOVA (Appendix G) with post-hoc test indicates 
that, there were significant differences in the average concentrations of Cd, As, Pb, Zn and Cu 
depending on the sampling site. For example, sampling site A significantly differs in the 
concentration of Cd and Pb from sampling sites B and C. However, there was no statistical 
significant difference between the concentrations of Cd and Pb from sampling sites B and C. 
Again, the total content of As and Cu from sampling site A was significantly different from those 
of sampling sites B and C. Nevertheless, the concentration of As and Cu from sampling site C 
did not differ significantly as compared to those of sampling sites A and B. 
 
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5.4.2 Correlation analysis 
In order to quantitatively analyze and confirm the relationship among soil heavy metal content, a 
Pearson’s correlation analysis was applied to the dataset. The Pearson correlation coefficients 
were calculated for each pair of variables (Table 5.1). 
 
Table 5.1: Pearson’s correlation matrix of heavy metals in the soils, ** indicates correlation is 
significant at the 0.01 level (2-tailed). 
 
 Cr Cd Ni As Hg Pb Fe Zn 
Cd .537**        
Ni .802** .694**       
As .572** .565** .620**      
Hg .547** .476** .597** .939**     
Pb .550** .584** .617** .967** .964**    
Fe .459** .327* .461** 0.162 0.215 0.228   
Zn .745** .640** .834** .499** .464** .479** .331*  
Cu .521** .481** .652** .575** .565** .601** .395** .497** 
 
The correlation between the metal pairs was positive and linear in all cases. The correlation 
coefficient between the metal pair As-Pb is 0.967, which indicates a very strong linear 
correlation at the 0.01 significance level and a common origin of these metals. Hg also exhibited 
a very strong correlation with Pb (0.964) and As (0.939) suggesting they probably originated 
from a common source. Ni exhibited strong positive correlations with both Zn (0.834) and Cr 
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(0.802). These high correlations between soil heavy metals may reflect the fact that these heavy 
metals had similar pollution levels and similar pollution sources. 
Moderate correlation was also observed between some of the metal pairs. Cd correlated 
moderately with Ni (0.694), Zn (0.640), Pb (0.584) and As (0.565). Additionally, Ni depicted a 
similar moderate correlation with As (0.620), Pb (0.617), Cu (0.652) and Hg (0.597). 
Furthermore, Cu correlated moderately with Pb (0.601), As (0.575) and Hg (0.565). Such 
elemental association may signify that each paired of elements has identical source or common 
sink in the soil samples. 
Generally, Fe exhibited the lowest correlation with majority of the elements studied. Very low 
correlation was observed between Fe with Cr, Cd and Ni. There was no significant correlation 
between Fe and the heavy elements; As, Hg and Pb. The lack of significant correlation between 
Fe and these heavy metals suggests that, Fe had different sources from the other metals. The 
apparent lack of correlation of Fe with the other heavy metals, which are mainly from 
anthropogenic sources, suggests that, Fe occurs naturally at high levels and is hardly affected by 
human activities (Fig 5.8). 
Mean conc of heavy metals (mg/kg)
20
Site A Site B Site C
15.55 14.9 14.9
15 14.95 13.89
10.65 10.69
10 9.1 9.14 8.44
7.3 7.61
6.49
5.63 4.91 5.31
5 4.16 4.19
2.08 0.47
0.65 1.33 1.1 1.070.58 0.65 0.48
0
Cr Cd Ni As Hg Pb Fe Zn Cu
Heavy Metals
Figure 5.8: Mean levels of heavy metals at sites A, B and C.  
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Mean Concentration (mg/kg)
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5.4.3 Factor Analysis - Principal Component Analysis (PCA) 
For PCA, the value of KMO (Kaiser-Meyer-Olkin) measure of sample adequacy was 0.801, 
which meets the limit of 0.600 conventionally held as a critical value. Bartlett's test of sphericity 
showed that PCA could be applied to the data at the p < 0.001 level. Two principal components 
(PCs) were identified using Varimax with Kaiser Normalization for the seven variables (Table 
5.2).  
Table 5.2: Principal component loadings (Varimax-normilzed) for heavy metals in the soil 
samples  
Variables Rotated Components 
 
  PC1 PC2 
Cr 0.689  
Cd 0.568   
Ni 0.802 0 .308  
As 0.885 -0.338  
Hg 0.827 -0.448  
Pb 0.862 -0.377  
Fe 0.711  
Zn 0 .698 0.425  
Cu 0.682 0.348  
 
E igen Value  4.611 1 .459  
Total Variance (%) 51.238 16.209  
Commutative 
Variance (%) 51.238 67.447 
 
The PCs accounted for 67.45% of the total variance in the variable set. PCA was performed on 
normalized data sets to reduce the dimensions of the original data sets. This method enhances the 
identification of different groups of metals that correlate and hence can be considered as 
exhibiting a similar behaviour and also from a common origin. The number of significant PCs 
was determined based on both screen plot and eigenvalue-one criterion. The eigenvalue-one 
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criterion indicates that PCs with eigenvalues greater than one are regarded as significant when 
the correlation matrix is used in the analysis. 
According to these results (Table 5.2), the eigenvalues of the two extracted components are 
greater than those before and after the matrix rotation. Consequently, heavy metals could be 
grouped into a two-component model that accounts for 67.45% of all the data variation. In the 
rotated component matrix, the first principal component (PC1) had an eigenvalue of 4.61, 
accounting for 51.24% of the total variance. It had strong positive loadings on As, Pb, Hg, Ni, 
Cr, Zn and Cu. However, PC1 was mainly dominated by four of the afore-mentioned heavy 
elements (As, Pb, Hg and Ni) reflecting the anthropogenic contamination in the soil samples. 
This implies that these heavy metals in the soil may have originated from similar pollution 
sources. The second principal component (PC2) had an eigenvalue of 1.46 and accounts for 
16.21% of the total variance. It is constituted by only Fe suggesting a possible natural source in 
the soil.   
PC1 with 51.24% variance is heavily loaded on As, Pb, Hg and Ni and can be defined as 
anthropogenic component which confirms the claim on the components of mobile 
phones/laptops/computers made by Hermann et al. (2000).  
PC2 on the other hand is heavily depended on Fe with a total variance of 16.21%. Fe being the 
only component in PC1 confirms that, it is weakly correlated; thus, separate from the other heavy 
metals regarding their correlation coefficient analysis and PCA. This separation between Fe and 
other heavy metals may suggest Fe originated from local natural sources. 
From this study, it can be suggested that, the unregulated recycling techniques (open-pit acid 
baths, heating, chipping and melting, burning etc) at sampling sites have contributed to the 
elevated heavy metal content. Considering this, it seems reasonable to conclude that PC1 
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constitute an anthropogenic component, whereas PC2 appears to be within the parent rock 
(natural source). 
 
5.4.4 Hierarchical Cluster Analysis (HCA) 
Performing HCA on variables rather than on cases is preferred in most research studies Kaufman 
and Rousseeuw (1990). HCA was developed in the present study on soil samples in order to 
identify similarities in metal contents between the analyzed soil samples. This approach was 
selected instead of trying to discriminate between the different sources of metals as already 
accounted for by PCA.  
Thus, the aim in performing HCA was to identify the samples which represented different areas 
where metal content followed a similar pattern. This different approach was also considered 
since the results obtained by both the HCA and PCA are complementary. PCA helped to group 
metals according to their different origin. Once this information is known, HCA allowed 
clustering the areas affected by the different metals, that is, affected or not affected by 
anthropogenic activities.  
 
Three main clusters can be distinguished in the dendrogram (Appendix H), performed with the 
Ward method, which uses the squared Euclidean distance as a similarity measure. Cluster 1 
includes fourteen soil samples (sample IDs; A1, A4, A11, A15, B1, B2, B3, B8, B9, B12, C3, 
C4, C10 and C11) affected by higher Cu and Fe contents. Soils associated with this cluster had a 
local natural origin as the predominant source. Cluster 2 comprises of twelve soil samples 
(sample IDs; A8, B4, B5, B7, B10, B13, B15, C1, C2, C12, C13, and C14). This cluster contains 
the lowest contents of all the heavy metals under investigation. In these cases, normal levels of 
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some of the heavy metals (especially Cd) were identified. Soil samples belonging to cluster 2 are 
the least contaminated. Soils samples (sample IDs; A2, A3, A5, A6, A7, A9, A10, A12, A13, 
A14, B6, B11, B14, C5, C6, C7, C8, C9, and C15) belonged to clusters 3. The soils in this 
cluster contain the highest concentrations of all the nine heavy elements under study. Cluster 3 
soil samples were the most polluted with the heavy metals.  
 
This study, though supports critical monitoring, it considers the adoption of the EU Directive 
2002/96/EC on WEEE which imposes recovery, reuse and recycling as a reliable long term 
option. Presently, as every individual is vulnerable to heavy metals and as global annual new 
cases of cancer increases, Stewart and Kleihues (2003), this research suggests that serious 
investment be made in recycling. 
 
5.5 Discussion of questionnaire responses 
5.5.1. Discussion of responses from residential areas 
In this study, residents were sampled according to the distance (20m, 50m, 100m and 150m) they 
reside from BSs. It was assumed that there is no significant difference among the residents’ 
levels of satisfaction (content) or their levels of satisfaction (content) are the same. 
The chi-square test for independence was used to determine whether a resident’s level of 
satisfaction (content) for living nearby a BS is related to distance from the BS. This test 
compared the frequency of cases found in the resident’s level of satisfaction with the BS in the 
neighbourhood across the different categories of distance from the BSs. On the other hand, is the 
proportion of residents at 20m, 50m, 100m and 150m from the BS the same for residents who are 
either satisfied or not satisfied living in the neighbourhood? 
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This test was used because the study wanted to explore the relationship between two categorical 
variables (resident’s level of content with a BS and distance from the BS).  
From table 4.6 (b) the assumptions of chi-square concerning the ‘minimum expected cell 
frequency’, which should be ≥ 5 (or at least 80% of cells have expected frequencies of ≥ 5). This 
means that, the data have not violated the assumption as all expected cell sizes were ≥ 5.  
The main value of interest is the Pearson chi square value, which is presented in the final table, 
titled “Chi-Square Tests”. In this study the Chi-square value is 39.231 with an associated 
significance level of .000. 
To be significant, the Sig. value needs to be ≤ 0.05, but in this case the value of 0.000 is less than 
the alpha value of 0.05, hence it can be concluded that the result is significant. 
Therefore, it has been statistically proven from the Chi-Square Test that;  
  There is significant risk associated with living close to BSs. 
 There is no significant difference between risk perception and the involvement of 
residents in the selection of sites for mounting BSs. 
 Distance has significant effect on risk to the public. 
 Distance has no significant effect on health risk in children and neighbourhoods. 
 
5.6 Discussion of data from questionnaire responses 
Strategic procedures are required to fulfill all the relevant principles identified in EIA either on 
short or long term durations. These procedures ought to take into consideration; 
 The knowledge level of all stakeholders or targeted groups 
How to involve all stakeholders to enhance transparency in all procedures 
 How to communicate what is either known, unknown or suspected to stakeholders 
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 Evaluation of implemented measures 
 
Concepts of EIA principles relevant to this study include; 
 
5.6.1. Participative 
The process should provide appropriate opportunities to inform and involve the interested and 
affected stakeholders. Additionally, the inputs and concerns of stakeholders should be addressed 
explicitly at the decision making stage or in any documentation. 
 
5.6.1.1 Consultation 
This study considers the claim that public participation is an indicator of an effective EIA 
procedure (Wood, 2003) and the argument that perception equals reality, Covello et al. (1991) as 
critical factors as the MTT is still new to Ghanaians. This study supports the adoption of the 
Seven Cardinal Rules of Risk Communication highlighted by Covello et al. (1991) as sited in 
chapter two.  
There is virtually in most instances no correlation between public perceptions of risk and the 
available scientific evidence, hence consultation is expected to; increase the knowledge level of 
all stakeholders as well as eliminate or reduce hypersensitivity.  
It is worth noting that, according to the Guidelines on Communication Towers (2010); 
neighbourhood consultation should be done within a radius of 500m (involving 11,775 persons). 
In this study (within a radius of 150m), out of 129 respondents forming 90.8% who were in 
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residence before BSs were mounted only 26 admitted they were consulted. The failure to consult 
is considered by this study as the major contributing factor that has led to the misconceptions, 
doubts and perceptions that has created discontent (dissatisfaction) in neighbourhoods. Though 
all stakeholders admitted perceptions exist, this study however, does not accept “collapsing of 
towers and noise” as perceptions. Therefore, this study suggests a critical scrutiny of the factors 
that affect laypeople’s perception of risk as considered by Siegrist (2000) and Krewski et al. 
(2006). This study can therefore support prior reviews cited by WHO (2002), Slovic (1987) 
andSlovic et al. (1990) and caution that risk perception could be extremely high if the present 
pattern of consultation does not change. 
Again, only 2 residents admitted their suggestions were taken into consideration, hence 
confirming a study by Palerm (1999) and Bond et al. (2004)that, practically, public participation 
has been reduced to a mere formality in EIA processes. However, another dimension highlighted 
by Hokkanen (2007) and Pölönen (2007) is that, the public's contribution is not used in decision 
making and this has been confirmed by this research.                                                                                                                                        
This research further revealed that, out of a total of 144 respondents, 88 forming 61.1% insisted 
they are familiar with the MTT. When requested to indicate where they acquired this knowledge 
from, a majority 46 forming 52.9% mentioned the “news media” as their source. This therefore 
confirms the assertion by Krewski et al. (2006) that, the most important source of information 
about health issues and risks for laypeople seems to be the news media. This study can confirm 
that, the discontent in neighbourhoods (even at 150m from BSs) is partly due to the news media 
which according to Koren and Klein (1991) are more likely to report studies suggesting that a 
technology is risky than studies suggesting that a technology is safe. 
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Again, the argument by Siegrist and Cvetkovich (2001) that, people have more confidence in 
hypothetical scientific results suggesting a danger than in results indicating a low level of risk 
has also been demonstrated in this research. 
The NCA emphasized that, neighbourhoods are consulted before BSs are mounted close to their 
homes. To verify this assertion, the NCA was asked to list the complaints they receive from 
residents dwelling close to BSs. Though the NCA admitted receiving complaints, they did not 
state the nature of complaints. Indeed, when institutions that issue permits do not provide 
answers to specific questions in such studies, it therefore becomes very difficult to objectively 
vindicate such institutions.  
The EPA affirmed that, neighbourhoods are consulted before BSs are mounted close to their 
homes. To verify this assertion, the EPA was asked to list the complaints they receive from 
residents dwelling close to BSs. Though the EPA claimed consultations are done, the EPA 
furthered listed “no consultation” as a complaint among others they receive from residents. This 
therefore contradicts a major EIA procedure highlighted by Wood (2003) which has further been 
described as fundamental. Indeed, the application for EPA permit requires among others a 
written “evidence of consultation with neighbours” within up to a distance of 500m from a BS 
Guidelines on Communication Towers (2010). This study can suggest that, since the EPA is not 
represented at the MMDA, participation in activities at the community level therefore become 
very challenging for the EPA. This study, finds it difficult to single out either political or 
organizational factor or even both as reiterated by Meredith and Mantel (1995) and Cicmil 
(2000) as being responsible for this weakness.               
Out of the 8 responses from the MMDA who participated in this study, 6 respondents forming 
75% maintained that, residents are consulted whilst the remaining 2 respondents forming 25% 
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gave a negative response. However, this research cannot fully associate with the MMDA after 
critically considering the responses obtained at residential areas. 
 
5.6.2 Credibility 
This describes how the process should be carried out with professionalism, firmness, fairness, 
objectivity, impartiality and balance, and be subjected to independent checks and verification. 
 
5.6.2.1 Possible adverse impacts  
This study accepts the claim that, in environmental management, it is neither possible to 
anticipate everything beforehand, nor is it possible to write a condition that covers everything 
Lindström et al. (2007). Therefore, complaints received by institutions that issue permits could 
possibly indicate that, significant adverse impacts other than those anticipated during the design 
phase might have been encountered. This could possibly confirm a basic constraint such as 
scientific uncertainty about environmental effects of a specific technology in EIA processes 
Stewart-Oaten et al. (1986).   
Though the NCA confirmed receiving complaints from residents, they declined to specifically 
elaborate on the nature of complaints received. The NCA in responding specifically to whether 
BSs have significant adverse impacts other than those anticipated during the design phase, 
responded “enquire from EPA”. This response could be interpreted objectively as the NCA not 
being aware of any adverse impacts other than those anticipated during the design phase if any 
exists. The improvement upon the performance of projects in terms of environmental concerns 
and safety Saad et al. (2002) and Flyvbjerg et al. (2003) cannot become relevant with this 
response. However, this research views the recommendation by Quigley et al. (2006) to careful 
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examine all uncertainties and the transparency of assumptions and limitations as very necessary 
at this stage. 
The EPA has in this study stated that, they continuously receive complaints from residents 
dwelling close to BSs and has also admitted that, there are possible risks associated with the 
MTT. This reveals the EPA’s professional awareness of the characteristics of risk; unknown, 
uncertain, involuntary, unfamiliar among others Gregory and Mendelsohn (1993) and possibly as 
risk is related to; health, safety, environment among others Covello (2003). The EPA listed some 
possible risks, however; leakages from fuel tanks could lead to possible fire outbreaks at BSs. 
This indeed verifies informal discussions with environmental expects who mentioned the 
incidence of self propagating battery fires as battery jars do craze and crack. This the ITU (2008) 
have already explained could be caused by; 
 Aging and manufacturing defect(s) 
 Chemical degradation between jar material, sulfuric acid, and other chemicals 
 Abuse, accidents, earthquakes, fires and improper installation                                              
Though traces of fuel indicating leakages were observed on the concrete floors at some BSs (Fig. 
4.1), samples of concrete could not be taken for quantitative analysis as has been recommended 
in risk assessment Veerman et al. (2005) and WHO (1998). Indeed, further discussions at 
neighbourhoods confirmed that, such fire outbreaks have been reported at some sites in some 
towns including Agona Swedru. However, this study cannot support any effective practical 
intervention or policy to mitigate even the anticipated significant adverse impacts and this indeed 
contradicts the fundamentals of an effective EIA system Heinma and Põder (2010) and Glasson 
et al. (2005).  
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This study revealed that indeed almost all the MMDA are aware of some possible risks 
associated with the MTT except only one assembly (Ga East-WD) forming 12.5%. For example, 
the AMA confirmed instances of tower collapse, but (Adentan-TP) mentioned a collapse leading 
to two deaths in the assembly’s jurisdiction, therefore prompting the need for periodic inspection 
and maintenance Hale et al. (2004). The responses from the MMDA suggest the strict 
enforcement of policies on public education Wood (2003), the creation of physical barriers 
Hollnagel (1999) and Hale et al. (2004) and inter and intra agency collaboration as well as 
institutional coordination Saarikoski (2000). Therefore, this indicates that, there could possibly 
be other significant adverse impacts other than those anticipated during the design phase. Again, 
this study cannot confirm any effective practical measure aimed at mitigating the adverse 
impacts mentioned by the MMDA.  
At residential areas, a basic indicator such as HHRA which according to Steinemann (2000) is 
used in EIA practice and also for estimation in HIA O'Connell and Hurley (2009) was used to 
determine significant adverse impacts. Therefore, residents were to confirm or otherwise whether 
they have identified any health risk in their children that they can link to BSs. Only 2 
respondents forming 1.4% out of a total number of 144 respondents claimed they have identified 
headache as a health risk in their children. Their claim contradicted the door-to-door interviews 
performed by Eger and Neppe (2009) which recorded 23 cases of cancer and also a study by 
Trower (2001) which recorded cases of pain, headache, general weakness and anaemia.However, 
because HIA is expected to involve many samples Mindell et al. (2001) and O’Connell and 
Hurley (2009), the study further sought from residents any health risk in their neighbourhood 
that they can link to BSs. Again, only 2 respondents forming 1.4% out of a total number of 144 
respondents claimed they have identified two individuals with health risks they suspect can be 
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linked to BSs. Unfortunately for this study, these respondents could not assist the research team 
to locate these persons for further interrogation. Furthermore, this claim contradicts two studies 
conducted by Santini and Santini (2001) and Santini et al. (2002) who surveyed people living up 
to 300m from BSs and recorded statistical significant correlations between distance from BSs 
and health problems. Tiredness was recorded up to a distance of 300m; headache, sleep 
disruption and “discomfort” up to 200m; depression, memory loss, dizziness and visual 
perturbations up to 100m. Therefore, they concluded that, BSs should be sited more than 300m 
from residential dwellings. Therefore, this research at this stage cannot confirm a correlation 
between RF from BSs and any health risk as confirmed by WHO (2005), Trower (2001), Hutter 
et al. (2006), Frey (1998) and Santini et al. (2002) and also contradicted by Rubin et al. (2005), 
Koivisto et al. (2001) and Seitz et al. (2005). 
If indeed possible health risks exist around BSs, then currently, the issue of age, sex and 
ethnicity among others argued by Pelkonen, et al. (1997) as influencing the response to hazards 
cannot be cited as possible reasons for not identifying significant health risk in this study. 
However, the assertion that, short duration may produce no effects Chagnaud et al. (1999) and 
Heikkinen et al. (2001) and especially ignorance of residents Songsore and McGranahan (1993) 
might have led to their inability to directly link possible symptoms to BSs. 
 
5.6.2.2 Monitoring and evaluation 
This study supports the views of researchers Marshall et al. (2005) and Stewart-Oaten et al. 
(1986) who have outlined the importance of monitoring and evaluation as significant in EIA 
procedures. Consequently, monitoring and evaluation is expected to; 
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 Improve effective consultation and reduce the number of complaints from residents  
 Ensure compliance with local and international regulations 
The NCA responded “enquire from EPA” in relation to monitoring and evaluation of measures 
implemented to mitigate environmental impacts. This response was not considered as due to 
legal barriers Buckley (1994a, b) but considered as the NCA not realizing the need for 
monitoring and evaluation. Certainly, the possible reason that can be assigned by this study is 
scientific uncertainty about environmental effects as identified by Stewart-Oaten et al. (1986). 
All responses provided by the NCA indicated an indirect involvement in environmental issues 
despite all the controversies. This attests to the argument that, the treadmill of production model 
has also affected the MTT Gould et al. (2004). 
The EPA gave a positive response in relation to monitoring and evaluation of measures 
implemented to mitigate environmental impacts and insisted it is done once in every 18 months. 
A critical consideration of the Guidelines on Communication Towers (2010), can categorically 
stress that, the EPA is not performing effectively. For example, the guidelines insists that, the 
EPA and MMDA issue separate permits before BSs are constructed, however, the EPA and 
MMDA have also complained of constructions without their permits. Perhaps what might have 
accounted for this lapse as evaluated by this research could be the absence of the EPA at the 
local level whilst the MMDA are virtually weak. In fact, this study supports the argument by 
researchers Gough and Yankson (2000), Grant (2009), Antwi and Adams (2003), Owusu (2008) 
and Yeboah and Obeng-Odoom (2010) who have attributed construction without permits to 
delays in obtaining permits from institutions that issue permits.  
However, this lapse could possibly be attributed directly or indirectly to the intertwining of 
political, technological, cultural, organizational and social factors as argued by Meredith and 
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Mantel (1995) and Cicmil (2000) whilst many studies have simply described these institutions as 
weak Farvacque-Vitkovic et al. (2008). 
A critical consideration of the responsibility assigned the MMDA Guidelines on Communication 
Towers (2010); stipulates that, the MMDA are to perform a supervisory role for all the 
institutions or agencies that issue permits. For example, the MMDA shall among others; 
 Be the receiving and/or collection points for building and environmental permits in 
respect of the construction of towers after the requisite approvals have been obtained 
from the GCAA and RPI. 
 Verify all submitted documents (including evidence of neighbourhood consultation) at 
the time of submission for compliance.  
Therefore in assessing to what extent the MMDA are involved in monitoring and evaluation, the 
response revealed a split disclosure; out of the 8 responses, 4 forming 50% gave an affirmative 
response whilst the other 4 gave a response to the contrary. This indeed confirms the constraint 
posed by the fragmentation in licensing procedures Kennett and Perl (1995) and firmly suggests 
the implementation of inter and intra agency collaboration and institutional coordination 
Saarikoski (2000). For example, Ga East and Adenta Assemblies had their town planning and 
engineering departments providing different figures to the number of BS permits they have 
issued (Appendix F).  Moreover the MMDA who responded to the contrary further explained 
that, they are not technically equipped to monitor and evaluate mitigating measures. 
Controversially, it becomes very difficult to explain why towers are not inspected every 6 
months to assess especially their structural integrity. Certainly, this study again cannot ignore the 
influence of political, technological, cultural, social and organizational factors as earlier 
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mentioned Meredith and Mantel (1995) and Cicmil (2000). However, informal interviews and 
discussions done revealed frustrations from the MMDA and hence support the argument that 
political factors are probably the most influential Ahmad (1996).                             
In assessing to what extent institutions monitor and evaluate measures implemented, some 
indirect questions were posed. This strategy was used in agreement with the ideals of Hilding-
Rydevik (2006) and Similä (2007) that directly linked the “effectiveness” of environmental 
policy tools to the achievement of policy goals. For example, compliance with regulations 
governing the mounting of BSs was used as a fundamental indicator. However, all residents who 
claimed they were aware of some regulations were categorical that, operators do not fully 
comply. In fact, 65 residents cited “not to mount closer to homes” whilst 20 sited “to seek public 
consent” among others as regulations operators do not comply. 
 
5.6.3 Focused 
This describes the ability of the EIA process to concentrate on significant environmental effects 
and relevant issues that ought to be taken into account in making decisions. 
 
5.6.3.1 Environmental requirements 
This study consents to the argument by Pölönen (2007) that, the preconditions for granting 
environmental permits are intended to prevent significant negative effects on the environment 
and unreasonable burdens on properties. However, considering the main responsibilities of the 
NCA, this study did not identify any direct responsibility linked to environmental concerns. This 
further suggests that perhaps because most studies have produced no adverse results, as 
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emphasized by Chagnaud et al. (1999) and Heikkinen et al. (2001) permitting agencies are still 
not critical on precautionary measures. 
The EPA listed “distance of BSs from residents, schools or hospitals” and “plot size” Guidelines 
on Communication Towers (2010) as the major environmental factors they consider before 
issuing permits. Though, this study can confidently assert that, this is practically to the contrary 
as observed in residential areas, nevertheless, the EPA at the time of this study insisted that, all 
operators will comply by May, 2015. Indeed, as this research directly links “effectiveness” of 
performance to achievement of goalsHilding-Rydevik (2006) and Similä (2007), it yet cannot 
point to the cause of this “ineffectiveness”.  
The MMDA are to issue permits only when twelve conditions are satisfied; however, those 
below are strictly designed to minimize environmental impacts, consequently, strengthening the 
principles of Glasson et al. (2005). 
They are;  
 A design of the structure showing its effective height, foundation, guys used, members, 
ladders, rest and work platforms, earthing, lighting protection and aviation lighting.  
 Permit issued by the GCAA for the installation of the tower in the proposed location. 
 Evidence of accident insurance policy and neighbourhood consultation.  
 Structural integrity report and geo-technical investigation report 
On the contrary, objective field survey conducted during this research cannot attest to operators 
fully satisfying all these conditions. Therefore in further assessing the achievement of goals as 
proposed by Hilding-Rydevik (2006) and Similä (2007) this research cancontend the 
effectiveness of the MMDA. However, Greig et al. (2004) concluded that, the limited ability to 
influence national policy at the local level, regardless of its importance hinders the value of EIA. 
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Residents were required to confirm whether operators comply with regulations governing the 
mounting of BSs as insisted in EIA Wood (2003). Eighty one respondents forming 56.3% who 
claimed being aware of the basic requirements, insisted that operators do not comply with 
regulations governing the mounting of BSs. This indeed to some extent contradicts the assertion 
that, many citizens are “rationally ignorant” in urban planning Krek (2005). Residents 
subsequently listed the regulations that companies flout in a descending order as;                                                               
 Mounting closer to homes or residential areas.    
 To seek public consent. 
 To educate the public.       
 Co-Location 
Therefore, if honestly, negativity bias has not been demonstrated as at times suspected by 
Rozin and Royzman (2001), then the public’s view does not contradict that of the EPA and 
the MMDA. 
 
5.6.3.2 Minimum legal distance 
The maintaining of physical barriersto reduce harm, as recommended by Haddon (1973) is 
indeed very relevant in developing countries as other restrictions Hale et al. (2004) 
andHollnagel(1999) are more likely to be flouted.              
The NCA and the EPA confirmed that, there are specific minimum legal distances for locating 
BSs from residents Guidelines on Communication Towers (2010). This suggests that, these 
agencies perhaps are not in a position to effectively enforce the mitigating measures 
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implemented. Though other factors could account for this, however, political factors as observed 
are the most influential Ahmad (1996) and is still perceived or suspected by this study.                             
The response from the MMDA revealed almost a split disclosure; out of the 8 responses, 5 
forming 62.5% insisted minimum setbacks exist whilst the others surprisingly maintained their 
ignorance. This further reveals the lack of an integrated model as asserted by Clarke (1984) and 
Hollick (1986) and hence suggests an approach which builds on the concept of EIA as an input 
to decision making Brown and Hill (1995).  
This study can associate the inability of the MMDA to enforce minimum distances with political, 
cultural, social and organizational factors Meredith and Mantel (1995) and Cicmil (2000). 
Residents as discussed earlier have already cited the mounting of BSs to homes as the regulation 
mostly flouted by operators. Therefore, this study can confirm that, distance is a critical factor in 
terms of the siting of certain sensitive facilities as further argued by Petts and Eduljee (1994), 
Löscher and Käs(1998) and Guidelines on Communications Towers (2010).  
 
5.6.4 Systematic 
The process should result in full consideration of all relevant information on the affected 
environment. This should include proposed alternatives, their impacts and measures necessary to 
monitor and investigate residual effects. 
 
5.6.4.1 Waste Management 
The NCA’s response to all EIA related questions and specific questions on WEEE of the MTT 
were simply “enquire from EPA”.  
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The EPA unfortunately did not mention any activity performed in managing WEEE as far as the 
MTT is concerned, however, the Guidelines on Communications Towers (2010) specified that, 
the disposal of used batteries should be supervised by the EPA. Controversially, the EPA stated 
that, WEEE of the industry is not scientifically managed in Ghana, hence confirming the 
assertion by Amoyaw-Osei et al. (2011) that, Ghana has no policy on WEEE. Indeed, Oteng-
Ababio (2010) has further concluded that, despite a wide range of environmental legislation in 
Ghana, there are no specific laws for WEEE recycling. This consequently reinforces other 
studies reporting of difficulties in WEEE management in most countries Agamuthu et al. (2009) 
and Hiramatsu et al. (2009).  
The MMDA responded “no” to this specific question except the Ga East Assembly (WD) that 
responded to the affirmative and subsequently pointed that, this measure is in the environmental 
permit issued by the EPA. Though the application for EPA permit requires the performance of 
EIA to avert any detrimental effect to the environment Guidelines on Communication Towers 
(2010), the guidelines failed to highlight the holistic management of WEEE.  
Residents were not to respond to this question as they know of the commonly adopted methods 
of open burning and dumping at uncontrolled dumpsites Agamuthu (2001) which scientists claim 
cause serious environmental problems including health hazards Ball and Denhann (2003). 
However, almost all stakeholders involved in this research expressed serious concern about the 
indiscriminate dumping of WEEE and suggested that the EPA should play a leading role in the 
management.  
 
 
 
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5.6.5 Adaptive 
The process should be adjusted to the realities, issues and circumstances of the proposal 
underreview. Therefore, relevant techniques and experts in diverse disciplines including 
traditional knowledge should be used in achieving accepted objectives. 
 
5.6.5.1 Commitment of stakeholders to EIA principles 
The main duty of the NCA that this study can confirm the agency attaches great commitment to 
is “equipment standards and type approval”. Indeed, this research revealed that, radiation levels 
measured are indeed in compliance with the ICNIRP threshold as confirmed by levels measured 
by Amoako et al. (2009). In responding directly to whether operators are committed to EIA 
principles, the NCA responded “enquire from EPA”. This response clearly indicates 
inconsistencies to the quality of regulatory governance and confidence as cited by Melody 
(1997) and Stern and Holder (1999). 
This study sought the capacity of the EPA relative to its duties as argued by Ridgeway et al. 
(1996) and discussed earlier. A direct question demanding whether operators are committed to 
EIA principles and compliance with local and international regulations were replied in the 
affirmative. 
All the MMDA indicated a direct link to environmental concerns as far as their duties are 
concerned. This therefore, places the MMDA in a better position to confirm whether operators 
are really committed to EIA principles. All the eight respondents except one (Adentan TP) which 
forms 12.5% gave an affirmative response to this question. If this claim can be fully supported 
by this study, then complaints and risk perception from residents should be at a minimal as 
sought by effective EIA principles Sadler (1996) and Mickwitz (2003). 
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Complaints and risk perception from residents as discussed above can be used as a measure to 
indicate the level of commitment of operators and institutions that issue permits. Results of this 
study contravene the claim by Slovic et al. (1990) that, a qualitative factor such as unfamiliarity 
could increase discontent as 81 respondents forming 61.1% out of 144 respondents claimed they 
are familiar with the MTT.  
Therefore, discontent for the MTT by residents could subsequently be linked to “electromagnetic 
hypersensitivity” Ahlbom et al. (2004) and Koivisto et al. (2001), especially with those in close 
proximity to facilities of the technology McGee et al. (2002). As a result of this, “compensating 
people to relocate” is considered by this study as a relevant option since a causal relationship 
between EMF exposure and symptoms is yet to be scientifically established WHO (2005), 
Koivisto et al. (2001), Seitz et al. (2005) and Rubin et al. (2005). However, this study has also 
revealed that relocation is indeed not acceptable to residents Hayes and Morrison-Saunders 
(2007) as only 28 respondents forming 19.4% claimed they will relocate when compensated. 
 
5.6.5.2 Achievement of EIA objectives 
The achievement of EIA objectives is considered in line with the claim by Glasson et al. (2005) 
to be linked directly to the effective implementation of EIA measures. Therefore, this study 
considers the reduction in the number of complaints from all stakeholders especially residents as 
a practical measure of achievement. Additionally, issues of scientific uncertainty Stewart-Oaten 
et al. (1986) as well as that of statistical and nonscientific constraints Osenberg et al. (1992) 
about the MTT should be resolved. In resolving, this study recommends the research tool 
“Before-After-Control-Impact” as prescribed by Stewart-Oaten et al. (1986) to enhance 
understanding of environmental effects.   
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Therefore, achievement of EIA objectives of the MTT can be judged based on all the discussions 
done in this chapter as further reiterated by the EPA “not fully”, hence suggesting that, more 
should be done. 
 
5.7 Risk vulnerability index (RVI) 
Based on the results obtained from this study, the following variables have been identified to 
increase the RVI of the MTT; 
X1 = Heavy metals 
X2 = Noise 
X3 = Possible collapse 
X4 = Possible fire outbreak 
X5 = Geohazard 
X6 = Perception 
X7 = Absence of aerial bulb 
Therefore, employing equation (4.7), the RVI for the MTT can be calculated with all the 
variables assigned a mathematical value of 1 and RVI expected to be greater than zero (0). 
Where X1 is considered as a constant. 
Xn may alter as risk perceptions vary over time and are interpreted on the basis of context. This 
model does not have any denominator to reduce the RVI; because all individuals including 
babies are susceptible Koranteng-Addo et al. (2010) and Cobbinah et al. (2013) and also cannot 
resist the constant X1. This RVI, though results in negative emotions it is expected to promote 
sustainable behavior Malott (2010) since sustainability does not come naturally Dawkins (2001). 
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CHAPTER SIX 
6.0 CONCLUSIONS AND RECOMMENDATIONS 
 
6.1 Conclusions  
This chapter provides the conclusions to significant findings of this study with the main intention 
of revealing and resolving misconceptions, doubts and perceptions about the MTT. Though few 
studies have focused on quantitative analysis, this research has gone further to include qualitative 
variables for analyses to arrive at relevant techniques to assist reduce risks. 
 
RF emissions from BSs were ranging from 2.23E-07 to 3.11E-01 for the 900MHz and also from 
1.58E-08 to 5.60E-01 for the 1800MHz, hence suggesting that RFs do not pose any health threat 
to the public as confirmed by (Amoako et al., 2009). The suspicion based on the “belief” that, 
radiation from BS antennae could in the long term cause adverse health effects could not be 
supported by this study. Indeed, this research revealed that, radiation levels are in compliance 
with the ICNIRP threshold. However, this public concern could be attributed to the fact that, 
there is no clear and definitive assessment as to whether there exists a health risk from long-term 
exposure to RFs. 
Possible adverse effects of noise could be experienced by people within a radius of 50m from 
BSs as noise levels were above the EPA standards around some BSs between the hours of 
22:00pm to 06:00am. Hence, considering only a BS sited in a residential zoning, about 118 
individuals are therefore prone to the adverse effects of noise. However, this number of 
individuals increases as the number of BSs as well increase. 
 
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This research focused on the presence of heavy metals as they are bio-accumulative, persistent 
and can virtually damage any organ. All the heavy metals analyzed were detected at low levels, 
except Hg which has exceeded its threshold whist Cd has also reached its threshold and may 
possibly pose health risk to the public. This confirms the claim by Koger et al. (2005) and Obiri 
et al. (2010) that, there is increasing evidence linking toxicants such as Hg, Pb, As, and Cd to the 
incidence of cognitive impairments, especially in children, and cancers of all sorts. Though Fe 
recorded high values in the soil samples, the statistical analysis performed suggested that, only 
Fe occurred naturally and is hardly affected by human activities. Indeed, the application for EPA 
permit requires the performance of EIA to avert any detrimental effect to the environment, the 
guidelines failed to highlight the holistic management of WEEE. 
Finally, this study cannot support any effective practical intervention or policy to mitigate even 
the anticipated significant adverse impacts and this indeed contradicts the fundamentals of an 
effective EIA system. Objective deductions from all respondents and observations pointed 
categorical to the fact that, operators do not fully comply with the rules and regulations 
governing the MTT.  However, the inability to influence national policy at the local level, 
regardless of its importance hinders the value of EIA. 
Additionally, ineffective strategic environmental communication has been highlighted as the 
major factor required to increase the knowledge level and content of all stakeholders, therefore 
reducing perception among populations and not necessarily distance from BSs. As such research 
fatigue was expressed by almost all residents indicating the desire to have access to credible 
information on the MTT and not necessarily to participate or contribute to what is not 
meaningful to them. 
 
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6.2 Recommendations 
Considering the diverse significant importance of the MTT to individuals and the society at 
large, it is therefore necessary to have regulations and practices that are relevant to encourage all 
stakeholders to voluntarily improve upon their environmental performance. 
Therefore the following recommendations are made; 
  
 To EPA 
I. Inter and intra agency/institutional collaboration should be initiated at the local level to 
ensure the effective implementation of all policies. 
II. An independent telecommunications regulator (ITR) should therefore be mandatory for 
the effective regulation of the MTT. The ITR should be deemed truthful, honest, frank 
and transparent by all stakeholders and be supervised by the EPA. 
 
 To MMDA 
I. Should supervise public consultation in all the EIA procedures (from decision making to 
evaluation) as this is crucial to cooperative risk management and the resolution of 
controversial risk-related issues.  
II. Should strictly enforce a minimum setback distance of not less than 50m of BSs from the 
nearest residential structure. 
III. Should invest in the recovery and recycling of all WEEE to reduce the extraction of raw 
metals from the earth and the exposure to heavy metals. 
 
 
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 To Operators 
I. Should comply with all the conditions in the guidelines provided by government for 
mounting BSs. 
II. Should compensate residents who would wish to relocate under very verifiable and 
acceptable conditions to all stakeholders. This is aimed at eliminating hypersensitivity 
especially in individuals who were in residence before BSs were mounted. 
 
 To Government 
I. Should initiate a research project that would really quantify radiation levels absorbed by 
individuals to provide a consistent database which will be needed for future studies since 
it is not always reliable to depend only on background scientific knowledge. 
II. Should initiate a research project that would test for heavy metals in body fluids of 
residents around dumpsites. 
 
 
 
 
 
 
 
 
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APPENDIX A 
Locationsof BSs and results of radiation levels in Accra 
SITE NAME MEASUREMENT GPS COORDINATES ELECTRIC ELECTRIC 
POSITIONS FIELD  FIELD 
(V/m) (V/m) 
LATITUDE LONGITUDE 900MHz 1800MHz 
Anyaa_NIC_Top Point A N05°36.792´ W000°18.288´ 5.30E-03 3.82E-03 
N05°36.793´, 
W000°18.289´ 
 Point B N05°36.801´ W000°18.310´ 6.33E-03 5.86E-04 
 Point C N05°36.777´ W000°18.209´ 1.21E-02 2.42E-03 
 Point D N05°36.717´ W000°18.174´ 6.07E-03 3.54E-04 
 Point E N05°36.711´ W000°18.142´ 2.12E-03 8.05E-04 
 
 
 
 
SITE NAME MEASUREMENT GPS COORDINATES ELECTRIC ELECTRIC 
POSITIONS FIELD FIELD 
(V/m) (V/m) 
LATITUDE LONGITUDE 900MHz 1800MHz 
Ofankor Point A N05°39.440´ W000°16.236´ 3.00E-03 1.28E-03 
N05°39.444´, 
W000°16.235
´ 
 Point B N05°39.512´ W000°16.215´ 8.14E-03 3.24E-03 
 Point C N05°39.539´ W000°16.229´ 1.60E-03 5.77E-04 
 Point D N05°39.535´ W000°16.352´ 1.14E-03 1.93E-03 
 Point E N05°39.453´ W000°16.470´ 4.79E-04 2.77E-04 
 
 
 
 
 
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SITE NAME MEASUREMENT GPS COORDINATES ELECTRIC ELECTRIC 
POSITIONS FIELD FIELD 
(V/m) (V/m)  
LATITUDE LONGITUDE 900MHz 1800MHz 
Spintex_RD_ Point A N05°38.459´ W000°06.373´ 2.27E-02 2.89E-02 
GT 
N05°38.459´, 
W000°06.371 
 Point B N05°38.527´ W000°06.317´ 2.19E-02 1.92E-02 
 Point C N05°38.459´ W000°06.201´ 2.21E-02 1.91E-02 
 Point D N05°38.432´ W000°06.461´ 3.07E-02 2.14E-02 
 Point E N05°38.369´ W000°06.401´ 7.39E-02 3.80E-02 
 
 
SITE NAME MEASUREMENT GPS COORDINATES ELECTRIC ELECTRIC 
POSITIONS FIELD FIELD 
(V/m) (V/m)  
LATITUDE LONGITUDE 900MHz 1800MHz 
Abelemkpe Point A N05° 36.234' W000° 12.822' 3.93E-02 3.51E-02 
N5°36.139´, 
W0°12.868´ 
 Point B N05° 36.304' W000° 12.775' 3.64E-02 1.58E-02 
 Point C N05° 36.181' W000° 12.911' 5.32E-02 1.58E-02 
 Point D N05° 36.059' W000° 12.876' 6.60E-02 4.44E-02 
 Point E N05° 36.053' W000° 12.838' 2.17E-02 2.05E-02 
 
SITE NAME MEASUREMENT GPS COORDINATES ELECTRIC ELECTRIC 
POSITIONS FIELD FIELD 
(V/m) (V/m)  
LATITUDE LONGITUDE 900MHz 1800MHz 
Haatso Point A N05° 40.007' W000° 11.613' 1.75E-02 7.70E-07 
N5°39.992´, 
W0°11.628´ 
 Point B N05° 39.969' W000° 11.683' 1.58E-02 1.12E-02 
 Point C N05° 39.991' W000° 11.720 1.26E-02 1.58E-02 
 Point D N05° 39.931' W000° 11.664' 1.78E-02 1.58E-02 
 Point E N05° 39.979' W000° 11.555' 5.01E-02 2.93E-02 
 
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SITE NAME MEASUREMENT GPS COORDINATES ELECTRIC ELECTRIC 
POSITIONS FIELD FIELD 
(V/m) (V/m)  
LATITUDE LONGITUDE 900MHz 1800MHz 
New Russia Point A N05° 33.020' W000° 15.444' 2.42E-02 1.58E-08 
N5°33.006´, 
W0°15.444´ 
 Point B N05° 33.086' W000° 15.454' 2.49E-02 2.73E-02 
 Point C N05° 32.975' W000° 15.467' 1.95E-02 1.58E-02 
 Point D N05° 32.970' W000° 15.502' 3.02E-02 1.58E-02 
 Point E N05° 32.996' W000° 15.543' 2.05E-02 2.93E-02 
 
 
SITE NAME MEASUREMENT GPS COORDINATES ELECTRIC ELECTRIC 
POSITIONS FIELD FIELD 
(V/m) (V/m)  
LATITUDE LONGITUDE 900MHz 1800MHz 
Legon Point A N05°38.527´ W00°10.663´ 2.62E-02 1.01E-02 
N5°38.532´, 
W0°10.665´ 
 Point B N05°38.504´ W00°10.663´ 3.98E-02 8.91E-03 
 Point C N05°38.499´ W00°10.624´ 1.61E-02 1.05E-02 
 Point D N05°38.494´ W00°10.583´ 2.62E-02 1.01E-02 
 Point E N05°38.440´ W00°10.586´ 1.26E-02 1.14E-04 
 
SITE NAME MEASUREMENT GPS COORDINATES ELECTRIC ELECTRIC 
POSITIONS FIELD FIELD 
(V/m) (V/m)  
LATITUDE LONGITUDE 900MHz 1800MHz 
Oyarifa Point A N05°44.746´ W00°10.604´ 5.56E-03 1.01E-02 
N5°44.746´, 
W0°10.604´ 
 Point B N05°44.706´ W00°10.615´ 4.58E-03 1.04E-02 
 Point C N05°44.915´ W00°10.529´ 4.13E-03 9.29E-03 
 Point D N05°44.738´ W00°10.641´ 7.24E-03 1.58E-02 
 Point E N05°44.646´ W00°10.623´ 6.24E-03 9.15E-03 
 
 
Source: Field Survey Data, 2014 
 
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Figure I: Map indicating locations where radiation levels were measured at “Awudome”  
 
 
 
 
 
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Figure II: Map indicating locations where radiation levels were measured at “Gloryland”  
 
 
 
 
 
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APPENDIX B 
Four (4) positions at approximately the geographical N, S, E and W positions on a radius 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table I: GPS coordinates of positions where noise levels were measured. 
 
BS site 20m 50m 100m 150m 
A P1    W0.34695 P1    W0.346637  P1     W0.346045 P1   W0.345926 
       N5.522295        N5.522441        N5.522116     N5.521229 
Laterbiokoshie 
P2    W0. 34677 P2 W0.34662 P2     W0.346944 P2    W0.348 
(Black Smith) 
        N5.522118         N5.521817        N5.521213      N5.52125 
   N 5.60537000  P3      W0.346947 P3 W0.347312 P3     W0.34785 P3    W0.34795 
W0.25021000 
        N5.521931          N5.521844        N5.522116      N5.523018 
P4   W0.347127 P4     W0.34728 P4    W0.346947 P4   W0.345952 
       N5.522116          N5.522412         N5.523021     N5.523034 
B P1    W0.368401 P1    W0.368087 P1     W0.367496 P1  W0.367376 
       N5.500128         N5.500274        N5.499949       N5.499062 
Kokrobite 
 P2    W0.368221 P2     W0.36807 P2      W0.368395 P2   W0.36945 
    N05°29.997 
       N5.49995          N5.49965         N5.499046      N5.499083 
W000°22.104 
P3    W0.368398 P3     W0.368762 P3     W0.369301 P3    W0.3694 
       N5.499764        N5.499677        N5.499948     N5.500851 
P4    W0.368578 P4   W0.368731 P4   W0.368398 P4   W0.367403 
       N5.499948        N5.500245        N5.500853       N5.500867 
C P1  W0.346959 P1  W0.346646 P1  W0.346054 P1  W0.345934 
      N5.522293       N5.522439       N5.522114       N5.521227 
N05°31.327 
W000°20.817 P2  W0.346779 P2  W0.346629 P2  W0.346953 P2  W0.348008 
     N5.522115       N5.521815       N5.521211       N5.521248  
P3   W0.346956 P3   W0.34732 P3   W0.347859 P3   W0.347959 
     N5.521929      N5.521842      N5.522114      N5.523016 
P4 W0.347136 P4 W0.347289 P4 W0.346956 P4 W0.345961 
    N5.522114     N5.52241     N5.523018     N5.523032 
D P1    W0.374887 P1  W0.374887 P1  W0.374295 P1     W0.374176 
        N5.535083       N5.535083       N5.534759         N5.533871 
New Bortianor 
 P2    W0.37487 P2     W0.37487 P2   W0.375194 P2      W0.37625 
     N5°32.086 
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W0°22.512        N5.534459          N5.534459        N5.533855         N5.533892 
P3    W0.375198 P3   W0.375562 P3    W0.376101 P3    W0.3762 
       N5.534573       N5.534486        N5.534758        N5.53566 
P4   W0.375377 P4   W0.37553 P4   W0.375197 P4   W0.374202 
       N5.534758       N5.535054        N5.535663        N5.535676  
E P1W0.124367 P1  W0.123954 P1  W0123362 P1  W0.123242 
      N5.601561       N5.601706       N5.601382       N5.600494 
 
P2W0.124087 P2  W0.123937 P2  W0.124261 P2  W0.125316 
N5°36.083 
     N5.601383       N5.601082       N5.600478       N5.600515  
W0°07.456 
P3W0.124264 P3   W0.124628 P3   W0.125167 P3   W0.125267 
     N5.601197      N5.601109      N5.601381      N5.602283 
P4W0.124444 P4 W0124597 P4 W0.124264 P4 W0.123269 
    N5.601381     N5.601677     N5.602286     N5.602299 
F P1      W0.25021 P1      W0.249896 P1   W0.249305 P1   W0.249185 
          N5.605548           N5.605693       N5.605369       N5.604481 
Abeka-Lapaz 
 P2 W0.25003 P2 W0.249879 P2    W0.250204 P2    W0.251259 
 
         N5.60537          N5.605069        N5.604465        N5.604502 
N 5.60537 
W0.25021 P3    W0.250207 P3    W0.250571 P3     W0.25111 P3     W0.251209 
          N5.605183           N5.605096        N5.605368        N5.60627 
P4     W0.250387 P4     W0.25054 P4    W0.250207 P4    W0.249212 
          N5.605368           N5.605664        N5.606273        N5.606286  
G P1  W0.2575 P1  W0.257187 P1  W0.256595 P1  W0.256476 
      N5.629727       N5.629873       N5.629548       N5.628661 
 
P2  W0.25732 P2  W0.25717 P2  W0.257494 P2  W0.25855 
N5°37.773 
     N5.629549       N5.629249       N5.628644       N5.628681  
W0°15.450 
P3   W0.257498 P3   W0.257862 P3   W0.258401 P3   W0.2585 
     N5.629363      N5.629275      N5.629547      N5.63045 
P4 W0.257677 P4 W0.25783 P4 W0.257497 P4 W0.256502 
    N5.629547     N5.629843     N5.630452     N5.630465 
H P1  W0.21008 P1  W0.209767 P1  W0.209175 P1  W0.209056 
      N5.556454       N5.5566        N5.556275       N5.555388 
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 P2  W0.2099  P2  W0.20975  P2  W0.210074 P2  W0.21113  
     N5.556277       N5.555976       N5.555372       N5.555409  
N 5.55627717 
W0.21008018 P3   W0.210078 P3   W0.210442 P3   W0.210981 P3   W0.21108 
     N5.55609      N5.556003      N5.556275      N5.557177 
P4 W0.210257 P4 W0.21041 P4 W0.210077 P4 W0209082 
    N5.556275     N5.556571     N5.55718     N5.557193 
I P1  W0.241067 P1  W0.240754 P1  W0.240162 P1  W0.240043 
      N5.531299       N5.531445       N5.53112        N5.530233 
 
P2  W0.240887 P2  W0.240737 P2  W0.241061 P2  W0.242117 
N 5.53112175  
     N5.531121       N5.530821       N5.530216       N5.530254  
W0.24106715 
P3   W0.241065 P3   W0.241429 P3   W0.241968 P3   W0.242067 
     N5.530935      N5.530848      N5.531119      N5.532022 
P4 W0.241244 P4 W0.241397 P4 W0.241064 P4 W0.240069 
    N5.531119     N5.531416     N5.532024     N5.532038 
J P1  W0.1943 P1  W0.193987 P1  W0.193395 P1  W0.193276 
      N5.616343       N5.616489       N5.616164       N5.615277 
 
P2  W0.19412  P2  W0.19397  P2  W0.194294 P2  W0.19535  
N5°36.970 
     N5.616166       N5.615865       N5.615261       N5.615298  
W0°11.658 
P3   W0.194298 P3   W0.194662 P3   W0.195201 P3   W0.1953 
     N5.615979      N5.615892      N5.616164      N5.617066 
P4 W0.194477 P4 W0.19463 P4 W0.194297 P4 W0.193302 
    N5.616164     N5.61646     N5.617069     N5.617082 
K P1  W0.3021 P1  W0.301787 P1  W0.301195 P1  W0.301076 
      N5.60236        N5.602506       N5.602181       N5.60129  
 
P2  W0.30192  P2  W0.30177  P2  W0.302094 P2  W0.30315  
N5°36.136 
     N5.602183       N5.601882       N5.601278       N5.601315  
W0°18.121 
P3   W0.302097 P3   W0.302462 P3   W0.303 P3   W0.3031 
     N5.601996      N5.601909      N5.602181      N5.603083 
P4 W0.302277 P4 W0.30243 P4 W0.302097 P4 W0.301102 
    N5.602182     N5.6024477     N5.603085     N5.603099 
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L P1  W0.204135 P1  W0.203822 P1  W0.20323 P1  W0.203111 
      N5.668921       N5.669067       N5.668742       N5.667855 
 
P2  W0.203955 P2  W0.203805 P2  W0.204129 P2  W0.205184 
N5°39.992 
     N5.668744       N5.668443       N5.667839       N5.667876  
W0°11.628 
P3   W0.204132 P3   W0.204496 P3   W0.205035 P3   W0.205135 
     N5.668557      N5.66847      N5.668742      N5.669644 
P4 W0.204312 P4 W0.204465 P4 W0.204132 P4 W0.203137 
    N5.669038     N5.669038     N5.669646     N5.66966 
M P1  W0.183749 P1  W0.183436 P1  W0.182844 P1  W0.182725 
      N5.56176        N5.561582       N5.561581       N5.560694 
 
P2  W0.183569 P2  W0.183419 P2  W0.183743 P2  W0.184799 
   N5.56158253 
     N5.561582       N5.561282       N5.560677       N5.560714  
   W0.18374881 
P3   W0.183747 P3   W0.184111 P3   W0.18465 P3   W0.184749 
     N5.561396      N5.561308      N5.56158      N5.562483 
P4 W0.183926 P4 W0.184079 P4 W0.183746 P4 W0.182751 
    N5.56158     N5.561876     N5.562485     N5.562498 
N P1  W0.1666 P1  W0.166287 P1  W0.165695 P1  W0.165576 
      N5.67876        N5.678906       N5.678581       N5.677694 
 
P2  W0.16642  P2  W0.16627  P2  W0.166594 P2  W0.16765  
N5°40.715 
     N5.678582       N5.678282       N5.677677       N5.677714  
W0°09.996 
P3   W0.166598 P3   W0.166962 P3   W0.167501 P3   W0.1676 
     N5.678396      N5.678308      N5.67858      N5.679483 
P4 W0.166777 P4 W0.16693 P4 W0.166597 P4 W0.165602 
    N5.67858     N5.678876     N5.679485     N5.679498 
O P1  W0.196586 P1  W0.196273 P1  W0.195681 P1  W0.195562 
      N5.596898       N5.597044       N5.596719       N5.595832 
 
P2  W0.196406 P2  W0.196256 P2  W0.19658  P2  W0.197636 
    N5.59672054  
     N5.59672       N5.59642       N5.595815       N5.595852  
W0.19658616 
P3   W0.196584 P3   W0.196948 P3   W0.197487 P3   W0.197586 
     N5.596534      N5.596447      N5.596718      N5.597621 
P4 W0.196763 P4 W0.196916 P4 W0.196583 P4 W0.195588 
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    N5.596718     N5.597015     N5.597623     N5.597636 
P P1  W0.257833 P1  W0.25752 P1  W0.256928 P1  W0.256809 
      N5.642594       N5.642116       N5.642415       N5.641528 
 
P2  W0.257653 P2  W0.257503 P2  W0.257827 P2  W0.258883 
N05°38.545 
     N5.642416       N5.642116       N5.641511       N5.641548  
W000°15.470 
P3   W0.25783 P3   W0.258195 P3   W0.258733 P3   W0.258833 
     N5.64223      N5.642142      N5.642414      N5.643317 
P4 W0.25801 P4 W0.258163 P4 W0.25783 P4 W0.256835 
    N5.642414     N5.64271     N5.643319     N5.643332 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table II: Noise levels recorded at specific locations. 
 
BS site 20m 50m 100m 150m 
A P1           62 P1 50 P1          ** P1             ** 
P2 67 P2 56 P2           ** P2            ** 
P3 70 P3 50 P3            ** P3              ** 
P4          64   P4             52 P4        ** P4     ** 
66 52 
B P1 63 P1  45 P1             ** P1 ** 
P2 66 P2              52 P2              ** P2              ** 
P3 75 P3 55 P3              ** P3              ** 
P4             67 P4             49 P4 ** P4  ** 
68 50 
C P1 74 P1             55 P1        ** P1              ** 
P2 77 P2             62 P2         45 P2              ** 
P3             73 P3             61 P3 42 P3          ** 
P4             81    P4  63 P4  46 P4     ** 
 
76 60 
44 
D P1 67 P1      56 P1   43 P1             ** 
P2 70 P2      59 P2   41 P2             ** 
P3 73 P3  61 P3   41 P3         ** 
P4              71 P4 57 P4  ** P4     **  
70 58 42 
E P1                    59 P1              43 P1             ** P1            ** 
P2                 61 P2               47 P2         ** P2           ** 
P3               63 P3               49 P3            ** P3            ** 
P4               62 P4  42 P4         ** P4         ** 
61 45  
F P1            69  P1                   48 P1 ** P1           ** 
P2 68 P2                48 P2 ** P2            ** 
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P3 66 P3                   46 P3            ** P3             ** 
P4             66 P4              46 P4         ** P4        ** 
67 47 
G P1               71 P1       59 P1           ** P1              ** 
P2 71 P2                   62 P2           43 P2              ** 
P3 68 P3                  59 P3         ** P3              ** 
P4               71 P4              61 P4         45 P4           ** 
70 60 44 
H P1 60 P1                  50 P1             ** P1            ** 
P2 58 P2              42 P2             ** P2            ** 
P3 50 P3                   ** P3             ** P3             ** 
P4              57 P4     **  P4         ** P4         ** 
56 46   
I P1 58 P1                52 P1             ** P1             ** 
P2                       52 P2                 47 P2             ** P2             ** 
P3                  45 P3                   ** P3             ** P3             ** 
P4               50 P4              44 P4         ** P4        ** 
51 48 
J P1                  53 P1                   ** P1              ** P1              ** 
P2 71 P2   57 P2            44 P2 ** 
P3                      60 P3                    49 P3              ** P3 ** 
P4               64     P  4              45 P4         ** P4          ** 
62 50 44  
K P1 69 P1              45 P1 ** P1 ** 
P2                    66 P2      ** P2 ** P2             ** 
P3                       70 P3      ** P3 ** P3 ** 
P4               56 P4            50 P4         ** P4   ** 
65 48 
L P1                44 P1              ** P1            ** P1 ** 
P2                       50 P2                    43 P2            ** P2             ** 
P3                 65 P3 44 P3 ** P3 ** 
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P4               58 P4      47 P4 ** P4  ** 
54 45 
M P1                56 P1                    49 P1              ** P1              ** 
P2 55 P2 50 P2          42 P2              ** 
P3                55 P3              50 P3          ** P3 ** 
P4               65 P4 52 P4  44 P4         ** 
58 50 43 
N P1   71 P1                  50 P1           ** P1 ** 
P2 54 P2             ** P2           ** P2 ** 
P3                    59 P3 46 P3             ** P3   ** 
P4              57 P4              ** P4     ** P4          ** 
60 48 
O P1                    59 P1              41 P1         ** P1 ** 
P2               58 P2                  ** P2            ** P2 ** 
P3                  71 P3              41 P3           44 P3 ** 
P4               41 P4             **    P 4         ** P4       ** 
57 41 44  
P P1                   64 P1                 55 P1   ** P1           ** 
P2                68 P2                 60 P2            45 P2             ** 
P3                64 P3              59 P3          43 P3   ** 
P4              64 P4            58 P4         ** P4           ** 
65 58 44 
 62.9Db 51Db 43.6dB ** 
 
 
 
 
 
 
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APPENDIX C 
Site “A” 
PLOT COORDINATES HEIGHT ABOVE SEA 
LEVEL 
LATITUDE LONGITUDE 
    A1 N 05033.179’  W 000013.578’ 33m 
    A2 N 05033.179’  W 000013.579’  34m 
    A3 N 05033.181’ W 000013.579’ 33m 
    A4 N 05033.184’ W 000013.579’ 35m 
    A5 N 05033.189’ W 000013.581’ 32m 
    A6 N 05033.189’ W 000013.582’ 33m 
    A7 N 05033.185’ W 000013.582’  35m 
    A8 N 05033.184’ W 000013.581’ 35m 
    A9 N 05033.181’ W 000013.581’ 34m 
    A10 N 05033.179’ W 000013.579’ 35m 
    A11 N 05033.176’ W 000013.580’ 41m 
    A12 N 05033.180’ W 000013.583’ 37m 
    A13 N 05033.181’ W 000013.583’ 34m 
    A14 N 05033.185’ W 000013.584’  35m 
    A15 N 05033.188’ W 000013.587’  37m 
 
Source: Field Survey Data, 2014 
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Site “B” 
PLOT COORDINATES HEIGHT ABOVE SEA 
LEVEL 
  LATITUDE LONGITUDE 
    B1 N 05033.106’  W 000013.556’ 33m 
    B2 N 05033.110’  W 000013.557’  35m 
    B3 N 05033.114’ W 000013.559’ 33m 
    B4 N 05033.118’ W 000013.560’ 33m 
    B5 N 05033.121’ W 000013.563’ 34m 
    B6 N 05033.124’ W 000013.562’ 33m 
    B7 N 05033.122’ W 000013.559’  34m 
    B8 N 05033.120’ W 000013.557’ 33m 
    B9 N 05033.117’ W 000013.554’ 34m 
    B10 N 05033.109’ W 000013.552’ 31m 
    B11 N 05033.113’ W 000013.550’ 35m 
    B12 N 05033.116’ W 000013.552’ 38m 
    B13 N 05033.118’ W 000013.553’ 34m 
    B14 N 05033.124’ W 000013.558’  34m 
    B15 N 05033.129’ W 000013.561’  35m 
 
Source: Field Survey Data, 2014 
 
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Site “C” 
PLOT COORDINATES HEIGHT ABOVE SEA 
LEVEL 
LATITUDE LONGITUDE 
    C1 N 05033.182’  W 000013.459’ 32m 
    C2 N 05033.182’  W 000013.458’  37m 
    C3 N 05033.187’ W 000013.454’ 37m 
    C4 N 05033.191’ W 000013.452’ 34m 
    C5 N 05033.192’ W 000013.446’ 29m 
    C6 N 05033.195’ W 000013.448’ 32m 
    C7 N 05033.193’ W 000013.453’  31m 
    C8 N 05033.189’ W 000013.455’ 32m 
    C9 N 05033.187’ W 000013.457’ 32m 
    C10 N 05033.186’ W 000013.461’ 34m 
    C11 N 05033.192’ W 000013.460’ 31m 
    C12 N 05033.193’ W 000013.459’ 32m 
    C13 N 05033.197’ W 000013.455’ 35m 
    C14 N 05033.199’ W 000013.453’  34m 
    C15 N 05033.202’ W 000013.451’  34m 
 
Source: Field Survey Data, 2014 
 
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APPENDIX D 
Wood (2003) proposed “14 pointevaluation criteria”. 
 
1. Is the EIA system based on a clear and specific legal provision? 
2. Must the relevant environmental impacts of all significant actions be assessed? 
3. Must evidence of the consideration, by the proponent of the environmental impacts of 
reasonable alternative actions for environmental significance take place? 
4. Must screening of actions for environmental significance take place? 
5. Must scoping of the environmental impacts of actions take place and specific guidelines be 
produced? 
6. Must EIA reports meet prescribed content requirements, and do checks to prevent the release 
of inadequate EIA reports exist? 
7. Must EIA reports be publicly reviewed and the proponent respond to the points raised? 
8. Must the findings of the EIA report and the review be a central determinant of the decision on 
the action? 
9. Must monitoring of action impacts be undertaken and is it linked to the earlier stages of the 
EIA process? 
10. Must the mitigation of action impacts be considered at the various stages of the EIA process? 
11. Must consultation and participation take place prior to, and following, EIA report 
publication? 
12. Must the EIA system be monitored and, if necessary, be amended to incorporate feedback 
from experience? 
13. Are the financial costs and time requirements of the EIA system acceptable to those involved 
and are they believed to be outweighed by discernible environmental benefits? 
14. Does the EIA system apply to significant programmes, plans and policies, as well as to 
projects? 
 
 
 
 
 
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APPENDIX E 
Questionnaires used to collect data 
 
UNIVERSITY OF GHANA 
Dear Respondent, 
We humbly request your kind assistance in the successful completion of a project entitled 
“Environmental impact of the mobile telecommunication technology in Ghana (A case study in 
Accra)”. 
The researcher is a PhD student from the Environmental Science Department. 
District Assemblies 
1. Name of assembly;__________________________________________________________________________________ 
2. What are your main duties as well as the mobile telecommunication industry is 
concerned?___________________________________________________________________________________________ 
3(a). How many base stations have you issued permits to in this 
assembly?_______________________________________________________________________________________________ 
(b). How fast has the number of base stations increased over the years?_______________________ 
4(a). Are there possible risks associated with the mobile telecommunication industry? 
                                   Yes                              No  
  
(b). If yes, what are the risks;______________________________________________________________ 
5. Are residents consulted/educated during the siting of base stations? 
                               Yes                                  No  
  
6(a). Do you receive complaints from residents dwelling close to base stations?      
                                Yes                                No 
  
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(b). If yes, what are the complaints?______________________________________________________________ 
(c). How do you resolve the complaints?__________________________________________________________ 
7(a). Do telecommunication operators comply with local and international regulations governing 
the mounting of base stations? 
                                      Yes                          No 
  
(b). If no, which regulation(s) do operators flout?_________________________________________________ 
(c). What sanctions are imposed against operators?______________________________________________ 
8. Are telecommunication operators really committed to EIA principles? 
                                   Yes                             No  
  
9. Do the EIA measures implemented actually attain their expected effects? 
                                        Yes                     No 
  
10 (a). Do base stations have significant adverse impacts? 
                              Yes                        No  
(b). If yes, what are the significant adverse impacts?____________________________________________ 
(c). Have you identified any riskin neighbourhoods that you can link to base stations? 
                                         Yes  No   
  
(d). If yes, what are the identified risks?                            __________________________ 
11(a). Is there an issue of risk perception among people living close to base stations? 
                                Yes                             No   
(b). What are the risk perceptions? ________________________________________________ 
12. Will you consider compensation for people to relocate? 
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                                  Yes                        No 
  
13.What major environmental factors do you consider before permitting base station proposals? 
__________________________________________________________________________________ 
14. Is there a minimum legal distance for locating base stations from the public? 
_______________________________________________________________________________________________ 
15(a). Are material wastes or products of the telecommunication industry (plastics, metals, 
glasses, batteries, SIM cards and chargers) properly managed? 
                          Yes                  No  
(b). If yes, how are they managed? _________________________________________________________ 
16(a). Does the assembly monitor and evaluate measures implemented to mitigate environmental 
impacts? 
                             Yes                        No  
  
(b). If yes, how often? ______________________________________________________________________ 
(c). If no, why?_________________________________________________________________________________ 
 
Wood (2003) proposed “14 pointevaluation criteria”. 
1. Is the EIA system based on a clear and specific legal provision? 
Yes                   No 
  
2. Must the relevant environmental impacts of all significant actions be assessed? 
Yes                   No 
  
3. Must evidence of the consideration, by the proponent of the environmental impacts of 
reasonable alternative actions for environmental significance take place? 
Yes                   No 
  
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4. Must screening of actions for environmental significance take place? 
Yes                   No 
  
5. Must scoping of the environmental impacts of actions take place and specific guidelines be 
produced? 
Yes                     No  
6. Must EIA reports meet prescribed content requirements, and do checks to prevent the release 
of inadequate EIA reports exist? 
Yes                         No    
7. Must EIA reports be publicly reviewed and the proponent respond to the points raised? 
Yes                       No  
  
8. Must the findings of the EIA report and the review be a central determinant of the decision on 
the action? 
Yes                        No  
  
9. Must monitoring of action impacts be undertaken and is it linked to the earlier stages of the 
EIA process? 
Yes                       No  
  
10. Must the mitigation of action impacts be considered at the various stages of the EIA process? 
Yes                        No   
11. Must consultation and participation take place prior to, and following, EIA report 
publication? 
Yes                      No   
12. Must the EIA system be monitored and, if necessary, be amended to incorporate feedback 
from experience? 
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Yes                      No  
  
13. Are the financial costs and time requirements of the EIA system acceptable to those involved 
and are they believed to be outweighed by discernible environmental benefits? 
Yes                       No  
14. Does the EIA system apply to significant programmes, plans and policies, as well as to 
projects? 
Yes                     No  
  
 
THANK YOU 
 
 
 
 
 
 
 
 
 
 
 
 
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UNIVERSITY OF GHANA 
Dear Respondent, 
We humbly request your kind assistance in the successful completion of a project entitled 
“Environmental impact of the mobile telecommunication technology in Ghana (A case study in 
Accra)”. 
The researcher is a PhD student from the Environmental Science Department. 
1. How many years have you lived in this neighbourhood?  
________________________________________________________________________ 
2. Were you living here before this telecommunication base station was mounted? 
            Yes  No              
  
3. If yes, were you consulted before the siting of the base station?  
 Yes     No              
4. If yes, how was the consultation done? 
____________________________________________________________________________________________________________ 
5. What suggestions did you make? 
____________________________________________________________________________________________________________ 
6. Were your suggestions taken into consideration when the base station was mounted? 
 Yes      No              
7. Are you content living closer to a base station? 
Yes      No  
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8. Why?  _____________________________________________________________________________________ 
9. Have you sent any complaint to any authority? 
Yes      No  
10. What is the name of that authority?  
_________________________________________________________ 
11. How were your complaints resolved? 
________________________________________________________________________ 
12. Are you aware of any regulation governing the mounting of base stations?  
              Yes      No  
13. Do telecommunication companies comply with the regulations governing the mounting of 
base stations? 
Yes      No  
14. If no, which regulation(s) do telecommunication companies flout? 
________________________________________________________________________ 
15. Have you identified any health risk in your children that you can link to base stations? 
Yes      No   
16. If yes, can you list them? 
17. Would you like your child∕children to be medically examined? 
Yes      No   
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18. Have you identified any health risk in your neighbourhood that you can link to base stations? 
Yes      No   
19. If yes, can you help us locate the person? 
Yes     No  
  
20. Are you familiar with the mobile telecommunication technology? 
               Yes                                                         No 
  
21. Where did you acquire this knowledge from? 
22. Will you relocate when compensated? 
                Yes     No  
23. Do you have any further suggestions?____________________________ 
PERSONAL DETAILS 
24. How old are you? 
(i) 21 - 40 year        (ii) 41 - 50 years         (iii) over 50 years  
25. Sex:  (i) Male    (ii) Female 
26.Are you a landlord or tenant? ------------------- 
27. What is your highest level of education? 
(i) None       (ii) JHS    (iii) SHS      (iv) Post-secondary (v) Tertiary. 
28. What is your main occupation? 
 
THANK YOU 
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UNIVERSITY OF GHANA 
Dear Respondent, 
We humbly request your kind assistance in the successful completion of a project entitled 
“Environmental impact of the mobile telecommunication technology in Ghana (A case study in 
Accra)”. 
The researcher is a PhD student from the Environmental Science Department. 
EPA/NCA 
1. What are your main duties as far as the mobile telecommunication industry is 
concerned?_________________________________________________________________________ 
2 (a). How many base stations have you issued permits to in Ghana? _________________________ 
(b). How fast has the number of base stations increased over the years?______________________ 
3 (a). How many base stations are in Accra? ____________________________________________________  
(b). How fast has the number of base stations increased over the years?_______________________ 
4 (a). Are there possible risks associated with the mobile telecommunication industry? 
                           Yes                     No    
(b)If yes, what are the risks? _________________________________________________________________________ 
5. Are residents consulted/educated during the siting of base stations? 
                             Yes                   No  
6 (a). Do you receive complaints from residents dwelling close to base stations?      
                             Yes                    No  
(b). If yes, what are the complaints?________________________________________________________________ 
(c). How do you resolve the complaints?_________________________________________________________ 
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7(a). Do telecommunication operators comply with local and international regulations governing 
the mounting of base stations? 
                                    Yes                  No  
  
(b). If no, which regulation(s) do companies flout and why?__________________________________ 
(c). What sanctions does the EPA impose against operators?_________________________________ 
8(a). Are telecommunication operators really committed to EIA principles? 
                                    Yes                     No  
(b). Do the EIA measures implemented actually attain their expected effects? 
                                    Yes                    No 
  
9(a). Are there possible risks associated with the mobile telecommunication industry? 
                                    Yes                    No 
  
(b). If yes, what are the risks? ____________________________________________________________ 
(c). Do base stations have significant adverse impacts? 
                              Yes                   No  
(d). If yes, what are the significant adverse impacts?_______________________________________ 
(e). Does the EPA monitor and evaluate measures implemented to mitigate environmental 
impacts? 
                             Yes                   No  
(f). How often? ______________________________________________________________________________ 
10(a).Is there an issue of risk perception among people living close to base stations? 
                           Yes                     No  
(b). What are some of the of risk perceptions?______________________________________________ 
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11. Will you consider compensation for people to relocate? 
                         Yes                     No 
  
12. What major environmental factors does the EPA take into consideration before permitting 
base station proposals?_________________________________________________________ 
13(a). Is there a minimum legal distance for locating base stations from residential areas? 
  
                      Yes                      No 
(b). If yes, what is the minimum distance?_____________________________________________________ 
14(a). Are material wastes or products of the telecommunication industry (plastics, metals, 
glasses, batteries, SIM cards and chargers) properly managed? 
                   Yes                    No   
 (b). If yes, how are they managed?_________________________________________________________ 
 
Wood (2003) proposed “14 pointevaluation criteria”. 
1. Is the EIA system based on a clear and specific legal provision? 
Yes                   No 
  
2. Must the relevant environmental impacts of all significant actions be assessed? 
Yes                   No 
  
3. Must evidence of the consideration, by the proponent of the environmental impacts of 
reasonable alternative actions for environmental significance take place? 
Yes                    No  
4. Must screening of actions for environmental significance take place? 
Yes                    No  
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5. Must scoping of the environmental impacts of actions take place and specific guidelines be 
produced? 
Yes                    No 
  
6. Must EIA reports meet prescribed content requirements, and do checks to prevent the release 
of inadequate EIA reports exist? 
Yes                        No  
  
7. Must EIA reports be publicly reviewed and the proponent respond to the points raised? 
Yes                       No  
  
8. Must the findings of the EIA report and the review be a central determinant of the decision on 
the action? 
Yes                         No   
9. Must monitoring of action impacts be undertaken and is it linked to the earlier stages of the 
EIA process? 
Yes                       No  
  
10. Must the mitigation of action impacts be considered at the various stages of the EIA process? 
Yes                       No    
11. Must consultation and participation take place prior to, and following, EIA report 
publication? 
Yes                      No   
12. Must the EIA system be monitored and, if necessary, be amended to incorporate feedback 
from experience? 
Yes                       No   
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13. Are the financial costs and time requirements of the EIA system acceptable to those involved 
and are they believed to be outweighed by discernible environmental benefits? 
Yes                       No  
14. Does the EIA system apply to significant programmes, plans and policies, as well as to 
projects? 
Yes                      No   
 
THANK YOU 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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APPENDIX F 
Table. I: The main duties of the NCA as well as the MTT is concerned 
 
 Main Duties 
NCA 1.  Grant licenses and authorizations for operation of 
communication systems and services. 
2. Ensure fair competition among licensees. 
3. Establish and monitor quality of service indicators for 
operators and service  providers. 
4. Consumer education and protection. 
5. Equipment standards and type approval. 
6. International frequency coordination. 
Source: Field Survey Data, 2014 
 
 
 
 
 
 
 
 
 
 
 
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Table. II: The main duties of the MMDAs as well as the MTT is concerned 
MMDA Main Duties 
Ga East (TP) To ensure that tower masts erected acquire all necessarily official 
permits. 
Adentan (TP)  Ensuring that communication base stations are sited according to land 
use conformity and technical specification. 
AMA 1. Issuance of business operating permit (B.O.P)   
 
Ga Central (WD) 1. Inspect site and also issue permit for the construction of base stations.
  
2. Drawings are checked for structure purposes before the approval of 
construction is done. 
Ga East (WD) Receive and process applications by town and country planning dept for 
approval by the statutory planning committee. 
La Nkwantang- Issuing of development permit. 
Madina (TP) 
Adentan(WD) 1. Approve the area for sighting the telecom base station. 
2. Grant structure permit for the construction of masts. 
3. Ensure structure safety for the permit granted. 
Ga West Receive development permit application from operators 
Source: Field Survey Data, 2014 
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Table. III: Rate of increase in the number of BSs over the years and the number of BSs 
issued with permits 
How fast has the number of BSs increased over the years? 
 
MMDA Base stations issued with Rate of increase in base stations over the 
permits years 
Ga West About 50 Not too fast 
Ga East (TP) Over 100 Very fast 
Adentan (TP)  About 5 Mast erection has increased at a fast rate 
 
AMA About 400 Not too fast within the Accra metropolis   
  
Ga Central  About 14 Not frequent   
Ga East (WD) About 44 Very fast 
La Nkwantanang- About 8 Occurred in last 3 years in newly 
Madina  developing communities  
Adentan(WD) No response Very fast 
Source: Field Survey Data, 2014 
 
 
 
 
 
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Table. IV: Compliance of telecommunication operators to local and international 
regulations and sanctions imposed against operators 
 
Do telecommunication operators comply with local and international regulations governing the 
mounting of BS? 
MMDA Response If no which regulation(s) do operators What sanctions are 
flout? imposed against 
 operators? 
Ga East (WD) Yes - - 
Adentan (TP) No 1. Acquisition of building permit 1. Refusal to grant 
which requires issues like; Land title, permits                        2. 
Assemblies and other 
EPA  report, Fire report and 
agencies do nothing  
Structural audit (of existing base 
NOTE: But facilities 
stations) 
remain operational                             
 
Adentan (WD) Yes - - 
  
AMA Yes - - 
 
 
Ga Central Yes - - 
  
Ga East (TP) No 1. EPA report  1. Removal of structures  
2. Supervision of constructing base of 
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structure 2. Penalties  
 
La No The permissible setback for siting Sanctions are contained 
Nkwantanang- base stations in the NCA Guidelines 
Madina  
Ga West Yes - - 
 
Source: Field Survey Data, 2014 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Table. V: Monitoring and evaluation of measures implemented to mitigate environmental 
impacts and effects of the EIA measures implemented 
 
MMD As Does the assembly monitor and evaluate Do the EIA measures 
measures implemented to mitigate implemented actually 
environmental impacts? attain their expected 
Response If yes, how often? /If no, why? effects? 
Ga East(TP) No Various logistics to do the No idea  
monitoring is a challenge to the 
  
assembly. 
Adentan (TP)   No EPA is supposed to monitor and May be 
evaluate measures implemented. 
Ga West Yes Once a while Yes 
AMA  Yes Not very regular No 
Ga Central  No The EPA is mandated to ensure Yes 
 the mitigation of any 
environmental impact ofbase 
stations. 
Ga East (WD)  Yes Once a year Yes 
Adentan(WD) No The assembly does not have the Yes 
capacity to manage or regulate 
such technical  area. 
La Nkwantanang- Yes From project commencement to Yes 
Madina 
completion before certificate of 
  
operation is  issued to the 
operator. 
 
Source: Field Survey Data, 2014 
 
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Descriptive Statistics 
Table. VI: Number of years lived in neighbourhood 
 N Minimum Maximum Mean Std. 
Deviation 
How many years have you 142 6 35 15.78 5.581 
lived in this neighborhood? 
Source: Field Survey Data, 2014 
 
Table.VII: Residents on site before the mounting of BSs 
 Frequency Percent 
Yes 129 90.8 
No 13 9.2 
Total 142         100.0 
Source: Field Survey Data, 2014 
 
Table. VIII: Residents consulted before the siting of BSs 
 
Were you consulted before the siting of Frequency Percent  
the base station? 
 
Yes 26 20.2 
No 103 79.8  
Total 129           100.0  
 
Source: Field Survey Data, 2014 
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Table. IX: Methods of consultation 
How was the consultation done? Frequency Percent 
Individual consultation/discussion 16 64.0 
General meeting with landlords 3 12.0 
 
I was just  informed without any discussion(Landlord) 6 24.0 
Total 25 100.0 
 
Source: Field Survey Data, 2014 
Table. X: Suggestions from residents 
 Frequency Percent 
What suggestions did you make? 
 
I told them it should be mounted far away from residential 7 53.8 
home/neighborhood 
I told them it should be mounted above 35 meters 1 7.7 
 
I told them it should be mounted out of town 4 30.8 
We rejected the mounting of the base station 1 7.7 
Total 13 100.0 
 
Source: Field Survey Data, 2014 
 
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Table. XI: Reasons for comfortably living closer toBSs 
 Why are you contentliving closer to BSs?  Frequency Percent 
Not aware of any consequences/side/harmful effects 36 73.5 
No disturbance (not harmful)  to people 1 2.0 
I think I am far away from it (I am at a reasonable distance) 7 14.3 
I feel OK, not aware of any side effects, however, noise/fumes are my 3 6.1 
 
problem 
Not aware of side effects, however, heard it has collapsed in one or two 1 2.0 
places 
I think am far away from it though heard it causes diseases 1 2.0 
Total 49 100.0 
 
Source: Field Survey Data, 2014 
 
 
 
  
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Table. XII: Suggestion(s) from neighbourhoods  
Do you have any suggestion(s) Frequency Percent 
Government should educate the public/clarify masts related issues 10 5.1 
Base stations should be sited far away from homes 59 30.3 
There should be public education 71 36.4 
Service providers should be truthful in discussing radiation issues with 9 4.6 
the public 
Government should enforce all laws to ensure compliance 2 1.0 
EPA should come out and clarify mast  related issues brought before it 1 .5 
EPA should be strict on the rules about the mast 1 .5 
Service providers should resolve all complaints brought before it 1 .5 
Service providers should consult/seek the consent of the neighborhood 27 13.8 
 Activities of service providers should be seriously monitored 1 .5 
Mast should be strong enough to stand the test of time/not collapse 2 1.0 
Warning lights should always be on 2 1.0 
Noisy generators should be replaced 1 .5 
Medical checkups be conducted regularly 2 1.0 
Residents should demonstrate/protest against sitting of mast closer to 1 .5 
homes 
No, even if I have suggestions, nothing will be done about it 2 1.0 
Stakeholders should ensure that mast has no any health implications 2 1.0 
Landlords should not just collect monies and let others suffer 1 .5 
Total 195 100.0 
 
Source: Field Survey Data, 2014 
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Table. XIII: Consultation before the siting of BSs * Residentsfamiliar with the MTT 
 Were you consulted before the siting of the BS? Total 
Yes No 
N % N % N % 
Yes 19 73.1 63 61.2 82 63.6 
 
No 7 26.9 40 38.8 47 36.4 
Total 26 100.0 103 100.0 129 100.0 
Source: Field Survey Data, 2014 
 
 
Table. XIV: Landlords and tenants * Residents comfortable living closer toBSs 
 
 
 
 Are you a Landlord or a Total 
 tenant? 
 
Landlord Tenant 
 
Count % Count % Count % 
 
Are you content living Yes 28 41.2 23 30.3 51 35.4 
 closer to the base 
station? No 40 58.8 53 69.7 93 64.6 
 
Total 68 100.0 76 100.0 144 100.0 
 
 
Source: Field Survey Data, 2014 
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Table. XV (a): Suggestions included in making decision * Residents comfortable living 
closer toBSs 
 
 
 Were your suggestions taken into Total 
consideration? 
Yes No 
N % N % N % 
Yes 2 100.0 0 .0 2 14.3 
 
No 0 .0 12 100.0 12 85.7 
Total 2 100.0 12 100.0 14 100.0 
Source: Field Survey Data, 2014 
 
Table. XV (b): Chi-Square Tests 
 
 Value Df Asymp. Sig. Exact Sig. (2- Exact Sig. 
(2-sided) sided) (1-sided) 
Pearson Chi-Square 14.000a 1 .000   
Continuity Correctionb 7.024 1 .008   
Likelihood Ratio 11.483 1 .001   
Fisher's Exact Test    .011 .011 
Linear-by-Linear 13.000 1 .000   
Association 
N of Valid Cases 14     
a. 3 cells (75.0%) have expected count less than 5. The minimum expected count is .29. 
b. Computed only for a 2x2 table 
 
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Table. XVI (a): Residents willing to relocate when compensated * Landlords and tenants 
 
 Will you relocate when compensated? Total 
Yes No 
N % N % N % 
Landlord 6 21.4 62 53.4 68 47.2 
 
Tenant 22 78.6 54 46.6 76 52.8 
Total 28 100.0 116 100.0 144 100.0 
Source: Field Survey Data, 2014 
 
Table. XVI (b): Chi-Square Tests 
 Value Df Asymp. Sig. Exact Sig. (2- Exact Sig. 
(2-sided) sided) (1-sided) 
Pearson Chi-Square 9.279a 1 .002   
Continuity Correctionb 8.038 1 .005   
Likelihood Ratio 9.827 1 .002   
Fisher's Exact Test    .003 .002 
Linear-by-Linear 9.214 1 .002   
Association 
N of Valid Cases 144     
a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 13.22. 
b. Computed only for a 2x2 table 
 
 
 
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Table. XVII (a) Familiarity with the MTT * Highest level of education 
  
Crosstabulation 
 Are you familiar with the MTT? Total 
Yes No 
N % N % N % 
None 2 2.3 0 .0 2 1.4 
JHS 2 2.3 7 12.5 9 6.3 
SHS 12 13.6 8 14.3 20 13.9 
 
Post- 40 45.5 29 51.8 69 47.9 
secondary 
Tertiary 32 36.4 12 21.4 44 30.6 
Total 88 100.0 56 100.0 144 100.0 
Source: Field Survey Data, 2014 
 
Table. XVII(b) Chi-Square Tests 
 Value df Asymp. Sig. 
(2-sided) 
Pearson Chi-Square 9.795a 4 .044 
Likelihood Ratio 10.543 4 .032 
Linear-by-Linear 3.541 1 .060 
Association 
N of Valid Cases 144   
a. 3 cells (30.0%) have expected count less than 5. The 
minimum expected count is .78. 
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APPENDIX G 
One - way between – groups ANOVA for heavy metal content and sampling sites as 
grouping variables 
Dependent Mean 
Categorical Variable Sig. 
Variable Difference (I-J) 
 A1 - A15 B1 - B15 1.47400* .028 
C1 - C15 1.73200* .009 
B1 - B15 A1 - A15 -1.47400* .028 
Cd 
C1 - C15 .25800 .887 
C1 - C15 A1 - A15 -1.73200* .009 
B1 - B15 -.25800 .887 
A1 - A15 B1 - B15 2.33200* .004 
C1 - C15 1.58467 .063 
B1 - B15 A1 - A15 -2.33200* .004 
As 
C1 - C15 -.74733 .522 
C1 - C15 A1 - A15 -1.58467 .063 
B1 - B15 .74733 .522 
A1 - A15 B1 - B15 4.94667* .005 
C1 - C15 3.82867* .037 
B1 - B15 A1 - A15 -4.94667* .005 
Pb 
C1 - C15 -1.11800 .737 
C1 - C15 A1 - A15 -3.82867* .037 
B1 - B15 1.11800 .737 
A1 - A15 B1 - B15 .22933 .068 
C1 - C15 .25867* .035 
B1 - B15 A1 - A15 -.22933 .068 
Zn 
C1 - C15 .02933 .954 
C1 - C15 A1 - A15 -.25867* .035 
B1 - B15 -.02933 .954 
A1 - A15 B1 - B15 5.45133* .002 
C1 - C15 3.19800 .086 
B1 - B15 A1 - A15 -5.45133* .002 
Cu 
C1 - C15 -2.25333 .283 
C1 - C15 A1 - A15 -3.19800 .086 
B1 - B15 2.25333 .283 
    
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APPENDIX H 
Dendrogram obtained by HCA for heavy metal contents in soil samples (Ward method). 
LINKAGES 
SAMPL    E     I  D              B2 
                    B3 
                      B1 
                    C10 
                    A15 
                    C11 
                      B9 
                  B12 
                      B8 
                    C3 
                    C4 
                    A4 
                    A11 
                      A1 
                     B5 
                    C14 
                   C13 
                   B13 
                     C2 
                   C12 
                       B4 
                   B15 
                     C1 
                    B7 
                      A8 
                   B10 
                    B11 
                   B14 
                     A2 
                     A3 
                     C9 
                     A5 
                   A10 
                       C5 
                    C6 
                     A9 
                    A6 
                   A12 
                     A7  
                   A13 
                   C15 
                     A14 
                       C7 
                        C8 
                        B6 
 
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