UNIVERSITY OF GHANA, LEGON 
 
COLLEGE OF BASIC AND APPLIED SCIENCES 
 
 
ECOLOGICAL FOOTPRINT OF ARTISANAL AND SMALL-
SCALE GOLD MINING ON SOIL AND PROVISIONING 
ECOSYSTEM SERVICES IN MPOHOR WASSA EAST AND 
AMANSIE WEST DISTRICTS, GHANA 
 
 
 
BY 
 
STEPHEN TWUMASI ANNAN  
 (10329151)  
 
 
 
THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, 
LEGON IN PARTIAL FULFILLMENT OF THE REQUIREMENT 
FOR THE AWARD OF DOCTOR OF PHILOSOPHY DEGREE IN 
ENVIRONMENTAL SCIENCE 
 
INSTITUTE FOR ENVIRONMENT AND SANITATION STUDIES 
 
SEPTEMBER, 2021
DECLARATION 
I, Stephen Twumasi Annan, hereby declare that this thesis is my own original work 
towards the award of Doctor of Philosophy in Environment Science. With the exception 
of quotations and references from other publications which have been duly acknowledged, 
this work has not been submitted, either in part or full for any other degree elsewhere. 
 
 
 
 
  
i 
 
ABSTRACT 
Issues of impacts of small-scale gold mining activities on the environment continue to take 
a center stage in environmental discourse in developing countries more especially sub-
Saharan Africa. This study was carried out to assess ecological footprint of artisanal and 
small-scale gold mining on soil and provisioning ecosystem services in the Mpohor Wassa 
East and Amansie West Districts, Ghana. Composite samples of soil and water were taken 
in selected artisanal and small-scale mining areas for analysis. A total of one hundred and 
sixty-two (162) soil samples including control soil sample were analyzed over a period of 
three-months. Seventy-five (75) and eighty-seven (87) composite soil samples were taken 
from Amansie West and Mpohor Wassa East Districts, respectively. In addition, twenty-
seven (27) water samples including control were taken and analyzed during the entire 
study period. From Mpohor Wassa East water samples, the mean pH ranged from 6.5 
to7.1; Electrical Conductivity (EC); 58.7 to 152.3µS/cm; Total Dissolved Solids (TDS); 
33.3 to 101mg/L, Total Suspended Solids (TSS); 5.3 to 645 mg/L; Dissolved Oxygen 
(DO); 5.4 to 12.9mg/L, Biological Oxygen Demand (BOD); 1.0 to 1.7mg/L. The DO, 
BOD and EC in most sampling sites exceeded the WHO permissible limit. The EC, TDS, 
alkalinity, and salinity values, however, were all within WHO recommended limits. The 
results of analysis of the water samples from Amansie West were also as follows: pH; 4.6 
to 7.8; EC; 42.7 to 484.8µS/cm; TDS; 25.3 to 221.2mg/L; TSS; 12.5 to 390.7mg/L, DO; 
4.3 to 11.8mg/L; BOD; 1.4 to 2.5mg/L. Mercury, arsenic, cadmium and nickel in water 
samples at both Mpohor Wassa East District sampling sites and Amansie West sampling 
sites exceeded the WHO and EPA (Ghana) acceptable limits. The concentrations of the 
following heavy metals in soils; Fe, Hg, Ni, Cu, Pb, Cr and As were above the FAO 
acceptable limit for agricultural soils. This suggests (which specifically?) that artisanal and 
small-scale gold activities have impacted on the soil. The study further revealed that 
ii 
 
change in land use due to artisanal and small-scale mining activities significantly 
influenced the following ecosystem services; drinking water, wood fuel, medicinal plants, 
raw material for construction and food crop production. However, in this study, all heavy 
metals analyzed with Atomic Absorption Spectrometer had Threshold Exceedance Ratio 
(TER) less than the total concentration when extracted with nitric acid. This translates that 
limited soil function might not occur since the TER values are smaller compared to the 
total concentrations and could not limit the function of the soil for agriculture purposes 
however, the re-mobility percentage especially, Cu was high and had higher percentage 
mobility in all sampling sites above 20% which suggest that, Cu has a higher potential to 
remobilized into the soil structure when environmental conditions are favorable. The geo-
accumulation index showed that the soils in both study districts are moderately 
contaminated. The overall conclusion is that artisanal and small-scale mining activities 
have impacted on provisioning ecosystem services in the two study areas. Efforts aimed 
at restoring the provisioning ecosystem services therefore need to be considered by 
relevant authorities. Recommendations made from the study include mandated agencies 
such as Environmental Protection Agency (EPA) and Ministry of Minerals and Land 
Commission should regulate the activities of ASGM to stop the discharge of poisonous 
heavy metals into soil and water bodies. Also, Phytoextraction ability plants such as Sun 
flower (Helianthus annuus), Cannabis sativa, Tobacco (Nicotiana tabacum), Maize (Zea 
mays) can be cultivated to demobilize Cu and Hg in the soil and this can be championed 
by EPA and the Ministry of Agriculture in Ghana. Lastly, it was recommended that the 
Forestry Commission, relevant stakeholders or NGOs should champion Land reclamation 
activities such as reafforestation and afforestation should be encouraged at the mined sites 
to revamp provisioning ecosystem services supply. 
 
iii 
 
DEDICATION 
 
This work is dedicated to my lovely wife Rose line Addae and my children Boahen 
Twumasi Annan and Grace Twumasi Annan for their support and encouragement. 
  
iv 
 
ACKNOWLEDGEMENT 
I acknowledge the immense support, general guidance, suggestions and directions from 
my supervisors, Prof. F.K. Nyame, Dr. J.S. Ayivor and Dr. Yaw Agyeman Boafo. Their 
remarkable comments indeed taught me the basic tenets of research work. I will forever 
remain grateful to them. I owe a debt of gratitude to Mr. Bright Frimpong of Research 
Consults and his entire family for their selfless and valued support that contributed 
immensely to the successful completion of this study. My sincere gratitude goes to my 
wife Roseline Addae and my children Boahen and Twumwaa for their wonderful support 
and encouragement. I am thankful to Mr. and Mrs. Bondzie, Augustina Adjei, Francis 
Adarkwa, Collins Owusu-Fordjour, Dr. Charles K. Koomson, Prof. Ruby Hanson (UEW) 
and Mr. Samuel Armah for their valued support and contributions to the successful 
completion of this work. My appreciation also goes to Mr. Moses Mensah-Nyumutei, Mr. 
Akwasi Adu Frimpong, Dr. Ted Annang, Dr. D. Nukpezah, Dr. Benjamin Denkyira Ofori, 
Prof. Chris Gordon, Prof. Kwasi Appeaning Addo. Dr. Adelina Maria Mensah, Dr. 
Dzodzor Yirenya-Tawiah Dr. Benedicta Fosu-Mensah, Dr. Daniel Amoako Darko, Dr. 
Opoku Pabi, Dr. Samuel Senyo Koranteng, Dr. Philip-Neri Jayson-Quashigah all of IESS, 
University of Ghana, Legon. I am also grateful to my mother Miss. Christiana Gyan, my 
late father Mr. Thomas Kofi Annan of blessed memory and my siblings Thomas, Felicia, 
Veronica, Sarah and Michael for making me what I am today. Also, to the family of Mr. 
and Mrs. David Kumi Gyan, Mr. Okpoti Konney and Mr. Stephen Acheampong. I would 
like to thank the following personalities; Mr. Emmanuel Ansah, Robert Twene ESQ and 
Samuel Amoah for their encouragement and support. I acknowledge the financial support 
of the Carnegie Corporation of New York (CCNY) BANGA Project. Lastly, I am grateful 
to the authors of the books and materials I consulted during my study and all who 
contributed in diverse ways to ensure the successful completion of this research wo 
v 
 
 TABLE OF CONTENTS 
 
DECLARATION i 
ABSTRACT ii 
DEDICATION iv 
ACKNOWLEDGEMENT v 
TABLE OF CONTENTS vi 
LIST OF TABLES xiii 
LIST OF FIGURES xiv 
LIST OF PLATES xv 
LIST OF ABBREVIATIONS xvi 
 
CHAPTER ONE 1 
INTRODUCTION 1 
1.1 Background of the Study 1 
1.2 Problem Statement 3 
1.3 Research Questions 5 
1.4 Study Objectives 5 
1.4.1 Objective of the Study 5 
1.5 Significance of Study 6 
 
CHAPTER TWO 7 
LITERATURE REVIEW 7 
2.1 Overview 7 
2.2 Overview of the Mining Industry in Ghana 7 
2.3 Small-Scale Mining in Ghana 9 
2.4 Artisanal Gold Mining 9 
2.5 Artisanal and Small-Scale Mining (ASM) Sector 10 
2.6 Historical Background of Small-scale Mining in Ghana 11 
2.7 An Overview of the Modern Ghanaian Small-scale Mining Industry 13 
2.8 Major Processes in Small-Scale Gold Mining Activities 14 
2.9 Socio - Economic Effects of Small-scale Mining Activities 15 
2.9.1 Socio-economic Impacts of Artisanal Mining 17 
vi 
 
2.10 Environmental Impacts of Artisanal Small-Scale Gold Mining 18 
2.10.1 Mining and Land Degradation. 19 
2.10.2 Illegal Mining Activities and Effect on Water Bodies 21 
2.10.3 Air Pollution and Transport of Earth Material 23 
2.10.4 Heavy Metals in the Environment 24 
2.11 Methods of Mining by Small-scale Miners 25 
2.12 Methods of Processing 26 
2.13 Regulations on Mining Activities in Ghana 27 
2.14 Mineral and Mining Law 1986 (PNDCL 153) 27 
2.15 The Mineral Commission Law 1986 (PNDCL 154) 28 
2.16 Small-Scale Gold Mining Law 1989 (PNDCL 218) 28 
2.17 Legal Framework for Small-Scale Mining Activities in Ghana 28 
2.18 Processes of Application for Small-Scale Mining Permit/License 29 
2.19 Definition and Concepts of Ecosystem 30 
2.19.1 Ecosystem and Ecosystem Services 30 
2.20 Ecosystem Services and Livelihoods 32 
2.20.1 Ecosystem Services 32 
2.20.2 Ecosystem Services and Land Use Change 33 
2.20.3 Provisioning Ecosystem Services 34 
2.20.4 Forest Provisioning Ecosystem Services 37 
2.20.5 Vulnerability of Rural Households to Provisioning Ecosystem Loss 38 
2.20.6 The contribution of Provisioning Ecosystem Services to Rural Livelihoods 39 
2.21 Loss of Land and Livelihood from Mining Activities 40 
2.22 Impact of Mining on Ecosystem Services 43 
2.23 Impact of Illegal Mining Activity on Ecosystem Services in Ghana 45 
2.24 Livelihood 48 
2.24.1 Sustainable Livelihood 50 
2.24.2 Conceptual Framework on Livelihood 50 
2.24.3 Livelihood Promotion 51 
2.24.4 Livelihood Protection 52 
2.24.5 Livelihood Assets 52 
2.24.5.1 Natural Capital 52 
2.24.5.2 Human Capital 52 
2.24.5.3 Financial Capital 53 
vii 
 
2.24.5.4 Social Capital 53 
2.24.5.5 Physical capital 54 
2.25 Geographical Information System, Remote Sensing and Land Cover Change 
Detection. 54 
2.25.1 Land Use and Land Cover Change in Mining Landscapes 56 
2.25.2 Land Use and Land Cover Change Analysis 57 
 
CHAPTER THREE 58 
MATERIALS AND METHODS 58 
3.1 Overview 58 
3.2 Study Area 58 
3.2.1 Mpohor Wassa East District 58 
3.2.1.1 Location of the District 58 
3.2.1.2 Vegetation and Agriculture 59 
3.2.1.3 Spatial Distribution and Occupation 59 
3.2.1.4 Climate 61 
3.2.1.5 Relief and Drainage 61 
3.2.2 Amansie West District (Ashanti Region) 63 
3.2.2.1 Location and Size of the District 63 
3.2.2.2 Relief and Drainage 64 
3.2.2.3 Climatic Condition 64 
3.2.2.4 Vegetation of the Area 65 
3.2.2.5 Soil Condition 65 
3.2.2.6 Mineral Deposits 66 
3.2.2.7 Spatial Distribution 67 
3.3 Research Approach 69 
3.4 Research Design 70 
3.5 Sampling Procedure 70 
3.6 Parameters Measured and Analytical Procedure 73 
3.6.1 Soil sample Collection 73 
3.6.2 Soil Sample Preparation and Acid Digestion 73 
3.6.3 Soil pH and Electrical Conductivity 74 
3.6.4 Soil Particle Size 74 
viii 
 
3.6.5 Soil Organic Carbon/ Organic Matter 76 
3.6.6 Total Nitrogen 77 
3.6.7 Available Phosphorus 78 
3.7 Determination of Heavy Metals in Soils 80 
3.7.1 Extraction of Heavy Metals from Soils using NH4NO3-Solution Extraction 
Technique 80 
3.7.2 Acid Digestion of Soil 81 
3.7.3 Determination of Heavy Metals using AAS 81 
3.8 Land Use and Land Cover (LULC) Change Analysis 82 
3.8.1 Data and Methodology 82 
3.9 Study Population for Social Survey 84 
3.10 Sampling for Qualitative Respondents 85 
3.10.1 Focus Group Discussion 86 
3.10.2 Key Informant Interview 86 
3.11 Data Collection Instruments 87 
3.11.1 Face-to-Face Interview 87 
3.12 Sampling Size for Quantitative Study 87 
3.12.1 Sample Size Determination 88 
3.13 Qualitative Data Handling 89 
3.14 Quantitative Data Analysis and Processing 89 
3.15 Ethical Issues 89 
3.16 Contamination Assessment and Hazard Rating 90 
3.17 Geoaccumulation Index (Igeo) 91 
3.18 Quality Control 92 
3.18.1 Analytical Technique and Accuracy Check 92 
3.18.2 Chemical and Sample Digestion 93 
3.18.3 Quality Control for Social Survey 93 
 
CHAPTER FOUR 94 
RESULTS 94 
4.0 Overview 94 
4.1 Physical Parameters in Surface water Samples, Mpohor Wassa East District 94 
4.1.1 pH 94 
ix 
 
4.1.2 Electrical Conductivity (EC) 95 
4.1.3 Total Dissolved Solids (TDS) 95 
4.1.4 Total Suspended Solids (TSS) 96 
4.1.6 Temperature 97 
4.1.7 Dissolved Oxygen (DO) 97 
4.1.8 Biological Oxygen Demand (BOD) 97 
4.1.9 Salinity 97 
4.1.10 Total Hardness 98 
4.1.11 Turbidity 98 
4.2 Heavy Metals in Water, Mpohor Wassa East District 98 
4.3 Factor Analysis of Physicochemical Parameters of Surface Water at Mpohor 
Wassa East District 99 
4.3.1 Scree Plot 101 
4.4 Correlation of Physico-Chemical Parameters of Surface Water at Mpohor Wassa 
East District 102 
4.5 Soil Physical Parameters, Mpohor Wassa East District 103 
4.5.1 pH 103 
4.5.2 Conductivity 103 
4.5.3 Available Phosphorus 103 
4.5.4 Organic Carbon (%) 104 
4.5.5 Percentage Sand, Silt and Clay 104 
4.5.6 Exchangeable K, Ca, Mg and Na 105 
4.6 Geoaccumulation Index of Heavy Metal Contaminations in Soils, Mpohor Wassa 
East District 107 
4.7 Contamination Assessment and Hazard Rating of Heavy Metals in Soils, Mpohor 
Wassa East District 108 
4.7.1 Physical Parameters of Surface Water, Amansie West District 110 
4.7.2 Electrical Conductivity (EC) 110 
4.7.3 Total Dissolved Solids (TDS) 111 
4.7.4 Total Suspended Solids (TSS) 112 
4.7.5 Total Alkalinity 112 
4.7.6 Dissolved Oxygen (DO) 113 
4.7.7 Biological Oxygen demand (BOD) 113 
4.7.8 Salinity 113 
x 
 
4.7.10 Turbidity 114 
4.8 Heavy Metals in Water, Amansie West District 114 
4.9 Factor Analysis 116 
4.9.1 Factor Analysis of Physicochemical Parameters of Surface Water, Amansie 
West District 117 
4.10 Correlation of Physico-Chemical Parameters of Surface Water, Amansie West119 
4.11 Soil Physical Parameters, Amansie West District 120 
4.11.1 pH 120 
4.11.2 Conductivity 120 
4. 11.3 Available Phosphorus 121 
4.11.4 Organic Carbon (%) 121 
4.11.5 Percentage Sand, Silt and Clay 121 
4.11.6 Exchangeable K, Ca, Mg and Na 122 
4.12 Geoaccumulation Index of Heavy Metal Contaminations in Soils, Amansie West 
District 124 
4.13 Contamination Assessment and Hazard Rating of soils, Amansie West District
 125 
4.14 Land use and Land Cover Change 127 
4.15 Socio-Demographic Characteristics of Respondents (Mpohor Wassa East and 
Amansie West District) 134 
4.16 Effect of Artisanal and Small-scale Mining on Provisioning Ecosystem Services
 136 
4.17 Interview with Key Stakeholders 139 
 
CHAPTER FIVE 144 
DISCUSSION 144 
5.0 Overview 144 
5.1 Physico-Chemical Quality of Surface Water 144 
5.2 Heavy Metals in Water 149 
5.3 Heavy Metals in Soil 151 
5.4 Assessment of Impact of Illegal Mining Activities on Provisioning Ecosystem 154 
 
 
xi 
 
CHAPTER SIX 157 
CONCLUSION AND RECOMMENDATION 157 
6.1 Conclusion 157 
6.1.1 Heavy Metals in Water 157 
6.1.2 Hazard Rating of Heavy Metals in Soils 157 
6.1.3 Impact of Artisanal and Small-Scale Mining Activities on Provisioning 
Ecosystem 158 
6.1.4 Impact of Artisanal and Small-scale Gold mine on Land use and Land Cover 
Changes 158 
6.2 Recommendation 158 
6.2.1 Recommendations for Government (Policy and Decision-makers) 158 
6.2.2 Recommendation for Academia 159 
6.2.3 Recommendation for Local Communities and Households 159 
 
REFERENCES 160 
 
APPENDICES 179 
 
 
  
xii 
 
LIST OF TABLES 
Table 2.1: Classification of Ecosystem Services 31 
Table 3.1: Matrix Showing Objectives, Methods Used and the Analytical Tool 69 
Table 3.2: Categorization of Igeo 92 
Table 4.1: Heavy Metals in Surface Water Samples from the Mpohor Wassa East District
 99 
Table 4. 2: Rotated Component Matrix of physico-Chemical Parameters, Mpohor Wassa 
East 101 
Table 4.3: Correlation between Physicochemical Parameters in Water Samples, Mpohor 
Wassa East District 103 
Table 4.4: Physico-Chemical Parameters in Soil, Mpohor Wassa East District 106 
Table 4.5: Geoaccumulation Index (Igeo) Values for Soil Samples in Mpohor Wassa 
District 108 
Table 4.6: Hazard Rating of Heavy Metals in Soil, Mpohor Wassa Sampling Site 109 
Table 4.7: Heavy Metals in Surface Water, Amansie West District 116 
Table 4.8: Component Matrix of physico-Chemical parameters, Amansie West 118 
Table 4.9: Correlation between Physico-Chemical Parameters in Water Samples, 
Amansie West District 120 
Table 4.10: Physico-Chemical Parameters in Soil, Amansie West District 123 
Table 4.11: Geoaccumulation Index (Igeo) Values for Soil Samples in Amansie West 
District 124 
Table 4.12: Suggested Igeo classification and absolutes 125 
Table 4.13: Hazard rating of heavy metals in soil, Amansie West District sampling sites
 126 
Table 4.14: Percentage (%) Cover of the Various Land Cover Classes 133 
Table 4.15: Socio-Demographic Characteristics of Respondent’s (N=404) 135 
Table 4.16: Effects of Artisanal and Small-scale Mining on Provisioning Ecosystem 
Services 136 
 
  
xiii 
 
LIST OF FIGURES 
Figure 1: Study Area and Sampling Point Map of Mpohor Wassa East District 62 
Figure 2: Study area and Sampling points Map of Amansie West District 68 
Figure 3: Methodology for Land Use Cover Change 83 
Figure 4: pH Variations Across Surface Water Locations, Mpohor Wassa East 94 
Figure 5: Conductivity Variations Across surface water Locations, Mpohor Wassa East
 95 
Figure 6: TDS Variations Across Surface Water Locations, Mpohor Wassa East District
 96 
Figure 7: TSS Variations Across Surface Water Locations, Mpohor Wassa East District
 96 
Figure 8: Scree Plot of Physico-Chemical Parameters, Mpohor Wassa East 102 
Figure 9: pH Variations Across Surface Water Locations at Amansie West District 110 
Figure 10: Conductivity Variations Across Surface Water Locations, Amansie West 
District 111 
Figure 11: TDS Variations across Surface Water Locations, Amansie West District 112 
Figure 12: TSS Variations Across Surface Water Locations, Amansie West District 112 
Figure 13: Scree Plot of Physico-Chemical Parameters, Amansie West District 119 
Figure 14:  Land Cover Classification Map for 2015 and 2020, Amansie East District 
(Top Row, a and b) and Mpohor Wassa East District (Bottom Row, c and d)
 128 
Figure 15: Change detection Map of Selected Area in the Amansie West District 130 
Figure 16:  Change Detection Map of Selected Area in the Mpohor Wassa East District
 132 
Figure 17: Bar Chart Showing the Extent of each Class in km² for 2015 and 2020 133 
Figure 18: Ecosystem Services Affected Due to Artisanal and Small-scale Mining 
Activities in the Study Area 137 
Figure 19: Cost of Living in Artisanal and Small-scale Mining Communities 138 
Figure 20: Source of Water in Artisanal and Small-Scale Mining Communities 5-10 
Years Ago 139 
Figure 21: Source of Water Prior to Artisanal and Small-Scale Mining Activities 139 
 
 
xiv 
 
LIST OF PLATES 
Plate 4.2: Destruction of food crops by small - scale gold mining activities at the study 
site 142 
Plate 4.1: Destruction of oil palm crops by illegal small - scale gold miners at the study 
site 142 
Plate 4.4: Land degradation as a result of small - scale mining activities at study site 142 
Plate 4.3: Casual workers at the mining site for their source of livelihood 142 
Plate 4.6: Depletion of vegetation cover due to small - scale gold mining activities 143 
Plate 4.5: Water pollution as result of small - scale mining activities at the mining site
 143 
Plate 4.8: Children engaging in small - scale gold mining activities 143 
Plate 4.7: Dug pit left after mining serving as a dead trap to human and animals 143 
 
 
 
  
xv 
 
LIST OF ABBREVIATIONS  
AAS             Atomic Absorption Spectrophotometer 
AGM            Artisanal Gold Mining 
AIDS            Acquired Immunodeficiency Syndrome 
ASM             Artisanal Small-Scale Mining 
AWWA        American Water Works Association  
BOPP           Benso Oil Palm Plantations  
DFID            Department for International Development  
DMC            Diamond Marketing Corporation 
EPA              Environmental Protection Agency 
ERP              Economic Recovery Programme 
FCPF            Forest Carbon Partnership Project 
GAEC          Ghana Atomic Energy Commission 
GDP             Gross Domestic Product 
GEPA           Ghana Export Promotion Authority 
GIS               Geographic Information Systems  
GNA             Ghana News Agency 
GREL           Ghana Rubber Estates Limited 
GSS              Ghana Statistical Service 
 HIV              Sexually Transmitted Diseases  
ILO              International Labour Organization 
INAA           Instrumental Neutron Activation Analysis  
IQ                 Intelligent Quotient 
LI                 Legislative Instrument  
LULC          Land use and Land Cover Change 
MES            Medical and Equipment Suppliers  
NGOs          Non-Governmental Organization  
xvi 
 
NSR             National Skills Registry 
PMMC         Precious Minerals Marketing Company  
PNDC          Provisional National Defence Council  
PNDCL        Provisional National Defence Council Law 
PTFE            Polytetrafluoroethylene  
SESA         Environmental and Social Assessment 
SL                Sustainable Livelihood 
UN               United Nation 
UNCBD       United Nations Convention on Biological Diversity 
UNCEP        United Nations Conference on Environment  
UNEP           United Nation Environmental Program 
UNESCO     United Nations Educational, Scientific, Cultural Organization  
UNIDO        United Nation Industrial Development Organization 
USAID         United State Agency for International Development 
WACAM      Wassa Association of Communities Affected by Mining Changes  
WHO            World Health Organization
xvii 
 
CHAPTER ONE 
INTRODUCTION 
1.1 Background of the study 
Ghana’s geological space is endowed with various mineral resources including gold, 
diamond, bauxite and manganese. Additionally, there are other minerals of industrial value 
such as salt, petroleum, limestone, kaolin, lime, silica, granite and iron ore (Aubynn, 
2016). Among all minerals mined in Ghana, gold dominates the mining sector and Ghana 
is Africa’s foremost important producer of gold (Bloch & Owusu, 2017). Although small-
scale mining is legalized and regulated under the minerals and mining Amendment Act, 
2015, the sector has a larger component of artisanal small-scale mining popularly known 
as ‘galamsey’ (Rudke et al., 2020). Artisanal and small-scale mining are estimated to 
provide direct and indirect employment to over one million people and contribute 
significantly to the national economy (Dzigbodi-Adjimah & Bansah, 2018). It is estimated 
that gold mining contributes approximately 7.2% to Ghana’s GDP annually (2006-2014) 
and employs a large proportion of the labour force (GSS, 2010). Despite the fact that gold 
mining provides thousands of indigenous peoples with employment, environmental 
problems including land degradation, water pollution, and biodiversity loss have 
intensified within the regions of the mining sites (Akosa et al., 2018).  
The Economic Recovery Programme (ERP) which was launched in 1983 by the 
government of the Provisional National Defence Council (PNDC) was responsible for the 
increase in gold output from both the small- and large-scale mining (Minerals 
Commission, 2006). Among the objectives of the Economic Recovery Programme (ERP) 
was the rejuvenation of mining activities in Ghana. These laws encouraged increased 
small-scale mining activities and many people, including the youth, women and children 
engaged in mining in many parts of the country. In 2006, the Minerals and Mining Act, 
1 
 
2006 (Act 703) was also enacted which again stated, among others, that despite a law to 
the contrary, a person shall not engage in or undertake a small-scale mining operation for 
a mineral unless there is in existence of the mining operation license granted by the 
Minister for Mines or by an officer authorized by the Minister (Minerals Commission, 
2006).  
Although Ghana’s economy is predominantly agriculture based, many small-scale miners 
depend on mining for their livelihood (Aryee et al., 2017; Akosa et al., 2018; Ntibery et 
al., 2003). Small-scale mining has been reported to be one of the major contributing factors 
to the rapid decline of forest resources in Ghana (FCPF, 2014). Forests play enormous 
roles in the maintenance and provision of goods and services that are beneficial to 
ecosystem and human livelihood. The rate of forest deforestation and degradation through 
mining in Ghana is very alarming knowing the various ecosystem services that the forest 
provides to the environment. From the country's 1950s forest cover of 8.2 million hectares 
from the onset of the last century, only an estimated 1.6 million hectares remain. Currently, 
the deforestation rate is about 2.5% of the total land area of Ghana leading to an annual 
loss of about 135,000 ha (Rudke et al., 2020).  
According to Akabzaa and Darimani (2001), the forests that are cleared for mining 
purposes are home to a large number of organisms and as such indiscriminate clearing of 
the forests leads to loss of habitat, loss of biodiversity and ecosystem services.   
Predominant mainly large-scale mining regions of Ghana were for a long time the Ashanti, 
Eastern and Western Regions (Hilson, 2011). For the past two decades, however, there has 
been intensive Artisanal and Small-scale Mining (ASM) activities in these regions 
(Dzigbodi-Adjimah, 2018). As part of efforts to deal with the effects of artisanal gold 
mining on the environment and water bodies across Ghana, the government of Ghana 
2 
 
through the Ministry of Lands and Natural Resources placed a ban through an executive 
instrument on all small-scale mining activities. The ban was to enable the government of 
Ghana to deal properly in order to streamline the activities of the licensed miners. Ghana 
government launched a joint military and police taskforce called Operation Vanguard to 
fight recalcitrant small-scale miners (Bloch & Owusu, 2017). Although mining is 
important for a country’s development, it is said to be illegal when it is practiced without 
permit or in unapproved areas such as forest reserves, game reserves or near water 
resources even with secured permit (World Bank, 2002). However, issues of ecological 
footprint of artisanal and small-scale gold mining and the environmental consequences 
remain a significant challenge to be addressed because most of the communities around 
the mining areas potentially lose their livelihood through the degradation of natural 
ecosystem services that were readily available in their vicinity. Furthermore, the 
community members around these mining areas are also faced with environmental issues 
due to unsustainable use of environmental resources. It is against this backdrop that the 
project was initiated to assess ecological footprint of artisanal and small-scale gold mining 
on soil and provisioning ecosystems in the study areas in Ghana. 
1.2 Problem Statement 
Gold mining is one of the key areas of natural resource exploitation in most tropical 
countries that contributes immensely to the Gross Domestic Product of Ghana (Yaro, 
2010). These contributions include employment opportunities, corporate social 
responsibilities through the provision of potable water and schools, among others. The 
environmental effects of artisanal gold mining footprints in most developing countries 
including Ghana have been well documented (Akosa et al., 2018; Amponsah-Tawiah et 
al., 2017; Agyensaim, 2016; Tetteh, 2010). The Mpohor Wassa East District of the 
Western Region and Amansie West District of the Ashanti Region have witnessed 
increased artisanal gold mining activity over the past three decades and currently receive 
3 
 
national attention with many people including women and children actively engaged in it 
(Agyapong, 2018; Aryee et al., 2003). During the mining processes, there are several 
wastes that are associated with the activities due to the mining methods and materials used 
in extracting and processing the gold bearing materials (Ntibery et al., 2003). The small-
scale and artisanal gold miners operate mostly along river banks and sometimes within 
river beds thus, affecting water quality, sediments load, turbidity and heavy metal 
concentrations. Major streams affected by small-scale mining operations in Ghana include 
Birim, Ankobra and Aboshyenso Rivers and their tributaries which serve as sources of 
drinking water for some communities (Dzigbodi-Adjimah & Bansah, 2018; Ntibery, 
2004). 
A lot of research work has been done in some mining communities in Ghana to emphasize 
the problem associated with mining. For instance, Bloch and Owusu (2017) reported major 
environmental problems faced by mining communities in the Tarkwa area in the Western 
Region resulting from lead, copper, nickel, mercury and cyanide and other heavy metal 
pollutions generally above recommended levels in both soil and water samples.  Negative 
effects of mining including destruction of farmlands and water bodies, high cost of living 
and increase in social vices have also been reported in mining communities such as Obuasi, 
Tarkwa and Prestea (Agyapong, 2018; Dzigbodi-Adjimah & Bansah, 2018). Although 
numerous and quite exhaustive studies have been done on Artisanal and Small-Scale Gold 
(ASGM) activities in Ghana, especially on the socio-economic and environmental impacts, 
many of such studies did not specifically assess or address the issue of ecological footprint 
of artisanal and small-scale mining on soil and provisioning ecosystem services, let alone 
in the Amansie West and Mpohor Wassa East Districts in the Ashanti and Western 
Regions, respectively, in spite of their long history of ASGM. Thus, very little is known 
of the possible relationship between ecological footprint from ASGM activities, soil and 
provisioning ecosystem services in the two areas identified. In addition, the levels of 
4 
 
contamination of the activities on the immediate environment using the threshold 
exceedance ratio (TER) and the heavy metal mobility coefficient potential have similarly 
not been studied and/or given much attention in research work. This study was therefore 
undertaken to partly address these knowledge gaps and to also contribute to existing 
literature on the subject. 
1.3 Research questions 
The following questions were formulated to guide the study: 
1. What are the levels of physico-chemical parameters in soils and water in or near 
artisanal and small-scale gold mining sites? 
2. What are the levels of heavy metals in artisanal and small-scale gold mined-out 
lands in Mpohor Wassa East and Amansie West Districts of Ghana?  
3. What are the land use changes in the artisanal and small-scale mining areas over 
the past five (5) years? 
4. What are the implications of artisanal and small-scale gold mining on provisional 
ecosystem services on the lives of people in the Mpohor Wassa East and Amansie 
West Districts? 
1.4 Study Objectives 
1.4.1 Objective of the study 
The overall objective of the study was to assess the ecological footprint of artisanal and 
small-scale gold mining on soil and provisioning ecosystem services in the Mpohor 
Wassa East and Amansie West Districts, Ghana. Specifically, the study sought to; 
⮚ determine the levels of physico-chemical parameters in soils and water in artisanal 
and small-scale gold mine sites in the Mpohor Wassa East and Amansie West 
Districts. 
⮚ assess the hazard rating of heavy metals in soils in artisanal and small-scale gold 
mine lands in the study areas. 
5 
 
⮚ explore the impact of small-scale and artisanal gold mining on land use and land 
cover changes. 
⮚ identify and examine the provisioning ecosystem services and livelihood activities 
likely impacted by small-scale and artisanal gold mining. 
 
1.5 Significance of Study 
 In Ghana, there have been a number of studies on the activities of small-scale mining 
(Bloch & Owusu, 2017). However, little data is available on ecological footprint of 
artisanal and small-scale gold mining on soil and provisioning ecosystem services in areas 
such as Mpohor Wassa East and Amansie West District. This research therefore sought to 
bridge the knowledge gap by acquisition of data on physico-chemical parameters including 
heavy metal concentrations in water and soil as well as on land use and ecosystem services 
in the study areas. Analysis and interpretation of the data gathered have provided 
potentially valuable information for the government, environmental managers and other 
decision makers or stakeholders on the state and impacts of small-scale mining activities 
in the study area. The data obtained has, in particular, contributed to the body of knowledge 
on the impacts of artisanal and small-scale gold mining especially the illegal or ‘galamsey’ 
segment of mining activity and the extent of loss of land use and land cover due to illegal 
mining activities.  
Direct interaction with people in the study areas also provided in-depth knowledge on their 
livelihood status which could serve as a valuable source of information to inform policy-
making towards the achievement of the United Nations Sustainable Development Goals, 
particularly poverty alleviation and food security which are goal one and goal two, 
respectively.  
  
6 
 
CHAPTER TWO 
LITERATURE REVIEW 
 
2.1 Overview 
This chapter deals with the review of pertinent literature in line with the objectives of the 
study. It focused on global perspectives of small-scale mining, overview of the modern 
Small-scale mining industry, major processes in small-scale mining activities, socio - 
economic effects of small-scale mining activities, methods of mining by small-scale 
miners, environmental impacts of artisanal small-scale gold mining, mining and land 
degradation, Illegal mining activities and their effect on water bodies, air pollution and 
transport of earth material, regulations on mining activities in Ghana. 
2.2 Overview of the Mining industry in Ghana  
Worldwide, artisanal gold miners (AGM) are the main consumers of mercury, using and 
losing almost 1000 tonnes of metallic mercury per annum or more than 30% of all mercury 
annually used by different industrial applications (Creek., 2016). Mercury emitted to the 
atmosphere and released to the environment has serious and detrimental environmental 
and health implications. There are now about 10 to 15 million artisanal and small gold 
miners worldwide. Their operations span more than 70 countries and they produce in the 
range of 350 tonnes of gold annually (Zhang et al., 2014). Furthermore, Rudke et al., 
(2020) suggested that organizations which aim to mitigate mercury releases require a 
deeper understanding of mine community dynamics, the organization of processing 
activities, the needs of operators and the nature of the ore (Lunt et al., 1995). 
Ghana abounds in a number of minerals especially in the south-west of the country and 
the most developed and sought-after mineral is gold. Gold mining has been associated with 
Ghana since time immemorial but documentation of this activity was captured by the 
7 
 
Portuguese around the 1470s (Junner, 2010). In the pre-independence period, it is 
documented that gold mining by indigenous people is said to pre-date Christian times and 
Ghana's modern mining history spans over six centuries, private Ghanaian gold miners 
were banned after 1933 from operating mines due to the promulgation of the Mercury 
Law. Large-scale mining by British and other foreign investors began in the late 19th 
century. British mining interests were a significant source of influence on the Colonial 
Office in London and its representatives in the territory and shaped the formulation and 
implementation of mineral policy in the colony (Akabzaa et al., 2007). 
Arah (2015) notes that the mining industry in Ghana dates back to pre-colonial times and 
some studies have shown that the industry has contributed significantly to the socio-
economic development of the country in terms of employment and social infrastructure 
(Amponsh-Tawiah et al., 2017; Ghana Chamber of Mines, 2008). Others argue that the 
negative impacts of mining, such as water, air and noise pollution, and the general 
deprivation of fertile land for agriculture, have left the population relatively much poorer 
than before (Queensland, 2019; Hilson et al., 2009). This has had a general impact on the 
overall livelihoods of people living in these areas and working with pastoralists. It is 
therefore unclear whether mining in Ghana has actually contributed positively to the 
country's development. It is therefore important to determine the sustainability of the 
mining industry in Ghana by weighing the socio-economic benefits and negative impacts, 
particularly on the ecosystem services and livelihoods, examining the environmental, 
social and economic characteristics of small-scale gold mining and its impact on mining 
communities (Amansie West District) and suggesting possible solutions to improve the 
situation (Asante et al., 2017). 
 
8 
 
2.3 Small-scale Mining in Ghana 
 In Ghana, small-scale mining is defined as mining by any method not involving 
substantial expenditure by an individual or group of persons not exceeding nine or by a 
cooperative society consisting of ten or more persons (Government of Ghana, 1989). 
According to a United Nations (UN) report, the definition includes what has been termed 
"artisanal," i.e., operations using only rudimentary/craft implements, as well as more 
sophisticated mining activities that operate at a relatively low level of production and 
generally require limited capital investment (UN, 2009). One country where the 
environmental impacts of small-scale gold mining activities are increasingly 
unmanageable is Ghana. Despite providing employment for thousands of indigenous 
people and contributing significantly to foreign exchange earnings, problems of mercury 
pollution, cyanide and soil degradation have intensified in the sector over the years 
(Hilson, 2011). According to Rambaud et al., (2016), the first and obvious environmental 
problem in the area in question is related to the significant changes in land use. Most of 
this is due to industrial mining operations. The installation of sinkholes along local rivers 
strongly alters the discharge regime and increases the turbidity of downstream rivers. 
Erosion, emanating from excavations and tailings piles, is also easily identified by the 
orange colour of the water and can result in acid rock drainage through rainwater leachates 
from excavated soils. However, Sumarga et al., (2020) went on to indicate that several 
metals from ore minerals dissolve in acidic water and can be released into adjacent rivers 
to cause water pollution. 
2.4 Artisanal Gold Mining 
Artisanal gold mining is often unplanned and carried out by workers who do not have the 
technical know-how. As a result, the pits, which can be up to 30 meters deep, are not 
secured and landslides claim many lives. A study by Babut et al., (2002) clearly shows 
9 
 
that abandoned pits are left unmarked, posing a danger to the population and livestock. 
Abandoned sites are not rehabilitated, leaving a barren and permanently destroyed habitat. 
For most miners, amalgamation is the simplest and most effective method of recovering 
the finest gold fraction. However, the process is known to be devastating to health, not 
only for the users, but also for those indirectly involved, including the unborn, through 
peripheral contamination and entry into the food chain. In recent years, life-threatening 
mercury contamination has been found in most developing countries where artisanal gold 
mining is practiced. In addition, gold panning and amalgamation are often conducted along 
rivers, resulting in water pollution and destruction of river banks. The resulting siltation 
reduces the quality of drinking water and affects all types of aquatic life (Creek, 2016; 
Armah et al., 2013; Babut et al., 2002). 
2.5 Artisanal and Small-Scale Mining (ASM) Sector  
According to Nyame and Danso (2006), small-scale miners are artisanal miners who are 
licensed to operate on a small parcel of land and who must market their production through 
the Precious Minerals Marketing Company (PMMC) or designated agents affiliated with 
the PMMC. In contrast, illegal miners, known as ‘galamsey,’ pursue their trade without 
any regularization or licensing from regulatory agencies. They emphasized that the 
"theoretical" distinction between the two is, for all intents and purposes, blurred when 
visiting the mining sites where these individuals or groups of individuals operate. Again, 
Amponsah-Tawiah et al., (2017) found it more convenient in their study to group the 
activities of both under the heading of ASM. However, they also frequently used the terms 
illegal mining (or miners), ‘galamsey’ (the local terminology for illegal miners or mining) 
and ASM interchangeably (Tetteh, 2010). ASM is one of the fastest growing more or less 
informal economic sectors in the country. The ASM sector is the most difficult to regulate 
due to, among other things, the nomadic and often seasonal nature of its activities. In 
10 
 
addition, these activities often take place outside the control of government or regulatory 
agencies (Nyame & Danso, 2006; Noestaller, 2011). Not only is the sector largely 
unregulated, but it is profusely "polluted" with illegal artisanal or ‘galamsey’ miners, 
whose numerical strength and areas of operation remain largely unknown, although one 
estimate puts the figure at approximately 100,000 to 200,000 (Aubynn, 2016). Apart from 
the few registered or licensed small-scale operators, no one knows exactly how many 
people are engaged in illegal gold and diamond mining in the country. The Ghana Minerals 
Commission estimates that small-scale miners generated about 4,500 jobs at the end of 
2006. However, conservatively, it is possible that more than half a million participants are 
engaged in the illicit business at any given time throughout the country, especially in the 
dry season when these activities are most prominent. During peak periods, the sector 
attracts an army of migrants from across the subregion, partly indicative of the severe 
unemployment conditions in the region as a whole (Anon, 2010). 
2.6 Historical background of Small-scale Mining in Ghana 
Small-scale mining, also called artisanal or subsistence mining has been engaged in by the 
native people of Ghana well over 1500 years now. According to Agyapong (2018), 
vestiges of alluvial gold extraction and winning have been found as far back as the sixth 
century in the shores and forest belts of the Gold Coast. He asserted there was evidence 
that precious metals recovered from regional artisan activities attracted Arab traders to 
certain areas of the country as early as the seventh and eighth century AD, and that gold 
deposits in Western Sahara were largely responsible for the wealth and strength of ancient 
Ghana empires and cultures. Thus, by the fifteenth and sixteenth centuries, of the European 
colonial exploration, the country was agreeably labeled the Gold Coast. Small-scale 
mining in Ghana had been treated as an informal sector up until the 1980s, and this resulted 
in the decline of mineral production, as observed by (Botchway, 2015). Small-scale mining 
11 
 
is often done by local people as well as nomadic immigrants who move from place to place 
for greener pastures (Amponsah-Tawiah et al., 2011). 
Nevertheless, this trend has taken a new dimension as the industry has now largely 
attracted foreigners, especially of Chinese origin, who undertake small-scale mining 
activities not only with improved local machines, but also with complex forms which are 
imported purposely for the operations (Appiah, 2015). This is especially true in developing 
countries such as Ghana where activities of Chinese citizens have called for much concern. 
More often, the foreigner may hide under the auspices of one or very few but highly 
influential citizens within the local setting to carry out such an obnoxious task with 
incomparable environmental pollution and related damages. Until the beginning of the new 
millennium, the activities of small-scale miners were much less, relative to large scale 
miners (Andoh, 2002). 
Gold was mainly mined in large quantities by the colonial masters in most parts of 
Southern Ghana. Small-scale mining activities which have gained much popularity in 
recent years had only had a major turn at the beginning of the 21st century. It comes as a 
huge solution and indeed relief to the increasing amount of unemployment in most 
developing countries and the world at large (Appiah, 2015). More often than not, small-
scale mining activities within a geographically defined region may be associated with a 
water body. Water is used in washing away the dirt (mostly soil) from which pure gold is 
extracted. As a result of this realization, small-scale mining activities have always had a 
major toll on water bodies from which their activities are carried out. The process is most 
detrimental to major water bodies which serve as the main recipient for other smaller 
streams and rivers which empty their contents into these large rivers. Among the major 
destructions caused is water pollution by chemicals such as mercury, cadmium, arsenic, 
12 
 
copper and lead, and damage to vegetation cover. As a result of the nation’s favourable 
geographical setting, which makes precious minerals available in most sections of the 
Ghanaian soil, small-scale mining of minerals, especially gold, is scattered across sections 
of the nation (Mineral Commission, 2006).  
2.7 An Overview of the Modern Ghanaian Small-scale Mining Industry 
The abundance of gold in the Ghanaian soil has been attributed mainly to tectonic 
processes several years ago, which resulted in folds and faults, as well as a series of 
metamorphic and igneous, sedimentary and erosion activities (Simon et al., 2004). Erosion 
activities have resulted in the spread of rich soils which cover a sizable portion of the 
nation. Several gold belts cover Ghana’s land surface. The first belt, covering about 15-
40km in width, contains the Birimian gold. However, Birimian gold is found in West 
African rocks extending from Ghana to as far as Burkina Faso in the north, and Senegal 
and Mauritania in the western parts of the region. The belt contains such fine gold as 
Proterozoic greenstone type lobe gold deposits. This is variably complex and occurs as 
quartz-filled shear zone and altered shear zone forms (Tetteh, 2010). The Tarkwaian gold 
is found in the second gold belt. About 90% of this gold belt comprises Vein-quartz-
pebbles and auriferous pebble deposits. Quartzite and phyllite particles constitute the 
remaining 10% (Hammond & Tabat, 2017).  Due to the mineral-rich contents of the 
Ghanaian soil, huge monies are accrued from the modern Ghanaian small-scale mining 
industry. A good amount of gold either in fine particle forms or lump forms are mined 
from small-scale mining sites across the nation (Appiah, 2015). Generally, wealthy 
persons buy some acres of land after prospects are made on them to assess the level of 
their gold mineral deposits. The mineral is very costly. A small part of it may sell millions 
of cedi. Hence the vigorous involvement of the youth. The prospectors then hire high men 
and pay them wages (daily, weekly or specified days’ interval). Currently, a minimum 
13 
 
average daily wage may support an unmarried young man or a woman’s moderate 
expenditure for at least four days, though the specific amount paid may differ widely from 
place to place. However, small-scale miners hardly economize. Thus, a day’s wage may 
be spent on that particular day with very little or no amount kept. It is worth noting though, 
that few people involved enter this business with specific aims. Another important factor 
that makes modern small-scale mining a lucrative business is the high demands for gold 
and golden products both on the local and international markets (Hilson, 2009).  
2.8 Major Processes in Small-scale Gold Mining Activities  
A licensed operator may employ between five to twenty groups of tributes made up of 
between five to ten workers. Each group excavates the ore to process the mineral. Usually, 
the tributes keep two-thirds of the profit and give the remaining one-third to the 
concessionaire (Appiah, 2015). Small-scale mining activities in Ghana employ very simple 
implements and devices such as pick-axes, shovels, mattocks, sluice boxes and cutlasses. 
In some instances, mechanized machinery such as washing plants, Honda water pumps 
and explosives are employed (Hilson, 2009). Nonetheless, small-scale mining sites of such 
mechanized machinery operate largely rudimentarily. Generally, the processes involved in 
small-scale gold mining are crushing the ore into pebbles or powder under various stages, 
washing the crushed sediments with washing blanket or hands along riverbanks to separate 
the mineral, and panning (Amponsah-Tawiah et al., 2017). Finally, as an inexpensive 
substitute for a gold pan, a krowa - a wooden bowl carved from a tree branch is used to 
further wash and separate crushed material. The concentrate obtained is amalgamated with 
mercury (Appiah, 2015; Bloch & Owusu, 2017). Mercury is used in the planning process, 
and the amalgamated gold is roasted on charcoal fire in the open air (Arah, 2015).  
 
14 
 
2.9 Socio - Economic Effects of Small-scale Mining Activities  
Small-scale mining activities have immense contributions within the communities where 
they are operated (Aubynn, 2016). The impacts may be both positive and negative. These 
influences are realized in the increase in population in the area of work, due to increased 
employment opportunities, boost in economic activities and reduction in crime. Negative 
influences resulting from small-scale mining activities include child labour, promiscuity 
and high cost of living (Creek, 2016). 
Small-scale mining activities bring about the influx of people to the areas of operation 
(Ayitey-Smith, 2012). These people are mostly young men and women who are aggressive 
in making quick money. Some of these people are apprentices in various vacations who 
may previously be learning one form of trade or the other. Students who fend for 
themselves or have minimal support from home are also sometimes involved. These 
people who come to the region for small-scale mining activities to work also take women, 
especially teenagers from the area and befriend them. This interaction often results in 
teenage pregnancy and birth to more children, most of whom do not receive the necessary 
support and parental guidance. Subsequently, this results in population increase. People, 
who previously farmed, also see the small-scale mining of gold as a quick form of 
acquiring wealth. Thus, farmers also abandon their farming activities and move to 
‘galamsey’ sites in search of quick wealth (ILO, 2015).   
Small-scale mining activities provide employment opportunities to the people within the 
area of operation, favourably in very remote settings where there are less or no formal job 
opportunities (Aidoo, 2015). In developing countries, where employment is very much 
limited, and where farming is viewed as a job for the aged, the uneducated and the less 
active in the community, small-scale mining activities are seen as huge sources of 
alternative employment for the youth and the more active within the society. A United 
15 
 
Nations‟ Report on Small-scale Mining Activities (UN, 2009) indicated that more than 
over thirteen million or 20% of the world’s mining population was involved in small-scale 
mining operations. This was confirmed by research carried out by the International Labour 
Organization which stated in its Global Report on Artisanal and Small-scale Mining, that 
the number of the world’s population directly involved in small-scale mining activities 
was over thirteen million (ILO, 2015). Besides the direct job opportunities to people, 
small-scale mining activities create alongside much other employment through the boost 
in marketing activities (Agyapong, 2018). Women who were previously jobless now 
acquire a form of a job through petty trading or running errands at the sites of operation. 
Thus, all forms of population structure are at least able to meet their economic needs from 
the job opportunities created by the mining operation. Whilst it is not capital intensive, 
small-scale mining activities require sufficient manpower. Intensive- small-scale mining 
operations are economically beneficial; investment cost per job is estimated to be 10-12 
percent the costs in large mining operations (Akabzaa et al., 2007).  
The cost of living in small-scale mining regions is often very high (Aryee et al., 2017). 
This is because the operation fetches relatively much quicker money for its workers. 
Small-scale miners are often spindrifts. This is because they hardly plan ahead, and since 
the business is very fetching, they always have enough to spend. Traders in the area of 
operation sell their goods at very high prices, and this makes the cost of living very high 
for the ordinary people living in the area. Small-scale gold mining is a continuously 
growing business, very attractive to the young and active populations (Hilson et al., 2009). 
Since these groups of people would prefer this business to farm, food cost is often very 
high as only fewer and less active people get involved in the production of crops. While 
the standard of living has very minimal effects on those engaged in small-scale mining due 
16 
 
to the money earned, it adversely affects the vulnerable and inactive, native population 
who are not involved in this business (Amponsah-Tawiah et al., 2011).  
2.9.1 Socio-economic Impacts of Artisanal Mining 
Artisanal mining is an attractive employment option for many in rural areas; the barriers 
to entry are minimal - low technology and little capital are needed. Activity levels are 
dynamic as precious minerals are often inversely correlated with economic opportunity 
and periods of economic crisis. Artisanal and small-scale mining generates income; 
minerals provide higher income than other traditional activities within rural mining 
communities in Sub- Saharan Africa (Agyensaim, 2016).  However, Appiah (2015) stated 
that the presence of mining activities in Geita district in Tanzania has created market 
opportunities for local farmers and small traders, and employment opportunities for others. 
However, artisanal gold mining is associated with many social problems. The local 
populations are marginalized and oppressed, especially those from lower economic 
classes, by miners in the Geita district in Tanzania. Communities are exposed to chemical 
contaminants, heat stress, unsanitary conditions, malaria, prostitution, poor diets, drug-
taking and alcoholism (Creek, 2016). Women and children are the most vulnerable and 
affected. The participation of women in artisanal gold mining varies depending on local 
beliefs from different countries and communities. Their role is not limited to mining 
activities and includes the supply of food, tools and equipment and sex services that expose 
them to sexually transmitted diseases and HIV/AIDS (ILO, 2015). 
The environmental impacts of small-scale mining have been studied worldwide. The main 
impacts are deforestation and land degradation, open pits which are animal traps and health 
hazards, stagnant water in excavated holes that are abandoned by the miners that often 
serve as breeding grounds for mosquitoes, mercury use for gold amalgamation, inefficient 
17 
 
extraction, dust and noise (Anon, 2010; Bloch & Owusu, 2017; Queenshland, 2019). One 
of the most significant environmental impacts is derived from the use of mercury (Hg). It 
is a pollutant causing growing concern because of its long-term impacts on ecosystems 
and human health. Artisanal and small-scale mining, in contrast to other sectors where 
mercury utilization is decreasing, remains a dangerous source of mercury pollution 
(Durkin et al., 2008). 
2.10 Environmental Impacts of Artisanal Small-Scale Gold Mining    
There is a global consensus that mining and the waste generated by ore processing at active 
and inactive mining sites, and its impacts on human health and the environment, are a 
serious and persistent problem faced by government agencies, industry and the public 
worldwide (Durkin & Herrmann, 2008). They also emphasized that poverty is most 
prevalent in communities directly affected by mining activities. Spatial analysis of field 
data from districts with mining activities suggests that communities close to mining 
projects are generally poorer than those further away from mining, and also show a clear 
trend of decreasing poverty as a function of distance from the mine. 
Gold mine production in developing countries has had a positive impact on employment, 
but at the same time has caused a multitude of environmental complications. A general 
problem is that most developing countries have only recently introduced national 
environmental legislation, and of the laws that pertain to mine-related activities, most are 
far from rigorous and do not regulate all aspects of the industry appropriately and 
effectively (Hilson et al., 2009). 
In the case of small-scale gold mining, environmental complications generally occur due 
to low safety awareness and low levels of training, poor exploitation of available resources 
due to selective mining of rich ores, low wages and chronic lack of capital, lack of 
18 
 
environmental standards and use of very inefficient equipment. It is clear that small-scale 
resource exploitation is a source of livelihood for a significant number of people in sub-
Saharan Africa (Nartey et al., 2011; Lombe, 2003). However, it has the greatest impact on 
sustaining rural economies. To the extent that it is a source of livelihood, it also has an 
impact on the environment. The environmental effects associated with artisanal gold 
mining include the following; 
2.10.1 Mining and Land Degradation. 
According to UNEP (2006) land degradation is the temporary or permanent reduction in 
the productive capacity of land. It thus includes the various forms of land degradation, 
human encroachment on water resources, deforestation and reduction in the productive 
capacity of rangelands. According to Kusi-Ampofo et al., (2012) rich vegetation has been 
cleared and ridges have been targeted and degraded from top to bottom through a series of 
benches. The degradation of large tracts of land by small-scale mining poses a major threat 
to community agriculture and economic survival. 
Land degradation through illegal mining activities reduces biodiversity and may 
subsequently reduce the availability of medicinal plants (Bagstad et al., 2016; Biodiversity 
Support Programme, 1993; Ayitey-Smith, 2012). Meanwhile, Forkuor and Coffie (2011) 
noted that the huge scale of mining has led to a complete change of landform suitable for 
agricultural and other subsistence activities. Huge craters have been formed and slopes and 
parts of mountains have been eroded, affecting the sources of many rivers and streams and 
also leading to deforestation. On the other hand, mine waste dumped were often occupying 
large areas and disfiguring the landscape, resulting in massive water pollution from 
rainfall. 
19 
 
With regard to artisanal gold mining, the World Bank (2002) pointed out that one of the 
environmental impacts of artisanal gold mining in Ghana is land degradation, more 
specifically the clearing of large areas of forest, the digging of ditches and the clearing of 
vegetation, which in turn leaves the land bare and exposed to erosion factors. About 15,000 
ha of land is potentially affected by small-scale mining activities. Furthermore, Appiah 
(2015) explains, it is common for potential mining sites to be cleared and where there has 
been deep underground mining, the shafts are left uncovered and abandoned. However, 
Agyapong (2018), who conducted field research in Tarkwa in the Western Region of 
Ghana, reported that large areas of the region had been deforested as a result of small-scale 
gold mining. Artisanal miners, who are supposed to clear vegetation and then dig to extract 
ore, have left the landscape with ditches and graves dug which, in turn, makes the land 
unusable for any other purpose. Many of these pits are filled with water and serve as 
breeding grounds for malaria-infected mosquitoes. In addition, large areas of forest were 
removed in some places to create neighborhoods or "rest areas" for miners. Studies on the 
effects of small-scale resource exploitation have mainly focused on land use change 
studies (Pearson, 2019). However, Forest Carbon Partnership Facility (2014) reported that 
artisanal small-scale gold mining activities in Ghana resulted in the clearing of extensive 
forest areas, which in turn left potentially productive land bare and exposed mainly to 
erosion, heavy rainfall and wind. In addition to this, the World Bank (2002) reported that 
about 15,000 hectares were potentially affected by small-scale mining by residents. 
In this regard, Agyapong (2018) explained that potential areas are generally devoid of 
vegetation and topsoil, and where there is deep underground mining, the pits tend to remain 
bare and abandoned. Furthermore, Aryee et al., (2017), who carried out fieldwork in the 
Tarkwa area of Ghana, reports that large areas of the area have been deforested as a result 
of small-scale gold mining. The artisanal miners, who reportedly cleared the vegetation 
20 
 
and then excavated mineral ore, marked the landscape with dug holes and trenches, which 
in turn made the land unusable for any other purpose. 
2.10.2 Illegal Mining Activities and Effect on Water Bodies  
Illegal mining activities of water bodies have been identified as one of the major causes 
affecting the production and supply of potable water in the Western Region. The Pra River, 
which serves as the major source of water for the production of potable water in the region, 
has been taken over by ‘galamsey’ operators, whose inhuman activities pose a great danger 
to the continuous availability of potable water (Amponsah-Tawiah et al., 2017). The 
deputy minister for Water Resources, Works and Housing on a working visit to assess the 
level of the water crisis in the region, admitted that illegal mining activities pose a great 
challenge to the operations of Ghana Water Company. She said that both the Ntweaban 
and the Daboase treatment plants have the capacity to produce six million gallons of water 
a day to serve the twin-city but production is low and inadequate. These illegal mining 
activities pose problems for the treatment plants and make the cost of production very 
high. She admitted that the current situation in the region was bad and could affect the 
industry and even household consumption (GNA, 2011). 
Determination of heavy metals in water bodies in Tarkwa and Obuasi areas by the Wassa 
Association of Communities Affected by Mining Changes (WACAM), particularly on 
Nyam River in Obuasi showed that Arsenic concentration of 13.56mg/L as against 
0.01mg/L required by the WHO and Environmental Protection Agency (EPA). This was 
due to pollution by small-scale mining activities. Data from Asuakoo River revealed that 
it had 22.72mg/L as against 0.4mg/L of Manganese prescribed by the WHO permissible 
guideline. The Executive Director, WACAM, said pollution of water bodies in the mining 
communities posed serious health implications to the people who were found to be 
21 
 
suffering from various illnesses. He said the result of the research showed that most water 
bodies in the study areas were polluted with a high arsenic level ranging from 0.005 to 
35.4mg/L. Manganese, lead and mercury are neurotoxic metals which could affect the IQ 
of children exposed to high levels in drinking water. In all 400 water samples, made up of 
200 from Obuasi and 200 from Tarkwa areas were collected between May and September 
2008 and each sample was analyzed separately for toxic chemicals including arsenic, 
manganese, cadmium, iron, copper, mercury, zinc and lead (Agyapong, 2018). 
The physicochemical parameters such as pH, conductivity, turbidity and total dissolved 
solids were measured using standard methods of analysis as prescribed by the American 
Water Works Association (AWWA, 2000). The turbidity of some of the water bodies and 
alternate sources of water provided had low pH and high turbidity values, which exceeded 
the WHO and GEPA permissible limits (GNA, 2009).  Studies conducted by the Ghana 
Atomic Energy Commission (GAEC) on water bodies and stream sediments as a result of 
small-scale mining activities at Tarkwa and its environs in the Western Region indicated 
excessive pollution of high mercury concentration, a toxic element that affects human 
health. The results suggested that the level of mercury detected from the water samples 
from the Western Region gold mining towns exceeded the WHO tolerable limit of 
0.001mg/L for drinking water (GNA, 2009). 
According to the GAEC, areas that contained high concentration of mercury are sites that 
experienced extensive ‘galamsey’ gold mining activities, showing that mercury 
concentration varied between 6.80 and 19.82 for water, and 28.90 and 84.30mg/Kg in 
sediment at sites with extensive small-scale mining activities (Akosa et al., 2018). At 
Prestea and its environs, total mercury concentrations in water were measured. The 
samples were analyzed by instrumental neutron activation analysis (INAA). Higher levels 
22 
 
of total mercury concentration were found in samples at the sites with extensive small-
scale gold mining activities than at the sites with low small-scale mining activities. 
Concentrations varied between 7.5 and 20.6mg/L with extensive small-scale mining 
activities. At low small-scale mining sites, mercury concentration varies between 0.50 and 
9.10mg/L (Serfor-Armah et al., 2006). At Bibiani-Anhwiaso-Bekwai District (a typical 
mining community in the southwestern part of Ghana), surface water and sediment 
samples were collected from seven streams that drain this mining community and analyzed 
for mercury concentration. The total mercury content of the water ranged between 0.125 
and 1.341µg/l while sediment values ranged between 0.169 and 1.739 mg/Kg. Physico-
chemical parameters were also determined for the water samples. The pH range varied 
from 8.4 to 7.1. Temperature also ranged from 32 22.7°C to 31.6°C. Conductivity also 
ranged from 2.77 to 0.21 µs/cm. Total dissolved solids were from 185.9 to 111 mg/L 
(Nartey et al., 2011). 
2.10.3 Air Pollution and Transport of Earth Material 
According to Armah et al., (2013), site clearance and road construction, topsoil removal 
and dumping, and transport of earth material in open-pit mines result in air pollution. The 
dust emitted pollutes the air in most mining communities, especially during the dry season. 
The dust produced during gold mining contains a high proportion of silica and can cause 
respiratory diseases such as silicosis, colds, tuberculosis and silico-tuberculosis (Aubynn, 
2016; Owusu & Dwomoh, 2012). 
Another issue of concern is social hazard. Issues about social hazards on gold mining have 
been raised by various authors however, Agyensaim (2016) stated that small-scale gold 
mining activities in Ghana are taking place in areas where agriculture is predominant. 
Miners compete with community members for agricultural land, forest products, domestic 
water resources, consumption, fishing and other aquatic products that are the source of 
23 
 
livelihood for community members. Referring to Andoh (2002), most farmers are 
abandoning their land because the economic argument weighs in favour of the mining 
industry. Residents are therefore forced to free up agricultural land and settlements for 
open-pit mining. The influx of migrant workers leads to high costs of living, especially 
higher food and housing rents, disorientation of communities and distortion of cultural 
values, among others (Aidoo, 2015; Agyensaim, 2016). The focus on small-scale gold 
mining has shifted attention away from food production, primarily to ‘galamsey’ 
operators. Land previously used for farming and other agricultural activities has been taken 
over by mining concessions, reducing food production and creating conditions for higher 
food prices (Bloch & Owusu, 2017; Tetteh, 2010; Noestaller, 2011). The business of 
‘galamsey’ which involves excessive physical exertion, is a challenge, and hence risky, 
due to high drug consumption. Smoking cigarettes increases exposure of the lungs to dust, 
making them more susceptible to silicosis, tuberculosis and pneumonia. Alcohol and drug 
abuse are important causes of mining accidents. Sexually transmitted diseases are 
prevalent in mining communities because of the activities of sex workers (Chambers & 
Conway, 2017).  
2.10.4 Heavy Metals in the Environment  
The presence of heavy metals in the environment has adverse effects on ecosystems 
(Mamo et al., 2007). Minute amounts of heavy metals available in the environment are 
basically due to the weathering of rocks. This is the process by which rocks break down 
into pieces to generate soil. Like all metals, heavy metals circulate within the environment 
and are eventually assimilated by plants and animals (Armah et al., 2013; Akabzaa et al., 
2007). 
 
24 
 
2.11 Methods of Mining by Small-scale Miners 
 Methods employed by small-scale gold miners vary according to the type of deposit being 
exploited and its location Due to the poor financial status of small-scale gold miners, the 
majority rely solely on traditional/manual methods of mining, which use simple equipment 
like shovels, pick-axes, pans, chisels and hammers (Ntibrey, 2004).  One method is the 
shallow alluvial mining techniques, which are popularly called -dig and wash, or the 
Krowa method (wooden bowl carved out of tree stem to serve as a pan) are used to mine 
shallow alluvial deposits usually found in valleys or low-lying areas. Such deposits have 
depths not exceeding three meters. The area is initially cleared and the soil excavated until 
the gold-rich layer is reached. The mineralized material is removed and transported to 
nearby streams for sluicing to recover the gold. Gold from sluices is concentrated by using 
a smaller ‘krowa’ or the gold pan. Women are very effective in using the ‘krowa’ for the 
recovery of gold (Appiah, 2015). Illegal small-scale gold miners practice this method 
because of easy access. 
Deep alluvial mining techniques or land dredges are also other types used to mine deep 
alluvial deposits found along the banks of major rivers such as the Ankobra, Tano, and 
Offin and certain older river courses. These methods involve excavating a pit and digging 
until the gold-bearing gravel horizon, which is typically located at depths of 7 to 12 metres, 
is reached (Noetstaller, 2011). The gold-bearing rocks are then removed and sluiced to 
recover the gold. In recent years, some of the richer owners have introduced large 
machinery to this method, bulldozing and back-hoeing pits to access layers of gold-bearing 
gravels more quickly or those formerly inaccessible by manual methods alone (Creek., 
2016). In areas where hard rocks are encountered the ore is excavated manually and size 
reduction is carried out using a combination of jaw and rocker crushers and hammer 
25 
 
(Amankwah & Anim-Sackey, 2003). In some cases, explosives are commonly used, 
despite being prohibited throughout Ghana (Ntribrey, 2004). 
2.12 Methods of Processing  
Firstly, the ore is crushed into pebbles by physical or mechanical means. The pebbles 
undergo primary, secondary and tertiary grinding in preparation for washing. The ground 
ore is transferred to the riverside or pond in cloth bags to be refined (Amegbey & Eshun, 
2003). The gold-containing material is washed on sluices where the heavier gold particles 
are caught and concentrated on carpets or jute sacks, due to gravity. The concentrate from 
the sluice box is re-assembled in rubber dishes or wooden pans (Krowa). Through panning, 
the undesirable sediments are separated from the gold particles until the latter clearly 
appear in the final concentrate. Next, mercury is poured into the concentrate inside the 
pan. Mercury is usually mixed by hand with the concentrate, forming a lump or ball of 
mercury-gold amalgam. Water is added several times to discard tailings and remove lighter 
particles until only the amalgam remains. The amalgam is then squeezed in a piece of cloth 
to recover excess mercury (often re-bottled and used again). Some miners put the fabric 
with the amalgam into their mouth to suck out additional mercury (Hilson et al., 2009). 
Finally, the amalgam is roasted in a coal pot for 15-40 min, depending on size. Burning 
can also take place with a blowtorch. During gold production, mercury losses occur at 
various stages: amalgamation, where mercury may be washed out during the gravity 
washing; and burning, where mercury, with its high volatility, is released into the 
atmosphere. After burning, a sponge-like gold substance stays behind in the tin. When the 
gold has cooled, it is weighed and at the end of the day sold (Akosa et al., 2018). 
  
26 
 
2.13 Regulations on Mining Activities in Ghana 
Mining activities in Ghana are regulated by a number of legal frameworks (Noetstaller, 
2011). That mining activities are not properly monitored cannot be attributed to the lack 
of regulations on mining processes. Rather, this can be attributed to the fact that there are 
weaknesses in existing laws, and also the lack of commitment on the part of environmental 
agencies to enforce these laws. Environmental agencies may sometimes also lack the 
necessary equipment for monitoring. This makes monitoring very difficult, and the agency 
becomes limited in its mandates (Agyensaim, 2016). Some important mining regulations 
enshrined in the constitution of Ghana are reviewed here. 
2.14 Mineral and Mining Law 1986 (PNDCL 153)  
Section I “All minerals are the property of the Republic of Ghana, and the Government 
has the power to acquire compulsorily any land which may be required to secure the 
development or utilization of any mineral resources”. This law makes the Government of 
Ghana the legal owner of minerals taken from the Ghanaian soil, and all lands suitable for 
mining processes. Section 14 subsection 2 (PNDCL 153, 1986) “The Secretary (now the 
Minister) for Lands and Natural Resources shall, on behalf of the Republic, have the power 
to negotiate, grant, revoke, suspend or renew any mineral right under this law”. This makes 
it possible for the Minister of Lands and Natural Resources, on behalf of the Government 
of Ghana, to enact by-laws and prevent any such move or mining activity deemed 
detrimental to the environment, and ultimately citizens of the nation. PNDC Law 153 also 
makes it possible for mining organizations to have inspectors and monitors who sample 
materials and systems such as soil, tailings and water bodies in and around the site of work, 
so as to ensure that environmental standards are met.  
 
 
27 
 
2.15 The Mineral Commission Law 1986 (PNDCL 154) 
The law was enacted to establish the Minerals Commission. The Minerals Commission is 
responsible for the formulation of policies regarding mineral exploration and its mining 
on Ghanaian soils. The Commission, therefore, has a contractual obligation with 
proponents in the country. Individuals holding the chairmanship, as well as the Chief 
Executive Officer of this Commission are appointed by the Government.  
2.16 Small-scale Gold Mining Law 1989 (PNDCL 218)  
Section I “No person shall engage in or undertake any small-scale mining operations 
unless there is existence, in respect of such operation, a license and granted by the 
Secretary (now the Minister) for Lands and Natural Resources or by an officer auditioned 
on that behalf”. It is evidently clear from this law that small-scale mining operations 
without permits are not allowed to operate. The story, however, differs from current 
observation, as almost all small-scale mining activities across the nation either operate 
illegally or have not adequately followed due processes to acquire permits. This law does 
not make it possible for non-Ghanaians to have a license to operate, as stated in Section 2 
of this regulation, except that such Ghanaians are 18 years and above. Nonetheless, they 
may participate in such operations where Ghanaian citizens are the majority stakeholders, 
as directed by the firm’s code. Section 13 of the law forbids using explosives in small-
scale gold mining operations. It states, “No small-scale gold miner shall use any explosive 
in his operations” Unfortunately, the lack of adequate supervision and monitoring has 
resulted in this aspect of the law being highly compromised. The purchase and use of 
mercury are however permitted, as specified in section 14 of this law 46.  
2.17 Legal Framework for Small-scale Mining Activities in Ghana 
An important move was made by the PNDC Government in 1989 to officially allow the 
mining of important minerals such as gold. This legalization of small-scale mining activity 
28 
 
was an important landmark over previous regulations, which only permitted the small-
scale mining of diamonds. Three important mining laws were passed as follows: The 
Small-scale Gold Mining Law (PNDCL 218): Provides for the registration of activity; the 
granting of gold-mining licenses to individuals or groups; the licensing of buyers to 
purchase products; and the establishment of district-assistance centers. 
The Mercury Law (PNDCL 217): Legalized the purchasing of mercury (for mineral 
processing purposes) from authorized dealers. The Precious Minerals Marketing 
Corporation Law (PNDC Law 219): This law transformed the Diamond Marketing 
Corporation (DMC) into the Precious Minerals Marketing Corporation (PMMC), which 
was authorized to buy and sell gold.  
2.18 Processes of Application for Small-scale Mining Permit/License  
Firstly, a notification is given to the Officer responsible for small-scale mining in the 
district. The Officer then follows up to the region to assess how suitable it would be for 
the activity before demarcating the site. The prospective miner prepares a site plan, and a 
notice of the intention is published at the Assembly, the local information center and the 
magistrate’s court for 21 days. After the 21 days, and if no contrary opinions are expressed, 
the applicant completes an application form to fulfill other requirements of the Mineral 
Commission. Recommendations are submitted to the Secretary or Minister for Mines and 
Energy who then approves or rejects the application. A successful applicant is handed a 
code of environmental safety practices, and these include safety precautions at workplaces 
(protection at workplaces), land surface protection and general environmental protection 
guidelines (Mineral Commission, 2006). 
 
 
29 
 
2.19 Definition and Concepts of Ecosystem 
2.19.1 Ecosystem and Ecosystem Services   
The United Nations Convention on Biological Diversity defines ecosystems as a dynamic 
complex of interacting plant, animal and microbial communities and their abiotic 
environment as a functional unit. Ecosystems can generally be divided into two main 
categories: terrestrial and marine ecosystems. Humans are part of ecosystems and benefit 
from them, as their lives depend on ecosystem services. These systems interact and 
interconnect through various processes that create ecological balance and are linked at 
different scales to provide valuable ecosystem services to people (Daily et al., 2009; 
Costanza et al., 2014). Ecosystem services provide outputs or outcomes that directly and 
indirectly affect human well-being because these are the benefits that people derive from 
ecosystems. Therefore, ecosystem services are an integral part of nature that are enjoyed, 
consumed, or used directly or indirectly to create and satisfy human well-being (Mitchell 
et al., 2017).  Ecosystem processes, sometimes referred to as functions, express the 
complex physical and biological cycles, processes and interactions that underpin the nature 
we observe and result in ecosystem services. The specific outcome of these processes is 
the direct maintenance or enhancement of human life (de Groot et al., 2002). Ecosystem 
services are the ability of natural processes and elements to provide goods and services 
that directly or indirectly meet human needs. Several competing definitions of ecosystem 
services come from different disciplines and approaches (Fisher et al., 2017; Fu et al., 
2018). However, there are some general definitions of ecosystem services that are 
frequently used and cited. 
- Daily et al., (2009) defines ecosystem services as the conditions and processes by which 
natural ecosystems and their component species support and enable human life. 
30 
 
- Costanza et al., (2014) define ecosystem services as the benefits that humans derive 
directly or indirectly from ecosystem functions. 
- The Millennium Ecosystem Assessment (2005) defines ecosystem services as the 
benefits that ecosystems provide to humans, which include the following services: 
nutrition, regulation, support and cultural services. The definition shows that although 
there is broad agreement on the general definition of ecosystem services, some critical 
differences can be highlighted. Daily et al., (2009) define ecosystem services as "states 
and processes" and "actual life-supporting functions". Again, Costanza et al., (2014) 
define ecosystem services as functions provided by and used by people. Basically, 
provisioning services include firewood, bushmeat, freshwater, fruits, medicines, and fish. 
Once the functioning of ecosystems is known, the value of goods and services provided to 
human society can be analysed and assessed according to the functional aspects of 
ecosystems. To avoid confusion between the two concepts, it is argued that the difference 
lies in the fact that human beneficiaries are related to services and not to the activities 
considered in this paper. Those activities that benefit individuals and society are referred 
to as 'ecosystem goods and services. Ecosystem goods and services are based on the natural 
environment, such as soil, water and air (Hein et al., 2018). Natural ecosystems provide a 
wide range of direct and indirect services and tangible benefits to humans and other 
organisms (Shackleton et al., 2019). 
Table 2.1: Classification of Ecosystem Services 
Provisioning services:   Cultural services  
 Products obtained from ecosystems, River for recreational use including non-
such as water, timber and non-timber material benefits, such as cultural, 
forest products, or genetic resources recreational or spiritual values  
including the following:  
Water, Medicinal plants, Bushmeat 
Food (Wildlife, fish, fruits, mushrooms 
and others) 
Raw materials such as building materials 
Supporting services: Services needed to  Regulatory services: Benefits from the 
produce the other three categories, ecological processes such as carbon 
primary production/soil and nutrient regulation 
recycling. 
31 
 
2.20 Ecosystem Services and Livelihoods  
2.20.1 Ecosystem Services  
According to Costanza et al., (2011) ecosystem services benefit humans by transforming 
resources or environmental assets, including land, water, vegetation and atmosphere, into 
a flow of essential goods and services such as clean air, water and food. The Millennium 
Ecosystem Assessment (2005) also defines ecosystem service as the benefits derived from 
nature that are important for human well-being. In 2005, the Millennium Ecosystem 
Assessment identified and categorized ecosystems and their resulting services, identified 
the links between these services and human societies, and the direct and indirect drivers 
and feedback loops. The Millennium Ecosystem Assessment framework identified 
ecosystem services within four categories, namely; provisioning services such as food and 
water, regulating services, such as flood and disease control, support services, such as 
nutrient cycling, that maintain the conditions for life on Earth, and cultural services, such 
as spiritual, recreational, and cultural benefits (Wang et al.,2018). 
Ecosystems are increasingly recognized for their contribution of services to human well-
being. This has led to an interest by many researchers in understanding human-
environment interactions against the backdrop of dwindling ecosystems (MA, 2005). 
Across the world, understanding ecosystems is an essential subject for scientific enquiry 
(Cowie et al., 2011; Rounsevell et al., 2010), mainly due to the growing costs of 
biodiversity loss and ecosystem degradation (TEEB, 2008). This is particularly true for 
developing countries whose population heavily depend on ecosystems for survival (due to 
high poverty levels) and have the highest rates of ecosystem degradation (MA, 2005), and 
is especially the case for the dryland systems of Sub-Saharan Africa (Bagstad et al., 2018) 
 
32 
 
2.20.2 Ecosystem Services and Land Use Change  
It has been noted in the Millennium Ecosystem Assessment (MA) Synthesis Report (2005) 
that degradation of ecosystem services significantly impacts human well-being and poses 
a direct threat to regional and global eco-safety (Daily et al., 2009). Land use refers to the 
deliberate management of land to achieve specified outcomes that are partially influenced 
by the natural characteristics of the land in question (Fu et al., 2013). Human activity has 
substantially altered land cover globally and Crop land now accounts for almost 11 % of 
the total global land area. The area used for grazing livestock has increased from 324 
million ha in 1700 to 3429 million ha in 2000, representing 25 % of the total global land 
area (Pielke et al., 2011). Such land-use change strongly influences an ecosystem’s 
capacity to provide services (MA, 2005; GLP, 2005; Lawler et al., 2014). However, 
Costanza et al., (2014) estimate that total global land-use changes between 1997 and 2011 
resulted in a loss of ecosystem services worth $4.3–20.2 trillion every year. Researchers 
and international groups increasingly view ecosystem services, land use change, and the 
links between them as of central importance for ecosystem restoration, management, and 
conservation (Palmer et al., 2004; GLP, 2005; MA, 2005; Sutherland et al., 2006; 
Crossman et al., 2013; Maes et al., 2013). 
Initial research conducted on ESs was heavily focused on defining essential concepts and 
terminology (De Groot et al., 2002; Haines-Young et al., 2011) and on attempting to value 
the ongoing provision of these services (Van Wilgen et al., 1996; Costanza et al., 2014; 
Ghaley et al., 2014). More recently, research has focused on the quantitative modelling 
and spatially explicit mapping of ESs (Ayanu et al., 2012; Martínez-Harms & Balvanera 
2012; Hu et al., 2014; Larondelle et al., 2014; Sumarga & Hein, 2020), trade-off and 
synergy analysis between multiple ecosystem services (Rodríguez et al. 2006; Fu & Wang, 
2018; Jia et al., 2014; Kragt & Robertson, 2014; Lu et al., 2014; Zheng et al., 2014), spatial 
33 
 
flow analysis of ecosystem services (Bagstad et al., 2018; Palomo et al., 2013; Villamagna 
et al., 2013; Schröter et al., 2014; Serna-Chavez et al., 2014), incorporation of ecosystem 
services into conservation and restoration programs (Egoh et al., 2007; Trabucchi et al., 
2012; Zheng et al., 2014), environmental decision-making and its implications (Daily et 
al., 2009; Bateman et al., 2013; von Stackelberg, 2013; Zheng et al., 2014), and assessing 
payment options to account for ESs (Derissen & Latacz-Lohmann, 2013; Schomers & 
Matzdorf, 2013).  
While ecosystem services have received increasing attention in the literature, much less 
attention has been given to studies linking ecosystem services with land-use changes.  The 
studies that examined such links, most focus on the changing values of ecosystem services 
under altered patterns of land use. In practice, these valuations help raise awareness of the 
importance of ecosystem services to society and serve as a powerful and essential 
communication tool for informing decisions regarding the inherent trade-offs associated 
with policies that enhance the gross domestic product (GDP) but damage ecosystems as 
opposed to simply treating ecosystem services as commodities for trade-in established 
markets (Costanza et al., 2014).   
2.20.3 Provisioning Ecosystem Services 
Provisioning services are the tangible goods or products obtained from ecosystems such 
as food, fresh water, timber and fiber (Fu et al., 2018).  Indeed, provisioning ecosystem 
services are vital to human survival, especially in rural communities in Africa that depend 
primarily on ecosystem services. Without provisioning ecosystem services such as wild 
fruits, bushmeat and other essential consumptive materials, their survival and livelihood 
will be highly affected. The question of food security under-provisioning ecosystem 
service in a primarily rural community is vital as demand increases and commodity 
markets become more volatile (Ghaley et al., 2014). Over the years, access to gold through 
34 
 
heavy machines and tools has significantly impacted the provisioning ecosystem services 
of the most vulnerable farmers across Africa. Ghana is among the countries in Africa that 
have suffered from over-exploitation of natural resources such as gold.  Small-scale 
mining activities, popularly called ‘galamsey’ has had a severe toll on the agricultural 
activity of community members. This situation has had dying down consequences on 
access to arable land for agricultural activities.  
Agro-ecosystems, ranging from small-holdings to commercial scale, provide food for 
human consumption and underpin global food security have been affected due to land-use 
change activities. Much of the Earth’s land surface is used for food production through 
crop cultivation and livestock rearing. Marine and freshwater fisheries and aquaculture 
also provide significant protein sources to the global population (FAO, 2014). Aquaculture 
depends on nutrient recycling and water purification services in coastal areas and inland 
water bodies (Asante et al., 2017). In urban contexts, ecosystems can help meet energy 
needs and support agriculture (Martinez-Harms et al., 2012). As well as the production of 
sufficient food in terms of quantity, the nutritional quality of food produced is also critical 
to human health and an essential component of food security (FAO, 2014). A range of 
ecosystems provides both wild and domestic sources of nutrition for humans (Obeng et 
al., 2018). Where these resources are in decline, malnutrition can occur, for example, 
coastal communities relying on dwindling fisheries for protein intake. For communities 
worldwide, nutritional needs can be met through wild products identified and located 
through ecological knowledge, another ecosystem service (FAO, 2014). Water is used 
predominantly for agriculture, including livestock production, followed by industry and 
domestic uses (Fu et al., 2013). Forest and mountain ecosystems are source areas for most 
renewable water supplies and regulate pollution and water quality. The link between the 
regulation of water supply and water quality is strong. However, vegetation, soils and soil 
35 
 
organism activity are significant determinants of water flows and quality, and micro-
organisms play an important role in groundwater quality.  
While the general relationship between more intact biodiversity and water regulation is 
understood, the relationships between discrete species and changes in biodiversity with 
changes in water regulation are not. Land-use change, significant deforestation can affect 
the capacities of ecosystems to regulate and provide fresh water, which can be difficult to 
reverse (GLP, 2005). Large-scale land-use change activities such as mining and other 
related activities can potentially affect vapour formation and rainfall patterns in locally 
specific and highly variable ways. Rain-fed agricultural systems will potentially be 
influenced, in turn impacting food production and quality. 
Agro-ecosystems can also interplay with human resilience in negative ways; for example, 
by impacting on ecosystem services nearby through nutrient pollution and barriers to 
migration and dispersal of organisms, sedimentation of waterways and loss of wildlife 
habitat (Schomers, 2013). The provision of fuels and fibres including timber, cotton, sisal, 
sugars and oils is an important ecosystem service for humans. Such natural materials are 
used to construct shelter and fuel for cooking and heating (MA, 2005). Biochemicals 
produced by plants, animals, and microorganisms are high-value medicinal resources for 
the production of pharmaceuticals and pesticides and other products. Pharmaceutical 
compounds have been derived from a range of ecosystems, including oceans, coastal areas, 
freshwater systems, forests, grasslands and agricultural land. Crop genetic diversity is 
critical for increasing and sustaining production levels and nutritional diversity throughout 
the full range of agro-ecological conditions (FAO, 2014).  
Genetic diversity within crops contributes to food security by increasing yields and 
nutritional values. Humans have had a long history of improving varieties and replacing 
36 
 
local varieties of domesticated plant species with high-yielding crops, thus eroding genetic 
resources. Agricultural genetic diversity also provides services for genetic diversity in non-
domesticated species of plants, animals, and microorganisms linked to them within 
ecosystems. Advances in genetic modification are opening up opportunities to increase 
these effects by preserving genetic diversity in gene banks and creating improved strains 
or breeds. Genetic diversity of crops also decreases susceptibility to pests. Genetic 
resources in crop plants, livestock and fisheries will be increasingly crucial for resistance 
to diseases and adaptation to novel climatic conditions. Access to green space has been 
linked to reduced mortality, improved perceived and actual general health and 
psychological benefits (Tesfaye et al., 2011). 
2.20.4 Forest Provisioning Ecosystem Services 
Ecosystems services are essential benefit that people obtain from ecosystems (MA, 2005). 
Provisioning ecosystem services are those products that can be harvested and quantified, 
such as food, fibre and fuel (Maass et al., 2005). In Africa, various land-use change 
activities have rendered most forest lands useless.  Most forests in Africa provide an 
essential day-to-day living for their inhabitants. They are a source of mushrooms, edible 
insects, indigenous fruits, seeds, wild vegetables, honey and oils (Shackleton & Gumbo, 
2010). The woodlands are also a source of traditional medicine for primary health care 
(Cristecu, 2012) and poles, fibers and other materials used for constructing houses and 
barns (Clarke et al., 1996). Wood fuel (firewood and charcoal) from the forest is an 
essential energy source, providing over 75% of Africa's total energy needs for urban and 
rural dwellers. The forest is a pharmacy, a supermarket, a building supply store, and a 
grazing resource. Over the last four decades, the accelerated conversion of forest and 
wetlands to mining areas has resulted in the total collapse of provisioning ecosystem 
37 
 
supply by most forests in Africa. This situation has deepened the poverty situation of the 
rural Africa population.   
2.20.5 Vulnerability of Rural Households to Provisioning Ecosystem Loss 
Rural households are vulnerable to various stresses and shocks that affect their livelihood 
assets and options due to loss to provisioning ecosystem services (Debela et al., 2012). 
Households experience different frequencies and types of idiosyncratic shocks such as 
death, sicknesses, loss of property and covariate shocks such as droughts, flooding, 
outbreaks of human and livestock diseases (McSweeney, 2004; Paumgarten & Shackleton, 
2011). Rural households rarely have access to formal insurance institutions to help them 
cope with stresses and shocks. Therefore, a loss to ecosystem services provision due to 
land-use change activities such as mining often rendered most rural households vulnerable. 
In most parts of Africa, rural households cope with these stresses and shocks from selling 
productive assets, kinship, engaging in off-farm employment, or reducing the frequency 
and amount of consumption (Debela et al., 2012; Dercon, 2002).  
The coping capacity of households is determined by several factors such as nature and 
intensity of shock (Pattanayak & Sills, 2001), local environmental endowments and 
household socio-economic factors. Although households use various strategies to cope 
with idiosyncratic shocks (Heemskerk et al., 2004; Maxwell et al., 1999; Paumgarten & 
Shackleton, 2011), these strategies are often inadequate cope with extreme covariate 
shocks (Dercon, 2002; Heemskerk et al., 2004). High frequency and intensity of shocks 
coupled with inadequate household's coping strategies are a common poverty trap for 
many rural households (Carter & Barrett, 2006; Zimmerman & Carter, 2003). The rising 
levels of human vulnerability to multiple stressors increase rural people's dependence on 
ecosystem services (Shackleton & Shackleton, 2012).  Unfortunately, the forest and 
accompanying ecosystem services are being destroyed to accommodate other land uses 
38 
 
such as mining, housing, etc. Although the use of forests to cope with stresses and shocks 
has been reported in some empirical studies, mainly in Latin America's tropical forests 
(Godoy et al., 1998; McSweeney, 2004), only a few studies have been conducted in the 
dry forests of southern Africa (Paumgarten & Shackleton, 2011). 
2.20.6 The contribution of provisioning ecosystem services to rural livelihood  
Ecosystem services are defined as the benefits that people obtain from ecosystems (MA, 
2005). Global policy interest in forest ecosystem services has increased due to their role 
in mitigating climate change and providing essential services to rural livelihoods in 
developing countries. The economical use of forest ecosystems has long been recognized 
(Whitford, 2015); however, forests worldwide are disappearing at alarming rates (FAO, 
2014). This trend has prompted policymakers, researchers, and development agencies to 
promote the sustainable management of forests to reconcile economic development and 
biodiversity conservation (Paumgarten & Shackleton, 2011). Forests provide several 
products that underpin many rural livelihood strategies (Shackleton & Shackleton, 2004). 
These products are collectively referred to as ‘provisioning services’, defined as ‘services 
supplying tangible goods, finite though renewable, that can be appropriated by people, 
quantified and traded’ (Maass et al., 2005). Because the value of vegetation to rural 
livelihoods is socially constructed and contested (Kragt, et al., 2014), the direct-use value 
of FPES in households is a crucial determinant of their value, both in consumption and as 
a source of income (Mamo et al., 2007; Shackleton & Shackleton 2006; Sunderlin et al., 
2005; Tesfaye et al., 2011).  
Although the importance of FPES to millions of rural households is increasingly being 
acknowledged, research regarding the impact of socio-economic factors on forest use 
shows mixed evidence. Wealthy households have been reported to consume more forest 
products than poorer households in Zimbabwe (Crossman et al., 2013) however, studies 
39 
 
in South Africa have reported that wealth does not significantly influence the consumption 
of forest products (Paumgarten & Shackleton, 2009; Shackleton & Shackleton, 2012). In 
terms of household income, middle-class and wealthy households have been reported to 
earn more income from the sale of FPES in Cameroon (Lawler et al., 2014) and the 
Democratic Republic of Congo (Debela et al., 2012), while a study in Dixie village in 
South Africa reported that household wealth did not influence the sale of FPES 
(Paumgarten & Shackleton, 2009). Research results concerning the influence of the head 
of the household gender on the use and sale of FPES are also mixed. Households headed 
by females have been reported to rely more on forest products in Cameroon (Fisher et al., 
2017) and southern Ethiopia (Zheng et al., 2014), while in South Africa, studies have 
indicated a negligible gender effect (Paumgarten & Shackleton, 2009). It is evident that 
the use and sale of FPES about household wealth and head of the household gender varies 
across different case studies, and further empirical studies are required to explore these 
relationships and inform local policies and programmes. Understanding how the use and 
sale of provisioning services differ according to wealth and gender is essential in 
understanding people’s reliance on forest ecosystems and their contributions to their 
livelihoods (Heubach et al., 2011; Shackleton et al., 2019). Research on the socio-
economic differentiation of FPES use is essential in developing local management 
interventions to protect rural livelihoods and ensure sustainable forest use (Shackleton & 
Shackleton, 2006). 
2.21 Loss of Land and Livelihood from Mining Activities  
Global changes pressure people’s ability to access and utilize land and natural resources 
in rural communal areas. Several ecological (drought, floods, exhausted soils) and social 
factors (legislation, privatization, over exploitation) govern the ability of people to access 
and derive livelihoods benefits from agriculture, livestock and natural resources in 
40 
 
communal land ecosystems (Chirwa, 2008; Bagstad et al., 2018). Losing the ability to 
derive these land-based benefits can increase the vulnerability of local communities and 
have devastating effects on their well-being and resilience. One such driver affecting land 
access and people’s ability to utilize natural resources is mining, which has negatively 
affected rural communities and their livelihoods all around the world, leading to social-
ecological change and conflict (Hilson, 2011; Kragt et al., 2014; Bagstad et al., 2014). 
Globally, rural communities rely heavily on access to land and the natural resources they 
provide to maintain their livelihoods. Agriculture, livestock and non-timber forest products 
(NTFPs) used on communal lands support the livelihoods of millions of people (Sumarga 
et al., 2020; Shackleton et al., 2019).  
Livestock and crops provide poor rural households with food security, allow for cash 
savings, and in some cases, provide additional incomes; they are also important culturally 
in many communities (Bagstad et al., 2014). Furthermore, ecosystems provide natural 
resources such as fruit, fuelwood, medicinal plants and others, commonly referred to as 
provisioning services (Shackleton et al., 2019), as well as other supporting and cultural 
services which are important for people (De Groot et al., 2002). However, non- timber 
forest products are harvested both for subsistence or personal use and for commercial use 
and can, therefore, provide poor rural households with a means to make an income 
(Shackleton et al., 2019). Furthermore, these resources act as a safety net or a fallback 
option for households going through times of high vulnerability, often in response to socio-
economic and ecological shocks (Shackleton & Shackleton, 2004). This fall-back option 
is significant for poor southern African households commonly affected by HIV/AIDS and 
drought shocks. In South Africa, communal land livelihoods still rely heavily on 
agriculture, livestock and the collection of non- timber forest products which act as 
primary livelihood strategies (Shackleton et al., 2019). Subsistence agriculture, 
41 
 
particularly maize production, in South Africa is important for local livelihoods within 
communal lands. It accounts for, on average, between a quarter to half of the household’s 
food requirements (Mensah et al., 2015). Most crop production is for home consumption 
or supplementing livestock diets, with very few households selling crops. Small amounts 
of maize or other produce are commonly given away in acknowledgement of kinship and 
community ties which aids households in building social and cultural assets (McSweeney, 
2004). Livestock is also important and contributes to cash income or savings and is 
culturally significant for local people (Alvarez-Berrios et al., 2015). Similarly, 
provisioning services in the form of non- timber forest products contribute an average of 
20 % of households’ total income in rural communal lands of southern Africa (Cristescu, 
2012; Shackleton et al., 2019).  
Furthermore, non- timber forest products contribute between 100 and 12000 with an 
average of around 3500 per household per annum in communal areas (Shackleton, 2019). 
Ecosystems within communal lands also have significant cultural value (sacred sites), 
important for local culture and identity and well-being (Armstrong, 2018). Long-standing 
institutional arrangements, social and cultural norms and practices revolving around 
communal lands are key for managing local natural resources. They are important for local 
cultural identity, community cohesion, and social capital and resilience (Cowie et al., 
2011). This highlights the highly dependent and integrated nature of rural communal 
livelihoods to land and the services it provides in southern Africa and the potential 
detrimental impacts of land loss or degradation on people's well-being. Mining is one 
major change that can affect land-based livelihoods negatively, through land rights and 
land tenure issues, degradation of the environment, human health challenges, social change 
and undermining of local institutions and practices, often leading to local conflicts (Hilson, 
2009; Simon et al., 2004; Li et al., 2018). Furthermore, mines are increasingly 
42 
 
mechanizing, reducing the demand for unskilled labour, thus leading to less local benefit 
to surrounding communities (TEEB, 2010; Bagstad et al., 2018). Due to these negative 
effects, it is crucial that local people are properly compensated for their losses and that 
corporate social responsibility (CSR) is adequately implemented to ensure that it 
contributes positively to local livelihoods and development (Hussain, 2013).  
This study assesses how the loss of communal land due to opencast mining has affected 
villagers' livelihoods in the Limpopo Province, South Africa. This is done by comparing a 
village that lost access to land and an adjacent village that did not. Based on the findings 
key, research and policy implications are highlighted to help mitigate harm to local 
communities impacted by mining. 
2.22 Impact of Mining on Ecosystem Services  
Mining activities throughout my life, from exploration and development to post-closure, 
impact social and environmental systems, including ecosystem services that contribute to 
human well-being (Crossman et al., 2013; MEA, 2005). Although growing recognition of 
the value of ecosystem services and global efforts to conserve and manage them (Costanza 
et al., 2014), the supply of many ecosystems service is declining (Creek, 2016; MEA, 
2005) while demand is increasing due to population growth and economic development 
(Crossman et al., 2013; MEA, 2005). As a result, managing threats to ecosystem services 
is increasingly included in mining industry performance standards (Hussain, 2013). 
Maintaining human wellbeing and the benefits of ecosystem services to people requires a 
comprehensive assessment of threats posed by mining activities. Current literature 
emphasizes a strong focus on the environmental and social impacts of mining, often due 
to land degradation (Sumarga, 2020), biodiversity loss (Hein et al., 2018) and livelihood 
displacement (Hu et al., 2014). While literature on the impacts to ecosystem services as a 
result of mining is emerging, little assessment on its remobilization after some time has 
43 
 
been performed. The thesis section reviewed the impacts of mining on ecosystem services, 
a specific type of mining termed ‘galamsey’ in Ghana or illegal mining. The section 
focuses on provisioning ecosystem services threats and illegal mining activity impacts on 
a global scale. Understanding the effects of mining on ecosystem services requires 
identifying and conceptualizing the complete ecosystem services chain, including four key 
components – supply, demand, flow and benefits (Jiang, 2019). Extracting mineral 
resources results in removing or modifying areas of natural ecosystems (Creek, 2016), 
affecting their capacity to supply ecosystem services.  
The supply of ecosystem services is biophysical conditions and processes derived from 
natural capital to benefit society, irrespective of being realized or used by society (Haase 
et al., 2018). Mining activities also can interact with human and social capital, which can 
impact the demand for ES by society (Lei et al., 2018). For society to receive these 
benefits, there must be an interaction between people and ecosystems – i.e., an ecosystem 
services flow (Mitchell et al., 2017). This interaction can be spatial or non-spatial, 
connecting areas supplying ecosystem services with people who demand them and 
resulting in the movement of people, species or matter across a landscape (Mitchell et al., 
2017). Ecosystem services flow can be affected by mediating factors, such as 
manufactured capital (building fences around a mining lease), which ultimately affect 
human access to the benefits of ecosystem services. The modification of natural areas 
resulting from mining activities can potentially impact each of these four components 
differently. It is critical to consider the drivers of ecosystem services change (through 
specific mining activities), which ES are impacted by these changes to better comprehend 
the impacts mining may have on people’s wellbeing. The severity and distribution of 
impacts to ecosystem services are dynamic and depend on my type and stage. 
44 
 
Furthermore, damage to ecosystem services over the mine life is not limited to the grounds 
of a mine site but can extend spatially and temporally across the landscape (Strauss., 2017). 
External but related drivers of land-use change, such as associated railroads and roads 
constructed to facilitate mining operations, also have the potential to threaten ecosystem 
services supply and delivery through landscape fragmentation, biodiversity loss and shifts 
in ecosystem function (Fisher et al., 2017). This makes it necessary to identify and manage 
these diverse impacts and alter ecosystem services between individual mine sites. Just as 
mining is dynamic land use, the supply and demand for ecosystem services may vary 
across landscapes and social circumstances. Mining operations may have different impacts 
on different ecosystem services (and their human beneficiaries) at different locations.  
The surrounding landscape may exhibit various categories of ecosystem services - 
provisioning, regulating, supporting and cultural services (based on TEEB classification 
complete definitions see Supplementary Information). Clear identification of the 
beneficiaries of ecosystem services impacted by mining operations and engaging with 
local stakeholders to determine how people benefit from their surrounding environment 
and ensure that all ecosystem services desired by individuals, particularly cultural services, 
which are often neglected, are managed (Leimona, 2015). Understanding the preferences 
of beneficiaries of ecosystem services in mining landscapes is crucial to ensure that mining 
operations can prevent, minimize or mitigate against any damages to ecosystem services 
that may arise across the life of a mine. This is particularly important for communities and 
people, such as those traditionally marginalized, who are dependent on particular 
ecosystem services for their wellbeing (Liiri et al., 2018).  
2.23 Impact of Illegal Mining Activity on Ecosystem Services in Ghana 
Tropical forest ecosystems worldwide are being wiped out at a rate of 25 million acres per 
year (Bagstad et al., 2016). While agricultural activities and wood extraction are identified 
45 
 
as significant drivers of deforestation and forest degradation in terms of spatial coverage 
(Lamarque et al., 2017), degradation caused by mining tends to have long-term adverse 
effects on flora and fauna (Cristescu et al., 2012). This is often due to the dumping of toxic 
chemicals and the severe mutilation of the earth's crust (Lei et al., 2018), the combined 
effects of which inhibit vegetation growth for a long time. Moreover, Alvarez-Berríos and 
Aide (2015) identified mining as an activity that causes significant change to the 
environment but is often ignored in deforestation analysis. It mainly covers small areas 
compared to agriculture or wood extraction activities. The rise in demand and prices of 
gold in the last two decades triggered a wave of intense mining activities worldwide 
(World Gold Council, 2012).  
Small-scale mining activities were still being carried out by illegal miners (Creek, 2016; 
Alvarez-Berríos & Aide, 2015), particularly in developing countries where regulatory 
capacities and institutions are weak. The small-scale mining sector in Ghana contributes 
to job creation for people in rural communities due to the lack of good-paying alternative 
jobs (Hilson & Banchirigah, 2009; Amponsah & Dartey-Baah, 2017). About 85% of the 
estimated one million people who are directly or indirectly employed in the small-scale 
mining sector are identified as illegal because they operate without a license (Akabzaa et 
al., 2007; Ofosu-Mensah, 2010); a phenomenon popularly referred to as ‘galamsey’ in 
Ghana. The havoc caused by ‘galamsey’ activities includes destroying forest cover and 
soils by introducing toxic waste into soil and water bodies that often lead to health 
problems (Opoku-Ware, 2010). Furthermore, after mining, the restoration of mined areas 
is necessary to ensure that disturbed lands are returned to environmental conditions 
suitable for recommencement of one-time or new use (Tetteh, 2010).  
Fundamentally, restoring mined sites aims to re-establish vegetal cover, stabilize the soil 
and water conditions, and bring back ecosystem goods and services (Asiedu, 2013). The 
46 
 
Minerals and Mining Act 2006 (Act 703) of Ghana requires all licensed operators to secure 
environmental impact assessment (EIA), which specifies the environmental safety for any 
intended mining projects in Ghana. The EIA should be accompanied by a land reclamation 
plan which must indicate, among others, how topsoil will be preserved, slopes will be 
stabilized and restored, progressive reclamation will be carried out, and how revegetation 
will be affected (Aseidu, 2013). Despite these legal items for protecting the environment, 
illegal mining and non-compliance remain the cause of mining-related environmental 
degradation in Ghana. Even though the extent of environmental degradation caused by 
mining in Ghana is well documented (Aryee et al., 2003; Armah et al., 2011; Armah et 
al., 2013; Mensah et al., 2015), little research has gone into looking at solutions to mining-
imposed environmental problems such as chemical-laden effluent usually discharged by 
mining companies and chemical remobilization after. For instance, several years.   
Again, Aseidu (2013) looked at reclamation of small-scale surface-mined lands in Ghana, 
focusing on the restoration process, methods and costs. The study seeks to broaden the 
horizon of knowledge on the subject matter by looking at how local communities will 
accept the challenge and responsibility of maintaining degraded landscapes as de facto 
owners and prime beneficiaries of natural resources within the landscapes. The feasibility 
(likely participation) of the concept of payment for ecosystem services (PES), a market-
based compensatory program aimed at reducing the market imperfection brought by 
positive externalities associated with non-market ecosystem services (Engel et al., 2008; 
Obeng et al., 2018) is explored. PES programs encourage participation by providing 
monetary compensation to owners or managers for behaviours that protect and enhance 
the flow and quality of non-market ecosystem services and, ultimately, well-being 
(Leimona et al., 2015; Wunder, 2015; Obeng et al., 2018). Specifically, the study assessed 
the importance that mining communities place on forest ecosystem services and 
47 
 
determined the perception and attitudes towards the impact of ‘galamsey’ activities on 
forest ecosystem services at the community level. It further assessed the factors 
influencing communities willing to participate in restoration activities for improved 
ecosystem services within a PES framework. However, people are motivated by personal 
values to cherish the natural environment. Those who attach much importance to non-
market ecosystem services are likely to subscribe to a PES scheme to restore degraded 
lands. Also, we expected people involved in illegal mining activities to show less likeliness 
to subscribe to a PES scheme for the restoration of degraded lands and demographic factors 
(age, gender, family size, education, residential status, income level) not to affect likeliness 
to subscribe to a degraded-land restoration PES scheme (Bagstad et al., 2018). 
2.24 Livelihood 
Livelihoods are how people sustain their income. They result from how and why people 
organize themselves to use technology, labour, power, knowledge and social relationships 
to shape the environment to better meet their needs (Mitchell, 2017). People's livelihoods 
can be defined as "the skills, resources (physical and social) and activities necessary for 
their way of life" (Chambers & Conway, 2017). 
In the last decade, the study of ecosystem services and resources has become an important 
research area, with an explosion in many publications on ecosystem services. Humans 
have always depended on the biosphere and its ecosystems to enjoy the services derived 
from various ecological processes. This explains why ecosystem services affect human 
well-being and are of high value to society (Fisher et al., 2017). 
Thus, people are an integral part of ecosystems and benefit more from ecosystem services. 
The concept of ecosystem services is a basic model for linking ecosystem functioning and 
human well-being. It is essential to understand this link in the broader context of decision-
48 
 
making. However, people, who are protected from the environment by cultural and 
technological developments and advances, ultimately depend entirely on ecosystem 
service flows. For more effective management, the link between society and ecosystem 
change needs to be emphasized, including indirect drivers of ecosystem change such as 
demographic and cultural factors (Derissen et al., 2013). 
Livelihood also means, activities, entitlements and assets by which people make a living, 
which is immediate and continuous. It is also a framework that seeks to build the capacity 
of people to continuously make a living and improve their quality of life without 
jeopardizing the livelihood option of others, either now or in the future by copying and 
adaptive strategies (Aubynn, 2016; Chirwa et al., 2008). Moreover, Chambers and 
Conway (2017) also asserted that “A livelihood comprises the capabilities, assets 
(including both material and social resources) and activities required for a means of living. 
A livelihood is sustainable when “it can cope with and recover from stresses and shocks.” 
To function as an individual, therefore, demands some basic requirements, Moreover, the 
various contexts within which most people in developing countries earn their means of 
livelihood render them more vulnerable. Such contexts include economic, social and 
environmental factors. A farmer may work extremely hard to earn his livelihood but can 
lose it to the rigours of the weather such as drought and flooding. The market under which 
the poor person produces can be highly unpredictable and can offer very low prices to the 
poor person's produce and even push him into debt. This is especially for economies that 
allow the free market to allocate resources and this is why the Department for International 
Development (DFID) sustainable livelihood approach should have emphasized the 
component of market influence. 
49 
 
2.24.1 Sustainable Livelihood 
The term 'sustainable livelihood' came to prominence as a development concept in the 
early 1990s, drawing on advances in the understanding of famine and food insecurity 
during the 1980s (Cowie et al., 2011). The December 2000 DFID conference in 
Kathmandu sought to address striking issues of livelihood on the six main principles of 
the sustainable livelihoods approach people-cantered, responsive and participatory, multi-
level, conducted in partnership, sustainable and dynamic linked as a framework and 
conceptual tool for understanding the context in which people make a living (Ellis, 2000).   
2.24.2 Conceptual Framework on Livelihood 
The sustainable livelihoods framework was adopted in this study. It is a tool for improving 
the general understanding of livelihoods of the poor and the vulnerable in society. The idea 
of sustainable livelihoods was first introduced by the Brundtland Commission on 
Environment and Development to link both socio-economic and ecological factors 
together for policy implementation. The United Nations Conference on Environment 
(UNCEP) in 1992 adopted and expanded the concept and advocated for the achievement 
of sustainable livelihoods as a broad goal for poverty eradication (Armstrong, 2008). It 
was later developed by the Sustainable Rural Livelihoods Advisory Committee of the 
United Kingdom after several months of deliberations. The committee relied on an earlier 
work of the Institute of Development Studies (Sumarga et al., 2020). In a Natural Resource 
Advisors conference organized by the UK’s Department for International Development 
(DFID) in 1999 which discussed early experience with implementing sustainable 
livelihoods approaches to poverty elimination, four agencies; DFID, Oxfam, CARE and 
UNDP discussed early experience with implementing sustainable livelihoods approaches 
to poverty elimination. 
50 
 
According to Armstrong (2008) one of the theorists that developed the framework, 
researchers and policymakers can adopt the framework to suit a particular study or 
circumstance. With this permission, the researcher adopted the conceptual sustainable 
livelihood form (Fisher et al., 2017) approaches to examine and explains the impact of 
illegal mining activities in the environment. The framework shows the key factors that 
affect people’s livelihood and typical relationships that exist between them. It can be used 
in both planning new development activities and assessing the contribution to livelihood 
sustainability made by existing activities.  At the conference, the livelihood definition by 
Ellis (2000) was the capabilities, assets (stores, resources, claims and access) and activities 
required for a means of living: a livelihood is sustainable when it can cope with and recover 
from stress and shocks, maintain or enhance its capabilities and assets, and provide 
sustainable livelihood opportunities for the next generation; and which contributes net 
benefits to other livelihoods at the local and global levels and in the long and short term 
(Chambers & Conway, 2017). However, Hilson (2016) identified different types of 
livelihood activities namely; livelihood promotion, livelihood protection and livelihood 
provisioning. 
2.24.3 Livelihood Promotion 
This refers to the strategies for improving the resilience of households, for example 
through programs which focus on savings and credit, crop diversification and marketing, 
reproductive health, institutional development, personal empowerment or community 
involvement in service delivery activities. Most livelihood promotion activities are long 
term development projects that increasingly involve participatory methodologies and an 
empowerment philosophy (Nyame & Danso, 2006). 
51 
 
2.24.4 Livelihood Protection  
This aims at helping to prevent a decline in household livelihood security, for example, 
programs which focus on early warning systems, cash or food for work, seeds and tools, 
health education and flood prevention. Members in a community may be informed in 
advance by specialists about the outbreak of diseases such as the Saint Paul Wilt disease 
which affects coconut plantation, the armyworm that attacks farm products and completely 
destroys them. Other examples that fall within this category are the announcement given 
to community members about the outbreak of diseases like cholera, project construction, 
chicken pox and measles (Yaro, 2010). 
2.24.5 Livelihood Assets  
The Sustainable Livelihood (SL) is centered on five major categories of livelihood assets 
represented by a pentagon to show interconnection and to mean also that livelihoods 
depend on a combination of assets of different kinds. It is important for analysts to find 
out how people get access to these assets; physical, human, financial, natural and social 
capitals (Ayanu et al., 2012). 
2.24.5.1 Natural Capital  
This is made up of the natural resource stocks from which resource flows which are useful 
for livelihoods or survival such as land, water, wildlife, biodiversity and environmental 
resources are derived (Haase et al., 2018).  
2.24.5.2 Human Capital  
Human capital refers to the skills, educational qualification and knowledge, ability to work 
and good health of an individual or a group deemed necessary for use to pursue different 
livelihood strategies. Households with high-quality human capital can use it to improve 
52 
 
the economy when a natural resource is discovered in an area as reported by the (Heubach 
et al., 2011). 
2.24.5.3 Financial Capital  
Financial capital refers to the financial resources which are available to people to access 
credit facilities, good pension or even regular remittances which give access to different 
livelihoods options. Access to credit facilities, livestock rearing and saving also forms part 
of the financial capital. The ability to generate Financial Capital is also dependent on 
wages or proceeds of work and living costs in a household’s ability to develop a livelihood 
strategy. In rural communities, income is usually earned by subsistence. Rural 
communities also need financial resources for development (Ellis, 2000). 
 2.24.5.4 Social Capital  
This refers to the social networks or resources such as membership to a group, relationships 
of trust and access to broader institutions, interactions among individuals and households 
from which people derive their livelihood. As a result of the influx of people to mining 
communities in search of employment (Owusu et al., 2012), and the pressure on 
infrastructure, landlords increase the cost of renting forcing those local residences who 
cannot afford such high cost to relocate to surrounding villages (Tetteh, 2010). 
This also includes the migration of people from one community or area to the other. The 
influx of foreigners and workers from different backgrounds can have health consequences 
on oil producing countries (Serfor-Armah et al., 2006) and it also disrupts the social 
network of community members thereby affecting the socio-cultural patterns of the 
community. Poverty and economic hardship lure some ladies into prostitution which can 
result in being infected with the HIV/AIDS virus (Aryee et al., 2017).  
53 
 
2.24.5.5 Physical capital  
Physical capital is the basic infrastructure such as transport, shelter, water, energy and 
communications and the assets like farming equipment and sewing machine tools and 
machines for the cultivation of the land for human survival. The influx of people in oil 
extracting communities also puts pressure on communities. This makes life unbearable for 
community members who cannot afford it. Some are even forcefully evacuated to new 
locations (Ellis, 2000). 
2.25 Geographical Information System, Remote Sensing and Land Cover Change 
Detection. 
Remote sensing and Geographic Information Systems (GIS) have become powerful tools 
for deriving accurate and timely information on the spatial distribution of land use/land 
cover change over large areas. Remote sensing imagery and image processing techniques 
also offer possible solutions to some of the problems of generating and updating shoreline 
maps (Prakasam, 2010). High resolution imagery from satellites such as IKONOS and 
Quickbird has been available for several years and has proven its usefulness in mapping 
and monitoring remote areas and developing countries. Geographic Information Systems 
(GIS) are an important tool for detecting land use change and play a role in improving the 
environment. Change detection involves the use of multi-date (time series) aerial 
photographs, topographic maps or satellite images of the study area, from which land use 
maps can be generated through visual interpretation or digital image processing 
(Prakasam, 2010). 
Remote sensing is widely used to observe land use and land cover change and dynamics 
(LULC). It offers multiple advantages in LULC research and allows assessment of 
inaccessible locations such as very steep mountains, determination of the most recent land 
cover and investigation of historical LULC. To more effectively detect changes in land 
54 
 
cover, remote sensing is often coupled with geographic information systems (GIS) 
technology. GIS is a technology used to create, store, analyze and manage data related to 
its attributes GIS is a technology used to create, store, analyze and manage spatial and 
temporal data related to its attributes (Longley, 2005). Both technologies provide the 
ability to map land use characteristics and dynamics by combining existing remote sensing 
data with historical maps in different contexts, such as tropical forests, urban areas and 
coastal zones, as well as different land transformations such as deforestation, urban 
development and desertification (Forest Partnership Facility, 2014). Land use and land 
cover (LULC) changes, especially those caused by human activities, is the most important 
component of global environmental change with impacts possibly greater than the other 
global changes (Jensen, 2005). Since the 1990s, global, regional, and local studies of 
LULC have greatly increased due to advances in observation and detection methods 
including remote sensing and related techniques.  
The issue of land use changes has been considered in many international and 
interdisciplinary researches such as remote sensing, political ecology, and biogeography 
(Jensen, 2005; Kachhwala, 2015). Recent global data indicate that fifty percent of the ice-
free terrestrial surface has been changed to other land uses; approximately forty percent of 
land is transformed to agricultural lands (Dwivedi et al., 2005). Land use is generating 
worldwide interest as changes in land use are at a rapid rate and it is estimated by the 
Longley (2005) that by the year 2025, 80% of the world's population will live in cities. 
Most major metropolitan areas face the growing problems of land development; residential 
and commercial development is replacing undeveloped land at an unprecedented rate. 
Information on land in relation to how it is being used as well as changes in such land use 
has become a prime pre-requisite for the growth and development of any nation (Dwivedi 
et al., 2005).   
55 
 
2.25.1 Land Use and Land Cover Change in Mining Landscapes 
Mining areas are hotspots for the exploration of minerals which are important. However, 
these areas activities subsequently deteriorate the natural environment (Sun, et al., 2018). 
Mining activities significantly damage the ecological environment, such as the landscape, 
vegetation, and agriculture. It also deposits metal particles into the soil, and can lead to 
soil erosion and pollution. The surface water, groundwater as well as air are also polluted 
(Sengupta, 1993). Remote sensing provides convenient conditions for monitoring 
environmental changes that take place in mining areas (Song et al., 2020). With the further 
development and numerous applications of remote sensing technologies in recent years, 
remote sensing has been applied frequently in the monitoring of the ecological 
environment in mining sites.  For example, some researchers have analyzed vegetation and 
its ecological environment in mining areas using hyperspectral remote sensing which has 
a higher spectral resolution hence provides detailed information. The result of this study 
was able to distinguish between stressed and unstressed vegetation within the mining areas 
(Zhang et al., 2014). 
In another study, southwest of the Brazilian Amazon state of Rondônia, the researchers 
made use of GIS and remote sensing to assess the impact of mining activities on 
environmental protected areas. Remote sensing imageries, Landsat 8 OLI imageries, were 
used to map the diverse land covers in the region and also evaluated the corresponding 
impact of mining activities. The research was able to assess that 26 km² of mining areas 
were within protected areas (Rudke et al., 2020). In Song et al., (2020) study, the 
boundaries of mining areas were identified and the land use or land cover changes in 
mining areas were monitored using remote sensing. Remote sensing was used in 
monitoring the biodiversity, landscape structure, vegetation change, soil environment, 
surface runoff conditions, and the atmospheric environment in mining areas (Turner et al., 
2007). 
56 
 
2.25.2 Land Use and Land Cover Change Analysis  
The land cover according to Prakasam (2010), is the most important property of the earth’s 
surface which is defined by the physical condition and biotic component. Land use, on the 
other hand, is the modification of land cover as per human needs and actions. These 
modifications of the land cover bring about land use and land cover (LULC) changes. Land 
cover is influenced by both anthropogenic and natural changes which affect what use land 
is put to. It is also a matter of historical process as to how land is used by people (Tewabe 
& Fentahun, 2020). Therefore, there is the need to have prior knowledge about the LULC 
of an area to be able to detect changes that occur on the land over time. 
The change detection technique is very valuable in many applications such as urban 
expansion and other fields that relate to LULC changes (Hegazy & Kaloop, 2015; 
Solaimani et al., 2010). Using remote sensing images to detect land use and land cover 
changes detection have been widely applied in the field of research for monitoring and 
protecting the environment (Zhang et al., 2014). Through the use of remotely sensed data 
studies on land, cover can be done in less time, cost-efficient and produces better results 
(Kachhwala, 2015). Combining remote sensing with GIS tends to provide a suitable 
platform for data analysis, updating and retrieval (Chilar, 2000).  Understanding these 
changes is also essential for taking the appropriate measures and gives room for decision 
improvement (Rawat & Kumar, 2015). Classification of LULC is of importance because 
the information about LULC is needed in planning and to be able to deal with problems 
that may occur as a result of changes occurring on the land and in relation to this study the 
changes that ecological footprint of artisanal and small-scale gold mining has on the 
environment. 
 
  
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CHAPTER THREE 
MATERIALS AND METHODS 
 
3.1 Overview  
This chapter presents the research methods used to collect data for the study. These are the 
approach, the design, population size, sample size, target and study population, data 
collection instruments, data handling, analysis and interpretation. The mixed method 
approach and multistage sampling technique (Kothari, 2004) supplemented by household 
questionnaires, in-depth interviews and Focus Group Discussions were used to collect the 
data for the study.  
3.2 Study Area 
3.2.1 Mpohor Wassa East District  
3.2.1.1 Location of the District  
This district was selected for the study because in literature, it was found that numerous 
studies have been done on ASGM activities in Ghana, especially on the socio-economic 
and environmental impact, but there is no research that specifically assesses or addresses 
the issue of ecological footprint of artisanal and small-scale gold mining on soil and 
provisioning ecosystem services in this district. Mpohor Wassa East District is located at 
the South-Eastern end of the Western Region. It is bound to the North East and South East 
by the Twifo Hemang Lower Denkyira and Komenda Edina Eguafo Abrem Districts in 
the Central Region, in the west and northwest by the Tarkwa Nsuaem Municipality and 
Prestea Huni-Valley District and in the south by Sekondi-Takoradi Metropolis, Shama and 
Ahanta West Districts (Figure 1). The district falls within the tropical climate zone. The 
mean annual rainfall is 1500mm and ranges from 1300 to 2000mm, with an average annual 
temperature of 30⁰С. The landscape is generally undulating with an average height of 
58 
 
about 70 m (above mean sea level). The highest elevation ranges between 150 and 200 m 
above sea level (GSS, 2010). The drainage pattern is largely dendritic and there are 
medium and small rivers and streams distributed throughout the district most of which 
originate from the Akwapim Ranges and flow southwards towards the coast. The main 
rivers are the Pra, Subri, Butre, Brempong, Suhyen, Abetumaso, Hwini and Tipae.  
3.2.1.2 Vegetation and Agriculture 
The vegetation cover in the area is mainly tropical rainforest interspersed with 
shrubs. There are two large forest reserves namely, Subri River Forest Reserve which 
occupies 375 sq km and the Pra Suhyen Forest Reserve with 204 sq km. Just over half 
(64.0%) of the households in this district are engaged in agriculture. They are engaged in 
agriculture. Cultivation of crops is the main agricultural activity of more than 9 out of 10 
households (95.6%). (95.4%) of the households are engaged in agriculture (Figure 1). 
Livestock rearing is 39.2% and plantation is 0.4%. In rural areas more than 7 out of 10 
households (72.0%) are engaged in agriculture, while in urban areas 40.9% are. Poultry 
(chickens - 61.5%) are the main animals kept in the district. 
3.2.1.3 Spatial Distribution and Occupation 
According to the 2000 population census, the population of the district in 1970 was 27,573 
and 55,801 in 1984. The total population in 2000 was 122,595 and estimated to be 163,512 
in 2009 with an inter-censual growth rate of 3.2 percent, which is the same as the regional 
growth rate. Males form 52.5 percent of the total population (85,844) as against 47.5 
percent (77,668) for females due to the mining and agricultural activities in the district. 
Subsistence and large-scale agriculture employ 71.5% of the workforce according to the 
2000 population and housing census. The major staple food crops produced in the district 
include cassava, plantain, maize, cocoyam and vegetables. About 98 percent of the farmers 
rely on traditional methods of farming using slash and burn, simple farm tools such as hoe, 
59 
 
cutlass and relying on natural climate conditions for cropping. The use of tractors and other 
heavy machinery is limited to the oil palm plantation companies. The predominant cash 
crops are cocoa, oil palm and coffee in some cases. Cocoa is usually cultivated in small to 
medium sized plantations, mostly by settler farmers while oil palm is cultivated on a large-
scale by Benso Oil Palm Plantations (BOPP), NORPALM and Ayiem Oil Mills. The 
Ghana Rubber Estates Ltd (GREL) is promoting and encouraging the cultivation of rubber 
in the district and a number of out-grower farmers have cultivated rubber on medium and 
small-scale plantations in various parts of the district (Figure 1).  
Agro-processing Company which is located in Ayiem, is involved in vegetable processing. 
Non-traditional crops like black pepper and pineapple which are cultivated in the district 
have high potential of becoming export crops. Other non-traditional crops with potential 
for high production are citrus, cashew and banana. The main staple food crops widely 
cultivated in the district are maize, rice, cowpea, cassava, cocoyam, sweet potatoes, yam 
and plantain. Local vegetables such as pepper, garden eggs, okra, tomatoes and other 
exotic types like cabbage are grown on a comparatively smaller scale. Livestock 
production forms an important agricultural activity in the district though not on a large 
scale as compared to crop production.  It involves predominantly sheep and goat, pigs 
(mainly improved breeds), poultry (local and improved breeds) and few cattle (Asante et 
al., 2017). 
Some non-traditional stock such as grass-cutter, rabbits and bees are reared/kept on a 
comparatively small-scale by farmers and training is organized by some NGOs who also 
provide breeding stock and cages to farmers after the training. Important market centers in 
the district are located in Daboase, Mpohor, Sekyere Krobo, Senchem, Ateiku, Krofof rom, 
Adum Banso, Edwinase, New Subri, Atobasie, Ebukrom and Apeasuman.  Very few of 
60 
 
these market centers have well- developed structures. Large and medium scale 
Agricultural enterprises operating in the district include Benso Oil Palm Plantation 
(BOPP) in Adum Banso, Norpalm Ghana Limited in Mpohor, Golden Star Wassa Oil Palm 
Plantation in Ateiku, Ayiem Oil Mills in Ayiem and West- West Agro-processing factory 
in Ayiem. Out-grower farmer’s schemes have been established by the large-scale 
agricultural enterprises for farmers in various areas.  Through these schemes farmers are 
assisted with special packages including inputs, credit and capacity building.  The Ghana 
Rubber Estates Limited (GREL) has also established a similar scheme for out-grower 
farmers involved in rubber cultivation. A number of micro- enterprises for agro-processing 
that are located in various parts of the districts include processing facilities for oil palm in 
Adum Banso, Mpeasem, Ateiku, Aboaboso, Atobiase and Ayiem and facilities for cassava 
processing in Kwabaa, Awiadaso, Akotosu, Adiembra and Abroadzewurum (GSS, 2010). 
3.2.1.4 Climate 
The district falls within the tropical climate zone. The average annual rainfall is 1500 mm 
and varies from 1300 to 2000 mm. The wet season in the district is between March and 
July while November to January is dry. In general, the rainfall pattern favours agricultural 
activities (GSS, 2010). 
3.2.1.5 Relief and drainage 
The district is located in the low-lying areas of the country, with most parts below 150 
meters above sea level. The landscape is generally undulating with an average height of 
about 70 metres. The maximum height varies between 150 and 200 metres above sea level. 
The drainage pattern of the Mpohor district is largely dendritic. There are a number of 
rivers and streams in the district (Subri, Butre and Hwini) (GSS, 2010).
61 
 
 
Figure 1: Study Area and Sampling Point Map of Mpohor Wassa East District 
62 
 
3.2.2 Amansie West District (Ashanti Region) 
3.2.2.1 Location and Size of the District 
This district was selected because in literature, it was found that numerous studies have 
been done on ASGM activities in Ghana, especially on the socio-economic and 
environmental impact, but there is no research that specifically assess or address the issue 
of ecological footprint of artisanal and small-scale mining on soil and provisioning 
ecosystem services in this district. Amansie West District was separated from the former 
Amansie District in 1988. It shares a common boundary with eight other districts, Atwima 
Nuwabiagya and Atwima Mponua, to the west, Bekwai Municipality, Amansie Central 
and Obuasi Municipality to the east, Atwima Kwawhoma to the north, Upper Denkyira 
and Bibiani to the south (Figure 2) This district serves as the regional boundary between 
the Ashanti Region on the one hand and the Central and Western Regions on the other. 
Specifically, it is located at latitudes 6.05°W, 6.35°N, 1.40°S and 2.35°N. It lies between 
latitudes 1.40°S and 2.05°E. The Amansie West District is one of the largest districts in 
the Ashanti Region, covering an area of about 1,364 square kilometers and accounting for 
about 5.4% of the total area of the Ashanti Region Manso Nkwanta is the capital of the 
district and is located about 65 kilometers from Kumasi. Other major settlements include 
Abole, Agroisum, Awerewa, Ankamu, Antakrom, Aponapong, Datano, Esase, Esowin, 
Kenyago, Mpatuam, Moseaso, Nipankieremia, Odaho, Pakii No.1 and No.2 and Watreso. 
This district is the gateway to Ashanti from the western and central regions. As such, it 
has great potential for the promotion of the hospitality industry, including hotels, 
restaurants and handicrafts. Large areas of land are planted to rice, citronella, cocoa, 
orange and oil palm plantations, contributing to the local agricultural-based industry 
(Hilson et al., 2016). 
63 
 
3.2.2.2 Relief and Drainage 
The terrain of the district is generally hilly and lies at an altitude of 210 metres above sea 
level (Figure 2). The most striking feature is the hilly area in the north-western part of the 
district, especially around Manso-Nkwanta and Arbor. The rivers Offin and Oda and their 
tributaries, such as the Jeni, Pumpim and Emuna, flow through the northern part of the 
district. The drainage pattern of the district allows for irrigated rice cultivation, vegetable 
cultivation and fish farming. 
3.2.2.3 Climatic Condition 
The climate is humid and semi-equatorial. It has a dual maximum rainfall regime, with the 
main rainy season occurring between March and July. The minor rainy season occurs 
between September and November. The average annual rainfall varies between 855 mm 
and 1500 mm. The average number of rainy days during the year varies between 110 and 
120 days. December to March is usually dry and is characterized by high temperatures, 
humidity/early morning fog and cold weather conditions. Temperatures are generally high 
throughout the year, with monthly averages of around 27ºC. The rainy season is 
characterized by high humidity. However, records from December to February show very 
low humidity. These climatic conditions are suitable for growing commercial and food 
crops such as cocoa, lemongrass, oranges, bananas and vegetables for the agro-based 
industries in the region and beyond. 
However, it should be noted that current trends in climatic conditions in the region are 
becoming unpredictable due to climate change. However, this has also affected 
agricultural planning. This situation requires measures to reduce the over-dependence of 
agricultural production on climate, such as irrigation. The region's climate is humid and 
semi-equatorial. It has a dual maximum rainfall regime, with the main rainy season 
occurring between March and July. The minor rainy season occurs between September 
64 
 
and November. The average annual rainfall varies between 855 mm and 1500 mm. The 
average number of rainy days during the year varies between 110 and 120 days. December 
to March is usually dry and is characterized by high temperatures, humidity/early morning 
fog and cold weather conditions. Temperatures are generally high throughout the year, 
with monthly averages of around 27ºC. The rainy season is characterized by high humidity. 
However, records from December to February show very low humidity. These climatic 
conditions are suitable for growing commercial and food crops such as cocoa, lemongrass, 
oranges, bananas and vegetables for the agro-based industries in the region and beyond. 
However, it should be noted that current trends in climatic conditions in the region are 
becoming unpredictable due to climate change. However, this has also affected 
agricultural planning. This situation requires measures to reduce the over-dependence of 
agricultural production on climate, such as irrigation. 
3.2.2.4 Vegetation of the Area 
The vegetation of the region is mainly of the rainforest type and has characteristics of a 
semi-glacial wetland. This makes the land very fertile and suitable for agricultural 
investment. Food and commercial crops such as cassava, rice, maize, cocoa, cocoa, citrus, 
palm oil, lemongrass and others are widely grown in the area. As a result of poor practices 
such as shifting cultivation, the method of curtailment, illegal mining and illegal logging, 
it was gradually destroyed and replaced by a mosaic of secondary forests. Fortunately for 
the region, there are four main forest reserves in the area (Figure 2). These are Oda River 
Forest Reserve, Apanprama Forest Reserve, Jimira Forest Reserve and Gyeni River Forest 
Reserve. 
3.2.2.5 Soil Condition 
There are six (6) main soil types in the area. These are the Bekwai-Oda compound 
association. The constituents of this association occur in a definite topographical sequence. 
On the summits, upper and middle slopes are found the red, well drained Bekwai series 
65 
 
and brown moderately well drained Nzima series. The valley bottoms are occupied by grey 
poorly drained clays (Oda series). The second soil type is the Ahawam-Kakum-Chichiwere 
structure. This soil type is reddish brown, deep, well-drained clay to loam. This series may 
be found in the district's south-western corner, as well as in the Nyamebekyere, 
Britcherkrom, and Adagya localities (Figure 2). 
The third group of soils found in the district is the Mim-Oda compound association. This 
is slightly different from Bekwai-Oda because of the presence of large amounts of gravel. 
This soil is found in the southern part of the Datano and Aboaboso areas. The fourth is the 
Bekwai-Zongo-Oda complex, found primarily in the Esaase's northern region The fifth 
soil type is the Nyanoo-Tinkong complex. This range is characterized by relatively thin 
soil on eroded hilltops and slopes. They are found in the Abore's hilly terrain. The sixth 
type is the Kobeda-Eschiem-Subinso-Oda complex. The distinctive feature of this range 
is that its use is limited by its shallow depth, which makes it vulnerable to drought. They 
are found in the northern part of Manso-Nkwanta and in the areas around Essuowin and 
Bayerebon. The soil types have the potential to support food and cash crops such as 
cassava, plantain, coca, lemongrass, palm oil, etc. It is no wonder that the region ranks 
third in cocoa production. However, where soil fertility is low, soil fertility practices and 
the use of fertilizers are needed to increase and sustain production and productivity. 
3.2.2.6 Mineral Deposits 
Among the resources found in the area are potentially rich deposits (gold). Areas with such 
deposits include Bontesso, Jeninso, Mpatum, Esouwen, Tonto Krom and others (Figure 
2). A significant area of the district has been acquired by a number of companies and 
concessions for prospecting have been granted. However, there are still areas in the district 
with gold deposits that have not yet been acquired. These areas include, in particular, the 
massive deposits of the Jeni bonte River, estimated to contain approximately 21,361,400 
cubic metres and 5,209,866 grams of gold in the Jeni bonte River. In addition to the 
66 
 
companies with large concessions in the area, there are other parties interested in mining. 
There are small mining groups in the area that use very crude methods to extract gold, 
although a large proportion of young people are involved in their activities. The activities 
of these different groups are not well regulated or organized and cannot be considered as 
part of the overall development efforts in the area (Asante et al., 2017). 
3.2.2.7 Spatial Distribution 
Population and Housing Census has shown that the total population of the district was 
108,726. This population is spread over more than three hundred towns, villages and 
hamlets in the district. The most populous town in the district is Mpatuam, with a 
population of 5,425. The distribution of the district's population is tilting towards the north-
eastern part of the district. These areas include Pakyi No.1, Antoakrom and Esuowin. The 
population growth in these areas can be attributed to the very good road network. For 
example, the Kumasi-Obuasi highway passes through Pakyi No. 1 and Pakyi No. 2. These 
two communities have become dormitory towns, providing accommodation for the 
Kumasi workforce (GSS, 2010). This situation has provided the conditions for the creation 
of weekend and night markets to cater for this workforce, in addition to the intervention 
of real estate developers. The hinterland has been reduced to scattered agricultural villages, 
some of which are connected to the communities by roads in poor condition. These areas 
are characterized by migrant farmers working on the farms of the local population. These 
areas coincide with the food basket of the district. However, some larger communities 
have emerged in these areas as a result of mining activities (Figure 2). These communities 
include Daatano, Watereso, Abore, Bonsaaso and Tontokrom. mining activities have 
attracted the population of these areas. These communities are centers of growth in the 
surrounding hinterland. The area needs effective and efficient planning to cater for the 
growing population and to prevent pressure on the few existing facilities. 
67 
 
 
Figure 2: Study area and Sampling points Map of Amansie West District
68 
 
3.3 Research Approach 
The mixed methods approach of combining both qualitative and quantitative methods in a 
particular study was used as the main research method. The qualitative and quantitative 
approach was embedded in collecting and analyzing the data. This approach generally uses 
qualitative and quantitative methods as a means to offset the weakness within one method 
with the strength of the other (or conversely, the strength of one adds to the strength of the 
other (Creswell, 2009). Combining the two methods provides better results and 
understanding of the phenomenon than just relying on one method. Furthermore, Johnson 
et al., (2007) defined mixed methods research as the type of research in which a researcher 
or team of researchers combines elements of qualitative and quantitative research 
approaches for the broad purposes of breadth and depth of understanding and 
corroboration. The matrix in (Table 3.1) shows the objectives of the study, the methods 
used and the analytical tool employed. 
Table 3.1: Matrix Showing Objectives, Methods Used and the Analytical Tool 
Objectives Methods Used Analytical tool 
1. Determine the concentrations of Soil analysis using Atomic Descriptive statistics, 
physico-chemical parameters in Absorption Analysis of variance 
soils and water at artisanal and Spectrophotometry (AAS) (ANOVA), Pearson’s 
small-scale gold mine sites. product moment 
correlation, Principal 
component analysis 
(PCA) 
2. Assess the hazard rating of Chemical extraction of Regression analysis 
heavy metals in soils at artisanal heavy metals using 
and small-scale gold mine sites. Ammonium Nitrate and 
Nitric acid. 
3. Explore the impact of small- GIS Mapping ArcGIS (Geographic 
scale and artisanal gold mining on information System) 
land use and land cover changes 
4. To identify and examine the Use of structured Use of chi-square and 
socio-economic and livelihood questionnaire and key regression analysis 
activities impacted by small-scale informant interviews 
and artisanal gold mining 
Source: Authors Fieldwork, 2020 
 
69 
 
3.4 Research Design  
The study used the concurrent triangulation design, one of the options in mixed method 
study. The purpose of this design is “to obtain different but complementary data on the 
same topic” (Morse, 1991) to best understand the research problem. The intent in using 
this design is to bring together the differing strengths and nonoverlapping weaknesses of 
quantitative methods with those of qualitative methods. Combining the two methods 
provides better results and understanding of the phenomenon than just relying on one 
method. 
3.5 Sampling Procedure 
The research involved both cluster sampling and purposive sampling. For administrative 
purposes, the Mpohor Wassa East District was divided into zonal councils. All the 
communities in the zonal councils that had illegal mining activities within the district were 
visited. Based on the reconnaissance survey, five (5) communities were purposively 
selected namely, Asowuo Ayipa (ASA), Adum Tokoro (ADT), Mpohor Adansi (MA), 
Mpohor Motorway (MM) and Mpohor Adawotwe (ADW). These communities were 
purposely selected because active illegal mining activities had taken place in those 
communities. Site evaluation was done and it was observed that the control site was a 
forested area and had not been cultivated. The control site was selected to eliminate any 
extraneous factors that could influence the parameters under investigation. At each 
sampling site in the Mpohor Wassa East District, the area was gridded and divided into 
various segments. The grid was done by using wooden pegs. Four square pegs were made 
and a composite sample was taken within each grid. Based on this technique, seven (7) 
sites were identified at ASA, six (6) at ADT, five (5) at MA, five (5) at MM and five (5) 
at ADW (Figures 1 and 2).  
70 
 
In each segment soil samples were taken at different positions within the grid at a depth of 
15cm with soil auger and put into a cleaned plastic bowl and mixed thoroughly to form a 
composite sample to represent that particular site. The 15cm depth was taken since most 
heavy metals have been found to be within the first 15cm of the soil profile. 
The auger was cleaned immediately after each sampling point to avoid cross contamination 
which could influence the results. Furthermore, about 30g of each soil composite sample 
from each segment was taken and put into a labeled plastic zip lock polythene bag. 
A control site was selected in Mpohor Wassa East District where soil samples were picked 
in triplicates. Together with the soil samples from the other sites, a total of eighty-seven 
(87) soil samples were collected during the entire study period in the Mpohor Wassa East 
District. The same sampling procedure was repeated at Amansie West District in the 
Ashanti Region. Four (4) communities were purposely selected based on active illegal 
mining activities that had taken place namely; Manso Nkwanta (MN), Domenase (DO), 
Brofoyedru (BYO), and Asaman (AS). Apparently, nine (9) sites were identified at AS, 
five (5) at MN, five (5) at BYO, three (3) at DO, and the researcher had access to 3 control 
sites where control soil samples were taken at Manso. In all, twenty-five (25) soil samples 
were taken from Amansie West District in triplicates summing up to seventy-five (75) soil 
samples. Within these two study areas, a total of one hundred and sixty-two (162) soil 
samples were taken for analysis over a period of three-month.  
In water sampling, out of the four (4) communities identified in Amansie West district, 
one community (BYO) did not have a water body (river, stream, lakes) and so relied on 
water tanks, a control site was also selected. Water samples were picked in triplicates from 
the control site and the 3 communities namely DO, AS and MN giving a total of 12 water 
samples for analysis. The same sampling procedure was repeated in Mpohor Wassa East 
district, out of the 5 communities identified, one community (MA) was not close to a water 
71 
 
body (river, stream, lake) and relied on water tank, a control site was selected and water 
samples were collected in triplicates from the control and the four sites giving a total of 15 
water samples for analysis. In all, a total of twenty-seven (27) water samples were taken 
and analyzed during the entire study period.  
The geographical locations of the sampling sites were determined using a global 
positioning system (GPS) device (Model, GARMIN etrex 20). Both soil and water samples 
were taken monthly within a period of three months. The sampling sites with their 
geographical coordinates are shown in (Appendix C). For the water sampling, 
polyethylene sample containers of 500ml capacity with stoppers were used for collecting 
the water samples. The containers were pretreated by washing with acetone to get rid of 
organic substances such as grease and fat residues. Each bottle was then washed with 
detergents and finally rinsed with deionized water. The sampling containers were then 
soaked in 1.0M nitric acid solution for 24 hours. The containers were finally rinsed three 
times with distilled water before transporting them to the sampling site. At the sampling 
site, the sampling bottles were rinsed thoroughly with the source water from where the 
sample was to be collected and the rinsed water was discarded away from the area being 
sampled. A clean water sampler was introduced into the water and the water was fetched 
out. At each of the sampling sites, the water sample was collected into a pretreated clean 
plastic bucket for in-situ measurements. For the in-situ measurement, the water was 
fetched into a pre-treated plastic bucket and the electrodes of a multiprobe instrument 
(Horiber H 50 series) was inserted into the water for measurement. The samples that were 
not measured in-situ were put into an ice chest preserved with ice blocks and transported 
to the laboratory for analysis. The analysis was done at the Ecological Laboratory of the 
Institute for Environment and Sanitation Studies of University of Ghana, Legon.  
 
72 
 
3.6 Parameters Measured and Analytical Procedure 
The parameters that were determined are: pH, Electrical conductivity (EC), moisture 
content, total nitrogen (N), available phosphorus (P), Cation Exchange capacity or 
Exchangeable bases (Mg, Na, Ca, K), soil particle size distribution (% clay, % silt and % 
sand), organic carbon/organic matter content of the soils and heavy metals (Fe, Hg, As, 
Co, Cr, Cu, Ni, Cd, Pb, Zn). 
3.6.1 Soil sample Collection 
Three soil samples were collected within a particular radius at the sampling sites and were 
composited. However, 30g of each soil composite sample was taken in triplicates and put 
into a labeled plastic zip lock polythene bag. In all, a total number of 162 soil samples 
were analyzed during the entire study period in the two study areas.  The samples were 
transported to Ecological Laboratory of University of Ghana for analysys. 
3.6.2 Soil sample preparation and Acid digestion 
The soil samples were initially dried, composited and ground, using a pestle and mortar to 
ensure homogeneity and representativeness. The soil samples were then sieved through 2 
mm steel sieves to remove all debris and stones. Approximately 0.4 g (0.24 - 0.26 mg/Kg) 
of the dry sample was weighed into Teflon tubes and 4 ml of concentrated nitric acid 
(HNO3) was added to the Teflon tubes slowly. Nitric acid was used in order to prevent 
adsorption to the container wall and the capillary tube of the Atomic Absorption 
Spectrophotometer (AAS). Due to the oxidizing nature of nitric acid, it is also able to 
convert metal ions in the soil into their nitrate salts which are highly soluble. These were 
to ensure that all heavy metals in soils are dissolved completely and are able to be assessed 
by the AAS. The tubes were closed and placed in stainless steel bombs. The bombs were 
placed on a hot plate and heated at 600C for 1 hour by which time all the metal ions would 
have dissolved into solution. Before opening the bombs, the samples were allowed to cool 
73 
 
to room temperature and the pressure released carefully. The samples were transferred into 
graduated polypropylene tubes. The Teflon tube was rinsed three times with deionized 
water to ensure that any species that could have formed and been deposited on the inner 
walls of the tube and the lid were transferred to the polypropylene tube. The sample was 
diluted to the 50 ml mark with deionized water and mixed thoroughly. The sample was 
left overnight to allow all the particles to settle before the final analysis. Three (3) blanks 
were used for high accuracy and precision of analytical measurements and one certified 
material (triplicate) was carried through the sample procedure. Certified materials are 
‘controls or standards used to check the quality of data, to validate analytical measurement 
methods, or for the calibration of instruments (Atiemo et al., 2011). 
3.6.3 Soil pH and Electrical conductivity  
The pH and EC of the fine earth fraction (< 2 mm) of each air-dried soil sample was 
determined in a 1:1 soil to distilled water ratio using a microprocessor pH meter. A 10 g 
soil was weighed into a 50 ml polythene beaker and 10 ml of distilled water added. The 
soil-distilled water solution was stirred vigorously with a magnetic stirrer for 30 minutes 
and allowed to stand for one hour for the suspended soil particles to settle. After calibrating 
the pH meter with standard buffer solution of pH 4.0 and pH 7.0, the electrode was then 
inserted into the supernatant (the upper part) of the soil solution. Soil pH and conductivity 
value was then recorded. The test was then duplicated for each sample and the means 
taken. Readings were recorded after stabilization. The stabilization state was determined 
when the signal became steady after 2 minutes. The electrode was rinsed with distilled 
water after each sample measurement before being used for other measurements.  
3.6.4 Soil particle size  
The particle size analysis of the soil was determined using the Bouyoucos Hydrometer 
method modified by (Day, 1965). Forty (40) grams of the air-dried and sieved soil sample 
74 
 
was weighed into a plastic bottle and 100 ml of 5% Sodium hexametaphosphate (NaPO3)6 
solution was added. The content of the bottle was shaken on a mechanical shaker for 2 
hours after which it was transferred into a 1.0 litre measuring cylinder and topped up to 
the mark with distilled water. The suspension was agitated with a plunger and five minutes 
after, the density of the suspension (silt and clay) was taken using a hydrometer. The 
hydrometer reading of the suspension was taken again after eight hours. The contents of 
the cylinder after the eight-hour reading were emptied onto a 47-μm sieve and effluent 
discarded. The sand retained on the sieve was washed off into a moisture can and dried at 
105 ºC for 24 hours, after which the dry weight of the sand was recorded (Day, 1965; FAO, 
2014). Blank sample hydrometer readings at five minutes and five hours were also taken 
for the 5 % calgon solution topped up to 1.0 L. The particle size distribution was then 
determined using the formulae below. Temperature of the suspensions at T1 and T2 = 28 
°C  
ℎ𝑦𝑑𝑟𝑜𝑚𝑒𝑡𝑒𝑟 𝑟𝑒𝑎𝑑𝑖𝑛𝑔 𝑎𝑡 5 ℎ𝑟𝑠  
% 𝑐𝑙𝑎𝑦 =    ×  100     
40 𝑔
ℎ𝑦𝑑𝑟𝑜𝑚𝑒𝑡𝑒𝑟 𝑟𝑒𝑎𝑑𝑖𝑛𝑔 𝑎𝑡 5 𝑚𝑖𝑛 −  ℎ𝑦𝑑𝑟𝑜𝑚𝑒𝑡𝑒𝑟 𝑟𝑒𝑎𝑑𝑖𝑛𝑔 𝑎𝑡 5ℎ𝑟𝑠   
% 𝑠𝑖𝑙𝑡 =   ×  100 
40 𝑔
𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑜𝑣𝑒𝑛 𝑑𝑟𝑖𝑒𝑑 𝑠𝑎𝑚𝑝𝑙𝑒    
% 𝑠𝑎𝑛𝑑 =   ×  100    
40 𝑔
Where 40 = weight of soil sample in grams  
Temperature effect on density of the soil particles was accounted for using the relation 
provided by Day (1965): for every 1 °C increase in temperature, above 19.5°C, there is an 
increase of 0.3 in the density of the particles in suspension.  
Hence, increase in weight = (T2 – T1) × 0.3 = (28 – 28) × 0.3 = 0.  
Correction for temperature = blank hydrometer reading – increase in weight of particles.  
75 
 
Thus = blank hydrometer reading - 0.  
Hence, Correction for temperature = blank hydrometer reading.  
With the percentages of sand, silt and clay, each soil sample was assigned a textural class 
using the United States Department of Agriculture textural triangle. Average proportions 
of the soil types in each soil core were determined and the corresponding average textural 
class was determined.  
3.6.5 Soil organic carbon/ Organic matter  
The organic carbon content of the soil was determined using the wet combustion method 
of (Walkley & Black, 1934). Approximately 0.5g of soil was sieved and measured into 
250 titration flask after which 10ml of 0.167 M potassium dichromate (K2Cr2O7) solution 
and 20 ml of concentrated sulphuric acid (H2SO4) were added to it. The flask was then 
swirled to ensure full contact of the soil with the solution after which the mixture was 
allowed to stand for 30 minutes. The unreduced K2Cr2O7 remaining in solution after the 
oxidation of the oxidizable organic material in the soil sample was titrated with 0.2 M 
ferrous ammonium sulphate solution after adding 10ml of orthophosphoric acid and 2ml 
of barium diphenylamine sulphonate indicator from a dirty brown colour to a bright green 
end point. A standardization titration of the K2Cr2O7 with the ferrous ammonium sulphate 
was done and the amount of oxidizable organic carbon was then calculated by subtracting 
the moles of unreduced K2Cr2O7 from that of K2Cr2O7 present in the standardized titration. 
The titre value was used to calculate the percent carbon (% C) as:  
0.3 × (10 – 𝑇𝑁)  × 1.33  
%𝑂𝐶 =   ×  100      
𝑊
Where %OC = Percent organic carbon 
X = Titre value of the ferrous ammonium sulphate  
N = Molar mass of the ferrous ammonium sulphate (0.2M)  
76 
 
W = The weight of the soil sample.  
0.3= 0.003 x 100 
0.003= Milliequivalent weight of carbon (g) 
1.33= correction factor (f) 
The % Organic C was then converted to organic matter using the equation:  
% Organic Matter (OM) = % Organic carbon x 1.724.   
3.6.6 Total Nitrogen  
The Kjeldahl (1983) method was used in the determination of total nitrogen. One (1) gram 
of soil will be weighed into a Kjeldahl flask and a tablet of a digestion accelerator 
(selenium catalyst) will be added. This was followed by the addition of 5ml of concentrated 
H2SO4. The mixture was digested until the digest became clear. The test tube was cooled 
and its content transferred into a 100 ml volumetric flask. Distilled water was added to the 
digest in the volumetric flask till it got to the 100 ml mark. An aliquot of 5 ml of the digest 
was taken into a Markham distillation apparatus and 10 ml of 40 % NaOH was added and 
the mixture distilled. The distillate (liberated ammonia) was collected in 5 ml of 2 % boric 
acid (H3BO3). Three drops of a mixed indicator containing methylene blue and methyl red 
were added to the solution and then back titrated with 0.01 M HCl from green to reddish 
end point. The percent N was calculated as follows:  
0.01 × 𝑡𝑖𝑡𝑟𝑒 𝑣𝑎𝑙𝑢𝑒 × 0.014 × 𝑣𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑒𝑥𝑡𝑟𝑎𝑐𝑡   ×100 
% 𝑁 =                                        
𝑆𝑎𝑚𝑝𝑙𝑒 𝑤𝑒𝑖𝑔ℎ𝑡 (𝑔)× 𝑣𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑎𝑙𝑖𝑞𝑢𝑜𝑡 (𝑚𝐿)
Where; 0.01 = Molarity of HCl, and 0.014 = Milliequivalent of Nitrogen  
77 
 
3.6.7 Available phosphorus  
The available phosphorus in soil samples was determined using 0.03 M ammonium 
fluoride (NH4F) in acid solution, potassium hydroxide, sulphuric acid and hydrochloric 
acid (Bray & Kurtz, 1945). A 0.1 g of soil sample was weighed and put into a centrifuge 
bottle and 50 ml of Bray 1 solution (0.03N NH4F + 0.025N HCL) was added. The 
mechanical shaker was used to mix the suspension by shaking for five minutes and left to 
settle overnight for the suspension. The suspension was then filtered into a 100 ml 
volumetric flask and made up to the volume. The available phosphorus in the filtrate was 
determined using molybdate-ascorbic acid method. Five ml of the aliquot was taken into 
a 50 ml volumetric flask and the pH was adjusted by adding P-nitrophenol indicator and 
drops of 4M NH4OH until the colour changed to yellow. Then 40 ml of distilled water was 
added to dilute the solution. A solution which is made from a mixture of 12 g ammonium, 
0.29 g potassium antimony tartrate, 140 ml of concentrated H2SO4 and 1.056 g of ascorbic 
acid (reagent B) was prepared. Eight ml of the reagent B was added to the solution and 
mixed thoroughly by shaking and allowing it to settle for 15 minutes until the colour 
changed to different shades of blue depending on the P content in the samples. A blank of 
was prepared using distilled water and 8 ml of reagent B. A Philips PU 8620 
spectrophotometer was used to measure the intensity of the P content at a wavelength of 
712 nm this was calculated using: 
𝑚𝑔 𝑠𝑝𝑒𝑐𝑡𝑟𝑜𝑝ℎ𝑜𝑡𝑜𝑚𝑒𝑡𝑒𝑟 𝑟𝑒𝑎𝑑𝑖𝑛𝑔 (𝑚𝑔𝐿−1)× 𝑣𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑒𝑥𝑡𝑟𝑎𝑐𝑡
𝑃 ( ) =            
𝐾𝑔  𝑣𝑜𝑙𝑢𝑚𝑒 𝑜𝑓 𝑎𝑙𝑖𝑞𝑢𝑜𝑡 × 𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑠𝑜𝑖𝑙 𝑠𝑎𝑚𝑝𝑙𝑒 
3.6.8 Extraction of Exchangeable bases (Ca, Mg, Na, K) 
Ten grams (10 g) of the soil samples (2 mm sieved) was weighed into 100 ml extraction 
bottles. Hundred (100) ml of 1M ammonium acetate (NH4OAc) solution buffered at pH 
7.0 was added. The bottles were covered and then placed on a reciprocating shaker and 
shaken for 1 hour at 180 strokes per min. The soil suspension was then decanted and 
78 
 
filtered. The filtrates were used for the determination of Ca, Mg, K and Na however, 5 ml 
aliquot of the filtrates was pipetted into 50 ml volumetric flask and made up to the mark 
with deionized water. The Perkin Elmer atomic absorption spectrometer (Analyst 800) was 
calibrated with the appropriate standards for Ca, Mg and Na respectively and the 
absorbance for each element in the filtrate determined. Exchangeable bases were 
calculated as below: 
𝑅  ×  𝑉𝑜𝑙.𝑜𝑓 𝑒𝑥𝑡𝑟𝑎𝑐𝑡 ×103 (𝑔) × 102(𝑐𝑚𝑜𝑙)×𝐸
𝐶𝑎 (𝑐𝑚𝑜𝑙𝑐𝐾𝑔
−1) =                
 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑠𝑜𝑖𝑙 ×106 (µ𝑔)× 40
Where 40 = Atomic mass of Ca and  
R = AAS (Atomic absorption spectroscopy) reading in mg L-1 
E = Charge of Ca 
𝑅   × 𝑉𝑜𝑙.𝑜𝑓 𝑒𝑥𝑡𝑟𝑎𝑐𝑡 × 103(𝑔) × 102(𝑐𝑚𝑜𝑙) ×𝐸
𝑀𝑔 (𝑐𝑚𝑜𝑙𝑐𝐾𝑔
−1) =  6                 𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑠𝑜𝑖𝑙 ×10 (µ𝑔)× 24
Where 24 = Atomic mass of Mg 
R = AAS (Atomic absorption spectroscopy) reading in mg L-1 
E = Charge of Mg 
−1 𝑅 ×  𝑉𝑜𝑙.𝑜𝑓 𝑒𝑥𝑡𝑟𝑎𝑐𝑡 × 10
3 (𝑔)× 102 (𝑐𝑚𝑜𝑙) × 𝐸
𝑁𝑎 (𝑐𝑚𝑜𝑙𝑐𝐾𝑔 ) =   𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑠𝑜𝑖𝑙 × 106  (µ𝑔) × 23
Where; 
R = AAS (Atomic absorption spectroscopy) reading on mg L-1 
23 = Atomic weight of Na 
E = Charge of Na 
The K content in the diluted soil extracts was measured with the standardized flame 
photometer. The flame photometer was standardized to give a 100 full scale deflection at 
79 
 
10 mg/Kg of K. The values obtained was then be used to calculate the amount of potassium 
contained in the soils as shown in the formula below: 
−1 𝑅 ×  𝑉𝑜𝑙.𝑜𝑓 𝑒𝑥𝑡𝑟𝑎𝑐𝑡 × 10
3(𝑔)× 102(𝑐𝑚𝑜𝑙)× 𝐸
𝐾 (𝑐𝑚𝑜𝑙𝑐𝐾𝑔 ) =                𝑊𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑠𝑜𝑖𝑙 × 106(µ𝑔) × 39.1
Where; 
R is the flame photometer reading (ppm) 
39.1 = Atomic weight of K 
E = Charge of K 
3.7 Determination of Heavy Metals in Soils 
3.7.1 Extraction of heavy metals from soils using NH4NO3-Solution Extraction 
Technique 
The German DIN 19730 (1997) described the use of NH4NO3 method for the extraction 
of readily available heavy metals from soil as one of the most effective methods especially 
for sequential extraction of soils. The extraction was done by shaking the soil with 1 M 
NH4NO3-solution. Cadmium (Cd), Pb, Zn, Ni, Cu, As, Co and Hg were extracted by 1 M 
NH4NO3 (Gryschko et al., 2000). The resulting solution was then determined using 
VARIAN AA240FS-Atomic Absorption Spectrophotometer AA240FS. 
The chemical mechanisms involved in heavy metal extraction in mine soils using this 
technique have been documented across the world. This has been used to create 
requirements to improve environmental risk assessment for soil contaminations. The 
chemical mechanisms involved when soil is extracted with 1 M NH4NO3-solution have 
been evaluated and this was followed by a laboratory experiment to quantify the formation 
of soluble metal ammine complexes during the extraction. 
80 
 
3.7.2 Acid Digestion of Soil 
A 1.5g of the dried soil sample was weighed into an acid wash labeled 100ml 
polytetrafluoroethylene (PTFE) Teflon beaker. About 6ml of concentrated nitric acid 
(HNO3), 65%), 3ml of concentrated hydrochloric acid (HCl, 35%) and 0.25ml of hydrogen 
peroxide (H2O2, 30%) was added to each sample in a fume chamber. The sample was then 
placed on a microwave carousel. The vessel cap was then tightly secured using a wrench. 
The complete assembly was microwave irradiated for 26minutes using milestone 
microwave lab station ETHOS 900, INSTR: MLS-1200 MEGA (Gryschko et al., 2000). 
3.7.3 Determination of Heavy Metals using AAS 
After digestion of soil sample, the Teflon beaker was mounted on the microwave carousel 
and was cooled in a water bath to reduce internal pressure to allow the volatilized material 
to re-stabilize. The digestate was made up to 20ml with double distilled water and an 
analytical blank was prepared for each sample. A series of calibration solutions (standard) 
containing a known amount of analyte elements were also prepared and used to calibrate 
the VARIAN AA240FS-Atomic Absorption Spectrophotometer AA240FS. 
Blanks were atomized followed by the standards and calibration graphs plotted showing 
response from the AAS. The concentrations were then calculated based on the absorbance 
obtained using the Beer Lambert law. Responses of standards were used to establish 
accurate performance of the machine and accurate values of elements. The machine was 
calibrated after every three elemental analyses. Light was generated from hollow cathode 
lamps at wavelength characteristics to each analyte. Each analyte was then atomized using 
an atomizer to create free atoms from the samples. Air-acetylene gas was used as a source 
of energy for the production of free atoms for the elements, Fe, Hg, As, Ba, Co, Cr, Cu, 
Ni, Cd, Pb, Sn and Zn. The sample was introduced as an aerosol into the flame and the 
burner aligned in the optical path to allow the light beam to pass through the flame where 
81 
 
the light was absorbed. The light was then directed into a monochromator which then 
isolates the specific analytical wavelength of the light emitted by the hollow cathode lamp 
from the non-analytical. The sensitive light detector then measures the light and translates 
the response into the analytical measurements. Calculation of the concentration of heavy 
metals was determined using; 
Final conc (mg/L or mg/Kg) = Conc. (analytical measurement) ×Nominal volume/Sample 
weight in grammes. 
Where conc. =instrumental measurement 
Nominal volume=final volume of digestate sample solution 
Conc. (mg/Kg) = concentration of metals in soil 
3.8 Land Use and Land Cover (LULC) Change Analysis 
3.8.1 Data and Methodology 
This section identifies and analyzes land use and land cover (LULC) change patterns in 
the studied communities. The primary focus of this analysis was to better appreciate and 
understand the role of mining in driving LULC change trends over time.  
The workflow to detect changes that have occurred over the years is presented 
schematically in (Figure 3). The steps involved were, data acquisition, pre-processing, 
image classification and change detection using ArcGIS Pro. The Satellite scenes used for 
this were Landsat 8 OLI images for 2015 and 2020 acquired from the United States 
Geological Service (USGS) website (www.usgs.glovis.com). The Landsat 8 Collection 2 
level-2 products which were already radiometrically, geometrically and atmospherically 
corrected were acquired for this analysis. Pre-processing for this study therefore focused 
on sub-setting.  The images have a resolution of 30 m for the multispectral band. 
82 
 
 GENERATE TRAING DATA 
 
(Classes: Vegetation, Water, 
 
DATA ACQUISITION Impervious surfaces and Mining 
 sites) 
 Landsat 8 OLI scenes 
 (2015_2020) 
 SUPERVISED CLASSIFICATION  
 Random Forest  
 
 
PRE-PROCESSING 
 
ACCURACY ASSESSMENT 
 Image Subsetting  
 
 
CHANGE DETECTION 
 
Figure 3: Methodology for land use cover change 
 
A total of four classes were identified for training namely (i) vegetation, (ii) water, (iii) 
impervious surfaces and (iv) mining sites. Training data was generated from ground 
truthing data and high-resolution images from google earth pro. An average of 10 training 
sites was identified for each class. Here there is a need to have prior knowledge about the 
terrain so as to avoid misclassification. The Random Forest algorithm was used for the 
image classification, producing a land use land cover map for each year in respect to the 
selected study areas.  
The accuracy of each classification map was assessed using the confusion matrix model. 
This compares the pixels in the classified map to those within the training samples. 
Producer and user accuracies and Kappa coefficient which measures the agreement and 
reliability of the classification were calculated (Forkuor & Cofie, 2011). The land cover 
classification of 2015 and 2020 was used for the change detection analysis. The researcher 
was interested in accessing land cover change between 2015 and 2020 to get the recent 
83 
 
changes in land cover. The raster was converted to a shape file (feature type) using the 
Raster to Polygon tool in ArcGIS Pro. This allows calculation of the area for each class to 
be able to detect changes that have occurred over the years. A unique ID is generated for 
each polygon. The merge tool was used to join polygons of the same class. After merging, 
a new field was added to the attribute table and the area of each class in square meters was 
calculated using Calculate Geometry. 
The Intersect tool was then used to compute the geometric intersection of 2015 and 2020 
input features. Classes that overlap in all layers were written to an output feature class, 
which is the change detection. The output consists of areas where a polygon from one of 
the input features classes (vegetation, water, impervious surfaces or mining areas) or a 
layer (2015 or 2020) intersects a polygon from the other input feature class or layer. This 
generated classes with changes and without changes. For instance, with the vegetation 
class, the change detection generated were Vegetation_Vegetation (no change occurred), 
Vegetation_Water (changes occurred), Vegetation_Impevious surfaces (changes occurred) 
and Vegetation_Mining sites (changes occurred). A change detection map was generated 
from the output, this was presented and discussed in the following chapters. The 
importance of detecting the changes is to help determine which land cover is changing to 
what (Rawat & Kumar, 2015). 
3.9 Study Population for Social Survey 
According to this research, the study population was Ghanaians from the age of 30 years 
to 70 years and above who have stayed in the selected study communities for at least ten 
years. That is, people who have stayed in the area from the ten years and beyond who meet 
the inclusion criteria were recruited into the study. The basis for choosing this age range 
is cognizance of the researcher’s aim of studying the prevailing conditions before and after 
the ban of illegal mining activities by the government of Ghana. A thirty (30) year old 
84 
 
person was at least 18 years then, an age recognized in Ghana for someone to vote or to 
make a decision (Article 42 of the 1992 constitution of Ghana). Again, a 70-year-old 
person today was also still less than 60 years of which he or she was still in active service 
(even if that person was working in the formal sector) in his or her occupation and could 
therefore tell the prevailing condition before and after the illegal mining activities. The 
researcher is interested in this study population because it takes someone who has stayed 
in the study area for a certain number of years to be able to tell the “Before” and “After” 
conditions of the impact of mining on their livelihoods in the study area. It is this 
population therefore that the researcher from whom information was gathered in order to 
draw a valid conclusion. 
3.10 Sampling for Qualitative Respondents  
There are four major types of qualitative sampling which are snowballing, convenience 
sampling, purposeful sampling and quota sampling (Scheyvens & Storey, 2003) of which 
a researcher can use to sample his or her respondents depending on the objectives and 
research questions of the study. Many researchers however, opt for purposive sampling to 
select respondents for their studies because they are of the conviction that such respondents 
are of relevance to the study and can also provide them with the information needed 
(Campbell et al., 2020). The study adopted purposive and snowballing sampling to sample 
respondents for the qualitative data because the researcher is of the conviction that these 
respondents were of relevance to the study and could provide him with the information 
needed (Kitchin & Tate, 2000). These respondents were selected during questionnaire 
administration. The researcher was directed to other respondents who are of relevance to 
the study; that is, the snowball sampling where a researcher is led by a respondent to 
another respondent of relevance to the study. The purposive sampling method was adopted 
to select the mining communities because not all the communities in the districts had 
85 
 
experienced ASGM and the researcher was interested in locating communities that had 
mining activities. Qualitative data was collected using Focus Group Discussion, Key 
informant interview and observations.  
3.10.1 Focus Group Discussion  
Separate Focus Group Discussions (FGD) for community members, farmers from each 
community were conducted. Focus Group Discussion (a minimum of 4 and a maximum 
of 5) was organized for these groups because they are the people whose economic activities 
have been mostly affected by the ‘galamsey’ operations. In Amansie West and Mpohor 
Wassa East District. In all, a total of forty-five (45) people were involved in the Focus 
Group Discussions for farmers in the nine (9) selected communities. The reason for using 
Focus Group Discussion (FGD) was to enable the researcher to organize the discussion 
with a selected group of individuals in order to obtain information about their views on the 
topic under study. It was also to gather diverse views from respondents about the topic 
being studied. Also, attitudes, feelings, beliefs and experiences of some individuals are 
properly exhibited in a group rather than on an individual basis. With this method, a large 
amount of information can be gathered within a very short period. Each was conducted 
separately for males and females to avoid the traditional male dominance over females in 
matters that affect them. Mixing males and females together in studies like this could 
prevent females from freely expressing their opinions on the issues being discussed. 
3.10.2 Key Informant Interview  
Key informant interviews also gave the researcher in-depth information about issues of 
relevance to the study which could not be provided by other respondents. This method was 
used for relevant stakeholders in the mining industry including community leaders and 
opinion leaders such as assemblymen, unit committee members, women leaders and 
86 
 
traditional rulers and the District Assemblies, in all thirty (30) individuals were selected 
and interviewed as key informants among the two districts. 
3.11 Data Collection Instruments 
Instruments for data collection are fact finding strategies and tools used by researchers to 
collect data for their study. Some of them are interviews, questionnaires and observations 
(Annum, 2017). In every research, the researcher adopts a certain approach for the study 
(Silverman, 2012). Questionnaire was used to collect the quantitative data. Questionnaires 
are used to collect key information about the population; besides, it is also used to cover a 
wide population in a short time (Mugenda & Mugenda, 2005). A survey instrument with 
both closed- ended and open-ended questionnaires was used in collecting the data. 
Questionnaires also serve as efficient data collection tools especially when the researcher 
knows exactly what he or she wants and knows what variables to use (Sekaran, 2003). 
With this in mind, the researcher carefully prepared questionnaires that answered the 
research objectives.  
3.11.1 Face-to-Face Interview  
In the course of administering the questionnaire, the researcher used face-to-face 
interviews for respondents who could not read nor write and asked the researcher to guide 
them fill the questionnaire. This ensured the quality of the data obtained through the 
information acquired during the conversation. Interviews were constructed in the English 
language and then translated into the various languages by the researcher himself for better 
understanding of the questions by respondents.  
3.12 Sampling Size for Quantitative Study  
Researchers may have the desire to collect information from as many people as they can 
but are constrained in many instances by time and resources. As a result, they devise means 
87 
 
to select a small group of the population which is representative and is of relevance to the 
study (Bryman & Cramer, 1995). Sample size refers to the number of items or respondents 
to be selected from the population to be studied (Kothari, 2004). These respondents are 
made up of both males and females but of different socio-economic background. 
3.12.1 Sample Size Determination 
𝑍2×𝑃𝑄
The sample size for the study was calculated using the Cochran (1977) formula; n =  2  𝑑
Where: n= desired sample size 
Z= Reliability coefficient for 95% confidence level usually set at 1.96 
P = the proportion of respondents in the study area that was interviewed, as a rule of thumb, 
50 % (0.5) was used because there is no reasonable estimate available in literature about 
the number of people affected by the illegal mining operations in each household in the 
Ghana Demographic health survey report in the communities selected for the study. 
Q=1-p 
d=degree of accuracy desired set at 0.05 probability level. 
Based on the formula above, a sample size of 404 was used for the study after allowing 
5% margin of error for non-responses. The respondents were selected from each 
community using a ratio. A ratio of sampling sites in a community to sampling sites in 
both districts was used to share the total questionnaire. Using the ratio, the questionnaire 
distribution in Mpohor Wassa District was ASA (57), ADT (48), MM (40), ADW (40) and 
MA (40) giving a total of 225 questionnaires administered. However, using the same ratio 
in Amansie West District, the questionnaire distribution was DO (24), AS (75), MN (40) 
and DYO (40) giving a total of 179 questionnaires administered. 
 
88 
 
3.13 Qualitative Data Handling  
Data from qualitative sources was handled through editing, coding and transcribing of 
information. The data collected was edited and grouped into themes, analyzed, interpreted 
and discussed. For the qualitative part of the study, interviews were recorded digitally and 
the audio files labeled appropriately for easy retrieval. Each recording was transcribed into 
English. The researcher validated the transcripts by listening to a sample of the tapes to 
check accuracy of content and translation quality. The transcripts were analyzed using 
qualitative data analysis software (NVIVO 11). The software is good for data organization 
and retrieval and allows easy and efficient retrieval of data. The transcriptions were coded 
using identified themes from the interview guide and themes that emerged from the data. 
3.14 Quantitative Data Analysis and Processing 
The data collected from quantitative data was edited, coded and entered into Microsoft 
Excel and finally imported onto STATA software version 16 for statistical analysis. 
Descriptive statistics such as mean ± SD, range, 95% confidence interval and tables were 
used to describe the data. One-way analysis of variance (ANOVA) at 95% confidence 
level was used to test differences in means among the sampling sites in each of the two 
studied regions. Where differences exist, Tukey’s HSD multiple comparisons was used to 
determine where the differences lie. The Pearson product moment correlation coefficient 
(r) was estimated to test the degree of relationship between the parameters. Factor analysis 
was conducted by using Principal Component Analysis (PCA) as the extraction technique 
with an Eigenvalue greater than 1 to obtain the latent factors or components responsible 
for water quality variations. Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity 
were used to determine the factorability of data. 
3.15 Ethical Issues  
Ethical clearance was sought from the Ethics Committee of the University of Ghana, 
Legon. This committee is tasked with the responsibility of ensuring that graduate students 
89 
 
follow ethical issues to ensure the safety and confidentiality of respondents. The 
committee also looks at data collection methods and any other issues related to the study. 
Clearance is given to the researcher (student) upon meeting the requirements and 
satisfaction of the committee with the issuing of a letter of clearance (Appendix M).  
The researcher met participants and discussed the purpose of the research, the expected 
time commitments and the procedure for the research activities. All participants were 
given a guarantee of confidentiality and anonymity in reporting the information provided 
for the study. Additionally, ethical issues governing human subjects in research were 
strictly adhered to. The names of respondents were not captured in the research. The 
features of the questionnaires such as ease of completion and sensitivity of the 
questionnaire were all considered. There were no biases towards any religion, race or 
culture. Permission was sought from participants to involve them in the study.  One cannot 
conduct research among any group of people without their consent. This is part of the 
ethical considerations in social research. In this regard, the researcher sought the consent 
and permission of respondents before obtaining information from them. This was done 
through self-introduction by the researcher, the purpose of the study, what it would be used 
for and how beneficial it is expected to be to the communities under study. They were also 
assured that the information being sought from them would be strictly kept in 
confidentiality and that their identities were not be disclosed anywhere. 
3.16 Contamination Assessment and Hazard Rating  
The current contamination situation at the illegal mining sites in the study areas was 
investigated by using the threshold exceedance ratio (TER) and the trace element mobility 
coefficient. The threshold exceedance ratio was calculated as follows (Prüeß et al., 1991):  
𝐸×𝐶
𝑇𝐸𝑅 = ……………………………………………………………………. Eq (1) 
𝐵𝐶
Where ExC is the NH4NO3 extractable concentration and BC is a given background 
concentration. The rule of thumb is that a threshold exceedance ratio (TER) value higher 
90 
 
than the total concentration (TC) can limit the functioning of the soil. Limited soil 
functioning might occur if the threshold value is exceeded, causing a reduction in plant 
growth and, thus, increased soil erosion. The mobility (bioavailability) of trace elements 
(MOB in %) was derived by comparing the extractable ratio of an element with the total 
concentration by using the following formula: 
𝐸×𝐶
𝑀𝑂𝐵 = …………………………………………………………………… Eq (2) 
𝑇𝑜𝑡 𝐶
Where TotC is the total concentration measured in soil samples. The MOB value gives the 
percentage value of the concentration that could be remobilized and is thus bioavailable in 
the soil.  
3.17 Geoaccumulation Index (Igeo) 
The Igeo values were calculated in order to determine metal contamination in soils at the 
study sites. The index of geoaccumulation is a widely used index in the estimation and 
evaluation of soil contamination by comparing the levels of metals obtained to the 
background levels originally used with bottom sediment (Atiemo, 2011). It was estimated 
by using the modified equation (Taylor & Meclennan, 2010). The Igeo values were 
calculated in order to determine metal contamination in soils at the study sites. The index 
of geoaccumulation is a widely used index in the estimation and evaluation of soil 
contamination by comparing the levels of metals obtained to the background levels 
originally used with bottom sediment (Atiemo, 2011). It was estimated by using the 
modified equation (Taylor & Meclennan, 2010). 
Igeo = log2 (Cn/1.5Bn) 
Where Cn is the measured heavy metal levels in the soil sample. Bn is the geochemical 
background levels of the heavy metal. The constant 1.5 is introduced to minimize the 
effects of possible variations in the background value, which may be attributed to 
91 
 
lithologic variation in the sediments (Lu et al., 2009). The geochemical background values 
for metals are Cr (67.30 mg/Kg), Cd (0.10 mg/Kg), Zn (65.40 mg/Kg), Pb (21 mg/Kg), Cu 
(22.50 mg/Kg) and Fe (125) (Taylor & Meclenan, 2010). The following classification is 
given for the geoaccumulation index as shown in Table 3.2. 
Table 3.2: Categorization of Igeo 
Igeo values Igeo class Designation of soil quality 
>5 6 Extremely contaminated 
4-5 5 Strongly to extremely contaminated 
3-4 4 Strongly contaminated 
2-3 3 Moderately to strongly contaminated 
1-2 2 Moderately contaminated 
0-1 1 Uncontaminated to moderately contaminated 
0 0 Uncontaminated 
Source: Muller, 1997 
3.18 Quality Control 
The sensitivity of methods used in the analysis of the metals was determined using 
recovery and reproducibility studies, which were conducted using certified standard 
reference solutions for As, Cd, Pb and Hg manufactured by BDH Chemicals (London, 
UK). In between the analysis, certified reference materials of the toxicants were analyzed 
to verify the calibration curve. The standard for the ASS calibration was prepared by 
diluting standard (1000 ppm) supplied by MES Equipment Ghana. All the results obtained 
were expressed in mg/L. 
3.18.1 Analytical Technique and Accuracy Check 
Nine (9) heavy metals namely arsenic (As), total chromium (Cr), cadmium (Cd), copper 
(Cu), lead (Pb), manganese (Mn), nickel (Ni), zinc (Zn), and iron (Fe) was measured using 
dual atomizer and hydride generator atomic absorption spectrophotometer (model ASC-
7000 No A309654, Shimadzu, Japan). All reagents used were of the analytical grade from 
MES Equipment Ghana. Ultrapure metal free deionized water was used for all analysis. 
92 
 
All glassware and plastic were cleaned by soaking them in warm 5% (V/V) aqueous nitric 
acid for 6-7 hours and rinsed with ultrapure deionized water.  
3.18.2 Chemical and Sample Digestion 
Deionized water supplied by Medical and Equipment Suppliers (MES) Equipment Limited 
Ghana was used in all the analysis. All standard solutions used were of the highest purity 
supplied. The nitric and hydrochloric acids that were used for the digestion were of 
analytical grades and supplied by MES equipment. 
3.18.3 Quality Control for Social Survey 
The structured questionnaire for the social survey was pretested at Manso Nkwanta and 
Mpohor Wassa and all errors detected were rectified. The questionnaire was validated by 
allowing an expert to go through. The internal consistency of the questionnaire was tested 
with Cronbach alpha and a value of 0.85 was obtained which indicates a good reliability 
of the questionnaire. 
 
 
 
 
 
 
  
93 
 
CHAPTER FOUR 
RESULTS  
4.0 Overview 
This chapter presents the results of water quality and soil analyses, social survey of 
households, land use and land cover change assessments from the studied locations. The 
first section explored the physical parameters of the surface water, the second section deals 
with physical parameters of soils and the third section deals with land use and land cover 
changes and the last deals with social survey of impacts of mainly artisanal and small-
scale mining on ecosystem services. 
4.1 Physical Parameters in Surface water Samples, Mpohor Wassa East District 
4.1.1 pH 
The mean pH values of the surface water sample from Mpohor Wassa East ranged from 
6.5 at sampling site ASA (Asowuo Ayipa) and MM (Mpohor Motorway) to 7.1 SD at 
control sampling site (Mpohor Anomabo) with standard deviations of 0.32 and 0.17 
respectively (Figure 4). Analysis of variance at 95% confidence level did not show any 
statistically significant differences in pH among sampling locations and the control sites 
(p>0.05) (Appendix B). 
10
8 76.7 7.16.5 6.5
6
4
GS 1212:2019
2
0
ADW ADT ASA MM Control
Sampling sites
 
Figure 4: pH Variations across surface water locations, Mpohor Wassa East  
 
94 
 
Mean pH
4.1.2 Electrical Conductivity 
The highest conductivity value was recorded in surface water samples at sampling site 
MM (Mpohor Motorway) with mean value of 152.3±24.4 μS/cm and the lowest was 
recorded at the control sampling site with mean value of 58.7 ± 3.4μS/cm (Figure 5). There 
was a statistically significant difference in conductivity among sampling sites (Appendix 
B). Pair wise comparison using Tukey’s HSD revealed statistically significant differences 
among the following; ADW and control (p=0.001), ADT and control (p=0.002), MM and 
control (p=0.004) and (p=0.017) (Appendix C).  
300
250
200 152.3
150 123.5 128.3
92.7
100 58.7 GS 1212:2019. Limit
50
0
ADW ADT ASA MM Control
Sampling site
 
Figure 5: Conductivity Variations across surface water locations, Mpohor Wassa 
East 
 
4.1.3 Total Dissolved Solids 
At Mpohor Wassa East sampling sites in the Western region however, the highest TDs 
value was recorded in surface water samples at sampling site MM (Mpohor Motorway) 
with mean value of 101±17.2mg/L and the lowest was recorded at the control sampling 
site with mean value of 33.3± 9.1mg/L (Figure 6). The TDS of water samples differed 
significantly among sampling sites (F=21.8; p=0.0001) (Appendix B).  
95 
 
Mean EC(µS/cm)
1200
1000
800
600
400 GS 1212:2019 limit
200 69.6 82.3 61.7 101 33.3
0
ADW ADT ASA MM Control
Sampling site
 
Figure 6: TDS Variations across surface water locations, Mpohor Wassa East 
District 
 
4.1.4 Total Suspended Solids 
The TSS of water samples obtained from the Mpohor Wassa East surface water in the 
Western Region is illustrated in Figure 7. The highest TSS value was recorded in surface 
water samples at sampling site MM with mean value of 645.3 ±14.8mg/L and the lowest 
was recorded at the control sampling site with mean value of 5.3± 0.5 mg/L (Figure 7). 
There was a statistically significant differences in TSS among sampling sites (F=178; 
p=0.0001) (Appendix B). Pair wise comparison using Tukey’s HSD revealed statistically 
significant differences and the results are shown in (Appendix C). 
800 645.3
600 399
400
145.3
200 15.8 5.3 GS 1212:2019
0
ADW ADT ASA MM Control
sampling site  
Figure 7: TSS Variations across surface water locations, Mpohor Wassa East 
District 
 
4.1.5 Total Alkalinity 
The highest total alkalinity of the surface water samples from Mpohor Wassa East 
recorded at control sampling site with mean value of 64.3±8.0mg/L whilst the lowest was 
recorded at sampling site ASA with mean value of 9.0 ± 2.6 mg/L (Appendix A). The total 
96 
 
Mean TSS (mg/L) Mean TDS (mg/L)
alkalinity of the water samples differed significantly among sampling sites (F=160.4, 
p=0.0001) (Appendix B). The pairwise comparison using Tukey’s HSD significant 
differences are shown in (Appendix C). 
4.1.6 Temperature 
The lowest temperature of water samples at Mpohor Wassa East District of Western region 
however, was recorded at sampling site ADT with mean value of 24.6 0C and the highest 
mean value of 25.4 0C at sampling site ADW (Appendix A). The temperature values did 
not differ significantly among sampling sites (p>0.05) (Appendix B). 
4.1.7 Dissolved Oxygen (DO) 
The highest DO of the surface water samples from Mpohor Wassa East was recorded at 
control sampling site with mean value of 12.9±3.7 mg/L whilst the lowest was recorded at 
sampling site ASA with mean value of 5.4 ± 0.91 mg/L (Appendix A). Analysis of 
variance at 95% confidence level revealed that the dissolved oxygen differed significantly 
among sampling sites (p=0.02) (Appendix B). 
4.1.8 Biological Oxygen demand (BOD) 
The highest BOD of the surface water samples from Mpohor Wassa East was recorded at 
sampling site ADW with mean value of 1.7±0.60 mg/L whilst the lowest was recorded at 
control sampling site with mean value of 1.0 ± 0.42 mg/L (Appendix A). There were no 
statistically significant differences in BOD among sampling sites (p=0.349) (Appendix B). 
4.1.9 Salinity 
The lowest salinity of the surface water samples from Mpohor Wassa recorded at control 
sampling site with mean value of 0.00mg/L whilst the highest was recorded at sampling 
site ADT with mean value of 2.0 ± 0.06mg/L (Appendix A). There was a statistically 
significant difference in salinity among sampling sites (p=0.013) (Appendix B). 
97 
 
4.1.10 Total Hardness 
Concerning water samples at Mpohor Wassa sampling site, the highest total hardness of 
the surface water samples was recorded at sampling site ADT with mean value of 
72.6±0.67 mg/L whilst the lowest was recorded at control sampling site with mean value 
of 11.7 ± 2.8 SD mg/L (Appendix A). There was a statistically significant differences in 
total harness among sampling sites (p=0.001) (Appendix B). 
4.1.11 Turbidity 
The mean turbidity values in surface water samples obtained from Mpohor Wassa East 
District however, also ranged from 2.5±0.7 SD at the control sampling site and 299.2 ±12.2 
SD. There was a statistically significant difference in total hardness among sampling sites 
(p=0.001) (Appendix B). 
4.2 Heavy metals in water, Mpohor Wassa East District 
The heavy metals in surface water samples obtained from Mpohor Wassa East sampling 
sites are as illustrated in Table 4.1. The lowest Fe concentration was recorded in water 
samples obtained from ASA and ranged from 0.41 to 0.49mg/L with a mean value of 
0.460.04SD mg/L, whilst the highest Fe concentrations were recorded at the control 
sampling site and ranged from 1.28 to 3.17mg/L with a mean value of 2.40.97SD mg/L. 
Cobalt was not detected in control water samples and the highest was recorded at sampling 
site ADT and ranged from 0.01 to 0.04 mg/L with a mean value of 0.030.00 SD mg/L. 
Copper was not detected in water samples and the highest was recorded at sampling site 
ADT and ranged from 0.20 to 0.38 mg/L with a mean value of 0.320.01SD mg/L. 
Chromium was not detected both at sampling site ADW and the control sampling site. The 
highest value, however, was recorded at sampling site ADT and ranged from 0.004 to 
0.008 mg/L with a mean value of 0.006±0.00 SD mg/L. The highest Ni concentration was 
recorded at sampling site ADT and ranged from 0.22 to 0.39 mg/L with a mean value of 
0.31±0.08 SD mg/L. The lowest Zn concentration was recorded in water samples from 
98 
 
control sampling sites and ranged from ND to 0.01 mg/L with a mean value of 0.003±0.001 
SD mg/L, whilst the highest was recorded at sampling site ADT and ranged from 0.27 to 
0.93 mg/L with a mean value of 0.49±0.01SD mg/L. Mercury (Hg) and arsenic were not 
detected in water samples obtained from control sampling sites. However, mean values of 
0.0003±0.00 SD mg/L each were recorded for mercury and arsenic. The lowest Cd 
concentration was recorded in water samples from control sampling sites and ranged from 
0.05 to 0.07 mg/L with a mean value of 0.06±0.01SD mg/L and the highest was recorded 
in water samples at sampling site ADW and ranged from 0.11 to 0.54 mg/L with a mean 
value of 0.3±0.02 SD mg/L. Lastly, the lowest Pb concentration was recorded in water 
samples from control sampling sites with a mean value of 0.0003±0.00 mg/L and the 
highest was recorded in water samples at sampling site ASA with a mean value of 
0.02±0.0001SD mg/L. 
Table 4.1: Heavy Metals in Surface Water Samples from the Mpohor Wassa East 
District 
Var ADW ADT ASA MM Control 
Fe 1.44±1.0.2(1.30-1.54) 0.75±0.2(0.64-0.88) 0.46±0.04(0.41-0.49) 1.8±0.1(1.72-1.93) 2.4±0.97(1.28-
  3.17) 
Co 0.01±0.00 (0.001-0.02) 0.03±0.00(0.01-0.04) 0.02±0.00(0.01-0.03) 0.01±0.00 (0.004-0.008). ND 
 
Cu 0.0002±0.03(0.0001- 0.32±0.01(0.20-0.38) 0.0002±0.01(0.0001- 0.0001±0.00 (ND- ND 
0.0003) 0.0003) 0.0003) 
 
Cr ND 0.006±0.00(0.004-0.008) 0.001±0.00(0.002-0.001) 0.003±0.00 (0.0001- ND 
0.0005)  
Ni 0.02± 0.01 (0.01-0.03) 0.31±0.08(0.22-0.39) 0.29±0.03(0.28-0.233) 0.21±0.06(0.17-0.27) 0.02±0.01(0.01-
 0.03) 
Zn    
0.08± 0.02 (0.06-0.09) 0.49±0.01(0.27-0.93) 0.04±0.002(0.002-0.0.006) 0.002±0.00(0.001-0.005) 0.003±0.001(ND-
  0.01) 
As     
0.00006±0.00 0.0003±0.00 0.0002±0.00 0.00001±0.00 ND 
  
Hg 0.0002±0.00 0.0003±0.00 0.0002±0.00   ND 
 
Mn 0.4±0.05(0.42-0.52) 0.41±0.07(0.35-0.49) 0.38±0.33(0.37-0.43) 0.43±0.08(0.35-0.53) 0.2±0.02(0.17-
  0.22) 
Cd 0.3±0.02(0.11-0.54) 0.2±0.06 (0.12-0.54) 0.1±0.009(0.11-0.28) 0.2±0.04(0.12-0.29) 0.06±0.01(0.05-
 0.07) 
Pb 0.006±0.002  0.005±0.001 0.02±0.0001 0.003±0.000 0.0003±0.00 
 
Mean ± SD (Range) ADW: Adawotwe; ADT: Adum Tokoro; ASA: Asowuo Ayipa  
MM: Mpohor motorway  
Source: Field Data, Annan (2021) 
 
4.3 Factor Analysis of Physicochemical Parameters of Surface Water at Mpohor 
Wassa East District 
99 
 
Factor Analysis using Principal Component Analysis (PCA) and rotated with Varimax 
rotation with Kaiser Normalization using an Eigenvalue of 1 was used to identify which 
parameters mostly or significantly influence the water quality variations within and source 
apportionment at Mpohor Wassa East (Table 4.2). In all, a total of 25 principal components 
were extracted. However, the first six components cumulatively explained 88.611% of the 
total variance and were therefore retained in the model. These six principal components 
whose eigenvalues were greater than one (>1) were selected in accordance with Liang et 
al., (2014); Bhat et al., 2014; Winkler et al., (2013) and Köse (2016), who reported that for 
PCA analysis, any factor with an eigenvalue greater than one (>1) is considered significant 
for further analysis. Each of the remaining six factors had eigenvalues of less than one and 
were considered insignificant and were therefore rejected.  
The first principal component (PC-1) contributed 22.501% of the total variance and was 
strongly loaded by turbidity, Co, Cr, Cu, As and Cd and moderately loaded by total 
hardness, Ni, Zn, Hg, Mg, Pb and Fe. However, the 2nd principal component, PC-2, 
explains that 21.490% of the total variance is highly loaded by EC, TDS, salinity, Mn and 
Na and moderately loaded by total alkalinity, As, Mg, Pb and Cd. The 3rd principal 
component, PC-3, contributed 20.185% of the total variance and was strongly loaded by 
alkalinity, bicarbonates, Ca and Pb and moderately loaded by total Fe and Zn. 
Furthermore, the 4th component (PC-4) explains 9.273% of the total variance, and was 
strongly loaded by pH and moderately loaded by Zn. Again, the 5th component (PC-5) 
contributed 7.830% of the total variance and was highly loaded by TSS. The last principal 
component, which is PC-6, explains 7.330% of the total variance and is strongly loaded 
by Hg and K. With the exception of the parameters mentioned above that had strong and 
moderate factor loadings and contributed significantly to the variation in water quality 
parameters in the surface water, all other variables were observed to have weak factor 
100 
 
loadings. The communalities of the data set also revealed that with the exception of As that 
contributed 69.2% to the variation in water quality parameters, all the other individual 
physicochemical parameters contributed more than 70% each of the total variance. Table 
4.2 illustrates the rotated component matrix. For the purposes of making interpretation of 
the relevant factors, factor rotation was computed. Mostly, factors with loadings of 0.5 are 
qualified to be retained. However, to obtain stronger loadings, this study adopted a factor 
loading of 0.5 as the minimum and classified as moderately loaded. 
Table 4. 2: Rotated Component Matrix of physico-Chemical Parameters, Mpohor 
Wassa East 
Variable                                                Components  communalities 
 PC1 PC2 PC3 PC4 PC5 PC6  
Temperature -0.169 0.149 -0.119 0.409 -0.205 0.142 0.782 
EC 0.135 0.912* -0.117 0.134 0.211 0.147 0.947 
TDS 0.255 0.720* -0.076 -0.037 0.507 0.049 0.850 
pH 0.395 -0.188 0.278 0.742* -0.004 -0.161 0.846 
Salinity 0.286 0.855* 0.188 -0.260 -0.065 -0.023 0.921 
TSS -0.140 0.327 -0.110 -0.041 0.875* 0.242 0.965 
Alkalinity 0.063 -0.005 0.970* -0.011 0.046 -0.055 0.950 
Bicarbonates 0.098 0.023 0.965* -0.069 0.085 0.046 0.956 
Turbidity 0.894* 0.111 0.042 0.342 0.154 -0.026 0.954 
Total Hardness 0.609** 0.694** 0.200 -0.150 0.142 0.120 0.950 
Fe -0.553** -0.001 0.689** 0.164 -0.157 -0.105 0.842 
Co 0.750* 0.040 -0.436 -0.301 0.032 0.309 0.941 
Cu 0.910* 0.223 0.176 -0.216 -0.160 0.018 0.981 
Cr 0.961* 0.180 0.143 0.051 -0.054 -0.005 0.982 
Ni 0.553** 0.227 -0.315 -0.254 0.458 0.214 0.777 
Zn 0.566** 0.305 0.052** -0.661** -0.338 0.068 0.971 
As 0.723* 0.585** 0.033 0.347 0.087 0.393 0.692 
Hg 0.624** 0.197 -0.531 0.121 0.005 0.715** 0.917 
Mg 0.669** 0.656** 0.174 -0.081 0.059 0.111 0.930 
Mn 0.107 0.732* -0.552 -0.141 0.057 0.150 0.898 
Ca 0.254 -0.157 0.903* -0.071 -0.113 -0.018 0.923 
K 0.075 0.064 0.254 -0.065 0.267 0.776* 0.751 
Na 0.399 0.760* -0.001 -0.039 0.461 0.175 0.982 
Pb 0.603** -0.540** -0.721* -0.173 0.288 0.321 0.737 
Cd 0.757* 0.670** -0.370 0.095 -0.138 -0.301 0.708 
Eigenvalue 8.530 5.218 3.308 2.082 1.806 1.209  
% of variance 22.501 21.490 20.185 9.273 7.830 7.330  
% Cumulative 22.501 43.992 64.177 73.450 81.281 88.611  
*Strong (factor score>0.75); **Moderate (0.5≤score≤0.75), Weak (Score<0.5) 
Source: Field data, Annan (2021) 
 
4.3.1 Scree plot 
Scree plot is a technique that can be used to determine the exact number of factors to retain 
for further analysis. The scree plot helps the researcher to know the number of principal 
components that summarize the data by plotting a graph. According to Cartell (1997) the 
101 
 
scree plot gives a plot of the eigenvalues of the various factors. The elbow effect is always 
used to determine the exact number of factors to extract. It is a rule of thumb that all factors 
above the elbow are to be retained. In this study, even though six factors had eigenvalues 
> 1 the elbow effect was very clear at the fourth factor (Figure 8). 
 
Figure 8: Scree plot of physico-chemical parameters, Mpohor Wassa East 
 
 
4.4 Correlation of Physico-Chemical Parameters of Surface Water at Mpohor 
Wassa East District 
To investigate the association, the direction and strength of the physico-chemical 
parameters of the surface water samples from Mpohor Wassa East sampling sites, Karl 
Pearson’s correlation coefficient was used. Table 4.3 shows the correlation matrix of the 
physico-chemical parameters. Significant positive correlation was observed between the 
following physico-chemical variables; EC and TDS (r=0.820), EC and salinity (r=0.720), 
EC and total hardness (r=0.727), EC and manganese (r=0.737), EC and magnesium 
(r=0.708), EC and Na (r=0.870), Alkalinity and Bicarbonates (r=0.983), TDS and TSS 
(r=0.632), Salinity and Total hardness (r=0.790), Ca and hardness (r=0.884), Ca and 
bicarbonates(r=0.888), Na and TDS (r=0.910). There was a negative weak correlation 
between pH and all heavy metals analyzed in surface water samples obtained from the 
Mpohor Wassa Sampling site.  
102 
 
Table 4.3: Correlation between Physicochemical Parameters in Water Samples, 
Mpohor Wassa East District 
Parameter              
EC TDS TSS Sal Na Mg Ca Alkal Bicar Hardness 
EC 1          
TDS 0.820** 1         
TSS 0.487 0.632* 1        
Sal 0.720* 0.646* 0.195 1       
Na 0.870** 0.940** 0.631* 0.711* 1      
Mg 0.708  0.700* 0.171 0.754* 0.837* 1     
Ca -0.280 0.228 -0.285 0.122 0.070 0.232 1    
Alkalinity -0.112 0.078 -0.091 0.202 0.016 0.166 0.884* 1   
Bicarbonates -0.049 -0.022 -0.039 0.232 0.096 0.235 0.888* 0.983** 1  
Hardness 0.727 0.775 -0.265 0.790* 0.248 0.977* 0.211 0.210  1 
**Correlation is significant at p < 0.01 level (2-tailed).  
*Correlation is significant at p < 0.05 level (2-tailed) 
Source: Field data, Annan (2021) 
 
4.5 Soil Physical Parameters, Mpohor Wassa East District 
4.5.1 pH 
Soil samples from sampling site ADW Mpohor at Wassa East recorded the lowest pH with 
mean value of 4.6±0.4 whilst the highest value was recorded in soils samples from the 
control sample with mean value of 7.3±0.3 (Table 4.4). Analysis of variance at 95% 
confidence level revealed a statistically significant difference in pH among soil samples 
obtained from the sampling sites (p=0.0001) (Appendix A).  
4.5.2 Conductivity  
The highest value of conductivity was recorded in soils samples obtained from ADW with 
mean value of 38.9±5.9 µS/cm whilst the lowest value was recorded in soils samples from 
the control samples with mean value of 14.2±1.7µS/cm (Table 4.4). There was a 
statistically significant difference in conductivity among soil samples obtained from the 
sampled sites (P =0.0001). 
4.5.3 Available phosphorus  
At Mpohor Wassa East, the highest available phosphorus in soil was recorded in soil 
samples obtained from the control sampling site with mean value of 43.3±3.9 mg/Kg 
whilst the lowest value was recorded in soil samples from MM sampling site with a mean 
103 
 
value of 18.4±4.2mg/Kg (Table 4.4). There was a statistically significant difference in 
available phosphorus levels among soil samples obtained from the sampled sites 
(p=0.0001). 
4.5.4 Organic Carbon (%) 
Table 4.4 shows the mean values of % organic carbon in soil obtained from Mpohor Wassa 
East sampling sites. The highest percentage organic carbon was recorded in soils from the 
control site with mean value of 5.8±0.94 % whilst the lowest value was recorded in ADT 
sampling site with mean value of 0.54±0.2%. There was a statistically significant 
difference in % organic carbon among soils from the various sampling sites (p = 0.0001).  
4.5.5 Percentage Sand, Silt and Clay 
With reference to Mpohor Wassa East, the highest value of % sand was recorded in soils 
from control sampling site with mean value 49.5±2.7 % whilst the lowest was recorded in 
ADT sampling site with mean value of 35.8±7.2 %. The percentage sand differed 
significantly among the sampling sites at 95% confidence level (p=0.04) (Appendix A).  
The highest percentage of silt was recorded in soils from the control sampling site with 
mean value 47.0±5.3 % whilst the lowest was recorded in soil samples from ADT with 
mean value of 33.8±6.1%. There was a statistically significant difference in percentage silt 
among soil samples obtained from the various sampling sites (p=0.01) (Appendix A). 
The highest percentage clay was recorded in soils obtained from control sampling sites 
with mean value of 37.3±1.8% whilst the lowest was recorded at ADT sampling site with 
mean value of 14.4±2.0%. The percentage clay in soil samples obtained from the sampling 
sites daggered significantly (p=0.001) (Appendix A).  
104 
 
4.5.6 Exchangeable K, Ca, Mg and Na  
The highest exchangeable potassium in soil was recorded in the control site with mean 
value of 1.4±0.32 cmol (+)/Kg whilst the lowest value with mean value of 0.06±0.01 cmol 
(+)/Kg each was recorded at sampling sites ADT and MM. There was no statistically 
significant difference in exchangeable potassium in soils obtained from Mpohor Wassa 
East sampling sites (p=0.0001) (Appendix A).  
The control sampling site recorded the highest exchangeable calcium in soil and ranged 
from mean value of 8.1±1.8Cmol (+)/Kg whilst the lowest value was recorded in sampling 
site ASA with mean value of 4.71±0.8 cmol (+)/Kg. The exchangeable calcium differed 
significantly among the sampling sites at 95% confidence level (p=0.0001) (Appendix A).  
The highest exchangeable magnesium in soil was recorded in control sample sites with 
mean value of 9.52±0.56 cmol (+)/Kg whilst the lowest value was recorded at ASA sample 
site AS with mean value of 4.7±0.8Cmol (+)/Kg. Analysis of variance revealed a 
statistically significant difference in exchangeable magnesium in soils obtained from the 
sampling sites (p=0.0001).  
The highest exchangeable sodium in soil was recorded in soil samples obtained from ASA 
sample site with mean value of 0.70±0.3 cmol (+)/Kg whilst the lowest value ranged from 
control sample site with mean value 0.23±0.04 cmol (+)/Kg. There was no statistically 
significant difference in exchangeable sodium in soils obtained from the sampling sites 
(p=0.14). 
  
105 
 
Table 4.4: Physico-Chemical Parameters in Soil, Mpohor Wassa East District 
 ASA ADT MA MM ADW Control 
Cr 0.69±0.015 0.75±0.75(0.51- 0.67±0.14(0.4 0.70±0.17(0.41- 0.64±0.11(0 0.07±0.01(ND-
(0.41-0.0.95) 0.95) 6-0.95) 0.94) .46-0.85) 0.11) 
As 5.5±0.7 (4.1- 5.8±0.8(3.8-6.9) 5.5±0.5(2.5- 5.3±0.6(1.8-6.2) 4.8±0.3(2.7 ND 
6.7) 6.1) -6.6) 
Cd 32.2±1.8 (18.9- 30.7±1.2(20.0- 32.8±2.1(22.0 34.8±2.2(18.7- 31.6±2.2(22 ND 
50.6) 38.7) -50.8) 50.9) .1-50.2) 
Hg 5.7±0.8(4.8- 6.4±0.4(5.5-6.7) 5.5±0.9(4.2- 4.6±0.7 (4.4-6.4) 5.7±0.9 ND 
6.9) 6.5) (4.1-6.9 
Ni 0.51± 0.15 0.65±0.11(0.42- 0.47±0.12(0.2 0.48±0.16(0.21- 0.32±0.11(0 0.10±0.01(0.05-
(0.31-0.72) 0.92) 7-0.75) 0.82) .28-0.62 0.18) 
Zn 41.0± 2.2 36.9±2.2(22.7- 42.9±2.9(22.3 42.7±2.4(29.5- 41.7±2.8(22 70.3±2.2(65-73.2) 
(22.5-57.9) 49.2) -57.6) 57.8) .8-57.6 
Cu 0.68± 0.1 0.67±0.1 (0.53- 0.67±0.1(0.56 0.65±0.2(0.53- 062±0.22(0. ND-0.005 
(0.43-0.87) 0.93) -0.84) 0.88) 51-0.0.89) 
Pb 1.2± 0.3 (0.58- 1.3±0.5(0.50- 1.1±0.3(0.55- 1.1±0.4(0.54- 1.2±0.4(0.5 ND 
1.77) 1.93) 1.73) 1.86) 8-1.74) 
Fe 3916±201(229 3940±144(2869- 3913±248(23 3878±223(2395- 4048±146(2 1534±128(1352-
5-5287) 5277) 95-5277) 5277) 495-5267) 1824 
Co 5.7±1.3(3.2- 6.4±0.9 (4.7-7.8) 5.7±1.4(3.1- 5.2±0.9(3.3-7.2) 5.8±1.6(3.5 0.01±0.0(ND-
9.0) 9.0) -9.5 0.04 
 
Ca 2.4±0.8 (1.4- 3.4±0.8 (1.7-4.6) 2.5±0.9(1.3- 2.4±0.9(1.4-4.3) 2.4±0.9(1.5 8.1±1.8(5.2-11.5) 
4.7) 4.9) -4.6) 
Mg 4.71±0.8(3.2- 4.94±0.6(4.1- 4.76±0.9(3.1- 4.91±0.8(3.1- 4.82±0.8(3. 9.52±2.4(7.9-
6.8) 6.2) 6.2) 6.4) 3-6.8) 12.3) 
Na 0.70±0.3 (0.35- 0.63±0.1(0.32- 0.69±0.3(0.37 0.59±0.2(0.34- 0.76±0.4(0. 0.23±0.04(0.19-
1.81) 0.93) -1.71) 0.87) 35-1.91) 0.28) 
K 0.07±0.01(0.05 0.06±0.01(0.004 0.07±0.01(0.0 0.06±0.01(0.05- 0.07±0.01(0 1.4±0.32(0.04-
-0.09) -0.08) 5-0.08) 0.09 .05-0.09 0.08) 
  
% silt 39.5±6.2(28.0- 33.8±6.1(23.0- 38.4±3.6(32.0 39.1±5.6(28.0- 39.2±3.3(33 47.0±5.3(42.0-
54.0) 44.0) -44.0) 49.0) .0-44.0) 52.8 
% sand 37.2±7.1(18.0-    36.2±4.5(34 49.5±2.7(46.0-
49.0) 35.8±7.2(24.0- 37.9±5.2(32.0 36.3±7.6(17.0- .0-45.0) 53.3) 
47.0) -45.0) 42.0) 
% clay 27.4±1.9 (12.0-    27.9±1.2(25  
48.0) 37.3±1.8(22.0- 28.1±1.2(22.0 28.6±2.5(15.0- .0-38.0) 14.4±2.0(11.7-
49.0) -38.0) 46.0) 18.5) 
% OC 0.73±0.1(0.22- 0.54±0.2(0.25-   0.70±0.2(0.  
0.97) 0.98) 0.73±0.2(0.23 0.74±0.1(0.26- 27-0.91) 5.8±0.9(4.88-
  -0.28) 0.97) 6.40) 
EC 38.4±5.5 (31.3- 37.7±5.3(32.3- 38.1±5.3(30.3 38.3±5.9(31.3- 38.9±5.9(34  
49.5) 47.4) -55.5) 46.5) .3-43.5) 14.2±1.7(12.3-
15.8) 
 4.7±0.4(4.1-      
pH 5.4) 4.9±0.5(4.2-5.2) 4.8±0.5 (4.4- 4.6±0.4(4.5-5.5) 4.8±0.4(4.6 7.2±0.3(6.9-7.5) 
5.1) -5.9) 
     20.6±4.8(13  
Avail P 20.2±4.9(11.6- 24.8±3.2(18.4- 20.5±4.9(13.5 18.4±4.2(11.2- .8-29.6) 43.3±3.9(39.5-
29.8) 29.8) -29.8) 25.7) 47.4) 
 
     0.05±0.011(  
Total N 0.06±0.01(0.02 0.06±0.02(0.02- 0.05±0.01(0.0 0.06±0.01(0.04- 0.03-0.08) 4.4±0.7(3.8-5.2) 
-0.08) 0.09) 1-0.07) 0.09) 
 
Mean ± SD (Range) ND: Not detected 
Source: Field data, Annan (2021) 
 
 
 
106 
 
4.6 Geoaccumulation Index of Heavy Metal Contaminations in Soils, Mpohor 
Wassa East District 
 From Mpohor Wassa East, the Igeo values recorded for Cr ranged from a minimum of 0.1 
in soils at control sample site and the highest value of 2.6 was recorded at MM (Mpohor 
Motorway) sampling site.  The Igeo values for Arsenic levels ranged from 0.3 in soils at 
control sampling site to a maximum of 2.5 at sampling site ADT. The Igeo values for Cd 
ranged from a minimum of 0.2 at control sampling site to a maximum of 3.2 at Sampling 
site MM. The Igeo values recorded for mercury varied from a minimum of 0.05 in soils at 
the control site and the highest value of 2.3 was recorded at sample site ASA. The Igeo 
values recorded for nickel also ranged from a minimum of 0.1 in soils at the control sample 
site and the highest value of 0.8 was recorded at sampling site ASA. The Igeo values for 
cobalt ranged from 0.1 in soils at control sample sites and the highest value of 1.1 was 
recorded in soils sampled at ADT sampling site. The Igeo values recorded for copper 
varied from a minimum of 0.5 in soils at control sampling and the highest value of 2.8 was 
recorded in soils at sampling site MM. Lastly, the Igeo values recorded for lead ranged 
from a minimum of 0.6 in soils at the control sampling site and the highest value of 2.2 
was recorded in soils at sampling site MM (Table 4.5). However, Table 4.12 shows the 
Igeo classification with their absolute values.   
  
107 
 
Table 4.5: Geoaccumulation Index (Igeo) Values for Soil Samples in Mpohor 
Wassa District 
Community Cr As Cd Hg   Ni Co   Cu Pb    
ASA 1.3 1.8 1.9 2.3 0.8 0.4 1.2 1.4  
ADT 2.2 2.5 1.5 1.4 0.6 1.1 1.7 1.9  
ADW 2.3 1.8 1.5 2.1 1.7 0.8 1.9 1.2  
MA 1.7 1.2 2.7 1.3 0.4 0.8 2.4 2.1  
MM 2.6 1.6 3.2 1.8 0.5 0.5 2.8 2.2 
Control 0.1 0.3 0.2 0.05 0.1 0.1 0.5 0.6 
 
Source: Field data, Annan (2021) 
 
4.7 Contamination Assessment and Hazard Rating of Heavy Metals in Soils, 
Mpohor Wassa East District 
Table 4.6 gives the threshold exceedance ratio and the percentage mobility of the various 
heavy metals in soil samples in selected sampling sites at Mpohor Wassa East District 
sampling site in the Western region. The results showed that cobalt has a percentage 
mobility of 5.2, 4.8, 4, 4.3 and 5.0% for sampling sites ADW, ADT, ASA, MM, and MA 
respectively. Copper has percentage mobility of 22.9, 37.8, 30.7, 18.6 and 29.2 
respectively. Chromium has % mobility of 3.8, 5.8, 8.8,4.9 and 9.4 respectively. Nickel 
has % mobility of 8.9, 5.1, 9.3, 2.4, and 3.3% respectively. Arsenic has percentage mobility 
of 15.9, 8.6, 8.3, 9.7and 14.7% respectively. 
  
108 
 
Table 4.6: Hazard Rating of Heavy Metals in Soil, Mpohor Wassa Sampling Site 
Parameter ADW ADT ASA MM MA 
Cobalt      
NH4NO3 Extractable 6.8 14.8 9.5 11.7 9.2 
HNO3 Extractable 3.5 8.2 6.3 7.2 4.9 
TER 0.18 0.395 0.25 0.312 0.245 
MOB 0.05 0.048 0.041 0.043 0.05 
MOB (%) 5.2 4.8 4.1 4.3 5.0 
Copper      
NH4NO3 Extractable 2.48 1.62 1.52 1.43 1.85 
HNO3 Extractable 0.48 0.19 0.22 0.34 0.28 
TER 0.11 0.072 0.07 0.063 0.082 
MOB 0.229 0.378 0.307 0.186 0.292 
MOB (%) 22.9 37.8 30.7 18.6 29.2 
Chromium      
NH4NO3 Extractable 0.72 1.38 2.92 1.72 1.85 
HNO3 Extractable 0.28 0.35 0.49 0.52 0.29 
TER 0.011 0.021 0.043 0.026 0.027 
MOB 0.038 0.058 0.088 0.049 0.094 
MOB (%) 3.8 5.8 8.88 4.9 9.4 
Nickel      
NH4NO3 Extractable 1.46 1.48 1.65 0.82 0.92 
HNO3 Extractable 0.25 0.44 0.27 0.54 0.42 
TER 0.022 0.023 0.025 0.013 0.014 
MOB 0.089 0.051 0.093 0.023 0.033 
MOB (%) 8.9 5.1 9.3 2.4 3.3 
Arsenic      
NH4NO3 Extractable 5.85 4.92 4.82 3.91 4.98 
HNO3 Extractable 2.37 3.68 3.76 2.58 2.18 
TER 0.377 0.317 0.311 0.252 0.321 
MOB 0.159 0.086 0.083 0.097 0.147 
MOB (%) 15.9 8.6 8.3 9.7 14.7 
Mercury      
NH4NO3 Extractable 6.14 4.29 7.22 5.84 5.91 
HNO3 Extractable 5.48 3.27 4.28 3.63 2.75 
TER 2.456 2.552 3.276 2.28 1.73 
MOB 0.314 0.346 0.228 0.349 0.282 
MOB (%) 31.4 34.6 22.8 34.9 28.2 
Cadmium      
NH4NO3 Extractable 35.9 27.3 55.2 62.9 54.3 
HNO3 Extractable 19.2 17.4 45.8 47.4 38.9 
TER 2.53 1.73 5.47 6.25 4.28 
MOB 0.163 0.123 0.173 0.152 0.143 
MOB (%) 16.3 12.3 17.3 15.2 14.3 
Lead      
NH4NO3 Extractable 1.43 1.82 0.95 1.35 0.76 
HNO3 Extractable 0.38 1.22 0.26 0.21 0.35 
TER 0.08 0.05 0.09 0.05 0.032 
MOB 0.180 0.072 0.146 0.161 0.142 
MOB (%) 18.0 7.2 14.6 16.1 14.2 
Source: Field data, Annan (2021) 
109 
 
4.7.1 Physical Parameters of Surface Water, Amansie West District 
The mean pH values of the surface water sample (Suben river) from Amansie West ranged 
from 4.6± 0.25 SD at sampling site DO (Domenase community) to 7.8± 0.5 SD at the 
control sampling site (Manso) (Figure 9). The pH of surface water samples differed 
significantly among the sampling locations at a 95% confidence level (F = 64.835; p = 
0.0001) (Appendix E). The pairwise comparison using Tukey’s HSD showed that there 
were differences among the following sampling locations: DO and control (p = 0.001); 
MN (Manso) and control (p = 0.002); AS (Asaman community) and control (p = 0.017). 
There were, however, no statistically significant differences between sampling sites DO 
and AS (p = 0.819) and DO and MN (p = 0.549) (Appendix F). 
 
10
9
7.8
8
7
6
4.9 4.7
5 4.6
4
3 GS 1212:2019
2 limit
1
0
DO AS MN Control
Sampling site
 
 
Figure 9: pH Variations across Surface Water Locations at Amansie West District 
 
4.7.2 Electrical Conductivity 
With reference to the Amansie West District, the highest conductivity value was recorded 
at sampling site MN (Manso Nkwanta) with a mean value of 484.8±34.4 μS/cm and the 
lowest was recorded at the control sampling site with a mean value of 42.7±4.5 S/cm 
(Figure 10). There was a statistically significant differences in conductivity among 
sampling sites at 95% confidence level (F=284.146; p=0.0001) (Appendix E). Pair wise 
110 
 
pH
comparison using Tukey’s HSD revealed statistically significant differences among the 
following; DO and control (p=0.001), MN (Manso Nkwanta) and control (p=0.002), AS 
(Asaman community) and control (p=0.017). There were also differences between DO and 
AS and DO and MN (p=0.029) (Appendix F). 
600
443.7 484.8
500
400
277.3
300
200 GS 1212:2019 Limit
100 42.7
0
DO AS MN Control
Sampling  Site
 
Figure 10: Conductivity Variations across Surface Water Locations, Amansie West 
District 
 
4.7.3 Total Dissolved Solids 
At Amansie West District, the highest conductivity value was recorded in surface water 
samples at sampling site MN (Manso Nkwanta) with mean value of 226±20.7mg/L and 
the lowest was recorded at the control sampling site with mean value of 25.33± 6.4mg/L 
(Figure 11). There was statistically significant difference in conductivity among sampling 
sites at 95% confidence level (F=124.523; p=0.0001) (Appendix D). Pairwise comparison 
using Tukey’s HSD are shown in (Appendix F).  
1200
1000
800
600
400 226 221.2 GS 1212:2019 limit
200 132.3 25.3
0
DO AS MN Control
Sampling site
 
111 
 
Mean TDS (mg/L) Mean EC(µS/cm)
Figure 11: TDS Variations across Surface Water Locations, Amansie West District 
 
4.7.4 Total Suspended Solids 
At Amansie West District sampling site in the Ashanti Region, the highest TSS value was 
recorded in surface water samples at sampling site AS with mean value of 390.7±19.4mg/L 
and the lowest was recorded at the control sampling site with mean value of 12.5± 0.7mg/L 
(Figure 12). There was statistically significant difference in TSS among sampling sites at 
95% confidence level (F=191.9; p=0.0001) (Appendix E). Pair wise comparison using 
Tukey’s HSD revealed statistically significant differences among the following; DO and 
control (p=0.001), DO and AS (p=0.001). There were however no differences between DO 
and MN (p=0.830) (Appendix F). 
600
390.7
400
247.3 261
200
12.5 GS 1212:2019 limit
0
DO AS MN Control
Sampling site
 
Figure 12: TSS Variations Across Surface Water Locations, Amansie West District 
 
4.7.5 Total Alkalinity 
The highest total alkalinity of the surface water samples from Amansie West District was 
recorded at control sampling site with mean value of 53.3±9.01mg/L whilst the lowest was 
recorded at sampling site DO with mean value of 2.2 ± 0.72mg/L (Appendix D). The total 
alkalinity of the water samples differed significantly among sampling sites (F=95.408, 
p=0.0001) (Appendix E). The pairwise comparison using Tukey’s HSD showed that there 
were differences among the following sampling sites; DO and control (p=0.001), MN 
(Manso) and control(p=0.0001), AS (Asaman community) and control (p=0.0001). There 
were however, no statistically significant differences between sampling sites DO and AS 
and DO and MN as shown in (Appendix F). 
112 
 
Mean TSS (mg/L)
4.7.6 Dissolved Oxygen (DO) 
The highest DO of the surface water samples from Amansie West District was recorded at 
control sampling site with mean value of 11.8±2.12mg/L whilst the lowest was recorded 
at sampling site DO with mean value of 4.3 ± 0.56 mg/L (Appendix D). The Dissolved 
oxygen differed significantly among sampling sites at 95% confidence level (p=0.001) 
(Appendix E). There were differences among all sampling sites and the control samples 
(p<0.05) (Appendix F). 
4.7.7 Biological Oxygen demand (BOD) 
The lowest BOD of the surface water samples from Amansie West District was recorded 
at control sampling site with mean value of 1.4±0.06mg/L whilst the highest was recorded 
at sampling site DO (Dominase) with mean value of 2.5±0.57 mg/L (Appendix D). The 
BOD did not differ significantly among sampling sites (p=0.530) (Appendix E). 
4.7.8 Salinity 
The lowest salinity of the surface water samples from Amansie West district was recorded 
at control sampling site with mean value of 0.00mg/L whilst the highest was recorded at 
sampling site AS with mean value of 0.3 ± 0.01mg/ l (Appendix D). The salinity of the 
water samples differed significantly among sampling sites (p=0.008) (Appendix E).  
4.7.9 Total Hardness 
At Amansie West District sampling site in the Ashanti Region, the highest total hardness 
in water samples was recorded at sampling site MN with mean value of 935.2±48.4mg/L 
and the lowest was recorded at the control sampling site with mean value of 11.9±4.2mg/L. 
There was statistically significant difference in total hardness among sampling sites 
(p=0.0001) (Appendices E and E). 
113 
 
4.7.10 Turbidity 
The mean turbidity values of the surface water sample from Amansie West District ranged 
from a minimum of 2.1±0.7 SD at the control sampling site to a maximum of 822.7± 70.59 
SD at sampling site AS. The turbidity values differed significantly among sampling sites 
(p=0.001) (Appendices E and E). 
4.8 Heavy Metals in Water, Amansie West District 
The heavy metals in surface water samples obtained from Amansie West District sampling 
sites are as illustrated in Table 4.7. The lowest Fe concentration was recorded in water 
samples obtained from MN sampling site and ranged from 1.07 to 1.53mg/L with mean 
value of 1.36±0.25 SD mg/L whilst the highest Fe concentrations was recorded at sampling 
site AS and varied from (9.73 to 12.28mg/L with mean value of 30.7±2.41 SD mg/L.  The 
lowest Co concentration was recorded in water samples obtained from control sampling 
sites with values below detection limit (<0.0001) and the highest was recorded at sampling 
site DO and ranged from 0.13 to 0.27mg/L with mean value of 0.19±0.07SD mg/L.  
The lowest Cu concentration was recorded in water samples obtained from control 
sampling sites with values below detection limit (<0.0001) and the highest was recorded 
at sampling site DO and ranged from 0.17 to 0.23mg/L with mean value of 0.20±0.03SD 
mg/L.  
With regards to Cr, it was not detected at control sampling sites and the highest value was 
recorded at sampling site AS and ranged from 0.01 to 0.04 mg/L with mean value of 
0.02±0.00 SD mg/L. The lowest Ni concentration was recorded in water samples from 
control sampling sites and ranged from 0.01 to 0.03mg/L with mean value of 
0.02±0.001SD mg/L whilst the highest was recorded at sampling site DO and ranged from 
0.52 to 0.63mg/L with mean value of 0.59± 0.06 SD mg/L. 
114 
 
The lowest Zn concentration was recorded in water samples from control sampling sites 
and ranged from ND to 0.01mg/L with mean value of 0.003±0.001 SD mg/L whilst the 
highest was recorded at sampling site AS and ranged from 0.35 to 0.45 mg/L with mean 
value of 0.39±0.05 SD mg/L. Mercury (Hg) was not detected at the control sampling sites 
whilst the highest Hg concentrations were recorded at sampling site AS and ranged from 
0.0019 to 0.0037 mg/L with mean value of 0.0028±0.00 SD mg/L. The lowest Mn 
concentration was recorded in water samples from control sampling sites and ranged from 
0.17 to 0.22mg/L with mean value of 0.2±0.02 SD mg/L whilst the highest was recorded 
at sampling site AS and ranged from 14.22 to 15.63 mg/L with mean value of 14.9±0.7 
SD mg/L (Table 4.7).  
Last but not the least, the lowest Cd concentration was recorded in water samples from 
control sampling sites and ranged from 0.03 to 0.06 mg/L with mean value of 0.04±0.01 
SD mg/L and the highest was recorded in water samples at sampling site AS and ranged 
from 0.27 to 0.39 with mean value of 0.33±0.03SD mg/L. 
Lastly, the lowest Pb concentration was recorded in water samples from control sampling 
sites with mean value of 0.0002±0.00 mg/L and the highest was recorded in water samples 
at sampling site MN and ranged from 0.005 to 0.04 mg/L with mean value of 0.02±0.01 
SD mg/L. 
  
115 
 
Table 4.7: Heavy Metals in Surface Water, Amansie West District  
Var DO AS MN Control 
Fe 10.7±1.36(9.73-12.28) 30.7±2.41(9.73-12.28) 1.36±0.25(1.07-1.53) 22.0±6.7(15.17-28.67) 
 
Co 0.19±0.07 (0.13-0.27) 0.13±0.008(0.13-0.14) 0.003±0.00(ND-0.0001) <0.0001 
 
Cu 0.20±0.03(0.17-0.23) 0.16±0.07(0.113-0.24) 0.04±0.01(0.03-0.04) <0.0001 
 
Cr 0.02±0.00 (0.01-0.03) 0.02±0.00(0.01-0.04) 0.01±0.00(0.01-0.02) ND 
 
Ni 0.59± 0.06 (0.52-0.63) 0.73±0.11(0.63-0.85) 0.06±0.01(0.05-0.07) 0.02±0.001(0.01-0.03) 
 
Zn   0.03±0.01(0.01-0.04)  
0.38± 0.04 (0.35-0.42) 0.39±0.05(0.35-0.45)  0.003±0.001(ND-0.01) 
As 0.004±0.00(0.003-0.004) 0.004±0.00(0.003-0.005) 0.011±0.00(0.002-0.003) 0.0001±0.00 
 
Hg 0.0028±0.00(0.0019-0.0037) 0.0027±0.00(0.002-0.003) 0.0004±0.00 (0.002-0.006) ND 
 
Mn 14.0±1.3(12.68-15.28) 14.9±0.7(14.22-15.63) 13.4±0.8(12.65-14.32) 0.2±0.02(0.17-0.22) 
 
Cd 0.30±0.07 (0.18-0.50) 0.33±0.03 (0.27-0.39) 0.23±0.06(0.16-0.28) 0.04±0.01(0.03-0.06) 
 
Pb 0.002±0.00 (0.001-0.003) 0.004±0.00 (0.003-0.005) 0.02±0.01(0.005-0.04) 0.0002±0.00 
Mean ± SD (Range) DO: Dominase, AS: Asaman   MN: Manso Nkwanta  
Source: Field data, Annan (2021)    
 
4.9 Factor Analysis  
Principal Component Analysis (PCA) is a data reduction technique. It checks the features 
of the data that are redundant and condenses the information into principal components 
without losing information from the original data (Pallant, 2005). Factor Analysis (FA) is 
an interdependent approach that defines the underlying variables or dimensions that 
explain most of the variance in the large data set. In this study, PCA was computed to 
reduce the large data set of physico-chemical parameters into principal components. After 
which Factor Analysis was computed to find the distinct number of factors to retain.  
A data set must satisfy some statistical assumptions before factor analysis can be computed 
(Pallant, 2005). One of the assumptions to determine whether a data set is suitable for 
factor analysis is the sample size. A sample size of 150 or more is ideal for factor analysis 
(Pallant, 2005). Furthermore, it is a rule of thumb that a correlation coefficient of r = 0.3 
or better is required for factor analysis (Pallant, 2005). Kaiser Meyer-Olkin (KMO) and 
Bartlett’s test of sphericity can also be used to determine the factorability of data. Bartlett's 
116 
 
test of sphericity must be significant (p < 0.05), before factor analysis can be computed 
(Pallant, 2005). The coefficient of the KMO test must be between 0 and 1, with 0.6 as the 
threshold for factor analysis (Tabachnick & Fidell, 2007). The data set for this study 
satisfied all the assumptions stated above. Bartlett’s test of sphericity was found significant 
(p= 0.000) and the coefficient of the KMO test was also high (0.817). 
4.9.1 Factor Analysis of Physicochemical Parameters of Surface Water, Amansie 
West District 
In all, a total of 25 principal components were extracted from Amansie West District; 
however, the first five components cumulatively explained 95.327% of the total variance 
and were therefore retained in the model. The first principal component (PC-1) contributed 
43.835 % of the total variance and was strongly loaded by the following variables; EC, 
TDS, pH, salinity, TSS, Alkalinity, Bicarbonates, Total hardness, Cr, Hg, Mg, Mn, Ca and 
K. Nickel, As, Pb, and Cd were also moderately loaded in the first PC-1.  The 2nd principal 
component, PC-2 contributed 29.484 % of the total variance and was highly loaded by 
turbidity, Co, Cu, Ni, Zn, As, Hg and Na.  The 3rd principal component, PC-3 contributed 
8.863 % of the total variance and was strongly loaded by two heavy metals; Arsenic and 
lead. The 4th component (PC-4) explains 8.213% of the total variance and was strongly 
loaded by only iron (Fe). The 5th component (PC-5) contributed 4.932% of the total 
variance and all variables were observed to have weak factor loadings. The communalities 
of the data set also revealed that the individual physicochemical parameters contributed 
more than 70% each of the total variance (Table 4.8). Concerning the scree plot, even 
though five factors had eigenvalues > 1 the elbow effect was very clear at the fourth factor 
(see Figure 13). 
  
117 
 
Table 4.8: Component Matrix of physico-Chemical parameters, Amansie West  
    Variable           Components 
     PC1 PC2 PC3 PC4 PC5 Communalities 
Temperature 0.197 -0.082 0.288 -0.049 0.427 0.950 
EC 0.972* 0.055 0.205 -0.057 0.064 0.997 
TDS 0.935* 0.129 0.306 0.058 0.048 0.990 
pH -0.878* -0.338 -0.127 0.248 -0.127 0.980 
Salinity 0.872* 0.236 -0.210 0.218 0.177 0.940 
TSS 0.874* 0.432 0.109 0.133 -0.049 0.983 
Alkalinity -0.821* -0.427 -0.198 0.248 0.056 0.961 
Bicarbonates -0.830* -0.401 -0.184 0.287 0.005 0.967 
Turbidity 0.417 0.794* -0.092 0.402 -0.042 0.976 
Total Hardness 0.939* 0.268 0.166 0.088 -0.010 0.989 
Fe -0.177 0.291 -0.232 0.892* -0.072 0.970 
Co 0.154 0.948* -0.120 -0.068 0.038 0.943 
Cu 0.316 0.900* -0.080 -0.020 0.150 0.939 
Cr 0.743* 0.199 -0.127 -0.249 0.320 0.773 
Ni 0.670** 0.874* -0.116 0.283 -0.017 0.994 
Zn 0.310 0.927* -0.103 0.157 -0.059 0.994 
As 0.725** 0.747* 0.912* -0.080 0.203 0.932 
Hg 0.818* 0.909* -0.011 0.096 -0.136 0.955 
Mg 0.858* 0.361 0.257 -0.120 0.025 0.948 
Mn 0.815* 0.496 0.203 -0.186 0.058 0.990 
Ca -0.825* -0.303 -0.172 0.284 -0.246 0.942 
K 0.811* 0.397 -0.010 0.408 -0.062 0.986 
Na 0.313 0.774* 0.055 -0.526 -0.032 0.978 
Pb 0.538** -0.336 0.821* -0.219 0.089 0.958 
Cd 0.583** 0.568** 0.234 0.014 -0.284 0.799 
Eigenvalue 15.136 4.776 1.779 1.115 1.026  
% of variance 43.835 29.484 8.863 8.213 4.932  
% Cumulative 43.835 73.318 82.181 90.394 95.327  
*Strong (factor score>0.75); **Moderate (0.5≤score≤0.75), Weak 
Source: Field data, Annan (2021) 
 
118 
 
 
Figure 13: Scree plot of physico-Chemical Parameters, Amansie West District 
 
4.10 Correlation of Physico-Chemical Parameters of Surface Water, Amansie West 
Table 4.10 illustrates the summary of Karl Pearson’s product moment correlation matrix 
between physico-chemical parameters and surface water samples from Amansie West 
District sampling sites. The correlation analysis of samples obtained from Amansie West 
surface water samples revealed that there was a significant positive correlation between 
the following variables; EC and TDS (r=0.976), EC and salinity (r=0.720), EC and total 
hardness (r=0.959), EC and magnesium (r=0.876), Alkalinity and Bicarbonates (r=0.936), 
TDS and TSS (r=0.632), Salinity and Total hardness (r=0.850), Ca and hardness (r=0.866), 
Ca and bicarbonates(r=0.944). There was a strong negative correlation between pH and 
the following heavy metals (Cr, Ni, Cd and Zn with correlation coefficient of at least 
(r≥0.5) in surface water samples obtained from Amansie West Sampling sites, pH- Fe 
119 
 
(r=0.311), pH- Co(r=-0.463), pH- Cu (r-0.573), pH - Cr(r=-0.781), pH- Ni(r=0.615), pH-
Zn (r=-0.524), pH-As (r=-0.326), pH-Hg (r=-0.543), pH-Pb (r=-0.348) and pH-Cd(r=-
0.688). 
Table 4.9: Correlation between Physico-Chemical Parameters in Water Samples, 
Amansie West District 
Parameter              
EC TDS TSS Sal Na Mg Ca Alkal Bicar Hardne
ss 
EC 1          
TDS 0.976** 1         
TSS 0.888** 0.902** 1        
Sal 0.720* 0.818** 0.833* 1       
Na 0.390 0.372 0.554 0.304 1      
Mg 0.876* 0.881** 0.920* 0.762* 0.643 1     
Ca -0.088* -0.871 0.823* -0.723 0.637 0.883* 1    
Alkalinity -0.877* -0.865* 0.889* 0.713* -0.752 -0.96* 0.875 1   
Bicarbonates -0.881* -0.871* 0.568 0.714 -0.420 -0.93* 0.94** 0.936** 1  
Hardness 0.959** 0.962** 0.977* 0.850* 0.475 -0.94* 0.86* 0.898* 0.896* 1 
**Correlation is significant at p < 0.01 level (2-tailed).  
*Correlation is significant at p < 0.05 level (2-tailed) 
Source: Field data, Annan (2021) 
 
 
4.11 Soil Physical Parameters, Amansie West District  
4.11.1 pH 
With regards to Amansie West sampling sites, MN recorded the lowest pH with mean 
value of 4.56±0.51 whilst the highest value was recorded in the control sample with mean 
value of 7.2±1.1 (Table 4.10). Analysis of variance at 95% confidence level revealed 
statistically significant difference in pH among soil samples obtained from the sampling 
sites (P < 0.05) (Appendix K).  
4.11.2 Conductivity  
From Amansie West District, the highest value of conductivity was recorded in soil 
samples obtained from BYO with mean value of 74±2.5 µS/cm whilst the lowest value 
was recorded in soils samples from the control samples with mean value of 3.5±0.05µS/cm 
(Table 4.10). There was statistically significant difference in conductivity among soil 
samples obtained from the sampled sites (P <0.05) (Appendix K). 
120 
 
4. 11.3 Available phosphorus  
The highest available phosphorus in soil was recorded from the control soil samples at 
Amansie West District with mean value of 18.2±1.24 mg/Kg whilst the lowest value was 
recorded in soil samples from BYO with a mean value of 12.4±0.50mg/Kg (Table 4.10). 
There was a statistically significant difference in available phosphorus levels among soil 
samples obtained from the sampled sites' waste dump sites (P < 0.05). 
4.11.4 Organic Carbon (%) 
Table 4.4 from Amansie West District, indicates the mean values of % organic carbon in 
soil obtained from the sampling sites. The highest percentage organic carbon was recorded 
in soils from the control site with mean value of 2.8±0.09 % whilst the lowest value was 
recorded in DO sampling site with mean value of 0.05±0.01% (Table 4.10). There was a 
statistically significant difference in % organic carbon among soils from the various 
sampling sites (P < 0.05) (Appendix K).  
4.11.5 Percentage Sand, Silt and Clay 
From Amansie West District, the highest value of % sand was recorded in soils from DO 
sampling site with mean value 62.1±5.1 % whilst the lowest was recorded in MN sampling 
site with mean value of 31.86±5.16 %. The percentage sand differed significantly among 
the sampling sites studied at 95% confidence level (P <0.05) (Appendix K).  
The highest percentage of silt was recorded in soils from the control sampling site with 
mean value 45.0±4.2 % whilst the lowest was recorded in soil samples from BYO with 
mean value of 15.0±2. % (Table 4.10). There was a statistically significant difference in 
percentage silt among soil samples obtained from the various sampling sites (P <0.05) 
(Appendix K). 
121 
 
The highest % clay was recorded in soils obtained from sampling site BYO with mean 
value of 26.0±3.8% whilst the lowest was recorded in the control sampling point with 
mean value of 16.0±1.4%. The percentage of sand in soil samples obtained from the 
sampling sites daggered significantly (P <0.05) (Appendix K).  
4.11.6 Exchangeable K, Ca, Mg and Na 
The highest exchangeable potassium in soil was recorded in the control site at Amansie 
West District ranged from 0.23 to 0.35 cmol (+)/Kg with mean value of 0.28 cmol (+)/Kg 
whilst the lowest value ranged from 0.01 to 0.04 cmol (+)/Kg with mean value of 
0.02±0.001 cmol (+)/Kg (Table 4.10). There was no statistically significant difference in 
exchangeable potassium in soils obtained from sampling sites (P >0.05) (Appendix K).  
The control sampling site recorded the highest exchangeable calcium in soil and ranged 
from 5.72 to 7.48 cmol (+)/Kg with mean value of 6.72±0.59 cmol (+)/Kg whilst the lowest 
value ranged from 0.25 to 0.78 (4.85 to 4.94 cmol (+)/Kg and was recorded in sampling 
site AS with mean value of 0.41±0.08 cmol(+)/Kg (Figure 4.10). The exchangeable 
calcium differed significantly among the sampling sites at 95% confidence level (P <0.05) 
(Appendix K).  
The highest exchangeable potassium in soil was recorded in the DO Sampling site and 
ranged from 3.91 to 5.89 cmol (+)/Kg with mean value of 4.57±0.56 cmol (+)/Kg whilst 
the lowest value ranged from 0.21 to 0. cmol (+)/Kg and was recorded in sampling site AS 
with mean value of 0.28±0.07 cmol (+)/Kg. Analysis of variance revealed a statistically 
significant difference in exchangeable magnesium in soils obtained from the sampling 
sites (P <0.05) (Appendix A). 
The highest exchangeable sodium in soil was recorded in soil samples obtained from DO 
and ranged from 0.24 to 0.62 cmol (+)/Kg with mean value of 0.40±0.13 cmol (+)/Kg 
whilst the lowest value ranged from 0.12 to 0.28 cmol (+)/Kg and was recorded in BYO 
sampling site with mean value of 0.16±0.03 cmol (+)/Kg (Table 4.10). There was a 
122 
 
statistically significant difference in exchangeable sodium in soils obtained from the 
sampling sites (P <0.05). 
Table 4.10: Physico-Chemical Parameters in Soil, Amansie West District 
 MN DO BYO AS Control 
Cr 0.47±0.06 (0.37-0.59) 0.42±0.09(0.28-0.56) 0.64±0.08(0.50-0.80) 0.58±0.14(0.33-0.43) 0.28±0.02(0.12-
0.42) 
As 3.58±0.57 (2.5-4.5) 3.49±1.04(1.8-4.9) 3.26±0.42(2.40-3.80) 2.95±0.41(2.10-3.70) 1.11±0.43(0.5-1.17 
Cd 28.9±5.77 (22.8-40.2) 23.01±1.6(20.7-25.3) 58.02±5.06(50.2-67.2) 59.5±14.4(39.5-436) 8.16±1.24(4.8-
15.8) 
Hg 6.44±0.47 (5.30-7.20) 4.67±0.55(3.9-5.5) 6.2±0.68(5.2-7.2) 4.6±0.75(3.2-6.3) ND 
 
Ni 0.39± 0.05 (0.30-0.49) 0.54±011(0.30-0.72) 0.25±0.01(0.16-0.380) 0.46±0.15(0.20-0.65) 0.18±0.04(0.18-0.) 
Zn 48.4± 6.45 (37.8-59.5) 5.1±1.52(1.8-6.9) 15.7±1.36(13.47-18.5) 20.7±4.7(12.48-27.4) 68.5±4.8(6.5-75.4) 
Cu 0.25± 0.02 (0.21-0.35) 0.15±0.04 (0.10-0.22) 0.18±0.05(0.10-0.27) 0.26±0.04(0.1-0.35) 0.06±0.01(0.01-
0.08) 
Pb 0.53± 0.11 (0.37-0.72) 1.5±0.31(1.1-2.2) 0.31±0.08 (0.21-0.46) 0.34±0.09(0.23-0.56) 0.03±0.0(0.01-
0.04) 
Fe 4251.5±363.8 (3549- 5374.6±1054(4016- 4868±592.8(4164-5723) 3847±428(3175-4734) 5796.1±587(4835-
4727) 6725) 6745 
Co 4.20±0.79 (3.20-6.20) 9.6±2.0 (7.4-12.4) 7.3±0.85(5.4-8.8) 8.9±0.84(7.2-11.4) 2.6±0.7(1.3-3.9) 
 
Ca 2.05±0.39 (1.59-2.74) 3.99±0.62 (3.02-4.7) 0.62±0.10(0.47-0.78) 0.41±0.08 (0.25-0.78) 6.72±0.59(5.72-
7.48) 
Mg 4.19±0.71(2.68-2.740) 4.57±0.56(3.91-5.89) 0.41±0.08(0.28-0.51) 0.28±0.07(0.21-0.54) 4.55±0.46(4.0-5.3) 
Na 0.26±0.04 (0.212-0.372) 0.40±0.13(0.24-0.62) 0.16±0.03(0.12-0.28) 0.22±0.11(0.00-0.482) 0.29±0.1(0.16-
0.34) 
K 0.04±0.007(0.027-0.052) 0.03±0.01(0.01-0.08) 0.06±0.003(0.04-0.08) 0.02±0.001(0.01-0.04 0.28±0.01(0.23-
 0.35) 
% 40.0±5.76 (28.0-49.0) 24.8±5.7(21.0-29.0) 15.0±2.7(11.0-22.0) 34.0±1.8(22.0-38.0) 45.0±4.2(12.0-18.0 
silt 
% 31.86±5.16 (23.0-41.0)    39±2.1(32-49) 
sand 62.1±5.1(53.0-68.0) 59.1±4.3(51.0-62.5) 44.0±4.8(33.0-48.0) 
% 25.4±4.03 (18.0-32.0)     
clay 17.1±4.0 (12.0-23.0) 26.0±3.8(14.0-38.0) 23±2.5(18.0-41.0) 16.0±1.4(12-25) 
% 0.62±0.17 (0.36-0.92)     
OC 0.05±0.01(0.01-0.08) 0.64±0.12(0.44-0.92) 0.15±0.04(0.12-0.28) 2.8±0.09(1.2-3.8) 
 
EC 45.0±5.7 (36.4-56.70)     
25±1.6(22.8-27.9) 74±2.5(62.4-82.5) 13.0±0.9(11.2-18.6) 3.5±0.05(2.5-4.9) 
      
pH 4.56±0.51(3.80-5.30) 6.5±0.5(6.2-6.9) 4.8±0.4 (3.8-5.4) 5.8±0.8(5.2-6.4) 7.2±1.1(6.8-7.5) 
      
Avail 15.86±0.93(14.20-17.10) 15.2±0.93(14.7-16.3) 12.4±0.50(10.4-13.8) 14.6±0.82(11.8-16.2) 18.2±1.24(16.5-
P  24.3) 
      
Total 0.05±0.002(0.01-0.09) 0.04±0.01(0.01-0.08) 0.08±0.02(0.03-1.24) 0.02±0.00(0.0-0.04) 2.8±0.04(1.25-
N  3.26) 
Mean ± SD (Range) ND: Not detected 
Source: Field data, Annan (2021) 
 
 
  
123 
 
4.12 Geoaccumulation Index of Heavy Metal Contaminations in Soils, Amansie 
West District 
Table 4.11 illustrates the Igeo values for soils in the various sampling sites at Amansie 
West District and Table 4.12 shows the Igeo classification with their absolute values. The 
Igeo values recorded for Cr ranged from a minimum of 0.8 in soils at control sampling site 
and the highest value of 2.0 was recorded at BYO sampling site.  The Igeo values for 
Arsenic levels ranged from 0.0 in soils at control sampling sites to a maximum of 0.1 at 
sampling sites MN and DO. The Igeo values for Cd ranged from a minimum of 0.1 at 
controlling sampling site to a maximum of 1.2 at Sampling site BYO. The Igeo values 
recorded for mercury varied from a minimum of 0.1 in soils at the control site and the 
highest value of 0.5 was recorded at sampling sites BYO and MN. The Igeo values 
recorded for nickel also ranged from a minimum of 0.2 in soils at the control sampling site 
and the highest value of 5.0 was recorded at sampling site DO. The Igeo values for cobalt 
ranged from 0.3 in soils at control sampling sites and the highest value of 1.2 was recorded 
in soils sampled at DO sampling site. Last but not the least, the Igeo values recorded for 
copper varied from a minimum of 0.01 in soils at sampling site DO and the control 
sampling site and the highest value of 0.03 was recorded in soils at sampling site AS. 
Lastly, the Igeo values recorded for lead ranged from a minimum of 0.03 in soils at the 
control sampling site and the highest value of 0.2 was recorded in soils at sampling site 
DO. 
Table 4.11: Geoaccumulation Index (Igeo) Values for Soil Samples in Amansie 
West District 
Community Cr As Cd Hg   Ni Co   Cu Pb    
MN 1.4 0.1 0.6 0.5 3.6 0.7 0.02 0.07  
DO 1.3 0.1 0.5 0.4 5.0 1.2 0.01 0.2 
BYO 2.0 0.0 1.2 0.5 2.1 0.6 0.02 0.04 
AS 1.7 0.0 1.1 0.4 4.2 0.5 0.03 0.05 
Control 0.8 0.0 0.1 0.1 0.2 0.3 0.01 0.03 
Source: Field data, Annan (2021) 
 
124 
 
Table 4.12: Suggested Igeo classification and absolutes  
Igeo values Igeo class Designation of soil quality 
>5 6 Extremely contaminated 
4-5 5 Strongly to extremely contaminated 
3-4 4 Strongly contaminated 
2-3 3 Moderately to strongly contaminated 
1-2 2 Moderately contaminated 
0-1 1 Uncontaminated to moderately contaminated 
0 0 Uncontaminated 
Source: Atiemo, (2011) 
 
4.13 Contamination Assessment and Hazard Rating of soils, Amansie West District 
The current contamination situation at Amansie West District was investigated by using 
the threshold excedance ratio (TER) and the trace element mobility coefficient (MOB) 
which was expressed in percentage points. “The geochemical background values for heavy 
metals used are; Cr (67.30mg/Kg), Cd (10 mg/Kg), Ni (65.40mg/Kg) and Pb (21mg/Kg), 
Cu (22.50mg/Kg) and As (15.5mg/Kg), Hg (2.5 mg/Kg) and Co (37.5mg/Kg) (Taylor and 
Meclenan, 1985). Table 4.13 illustrates the results of the threshold excedance ratio and the 
percentage mobility of the various heavy metals in soil samples in selected sampling sites 
at Amansie West in the Ashanti Region. The results showed that cobalt has a percentage 
mobility of 4.76, 3.55, 4.23, 3.83 and 5.02 for sampling sites MN, DO, BYO and AS 
respectively. This is an indication that when environmental conditions are favorable, 
cobalt has the percentage potential of remobilizing back into the soil the above percentage 
points in the studied sampling communities. Copper has % mobility of 22.7, 45.3, 33.33% 
and 24.78 for sampling sites; MN, DO, BYO and AS respectively. Chromium has % 
mobility of 2.91, 4.57, 4.48, and 3.56 for sampling sites; MN, DO, BYO and AS 
respectively. Nickel has % mobility of 4.94, 3.91, 3.64 and 3.10 for sampling sites; MN, 
DO, BYO and AS respectively. Arsenic has %mobility of 9.42, 8.73, 9.54 and 8.55 for 
sampling sites; MN, DO, BYO and AS respectively. Finally, Mercury has % mobility of 
5.14, 5.46, 5.28 and 4.90 for sampling sites; MN, DO, BYO and AS respectively (Table 
4.13). 
125 
 
Table 4.13: Hazard rating of heavy metals in soil, Amansie West District sampling 
sites 
Parameter MN DO BYO AS 
Cobalt     
NH4NO3 Extractable 7.5 12.8 11.6 12.8 
HNO3 Extractable 4.2 9.6 7.3 8.9 
TER 0.20 0.341 0.309 0.341 
MOB 0.047 0.035 0.042 0.038 
MOB (%) 4.76 3.55 4.23 3.83 
Copper     
NH4NO3 Extractable 1.28 1.53 1.35 1.45 
HNO3 Extractable 0.25 0.15 0.18 0.26 
TER 0.06 0.068 0.06 0.064 
MOB 0.228 0.453 0.333 0.247 
MOB (%) 22.71 45.33 33.33 24.78 
Chromium     
NH4NO3 Extractable 0.92 1.26 1.93 1.39 
HNO3 Extractable 0.47 0.42 0.64 0.58 
TER 0.013 0.018 0.028 0.020 
MOB 0.029 0.044 0.044 0.035 
MOB (%) 2.91 4.57 4.48 3.56 
Nickel     
NH4NO3 Extractable 1.26 1.38 1.45 0.92 
HNO3 Extractable 0.39 0.54 0.23 0.46 
TER 0.019 0.021 0.022 0.014 
MOB 0.049 0.039 0.096 0.031 
MOB (%) 4.94 3.91 9.64 3.1 
Arsenic     
NH4NO3 Extractable 5.28 4.72 4.82 3.91 
HNO3 Extractable 3.58 3.49 3.26 2.95 
TER 0.337 0.305 0.311 0.25 
MOB 0.094 0.087 0.095 0.085 
MOB (%) 9.42 8.73 9.54 8.55 
Mercury     
NH4NO3 Extractable 8.28 6.38 8.19 5.72 
HNO3 Extractable 6.44 4.67 6.20 4.6 
TER 3.312 2.552 3.276 2.28 
MOB 0.0514 0.0546 0.0528 0.049 
MOB (%) 5.14 5.46 5.28 4.90 
Cadmium      
NH4NO3 Extractable      35.3  64.7 62.5 9.4 
HNO3 Extractable     28.9  58.02 59.5 8.16 
TER     3.53  6.47 6.25 0.94 
MOB     0.122  0.112 0.111 0.115 
MOB (%)     12.21  11.20 11.10 11.52 
Lead      
NH4NO3 Extractable    1.38  0.82 1.22 0.59 
HNO3 Extractable    0.53  0.31 0.34 0.23 
TER    0.06  0.04 0.06 0.028 
MOB    0.130  0.126 0.171 0.122 
MOB (%)    13.01  12.6 17.00 12.22 
TER: Threshold Exceedance Ratio; MOB:  Mobility 
Source: Field data, Annan (2021) 
 
126 
 
4.14 Land use and Land Cover Change 
The Land use and Land cover changes in the two studied artisanal and small-scale mining 
sites were investigated. The results of the land cover classification are shown in Figure 
4.11 For the study area in Amansie East District (a and b in Figure 4.11), an overall 
accuracy of 97% was obtained for 2015 with an overall Kappa statistic of 0.95. With 2020, 
an overall accuracy of 95% was attained and a Kappa statistic of 0.93 was attained. A 
Kappa value ranging between 0.81 and 1.00 shows that there is almost a perfect agreement 
with the classification. For the study area in Mpohor Wassa East District in the graphical 
representation (c and d in Figure 14). An overall accuracy of 95% was obtained for both 
years with an overall Kappa statistic of 0.91. 
Through the analyses of temporal Landsat TM and ETM satellite images, field verification, 
and monitoring of LULC changes, it has become clear that there have been changes in the 
land cover in the studied area.  Comparing the two study areas, it shows that the study area 
in Amansie East District is more developed than that of Mpohor Wassa East District. The 
impervious surfaces in the former are greater than the latter. The impervious surfaces 
comprise mainly of settlement and roads and these are the areas in red (Figure 14). 
However, from both classifications, the dominant land cover was the vegetation cover.
127 
 
 
 
 
 
 
 
 
 
 
 
(a) (b) 
 
 
 
 
 
 
 
 
 
(c) (d) 
Figure 14:  Land cover classification map for 2015 and 2020, Amansie East District (Top Row, a and b) and Mpohor Wassa East 
District (Bottom Row, c and d) 
Source: Field data, Annan (2021) 
128 
 
It can also be seen from the results of the land cover classification maps that the Amansie 
East District is engaged in mining more than the Mpohor Wassa East District. This land 
cover is represented by the gold colour in the classification maps. Due to the fact that 
impervious surfaces and mining sites in the study area in the Amansie East District are 
more than those in the second study area in the Mpohor Wassa East District, the majority 
of the vegetation cover in the latter is intact. Therefore, not many trees have been cut down 
to make way for settlement or mining in this area. However, Figure 15 and 16 are the 
change detection maps of the selected areas in the Amansie East District and Mpohor 
Wassa for the five years. They describe the land cover change patterns and changes in the 
land use over the span of five years. Again, Figure 17 is a graphical representation of the 
area of each class in km².
129 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
   
Fi gure 15: Change detection map of selected area in the Amansie West District 
 
Source: Field data, Annan (2021) 
130 
 
The result of the change detection shows that in the area that lies within the Amansie West 
District, within five years, the vegetation cover reduced from 67.46 to 63.89 km² which is 
about 4% reduction whereas the other classes generally increased. Mining sites which are 
the focus of this study increased from 7.70 to 9.79 km² representing approximately a 3% 
increase. With the reduction in the vegetation cover, a greater portion of vegetation lost 
was taken over by mining. The increase in the water class from 1.96 to 2.02 km² can be 
attributed to the clearing of vegetation cover and diverting water to those areas near the 
mining sites. Impervious surfaces increased from 7.76 to 9.20 km² showing the expansion 
of settlement in this region. 
 
 
131 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 16:  Change detection map of selected area in the Mpohor Wassa East district 
Source: Field data, Annan (2021) 
132 
 
On the other hand, in the area that lies within the Mpohor Wassa East district, the 
vegetation cover increased from 77.76 to 81.90 km² whereas that of the mining sites 
decreased from 4.87 to 0.42 km2. Water increased from 1.01 to 1.78 km² and impervious 
surfaces on the other hand decreased. This can be attributed to the misclassification for 
2015 which was a result of the issue with cloud cover. 
Amansie 2015 Amansie 2020 Mpohor 2015 Mpohor 2020
90
80
70
60
50
40
30
20
10
0
Vegetation Water Impervious surfaces Mining sites
Amansie 2015 67.46 1.96 7.76 7.70
Amansie 2020 63.89 2.02 9.20 9.79
Mpohor 2015 77.76 1.01 2.29 4.87
Mpohor 2020 81.90 1.78 1.89 0.36
Classes
 
Figure 17: Bar Chart Showing the Extent of each Class in km² for 2015 and 2020 
Source: Field data Annan (2021) 
 
Table 4.14: Percentage (%) Cover of the Various Land Cover Classes  
Class 
Vegetation Water Impervious Surfaces Mining Sites 
Amansie 2015 
79.47 2.31 9.14 9.08 
Amansie 2020 
75.26 2.38 10.84 11.53 
Mpohor 2015 
90.49 1.18 2.66 5.67 
Mpohor 2020 
95.30 2.07 2.20 0.42 
Source: Field data Annan (2021) 
 
  
133 
 
Area in km²
4.15 Socio-Demographic Characteristics of Respondents (Mpohor Wassa East and 
Amansie West District) 
The socio-demographic data of the respondents were illustrated in Table 4.15. The results 
showed that majority 142 (35.1%) of respondents were between the ages of 30-39, 128 
(31.7%) aged 20-29 years, 75 (18.6%) aged less than 20 years, 42 (10.4%) aged 40-49 
years and 17(4.2%) were between the ages of 50 and 59 years. The mean age was 35.2± 
0.34SD. Majority, 292 (72.3%) were males and the remaining 112 (27.7%) were females. 
Most respondents 122 (30.2%) were single, 103 (25.5%) were cohabiting, 96 (23.8%) were 
married, 45 (11.1%) were widowed and 38(9.4%) were divorced. With regards to the 
educational level, 147 (36.4%) had had basic education, 122 (30.2%) had secondary 
education, 88 (21.8%) had no formal education, 28 (6.9%) had vocational/Technical 
education and 19 (4.7%) had tertiary education.  Concerning family size, most respondents 
159 (39.4%) had a family size of 2-4, 127(31.4%) had a family size of 5-6 and 118 (29.2%) 
had a family size of at least 7 (Table 4.15). 
  
134 
 
Table 4.15: Socio-Demographic Characteristics of Respondent’s (N=404) 
Variable Frequency  % 
Age of respondents (Yrs.)   
>20 75 18.6 
20-29 128 31.7 
30-39 142 35.1 
40-49 42 10.4 
50-59 17 4.2 
Mean ± SD  35.2± 0.34 (95% CI: 33.6-36.5)   
Sex   
Male 292 72.3 
Female 112 27.7 
Marital status    
Married 96 23.8 
Single 122 30.2 
Cohabitation 103 25.5 
Divorced 38 9.4 
Widowed 45 11.1 
Educational level   
No formal education 88 21.8 
Basic 147 36.4 
Secondary 122 30.2 
Vocational/Technical 28 6.9 
Tertiary 19 4.7 
Family size   
Small (2-4) 159 39.4 
Medium (5-6) 127 31.4 
Large (≥7) 118 29.2 
SD represent standard deviation; CI: Confidence interval 
Source: Field data, Annan (2021) 
 
 
 
 
 
 
 
 
 
135 
 
4.16 Effect of Artisanal and Small-scale Mining on Provisioning Ecosystem Services 
Table 4.16: Effects of Artisanal and Small-scale Mining on Provisioning Ecosystem 
Services 
Ecosystem Provisioning Coefficient Std. Err. t-value p-value [95% Conf.    
services Interval] 
Drinking water 2.573 0.261 2.20 0.027* 1.742-3.889 
Wood fuel -0.007 0.271 -0.23 0.035* 1.451-3.436 
Medicinal plants 1.425 0.240 -0.52 0.028* 1.225-4.832 
Raw material for -0.463 0.223 -2.07 0.017* 1.307-5.381 
construction 
Recreational and Tourism 0.360 0.295 1.22 0.437 -3.389-4.110 
Spiritual benefits 0.264 0.401 0.66 0.629 -4.842-5.371 
Food crop 1.343 0.141 1.41 0.003* 1.237-2.475 
Loss of Timber -0.198 0.304 -0.65 0.633 -4.072-3.675 
Bush meat 0.948 0.398 2.38 0.253 -4.119-6.016 
_constant 0.426 0.794 0.54 0.686 -9.663-10.516 
Adjusted R2= 0.873   * Significant (p<0.05) 
Source: Field data, Annan (2021) 
 
Land use due to Artisanal and small-scale mining activities were observed to significantly 
influence provisional ecosystem services. This was tested at 95% confidence level. The 
results revealed that, the following ecosystem services were significantly influenced by 
change in land use due to Artisanal and small-scale mining; drinking water, wood fuel, 
medicinal plants, raw material for construction and food crop. The coefficient of 
determination was determined and from the regression results, an adjusted R2 value of 
0.873 was produced, which translates that these ecosystem provisional services accounted 
for 87.3% of the total variation of services affected by illegal mining activities in the study 
area (Table 4.16). 
 
 
 
 
136 
 
 
Figure 18: Ecosystem Services Affected Due to Artisanal and Small-scale Mining 
Activities in the Study Area 
 
Figure 18 shows how respondents perceived that ecosystem services have been affected in 
both Mpohor Wassa East District and Amansie West District of Ghana. The results showed 
that with respect to the provision of bush meat, 78% of respondents in communities at 
Amansie West District were affected and 75% at Mpohor Wassa District were affected. 
Eighty-seven (87%) and 84% asserted that there is a loss of timber for Amansie West 
District Mpohor and Wassa District respectively. Most people also asserted that their 
source of drinking water, food crop, medical plants among other ecosystem services have 
been affected. The respondents were asked whether the cost of living had increased 
compared to the commencement of the intensive artisanal and small-scale mining 
activities, the results indicated that, 45% perceived it was high, 25% asserted it was very 
high, 18% moderate and 12% perceived cost of living was very low. This is a clear 
137 
 
indication that the livelihoods of the inhabitants have been significantly affected (Figure 
19 and Plate 4.1 and 4.2).  
12%
18%
25%
Low
Moderate
High
Very hifgh
45%
 
Figure 19: Cost of Living in Artisanal and Small-scale Mining Communities 
 
The respondents were further asked the type of water source they used for drinking 
purposes prior to the intensive illegal mining activities. The results showed that 65% were 
drinking water from rivers, 18% from boreholes, 12% from hand-dug wells and 5% from 
pipe- borne water (Figure 20).  However, after the intensive illegal mining activities, the 
majority has shifted to the use of boreholes as a water source representing 83% and only 
2% depends on rivers (Figure 21). This is the clear indication of the impact of illegal 
mining activities on the water provisioning ecosystem (Plate 4.5). 
138 
 
5%
18%
River
Hand dug well
Borehole
Pipeborne water
12%
65%
 
Figure 20: Source of Water in Artisanal and Small-scale mining communities 5-10 
years ago 
 
3% 2%
12%
River
Hand dug well
Borehole
Pipeborne water
83%
 
Figure 21: Source of Water prior to Artisanal and Small-scale mining activities 
 
4.17 Interview with Key stakeholders 
On the extent of perception of stakeholders on the impacts of artisanal and small-scale 
mining activities on livelihoods; the following views were expressed. The interviewee was 
asked how the illegal mining activities in the communities have affected their livelihoods 
and ecosystem services. The following were the quotes; 
“Interviewee had extensive knowledge of how the ‘galemsey’ activities have 
impacted significantly on livelihoods in the study area. First, we used to get a lot 
of resources from the forest, things that we got included; bush meat, medicinal 
plants, fuel wood, food crop and timber for construction, but due to the mining 
activities, there is hunger since all these resources are no more (Assembly man, 
Mpohor Motorway-Mpohor Wassa District)”.  
139 
 
 “Interviewee highlighted that as part of the ‘galamsey’ activities, some of our 
farmlands have been sold and we were compensated and now they have destroyed 
our food crops including plantain, palm tree and cocoa trees (Plate 4.1 and plate 
4.2). Our water bodies have been contaminated and now we cannot drink the water 
anymore so we have to depend on boreholes and hand-dug wells as drinking water 
sources, we are really suffering” (Assembly Man, Dominase- Amansie West 
District).  
Asked whether Artisanal and small-scale mining activities have benefited them in any 
way? the following represent the quotes; 
 “Interviewee responded Yes. He however stated that the only way it has benefited 
us is by creating employment (Plate 4.3). Though we get money from ‘galamsey’ 
activities, food is scarce here, you have money but there is no food to buy and it is 
worrying. We also don’t have good and portable drinking water in our community 
since they are all polluted by the ‘galamsey’ activities (Plate 4.5 and 4.8). My 
husband used to get medicinal plants for sale since he is a herbalist but now they 
are all destroyed. So, the ‘galamsey’ activities have really affected us” (FGD: 
Female participant at Asaman, Mpohor Wassa East District). 
 
“We get money from the ‘galamsey’ activities, but it is not sustainable. We don’t 
have water to drink and other domestic purposes. All our water is polluted by the 
‘galamsey’ activities. First, I used to get fuel wood from my farm but now my farm 
is no more so I have to resort to the use of kerosene stoves and gas which has 
increased my expenditure. Also, during the afternoon, you will have no food to buy 
as the young ones that farms are all into mining activities and only the aged go to 
farm (Plate 4.8). So, the ‘galamsey’ activities have really caused more harm than 
good to us” (FGD: Male participant at Amansie West District). 
 Key personality from the Environmental Protection Agency, Forestry commission was 
asked if there is any legal requirement available to stop artisanal and small-scale mining 
activities and the role the EPA is playing in the fight against artisanal and small-scale 
mining activities. The following quote were observed; 
140 
 
“Interviewee responded that mostly, those that engage in artisanal and small-scale 
mining activities often come for prospecting license as part of the licensing regime. 
But they later end up mining illegally though they have not secured the requisite 
permit from the appropriate organization and this is a source of worry. Instead of 
doing the prospecting and later proceed to conduct environmental Impact 
assessment, they finally end up doing ‘galamsey’. I believe fighting these illegal 
activities is a collective effort and not only the government. The communities, 
traditional authorities all have a role to play so as to compliment government 
efforts” (Participant A, Environmental Protection Agency). 
 
“There is a legal requirement that needs to be fulfilled by parties who engage in 
activities that encroach into forest lands. He concluded by stating that, the Forestry 
Commission has an intensive monitoring programme to ensure companies who 
work in forest areas strictly comply with permit requirements issued by the 
commission or risk having their permits revoked” (Participant A, Forestry 
Commission). 
 
 
 
 
 
  
141 
 
 
 
 
 
 
 
 
 
 
Plate 4.2: Destruction of oil palm crops by Plate 4.1: Destruction of food crops by small - 
illegal small - scale gold miners at scale gold mining activities at the 
the study site study site 
   
 
 
 
 
 
 
 
 
 
 
 
Plate 4.4: Casual workers at the mining site Plate 4.3: Land degradation as a result of 
for their source of livelihood small - scale mining activities at 
  study site 
 
 
  
142 
 
 
 
 
 
  
 
 
 
 
Plate 4.6: Water pollution as result of small - Plate 4.5: Depletion of vegetation cover due 
scale mining activities at the mining to small - scale gold mining 
site activities 
  
 
 
 
 
  
 
 
 
 
 
 
Plate 4.8: Dug pit left after mining serving as Plate 4.7: Children engaging in small - 
a dead trap to human and animals  scale gold  mining activities 
143 
 
CHAPTER FIVE 
DISCUSSION 
5.0 Overview  
This chapter presents the discussions of the study. The research objectives have been 
addressed in relation with the findings of the study, the physico-chemical quality of water, 
heavy metals in water and soil and the assessment of the impact of illegal mining activities 
on provisioning ecosystems. 
5.1 Physico-Chemical Quality of Surface Water 
Water samples obtained from Mpohor Wassa sampling sites were observed to be neutral 
to alkaline in nature and were within acceptable range of pH 6.0-9.0 as set by the WHO 
(2010) and Ghana Standard authority standard (GS 1212:2019) for drinking water. 
Comparatively pH of surface water samples obtained from communities in Amansie West 
District tends to be acidic compared to those that are found in Mpohor Wassa District. 
However, Akosa et al., (2017) reported that pH values lower than 6.5 are considered too 
acidic for human consumption and can cause significant health problems such as acidosis 
and adverse effects on the digestive and lymphatic system. A drop in pH could also be 
harmful since metals have higher dissolution in low pH waters and thus could contain 
elevated levels of toxic metals posing a health hazard to consumers (Annan et al., 2018). 
The lower pH obtained in Amansie West District could be an indication of the excessive 
artisanal and small-scale gold mining activities in the district. This finding is not surprising 
since acid mine drainage or “yellow boy” were observed during field observation in the 
artisanal and small-scale gold mining sites and this could have entered into the surface 
water sources (Plate 4.5). This implies that the artisanal and small-scale gold mining 
activities were significantly impacting on water resources which is a provisioning 
ecosystem services in the Amansie West Districts compared to Mpohor Wassa District 
144 
 
(Plate 4.5). The pH values obtained in surface water samples at Mpohor Wassa sampling 
sites was good since a drop in pH could be harmful due to higher dissolution of heavy 
metals in low pH waters which could contain elevated levels of toxic heavy metals posing 
a health hazard to consumers and aquatic organisms (Asante et al., 2017). However, the 
pH of water obtained from Amansie West District sampling sites were acidic and not 
suitable for human consumption and aquatic life in general.   
The results for temperature of water showed no significant variation among sampling sites 
at both Mpohor Wassa District and Amansie West District. The natural background limit 
for WHO and GS 1212:2019 limit is between 22 to 27℃ (WHO, 2010). The temperature 
values obtained from sampled water for the entire study period were within the natural 
background limit. Temperature is a critical factor of significant importance for aquatic 
ecosystems as it affects the water organism as well as the physico-chemical properties of 
water (Bloch & Owusu, 2017; Attuah et al., 2014).   
With regards to water samples at Mpohor Wassa East District sampling site, the highest 
Dissolve oxygen (DO) of the surface water samples was recorded at control sampling site 
with mean value of 12.9 mg/L whilst the lowest was recorded at sampling site ASA with 
mean value of 5.4 mg/L.  The DO obtained for water samples in the two districts were 
comparatively lower and this is an indication of higher organic loads in the water due to 
anthropogenic effects possibly from the illegal mining activities.  
The highest dissolved oxygen (DO) of the surface water samples from Amansie West 
District however was recorded at a control sampling site. The World Health Organization 
does not have a specific guideline for DO in drinking water. However, a provisional health-
based guideline value of at least 7.5mg/L was indicated for the purpose of public health 
protection (WHO, 2010). DO of almost all the water samples analyzed for the entire study 
145 
 
period fell below the guideline value. The concentration of dissolved oxygen is a critical 
parameter relating to the overall quality of any watercourse. According to Akosa et al., 
(2018) the minimum acceptable limit for maintaining life in an aquatic environment is 
about 5 mg/L. The low DO recorded in the water samples may be due to high levels of 
oxidizable substances in the water sample such as organic matter. Oxygen levels are 
naturally low where organic matter accumulates, because aerobic decomposer 
microorganisms require and so consume oxygen (Annan et al., 2018; Hutchinson & 
Meema, 2017). This low DO levels may encourage anaerobic respiration activities and 
could lead to bad odour in the water rendering it unwholesome for human consumption 
(Lenn, 2017). According to Amegbey and Eshun (2003), the parameter is noted as the most 
essential factor with regards to the study of lakes and other freshwater or inland water 
bodies. Even though the diversity of organisms in water is greatest at higher DO 
concentrations, DO levels, when too low (< 3 mg/L), can be very detrimental to aquatic 
life and subsequently affect the quality of water (FEI, 2014).  
Biological Oxygen demand (BOD) is a measure of organic matter contamination of water 
and therefore a good measure of the relative oxygen depleting effect of biodegradable 
pollutants in water. The World Health Organization recommends that water considered to 
be safe for drinking should not have any material of organic origin (WHO, 2010). Though 
the BOD at the control sites were relatively lower all the values exceeded the WHO 
permissible zero limit per 100ml rendering the water unsuitable for drinking purposes as 
it could pose health problems if not treated. The higher levels recorded may be due to the 
possibility of poor mixing of atmospheric oxygen into the water due to higher levels of 
organic matter pollution as a result of anthropogenic inputs possibly from illegal mining 
activities. The use of the water samples in communities in the two study areas without 
146 
 
adequate treatment may pose a public health hazard to consumers (Annan et al., 2018; 
Attuah et al., 2014). 
Alkalinity of water is its capacity and ability to resist changes in acidity. Total alkalinity 
for portable drinking water according to WHO (2014) is 400mg/L. The concentrations in 
the water for the entire study period was within the WHO permissible limit. Alkalinity of 
500 mg/L is also accepted by the USEPA and Ghana water company (GWC). Alkalinity 
is apparently unrelated to public health but is very important in pH control. The major 
ionic species that contribute to alkalinity in water include carbonates, bicarbonates, 
hydroxides, phosphates, borate and organic acids. The low levels of alkalinity recorded for 
the entire study period may be attributed to the geology of the study area. The study area 
is characterized by sedimentary rocks composed of limestone and sandstone which is very 
rich in carbonates and bicarbonate ions (Akabzaa et al., 2007).  
Salinity is a measure of the total soluble or dissolved salt in water. It is normally a measure 
of the total dissolved solids or the electrical conductivity of the water. The WHO 
recommends that water that is considered to be safe for human consumption should not 
have salinity levels exceeding 200 mg/L as it will induce a salty taste in the water (WHO, 
2014). All the water samples analyzed for the entire study period were within the WHO 
permissible limit. This confirms results obtained from the social survey where 85% of the 
respondents asserted that the water is not saline.  
Electrical conductivity is the measure of the total dissolved ions in water. The conductivity 
of all water samples obtained from Mpohor Wassa East District sampling sites were within 
the acceptable limit of 250µS/cm set by WHO and Ghana standard. The values recorded 
during the study period for surface water samples at Amansie West District sampling sites 
however, far exceeded the WHO regulatory limit and it ranged from a minimum of 277.3 
147 
 
µS/cm at Dominase community to a maximum of 484.8 µS/cm at Manso Nkwanta 
community. The high values indicated that the dissolved ions in the water were too high 
for human consumption. Research has found that there is a positive correlation between 
conductivity and total dissolved solids (TDS) and the latter may be obtained by 
multiplying conductivity by a factor between the ranges of 0.55 to 0.75 (Chapman, 1992).  
Total dissolved solids are a measure of the total organic and inorganic substances dissolved 
in water (Akosa et al., 2018). Water samples from sampling sites in communities within 
the two study areas had TDS values within the WHO guideline limit of 1000mg/L. 
Consumers may object to total dissolved solids levels above 1000 mg/L (WHO, 2010).  
Total suspended solids relatively measure the visual observation of a water sample. The 
WHO value guideline dictates that water must have a TSS value not exceeding 500 mg/L 
for it to be considered safe (WHO, 2014). The highest TSS of the sampled water was 
recorded at Mpohor Motorway (MM) in Mpohor Wassa district, with a mean value of 
645.3 mg/L. Water samples obtained from Amansie West District, however, were within 
the WHO acceptable limit, an indication of an excellent measure of the quality of the water.  
The WHO guideline for turbidity in drinking water is 5 NTU (Nephelometric turbidity 
units). The highest turbidity value of 299.2NTU was recorded at the Mpohor Wassa 
sampling site and 822.7 NTU was recorded at Amansie West sampling site. With the 
exception of the control sampling site, all water samples obtained in communities from 
both study areas fell above the WHO and Ghana Standard Authority permissible limit of 
5NTU. The value recorded is very alarming as far as this research is concerned, the 
increase may have arisen from soil disturbance from illegal mining activities that were 
impacting significantly on the surface water sources. During rainfall, soil and other 
particulate matter (silt, clay, and organic materials) from the illegal mining sites could 
148 
 
enter the surface water sources thereby increasing the turbidity subsequently. In 
communities where the turbidity is high, if persistent without interventions, it may cause 
sedimentation and siltation (Armah et al., 2013). This may subsequently affect the habitat 
for the fisheries and other aquatic life resulting in low productivity and loss of species. 
5.2 Heavy Metals in Water 
A typical mining effluent has arsenic (As), mercury (Hg), lead (Pb), copper (Cu), and 
cadmium (Cd) as its constituents (Creek, 2016).  These heavy metals are among ten (10) 
toxic heavy metals with major public health concerns (WHO, 2014). Heavy metal 
pollution in rivers and streams is primarily caused due to industrialization with which 
illegal mining is a significant source of concern (Queensland, 2019).  According to the 
World Health Organization, Hg, As, Pb, Cd and Cu in drinking water sources should be 
0.002, 0.01, 0.01, 0.005 and 1.3 mg/L respectively.  
The level of mercury from the water samples at both Mpohor Wassa District sampling 
sites and Amansie West sampling sites exceeded the acceptable limit of EPA guideline 
value and WHO limits of 0.002mg/L. The pollution of mercury in surface water bodies 
was an indication of anthropogenic impacts of illegal mining footprints on provisioning 
ecosystem services in the study area. The mean value for the control sample fell within 
WHO and EPA limits (Amegbey et al., 2003).  
The Arsenic concentrations in surface water samples in communities in the two study areas 
were above the WHO recommended limit of 0.01mg/L. The high arsenic concentration in 
the surface water could be due to runoff from the illegal mining operations. At Mpohor 
Motorway where a significant arsenic level was recorded, it was realized that the majority 
of the inhabitants depend on this stream of water for bathing and other domestic use. The 
arsenic contamination is not surprising since the gold bearing ores in the two study areas 
149 
 
thus the Mpohor Wassa district and Amansie West district happened to be mineralized 
pyrites and arsenopyrite (Rambaud et al., 2016; Simon et al., 2004; Serfor-Armah et al., 
2006). Processing of the ore such as roasting, it leads to the production of arsenic trioxide 
gas which is distributed throughout the study area and may drop onto soils through 
atmospheric deposition and can contaminate soils and subsequently leach into surface 
water and groundwater aquifers (Sumaraga et al., 2020; Annan et al., 2018). During 
oxidation, arsenic is released into the environment which can contaminate environmental 
media (Amponsah-Tawiah et al., 2017). According to WHO (2010), arsenic may be found 
in water which has flowed through arsenic rich rocks. Arsenic is released into ground and 
surface waters by the erosion, dissolution and weathering of rocks. The high level of 
arsenic in surface water samples suggest that the illegal mining activities have released 
arsenic footprints which have impact on the provisioning ecosystem services in the study 
area. 
Arsenic compounds cause acute and chronic effects in individuals, populations and 
communities at concentrations ranging from a few micrograms to milligrams per liter, 
depending on species, time of exposure and end-points measured. These effects include 
lethality, inhibition of growth, photosynthesis and reproduction, and behavioral effects 
such as mental retardation (FEI, 2014). 
The cadmium and lead levels in surface water samples, both Mpohor Wassa East and 
Amansie West sampling sites exceeded the WHO recommended limit of 0.005 and 
0.01mg/L, respectively. This suggests anthropogenic impacts possibly from the illegal 
mining activities since the principal component analysis (PCA) showed that both cadmium 
and lead were of the same origin. Nickel in this study was higher at Mpohor Wassa 
sampling sites compared to sampling communities in the Amansie West District. This 
finding is consistent with the study of Asante et al., (2017) in a mining community in 
150 
 
Jharkhand, India. The levels of Ni obtained in surface water samples in this study are 
higher than the study by Kacmaz and Nakoman (2010) in Turkey and lower compared to 
that of Asante et al., (2017) in Tarkwa, Ghana. 
The findings obtained for heavy metals in this study are consistent with those of Asante et 
al., (2017), and Wongsasuluk et al., (2013) who reported high levels of As, Fe, As, Cr, Pb 
and Ni in surface water samples in some illegal mining communities at Obuasi. The high 
levels of heavy metals detected in the water samples could be attributed to illegal mining 
operations in the study area (Obiri, 2007). 
The PCA of water samples in Mpohor Wassa sampling site showed that the first principal 
component 1 (PC-1) contributed 22.5% of the total variance and was strongly loaded by 
Co, Cr, Cu, As and Cd and moderately loaded by Ni, Zn, Hg, Mg, Pb and Fe. The PCA of 
water samples from Amansie West district also showed that the first principal component 
(PC-1) contributed 43.8% of the total variance and was strongly loaded by the following 
heavy metals; Cr, Hg, Mg, Mn, Nickel, As, Pb, and Cd.  This suggests that these heavy 
metals were considered most significant parameters which always contribute to water 
quality variations in the studied surface water. Therefore, based on a correlation coefficient 
selection criterion, the physico-chemical parameters considered important in contributing 
to water quality variations in a particular district may or may not be considered important 
in another district due to differences in mining activities. Hence, this may serve as 
information for the management of the surface waters so as to find out which parameters 
need to be monitored in different districts in Ghana.  
5.3 Heavy Metals in Soil  
Heavy metals pose potential and significant human health challenges when levels are high 
and exceed recommended limits established by regulatory agencies such as the World 
151 
 
Health organization (WHO) and Food and Agriculture Organization) (FAO) in agricultural 
soils. The concentrations of heavy metals determined in soils for Fe, Hg, Ni, Cu, Pb, Cr 
and As were above the FAO acceptable limit with the exception of the control sampling 
sites (WHO/FAO, 2014). However, WHO/FAO (2014) guidelines stipulates that Ni, Cu, 
Pb, Cr, As, Zn and Hg levels in agricultural soils should not be more than 0.20mg/Kg, 
0.30, 0.50, 0.20, 0.01, 300 and 0.001mg/Kg respectively. The findings for high levels of 
Hg, As, Cu and Pb and Ni in this study agrees and is consistent with the study of Akosa et 
al., (2018) who found increased levels of Cu, Hg, As and Pb in soils obtained at some 
illegal mining sites in Dhaka city. The higher concentrations of Pb, Hg, As, Cr, Ni and Cu 
recorded in this study could partly be attributed to the illegal mining operations since these 
metals are typical components in a mining effluent (Bloch & Owusu, 2017). The arsenic 
and mercury levels in soil exceeded the WHO and EPA limits of 0.01mg/Kg and 
0.001mg/Kg respectively. This could be attributed to the exposure of underlying rocks in 
the soil. Mining, smelting of non-ferrous metals and burning of fossil fuel are the major 
industrial processes that contribute to anthropogenic arsenic contamination of air, water 
and soil. The mean Fe concentrations in soils obtained from illegal mining sites at both 
Amansie West District and Mpohor Wassa East District in this study were below the 
recommended guideline by WHO/FAO (2014) permissible limit of 425.5 mg/Kg for 
agricultural soils. Iron (Fe) is abundant in nature in the earth crust and useful in the 
formation of red blood cells. Elevated levels therefore could partly be attributed to 
lithogenic influence. The presence of mercury in the soil could be due to washing of mine 
waste onto soils (Annan et al., 2018; Hutchinson et al., 2017).  Mercury can harm fish 
even at low concentrations. It finds its way into fish and shellfish and eventually into the 
human food chain (Kachhwala, 2015). Over exposure to mercury can cause a variety of 
ailments, including dizziness, fatigue, loss of appetite, headaches, convulsions and even 
152 
 
death (Andoh, 2002). Mercury also weakens the immune system, potentially making 
miners and dependents more susceptible to other ailments, such as malaria, which 
necessitate the purchase of costly medicines (Bagstad et al., 2016).  
General health effects associated with arsenic exposure include cardiovascular and 
peripheral vascular disease, developmental anomalies, neurologic and neurobehavioral 
disorders, diabetes, hearing loss, portal fibrosis, hematological disorders and cancers 
(Centeno et al., 2018; Abenathy et al., 2017). 
Comparatively, this study found higher levels of heavy metals occurring in mine waste 
soil than in surface waters. In aquatic environments, the main processes governing 
distribution of metals include adsorption and sedimentation (Hughes, 2020). Thus, in the 
course of distribution of metals, permanent or temporary storage takes place in the soils or 
sediments. The highest concentrations of heavy metals occurred in soils compared to water 
which may have possible adverse impacts on ecosystem services (Belton et al., 2005). The 
results from the land use land cover change (LULC) analysis show that vegetation is the 
predominant land cover in both areas including forest and grassland. For Amansie West, 
the vegetation cover reduced over the period being converted to mining areas which 
increased by about 3%. This finding is supported by data from the social survey which 
revealed that drinking water, wood fuel, medicinal plants, raw materials for construction 
and food crops were affected due to change in land use (Plates 4.1 and 4.2). There was 
also an increase in impervious surfaces which give an indication of expansion in settlement 
and other construction activities. Site visits confirm active ‘galamsey’ activities in the area 
(Song et al., 2020).  
For the area in Mpohor Wassa East, there was an increase in vegetation cover and a 
reduction in mining sites by over 5%. Field observations revealed that some of the mining 
153 
 
sites had been abandoned and vegetation was slowly recovering or growing back at some 
of the mined-out sites. Comparison of the two sites analyzed show that there were more 
mining activities within the Amansie West District compared to the Mpohor Wassa East 
area (Annan et al., 2019). In all the heavy metal analysis, the threshold exceedance ratio 
(TER) was less than the total concentration when extracted with Nitric acid. The rule of 
thumb is that a threshold exceedance ratio (TER) value higher than the total concentration 
(TC) can limit the functioning of the soil (Liang et al., 2014). Limited soil functioning 
might occur if the threshold value is exceeded, causing a reduction in plant growth and, 
thus, increased soil erosion. In this study, limited soil function might not occur since the 
TER values are smaller compared to the total concentrations and could not limit the 
function of the soil for agriculture purposes. However, the re-mobility percentage, 
especially Cu, was high with higher percentage mobility in all sampling sites above 20% 
which could indicate that they have a higher potential to be remobilized back into the soil 
structure when environmental conditions are favourable. 
5.4 Assessment of Impact of Illegal Mining Activities on Provisioning Ecosystem 
In illegal mining activities, the vegetation cover and topsoil are often removed which led 
to degradation of the environment and resulting in soil erosion, among others. In addition, 
soil compaction may also occur due to use of heavy machines or equipment by miners. 
Notwithstanding, World Bank (2002) reported that one of the environmental effects of 
illegal mining of gold mining in Ghana is land degradation (Plate 4.4), more specifically, 
clearing vast forest, digging trenches and up-turning of vegetation which in turn leaves 
land bare and exposed to agents of erosion. There is also the destruction of beneficial 
microorganisms in soil that could help in the decomposition of organic materials in the 
soil to enrich it fertility. Furthermore, Akabzaa (2000) reported that degradation of large 
tracts of land by illegal mining constitutes a major threat to agriculture in the communities 
154 
 
and their economic survival (Plates 4.1, 4.2, 4.4 and 4.4.6). Moreover, studies by the 
Biodiversity Support Program (2014) and Ayitey-Smith (2012) revealed that land 
degradation from illicit mining activities reduces biodiversity and can subsequently 
decrease the availability of medicinal plants in the environment. In this current study, the 
findings of the social survey revealed that residents in the study area used to get a lot of 
resources from the forest such as bush meat, medicinal plants, fuel wood, food crops and 
timber for construction. According to people interviewed in the study communities, the 
illegal mining activities had seriously affected ready availability of these vital ecosystem 
resources to the extent that they have increasingly become rare to obtain. Some of these 
resources served as medicinal plants for a variety of ailments including anemia, asthma, 
gonorrhea, measles, and typhoid in the study area. One participant at the time of the in-
depth interviews confirmed even though it was still possible to find the herbs or plants, he 
often had to travel longer distances to obtain plants that were once found near the village 
or in his farm (Amponsah-Tawiah et al., 2017). 
The activities of the illegal or “galamsey” mining operators in the studied communities 
were also observed to impact significantly on the provisioning ecosystem including soil 
and surface water bodies which may also have greatly affected their ability to support 
aquatic life (Plate 4.5). Interpretations of data from the social surveys indicate that many 
respondents in the study area are of the opinion dug pits from illegal mining (Plate 4.8) 
serves as breeding grounds for mosquitoes. The environmental implications of the mining 
operations are best viewed from the environmental effects on health. However, Agyapong 
(2018) estimated that for daily reported illnesses in Ghana, 72% including malaria are 
environmentally related. Malaria accounted for an average of 32% of all cases followed 
by acute respiratory infection, skin diseases, ulcers and typhoid fever. With the use of 
mercury in illegal mining, the respondents admitted that they use mercury to extract gold 
155 
 
from the ore. Interestingly, they obtain the mercury from friends, drug stores and gold 
dealers. There is a convention called the Minamata Convention on Mercury which is a 
global treaty to protect human health and the environment from the adverse effects of 
mercury. What the convention seeks to achieve include a ban on new mercury mines, the 
phase-out of existing ones, the phase-out and phase-down of mercury use in a number of 
products and processes, control measures on emissions to air and releases to land and 
water, and the regulation of the informal sector of artisanal and small-scale gold mining. 
The convention also addresses interim storage of mercury and its disposal once it becomes 
waste after use, sites contaminated by mercury as well as public health implications. All 
these regulations are not duly followed and adhered to in the mining communities studied 
suggesting that mercury pollution may be rampant. Once in the body, inorganic mercury 
is transformed into toxic methylmercury, which poses a serious threat to humans. Over-
exposure to mercury can cause a variety of ailments, including dizziness, fatigue, and a 
loss of appetite, headaches, convulsions and even death (Belton et al., 2005). During the 
course of data collection, poor mercury management practices were observed throughout 
the studied communities in both Mpohor Wassa East and Amansie west Districts of the 
Western and Ashanti Regions, respectively. 
 
 
 
 
 
 
156 
 
CHAPTER SIX 
CONCLUSION AND RECOMMENDATION 
6.1 Conclusion 
6.1.1 Heavy Metals in water 
Arsenic, Cd, Ni, and Mn concentrations in surface water samples at Amansie West and Ni 
and Cd at Mpohor Wassa East were above the WHO recommended limit. The pollution of 
mercury in surface water bodies was an indication of anthropogenic impacts of illegal 
mining footprints on provisioning ecosystem services in the study area. The high arsenic 
concentration in the surface water could be due to runoff from the illegal mining 
operations, and this could possibly suggest that the illegal mining activities have released 
arsenic footprints which have impact on the provisioning ecosystem services in the study 
area. The high level of cadmium suggests anthropogenic impacts possibly from the illegal 
mining activities since the principal component analysis (PCA) showed that both cadmium 
and lead were of the same origin. Nickel in this study was higher at Mpohor Wassa 
sampling sites compared to sampling communities in the Amansie West District. 
6.1.2 Hazard Rating of Heavy Metals in Soils 
The concentrations of Fe, Hg, Ni, Cu, Pb, Cr and As determined in soils at Mpohor Wassa 
sampling sites were above the FAO acceptable limit with the exception of the control 
sampling sites. In all the heavy metal analysis, threshold exceedance ratio (TER) was less 
than the total concentration when extracted with nitric acid and may indicate that limited 
soil function for agricultural purposes might not occur since the TER values are smaller 
compared to the total concentrations. The re-mobility percentages of Cu and Hg were 
above 29% and 19% respectively at all study areas which suggests higher potential for 
remobilization back into the soil structure when environmental conditions are favorable. 
The geo-accumulation index of heavy metals in soils at both study areas were found to 
157 
 
range from Igeo values (1-2), uncontaminated to moderate contamination; this suggests 
potentially moderate contamination of heavy metals in soils in both sampling sites. 
6.1.3 Impact of Artisanal and Small-Scale Mining Activities on Provisioning 
Ecosystem 
ASGM affected 87.3% of the provisioning ecosystem services supply. The following 
provisioning ecosystem services were significantly influenced by change in land use due 
to illegal mining; drinking water, wood fuel, medicinal plants, raw material for 
construction and food crops. It can therefore be concluded that illegal mining footprints 
have impacted significantly on provisioning ecosystem services in the two study areas.  
6.1.4 Impact of Artisanal and Small-scale Gold mine on Land use and Land Cover 
Changes 
Vegetation is the predominant land cover in both areas including forest and grassland. 
Within a period of (2015-2020) the vegetative cover has reduced by 4.21% and mining 
sites increased by 2.45% at Amansie West District. Comparison of the two sites studied 
shows that there are more mining activities within the Amansie West as compared to the 
Mpohor Wassa East district. For the area in Mpohor Wassa East, there was an increase in 
vegetation cover and a reduction in mining sites by over 5%. Observations during the 
period of study revealed that some of the mining sites had been abandoned and vegetation 
was recovering at some of the sites. 
6.2 Recommendation 
6.2.1 Recommendations for Government (Policy and Decision-makers) 
● Based on the study, the pH of water samples was high at Amansie West. It is 
therefore recommended that periodic monitoring should be done by the Water 
Resources Commission and other relevant institutions.  
158 
 
● The Environmental Protection Agency needs to come out with a stiffer punishment 
on the indiscriminate use of mercury in illegal mining activities which tend to 
pollute water and soil resources.   
● The forestry commission must ensure that reforestation projects should be 
undertaken in all communities where these illegal mining activities have taken 
place so as to restore the land use.  
● Phytoextraction-ability plants such as Sun flower (Helianthus annuus), Cannabis 
sativa, Tobacco (Nicotiana tabacum), Maize (Zea mays) can be cultivated to 
demobilize Cu and Hg in the soil and this can be championed by EPA and the 
Ministry of Agriculture in Ghana 
● The Environmental Protection Agency (EPA), Ministry of Lands and Natural 
Resources and Land Commission should regulate the activities of ASGM to stop 
the discharge of poisonous heavy metals into soil and water bodies. 
6.2.2 Recommendation for Academia  
Academic institutions need to collaborate with the government to conduct intensive studies 
in all illegal mining sites in Ghana so as to establish the extent of contamination and its 
impacts on the ecosystem services. 
6.2.3 Recommendation for Local Communities and Households 
Since livelihoods have been significantly affected as a result of change in land use, the 
district assemblies should have alternative livelihood projects for affected community 
members so as to improve their livelihoods 
 
 
 
159 
 
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APPENDICES 
Appendix A: Descriptive Statistics of Physico-Chemical Parameters of Water 
Sample, Mpohor Wassa 
Descriptive 
95% Confidence 
Interval for Mean 
Std. Lower Upper Minimu
 N Mean Deviation Std. Error Bound Bound m 
Temperature ADW 3 25.3967 1.00271 .57892 22.9058 27.8875 24.33 
ADT 3 24.5833 1.22255 .70584 21.5463 27.6203 23.29 
ASA 3 25.1133 .54638 .31545 23.7560 26.4706 24.66 
MM 3 24.9567 1.31546 .75948 21.6889 28.2244 23.90 
Control 3 24.9733 .81027 .46781 22.9605 26.9861 24.32 
Total 15 25.0047 .90277 .23309 24.5047 25.5046 23.29 
Conductivity ADW 3 123.5000 15.74008 9.08754 84.3995 162.6005 106.00 
ADT 3 128.3000 7.04060 4.06489 110.8102 145.7898 122.00 
ASA 3 92.6667 4.50925 2.60342 81.4651 103.8683 88.00 
MM 3 152.3333 26.57693 15.34420 86.3126 218.3541 127.00 
Control 3 58.6667 13.50309 7.79601 25.1231 92.2102 45.00 
Total 15 111.0933 35.98524 9.29135 91.1654 131.0213 45.00 
Total Dissolved ADW 3 69.6333 2.12211 1.22520 64.3617 74.9049 67.90 
solids ADT 3 82.3333 6.02771 3.48010 67.3597 97.3070 76.00 
ASA 3 61.6667 3.05505 1.76383 54.0775 69.2558 59.00 
MM 3 101.0000 17.34935 10.01665 57.9018 144.0982 86.00 
Control 3 33.3333 9.07377 5.23874 10.7928 55.8738 25.00 
Total 15 69.5933 24.56177 6.34182 55.9915 83.1952 25.00 
pH ADW 3 6.7000 .26458 .15275 6.0428 7.3572 6.40 
ADT 3 7.0000 2.00000 1.15470 2.0317 11.9683 5.00 
ASA 3 6.5000 .36056 .20817 5.6043 7.3957 6.20 
MM 3 6.5333 .32146 .18559 5.7348 7.3319 6.30 
Control 3 7.1000 .17321 .10000 6.6697 7.5303 6.90 
Total 15 6.7667 .82606 .21329 6.3092 7.2241 5.00 
Salinity ADW 3 .1000 .10000 .05774 -.1484 .3484 .00 
ADT 3 .2000 .10000 .05774 -.0484 .4484 .10 
ASA 3 .0000 .00000 .00000 .0000 .0000 .00 
MM 3 .1667 .05774 .03333 .0232 .3101 .10 
Control 3 .0000 .00000 .00000 .0000 .0000 .00 
Total 15 .0933 .10328 .02667 .0361 .1505 .00 
Total Suspended ADW 3 5.3333 .57735 .33333 3.8991 6.7676 5.00 
solids ADT 3 145.3333 3.51188 2.02759 136.6093 154.0573 142.00 
ASA 3 399.0000 20.07486 11.59023 349.1313 448.8687 385.00 
MM 3 645.3333 14.84363 8.56997 608.4597 682.2070 629.00 
Control 3 15.8000 1.96723 1.13578 10.9131 20.6869 14.00 
Total 15 242.1600 255.34650 65.93018 100.7538 383.5662 5.00 
Alkalinity ADW 3 18.0000 4.00000 2.30940 8.0634 27.9366 14.00 
ADT 3 47.6667 4.50925 2.60342 36.4651 58.8683 43.00 
ASA 3 9.0000 2.64575 1.52753 2.4276 15.5724 7.00 
MM 3 48.6667 3.05505 1.76383 41.0775 56.2558 46.00 
Control 3 64.3333 8.02081 4.63081 44.4085 84.2581 56.00 
Total 15 37.5333 21.80389 5.62974 25.4587 49.6079 7.00 
Bicarbonates ADW 3 18.3560 1.15508 .66689 15.4866 21.2254 17.07 
ADT 3 55.6537 2.90107 1.67493 48.4470 62.8603 52.72 
ASA 3 9.2940 .51125 .29517 8.0240 10.5640 8.74 
MM 3 56.8163 .90259 .52111 54.5742 59.0585 56.08 
Control 3 69.6667 7.02377 4.05518 52.2187 87.1147 63.00 
179 
 
Total 15 41.9573 24.67015 6.36981 28.2955 55.6192 8.74 
Turbidity ADW 3 23.8667 1.05040 .60645 21.2573 26.4760 22.80 
ADT 3 299.2667 220.44322 127.27295 -248.3446 846.8780 44.80 
ASA 3 89.9667 3.61156 2.08513 80.9951 98.9383 85.90 
MM 3 59.6667 2.51661 1.45297 53.4151 65.9183 57.00 
Control 3 2.5000 .65574 .37859 .8710 4.1290 1.90 
Total 15 95.0533 138.11919 35.66222 18.5655 171.5412 1.90 
Total hardness ADW 24.89954 42.24112
3 33.570333 3.4904626 2.0152195 29.5430 
3 3 
ADT 70.94828 74.30771
3 72.628000 .6761745 .3903895 71.9300 
9 1 
ASA 16.27634 19.08966
3 17.683000 .5662570 .3269286 17.1690 
0 0 
MM 48.38900 62.52832
3 55.458667 2.8459208 1.6430932 52.9180 
7 6 
Control 23.76095
3 11.666667 4.8686069 2.8108915 -.427623 6.3000 
7 
Total 24.98149 51.42117
15 38.201333 23.8719358 6.1637073 6.3000 
6 1 
Iron ADW 3 1.432667 .1178233 .0680253 1.139977 1.725356 1.3060 
ADT 3 .746333 .1216895 .0702575 .444040 1.048627 .6380 
ASA 3 .465333 .0470567 .0271682 .348438 .582229 .4110 
MM 3 1.826333 .1010264 .0583276 1.575370 2.077297 1.7240 
Control 3 2.373333 .9793025 .5654005 -.059389 4.806056 1.2800 
Total 15 1.368800 .8141143 .2102034 .917959 1.819641 .4110 
Cobalt ADW 3 .008133 .0085594 .0049418 -.013129 .029396 .0027 
ADT 3 .026000 .0065574 .0037859 .009710 .042290 .0190 
ASA 3 .019333 .0077675 .0044845 .000038 .038629 .0130 
MM 3 .006333 .0020817 .0012019 .001162 .011504 .0040 
Control 3 .000000 .0000000 .0000000 .000000 .000000 .0000 
Total 15 .011960 .0109686 .0028321 .005886 .018034 .0000 
Copper ADW 3 .000230 .0001127 .0000651 -.000050 .000510 .0001 
ADT 18.31159 24.32847
3 21.320033 1.2110607 .6992062 20.0001 
2 5 
ASA 3 .000200 .0001000 .0000577 -.000048 .000448 .0001 
MM 3 .000133 .0001528 .0000882 -.000246 .000513 .0000 
Control 3 .000000 .0000000 .0000000 .000000 .000000 .0000 
Total 15 4.264119 8.8391341 2.2822546 -.630830 9.159069 .0000 
Chromium  ADW 3 .000000 .0000000 .0000000 .000000 .000000 .0000 
ADT 3 .006000 .0020000 .0011547 .001032 .010968 .0040 
ASA 3 .000567 .0004041 .0002333 -.000437 .001571 .0002 
MM 3 .000267 .0002082 .0001202 -.000250 .000784 .0001 
Control 3 .000000 .0000000 .0000000 .000000 .000000 .0000 
Total 15 .001367 .0025294 .0006531 -.000034 .002767 .0000 
Nickel ADW 3 .020333 .0070946 .0040961 .002709 .037957 .0140 
ADT 3 .310000 .0867583 .0500899 .094480 .525520 .2160 
ASA 3 .295000 .0302655 .0174738 .219816 .370184 .2710 
MM 3 .210300 .0559917 .0323268 .071209 .349391 .1650 
Control 3 .020000 .0100000 .0057735 -.004841 .044841 .0100 
Total 15 .171127 .1385345 .0357695 .094409 .247845 .0100 
Zinc ADW 3 .080667 .0172143 .0099387 .037904 .123429 .0610 
ADT 3 .495000 .3733135 .2155327 -.432362 1.422362 .2730 
ASA 3 .003667 .0020817 .0012019 -.001504 .008838 .0020 
MM 3 .002667 .0020817 .0012019 -.002504 .007838 .0010 
Control 3 .003333 .0057735 .0033333 -.011009 .017676 .0000 
Total 15 .117067 .2432701 .0628121 -.017652 .251785 .0000 
Arsenic ADW 3 .000567 .0001528 .0000882 .000187 .000946 .0004 
ADT 3 .000333 .0002082 .0001202 -.000184 .000850 .0001 
ASA 3 .000267 .0001528 .0000882 -.000113 .000646 .0001 
MM 3 .001533 .0021385 .0012347 -.003779 .006846 .0002 
Control 3 .000133 .0000577 .0000333 -.000010 .000277 .0001 
Total 15 .000567 .0009686 .0002501 .000030 .001103 .0001 
180 
 
Mercury ADW 3 .000233 .0001528 .0000882 -.000146 .000613 .0001 
ADT 3 .000233 .0000577 .0000333 .000090 .000377 .0002 
ASA 3 .000267 .0002082 .0001202 -.000250 .000784 .0001 
MM 3 .000200 .0001000 .0000577 -.000048 .000448 .0001 
Control 3 .000000 .0000000 .0000000 .000000 .000000 .0000 
Total 15 .000187 .0001457 .0000376 .000106 .000267 .0000 
Magnesium ADW 3 5.780000 .4950758 .2858321 4.550164 7.009836 5.2900 
ADT 13.76181
3 11.506667 .9078179 .5241289 9.251522 10.7200 
1 
ASA 3 3.001000 .2272356 .1311945 2.436516 3.565484 2.8070 
MM 10.11943
3 8.015333 .8470138 .4890236 5.911234 7.2750 
2 
Control 3 2.166667 1.1930353 .6887993 -.796997 5.130331 1.2000 
Total 15 6.093933 3.5932108 .9277630 4.104080 8.083787 1.2000 
Manganese ADW 3 .454000 .0540278 .0311929 .319788 .588212 .4170 
ADT 3 .412667 .0712835 .0411555 .235589 .589745 .3540 
ASA 3 .389667 .0332315 .0191862 .307115 .472218 .3690 
MM 3 .437333 .0877971 .0506897 .219233 .655433 .3540 
Control 3 .192667 .0241937 .0139682 .132566 .252767 .1740 
Total 15 .377267 .1101093 .0284301 .316290 .438243 .1740 
Calcium ADW 3 4.166000 .7624356 .4401924 2.272005 6.059995 3.2900 
ADT 12.87518
3 10.546667 .9373544 .5411818 8.218149 9.6500 
4 
ASA 3 2.587000 .3456313 .1995503 1.728404 3.445596 2.2470 
MM 3 7.639667 .5536030 .3196229 6.264440 9.014893 7.2540 
Control 19.12184
3 12.566667 2.6388129 1.5235193 6.011492 10.8000 
1 
Total 15 7.501200 4.0388839 1.0428353 5.264541 9.737859 2.2470 
Potassium ADW 3 .295667 .0320052 .0184782 .216161 .375172 .2640 
ADT 3 .511000 .0671789 .0387857 .344118 .677882 .4390 
ASA 3 .455333 .4084046 .2357925 -.559200 1.469867 .1580 
MM 3 .624333 .0461122 .0266229 .509784 .738882 .5720 
Control 3 .456667 .1517674 .0876229 .079656 .833678 .3200 
Total 15 .468600 .2006788 .0518150 .357468 .579732 .1580 
Sodium ADW 10.92198
3 8.503333 .9736387 .5621306 6.084681 7.4370 
6 
ADT 12.75493 18.41840
3 15.586667 1.1399269 .6581371 14.3200 
1 2 
ASA 10.04306
3 8.344000 .6839656 .3948877 6.644935 7.5920 
5 
MM 14.73250 20.27415
3 17.503333 1.1154072 .6439807 16.5500 
8 9 
Control 3 2.200000 .4358899 .2516611 1.117189 3.282811 1.7000 
Total 13.62092
15 10.427467 5.7666367 1.4889392 7.234010 1.7000 
4 
Lead ADW 3 .006333 .0020817 .0012019 .001162 .011504 .0040 
ADT 3 .004667 .0030551 .0017638 -.002922 .012256 .0020 
ASA 3 .015933 .0142553 .0082303 -.019479 .051345 .0048 
MM 3 .003667 .0020817 .0012019 -.001504 .008838 .0020 
Control 3 .000267 .0001528 .0000882 -.000113 .000646 .0001 
Total 15 .006173 .0078315 .0020221 .001836 .010510 .0001 
Cadmium ADW 3 .281200 .2250893 .1299554 -.277953 .840353 .1143 
ADT 3 .191033 .0642965 .0371216 .031312 .350755 .1168 
ASA 3 .190400 .0900457 .0519879 -.033286 .414086 .1093 
MM 3 .209300 .0902910 .0521296 -.014995 .433595 .1079 
Control 3 .060000 .0120000 .0069282 .030190 .089810 .0480 
Total 15 .186387 .1250673 .0322922 .117127 .255647 .0480 
 
 
 
181 
 
Appendix B: Analysis of Variance (ANOVA) of Physico-Chemical Parameters, 
Mpohor Wassa East 
ANOVA 
 Sum of Squares df Mean Square F p-value 
Temperature Between Groups 1.039 4 .260 .250 .903 
Within Groups 10.371 10 1.037   
Total 11.410 14    
Conductivity Between Groups 15716.489 4 3929.122 16.286 .000 
Within Groups 2412.640 10 241.264   
Total 18129.129 14    
Total Dissolved Between Groups 7578.923 4 1894.731 21.854 .000 
solids Within Groups 867.007 10 86.701   
Total 8445.929 14    
pH Between Groups .887 4 .222 .256 .900 
Within Groups 8.667 10 .867   
Total 9.553 14    
Salinity Between Groups .103 4 .026 5.500 .013 
Within Groups .047 10 .005   
Total .149 14    
Total Between Groups 911545.936 4 227886.484 1780.725 .000 
Suspended Within Groups 1279.740 10 127.974   
solids 
Total 912825.676 14    
Alkalinity Between Groups 6421.733 4 1605.433 68.608 .000 
Within Groups 234.000 10 23.400   
Total 6655.733 14    
Bicarbonates Between Groups 8400.309 4 2100.077 174.542 .000 
Within Groups 120.320 10 12.032   
Total 8520.628 14    
Turbidity Between Groups 169844.511 4 42461.128 4.367 .027 
Within Groups 97232.247 10 9723.225   
Total 267076.757 14    
Total hardness Between Groups 7888.643 4 1972.161 220.285 .000 
Within Groups 89.528 10 8.953   
Total 7978.170 14    
iron Between Groups 7.279 4 1.820 9.097 .002 
Within Groups 2.000 10 .200   
Total 9.279 14    
Cobalt Between Groups .001 4 .000 9.137 .002 
Within Groups .000 10 .000   
Total .002 14    
Copper Between Groups 1090.891 4 272.723 929.736 .000 
Within Groups 2.933 10 .293   
Total 1093.824 14    
Chromium Between Groups .000 4 .000 24.116 .000 
Within Groups .000 10 .000   
Total .000 14    
Nickel Between Groups .245 4 .061 26.136 .000 
Within Groups .023 10 .002   
Total .269 14    
Zinc Between Groups .549 4 .137 4.913 .019 
182 
 
Within Groups .279 10 .028   
Total .829 14    
Arsenic Between Groups .000 4 .000 1.018 .443 
Within Groups .000 10 .000   
Total .000 14    
Mercury Between Groups .000 4 .000 2.146 .149 
Within Groups .000 10 .000   
Total .000 14    
Magnesium Between Groups 174.233 4 43.558 66.774 .000 
Within Groups 6.523 10 .652   
Total 180.756 14    
Manganese Between Groups .135 4 .034 9.695 .002 
Within Groups .035 10 .003   
Total .170 14    
Calcium Between Groups 210.678 4 52.669 29.759 .000 
Within Groups 17.698 10 1.770   
Total 228.376 14    
Potassium Between Groups .169 4 .042 1.069 .421 
Within Groups .395 10 .039   
Total .564 14    
Sodium Between Groups 457.259 4 114.315 137.750 .000 
Within Groups 8.299 10 .830   
Total 465.557 14    
Lead Between Groups .000 4 .000 2.351 .124 
Within Groups .000 10 .000   
Total .001 14    
Cadmium Between Groups .077 4 .019 1.344 .320 
Within Groups .142 10 .014   
Total .219 14    
 
 
 
 
 
 
 
183 
 
Appendix C: Tukey’s HSD Comparison, Mpohor Wassa East 
 
 
Dependent Variable (I) Sampling site (J) Sampling site Mean Difference Std. Error Sig. 95% Confidence Interval 
(I-J) Lower Bound Upper Bound 
Temperature ADW ADT .81333 .83151 .859 -1.9232 3.5499 
ASA .28333 .83151 .997 -2.4532 3.0199 
MM .44000 .83151 .982 -2.2966 3.1766 
Control .42333 .83151 .985 -2.3132 3.1599 
ADT ADW -.81333 .83151 .859 -3.5499 1.9232 
ASA -.53000 .83151 .965 -3.2666 2.2066 
MM -.37333 .83151 .990 -3.1099 2.3632 
Control -.39000 .83151 .989 -3.1266 2.3466 
ASA ADW -.28333 .83151 .997 -3.0199 2.4532 
ADT .53000 .83151 .965 -2.2066 3.2666 
MM .15667 .83151 1.000 -2.5799 2.8932 
Control .14000 .83151 1.000 -2.5966 2.8766 
MM ADW -.44000 .83151 .982 -3.1766 2.2966 
ADT .37333 .83151 .990 -2.3632 3.1099 
ASA -.15667 .83151 1.000 -2.8932 2.5799 
Control -.01667 .83151 1.000 -2.7532 2.7199 
Control ADW -.42333 .83151 .985 -3.1599 2.3132 
ADT .39000 .83151 .989 -2.3466 3.1266 
ASA -.14000 .83151 1.000 -2.8766 2.5966 
MM .01667 .83151 1.000 -2.7199 2.7532 
Conductivity ADW ADT -4.80000 12.68238 .995 -46.5387 36.9387 
ASA 30.83333 12.68238 .184 -10.9054 72.5721 
MM -28.83333 12.68238 .230 -70.5721 12.9054 
Control 64.83333* 12.68238 .003 23.0946 106.5721 
ADT ADW 4.80000 12.68238 .995 -36.9387 46.5387 
ASA 35.63333 12.68238 .105 -6.1054 77.3721 
MM -24.03333 12.68238 .378 -65.7721 17.7054 
184 
 
Control 69.63333* 12.68238 .002 27.8946 111.3721 
ASA ADW -30.83333 12.68238 .184 -72.5721 10.9054 
ADT -35.63333 12.68238 .105 -77.3721 6.1054 
MM -59.66667* 12.68238 .006 -101.4054 -17.9279 
Control 34.00000 12.68238 .127 -7.7387 75.7387 
MM ADW 28.83333 12.68238 .230 -12.9054 70.5721 
ADT 24.03333 12.68238 .378 -17.7054 65.7721 
ASA 59.66667* 12.68238 .006 17.9279 101.4054 
Control 93.66667* 12.68238 .000 51.9279 135.4054 
Control ADW -64.83333* 12.68238 .003 -106.5721 -23.0946 
ADT -69.63333* 12.68238 .002 -111.3721 -27.8946 
ASA -34.00000 12.68238 .127 -75.7387 7.7387 
MM -93.66667* 12.68238 .000 -135.4054 -51.9279 
Total Dissolved solids ADW ADT -12.70000 7.60266 .491 -37.7210 12.3210 
ASA 7.96667 7.60266 .828 -17.0543 32.9876 
MM -31.36667* 7.60266 .014 -56.3876 -6.3457 
Control 36.30000* 7.60266 .005 11.2790 61.3210 
ADT ADW 12.70000 7.60266 .491 -12.3210 37.7210 
ASA 20.66667 7.60266 .120 -4.3543 45.6876 
MM -18.66667 7.60266 .178 -43.6876 6.3543 
Control 49.00000* 7.60266 .001 23.9790 74.0210 
ASA ADW -7.96667 7.60266 .828 -32.9876 17.0543 
ADT -20.66667 7.60266 .120 -45.6876 4.3543 
MM -39.33333* 7.60266 .003 -64.3543 -14.3124 
Control 28.33333* 7.60266 .025 3.3124 53.3543 
MM ADW 31.36667* 7.60266 .014 6.3457 56.3876 
ADT 18.66667 7.60266 .178 -6.3543 43.6876 
ASA 39.33333* 7.60266 .003 14.3124 64.3543 
Control 67.66667* 7.60266 .000 42.6457 92.6876 
Control ADW -36.30000* 7.60266 .005 -61.3210 -11.2790 
ADT -49.00000* 7.60266 .001 -74.0210 -23.9790 
ASA -28.33333* 7.60266 .025 -53.3543 -3.3124 
MM -67.66667* 7.60266 .000 -92.6876 -42.6457 
pH ADW ADT -.30000 .76012 .994 -2.8016 2.2016 
185 
 
ASA .20000 .76012 .999 -2.3016 2.7016 
MM .16667 .76012 .999 -2.3349 2.6683 
Control -.40000 .76012 .983 -2.9016 2.1016 
ADT ADW .30000 .76012 .994 -2.2016 2.8016 
ASA .50000 .76012 .961 -2.0016 3.0016 
MM .46667 .76012 .970 -2.0349 2.9683 
Control -.10000 .76012 1.000 -2.6016 2.4016 
ASA ADW -.20000 .76012 .999 -2.7016 2.3016 
ADT -.50000 .76012 .961 -3.0016 2.0016 
MM -.03333 .76012 1.000 -2.5349 2.4683 
Control -.60000 .76012 .928 -3.1016 1.9016 
MM ADW -.16667 .76012 .999 -2.6683 2.3349 
ADT -.46667 .76012 .970 -2.9683 2.0349 
ASA .03333 .76012 1.000 -2.4683 2.5349 
Control -.56667 .76012 .940 -3.0683 1.9349 
Control ADW .40000 .76012 .983 -2.1016 2.9016 
ADT .10000 .76012 1.000 -2.4016 2.6016 
ASA .60000 .76012 .928 -1.9016 3.1016 
MM .56667 .76012 .940 -1.9349 3.0683 
Salinity ADW ADT -.10000 .05578 .427 -.2836 .0836 
ASA .10000 .05578 .427 -.0836 .2836 
MM -.06667 .05578 .754 -.2502 .1169 
Control .10000 .05578 .427 -.0836 .2836 
ADT ADW .10000 .05578 .427 -.0836 .2836 
ASA .20000* .05578 .032 .0164 .3836 
MM .03333 .05578 .972 -.1502 .2169 
Control .20000* .05578 .032 .0164 .3836 
ASA ADW -.10000 .05578 .427 -.2836 .0836 
ADT -.20000* .05578 .032 -.3836 -.0164 
MM -.16667 .05578 .080 -.3502 .0169 
Control .00000 .05578 1.000 -.1836 .1836 
MM ADW .06667 .05578 .754 -.1169 .2502 
ADT -.03333 .05578 .972 -.2169 .1502 
ASA .16667 .05578 .080 -.0169 .3502 
186 
 
Control .16667 .05578 .080 -.0169 .3502 
Control ADW -.10000 .05578 .427 -.2836 .0836 
ADT -.20000* .05578 .032 -.3836 -.0164 
ASA .00000 .05578 1.000 -.1836 .1836 
MM -.16667 .05578 .080 -.3502 .0169 
Total Suspended solids ADW ADT -140.00000* 9.23667 .000 -170.3986 -109.6014 
ASA -393.66667* 9.23667 .000 -424.0653 -363.2680 
MM -640.00000* 9.23667 .000 -670.3986 -609.6014 
Control -10.46667 9.23667 .786 -40.8653 19.9320 
ADT ADW 140.00000* 9.23667 .000 109.6014 170.3986 
ASA -253.66667* 9.23667 .000 -284.0653 -223.2680 
MM -500.00000* 9.23667 .000 -530.3986 -469.6014 
Control 129.53333* 9.23667 .000 99.1347 159.9320 
ASA ADW 393.66667* 9.23667 .000 363.2680 424.0653 
ADT 253.66667* 9.23667 .000 223.2680 284.0653 
MM -246.33333* 9.23667 .000 -276.7320 -215.9347 
Control 383.20000* 9.23667 .000 352.8014 413.5986 
MM ADW 640.00000* 9.23667 .000 609.6014 670.3986 
ADT 500.00000* 9.23667 .000 469.6014 530.3986 
ASA 246.33333* 9.23667 .000 215.9347 276.7320 
Control 629.53333* 9.23667 .000 599.1347 659.9320 
Control ADW 10.46667 9.23667 .786 -19.9320 40.8653 
ADT -129.53333* 9.23667 .000 -159.9320 -99.1347 
ASA -383.20000* 9.23667 .000 -413.5986 -352.8014 
MM -629.53333* 9.23667 .000 -659.9320 -599.1347 
Alkalinity ADW ADT -29.66667* 3.94968 .000 -42.6654 -16.6679 
ASA 9.00000 3.94968 .228 -3.9987 21.9987 
MM -30.66667* 3.94968 .000 -43.6654 -17.6679 
Control -46.33333* 3.94968 .000 -59.3321 -33.3346 
ADT ADW 29.66667* 3.94968 .000 16.6679 42.6654 
ASA 38.66667* 3.94968 .000 25.6679 51.6654 
MM -1.00000 3.94968 .999 -13.9987 11.9987 
Control -16.66667* 3.94968 .012 -29.6654 -3.6679 
ASA ADW -9.00000 3.94968 .228 -21.9987 3.9987 
187 
 
ADT -38.66667* 3.94968 .000 -51.6654 -25.6679 
MM -39.66667* 3.94968 .000 -52.6654 -26.6679 
Control -55.33333* 3.94968 .000 -68.3321 -42.3346 
MM ADW 30.66667* 3.94968 .000 17.6679 43.6654 
ADT 1.00000 3.94968 .999 -11.9987 13.9987 
ASA 39.66667* 3.94968 .000 26.6679 52.6654 
Control -15.66667* 3.94968 .018 -28.6654 -2.6679 
Control ADW 46.33333* 3.94968 .000 33.3346 59.3321 
ADT 16.66667* 3.94968 .012 3.6679 29.6654 
ASA 55.33333* 3.94968 .000 42.3346 68.3321 
MM 15.66667* 3.94968 .018 2.6679 28.6654 
Bicarbonates ADW ADT -37.29767* 2.83219 .000 -46.6186 -27.9767 
ASA 9.06200 2.83219 .058 -.2590 18.3830 
MM -38.46033* 2.83219 .000 -47.7813 -29.1394 
Control -51.31067* 2.83219 .000 -60.6316 -41.9897 
ADT ADW 37.29767* 2.83219 .000 27.9767 46.6186 
ASA 46.35967* 2.83219 .000 37.0387 55.6806 
MM -1.16267 2.83219 .993 -10.4836 8.1583 
Control -14.01300* 2.83219 .004 -23.3340 -4.6920 
ASA ADW -9.06200 2.83219 .058 -18.3830 .2590 
ADT -46.35967* 2.83219 .000 -55.6806 -37.0387 
MM -47.52233* 2.83219 .000 -56.8433 -38.2014 
Control -60.37267* 2.83219 .000 -69.6936 -51.0517 
MM ADW 38.46033* 2.83219 .000 29.1394 47.7813 
ADT 1.16267 2.83219 .993 -8.1583 10.4836 
ASA 47.52233* 2.83219 .000 38.2014 56.8433 
Control -12.85033* 2.83219 .007 -22.1713 -3.5294 
Control ADW 51.31067* 2.83219 .000 41.9897 60.6316 
ADT 14.01300* 2.83219 .004 4.6920 23.3340 
ASA 60.37267* 2.83219 .000 51.0517 69.6936 
MM 12.85033* 2.83219 .007 3.5294 22.1713 
Turbidity ADW ADT -275.40000* 80.51180 .041 -540.3709 -10.4291 
ASA -66.10000 80.51180 .918 -331.0709 198.8709 
MM -35.80000 80.51180 .991 -300.7709 229.1709 
188 
 
Control 21.36667 80.51180 .999 -243.6043 286.3376 
ADT ADW 275.40000* 80.51180 .041 10.4291 540.3709 
ASA 209.30000 80.51180 .144 -55.6709 474.2709 
MM 239.60000 80.51180 .081 -25.3709 504.5709 
Control 296.76667* 80.51180 .027 31.7957 561.7376 
ASA ADW 66.10000 80.51180 .918 -198.8709 331.0709 
ADT -209.30000 80.51180 .144 -474.2709 55.6709 
MM 30.30000 80.51180 .995 -234.6709 295.2709 
Control 87.46667 80.51180 .810 -177.5043 352.4376 
MM ADW 35.80000 80.51180 .991 -229.1709 300.7709 
ADT -239.60000 80.51180 .081 -504.5709 25.3709 
ASA -30.30000 80.51180 .995 -295.2709 234.6709 
Control 57.16667 80.51180 .949 -207.8043 322.1376 
Control ADW -21.36667 80.51180 .999 -286.3376 243.6043 
ADT -296.76667* 80.51180 .027 -561.7376 -31.7957 
ASA -87.46667 80.51180 .810 -352.4376 177.5043 
MM -57.16667 80.51180 .949 -322.1376 207.8043 
Total hardness ADW ADT -39.0576667* 2.4430524 .000 -47.097953 -31.017381 
ASA 15.8873333* 2.4430524 .001 7.847047 23.927619 
MM -21.8883333* 2.4430524 .000 -29.928619 -13.848047 
Control 21.9036667* 2.4430524 .000 13.863381 29.943953 
ADT ADW 39.0576667* 2.4430524 .000 31.017381 47.097953 
ASA 54.9450000* 2.4430524 .000 46.904714 62.985286 
MM 17.1693333* 2.4430524 .000 9.129047 25.209619 
Control 60.9613333* 2.4430524 .000 52.921047 69.001619 
ASA ADW -15.8873333* 2.4430524 .001 -23.927619 -7.847047 
ADT -54.9450000* 2.4430524 .000 -62.985286 -46.904714 
MM -37.7756667* 2.4430524 .000 -45.815953 -29.735381 
Control 6.0163333 2.4430524 .176 -2.023953 14.056619 
MM ADW 21.8883333* 2.4430524 .000 13.848047 29.928619 
ADT -17.1693333* 2.4430524 .000 -25.209619 -9.129047 
ASA 37.7756667* 2.4430524 .000 29.735381 45.815953 
Control 43.7920000* 2.4430524 .000 35.751714 51.832286 
Control ADW -21.9036667* 2.4430524 .000 -29.943953 -13.863381 
189 
 
ADT -60.9613333* 2.4430524 .000 -69.001619 -52.921047 
ASA -6.0163333 2.4430524 .176 -14.056619 2.023953 
MM -43.7920000* 2.4430524 .000 -51.832286 -35.751714 
Iron ADW ADT .6863333 .3651748 .385 -.515487 1.888154 
ASA .9673333 .3651748 .134 -.234487 2.169154 
MM -.3936667 .3651748 .814 -1.595487 .808154 
Control -.9406667 .3651748 .149 -2.142487 .261154 
ADT ADW -.6863333 .3651748 .385 -1.888154 .515487 
ASA .2810000 .3651748 .934 -.920820 1.482820 
MM -1.0800000 .3651748 .084 -2.281820 .121820 
Control -1.6270000* .3651748 .008 -2.828820 -.425180 
ASA ADW -.9673333 .3651748 .134 -2.169154 .234487 
ADT -.2810000 .3651748 .934 -1.482820 .920820 
MM -1.3610000* .3651748 .025 -2.562820 -.159180 
Control -1.9080000* .3651748 .003 -3.109820 -.706180 
MM ADW .3936667 .3651748 .814 -.808154 1.595487 
ADT 1.0800000 .3651748 .084 -.121820 2.281820 
ASA 1.3610000* .3651748 .025 .159180 2.562820 
Control -.5470000 .3651748 .586 -1.748820 .654820 
Control ADW .9406667 .3651748 .149 -.261154 2.142487 
ADT 1.6270000* .3651748 .008 .425180 2.828820 
ASA 1.9080000* .3651748 .003 .706180 3.109820 
MM .5470000 .3651748 .586 -.654820 1.748820 
Cobalt ADW ADT -.0178667* .0049116 .029 -.034031 -.001702 
ASA -.0112000 .0049116 .228 -.027365 .004965 
MM .0018000 .0049116 .996 -.014365 .017965 
Control .0081333 .0049116 .498 -.008031 .024298 
ADT ADW .0178667* .0049116 .029 .001702 .034031 
ASA .0066667 .0049116 .665 -.009498 .022831 
MM .0196667* .0049116 .017 .003502 .035831 
Control .0260000* .0049116 .003 .009835 .042165 
ASA ADW .0112000 .0049116 .228 -.004965 .027365 
ADT -.0066667 .0049116 .665 -.022831 .009498 
MM .0130000 .0049116 .134 -.003165 .029165 
190 
 
Control .0193333* .0049116 .018 .003169 .035498 
MM ADW -.0018000 .0049116 .996 -.017965 .014365 
ADT -.0196667* .0049116 .017 -.035831 -.003502 
ASA -.0130000 .0049116 .134 -.029165 .003165 
Control .0063333 .0049116 .703 -.009831 .022498 
Control ADW -.0081333 .0049116 .498 -.024298 .008031 
ADT -.0260000* .0049116 .003 -.042165 -.009835 
ASA -.0193333* .0049116 .018 -.035498 -.003169 
MM -.0063333 .0049116 .703 -.022498 .009831 
Copper ADW ADT -21.3198033* .4422168 .000 -22.775175 -19.864431 
ASA .0000300 .4422168 1.000 -1.455342 1.455402 
MM .0000967 .4422168 1.000 -1.455275 1.455469 
Control .0002300 .4422168 1.000 -1.455142 1.455602 
ADT ADW 21.3198033* .4422168 .000 19.864431 22.775175 
ASA 21.3198333* .4422168 .000 19.864461 22.775205 
MM 21.3199000* .4422168 .000 19.864528 22.775272 
Control 21.3200333* .4422168 .000 19.864661 22.775405 
ASA ADW -.0000300 .4422168 1.000 -1.455402 1.455342 
ADT -21.3198333* .4422168 .000 -22.775205 -19.864461 
MM .0000667 .4422168 1.000 -1.455305 1.455439 
Control .0002000 .4422168 1.000 -1.455172 1.455572 
MM ADW -.0000967 .4422168 1.000 -1.455469 1.455275 
ADT -21.3199000* .4422168 .000 -22.775272 -19.864528 
ASA -.0000667 .4422168 1.000 -1.455439 1.455305 
Control .0001333 .4422168 1.000 -1.455239 1.455505 
Control ADW -.0002300 .4422168 1.000 -1.455602 1.455142 
ADT -21.3200333* .4422168 .000 -22.775405 -19.864661 
ASA -.0002000 .4422168 1.000 -1.455572 1.455172 
MM -.0001333 .4422168 1.000 -1.455505 1.455239 
Chromium ADW ADT -.0060000* .0007489 .000 -.008465 -.003535 
ASA -.0005667 .0007489 .937 -.003031 .001898 
MM -.0002667 .0007489 .996 -.002731 .002198 
Control .0000000 .0007489 1.000 -.002465 .002465 
ADT ADW .0060000* .0007489 .000 .003535 .008465 
191 
 
ASA .0054333* .0007489 .000 .002969 .007898 
MM .0057333* .0007489 .000 .003269 .008198 
Control .0060000* .0007489 .000 .003535 .008465 
ASA ADW .0005667 .0007489 .937 -.001898 .003031 
ADT -.0054333* .0007489 .000 -.007898 -.002969 
MM .0003000 .0007489 .994 -.002165 .002765 
Control .0005667 .0007489 .937 -.001898 .003031 
MM ADW .0002667 .0007489 .996 -.002198 .002731 
ADT -.0057333* .0007489 .000 -.008198 -.003269 
ASA -.0003000 .0007489 .994 -.002765 .002165 
Control .0002667 .0007489 .996 -.002198 .002731 
Control ADW .0000000 .0007489 1.000 -.002465 .002465 
ADT -.0060000* .0007489 .000 -.008465 -.003535 
ASA -.0005667 .0007489 .937 -.003031 .001898 
MM -.0002667 .0007489 .996 -.002731 .002198 
Nickel ADW ADT -.2896667* .0395447 .000 -.419812 -.159522 
ASA -.2746667* .0395447 .000 -.404812 -.144522 
MM -.1899667* .0395447 .005 -.320112 -.059822 
Control .0003333 .0395447 1.000 -.129812 .130478 
ADT ADW .2896667* .0395447 .000 .159522 .419812 
ASA .0150000 .0395447 .995 -.115145 .145145 
MM .0997000 .0395447 .161 -.030445 .229845 
Control .2900000* .0395447 .000 .159855 .420145 
ASA ADW .2746667* .0395447 .000 .144522 .404812 
ADT -.0150000 .0395447 .995 -.145145 .115145 
MM .0847000 .0395447 .275 -.045445 .214845 
Control .2750000* .0395447 .000 .144855 .405145 
MM ADW .1899667* .0395447 .005 .059822 .320112 
ADT -.0997000 .0395447 .161 -.229845 .030445 
ASA -.0847000 .0395447 .275 -.214845 .045445 
Control .1903000* .0395447 .005 .060155 .320445 
Control ADW -.0003333 .0395447 1.000 -.130478 .129812 
ADT -.2900000* .0395447 .000 -.420145 -.159855 
ASA -.2750000* .0395447 .000 -.405145 -.144855 
192 
 
MM -.1903000* .0395447 .005 -.320445 -.060155 
Zinc ADW ADT -.4143333 .1364802 .074 -.863501 .034834 
ASA .0770000 .1364802 .977 -.372168 .526168 
MM .0780000 .1364802 .976 -.371168 .527168 
Control .0773333 .1364802 .977 -.371834 .526501 
ADT ADW .4143333 .1364802 .074 -.034834 .863501 
ASA .4913333* .1364802 .031 .042166 .940501 
MM .4923333* .1364802 .031 .043166 .941501 
Control .4916667* .1364802 .031 .042499 .940834 
ASA ADW -.0770000 .1364802 .977 -.526168 .372168 
ADT -.4913333* .1364802 .031 -.940501 -.042166 
MM .0010000 .1364802 1.000 -.448168 .450168 
Control .0003333 .1364802 1.000 -.448834 .449501 
MM ADW -.0780000 .1364802 .976 -.527168 .371168 
ADT -.4923333* .1364802 .031 -.941501 -.043166 
ASA -.0010000 .1364802 1.000 -.450168 .448168 
Control -.0006667 .1364802 1.000 -.449834 .448501 
Control ADW -.0773333 .1364802 .977 -.526501 .371834 
ADT -.4916667* .1364802 .031 -.940834 -.042499 
ASA -.0003333 .1364802 1.000 -.449501 .448834 
MM .0006667 .1364802 1.000 -.448501 .449834 
Arsenic ADW ADT .0002333 .0007888 .998 -.002363 .002829 
ASA .0003000 .0007888 .995 -.002296 .002896 
MM -.0009667 .0007888 .738 -.003563 .001629 
Control .0004333 .0007888 .980 -.002163 .003029 
ADT ADW -.0002333 .0007888 .998 -.002829 .002363 
ASA .0000667 .0007888 1.000 -.002529 .002663 
MM -.0012000 .0007888 .573 -.003796 .001396 
Control .0002000 .0007888 .999 -.002396 .002796 
ASA ADW -.0003000 .0007888 .995 -.002896 .002296 
ADT -.0000667 .0007888 1.000 -.002663 .002529 
MM -.0012667 .0007888 .526 -.003863 .001329 
Control .0001333 .0007888 1.000 -.002463 .002729 
MM ADW .0009667 .0007888 .738 -.001629 .003563 
193 
 
ADT .0012000 .0007888 .573 -.001396 .003796 
ASA .0012667 .0007888 .526 -.001329 .003863 
Control .0014000 .0007888 .436 -.001196 .003996 
Control ADW -.0004333 .0007888 .980 -.003029 .002163 
ADT -.0002000 .0007888 .999 -.002796 .002396 
ASA -.0001333 .0007888 1.000 -.002729 .002463 
MM -.0014000 .0007888 .436 -.003996 .001196 
Mercury ADW ADT .0000000 .0001033 1.000 -.000340 .000340 
ASA -.0000333 .0001033 .997 -.000373 .000307 
MM .0000333 .0001033 .997 -.000307 .000373 
Control .0002333 .0001033 .235 -.000107 .000573 
ADT ADW .0000000 .0001033 1.000 -.000340 .000340 
ASA -.0000333 .0001033 .997 -.000373 .000307 
MM .0000333 .0001033 .997 -.000307 .000373 
Control .0002333 .0001033 .235 -.000107 .000573 
ASA ADW .0000333 .0001033 .997 -.000307 .000373 
ADT .0000333 .0001033 .997 -.000307 .000373 
MM .0000667 .0001033 .964 -.000273 .000407 
Control .0002667 .0001033 .148 -.000073 .000607 
MM ADW -.0000333 .0001033 .997 -.000373 .000307 
ADT -.0000333 .0001033 .997 -.000373 .000307 
ASA -.0000667 .0001033 .964 -.000407 .000273 
Control .0002000 .0001033 .359 -.000140 .000540 
Control ADW -.0002333 .0001033 .235 -.000573 .000107 
ADT -.0002333 .0001033 .235 -.000573 .000107 
ASA -.0002667 .0001033 .148 -.000607 .000073 
MM -.0002000 .0001033 .359 -.000540 .000140 
Magnesium ADW ADT -5.7266667* .6594579 .000 -7.896997 -3.556337 
ASA 2.7790000* .6594579 .012 .608670 4.949330 
MM -2.2353333* .6594579 .043 -4.405663 -.065003 
Control 3.6133333* .6594579 .002 1.443003 5.783663 
ADT ADW 5.7266667* .6594579 .000 3.556337 7.896997 
ASA 8.5056667* .6594579 .000 6.335337 10.675997 
MM 3.4913333* .6594579 .003 1.321003 5.661663 
194 
 
Control 9.3400000* .6594579 .000 7.169670 11.510330 
ASA ADW -2.7790000* .6594579 .012 -4.949330 -.608670 
ADT -8.5056667* .6594579 .000 -10.675997 -6.335337 
MM -5.0143333* .6594579 .000 -7.184663 -2.844003 
Control .8343333 .6594579 .717 -1.335997 3.004663 
MM ADW 2.2353333* .6594579 .043 .065003 4.405663 
ADT -3.4913333* .6594579 .003 -5.661663 -1.321003 
ASA 5.0143333* .6594579 .000 2.844003 7.184663 
Control 5.8486667* .6594579 .000 3.678337 8.018997 
Control ADW -3.6133333* .6594579 .002 -5.783663 -1.443003 
ADT -9.3400000* .6594579 .000 -11.510330 -7.169670 
ASA -.8343333 .6594579 .717 -3.004663 1.335997 
MM -5.8486667* .6594579 .000 -8.018997 -3.678337 
Manganese ADW ADT .0413333 .0481641 .906 -.117179 .199845 
ASA .0643333 .0481641 .677 -.094179 .222845 
MM .0166667 .0481641 .996 -.141845 .175179 
Control .2613333* .0481641 .002 .102821 .419845 
ADT ADW -.0413333 .0481641 .906 -.199845 .117179 
ASA .0230000 .0481641 .988 -.135512 .181512 
MM -.0246667 .0481641 .984 -.183179 .133845 
Control .2200000* .0481641 .007 .061488 .378512 
ASA ADW -.0643333 .0481641 .677 -.222845 .094179 
ADT -.0230000 .0481641 .988 -.181512 .135512 
MM -.0476667 .0481641 .854 -.206179 .110845 
Control .1970000* .0481641 .015 .038488 .355512 
MM ADW -.0166667 .0481641 .996 -.175179 .141845 
ADT .0246667 .0481641 .984 -.133845 .183179 
ASA .0476667 .0481641 .854 -.110845 .206179 
Control .2446667* .0481641 .003 .086155 .403179 
Control ADW -.2613333* .0481641 .002 -.419845 -.102821 
ADT -.2200000* .0481641 .007 -.378512 -.061488 
ASA -.1970000* .0481641 .015 -.355512 -.038488 
MM -.2446667* .0481641 .003 -.403179 -.086155 
Calcium ADW ADT -6.3806667* 1.0862297 .001 -9.955538 -2.805796 
195 
 
ASA 1.5790000 1.0862297 .611 -1.995871 5.153871 
MM -3.4736667 1.0862297 .058 -7.048538 .101204 
Control -8.4006667* 1.0862297 .000 -11.975538 -4.825796 
ADT ADW 6.3806667* 1.0862297 .001 2.805796 9.955538 
ASA 7.9596667* 1.0862297 .000 4.384796 11.534538 
MM 2.9070000 1.0862297 .128 -.667871 6.481871 
Control -2.0200000 1.0862297 .395 -5.594871 1.554871 
ASA ADW -1.5790000 1.0862297 .611 -5.153871 1.995871 
ADT -7.9596667* 1.0862297 .000 -11.534538 -4.384796 
MM -5.0526667* 1.0862297 .006 -8.627538 -1.477796 
Control -9.9796667* 1.0862297 .000 -13.554538 -6.404796 
MM ADW 3.4736667 1.0862297 .058 -.101204 7.048538 
ADT -2.9070000 1.0862297 .128 -6.481871 .667871 
ASA 5.0526667* 1.0862297 .006 1.477796 8.627538 
Control -4.9270000* 1.0862297 .007 -8.501871 -1.352129 
Control ADW 8.4006667* 1.0862297 .000 4.825796 11.975538 
ADT 2.0200000 1.0862297 .395 -1.554871 5.594871 
ASA 9.9796667* 1.0862297 .000 6.404796 13.554538 
MM 4.9270000* 1.0862297 .007 1.352129 8.501871 
Potassium ADW ADT -.2153333 .1622719 .682 -.749384 .318717 
ASA -.1596667 .1622719 .857 -.693717 .374384 
MM -.3286667 .1622719 .321 -.862717 .205384 
Control -.1610000 .1622719 .853 -.695050 .373050 
ADT ADW .2153333 .1622719 .682 -.318717 .749384 
ASA .0556667 .1622719 .997 -.478384 .589717 
MM -.1133333 .1622719 .952 -.647384 .420717 
Control .0543333 .1622719 .997 -.479717 .588384 
ASA ADW .1596667 .1622719 .857 -.374384 .693717 
ADT -.0556667 .1622719 .997 -.589717 .478384 
MM -.1690000 .1622719 .831 -.703050 .365050 
Control -.0013333 .1622719 1.000 -.535384 .532717 
MM ADW .3286667 .1622719 .321 -.205384 .862717 
ADT .1133333 .1622719 .952 -.420717 .647384 
ASA .1690000 .1622719 .831 -.365050 .703050 
196 
 
Control .1676667 .1622719 .835 -.366384 .701717 
Control ADW .1610000 .1622719 .853 -.373050 .695050 
ADT -.0543333 .1622719 .997 -.588384 .479717 
ASA .0013333 .1622719 1.000 -.532717 .535384 
MM -.1676667 .1622719 .835 -.701717 .366384 
Sodium ADW ADT -7.0833333* .7438054 .000 -9.531258 -4.635409 
ASA .1593333 .7438054 .999 -2.288591 2.607258 
MM -9.0000000* .7438054 .000 -11.447925 -6.552075 
Control 6.3033333* .7438054 .000 3.855409 8.751258 
ADT ADW 7.0833333* .7438054 .000 4.635409 9.531258 
ASA 7.2426667* .7438054 .000 4.794742 9.690591 
MM -1.9166667 .7438054 .149 -4.364591 .531258 
Control 13.3866667* .7438054 .000 10.938742 15.834591 
ASA ADW -.1593333 .7438054 .999 -2.607258 2.288591 
ADT -7.2426667* .7438054 .000 -9.690591 -4.794742 
MM -9.1593333* .7438054 .000 -11.607258 -6.711409 
Control 6.1440000* .7438054 .000 3.696075 8.591925 
MM ADW 9.0000000* .7438054 .000 6.552075 11.447925 
ADT 1.9166667 .7438054 .149 -.531258 4.364591 
ASA 9.1593333* .7438054 .000 6.711409 11.607258 
Control 15.3033333* .7438054 .000 12.855409 17.751258 
Control ADW -6.3033333* .7438054 .000 -8.751258 -3.855409 
ADT -13.3866667* .7438054 .000 -15.834591 -10.938742 
ASA -6.1440000* .7438054 .000 -8.591925 -3.696075 
MM -15.3033333* .7438054 .000 -17.751258 -12.855409 
Lead ADW ADT .0016667 .0054312 .998 -.016208 .019541 
ASA -.0096000 .0054312 .440 -.027475 .008275 
MM .0026667 .0054312 .986 -.015208 .020541 
Control .0060667 .0054312 .795 -.011808 .023941 
ADT ADW -.0016667 .0054312 .998 -.019541 .016208 
ASA -.0112667 .0054312 .301 -.029141 .006608 
MM .0010000 .0054312 1.000 -.016875 .018875 
Control .0044000 .0054312 .922 -.013475 .022275 
ASA ADW .0096000 .0054312 .440 -.008275 .027475 
197 
 
ADT .0112667 .0054312 .301 -.006608 .029141 
MM .0122667 .0054312 .235 -.005608 .030141 
Control .0156667 .0054312 .094 -.002208 .033541 
MM ADW -.0026667 .0054312 .986 -.020541 .015208 
ADT -.0010000 .0054312 1.000 -.018875 .016875 
ASA -.0122667 .0054312 .235 -.030141 .005608 
Control .0034000 .0054312 .967 -.014475 .021275 
Control ADW -.0060667 .0054312 .795 -.023941 .011808 
ADT -.0044000 .0054312 .922 -.022275 .013475 
ASA -.0156667 .0054312 .094 -.033541 .002208 
MM -.0034000 .0054312 .967 -.021275 .014475 
Cadmium ADW ADT .0901667 .0974364 .881 -.230505 .410838 
ASA .0908000 .0974364 .878 -.229871 .411471 
MM .0719000 .0974364 .942 -.248771 .392571 
Control .2212000 .0974364 .231 -.099471 .541871 
ADT ADW -.0901667 .0974364 .881 -.410838 .230505 
ASA .0006333 .0974364 1.000 -.320038 .321305 
MM -.0182667 .0974364 1.000 -.338938 .302405 
Control .1310333 .0974364 .672 -.189638 .451705 
ASA ADW -.0908000 .0974364 .878 -.411471 .229871 
ADT -.0006333 .0974364 1.000 -.321305 .320038 
MM -.0189000 .0974364 1.000 -.339571 .301771 
Control .1304000 .0974364 .676 -.190271 .451071 
MM ADW -.0719000 .0974364 .942 -.392571 .248771 
ADT .0182667 .0974364 1.000 -.302405 .338938 
ASA .0189000 .0974364 1.000 -.301771 .339571 
Control .1493000 .0974364 .566 -.171371 .469971 
Control ADW -.2212000 .0974364 .231 -.541871 .099471 
ADT -.1310333 .0974364 .672 -.451705 .189638 
ASA -.1304000 .0974364 .676 -.451071 .190271 
MM -.1493000 .0974364 .566 -.469971 .171371 
*. The mean difference is significant at the 0.05 level. 
 
198 
 
Appendix D: Descriptives for physico-Chemical Parameters in Water, Amansie West  
 
95% Confidence Interval for Mean 
 N Mean Std. Deviation Std. Error Lower Bound Upper Bound Minimum Maximum 
Temperature DO 3 23.4800 1.02059 .58924 20.9447 26.0153 22.48 24.52 
AS 3 23.6467 .97675 .56392 21.2203 26.0730 22.81 24.72 
MN 3 24.5367 1.23525 .71317 21.4681 27.6052 23.22 25.67 
Control 3 23.3033 2.22541 1.28484 17.7751 28.8315 21.32 25.71 
Total 12 23.7417 1.33664 .38586 22.8924 24.5909 21.32 25.71 
Conductivity DO 3 277.3333 13.65040 7.88106 243.4239 311.2428 265.00 292.00 
AS 3 443.6667 14.57166 8.41295 407.4687 479.8647 430.00 459.00 
MN 3 484.8333 34.38958 19.85483 399.4049 570.2618 458.00 523.60 
Control 3 42.6667 11.01514 6.35959 15.3035 70.0298 32.00 54.00 
Total 12 312.1250 182.47146 52.67497 196.1882 428.0618 32.00 523.60 
Total Dissolved solids DO 3 132.3333 8.73689 5.04425 110.6297 154.0370 125.00 142.00 
AS 3 226.0000 20.66398 11.93035 174.6678 277.3322 204.00 245.00 
MN 3 222.1733 17.81000 10.28261 177.9308 266.4158 210.82 242.70 
Control 3 25.3333 6.42910 3.71184 9.3626 41.3041 18.00 30.00 
Total 12 151.4600 86.45271 24.95675 96.5306 206.3894 18.00 245.00 
pH DO 3 4.9333 .25166 .14530 4.3082 5.5585 4.70 5.20 
AS 3 4.7000 .20000 .11547 4.2032 5.1968 4.50 4.90 
MN 3 4.5667 .25166 .14530 3.9415 5.1918 4.30 4.80 
Control 3 7.7667 .51316 .29627 6.4919 9.0414 7.20 8.20 
Total 12 5.4917 1.40677 .40610 4.5978 6.3855 4.30 8.20 
Salinity DO 3 .1333 .05774 .03333 -.0101 .2768 .10 .20 
AS 3 .3000 .10000 .05774 .0516 .5484 .20 .40 
MN 3 .2000 .10000 .05774 -.0484 .4484 .10 .30 
Control 3 .0000 .00000 .00000 .0000 .0000 .00 .00 
Total 12 .1583 .13114 .03786 .0750 .2417 .00 .40 
Total Suspended solids DO 3 247.3333 5.68624 3.28295 233.2079 261.4587 241.00 252.00 
AS 3 390.6667 33.60556 19.40218 307.1858 474.1475 354.00 420.00 
MN 3 261.0000 19.67232 11.35782 212.1313 309.8687 243.00 282.00 
Control 3 12.4667 1.26623 .73106 9.3212 15.6122 11.50 13.90 
Total 12 227.8667 143.41099 41.39919 136.7477 318.9857 11.50 420.00 
Alkalinity DO 3 2.2000 .72111 .41633 .4087 3.9913 1.60 3.00 
AS 3 2.3333 .57735 .33333 .8991 3.7676 2.00 3.00 
MN 3 1.7000 .65574 .37859 .0710 3.3290 1.00 2.30 
Control 3 53.3333 9.01850 5.20683 30.9301 75.7365 44.00 62.00 
199 
 
Total 12 14.8917 23.50425 6.78509 -.0422 29.8256 1.00 62.00 
Bicarbonates DO 3 2.7147 .40700 .23498 1.7036 3.7257 2.44 3.18 
AS 3 2.4153 .13012 .07513 2.0921 2.7386 2.28 2.53 
MN 3 1.3480 .11849 .06841 1.0536 1.6424 1.22 1.45 
Control 3 42.3333 8.96289 5.17472 20.0683 64.5984 32.00 48.00 
Total 12 12.2028 18.57551 5.36229 .4005 24.0051 1.22 48.00 
Turbidity DO 3 513.5600 17.86795 10.31607 469.1736 557.9464 493.86 528.72 
AS 3 822.6667 70.59981 40.76082 647.2870 998.0463 750.00 891.00 
MN 3 44.5333 1.50111 .86667 40.8044 48.2623 43.00 46.00 
Control 3 2.1667 .75056 .43333 .3022 4.0311 1.40 2.90 
Total 12 345.7317 357.23495 103.12485 118.7554 572.7079 1.40 891.00 
Total hardness DO 3 539.320046 30.2014776 17.4368312 464.295416 614.344675 510.4800 570.7200 
AS 3 935.173339 48.3781803 27.9311554 814.995277 1055.351401 880.5800 972.7230 
MN 3 758.062867 13.8039978 7.9697418 723.771836 792.353898 745.6000 772.9000 
Control 3 11.966667 4.1645328 2.4043941 1.621394 22.311940 7.2000 14.9000 
Total 12 561.130730 362.9628062 104.7783369 330.515165 791.746294 7.2000 972.7230 
iron DO 3 10.723333 1.3651496 .7881695 7.332114 14.114553 9.7300 12.2800 
AS 3 30.726667 2.4160781 1.3949233 24.724796 36.728537 28.7500 33.4200 
MN 3 1.360000 .2505674 .1446651 .737556 1.982444 1.0720 1.5280 
Control 3 22.040000 6.7531992 3.8989614 5.264123 38.815877 15.1700 28.6700 
Total 12 16.212500 12.0332290 3.4736940 8.566951 23.858049 1.0720 33.4200 
Cobalt DO 3 .195000 .0739121 .0426732 .011392 .378608 .1260 .2730 
AS 3 .136667 .0087369 .0050442 .114963 .158370 .1270 .1440 
MN 3 .002633 .0009609 .0005548 .000246 .005020 .0016 .0035 
Control 3 .000000 .0000000 .0000000 .000000 .000000 .0000 .0000 
Total 12 .083575 .0940949 .0271628 .023790 .143360 .0000 .2730 
Copper DO 3 .190667 .0330807 .0190992 .108490 .272844 .1650 .2280 
AS 3 .158000 .0728080 .0420357 -.022865 .338865 .1130 .2420 
MN 3 .039667 .0020817 .0012019 .034496 .044838 .0380 .0420 
Control 3 .000000 .0000000 .0000000 .000000 .000000 .0000 .0000 
Total 12 .097083 .0896310 .0258742 .040135 .154032 .0000 .2420 
Chromium DO 3 .020000 .0100000 .0057735 -.004841 .044841 .0100 .0300 
AS 3 .020000 .0100000 .0057735 -.004841 .044841 .0100 .0300 
MN 3 .023333 .0152753 .0088192 -.014612 .061279 .0100 .0400 
Control 3 .000000 .0000000 .0000000 .000000 .000000 .0000 .0000 
Total 12 .015833 .0131137 .0037856 .007501 .024165 .0000 .0400 
Nickel DO 3 .580667 .0578389 .0333933 .436987 .724346 .5180 .6320 
AS 3 .731667 .1101514 .0635959 .458035 1.005298 .6250 .8450 
MN 3 .057800 .0128888 .0074413 .025783 .089817 .0480 .0724 
200 
 
Control 3 .020000 .0100000 .0057735 -.004841 .044841 .0100 .0300 
Total 12 .347533 .3317843 .0957779 .136728 .558339 .0100 .8450 
Zinc DO 3 .383333 .0351188 .0202759 .296093 .470573 .3500 .4200 
AS 3 .390333 .0558062 .0322197 .251703 .528964 .3460 .4530 
MN 3 .027000 .0155242 .0089629 -.011564 .065564 .0110 .0420 
Control 3 .003333 .0057735 .0033333 -.011009 .017676 .0000 .0100 
Total 12 .201000 .1964610 .0567134 .076175 .325825 .0000 .4530 
Arsenic DO 3 .003733 .0008021 .0004631 .001741 .005726 .0029 .0045 
AS 3 .004467 .0007024 .0004055 .002722 .006211 .0038 .0052 
MN 3 .011667 .0167479 .0096694 -.029938 .053271 .0016 .0310 
Control 3 .000133 .0000577 .0000333 -.000010 .000277 .0001 .0002 
Total 12 .005000 .0083846 .0024204 -.000327 .010327 .0001 .0310 
Mercury DO 3 .002833 .0009018 .0005207 .000593 .005074 .0019 .0037 
AS 3 .002733 .0005686 .0003283 .001321 .004146 .0021 .0032 
MN 3 .000400 .0002000 .0001155 -.000097 .000897 .0002 .0006 
Control 3 .000000 .0000000 .0000000 .000000 .000000 .0000 .0000 
Total 12 .001492 .0014343 .0004140 .000580 .002403 .0000 .0037 
Magnesium DO 3 11.703333 1.4108272 .8145415 8.198644 15.208022 10.5600 13.2800 
AS 3 13.943333 1.3469348 .7776532 10.597362 17.289305 12.4600 15.0900 
MN 3 13.433333 .8551218 .4937048 11.309093 15.557574 12.5700 14.2800 
Control 3 2.066667 1.2423097 .7172478 -1.019402 5.152735 1.3000 3.5000 
Total 12 10.286667 5.1407699 1.4840124 7.020377 13.552956 1.3000 15.0900 
Manganese DO 3 14.040000 1.3041472 .7529498 10.800319 17.279681 12.6800 15.2800 
AS 3 14.853333 .7229338 .4173860 13.057466 16.649201 14.2000 15.6300 
MN 3 13.400000 .8478797 .4895236 11.293750 15.506250 12.6500 14.3200 
Control 3 .192667 .0241937 .0139682 .132566 .252767 .1740 .2200 
Total 12 10.621500 6.3540351 1.8342519 6.584339 14.658661 .1740 15.6300 
Calcium DO 3 5.701667 .5452140 .3147795 4.347280 7.056053 5.2900 6.3200 
AS 3 5.407667 .7081288 .4088383 3.648577 7.166756 4.5900 5.8200 
MN 3 4.469333 .2219039 .1281163 3.918094 5.020573 4.2980 4.7200 
Control 3 12.516667 2.6899504 1.5530436 5.834459 19.198874 10.6500 15.6000 
Total 12 7.023833 3.5590855 1.0274195 4.762498 9.285168 4.2980 15.6000 
Potassium DO 3 2.675667 .4583474 .2646270 1.537069 3.814265 2.3360 3.1970 
AS 3 5.999000 .2824376 .1630654 5.297386 6.700614 5.8280 6.3250 
MN 3 2.926000 .6789904 .3920153 1.239294 4.612706 2.5280 3.7100 
Control 3 .413333 .1001665 .0578312 .164506 .662161 .3100 .5100 
Total 12 3.003500 2.1086019 .6087009 1.663758 4.343242 .3100 6.3250 
Sodium DO 3 17.426667 .8161699 .4712159 15.399188 19.454145 16.7200 18.3200 
AS 3 9.201333 .7731813 .4463964 7.280644 11.122022 8.4380 9.9840 
201 
 
MN 3 8.435000 .9579796 .5530898 6.055247 10.814753 7.4600 9.3750 
Control 3 2.033333 .6350853 .3666667 .455694 3.610973 1.3000 2.4000 
Total 12 9.274083 5.7522904 1.6605432 5.619252 12.928914 1.3000 18.3200 
Lead DO 3 .002000 .0010000 .0005774 -.000484 .004484 .0010 .0030 
AS 3 .004000 .0010000 .0005774 .001516 .006484 .0030 .0050 
MN 3 .022733 .0179113 .0103411 -.021761 .067227 .0052 .0410 
Control 3 .000233 .0001528 .0000882 -.000146 .000613 .0001 .0004 
Total 12 .007242 .0121616 .0035108 -.000485 .014969 .0001 .0410 
Cadmium DO 3 .300067 .1760878 .1016643 -.137360 .737493 .1842 .5027 
AS 3 .328933 .0560019 .0323327 .189817 .468050 .2732 .3852 
MN 3 .226133 .0579346 .0334485 .082216 .370051 .1642 .2790 
Control 3 .043333 .0160416 .0092616 .003484 .083183 .0280 .0600 
Total 12 .224617 .1426488 .0411792 .133982 .315251 .0280 .5027 
Dissolved oxygen DO 3 4.3000 .55678 .32146 2.9169 5.6831 3.80 4.90 
AS 3 5.0667 .51316 .29627 3.7919 6.3414 4.50 5.50 
MN 3 4.7333 .95044 .54874 2.3723 7.0944 3.80 5.70 
Control 3 4.5000 .40000 .23094 3.5063 5.4937 4.10 4.90 
Total 12 4.6500 .62158 .17944 4.2551 5.0449 3.80 5.70 
Biological Oxygen demand DO 3 2.5667 .57735 .33333 1.1324 4.0009 1.90 2.90 
AS 3 1.7000 .20000 .11547 1.2032 2.1968 1.50 1.90 
MN 3 2.0333 .20817 .12019 1.5162 2.5504 1.80 2.20 
Control 3 1.4333 .05774 .03333 1.2899 1.5768 1.40 1.50 
Total 12 1.9333 .52107 .15042 1.6023 2.2644 1.40 2.90 
 
  
202 
 
Appendix E: ANOVA for Physico-Chemical Parameters, Amansie West 
ANOVA 
 Sum of Squares df Mean Square F Sig. 
Temperature Between Groups 2.705 3 .902 .426 .740 
Within Groups 16.948 8 2.118   
Total 19.653 11    
Conductivity Between Groups 362848.896 3 120949.632 284.146 .000 
Within Groups 3405.287 8 425.661   
Total 366254.183 11    
Total Dissolved solids Between Groups 80491.058 3 26830.353 124.523 .000 
Within Groups 1723.726 8 215.466   
Total 82214.783 11    
pH Between Groups 20.909 3 6.970 64.835 .000 
Within Groups .860 8 .108   
Total 21.769 11    
Salinity Between Groups .143 3 .048 8.143 .008 
Within Groups .047 8 .006   
Total .189 11    
Total Suspended solids Between Groups 223133.307 3 74377.769 191.909 .000 
Within Groups 3100.540 8 387.567   
Total 226233.847 11    
Alkalinity Between Groups 5911.716 3 1970.572 95.408 .000 
Within Groups 165.233 8 20.654   
Total 6076.949 11    
Bicarbonates Between Groups 3634.485 3 1211.495 60.176 .000 
Within Groups 161.060 8 20.132   
Total 3795.545 11    
Turbidity Between Groups 1393172.067 3 464390.689 350.060 .000 
Within Groups 10612.827 8 1326.603   
Total 1403784.894 11    
Total hardness Between Groups 1442241.043 3 480747.014 555.701 .000 
Within Groups 6920.943 8 865.118   
203 
 
Total 1449161.985 11    
iron Between Groups 1486.045 3 495.348 37.126 .000 
Within Groups 106.739 8 13.342   
Total 1592.785 11    
Cobalt Between Groups .086 3 .029 20.772 .000 
Within Groups .011 8 .001   
Total .097 11    
Copper Between Groups .076 3 .025 15.745 .001 
Within Groups .013 8 .002   
Total .088 11    
Chromium Between Groups .001 3 .000 3.154 .086 
Within Groups .001 8 .000   
Total .002 11    
Nickel Between Groups 1.179 3 .393 99.876 .000 
Within Groups .031 8 .004   
Total 1.211 11    
Zinc Between Groups .415 3 .138 119.810 .000 
Within Groups .009 8 .001   
Total .425 11    
Arsenic Between Groups .000 3 .000 .994 .443 
Within Groups .001 8 .000   
Total .001 11    
Mercury Between Groups .000 3 .000 22.975 .000 
Within Groups .000 8 .000   
Total .000 11    
Magnesium Between Groups 278.544 3 92.848 61.092 .000 
Within Groups 12.158 8 1.520   
Total 290.703 11    
Manganese Between Groups 438.226 3 146.075 198.545 .000 
Within Groups 5.886 8 .736   
Total 444.111 11    
Calcium Between Groups 123.170 3 41.057 20.316 .000 
204 
 
Within Groups 16.168 8 2.021   
Total 139.338 11    
Potassium Between Groups 47.386 3 15.795 83.034 .000 
Within Groups 1.522 8 .190   
Total 48.908 11    
Sodium Between Groups 358.807 3 119.602 185.071 .000 
Within Groups 5.170 8 .646   
Total 363.977 11    
Lead Between Groups .001 3 .000 4.053 .050 
Within Groups .001 8 .000   
Total .002 11    
Cadmium Between Groups .148 3 .049 5.238 .027 
Within Groups .076 8 .009   
Total .224 11    
Dissolved oxygen Between Groups .977 3 .326 .796 .530 
Within Groups 3.273 8 .409   
Total 4.250 11    
Biological Oxygen demand Between Groups 2.147 3 .716 6.815 .014 
Within Groups .840 8 .105   
Total 2.987 11    
 
 
  
205 
 
Appendix F: Tukey’s HSD Comparison of Physico-Chemical Parameters, Amansie West  
Mean Difference 95% Confidence Interval 
Dependent Variable (I) Sampling site (J) Sampling site (I-J) Std. Error Sig. Lower Bound Upper Bound 
Temperature DO AS -.16667 1.18841 .999 -3.9724 3.6390 
MN -1.05667 1.18841 .811 -4.8624 2.7490 
Control .17667 1.18841 .999 -3.6290 3.9824 
AS DO .16667 1.18841 .999 -3.6390 3.9724 
MN -.89000 1.18841 .875 -4.6957 2.9157 
Control .34333 1.18841 .991 -3.4624 4.1490 
MN DO 1.05667 1.18841 .811 -2.7490 4.8624 
AS .89000 1.18841 .875 -2.9157 4.6957 
Control 1.23333 1.18841 .734 -2.5724 5.0390 
Control DO -.17667 1.18841 .999 -3.9824 3.6290 
AS -.34333 1.18841 .991 -4.1490 3.4624 
MN -1.23333 1.18841 .734 -5.0390 2.5724 
Conductivity DO AS -166.33333* 16.84559 .000 -220.2788 -112.3878 
MN -207.50000* 16.84559 .000 -261.4455 -153.5545 
Control 234.66667* 16.84559 .000 180.7212 288.6122 
AS DO 166.33333* 16.84559 .000 112.3878 220.2788 
MN -41.16667 16.84559 .145 -95.1122 12.7788 
Control 401.00000* 16.84559 .000 347.0545 454.9455 
MN DO 207.50000* 16.84559 .000 153.5545 261.4455 
AS 41.16667 16.84559 .145 -12.7788 95.1122 
Control 442.16667* 16.84559 .000 388.2212 496.1122 
Control DO -234.66667* 16.84559 .000 -288.6122 -180.7212 
AS -401.00000* 16.84559 .000 -454.9455 -347.0545 
MN -442.16667* 16.84559 .000 -496.1122 -388.2212 
Total Dissolved solids DO AS -93.66667* 11.98515 .000 -132.0473 -55.2860 
MN -89.84000* 11.98515 .000 -128.2207 -51.4593 
Control 107.00000* 11.98515 .000 68.6193 145.3807 
AS DO 93.66667* 11.98515 .000 55.2860 132.0473 
MN 3.82667 11.98515 .988 -34.5540 42.2073 
Control 200.66667* 11.98515 .000 162.2860 239.0473 
MN DO 89.84000* 11.98515 .000 51.4593 128.2207 
AS -3.82667 11.98515 .988 -42.2073 34.5540 
Control 196.84000* 11.98515 .000 158.4593 235.2207 
Control DO -107.00000* 11.98515 .000 -145.3807 -68.6193 
206 
 
AS -200.66667* 11.98515 .000 -239.0473 -162.2860 
MN -196.84000* 11.98515 .000 -235.2207 -158.4593 
pH DO AS .23333 .26771 .819 -.6240 1.0906 
MN .36667 .26771 .549 -.4906 1.2240 
Control -2.83333* .26771 .000 -3.6906 -1.9760 
AS DO -.23333 .26771 .819 -1.0906 .6240 
MN .13333 .26771 .957 -.7240 .9906 
Control -3.06667* .26771 .000 -3.9240 -2.2094 
MN DO -.36667 .26771 .549 -1.2240 .4906 
AS -.13333 .26771 .957 -.9906 .7240 
Control -3.20000* .26771 .000 -4.0573 -2.3427 
Control DO 2.83333* .26771 .000 1.9760 3.6906 
AS 3.06667* .26771 .000 2.2094 3.9240 
MN 3.20000* .26771 .000 2.3427 4.0573 
Salinity DO AS -.16667 .06236 .106 -.3664 .0330 
MN -.06667 .06236 .717 -.2664 .1330 
Control .13333 .06236 .220 -.0664 .3330 
AS DO .16667 .06236 .106 -.0330 .3664 
MN .10000 .06236 .428 -.0997 .2997 
Control .30000* .06236 .006 .1003 .4997 
MN DO .06667 .06236 .717 -.1330 .2664 
AS -.10000 .06236 .428 -.2997 .0997 
Control .20000* .06236 .050 .0003 .3997 
Control DO -.13333 .06236 .220 -.3330 .0664 
AS -.30000* .06236 .006 -.4997 -.1003 
MN -.20000* .06236 .050 -.3997 -.0003 
Total Suspended solids DO AS -143.33333* 16.07415 .000 -194.8084 -91.8582 
MN -13.66667 16.07415 .830 -65.1418 37.8084 
Control 234.86667* 16.07415 .000 183.3916 286.3418 
AS DO 143.33333* 16.07415 .000 91.8582 194.8084 
MN 129.66667* 16.07415 .000 78.1916 181.1418 
Control 378.20000* 16.07415 .000 326.7249 429.6751 
MN DO 13.66667 16.07415 .830 -37.8084 65.1418 
AS -129.66667* 16.07415 .000 -181.1418 -78.1916 
Control 248.53333* 16.07415 .000 197.0582 300.0084 
Control DO -234.86667* 16.07415 .000 -286.3418 -183.3916 
AS -378.20000* 16.07415 .000 -429.6751 -326.7249 
MN -248.53333* 16.07415 .000 -300.0084 -197.0582 
Alkalinity DO AS -.13333 3.71072 1.000 -12.0164 11.7497 
207 
 
MN .50000 3.71072 .999 -11.3830 12.3830 
Control -51.13333* 3.71072 .000 -63.0164 -39.2503 
AS DO .13333 3.71072 1.000 -11.7497 12.0164 
MN .63333 3.71072 .998 -11.2497 12.5164 
Control -51.00000* 3.71072 .000 -62.8830 -39.1170 
MN DO -.50000 3.71072 .999 -12.3830 11.3830 
AS -.63333 3.71072 .998 -12.5164 11.2497 
Control -51.63333* 3.71072 .000 -63.5164 -39.7503 
Control DO 51.13333* 3.71072 .000 39.2503 63.0164 
AS 51.00000* 3.71072 .000 39.1170 62.8830 
MN 51.63333* 3.71072 .000 39.7503 63.5164 
Bicarbonates DO AS .29933 3.66356 1.000 -11.4327 12.0313 
MN 1.36667 3.66356 .981 -10.3653 13.0987 
Control -39.61867* 3.66356 .000 -51.3507 -27.8867 
AS DO -.29933 3.66356 1.000 -12.0313 11.4327 
MN 1.06733 3.66356 .991 -10.6647 12.7993 
Control -39.91800* 3.66356 .000 -51.6500 -28.1860 
MN DO -1.36667 3.66356 .981 -13.0987 10.3653 
AS -1.06733 3.66356 .991 -12.7993 10.6647 
Control -40.98533* 3.66356 .000 -52.7173 -29.2533 
Control DO 39.61867* 3.66356 .000 27.8867 51.3507 
AS 39.91800* 3.66356 .000 28.1860 51.6500 
MN 40.98533* 3.66356 .000 29.2533 52.7173 
Turbidity DO AS -309.10667* 29.73890 .000 -404.3411 -213.8722 
MN 469.02667* 29.73890 .000 373.7922 564.2611 
Control 511.39333* 29.73890 .000 416.1589 606.6278 
AS DO 309.10667* 29.73890 .000 213.8722 404.3411 
MN 778.13333* 29.73890 .000 682.8989 873.3678 
Control 820.50000* 29.73890 .000 725.2656 915.7344 
MN DO -469.02667* 29.73890 .000 -564.2611 -373.7922 
AS -778.13333* 29.73890 .000 -873.3678 -682.8989 
Control 42.36667 29.73890 .520 -52.8678 137.6011 
Control DO -511.39333* 29.73890 .000 -606.6278 -416.1589 
AS -820.50000* 29.73890 .000 -915.7344 -725.2656 
MN -42.36667 29.73890 .520 -137.6011 52.8678 
Total hardness DO AS -395.8532932* 24.0155202 .000 -472.759442 -318.947144 
MN -218.7428213* 24.0155202 .000 -295.648971 -141.836672 
Control 527.3533791* 24.0155202 .000 450.447230 604.259528 
AS DO 395.8532932* 24.0155202 .000 318.947144 472.759442 
208 
 
MN 177.1104719* 24.0155202 .000 100.204323 254.016621 
Control 923.2066723* 24.0155202 .000 846.300523 1000.112822 
MN DO 218.7428213* 24.0155202 .000 141.836672 295.648971 
AS -177.1104719* 24.0155202 .000 -254.016621 -100.204323 
Control 746.0962003* 24.0155202 .000 669.190051 823.002350 
Control DO -527.3533791* 24.0155202 .000 -604.259528 -450.447230 
AS -923.2066723* 24.0155202 .000 -1000.112822 -846.300523 
MN -746.0962003* 24.0155202 .000 -823.002350 -669.190051 
iron DO AS -20.0033333* 2.9824361 .001 -29.554144 -10.452523 
MN 9.3633333 2.9824361 .055 -.187477 18.914144 
Control -11.3166667* 2.9824361 .022 -20.867477 -1.765856 
AS DO 20.0033333* 2.9824361 .001 10.452523 29.554144 
MN 29.3666667* 2.9824361 .000 19.815856 38.917477 
Control 8.6866667 2.9824361 .075 -.864144 18.237477 
MN DO -9.3633333 2.9824361 .055 -18.914144 .187477 
AS -29.3666667* 2.9824361 .000 -38.917477 -19.815856 
Control -20.6800000* 2.9824361 .001 -30.230810 -11.129190 
Control DO 11.3166667* 2.9824361 .022 1.765856 20.867477 
AS -8.6866667 2.9824361 .075 -18.237477 .864144 
MN 20.6800000* 2.9824361 .001 11.129190 30.230810 
Cobalt DO AS .0583333 .0303871 .293 -.038977 .155644 
MN .1923667* .0303871 .001 .095056 .289677 
Control .1950000* .0303871 .001 .097690 .292310 
AS DO -.0583333 .0303871 .293 -.155644 .038977 
MN .1340333* .0303871 .010 .036723 .231344 
Control .1366667* .0303871 .009 .039356 .233977 
MN DO -.1923667* .0303871 .001 -.289677 -.095056 
AS -.1340333* .0303871 .010 -.231344 -.036723 
Control .0026333 .0303871 1.000 -.094677 .099944 
Control DO -.1950000* .0303871 .001 -.292310 -.097690 
AS -.1366667* .0303871 .009 -.233977 -.039356 
MN -.0026333 .0303871 1.000 -.099944 .094677 
Copper DO AS .0326667 .0326590 .754 -.071919 .137252 
MN .1510000* .0326590 .007 .046414 .255586 
Control .1906667* .0326590 .002 .086081 .295252 
AS DO -.0326667 .0326590 .754 -.137252 .071919 
MN .1183333* .0326590 .028 .013748 .222919 
Control .1580000* .0326590 .006 .053414 .262586 
MN DO -.1510000* .0326590 .007 -.255586 -.046414 
209 
 
AS -.1183333* .0326590 .028 -.222919 -.013748 
Control .0396667 .0326590 .635 -.064919 .144252 
Control DO -.1906667* .0326590 .002 -.295252 -.086081 
AS -.1580000* .0326590 .006 -.262586 -.053414 
MN -.0396667 .0326590 .635 -.144252 .064919 
Chromium DO AS .0000000 .0084984 1.000 -.027215 .027215 
MN -.0033333 .0084984 .978 -.030548 .023881 
Control .0200000 .0084984 .165 -.007215 .047215 
AS DO .0000000 .0084984 1.000 -.027215 .027215 
MN -.0033333 .0084984 .978 -.030548 .023881 
Control .0200000 .0084984 .165 -.007215 .047215 
MN DO .0033333 .0084984 .978 -.023881 .030548 
AS .0033333 .0084984 .978 -.023881 .030548 
Control .0233333 .0084984 .095 -.003881 .050548 
Control DO -.0200000 .0084984 .165 -.047215 .007215 
AS -.0200000 .0084984 .165 -.047215 .007215 
MN -.0233333 .0084984 .095 -.050548 .003881 
Nickel DO AS -.1510000 .0512263 .072 -.315045 .013045 
MN .5228667* .0512263 .000 .358822 .686911 
Control .5606667* .0512263 .000 .396622 .724711 
AS DO .1510000 .0512263 .072 -.013045 .315045 
MN .6738667* .0512263 .000 .509822 .837911 
Control .7116667* .0512263 .000 .547622 .875711 
MN DO -.5228667* .0512263 .000 -.686911 -.358822 
AS -.6738667* .0512263 .000 -.837911 -.509822 
Control .0378000 .0512263 .879 -.126245 .201845 
Control DO -.5606667* .0512263 .000 -.724711 -.396622 
AS -.7116667* .0512263 .000 -.875711 -.547622 
MN -.0378000 .0512263 .879 -.201845 .126245 
Zinc DO AS -.0070000 .0277549 .994 -.095881 .081881 
MN .3563333* .0277549 .000 .267452 .445214 
Control .3800000* .0277549 .000 .291119 .468881 
AS DO .0070000 .0277549 .994 -.081881 .095881 
MN .3633333* .0277549 .000 .274452 .452214 
Control .3870000* .0277549 .000 .298119 .475881 
MN DO -.3563333* .0277549 .000 -.445214 -.267452 
AS -.3633333* .0277549 .000 -.452214 -.274452 
Control .0236667 .0277549 .828 -.065214 .112548 
Control DO -.3800000* .0277549 .000 -.468881 -.291119 
210 
 
AS -.3870000* .0277549 .000 -.475881 -.298119 
MN -.0236667 .0277549 .828 -.112548 .065214 
Arsenic DO AS -.0007333 .0068512 1.000 -.022673 .021207 
MN -.0079333 .0068512 .667 -.029873 .014007 
Control .0036000 .0068512 .951 -.018340 .025540 
AS DO .0007333 .0068512 1.000 -.021207 .022673 
MN -.0072000 .0068512 .726 -.029140 .014740 
Control .0043333 .0068512 .919 -.017607 .026273 
MN DO .0079333 .0068512 .667 -.014007 .029873 
AS .0072000 .0068512 .726 -.014740 .029140 
Control .0115333 .0068512 .391 -.010407 .033473 
Control DO -.0036000 .0068512 .951 -.025540 .018340 
AS -.0043333 .0068512 .919 -.026273 .017607 
MN -.0115333 .0068512 .391 -.033473 .010407 
Mercury DO AS .0001000 .0004428 .996 -.001318 .001518 
MN .0024333* .0004428 .003 .001015 .003851 
Control .0028333* .0004428 .001 .001415 .004251 
AS DO -.0001000 .0004428 .996 -.001518 .001318 
MN .0023333* .0004428 .003 .000915 .003751 
Control .0027333* .0004428 .001 .001315 .004151 
MN DO -.0024333* .0004428 .003 -.003851 -.001015 
AS -.0023333* .0004428 .003 -.003751 -.000915 
Control .0004000 .0004428 .804 -.001018 .001818 
Control DO -.0028333* .0004428 .001 -.004251 -.001415 
AS -.0027333* .0004428 .001 -.004151 -.001315 
MN -.0004000 .0004428 .804 -.001818 .001018 
Magnesium DO AS -2.2400000 1.0065811 .196 -5.463427 .983427 
MN -1.7300000 1.0065811 .375 -4.953427 1.493427 
Control 9.6366667* 1.0065811 .000 6.413240 12.860094 
AS DO 2.2400000 1.0065811 .196 -.983427 5.463427 
MN .5100000 1.0065811 .955 -2.713427 3.733427 
Control 11.8766667* 1.0065811 .000 8.653240 15.100094 
MN DO 1.7300000 1.0065811 .375 -1.493427 4.953427 
AS -.5100000 1.0065811 .955 -3.733427 2.713427 
Control 11.3666667* 1.0065811 .000 8.143240 14.590094 
Control DO -9.6366667* 1.0065811 .000 -12.860094 -6.413240 
AS -11.8766667* 1.0065811 .000 -15.100094 -8.653240 
MN -11.3666667* 1.0065811 .000 -14.590094 -8.143240 
Manganese DO AS -.8133333 .7003474 .665 -3.056092 1.429425 
211 
 
MN .6400000 .7003474 .798 -1.602759 2.882759 
Control 13.8473333* .7003474 .000 11.604575 16.090092 
AS DO .8133333 .7003474 .665 -1.429425 3.056092 
MN 1.4533333 .7003474 .239 -.789425 3.696092 
Control 14.6606667* .7003474 .000 12.417908 16.903425 
MN DO -.6400000 .7003474 .798 -2.882759 1.602759 
AS -1.4533333 .7003474 .239 -3.696092 .789425 
Control 13.2073333* .7003474 .000 10.964575 15.450092 
Control DO -13.8473333* .7003474 .000 -16.090092 -11.604575 
AS -14.6606667* .7003474 .000 -16.903425 -12.417908 
MN -13.2073333* .7003474 .000 -15.450092 -10.964575 
Calcium DO AS .2940000 1.1607310 .994 -3.423069 4.011069 
MN 1.2323333 1.1607310 .721 -2.484736 4.949403 
Control -6.8150000* 1.1607310 .002 -10.532069 -3.097931 
AS DO -.2940000 1.1607310 .994 -4.011069 3.423069 
MN .9383333 1.1607310 .849 -2.778736 4.655403 
Control -7.1090000* 1.1607310 .001 -10.826069 -3.391931 
MN DO -1.2323333 1.1607310 .721 -4.949403 2.484736 
AS -.9383333 1.1607310 .849 -4.655403 2.778736 
Control -8.0473333* 1.1607310 .001 -11.764403 -4.330264 
Control DO 6.8150000* 1.1607310 .002 3.097931 10.532069 
AS 7.1090000* 1.1607310 .001 3.391931 10.826069 
MN 8.0473333* 1.1607310 .001 4.330264 11.764403 
Potassium DO AS -3.3233333* .3561167 .000 -4.463744 -2.182922 
MN -.2503333 .3561167 .893 -1.390744 .890078 
Control 2.2623333* .3561167 .001 1.121922 3.402744 
AS DO 3.3233333* .3561167 .000 2.182922 4.463744 
MN 3.0730000* .3561167 .000 1.932589 4.213411 
Control 5.5856667* .3561167 .000 4.445256 6.726078 
MN DO .2503333 .3561167 .893 -.890078 1.390744 
AS -3.0730000* .3561167 .000 -4.213411 -1.932589 
Control 2.5126667* .3561167 .000 1.372256 3.653078 
Control DO -2.2623333* .3561167 .001 -3.402744 -1.121922 
AS -5.5856667* .3561167 .000 -6.726078 -4.445256 
MN -2.5126667* .3561167 .000 -3.653078 -1.372256 
Sodium DO AS 8.2253333* .6563791 .000 6.123376 10.327290 
MN 8.9916667* .6563791 .000 6.889710 11.093624 
Control 15.3933333* .6563791 .000 13.291376 17.495290 
AS DO -8.2253333* .6563791 .000 -10.327290 -6.123376 
212 
 
MN .7663333 .6563791 .662 -1.335624 2.868290 
Control 7.1680000* .6563791 .000 5.066043 9.269957 
MN DO -8.9916667* .6563791 .000 -11.093624 -6.889710 
AS -.7663333 .6563791 .662 -2.868290 1.335624 
Control 6.4016667* .6563791 .000 4.299710 8.503624 
Control DO -15.3933333* .6563791 .000 -17.495290 -13.291376 
AS -7.1680000* .6563791 .000 -9.269957 -5.066043 
MN -6.4016667* .6563791 .000 -8.503624 -4.299710 
Lead DO AS -.0020000 .0073353 .992 -.025490 .021490 
MN -.0207333 .0073353 .085 -.044223 .002757 
Control .0017667 .0073353 .995 -.021723 .025257 
AS DO .0020000 .0073353 .992 -.021490 .025490 
MN -.0187333 .0073353 .125 -.042223 .004757 
Control .0037667 .0073353 .954 -.019723 .027257 
MN DO .0207333 .0073353 .085 -.002757 .044223 
AS .0187333 .0073353 .125 -.004757 .042223 
Control .0225000 .0073353 .060 -.000990 .045990 
Control DO -.0017667 .0073353 .995 -.025257 .021723 
AS -.0037667 .0073353 .954 -.027257 .019723 
MN -.0225000 .0073353 .060 -.045990 .000990 
Cadmium DO AS -.0288667 .0793272 .982 -.282900 .225167 
MN .0739333 .0793272 .789 -.180100 .327967 
Control .2567333* .0793272 .048 .002700 .510767 
AS DO .0288667 .0793272 .982 -.225167 .282900 
MN .1028000 .0793272 .590 -.151234 .356834 
Control .2856000* .0793272 .029 .031566 .539634 
MN DO -.0739333 .0793272 .789 -.327967 .180100 
AS -.1028000 .0793272 .590 -.356834 .151234 
Control .1828000 .0793272 .176 -.071234 .436834 
Control DO -.2567333* .0793272 .048 -.510767 -.002700 
AS -.2856000* .0793272 .029 -.539634 -.031566 
MN -.1828000 .0793272 .176 -.436834 .071234 
Dissolved oxygen DO AS -.76667 .52228 .497 -2.4392 .9059 
MN -.43333 .52228 .839 -2.1059 1.2392 
Control -.20000 .52228 .980 -1.8725 1.4725 
AS DO .76667 .52228 .497 -.9059 2.4392 
MN .33333 .52228 .917 -1.3392 2.0059 
Control .56667 .52228 .708 -1.1059 2.2392 
MN DO .43333 .52228 .839 -1.2392 2.1059 
213 
 
AS -.33333 .52228 .917 -2.0059 1.3392 
Control .23333 .52228 .968 -1.4392 1.9059 
Control DO .20000 .52228 .980 -1.4725 1.8725 
AS -.56667 .52228 .708 -2.2392 1.1059 
MN -.23333 .52228 .968 -1.9059 1.4392 
Biological Oxygen demand DO AS .86667* .26458 .045 .0194 1.7139 
MN .53333 .26458 .259 -.3139 1.3806 
Control 1.13333* .26458 .011 .2861 1.9806 
AS DO -.86667* .26458 .045 -1.7139 -.0194 
MN -.33333 .26458 .610 -1.1806 .5139 
Control .26667 .26458 .750 -.5806 1.1139 
MN DO -.53333 .26458 .259 -1.3806 .3139 
AS .33333 .26458 .610 -.5139 1.1806 
Control .60000 .26458 .185 -.2473 1.4473 
Control DO -1.13333* .26458 .011 -1.9806 -.2861 
AS -.26667 .26458 .750 -1.1139 .5806 
MN -.60000 .26458 .185 -1.4473 .2473 
*. The mean difference is significant at the 0.05 level. 
 
214 
 
Appendix G: PCA of Physico-Chemical Parameters of Water Sample from Mpohor 
Wassa 
 
Total Variance Explained 
Component Initial Eigenvalues Extraction Sums of Squared Rotation Sums of Squared 
Loadings Loadings 
Total % of Variance Cumulative Total % of Cumulativ Total % of Cumulati
% Variance e % Varianc ve % 
e 
1 8.530 34.118 34.118 8.530 34.118 34.118 5.625 22.501 22.501 
2 5.218 20.873 54.991 5.218 20.873 54.991 5.373 21.490 43.992 
3 3.308 13.231 68.222 3.308 13.231 68.222 5.046 20.185 64.177 
4 2.082 8.328 76.551 2.082 8.328 76.551 2.318 9.273 73.450 
5 1.806 7.225 83.775 1.806 7.225 83.775 1.958 7.830 81.281 
6 1.209 4.836 88.611 1.209 4.836 88.611 1.833 7.330 88.611 
7 .970 3.881 92.492       
8 .791 3.163 95.656       
9 .335 1.341 96.997       
10 .257 1.029 98.026       
11 .212 .848 98.874       
12 .141 .564 99.438       
13 .089 .355 99.794       
14 .052 .206 100.000       
15 1.050E-15 4.200E-15 100.000       
16 5.042E-16 2.017E-15 100.000       
17 2.968E-16 1.187E-15 100.000       
18 1.938E-16 7.753E-16 100.000       
19 1.173E-16 4.694E-16 100.000       
20 5.021E-17 2.009E-16 100.000       
21 -1.477E-16 -5.906E-16 100.000       
22 -2.530E-16 -1.012E-15 100.000       
23 -2.749E-16 -1.100E-15 100.000       
24 -5.010E-16 -2.004E-15 100.000       
25 -8.791E-16 -3.516E-15 100.000       
Extraction Method: Principal Component Analysis. 
 
  
215 
 
Appendix H: Descriptive statistics for parameters in soil 
 Descriptives 
95% Confidence 
Interval for Mean 
Std. Std. Lower Upper 
 N Mean Deviation Error Bound Bound Minimum Maximum 
Calcium Asowuo 
21 2.39543 .841992 .183738 2.01216 2.77870 1.339 4.680 
Ayipa(ASA) 
Adum 
18 3.38494 .854537 .201416 2.95999 3.80990 1.750 4.560 
Tokoro(ADT) 
Mpohor 
15 2.48073 .909360 .234796 1.97715 2.98432 1.284 4.680 
Adansi(MA) 
Mpohor 
15 2.46733 .917148 .236807 1.95943 2.97523 1.424 4.350 
Motorway(MM) 
Mpohor 
Adawotwe 15 2.49593 .954074 .246341 1.96758 3.02428 1.529 4.480 
(ADW) 
control 1.83881 15.9817
3 8.07000 3.184918 .15823 5.280 11.540 
3 7 
Total 87 2.84026 1.448042 .155246 2.53164 3.14888 1.339 11.540 
Magnesium Asowuo 
21 4.71971 .877245 .191430 4.32040 5.11903 3.286 6.852 
Ayipa(ASA) 
Adum 
18 4.94739 .593508 .139891 4.65224 5.24253 4.196 6.220 
Tokoro(ADT) 
Mpohor 
15 4.76340 .922011 .238062 4.25281 5.27399 3.290 6.552 
Adansi(MA) 
Mpohor 
15 4.90793 .895035 .231097 4.41228 5.40359 3.290 6.852 
Motorway(MM) 
Mpohor 
Adawotwe 15 4.82700 .854184 .220549 4.35397 5.30003 3.290 6.752 
(ADW) 
control 1.38713 15.4890
3 9.52067 2.402591 3.55230 7.892 12.280 
6 3 
Total 87 4.99085 1.232662 .132155 4.72813 5.25357 3.286 12.280 
Sodium Asowuo 
21 .70059 .321657 .070191 .55417 .84700 .346 1.812 
Ayipa(ASA) 
Adum 
18 .63800 .152735 .036000 .56205 .71395 .321 .926 
Tokoro(ADT) 
Mpohor 
15 .69493 .343568 .088709 .50467 .88520 .346 1.812 
Adansi(MA) 
Mpohor 
15 .59569 .157637 .040702 .50839 .68298 .346 .872 
Motorway(MM) 
Mpohor 
Adawotwe 15 .76780 .449798 .116137 .51871 1.01689 .346 1.812 
(ADW) 
control 3 .23000 .045826 .026458 .11616 .34384 .190 .280 
Total 87 .66394 .307924 .033013 .59831 .72957 .190 1.812 
Potassium Asowuo 
21 .07314 .015612 .003407 .06604 .08025 .051 .097 
Ayipa(ASA) 
Adum 
18 .06589 .014016 .003304 .05892 .07286 .041 .082 
Tokoro(ADT) 
Mpohor 
15 .07367 .016766 .004329 .06438 .08295 .050 .097 
Adansi(MA) 
Mpohor 
15 .06947 .014861 .003837 .06124 .07770 .051 .092 
Motorway(MM) 
Mpohor 
Adawotwe 15 .07207 .015854 .004094 .06329 .08085 .041 .097 
(ADW) 
control 3 1.43333 .321455 .185592 .63479 2.23187 1.200 1.800 
Total 87 .11782 .255255 .027366 .06341 .17222 .041 1.800 
Iron Asowuo 3916.142 201.899 3494.988 4337.29
21 925.217936 2295.000 5287.000 
Ayipa(ASA) 86 106 70 701 
Adum 3940.222 144.076 3636.247 4244.19
18 611.264798 2869.000 4972.000 
Tokoro(ADT) 22 495 39 706 
Mpohor 3913.933 223.288 3435.027 4392.83
15 864.791838 2595.000 5287.000 
Adansi(MA) 33 293 58 909 
216 
 
Mpohor 3878.600 248.719 3345.149 4412.05
15 963.286695 2295.000 5287.000 
Motorway(MM) 00 555 61 039 
Mpohor 
4058.933 223.043 3580.552 4537.31
Adawotwe 15 863.843113 2595.000 5287.000 
33 333 96 370 
(ADW) 
control 1534.666 146.320 905.0990 2164.23
3 253.435067 1352.000 1824.000 
67 804 6 427 
Total 3856.770 99.8222 3658.330 4055.21
87 931.079814 1352.000 5287.000 
11 34 08 015 
Zinc Asowuo 2.24254 45.7040
21 41.02619 10.276607 36.34833 22.500 57.600 
Ayipa(ASA) 0 5 
Adum 2.20367 41.5521
18 36.90278 9.349418 32.25342 22.500 49.200 
Tokoro(ADT) 9 3 
Mpohor 2.85587 49.0319
15 42.90667 11.060755 36.78142 22.500 57.600 
Adansi(MA) 5 1 
Mpohor 2.41750 47.9383
15 42.75333 9.362968 37.56829 29.500 57.600 
Motorway(MM) 8 7 
Mpohor 
2.87216 47.4335
Adawotwe 15 41.27333 11.123862 35.11314 22.500 57.600 
9 2 
(ADW) 
control 2.24820 80.0066
3 70.33333 3.894012 60.66007 65.900 73.200 
9 0 
Total 1.22477 44.2830
87 41.84828 11.423974 39.41350 22.500 73.200 
9 6 
Nickel Asowuo 
21 .51762 .158994 .034695 .44525 .58999 .310 .720 
Ayipa(ASA) 
Adum 
18 .65278 .112397 .026492 .59688 .70867 .420 .920 
Tokoro(ADT) 
Mpohor 
15 .45400 .124258 .032083 .38519 .52281 .270 .720 
Adansi(MA) 
Mpohor 
15 .47933 .163028 .042094 .38905 .56962 .310 .820 
Motorway(MM) 
Mpohor 
Adawotwe 15 .47067 .126065 .032550 .40085 .54048 .280 .620 
(ADW) 
control 3 .10333 .068069 .039299 -.06576 .27243 .050 .180 
Total 87 .50563 .170066 .018233 .46939 .54188 .050 .820 
Cobalt Asowuo 
21 5.76667 1.302050 .284131 5.17398 6.35935 3.200 9.000 
Ayipa(ASA) 
Adum 
18 6.44444 .970883 .228839 5.96164 6.92725 4.700 7.800 
Tokoro(ADT) 
Mpohor 
15 5.68667 1.435204 .370568 4.89188 6.48146 3.100 9.000 
Adansi(MA) 
Mpohor 
15 5.21333 .956830 .247052 4.68346 5.74321 3.300 7.200 
Motorway(MM) 
Mpohor 
Adawotwe 15 5.85333 1.653078 .426823 4.93789 6.76878 3.500 9.500 
(ADW) 
control 3 .01433 .022279 .012863 -.04101 .06968 .000 .040 
Total 87 5.61429 1.671103 .179161 5.25813 5.97045 .000 9.000 
Copper Asowuo 
21 .67905 .100693 .021973 .63321 .72488 .530 .870 
Ayipa(ASA) 
Adum 
18 .67056 .115324 .027182 .61321 .72790 .530 .930 
Tokoro(ADT) 
Mpohor 
15 .66600 .095454 .024646 .61314 .71886 .530 .840 
Adansi(MA) 
Mpohor 
15 .67533 .105618 .027271 .61684 .73382 .530 .870 
Motorway(MM) 
Mpohor 
Adawotwe 15 .65800 .099585 .025713 .60285 .71315 .530 .840 
(ADW) 
control 3 .00167 .002887 .001667 -.00550 .00884 .000 .005 
Total 87 .64741 .158174 .016958 .61370 .68113 .000 .930 
Lead Asowuo 
21 1.18871 .390221 .085153 1.01109 1.36634 .582 1.767 
Ayipa(ASA) 
Adum 
18 1.30283 .479052 .112914 1.06461 1.54106 .500 1.932 
Tokoro(ADT) 
Mpohor 
15 1.12780 .388199 .100233 .91282 1.34278 .582 1.737 
Adansi(MA) 
Mpohor 
15 1.14500 .408449 .105461 .91881 1.37119 .582 1.767 
Motorway(MM) 
217 
 
Mpohor 
Adawotwe 15 1.20233 .403510 .104186 .97888 1.42579 .582 1.737 
(ADW) 
control 3 .00000 .000000 .000000 .00000 .00000 .000 .000 
Total 87 1.15564 .459236 .049235 1.05777 1.25352 .000 1.932 
Chromium Asowuo 
21 .69452 .156897 .034238 .62311 .76594 .410 .950 
Ayipa(ASA) 
Adum 
18 .74789 .140220 .033050 .67816 .81762 .514 .952 
Tokoro(ADT) 
Mpohor 
15 .66833 .142808 .036873 .58925 .74742 .462 .950 
Adansi(MA) 
Mpohor 
15 .70100 .169673 .043809 .60704 .79496 .410 .950 
Motorway(MM) 
Mpohor 
Adawotwe 15 .63833 .107244 .027690 .57894 .69772 .462 .852 
(ADW) 
control 3 .06667 .058595 .033830 -.07889 .21222 .000 .110 
Total 87 .67083 .184464 .019777 .63151 .71014 .000 .952 
Cadmium Asowuo 1.82593 36.0374
21 32.22857 8.367505 28.41973 18.900 50.600 
Ayipa(ASA) 9 1 
Adum 1.28065 33.4408
18 30.73889 5.433354 28.03695 20.900 38.700 
Tokoro(ADT) 4 3 
Mpohor 2.16065 37.4274
15 32.79333 8.368177 28.15919 22.300 50.800 
Adansi(MA) 4 8 
Mpohor 2.19551 39.5489
15 34.84000 8.503176 30.13110 18.700 50.900 
Motorway(MM) 1 0 
Mpohor 
2.22211 36.3793
Adawotwe 15 31.61333 8.606216 26.84737 22.100 50.200 
5 0 
(ADW) 
control 3 .00000 .000000 .000000 .00000 .00000 .000 .000 
Total 1.04158 33.3211
87 31.25057 9.715283 29.17997 .000 50.200 
8 8 
Mercury Asowuo 
21 5.70905 .896838 .195706 5.30081 6.11728 4.200 6.900 
Ayipa(ASA) 
Adum 
18 6.38778 .411990 .097107 6.18290 6.59266 5.500 6.900 
Tokoro(ADT) 
Mpohor 
15 5.56600 .934985 .241412 5.04822 6.08378 4.200 6.900 
Adansi(MA) 
Mpohor 
15 5.55933 .947979 .244767 5.03436 6.08431 4.200 6.900 
Motorway(MM) 
Mpohor 
Adawotwe 15 5.70600 .968709 .250120 5.16955 6.24245 4.200 6.900 
(ADW) 
control 3 .00000 .000000 .000000 .00000 .00000 .000 .000 
Total 87 5.60161 1.375197 .147437 5.30851 5.89470 .000 6.900 
Arsenic Asowuo 
21 5.51429 .750524 .163778 5.17265 5.85592 4.100 6.700 
Ayipa(ASA) 
Adum 
18 5.84444 .746145 .175868 5.47340 6.21549 4.100 6.700 
Tokoro(ADT) 
Mpohor 
15 5.58000 .771085 .199093 5.15299 6.00701 4.100 6.500 
Adansi(MA) 
Mpohor 
15 5.27333 .699456 .180599 4.88599 5.66068 4.100 6.700 
Motorway(MM) 
Mpohor 
Adawotwe 15 5.64667 .779988 .201392 5.21472 6.07861 4.100 6.500 
(ADW) 
control 3 .00000 .000000 .000000 .00000 .00000 .000 .000 
Total 87 5.38506 1.263764 .135490 5.11571 5.65440 .000 6.700 
%  Silt Asowuo 
21 39.5714 6.20944 1.35501 36.7449 42.3979 28.00 54.00 
Ayipa(ASA) 
Adum 
18 33.8333 6.16680 1.45353 30.7667 36.9000 23.00 44.00 
Tokoro(ADT) 
Mpohor 
15 38.4000 3.56170 .91963 36.4276 40.3724 32.00 44.00 
Adansi(MA) 
Mpohor 
15 39.0667 5.68792 1.46861 35.9168 42.2165 28.00 49.00 
Motorway(MM) 
Mpohor 
Adawotwe 15 39.2000 3.36367 .86850 37.3373 41.0627 33.00 44.00 
(ADW) 
control 3 47.0000 5.33573 3.08058 33.7453 60.2547 42.30 52.80 
Total 87 38.2874 5.81173 .62308 37.0487 39.5260 23.00 54.00 
218 
 
% clay Asowuo 
21 27.4286 9.09710 1.98515 23.2876 31.5695 12.00 48.00 
Ayipa(ASA) 
Adum 
18 37.3333 7.97053 1.87867 33.3697 41.2970 22.00 48.00 
Tokoro(ADT) 
Mpohor 
15 28.0667 4.80278 1.24007 25.4070 30.7264 22.00 38.00 
Adansi(MA) 
Mpohor 
15 28.6000 9.86190 2.54633 23.1387 34.0613 15.00 48.00 
Motorway(MM) 
Mpohor 
Adawotwe 15 27.9333 4.80278 1.24007 25.2736 30.5930 22.00 38.00 
(ADW) 
control 3 14.3667 3.62951 2.09550 5.3505 23.3829 11.70 18.50 
Total 87 29.4264 8.84619 .94841 27.5411 31.3118 11.70 48.00 
% sand Asowuo 
21 37.2381 7.18961 1.56890 33.9654 40.5108 18.00 49.00 
Ayipa(ASA) 
Adum 
18 35.8333 7.20498 1.69823 32.2504 39.4163 24.00 49.00 
Tokoro(ADT) 
Mpohor 
15 37.8667 5.20805 1.34471 34.9825 40.7508 32.00 49.00 
Adansi(MA) 
Mpohor 
15 36.9333 7.61077 1.96509 32.7186 41.1480 18.00 45.00 
Motorway(MM) 
Mpohor 
Adawotwe 15 36.2667 4.51136 1.16483 33.7684 38.7650 32.00 45.00 
(ADW) 
control 3 49.4667 2.70986 1.56454 42.7350 56.1983 46.90 52.30 
Total 87 37.2575 6.74466 .72310 35.8200 38.6950 18.00 52.30 
Moisture content Asowuo 
21 1.6976 .28721 .06267 1.5669 1.8284 1.29 2.57 
Ayipa(ASA) 
Adum 
18 2.0050 .38181 .08999 1.8151 2.1949 1.47 2.61 
Tokoro(ADT) 
Mpohor 
15 1.6053 .14317 .03697 1.5260 1.6846 1.44 1.97 
Adansi(MA) 
Mpohor 
15 1.7787 .29672 .07661 1.6144 1.9430 1.44 2.57 
Motorway(MM) 
Mpohor 
Adawotwe 15 1.5787 .10329 .02667 1.5215 1.6359 1.44 1.77 
(ADW) 
control 3 4.6000 .81854 .47258 2.5666 6.6334 3.70 5.30 
Total 87 1.8389 .61769 .06622 1.7072 1.9705 1.29 5.30 
%organic matter Asowuo 
21 1.9601 .32355 .07060 1.8129 2.1074 1.55 2.54 
Ayipa(ASA) 
Adum 
18 2.0400 .44607 .10514 1.8182 2.2618 1.24 2.71 
Tokoro(ADT) 
Mpohor 
15 1.9547 .29756 .07683 1.7899 2.1194 1.63 2.54 
Adansi(MA) 
Mpohor 
15 1.8780 .28013 .07233 1.7229 2.0331 1.55 2.54 
Motorway(MM) 
Mpohor 
Adawotwe 15 1.9027 .26464 .06833 1.7561 2.0492 1.63 2.54 
(ADW) 
control 3 4.8667 1.05040 .60645 2.2573 7.4760 3.80 5.90 
Total 87 2.0519 .64591 .06925 1.9142 2.1895 1.24 5.90 
% organic carbon Asowuo 
21 .7389 .16739 .03653 .6627 .8151 .22 .97 
Ayipa(ASA) 
Adum 
18 .5422 .21295 .05019 .4363 .6481 .25 .98 
Tokoro(ADT) 
Mpohor 
15 .7333 .19352 .04997 .6262 .8405 .22 .97 
Adansi(MA) 
Mpohor 
15 .7493 .19241 .04968 .6428 .8559 .26 .97 
Motorway(MM) 
Mpohor 
Adawotwe 15 .7323 .17878 .04616 .6333 .8313 .22 .97 
(ADW) 
control 3 5.8273 .90666 .52346 3.5751 8.0796 4.88 6.40 
Total 87 .8734 .97210 .10422 .6662 1.0806 .22 6.40 
Available Asowuo 
21 20.2524 4.98775 1.08842 17.9820 22.5228 11.60 29.80 
phosphorus Ayipa(ASA) 
Adum 
18 24.7500 3.22294 .75965 23.1473 26.3527 18.40 29.80 
Tokoro(ADT) 
Mpohor 
15 20.5000 4.90772 1.26717 17.7822 23.2178 13.50 29.80 
Adansi(MA) 
219 
 
Mpohor 
15 18.4133 4.17405 1.07773 16.1018 20.7248 11.20 25.70 
Motorway(MM) 
Mpohor 
Adawotwe 15 20.6467 4.87499 1.25872 17.9470 23.3463 13.80 29.60 
(ADW) 
control 3 43.2667 3.96274 2.28789 33.4227 53.1107 39.50 47.40 
Total 87 21.7701 6.31171 .67669 20.4249 23.1153 11.60 47.40 
Total Nitrogen Asowuo 
21 .0581 .01778 .00388 .0500 .0662 .02 .08 
Ayipa(ASA) 
Adum 
18 .0556 .02064 .00487 .0453 .0658 .02 .09 
Tokoro(ADT) 
Mpohor 
15 .0540 .01724 .00445 .0445 .0635 .02 .08 
Adansi(MA) 
Mpohor 
15 .0640 .01298 .00335 .0568 .0712 .04 .08 
Motorway(MM) 
Mpohor 
Adawotwe 15 .0527 .01870 .00483 .0423 .0630 .02 .08 
(ADW) 
control 3 4.4333 .70946 .40961 2.6709 6.1957 3.80 5.20 
Total 87 .2078 .81062 .08691 .0350 .3806 .02 5.20 
Conductivity Asowuo 
21 38.3981 5.51189 1.20279 35.8891 40.9071 31.30 49.50 
Ayipa(ASA) 
Adum 
18 37.7100 5.32806 1.25584 35.0604 40.3596 32.30 47.40 
Tokoro(ADT) 
Mpohor 
15 38.1253 5.53307 1.42863 35.0612 41.1894 30.30 44.50 
Adansi(MA) 
Mpohor 
15 38.3187 5.95981 1.53882 35.0182 41.6191 31.30 46.50 
Motorway(MM) 
Mpohor 
Adawotwe 15 38.9147 5.94109 1.53398 35.6246 42.2047 34.30 43.50 
(ADW) 
control 3 14.2333 1.77858 1.02686 9.8151 18.6516 12.30 15.80 
Total 87 37.4508 6.99173 .74959 35.9607 38.9409 12.30 49.50 
pH Asowuo 
21 4.7895 .47069 .10271 4.5753 5.0038 4.10 5.40 
Ayipa(ASA) 
Adum 
18 4.9744 .50479 .11898 4.7234 5.2255 4.20 5.20 
Tokoro(ADT) 
Mpohor 
15 4.7893 .53130 .13718 4.4951 5.0836 4.40 5.10 
Adansi(MA) 
Mpohor 
15 4.6800 .43785 .11305 4.4375 4.9225 4.50 5.50 
Motorway(MM) 
Mpohor 
Adawotwe 15 4.8787 .59907 .15468 4.5469 5.2104 4.60 5.90 
(ADW) 
control 3 7.2000 .30000 .17321 6.4548 7.9452 6.90 7.50 
Total 87 4.9074 .66246 .07102 4.7662 5.0485 4.20 7.50 
 
 
ANOVA for Parameters in Soil, Amansie West  
 Sum of Squares df Mean Square F Sig. 
Calcium Between Groups 333.700 4 83.425 688.878 .000 
Within Groups 8.477 70 .121   
Total 342.177 74    
Magnesium Between Groups 306.913 4 76.728 464.546 .000 
Within Groups 11.562 70 .165   
Total 318.475 74    
Sodium Between Groups .374 4 .094 10.379 .000 
Within Groups .631 70 .009   
Total 1.005 74    
Potassium Between Groups .461 4 .115 324.011 .000 
Within Groups .025 70 .000   
Total .485 74    
Iron Between Groups 36296723.339 4 9074180.835 27.371 .000 
220 
 
Within Groups 23206581.407 70 331522.592   
Total 59503304.747 74    
Zinc Between Groups 29245.727 4 7311.432 368.719 .000 
Within Groups 1388.048 70 19.829   
Total 30633.775 74    
Nickel Between Groups .910 4 .227 46.548 .000 
Within Groups .342 70 .005   
Total 1.252 74    
Cobalt Between Groups 457.559 4 114.390 103.145 .000 
Within Groups 77.632 70 1.109   
Total 535.191 74    
Copper Between Groups .144 4 .036 16.896 .000 
Within Groups .149 70 .002   
Total .293 74    
Lead Between Groups 10.981 4 2.745 150.543 .000 
Within Groups 1.276 70 .018   
Total 12.257 74    
Chromium Between Groups .964 4 .241 20.983 .000 
Within Groups .804 70 .011   
Total 1.768 74    
Cadmium Between Groups 28248.900 4 7062.225 3.331 .015 
Within Groups 148411.964 70 2120.171   
Total 176660.864 74    
Mercury Between Groups 199.201 4 49.800 121.793 .000 
Within Groups 28.622 70 .409   
Total 227.823 74    
Arsenic Between Groups 40.526 4 10.132 32.630 .000 
Within Groups 21.735 70 .310   
Total 62.261 74    
% silt Between Groups 1793.230 4 448.307 18.800 .000 
Within Groups 524.622 22 23.846   
Total 2317.852 26    
% clay Between Groups 440.178 4 110.044 6.947 .001 
Within Groups 348.489 22 15.840   
Total 788.667 26    
%sand Between Groups 5407.378 4 1351.844 55.216 .000 
Within Groups 538.622 22 24.483   
Total 5946.000 26    
% moisture Between Groups .944 3 .315 17.240 .000 
Within Groups .402 22 .018   
Total 1.346 25    
% organic matter Between Groups 9.609 3 3.203 178.215 .000 
Within Groups .395 22 .018   
Total 10.005 25    
% organic crabon Between Groups 1.890 3 .630 31.851 .000 
Within Groups .435 22 .020   
Total 2.326 25    
conductivity Between Groups 4092.318 3 1364.106 61.960 .000 
Within Groups 484.354 22 22.016   
Total 4576.672 25    
pH Between Groups 21.744 3 7.248 37.908 .000 
221 
 
Within Groups 4.206 22 .191   
Total 25.950 25    
available phosphorus Between Groups 13.093 3 4.364 6.729 .002 
Within Groups 14.269 22 .649   
Total 27.362 25    
Total Nitrogen Between Groups .002 3 .001 .970 .424 
Within Groups .014 22 .001   
Total .016 25    
 
 
 
 
 
 
222 
 
Tukey’s HSD Comparison in soil, Amansie West 
  
Mean Difference 95% Confidence Interval 
Dependent Variable (I) Sampling site (J) Sampling site (I-J) Std. Error Sig. Lower Bound Upper Bound 
Calcium Manso(MN) Dominase(DO) -1.938933* .146729 .000 -2.34980 -1.52807 
Brofoyedru(BYO) 1.436533* .127071 .000 1.08072 1.79235 
Asaman(AS) 1.652119* .112066 .000 1.33832 1.96592 
Control -4.664489* .146729 .000 -5.07535 -4.25363 
Dominase(DO) Manso(MN) 1.938933* .146729 .000 1.52807 2.34980 
Brofoyedru(BYO) 3.375467* .146729 .000 2.96460 3.78633 
Asaman(AS) 3.591052* .133945 .000 3.21599 3.96612 
Control -2.725556* .164048 .000 -3.18491 -2.26620 
Brofoyedru(BYO) Manso(MN) -1.436533* .127071 .000 -1.79235 -1.08072 
Dominase(DO) -3.375467* .146729 .000 -3.78633 -2.96460 
Asaman(AS) .215585 .112066 .315 -.09822 .52939 
Control -6.101022* .146729 .000 -6.51189 -5.69016 
Asaman(AS) Manso(MN) -1.652119* .112066 .000 -1.96592 -1.33832 
Dominase(DO) -3.591052* .133945 .000 -3.96612 -3.21599 
Brofoyedru(BYO) -.215585 .112066 .315 -.52939 .09822 
Control -6.316607* .133945 .000 -6.69167 -5.94154 
Control Manso(MN) 4.664489* .146729 .000 4.25363 5.07535 
Dominase(DO) 2.725556* .164048 .000 2.26620 3.18491 
Brofoyedru(BYO) 6.101022* .146729 .000 5.69016 6.51189 
Asaman(AS) 6.316607* .133945 .000 5.94154 6.69167 
Magnesium Manso(MN) Dominase(DO) -.384244 .171357 .177 -.86407 .09558 
Brofoyedru(BYO) 3.787200* .148400 .000 3.37166 4.20274 
Asaman(AS) 3.906015* .130876 .000 3.53954 4.27249 
Control -.360911 .171357 .229 -.84074 .11892 
Dominase(DO) Manso(MN) .384244 .171357 .177 -.09558 .86407 
Brofoyedru(BYO) 4.171444* .171357 .000 3.69162 4.65127 
Asaman(AS) 4.290259* .156427 .000 3.85224 4.72828 
Control .023333 .191583 1.000 -.51313 .55980 
Brofoyedru(BYO) Manso(MN) -3.787200* .148400 .000 -4.20274 -3.37166 
Dominase(DO) -4.171444* .171357 .000 -4.65127 -3.69162 
Asaman(AS) .118815 .130876 .893 -.24766 .48529 
223 
 
Control -4.148111* .171357 .000 -4.62794 -3.66828 
Asaman(AS) Manso(MN) -3.906015* .130876 .000 -4.27249 -3.53954 
Dominase(DO) -4.290259* .156427 .000 -4.72828 -3.85224 
Brofoyedru(BYO) -.118815 .130876 .893 -.48529 .24766 
Control -4.266926* .156427 .000 -4.70495 -3.82891 
Control Manso(MN) .360911 .171357 .229 -.11892 .84074 
Dominase(DO) -.023333 .191583 1.000 -.55980 .51313 
Brofoyedru(BYO) 4.148111* .171357 .000 3.66828 4.62794 
Asaman(AS) 4.266926* .156427 .000 3.82891 4.70495 
Sodium Manso(MN) Dominase(DO) -.138189* .040023 .008 -.25026 -.02612 
Brofoyedru(BYO) .103480* .034661 .031 .00642 .20054 
Asaman(AS) .044800 .030568 .588 -.04080 .13040 
Control -.030078 .040023 .943 -.14215 .08199 
Dominase(DO) Manso(MN) .138189* .040023 .008 .02612 .25026 
Brofoyedru(BYO) .241669* .040023 .000 .12960 .35374 
Asaman(AS) .182989* .036536 .000 .08068 .28530 
Control .108111 .044747 .123 -.01719 .23341 
Brofoyedru(BYO) Manso(MN) -.103480* .034661 .031 -.20054 -.00642 
Dominase(DO) -.241669* .040023 .000 -.35374 -.12960 
Asaman(AS) -.058680 .030568 .317 -.14428 .02692 
Control -.133558* .040023 .012 -.24563 -.02149 
Asaman(AS) Manso(MN) -.044800 .030568 .588 -.13040 .04080 
Dominase(DO) -.182989* .036536 .000 -.28530 -.08068 
Brofoyedru(BYO) .058680 .030568 .317 -.02692 .14428 
Control -.074878 .036536 .254 -.17718 .02743 
Control Manso(MN) .030078 .040023 .943 -.08199 .14215 
Dominase(DO) -.108111 .044747 .123 -.23341 .01719 
Brofoyedru(BYO) .133558* .040023 .012 .02149 .24563 
Asaman(AS) .074878 .036536 .254 -.02743 .17718 
Potassium Manso(MN) Dominase(DO) .009647 .007948 .743 -.01261 .03190 
Brofoyedru(BYO) -.021187* .006883 .024 -.04046 -.00191 
Asaman(AS) .012176 .006071 .274 -.00482 .02917 
Control -.236653* .007948 .000 -.25891 -.21440 
Dominase(DO) Manso(MN) -.009647 .007948 .743 -.03190 .01261 
Brofoyedru(BYO) -.030833* .007948 .002 -.05309 -.00858 
Asaman(AS) .002530 .007256 .997 -.01779 .02285 
Control -.246300* .008886 .000 -.27118 -.22142 
224 
 
Brofoyedru(BYO) Manso(MN) .021187* .006883 .024 .00191 .04046 
Dominase(DO) .030833* .007948 .002 .00858 .05309 
Asaman(AS) .033363* .006071 .000 .01636 .05036 
Control -.215467* .007948 .000 -.23772 -.19321 
Asaman(AS) Manso(MN) -.012176 .006071 .274 -.02917 .00482 
Dominase(DO) -.002530 .007256 .997 -.02285 .01779 
Brofoyedru(BYO) -.033363* .006071 .000 -.05036 -.01636 
Control -.248830* .007256 .000 -.26915 -.22851 
Control Manso(MN) .236653* .007948 .000 .21440 .25891 
Dominase(DO) .246300* .008886 .000 .22142 .27118 
Brofoyedru(BYO) .215467* .007948 .000 .19321 .23772 
Asaman(AS) .248830* .007256 .000 .22851 .26915 
Iron Manso(MN) Dominase(DO) -1123.133333* 242.770158 .000 -1802.92691 -443.33976 
Brofoyedru(BYO) -617.200000* 210.245124 .035 -1205.91851 -28.48149 
Asaman(AS) 404.459259 185.418771 .199 -114.74166 923.66018 
Control -1544.577778* 242.770158 .000 -2224.37135 -864.78420 
Dominase(DO) Manso(MN) 1123.133333* 242.770158 .000 443.33976 1802.92691 
Brofoyedru(BYO) 505.933333 242.770158 .239 -173.86024 1185.72691 
Asaman(AS) 1527.592593* 221.617820 .000 907.02880 2148.15639 
Control -421.444444 271.425288 .532 -1181.47677 338.58788 
Brofoyedru(BYO) Manso(MN) 617.200000* 210.245124 .035 28.48149 1205.91851 
Dominase(DO) -505.933333 242.770158 .239 -1185.72691 173.86024 
Asaman(AS) 1021.659259* 185.418771 .000 502.45834 1540.86018 
Control -927.377778* 242.770158 .003 -1607.17135 -247.58420 
Asaman(AS) Manso(MN) -404.459259 185.418771 .199 -923.66018 114.74166 
Dominase(DO) -1527.592593* 221.617820 .000 -2148.15639 -907.02880 
Brofoyedru(BYO) -1021.659259* 185.418771 .000 -1540.86018 -502.45834 
Control -1949.037037* 221.617820 .000 -2569.60083 -1328.47324 
Control Manso(MN) 1544.577778* 242.770158 .000 864.78420 2224.37135 
Dominase(DO) 421.444444 271.425288 .532 -338.58788 1181.47677 
Brofoyedru(BYO) 927.377778* 242.770158 .003 247.58420 1607.17135 
Asaman(AS) 1949.037037* 221.617820 .000 1328.47324 2569.60083 
Zinc Manso(MN) Dominase(DO) 43.246667* 1.877552 .000 37.98923 48.50410 
Brofoyedru(BYO) 32.641800* 1.626008 .000 28.08873 37.19487 
Asaman(AS) 27.644074* 1.434004 .000 23.62864 31.65950 
Control -20.066667* 1.877552 .000 -25.32410 -14.80923 
Dominase(DO) Manso(MN) -43.246667* 1.877552 .000 -48.50410 -37.98923 
225 
 
Brofoyedru(BYO) -10.604867* 1.877552 .000 -15.86230 -5.34743 
Asaman(AS) -15.602593* 1.713963 .000 -20.40195 -10.80324 
Control -63.313333* 2.099167 .000 -69.19132 -57.43534 
Brofoyedru(BYO) Manso(MN) -32.641800* 1.626008 .000 -37.19487 -28.08873 
Dominase(DO) 10.604867* 1.877552 .000 5.34743 15.86230 
Asaman(AS) -4.997726* 1.434004 .007 -9.01316 -.98230 
Control -52.708467* 1.877552 .000 -57.96590 -47.45103 
Asaman(AS) Manso(MN) -27.644074* 1.434004 .000 -31.65950 -23.62864 
Dominase(DO) 15.602593* 1.713963 .000 10.80324 20.40195 
Brofoyedru(BYO) 4.997726* 1.434004 .007 .98230 9.01316 
Control -47.710741* 1.713963 .000 -52.51010 -42.91138 
Control Manso(MN) 20.066667* 1.877552 .000 14.80923 25.32410 
Dominase(DO) 63.313333* 2.099167 .000 57.43534 69.19132 
Brofoyedru(BYO) 52.708467* 1.877552 .000 47.45103 57.96590 
Asaman(AS) 47.710741* 1.713963 .000 42.91138 52.51010 
Nickel Manso(MN) Dominase(DO) -.156000* .029470 .000 -.23852 -.07348 
Brofoyedru(BYO) .158267* .025521 .000 .08680 .22973 
Asaman(AS) -.070815* .022508 .020 -.13384 -.00779 
Control .141778* .029470 .000 .05926 .22430 
Dominase(DO) Manso(MN) .156000* .029470 .000 .07348 .23852 
Brofoyedru(BYO) .314267* .029470 .000 .23175 .39679 
Asaman(AS) .085185* .026902 .019 .00986 .16051 
Control .297778* .032948 .000 .20552 .39004 
Brofoyedru(BYO) Manso(MN) -.158267* .025521 .000 -.22973 -.08680 
Dominase(DO) -.314267* .029470 .000 -.39679 -.23175 
Asaman(AS) -.229081* .022508 .000 -.29211 -.16606 
Control -.016489 .029470 .980 -.09901 .06603 
Asaman(AS) Manso(MN) .070815* .022508 .020 .00779 .13384 
Dominase(DO) -.085185* .026902 .019 -.16051 -.00986 
Brofoyedru(BYO) .229081* .022508 .000 .16606 .29211 
Control .212593* .026902 .000 .13726 .28792 
Control Manso(MN) -.141778* .029470 .000 -.22430 -.05926 
Dominase(DO) -.297778* .032948 .000 -.39004 -.20552 
Brofoyedru(BYO) .016489 .029470 .980 -.06603 .09901 
Asaman(AS) -.212593* .026902 .000 -.28792 -.13726 
Cobalt Manso(MN) Dominase(DO) -5.488889* .444027 .000 -6.73223 -4.24555 
Brofoyedru(BYO) -3.104000* .384538 .000 -4.18077 -2.02723 
226 
 
Asaman(AS) -4.725926* .339131 .000 -5.67554 -3.77631 
Control 1.588889* .444027 .006 .34555 2.83223 
Dominase(DO) Manso(MN) 5.488889* .444027 .000 4.24555 6.73223 
Brofoyedru(BYO) 2.384889* .444027 .000 1.14155 3.62823 
Asaman(AS) .762963 .405339 .336 -.37205 1.89797 
Control 7.077778* .496437 .000 5.68768 8.46788 
Brofoyedru(BYO) Manso(MN) 3.104000* .384538 .000 2.02723 4.18077 
Dominase(DO) -2.384889* .444027 .000 -3.62823 -1.14155 
Asaman(AS) -1.621926* .339131 .000 -2.57154 -.67231 
Control 4.692889* .444027 .000 3.44955 5.93623 
Asaman(AS) Manso(MN) 4.725926* .339131 .000 3.77631 5.67554 
Dominase(DO) -.762963 .405339 .336 -1.89797 .37205 
Brofoyedru(BYO) 1.621926* .339131 .000 .67231 2.57154 
Control 6.314815* .405339 .000 5.17980 7.44983 
Control Manso(MN) -1.588889* .444027 .006 -2.83223 -.34555 
Dominase(DO) -7.077778* .496437 .000 -8.46788 -5.68768 
Brofoyedru(BYO) -4.692889* .444027 .000 -5.93623 -3.44955 
Asaman(AS) -6.314815* .405339 .000 -7.44983 -5.17980 
Copper Manso(MN) Dominase(DO) .107556* .019448 .000 .05310 .16201 
Brofoyedru(BYO) .075000* .016843 .000 .02784 .12216 
Asaman(AS) .003481 .014854 .999 -.03811 .04507 
Control .092000* .019448 .000 .03754 .14646 
Dominase(DO) Manso(MN) -.107556* .019448 .000 -.16201 -.05310 
Brofoyedru(BYO) -.032556 .019448 .456 -.08701 .02190 
Asaman(AS) -.104074* .017754 .000 -.15379 -.05436 
Control -.015556 .021744 .952 -.07644 .04533 
Brofoyedru(BYO) Manso(MN) -.075000* .016843 .000 -.12216 -.02784 
Dominase(DO) .032556 .019448 .456 -.02190 .08701 
Asaman(AS) -.071519* .014854 .000 -.11311 -.02993 
Control .017000 .019448 .905 -.03746 .07146 
Asaman(AS) Manso(MN) -.003481 .014854 .999 -.04507 .03811 
Dominase(DO) .104074* .017754 .000 .05436 .15379 
Brofoyedru(BYO) .071519* .014854 .000 .02993 .11311 
Control .088519* .017754 .000 .03881 .13823 
Control Manso(MN) -.092000* .019448 .000 -.14646 -.03754 
Dominase(DO) .015556 .021744 .952 -.04533 .07644 
Brofoyedru(BYO) -.017000 .019448 .905 -.07146 .03746 
227 
 
Asaman(AS) -.088519* .017754 .000 -.13823 -.03881 
Lead Manso(MN) Dominase(DO) -.969000* .056937 .000 -1.12843 -.80957 
Brofoyedru(BYO) .230667* .049309 .000 .09259 .36874 
Asaman(AS) .193778* .043486 .000 .07201 .31555 
Control .300667* .056937 .000 .14124 .46010 
Dominase(DO) Manso(MN) .969000* .056937 .000 .80957 1.12843 
Brofoyedru(BYO) 1.199667* .056937 .000 1.04024 1.35910 
Asaman(AS) 1.162778* .051976 .000 1.01724 1.30832 
Control 1.269667* .063657 .000 1.09142 1.44792 
Brofoyedru(BYO) Manso(MN) -.230667* .049309 .000 -.36874 -.09259 
Dominase(DO) -1.199667* .056937 .000 -1.35910 -1.04024 
Asaman(AS) -.036889 .043486 .914 -.15866 .08488 
Control .070000 .056937 .734 -.08943 .22943 
Asaman(AS) Manso(MN) -.193778* .043486 .000 -.31555 -.07201 
Dominase(DO) -1.162778* .051976 .000 -1.30832 -1.01724 
Brofoyedru(BYO) .036889 .043486 .914 -.08488 .15866 
Control .106889 .051976 .251 -.03865 .25243 
Control Manso(MN) -.300667* .056937 .000 -.46010 -.14124 
Dominase(DO) -1.269667* .063657 .000 -1.44792 -1.09142 
Brofoyedru(BYO) -.070000 .056937 .734 -.22943 .08943 
Asaman(AS) -.106889 .051976 .251 -.25243 .03865 
Chromium Manso(MN) Dominase(DO) .042911 .045186 .876 -.08362 .16944 
Brofoyedru(BYO) -.171133* .039132 .000 -.28071 -.06156 
Asaman(AS) -.110274* .034511 .017 -.20691 -.01364 
Control .193133* .045186 .001 .06661 .31966 
Dominase(DO) Manso(MN) -.042911 .045186 .876 -.16944 .08362 
Brofoyedru(BYO) -.214044* .045186 .000 -.34057 -.08752 
Asaman(AS) -.153185* .041249 .004 -.26869 -.03768 
Control .150222* .050519 .032 .00876 .29168 
Brofoyedru(BYO) Manso(MN) .171133* .039132 .000 .06156 .28071 
Dominase(DO) .214044* .045186 .000 .08752 .34057 
Asaman(AS) .060859 .034511 .403 -.03578 .15750 
Control .364267* .045186 .000 .23774 .49079 
Asaman(AS) Manso(MN) .110274* .034511 .017 .01364 .20691 
Dominase(DO) .153185* .041249 .004 .03768 .26869 
Brofoyedru(BYO) -.060859 .034511 .403 -.15750 .03578 
Control .303407* .041249 .000 .18790 .41891 
228 
 
Control Manso(MN) -.193133* .045186 .001 -.31966 -.06661 
Dominase(DO) -.150222* .050519 .032 -.29168 -.00876 
Brofoyedru(BYO) -.364267* .045186 .000 -.49079 -.23774 
Asaman(AS) -.303407* .041249 .000 -.41891 -.18790 
Cadmium Manso(MN) Dominase(DO) 5.908889 19.414409 .998 -48.45443 60.27220 
Brofoyedru(BYO) -29.100000 16.813371 .422 -76.18001 17.98001 
Asaman(AS) -30.617037 14.828000 .247 -72.13770 10.90363 
Control 20.762222 19.414409 .821 -33.60109 75.12554 
Dominase(DO) Manso(MN) -5.908889 19.414409 .998 -60.27220 48.45443 
Brofoyedru(BYO) -35.008889 19.414409 .380 -89.37220 19.35443 
Asaman(AS) -36.525926 17.722850 .249 -86.15262 13.10076 
Control 14.853333 21.705969 .959 -45.92670 75.63337 
Brofoyedru(BYO) Manso(MN) 29.100000 16.813371 .422 -17.98001 76.18001 
Dominase(DO) 35.008889 19.414409 .380 -19.35443 89.37220 
Asaman(AS) -1.517037 14.828000 1.000 -43.03770 40.00363 
Control 49.862222 19.414409 .088 -4.50109 104.22554 
Asaman(AS) Manso(MN) 30.617037 14.828000 .247 -10.90363 72.13770 
Dominase(DO) 36.525926 17.722850 .249 -13.10076 86.15262 
Brofoyedru(BYO) 1.517037 14.828000 1.000 -40.00363 43.03770 
Control 51.379259* 17.722850 .039 1.75257 101.00595 
Control Manso(MN) -20.762222 19.414409 .821 -75.12554 33.60109 
Dominase(DO) -14.853333 21.705969 .959 -75.63337 45.92670 
Brofoyedru(BYO) -49.862222 19.414409 .088 -104.22554 4.50109 
Asaman(AS) -51.379259* 17.722850 .039 -101.00595 -1.75257 
Mercury Manso(MN) Dominase(DO) 1.768889* .269614 .000 1.01393 2.52385 
Brofoyedru(BYO) .213333 .233493 .891 -.44048 .86715 
Asaman(AS) 1.793704* .205921 .000 1.21709 2.37031 
Control 5.413333* .269614 .000 4.65837 6.16829 
Dominase(DO) Manso(MN) -1.768889* .269614 .000 -2.52385 -1.01393 
Brofoyedru(BYO) -1.555556* .269614 .000 -2.31052 -.80059 
Asaman(AS) .024815 .246123 1.000 -.66437 .71400 
Control 3.644444* .301438 .000 2.80037 4.48852 
Brofoyedru(BYO) Manso(MN) -.213333 .233493 .891 -.86715 .44048 
Dominase(DO) 1.555556* .269614 .000 .80059 2.31052 
Asaman(AS) 1.580370* .205921 .000 1.00376 2.15698 
Control 5.200000* .269614 .000 4.44504 5.95496 
Asaman(AS) Manso(MN) -1.793704* .205921 .000 -2.37031 -1.21709 
229 
 
Dominase(DO) -.024815 .246123 1.000 -.71400 .66437 
Brofoyedru(BYO) -1.580370* .205921 .000 -2.15698 -1.00376 
Control 3.619630* .246123 .000 2.93045 4.30881 
Control Manso(MN) -5.413333* .269614 .000 -6.16829 -4.65837 
Dominase(DO) -3.644444* .301438 .000 -4.48852 -2.80037 
Brofoyedru(BYO) -5.200000* .269614 .000 -5.95496 -4.44504 
Asaman(AS) -3.619630* .246123 .000 -4.30881 -2.93045 
Arsenic Manso(MN) Dominase(DO) .097778 .234946 .994 -.56011 .75566 
Brofoyedru(BYO) .326667 .203469 .499 -.24308 .89641 
Asaman(AS) .638519* .179443 .006 .13605 1.14099 
Control 2.476667* .234946 .000 1.81878 3.13455 
Dominase(DO) Manso(MN) -.097778 .234946 .994 -.75566 .56011 
Brofoyedru(BYO) .228889 .234946 .866 -.42900 .88677 
Asaman(AS) .540741 .214475 .098 -.05982 1.14130 
Control 2.378889* .262677 .000 1.64335 3.11443 
Brofoyedru(BYO) Manso(MN) -.326667 .203469 .499 -.89641 .24308 
Dominase(DO) -.228889 .234946 .866 -.88677 .42900 
Asaman(AS) .311852 .179443 .418 -.19062 .81432 
Control 2.150000* .234946 .000 1.49212 2.80788 
Asaman(AS) Manso(MN) -.638519* .179443 .006 -1.14099 -.13605 
Dominase(DO) -.540741 .214475 .098 -1.14130 .05982 
Brofoyedru(BYO) -.311852 .179443 .418 -.81432 .19062 
Control 1.838148* .214475 .000 1.23758 2.43871 
Control Manso(MN) -2.476667* .234946 .000 -3.13455 -1.81878 
Dominase(DO) -2.378889* .262677 .000 -3.11443 -1.64335 
Brofoyedru(BYO) -2.150000* .234946 .000 -2.80788 -1.49212 
Asaman(AS) -1.838148* .214475 .000 -2.43871 -1.23758 
*. The mean difference is significant at the 0.05 level. 
 
 
  
230 
 
Appendix I: ANOVA of Parameters in Soil, Mpohor Wassa East 
  
  Sum of Squares df Mean Square F Sig. 
 Calcium Between Groups 97.350 5 19.470 19.006 .000 
 Within Groups 82.977 81 1.024   
 Total 180.327 86    
 Magnesium Between Groups 64.417 5 12.883 15.750 .000 
 Within Groups 66.256 81 .818   
 Total 130.673 86    
 Sodium Between Groups .851 5 .170 1.888 .105 
 Within Groups 7.303 81 .090   
 Total 8.154 86    
 Potassium Between Groups 5.378 5 1.076 386.475 .000 
 Within Groups .225 81 .003   
 Total 5.603 86    
 Iron Between Groups 17045089.586 5 3409017.917 4.802 .001 
 Within Groups 57509137.816 81 709989.356   
 Total 74554227.402 86    
 Zinc Between Groups 2922.679 5 584.536 5.704 .000 
 Within Groups 8300.938 81 102.481   
 Total 11223.617 86    
 Nickel Between Groups .947 5 .189 9.959 .000 
 Within Groups 1.540 81 .019   
 Total 2.487 86    
 Cobalt Between Groups 110.318 5 22.064 13.764 .000 
 Within Groups 129.844 81 1.603   
 Total 240.162 86    
 Copper Between Groups 1.300 5 .260 24.737 .000 
 Within Groups .851 81 .011   
231 
 
 Total 2.152 86    
 Lead Between Groups 4.465 5 .893 5.291 .000 
 Within Groups 13.672 81 .169   
 Total 18.137 86    
 Chromium Between Groups 1.243 5 .249 11.967 .000 
 Within Groups 1.683 81 .021   
 Total 2.926 86    
 Cadmium Between Groups 3185.529 5 637.106 10.464 .000 
 Within Groups 4931.728 81 60.886   
 Total 8117.257 86    
 Mecury Between Groups 105.711 5 21.142 30.081 .000 
 Within Groups 56.930 81 .703   
 Total 162.640 86    
 Arsenic Between Groups 92.930 5 18.586 33.891 .000 
 Within Groups 44.421 81 .548   
 Total 137.351 86    
 %  Silt Between Groups 641.240 5 128.248 4.589 .001 
 Within Groups 2263.516 81 27.945   
 Total 2904.756 86    
 % clay Between Groups 1960.973 5 392.195 6.661 .000 
 Within Groups 4768.956 81 58.876   
 Total 6729.929 86    
 % sand Between Groups 505.576 5 101.115 2.404 .044 
 Within Groups 3406.596 81 42.057   
 Total 3912.173 86    
 Moisture content Between Groups 25.675 5 5.135 58.280 .000 
 Within Groups 7.137 81 .088   
 Total 32.812 86    
232 
 
 %organic matter Between Groups 24.878 5 4.976 36.632 .000 
 Within Groups 11.002 81 .136   
 Total 35.879 86    
 % organic carbon Between Groups 76.802 5 15.360 278.626 .000 
 Within Groups 4.465 81 .055   
 Total 81.268 86    
 Available phosphorus Between Groups 1806.664 5 361.333 18.074 .000 
 Within Groups 1619.379 81 19.992   
 Total 3426.042 86    
 Total Nitrogen Between Groups 55.479 5 11.096 871.192 .000 
 Within Groups 1.032 81 .013   
 Total 56.511 86    
 Conductivity Between Groups 1687.473 5 337.495 10.863 .000 
 Within Groups 2516.574 81 31.069   
 Total 4204.046 86    
 pH Between Groups 17.138 5 3.428 13.475 .000 
 Within Groups 20.603 81 .254   
 Total 37.741 86    
  
233 
 
Appendix J: Tukey’s HSD Comparison of Parameters in soil, Mpohor Wassa East 
 
Mean Difference 95% Confidence Interval 
Dependent Variable (I) Sampling site (J) Sampling site (I-J) Std. Error Sig. Lower Bound Upper Bound 
Calcium Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.989516* .325105 .036 -1.93852 -.04052 
Mpohor Adansi(MA) -.085305 .342163 1.000 -1.08410 .91349 
Mpohor Motorway(MM) -.071905 .342163 1.000 -1.07070 .92689 
Mpohor Adawotwe (ADW) -.100505 .342163 1.000 -1.09930 .89829 
control -5.674571* .624702 .000 -7.49811 -3.85103 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .989516* .325105 .036 .04052 1.93852 
Mpohor Adansi(MA) .904211 .353844 .121 -.12868 1.93710 
Mpohor Motorway(MM) .917611 .353844 .111 -.11528 1.95050 
Mpohor Adawotwe (ADW) .889011 .353844 .133 -.14388 1.92190 
control -4.685056* .631175 .000 -6.52749 -2.84262 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) .085305 .342163 1.000 -.91349 1.08410 
Adum Tokoro(ADT) -.904211 .353844 .121 -1.93710 .12868 
Mpohor Motorway(MM) .013400 .369578 1.000 -1.06542 1.09222 
Mpohor Adawotwe (ADW) -.015200 .369578 1.000 -1.09402 1.06362 
control -5.589267* .640129 .000 -7.45784 -3.72070 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) .071905 .342163 1.000 -.92689 1.07070 
Adum Tokoro(ADT) -.917611 .353844 .111 -1.95050 .11528 
Mpohor Adansi(MA) -.013400 .369578 1.000 -1.09222 1.06542 
Mpohor Adawotwe (ADW) -.028600 .369578 1.000 -1.10742 1.05022 
control -5.602667* .640129 .000 -7.47124 -3.73410 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .100505 .342163 1.000 -.89829 1.09930 
Adum Tokoro(ADT) -.889011 .353844 .133 -1.92190 .14388 
Mpohor Adansi(MA) .015200 .369578 1.000 -1.06362 1.09402 
Mpohor Motorway(MM) .028600 .369578 1.000 -1.05022 1.10742 
control -5.574067* .640129 .000 -7.44264 -3.70550 
control Asowuo Ayipa(ASA) 5.674571* .624702 .000 3.85103 7.49811 
Adum Tokoro(ADT) 4.685056* .631175 .000 2.84262 6.52749 
Mpohor Adansi(MA) 5.589267* .640129 .000 3.72070 7.45784 
Mpohor Motorway(MM) 5.602667* .640129 .000 3.73410 7.47124 
Mpohor Adawotwe (ADW) 5.574067* .640129 .000 3.70550 7.44264 
234 
 
Magnesium Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.227675 .290507 .970 -1.07568 .62033 
Mpohor Adansi(MA) -.043686 .305749 1.000 -.93619 .84881 
Mpohor Motorway(MM) -.188219 .305749 .990 -1.08072 .70428 
Mpohor Adawotwe (ADW) -.107286 .305749 .999 -.99979 .78521 
control -4.800952* .558219 .000 -6.43043 -3.17148 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .227675 .290507 .970 -.62033 1.07568 
Mpohor Adansi(MA) .183989 .316188 .992 -.73898 1.10696 
Mpohor Motorway(MM) .039456 .316188 1.000 -.88351 .96242 
Mpohor Adawotwe (ADW) .120389 .316188 .999 -.80258 1.04336 
control -4.573278* .564004 .000 -6.21964 -2.92692 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) .043686 .305749 1.000 -.84881 .93619 
Adum Tokoro(ADT) -.183989 .316188 .992 -1.10696 .73898 
Mpohor Motorway(MM) -.144533 .330247 .998 -1.10854 .81948 
Mpohor Adawotwe (ADW) -.063600 .330247 1.000 -1.02761 .90041 
control -4.757267* .572005 .000 -6.42698 -3.08755 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) .188219 .305749 .990 -.70428 1.08072 
Adum Tokoro(ADT) -.039456 .316188 1.000 -.96242 .88351 
Mpohor Adansi(MA) .144533 .330247 .998 -.81948 1.10854 
Mpohor Adawotwe (ADW) .080933 .330247 1.000 -.88308 1.04494 
control -4.612733* .572005 .000 -6.28245 -2.94302 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .107286 .305749 .999 -.78521 .99979 
Adum Tokoro(ADT) -.120389 .316188 .999 -1.04336 .80258 
Mpohor Adansi(MA) .063600 .330247 1.000 -.90041 1.02761 
Mpohor Motorway(MM) -.080933 .330247 1.000 -1.04494 .88308 
control -4.693667* .572005 .000 -6.36338 -3.02395 
control Asowuo Ayipa(ASA) 4.800952* .558219 .000 3.17148 6.43043 
Adum Tokoro(ADT) 4.573278* .564004 .000 2.92692 6.21964 
Mpohor Adansi(MA) 4.757267* .572005 .000 3.08755 6.42698 
Mpohor Motorway(MM) 4.612733* .572005 .000 2.94302 6.28245 
Mpohor Adawotwe (ADW) 4.693667* .572005 .000 3.02395 6.36338 
Sodium Asowuo Ayipa(ASA) Adum Tokoro(ADT) .062586 .096448 .987 -.21895 .34412 
Mpohor Adansi(MA) .005652 .101508 1.000 -.29066 .30196 
Mpohor Motorway(MM) .104899 .101508 .905 -.19141 .40121 
Mpohor Adawotwe (ADW) -.067214 .101508 .986 -.36352 .22909 
control .470586 .185328 .125 -.07040 1.01157 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -.062586 .096448 .987 -.34412 .21895 
Mpohor Adansi(MA) -.056933 .104974 .994 -.36336 .24949 
235 
 
Mpohor Motorway(MM) .042313 .104974 .999 -.26411 .34874 
Mpohor Adawotwe (ADW) -.129800 .104974 .818 -.43622 .17662 
control .408000 .187249 .259 -.13859 .95459 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.005652 .101508 1.000 -.30196 .29066 
Adum Tokoro(ADT) .056933 .104974 .994 -.24949 .36336 
Mpohor Motorway(MM) .099247 .109642 .944 -.22080 .41930 
Mpohor Adawotwe (ADW) -.072867 .109642 .985 -.39292 .24718 
control .464933 .189905 .152 -.08941 1.01928 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.104899 .101508 .905 -.40121 .19141 
Adum Tokoro(ADT) -.042313 .104974 .999 -.34874 .26411 
Mpohor Adansi(MA) -.099247 .109642 .944 -.41930 .22080 
Mpohor Adawotwe (ADW) -.172113 .109642 .621 -.49216 .14794 
control .365687 .189905 .394 -.18866 .92003 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .067214 .101508 .986 -.22909 .36352 
Adum Tokoro(ADT) .129800 .104974 .818 -.17662 .43622 
Mpohor Adansi(MA) .072867 .109642 .985 -.24718 .39292 
Mpohor Motorway(MM) .172113 .109642 .621 -.14794 .49216 
control .537800 .189905 .062 -.01654 1.09214 
control Asowuo Ayipa(ASA) -.470586 .185328 .125 -1.01157 .07040 
Adum Tokoro(ADT) -.408000 .187249 .259 -.95459 .13859 
Mpohor Adansi(MA) -.464933 .189905 .152 -1.01928 .08941 
Mpohor Motorway(MM) -.365687 .189905 .394 -.92003 .18866 
Mpohor Adawotwe (ADW) -.537800 .189905 .062 -1.09214 .01654 
Potassium Asowuo Ayipa(ASA) Adum Tokoro(ADT) .007254 .016945 .998 -.04221 .05672 
Mpohor Adansi(MA) -.000524 .017834 1.000 -.05258 .05154 
Mpohor Motorway(MM) .003676 .017834 1.000 -.04838 .05574 
Mpohor Adawotwe (ADW) .001076 .017834 1.000 -.05098 .05314 
control -1.360190* .032561 .000 -1.45524 -1.26514 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -.007254 .016945 .998 -.05672 .04221 
Mpohor Adansi(MA) -.007778 .018443 .998 -.06161 .04606 
Mpohor Motorway(MM) -.003578 .018443 1.000 -.05741 .05026 
Mpohor Adawotwe (ADW) -.006178 .018443 .999 -.06001 .04766 
control -1.367444* .032898 .000 -1.46348 -1.27141 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) .000524 .017834 1.000 -.05154 .05258 
Adum Tokoro(ADT) .007778 .018443 .998 -.04606 .06161 
Mpohor Motorway(MM) .004200 .019263 1.000 -.05203 .06043 
Mpohor Adawotwe (ADW) .001600 .019263 1.000 -.05463 .05783 
236 
 
control -1.359667* .033365 .000 -1.45706 -1.26227 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.003676 .017834 1.000 -.05574 .04838 
Adum Tokoro(ADT) .003578 .018443 1.000 -.05026 .05741 
Mpohor Adansi(MA) -.004200 .019263 1.000 -.06043 .05203 
Mpohor Adawotwe (ADW) -.002600 .019263 1.000 -.05883 .05363 
control -1.363867* .033365 .000 -1.46126 -1.26647 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.001076 .017834 1.000 -.05314 .05098 
Adum Tokoro(ADT) .006178 .018443 .999 -.04766 .06001 
Mpohor Adansi(MA) -.001600 .019263 1.000 -.05783 .05463 
Mpohor Motorway(MM) .002600 .019263 1.000 -.05363 .05883 
control -1.361267* .033365 .000 -1.45866 -1.26387 
control Asowuo Ayipa(ASA) 1.360190* .032561 .000 1.26514 1.45524 
Adum Tokoro(ADT) 1.367444* .032898 .000 1.27141 1.46348 
Mpohor Adansi(MA) 1.359667* .033365 .000 1.26227 1.45706 
Mpohor Motorway(MM) 1.363867* .033365 .000 1.26647 1.46126 
Mpohor Adawotwe (ADW) 1.361267* .033365 .000 1.26387 1.45866 
Iron Asowuo Ayipa(ASA) Adum Tokoro(ADT) -24.079365 270.652674 1.000 -814.12954 765.97081 
Mpohor Adansi(MA) 2.209524 284.853718 1.000 -829.29429 833.71334 
Mpohor Motorway(MM) 37.542857 284.853718 1.000 -793.96096 869.04667 
Mpohor Adawotwe (ADW) -142.790476 284.853718 .996 -974.29429 688.71334 
control 2381.476190* 520.069356 .000 863.36487 3899.58751 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) 24.079365 270.652674 1.000 -765.97081 814.12954 
Mpohor Adansi(MA) 26.288889 294.578473 1.000 -833.60202 886.17980 
Mpohor Motorway(MM) 61.622222 294.578473 1.000 -798.26869 921.51314 
Mpohor Adawotwe (ADW) -118.711111 294.578473 .999 -978.60202 741.17980 
control 2405.555556* 525.458820 .000 871.71209 3939.39902 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -2.209524 284.853718 1.000 -833.71334 829.29429 
Adum Tokoro(ADT) -26.288889 294.578473 1.000 -886.17980 833.60202 
Mpohor Motorway(MM) 35.333333 307.677181 1.000 -862.79343 933.46010 
Mpohor Adawotwe (ADW) -145.000000 307.677181 .997 -1043.12677 753.12677 
control 2379.266667* 532.912509 .000 823.66547 3934.86786 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -37.542857 284.853718 1.000 -869.04667 793.96096 
Adum Tokoro(ADT) -61.622222 294.578473 1.000 -921.51314 798.26869 
Mpohor Adansi(MA) -35.333333 307.677181 1.000 -933.46010 862.79343 
Mpohor Adawotwe (ADW) -180.333333 307.677181 .992 -1078.46010 717.79343 
control 2343.933333* 532.912509 .000 788.33214 3899.53453 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) 142.790476 284.853718 .996 -688.71334 974.29429 
237 
 
Adum Tokoro(ADT) 118.711111 294.578473 .999 -741.17980 978.60202 
Mpohor Adansi(MA) 145.000000 307.677181 .997 -753.12677 1043.12677 
Mpohor Motorway(MM) 180.333333 307.677181 .992 -717.79343 1078.46010 
control 2524.266667* 532.912509 .000 968.66547 4079.86786 
control Asowuo Ayipa(ASA) -2381.476190* 520.069356 .000 -3899.58751 -863.36487 
Adum Tokoro(ADT) -2405.555556* 525.458820 .000 -3939.39902 -871.71209 
Mpohor Adansi(MA) -2379.266667* 532.912509 .000 -3934.86786 -823.66547 
Mpohor Motorway(MM) -2343.933333* 532.912509 .000 -3899.53453 -788.33214 
Mpohor Adawotwe (ADW) -2524.266667* 532.912509 .000 -4079.86786 -968.66547 
Zinc Asowuo Ayipa(ASA) Adum Tokoro(ADT) 4.123413 3.251678 .801 -5.36841 13.61524 
Mpohor Adansi(MA) -1.880476 3.422292 .994 -11.87034 8.10938 
Mpohor Motorway(MM) -1.727143 3.422292 .996 -11.71700 8.26272 
Mpohor Adawotwe (ADW) -.247143 3.422292 1.000 -10.23700 9.74272 
control -29.307143* 6.248222 .000 -47.54605 -11.06824 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -4.123413 3.251678 .801 -13.61524 5.36841 
Mpohor Adansi(MA) -6.003889 3.539127 .538 -16.33480 4.32702 
Mpohor Motorway(MM) -5.850556 3.539127 .566 -16.18146 4.48035 
Mpohor Adawotwe (ADW) -4.370556 3.539127 .818 -14.70146 5.96035 
control -33.430556* 6.312972 .000 -51.85847 -15.00264 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) 1.880476 3.422292 .994 -8.10938 11.87034 
Adum Tokoro(ADT) 6.003889 3.539127 .538 -4.32702 16.33480 
Mpohor Motorway(MM) .153333 3.696498 1.000 -10.63695 10.94362 
Mpohor Adawotwe (ADW) 1.633333 3.696498 .998 -9.15695 12.42362 
control -27.426667* 6.402522 .001 -46.11598 -8.73735 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) 1.727143 3.422292 .996 -8.26272 11.71700 
Adum Tokoro(ADT) 5.850556 3.539127 .566 -4.48035 16.18146 
Mpohor Adansi(MA) -.153333 3.696498 1.000 -10.94362 10.63695 
Mpohor Adawotwe (ADW) 1.480000 3.696498 .999 -9.31028 12.27028 
control -27.580000* 6.402522 .001 -46.26932 -8.89068 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .247143 3.422292 1.000 -9.74272 10.23700 
Adum Tokoro(ADT) 4.370556 3.539127 .818 -5.96035 14.70146 
Mpohor Adansi(MA) -1.633333 3.696498 .998 -12.42362 9.15695 
Mpohor Motorway(MM) -1.480000 3.696498 .999 -12.27028 9.31028 
control -29.060000* 6.402522 .000 -47.74932 -10.37068 
control Asowuo Ayipa(ASA) 29.307143* 6.248222 .000 11.06824 47.54605 
Adum Tokoro(ADT) 33.430556* 6.312972 .000 15.00264 51.85847 
Mpohor Adansi(MA) 27.426667* 6.402522 .001 8.73735 46.11598 
238 
 
Mpohor Motorway(MM) 27.580000* 6.402522 .001 8.89068 46.26932 
Mpohor Adawotwe (ADW) 29.060000* 6.402522 .000 10.37068 47.74932 
Nickel Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.135159* .044295 .035 -.26446 -.00586 
Mpohor Adansi(MA) .063619 .046619 .748 -.07246 .19970 
Mpohor Motorway(MM) .038286 .046619 .963 -.09780 .17437 
Mpohor Adawotwe (ADW) .046952 .046619 .914 -.08913 .18304 
control .414286* .085114 .000 .16583 .66274 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .135159* .044295 .035 .00586 .26446 
Mpohor Adansi(MA) .198778* .048211 .001 .05805 .33951 
Mpohor Motorway(MM) .173444* .048211 .007 .03271 .31417 
Mpohor Adawotwe (ADW) .182111* .048211 .004 .04138 .32284 
control .549444* .085996 .000 .29842 .80047 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.063619 .046619 .748 -.19970 .07246 
Adum Tokoro(ADT) -.198778* .048211 .001 -.33951 -.05805 
Mpohor Motorway(MM) -.025333 .050354 .996 -.17232 .12165 
Mpohor Adawotwe (ADW) -.016667 .050354 .999 -.16365 .13032 
control .350667* .087216 .002 .09608 .60526 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.038286 .046619 .963 -.17437 .09780 
Adum Tokoro(ADT) -.173444* .048211 .007 -.31417 -.03271 
Mpohor Adansi(MA) .025333 .050354 .996 -.12165 .17232 
Mpohor Adawotwe (ADW) .008667 .050354 1.000 -.13832 .15565 
control .376000* .087216 .001 .12141 .63059 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.046952 .046619 .914 -.18304 .08913 
Adum Tokoro(ADT) -.182111* .048211 .004 -.32284 -.04138 
Mpohor Adansi(MA) .016667 .050354 .999 -.13032 .16365 
Mpohor Motorway(MM) -.008667 .050354 1.000 -.15565 .13832 
control .367333* .087216 .001 .11274 .62192 
control Asowuo Ayipa(ASA) -.414286* .085114 .000 -.66274 -.16583 
Adum Tokoro(ADT) -.549444* .085996 .000 -.80047 -.29842 
Mpohor Adansi(MA) -.350667* .087216 .002 -.60526 -.09608 
Mpohor Motorway(MM) -.376000* .087216 .001 -.63059 -.12141 
Mpohor Adawotwe (ADW) -.367333* .087216 .001 -.62192 -.11274 
Cobalt Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.677778 .406682 .558 -1.86491 .50935 
Mpohor Adansi(MA) .080000 .428021 1.000 -1.16942 1.32942 
Mpohor Motorway(MM) .553333 .428021 .788 -.69608 1.80275 
Mpohor Adawotwe (ADW) -.086667 .428021 1.000 -1.33608 1.16275 
control 5.752333* .781455 .000 3.47122 8.03344 
239 
 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .677778 .406682 .558 -.50935 1.86491 
Mpohor Adansi(MA) .757778 .442633 .528 -.53429 2.04985 
Mpohor Motorway(MM) 1.231111 .442633 .071 -.06096 2.52318 
Mpohor Adawotwe (ADW) .591111 .442633 .764 -.70096 1.88318 
control 6.430111* .789553 .000 4.12536 8.73486 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.080000 .428021 1.000 -1.32942 1.16942 
Adum Tokoro(ADT) -.757778 .442633 .528 -2.04985 .53429 
Mpohor Motorway(MM) .473333 .462315 .909 -.87619 1.82286 
Mpohor Adawotwe (ADW) -.166667 .462315 .999 -1.51619 1.18286 
control 5.672333* .800753 .000 3.33489 8.00978 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.553333 .428021 .788 -1.80275 .69608 
Adum Tokoro(ADT) -1.231111 .442633 .071 -2.52318 .06096 
Mpohor Adansi(MA) -.473333 .462315 .909 -1.82286 .87619 
Mpohor Adawotwe (ADW) -.640000 .462315 .736 -1.98952 .70952 
control 5.199000* .800753 .000 2.86156 7.53644 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .086667 .428021 1.000 -1.16275 1.33608 
Adum Tokoro(ADT) -.591111 .442633 .764 -1.88318 .70096 
Mpohor Adansi(MA) .166667 .462315 .999 -1.18286 1.51619 
Mpohor Motorway(MM) .640000 .462315 .736 -.70952 1.98952 
control 5.839000* .800753 .000 3.50156 8.17644 
control Asowuo Ayipa(ASA) -5.752333* .781455 .000 -8.03344 -3.47122 
Adum Tokoro(ADT) -6.430111* .789553 .000 -8.73486 -4.12536 
Mpohor Adansi(MA) -5.672333* .800753 .000 -8.00978 -3.33489 
Mpohor Motorway(MM) -5.199000* .800753 .000 -7.53644 -2.86156 
Mpohor Adawotwe (ADW) -5.839000* .800753 .000 -8.17644 -3.50156 
Copper Asowuo Ayipa(ASA) Adum Tokoro(ADT) .008492 .032933 1.000 -.08764 .10462 
Mpohor Adansi(MA) .013048 .034661 .999 -.08813 .11422 
Mpohor Motorway(MM) .003714 .034661 1.000 -.09746 .10489 
Mpohor Adawotwe (ADW) .021048 .034661 .990 -.08013 .12222 
control .677381* .063281 .000 .49266 .86210 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -.008492 .032933 1.000 -.10462 .08764 
Mpohor Adansi(MA) .004556 .035844 1.000 -.10007 .10919 
Mpohor Motorway(MM) -.004778 .035844 1.000 -.10941 .09985 
Mpohor Adawotwe (ADW) .012556 .035844 .999 -.09207 .11719 
control .668889* .063937 .000 .48225 .85553 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.013048 .034661 .999 -.11422 .08813 
Adum Tokoro(ADT) -.004556 .035844 1.000 -.10919 .10007 
240 
 
Mpohor Motorway(MM) -.009333 .037438 1.000 -.11862 .09995 
Mpohor Adawotwe (ADW) .008000 .037438 1.000 -.10128 .11728 
control .664333* .064844 .000 .47505 .85362 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.003714 .034661 1.000 -.10489 .09746 
Adum Tokoro(ADT) .004778 .035844 1.000 -.09985 .10941 
Mpohor Adansi(MA) .009333 .037438 1.000 -.09995 .11862 
Mpohor Adawotwe (ADW) .017333 .037438 .997 -.09195 .12662 
control .673667* .064844 .000 .48438 .86295 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.021048 .034661 .990 -.12222 .08013 
Adum Tokoro(ADT) -.012556 .035844 .999 -.11719 .09207 
Mpohor Adansi(MA) -.008000 .037438 1.000 -.11728 .10128 
Mpohor Motorway(MM) -.017333 .037438 .997 -.12662 .09195 
control .656333* .064844 .000 .46705 .84562 
control Asowuo Ayipa(ASA) -.677381* .063281 .000 -.86210 -.49266 
Adum Tokoro(ADT) -.668889* .063937 .000 -.85553 -.48225 
Mpohor Adansi(MA) -.664333* .064844 .000 -.85362 -.47505 
Mpohor Motorway(MM) -.673667* .064844 .000 -.86295 -.48438 
Mpohor Adawotwe (ADW) -.656333* .064844 .000 -.84562 -.46705 
Lead Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.114119 .131964 .954 -.49933 .27109 
Mpohor Adansi(MA) .060914 .138888 .998 -.34451 .46634 
Mpohor Motorway(MM) .043714 .138888 1.000 -.36171 .44914 
Mpohor Adawotwe (ADW) -.013619 .138888 1.000 -.41904 .39180 
control 1.188714* .253574 .000 .44852 1.92891 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .114119 .131964 .954 -.27109 .49933 
Mpohor Adansi(MA) .175033 .143629 .827 -.24423 .59430 
Mpohor Motorway(MM) .157833 .143629 .880 -.26143 .57710 
Mpohor Adawotwe (ADW) .100500 .143629 .981 -.31876 .51976 
control 1.302833* .256201 .000 .55497 2.05070 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.060914 .138888 .998 -.46634 .34451 
Adum Tokoro(ADT) -.175033 .143629 .827 -.59430 .24423 
Mpohor Motorway(MM) -.017200 .150016 1.000 -.45511 .42071 
Mpohor Adawotwe (ADW) -.074533 .150016 .996 -.51244 .36337 
control 1.127800* .259836 .001 .36933 1.88627 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.043714 .138888 1.000 -.44914 .36171 
Adum Tokoro(ADT) -.157833 .143629 .880 -.57710 .26143 
Mpohor Adansi(MA) .017200 .150016 1.000 -.42071 .45511 
Mpohor Adawotwe (ADW) -.057333 .150016 .999 -.49524 .38057 
241 
 
control 1.145000* .259836 .000 .38653 1.90347 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .013619 .138888 1.000 -.39180 .41904 
Adum Tokoro(ADT) -.100500 .143629 .981 -.51976 .31876 
Mpohor Adansi(MA) .074533 .150016 .996 -.36337 .51244 
Mpohor Motorway(MM) .057333 .150016 .999 -.38057 .49524 
control 1.202333* .259836 .000 .44386 1.96081 
control Asowuo Ayipa(ASA) -1.188714* .253574 .000 -1.92891 -.44852 
Adum Tokoro(ADT) -1.302833* .256201 .000 -2.05070 -.55497 
Mpohor Adansi(MA) -1.127800* .259836 .001 -1.88627 -.36933 
Mpohor Motorway(MM) -1.145000* .259836 .000 -1.90347 -.38653 
Mpohor Adawotwe (ADW) -1.202333* .259836 .000 -1.96081 -.44386 
Chromium Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.053365 .046301 .858 -.18852 .08179 
Mpohor Adansi(MA) .026190 .048730 .994 -.11606 .16844 
Mpohor Motorway(MM) -.006476 .048730 1.000 -.14872 .13577 
Mpohor Adawotwe (ADW) .056190 .048730 .857 -.08606 .19844 
Control .627857* .088969 .000 .36815 .88756 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .053365 .046301 .858 -.08179 .18852 
Mpohor Adansi(MA) .079556 .050394 .615 -.06755 .22666 
Mpohor Motorway(MM) .046889 .050394 .937 -.10021 .19399 
Mpohor Adawotwe (ADW) .109556 .050394 .261 -.03755 .25666 
Control .681222* .089891 .000 .41883 .94362 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.026190 .048730 .994 -.16844 .11606 
Adum Tokoro(ADT) -.079556 .050394 .615 -.22666 .06755 
Mpohor Motorway(MM) -.032667 .052635 .989 -.18631 .12098 
Mpohor Adawotwe (ADW) .030000 .052635 .993 -.12364 .18364 
Control .601667* .091166 .000 .33555 .86779 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) .006476 .048730 1.000 -.13577 .14872 
Adum Tokoro(ADT) -.046889 .050394 .937 -.19399 .10021 
Mpohor Adansi(MA) .032667 .052635 .989 -.12098 .18631 
Mpohor Adawotwe (ADW) .062667 .052635 .840 -.09098 .21631 
Control .634333* .091166 .000 .36821 .90045 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.056190 .048730 .857 -.19844 .08606 
Adum Tokoro(ADT) -.109556 .050394 .261 -.25666 .03755 
Mpohor Adansi(MA) -.030000 .052635 .993 -.18364 .12364 
Mpohor Motorway(MM) -.062667 .052635 .840 -.21631 .09098 
Control .571667* .091166 .000 .30555 .83779 
control Asowuo Ayipa(ASA) -.627857* .088969 .000 -.88756 -.36815 
242 
 
Adum Tokoro(ADT) -.681222* .089891 .000 -.94362 -.41883 
Mpohor Adansi(MA) -.601667* .091166 .000 -.86779 -.33555 
Mpohor Motorway(MM) -.634333* .091166 .000 -.90045 -.36821 
Mpohor Adawotwe (ADW) -.571667* .091166 .000 -.83779 -.30555 
Cadmium Asowuo Ayipa(ASA) Adum Tokoro(ADT) 1.489683 2.506360 .991 -5.82652 8.80589 
Mpohor Adansi(MA) -.564762 2.637868 1.000 -8.26484 7.13532 
Mpohor Motorway(MM) -2.611429 2.637868 .920 -10.31151 5.08865 
Mpohor Adawotwe (ADW) .615238 2.637868 1.000 -7.08484 8.31532 
Control 32.228571* 4.816066 .000 18.17021 46.28693 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -1.489683 2.506360 .991 -8.80589 5.82652 
Mpohor Adansi(MA) -2.054444 2.727923 .974 -10.01740 5.90851 
Mpohor Motorway(MM) -4.101111 2.727923 .663 -12.06407 3.86185 
Mpohor Adawotwe (ADW) -.874444 2.727923 1.000 -8.83740 7.08851 
Control 30.738889* 4.865975 .000 16.53484 44.94294 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) .564762 2.637868 1.000 -7.13532 8.26484 
Adum Tokoro(ADT) 2.054444 2.727923 .974 -5.90851 10.01740 
Mpohor Motorway(MM) -2.046667 2.849223 .979 -10.36371 6.27037 
Mpohor Adawotwe (ADW) 1.180000 2.849223 .998 -7.13704 9.49704 
Control 32.793333* 4.934999 .000 18.38780 47.19887 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) 2.611429 2.637868 .920 -5.08865 10.31151 
Adum Tokoro(ADT) 4.101111 2.727923 .663 -3.86185 12.06407 
Mpohor Adansi(MA) 2.046667 2.849223 .979 -6.27037 10.36371 
Mpohor Adawotwe (ADW) 3.226667 2.849223 .866 -5.09037 11.54371 
Control 34.840000* 4.934999 .000 20.43446 49.24554 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.615238 2.637868 1.000 -8.31532 7.08484 
Adum Tokoro(ADT) .874444 2.727923 1.000 -7.08851 8.83740 
Mpohor Adansi(MA) -1.180000 2.849223 .998 -9.49704 7.13704 
Mpohor Motorway(MM) -3.226667 2.849223 .866 -11.54371 5.09037 
Control 31.613333* 4.934999 .000 17.20780 46.01887 
control Asowuo Ayipa(ASA) -32.228571* 4.816066 .000 -46.28693 -18.17021 
Adum Tokoro(ADT) -30.738889* 4.865975 .000 -44.94294 -16.53484 
Mpohor Adansi(MA) -32.793333* 4.934999 .000 -47.19887 -18.38780 
Mpohor Motorway(MM) -34.840000* 4.934999 .000 -49.24554 -20.43446 
Mpohor Adawotwe (ADW) -31.613333* 4.934999 .000 -46.01887 -17.20780 
Mecury Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.678730 .269285 .130 -1.46479 .10733 
Mpohor Adansi(MA) .143048 .283415 .996 -.68426 .97035 
Mpohor Motorway(MM) .149714 .283415 .995 -.67759 .97702 
243 
 
Mpohor Adawotwe (ADW) .003048 .283415 1.000 -.82426 .83035 
Control 5.709048* .517442 .000 4.19861 7.21949 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .678730 .269285 .130 -.10733 1.46479 
Mpohor Adansi(MA) .821778 .293090 .067 -.03377 1.67732 
Mpohor Motorway(MM) .828444 .293090 .063 -.02710 1.68399 
Mpohor Adawotwe (ADW) .681778 .293090 .196 -.17377 1.53732 
Control 6.387778* .522804 .000 4.86168 7.91387 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.143048 .283415 .996 -.97035 .68426 
Adum Tokoro(ADT) -.821778 .293090 .067 -1.67732 .03377 
Mpohor Motorway(MM) .006667 .306123 1.000 -.88692 .90026 
Mpohor Adawotwe (ADW) -.140000 .306123 .997 -1.03359 .75359 
Control 5.566000* .530220 .000 4.01826 7.11374 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.149714 .283415 .995 -.97702 .67759 
Adum Tokoro(ADT) -.828444 .293090 .063 -1.68399 .02710 
Mpohor Adansi(MA) -.006667 .306123 1.000 -.90026 .88692 
Mpohor Adawotwe (ADW) -.146667 .306123 .997 -1.04026 .74692 
Control 5.559333* .530220 .000 4.01159 7.10708 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.003048 .283415 1.000 -.83035 .82426 
Adum Tokoro(ADT) -.681778 .293090 .196 -1.53732 .17377 
Mpohor Adansi(MA) .140000 .306123 .997 -.75359 1.03359 
Mpohor Motorway(MM) .146667 .306123 .997 -.74692 1.04026 
Control 5.706000* .530220 .000 4.15826 7.25374 
control Asowuo Ayipa(ASA) -5.709048* .517442 .000 -7.21949 -4.19861 
Adum Tokoro(ADT) -6.387778* .522804 .000 -7.91387 -4.86168 
Mpohor Adansi(MA) -5.566000* .530220 .000 -7.11374 -4.01826 
Mpohor Motorway(MM) -5.559333* .530220 .000 -7.10708 -4.01159 
Mpohor Adawotwe (ADW) -5.706000* .530220 .000 -7.25374 -4.15826 
Arsenic Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.330159 .237869 .734 -1.02451 .36419 
Mpohor Adansi(MA) -.065714 .250350 1.000 -.79650 .66507 
Mpohor Motorway(MM) .240952 .250350 .928 -.48983 .97174 
Mpohor Adawotwe (ADW) -.132381 .250350 .995 -.86317 .59840 
Control 5.514286* .457074 .000 4.18006 6.84851 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .330159 .237869 .734 -.36419 1.02451 
Mpohor Adansi(MA) .264444 .258896 .909 -.49129 1.02018 
Mpohor Motorway(MM) .571111 .258896 .246 -.18462 1.32684 
Mpohor Adawotwe (ADW) .197778 .258896 .973 -.55795 .95351 
Control 5.844444* .461810 .000 4.49639 7.19249 
244 
 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) .065714 .250350 1.000 -.66507 .79650 
Adum Tokoro(ADT) -.264444 .258896 .909 -1.02018 .49129 
Mpohor Motorway(MM) .306667 .270408 .866 -.48267 1.09600 
Mpohor Adawotwe (ADW) -.066667 .270408 1.000 -.85600 .72267 
Control 5.580000* .468361 .000 4.21283 6.94717 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.240952 .250350 .928 -.97174 .48983 
Adum Tokoro(ADT) -.571111 .258896 .246 -1.32684 .18462 
Mpohor Adansi(MA) -.306667 .270408 .866 -1.09600 .48267 
Mpohor Adawotwe (ADW) -.373333 .270408 .738 -1.16267 .41600 
Control 5.273333* .468361 .000 3.90616 6.64051 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .132381 .250350 .995 -.59840 .86317 
Adum Tokoro(ADT) -.197778 .258896 .973 -.95351 .55795 
Mpohor Adansi(MA) .066667 .270408 1.000 -.72267 .85600 
Mpohor Motorway(MM) .373333 .270408 .738 -.41600 1.16267 
Control 5.646667* .468361 .000 4.27949 7.01384 
control Asowuo Ayipa(ASA) -5.514286* .457074 .000 -6.84851 -4.18006 
Adum Tokoro(ADT) -5.844444* .461810 .000 -7.19249 -4.49639 
Mpohor Adansi(MA) -5.580000* .468361 .000 -6.94717 -4.21283 
Mpohor Motorway(MM) -5.273333* .468361 .000 -6.64051 -3.90616 
Mpohor Adawotwe (ADW) -5.646667* .468361 .000 -7.01384 -4.27949 
%  Silt Asowuo Ayipa(ASA) Adum Tokoro(ADT) 5.73810* 1.69799 .014 .7816 10.6946 
Mpohor Adansi(MA) 1.17143 1.78709 .986 -4.0452 6.3880 
Mpohor Motorway(MM) .50476 1.78709 1.000 -4.7118 5.7214 
Mpohor Adawotwe (ADW) .37143 1.78709 1.000 -4.8452 5.5880 
Control -7.42857 3.26276 .216 -16.9527 2.0956 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -5.73810* 1.69799 .014 -10.6946 -.7816 
Mpohor Adansi(MA) -4.56667 1.84810 .145 -9.9614 .8280 
Mpohor Motorway(MM) -5.23333 1.84810 .063 -10.6280 .1614 
Mpohor Adawotwe (ADW) -5.36667 1.84810 .052 -10.7614 .0280 
Control -13.16667* 3.29657 .002 -22.7895 -3.5438 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -1.17143 1.78709 .986 -6.3880 4.0452 
Adum Tokoro(ADT) 4.56667 1.84810 .145 -.8280 9.9614 
Mpohor Motorway(MM) -.66667 1.93027 .999 -6.3012 4.9679 
Mpohor Adawotwe (ADW) -.80000 1.93027 .998 -6.4346 4.8346 
Control -8.60000 3.34333 .116 -18.3594 1.1594 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.50476 1.78709 1.000 -5.7214 4.7118 
Adum Tokoro(ADT) 5.23333 1.84810 .063 -.1614 10.6280 
245 
 
Mpohor Adansi(MA) .66667 1.93027 .999 -4.9679 6.3012 
Mpohor Adawotwe (ADW) -.13333 1.93027 1.000 -5.7679 5.5012 
Control -7.93333 3.34333 .178 -17.6927 1.8260 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.37143 1.78709 1.000 -5.5880 4.8452 
Adum Tokoro(ADT) 5.36667 1.84810 .052 -.0280 10.7614 
Mpohor Adansi(MA) .80000 1.93027 .998 -4.8346 6.4346 
Mpohor Motorway(MM) .13333 1.93027 1.000 -5.5012 5.7679 
Control -7.80000 3.34333 .193 -17.5594 1.9594 
control Asowuo Ayipa(ASA) 7.42857 3.26276 .216 -2.0956 16.9527 
Adum Tokoro(ADT) 13.16667* 3.29657 .002 3.5438 22.7895 
Mpohor Adansi(MA) 8.60000 3.34333 .116 -1.1594 18.3594 
Mpohor Motorway(MM) 7.93333 3.34333 .178 -1.8260 17.6927 
Mpohor Adawotwe (ADW) 7.80000 3.34333 .193 -1.9594 17.5594 
% clay Asowuo Ayipa(ASA) Adum Tokoro(ADT) -9.90476* 2.46465 .002 -17.0992 -2.7103 
Mpohor Adansi(MA) -.63810 2.59397 1.000 -8.2100 6.9339 
Mpohor Motorway(MM) -1.17143 2.59397 .998 -8.7434 6.4005 
Mpohor Adawotwe (ADW) -.50476 2.59397 1.000 -8.0767 7.0672 
Control 13.06190 4.73592 .075 -.7625 26.8863 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) 9.90476* 2.46465 .002 2.7103 17.0992 
Mpohor Adansi(MA) 9.26667* 2.68253 .011 1.4362 17.0971 
Mpohor Motorway(MM) 8.73333* 2.68253 .020 .9029 16.5638 
Mpohor Adawotwe (ADW) 9.40000* 2.68253 .009 1.5696 17.2304 
Control 22.96667* 4.78500 .000 8.9990 36.9343 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) .63810 2.59397 1.000 -6.9339 8.2100 
Adum Tokoro(ADT) -9.26667* 2.68253 .011 -17.0971 -1.4362 
Mpohor Motorway(MM) -.53333 2.80181 1.000 -8.7120 7.6453 
Mpohor Adawotwe (ADW) .13333 2.80181 1.000 -8.0453 8.3120 
Control 13.70000 4.85288 .064 -.4658 27.8658 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) 1.17143 2.59397 .998 -6.4005 8.7434 
Adum Tokoro(ADT) -8.73333* 2.68253 .020 -16.5638 -.9029 
Mpohor Adansi(MA) .53333 2.80181 1.000 -7.6453 8.7120 
Mpohor Adawotwe (ADW) .66667 2.80181 1.000 -7.5120 8.8453 
Control 14.23333* 4.85288 .048 .0675 28.3991 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .50476 2.59397 1.000 -7.0672 8.0767 
Adum Tokoro(ADT) -9.40000* 2.68253 .009 -17.2304 -1.5696 
Mpohor Adansi(MA) -.13333 2.80181 1.000 -8.3120 8.0453 
Mpohor Motorway(MM) -.66667 2.80181 1.000 -8.8453 7.5120 
246 
 
Control 13.56667 4.85288 .068 -.5991 27.7325 
control Asowuo Ayipa(ASA) -13.06190 4.73592 .075 -26.8863 .7625 
Adum Tokoro(ADT) -22.96667* 4.78500 .000 -36.9343 -8.9990 
Mpohor Adansi(MA) -13.70000 4.85288 .064 -27.8658 .4658 
Mpohor Motorway(MM) -14.23333* 4.85288 .048 -28.3991 -.0675 
Mpohor Adawotwe (ADW) -13.56667 4.85288 .068 -27.7325 .5991 
% sand Asowuo Ayipa(ASA) Adum Tokoro(ADT) 1.40476 2.08307 .984 -4.6758 7.4854 
Mpohor Adansi(MA) -.62857 2.19237 1.000 -7.0282 5.7711 
Mpohor Motorway(MM) .30476 2.19237 1.000 -6.0949 6.7044 
Mpohor Adawotwe (ADW) .97143 2.19237 .998 -5.4282 7.3711 
Control -12.22857* 4.00270 .035 -23.9127 -.5445 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -1.40476 2.08307 .984 -7.4854 4.6758 
Mpohor Adansi(MA) -2.03333 2.26722 .946 -8.6515 4.5848 
Mpohor Motorway(MM) -1.10000 2.26722 .997 -7.7181 5.5181 
Mpohor Adawotwe (ADW) -.43333 2.26722 1.000 -7.0515 6.1848 
Control -13.63333* 4.04418 .014 -25.4385 -1.8281 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) .62857 2.19237 1.000 -5.7711 7.0282 
Adum Tokoro(ADT) 2.03333 2.26722 .946 -4.5848 8.6515 
Mpohor Motorway(MM) .93333 2.36803 .999 -5.9791 7.8457 
Mpohor Adawotwe (ADW) 1.60000 2.36803 .984 -5.3124 8.5124 
Control -11.60000 4.10155 .063 -23.5726 .3726 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.30476 2.19237 1.000 -6.7044 6.0949 
Adum Tokoro(ADT) 1.10000 2.26722 .997 -5.5181 7.7181 
Mpohor Adansi(MA) -.93333 2.36803 .999 -7.8457 5.9791 
Mpohor Adawotwe (ADW) .66667 2.36803 1.000 -6.2457 7.5791 
Control -12.53333* 4.10155 .035 -24.5060 -.5607 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.97143 2.19237 .998 -7.3711 5.4282 
Adum Tokoro(ADT) .43333 2.26722 1.000 -6.1848 7.0515 
Mpohor Adansi(MA) -1.60000 2.36803 .984 -8.5124 5.3124 
Mpohor Motorway(MM) -.66667 2.36803 1.000 -7.5791 6.2457 
Control -13.20000* 4.10155 .022 -25.1726 -1.2274 
control Asowuo Ayipa(ASA) 12.22857* 4.00270 .035 .5445 23.9127 
Adum Tokoro(ADT) 13.63333* 4.04418 .014 1.8281 25.4385 
Mpohor Adansi(MA) 11.60000 4.10155 .063 -.3726 23.5726 
Mpohor Motorway(MM) 12.53333* 4.10155 .035 .5607 24.5060 
Mpohor Adawotwe (ADW) 13.20000* 4.10155 .022 1.2274 25.1726 
Moisture content Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.30738* .09535 .022 -.5857 -.0291 
247 
 
Mpohor Adansi(MA) .09229 .10035 .940 -.2006 .3852 
Mpohor Motorway(MM) -.08105 .10035 .965 -.3740 .2119 
Mpohor Adawotwe (ADW) .11895 .10035 .843 -.1740 .4119 
Control -2.90238* .18321 .000 -3.4372 -2.3676 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .30738* .09535 .022 .0291 .5857 
Mpohor Adansi(MA) .39967* .10377 .003 .0967 .7026 
Mpohor Motorway(MM) .22633 .10377 .258 -.0766 .5293 
Mpohor Adawotwe (ADW) .42633* .10377 .001 .1234 .7293 
Control -2.59500* .18511 .000 -3.1353 -2.0547 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.09229 .10035 .940 -.3852 .2006 
Adum Tokoro(ADT) -.39967* .10377 .003 -.7026 -.0967 
Mpohor Motorway(MM) -.17333 .10839 .601 -.4897 .1431 
Mpohor Adawotwe (ADW) .02667 .10839 1.000 -.2897 .3431 
Control -2.99467* .18773 .000 -3.5427 -2.4467 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) .08105 .10035 .965 -.2119 .3740 
Adum Tokoro(ADT) -.22633 .10377 .258 -.5293 .0766 
Mpohor Adansi(MA) .17333 .10839 .601 -.1431 .4897 
Mpohor Adawotwe (ADW) .20000 .10839 .443 -.1164 .5164 
Control -2.82133* .18773 .000 -3.3693 -2.2733 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.11895 .10035 .843 -.4119 .1740 
Adum Tokoro(ADT) -.42633* .10377 .001 -.7293 -.1234 
Mpohor Adansi(MA) -.02667 .10839 1.000 -.3431 .2897 
Mpohor Motorway(MM) -.20000 .10839 .443 -.5164 .1164 
Control -3.02133* .18773 .000 -3.5693 -2.4733 
control Asowuo Ayipa(ASA) 2.90238* .18321 .000 2.3676 3.4372 
Adum Tokoro(ADT) 2.59500* .18511 .000 2.0547 3.1353 
Mpohor Adansi(MA) 2.99467* .18773 .000 2.4467 3.5427 
Mpohor Motorway(MM) 2.82133* .18773 .000 2.2733 3.3693 
Mpohor Adawotwe (ADW) 3.02133* .18773 .000 2.4733 3.5693 
%organic matter Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.07986 .11838 .984 -.4254 .2657 
Mpohor Adansi(MA) .00548 .12459 1.000 -.3582 .3692 
Mpohor Motorway(MM) .08214 .12459 .986 -.2815 .4458 
Mpohor Adawotwe (ADW) .05748 .12459 .997 -.3062 .4212 
Control -2.90652* .22747 .000 -3.5705 -2.2425 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .07986 .11838 .984 -.2657 .4254 
Mpohor Adansi(MA) .08533 .12884 .986 -.2908 .4614 
Mpohor Motorway(MM) .16200 .12884 .807 -.2141 .5381 
248 
 
Mpohor Adawotwe (ADW) .13733 .12884 .893 -.2388 .5134 
Control -2.82667* .22983 .000 -3.4975 -2.1558 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.00548 .12459 1.000 -.3692 .3582 
Adum Tokoro(ADT) -.08533 .12884 .986 -.4614 .2908 
Mpohor Motorway(MM) .07667 .13457 .993 -.3162 .4695 
Mpohor Adawotwe (ADW) .05200 .13457 .999 -.3408 .4448 
Control -2.91200* .23309 .000 -3.5924 -2.2316 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.08214 .12459 .986 -.4458 .2815 
Adum Tokoro(ADT) -.16200 .12884 .807 -.5381 .2141 
Mpohor Adansi(MA) -.07667 .13457 .993 -.4695 .3162 
Mpohor Adawotwe (ADW) -.02467 .13457 1.000 -.4175 .3682 
Control -2.98867* .23309 .000 -3.6691 -2.3083 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.05748 .12459 .997 -.4212 .3062 
Adum Tokoro(ADT) -.13733 .12884 .893 -.5134 .2388 
Mpohor Adansi(MA) -.05200 .13457 .999 -.4448 .3408 
Mpohor Motorway(MM) .02467 .13457 1.000 -.3682 .4175 
Control -2.96400* .23309 .000 -3.6444 -2.2836 
control Asowuo Ayipa(ASA) 2.90652* .22747 .000 2.2425 3.5705 
Adum Tokoro(ADT) 2.82667* .22983 .000 2.1558 3.4975 
Mpohor Adansi(MA) 2.91200* .23309 .000 2.2316 3.5924 
Mpohor Motorway(MM) 2.98867* .23309 .000 2.3083 3.6691 
Mpohor Adawotwe (ADW) 2.96400* .23309 .000 2.2836 3.6444 
% organic carbon Asowuo Ayipa(ASA) Adum Tokoro(ADT) .19663 .07542 .107 -.0235 .4168 
Mpohor Adansi(MA) .00552 .07938 1.000 -.2262 .2372 
Mpohor Motorway(MM) -.01048 .07938 1.000 -.2422 .2212 
Mpohor Adawotwe (ADW) .00652 .07938 1.000 -.2252 .2382 
Control -5.08848* .14492 .000 -5.5115 -4.6654 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -.19663 .07542 .107 -.4168 .0235 
Mpohor Adansi(MA) -.19111 .08209 .195 -.4307 .0485 
Mpohor Motorway(MM) -.20711 .08209 .130 -.4467 .0325 
Mpohor Adawotwe (ADW) -.19011 .08209 .200 -.4297 .0495 
Control -5.28511* .14642 .000 -5.7125 -4.8577 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.00552 .07938 1.000 -.2372 .2262 
Adum Tokoro(ADT) .19111 .08209 .195 -.0485 .4307 
Mpohor Motorway(MM) -.01600 .08574 1.000 -.2663 .2343 
Mpohor Adawotwe (ADW) .00100 .08574 1.000 -.2493 .2513 
Control -5.09400* .14850 .000 -5.5275 -4.6605 
249 
 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) .01048 .07938 1.000 -.2212 .2422 
Adum Tokoro(ADT) .20711 .08209 .130 -.0325 .4467 
Mpohor Adansi(MA) .01600 .08574 1.000 -.2343 .2663 
Mpohor Adawotwe (ADW) .01700 .08574 1.000 -.2333 .2673 
Control -5.07800* .14850 .000 -5.5115 -4.6445 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.00652 .07938 1.000 -.2382 .2252 
Adum Tokoro(ADT) .19011 .08209 .200 -.0495 .4297 
Mpohor Adansi(MA) -.00100 .08574 1.000 -.2513 .2493 
Mpohor Motorway(MM) -.01700 .08574 1.000 -.2673 .2333 
Control -5.09500* .14850 .000 -5.5285 -4.6615 
control Asowuo Ayipa(ASA) 5.08848* .14492 .000 4.6654 5.5115 
Adum Tokoro(ADT) 5.28511* .14642 .000 4.8577 5.7125 
Mpohor Adansi(MA) 5.09400* .14850 .000 4.6605 5.5275 
Mpohor Motorway(MM) 5.07800* .14850 .000 4.6445 5.5115 
Mpohor Adawotwe (ADW) 5.09500* .14850 .000 4.6615 5.5285 
Available phosphorus Asowuo Ayipa(ASA) Adum Tokoro(ADT) -4.49762* 1.43621 .028 -8.6900 -.3052 
Mpohor Adansi(MA) -.24762 1.51157 1.000 -4.6600 4.1647 
Mpohor Motorway(MM) 1.83905 1.51157 .828 -2.5733 6.2514 
Mpohor Adawotwe (ADW) -.39429 1.51157 1.000 -4.8066 4.0181 
Control -23.01429* 2.75973 .000 -31.0701 -14.9585 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) 4.49762* 1.43621 .028 .3052 8.6900 
Mpohor Adansi(MA) 4.25000 1.56317 .083 -.3130 8.8130 
Mpohor Motorway(MM) 6.33667* 1.56317 .002 1.7737 10.8997 
Mpohor Adawotwe (ADW) 4.10333 1.56317 .103 -.4597 8.6663 
Control -18.51667* 2.78833 .000 -26.6560 -10.3774 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) .24762 1.51157 1.000 -4.1647 4.6600 
Adum Tokoro(ADT) -4.25000 1.56317 .083 -8.8130 .3130 
Mpohor Motorway(MM) 2.08667 1.63268 .796 -2.6792 6.8526 
Mpohor Adawotwe (ADW) -.14667 1.63268 1.000 -4.9126 4.6192 
Control -22.76667* 2.82788 .000 -31.0214 -14.5119 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -1.83905 1.51157 .828 -6.2514 2.5733 
Adum Tokoro(ADT) -6.33667* 1.56317 .002 -10.8997 -1.7737 
Mpohor Adansi(MA) -2.08667 1.63268 .796 -6.8526 2.6792 
Mpohor Adawotwe (ADW) -2.23333 1.63268 .746 -6.9992 2.5326 
Control -24.85333* 2.82788 .000 -33.1081 -16.5986 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .39429 1.51157 1.000 -4.0181 4.8066 
Adum Tokoro(ADT) -4.10333 1.56317 .103 -8.6663 .4597 
250 
 
Mpohor Adansi(MA) .14667 1.63268 1.000 -4.6192 4.9126 
Mpohor Motorway(MM) 2.23333 1.63268 .746 -2.5326 6.9992 
Control -22.62000* 2.82788 .000 -30.8748 -14.3652 
control Asowuo Ayipa(ASA) 23.01429* 2.75973 .000 14.9585 31.0701 
Adum Tokoro(ADT) 18.51667* 2.78833 .000 10.3774 26.6560 
Mpohor Adansi(MA) 22.76667* 2.82788 .000 14.5119 31.0214 
Mpohor Motorway(MM) 24.85333* 2.82788 .000 16.5986 33.1081 
Mpohor Adawotwe (ADW) 22.62000* 2.82788 .000 14.3652 30.8748 
Total Nitrogen Asowuo Ayipa(ASA) Adum Tokoro(ADT) .00254 .03625 1.000 -.1033 .1084 
Mpohor Adansi(MA) .00410 .03815 1.000 -.1073 .1155 
Mpohor Motorway(MM) -.00590 .03815 1.000 -.1173 .1055 
Mpohor Adawotwe (ADW) .00543 .03815 1.000 -.1059 .1168 
Control -4.37524* .06966 .000 -4.5786 -4.1719 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -.00254 .03625 1.000 -.1084 .1033 
Mpohor Adansi(MA) .00156 .03945 1.000 -.1136 .1167 
Mpohor Motorway(MM) -.00844 .03945 1.000 -.1236 .1067 
Mpohor Adawotwe (ADW) .00289 .03945 1.000 -.1123 .1181 
Control -4.37778* .07038 .000 -4.5832 -4.1723 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.00410 .03815 1.000 -.1155 .1073 
Adum Tokoro(ADT) -.00156 .03945 1.000 -.1167 .1136 
Mpohor Motorway(MM) -.01000 .04121 1.000 -.1303 .1103 
Mpohor Adawotwe (ADW) .00133 .04121 1.000 -.1190 .1216 
Control -4.37933* .07138 .000 -4.5877 -4.1710 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) .00590 .03815 1.000 -.1055 .1173 
Adum Tokoro(ADT) .00844 .03945 1.000 -.1067 .1236 
Mpohor Adansi(MA) .01000 .04121 1.000 -.1103 .1303 
Mpohor Adawotwe (ADW) .01133 .04121 1.000 -.1090 .1316 
Control -4.36933* .07138 .000 -4.5777 -4.1610 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) -.00543 .03815 1.000 -.1168 .1059 
Adum Tokoro(ADT) -.00289 .03945 1.000 -.1181 .1123 
Mpohor Adansi(MA) -.00133 .04121 1.000 -.1216 .1190 
Mpohor Motorway(MM) -.01133 .04121 1.000 -.1316 .1090 
Control -4.38067* .07138 .000 -4.5890 -4.1723 
control Asowuo Ayipa(ASA) 4.37524* .06966 .000 4.1719 4.5786 
Adum Tokoro(ADT) 4.37778* .07038 .000 4.1723 4.5832 
Mpohor Adansi(MA) 4.37933* .07138 .000 4.1710 4.5877 
Mpohor Motorway(MM) 4.36933* .07138 .000 4.1610 4.5777 
251 
 
Mpohor Adawotwe (ADW) 4.38067* .07138 .000 4.1723 4.5890 
Conductivity Asowuo Ayipa(ASA) Adum Tokoro(ADT) .68810 1.79039 .999 -4.5382 5.9144 
Mpohor Adansi(MA) .27276 1.88434 1.000 -5.2277 5.7732 
Mpohor Motorway(MM) .07943 1.88434 1.000 -5.4211 5.5799 
Mpohor Adawotwe (ADW) -.51657 1.88434 1.000 -6.0171 4.9839 
Control 24.16476* 3.44031 .000 14.1223 34.2072 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) -.68810 1.79039 .999 -5.9144 4.5382 
Mpohor Adansi(MA) -.41533 1.94867 1.000 -6.1036 5.2729 
Mpohor Motorway(MM) -.60867 1.94867 1.000 -6.2969 5.0796 
Mpohor Adawotwe (ADW) -1.20467 1.94867 .989 -6.8929 4.4836 
Control 23.47667* 3.47596 .000 13.3301 33.6232 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.27276 1.88434 1.000 -5.7732 5.2277 
Adum Tokoro(ADT) .41533 1.94867 1.000 -5.2729 6.1036 
Mpohor Motorway(MM) -.19333 2.03532 1.000 -6.1345 5.7479 
Mpohor Adawotwe (ADW) -.78933 2.03532 .999 -6.7305 5.1519 
Control 23.89200* 3.52527 .000 13.6015 34.1825 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.07943 1.88434 1.000 -5.5799 5.4211 
Adum Tokoro(ADT) .60867 1.94867 1.000 -5.0796 6.2969 
Mpohor Adansi(MA) .19333 2.03532 1.000 -5.7479 6.1345 
Mpohor Adawotwe (ADW) -.59600 2.03532 1.000 -6.5372 5.3452 
Control 24.08533* 3.52527 .000 13.7949 34.3758 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .51657 1.88434 1.000 -4.9839 6.0171 
Adum Tokoro(ADT) 1.20467 1.94867 .989 -4.4836 6.8929 
Mpohor Adansi(MA) .78933 2.03532 .999 -5.1519 6.7305 
Mpohor Motorway(MM) .59600 2.03532 1.000 -5.3452 6.5372 
Control 24.68133* 3.52527 .000 14.3909 34.9718 
control Asowuo Ayipa(ASA) -24.16476* 3.44031 .000 -34.2072 -14.1223 
Adum Tokoro(ADT) -23.47667* 3.47596 .000 -33.6232 -13.3301 
Mpohor Adansi(MA) -23.89200* 3.52527 .000 -34.1825 -13.6015 
Mpohor Motorway(MM) -24.08533* 3.52527 .000 -34.3758 -13.7949 
Mpohor Adawotwe (ADW) -24.68133* 3.52527 .000 -34.9718 -14.3909 
pH Asowuo Ayipa(ASA) Adum Tokoro(ADT) -.18492 .16200 .862 -.6578 .2880 
Mpohor Adansi(MA) .00019 .17050 1.000 -.4975 .4979 
Mpohor Motorway(MM) .10952 .17050 .987 -.3882 .6072 
Mpohor Adawotwe (ADW) -.08914 .17050 .995 -.5868 .4085 
control -2.41048* .31128 .000 -3.3191 -1.5018 
Adum Tokoro(ADT) Asowuo Ayipa(ASA) .18492 .16200 .862 -.2880 .6578 
252 
 
Mpohor Adansi(MA) .18511 .17632 .899 -.3296 .6998 
Mpohor Motorway(MM) .29444 .17632 .555 -.2202 .8091 
Mpohor Adawotwe (ADW) .09578 .17632 .994 -.4189 .6105 
control -2.22556* .31451 .000 -3.1436 -1.3075 
Mpohor Adansi(MA) Asowuo Ayipa(ASA) -.00019 .17050 1.000 -.4979 .4975 
Adum Tokoro(ADT) -.18511 .17632 .899 -.6998 .3296 
Mpohor Motorway(MM) .10933 .18416 .991 -.4282 .6469 
Mpohor Adawotwe (ADW) -.08933 .18416 .997 -.6269 .4482 
control -2.41067* .31897 .000 -3.3418 -1.4796 
Mpohor Motorway(MM) Asowuo Ayipa(ASA) -.10952 .17050 .987 -.6072 .3882 
Adum Tokoro(ADT) -.29444 .17632 .555 -.8091 .2202 
Mpohor Adansi(MA) -.10933 .18416 .991 -.6469 .4282 
Mpohor Adawotwe (ADW) -.19867 .18416 .888 -.7362 .3389 
control -2.52000* .31897 .000 -3.4511 -1.5889 
Mpohor Adawotwe (ADW) Asowuo Ayipa(ASA) .08914 .17050 .995 -.4085 .5868 
Adum Tokoro(ADT) -.09578 .17632 .994 -.6105 .4189 
Mpohor Adansi(MA) .08933 .18416 .997 -.4482 .6269 
Mpohor Motorway(MM) .19867 .18416 .888 -.3389 .7362 
control -2.32133* .31897 .000 -3.2524 -1.3902 
control Asowuo Ayipa(ASA) 2.41048* .31128 .000 1.5018 3.3191 
Adum Tokoro(ADT) 2.22556* .31451 .000 1.3075 3.1436 
Mpohor Adansi(MA) 2.41067* .31897 .000 1.4796 3.3418 
Mpohor Motorway(MM) 2.52000* .31897 .000 1.5889 3.4511 
Mpohor Adawotwe (ADW) 2.32133* .31897 .000 1.3902 3.2524 
*. The mean difference is significant at the 0.05 level. 
 
 
 
 
 
 
 
253 
 
Appendix K : Sampling Sites and the Geographical Coordinates, Mpohor Wassa 
East  
  Sampling point  Code GPS Location 
Latitude/Longitude 
  Asowuo Ayipa  ASA 1 N 040 58. 3241 
 W 0010 52. 0411 
 Asowuo Ayipa  ASA2 N 040 58.3101 
  W 0010 52.0971 
Asowuo Ayipa  ASA3 N 040 58.3131  
W 0010 52.1501 
Asowuo Ayipa  ASA4 N 040 58.3371 
 W 0010 52.1651 
Asowuo Ayipa  ASA5 N 040 58.3601 
  W 0010 52.1791 
Asowuo Ayipa  ASA6 N 040 58.1991 
   W 0010 52.1751 
Asowuo Ayipa  ASA7 N 040 58.0131 
 W 0010 52.3261 
Adum Tokoro     N 040 58.6391 
ADT1 W 0010 51.8931 
Adum Tokoro  ADT2 N 040 58.5791 
  W 0010 51.9501 
Adum Tokoro    N 040 58.545′ 
ADT3 W 0010 51.995′ 
Adum Tokoro  ADT4 N 040 58.557′ 
W 0010 52.033′ 
Adum Tokoro  ADT5 N 040 58.447′ 
W 0010 52.067′ 
Adum Tokoro  ADT6 N 040 58.479′ 
W 0010 52.107′ 
Mpohor Adansi  MA1 N 040 57.733′ 
W 0010 52.572′ 
Mpohor Adansi  MA2 N 040 57.777′ 
W 0010 52.591′ 
Mpohor Adansi  MA3 N 040 57.766′ 
W 0010 52.523′ 
Mpohor Adansi  MA4 N 040 57.823′ 
W 0010 52.483′ 
Mpohor Adansi  MA5 N 040 57.735′ 
W 0010 52.668′ 
Mpohor Motorway  MM1 N 040 57.7071 
W 0010 52.9571 
Mpohor Motorway  MM2 N 040 57.6651 
  W 0010 52.8791 
Mpohor Motorway  MM3 N 040 57.5821  
W 0010 52.8731 
Mpohor Motorway  MM4 N 040 57.5271 
 W 0010 52.8771 
Mpohor Motorway  MM5 N 040 57.7981 
  W 0010 52.9751 
Mpohor Adawotwe  ADW1 N 040 58.9781 
   W 0010 53.1371 
Mpohor Adawotwe  ADW2 N 040 58.9891 
 W 0010 53.1091 
Mpohor Adawotwe    ADW3 N 040 58.9961 
W 0010 53.0701 
Mpohor Adawotwe  ADW4 N 040 59.0571 
  W 0010 53.0381 
Mpohor Adawotwe   ADW5 N 040 59.127′ 
W 0010 53.093′ 
Mpohor Anomabo  Control N 040 59.607′ 
 W 0010 54.443′ 
 
 
254 
 
Sampling sites and the geographical coordinates at Amansie West  
Sampling point  Code GPS Location 
Latitude/Longitude 
Manso Nkwanta  MN01 N 060 29.22′ 
W010 52.561′ 
Manso Nkwanta  MN02 N 060 29.159′ 
  W010 52.560′ 
Manso Nkwanta  MN03 N 060 29.107′ 
W010 52.540′ 
Manso Nkwanta  MN04 N 060 29.065′ 
 W010 52.515′ 
Manso Nkwanta  MN05 N 060 29.021′ 
 W010 52.477′ 
Water (Manso suben  MN N 060 29.043′ 
River)   W010 52.499′ 
Domenase  DO1 N 060 27.555′ 
 W010 54.288′ 
Domenase    DO2 N 060 27.418′ 
W010 54.365′ 
Domenase  DO3 N 060 27.473′ 
  W010 54.346′ 
Domenase Suben  DO N 060 27.555′ 
River W010 54.288′ 
Brofoyedru   BY01 N 060 27.430′ 
W010 52.136′ 
Brofoyedru   N 060 27.389′ 
BY02 W010 52.159′ 
Brofoyedru  BY03 N 060 27.375′ 
W010 52.139′ 
Brofoyedru  BY04 N 060 27.357′ 
W010 52.127′ 
Brofoyedru  BY05 N 060 27.340′ 
 W010 52.102′ 
Asaman  AS01 N 060 26.263′ 
W010 52.074′ 
Asaman  AS02 N 060 26.246′ 
W010 52.084′ 
Asaman  AS03 N 060 26.281′ 
W010 52.112′ 
Asaman  AS04 N 060 26.304′ 
W010 52.130′ 
Asaman  AS05 N 060 26.273′ 
W010 52.063′ 
Asaman  AS06 N 060 26.300′ 
 W010 52.064′ 
Asaman  AS07 N 060 26.328′ 
W010 52.073′ 
Asaman  AS08 N 060 26.344′ 
W010 52.084′ 
Asaman  AS09 N 060 26.352′ 
W010 52.106′ 
Suben River  AS N 060 26.278′ 
W010 52.037′ 
Control soil (Manso)  Soil N 060 26.250′ 
W010 49.121′ 
 
255 
 
Appendix L: Questionaire 
I am STEPHEN TWUMASI ANNAN, a PhD student at the University of Ghana, Legon. 
I am conducting research on the Assessment of ecological footprint of artisanal and small-
scale gold mining on soil and provision ecosystem services at Mpohor Wassa East and 
Amansie West District, Ghana. I would be grateful if you could contribute to this research 
by responding to some questions. Be assured that whatever information you give will be 
strictly kept confidential. 
Name of Community/village ………………………………………………………………….. 
A. SOCIO – DEMOGRAPHIC BACKGROUND OF RESPONDENTS 
1.  Sex                               Male      [    ] 
                                                                                               Female    [     ] 
2.  Age …………………………………                    
3.  Marital Status      Single [    ] 
Married [    ] 
Divorced [    ] 
       Widow / Widower [    ] 
4.  Level of Education     None                 [    ] 
       Basic Level [    ] 
       Secondary Level [    ] 
                                                                                     Vocational/Technical   [    ] 
       Tertiary Level [    ] 
                                                                             
5.  Family Size     Small (2 – 4)          [    ] 
      Medium (5 – 6)          [    ] 
      Large (7 and above) [    ] 
 
B. MIGRATION, EMPLOYMENT AND INCOME 
 
6. Are you a native of this community          Yes   [    ] 
No   [    ] 
256 
 
(If yes to question 6, proceed to question 9. if no to question 6 continue the questions) 
7. If no, where do you come from? 
Town/village...……………………… District……………………………… 
Region………………………………..  Country……………………………. 
8. Reasons for moving to this community? 
……………………………………………………………………………………… 
……………………………………………………………………………………… 
9. What is your previous occupation?  Trader  [    ]   Farmer [   ]  Fisherman  [    ]  
Other, specify………………………………………………………………. 
10. What work do you do now?  …………………………………………………. 
11. Which other activities do you undertake to get income? 
Food crop farming [    ] 
Cocoa farming  [    ] 
Livestock farming [    ] 
Others ……………………. 
12. If others specify: …………………………………………………………………... 
13. How long have you engaged in your main occupation in the community 
Less than 1 yr  [    ] 
1 – 3 yrs   [    ] 
4 – 6 yrs above              [    ] 
14. How is the cost of living in your community?  Low  [     ] 
Moderate [     ] 
High  [     ] 
Very high [     ] 
15. If high / very high why 
……………………………………………………………………………………… 
……………………………………………………………………………………… 
……………………………………………………………………………………… 
16.  Do you have any knowledge about small-scale gold mining?               
                                                                                      Yes                                     [    ] 
                                                                                       No                                       [    ] 
17. If yes, what kind of knowledge?  …………………………………………… 
18. Do you do mining     Yes    [    ] 
No   [    ] 
19. Which type of mining  Small – Scale (“Galamsey”)               [    ] 
257 
 
                                                           Large Scale (Mining Company)            [    ] 
 
C.   Mention any five (5) common ailments that normally affect people in this community: 
……………………………………………………………………………………… 
……………………………………………………………………………………… 
……………………………………………………………………………………… 
20. Are there some accidents/injuries during the mining?        Yes [    ] 
No [    ] 
21. If yes, what type (s) of injuries/accidents have you witnessed or experienced? 
…………………………………………………………………………………                                                                     
      D.  ASSESSMENT OF SOIL, BIODIVERSITY AND WATER QUALITY. 
      22.  Has your farm land been destroyed/ reduced by the activities of these miners? 
                                                                                                       Yes           [    ] 
                                                                                                          No         [    ] 
      23.  Do you get access to farmland?  
                                                                                                          Yes        [    ] 
                                                                                                           No        [    ]  
      24. What is the state of the farmland in the community? 
              …………………………………………………….................................... 
             …………………………………………………………………………….. 
      25. Has your farm land been degraded?  
                                                                                                           Yes        [    ] 
                                                                                                            No        [    ] 
26. What indication shows that your farm land has been degraded? 
             …………………………………………………………………………. 
             ………………………………………………………………………… 
27. Has the forest been destroyed by the activities of these small-scale miners? 
                                                                                                      Yes       [    ] 
                                                                                                       No       [    ] 
28. Can you name any medicinal plant that was common in the environment but cannot be seen 
again? …………………………………………………………………………… 
 
29. What species of animals/fish used to be seen but cannot be seen in the environment? 
……………………………………………………………………………. 
258 
 
30. Do you use chemicals in your mining?                  Yes  [    ] 
                                                                                            No [    ] 
 
31. If yes, mention the type of chemicals you frequently use, where do you obtain them, how and 
where do you dispose of your waste? 
Type of Frequency of Sources Mode of disposal Site of disposal 
chemical use 
     
     
     
     
     
 
32. Indicate the sources of water to the people of your community over the past 5 years. 
Type of Last year 2 years ago 3 years ago 4 years ago 5 years ago 
facility 
Pond      
River      
Well      
Bore-hole      
Pipe-borne      
water 
 
 
33. Do you think mining activities affect the water quality? Yes  [     ] 
No [     ] 
34. If yes, how do these affect the quality of your water?      Colour [     ] 
Taste [     ] 
Scent [     ] 
Others [     ] 
  
35. Has the activities of these small-scale gold miners affected the taste of the water? 
                                                                                                         Yes   [    ] 
                                                                                                                No   [    ] 
       36. How has the taste been affected? 
            …………………………………………………………………………….. 
37.Has the mining activities affected the colour of the water?  
                                                                                                         Yes   [    ] 
                                                                                                         No    [    ] 
259 
 
38. How has the colour of the water been affected by the activities of the small-scale gold 
miners? 
…………………………………………………………………………………….                                                               
E.  CONCERNS 
39. What are some of the problems you face living in this small-scale mining area? 
a. …………………………………………………………………….. 
b. …………………………………………………………………….. 
c. …………………………………………………………………….. 
d. …………………………………………………………………….. 
40. What suggestions do you have for improvements 
a. …………………………………………………………………….. 
b. …………………………………………………………………….. 
c. …………………………………………………………………….. 
d. …………………………………………………………………….. 
 
Thank you for your cooperation!!! 
 
 
 
 
 
 
 
 
 
 
 
 
260 
 
Appendix M: Ethical Clearance 
 
261