Impacts of land-use change on the water quality

Impacts of land use change on some water quality parameters in the Barekese catchment

KWAME NKRUMAH UNIVERSITY OF SCIENCE AND TECHNOLOGY, KNUST-KUMASI

COLLEGE OF SCIENCE DEPARTMENT OF THEORETICAL AND APPLIED BIOLOGY

IMPACTS OF LAND USE CHANGE ON SOME WATER QUALITY PARAMETERS IN THE BAREKESE RESERVOIR CATCHMENT

TYHRA CAROLYN KUMASI

Dissertation submitted to the College of Science in partial fulfilment of the requirements for the Degree of Doctor of Philosophy

©Tyhra Carolyn Kumasi JUNE, 2009 1


DEDICATION

I unequivocally dedicate this work to my parents My Dad, Mr. Benedict Joseph Labre Kumasi My Mom, Mrs. Grace Tanii Kumasi For sharing in the many tears, laughter, despair, and joys of successes over the years, without whom my dreams would be bounded unreachable. You’ve created the space for possibilities.

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ABSTRACT

Anthropogenic modification adversely impact on water resources and cause of anthropogenic activities which demand regional solutions. The aim of the study was to identify land use changes in the Barekese catchment and assess its impacts on the reservoir’s water quality by evaluating some microbial and physicochemical, parameters in the Barekese reservoir and feeder streams with WHO guidelines and the GWRCTWQR. The study also aimed at exploring ways of sustainably managing the catchment to ameliorate the deteriorating water quality. Data was collected by three techniques employing social survey, satellite imagery and water quality analysis. A survey was conducted in seven communities bordering the catchment: Ayensua Fufuo, Ayensua Kokoo, Denase, Esaase, Pampatia, Penten and Nkwantakese. The questionnaire collected reported information on land use, water quality and perceptions of the impacts of anthropogenic alterations on the water resources. The data used to estimate land use change were extracted from two LANDSAT Multi-Spectral Scanner (1973, 1986) and one LANDSAT, Thematic Mapper imagery (2003) obtained from the US National Aeronautics and Space Administration. Monthly water samples were collected from thirteen sampling locations to include the reservoir water and feeder streams. Total coliforms, Faecal coliforms, E. coli, Alkalinity, Biochemical Oxygen Demand, Colour, Conductivity, Hardness, Oil and grease, 𝑝𝐻, Total Dissolved Solids, Total Suspended Solids, Temperature, Arsenic, Copper, Cyanide, Lead, Iron, Zinc, Chloride, Nitrate and Sulphate were measured. Farming was identified as the dominant (70.3%) economic activity in the local communities which were characterized by the lack of KVIP facilities, potable drinking water and escalating population. The logging of economic trees, farming on watercourses and in the reserve, the use of inorganic fertilizers and the indiscriminate

3


defecation was pervasive in the communities. Some of the adverse impacts of the human activities included widespread floods, drought, decline in fish catch per unit of effort and changes in the hydrology of rivers. The underlying factors to the use of the forest reserve were attributed to the non-payment of compensation to the communities who lost their lands to the construction of the Barekese Dam and the fact that the lands in the reserve were more fertile. The issue of negligence and lack of concern on the part of the communities in the management of the Barekese reservoir and reserve is as a result of the non involvement of the locals and the numerous unfulfilled promises that were made to the communities in 1969. The catchment has undergone tremendous changes in 19731986, 1986- 2003 as a result of anthropogenic activities. In 1973-1986 the closed forest decreased by 44.21% whereas the open forest increased by 34.60%. From 1986 to 2003, the open forest decreased by 55.25% and was replaced by grassland and open area/towns. The projections of the land cover change in the catchment reveal that vegetation cover will continue to experience a decline in area by the year 2043 resulting in the loss of the closed forest. Conversely grassland and open area/towns will significantly increase in size with a consequent impact on water resources. Mean bacterial indicator numbers (geometric mean 100 ml-1) at all the sampling sites ranged from 1.45x10 4 to 9.50 x 107 for total coliforms, 1.60x103 to 9.00x105 for faecal coliforms and 1.50x101 to 9.50x103 for E. coli exceeding the WHO guideline value of (0/100 ml) for E. coli and the GWRC-TWQR of 5-100/100ml for total coliforms and 0/100 ml for faecal coliforms in water for domestic purposes. Generally alkalinity, biochemical oxygen demand, conductivity, oil and grease, total hardness, total dissolved solids and total suspended solids in the feeder streams were higher than those of the reservoir water. Conductivity in reservoir water and the feeder streams exceeded the recommended GWRC-TWQR of 0 − 70𝜇𝑆/𝑐𝑚 for domestic purposes. Mean true colour in the feeder streams exceeded the WHO recommended

4


desirable guideline of (15mg/l) with the exception of Ntuma stream although the reservoir water was below the value. The 𝑝𝐻 for the feeder streams did not conform to the WHO range of 6.5-8.5 and GWRC-TWQR of 6-9. However the reservoir water was within the WHO guideline value. Total dissolved solids in all the sampling sites were less than the WHO guideline value of 1000𝑚𝑔/𝑙 and the GWRC-TWQR of 0-450 𝑚𝑔/𝑙 for domestic purposes. Concentrations of arsenic and lead in reservoir water and the feeder streams exceeded the WHO recommended guideline value of 0.01 𝑚𝑔/𝑙 but were within that of GWRC-TWQR (0-10𝑚𝑔/𝑙). Cyanide exceeded the WHO guideline value of 0.07𝑚𝑔/𝑙 for some sites. Levels of iron and nitrate in the feeder streams and reservoir water exceeded the GWRC-TWQR (0-0.01 𝑎𝑛𝑑 0 − 6𝑚𝑔/𝑙) however nitrate was within the maximum acceptable limits by the WHO (50 𝑚𝑔/𝑙). Levels of zinc, chloride and sulphate were within the GWRC-TWQR ‘no effect range’ of (0-3, 0 − 100 𝑎𝑛𝑑 0 − 200 𝑚𝑔/𝑙 ) and below the WHO guideline value of 3𝑚𝑔/𝑙 for zinc. The study concludes with some direct policy recommendations and applications to include the consideration of affected communities in the development of projects as shareholders of the project, empowerment of women under the communal land tenure, a review of existing environmental legislation, the adoption of indigenous knowledge, involvement of affected local communities and the public in proposed and ongoing projects and systematic ex post evaluation of the Barekese water project.

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ACKNOWLEDGEMENT

I gratefully acknowledge the contributions of those whose guidance, ideas, questions, and contributions have shaped this thesis. My first and foremost thanks go to the almighty God, who has graciously granted me the strength and the wisdom to embark on this academic endeavour successfully. Secondly, I would like to thank my mentor and supervisor (Prof. Kwasi ObiriDanso) for his continuous guidance and inspiration during the course of my doctoral studies and prior to it. You competently empowered me with the tools and confidence to “enter the conversation” as a scholar in my own right. I am indebted to Prof. J.H. Ephraim who cosupervised this study. The cordial relationship you extended to me granted me a lot of confidence and enthusiasm.

To Prof. K. Frimpong, Prof. Steve Amisah, Prof. S.K. Oppong, Dr. F. Ulzen-Appiah and Mr. A. L. Dassah I say thank you for your earlier direction and support. I would like to thank the academic staff of Dept of Theoretical and Applied Biology of KNUST for their extraordinary achievement in teaching courses, providing professional advice, and caring for their students. Among them are Prof. B.W.L. Lawson, Mr. W. G. Akanwariwiak, Dr. S. Aikins, Dr. P.K. Baidoo and Mr S. Acheampong. I am also grateful to the non-teaching staff of the Department. I am also indebted to Prof. Charles Quansah of the Faculty of Agriculture for his direction and comments on my thesis. I am appreciative of the support Dr. Anthony K. Edusei of the Department of Community Health, School of Medical Sciences, KNUST offered in the designing of my questionnaires.

I am also thankful to Dr. Paul Amuna of the Department of Life Sciences, University of Greenwich who has timelessly provided research guidance during and after my stay in the United Kingdom. Dr. Megan. H. Mehaffey of the Environmental Sciences Division, of the

6


U.S. EPA, Prof. Dibyendu Sarkar of the Environmental Geochemistry Laboratory, University of Texas and Dr. Chris J. Paddon of AMYRIS Biotechnologies, Emeryville, California have been very supportive to me during my PhD studies.

I acknowledge the support of the KNUST Staff Development Scholarship, CIDA for the CYEATA award and the West African Research Centre for awarding me a WARC travel grant. I am also indebted to Mr Lawrence Akpalu and Mr. Francis Akurugu of the Forestry Commission who readily assisted me secure satellite imagery and throughout the change detection analysis. I will like to express my profound gratitude and appreciation to Mr Fred Frimpong and Mr. Godwin Mensah who were selfless during the data collection.

I would like to express my deepest gratitude to many wonderful people that I have come in contact with. My life has been enriched so much through your friendship, ideas, and advice and the diverse activities on and off campus. The list may be endless but just to mention a few; Ms. Rhoda Bayeldeng, Dr. Mrs. Mizpah Rockson, Ms. Matilda Owusu Ansah,

Maxwell

Akple, Dr. Peter O. Sanful, Dr. Francis Awortwi, Dr. Denis D. Yar and Dr. George Rockson.

Most importantly, I am most grateful for the boundless love, care and support that my family has always given me, during my studies at KNUST, you are simply the best. I am proud of having the opportunity of being a student of KNUST and experiencing the truly unique spirit and enthusiasm of this great University.

And finally, my last words of thankfulness go to Ambrose Zimbawa, the love of my life. You have been my best supporter, and perhaps also the most straightforward critic. But most of all thank you for every time you have said that you are so proud of me. I’m joyfully indebted for inspiration, endurance, hope and a future. I could never have done this without you!

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TABLE OF CONTENT Content

Pages

CHAPTER ONE 1.0

INTRODUCTION

24

1.1

Conceptual Framework

24

1.2

Problem Statement and Rationale of the Study

30

1.3

Working Hypotheses

31

1.4

Research Questions

31

1.5

Main Objective

32

1.6

Specific Objectives

32

CHAPTER TWO 2.0

LITERATURE REVIEW

33

2.1

Water Quality

33

2.1.1

Threats to water quality

34

2.1.1.1 Point sources of pollution

35

2.1.1.2 Non-point sources of pollution

35

2.2

Land Use Change

36

2.2.1

38

Impacts of land use change on water quality

2.3

Water quality, Land Use and River Basin

40

2.4

Physio-Chemical Parameters

44

2.4.1

Alkalinity

44

2.4.1.1 Health consideration of alkalinity

45

Biochemical oxygen demand

45

2.4.2.1 Health consideration of biochemical oxygen demand

46

Chloride

47

2.4.3.1 Health considerations of chloride

48

Colour (True)

48

2.4.4.1 Health considerations of colour (true)

49

Conductivity

49

2.4.5.1 Health considerations of conductivity

51

Hardness (calcium carbonate)

51

2.4.2

2.4.3

2.4.4

2.4.5

2.4.6

2.4.6.1 Health considerations of hardness (calcium carbonate) 52

8


2.4.7

2.4.8

2.4.9

Nitrate

53

2.4.7.1 Health considerations of nitrate

53

Oil and grease

54

2.4.8.1 Health consideration of oil and grease

54

𝑝𝐻

55

2.4.9.1 Health considerations of 𝑝𝐻

56

2.4.10 Sulphates

57

2.4.10.1 Health considerations of sulphates 2.4.11 Total Dissolved Solids

58

2.4.11.1 Health considerations of total dissolved solids 2.4.12 Total suspended solids

60 60

2.4.12.1 Health considerations of total suspended solids 2.4.13 Temperature

61 61

2.4.13.1 Health considerations of temperature 2.4.14 Arsenic

62 63

2.4.14.1 Health considerations of arsenic 2.4.15 Copper

63 63

2.4.15.1Health considerations of copper 2.4.16 Cyanide

65 66

2.4.16.1Health considerations of cyanide 2.4.17 Iron

67 67

2.4.17.1Health considerations of iron 2.4.18 Lead

68 69

2.4.18.1Health considerations of lead 2.4.19 Zinc

70 71

2.4.19.1Health considerations of zinc 2.5

58

71

Biological Parameters

72

2.5.1

Total coliforms

72

2.5.2

Faecal coliforms

73

2.5.3

Escherichia coli

74

CHAPTER THREE 3.0

MATERIALS AND METHODS

76

3.1

76

Analytical Framework

9


3.1.1 3.2

3.3

3.4

Research Approach

76

Study Area

77

3.2.1

Climate

80

3.2.2

Geology

80

3.2.3

Topography

81

3.2.4

Soils

81

3.2.5

Geohydrology and drainage

83

Study Communities

83

3.3.1

Ayensua Fufuo

85

3.3.2

Ayensua Kokoo

85

3.3.3

Esaase

86

3.3.4

Denase

86

3.3.5

Nkwantakese

87

3.3.6

Pampatia

87

3.3.7

Penten

88

Research Design

88

3.4.1

Ethical consideration

89

3.4.2

Data collection techniques

89

3.4.3

Field survey

90

3.4.4

Questionnaire development

90

3.4.5

Informal surveys and observations

91

3.4.6

Formal survey

91

3.4.7

Data validation

92

3.5

Selection of Sampling Sites

92

3.6

Change Detection

94

3.7

Projecting Land Cover Change in the Barekese Catchment

96

3.8

Water Sampling

97

3.8.1

Alkalinity

97

3.8.2


98

3.8.3

Chloride, Nitrate and Sulphate

98

3.8.4

Colour (True)

99

3.8.5

Conductivity

99

3.8.6

Hardness (Calcium Carbonate)

100

3.8.7

Oil and grease

100

10


3.8.8

𝑝𝐻

102

3.8.9

Determination of total dissolved solids

103

3.8.10 Total suspended solids

103

3.8.11 Temperature

105

3.8.12 Determination of metals (Arsenic, Copper, Lead, Iron and Zinc) 105

3.9

3.8.13 Cyanide

106

3.8.14 Total coliforms enumeration

107

3.8.15 Faecal coliforms enumeration

108

3.8.15.1 Preparation and examination of stained smears

108

3.8.15.2 The Grain Stain

108

3.8.15.3 Characterization of microbial bacteria

109

3.8.15.4 Preparation of strip

109

3.8.15.5 Preparation of the inoculum

110

3.8.15.6 Inoculation of the strip

110

3.8.15.7 Reading of the strip

110

3.8.15.8 Interpretation of the strip

111

3.8.16 Enumeration of Escherichia coli

112

Data Analysis

112

CHAPTER FOUR 4.0

RESULTS

114

4.1

Social Survey

114

4.1.1

Demographic characteristics

114

4.1.2

Land use in the Barekese catchment

117

4.1.3

Impacts of land use change

121

4.1.4

Sustainable management of the Barekese catchment

126

4.2

4.3

Land Use Change of the Barekese Catchment

129

4.2.1

Change detection of Barekese catchment from 1973 to 1986

129

4.2.2

Change detection of Barekese catchment from 1986 to 2003

130

4.2.3

Projected land cover change in the Barekese catchment

135

Physico-chemical Parameters 4.3.1

137

Temporal mean of physico-chemical parameters in the feeder streams and reservoir water from the Barekese reservoir

4.4

Heavy Metals and Cyanide

137 150

11


4.4.1 Temporal mean of heavy metals and cyanide in the feeder streams and reservoir water from the Barekese Reservoir 4.5

Microbiological Parameters

150 154

4.5.1 Microbiological quality in the feeder streams and raw water from the Barekese reservoir

4.3.1.1 Speciation of faecal indicator isolates

154 158

4.3.1.2 Dendograms illustrating the clusters of bacteria Isolates

160

CHAPTER FIVE 5.0

DISCUSSION

162

5.1

Social Survey

162

5.1.1


162

5.1.2

Land use in the Barekese catchment

164

5.1.3


168

5.1.4

Sustainable management of the Barekese catchment

170

5.1.4.1 The involvement of local communities in the sustainable management of the Barekese catchment

171

5.1.4.2 The payment of compensation to local communities in the Barekese catchment

175

5.1.4.3 The adverse effects of the Barekese reservoir to the local

5.2

5.3

communities

178

Land Use Change of the Barekese Catchment

180

5.2.1

Change detection of Barekese catchment from 1973-1986

180

5.2.2

Change detection of Barekese catchment from 1986-2003

182

5.2.3

Projected land cover change in the Barekese catchment

184

Physico-chemical Parameters

185

5.3.1

Alkalinity

185

5.3.2


186

5.3.3

Colour

187

5.3.4

Conductivity

188

5.3.5

Total Hardness (Calcium Carbonate)

188

5.3.6

Oil and Grease

189

5.3.7

𝑝𝐻

190

5.3.8

Total dissolved solids

191

12


5.3.9

5.4

Total suspended solids

191

5.3.10 Temperature

192

5.3.11 Chloride

192

5.3.12 Nitrate

193

5.3.13 Sulphate

194

5.3.14 Arsenic

195

5.3.15 Copper

196

5.3.16 Cyanide

197

5.3.17 Iron

198

5.3.18 Lead

198

5.3.19 Zinc

199

Microbiological Parameters

199

5.4.1 Microbial quality of the Barekese reservoir and feeder streams

200

5.4.1.1 Speciation of faecal indicator isolates

202

CHAPTER SIX 6.0

CONCLUSION AND RECOMMENDATIONS

205

6.1

Social Survey

205

6.2

Land Use Change in the Barekese Catchment

206

6.3

Physico-Chemical Parameters

206

6.4

Microbial Quality of the Barekese Reservoir and Feeder Streams

208

6.5

Contribution to the Body of Knowledge

210

6.5.1

Social issues bordering the communities in the Barekese catchment

6.5.2

210

The use of satellite imagery to assess land use change in the Barekese catchment

6.5.3

210

The development of a forecast to predict land use change for 2003-2043 in the Barekese catchment

6.5.4

Water quality data for the Barekese reservoir and feeder streams

6.6

210

211

Recommendations from Research Findings

211

6.6.1

211

Recommendations for Government 6.6.1.1 The consideration of local communities as shareholders of a project

13

212


6.6.1.2 The empowerment of women under the communal land tenure

213

6.6.1.3 Review of existing environmental legislation

213

6.6.1.4 The incorporation of indigenous knowledge

214

6.6.1.5 The enactment of environmental bye-laws at the District Assembly level 6.6.1.6 Training of personnel at the District Assembly level

214 215

6.6.1.7 Conducting ex post evaluation to include environmental auditing and monitoring of the Barekese water project 6.6.2

6.6.3

215

Recommendations for Non-Governmental Organisations

216

6.6.2.1 The introduction of alternative livelihoods

216

6.6.2.2 Sustainable community education programmes

217

Recommendations for Traditional and community rulers

218

6.6.3.1 The re-introduction of norms and traditions within local communities 6.6.4

218

Recommendations for International Finance/Donors

218

6.6.4.1 The adoption of the Modified Taungya System in the Barekese reserve

219

6.7

Limitations of Study

220

6.8

Suggestions for Future Research

220

REFERENCES

221

APPENDIX

1: Survey instrument

244

APPENDIX

2: Published table for determining the sample size

251

APPENDIX

3: Unprocessed satellite imagery

252

APPENDIX

4: Physico-chemical statistical tables

255

APPENDIX

5: Heavy metals and cyanide statistical tables

268

APPENDIX

6: Microbial statistical tables

273

APPENDIX

7: Graphs on chloride, nitrate and sulphate

276

14


APPENDIX

8: Graphs on heavy metals and cyanide

APPENDIX

9: WHO guideline values and GWRC-TWQR for domestic use 280

APPENDIX

10: List of published papers from the thesis

15

278

281


LIST OF TABLES Table

Page

Table 1:

Location of sampling sites in the study area

Table 2:

Selected demographic parameters of households in the seven communities 115

Table 3:

Sources of drinking water in the seven communities

116

Table 4:

Accessibility to KVIP and toilet facility patronised by the communities

116

Table 5:

Economic activities in the communities

117

Table 6:

Average distance of farms from the river banks

118

Table 7:

Respondents reasons for farming on watercourses in the communities

118

Table 8:

Respondents reasons for farming or hunting in the reserve

119

Table 9:

Use of fertilizer and agrochemicals on farms

120

Table 10:

Sources of energy in the communities

120

Table 11:

Perception on population increase in the communities over the past ten years

Table 12:

83

121

Respondents reasons for flooding of rivers and streams during the wet season

122

Table 13:

Respondents reasons for seasonal drought of rivers and streams

123

Table 14:

Respondents reasons for changes in the hydrology of rivers and streams in the communities

Table 15:

124

Respondents reasons for the decline in the catch per unit of effort of the rivers and streams in the communities

125

Table 16:

Respondents reasons for changes in water quality

126

Table 17:

Willingness of local communities in sustainable management practices of the Barekese reservoir

Table 18:

127

Social, cultural and economic effects on residents within the Barekese catchment

Table 19:

128

Local communities’ opinion on the sustainable management of the Barekese reservoir

Table 20:

129

Statistical comparison of land use in the Barekese catchment from 1973-1986-2003

Table 21:

135 2

Actual and projected land cover (𝑘𝑚 ) in the Barekese catchment from 1973-2043

136

16


Table 22:

Monthly mean of selected Physico-chemical parameters in the Barekese reservoir and Akyekasu stream

Table 23:

140

Monthly mean of selected Physico-chemical parameters in Nsuta and Ntuma Stream

Table 24:

141

Monthly mean of selected Physico-chemical parameters in Abetesua and Amoadan Stream

Table 25:

142

Monthly mean of selected Physico-chemical parameters in River Offin and Amansie stream

Table 26:

143

Monthly mean of selected Physico-chemical parameters in Nwabi River and Buokese stream

Table 27:

144

Statistical comparison of mean Alkalinity, BOD and colour of ten sampling points in the Barekese catchment

Table 28:

Statistical comparison of mean conductivity, total hardness and oil and grease of ten sampling points in the Barekese catchment

Table 29:

147

Statistical comparison of mean chloride, nitrate and sulphate concentrations of ten sampling points in the Barekese catchment

Table 32:

155

Statistical comparisons of log mean total coliforms, faecal coliforms and E. coli in the ten sampling points in the Barekese catchment

Table 36:

153

Mean and range of total coliforms, faecal coliforms and E. coli at the ten sampling points in the Barekese catchment

Table 35:

152

Statistical comparison of mean lead, iron and zinc of ten sampling points in the Barekese catchment

Table 34:

149

Statistical comparison of mean arsenic, copper and cyanide of ten sampling points in the Barekese catchment

Table 33:

147

Statistical comparison of mean temperature of ten sampling points in the Barekese catchment

Table 31:

146

Statistical comparison of mean pH, total dissolved solids and total suspended solids of ten sampling points in the Barekese catchment

Table 30:

145

157

Phenotypic characterization of bacterial isolates in Barekese reservoir and feeder streams

159

17


LIST OF FIGURES Figure Figure 1:

Page An analytical conceptual framework of linkage between land use change and water quality

27

Figure 2:

A map of Ghana showing the Barekese catchment

79

Figure 3:

Barekese catchment and surrounding communities

84

Figure 4:

Sampling sites numbered 1-13 in the Barekese catchment

94

Figure 5:

Percent land use change in the Barekese catchment from 1973 - 1986 - 2003

131

Figure 6:

Land use change in the Barekese catchment in 1973

132

Figure 7:

Land use in the Barekese catchment in 1973

132

Figure 8:


133

Figure 9:


133

Figure 10:


134

Figure 11:


134

Figure 12:

Land use change in the Barekese catchment from 1973 – 1986, 1986 – 2003

135

Figure 13:

Projected land cover in the Barekese catchment for the next forty years 137

Figure 14:

Mean bacterial indicator numbers in Barekese reservoir (A) and the feeder streams Nsuta (B), Ntuma (C), Abetesua (D), Amoadan (E), River Offin (F), Amansie (G), Nwabi (H), Buokese (I) and Akyekasu(J) 156

Figure 15:

Dendogram illustrating the clusters of bacteria isolates in raw water and the feeder streams of the Barekese reservoir

Figure 16:

161

Land use changes identified in the Barekese catchment and their impacts on water quality

209

18


LIST OF ABBREVATIONS

AAS:

Atomic Absorption Spectrophotometer

AfDB:

African Development Bank

ADH:

Arginine DiHydrolase

ADWG:

Australian Drinking Water Guidelines

ANOVA:

Analysis of Variance

APHA:

American Public Health Association

API:

Analytical Profile Index

AWWA:

American Water Works Association

BOD:

Biochemical Oxygen Demand

BTW:

Buffered Tryptone Water

CAP:

Common Agricultural Policy

CBOD:

Carbonaceous Biochemical Oxygen Demand

CEC:

Cation Exchange Capacity

CEDAR:

Centre for Developing Areas Research

CIDA:

Canadian International Development Agency

CIT:

Citrate

CPUE

Catch per Unit Effort

CSIR:

Council for Scientific and Industrial Research

CBNRM:

Community Based Natural Resource Management

DFID:

Department for International Development

DO:

Dissolved Oxygen

DW:

Distilled Water

EC:

Electrical Conductivity

EDTA:

EthyleneDiamineTetraacetic Acid

EIA:

Environmental Impact Assessment

EM:

Environmental Management

GEPA:

Ghana Environmental Protection Agency

FAO:

Food and Agriculture Organization

FORIG:

Forestry Research Institute of Ghana

GCS:

Guidelines for Canadian Supply

GDP:

Gross Domestic Product

GEF:

Global Environment Facility

19


GEL:

GELatinase

GEPA:

Ghana Environmental Protection Agency

GIS:

Geographical Information System

GNP:

Gross National Product

GPRS:

Growth and Poverty Reduction Strategy

GPS:

Global Positioning System

GWCL:

Ghana Water Company Limited

GWRC:

Ghana Water Resources Commission

IARC:

International Agency for Research on Cancer

ICIMOD:

International Centre for Integrated Mountain Development

IND:

INDole Production

IPCC:

Intergovernmental Panel on Climate Change

IUCN:

International Union for Conservation of Nature

IWRM:

Integrated Water Resources Management

JECFA:

Joint FAO/WHO Expert Committee on Food Additives

KMA:

Kumasi Metropolitan Assembly

KVIP:

Kumasi Ventilated Improved Pit

LDC:

Lysine DeCarboxylase

LDCs:

Least Developed Countries

LLL:

Logs and Lumber Ltd

LSD:

Least Significance Difference

ITTO:

International Tropical Timber Organization

MDGs:

Millennium Development Goals

MPN:

Most Probable Number method

MSS:

Multi-Spectral Scanner

MTS:

Modified Taungya System

NAS:

National Academy of Sciences

NASA:

National Aeronautics and Space Administration

NGOs:

Non Governmental Organizations

NHMRC:

National Health and Medical Research Council

NPS:

Non Point source

NRC/SMC:

National Redemption Council/Supreme Military Council

ODC:

Ornithine DeCarboxylase

O&G:

Oil and Grease

20


PMTDI:

Provisional Maximum Tolerable Daily Intake

PRAs:

Participatory Rural Appraisals

SPSS:

Statistical Package for Social Sciences

STEDS:

Septic Tank Effluent Disposal

TDA:

Trptophane DeAminase

TDS:

Total Dissolved Solids

TM:

Thematic Mapper

TPH:

Total Petroleum Hydrocarbon

TRPH:

Total Recoverable Petroleum Hydrocarbon

TSS:

Total Suspended Solids

TWDB:

Texas Water Development Board

TWQR:

Target Water Quality Range

NGOs:

Non-Governmental Organisations

UK:

United Kingdom

UN:

United Nations

UNECA:

United Nations Economic Commission for Africa

UNCED:

The United Nations Conference on Environment and Development

UNICEF:

United Nations Children’s Fund

U.S.A:

United States of America

URE:

Urease

USEPA:

United States Environmental Protection Agency

USEGO:

United States Group on Earth Observations

UTM:

Universal Transverse Mercator

VIP:

Ventilated Improved Pit

VP:

Voges Proskauer

WCED:

World Commission on Environment and Development

WHO:

World Health Organization

WRI:

Water Research Institute

21


CHAPTER 1: INTRODUCTION 1.1 Conceptual Framework The impacts of climate change and land use changes on the regional water cycle are among the most urgent issues in contemporary hydrological research. Land use land cover change can have disastrous local impacts on societies and ecosystems. For the reason that important sources and drivers of global change are located at regional and local scales, a stronger emphasis is needed at these scales. Land use directly or indirectly influences many hydrological processes (Lahmer et al., 2001) and may have far-reaching impacts on the regional water cycle. Both biophysical characteristics of a region socio-economic factor strongly influence land use changes (Dong et al., 2009). Changes in land cover can affect ecosystem processes (Turner et al., 1995; Brown et al., 2000). Thus, land use and land cover are closely linked (Brown et al., 2000) and are often used in conjunction with one another. The fundamental objectives of studying change in land use are to investigate the social, economic, and spatial causes of changes (Batty and Longley, 1994; de Koning et al., 1999) and the process and direction of change (Theobald et al., 1997; Pijanowski et al,. 2002), to facilitate the making of proposals on the suitable use of land and patterns of development (Dai et al., 2005) and thus influencing the decision-making process with the aim of achieving sustainable development.

According to Helmer et al. (1998) the impact of human activities and population density, changes in water balance, land use, long-range transboundary transport of pollutants, pollutant sources and global climate change has significant implications for water quality degradation. Concentration of pollutants in cities such as pathogens, oxygen 22


consuming organic matter, nutrients, metals and organic micro pollutants constitute a major source of pollution to surface and groundwater. The pollution problem is further exacerbated by land use change through construction, deforestation and agriculture and associated use of fertilizers, defoliants and agrochemicals.

Microbiological contamination is probably the most important health-related water quality problem in the Danube region. Current health statistics are believed to record only a limited number of the actual incidents of water-borne diseases. Some information suggests that there are a number of epidemics each year and that thousands of people in the basin suffer each year from water-borne diseases including dysentery, hepatitis A, rotavirus and cholera (Task Force, 1994).

It is observed that both non-point sources such as agricultural and urban runoff, point sources are important contributors of contaminants with storm flows in waterways being significant in contaminant export (Cullen, 1991). Hunter et al. (1996) therefore suggested that land use change and land management are needed to address the many causes of water quality problems. While there is no doubt that point sources of pollution have a major impact on water quality in developing countries, the role of agriculture and other types of non point sources is not known and may be substantial. The absence of reliable data makes the assessment of agriculture relative to point sources difficult or impossible in such countries (Hassan, 2006).

The nexus between land use change and climate change thus provides a useful conceptual framework for analysing the impact of these changes on the quality and 23


quantity of water resources. This study is premised on this concept and is rooted in the framework presented in Fig 1. Land use changes affect water resources, biodiversity and the soil and these are interrelated. Water resources are impacted on by land use change through natural processes, point and non point sources which result in agrochemicals, faecal contamination, increased inorganic load, sediment transport, amplitude between river flow and damages caused by flooding. The underlying causes of water quality degradation by anthropogenic modifications include population density, changes in water balance, land use indicators, long-range transboundary atmospheric transport of pollutants, concentrated pollutant resources and global climate change. These underlying causes and the severe impacts of land use change on soil resources ultimately affect environmental change through increased climate variability, global emissions of greenhouse gases, deforestation and water scarcity.

24


land use change

Water resources

Natural processes Changes in temperature Organic carbon and salt loads

Point sources Irrigation drainage water Township effluent Urban storm drains Urban sewage Intensive animal Industrial

Impacts of land use change on water reources: Agrochemical contamination Feacal contamination Increase in organic load Increase on sediment transport and siltation Increase in the amplitude between river flow Increased damages caused by flooding and dry spells (water shortages)

Biodiversity

Non-point sources Agriculture (Agrochemicals) Forestry, Urban runoff, mining and construction

Underlying causes of water quality degradation by anthropogenic alterations: Popualtion density Changes in water balance Land use indicators Long-range transboundary atmospheric transport of pollutants Concentrated pollutant resources Global climate change

Figure 1 An analytical conceptual framework of the linkage between land use change and water quality

25

Soil

Severe impacts of land use change on soil resources: Depletion of soil organic matter Consequent loss of fertility Reduction of carbon stocks Erosion Desertification Biological degradation

Environmental Change Increased climate variability Global emissions of greenhouse gases Deforestation water scarcity


According to Nsiah-Gyabaah (2000a), in urban centres such as Kumasi, Accra and Sunyani, water rationing has become common and poor households are forced to buy water at exorbitant prices, thus putting more pressure on the family budget. Throughout the country, destruction of watersheds, contamination, and overexploitation of underground water resources pose a serious challenge. Agriculture, industrial development and the health of people in the urban and peri-urban areas are worse affected. Nsiah-Gyabaah (2000a) identified three major problems affecting water resources in the urban areas of Ghana with large population concentrations to include: Over exploitation and the cutting down of trees in the watershed. Misuse of water and wastage through burst pipes Pollution through agriculture and domestic waste disposal systems The survey revealed that the Kumasi city depended on the surrounding region or periurban area to act as sink and disposal sites for domestic and industrial wastes. The peri-urban areas were not affected only by air pollution but also suffered from unplanned disposal of polluted water and illegal dumping of other wastes especially night soil generated in the city. Water pollution can negatively affect agricultural productivity and compromise the safety of fresh farm produce. In many vegetableproducing areas, riverbanks are cultivated and large amount of water is pumped to water crops. The use of water to increase biomass production in agriculture and forestry affect both the quantity and quality of water in downstream communities. Unsustainable agricultural practices, bushfires, uncontrolled deforestation and weak institutional mechanisms are the factors responsible for water supply problems in Ghana.

28


The Barekese Reservoir, located 26km north of Kumasi in the Ashanti Region of Ghana is a facility created to serve the purpose of reserving water for treatment and consequent consumption of the populace in the Kumasi Metropolis and its environs. The reservoir provides over 80 percent of the total public water supply of the Kumasi metropolis and surrounding communities (Kumasi et al., 2010).

However over the past four decades there has been persistent degradation of the Barekese watershed through arable farming activities, logging activities and lack of adequate sewage treatment facilities in the catchment. This has created a major concern for the Ghana Water Company Limited (GWCL) since it is charged with the task of maintaining the reservoirs and treating the constantly deteriorating water quality for consumption (Ghana Water Company Limited, 2005).

The National Water Policy of Ghana is intended to provide a framework for the sustainable development of Ghana’s water resources. It is targeted at all water users, water managers and practitioners, investors, decision-makers and policy makers within the

central

Governmental

and

decentralised

structures,

non-Governmental

organisations and international agencies. The policy also recognises the various crosssectoral issues related to water-use and the links to other relevant sectoral policies such as those on sanitation, agriculture, transport, energy etc (Ghana National Policy, 2007).

Unlike many developed countries, Ghana has no legislation on the permitted microbial numbers in inland freshwater bodies. The need for higher inland water quality has been recognized by the Ghana Environmental Protection Agency (GEPA), which is in 29


the process of setting standards that will regulate the quality of inland water bodies. However the GEPA has set limits for the permitted amount of microbial contamination in liquid effluent discharges to water bodies (GEPA Act 490, 1994). Furthermore the Ghana Water Resources Commission (WRC) is formulating a policy document on how buffer zones are to be created, protected and maintained along Ghana's water bodies. The document serves to clarify the requirements for water quality and to outline a national policy on buffer zones as part of managing Ghana’s river basins in an integrated manner and in accordance with the Integrated Water Resources Management approach and to harmonize traditional and existing public institutional standards on buffer zones in Ghana (WRC, 2008).

1.2 Problem Statement and Rationale of the Study Water is one of the fundamental natural resources that a country must harness in the quest for rapid economic growth. It plays a vital role in the promotion of economic growth, improve health and livelihood, and reduce vulnerability. Current trends point to the fact that an integrated water resources management approach is needed to ensure that water does not become a constraint to national development (Ghana National Policy, 2007). The challenge is to create awareness of the adverse impacts of water quality degradation through the use of improper waste disposal methods which may invariably increase the cost of water and decrease supply.

As the demand for water suitable for human production increases with population growth, the potential for risk of contamination also proportionately increases (Manz et al., 2005). The cost at which potable water is made accessible is very crucial especially 30


in developing countries with economic constraints. The cost of potable water production has been found to be a function of the quality of water supplies and the cost of water.

There is currently no study that links land use change and the quality of the Barekese reservoir and feeder streams. This study therefore aims to provide data on linking land use change and its associated impacts on water quality in the Barekese catchment. It is against this background that the Barekese catchment was chosen for this research.

1.3 Working Hypotheses The study uses the deductive approach to research with the following hypotheses: There are no significant land use changes in the Barekese catchment from 19731986, 1986-2003. There are no significant impacts of human activities on land use and water quality in the Barekese catchment. There are no significant differences in the Barekese reservoir water and the different feeder streams of the Reservoir.

1.4 Research Questions The thesis has the following research questions: Is there any difference in the physico-chemical and microbiological quality of the reservoir water and the feeder streams from the Barekese reservoir? Could the feeder streams contribute to the polluting of the Barekese reservoir and what could be some of the causes?

31


Does land use change in the Barekese catchment have any effects on the water quality parameters of the Barekese Reservoir?

1.5 Main Objective The principal objective of the study is to assess the impact of land use changes in the catchment on some water quality parameters of the Barekese Reservoir.

1.6 Specific Objectives The specific objectives are: To identify the main land use change in the catchment as a result of anthropogenic modification. To assess the land use changes in the Barekese catchment from 1973-1986, 1986-2003. To develop a forecast for the next forty years in the Barekese catchment. To assess the impacts of these land use change in the catchment on the physico-chemical water quality parameters of the Barekese Reservoir. To assess the microbiological quality of the water with respect WHO guidelines and GWRC-TWQR. To propose ways of sustainably managing the Barekese catchment to ameliorate the deteriorating water quality.

32


CHAPTER 2: LITERATURE REVIEW

2.1 Water Quality Day (1998) defines water quality as the combined physical attributes and chemical constituents of water that contribute to its usefulness for a particular purpose. Water quality must therefore be defined relative to a proposed use, e.g. human consumption, supporting aquatic fauna or flora, recreational use etc. O'Loughlin (1994) identifies five key parameters: Chemical water quality1 Physical parameters2 Concentrations of suspended solids3 Concentrations of microbial organisms4 Water quality problems arising from natural conditions occur in many continents and are linked to local characteristics of a humid climate. In most arid regions, surface and groundwater contains high concentrations of minerals than in humid areas and may exceed the drinking water health standards. In certain geological settings, where evaporites (halite, gypsum) and other easily soluble minerals such as fluoro-apatite or arsenic-bearing minerals occur, concentrations of fluoride, arsenic and other substances may well exceed the WHO drinking water limits. In these regions, such waters may be primary water resource that causes massive degradation of population health (Helmer et al., 1998).

1

Concentrations of anions, cations, heavy metals etc. 𝑝𝐻, temperature and electrical conductivity 3 Affecting clarity and turbidity 4 Bacteria, faecal coliforms 2

33


The risk of increased nitrate and phosphate concentrations in streams draining pastoral catchments is extremely high given the reliance on legume-based agriculture and increased surface flow to streams in contrast to forests, and is exacerbated where stream margins are grazed or accessible to stock. The volume of stream flow directly affects both water quality and the availability of water for use. A given amount of contamination, whether nutrients, pesticides, sediment or faecal coliforms, will have a greater impact on water quality when there is reduced flow (Cooper and Thomsen, 1988). Control and sustainable management of river catchments are major issues in Ghana because of a variety of pressures placed upon land and water resources. These include input of agricultural chemicals, sediment loading caused by deforestation, eutrophication, abstraction of water for human consumption and irrigation, essential requirements for rural and urban development and poverty alleviation (Amoako et al., 2010; Ansa-Asare, 1995).

2.1.1 Threats to water quality It has been suggested that the rising demand for water and the degradation of its quality, represent the most serious threat to the provision of various goods and services required by society (FAO, 2000). The qualities of water in waterways of a catchment are threatened by two broad categories of pollutants: point sources and non-point 5 (diffuse) sources (Chen et al., 2005).

5

Non-points is interchangeably used as diffuse sources

34


2.1.1.1 Point sources of pollution Point source pollutants are those where contaminants originate from an identifiable discharge point. These include: irrigation drainage water, urban effluent, urban storm water drains, and industrial effluent and intensive animal industries. Management of point sources is generally easier, as controls can be placed on the quantity and quality of pollution. Generally, for industrial activities, Environmental Protection Agencies such as USEPA license these activities and are able to prosecute in the event of unacceptable pollution events (Hassan, 2006).

Urban storm water runoff affects the quality of receiving water bodies by carrying a significant load of contaminants that have accumulated on urban surfaces (Wu et al., 1998). In urban areas storm water can wash litter, grease, heavy metals, pathogens, petroleum products etc, directly into waterways. Increases in pollutants can also severely stress stream biota. Undisturbed streambeds typically support a wide array of insects and other macro-invertebrates. The combination of habitat degradation and increased pollution can cause many sensitive species to disappear. Population increases lead to an increase in the volume of wastewater (Suraj, 2004).

2.1.1.2 Non-point sources of pollution The term "Non-Point Source pollution"(NPS) refers to pollutants that cannot be identified as coming from one discrete location or point (Hassan, 2006). Diffuse source pollutants are typically carried to surface waters by rainfall or runoff.

35


Examples are faecal coliform bacteria, heavy metals, oil and grease that enter the water with runoff from urban streets or nitrogen from fertilizers and animal waste that wash into surface waters from agricultural lands. NPS pollution from urban runoff has been established as a major cause of water degradation. In an effort to control this problem in the USA for example, new regulations have been passed and the federal, state, and local agencies have devised urban runoff management programs (Tsihrintzis and Hamid, 2004). Nonpoint source pollution in the United States causes nearly $10 billion in damage yearly (Bhaduri et al., 2000).

2.2

Land Use Change

The concept of land use is often considered a relatively stable subject related mainly to the use to which the land in a certain region at a certain time is put. A series of operations on land, carried out by man, with the intention to obtain products and/or benefits through using land resources is called land use (Huizing and Bronsveld, 1994). Turner et al. (1995) and Brown et al. (2000) describe land use as human activity on the land. The Intergovernmental Panel on Climate Change (IPCC) (2000) defines land use as the total of arrangements, activities, and inputs undertaken in a certain land cover type (a set of human actions). Land use is carried out in many different ways. The broadest categories include: Rural land use; including agriculture, forestry and wildlife. Urban and industrial land use including towns, villages and industrial complexes (Suraj, 2004). Land use change, dominated by an increase in urban/impervious areas, has a significant impact on water resources. Land use and land cover change therefore can 36


have significant impact on the hydrology and hydrological cycle in an area or a watershed. Since the landscape contains the natural resources on which humans depend, i.e. water, biomass, energy sources and minerals and other basic materials (Falkenmark et al., 1999), any use or exploitation of these resources can cause changes in the land cover, which consequently modifies the local ecosystem. Thus, the proposition by Falkenmark et al. (1999) that ‘a water decision is also a land use decision’, in relation to the effective management of water resources, which must take into consideration the surrounding, land. It is undeniable fact that as urbanization expands the land with its natural vegetative and forest covers are cleared to give way for residential and industrial purposes (Suraj, 2004). It has been shown by Jin-Yong et al. (2003) that there has been an increase in runoff after vegetation removal, as a result of urbanization.

Similarly, Barnes et al. (2003) noted in connection with landscape changes in Chesapeake Bay, that the increasing imperviousness of a landscape as a result of urbanization, has five broad interrelated impacts. These include: alteration of a local and regional hydrologic cycles (i.e. changes in water quantity; changes in water quality; changes to local energy balance and microclimates); habitat degradation, loss and fragmentation of forests; and changes to stream and landscape aesthetics. The increase in the use of water and other resources in combination with input of various chemicals, generate tremendous volumes of non-desirable by-products and pollution far in excess of what can be handled and what is being disposed and diluted. The most striking environmental problems and subsequent impacts of urbanization, aside water resources,

37


especially in the African setting are unsustainable land use changes, land degradation, deforestation and loss of biological diversity (UNECA, 2001).

The communities relying on polluted water sources tend to be poor and live in polluted environments with associated high health risk. Such communities occur in most cities in developing countries. Their occurrence is attributed to rapid urbanisation where urban growth is associated with rapid expansion of small, unplanned urban centres and periurban settlements (WHO and UNICEF, 2000; Vernon, 2002; Hardoy et al., 2002; Kulabako et al., 2007). It is paradoxical that humans, who are the worse affected by water shortage are the architect of their own misfortune. Humans engage in activities that tend to degrade the environment with adverse consequences on the environment. Unsustainable and unhealthy practices such as deforestation, indiscriminate bush burning and sand winning have had serious repercussions on the ecosystem culminating in the depletion of water resources, prolonged drought and water shortage (NsiahGyabaah, 2000a).

2.2.1

Impacts of land use change on water quality

The way in which urban development unfolded has caused major problems in water resources, among which are changes in the flow of streams, changes in the hydrological patterns of streams, changes in the amount of suspended sediment, sedimentation and siltation of reservoirs and difficulties in recycling potentially limiting resources such as phosphorus from wastewater back to agriculture (Anton, 1993). Increase in urban population can affect water quality in several ways.

38


Analysis of major ion chemistry in the Mill River watershed reveals the importance of anthropogenic modification in controlling stream water chemistry. Average concentrations of NO3- and SO42- show a positive correlation with percent catchment altered by human land uses, and concentrations of Cl - increase with road density. Water removal from municipal reservoirs increases the downstream concentration of NO3- and SO42- over that predicted by land use change, showing that removal of high quality upstream water concentrates pollutants downstream (Rhodes et al., 2001).

Quantitative supply and water quality problems are mounting and could constrain economic development and human well-being in general. It is in recognition of the existence of these problems, among others, that the United Nations (UN) Conference in Rio de Janeiro 1992 and subsequent World Summit in Johannesburg, advocated the adoption of strategies in order to identify trade-offs between economic, social and environmental interests in society in what has come to be known as the ‘Sustainable Development’. This is defined by the World Commission on Environment and Development (WCED) as development that meets the need of the present generation without compromising the ability of the future generations to meet their needs. Preservation and protection of water resources is a central imperative of sustainable development. Sustainable development thus, incorporates into its goals and methods the long range impacts of development on the natural environment and on its utility for human beings in the present and future generations (WCED, 1987; Lundqvist, 1998; Suraj, 2004).

39


In September 2000, Heads of State adopted the UN Millennium Declaration which was then translated into a roadmap setting out clear goals, time-bound targets for making real progress on the most pressing development issues to be addressed by 2015. The eight Millennium Development Goals (MDGs) build on agreements made at United Nations conferences in the 1990s and represented commitments to reduce poverty and hunger, and to tackle ill-health, gender inequality, lack of education, lack of access to clean water and environmental degradation. Achieving these targets will directly affect the lives and future prospects of billions of people around the globe. It will also set the world on a positive course at the start of the 21st century. Goal 7 is to ensure environmental sustainability. One of its targets is to halve, by 2015, the proportion of people without sustainable access to safe drinking water and basic sanitation. One billion people lack access to safe drinking water and 2.4 billion to adequate sanitation. To achieve this target, an additional 1.5 billion people will require access to some form of improved water supply by 2015, that is an additional 100 million people each year (or 274,000/day) until 2015. The results so far are mixed with the exception of subSaharan Africa; the world is well on its way to meeting the drinking water target however progress in sanitation is stalled in many developing regions (WHO/UNICEF, 2004).

2.3

Water Quality, Land Use and River Basin

In the developing world more than 90% of sewage water is discharged directly into rivers, lakes and coastal waters without any kind of treatment. The ongoing depletion of water quality is having consequences not only on human but also on ecological health.

40


The environmental concern for water is a pre-requisite for sustainable use, which was recognised in Agenda 21(Björklund, 2001).

Falkenmark et al. (1999) noted that the role of land use and change in a catchment is highly complex. Land use change may be gradual conversions to total modifications. In addition, there are structural complexities between different land uses, and functional complexities within them, all of which influence the movement of water through the landscape. However, there is often a dichotomy between land use and water resources considerations. Even in important policy documents such as United Nations Commission on Environment and Development (UNCED) Agenda 21, sections on land use and freshwater show little appreciation of water related phenomena as determinants of land use, or of land use practices as determining water pathways, water flow and water quality.

Until recently, pollution control in rural drainage basins of the United Kingdom (UK) consisted solely of water treatment at the point of abstraction. However, prevention of agricultural pollution at source is now a realistic option given the possibility of financing the necessary changes in land use through modification of the Common Agricultural Policy (CAP) (Burt and Johnes, 2007).

The Barron River Catchment overview study identified water quality, water supply and land use conflict as major issues in the Barron River catchment. The Barron catchment has been subjected to many anthropogenic influences, including land clearing for cropping and the establishment of urban areas. While water quality is generally 41


acceptable, the report identified areas within the catchment where resource health is damaged and has provided a basis for the evaluation of new management, in the form of clear baseline water chemistry and land use parameters (Cogle et al., 2000).

The increasing stress on Africa's freshwater resources is the result of natural and human causes. Rapid population growth, pollution from pesticides and fertilizers, and industrial effluent all contribute to Africa's water stress. Another cause of stress is environmental degradation. Forests, which serve as important water catchments are cleared for fuel wood, lumber, and agriculture. Existing agricultural land is being degraded by soil erosion and devegetation (Gordon, 1998).

McGregor et al. (2000) noted that water resources in the peri-urban area around Kumasi are affected by a variety of activities. Principal among these are: 1) river water pollution especially within and downstream from urban Kumasi, attributed to untreated sewerage and other domestic waste, hospital waste, industrial waste, including an assortment of chemicals and possibly heavy metals, oils from informal motor repair businesses; sawmills, brewery, abattoir, urban and rural runoff, including agricultural chemicals and residues, leachate from groundwater into the river system of any of the above pollutants; 2) contamination of boreholes and wells situated close to polluted watercourses by one or more of the pollutants listed under 1; 3) contamination of boreholes and wells by leachate from pit latrines located upslope from them; 4) unplanned and unregulated waste tipping, both by villagers and by urban dwellers, with inadequate if any management and mitigation measures; and 5) localised heavy resource exploitation e.g. sand winning, deforestation for agriculture 42


and wood use, and new urban and peri-urban housing and industrial/commercial premises. Pollution is a problem because controls on discharge are difficult to enforce. The rivers are used for washing (people, clothes and vehicles), and people drink from contaminated streams in times of shortage in more rural areas (CEDAR, 1999).

In Kumasi and the peri-urban areas the dumping of refuse was a cause for concern in almost all the villages. They were often poorly sited, upstream and close to rivers so that rubbish was washed into the streams. As villages grew, dumps that were previously on the edge of the village became surrounded with houses though all the villages had communal latrines. Though some of the larger and modern houses had their own private toilets, the private toilets were either pit, Ventilated Improved Pit (VIP) or (only in villages with piped water) flush toilets with septic tanks. Waste water from houses is thrown into open channels and drains. There was no drainage system in any of the villages other than gutters built along some roads which channelled the water down into the streams. Some of the modern houses had plastic guttering and pipes to carry the waste water away from the house, but these also fed into open gutters in the village (McGregor et al., 2000).

The "Kumasi Natural Resource Management at the Watershed Level" Project (R7330) commenced in 1999 with the purpose of developing a framework for sustainable and equitable water resource management at a watershed level (CEDAR, 1999). This concentrated on the examination of water resources in two sample watersheds in the Kumasi area (Owabi and Sisa-Oda), with the aim of investigating how catchments upstream of Kumasi affect Kumasi and how Kumasi affects rivers downstream. The 43


problems related to water quality in Kumasi and peri-urban areas included: High turbidity levels and sediments within important basins such as the Sisa and Oda in the Kumasi metropolis; increasing water resources pollution caused mainly by domestic wastes, industries, abattoir, garages and pollution caused by increasing use of chemical fertilizers and toxic substances especially in horticultural crop production for the urban market. Heavy metal pollution and other dangerous substances were threatening aquatic and human life (Brook and Davila, 2000; Nsiah-Gyabaah, 2000a).

2.4 Physio-Chemical Parameters In general, most physical characteristics of water are not of direct public health concern, but they do affect the aesthetic quality of the water, which largely determines whether or not people are prepared to drink it. A number of chemicals, both organic and inorganic, including some pesticides, are of concern in drinking water from the health perspective because they are toxic to humans or are suspected of causing cancer; some can also affect the aesthetic quality of water (ADWG, 2004).

2.4.1

Alkalinity

According to Murphy (2007), alkalinity refers to how well a water body can neutralize acids. Alkalinity measures the amount of alkaline compounds in water, such as carbonates (CO32-), bicarbonates (HCO3-) and hydroxides (OH-). These compounds are natural buffers that can remove excess hydrogen ions that have been added from sources such as acid rain or acid mine drainage. Alkalinity mitigates metals toxicity by using available HCO3- and CO32- to take metals out of solution, thus making it unavailable to fish. Alkalinity is affected by the geology of the watershed; watersheds containing 44


limestone will have a higher alkalinity than watersheds where granite is predominant. Waters with low alkalinity are very susceptible to changes in 𝑝𝐻. Waters with high alkalinity are able to resist major shifts in 𝑝𝐻. As increasing amounts of acid are added to a water body, the 𝑝𝐻 of the water decreases, and the buffering capacity of the water is decreased. If natural buffering materials are present, 𝑝𝐻 will drop slowly to around 6; then a rapid 𝑝𝐻 drop occur as the bicarbonate buffering capacity (CO32- and HCO3-) is used up. Alkalinity not only helps regulate the 𝑝𝐻 of a water body, but also the metal content. Bicarbonate and carbonate ions in water can remove toxic metals (such as lead, arsenic, and cadmium) by precipitating the metals out of solution.

2.4.1.1 Health consideration of alkalinity Alkalinity in the range of 30 to 500 𝑚𝑔/𝐿 CaCO3 is generally acceptable. Alkalinity may also affect water taste. When water with high alkalinity is boiled over an extended period of time it forms a deposit or unpleasant taste. Waters with very low alkalinity corrode pipes and plumbing (GCS Water Systems, 2004). Most aquatic organisms can live in a broad range of alkalinity concentrations. The desired total alkalinity level for most aquaculture species lies between 50-150 𝑚𝑔/𝑙 CaCO3, but no less than 20 𝑚𝑔/ 𝑙 (Wurts, 2000).

2.4.2


Biochemical Oxygen Demand (BOD) represents the amount of oxygen consumed by bacteria and other microorganisms while they decompose organic matter under aerobic conditions at a specified temperature. Accurate measurement of BOD requires an

45


accurate determination of Dissolved Oxygen (DO). The conversion of ammonia to nitrate requires more than four times the amount of oxygen as the conversion of an equal amount of sugar to carbon dioxide and water (Delzer and McKenzie, 2003).

BOD is one of the most common criteria of pollutant organic material in water. BOD indicates the amount of putrescible organic matter present in water. Therefore, a low BOD is an indicator of good quality water, while a high BOD indicates polluted water. This measurement is obtained over a period of five days, and is expressed in 𝑚𝑔/𝑙. Dissolved oxygen is consumed by bacteria when large amounts of organic matter from sewage or other discharges are present in the water. DO is the actual amount of oxygen available in dissolved form in the water. Human activities that affect DO levels include the removal of riparian vegetation, runoff from roads, and sewage discharge. High BOD loads are added to wastewater by food processing plants, dairy plants, canneries, distilleries and similar operations, and they are discharged into streams and other bodies of water (Robson, 2007).

2.4.2.1 Health consideration of biochemical oxygen demand If elevated levels of BOD are lower than the concentration of dissolved oxygen in a water body, there is a potential for profound effects on the water body itself, and the resident aquatic life. When the dissolved oxygen concentration falls below 5 (𝑚𝑔/𝑙 ), species intolerant of low oxygen levels become stressed. Low oxygen concentration increases the level of stress in organisms. Eventually, species sensitive to low dissolved oxygen levels are replaced by species that are more tolerant of adverse conditions, significantly reducing the diversity of aquatic life in a given body of water 46


(Robson, 2007). If dissolved oxygen levels fall below 2 mg/l for more than even a few hours, fish kills can result. At levels below 1𝑚𝑔/𝑙, anaerobic bacteria replace the aerobic bacteria. As the anaerobic bacteria break down organic matter, foul smelling hydrogen sulphide can be produced. There have been no direct health effects caused by low oxygen concentrations in drinking water. Indirect effects may result from the corrosion of fittings, which can give rise to higher concentrations of heavy metals such as lead, copper and cadmium, and by the anaerobic generation of hydrogen sulphide and nitrite (AWDG, 2004).

2.4.3 Chloride Chloride is present in natural waters from the dissolution of salt deposits, and contamination from effluent disposal. Sodium chloride is widely used in the production of industrial chemicals such as caustic soda, chlorine, and sodium chlorite and hypochlorite. Potassium chloride is used in the production of fertilizers. The taste threshold of chloride in water is dependent on the associated cation but is in the range 200–300 𝑚𝑔/𝑙 (IARC, 1990). The chloride content of water can affect corrosion of pipes and fittings. It can also affect the solubility of metal ions.

In surface water, the concentration of chloride is usually less than 100𝑚𝑔/𝑙 and frequently below 10𝑚𝑔/𝑙. Groundwater can have higher concentrations, particularly if there is salt water intrusion. Food is the major source of chloride intake. All plants and animals contain chloride. The addition of salt during processing or cooking can markedly increase the chloride content (APHA Method 4500-Cl- Part B, 1992).

47


Chloride in drinking-water originates from natural sources, sewage and industrial effluents, urban runoff containing de-icing salt and saline intrusion. The main source of human exposure to chloride is the addition of salt to food, and the intake from this source is usually greatly in excess of that from drinking-water. Excessive chloride concentrations increase rates of corrosion of metals in the distribution system, depending on the alkalinity of the water. This can lead to increased concentrations of metals in the supply. A guideline value 250 mg/litre is proposed for chloride (WHO, 2004).

2.4.3.1 Health considerations of chloride Chloride is essential for humans and animals. It contributes to the osmotic activity of body fluids. Chloride is absorbed almost completely by the gastrointestinal tract. Healthy individuals can tolerate the intake of large quantities of chloride provided there is a corresponding intake of fresh water. Little is known about the prolonged intake of large amounts of chloride by humans (APHA Method 4500-Cl- Part F, 1992). Large salt intake has been reported to increase blood pressure but this is attributed to the sodium content rather than chloride. Similar results have been reported in studies with animals, although long-term data are not available (WHO, 2004).

2.4.4 Colour (True) ‘True colour’ is the colour after particulate matter has been removed (usually by filtration through a 0.45 micrometer pore size filter). ‘Apparent colour’ is what one actually sees; it is the colour resulting from the combined effect of true colour and an y particulate matter, or turbidity. In turbid waters, the true colour is substantially less 48


than the apparent colour. In natural waters, colour is due mainly to the presence of dissolved organic matter including humic and fulvic acids, which originate from soil and decaying vegetable matter. Surface water can also be coloured by waste discharges, for example from dyeing operations in the textile industry, and paper manufacture. The dissolution of metals in pipes and fittings can also discolour drinking water (ADWG, 2004). Badly corroded iron pipes can produce a brownish colour whereas corrosion of copper pipes can produce a blue-green colouration on sanitary ware and a faint blue colour in water in extreme cases. The condition of household pipes can significantly influence water colour. A black discolouration in reservoirs and distribution systems can result from the action of bacteria on dissolved manganese to produce insoluble oxides (WHO, 2004). Some of these compounds form fine suspensions, or are only partially dissolved, and so contribute to apparent rather than true colour.

2.4.4.1 Health considerations of colour (true) Colour is generally related to organic content, and while colour derived from natural sources such as humic and fulvic acids is not a health consideration, chlorination of such water can produce a variety of chlorinated organic compounds as by products. It should be noted; however, that low colour at the time of disinfection does not necessarily mean that the concentration of disinfection by products will be low (WHO, 2004). Reactions between naturally occurring humic and fulvic material and water disinfectants (such as chlorine, ozone, chloramines and chlorine dioxide) can also cause difficulties in maintaining an adequate level of disinfectant thus creating the opportunity for bacterial re-infection or regrowth. 49


The solubility of some organic pollutants can also be increased through complex formation with humic material (ADWG, 2004). Coloured water may prompt people to seek other, perhaps less safe, sources of drinking water.

2.4.5 Conductivity Conductivity is the ability of the water to conduct an electrical current, and is an indirect measure of the ion concentration. The more ions present, the more electricity can be conducted by the water. This measurement is expressed in microsiemens per centimetre (𝜇𝑆/𝑐𝑚) at 250C. Conductivity can be used as a measure of Total Dissolved Solids (TDS). These solids are usually composed of the sulphate, bicarbonate, and chlorides of calcium, magnesium, and sodium (Bordin, 1997). Conductivity in water is affected by the presence of inorganic dissolved solids such as chloride, nitrate, sulphate, and phosphate anions (ions that carry a negative charge) or sodium, magnesium, calcium, iron, and aluminium cations (ions that carry a positive charge). Organic compounds like oil, phenol, alcohol, and sugar do not conduct electrical current very well and therefore have a low conductivity when in water. Conductivity is also affected by temperature: the warmer the water, the higher the conductivity. For this reason, conductivity is reported as conductivity at 25 degrees Celsius (25℃) (USEPA, 2001).

According to USEPA (2001), Conductivity in streams and rivers is affected primarily by the geology of the area through which the water flows. Streams that run through areas with granite bedrock tend to have lower conductivity because granite is composed of more inert materials that do not ionize (dissolve into ionic components) 50


when washed into the water. On the other hand, streams that run through areas with clay soils tend to have higher conductivity because of the presence of materials that ionize when washed into the water. Ground water inflows can have the same effects depending on the bedrock they flow through. Discharges to streams can change the conductivity depending on their make-up. A failing sewage system would raise the conductivity because of the presence of chloride, phosphate, and nitrate; an oil spill would lower the conductivity.

2.4.5.1 Health considerations of conductivity Significant changes in conductivity could be used an indicator that a discharge or some other source of pollution has entered a stream. Distilled water has conductivity in the range of 0.5 to 3𝜇𝑚ℎ𝑜𝑠/𝑐𝑚 . The conductivity of rivers in the United States generally ranges from 50 to 15003𝜇𝑚ℎ𝑜𝑠/𝑐𝑚. Studies of inland fresh waters indicate that streams supporting good mixed fisheries have a range between 150 and 500𝜇𝑚ℎ𝑜𝑠/ 𝑐𝑚. Conductivity outside this range could indicate that the water is not suitable for certain species of fish or macro invertebrates. Industrial waters can range as high as 10,000 𝜇𝑚ℎ𝑜𝑠/𝑐𝑚 (USEPA, 2001).

2.4.6 Hardness (Calcium carbonate) Total hardness is the sum of the concentrations of calcium and magnesium ions expressed as a calcium carbonate equivalent. Hardness may also be classified as carbonate (temporary) or noncarbonated (permanent) hardness (WHO, 2004). Carbonate hardness is the total alkalinity expressed as calcium carbonate, where

51


alkalinity is the sum of the carbonate, bicarbonate and hydroxide content. Non carbonate hardness is the difference between the total and carbonate hardness. Degrees of hardness can be described as follows: 500 𝑚𝑔/𝑙 CaCO3 severe scaling (ADWG, 2004).

To minimise undesirable build-up of scale in hot water systems, total hardness in drinking water should not exceed 200 𝑚𝑔/𝑙 (WHO, 2004). Hard water can cause scale to form on hot water pipes and fittings. Hardness is caused primarily by the presence of calcium and magnesium ions, although other cations such as strontium, iron, manganese and barium can also contribute. Total hardness above 200 𝑚𝑔/𝑙 may lead to excessive scaling of pipes and fittings, and cause blockage of safety relief valves in hot water systems. High total hardness may be a problem for supplies reliant on groundwater. Surface waters can generally be expected to have acceptable values (APHA Method 2340C, 1992).

2.4.6.1 Health considerations of hardness (Calcium carbonate) Some epidemiological studies have found that hard water may have a beneficial effect on health, particularly on some types of cardiovascular disease, but the data are inadequate to conclude that the association is causal. There is some indication that soft water, with a hardness of less than about 75𝑚𝑔/𝑙, may adversely affect mineral balance (ADWG, 2004). 52


2.4.7

Nitrate

Nitrate and nitrite are naturally occurring ions that are part of the nitrogen cycle. Nitrate is used mainly in inorganic fertilizers and sodium nitrite is used as a food preservative, especially in cured meats. The nitrate concentration in groundwater and surface water is normally low but can reach high levels as a result of leaching or runoff from agricultural land or contamination from human or animal wastes as a consequence of the oxidation of ammonia and similar sources (USEPA, 1990). Anaerobic conditions may result in the formation and persistence of nitrite. Chloramination may give rise to the formation of nitrite within the distribution system if the formation of chloramines is not sufficiently controlled. The formation of nitrite is as a consequence of microbial activity and may be intermittent. Nitrification in distribution systems can increase nitrite levels, usually by 0.2–1.5 𝑚𝑔/𝑙 (APHA Method 4500-NO2 Part B, 1992).

2.4.7.1 Health considerations of nitrate The toxicity of nitrate to humans is thought to be solely due to its reduction to nitrite. The major biological effect of nitrite in humans is its involvement in the oxidation of normal haemoglobin to methaemoglobin, which is unable to transport oxygen to the tissues. This condition is called methaemoglobinaemia. Young infants are more susceptible to methaemoglobin formation than older children and adults. Other susceptible groups include pregnant women and people with a deficiency of glucose-6phosphate dehydrogenase or methaemoglobin reductase (WHO, 2004).

53


In animals, laboratory experiments suggest that neither nitrite nor nitrate acts directly as a carcinogen. There is concern that nitrite may react with foods rich with secondary amines to form N-nitroso compounds in the stomach: many of these compounds are known to be carcinogenic in animals. Some epidemiological evidence suggests a relationship between nitrate and gastric cancer in humans, but this has not been confirmed in more definitive analytical studies. Nitrate is not mutagenic in tests with bacteria and mammalian cells in vitro. Chromosome aberrations have been observed in the bone marrow of rats but may be due to the formation of N-nitroso compounds. Nitrite is mutagenic in both in vivo and in vitro experiments using mammalian cells (ADWG, 2004).

2.4.8 Oil and grease Oil and grease includes a wide array of hydrocarbon compounds, some of which are toxic to aquatic organisms at low concentrations. Sources of oil and grease include leakage, spills, cleaning and sloughing associated with vehicle and equipment engines and suspensions, leaking and breaks in hydraulic systems, restaurants, and waste oil disposal (Stormwater BMP Handbook, 2003). Oil and grease includes not only petroleum oils but also vegetable and natural oils. Sediments, biota and decaying life forms are often high in natural oil lipids which make up part of the oil and grease measure (Irwin et al., 1997).

2.4.8.1 Health consideration of oil and grease Like Total Petroleum Hydrocarbon (TPH) and Total Recoverable Petroleum Hydrocarbon (TRPH) data, oil and grease data is very difficult (if not impossible) to 54


interpret related to ecological effects. However, oil and grease have some indirect value as one of the measures of oxygen demanding materials (Irwin et al., 1997). Concentration of oil and grease should not exceed 0.1 𝑚𝑔/𝑙 as it has effect on fish eggs and larvae (EPA, 1986).

2.4.9 𝒑𝑯 𝑝𝐻 is a measure of the hydrogen ion (H +) concentration in water and reported using an inverse logarithmic scale from 0 - 14, with a 𝑝𝐻 of 7.00 representing neutral or no excess of hydrogen ions (AWWA,1990). Excess hydrogen ions represent acid conditions, whereas a deficit of such ions (usually an excess of OH - ions) represent the opposite or base conditions. Conditions are said to be weak if close to neutral or 𝑝𝐻 7.0 and strong if far from neutral. 𝑝𝐻 is an important parameter for monitoring because of the influence 𝑝𝐻 has on chemical reactions in solutions. 𝑝𝐻 values also provide information on water chemistry and ecosystem function. High primary productivity by algae in the water results in dissolved CO2 being removed from the water by algal cells and used in photosynthesis. Because dissolved CO 2 is a weak acid, this process of removal causes the 𝑝𝐻 of the water to increase (ADWG, 2004).

Based on the need to reduce corrosion and encrustation in pipes and fittings, the 𝑝𝐻 of drinking water should be between 6.5 and 8.5 (ADWG, 2004). New concrete tanks and cement-mortar lined pipes can significantly increase 𝑝𝐻 and a value up to 9.2 may be tolerated, provided monitoring indicates no deterioration in microbiological quality One of the major objectives in controlling 𝑝𝐻 is to minimize corrosion and

55


encrustation in pipes and fittings. If the water is too alkaline (above 𝑝𝐻 8.5) the rapid deposition and build-up of calcium carbonate that can result may eventually block the pipe. When 𝑝𝐻 is below 6.5 or above 11, the water may corrode plumbing fittings and pipes. Chlorination of water supplies can decrease the 𝑝𝐻, while it can be significantly raised by lime leached from new concrete tanks or from pipes lined with asbestos cement or cement mortar. Values of 𝑝𝐻 above 9.5 can cause a bitter taste in drinking water, and can irritate skin if the water is used for ablutions (WHO, 2004).

2.5.9.1 Health considerations of 𝒑𝑯 A direct relationship between 𝑝𝐻 and human health is difficult to determine, as 𝑝𝐻 is closely associated with other aspects of water quality. Consumption of food and beverages with quite low or high 𝑝𝐻 is common and does not result in adverse health effects. In humans, extreme values of 𝑝𝐻 result in irritation of the eyes, skin and mucous membranes. Eye irritation and exacerbation of skin disorders have been associated with 𝑝𝐻 values above 11 (USEPA, 1989).

Gastrointestinal irritation may occur in sensitive individuals at 𝑝𝐻 values above 10. Below 𝑝𝐻 4, redness and irritation of the eyes have been reported, with the severity increasing with decreasing 𝑝𝐻. Below 𝑝𝐻 2.5, damage to the epithelium is irreversible and extensive (WHO, 2004).

`

56


2.4.10 Sulphates Sulphur is a non-metallic element that occurs naturally in numerous minerals, including barite (𝐵𝑎𝑆𝑂4 ) epsomite (𝑀𝑔𝑆𝑂4 7𝐻2 𝑂), and gypsum (CaSO4·2 H2O). Hexavalent sulphur combines with oxygen to form the divalent sulphate ion (SO 42-). The reversible reaction between sulphide and sulphate in the natural environment is often referred to as the "sulphur cycle." Natural sources of sulphur include volcanoes, decomposition and combustion of organic matter and from sea salt over the oceans. Particles of sea salt formed by the breaking of myriads of bubbles are an important source of atmospheric sulphate. The atmosphere is the main vehicle for transport of sulphur from various sources (Kellogg et al., 1972).

Sulphates are discharged into the aquatic environment in wastes from industries that use sulphates and sulphuric acid, such as mining and smelting operations, kraft pulp and paper mills, textile mills and tanneries. Iron pyrite (FeS) may be leached from abandoned coal mines and the sulphide ions converted in surface waters to sulphates. Sulphates are also released during blasting and the deposition of waste rock in dumps at metal mines (ADWG, 2004). This is a significant source of sulphate generation in British Columbia. The burning of fossil fuels is also a major source of sulphur to the atmosphere. Most of man's emissions of sulphur to the atmosphere (about 95%) are in the form of SO2. Sulphate fertilizers are identified as a major source of sulphate to ambient waters (Kellogg et al., 1972).

57


2.4.10.1 Health consideration of sulphates In addition to adversely affecting the taste of water, high sulphate levels can have a strong laxative effect, especially on people not used to water with increased sulphate levels. Concentrations above 200𝑚𝑔/𝑙 also increase the amount of lead leached from lead pipes. The USEPA has set acceptable sulphate levels at 250𝑚𝑔/𝑙 or less (Tarrant County Public Health, 2007).

The guideline of 500𝑚𝑔/𝑙 for sulphate has been established for health as well as aesthetic reasons. The major physiological effect from ingestion of water containing sulphate at concentrations in excess of this limit is catharsis (laxative effect) and gastrointestinal irritation. The presence of sulphate in drinking water may also result in noticeable taste. Individuals who complain of a sulphur or rotten egg like odour from their water often have a hydrogen sulphide problem. Such water is usually low in sulphate. The problem occurs when the groundwater exists in either low or negligible oxygen conditions and any sulphur present tends to be reduced to hydrogen sulphide gas. As this water is piped into the house, the gas separates from the water and as the tap is opened, the gas releases into the air causing a foul odour. Because this gas has been released into the air, laboratory measurement is almost meaningless. Hydrogen Sulphide can also be generated in a fouled water treatment device as well as in a domestic hot water heater (GCS Water Systems, 2004).

2.4.11 Total dissolved solids Total Dissolved Solids are the term used to describe the inorganic salts and small amounts of organic matter present in solution in water. TDS are solids in water that 58


can pass through a filter (with a pore size of 0.45µm). TDS is a measure of the amount of material dissolved in water. The principal constituents are usually calcium, magnesium, sodium, and potassium cations and carbonate, hydrogen carbonate, chloride, sulphate, nitrate anions, organic ions, and other ions (WHO, 1996a). A certain level of these ions in water is necessary for aquatic life.TDS consist of inorganic salts and small amounts of organic matter that are dissolved in water. Clay particles, colloidal iron, manganese oxides, and silica fine enough to pass through a 0.45µm filter membrane can also contribute to TDS (ADWG, 2004).

Changes in TDS concentrations can be harmful because the density of the water determines the flow of water into and out of an organism's cells (Mitchell and Stapp, 1992). TDS is used to estimate the quality of drinking water, because it represents the amount of ions in the water. The palatability of drinking water has been rated according to TDS concentrations as follows: (𝑚𝑔/𝑙 quality): 1000 unacceptable (Bruvold and Daniels, 1990).

Water with extremely low TDS may taste flat and insipid. High TDS values may be associated with excessive scaling in pipes, fittings and household appliances. Excessive corrosion may also occur with high TDS values (ADWG, 2004). The electrical conductivity of water, measured in EC units, increases with the concentration of dissolved solids. Electrical conductivity can be used as a measure of TDS, but the 59


factor used to convert EC into TDS will depend on the type of dissolved solids present in the water (APHA Method 2510A, 1992).

2.4.11.1 Health considerations of total dissolved solids The presence of dissolved solids in water may affect its taste. However, if TDS concentrations are too high or too low, the growth of many aquatic lives can be limited, and death may occur. High concentrations of TDS may also reduce water clarity, contribute to a decrease in photosynthesis, combine with toxic compounds and heavy metals, and lead to an increase in water temperature. Water with high TDS often has a bad taste and/or high water hardness, and could result in a laxative effect (Murphy, 2007). However according to (ADWG, 2004) no health effects have been associated specifically with high TDS concentrations.

2.4.12 Total suspended solids Total Suspended Solids (TSS) are solids in water that can be trapped by a filter. TSS can include a wide variety of material, such as silt, decaying plant and animal matter, industrial wastes, and sewage. High concentrations of suspended solids can cause many problems for stream health and aquatic life (Murphy, 2007). High TSS in a water body can often mean higher concentrations of bacteria, nutrients, pesticides, and metals in the water. These pollutants may attach to sediment particles on the land and be carried into water bodies with storm water. In the water, the pollutants may be released from the sediment or travel farther downstream (Federal Interagency Stream Restoration Working Group, 1998).

60


2.4.12.1

Health consideration of total suspended solids

High TSS can block light from reaching submerged vegetation. As the amount of light passing through the water is reduced, photosynthesis slows down. Reduced rates of photosynthesis causes less dissolved oxygen to be released into the water by plants. If light is completely blocked from bottom dwelling plants, the plants will stop producing oxygen and will die. As the plants are decomposed, bacteria will use up even more oxygen from the water. Low dissolved oxygen can lead to fish kills. High TSS can also cause an increase in surface water temperature, because the suspended particles absorb heat from sunlight. This can cause dissolved oxygen levels to fall even further (because warmer waters can hold less DO), and can harm aquatic life in many other ways, (Mitchell and Stapp, 1992). High TSS can cause problems for industrial use, because the solids may clog or scour pipes and machinery (Murphy, 2007).

According to Mitchell and Stapp (1992), the decrease in water clarity caused by TSS can affect the ability of fish to see and catch food. Suspended sediment can also clog fish gills, reduce growth rates, decrease resistance to disease, and prevent egg and larval development. When suspended solids settle to the bottom of a water body, they can smother the eggs of fish and aquatic insects, as well as suffocate newly hatched insect larvae. Settling sediments can fill in spaces between rocks which could have been used by aquatic organisms for homes.

2.4.13 Temperature Temperature is an easily measured parameter and important because of the influence it has on physico-chemical processes and the distribution of biota. The influence of 61


temperature on solubility of oxygen is an example of a physical influence (APHA Method 2550B, 1992). Key factors that contribute to changes in temperature of a water body are weather, bottom material, and depth. Temperature is a major factor that influences the metabolism and structure of the biological communities in rivers. Stream temperature can be influenced by many factors including: discharge (flow), stream gradient, depth, stream cover, water colour, time of day, season, stream segment, intensity of solar radiation, and human activities. Temperature is inversely related to dissolved oxygen levels. As temperature levels increase the solubility of oxygen decreases and this becomes more important as temperatures rise (WHO, 2004).

Temperature is primarily an aesthetic criterion for drinking water. The turbidity and colour of filtered water may be indirectly affected by temperature, as low water temperatures tend to decrease the efficiency of water treatment processes by, for instance, affecting floc formation rates and sedimentation efficiency. Chemical reaction rates increase with temperature, and this can lead to greater corrosion of pipes and fittings in closed systems. Scale formation in hard waters will also be greater at higher temperatures (AWWA, 1990).

2.4.13.1 Health considerations of temperature The effectiveness of chlorine as a disinfectant is influenced by the temperature of the water being dosed. Generally higher temperatures result in more effective disinfection at a particular chlorine dose, but this may be counterbalanced by a more rapid loss of chlorine to the atmosphere (AWWA, 1990). Temperature can directly affect the growth and survival of microorganisms. 62


In general the survival time of infectious bacteria and parasites is reduced as the temperature of the contaminated water increases. Naegleria fowleri, which can cause amoebic meningitis, grows between 18℃ and 46℃ and is likely to occur in non disinfected water supplies that reach 30℃ seasonally (ADWG, 2004). Legionella pneumophila and related bacteria are found in hot and cold water systems, with colonization occurring in stagnant water at temperatures between 20℃ and 45℃ (WHO, 2004). Increased temperatures can also promote the growth of taste and odourproducing organisms in lakes and impoundments, and in distribution systems.

2.4.14 Arsenic Arsenic is a naturally occurring element which can be introduced into water through the dissolution of minerals and ores (where it exists mainly in the sulphide form), or from industrial effluent, atmospheric deposition (burning of fossil fuels and waste incineration), drainage from old gold mines, or the use of some types of sheep dip. Natural sources can make a significant contribution to the arsenic concentration in drinking water (WHO, 1988). Pentavalent arsenic (As (V)) is generally the most common form in well oxygenated surface waters, but under reducing conditions, such as those found in deep lake sediments or ground waters, the trivalent form (As (III)) predominates. In natural waters the concentration of arsenic is generally less than 0.005 𝑚𝑔/𝑙, although some countries have reported very high concentrations, particularly in groundwater supplies. Food is a significant source of arsenic intake. It is difficult to make direct comparisons between the arsenic intake from food and water because the biological availability differs markedly (ADWG, 2004).

63


2.4.14.1

Health considerations of arsenic

The health considerations apply mainly to the inorganic arsenic compounds, as they are more likely than the organic compounds to be present in drinking water supplies. Although the results of studies indicate that arsenic may be essential for a number of animal species, there is no evidence that it is essential for humans. Soluble arsenic salts are readily absorbed by the gastrointestinal tract. After absorption inorganic arsenic binds to haemoglobin, and is deposited in the liver, kidney, lungs, spleen, and skin. Inorganic arsenic does not appear to cross the blood-brain barrier but can cross the placenta (ADWG, 2004).

Approximately 45-85% of ingested arsenic is excreted in the urine within 1 to 3 days. A number of epidemiological studies have looked at the effects of drinking water with high concentrations of arsenic (greater than 0.3𝑚𝑔/𝑙). Effects attributed to consumption of such water over periods of 5 to 25 years include skin lesions, skin cancer, vascular disease, effects on the nervous system, and possibly cancer of other organs (ADWG, 2004). Arsenic has however been shown to be the cause of cancer in humans through the consumption of drinking-water (WHO, 2004).

2.4.15 Copper Copper is widely distributed in rocks and soils as carbonate and sulphide minerals. Copper is relatively resistant to corrosion and is used in domestic water supply pipes and fittings. It is also used in the electroplating and chemical industries, and in many household goods. Copper sulphate is used extensively to control the growth of algae in water storages. Copper is present in uncontaminated surface waters at very low 64


concentrations, usually less than 0.01𝑚𝑔/𝑙. The concentration can rise substantially when water with a low 𝑝𝐻 and hardness remains in stagnant contact with copper pipes and fittings (WHO, 1998).

Under these conditions, the concentration of copper can reach 5𝑚𝑔/𝑙 or higher. The taste threshold for copper is in the range 1-5𝑚𝑔/𝑙, depending on the water purity. Concentrations above 1 𝑚𝑔/𝑙 may cause blue or green stains on sanitary ware. Such stains may also be due to slowly leaking taps, where copper corrosion occurs over a long time, and are not necessarily due to high concentrations of copper in drinking water. Food is the main source of copper intake. Intake from water would normally be less than 10% of total intake (ADWG, 2004). Copper concentrations in drinking-water vary widely, with the primary source most often being the corrosion of interior copper plumbing. Levels in running or fully flushed water tend to be low, whereas those in standing or partially flushed water samples are more variable and can be substantially higher (frequently > 1mg/litre) (Fitzgerald, 1998). Copper concentrations in treated water often increase during distribution, especially in systems with an acid 𝑝𝐻 or highcarbonate waters with an alkaline 𝑝𝐻.

2.4.15.1

Health considerations of copper

Copper is an essential trace element for humans. High doses of copper (above 50mg/kg bodyweight) can be lethal. The absorption of copper by the gastrointestinal tract is in the range of 25–60%, depending on a number of factors, including copper speciation and copper dietary status (Olivares et al., 1998). Copper is stored in the liver, brain

65


and muscle tissue. High concentrations can also be found in the kidneys, heart and hair. Copper is eliminated from the body mainly in the bile. Many cases of copper poisoning have been reported, including cases involving the poisoning of children who had their food prepared in copper or brass pots (Tanner, 1998).

Copper poisoning has resulted in cirrhosis of the liver and, in extreme cases, death. Other less severe symptoms associated with the consumption of water containing 3-5 mg/L copper (but not 1𝑚𝑔/𝑙) are gastrointestinal symptoms such as nausea, abdominal pain and vomiting (Pizarro et al., 1999). Infants are thought to be most susceptible, though in one study of 3-month-old infants given water containing 2 𝑚𝑔/𝑙 copper over 9 months there were no acute or chronic adverse consequences (Olivares et al., 1998). In the genetic disorders Wilson’s disease and idiopathic copper toxicosis, sufferers are particularly susceptible to copper (Lönnerdal and Uauy, 1998).

2.4.16 Cyanide Cyanide can be present in drinking water through the contamination of source water, or through the natural decomposition of some plants that synthesise cyanoglycosides. Some microorganisms, such as the cyanobacterium, Anacystis nidulans and the bacterium Chromobacterium violaceum, produce free cyanide. In uncontaminated water sources, free cyanide concentrations are usually less than 0.01𝑚𝑔/𝑙. Sodium cyanide is used in the extraction of gold and silver from low-grade ores. It is also used in the electroplating, steel and chemical industries. Some foods can contain quite high concentrations of cyanide. Green almonds and improperly treated cassava are of particular concern (Jackson et al., 1986). 66


2.4.16.1

Health considerations of cyanide

Cyanide is highly toxic. It is rapidly absorbed by the gastrointestinal tract and metabolised to thiocyanate. In humans, long-term consumption of improperly prepared cassava in the tropics has been linked with effects on the thyroid gland and particularly the nervous system. Cyanide may deplete vitamin B12 and result in a deficiency that can cause goitre and cretinism. People most at risk are those with a nutritionally inadequate diet (IARC, 1990).

Animal studies indicate that pigs may be more sensitive than rats to the effects of longterm exposure to cyanide. In a six-month study using pigs, exposure to cyanide was reported to increase ambivalence and result in slower response times to stimuli. Behaviour demanding high energy appeared to be more readily affected by cyanide exposure than low-energy behaviour. No data are available on the carcinogenic properties of cyanide. Tests for mutagenicity with different strains of bacteria have been mostly negative (Jackson et al., 1986).

2.4.17 Iron Iron is one of the most abundant metals in the Earth’s crust. Iron occurs commonly in soil and rocks as the oxide, sulphide and carbonate minerals. It is found in natural fresh waters at levels ranging from 0.5 to 50mg/litre. Iron may also be present in drinking-water as a result of the use of iron coagulants or the corrosion of steel and cast iron pipes during water distribution. Iron is an essential element in human nutrition (WHO, 2004). In water, it is present in oxidised forms as ferric (Fe (III)) or ferrous (Fe (II)) compounds. Iron has many domestic and industrial applications, 67


ranging from iron and steel products and pigments in paints to food colours and preparations for preventing iron deficiency in humans. Iron sulphate (hydroxylated ferrous sulphate) is used as a flocculant in water treatment. In aerated surface waters, iron is often complexed with organic matter such as humic material, or adsorbed onto suspended matter. Iron concentrations in uncontaminated surface waters are usually less than 1𝑚𝑔/𝑙. Iron has a taste threshold of about 0.3𝑚𝑔/𝑙 in water and becomes objectionable above 3𝑚𝑔/𝑙 (ADWG, 2004). High iron concentrations give water an undesirable rust-brown appearance and can cause staining of laundry and plumbing fittings, fouling of ion-exchange softeners, and blockages in irrigation systems.

2.4.17.1

Health considerations of iron

Iron deficiency is common and affects people throughout the world. The amount of iron absorbed from food by the gastrointestinal tract varies from 1% to 20%, according to individual requirements and the source of iron. It is used in the production of haemoglobin, myoglobin and a number of enzymes, and is stored in the spleen, liver, bone marrow and muscle (AWDG, 2004). The taste and appearance of drinking-water will usually be affected below 2mg/litre (WHO, 2004).

Numerous cases of iron poisoning have been reported, mainly among young children who ingest medicinal iron supplements formulated for adults. Physiological regulation of iron absorption confers a high degree of protection against iron toxicity and there are a number of reports of people, particularly adults, taking high doses of iron with no adverse effects. Studies with animals over long periods have reported only very mild adverse effects associated with a high iron intake. There is no evidence that iron 68


induces cancer in laboratory animals. Most iron salts have been inactive in tests for mutagenicity and do not induce chromosome aberrations in human cells. No guideline value for iron in drinking-water is proposed (WHO, 2004).

2.4.18 Lead Lead can be present in drinking water as a result of dissolution from natural sources, or from household plumbing systems containing lead. These may include lead in pipes, or in solder used to seal joints. The amount of lead dissolved will depend on a number of factors including 𝑝𝐻, water hardness and the standing time of the water (ADWG, 2004). Lead is the most common of the heavy metals and is mined widely throughout the world. It is used in the production of lead acid batteries, solder, alloys, cable sheathing, paint pigments, rust inhibitors, ammunition, glazes and plastic stabilisers. The organo-lead compounds tetramethyl and tetraethyl lead are used extensively as anti-knock and lubricating compounds in gasoline (NHMRC, 1993).

Drinking water concentrations of lead reported overseas are usually less than 0.002𝑚𝑔/𝑙, but concentrations of 0.1𝑚𝑔/𝑙 have been reported in Scotland where lead pipes and soft, acidic water are contributing factors. Approximately 80% of the daily intake of lead is from the ingestion of food, dirt and dust. Food contains small but significant quantities of lead, which can increase when acidic food is stored in leadglazed ceramic pottery or lead-soldered cans (WHO, 2004). The use of lead-free solders is becoming more widespread in the food processing industry.

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2.4.18.1 Health considerations of lead Lead can be absorbed by the body through inhalation, ingestion or placental transfer. In adults, approximately 10% of ingested lead is absorbed but in children this figure can be 4 to 5 times higher. After absorption, the lead is distributed in soft tissue such as the kidney, liver, and bone marrow where it has a biological half-life in adults of less than 40 days, and in skeletal bone where it can persist for 20 to 30 years (APHA Method 3500-Pb Part B, 1992).

In humans, lead is a cumulative poison that can severely affect the central nervous system. Infants, foetuses and pregnant women are most susceptible. Many epidemiological studies have been carried out on the effects of lead exposure on the intellectual development of children (Ryu et al., 1983). Although there are some conflicting results, on balance the studies demonstrate that exposure to lead can adversely affect intelligence. These results are supported by experiments using young primates, where exposure to lead causes significant behavioural and learning difficulties of the same type as those observed in children. Other adverse effects associated with exposure to high amounts of lead include kidney damage, interference with the production of red blood cells, and interference with the metabolism of calcium needed for bone formation (NHMRC, 1993). Lead salts given orally to rats have increased the carcinogenic activity of known carcinogens. Tests for mutagenicity using strains of bacteria have largely been negative. Tests using mammalian cells have been inconclusive, with some studies reporting negative results and some reporting chromosome damage.

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2.4.19 Zinc Zinc is widely distributed and occurs in small amounts in almost all rocks, commonly as the sulphide. It is used as a coating to prevent corrosion of iron and steel products and in the manufacture of brass. Zinc oxide is an important component in the manufacture of paint and rubber products, including tyres. In surface and ground waters, the concentration of zinc from natural leaching is usually less than 0.01𝑚𝑔/𝑙. Tap water can contain much higher concentrations as a result of corrosion of zinccoated pipes and fittings. Taste problems can occur if the zinc concentration in drinking water exceeds 3 𝑚𝑔/𝑙. Water with a zinc concentration above 5𝑚𝑔/𝑙 tends to be opalescent, develops a greasy film when boiled, and has an undesirable dry ‘metallic’ taste (ADWG, 2004).

Zinc is present in plant and animal tissues, and food is the major source of zinc intake. Drinking water usually makes a negligible contribution to total intake. Zinc is an essential trace element found in virtually all food and potable water in the form of salts or organic complexes. The diet is normally the principal source of zinc. Although levels of zinc in surface water and groundwater normally do not exceed 0.01 and 0.05 mg/litre, respectively, concentrations in tap water can be much higher as a result of dissolution of zinc from pipes (WHO, 2004).

2.4.19.1

Health considerations of zinc

Nutritional zinc deficiency results in retarded growth, anorexia, mental lethargy, skin changes and night blindness. Approximately 20-30% of dietary zinc is absorbed by the gastrointestinal tract. Highest concentrations are found in the liver, kidney, bone, and 71


retina, prostate and muscle (ADWG, 2004). In humans, consumption of very high amounts of zinc can result in nausea, vomiting, diarrhoea and abdominal cramps. The major effects of long-term exposure to zinc are copper deficiency, anaemia and gastric erosion. In animal studies, zinc has been reported to reduce the toxic effects of nickel and cadmium. High doses over long periods may, however, be toxic to nerve cells of mammals. There is no evidence that occupational exposure to zinc increases the risk of cancer. Zinc has been shown to induce chromosomal aberrations in mammalian cells, but is inactive in bacterial mutation tests (APHA Method 3500-Zn Part C, 1992).

2.5

Biological Parameters

The most common and deadly pollutants in the drinking water in developing countries are of biological origin. WHO states that the “infectious diseases caused by pathogenic bacteria, viruses and protozoa or by parasites are the most common and widespread health risk associated with drinking water”(Gadgil, 1998).

2.5.1

Total coliforms

Coliforms are a diverse group of bacteria including Escherichia coli and other thermotolerant coliforms. Faecal material contains large coliforms numbers of coliform bacteria but there are many species that occur naturally in the environment. Coliforms are gram negative nonsporing rod-shaped bacteria, capable of aerobic and facultative anaerobic growth in the presence of bile salts or other surface-active agents with similar growth-inhibiting properties (Adcock and Saint, 1997). They are able to ferment lactose with the production of acid within 48 hours at 35–37℃. Fermentation

72


by these organisms begins with the cleavage of lactose into galactose and glucose by the enzyme ß-galactosidase. Coliforms are oxidase-negative (Ashbolt et al., 2001).

Total coliform bacteria (excluding E. coli) occur in both sewage and natural waters. Some of these bacteria are excreted in the faeces of humans and animals, but many coliforms are heterotrophic and able to multiply in water and soil environments. Total coliforms can also survive and grow in water distribution systems, particularly in the presence of biofilms. The presence of total coliforms in distribution systems and stored water supplies can reveal regrowth and possible biofilm formation or contamination through ingress of foreign material, including soil or plants (George et al., 2001). As a disinfection indicator, the test for total coliforms is far slower and less reliable than direct measurement of disinfectant residual. In addition, total coliforms are far more sensitive to disinfection than are enteric viruses and protozoa (Grabow, 1996).

2.5.2

Faecal coliforms

Faecal coliform bacteria are microscopic organisms that live in the intestines of all warm blooded animals, and in animal wastes or faeces eliminated from the intestinal tract. Faecal coliform bacteria may indicate the presence of disease carrying organisms which live in the same environment as the faecal coliform bacteria (Bordin, 1997). The faecal coliform group includes all of the rod-shaped bacteria that are non-spore forming, GramNegative, lactose-fermenting in 24 hours at 44.5C, and which can grow with or without oxygen. Faecal coliform is a type of faecal bacteria. Another type of faecal bacteria is Streptococcus faecalis. Streptococcus faecalis is a group of bacteria normally present in

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large numbers in the intestinal tracts of warm-blooded animals other than humans (Murphy, 2007).

Swimming in waters with high levels of faecal coliform bacteria increases the chance of falling ill (fever, nausea and stomach cramps) as a result of pathogens entering the body through the mouth, nose, ears, or cuts in the skin. Diseases and illnesses that can be contracted in water with high faecal coliform counts include typhoid fever, hepatitis, gastroenteritis, dysentery and ear infections. Faecal coliform, like other bacteria, can usually be killed by boiling water or by treating it with chlorine. Washing thoroughly with soap after contact with contaminated water can also help prevent infections (Murphy, 2007).

2.5.3

Escherichia coli

Escherichia coli is considered the most suitable index of faecal contamination. In most circumstances, populations of thermotolerant coliforms are composed predominantly of E. coli; as a result, this group is regarded as a less reliable but acceptable index of faecal pollution. Escherichia coli (or, alternatively, thermotolerant coliforms) is the first organism of choice in monitoring programmes for verification, including surveillance of drinking-water quality. These organisms are also used as disinfection indicators, but testing is far slower and less reliable than direct measurement of disinfectant residual (ADWG, 2004). Escherichia coli occur in high numbers in human and animal faeces, sewage and water subject to recent faecal pollution. Water temperatures and nutrient conditions present in drinking-water distribution systems are highly unlikely to support the growth of these organisms (Ashbolt et al., 2001). 74


The presence of E. coli should lead to consideration of further action which could include further sampling and investigation of potential sources such as inadequate treatment in distribution system integrity (Sueiro et al., 2001).

75


CHAPTER 3: MATERIALS AND METHODS 3.1 Analytical Framework This study is considered as a social, natural and geographical information science research and falls under the scope of qualitative and quantitative paradigm. In order to generate diversity in its design and practice, it was imperative to consider the analytical framework within which the study is placed. This section consequently addresses the analytical framework within which the study is oriented.

3.1.1 Research approach The methodological question concerning every kind of research is best answered with cautiously taking into consideration the theoretical concepts 6 of the research; the objectives, hypothesis and research questions under investigation (Denzin and Lincoln, 2000). A review of methodologies adopted for previous related studies revealed that the most contemporary and regular strategies adopted by most researchers in social science, natural science research and Geographic information science were survey methods, case studies, interviews (Müller and Zeller, 2002; Munroe et al., 2002), water quality analysis and land use change detection (Andersson, 2006; Mehaffey et al., 2005; Tong and Chen, 2002). The choice of research methodology must not be influenced by more popular adopted scientific strategies, but rather careful consideration should be given to the relevance and usefulness of the research and the researcher must select the most befitted strategy to accomplish his purpose (Bryman, 1992).

6

No single method is always necessarily superior; it all depends on what questions we are exploring (Cresswell, 2003).

76


The nature of this research is entirely qualitatively and quantitatively oriented; the data collection method involved integrating three strategies; survey questionnaires, land use change detection and water quality analysis. Both qualitative and quantitative methods have their own strengths and weaknesses and the logical intersection of their respective strengths and weaknesses often justifies the rationale for integrating them, thereby achieving the best possible output (Bryman, 1992).

3.2 Study Area The study was conducted in the Barekese Basin located approximately 26km north of Kumasi. Kumasi is located 272 kilometers Northwest of Accra. Kumasi is the second largest city in Ghana. The metropolitan area covers an area of 245 square kilometers as a result of the expansion from the previous area of 150 square kilometers. Kumasi has been the cross roads between the northern and the southern sectors of the country, since its establishment as the heart of the Ashanti Empire around the turn of the eighteenth century (Salifu and Mumuni, 1998). Generally, the Metropolitan area is located at more or less the central part of the Ashanti region. It lies within latitudes 6o38’ north and 6o45’ north and longitudes 1o41’05’ west and 1o32’ west. It is bounded on the north by the Kwabre district and on the south by BosomtweKwanwoma district. On the West and the East, Ejisu-Juaben district and the Atwima district bound Kumasi Metropolitan Assembly (KMA), respectively. In relation to its fast physical and demographic growth as well as to the expansion of its role within the region, Kumasi is increasingly being considered as an entity extending beyond the administrative boundaries of the KMA to incorporate also the four neighbouring districts afore-mentioned (Suraj, 2004). 77


The Barekese reservoir lies on latitude 060 44’N and longitude 010 42’W. The overall length is 6.096 meters (13km) above sea level and has a maximum width of 91.74m (1.25km). It is a composite dam with a concrete gravity spillway and with earth fill flank embankment. It was formed by building an earth and concrete dam of 548.78m long transversely across the river Offin that takes it source from the Mampong ridge. The Barekese Reservoir was constructed in 1965 and became operational in 1970. Its full storage level is 224 meters above datum and it has a production capacity of 82000m3 per day, which is equivalent to 18million gallons per day. The treatment plant is designed to provide 218,000m 3 per day that is 48 million gallons per day (Blokhuis et al., 2005).

The reservoir has a surface area of approximately 6.4km 2 with average depth of 33m and a catchment of 564.8sqkm. The river Offin is the major inflowing and out flowing river of the reservoir though it comprises of undulating catchments of small tributaries of the Offin River which centre on the reservoir formed by a dam constructed across the river, which roughly bisects the catchment. The area extends from the Kumasi Barekese road to Kumasi–Offinso village to the east. The area is approximately 90km2 and is situated on a dissected plateau southwards (Fig 2) (Kumasi et al., 2010).

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Figure 2. A map of Ghana showing the Barekese catchment

The Barekese Head works which solely water from the Barekese Reservoir supplies over 80% of the daily water requirement of the Kumasi metropolitan area. The remaining is augmented by the Owabi Reservoir (GWCL, 2005). The reservoir and water works is therefore of immense importance to the populace of Kumasi and its environs in terms of water, sanitation and health needs, hence its selection.

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3.2.1 Climate Kumasi lies within the moist semi-humid climatic zone of the country. It experiences two rainfall maxima annually with an annual mean rainfall of about 1300mm (Meteorological Services Department Kumasi Airport Weather Station, 2000). The first rainy season is from mid-March to early July, and the second season begins from late August to October. The period between November to early March is much drier throughout the year. February is the warmest month of the year over the Ashanti Region.

Average monthly rainfall distribution for Kumasi from 1985 to 1998 showed that the mean annual temperature is about 28℃ with average monthly temperatures varying from 24℃ to 33℃. Humidity varied from about 50 percent in the dry season to about 76 percent at the end of the main wet season (Suraj, 2004). The vegetation of Kumasi has been characterized under the moist semi-deciduous forest zone of the country affirming the fact that it occurs within the wet semi-equatorial climatic region (Dickson and Benneh, 1998). Suraj (2004) noted that presently, due to rapid increases in population and the consequent land use change, very little of the original forest remains.

3.2.2 Geology The Kumasi Metropolis is characterized by two main geological formations. The first belongs to the lower Birimian System of metamorphosed sediments and is of PreCambrian origin. The second is a slightly later series of acid intrusive rocks. The latter consists of variably textural granitic rocks, which may be cut by pegmatites; 80


whilst the former is made up predominantly of phyllitic schists, phyllites and metagreywackes (Gogo, 1990).

Gogo (1990) established that the granites, which may be muscovite-rich or biotite-rich occur in large batholiths and as small masses that have usually intruded the lower Birimian sediments. The biotite-rich muscovite granites of Kumasi are foliated though not markedly in places. However, due to the variations in intensity of metamorphism in these granitic rocks, their texture and composition range from those of typical granites to granitic gneiss.

3.2.3 Topography The general topography of the Kumasi metropolitan area is undulating with gentle slopes, commonly of 5 o to 15o. Kumasi itself lies on top of a local watershed at approximately 282 m high (Nsiah-Gyabaah, 2000b), but altitudes in the peri-urban interface around Kumasi vary from 370 to 300 meters. The granitic areas are slightly hilly and the interfluve ridges are flat topped with varying widths. The landform is an advanced dissection of a tertiary erosion surfaces (Holland et al., 1996).

Soils According to Holland et al. (1996) the soils in the Kumasi metropolis belong to the Forest Ochrosol great group, though formerly high in organic matter, intensive agriculture has led to many areas now being low in organic matter. Observations made by Holland et al. (1996) with regards to the soil’s characteristics are as follows:

81


That the macro-nutrients are very low and micro-nutrients are deficient in some areas. Clay minerals are predominantly kaolin so Cation Exchange Capacity (CEC) is very low. Before intensive over-cultivation erosion, soil physical properties are favourable to crop growth but erosion is severe in some areas and physical properties are now poor. Structure of the soil becomes weak when organic matter is reduced. Seasonal water loggings occur in many valley bottoms with the soil becoming hard and structure less when dry. Erosion of topsoil is evident in some areas and can have a large effect on soil fertility.

A survey made by CEDAR (1999) in some part of the peri-urban areas of Kumasi reveal that soils in the areas are developed on granite or phyllites. The soils on the granites are quite acidic but those on the phyllites are less acidic. The dominant textures are sandy loams. Soil classes are: Ferric Acrisols (most common), Haplic Acrisols, Eutric Gleysols, Gleyic Arenosols and Gleyic Cambisols. Nitrogen and organic matter content tend to be moderate to high, at least when they are newly cultivated after fallow. However the soils are often seriously deficient in phosphorus and nitrogen. Soils in Kumasi belong to the Bekwai, Nzema, Kokofu and Oda series, which are poorly drained, and are usually found in the low-lying areas or valley bottom. These soils are clayey or silt loams and are described as Forest Oxysols because of their ‘sharp’ or acidic nature (Adu, 1992). 82


3.2.4 Geohydrology and drainage The Metropolis is located within the Pra basin (Dickson and Benneh, 1998). It is drained by a relatively dense network of streams whose natural drainage runs generally from north to south, and some of which include the Daban, Subin, Aboabo, Wiwi and Santang streams, exhibiting some dendritic patterns and stemming out of the Sisa, Oda, Sokoban and the Owabi rivers, whose valleys are flat-bottomed (Dickson and Benneh, 1998; Suraj, 2004). These converge into the Sisa, which flows into the Oda approximately 9 kilometers south of Kumasi. A small portion of the North West to the city, where a vehicle repair area in Kumasi is located, drains to the northwest into the catchment of the Owabi dam and thence into the Offin River (Cornish et al., 1999).

The Birimian rocks are generally strongly foliated and jointed, and where they outcrop or lie near the surface, considerable water may percolate through the joints, fractures or other partings. The granitic rocks associated with the Birimian rocks are not inherently permeable but have secondary permeability. The porosity developed as a result of jointing, fracturing and weathering contribute to the relatively average higher yields of groundwater found in wells within the Kumasi granitic batholiths (Kesse, 1985; Suraj, 2004).

3.3 Study Communities The Barekese reservoir is bordered by thirteen communities namely: Tonto Kokoben, Akuroforom, Patase, Nkwanta Penten, Nkwantakese, Pampatia, Esaase, Denasi, Ahenkro, Offinso, Ayensua Fufuo, Ayensua Kokoo and Barekese. 83


Figure 3: Barekese catchment and surrounding communities

The inhabitants of Asumenya and Amisare were relocated to Tonto Kokoben during the construction of the Barekese dam in 1969 and do not exist as Asumenya and Amisare. In all the thirteen communities Nkwanta Penten, Nkwantakese, Pampatia, Esaase, Denasi, Ayensua Fufuo and Ayensua Kokoo lost their lands during the

84


construction of the dam. Seven communities which are all located within the catchment were systematically sampled for the purpose of this research (Fig. 3).

3.3.1 Ayensua Fufuo Ayensua Fufuo is in the Offinso District, with a population of about 455 of which the males and females are 210 and 245, respectively. This community comprises of 43 houses and 74 households with an average household size of 6.1 (Ghana Statistical Services, 2002). It is characterized by high fertility rate, lack of electricity, Kumasi Ventilated Improved Pit (KVIP) facilities and pipe-borne water supply is not regular. The inhabitants often resort to the use of Nsuta and Ntuma streams. The Nsuta runs into the Ntuma stream which runs into the Offin River and finally ends up in the Barekese Reservoir. The Nsuta stream dries up in the harmattan. Bushfires are widespread in this village as it is used for hunting. The inhabitants are mostly farmers and most of them farm very close to the Offin River. It is the second village very close to the Offin River and the Barekese reserve.

3.3.2 Ayensua Kokoo Ayensua Kokoo has a population of about 346 of which 177 are males and 169 are females. There are a total of 34 houses, 60 households and an average household size of 5.1 (Ghana Statistical Service, 2002). Ayensua Kokoo is located in the Offinso District and it is characterized by pervasive bushfires, lack of KVIP facilities, widespread chainsaw operations and the encroachment of the Barekese reserve. The lack of electricity, high fertility rates and high levels of poverty has also compounded the problems of the villagers. 85


The community has pipe-borne water supply that is not regular compelling them to resort to the Abetesua and Amoadan streams. The Abetesua runs into the Amoadan which runs into the Offin River and finally ends up in the reservoir.

3.3.3 Esaase Esaase is in the Afiyga Sekyere District with a population of about 512 of which the males are 260 and the females 252. Esaase has the lowest average household size of 3.8 compared to the communities sampled with 81 houses and 135 households (Ghana Statistical Service, 2002). The inhabitants are mostly farmers. The dwellers have no access to pipe-borne water and depend solely on well/dugouts and the streams (Nwabi and Buokese). The dwellers have been provided with a KVIP facility, which is out of use and they resort to free range. The inhabitants in this village lost their lands in 1969 when the Dam was constructed and have since not been compensated.

3.3.4 Denasi Denasi is in the Afiyga Sekyere District with a population of about 1162 of which 542 are males and 620 females. A total of 113 and 236 were houses and households with an average household size being 4.9 (Ghana Statistical Service, 2002). The village is alienated by the highway that leads to the northern part of Ghana resulting in it being vibrant in trading. The village is characterized by a good road network, pipe-borne water, electricity and a KVIP facility. The inhabitants are primarily traders and a few farmers. The KVIP is situated on one side of the main road, as a result of its location it is not patronized by those on the other side of the road and they resort to indiscriminate open (free range) defecation. The community in this village lost their 86


lands during the construction of the dam. Some villagers from neighbouring villages who also lost their lands migrated to Denasi as a result.

3.3.5 Nkwantakese Nkwantakese is the largest of all the communities sampled and is within the Afiyga Sekyere District with a populace size of about 1455 of which 708 are males and 747 females. This community had the highest average household size of 6.7 with 152 houses and 217 households (Ghana Statistical Service, 2002). Habitat Ghana has put up over 70 houses to ensure affordable housing for the inhabitants who are predominantly farmers. This village is characterized by a deplorable road network, lack of potable drinking water, high literacy rate and lack of KVIP facilities. The only KVIP in the village is out of use and the people resort to open (free range) defecation. Though they have pipe-borne water it is not regular and they are forced to resort to the Amansie stream. The two bore-holes are also out of use. The inhabitants in this village lost their lands in 1969 when the Barekese Dam was constructed but only a few were compensated.

3.3.6 Pampatia Pampatia is the smallest of all the communities sampled for questionnaire administration and is in the Afiyga Sekyere district. According to the Ghana Statistical Service (2002) Pampatia has a population of 80 of which 30 are males and 50 females. It comprise of 14 houses, 18 households and an average household size of 4.4. It is characterized by bad roads network, high fertility rates and high povert y levels. The Habitat Ghana constructed about 20 affordable self contained 87


accommodation for some of the populace. Those in the Habitat Ghana self contained have household toilets and the rest have access to KVIP. A bore-hole constructed for the populace is obsolete and as a result they resort to the Nwabi stream. The Nwabi runs into the Offin River which finally ends up in the Reservoir. The inhabitants are essentially farmers and lost their lands during the construction of the dam and have since not been compensated.

3.3.7 Penten Penten is in the Afiyga Sekyere District and with a population of about 613 of which 294 are males and 319 females. There are about 73 houses, 158 households and an average household size of 3.9 (Ghana Statistical Service, 2002). The inhabitants are generally farmers with no access to pipe-borne water supply and KVIP facility. The villagers resort to indiscriminate open (free range) defecation. The farmers are mostly cocoa and vegetable farmers. They rely on well/ dugouts, the Akyekasu and Bohyen streams. The village is characterized by high fertility rates, poverty and deplorable roads. The populace lost their lands during the construction of the dam in 1969 and have since not been compensated.

3.4 Research Design Reconnaissance survey was carried out in all the communities in the Barekese catchment. Questionnaires were pre-tested and re-designed again. The adult population was estimated for each community with the help of the Ghana Population Census Survey 2000 document and from the District Assemblies.

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The sample size of each community was then calculated using the mathematical formula as described by Ghazizadeh (2005) and Israel (2003). 𝒏 =

𝒁𝟐 𝒑𝒒 𝒅𝟐

Where 𝒒 = (𝟏 − 𝒑) 𝒏 = Total number of populace administered questionnaire (370) 𝒑 = Population proportion 𝒁 = Reliability coefficient associated with the level of significance (95%). 𝒅 =The level of precision to be achieved (0.5%) A total of 370 questionnaires were administered in the seven communities. A sample of the questionnaire administered can be seen in Appendix (1).

3.4.1 Ethical consideration Prior to the pre-testing of the questionnaire, opinion leaders including the Chiefs, Assemblymen and Unit Committee members were consulted to explain the relevance of the research work in their various communities. They were also given the opportunity to ask questions. The research team was formally introduced to the communities and this made the administration of the questionnaire uncomplicated as they were neither apprehensive nor aggressive towards the interviewer.

3.4.2 Data collection techniques Informal, formal surveys and observations were employed in collecting data. In this research, a multiple source of information was used to address the research goals. However, this approach was time consuming and relatively expensive compared to 89


single source of data. Notwithstanding, the added benefits (such as the validity of the data gathered, etc) associated with multiple sources was enough motivation. The desk survey (literature review) forms an essential aspect of the research since it sets the pace for the development of field survey instruments. Secondary data was identified and collected in books, articles, journals, and internet and from databases.

3.4.3 Field survey The field survey involved the collection of empirical data. A multiple approach of data gathering was adopted for the purpose of this research which included; questionnaires and interviews. The obvious reason for adopting the multiple approaches is the possible combination and integration of strengths and weaknesses in each method.

3.4.4 Questionnaire development The questionnaires were designed to address the study concerns. Once the survey questionnaires were drafted, they were pre-tested on a small number of respondents having characteristics similar to those of the target group of respondents. This helped to re-design the questionnaires, making it more consistent and focused on strategic issues. The format of the questionnaires was guided by considerations of appeal to respondents, ease of comprehending and coding. The questionnaires were personally administered by the researcher and a trained research assistant. This approach was chosen because it was suitable for the exploratory stages of the research and the main advantage of this approach was the fact that the researcher could adapt to the questions easily. The researcher could also pick up non-verbal cues from the 90


respondents. The main disadvantages of the face-to face administration are inherent in the geographical limitations that may impose on the surveys and the vast resources needed if such surveys are to be carried out nationally or internationally; making it more expensive and time consuming (Frazer and Lawley, 2000). With this in mind, the questionnaires were in simple language, null and void of technical terms to minimize potential errors from respondents (Mangiome, 1995).

3.4.5 Informal surveys and observations Informal village appraisals were conducted in seven towns and villages of the catchment. Participatory Rural Appraisals (PRAs) tools were used to collect information on the local perceptions on land use, linkage to water quality and ways of improvement (Malley et al., 2007). The PRA tools used were: focused group discussions, problem analysis chart, flow diagramming, trend analysis and participant observations.

3.4.6 Formal survey In December 2005, a survey was conducted in seven communities. The following towns and villages in the Barekese catchment were selected for the questionnaire administration: Ayensua Fufuo, Ayensua Kokoo, Denasi, Esaase, Pampatia, Penten and Nkwantakese. These towns and villages were systematically selected to comprise those located in the upper, mid and lower part of the catchment. The questionnaire contained both closed and open-ended questions framed to satisfy the objectives of the study. The survey further explored reported PRAs on the land use, water quality

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and respondent’s perceptions of the impacts of anthropogenic activities on water resources.

3.4.7 Data validation According to Bell (1996); Sarantakos (2005) validity is the property of a research survey instrument that measures the quality of the research in terms of its relevance, precision and accuracy. According to these authors, it is a measure of the quality of the process of measurement and reflects the essential value of the study. Validity requires that a measure is precise. Precision implies accuracy (Sarantakos, 2005), but in addition it requires that measurements employ the smallest possible measure (Nachimias and Nachimias, 1996). Sarantakos (2005) reported that, reliability of survey instrument refers to the capacity of measurement to produce consistent results. Thus, a method is reliable if it produces the same results whenever it is repeated, and is not sensitive to the researcher, research conditions or the respondents. The study analysis compared results from different data gathering methods to examine whether similar decisions in different situations or different decisions in similar situations led to different consequences.

3.5 Selection of Sampling Sites A drainage map of the Barekese catchment was used to map out the thirteen7

7

The thirteen sampling locations became ten because Reservoir water 1 and 2, River Offin 1 and 2 and Nwabi 1 and 2 were merged for the purpose of analysis.

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sampling points selected. The coordinates of these points were estimated from the map and located on the ground using a GPS (Global Positioning System – GARMIN eTrex Vista) (Table 1). The thirteen sampling locations were at the Barekese reservoir as Reservoir Water 1, Reservoir Water 2, River Offin 1, River Offin 2, Abetesua, Amoadan, Nsuta, Ntuma, Amansie, Nwabi Stream 1, Nwabi Stream 2, Buokese and Akyekasu Stream. The River Offin and the Nwabi Stream were divided into two as River Offin 1, River Offin 2, Nwabi Stream1 and Nwabi stream 2 (Fig 4).

Table 1 Location of sampling sites in the study area Sampling Name of Name of stream Longitude

Latitude

location

village

/ Reservoir

S1R1

Barekese

Reservoir 1

-1.720131025

6.836796258

S1R2

Barekese

Reservoir 2

-1.712818201

6.841684226

S7O1

Offinso

River Offin 1

-1.700729031

6.852745852

S8O2

Offinso

River Offin 2

-1.649506316

6.895920448

S5K1

Ayensua Kokoo

Abetesua stream

-1.658451949

6.895789527

S6K2

Ayensua Kokoo

Amoadan stream

-1.661999999

6.907999998

S3F1

Ayensua Fufuo

Nsuta stream

-1.676307064

6.900964923

S4F2

Ayensua Fufuo

Ntuma stream

-1.678247838

6.899671069

S9N1

Nkwantakese

Amansie stream

-1.652823666

6.868129846

S10P1

Pampatia

Nwabi stream 1

-1.671811389

6.851587698

S11E1

Esaase

Buokese stream

-1.668116171

6.847116169

S12E2

Esaase

Nwabi stream 2

-1.660388154

6.835447382

S13A1

Penten

Akyekasu stream

-1.679353075

6.835646925

93


Figure 4. Sampling sites numbered 1-13 in the Barekese catchment

3.6 Change Detection Geographic Information System (GIS) technique was employed to establish land use8 change in the Barekese catchment. GIS provide a method of combining different layers of data that are relevant to the same spatial area. They allow not only visualisation and intuitive comprehension of different datasets but also statistical analysis of the interactions taking place (Aitkenhead and Aalders, 2009; Tong and Chen, 2002).

8

Land use and land cover is used interchangeably here to refer to general classes of land cover associated with specific land uses.

94


Spatial data analysis has been found to give valuable insights into processes of land use change and their underlying causes (Müller and Zeller, 2002). The data used to estimate land use changes were extracted from two LANDSAT Multi-Spectral Scanner (MSS) and one LANDSAT Thematic Mapper (TM) imagery obtained in 1973, 1986 and 2003. All the three imagery were registered to the Universal Transverse Mercator (UTM), Zone 31 geographic projection. The ERDAS Imagine 9.1 and ArcGIS 9.1 softwares (Tong and Chen, 2002) were employed in the change detection (Dai et al., 2005). Satellite scene used was 194055 on Satellite path 194 and row 055.The Landsat Thermatic Mapper satellite imagery was pre-processed (Andersson, 2006) to convert the image to reflectance for the correction of sun angle and seasonal differences. The Satellite image was first geometrically corrected to orient the pixels to the real world coordinates (Pouncey et al., 1999).

TM Ghanafeet projection was used because the base vector data (roads, forest, rivers, towns etc) is in TM Ghanafeet projection and also to ease the computation of the area. Erdas Imagine (a remote Sensing Software) was used to classify the image into classes. Using ERDAS imagine the data was stacked by date within the same path and principal components, a process that prepares the data for classification by reducing noise and redundancy. An unsupervised classification method was ran on the imagery, the field data used to identify spectral categories and finally verified on the field as described by Mehaffey et al. (2005) and Andersson (2006). After 3x3 filter passing to remove noise to define real boundaries for area calculation (Leica Geosystems, 2005). The three different images were analysed using sigmaPlot 2001 to determine the change over the thirty-three (33) year period. 95


3.7 Projecting Land Cover Change in the Barekese Catchment The area of the land cover in the satellite imagery was used to produce a 40-year forecast at an interval of 10 years using year 2003 as the base. Forecasting is fundamental in understanding what needs to be done to avoid environmental disasters and to promote sustainable development. In this regard, forecasting plays an important role in early warning. Forecasting has also been found to be an integral part in protecting and managing terrestrial, coastal, and marine ecosystems; understanding, assessing, and mitigating climate change impacts; identifying options for sustainable agriculture and reversing and combating land degradation and desertification; promoting human well being; protecting water resources, and understanding, monitoring, and preserving biological diversity (USGEO, 2004).

The forecasting was used to calculate and predict a future value by using quadratic linear regression from existing area values of the different classes of land cover in the Barekese catchment. The forecasting was done based on the following assumptions: The population of the communities increase at an annual growth rate of 3.4 per cent. There are no changes in interventions such as :  Social intervention e.g. technology, policy  Economic intervention e.g. Resource use, economic activities  Physical intervention e.g. climate, soil, water, vegetation  Natural disasters e.g. earthquakes, hurricanes wildfires

96


3.8

Water Sampling

Monthly water samples were collected in triplicate from January 2006 to December, 2007. At each site, the weather conditions and human activities occurring at the site were recorded. Sterilized 500ml Duran Schott bottles were used in collecting the water samples and transported to the laboratory in a cool box. For each of the thirteen sampling locations three replicate samples were analysed. To meet the objectives set out, nine feeder streams of the Barekese Reservoir including the Offin River were sampled. Reservoir water at the Barekese reservoir was also sampled. These feeder streams were monitored from the various communities in which they are located and just before they enter the Offin river and subsequently the Reservoir (Fig. 4).

3.8.1

Alkalinity

A sample of 100 mL was measured and put into a 250 mL beaker. A bar magnet was then inserted and the sample placed onto a stir plate. The initial pH of the sample is recorded. If the pH is above 8.3 several drops of phenolphthalein indicator was added. The sample was titrated with 0.02 N H2SO4 or HCl until the pH endpoint (colour change) was reached. This is the phenolphthalein alkalinity. The total volume of acid needed to reach the endpoint was recorded. The total alkalinity of the sample was calculated using: 𝑨𝒍𝒌𝒂𝒍𝒊𝒏𝒊𝒕𝒚 ( Where;

𝒎𝒈 𝑪𝒂𝑪𝑶𝟑 𝟓𝟎, 𝟎𝟎𝟎 )=𝑨𝒙𝑵𝒙 𝑳 𝒎𝑳 𝑺𝒂𝒎𝒑𝒍𝒆

A

= is the total volume in mL of the standard acid used

N

= is the normality of the standard acid used

97


50,000 = is a conversion factor to change the normality into units of mg CaCO3 /L (APHA, 1995).

3.8.2


The 5-day BOD test was used. This method consists of filling an airtight bottle with the sample of the specified size and incubating it at the specified temperature for 5 days. Dissolved oxygen was measured initially and after incubation, BOD was computed from the difference between the initial and the final DO. For this study all samples taken from the field for BOD analysis were diluted (the samples were diluted because BOD concentration in most waters exceeds the concentration of DO available in an air-saturated sample. Because the initial DO is determined immediately after the dilution is made, all oxygen uptake, including that occurring during the first 15 minutes, is included in the BOD measurement). The dilution was prepared by adding 1 millimeter each of phosphate buffer, 𝑀𝑔𝑆𝑂4 , 𝐶𝑎𝐶𝑙2 , 𝐹𝑒𝐶𝑙3 , solutions/l of water. 𝐂𝐚𝐥𝐜𝐮𝐥𝐚𝐭𝐢𝐨𝐧: Where;

𝑩𝑶𝑫𝟓

𝒎𝒈/𝒍 =

𝑫𝟏 − 𝑫𝟐 𝑷

𝑫𝟏 = DO of diluted sample immediately after preparation, 𝑚𝑔/𝑙 𝑫𝟐 = DO of diluted sample after 5 day incubation at 20℃, 𝑚𝑔/𝑙 𝑷=

3.8.3

Decimal volumetric fraction of sample used (APHA, 1995).

Chloride, nitrate and sulphate

The ion chromatography method was used in the determination of anions (chloride, nitrate and sulphate). The water sample was injected into a stream of carbonatebicarbonate eluent and passed through a series of ion exchangers. The anions of interest were separated on the basis of their relative affinities for a low capacity, 98


strongly basic anion exchanger (guard and separator columns). The separated anions were directed through a hollow fibre cation exchanger membrane (fibre suppressor) bathed in continuously flowing strong acidic solution (regenerant solution). In the suppressor the separated anions are converted to their conductive acid forms and the carbonate-bicarbonate eluent was converted to weakly conductive acid. The separated anions in their acidic forms were measured by conductivity. They were identified on the basis of retention time and compared to standards. Quantification was by measurement of peak area or peak heights (APHA, 1995).

3.8.4

Colour (True)

The spectrophotometric method was used because this method is applicable to potable and surface waters and to waste waters, both domestic and industrial use (Anon, 1992). Fifty (50) millilitre of demineralised water was filtered and 25𝑚𝑙 poured into a sample cell. The stored programme number, 120, was entered on the spectrophotometer and the 455nm wavelength used. The blank was placed into the cell holder and standardized. The prepared sample was placed into the cell holder and the result was displayed (Hach Company, 2001).

3.8.5

Conductivity

Conductivity was determined using a Cyberscan PC 510 conductivity meter. The conductivity cell and the beaker were rinsed thoroughly with a portion of the sample to be examined. The beaker was completely filled and the cell inserted into the beaker. When the water sample and the equipment had reached the same temperature, the

99


indicated value on the conductivity meter was read in 𝜇𝑆/𝑐𝑚. This is because the conductivity meter used had temperature compensation (CSIR WRI, 2005).

3.8.6

Hardness (Calcium carbonate)

A sample of 50 mL was measured and put into a 125 mL.

A bar magnet was then

inserted and the sample placed onto a stir plate. The pH was elevated to approximately 10.2 by adding 1 mL of the buffer solution. A minute amount was added to dry Eriochrome Black indicator. The indicator endpoint was reached when the red turned to blue. The sample was slowly titrated with (0.01M) EDTA until the last reddish tinge disappeared from the solution. A few drops were added at 3 - 5 second intervals to allow the endpoint reaction to go to completion. The total volume of EDTA used was recorded. Hardness of the sample was calculated using:

𝒎𝒈 𝑪𝒂𝑪𝑶𝟑 𝑨 𝒙 𝑩 𝒙 𝟏𝟎𝟎𝟎 𝑯𝒂𝒓𝒅𝒏𝒆𝒔𝒔 ( )= 𝑳 𝒎𝑳 𝑺𝒂𝒎𝒑𝒍𝒆 Where;

A

= is the mL of the EDTA used in the titration

B

= is the mg CaCO3 equivalent to 1 mL of EDTA solution

For a (0.01 M) EDTA solution, B = 1 mg CaCO3 / 1 mL of EDTA (APHA, 1995).

3.8.7

Oil and grease

The partition-gravimetric method was used in the determination of oil and grease. Oil and grease was extracted from the water samples by trichloro-trifluoroethane, after acidifying the water to 𝑝𝐻 2 or lower. The organic solvent was then distilled at 70℃ 100


and the amount of oil and grease, as the residue, determined by weight. A sample of about 1L of the water was poured into a glass jar and the sample level marked for later determination of sample volume. The sample was then acidified to 𝑝𝐻 2 or lower with about 5𝑚𝑙 𝐻𝐶𝑙; quantitatively the acidified sample was transferred to a separator funnel. The sample container was carefully rinsed with 30ml 1, 1, 2trichlorotrifluoroethane and solvent washings added to separator funnel. The funnel was vigorously shaken for at least 2 minutes and allowed for the layers to separate. A clean distilling flask was weighed and a filter paper was then folded and put into the glass funnel. The paper was made wet with about 15 𝑚𝑙 1, 1, 2-trichlorotrifluoroethane and the funnel put on the distilling flask. The solvent was drained through the moistened filter paper into the weighed distilling flask and 1g of 𝑁𝑎2 𝑆2 𝑂3 crystals weighed and added to the filter cone and slowly the emulsified solvent was drained onto the crystal.

Two more extractions were then carried out on the aqueous phase with 30 𝑚𝑙 solvent each but rinsing sample jar with each solvent portion. Extracts were then added to the distilling flask and the filter paper washed with an additional 15 𝑚𝑙 solvent. A water bath was subsequently plugged to an electrical outlet and the power turned on to heat to 70℃; the solvent was distilled from the distilling flask on water bath at 70℃. Using a clamp and a stand, the flask water bath was placed for 15 minutes and air drawn through the flask during the final one minute with a vacuum pump. The volume of the sample was determined by filling a 2L volumetric flask with water to the mark. The water was poured from the flask into the sample jar up to the mark.

101


The final volume of water in the flask was noted. The difference between the initial and final volume of water was recorded as the sample volume. 𝒎𝒈 𝒐𝒊𝒍 𝒂𝒏𝒅 𝒈𝒓𝒆𝒂𝒔𝒆/𝒍 = Where: 𝑾𝟐 =

3.8.8

𝑾𝟐 − 𝑾𝟏 × 𝟏𝟎𝟎𝟎 𝑽𝑺

Weight of distilling flask and residue, mg

𝑾𝟏 =

Weight of distilling flask only, mg

𝑽𝑺 =

Sample volume 𝑚𝑙 (Anon, 1992).

𝒑𝑯

𝑝𝐻 was measured using the Corning 𝑝𝐻 /℃ 107 meter. The calibration of the instrument with two buffer solutions was performed manually. The ℃ button on the Corning 𝑝𝐻 / ℃ 107 meter was pressed and the electrode placed in a buffer solution of 𝑝𝐻 value of 7 until the reading on the meter stabilized and the temperature displayed noted. The 𝑝𝐻 button on the meter was pressed and the value noted. The provided temperature chart was used to obtain the corresponding 𝑝𝐻 value at the displayed temperature. When the 𝑝𝐻 value was not equal to 7; a screw driver was used to adjust the CAL 1 to the 𝑝𝐻 value. The electrode was removed from the buffer solution, wiped with a tissue paper and rinsed with distilled water.

The electrode was immersed in a second buffer solution of 𝑝𝐻 value 4.0 until the reading on the instrument stabilized. The temperature displayed was noted and when the 𝑝𝐻 value indicated did not correspond to that on the chart, the screwdriver was used to adjust the CAL II to the 𝑝𝐻 value. The 𝑝𝐻 meter was thus calibrated. The electrode was then removed from the buffer solution, wiped with a tissue paper and rinsed with 102


distilled water. It was then immersed in the sample, allowing the reading to stabilize and the displayed 𝑝𝐻 value recorded (Anon, 1992).

3.8.9

Determination of total dissolved solids

A dry 370 millimeter beaker was put in an oven for 30 minutes and the beaker removed from the oven and put in a desiccator to cool to room temperature. The sample was then shaken thoroughly, and a reasonable quantity of it filtered through a 0.45𝜇𝑀 pore size filter paper using vacuum filtration. A beaker was weighed on a BDH analytical balance and the weight recorded. A filtrate of 200𝑀𝐿 was measured in a graduated cylinder, and the contents of the cylinder transferred into the beaker. The beaker and the contents were put in an electric oven to evaporate to dryness overnight. The beaker was then removed from the oven and quickly put in a desiccator to cool to room temperature. The beaker and its dry contents were then weighed on an analytical balance and the weight recorded. 𝐶𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑖𝑜𝑛: 𝑴𝒈 𝑻𝒐𝒕𝒂𝒍 𝑫𝒊𝒔𝒔𝒐𝒍𝒗𝒆𝒅 𝑺𝒐𝒍𝒊𝒅𝒔/𝒍 =

𝑾𝟐 − 𝑾𝟏 × 𝟏𝟎𝟔 𝑽𝒔

Where 𝑾𝟐 =Weight of beaker and solids in gram 𝑾𝟏 =Weight of beaker in gram 𝑽𝒔 =Volume of sample 𝑚𝐿 (Anon, 1992).

3.8.10

Total suspended solids

In determining the suspended solids of a sample of water, a vacuum filtration apparatus was set up and a 0.4 𝜇𝑀 pore size filter paper was placed on the stainless steel screen and wetted by filtering about 20 millimeter distilled water using the vacuum filtration. The wet filter paper was carefully removed using a pair of a stainless steel of plastic forceps, and placed on a watch glass. 103


The watch glass and its contents were put in an electric oven for 15 minutes to dry. The filter paper together with the watch glass were removed from the oven and placed in a desiccator for about 1 hour to cool. The filter paper was marked, weighed on a BDH analytical balance, and its weight recorded. The sample was shaken to obtain a homogeneous mixture, and thereafter, a reasonable quantity was measured in a graduated glass cylinder. The volume of the test sample was then recorded.

The test sample was put into the funnel and vacuum filtration applied. The contents of the measuring cylinder was rinsed with distilled water and poured into the funnel, which was later on rinsed with three portions of 20 millimeter distilled water whilst the vacuum filtration continued until the last drop of the filtrate was seen. The filter paper, with the solids, was removed using a pair of forceps and put on a clean dry watch glass. The watch glass and its contents were placed in an electric oven at a temperature of (104℃ < −107℃) for at least, 30 minutes, and thereafter removed into a desiccator to cool to room temperature. The filter paper and its contents were weighed, and the weight recorded. 𝐶𝑎𝑙𝑐𝑢𝑙𝑎𝑡𝑖𝑜𝑛: 𝑴𝒈𝑻𝑺𝑺/𝑳 = Where:

𝑾𝟐 − 𝑾𝟏 × 𝟏𝟎𝟔 𝑽𝒔

𝑾𝟐 = Weight of the filter paper and solids, in grams 𝑾𝟏 = Weight of the filter paper only, in grams 𝑽𝒔 = Volume of test sample, in 𝑚𝑙, (Anon, 1992).

104


3.8.11

Temperature

A glass in mercury-filled Celsius thermometer was used to record the temperature of the water sample. The thermometer was dipped into the stream and after a few minutes the temperature was recorded. The temperature reading was recorded to the nearest 0.2 or 1.0℃ (Anon, 1992).

3.8.12

Determination of metals (Arsenic, Copper, Lead, Iron and Zinc)

The Varian 220 Spectra Atomic Absorption Spectrophotometer (AAS) was used in the determination of Arsenic, Copper, Lead, Iron and Zinc. The samples were pre-treated by filtration and digestion and subsequently aspirated. The instrument was switched on and allowed to warm up for at least 30 minutes. An appropriate hollow cathode lamp was inserted in the lamp holder and aligned up for optimal light throughput. The knurled screw on the side of the lamp holder was adjusted in order to produce maximum output readings in the green zone of the energy meter.

The current for the selected Hollow Cathode Lamp for the instrumental parameters in the AAS was adjusted and the appropriate band width selected and the wavelength tuned using the band and wavelength selectors respectively. Subsequently the gas control selector was switched to the Air position, and then to the Air/fuel position. The fuel flow was also set to 4-6SCFH by means of the fuel flow control knob and the fuel flow reading sighted from the middle of the ball in the rotameter. The pilot was pushed to ignite the burner and the aspirator tube placed in a 10 millimeter graduated cylinder containing de-ionised water. The aspiration rate was measured and then set to 46𝑚𝐿/𝑚𝑖𝑛, by adjusting the nebulizer. 105


The de-ionised water was then aspirated and the instrument was zero by pressing the key A/Z and the READ key pressed. The recommended standard solutions and blanks were aspirated and the absorbance readings recorded. The sample was aspirated and the READ key pressed and the absorbance reading recorded (Anon, 1992).

Calculation: The concentration of the sample from the calibration data for the instrument was usually printed but below is how to calculate it from the absorbance readings: 𝑴𝒈 𝒂𝒏𝒂𝒍𝒚𝒕𝒆/𝑳 =

3.8.13

𝑨𝒃𝒔𝒐𝒓𝒃𝒂𝒏𝒄𝒆 𝒐𝒇𝒔𝒂𝒎𝒑𝒍𝒆 × 𝑺𝒕𝒂𝒏𝒅𝒂𝒓𝒅 𝒔𝒐𝒍𝒖𝒕𝒊𝒐𝒏 𝒄𝒐𝒏𝒄. 𝑨𝒃𝒔𝒐𝒓𝒃𝒂𝒏𝒄𝒆 𝒐𝒇 𝒔𝒕𝒂𝒏𝒅𝒂𝒓𝒅 𝒔𝒐𝒍𝒖𝒕𝒊𝒐𝒏

Cyanide

Cyanide was determined using a spectrophotometer set at a wave length of 578mm, instead of using the comparator. The Spectrophotometer was plugged in an electrical outlet and the instrument warmed for at least 30 minutes. An aliquot of the absorption solution was taken and diluted such that cyanide concentration fell in the measurable range of 0.1𝑚𝑔/𝑙 to 0.3 𝑚𝑔/𝑙 and that of the dilution factor. A 10mm cuvette was filled with 6 millimeter of the sample (after distillation). The microspoon provided in the cyanide kit was used to add a spoonful of each of the reagents CN-1A and CN-2A in that order to the contents in the cuvette.

Three drops of reagent CN-3A was added to the solution in the cell. A period of 5 minutes was allowed for the reactions to complete. The concentration of cyanide in the sample on the spectrophotometer was set at a wavelength of 578 and the concentration of cyanide measured as follows. Three standard working cyanide solutions, 0.1𝑚𝑔/𝑙, 106


0.2𝑚𝑔/𝑙 and 0.3𝑚𝑔/𝑙 was prepared from KCN standard solution and dissolved in 25.1mg KCN AnalaR grade, in IL deionized water to obtain 10 𝑚𝑔/𝑙 of cyanide solution. Using the three standard cyanide solutions and a deionized water blank treated as a sample, a calibration curve was prepared. The measure of the concentration or absorbance of the cyanide in the sample was determined using the calibration curve and the formula below, the cyanide concentration in the original sample was determined (Anon, 1992).

𝑪𝒂𝒍𝒄𝒖𝒍𝒂𝒕𝒊𝒐𝒏: 𝑪𝑵− , 𝒎𝒈/𝒍 = Where: 𝑪 =

𝑪 × 𝒅 × 𝑽𝟏 𝑽𝟐

Reading from calibration curve, 𝑚𝑔/𝐿

𝑽𝟏 =

Volume of absorbing solution from the distillation

𝑽𝟐 =

Volume of original sample used in distillation

𝒅=

Dilution factor

3.8.14 Total coliforms enumeration Total coliforms were estimated using the Most Probable Number method (MPN) according to Standard Methods (Anon, 1992). The multiple tube method was used because it gives an estimate of the number of organisms in a given volume of water based on the inoculation of that volume into a number of tubes of growth medium. After incubation the Most Probable Number of organisms in the original sample can be estimated from the tubes with a positive reaction (Chapman, 1992). Dilutions of 10-1 to 10-14 were prepared in 9ml sterilised distilled water (DW) and 1ml aliquots from each of the dilutions were inoculated in triplicates into 5ml of MacConkey Broth with inverted Durham tubes. Tubes showing acid and gas productions after incubation for 24 hours at 107


37℃ were recorded as presumptive total coliforms. These were then confirmed by plating on MacConkey No. 3 agar and examined for typical colonies. Counts per 100 ml were calculated from MPN tables and expressed as MPN 100ml-1 (Collins et al., 1989).

3.8.15 Faecal coliforms enumeration Faecal coliforms were estimated following the same procedure for total coliforms in 3.8.1 above. However, tubes were incubated at 44℃ for 24 hours. Tubes showing acid and gas production after incubation for 24 hours were confirmed by plating on MacConkey No. 3 agar and examined for typical colonies. Counts per 100ml were calculated from MPN tables and expressed as MPN 100ml-1 (Collins et al., 1989).

3.8.15.1 Preparation and examination of stained smears The slides were cleaned in chromic acid, washed in water and stored in alcohol. They were removed from the jar with forceps (the lid replaced immediately), drained and flamed to remove the alcohol. A drop of the liquid culture of bacteria to be examined was placed on a slide and spread with a flamed sterilized loop. This was allowed to dry and the bacteria fixed by passing the slides through a Bunsen flame 2 or 3 times (Atlas et al., 1997).

3.8.15.2

The grain stain

The bacterial smears on the slides were stained with 0.5% crystal violet for 2 minutes. This was washed with water, drained off and stained with dilute iodine for 2 minutes. Alcohol was carefully dripped onto the smear and allowed to run off for three times

108


and then washed off. The smears were finally counterstained with safranin, washed off and blotted with a filter paper to dry (Atlas et al., 1997).

3.8.15.3 Characterization of microbial bacteria The Analytical Profile Index (API) system (Bio Mérieux® SA 69280 Marcy I´Etiole/France or bioMérieux, Inc., Hazelwood, MO) is a standardized miniature version of conventional procedures for the identification of Enterobacteriaceae and other non-fastidious, Gram-negative bacilli which uses 21 miniaturized biochemical tests and a database (Popovic et al., 2007). The microorganisms to be identified were first isolated from colonies of bacteria on a culture medium adapted to the culture of Enterobacteriacease and/or non-fastidious, Gram-negative rods, according to standard microbiological techniques. The API 20E was employed in characterizing coliform isolates adhering to procedures recommended by the manufacturers and the results interpreted.

3.8.15.4 Preparation of strip For every isolate an incubation box (tray and lid) was prepared and sterile distilled water distributed into the honey-combed wells of the tray to create a humid atmosphere. The strain reference on the elongated flap of the tray was recorded. A strip was then removed from its packaging and placed in the incubation box (BioMe´rieux Vitek, 2007; Willey et al., 2008).

109


3.8.15.5 Preparation of the inoculum A single well isolated colony was removed from an isolation plate and cautiously emulsified in about 5ml of sterile distilled water to attain a homogeneous bacterial suspension using a sterile cotton wool bud to swap. For all the isolates young cultures, 18-24 hours old were used (BioMe´rieux Vitek, 2007).

3.8.15.6 Inoculation of the strip The tube and the cupule of the tests Citrate (CIT), Voges Proskauer (VP) and GELatinase (GEL) were filled with bacterial suspension using the same pipette. Unlike the other tests only the tubes (and not the cupule) were filled. Furthermore anaerobic conditions were created in the tests; Arginine DiHydrolase (ADH), Lysine DeCarboxylase (LDC), Ornithine DeCarboxylase (ODC), hydrogen sulphide production (𝐻2 𝑆) and urease (URE) by overlaying the bacterial suspension with mineral oil. The incubation box is then closed incubated at 36℃ ± 2℃

for 18-

24hours (BioMe´rieux Vitek, 2007; Willey et al., 2008).

3.8.15.7 Reading of the strip The strip was read after the incubation period by referring to the reading table and all spontaneous reactions (+ or -) recorded on the result sheet. Reactions which required additional reagents were performed as follows: Trptophane DeAminase (TDA) test: A drop of TDA reagent was added to the suspension in both the tube and the cupule. A reddish brown colour indicated a positive reaction recorded on the result sheet.

110


INDole Production (IND) test: A drop of JAMES reagent was added to the tube. The development of a pink colour in the whole cupule was an Indication of a positive reaction and recorded on the result sheet. Voges Proskauer (VP) test: a drop each of VP 1 and VP 2 reagents were added to the tube and left for 10 minutes. A reddish brown colour was an indication of a positive reaction (Willey et al., 2008).

3.8.15.8 Interpretation of the strip Identification of the bacterial isolates was determined by the numerical profile using the API. On the result sheet, the tests are separated into groups of 3 and a value of 1, 2 or 4 is indicated for each. By adding together the values corresponding to positive reactions within each group, a 7-digit profile number was obtained for the 20 tests of the API 20E strip. The oxidase reaction constitutes the 21 st test and has a value of 4 if it is positive and three if negative. In cases where the 7-digit profile was not discriminatory enough the following supplementary tests were carried out: Reduction of nitrates (𝑁𝑂2 ) and 𝑁2 gas (𝑁2 ): A drop of each of NIT 1 and NIT 2 reagents were added to the GLUcose (GLU) tube and remained for 2-5 minutes. A red colour is an indication of a positive reaction (𝑁𝑂2). A negative reaction (yellow) however may be due to the reduction to nitrogen (as sometimes evidenced by gas bubble): 2-3mg of Zn reagent is added to the GLU tube and 5 minutes later if the tube remains yellow this is an indication of a positive reaction (𝑁2 ) and is recorded on the result sheet. However if the test turns orange- red, this is a negative reaction. The nitrates still present in the tube have been reduced by the Zinc (BioMe´rieux Vitek, 2007). 111


3.8.16

Enumeration of Escherichia coli

In assaying for E. coli, tubes which were positive for faecal coliforms were selected and 1ml of each positive tube inoculated into corresponding 5ml tubes of Buffered Tryptone Water (BTW) and incubated at 44 ℃ for 24 hours. Two (2) drops of Kovacs Reagent using Pasteur pipette were added to the tubes whilst shaking gently. The samples were allowed to settle for two minutes and tubes with the formation of a red ring on top of the contents depicted the presence of E. coli and were recorded as positive. Counts were calculated from MPN tables and expressed as MPN 100ml,-1 (Anon, 1992).

3.9

Data Analysis

The statistical package for social sciences (SPSS) 16 for Windows was used for the data analysis. Pearson’s Chi-square (𝑋 2 ) tests (non- parametric) were also used to compare categorical variables. Responses on demographic characteristics, land use, impacts of land use and the sustainable management of the Barekese Catchment were tested using the Chi-square (𝑋 2 )model:𝑋 2 = ∑[(𝑄𝑖 − 𝐸𝑖 )2 ÷ 𝐸𝑖 ], where 𝑄𝑖 = observed frequencies and 𝐸𝑖 = expected frequencies as described by (Malley et al., 2007). Graphical presentation of the data used SigmaPlot 2001 to highlight any trends occurring.

The SPSS software was employed for testing if the means on a dependent variable were significantly different among groups with respect to the water analysis. If the overall Analysis of Variance (ANOVA) is significant and a factor has more than two levels a post-hoc multiple comparisons follow up test was carried out using Least Significance Difference (LSD). In all cases, significance was determined at the 95% 112


confidence level. One-way analysis of variance was performed to assess the differences among means, with a significance level of 5%(𝜌 < 0.05) as described by (Isobe et al., 2004). Results obtained from the water quality analysis were compared to the Ghana Water Resources Commission (GWRC) Target Water Quality Range (TWQR) standards for surface water quality, and the WHO guideline for surface water.

113


CHAPTER 4: RESULTS 4.1 Social Survey In this section the demographic characteristics of the seven communities, land use in the Barekese catchment, impacts of land use and the sustainable management of the Barekese catchment are presented.

4.1.1 Demographic characteristics In all the seven communities the number of females was about twice that of males (Table 2). Illiteracy rates amongst respondents from all the 7 communities were found to be high (94.3%), with Nkwantakese being the highest (28.4%) and Denase and Pampatia recording no literates (0.0%). The highest ethnic group in all the 7 communities were the Akans (90.5%) with only a few Ewes (3.8%) and Northerners9 (5.7%). However in Ayensua Fufuo, Ayensua Kokoo and Pampatia respondents were all Akans. Christianity was the principal religion (80%) in all the communities. Muslims formed only (4.9%) and traditionalist (15.1%). Most of the respondents had lived in their communities since their birth with 53.0% having been there for more than twenty years (Table 2).

9

All other tribes from the Northern, Upper East and Upper West regions were grouped as Northerners.

114


Table 2 Selected demographic parameters of households in the seven communities Parameters Sex: N=370 Female Male Mean=1.39 SE =.025 Total Literacy: N=370 Literate Illiterate Mean=1.94 SE =.012 Total Ethnicity: N=370 Akan Ewe Northern Mean=1.21 SE =.037

A. Fufuo

Community of Respondents A. Kokoo Denase Esaase

Nkwant

Pampat

Penten

Total

6.5 (24) 3.2 (12)

6.2 (23) 1.4 (5)

13.2 (49) 11.9 (44)

7.0 (26) 4.1 (15)

18.9 (70) 12.4 (46)

1.6 (6) 0.3 (1)

7.6 (28) 5.7 (21)

61.1 (226) 38.9 (144)

9.7(36)

7.6(28)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

1.4 (5) 8.4 (31)

0.5 (2) 7.0 (26)

0.0(0) 25.1 (93)

0.5 (2) 10.5 (39)

3.0(11) 28.4(105)

0.0 (0) 1.9(7)

0.2(1) 13.0(48)

5.7 (21) 94.3 (349)

9.7(36)

7.6(28)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

9.7 (36) 0.0(0) 0.0(0)

7.6 (28) 0.0(0) 0.0(0)

20.3 (75) 1.1 (4) 3.8 (14)

10.3 (38) 0.8(3) 0.0(0)

28.6(106) 1.1 (4) 1.6(6)

1.9 (7) 0.0(0) 0.0(0)

12.2 (45) 0.8 (3) 0.3(1)

90.5 (335) 3.8 (14) 5.7(21)

Total Religion: N=370 Christian Muslim (Islam) Traditionalist Mean=1.35 SE =.038 Total Length of stay: N=370 1-5 years 6-10 years 11-15 years 16- 20 years 20years+ Mean=4.07 SE =.064

9.7(36)

7.6(28)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

8.9 (33) 0.0(0) 0.8(3)

6.8 (25) 0.3(1) 0.5 (2)

18.6 (69) 3.8 (14) 2.7 (10)

8.6 (32) 0.0(0) 2.4 (9)

24.3 (90) 0.5 (2) 6.5 (24)

1.6 (6) 0.0(0) 0.3(1)

11.1 (41) 0.3(1) 1.9 (7)

80.0 (296) 4.9 (18) 15.1 (56)

9.7(36)

7.6(28)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

0.0(0) 0.5 (2) 2.7 (10) 1.6 (6) 4.9 (18)

0.0(0) 0.3(1) 0.5(2) 1.1(4) 5.7 (21)

2.2 (8) 1.1 (4) 2.4(9) 7.8 (29) 11.6 (43)

1.6(6) 0.5(2) 1.4 (5) 2.4 (9) 5.1 (19)

1.6 (6) 1.4(5) 5.9 (22) 4.6 (17) 17.8 (66)

0.0(0) 0.0(0) 0.3(1) 0.3 (1) 1.4 (5)

1.6 (6) 1.1 (4) 2.2(8) 1.9(7) 6.5 (24)

7.0 (26) 4.9 (18) 15.4 (57) 19.7 (73) 53.0 (196)

Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) Source: Household survey (2005). Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

Although Ayensua Fufuo, Nkwantakese and Denase communities had pipe-borne water supply, due to the irregularity in the supply, these communities often resorted to the use of streams and boreholes. However Ayensua Kokoo, Esaase, Pampatia and Penten did not have access to pipe-borne water and therefore depended solely on the streams. In Ayensua Fufuo and Denase 8.6% and 18.4% did use both pipe-borne and stream water. The differences in the sources of drinking water in the seven communities

were

statistically

significant

36) (Table 3). 115

at

𝜌 = 0.000(𝑋 2 = 511.578, 𝑑𝑓 =


Table 3 Sources of drinking water in the seven communities Community of Respondents Parameters A. Fufuo A. Kokoo Denase Esaase Nkwant Pampat Penten Total Source of drinking water: N=370 Pipe-borne 1.1(4) 0.0(0) 0.8 (3) 0.0(0) 0.5 (2) 0.0(0) 0.0(0) 2.4(9) Well/Dugout 0.0 (0) 0.0(0) 0.0(0) 0.0(0) 1.1(4) 0.0(0) 0.3 (1) 1.4 (5) Stream 0.0(0) 0.0(0) 1.6 (6) 3.0 (11) 7.6 (28) 0.0(0) 5.9(22) 18.1(67) Borehole 0.0(0) 0.0(0) 4.3 (16) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 4.3(16) Pipe-borne& stream 8.6 (32) 7.6 (28) 18.4 (68) 0.0(0) 20.5 (76) 0.0(0) 0.8 (3) 55.9 (207) Well/dugout& stream 0.0(0) 0.0(0) 0.0(0) 8.1 (30) 1.6(6) 0.0(0) 2.2 (8) 11.9 (44) Stream& borehole 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 1.9 (7) 4.1(15) 5.9 (22) Mean=4.69 SE =.065 X2=511.578 df=36 P value =.000 Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

The whole (100%) of the Pampatia community had access to KVIPs. In Nkwantakese and Esaase, the KVIP was out of use with the community resorting to open defecation. In general, only half (51.4%) of the communities had access to KVIP facilities but most of them did not bother using them (Table 4). Table 4 Accessibility to KVIP and toilet facility patronized by the communities Parameters KVIP Accessibility: N=370 KVIP accessible KVIP not accessible Mean=1.49 SE =.026 X2= 148.398 df=6 P value =.000 Total Toilet facility patronised: N=370 Household toilet Public KVIP Free range

A. Fufuo

Community of Respondents A. Kokoo Denase Esaase Nkwant

Pampat

1.1(4) 8.6 (32)

1.1(4) 6.5 (24)

25.1 (93) 0.0(0)

5.9 (22) 5.1 (19)

11.4 (42) 20.0 (74)

1.9 (7) 0.0(0)

4.9 (18) 8.4 (31)

51.4 (190) 48.6 (180)

9.7(36)

7.6(28)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

0.0(0) 0.0(0) 2.1 (4)

0.0(0) 1.0 (2) 0.0(0)

0.0(0) 27.2 (53) 20.5(40)

0.0(0) 0.0(0) 6.7 (13)

0.0(0) 3.6 (7) 14.4 (28)

1.5 (3) 2.1 (4) 0.0(0)

5.1 (10) 3.6 (7) 0.5(1)

6.7 (13) 37.4 (73) 44.1 (86)

0.0(0) 6.5 (24)

0.0(0) 0.0(0)

4.6 (9) 5.1 (19)

7.2 (14) 20.0 (74)

0.0(0) 0.0(0)

0.0(0) 8.4 (31)

11.8 (23) 48.6 (180)

Wooden structure 0.0(0) KVIP not accessible 8.6 (32) Mean=2.61 SE =.056 X2=176.724 df=18 P value =.000 Total 9.7(36) Source: Household survey (2005). X2 in parentheses are in percentages.

Penten

Total

7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not

116


4.1.2 Land use in the Barekese catchment The various anthropogenic modification identified in the Barekese catchment are presented in Table 5 with farming being the dominant occupation (70.3%). The variations in economic activities of the respondents were significantly different at 𝜌 = 0.000(𝑋 2 = 160.645, 𝑑𝑓 = 54). Table 5 Economic activities in the communities Community of Respondents Parameters A. Fufuo A. Kokoo Denase Esaase Nkwant Pampat Penten Total Economic activity: N=370 Farming 9.2 (34) 6.8 (25) 12.2 (45) 9.7 (36) 22.4 (83) 1.1(4) 8.9(33) 70.3(260) Hunting 0.0(0) 0.0(0) 0.0 (0) 0.0(0) 2.7 (10) 0.3(1) 0.3(1) 3.2(12) Craftsmanship 0.0(0) 0.0(0) 4.9(18) 0.0(0) 1.4(5) 0.0(0) 0.3(1) 6.5(24) Fishing 0.0(0) 0.0(0) 1.1(4) 0.0(0) 0.0(0) 0.0(0) 1.1(4) 2.2(8) Trading 0.0(0) 0.0(0) 0.0(0) 0.5 (2) 0.5(2) 0.0(0) 0.0(0) 0.5(2) Washing of vehicles 0.0(0) 0.0(0) 3.8 (14) 0.0(0) 1.1(4) 0.0(0) 0.3(1) 5.1(19) Cocoa Spraying 0.0(0) 0.0(0) 0.0(0) 0.5(2) 0.5(2) 0.3(1) 0.3(1) 1.6(6) Chain saw operation 0.5(2) 0.8(3) 2.2 (8) 0.8(3) 2.2(8) 0.3(1) 1.9(7) 6.5(24) Palm Kernel oil producer 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0 (0) 0.0(0) 0.0(0) 1.1(4) Unemployed 0.0(0) 0.0(0) 1.1(4) 0.0(0) 0.5(2) 0.0(0) 0.3(1) 3.0(11) Mean=2.39 SE =.132 X2=160.645 df=54 P value =.000 Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parenthesis are in percentages.

The proximity of farming activities to the two major river banks in the communities was variable, with 38.9% (n = 144) of farmers farming in River Offin basin (ca.5–10 m from shore) and 18.4% (n = 68) in the Nwabi basin (ca. 11–15 m from shore) (Table 6).

117


Table 6 Average distance of farms from the river banks Parameters

A. Fufuo

Community of Respondents A. Denase Esaase Nkwant Kokoo

Pampat

Penten

Total

Distance of farm from the stream: N =370 5-10m 7.3(27) 7.3(27) 1.1(4) 5.1(19) 11.1(41) 0.3(1) 6.8(25) 38.9(144) 11-15m 0.3(1) 0.0(0) 3.8(14) 1.6(6) 10.3(38) 0.5(2) 1.9(7) 18.4(68) 16-20m 0.5(2) 0.0(0) 6.2(23) 2.4(9) 4.9(18) 0.3(1) 0.5(2) 14.9(55) 21-25m 1.1(4) 0.0(0) 0.5(2) 0.5(2) 0.5(2) 0.0(0) 0.0(0) 2.7(10) Beyond 26m 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) Not applicable 0.5(2) 0.3(1) 13.5(50) 1.4(5) 4.6(17) 1.9(7) 4.1(15) 25.1(93) Mean=26.19 SE =2.196 X2 =173.830 df=24 P value =.000 Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

Unfortunately almost half (49.5%) of the communities were farming on watercourses with reasons being scarcity of land, non payment of compensation and as a form of protest (Table 7). Table 7 Respondents reasons for farming on watercourses in the communities Community of Respondents Parameters

A. Fufuo

A. Kokoo

Denase

Farming on watercourses: N=370 Farming on watercourses 9.2(34) 6.8(25) 6.8(25) Not farming on watercourses 0.5(2) 0.8(3) 18.4(68) Not applicable 0.0(0) 0.0(0) 0.0(0) Mean=2.55 SE =.526 X2=78.978 df=12 P value =.000 Total 9.7(36) 7.6(28) 25.1(93) Reasons: N=183 Non payment of compensation 2.2(4) 3.8(7) 0.0(0) Scarcity of land for agriculture 2.7(5) 5.4(10) 0.0(0) A form of protest 5.4(9) 4.3(8) 0.0(0) Lack of access to water for 0.5(1) 0.0(0) 7.0(13) farming Cultivation of vegetable 7.0(12) 0.0(0) 6.5(12) Increase in population 0.0(0) 0.0(0) 0.0(0) Non payment of compensation 0.5(1) 0.0(0) 0.0(0) and as a form of protest Mean=3.48 SE =.134 X2=146.947 df=36 P value =.000 Total 18.3(32) 13.4(25) 13.4(25) Source: Household survey (2005). X2 = Pearson Chi-square value. in parentheses are in percentages.

118

Esaase

Nkwant

Pampat

Penten

Total

5.4(20) 5.7(21) 0.0(0)

15.7(58) 14.6(54) 1.1(4)

0.8(3) 1.1(4) 0.0(0)

4.9(18) 8.4(31) 0.0(0)

49.5(183) 49.5(183) 1.1(4)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

0.5(1) 2.7(5) 0.0(0) 4.3(8)

4.3(8) 7.5(14) 2.7(5) 2.2(4)

1.1(2) 0.0(0) 0.0(0) 0.0(0)

4.8(9) 2.7(5) 1.1 (2) 1.1(2)

16.7(31) 21.0(38) 13.4(25) 15.1(28)

3.2(6) 0.0(0) 0.0(0)

6.5(12) 0.5(1) 8.6(15)

0.5(1) 0.0(0) 0.0(0)

0.0(0) 0.0(0) 0.5(1)

23.7(42) 0.5(1) 9.7(18)

10.8(20) 32.3(59) 1.6(3) 10.2(19) 100.0(183) Figures in parenthesis are the frequencies whilst those not


In Ayensua Kokoo, 7.6% of the respondents indicated that they farm, hunt and fell trees in the reserve. In Denase, only 1.4% of the farmers make use of the reserve. However, in Ayensua Fufuo (8.1%), Penten (9.2%), Nkwantakese (17.3) and Esaase (5.9%) the number of respondents using the reserve was alarmingly high. They had various reasons for using the reserve though they did know these activities were illegal. These include lack of land (21.7%), poverty (15.2%), non payment of compensation (29.9%) and the fact that the reserve was more fertile and rich in game (9.2%) (Table 8). Table 8 Respondents reasons for farming or hunting in the reserve Community of Respondents Parameters

A. Fufuo

A. Kokoo

Denase

Esaase

Nkwant

Pampat

Penten

Total

Farming or hunting in the reserve: N = 370 Farming/hunting in the reserve 8.1(30) 7.6(28) 1.4(5) 5.9(22) 17.3(64) 0.8(3) 9.2(34) 50.3(186) Not farming /hunting in 1.6(6) 0.0(0) 23.2(86) 5.1(19) 13.0(48) 1.1(4) 4.1(15) 48.1(178) reserve Not applicable 0.0(0) 0.0(0) 0.5(2) 0.0(0) 1.1(4) 0.0(0) 0.0(0) 1.6(6) Mean=3.07 SE =.642 X2=131.606 df=12 P value =.000 Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) Reasons: N=186 Non payment of compensation 8.7(17) 7.1(13) 0.0(0) 2.2(4) 5.4(10) 0.5(1) 6.0(11) 29.9(56) A form of protest 0.5(1) 2.2(4) 0.0(0) 3.3(6) 1.1(2) 0.5(1) 2.7(5) 10.3(19) Lack of land 2.7(5) 2.2(4) 0.5(1) 1.1(2) 9.8(19) 0.0(0) 5.4(10) 21.7(41) Poverty 3.3(6) 3.3(6) 0.0(0) 2.7(5) 4.3(8) 0.0(0) 1.6(3) 15.2(28) Inadequate compensation 0.0(0) 0.0(0) 0.0(0) 0.0(0) 2.7(5) 0.0(0) 0.0(0) 2.7(5) The land in the reserve is fertile & rich in game 1.1(2) 0.0(0) 0.0(0) 1.1(2) 6.0(11) 0.5(1) 0.5(1) 9.2(17) No compensation and as a form of protest 0.0(0) 0.5(1) 1.1(2) 0.0(0) 2.7(5) 0.0(0) 0.0(0) 4.3(8) No compensation, protest & lack of land 0.0(0) 0.0(0) 0.0(0) 0.5(1) 0.0(0) 0.0(0) 1.1(2) 1.6(3) No compensation, protest, lack of land and poverty 0.0(0) 0.0(0) 0.0(0) 0.5(1) 2.7(5) 0.0(0) 1.1(2) 4.3(8) Lack of land, Poverty, Inadequate compensation and 0.0(0) 0.0(0) 0.0(0) 0.5(1) 0.0(0) 0.0(0) 0.0(0) 0.5(1) the reserve is fertile Mean=3.34 SE =.169 X2=109.592 df=54 P value =.000 Total 16.3(31) 15.2(28) 1.6(3) 12.0(22) 34.8(65) 1.6(3) 18.5(34) 100(186) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

119


From all the communities a total of 34.1% of farmers admitted using fertilizers and agrochemicals in the cultivation of their vegetables and cocoa (Table 9). Table 9 Use of fertilizers and agrochemicals in farming by the respondents Parameters

A. Fufuo

Community of Respondents A. Denase Esaase Kokoo

Nkwant

Pampat

Penten

Total

Use of fertilizer& chemicals on farm: N=370 Use fertilizer and chemicals on 5.1(19) 5 .9(22) 2.7(10) 6.8(25) 8.1(30) 0.8(3) 4.6 (17) 34.1(126) farm Fertilizer and chemicals not 4.6(17) 22.4(83) 20.4(51) 4.3(16) 22.7(84) 1.1(4) 8.6(32) 65.4(242) used on farm Not applicable 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.5(2) 0.0(0) 0.0(0) 0.5(2) Mean=2.18 SE =.372 X2 =73.874 d f= 12 P value =.000 Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

In all the seven communities most (48.6%) of the respondents used fuel wood as their main source of energy followed by the use of charcoal (15.9%) (Table 10). Table 10 Sources of energy in the indicated communities in the study area Community of Respondents Parameters A. Fufuo A. Kokoo Denase Esaase Nkwant Pampat Penten Total Source of energy: N=370 Fuel wood 8.9(33) 6.8(25) 8.1(30) 4.3(16) 12.2(45) 0.8(3) 7.6(28) 48.6(180) Charcoal 0.8(3) 0.8(3) 4.9(18) 0.5(2) 4.6(17) 0.3(1) 4.1(15) 15.9 (59) Kerosene 0.0(0) 0.0(0) 0.0(0) 0.0(0) 2.2(8) 0.0(0) 0.0(0) 2.2(8) Electricity 0.0(0) 0.0(0) 0.5(2) 0.0(0) 0.5(2) 0.0(0) 0.0(0) 1.1(4) Charcoal & fuel wood 0.0(0) 0.0(0) 4.1(15) 5.9(22) 7.6(28) 0.8(3) 1.6(6) 20.0(74) All 0.0(0) 0.0(0) 7.6(28) 0.3(1) 4.3(16) 0.0(0) 0.0(0) 12.2(45) Mean=2.98 SE =.126 X2 = 148.927 d f= 30 P value =.000 Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

All (100%) the respondents were of the view that population had increased over the last ten years in the seven communities (Table 11).

120


Table 11Perception on population increase in the communities over the past ten years Parameters

A. Fufuo


Nkwant

Pampat

Penten

Total

Increase in population of the communities in the last ten years There is an increase 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 00(370) There is no increase 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) Mean10 =1.00 SE =.000 Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) Increase in population of the communities (%) N=370 10- 20 0.3(1) 0.5(1) 0.5(1) 0.0(0) 2.7(10) 0.3(1) 0.5(2) 4.3(16) 21 – 30 2.7(10) 2.2(8) 3.0(11) 0.8(3) 13.8(51) 0.3(1) 3.8(14) 26.5(98) 31 – 40 3.5(13) 5.1(19) 12.7(47) 6.2(23) 8.6(32) 1.4(5) 5.7(21) 43.2(160) 41 - 50 3.2(12) 0.0(0) 9.2(34) 4.1(15) 6.2(23) 0.0(0) 3.2(12) 25.9(96) Mean=2.91 SE =.043 X2 = 70.298 d f=18 P value =.000 Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.6(49) 100(370) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

4.1.3 Impacts of land use change Over 87.3% of persons interviewed within the communities had experienced flooding on their farms and attributed this to increased rainfall and siltation of the rivers and streams (Table 12).

121


Table 12 Respondents perceived reasons for flooding of rivers and streams Community of Respondents Parameters Flooding of stream: N=370 There is flooding There is no flooding Mean =1.13 SE =.017 X2= 75.501 d f=6 P value =.000 Total Reasons: N=323 No idea Raining season and heavy rains Smaller streams cause the river to overflow it's banks Siltation Small width of the river The construction of the dam

A. Fufuo

A. Kokoo

Denase

Esaase

Nkwant

Pampat

Penten

Total

5.9(22) 3.8(14)

7.6(28) 0.0(0)

25.1(93) 0.0(0)

8.6(32) 2.4(9)

29.7(110) 1.6(6)

1.9(7) 0.0(0)

8.4(31) 4.9(18)

87.3(323) 12.7(47)

9.7(36)

7.6(28)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

0.0(0) 1.5(5) 2.8(9)

0.0(0) 4.3(14) 4.3(14)

1.5(5) 12.7(41) 5.0(16)

0.0(0) 3.4(11) 3.1(10)

3.7(12) 11.5(37) 9.9(32)

0.0(0) 0.3(1) 0.6(2)

1.5(5) 1.2(4) 1.2(4)

6.8(22) 35.0(113) 26.9(87)

0.6(2) 0.0(0)

0.0(0) 0.0(0)

9.0(29) 0.6(2)

2.2(7) 0.3(1)

3.4(11) 2.5(8)

0.6(2) 0.0(0)

1.9(6) 0.9(3)

17.6(57) 4.3(14)

1.9(6)

0.0(0)

0.0(0)

0.9(3)

3.1(10)

0.6(2)

2.8(9)

9.3(30)

Mean=3.06 SE =.075 X2 = 98.832 d f=30 P value =.000 Total 6.8(22) 8.7(28) 28.8(93) 9.9(32) 32.1(110) 2.2(7) 9.6(31) 100(323) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

Over 7.1% of the respondents admitted to their farming close the streams and rivers as being the main reason for the seasonal drought of the streams although the cutting or felling of trees close to these rivers could be a contributing factor (Table 13).

122


Table 13 Respondents reasons for the seasonal drought of rivers and streams Parameters

A. Fufuo

A. Kokoo

4.9(18) 4.9(18)

5.1(19) 2.5(9)

Community of Respondents Denase Esaase Nkwant

Pampat

Penten

Total

24.8(92) 0.3(1)

Drying up of stream: N=370 There is drying up There is no drying up Mean =1.24 SE =.022 X2 = 63.618 d f=6 P value =.000

Total 9.7(36) 7.6(28) 25.1(93) Reasons: N=282 No idea 0.0(0) 0.0(0) 0.4(1) Dry season 1.1(3) 0.7(2) 11.3(32) Lack of tree cover and shade 4.2(12) 4.6(13) 12.7(36) Intense sunlight 1.1(3) 1.1(3) 3.9(11) Dry season, Lack of tree cover 0.0(0) 0.0(0) 0.7(2) and shade Lack of tree cover, shade and 0.0(0) 0.0(0) 1.4(4) Intense sunlight Farming activities 0.0(0) 0.4(1) 2.1(6) Mean=3.28 SE =.095 X2 = 140.790 d f=36 P value =.000 Total 6.4(18) 6.7(19) 32.5(92) Source: Household survey (2005). X2 = Pearson Chi-square value. in parentheses are in percentages.

8.6(32) 2.5(9)

24.3(90) 7.0(26)

1.9(7) 0.0(0)

6.5(24) 6.7(25)

76.2(282) 23.8(88)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

0.0(0) 1.4(4) 2.8(8) 2.5(7) 0.4(1)

5.3(15) 10.6(30) 6.4(18) 1.8(5) 2.1(6)

0.0(0) 1.1(3) 1.1(3) 0.0(0) 0.4(1)

0.7(2) 3.9(11) 1.4(4) 1.8(5) 0.4(1)

6.4(18) 30.0(85) 33.2(93) 12.0(34) 3.9(11)

0.0(0)

5.7(16)

0.0(0)

0.4(1)

7.4(21)

4.2(12)

0.0(0)

0.0(0)

0.4(1)

7.1(20)

11.3.0(32) 31.8(90) 2.5(7) 8.8(25) 100(282) Figures in parenthesis are the frequencies whilst those not

Additionally, the changes in hydrology of the rivers and streams were attributed to the impoundment of these water bodies as a result of the construction of the dam (Table 14).

123


Table 14 Respondents reasons for the changes in hydrology of rivers and streams in the communities Parameters Change in rate of flow of river: N=370 There is a change in flow rate There is no change in flow rate Not applicable Mean=2.37 SE =.526 X2 = 54.556 d f=12 P value =.000 Total Reasons: N=250 No idea The dam made the water stagnant The dam impedes the flow of the stream Farming activities along the stream impedes the flow rate Mean=2.67 SE =.074 X2 = 167.276 d f=18 P value =.000 Total Source: Household survey (2005). parentheses are in percentages.

A. Fufuo


Nkwant

Pampat

Penten

Total

9.7(36) 0.0(0) 0.0(0)

7.6(28) 0.0(0) 0.0(0)

14.6(54) 10.5(39) 0.0(0)

6.2(23) 4.9(18) 0.0(0)

17.6(65) 12.7(47) 1.1(4)

1.6(6) 0.3(1) 0.0(0)

10.3(38) 3.0(11) 0.0(0)

67.6(250) 31.4(116) 1.1(4)

9.7(36)

7.6(28)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

0.8(2) 0.0(0)

0.0(0) 0.0(0)

13.1(33) 0.0(0)

0.0(0) 0.0(0)

12.7(32) 1.6(4)

0.0(0) 0.4(1)

1.2(3) 5.6(14)

27.9(70) 7.6(19)

6.8(17)

6.0(15)

0.0(0)

6.0(15)

7.6(19)

0.4(1)

7.6(19)

34.3(85)

6.8(17)

5.2(13)

8.4(21)

3.2(8)

4.0(10)

1.6(4)

1.2(3)

30.3(76)

14.3(36) 11.2(28) 21.5(54) 9.2(23) 25.9(65) 2.4(6) 15.5(39) 100(250) X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in

Catch per unit effort (CPUE) by fishermen within the communities had decreased by 50.0% over the years which they claimed may be due to the construction of the dam, overexploitation of the fishery resources and failure to adhere to traditional taboos and norms (Table 15).

124


Table 15 Respondents reasons for the decline in the catch per unit effort in the rivers and streams in the communities Community of Respondents Parameters Decrease in catch per effort of the river: N=370 There is a decrease in catch per effort There is no decrease in catch per effort Not applicable

A. Fufuo

A. Kokoo

Denase

Esaase

Nkwant

Pampat

Penten

Total

8.1(30)

7.6(28)

2.4(9)

9.7(36)

10.5(39)

1.6(6)

10.0(37)

50.0(185)

0.5(2)

0.0(0)

1.9(7)

0.0(0)

7.8(29)

0.3(1)

1.9(7)

12.4(46)

1.1(4)

0.0(0)

20.8(77)

1.4(5)

13.0(48)

0.0(0)

1.4(5)

37.6(139)

9.7(36)

7.6(28)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49)

100(370)

0.0(0) 1.1(2)

0.0(0) 0.0(0)

4.8(9) 0.0(0)

0.0(0) 1.1(2)

10.7(20) 11.2(21)

0.0(0) 0.0(0)

0.0(0) 5.9(11)

15.5(29) 19.3(36)

1.1(2)

3.2(6)

0.0(0)

1.1(2)

0.0(0)

0.5(1)

2.1(4)

8.0(15)

8.0(15) 5.9(11)

10.2(19) 1.6(3)

0.0(0) 0.0(0)

17.1(30) 0.0(0)

0.0(0) 0.0(0)

1.6(3) 1.1(2)

10.2(19) 1.6(3)

47.1(86) 10.2(19)

16.0(30)

7.6(28)

4.8(9)

19.3(34)

21.9(41)

3.2(6)

19.8(37)

100(185)

Mean=37.94 SE =2.466 X2 = 192.724 d f=12 P value =.000 Total Reasons: N= 185 No idea The creation of the Barekese dam The presence of algae and water weeds Overexploitation of the fishery Traditional taboos not observed Mean=3.17 SE =.094 X2 = 214.134 d f=24 P value =.000 Total

2

Source: Household survey (2005). X = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

Over 71.1% of the residents had observed changes in the quality of the water in the streams and rivers using indicators such as change in colour, increased presence of algae, and water weed (Table 16).

125


Table 16 Respondents reasons for changes in water quality Parameters

A. Fufuo

A. Kokoo


Pampat

Penten

Total

Changes in the quality of water: N=370 No change observed 1.6(6) 0.3(1) 13.2(49) 3.8(14) 7.6(28) 0.5(2) 1.9(7) 28.9(107) Smell 1.4(5) 0.0(0) 4.9(18) 2.2(8) 2.2(8) 0.0(0) 0.8(3) 11.4(42) Taste 0.0(0) 0.0(0) 0.0(0) 0.0(0) 1.1(4) 0.0(0) 0.0(0) 1.1(4) Lathering ability 0.0(0) 0.0(0) 0.0(0) 0.0(0) 1.1(4) 0.0 (0) 0.5(2) 1.6(6) Colour change 4.3 (16) 3.0(11) 7.0(26) 2.4(9) 5.7(21) 0.8(3) 3.0(11) 26.2 (97) Grease& oil 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.3(1) 0.3(1) Algae and water weed 1.1(4) 1.9(7) 0.0(0) 0.8(3) 5.4(20) 0.0(0) 3.8(14) 13.0(48) Presence of insects 0.5(2) 1.6(6) 0.0(0) 0.0(0) 2.7(10) 0.3(1) 2.4(9) 7.628) Colour and taste 0.8(3) 0.8(3) 0.0(0) 1.9(7) 5.7(21) 0.3(1) 0.5(2) 10.0(37) Mean=4.35 SE =.148 X2 = 154.36 d f=48 P value =.000 Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.4(116) 1.9(7) 13.2(49) 100(370) Reasons: N=263 No idea 0.0(0) 0.0(0) 0.0(0) 0.0(0) 12.5(33) 0.0(0) 0.0(0) 12.5(33) The incidence of bushfires 5.3(14) 0.8(2) 0.0(0) 0.0(0) 0.4(1) 0.0(0) 0.8(2) 7.2(19) Lack of tree cover 0.8(2) 3.8(10) 2.3(6) 0.0(0) 2.3(6) 0.4(1) 1.5(4) 11.0(29) The creation of the dam 2.7(7) 4.6(12) 0.0(0) 0.4(1) 8.7(23) 0.8(2) 9.9(26) 27.0(71) Lack of tree cover and 0.4(1) 0.4(1) 0.0(0) 0.8(2) 0.8(2) 0.4(1) 0.4(1) 3.0(8) incidence of bushfires The growth of spirogyra, algae 2.3(6) 0.8(2) 0.0(0) 0.0(0) 3.4(9) 0.4(1) 1.1 (3) 8.0(21) and water weeds The washing of vehicles 0.0(0) 0.0(0) 3.8(10) 1.9(5) 5.3(14) 0.0(0) 2.3(6) 13.3(35) The brewing of alcoholic 0.0(0) 0.0(0) 0.0(0) 7.2(19) 0.0(0) 0.0(0) 0.0(0) 7.2(19) beverages The rearing of animals 0.0(0) 0.0(0) 10.6(28) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 10.6(28) Mean=4.78 SE =.155 X2 = 540.864 d f=48 P value =.000 Total 11.4(30) 10.3(27) 16.7(44) 10.3(27) 33.5(88) 1.9(5) 16.0(42) 100(263) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

4.1.4 Sustainable management of the Barekese catchment Unfortunately only 2.7% of residents in all the seven communities had been involved in the sustainable management of the Barekese reservoir. However, about 97.3% cited lack of knowledge and experience and technical know-how in natural resource management, and the fact that the local communities were not beneficiaries of the Barekese water project.

126


Interestingly almost all (97.8%) were willing to participate in sustainable management practices of the Barekese reservoir should they be trained (Table 17). Table 17 Willingness of local communities to participate in sustainable management practices of the Barekese reservoir Parameters Involvement of the communities: N=370 Involvement of the communities Non involvement of the communities Mean=1.97 SE =.090 X2 = 22.505 d f=6 P value =.001 Total Reasons: N=360 Lack of knowledge and experience of local communities Lack of technical know-how in natural resource management The local communities were not beneficiaries of the Barekese reservoir Mean=1.58 SE =.035 X2 = 50.655 d f=12 P value =.000 Total

A. Fufuo


0.0(0)

0.0(0)

0.0(0)

9.7(36)

7.6(28)

25.1(93)

9.7(36)

7.6(28)

5.0(18)

Nkwant

Pampat

Penten

Total

0.0(0)

2.7 (10)

0.0(0)

0.0(0)

2.7(10)

11.1(41)

28.7(106)

1.9(7)

13.2(49) 97.3(360)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49) 100(370)

3.1(11)

15.3(55)

7.8(28)

12.2 (44)

0.3(1)

8.3(30)

51.9(187)

5.0(18)

4.2(15)

6.4(23)

3.3(12)

13.1(47)

1.4(5)

5.0(18)

38.3(138)

0.0(0)

0.0(0)

3.6(13)

0.0(0)

6.1(22)

0.0(0)

0.0(0)

9.7(35)

10.0(36)

7.2(26)

25.3(91)

11.1(40)

31.4(113)

1.7(6)

13.3(48) 100(360)

Readiness of local communities to be involved in the Sustainable mgt of the reservoir (N=360) Will participate when involved 9.4(34) 6.7(24) 25.0(90) 11.1(40) 30.8(111) 1.9(7) 12.8(46) 97.8(352) Will not participate when involved 0.6(2) 0.6(2) 0.3(1) 0.3(1) 0.0(0) 0.0(0) 0.6(2) 2.2(8) Mean=1.02 SE =.008 X2=9.476a d f=6 P value =.149 Total 10.0(36) 7.2(26) 25.3(91) 11.4(41) 30.8(111) 1.9(7) 13.3(48) 100(360) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages. a7 cells (50.0%) have expected count less than 5. The minimum expected count is .16.

In all the seven communities, residents (99.7%) were of the view that the dam had adversely affected their social, economic and cultural lives as a result of increased incidence of malaria, lack of farm lands and increased cost of food (Table 18). Only one community, Nkwantakese, had been compensated (State Lands Act (1962)).

127


Table 18 Social, cultural and economic effects of the Barekese reservoir on the residents in the catchment Parameters Social, cultural and economic effect the dam on the respondents: N=370 There is an effect There is no effect Mean=1.00 SE =.003 X2= 2.196 d f=6 P value =.901 Total Some effects of the dam: N=369 Non payment of compensation High incidence of Bilharzia Inadequate compensation Unfulfilled promises of free electricity & water High incidence of mosquitoes Deprived of productive lands Deprived of livelihood Increased cost of food and poverty Deteriorated water quality High levels of unemployment The planting of flowers made the few lands unproductive Mean=5.28 SE =.148 X2= 207.078 d f=60 P value =.000


A. Fufuo

A. Kokoo

Pampat Penten

Total

9.7(36) 0.0(0)

7.6(28) 0.0(0)

25.1(93) 0.0(0)

11.1(41) 0.0(0)

31.1(115) 0.3(1)

1.9(7) 0.0(0)

13.2(49) 99.7(369) 0.0(0) 0.3(1)

9.7(36)

7.6(28)

25.1(93)

11.1(41)

31.4(116)

1.9(7)

13.2(49) 100(370)

0.5(2) 0.0(0) 2.4(9) 1.4(5)

0.5(2) 0.0(0) 3.5(13) 0.3(1)

0.0(0) 0.0(0) 13.0(47) 1.1(4)

1.1(4) 0.0(0) 5.9(22) 0.3(1)

0.5(2) 3.2(12) 4.6(17) 4.9(18)

0.3(1) 0.0(0) 0.0(0) 0.5(2)

1.6(6) 0.8(3) 3.5(13) 1.4(5)

4.6(17) 4.1(15) 33.0(121) 9.7(36)

1.1(4) 2.2(8) 0.0(0) 1.6(6) 0.0(0) 0.5(2) 0.0(0)

0.8(3) 2.4(9) 0.0(0) 0.0(0) 0.0(0) 0.0(0) 0.0(0)

0.0(0) 2.7(10) 0.0(0) 0.0(0) 0.0(0) 8.4(31) 0.0(0)

1.6(6) 1.1(4) 0.0(0) 1.1(4) 0.0(0) 0.0(0) 0.0(0)

1.6(6) 3.8(14) 0.8(3) 3.0(11) 1.4(5) 5.1(19) 2.4(9)

0.3(1) 0.5(2) 0.0(0) 0.0(0) 0.3(1) 0.0(0) 0.0(0)

0.8(3) 2.4(9) 0.0(0) 2.2(8) 0.0(0) 0.5(2) 0.0(0)

6.2(23) 15.1(56) 0.8(3) 7.8(29) 1.6(6) 14.6(54) 2.4(9)

Total 9.7(36) 7.6(28) 25.1(93) 11.1(41) 31.1(115) 1.9(7) 13.2(49) 100(369) Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

In the opinion of residents in the communities, the sustainable management of the Barekese reservoir and forest reserve could be salvaged through regular spraying, payment of realistic compensation for farmlands by government and the introduction of sustainable alternative livelihoods such as bead making, beekeeping, snail and grasscutter rearing (Table 19).

128


Table 19 Local communities’ opinion on the sustainable management of the Barekese reservoir Community of Respondents Parameters

A. Fufuo

A. Kokoo

Denase

Esaase

Nkwant

Pampat

Penten

Total

Release of some lands back

0.5(2)

0.0(0)

0.5(2)

0.0(0)

0.0(0)

0.0(0)

0.3(1)

5.1(19)

Relocation of the community

1.1(4)

0.3(1)

0.0(0)

2.2(8)

2.2(8)

0.0(0)

1.4(5)

7.0(26)

Regular spraying of dam

0.3(1)

0.0(0)

13.2(49)

2.2(8)

2.4(9)

0.3(1)

1.4(5)

19.7(73)

Release some of the land + adequate compensation Release some of the land, adequate compensation + Access to potable water

0.0(0)

0.0(0)

0.0(0)

0.0(0)

6.5(24)

0.0(0)

0.5(2)

7.0(26)

0.0(0)

0.0(0)

1.1(4)

0.0(0)

3.2(12)

1.1(4)

2.2(8)

7.6(28)

Adequate compensation

5.1(19)

4.1(15)

1.6(6)

1.6(6)

1.6(6)

0.0(0)

1.6(6)

15.7(58)

Access to potable water

0.8(3)

0.0(0)

5.9(22)

1.4(5)

0.3(1)

0.0(0)

1.1(4)

10.4(26)

Access to free pipe-borne water and electricity

0.0(0)

0.0(0)

0.0(0)

0.3(1)

0.5(2)

0.0(0)

0.3(1)

1.2(3)

Return our lands to us

0.0(0)

0.0(0)

0.0(0)

0.0(0)

4.1(15)

0.3(1)

1.1(4)

5.2 (13)

Afforestation projects

0.5(2)

0.0(0)

2.7(10)

0.8(3)

0.0(0)

0.3(1)

0.0(0)

4.3(16)

Scholarship for our children

0.3(1)

0.0(0)

0.0(0)

0.0(0)

1.1(4)

0.0(0)

0.5(2)

1.9 (7)

Involvement of community in the management of the catchment

0.0(0)

0.0(0)

0.5(2)

0.5(2)

2.7(10)

0.0(0)

0.8(3)

4.6(17)

Alternative livelihoods Mean=7.42 SE =.210 X2= 384.002 d f=72 P value =.000 Total

1.1(4)

2.2(8)

0.0(0)

2.2(8)

3.0(11)

0.0(0)

2.2(8)

11.1(41)

10.8(27)

7.6(19)

22.4(56)

12.0(30)

24.8(62)

8.8(22)

13.6(34)

100(250)

The sustainable mgt of the Barekese reservoir: N= 370

Source: Household survey (2005). X2 = Pearson Chi-square value. Figures in parenthesis are the frequencies whilst those not in parentheses are in percentages.

4.2 Land Use Change in the Barekese Catchment This sub-section presents the results of land use change in the Barekese catchment from 1973-1986, 1986-2003 and an ensuing forecast from 2003 to 2043.

129


4.2.1 Change detection of Barekese catchment from 1973 to 1986 From 1973 to 1986 closed 10 forest decreased by 44.21% from 11431.96 km2 to 6377.35 km2. Open11 forest increased by 16.47% from 7131.91 km2 to 10905.75 km2. Grassland increased by 34.60% from 823.59 km2 to 2359.78 km2 and open area/town increased from 163.55 km2 to 206.02 km2 representing 20.61%. The reservoir recorded a decrease in the surface area by 60.91% from 489.05 km2 to 191.16 km2 (Fig. 5-9).

4.2.2 Change detection in Barekese catchment from 1986 to 2003 The closed forest increased by a very small margin from 6377.35 km2 to 6445.30 km2 representing 1.05% from 1986 to 2003.

However, the open forest decreased

extensively from 10905.75 km2 to 4880.06 km2 representing 55.25% resulting in more grasslands and open area/towns. Grassland increased from 2359.78 km2 to 6688.09 km2 representing 64.72%. Concurrently, open areas/ towns increased from 206.02 km2 to 1762.29 km2 representing 88.31% (Fig. 5, 8-11). All differences in the land use land change were statistically significant (𝜌 = 0.001) (Table 20).

10 11

Closed forest here refers to forest with a canopy cover of more than 60%. Open forest here refers to forest with a canopy cover of less than 60%.

130


% land use land cover change in 1986 % land use land cover change in 2003 100

Land use land cover change %

80 60 40 20 0 -20 -40 -60 -80 Closed Forest Open forest

Grass land Open area / town Waterbody

Land use land cover

Figure 5 Percent land use land cover change in the Barekese catchment from 1973-1986, 1986-2003

131


Closed forest

Open area / town

forestfores Open forest t

Water body

Grassland Cloud cover

forestfores t

Figure 6 Land use change in the Barekese catchment in 1973 12000 10000

Area (km2)

8000 6000 4000 2000 0

Closed Forest

Open forest


Land use land cover

Figure 7 Land use land cover in the Barekese catchment in 1973

132


Closed forest

Open area / town

Grassland

forestfores Open forest t

Water body

Cloud cover

forestfores t

Figure 8 Land use change in the Barekese catchment in 1986

12000 10000

Area (km2)

8000 6000 4000 2000 0

Closed Forest Open forest


Land use land cover

Figure 9 Land use land cover in the Barekese catchemnt in 1986

133


Closed forest

Open area / town

forestfores Open t forest

Water body

Grassland Cloud cover

forestfores t

Figure 10 Land use change in the Barekese catchment in 2003

8000

Area (km2)

6000

4000

2000

0

Closed Forest Open forest

Grass landOpen area / townWaterbody

Land use land cover

Figure 11 Land use land cover in the Barekese catchment in 2003

134


The three satellite images of 1973, 1986 and 2003 were put side by side to depict the level of land use change in the Barekese catchment (Fig 12).

Closed forest

Open forest

forestforest

forestforest

Water body

Grassland

Open area / town Cloud cover

forestfore st

1973 1986 2003 Figure 12 Land use change in the Barekese catchment from 1973 – 1986 - 2003

Table 20 Statistical comparison of land use in the Barekese catchment from 1973-1986 and from 1986-2003 Source of Variation SS df MS F P-value F crit Between Years 2.02E+08 5 40494611 8.944473 0.000974 3.105875 Within Years 54328003 12 4527334 Total

4.2.3

2.57E+08

17

Projected land cover change in the Barekese catchment (2003-2043)

Projections from current land use trends reveal that closed forest will continue to experience a decline in area from 2003 with a subsequent negative decline in the year 2043 (0.00 km2) if current trends continue. Open forest will however increase from 135


2003 to 2013 (4880.06 to 5353.58 km2) and consequently decrease gradually in area. The area of grassland will experience a consequential increase from 2003 -2043 (6688.09 - 13574.77 km2). Open area or town will witness a considerable rise of 1762.29 to 3783.80 km2 with a peak in the year 2043. The Barekese reservoir’s surface will eventually be covered entirely by waterweeds to the extent that the surface area available water without waterweeds in the year 2033 and 2043 will be (0.00 km2) and (0.00 km2) respectively (Table 21 and Fig 13). Table 21 Actual and projected land cover (km2) in the Barekese catchment from 19732043 Actual Years 1973 1986 km2

2003

Closed forest

11431.96

6377.35

6445.30

4037.27

Open forest

7131.91

10905.75

4880.06

Grass land

823.59

2359.78

Open area / town

163.55

Water body

489.05

Land cover

Projected Years 2013 2023

2033

2043

2460.29

883.30

0.00

5353.58

4463.06

3572.54

2682.02

6688.09

8383.88

10368.32

12353.02

13574.77

206.02

1762.29

2127.60

2679.66

3231.73

3783.80

191.16

264.32

137.73

68.73

0.00

0.00

km2

136


Closed forest Open forest Grassland Open area / town Waterbody 16000 14000

2 Area (km )

12000 10000 8000 6000 4000 2000 0 1973

1986

2003

2013

2023

2033

2043

Year

Figure 13 Projected land use land cover in the Barekese catchment for the next forty years

4.3 Physico-chemical Parameters The results of temporal mean and analysis of variance of physico-chemical parameters are presented below.

4.3.1 Temporal mean of physico-chemical parameters in the feeder streams and reservoir water from the Barekese reservoir The alkalinity of the feeder streams were generally much higher (8.3 and 205.0𝑚𝑔/𝑙) compared to the reservoir water in the Barekese reservoir (16.2-152.2𝑚𝑔/𝑙) (Table 2226). Biochemical Oxygen Demand was high in the feeder streams (1.17 to 3.86𝑚𝑔/𝑙)

137


but much lower in the reservoir water (1.09 to 1.73 𝑚𝑔/𝑙) (Table 22-26). Statistically significant differences were also observed in alkalinity and biochemical oxygen demand levels between the reservoir water and the feeder streams and between the different feeder streams themselves (Appendix 4 and Table 27).

Mean true colour ranged from 5 to 70 𝑇𝐶𝑈 for the feeder streams with a bulk of these streams exceeding the WHO recommended limit of 15 for ‘no risk’ with the exception of Ntuma stream (5 to 12 𝑇𝐶𝑈) and the reservoir water in the Barekese reservoir (5 to 13 𝑇𝐶𝑈) (Table 22-26). Statistically, there were significant differences in colour between the different feeder streams and between the reservoir water and feeder streams (Table 27 and Appendix 4).

Conductivity in both the feeder streams (31 to 213𝜇𝑆/𝑐𝑚) and in the reservoir water (95 to 158𝜇𝑆/𝑐𝑚) in the Barekese reservoir was high exceeding the recommended GWRC-TWQR of 0 − 70𝜇𝑆/𝑐𝑚 for domestic purposes (Table 22-26). There were statistically significant differences in total hardness between the reservoir water and the different feeder streams (Table 28). Total hardness ranged from 9.7 to 82.7 𝑚𝑔/𝑙 in the feeder streams and 6.3 to 46.0 𝑚𝑔/𝑙 in the reservoir water (Table 22-26).

Oil and grease were present in both the feeder streams and the reservoir water of the Barekese reservoir and varied between 0.01 and 0.12𝑚𝑔/𝑙 (Table 22-26). The WHO recommends a 𝑝𝐻 range of 6.5-8.5 and the GWRC-TWQR 6-9 for drinking, domestic

138


and recreational purposes. The 𝑝𝐻 for both the feeder streams (5.27 to 7.10) and the reservoir water (6.50-7.37) were within the accepted range (Table 22-26).

Total dissolved solids in all the sampling sites were less than the WHO guideline value of 1000𝑚𝑔/𝑙 and the GWRC-TWQR of 0-450 𝑚𝑔/𝑙 for domestic purposes (Table 2226). TDS varied from 49 to 81 𝑚𝑔/𝑙 in reservoir water and 28 to 134 𝑚𝑔/𝑙 in the feeder streams.

Total suspended solids were high in the feeder streams (5 to 166𝑚𝑔/𝑙) but much lower in the reservoir water (14 to 41 𝑚𝑔/𝑙) in the Barekese reservoir (Table 22-26). Statistically there were significant differences in mean total dissolved solids and total suspended solids between the different feeder streams and between the reservoir water and the feeder streams (Table 29 and Appendix 4). Variations in mean temperature observed were between 22.0 to 28.8℃ for the feeder streams and 25.0 to 29.8℃ in the reservoir water (Table 22-26). Statistically there were significant differences between the reservoir water and the feeder streams and between the streams themselves (Table 30 and Appendix 4).

139


Table 22: Monthly mean of selected physico-chemical parameters in reservoir water at the Barekese reservoir and Akyekasu stream Sampling Site

Parameters

mpling Site month

Reservoir Jan water

Akyekasu Jan

Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Temp

SE

pH

SE

Tds SE

Cond SE

Tss

SE

Th

25.0

0.37

7.37

0.14

52

2.05

106

5.61

16

1.43

6.3

1.41

17.0

1.26

27.7 28.2 28.0 27.8 28.3 28.5 28.5 28.5 29.0 29.7 29.8

0.92 0.70 0.37 0.31 0.42 0.56 0.43 0.80 0.37 0.21 0.17

7.08 6.88 6.90 6.97 6.97 6.77 6.50 6.88 6.55 7.08 7.37

0.15 0.15 0.18 0.25 0.26 0.25 0.18 0.22 0.02 0.15 0.14

50 49 81 72 63 71 78 49 49 50 52

2.54 1.89 3.60 1.66 2.37 1.70 2.94 2.68 0.92 2.54 2.05

97 95 158 152 125 151 154 95 109 97 106

2.90 3.48 6.30 8.99 3.85 8.98 6.30 4.92 4.66 2.90 5.61

20 17 40 41 14 39 38 20 19 18 16

1.95 2.01 3.62 5.21 1.37 5.21 4.25 3.90 0.76 1.70 1.43

23.2 46.0 43.0 29.3 36.7 26.3 41.3 46.0 38.7 24.8 24.7

2.40 7.00 5.90 1.76 2.85 2.16 6.25 9.91 0.99 1.05 0.99

16.2 23.0 151.3 152.2 91.3 150.2 148.8 58.0 61.0 16.2 17.0

24.7 25.0 24.0 25.3 24.3 22.7 22.3 22.3 22.3 22.3 22.0 22.0

0.33 0.00 0.00 0.33 0.33 0.67 0.33 0.33 0.33 0.33 0.00 0.00

5.67 5.80 5.27 5.50 5.57 5.37 5.53 5.47 5.40 6.07 5.80 5.67

0.09 0.06 0.03 0.06 0.03 0.03 0.03 0.03 0.10 0.03 0.06 0.09

42 46 34 44 46 42 44 44 34 29 46 42

1.14 0.96 1.71 1.67 2.69 4.57 2.41 1.67 1.71 0.57 0.96 1.14

85 90 65 89 90 81 89 89 65 60 90 85

2.92 1.62 1.59 3.90 4.74 7.59 4.45 3.90 1.59 0.59 1.62 2.92

7 9 9 27 29 5 27 27 22 12 9 20

1.76 1.76 1.76 2.60 3.84 2.52 3.84 2.60 5.03 1.73 1.76 5.03

9.7 12.3 12.0 20.7 21.7 16.0 21.0 20.0 22.0 16.0 22.3 16.3

3.18 2.60 3.46 1.76 2.73 2.00 2.08 1.15 2.31 1.15 3.18 4.10

29.7 8.3 25.3 90.0 92.3 42.3 90.3 86.7 55.3 37.0 25.0 29.7

140

SE

Alk

SE

Colour

SE

Oil&G SE

BOD

SE

8

1.05

0.01

0.00

1.11

0.04

2.64 2.39 2.14 2.24 2.50 2.24 1.89 2.85 2.58 2.64 1.26

10 11 11 12 13 10 12 13 5 11 11

1.83 2.01 2.01 3.07 2.11 2.24 1.67 2.42 0.00 1.54 0.83

0.02 0.05 0.02 0.05 0.06 0.05 0.02 0.05 0.01 0.02 0.01

0.00 0.02 0.00 0.03 0.05 0.03 0.00 0.02 0.00 0.00 0.00

1.20 1.12 1.73 1.64 1.64 1.62 1.70 1.09 1.35 1.29 1.32

0.03 0.04 0.12 0.13 0.13 0.13 0.12 0.03 0.14 0.09 0.04

2.91 3.38 1.45 1.73 2.91 2.33 2.91 2.33 6.49 4.04 0.58 2.91

5 5 5 5 5 5 5 5 5 18 7 7

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 7.26 1.67 1.67

0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

1.17 1.24 1.27 1.28 1.34 1.45 1.59 1.58 1.55 1.35 1.33 1.27

0.06 0.01 0.01 0.02 0.02 0.03 0.02 0.02 0.39 0.02 0.06 0.03


Table 23: Monthly mean of selected physico-chemical parameters in Nsuta and Ntuma stream Sampling Site month Temp SE

pH

SE

Tds

SE

Cond

SE

Tss

Parameters SE Th

SE

Alk

SE

Colour

SE

Oil&G

SE

BOD

SE

Nsuta

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

23.0 27.3 22.7 22.3 23.3 25.7 25.7 25.7 25.7 25.7 25.7 25.7

0.58 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33

6.23 6.43 6.30 6.27 6.23 6.10 6.03 6.20 6.30 6.67 6.43 6.23

0.09 0.09 0.06 0.03 0.03 0.06 0.03 0.06 0.06 0.07 0.09 0.09

39 40 31 61 61 43 59 57 31 35 40 39

0.67 0.28 2.12 4.86 4.81 2.38 4.81 4.86 2.12 1.24 0.28 0.67

78 79 31 122 119 82 117 118 114 69 79 78

1.21 0.79 1.50 10.25 8.24 0.43 8.24 10.25 11.81 2.90 0.79 1.21

33 19 24 67 68 18 66 65 22 16 18 33

2.65 2.96 7.54 10.26 11.36 5.93 11.36 11.54 6.66 0.88 2.03 2.65

18.7 22.8 28.0 15.0 13.3 12.3 14.7 13.7 26.7 18.7 19.5 18.7

1.18 1.89 5.03 2.52 2.40 1.45 2.40 2.33 4.37 2.91 4.90 3.55

13.3 8.7 13.7 125.0 126.7 135.7 124.7 125.0 33.7 46.3 35.3 23.3

1.86 1.76 1.20 11.93 13.17 38.08 13.17 11.93 1.20 1.20 1.76 4.10

15 10 5 5 5 5 5 5 5 8 10 15

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.67 0.00 0.00

0.02 0.02 0.12 0.02 0.02 0.01 0.02 0.02 0.12 0.01 0.02 0.02

0.00 0.00 0.09 0.01 0.00 0.00 0.00 0.00 0.09 0.00 0.00 0.00

2.21 2.31 2.49 2.51 2.65 2.66 2.74 2.77 2.72 2.67 2.61 2.42

0.02 0.09 0.09 1.45 0.08 0.04 0.03 0.08 0.03 0.02 0.19 0.06

Ntuma


24.7 25.3 28.0 24.5 25.7 26.3 26.7 26.7 26.3 26.7 27.0 26.7

1.33 1.67 1.00 1.50 0.33 0.33 0.33 0.33 0.33 0.33 0.00 0.33

6.47 6.73 6.47 6.53 6.50 6.33 6.43 6.33 6.47 6.70 6.77 6.50

0.03 0.03 0.03 0.12 0.10 0.03 0.17 0.09 0.03 0.06 0.03 0.06

81 97 94 110 107 86 106 108 94 60 89 75

5.21 4.56 10.88 4.94 2.85 3.17 2.19 3.82 10.88 4.58 5.08 1.90

160 183 163 211 213 174 211 207 163 111 167 146

9.16 6.59 7.11 4.52 5.13 8.78 5.13 4.24 7.11 2.44 3.44 5.51

17 21 13 35 37 22 35 33 13 15 20 17

2.03 2.03 2.40 1.86 3.48 4.26 3.21 0.88 2.40 1.53 0.88 2.03

43.3 46.7 82.7 40.7 39.3 41.3 38.0 36.7 78.7 38.7 46.7 43.3

0.88 1.76 1.76 2.91 4.37 3.53 4.00 2.91 2.40 4.67 1.76 0.88

19.3 19.0 24.3 201.0 205.0 157.7 201.7 197.0 64.3 74.3 19.0 19.3

1.45 2.65 1.45 5.57 4.00 19.32 3.84 5.57 6.74 2.60 2.65 1.45

12 10 8 5 5 8 10 7 10 10 10 12

1.67 0.00 1.67 0.00 0.00 1.67 0.00 1.67 0.00 0.00 0.00 1.67

0.03 0.03 0.02 0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.03 0.03

0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.00

2.66 2.79 2.79 2.80 2.88 2.89 2.90 2.96 2.95 2.95 2.89 2.85

0.02 0.02 0.06 1.61 0.01 0.02 0.03 0.06 0.03 0.03 0.05 0.03

141


Table 24: Monthly mean of selected physico-chemical parameters in Abetesua and Amoadan stream Sampling Site

Parameters month

Temp

SE

pH

SE

Tds

SE

Cond

SE

Tss

SE

Th

SE

Alk

SE

Colour SE

Oil&G SE

BOD

SE

Abetesua


25.8 26.0 24.3 24.3 24.3 22.7 24.0 25.7 25.7 26.0 26.0 25.0

0.17 0.00 0.33 0.33 0.33 0.33 0.00 0.33 0.33 0.00 0.00 0.00

5.80 6.00 5.70 5.70 5.70 5.63 5.60 5.50 5.70 6.30 6.00 5.80

0.00 0.00 0.00 0.00 0.00 0.03 0.00 0.06 0.00 0.00 0.00 0.00

37 50 42 134 134 61 132 131 42 31 50 37

0.39 0.20 1.35 0.82 0.64 0.77 0.64 0.49 1.35 0.98 0.20 0.39

74 101 83 67 67 123 65 63 83 70 96 74

0.50 1.69 0.62 0.49 0.52 1.71 0.52 0.49 0.62 0.30 2.98 0.50

160 40 71 33 34 14 32 31 71 22 40 160

0.47 0.43 0.70 1.83 1.15 0.88 1.15 0.73 0.70 1.33 0.43 0.47

19.0 18.3 32.7 34.0 35.0 21.7 33.0 31.0 31.3 14.3 18.3 19.0

0.58 0.33 0.67 1.15 0.58 0.88 0.58 1.00 0.67 0.33 0.33 0.58

18.0 8.7 13.3 112.8 113.3 95.1 112.0 108.8 33.3 46.0 28.7 18.0

0.58 0.67 0.88 0.83 0.88 0.70 1.53 0.83 0.88 0.00 0.67 0.58

70 15 20 15 15 20 20 15 20 15 15 20

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.01 0.02 0.03 0.01 0.01 0.01 0.01 0.01 0.03 0.01 0.02 0.01

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.38 3.38 3.47 3.57 3.61 3.68 3.72 3.78 3.83 3.70 3.66 3.57

0.02 0.04 0.02 0.04 0.04 0.02 0.03 0.02 0.03 0.02 0.06 0.06

Amoadan


23.7 25.7 23.0 25.0 24.3 24.0 24.3 24.7 26.2 25.7 25.7 25.7

0.88 0.33 0.58 0.58 0.33 0.58 0.33 0.33 0.33 0.33 0.33 0.33

6.37 6.77 6.27 6.27 6.30 6.30 6.33 6.27 6.40 6.50 6.70 6.47

0.09 0.09 0.09 0.09 0.12 0.06 0.09 0.03 0.06 0.06 0.06 0.03

69 94 74 86 30 102 28 99 73 71 91 75

0.64 0.30 1.11 2.91 2.31 1.56 2.31 0.90 1.59 0.84 3.05 2.53

140 192 146 173 171 203 170 199 144 147 176 147

1.62 2.47 0.58 6.54 5.24 3.31 4.60 3.31 2.15 1.27 2.69 1.81

37 47 22 31 31 12 29 16 25 20 40 41

5.78 6.64 5.44 2.81 3.21 3.84 3.21 0.88 2.18 2.60 3.55 2.60

42.7 36.3 63.0 38.3 42.0 46.0 40.7 44.7 59.7 46.0 36.3 41.0

3.38 4.26 3.51 6.17 8.72 2.31 8.11 2.91 3.18 2.31 4.26 3.21

27.0 8.3 16.3 154.7 156.0 155.0 154.7 151.7 73.0 92.0 78.3 60.3

0.58 0.88 0.33 0.67 2.00 1.15 2.67 0.67 3.51 6.03 0.88 3.38

8 17 13 13 13 7 13 12 15 12 15 12

1.67 1.67 1.67 1.67 1.67 1.67 1.67 1.67 0.00 1.67 2.89 1.67

0.02 0.03 0.02 0.01 0.01 0.03 0.02 0.03 0.02 0.01 0.03 0.02

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.41 2.48 2.54 2.54 2.63 2.71 2.75 2.86 2.72 2.64 2.61 2.43

0.04 0.02 0.01 0.07 0.02 0.03 0.03 0.07 0.03 0.07 0.09 0.02

142


Table 25: Monthly mean of selected physico-chemical parameters in River Offin and Amansie stream Sampling Site

Parameters month

Temp

SE

pH

SE

Tds

SE

Cond

SE

Tss

SE

Th

SE

Alk

SE

Colour

SE

Oil&G

SE

BOD

SE

Offin


25.2 25.7 26.7 25.5 25.8 24.7 24.8 24.7 25.0 25.3 25.0 25.0

0.40 0.56 0.92 0.62 0.40 0.92 0.87 0.80 0.77 0.49 0.37 0.37

6.65 6.93 6.52 6.48 6.52 6.53 6.53 6.43 6.52 6.65 6.85 6.62

0.07 0.08 0.05 0.06 0.07 0.05 0.06 0.06 0.05 0.02 0.05 0.05

74 83 75 84 83 77 81 82 75 62 78 76

6.56 6.80 11.21 12.34 11.29 5.19 11.29 12.18 11.21 2.56 4.23 4.61

143 161 134 165 166 153 165 162 134 109 156 141

11.35 11.22 15.04 22.88 22.49 12.39 22.35 22.62 15.04 0.73 10.52 7.40

61 30 16 41 42 14 40 38 41 21 29 27

18.61 2.97 1.09 2.23 2.78 1.43 3.02 2.40 2.80 1.45 2.54 2.12

34.5 41.8 73.2 39.3 40.2 36.7 38.2 36.0 68.2 43.3 43.5 39.5

5.37 2.74 5.62 2.56 3.10 4.22 3.10 2.68 3.69 1.12 2.53 3.18

25.8 21.2 22.4 153.1 156.2 136.4 154.2 149.2 75.7 84.3 66.2 62.5

2.12 2.02 2.81 23.64 23.05 15.57 23.05 23.71 3.03 4.36 4.25 2.17

14 16 11 9 9 7 9 9 8 10 15 13

0.83 2.01 0.83 1.54 0.83 1.05 0.83 1.54 1.12 2.24 1.83 1.05

0.02 0.02 0.03 0.02 0.02 0.01 0.02 0.01 0.02 0.01 0.02 0.02

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3.44 3.48 3.56 3.61 3.71 3.72 3.85 3.86 3.78 3.76 3.59 3.50

0.03 0.03 0.03 0.05 0.02 0.02 0.02 0.03 0.03 0.02 0.06 0.04

Amansie


27.3 26.3 24.0 26.3 26.7 27.0 27.0 27.0 26.3 26.3 26.3 25.7

0.33 0.33 0.58 0.33 0.33 0.58 0.58 0.58 0.33 0.33 0.33 0.33

6.73 5.67 6.43 5.33 5.33 5.40 5.37 5.27 6.33 5.70 5.70 6.60

0.03 0.03 0.03 0.03 0.03 0.06 0.03 0.07 0.03 0.06 0.06 0.06

55 58 59 81 80 65 78 79 62 51 56 57

0.37 0.61 1.50 3.46 3.70 4.44 3.70 3.60 3.01 0.74 2.49 1.70

110 115 117 170 169 123 167 167 124 114 122 120

1.06 1.74 2.26 9.40 8.92 3.78 8.59 8.75 1.11 6.65 4.54 5.05

52 166 35 30 30 36 30 27 33 17 66 59

1.73 2.03 3.56 5.56 5.51 8.37 4.33 4.39 3.37 1.73 2.03 5.24

34.0 15.3 48.7 33.3 33.3 28.0 31.3 29.3 42.0 25.3 25.3 31.3

2.00 1.33 0.67 4.81 4.81 4.62 4.81 4.81 4.00 5.70 6.96 0.67

28.0 17.0 20.7 96.7 93.3 55.3 91.3 92.7 40.7 57.3 54.7 31.3

1.00 1.00 1.67 1.67 1.67 3.53 1.67 1.67 4.41 11.68 4.37 1.00

12 12 12 12 12 5 13 12 12 18 12 12

1.67 1.67 1.67 1.67 1.67 0.00 1.67 1.67 1.67 10.93 1.67 1.67

0.03 0.03 0.03 0.02 0.02 0.01 0.02 0.02 0.03 0.01 0.03 0.03

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.60 2.65 2.68 2.68 2.70 2.73 2.76 2.81 2.75 2.70 2.66 2.65

0.03 0.04 0.02 0.02 0.01 0.01 0.02 0.03 0.01 0.04 0.02 0.02

143


Table 26: Monthly mean of selected physico-chemical parameters in Nwabi River and Buokese stream Sampling Site

Parameters month

Temp

SE

pH

SE

Tds

SE

Cond

SE

Tss

SE

Th

SE

Alk

SE

Colour SE

Oil&G

SE

BOD

SE

Nwabi


28.8 25.5 25.0 25.0 25.3 24.7 27.5 25.5 24.7 24.3 24.8 24.2

0.31 0.22 0.26 0.26 0.21 0.42 0.85 0.22 0.21 0.21 0.31 0.31

6.05 6.83 6.50 6.12 6.13 6.52 6.22 6.12 6.55 6.68 6.78 6.05

0.28 0.04 0.04 0.04 0.05 0.03 0.03 0.04 0.02 0.19 0.05 0.28

66 64 59 91 93 81 91 88 62 63 66 66

2.42 2.05 1.40 1.59 2.27 1.85 2.27 1.94 1.77 1.37 1.13 2.42

137 131 118 183 182 162 180 180 123 131 134 132

3.97 1.97 1.95 3.50 2.97 3.41 2.85 3.10 1.89 0.64 1.26 3.30

59 60 31 75 80 16 77 72 31 26 58 79

19.49 1.03 1.20 6.82 6.27 1.14 6.41 7.14 1.20 2.36 1.58 8.51

30.3 34.7 67.8 54.3 54.5 41.5 53.0 51.7 67.8 49.0 34.7 30.3

1.52 1.41 3.75 4.39 4.51 2.06 4.58 3.95 3.75 4.46 1.41 1.52

21.0 17.0 30.2 129.7 129.8 133.7 127.8 125.7 30.2 75.7 27.0 26.0

2.91 2.07 2.46 8.66 8.65 4.26 8.65 8.66 2.46 5.29 3.60 1.46

14 13 13 12 12 18 13 12 14 36 13 16

2.71 1.12 1.12 1.05 1.05 1.12 1.12 1.05 0.83 2.71 1.12 2.01

0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.02 0.01

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.17 2.22 2.22 2.31 2.31 2.36 2.37 2.40 2.39 2.26 2.24 2.23

0.34 0.32 0.32 0.28 0.30 0.29 0.29 0.28 0.26 0.30 0.25 0.22

Buokese


28.3 25.3 23.7 23.3 25.3 24.7 25.0 24.3 24.3 24.7 24.3 24.3

0.33 0.67 0.33 0.67 0.33 0.33 0.00 0.33 0.33 0.33 0.33 0.33

6.80 7.10 5.40 6.23 6.23 6.63 6.30 6.27 5.40 7.00 7.10 6.73

0.06 0.06 0.06 0.03 0.03 0.03 0.06 0.03 0.06 0.00 0.06 0.03

63 70 63 90 90 85 88 86 63 63 70 63

3.61 2.29 3.68 2.56 2.59 1.22 2.59 2.66 3.68 1.42 2.29 3.61

120 133 126 180 180 168 178 176 126 131 133 120

5.93 5.26 7.33 4.45 4.07 1.08 4.07 4.20 7.33 0.32 5.26 5.93

133 166 96 71 64 11 63 69 96 38 166 133

5.93 3.18 19.97 7.22 3.53 4.06 3.84 7.31 19.97 2.65 3.18 5.93

44.0 30.0 25.3 45.0 46.7 40.7 45.3 43.3 25.3 35.3 30.0 44.0

7.21 5.29 5.21 12.90 14.11 1.76 13.48 12.02 5.21 1.33 5.29 7.21

18.3 20.3 26.3 135.3 134.7 132.3 132.7 131.7 26.3 101.3 20.3 25.0

3.18 3.18 3.84 5.84 5.17 3.93 5.17 5.78 3.84 5.61 3.18 1.53

20 12 12 13 15 13 18 17 13 17 13 20

5.77 3.33 3.33 4.41 5.00 1.67 1.67 1.67 1.67 1.67 1.67 5.77

0.01 0.01 0.02 0.02 0.02 0.01 0.02 0.02 0.02 0.01 0.01 0.01

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

2.50 2.52 2.54 2.57 2.63 2.64 2.81 2.86 2.84 2.84 2.78 2.70

0.02 0.01 0.02 0.02 0.01 0.03 0.06 0.02 0.04 0.03 0.03 0.04

144


Table 27: Statistical comparison of mean alkalinity, BOD and colour of ten sampling points in the Barekese catchment Sampling site

N

Mean Alkalinity

SE Alkalinity

Range Alkalinity

F

Sig.

Mean BOD

SE BOD

Range BOD

F

Sig

Mean Colour

SE Colour

Range Colour

F

Sig

reservoir water Nsuta stream

72

75.1806

6.87575

10.00-160.00

3.627

.000 *

1.4000

.03826

1.00-2.01

13.587

.000 *

10.4861

.54313

5.00-20.00

18.468

.000*

36

67.6111

9.27416

6.00-186.00

2.5631

.03324

2.18-2.97

7.7778

.64379

5.00-15.00

Ntuma stream Abetesua stream Amoadan stream River Offin

36

100.1667

13.72921

15.00-209.00

2.8656

.01455

2.67-3.00

8.8889

.45036

5.00-15.00

36

59.0111

7.26916

8.00-115.00

3.6133

.02514

3.33-3.89

21.6667

2.49603

15.00-70.00

36

93.9444

9.52265

7.00-160.00

2.6103

.02473

2.34-2.91

12.5000

.61399

5.00-20.00

72

92.2681

7.31094

14.00-210.00

3.6540

.01868

3.36-3.96

10.8333

.50429

5.00-20.00

Amansie stream Nwabi stream Buokese stream Akyekasu stream Total

36

56.5833

4.99084

16.00-100.00

2.6983

.01090

2.56-2.87

11.8056

.95852

5.00-40.00

72

72.8056

6.09698

11.00-154.00

2.2894

.07728

1.35-3.08

15.2083

.87029

5.00-45.00

36

75.3889

9.07651

13.00-147.00

2.6861

.02330

2.45-2.90

15.2778

.97477

5.00-30.00

36

52.1111

4.79811

15.00-97.00

1.3692

.02401

1.05-1.63

6.3889

.81189

5.00-30.00

468

75.7942

2.61570

6.00-210.00

2.5456

.03877

1.00-3.96

12.1047

.34393

5.00-70.00

*The mean difference is significant at the .05 level.

145


Table 28: Statistical comparison of mean conductivity, total hardness and oil and grease of ten sampling points in the Barekese catchment Sampling site

N

Mean Cond

SE Cond

Range Cond

F

Sig.

Mean TH

SE TH

Range TH

F

Sig

Mean O&G

SE O&G

Range O&G

F

Sig

reservoir water

72

120.4208

3.31144

84.70-175.20

60.243

.000 *

32.1944

1.72692

2.00-72.00

35.036

.000*

.0296

.00570

.01-.30

3.160

.001*

Nsuta stream

36

90.3778

4.74195

28.00-136.40

18.7778

1.13696

10.00-34.00

.0339

.01096

.01-.30

Ntuma stream

36

175.7972

5.25835

106.60-218.80

48.0000

2.62043

30.00-86.00

.0217

.00135

.01-.03

Abetesua stream

36

80.4222

2.92065

62.30-125.60

25.6389

1.26501

14.00-36.00

.0150

.00129

.01-.03

Amoadan stream

36

167.2556

3.74067

137.60-209.70

44.7222

1.78824

26.00-67.00

.0200

.00126

.01-.03

River Offin

72

148.9292

4.68281

96.90-220.80

44.5278

1.70817

20.00-90.00

.0168

.00084

.01-.03

Amansie stream

36

134.9750

4.27330

103.00-182.60

31.4444

1.69521

14.00-50.00

.0222

.00120

.01-.03

Nwabi stream

72

149.5097

3.02103

110.30-197.50

47.4722

1.75114

25.00-80.00

.0168

.00086

.01-.03

Buokese stream

36

147.3694

4.38497

109.00-188.40

37.9167

2.47026

16.00-68.00

.0147

.00101

.01-.03

Akyekasu stream

36

81.5861

2.01014

59.40-98.20

17.5000

.94994

4.00-28.00

.0100

.00000

.01-.01

Total

468

131.9618

1.89315

28.00-220.80

36.3376

.75921

2.00-90.00

.0203

.00127

.01-.30


146


Table 29: Statistical comparison of mean pH, total dissolved solids and total suspended solids of ten sampling points in the Barekese catchment Sampling site

N

Mean pH

SE pH

Range pH

F

Sig.

Mean TDS

SE TDS

Range TDS

F

Sig

Mean TSS

SE TSS

Range TSS

F

Sig

reservoir water Nsuta stream Ntuma stream Abetesua stream Amoadan stream River Offin Amansie stream Nwabi stream Buokese stream Akyekasu stream Total

72 36 36 36 36 72 36 72 36 36 468

6.9431 6.2861 6.5194 5.7861 6.4111 6.6028 5.8222 6.3792 6.4333 5.5917 6.3615

.05674 .03116 .02977 .03567 .03280 .02281 .08927 .04829 .09495 .03870 .02470

6.00-7.80 6.00-6.80 6.10-6.80 5.40-6.30 6.10-6.90 6.20-7.20 5.20-6.80 5.40-7.00 5.30-7.20 5.20-6.10 5.20-7.80

62.875

.000*

59.6917 44.7083 92.0222 73.5111 74.2806 77.5042 65.1750 74.1972 74.6139 41.1000 68.3229

1.54421 1.95639 2.82017 7.20406 3.89558 2.48717 1.96134 1.59686 2.07605 1.05391 1.07550

42.00-88.00 28.00-67.20 50.70-118.70 30.00-135.70 24.00-104.60 45.60-120.70 49.80-87.80 55.20-102.30 55.80-95.30 28.20-50.30 24.00-135.70

25.741

.000*

24.6806 37.4833 23.1111 59.1278 29.2250 33.1667 48.5444 55.3194 92.1944 20.1389 41.2429

1.50076 3.98268 1.58053 8.14144 1.95594 2.15689 6.51231 3.28401 8.25379 1.52292 1.54734

10.00-55.00 7.00-86.00 10.00-43.00 12.00-161.40 5.00-56.30 10.00-106.00 14.00-170.00 12.00-110.00 4.00-170.00 6.00-36.00 4.00-170.00

26.500

.000*

*The mean difference is significant at the .05 level

Table 30: Statistical comparison of mean temperature of ten sampling points in the Barekese catchment Sampling site

N

reservoir water Nsuta stream Ntuma stream Abetesua stream Amoadan stream River Offin Amansie stream Nwabi stream Buokese stream Akyekasu stream Total

72 36 36 36 36 72 36 72 36 36 468

Mean Temp 28.2500 24.8611 26.2083 24.9861 24.7500 25.2778 26.3611 25.4444 24.8056 23.2778 25.6303

Std. Error Temp .19049 .27066 .25925 .18092 .18420 .18683 .17436 .18224 .22474 .22043 .09031


147

Range Temp 24.00-30.00 22.00-28.00 21.50-29.00 22.00-26.00 22.00-26.00 22.00-29.00 23.00-28.00 23.00-30.00 22.00-29.00 22.00-26.00 21.50-30.00

F

Sig.

43.812

.000*


Chloride concentrations in the feeder streams varied between 2.76 and 15.18 𝑚𝑔/𝑙 but were much lower (4.72-5.81𝑚𝑔/𝑙) in reservoir water. Chloride was within the GWRC-TWQR ‘no effect range’ of

0 − 100𝑚𝑔/𝑙

for all the sampling sites.

Statistically significant differences were observed between reservoir and the feeder streams and between the feeder streams themselves (Table 31, Appendix 4 and 7). Levels of nitrate in the feeder streams ranged from 7.16 to 24.19 𝑚𝑔/𝑙 and from 10.26 to 14.26𝑚𝑔/𝑙 but exceeded the GWRC-TWQR ‘no effect range’ of 0-6𝑚𝑔/𝑙 in all the sampling sites. Incongruously nitrate in all the sites were within the maximum acceptable limits by WHO (50 𝑚𝑔/𝑙). Statistically there were significant differences in mean nitrate between reservoir and the feeder streams (Table 31, Appendix 4 and 7).

Variation in the concentration of sulphate in the feeder streams (10.36 to 25.49 𝑚𝑔/𝑙) and reservoir water (10.57 to 15.84𝑚𝑔/𝑙) were observed. Sulphates in all the sampling sites were within the GWRC-TWQR ‘no effect range’ of 0 − 200𝑚𝑔/𝑙 for domestic use of water. In general there were statistically significant differences in mean sulphate between reservoir and the feeder streams and between the feeder streams themselves (Table 31, Appendix 4 and 7).

148


Table 31: Statistical comparison of mean chloride, nitrate and sulphate concentrations of ten sampling points in the Barekese catchment Sampling site

N

Mean chloride

reservoir water

72

5.2993

SE Range chloride chloride .07371 4.02-6.32

Nsuta stream

36

3.8406

.15212

Ntuma stream

36

7.5989

Abetesua stream

36

Amoadan stream

F

Sig.

Mean nitrate 11.5764

SE nitrate .23842

Range nitrate 10.01-17.90

39.370

.000*

2.61-6.03

10.4592

.02710

.70144

2.59-15.25

10.4997

4.5219

.18108

3.32-7.03

36

8.2875

.34270

River Offin

72

7.6610

Amansie stream

36

Nwabi stream

Mean sulphate 12.2592

SE R Range sulphateS sulphate .30482 10.15-19.20

10.11-10.90

22.5847

.45819

20.39-27.97

.04970

10.17-11.52

12.8739

.51931

10.29-19.62

10.5083

.03910

10.11-10.87

12.1308

.09793

10.50-12.59

5.02-13.19

21.0758

.08720

20.11-21.89

15.4317

.37504

11.05-19.50

.24477

4.37-12.25

12.1014

.20576

10.15-14.90

17.4492

.29939

11.03-24.00

9.6139

.43196

5.08-16.12

11.4325

.14772

10.40-13.06

16.1128

.32913

11.09-19.90

72

9.6356

.30799

4.99-15.00

22.5964

.16496

20.38-24.99

17.6563

.38502

10.50-24.82

Buokese stream

36

8.5692

.29537

4.80-11.41

22.3353

.18599

20.21-23.99

15.5397

.30364

10.50-18.90

Akyekasu stream

36

6.9861

.38200

4.06-9.99

11.8517

.24121

10.11-13.99

14.3064

.35810

10.05-18.90

Total

468

7.2777

.13320

2.59-16.12

14.6701

.24046

10.01-24.99

15.6699

.17611

10.05-27.97


149

F

Sig

746.970

.000*

F

Sig

68.110

.000*


4.4 Heavy Metals and Cyanide The results of temporal mean and analysis of variance of heavy metals (arsenic, copper, lead, iron and zinc) and cyanide are presented below.

4.4.1 Temporal mean of heavy metals and cyanide in the feeder streams and reservoir water from the Barekese Reservoir Levels of arsenic in the different feeder streams varied between 0.01 and 0.33 𝑚𝑔/𝑙 but were much lower (0.01-0.29𝑚𝑔/𝑙) in reservoir water. Arsenic levels exceeded the WHO recommended guideline value of 0.01 𝑚𝑔/𝑙 but were within the GWRC-TWQR (010𝑚𝑔/𝑙). There were statistically significant differences in levels of arsenic between reservoir and the feeder streams and between the feeder streams themselves (Table 32, Appendix 5 and 8).

Copper concentrations were consistently constant for all the sampling points (0.01𝑚𝑔/𝑙) and within the GWRC-TWQR of 0.0 -0. 01𝑚𝑔/𝑙 but below the WHO guideline value of 2 𝑚𝑔/𝑙. On the whole there were no statistically significant differences between reservoir water and the nine feeder streams of the Barekese reservoir (Table 32, Appendix 5 and 8).

Cyanide was present in both the feeder streams and the reservoir water of the Barekese reservoir and varied between 0.01 and 0.11 𝑚𝑔/𝑙 and exceeded the WHO guideline value of 0.07𝑚𝑔/𝑙. The GWRC-TWQR recommends (0-0.01𝑚𝑔/𝑙) iron in water for domestic purposes whilst the WHO recommends a guideline value of 2 𝑚𝑔/𝑙. Levels of iron in the feeder streams varied from 0.01 to 5.02 𝑚𝑔/𝑙 and 0.10 to 0.47𝑚𝑔/𝑙 for reservoir water and exceeded the recommended range for all the sampling sites. The differences in cyanide and iron between the reservoir water and the feeder streams were statistically 150


significant and were observed between the feeder streams themselves (Table 32-33, Appendix 5 and 8).

The concentration of lead in the feeder streams varied between 0.01 and 0.28 𝑚𝑔/𝑙 and 0.01 to 0.37𝑚𝑔/𝑙 for reservoir water and exceeded the WHO maximum acceptable limits of 0.01𝑚𝑔/𝑙 for all the sampling sites. In contradiction it was within the GWRC-TWQR ‘no effect range’ of 0-10 𝑚𝑔/𝑙 for all the sites (Table 33, Appendix 5 and 8).

Zinc in the different feeder streams ranged between 0.01 and 0.73 𝑚𝑔/𝑙 but was much lower (0.02-0.20𝑚𝑔/𝑙) in reservoir water. Levels of zinc were within the GWRC-TWQR ‘no effect range’ of 0-3𝑚𝑔/𝑙 and below the WHO guideline value of 3 𝑚𝑔/𝑙 (Table 33, Appendix 5 and 8).

151


Table 32: Statistical comparison of mean arsenic, copper and cyanide of ten sampling points in the Barekese catchment Sampling site

N 72

Mean Arsenic .1874

SE Arsenic .01188

Range Arsenic .01-.40


36

.1564

.01567

Ntuma stream

36

.1303

Abetesua stream

36

Amoadan stream

F

Sig.

Mean Copper .0100

SE Copper .00000

Range Copper .01- .01

4.356

.000*

.01-.29

.0100

.00000

.01949

.01-.32

.0100

.1767

.02158

.01-.40

36

.1772

.02225

River Offin

72

.1211

Amansie stream

36

Nwabi stream

Mean Cyanide .0628

SE Cyanide .00592

Range Cyanide .01-.11

.01- .01

.0628

.00844

.01-.11

.00000

.01- .01

.0517

.00833

.01-.11

.0100

.00000

.01- .01

.0489

.00824

.01-.11

.01-.42

.0100

.00000

.01- .01

.0683

.00833

.01-.11

.01163

.01-.45

.0100

.00000

.01- .01

.0321

.00426

.01-.11

.1550

.01518

.01-.30

.0100

.00000

.01- .01

.0267

.00630

.01-.11

72

.1508

.01128

.01-.37

.0100

.00000

.01- .01

.0392

.00539

.01-.11

Buokese stream

36

.1289

.01342

.01-.30

.0100

.00000

.01- .01

.0350

.00732

.01-.11

Akyekasu stream

36

.0836

.00986

.01-.20

.0100

.00000

.01- .01

.0218

.00561

.01-.11

Total

468

.1482

.00483

.01-.45

.0100

.00000

.01- .01

.0450

.00218

.01-.11


152

F

Sig

.000

1.000

F

Sig

5.723

.000*


Table 33: Statistical comparison of mean lead, iron and zinc of ten sampling points in the Barekese catchment Sampling site

N 72

Mean Lead .2131

SE Lead .01691

Range Lead .01-.70


36

.0728

.01334

Ntuma stream

36

.0700

Abetesua stream

36

Amoadan stream

F

Sig.

Mean Iron .2754

SE Iron .01849

Range Iron .07-.80

13.303

.000*

.01-.29

.2475

.01242

.00774

.01-.19

.8500

.1017

.01482

.01-.35

36

.1308

.01261

River Offin

72

.1308

Amansie stream

36

Nwabi stream

Mean Zinc .0654

SE Zinc .01359

Range Zinc .01-.90

.06-.38

.0231

.00218

.01-.05

.05721

.47-1.87

.0833

.03383

.01-.90

2.3422

.31249

.30-5.23

.0403

.00654

.01-.15

.03-.37

2.2822

.29782

.38-5.20

.0850

.01420

.01-.36

.00605

.03-.29

1.7321

.09447

.50-3.55

.1229

.01829

.01-.90

.0886

.01284

.01-.30

.9644

.17797

.01-3.45

.0392

.00877

.01-.24

72

.1025

.00831

.02-.30

1.2788

.11408

.01-3.46

.0418

.00407

.01-.18

Buokese stream

36

.1008

.01397

.02-.34

.8028

.15713

.03-3.20

.0322

.00368

.01-.09

Akyekasu stream

36

.0936

.01447

.01-.34

.1453

.01551

.01-.31

.0222

.00239

.01-.07

Total

468

.1193

.00449

.01-.70

1.0928

.05559

.01-5.23

.0604

.00484

.01-.90

*The mean difference is significant at the .05 level..

153

F

Sig

30.093

.000*

F

Sig

5.633

.000*


4.5 Microbiological Parameters Geometric mean bacterial (total and faecal coliforms and E. coli) numbers in waters at the different sampling points is presented in Table 34.

4.5.1 Microbiological quality in the feeder streams and reservoir water from the Barekese reservoir Mean bacterial indicator numbers (geometric mean 100 ml -1) at all the ten sampling sites ranged from 1.45x104 to 9.50 x 107 for total coliforms, 1.60x10 3 to 9.00x105 for faecal coliforms and 1.50x101 to 9.50x103 for E. coli (Table 34).

Total coliform numbers in the different feeder streams were high varying between 1.45 x104 and 9.50 x 107 and were lower in the reservoir water (1.65 x 106 to 2.18 x107) (Figure 14). Statistically significant differences were observed in total coliform numbers between the reservoir water and the different feeder streams (Table 35 and Appendix 6).

Faecal coliform numbers were slightly lower compared to the total coliforms and varied between 1.60 x 103 to 9.00 x 105 for the feeder streams and 1.73 x 10 4 to 1.84 x 105 in the reservoir water (Figure 14). Statistically there were significant differences in faecal coliform numbers between reservoir water and the feeder streams (Table 35 and Appendix 6).

E. coli numbers in the feeder streams were between 1.50 x 10 1 and 9.50 x 103 and 2.00 x 102 to 2.85 x 102 in the reservoir water (Table 34 and Figure 14). Statistically, E. coli numbers were varied significantly between reservoir and the feeder streams (Table 35 and Appendix 6).

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Table 34 Mean and range of total coliforms, points in the Barekese catchment Bacterial Parameters Sampling site Total coliforms Reservoir water Nsuta stream Ntuma stream Abetesua stream Amoadan stream River Offin Amansie stream Nwabi stream Buokese stream Akyekasu stream Faecal coliforms Reservoir water Nsuta stream Ntuma stream Abetesua stream Amoadan stream River Offin Amansie stream Nwabi stream Buokese stream Akyekasu stream E. coli Reservoir water Nsuta stream Ntuma stream Abetesua stream Amoadan stream River Offin Amansie stream Nwabi stream Buokese stream Akyekasu stream

faecal coliforms and E. coli at the ten sampling Log Mean 9.69E+06 1.86E+07 1.15E+07 1.12E+07 1.93E+07 2.29E+06 1.87E+07 2.11E+07 2.26E+07 2.89E+06 8.90E+04 1.50E+05 1.23E+05 1.26E+05 1.30E+05 2.03E+04 1.21E+05 1.94E+05 2.15E+05 6.01E+04 2.28E+02 1.97E+03 1.92E+03 1.78E+03 1.71E+03 3.02E+02 1.25E+03 1.51E+03 2.44E+03 2.45E+02

155

Range 1.65E+06 - 2.18E+07 2.90E+06 - 9.30E+07 1.90E+06 - 2.50E+07 2.90E+05 - 2.60E+07 9.00E+05 - 9.30E+07 1.16E+06 - 5.83E+06 2.10E+06 - 9.40E+07 2.48E+06 - 6.70E+07 2.85E+06 - 9.50E+07 1.45E+04 - 6.40E+06 1.73E+04 - 1.84E+05 1.60E+04 - 3.55E+05 2.30E+04 - 2.90E+05 1.60E+03 - 2.85E+05 2.60E+03 - 2.60E+05 1.13E+04 - 4.38E+04 6.40E+03 - 3.90E+05 1.50E+04 - 4.75E+05 4.30E+03 - 9.00E+05 2.75E+03 - 4.13E+05 2.00E+02 - 2.85E+02 2.10E+02 - 4.30E+03 2.00E+02 - 4.30E+03 1.90E+02 - 4.30E+03 1.95E+02 - 4.40E+03 2.20E+02 - 4.60E+02 1.90E+02 - 3.50E+03 3.15E+02 - 3.40E+03 9.50E+01 - 9.50E+03 1.50E+01– 3.90E+02

2 0 Jan

Feb

Mar

Apr

May

Jun

Jul

Aug Sep

Oct

Nov Dec

X Data

Impacts of land use change on some water quality parameters in the Barekese catchment Total coliforms Faecal coliforms E. coli

B

A

4

2

Log No. bacteria /100ml

6

8

6

4

2


4

2

6

4

2



Sampling month

Sampling month

G

6

4

2 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Sampling month

I

H


Sampling month

8

8 Log No. bacteria /100ml

Log No. bacteria / 100ml

2


F

Log No. bacteria/100ml

6

8

Sampling month

8

8



8

4


E

6

4

Sampling month

D

8

6


Samplinng month


C

8 Log No. bacteria /100ml


8

6

4


Sampling month

6

4


Sampling month

J

6

4

2


Sampling month

Figure 14 Mean bacterial indicator numbers in the water samples from Barekese reservoir (A) and the feeder streams; Nsuta (B), Ntuma (C), Abetesua (D), Amoadan (E), River Offin (F), Amansie(G), Nwabi (H), Buokese (I) and Akyekasu(J).

156


Table 35: Statistical comparisons of log mean total coliforms, faecal coliforms and E. coli in the ten sampling points in the Barekese catchment Sampling site

N

TC Log mean 6.3292

SE TC .03144

Range TC 5.89-6.72

reservoir water

72

Nsuta stream

36

6.9486

.08656

Ntuma stream

36

6.8653

Abetesua stream

36

6.6872

Amoadan stream

36

River Offin

72

Amansie stream Nwabi stream

F

Sig.

FC Log mean 4.6663

SE FC .04478

Range FC 4.18-5.61

19.783

.000*

6.37-7.98

4.9231

.08673

.08139

6.15-7.59

4.8617

.11561

5.42-7.45

4.6456

6.8506

.09995

5.63-7.98

6.2276

.05045

5.08-6.98

36

6.9328

.09582

72

6.9567

.07015

Buokese stream

36

7.0544

Akyekasu stream

36

5.9783

EC Log mean 2.2274

SE EC .00760

Range EC 2.15-2.37

4.15-5.55

3.0086

.09532

2.30-3.72

.08289

4.11-5.55

3.0297

.09283

2.28-3.87

.13142

3.18-5.46

2.8986

.10090

2.23-3.72

4.7731

.12208

3.15-5.72

2.9703

.08943

2.27-3.64

4.1357

.05952

3.04-4.81

2.4618

.01198

2.28-2.72

6.19-7.98

4.7047

.12230

3.72-5.64

2.8483

.09108

2.20-3.59

6.20-7.98

4.8971

.08039

3.97-5.72

2.9565

.06072

2.06-3.64

.08797

6.28-7.98

4.6536

.15397

3.63-5.95

3.0031

.10032

1.88-3.98

.17463

4.16-6.88

4.4086

.06634

3.36-5.62

2.2944

.06367

1.04-2.64

Total 468 6.6419 .03089 *The mean difference is significant at the .05 level.

4.16-7.98

4.6437

.03061

3.04-5.95

2.7188

.02526

1.04-3.98

157

F

Sig

8.900

.000*

F

Sig

25.166

.000*


4.3.1.3 Speciation of faecal bacteria indicator isolates The identified isolates in the reservoir water and feeder streams of the Barekese reservoir belonged to five genera (Serratia, Enterobacter, Citrobacter, Salmonella and Klebsiella). The isolates were all positive for catalyse. All the faecal coliforms from the reservoir water and feeder streams samples were identified as Serratia liquefaciens (38%), Serratia marcescens (33%), Citrobacter braakii (5%) and Klebsiella pneumoniae ssp ozaenae (2%). Enterobacter identified were 7% Enterobacter sakazakii, 5% Enterobacter cloacae and 5% Enterobacter gergoviae. Salmonellae isolated included 2% Salmonella choleraesuis ssp arizonae and 2% Salmonella spp. Of these isolates, Serratia marcescens and Serratia liquefaciens were the most predominant, being isolated from all samples (Table 36).

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Table 36: Phenotypic characterization of bacterial isolates in Barekese reservoir and feeder streams Isolate Sampling site API 20E Result Numerical Profile 001 Amoadan 7316573 Serratia liquefaciens stream 001 Amoadan 7316573 Serratia marcescens stream 002 Amoadan 7312153 Enterobacter gergoviae stream 002 Amoadan 7312153 Enterobacter sakazakii stream 003 Amansie stream 7316573 Serratia liquefaciens 003 Amansie stream 7316573 Serratia marcescens 004 Amoadan 7316573 Serratia liquefaciens stream 004 Amoadan 7316573 Serratia marcescens stream 7317773 005 Abetesua Serratia liquefaciens stream 7317773 005 Abetesua Serratia marcescens stream 7317773 005 Abetesua Enterobacter sakazakii stream 006 Amansie stream 7316673 Serratia liquefaciens 006 Amansie stream 7316673 Serratia marcescens 007 River Offin 7716553 Citrobacter braakii 007 River Offin 7716553 Salmonella choleraesuis ssp arizonae 007 River Offin 7716553 Salmonella spp 008 Abetesua 7316453 Serratia liquefaciens stream 008 Abetesua 7316453 Enterobacter cloacae stream 008 Abetesua 7316453 Serratia marcescens stream 008 Abetesua 7316453 Klebsiella pneumoniae ssp stream ozaenae 008 Abetesua 7316453 Citrobacter braakii stream 009 Buokese stream 7716673 Serratia liquefaciens 009 Buokese stream 7716673 Serratia marcescens 010 Reservoir water 7716473 Serratia liquefaciens 159


011 011 011 011 012 012 013 013 013 014 014 015 015 016 016 017 018 018

River Offin River Offin River Offin River Offin Nsuta stream Nsuta stream Nsuta stream Nsuta stream Nsuta stream Nwabi stream Nwabi stream Ntuma stream Ntuma stream Akyekasu stream Akyekasu stream Nsuta stream Reservoir water Reservoir water

7716373 7716373 7716373 7716373 7716673 7716673 7316663 7316663 7316663 7716573 7716573 7716673 7716673 7316673

Enterobacter sakazakii Serratia liquefaciens Enterobacter gergoviae Serratia marcescens Serratia liquefaciens Serratia marcescens Serratia liquefaciens Serratia marcescens Enterobacter cloacae Serratia marcescens Serratia liquefaciens Serratia liquefaciens Serratia marcescens Serratia liquefaciens

7316673

Serratia marcescens

7716473 7316573 7316573

Serratia liquefaciens Serratia liquefaciens Serratia marcescens

4.3.1.4 Dendograms illustrating the clusters of bacteria isolates Figure 15 shows the relative size of the proximity coefficients at which faecal bacteria isolates from reservoir water and the feeder streams of Barekese reservoir were combined. The bigger the distance coefficient or the smaller the similarity coefficient, the more clustering involved combining unlike entities, which may be undesirable (Everitt et al., 2001). The isolates from the reservoir water and feeder streams belong to six clusters. The dendogram is depicted horizontally, with each row representing bacterial isolates on the Y axis, while the X axis is a rescaled version of the proximity coefficients. Bacterial isolates with low distance/high similarity are close together, showing low distance, with a line linking them a short distance from the left of the dendogram, indicating that they are agglomerated into a cluster at a low distance

160


coefficient, indicating a likeness. The identified cluster distance was design at A, B, C, etc while subgroups within a cluster were designed as A1, A2, B1, B2, C1, C2 etc Rescaled Distance Cluster Combine C A S E Label Num

A B

C

D

E

7316673 7316673 7716673 7716673 7716473 7312453 7716573 7716573 7316663 7316663 7316663 7716673 7716673 7716373 7716373 7716373 7716373 7316573 7316573 7316573 7316573 7312153 7312153 7316573 7316573 7316673 7316673 7313253 7716673 7716673 7716473 7316453 7316453 7316453 7316453 7316453 7716253

34 35 32 33 36 37 30 31 28 29 27 25 26 23 24 21 22 7 8 5 6 3 4 1 2 10 11 9 18 19 20 16 17 13 14 15 12

0 5 10 15 20 25 +---------+---------+---------+---------+---------+ ─┐A1 ─┼─┐ ─┤ ├───────┐ ─┘ │ │ ─┬─┘A2 │ ─┘ │ ─┐ ├─────────────────────────────────────┐ ─┼─┐ C1 │ │ ─┤ │ │ │ ─┤ │ │ │ ─┘ ├───────┘ │ ─┐ │ │ ─┤ │ │ ─┼─┘ │ ─┤ C2 │ ─┤ │ ─┘ │ ─┐ D1 │ ─┼─┐ │ ─┤ │ │ ─┘ ├───────────────┐ │ ─┐ │ │ │ ─┼─┘ │ │ ─┤ D2 │ │ ─┘ ├─────────────────────────────┘ ─┐ E1 │ ─┼───┐ │ ─┘ │ │ ─┐ ├─────────────┘ ─┤ │ ─┤ │ ─┼───┘ ─┤ ─┤ E2 ─┤ ─┤ ─┘

Figure 15 Dendogram illustrating the clusters of bacteria isolates in the Barekese reservoir and the feeder streams

161


CHAPTER 5: DISCUSSION 5.1

Social Survey

The findings of the social survey are discussed vis-à-vis its implications.

5.1.1


The study shows that the literacy levels amongst the communities bordering the Barekese catchment is low (5.7%) and much lower than the Ghana Population census figure of, 42.6%. In Ghana, literacy levels in rural areas is much lower (44.4%) compared to the urban areas (73.1%) (Ghana Statistical Service, 2005a) and this is an indicator of the general poverty, gender disparities and traditions and norms in the rural communities (Table 2). The low level of literacy in the country is a source of concern since it is not compatible with the national goals of sustainable social and economic development. Literacy is an important indicator of how effective a society can transmit its culture from generation to generation in a written form. Literacy significantly influences the socio-economic and cultural behaviour of communities and plays a role in determining the capacity of the individual to profit from the planned activities of formal and non-formal education (Ghana Statistical Service 2005a; University of Linköping, 1990). The Millennium Development Goals may never be achieved unless the low level of literacy in this country is significantly improved.

In all the seven communities the female population was higher (61.1%) compared to the males possibly because of the generally lower death rates amongst females and migration which is an important factor that produces differences in sex ratios within 162


communities (Ghana Statistical Service, 2005b). This agrees with the national sex ratio of 97 males to 100 females, which is what pertains in most African countries (Ghana Statistical Service, 2005b; Laar et al., 2004; Nobile et al., 2000). This can facilitate the use of women as target groups in empowering communities.

Most of the communities bordering the Barekese dam were Akans (90.5%) which is also the dominant ethnic group within the Ashanti region (78.9%) (Ghana Statistical Services, 2005c). The language can therefore be employed in educating the communities on issues of environmental degradation and the rational exploitation of the natural resources. Churches can also be engaged in educating the communities on various issues for the advancement of the communities since Christianity (77.5%) was the dominant religious group followed by Islam (13.2%) in the region (Ghana Statistical Service, 2005c).

Majority of the populace (53.0%) were indigenes of the local community and had lived there for over twenty years. There has been an increasing realization that indigenes are valuable sources of information (Ishaya and Abaje, 2008; Nyong et al., 2007).

In Ghana, water supply coverage for both the rural and urban areas in 2004 was 51.6% and 55%, respectively (Ghana National Policy, 2007). Notwithstanding the availability of water to meet water supply, there are deficits. It is regrettable to note that although the water supply to the whole of the Kumasi metropolis is obtained from the Barekese dam, the communities in the catchment do not have access to treated pipe-borne water and this may explain why most of these communities have resorted to the use of 163


streams (18.1%) and boreholes (4.3%) (Table 3). This supports the projected assertion by WHO and UNICEF (2000) that 1.1 billion poor people still lack reasonable access to improved drinking water supplies and 2.4 billion lack access to improved sanitation (Derman and Hellum, 2007).

Similarly, the rural communities (48.6%) along the dam lack access to KVIP facilities and have therefore resorted to open defecation (Table 4) This was higher than the average of 31% of rural inhabitants in developing regions without access to any type of improved sanitation (Freshwater, 2004; WHO/UNICEF, 2004).

5.1.2 Land use in the Barekese catchment The mainstay economic activities of the communities along the dam is farming (70.3%), probably because it is a decent generational occupation coupled with high unemployment, low literacy and poverty in the area (Table 5). In the Ashanti region of Ghana, agriculture provides employment to more than half of the economically active population followed by trade, which employs between 3.1% and 20.7% (Ghana Statistical Service, 2005b). It was also observed that farming in the catchment of the dam was done close to the river banks of the main River Offin and its tributary the Nwabi stream (Table 6). About 74.9% of the residents farm within 5-25m of the River Offin and Nwabi stream. These will receive nutrients, and pesticides from upland agricultural fields. However, riparian forest buffer zones have been reported to have a positive impact in filtering these nutrients and pesticides (Anbumozhi et al., 2005). It may therefore be important to establish buffer systems in the Barekese catchment to protect the feeder streams and the reservoir. Residents farm close to the watercourses 164


because of the scarcity of fertile agricultural land, non payment of compensation and as a form of protest (Table 7). Kumasi et al. (2007) argued that reasons for the increased anthropogenic alteration in the catchment could be numerous. Firstly, although most of the communities in the catchment lost their agricultural lands to the construction of the dam in 1969, they had not received adequate compensation from government. Secondly the promise of free pipe-borne water and electricity supply has not been realized. Thirdly, increasing population and lack of good governance and poverty could also be some of the main reasons for the environmental degradation (ICIMOD, 2002; Chalise and Sial, 2000; Liniger et al., 1998).

Changes in agricultural land use often induce changes in the hydrological behaviour of a dam’s catchment. Subsequently, conversion of tropical forest ecosystems such as the Barekese reserve to agricultural land use can also have drastic adverse impact on the quality of the water as this may result in temporal changes in the sediment load and concentrations of the dissolved nutrients (Bormann et al., 1999; Lai, 1997).

The rate at which the Barekese reserve was being used for farming and hunting (50.3%) was disquieting though the inhabitants of the surrounding communities admitted it was illegal (Table 8). Wildlife in tropical forests is an important resource for local communities. Hunting is done to meet the short-term economic needs of inhabitants of tropical forest. However, balancing these short-term economic needs with long-term developmental and conservation needs has to be evaluated by considering the sustainability of resource use (Bennett and Robinson, 2000).

165


Another critical problem for the fast degradation of the forest cover within the Barekese catchment could be the use of fuel wood only (48.6%) and charcoal only (15.9%) for cooking in the communities (Table 10). The fact that people in the rural communities have easy access to fuel wood, at relatively no cost (Ghana Statistical Service, 2005c), explains why they tend to use more of it than the other sources, particularly gas and electricity. By 1900, Ghana had 8.1 million hectares of forest, compared with 2.1 million hectares today (Ghartey, 1989). Similarly, Fair (1992) and Ebregt (1995) reported that Ghana's rainforests have been reduced from 8.2 million hectares to 2 million hectares since 1900. In keeping with these figures; the World Bank (1988) has estimated that Ghana’s closed forest has been lost at an annual rate of 75,000 hectares since the beginning of the twentieth century. If, as most authors point out, deforestation has recently slackened, this is presumably because little forest remains, and what remains is reserved (Leach and Fairhead, 2000) though there is still encroachment in these reserves.

Most of the vegetable producing farmers in the Barekese catchment use chemical fertilizers and other agrochemicals (34.1%) the residues end up in the reservoir to increase its nutrient status although the nutrient status of water bodies in the Ashanti Region is low (CEDAR, 1999; Harris, 1997). This was the situation at the River Offin and Nwabi stream as vegetable farmers resorted to the use of fertilizers and agrochemicals (Table 9). The use of water to increase agriculture and forestry production affects both the quantity and quality of water sent to the downstream communities.

166


The increased effects of deteriorated water quality on the health of humans (Suraj, 2004) could be potentially overwhelming, given the crucial role of water in human activities.

The conventional farming practices of slash and burn agriculture along watersheds has devastating effects on water resources. When watersheds are cleared of vegetation, several environmental changes may occur which include: increasing stream runoff, floods and increasing runoff concentration time after storms. Sediments may also be deposited on previously 'clean' streambeds and the migration and spawning of fish may be affected. Soil erosion spread, sediment in rivers increase, and river channels become silted and alter significantly the flow of rivers during floods. Sedimentation in water resources may alter their ecological characteristics and reduce their useful lifetime (Nsiah-Gyabaah, 2000a) as was observed in the feeder streams of the Barekese reservoir.

A catchment approach to the study of the Midwestern U.S.A. river system indicates that water quality, habitat and biotic integrity of the river are strongly influenced by land use through human alteration of the landscape affecting the riverine ecosystem (Doppelt et al., 1993). The study revealed unsustainable agricultural practices, bushfires, uncontrolled deforestation and enormous encroachment of the reserve as a result of rural poverty and weak institutional mechanisms as the factors responsible for the degraded water quality and reduced quantity of the reservoir.

167


5.1.3


The study shows that the various human centred activities by the residents along the Barekese catchment have adversely resulted in flooding (87.3%), seasonal drought conditions where tributary streams dried up (76.2%), decline in catch per effort (50.0%), changes in hydrology of rivers (67.6%) and changes in the quality of surface waters (71.1%) (Table 12-16). Bank erosion and severe flooding destroy valuable streamside or riparian habitat. Loss of tree cover leads to greater water temperature fluctuations, making the water warmer in the dry season and colder in the wet season. Most importantly, there is substantial loss of aquatic habitat as the natural ecosystem is varied (Whitehead et al., 2006). Flooding accounts for about 40% of all natural disasters that occur worldwide. In 2002-2003 many counties in England experienced severe floods. Floods are particularly important in public health terms (Euripidou and Murray, 2004) as they may have multiple environmental consequences. Anthropogenic influences on the generation of floods in river basins are one of the main reasons for the world-wide observed disasters due to such events. Moderate land use changes result in only small changes of various water balance components, while larger effects can be achieved only by the reforestation of larger areas (Lahmer et al., 2001).

The results of diverse human activities, which take place in the Barekese river basin and feeder streams, are reflected in the reservoir. Human influences frequently reduce infiltration, causing more direct runoff and thus increasing the likelihood of floods (Zain et al., 2003). Land use change to human settlement from agricultural land and water basin area, deforestation, overgrazing are some of the examples, of increasing rapid runoff of the Barekese catchment. 168


Buildings, roads and paved areas are effectively waterproof and also cause very rapid runoff, leading to conditions favourable for floods. Human interference with the Barekese reservoir and ecosystems has severely affected their natural physical characteristics and biological complexity, thus undermining their productivity and resilience (Nilsson et al., 2007). For example, in North America by the year 2000, 27 species of freshwater fish had gone extinct in the previous century and 37% of the more than 1200 freshwater fish species were at risk of extinction (Abell et al., 2000). Strikingly, similar erosion of biodiversity is underway in other parts of the world experiencing rapidly increasing economic growth (Dudgeon et al., 2006). In tropical Asia for example where there is no legislation specifically regulating environmental protection or conservation to protect biodiversity, overexploitation of rivers has caused the depletion of many fish populations (Dudgeon, 2005). In Ghana however, there is legislation but the lack of enforcement could account for the decline (50.0%) in catch per unit of effort (Table 15) of the rivers in the seven communities.

Using changes in the quality of water as an indicator of the quality, about 71.1% of respondents (Table 16) had observed some changes in the water. This has severe repercussions if the Millennium Development Goal aimed at halving the number of people without clean drinking water and emphasizing the critical importance of clean water and a rights-based approach are to be achieved (Derman and Hellum, 2007). It has been suggested that the rising demand for water and the degradation of its quality, represents the most serious threat to the provision of various goods and services required by society (FAO, 2000).

169


The presence of oil and grease (0.3%), insects (7.6%), algae and water weed (13.0%), changes in colour (26.2%), smell (11.4%) and lathering ability of the water (1.6%) were cited as some observed changes in the quality of the water (Table 16). The clearing of forests around the water bodies account for the growth of algae as it is directly related to temperature. The growth of algae in surface waters, estuaries and coastal waters is sensitive to temperature (Harvell et al., 1999; Epstein et al., 1993). It is also apparent that algal blooms facilitate the transmission of cholera. Electron microscopy has shown that algae and the zooplankton that feed upon them provide a natural refuge for Vibrio cholerae, where, under normal conditions, the bacteria exist in a nonculturable, dormant state (Hainees et al., 2000).

The use of the banks of the Barekese feeder streams as washing bay for vehicles could account for the presence of the observed oil and grease (Table 16) in the water. Oils can form films on the surface, and some oil derived substances, such as xylenes and ethylbenzene which are volatile may give rise to odours or tastes even though they are of low toxicity. Detergents can give rise to aesthetic problems if foaming occurs, particularly since this can be confused with foam caused by the by-products of algal growth (WHO, 2004; Bartram and Rees, 2000).

5.1.4 Sustainable management of the Barekese catchment There is the issue of negligence, lack of involvement of the communities (97.3%), non payment of compensation and the negative effects of the dam on the lives of the communities militating against the sustainable management of the reservoir and reserve. 170


5.1.4.1 The involvement of local communities in the sustainable management of the Barekese Catchment Despite the many promises that were made to the communities who lost access to their farmlands as a result of the construction of the dam in 1969, none of these promises have been fulfilled. The fact that only 2.7% (Table 17) perceived that their local communities had been involved in the sustainable management of the Barekese reservoir was disquieting. This may buttress the fact that if these communities were involved in the initial project planning and implementation with sufficient education on the benefits that may accrue to them, then this valuable resource could have been saved from unsustainable use. Projects are often designed without local input and consultation and efforts to gain local acceptance are sought later (Kumasi et al., 2010; Sharpe, 1998; Campbell and Vainio-Mattilia, 2003). This is not different from the situation of the local communities along the Barekese catchment.

Contrastingly, local cooperation should be central, not peripheral, as local objections can override the best conservation intentions. Joint priority-setting, planning and implementation can decrease conflict and thus reduce costs. Rather than viewing the local communities as part of the conservation challenge, to be educated, compensated or given economic alternatives, local priorities for conservation should be placed at the centre of joint conservation strategies. The involvement of indigenes to engage effectively in a range of planning activities is crucial to achieving land equity and community goals. This argument is also relevant in the face of long-standing tensions between indigenous peoples residing in post-settler societies and nation-states such as

171


Australia, Canada, and New Zealand over questions of land and natural resource use (Vermeulen and Sheil, 2007; Buckles, 1999; Lane, 2006).

The implication of the non involvement of local communities is hostility towards specific conservation initiatives frequently encountered among local communities (Sharpe, 1998). This usually results from neglect of their own concerns or from perceived abuses by executing agencies, rather than any genuinely anti-nature sentiments. Nonetheless what makes the entire situation reassuring is the fact that the local communities are willing to be involved in the sustainable management (97.8%) of the Barekese reservoir (Table 17). Ultimately, conservation is something that most people are willing to support to some degree. Even those who have to sacrifice their lands for projects accept the need for conservation interventions more generally (Kumasi et al., 2010; McLean and Stræde, 2003). Working with the local communities efficiently utilizes both local and external knowledge (Sheil et al., 2006).

The lack of knowledge and experience of local communities (51.9%), lack of technical know-how in natural resource management (38.3%) and the fact that the local communities were not the beneficiaries of the Barekese reservoir (9.7%) are reasons for their non involvement (Table 17) of the reservoir management. These have serious repercussions for the sustainable management of this fundamental reservoir. This is because community participation and commitment are necessary for projects involving natural resource management (CIDA, 1995). Local communities’ needs must be taken into consideration especially those that affect their livelihoods and resource base. Active participation of the affected communities in all stages of a project is needed for 172


the sustainability of the project. Ownership and/or control of the resource have been found to also affect the management of that resource. Many projects have failed because the ownership/control question was not addressed at the planning stage (Mathbor, 1990). This is because the local communities perceived the Barekese dam to be owned by the government.

It has been more than 20 years since the implementation of the first community based natural resource management (CBNRM) projects in Africa, which enhanced local user rights and stewardship over natural resources (Adams and Hulme, 2001; Hulme and Murphree, 2001; Songorwa et al., 2000; Songorwa, 1999). The approach arose from an outcry against excluding communities from resources and from the failure of governments to manage the resources efficiently (Musumali et al., 2007). This is not different from the management of the Barekese catchment. Significant inclusion and the fostering of responsibility cannot be instilled artificially in local communities. The local communities must be given opportunities to make decisions, control actions that affect their lives, and benefit from their involvement for them to appreciate community based natural resource management. Local institutional competence, capacity, and outreach must be improved to enable the evolution of societal responsibility (Musumali et al., 2007). Sustainable management of water resources should be through local participation. It is only when local people own and take responsibility for water supply that they will ensure its rational use (Nsiah-Gyabaah, 2000a). This presumably will then lead to the sustainable management of the Barekese reservoir and the reserve.

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The local communities will participate in environmental management in a meaningful way; if they are also given adequate information and knowledge about the natural and built environments to be able to make informed decisions. The success of environmental management programmes depends on a complex set of political– institutional and scientific factors. However, it also greatly depends on each citizen’s participation and understanding of the environment (Menegat, 2002).

Achieving effective conservation in the Barekese catchment is a global concern but implicates local communities. Despite considerable rhetoric about local participation the vast majority of conservation initiatives continue to be devised and controlled by a small group of powerful, external voices. What is widely overlooked is that local people often have positive conservation goals and preferences (Kumasi et al., 2010; Vermeulen and Sheil, 2007). The local people in the Barekese catchment can be part of a solution, rather than of the problem, if they are given the opportunity. Strong partnerships entailing shared decision making, shared risks and a balance of rights and responsibilities between external conservation agencies and local interest groups will offer conservation outcomes that are more ethical and often more realistic than contemporary models. Partnerships are today seen as a primary route to sustainable and equitable development (Commission on Sustainable Development, 2004).

Authoritarian approaches to imposing conservation may claim some success but are becoming increasingly indefensible. Partnerships provide a more democratic approach to decision making in conservation and have both ethical and pragmatic justifications. The ethical rational is that natural resource governance should be legitimate and 174


subject to democratic control; conservation costs and benefits should be distributed equitably. The pragmatic rationale is that partnership could lead to more effective and economical conservation by avoiding costs associated with conflict and leading to more intrinsically sustainable conservation programmes in the local communities (Vermeulen and Sheil, 2007).

5.1.4.2 The payment of compensation to local communities in the Barekese catchment The refusal of government to pay compensation to the farmers who lost their farmlands to the construction of the dam has compelled most of these communities to farm on the fertile lands in the reserve and on watercourses (Table 18). The 1992 Constitution vests all customary lands which constitute approximately 80% of the land in Ghana (Larbi et al., 1998; Alden and Hammond, 2001; Kasanga and Kotey, 2001) in the appropriate stool, skin or land owning family on behalf of and in trust for their people. This implies that such lands be managed according to the fiduciary duty of the traditional authorities towards their people on the basis of customary law, which is recognised as a source of Ghanaian law (Articles 267(1), 36(8) and 11, 1992 Constitution). However, the lack of specific provisions on how customary lands should be managed by traditional authorities, and increasing land values lead to widespread disputes over powers to allocate rights in customary land and entitlements to the proceeds of these land allocations (Ubink and Quan, 2008).

Many authors have blamed African land tenure systems for the poor agricultural production and environmental degradation in Africa, and therefore the resulting 175


hunger, environmental refugees and the lack of socio-economic progress (Bugri, 2008). Subsequently international land policy tends to emphasize the importance of recognizing and building on customary tenure systems in order to achieve equitable land management in developing countries. In Ghana, where customary transactions have become increasingly monetized, the equity of customary systems under the control of traditional chiefs is being questioned. At the heart of the problem are issues of authority to allocate land rights and the entitlements to the proceeds from such allocations (Ubink and Quan, 2008). Furthermore, land tenure and resource availability play a critical role in the land use decision-making process, resulting in different types of land use changes. Tenure insecurity is found to be associated with deforestation and forest encroachment (Wannasai and Shrestha, 2008).

Compulsory land acquisition powers have been used extensively in Ghana since colonial times, as the main means of the state’s access to land for development (Larbi et al., 2004). The underlying principle is supremacy of the state over people and their private property. This is aimed at providing land for public and social amenities, correcting economic and social inefficiencies in private market operations and providing greater equity and social justice in the distribution of land. This power provides the state with an overriding interest over access, control and management of land irrespective of the tenure category under which the land is held or owned (OkothOgendo, 2000). Though there are sound theoretical reasons why governments may acquire land compulsorily and the Barekese communities are not excluded (Larbi et al., 2004) it could have momentous cost if caution is not taken in the initial stage.

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A major change in procedure was introduced in the post-independence era detaching compensation payment from the acquisition process. The effect was that compensation for lands acquired ceased after 1966 and the NRC/SMC12 policy of repudiation of national debts worsened the situation. Compensation has not been paid for about 90% of all lands acquired after 1966 and this includes the communities in the Barekese catchment. This translates into a total of 82,563.24 ha of lands acquired13 for which compensation has not been paid. The Land Valuation Board estimates (actual current figures are lacking) that the state is currently indebted to a conservative figure of about 800 billion cedis (US$ 94.1 m) in compensation payment. This is the magnitude of the potential debt that should be paid by the state (Brobby, 1991).

Larbi et al. (2004) noted that under the Land Administration Project, an inventory of all compulsorily acquired lands throughout the country will be undertaken to assess the exact lands acquired, extent of development, extent of encroachments (if any), whether compensation has been paid or not, and current value of outstanding compensation. The data will enable the government to declare its policy on the compulsorily acquired lands and outstanding compensation. Whether the government will return unutilized lands, pay the outstanding compensation, or trade-off some of the outstanding compensation for a return of some of the lands is yet to be determined. It must however be emphasized that the non-payment of compensation, non-development and change of use of some of the acquired lands have resulted in massive encroachments

12

The National Redemption Council (NRC) later metamorphosed into the Supreme Military Council (SMCI) and SMCII which was overthrown on 4 June 1979 to usher in the 4 months regime of the AFRC (Larbi et al., 2004). 13 79.6% of all post independence acquisitions (Brobby, 1991).

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on acquired but unutilized lands by the indigenous owners. This is not different from the situation in the Barekese catchment where even the utilized lands (reserve) are encroached. There are hundreds of cases (Lands Commission, 2001) in the courts for the state to either pay the compensation or return the lands to their indigenous owners (Larbi et al., 2004).

5.1.4.3 The adverse effects of the Barekese reservoir to the local communities Dams have been found to affect the lives of communities living close to them. In the Barekese catchment a total of about 4.1% and 6.2% admitted that the incidence of Bilharzia and malaria respectively as a result mosquitoes were on the ascendancy in the local communities respectively (Table 18). The bilharzia was especially recorded in the children who bathed in the streams and rivers and even in adults who had contact with the infected streams.

It is estimated that the annual water availability per person in 2025 is likely to result in at least 40% of the world's 7.2 billion people facing serious problems with obtaining freshwater for agriculture and industry or human health (Gleick, 2001). To meet present and future needs with the currently available surface and groundwater resources, while at the same time preserving terrestrial and aquatic ecosystems, will require a sustainable approach to managing water (Hiscock et al., 2002). The range of options the local communities suggested for the sustainable management of the Barekese reservoir though laudable are not sustainable (Table 19). The issue of regular spraying of the dam (19.7%), realistic compensation packages (15.7%), returning the 178


lands (5.1%) and relocation of the local communities (7.0%), access to free pipe-borne water, and electricity (1.2%) may not be sustainable. This is because every generation of the local communities will keep demanding for realistic compensation packages. The introduction of alternative livelihoods, involvement of the local communities and the award of scholarship to brilliant but needy children of the local communities by the Ghana Water Company Limited will give the local communities a sense of commitment towards the sustainable management of the reservoir. In so doing, the local communities will become shareholders of the Barekese reservoir ensuring equity of the resource management and allocation as well as they feeling they are part of development.

The re-introduction of norms and traditions within the communities along the Barekese catchment could instil respect in the rural dwellers and consequently act as environmental protection measures. Beginning several decades ago, the idea that indigenous people and other small scale societies were exemplary conservationists gained widespread currency in popular media as well as academic circles. The basis of indigenous conservation has often been attributed to a spiritual respect for, and a practical understanding of, the natural world (Vecsey, 1980; Martinez, 1996; Berkes, 1999; Smith and Wishnie, 2000). Evidence offered in support of this characterization includes culturally expressed conservation ethics, animistic religious beliefs that conceptualize other species as social beings and the impressive environmental knowledge they possess (Nelson, 1982; Durning, 1992; Posey, 1992; Gadgil et al., 1993; Callicott, 1994; Alcorn, 1996; Bodley, 1996; Nabhan, 1997). These traditional taboos and norms observed decades ago ensured that farming, hunting and fishing 179


were forbidden on particular days and seasons and these norms eventually protected the natural resources.

5.2

Land Use Change in the Barekese Catchment

This study shows that the Barekese reservoir and its catchment have undergone tremendous changes from 1973 to 2003 as a result of anthropogenic modification and that this has had adverse effects on the quality and quantity of the water resources. Land cover changes in the Barekese catchment will be more severe during the next 40 years.

5.2.1 Change detection of Barekese catchment from 1973 -1986 The satellite imagery of the Barekese reservoir and its catchment shows that there has been decreases in the closed forests from 1973 -1986 which has resulted in an increase in open forest, grassland and open area/towns (Fig. 5). This can be attributed to human activities and also to the severe harmattan season bush fires experienced throughout the country from 1982 to 1983. The pervasive bush fire is on record to have destroyed 35% of crops and forest cover (Leach and Fairhead, 2000; Nsiah-Gyabaah, 1996). The total forest cover in Ghana has declined from 7.5 million hectares to 6.3 million hectares between 1990 and 2000, and the current alarming rate of deforestation, is estimated at three percent per year (IUCN, 2006).

Land use change in the Barekese catchment is a very important aspect of “global change”. This is because it is often induced by changes in population trends and economic environments intimately linked to other forms of change, including changes in climate, biological diversity, and accelerated land degradation (El-Swaify, 2002). 180


Comparably, the River Nile has also been shown to be of great importance to the population living along it, however, factors such as the rapidly growing population combined with the ecological consequences, and the increasing agricultural activities which demands more water as in the case of Barekese, are problems facing the river (Mohammed, 2007). In Ghana the Densu River is an important source of water supply for the people in its catchment area including urban cities. It is confronted with three agents of degradation: Unconventional farming practices, deforestation and pollution. The Densu River is presently one of the most polluted rivers in the country due to growing population densities, industrialization and intensification of agricultural activities (Amoako et al., 2010; WRC, 2003).

The changes in the land use patterns of the Barekese catchment certainly provide many social and economic benefits. However, they also come at a cost to the natural environment (Kumasi et al, 2007). One of the major direct environmental impacts of the catchment development is the degradation of water resources and water quality (USEPA, 2001). Land use change in the catchment can have significant effects on runoff volume and consequently non-point source pollution (Tang et al., 2005).

The satellite imagery showed that the size of the reservoir decreased by 60.91% and this could possibly be due to the fact that the surface of the water body itself was covered by water weeds and may have been captured as a decrease in size by the satellite. Amuzu (1973) observed that although the Barekese reservoir had a juvenile surface, about two thirds, had been extensively covered by weeds mainly of the floating type Pistia stratiotes and weed mats of about 3.1km across. One of the sources 181


of pollution of the Barekese reservoir was the decomposition of the water weeds and other vegetation. This could cause eutrophication if not removed and may generate a recycling of nutrients as the weeds decompose and return the nutrients absorbed into solution. An attempt was made to control the weeds in 1972 by spraying with paraquat but the results were disappointing. Besides, because the lake water was the main source of pipe borne water supply to the Kumasi metropolis, the residual effects of the herbicide on the water quality was of concern to health professionals.

5.2.2 Change detection of Barekese catchment from 1986-2003 Kumasi et al. (2007) indicated that the marginal increase of 1.05% in closed forest from 1986 to 2003 was as a result of the massive reforestation campaign by the Ghana Water Company in the mid 1980s with the help of a local timber industry Logs and Lumber Ltd (LLL) in Kumasi to protect the integrity of the reserve (Fig. 5). However because of inadequate financing the project was abolished before its key objectives could be met. Similarly, the open forest decreased substantially by 55.25% resulting in increased grassland area as a result of increased agricultural activities, commercial logging and small scale loggers (chain saw activities). The significant increase in grassland area (64.72%) may possibly be attributed to the activities of increased population of the communities in the catchment which has also resulted in increased encroachment of the reserve.

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It is estimated that environmental degradation, mainly soil degradation, due to agriculture costs the Ghanaian economy an amount of GH¢2.88 million14 or US$88.5 million on the Ghanaian economy which constitutes about 4% of GDP15 (Botchie et al., 2003). In Ghana, forests provide many products on which the local population subsists. However, these resources are depleting due to a variety of factors including agricultural expansion and over-exploitation of forest resources. The four most highly ranked causes of deforestation in the Barekese catchment are poverty-driven agriculture, lack of alternative rural wage employment other than farming, household population levels, and conflict in traditional land practices (Blay et al., 2008).

Furthermore climate change in the catchment is expected to lead to an intensification of the global hydrological cycle and have major impacts on regional water resources. Human activities16 in the catchment can lead to increasing the atmospheric concentrations of greenhouse gases, which alter radiative balances and tend to warm the atmosphere. In Africa today, tropical forests and rangelands are under threat from population pressures and systems of land use. Generally, apparent effects of these threats include loss of biodiversity, rapid deterioration in land cover and depletion of water availability through destruction of catchments and aquifers (IPCC, 1997).

28.8 billion old Ghana cedis The Gross Domestic Product (GDP) represents the total value of the goods and services produced by an economy over some unit of time (a month, a season, a year etc.). The "Domestic" part of the name comes from the fact, unlike GNP; it does not consider imports or exports in the calculation. 16 Primarily the burning of fossil fuels and changes in land use and land cover. 14 15

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Multiple linear regression analyses indicate that climate and land use are nearly equal in their importance in the regulation of river solute concentrations, suggesting that both factors must be taken into account when managing water quality in developing watersheds (Interlandi and Crockett, 2003). There is potential for synergy between the global environmental conventions on climate change, biodiversity and desertification: changes in land management and land use undertaken to reduce net greenhouse gas emissions can simultaneously deliver positive outcomes for conservation of biodiversity, and mitigation of desertification and land degradation (Cowie et al., 2007).

5.2.3 Projected land cover change in the Barekese catchment The impact of human activities as a result of land cover changes in the Barekese catchment will be more severe during the next 40 years. Accompanying the projected land cover change in the Barekese catchment will be significant shifts in regional climate and weather patterns. This is because land cover change will affect regional climate through changes in surface energy, water balances, and the division of energy into sensible and latent heat (Sohl and Sayler, 2008; Foley et al., 2005; Kalnay and Cai, 2003; Pielke et al., 2002; Snyder et al., 2004).

Closed forest is projected to suffer incalculably from year 2003 to 2043 (6445.30 to 0.00𝑘𝑚2 ) followed by the Barekese reservoir surface area (water body) from (264.32 to -0.00 𝑘𝑚2 ) (Table 21 and Fig.13). The possible reasons for this negative projected change could possibly be due to population growth and demand for wood products

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(Wear, and Greis, 2002; Sohl and Sayler, 2008). These changes may have adverse implications for water quality and quantity in the reservoir.

5.3 Physico-chemical Parameters In general, the quality of the main reservoir water and that of its feeder streams in terms of the physico-chemical parameters was not satisfactory. Alkalinity, biochemical oxygen demand, conductivity, oil and grease, total suspended solids, total dissolved solids and total hardness were higher in the feeder streams compared to the main reservoir water.

5.3.1 Alkalinity The alkalinity of the feeder streams were generally much higher (8.3 to 205.0𝑚𝑔/𝑙) compared to the reservoir water in the Barekese reservoir (16.2 to 152.2𝑚𝑔/𝑙) (Table 27). Alkalinity in rivers and streams is often as a result of the soils and bedrock in these water bodies. This is because soils and bedrocks have been shown to contain compounds that impact on alkalinity as the stream flow through. Additionally, the rocks that provide alkalinity also tend to provide other nutrients, such as phosphorus, that promote plant growth. Soils in Kumasi belong to the Bekwai, Nzema, Kokofu and Oda series, which are poorly drained, and are usually found in the low-lying areas or valley bottom. These soils are clayey or silt loams and are described as Forest Oxysols because of their ‘sharp’ or acidic nature (Adu, 1992). Alkalinity was highest in the Ntuma stream and lowest in the Akyekasu. Stewart and Skousen (2003) noted that alkalinity neutralizes acidity and complexes dissolved metals, therefore the Ntuma stream with high alkalinity level will be able to supply adequate amounts of carbonate, 185


bicarbonate, and hydroxide ions in solution to bind up free protons and metals. In other words, streams with high alkalinity would be able to resist changes in 𝑝𝐻 if an acid was added to it by acid rain, for example as alkalinity prevents drastic changes in the 𝑝𝐻 of a water body. In other related works, alkalinity was found to be much lower (88.0-572 𝑚𝑔/𝑙) in Lake Bosumtwi, 83.6-96. 0 𝑚𝑔/𝑙 in the Weija reservoir, 35.742.5 𝑚𝑔/𝑙 in the Brimusu river and 31.0-50.8 𝑚𝑔/𝑙 in the Inchaban Reservoir (Bosque-Hamilton et al., 2004; Karikari and Bosque-Hamilton, 2004).

5.3.2 Biochemical Oxygen Demand Biochemical Oxygen Demand levels in the feeder streams was high (1.17 to 3.86𝑚𝑔/ 𝑙) compared to lower levels (1.09 to 1.73 𝑚𝑔/𝑙) in the reservoir water (Table 27). The levels in the reservoir water were within the natural background concentration of 13 𝑚𝑔/𝑙 for BOD in freshwater bodies (Ansa-Asare and Asante, 1998). The reason for the high BOD in the feeder streams may be due to the high inputs of both domestic and agricultural wastes generated by the many human activities of the nearby residents in the Barekese catchment (Kazi et al., 2009; Robson and Neal, 1997).

The reasons for the highest and least BOD for Abetesua and Akyekasu streams respectively are several (Table 27). Abetesua stream is a small watershed located in a deforested area engulfed with farming on water courses, use of inorganic fertilizers, rapid bushfires and experiences drying up during the dry season. Divergently Akyekasu has a source that is a spring with modest human activities and this explains the moderate quality of the water as compared to Abetesua.

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5.3.3 Colour Mean true colour ranged from 5 to 70 𝑇𝐶𝑈 for the feeder streams with nine streams exceeding the WHO recommended limit of 15 for ‘no risk’ with the exception of Ntuma stream (5 to 12 𝑇𝐶𝑈). However, true colour for the reservoir water ranged from 5 to 13 𝑇𝐶𝑈 which was well below the WHO recommended value of 15TCU (Table 27). Colour has been shown to be an important physical property of water because consumers reject potable water with colour (Karikari and Ansa-Asare, 2006). Consequently increase in the colour of reservoir water at the Barekese reservoir will result in increase in treatment cost (Amoako et al., 2010) with the indication of a high propensity to produce by-products from disinfection processes.

Abetesua and Akyekasu recorded the maximum and minimum in colour respectively. The reason for the high colour in Abetesua was as a result of human activities and humus fraction of the soil. The low colour in Akyekasu was probably due to the low human population in the community and therefore little human activity and the stream source being a spring. Colour in natural water has been found to usually result from the leaching of organic materials and is primarily the result of dissolved and colloidal humic substances, primarily humic and fulvic acids. Colour is also strongly influenced by the presence of iron and other metals, either as natural impurities or as corrosion products and as a result of decaying vegetation (Karikari and Ansa-Asare, 2006).

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5.3.4

Conductivity

Conductivity in both the feeder streams (63 to 213𝜇𝑆/𝑐𝑚) and the Barekese reservoir r (95 to 158𝜇𝑆/𝑐𝑚) was high exceeding the recommended GWRC-TWQR of 0 − 70𝜇𝑆/𝑐𝑚 for domestic purposes (Table 28). The conductivity of most freshwaters often range from 10 to 1,000 𝜇𝑆/𝑐𝑚 but may sometimes exceed 1000 𝜇𝑆/𝑐𝑚. The sources of conductivity in the Barekese reservoir and its feeder streams could possibly be as a result of human activities. Additionally conductivity is influenced by both natural and anthropogenic processes (Sarkar et al., 2007). The gradual increase in conductivity observed in the feeder streams and the successive decline in River Offin and subsequently in the reservoir water at the reservoir is possibly a reflection of the accumulated impacts of domestic and agricultural wastes inputs in the feeder streams. Furthermore Karikari and Ansa-Asare (2006) and Ferrar (1989) have shown that conductivity of a river is lowest at the source of its catchments and as it flows along the course of the river, it is contaminated by leachate from the soils and also picks up organic material from the biota and detritus.

5.3.5

Total Hardness (Calcium Carbonate)

There were statistically significant differences in total hardness between the reservoir water and the different feeder streams. Total hardness ranged from 9.7 to 82.7 𝑚𝑔/𝑙 in the feeder streams and 6.3 to 46.0 𝑚𝑔/𝑙 in the reservoir water (Table 28 and Appendix 4). The reservoir can therefore be classified as soft which agrees with a previous work by Amuzu, (1973). In a related study, Bosque-Hamilton et al. (2004) observed that the waters of the Weija Reservoir was moderately soft (