PHYSICO-CHEMICAL CHARACTERISTICS AND METAL BIOACCUMULATION IN FOUR MAJOR RIVER SYSTEMS THAT TRANSECT THE KRUGER NATIONAL PARK, SOUTH AFRICA.

By

HARRY JONATHON BARKER

DISSERTATION

Submitted in Fulfillment of the Requirements for the Degree

MASTER OF SCIENCE

in

AQUATIC HEALTH

in the

FACULTY OF SCIENCE

at the

UNIVERSITY OF JOHANNESBURG

Supervisor: Professor V. Wepener Co-supervisor: Doctor T. Gyedu-Ababio Co-supervisor: Professor J.H.J. Van Vuren

November 2006

TO MY PARENTS.

‘I CAN NO OTHER ANSWER MAKE, BUT, THANKS, AND THANKS AND EVER THANKS’

WILLIAM SHAKESPEARE

Table of Contents

TABLE OF CONTENTS

Acknowledgements ...... i Summary...... ii Opsomming...... v List of Tables ...... vii List of Figures...... ix

Chapter 1 General Introduction.

1.1 Background ...... 1 1.2 Water quality and quantity with regard to South Africa...... 2 1.3 Metal ecotoxicology ...... 3 1.3.1 Emission of metals...... 4 1.3.2 Biomonitoring...... 4 1.4 Main objectives ...... 5 1.5 References...... 6

Chapter 2 Situation Analysis and Locality Descriptions.

2.1 Introduction ...... 9 2.2 Sabie River...... 11 2.2.1 General description...... 11 2.2.2 Water quality ...... 12 2.3 Letaba River...... 13 2.3.1 General description...... 13 2.3.2 Water quality ...... 14 2.4 Shingwedzi River ...... 16 2.4.1 General description...... 16 2.4.2 Water quality ...... 18 2.5 Luvuvhu River...... 19

Table of Contents

2.5.1 General description...... 19 2.5.2 Water quality ...... 20 2.6 References...... 21

Chapter 3 Physico-chemical and Nutrient Analysis of Water and Sediment in the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers.

3.1 Introduction ...... 24 3.2 Methods...... 25 3.2.1 Field sampling ...... 25 3.2.2 Laboratory techniques...... 26 3.2.2.1 Water...... 26 3.2.2.2 Sediment ...... 27 3.2.3 Data processing...... 27 3.3 Results...... 27 3.3.1 Water quality and nutrient analysis ...... 27 3.3.2 Sediment...... 53 3.3.3 Multivariate analysis...... 56 3.4 Discussion ...... 57 3.4.1 Water quality and nutrient analysis...... 57 3.4.2 Sediment...... 63 3.4.3 Multivariate analysis...... 64 3.5 Conclusions ...... 65 3.6 References...... 66

Chapter 4 Metal Accumulation in Water, Sediment and Fish Tissue from the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers.

4.1 Introduction ...... 70 4.2 Methods...... 72 4.2.1 Field sampling ...... 72 4.2.2 Laboratory techniques...... 73

Table of Contents

4.2.2.1 Water...... 73 4.2.2.2 Sediment ...... 73 4.2.2.3 Fish...... 75 4.2.3 Data processing...... 75 4.3 Results...... 75 4.4 Discussion ...... 86 4.5 Conclusion...... 92 4.6 References...... 93

Chapter 5 Setting Quality Criteria for Nine Metals in the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers by Applying the Equilibrium Partitioning Method.

5.1 Introduction ...... 100 5.2 Methods...... 101 5.2.1 Toxic effects data...... 102 5.2.2 Product standard data...... 103 5.3 Results...... 103 5.4 Discussion ...... 107 5.5 Conclusion...... 112 5.6 References...... 112

Chapter 6 Conclusions and Recommendations.

6.1 Conclusions ...... 115 6.2 Recommendations...... 117 6.3 References...... 119

Acknowledgments

ACKNOWLEDGEMENTS

Special thanks and appreciation to the following persons and organisations:

♦ Our Lord for granting me the privilege, opportunity, ability, patience and strength to complete this study.

♦ My supervisor Professor Victor Wepener for his professional assistance, guidance, support, advice and motivation throughout the study.

♦ My parents for their ever willing support, guidance and understanding in allowing me to turn a dream into reality.

♦ My best friend Tracy, a pillar of strength and someone I can trust to always be there for me.

♦ My Brother Graham and Candice for their patience, encouragement and advice.

♦ The Head of Department of Zoology (University of Johannesburg), Prof J.H.J van Vuren for the use of facilities, funding and the opportunity to conduct this study.

♦ The Kruger National Park personnel, especially Dr Gyedu-Ababio for assistance and guidance with numerous aspects of this project, without them it would not have been possible.

♦ All my friends at the Zoology Department for their support and assistance.

♦ Kruger Park Lodge, especially Gary and Michelle for the use of the lodge.

♦ Mpumalanga and Limpopo Provincial Government for permits.

♦ Resource Quality Services (RQS) at DWAF for metal analysis.

i Summary

SUMMARY

The escalating population growth and increased forestry, mining, agricultural and industrial development in the catchment areas over past years has had a profound effect upon water quality and quantity, resulting in increased pollution levels and a reduction in flow rates. This is cause for concern not only to water users in the upper catchments but also to the down stream user, the Kruger National Park, which is exceedingly dependent on good quality waters in order to maintain and sustain a large variety of ecosystems. This study was therefore aimed at investigating the physico- chemical, nutrient and metal concentrations of four major river systems that transect the park, namely the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers.

By virtue of its position along South Africa’s eastern border the Kruger National Park (KNP) receives waters from seven major river systems each of which enter via the western boundary, flow eastwards through the park and on into Mozambique. The majority of the catchment areas of these river systems are situated upstream of the park itself making water quality and quantity management an important yet challenging task.

Water quality is a significant and powerful determinant of health of aquatic ecosystems. Full assessment of chemical and physical attributes upon the river systems was supported by biological monitoring thus integrating changes in the system over time. Water and sediment samples were collected seasonally between September 2005 to March 2006 at sites outside and inside the borders of the Kruger National Park. Sites outside were located of sufficient distance from the border so as to be regarded as representative of water quality conditions before entering the Park. At each site physico-chemical variables were measured in the water. Two metal bioaccumulation indicator species (Labeobarbus marequensis and Barbus radiatus) were also sampled during this time period. These samples as well as water and sediment samples were analysed for Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn.

Physico-chemical analyses of water and sediment allowed for the separation of the rivers into two distinct groups. The Luvuvhu and Sabie Rivers grouped together

ii Summary showing little deviations from normal water quality guidelines. On the other hand the Shingwedzi and Letaba Rivers deviated from normal values sometimes by large degrees. This was particularly true for the Letaba 1a site for all physico-chemical variables barring temperature. Nutrient concentrations correlated with these findings. Nitrates and Sulphates can be identified as possible problem nutrients; however historic data suggest levels to be acceptable for these river systems. Multivariate analysis of sites with regards to physico-chemical and nutrient concentrations produced three major clusters. Determining factors were conductivity, nitrates, organic content and grain size of 53 µm.

Fish were utilised as bioindicator organisms in order to quantify the levels of metals available within each river system. These organisms accumulate metals in their tissues and thus provide not only instantaneous data but rather a time integrate of measure of the bioavailability of metals. Although metal concentrations within water, sediment and fish were not consistently high within one system, three distinct trends were observed. Firstly metal levels were generally found to occur in slightly higher concentrations during the high flow periods when waters were turbid; this could be attributed to increased run off from the surrounding lands as well as increased exposure to sediment bound chemicals released back into the systems. Secondly a trend of high concentrations of Cr, Cu, Fe, Pb, Mn, Ni and Zn was found occurring at the Luvuvhu 1, Luvuvhu 2 and Shingwedzi 1 sites during the high flow sampling. Metal levels at these sites were consistently higher than those recorded at other sampling sites. The last trend noted was that the Sabie River on a whole showed lower metal readings with regard to water, sediment as well as fish samples.

In order to integrate bioaccumulation data and metal concentrations in the different phases of the water and sediments an equilibrium partitioning (EP) technique was implemented. Toxic effect-derived quality data is the lowest for Al, Cr, Cu, Pb and Zn, whilst product standard-based criteria produce the lowest values for Fe, Mn and Ni. Cadmium values were found to be similar for both criteria. Results indicate that current water quality guidelines are over protective for Al and Fe, whilst those for Ni and Mn are too lenient. The EP method has not been utilised extensively in South Africa and was carried out for the first time upon these river systems, it is thus essential that further research be carried out.

iii Summary

Recommendations are made as to the continued monitoring of these diverse systems. It is suggested that each river be looked at in greater detail and that the development of a field biomonitoring programme should be implemented in order to determine the impacts upon the biological components and the aquatic ecosystem as a whole. Future studies should also look at incorporating the use of aquatic macroinvertebrates (SASS 5) as well as other available techniques in order to supply a more detailed assessment.

iv Opsomming

OPSOMMING

Die sewe hoof riviere wat water aan die Nasionale Krugerwildtuin (NKW) voorsien, vloei vanaf die ooste na die westelike grens in Mosambiek in. Dit beteken dat die opvanggebiede van hierdie riviere stroom-op van die NKW geleë is. Dit veroorsaak dat die bestuur van waterkwantiteit en kwaliteit aansienlik bemoeilik word.

Met die toenames in bevolkingsaanwas, bosbou, mynbou, landbou en industriële aktiwiteite in die opvanggebiede word daar al hoe meer druk op die waterkwaliteit en kwantiteit van die riviere geplaas. Hierdie is nie net ‘n bron van kommer vir diegene wat in die bo-lope van die opvanggebiede woon nie maar ook die NKW wat goeie waterbronne benodig om die groot verskeidenheid van ekosisteme te onderhou. Die doel van hierdie studie was dus om die fisies-chemiese, nutrient en metaalkonsentrasies in vier riviersisteme, nl. die Luvuvhu-, Shingwedzi-, Letaba- en Sabieriviere te ondersoek.

Waterkwaliteit van akwatiese ekosisteme is ‘n belangrike faktor wat die biologiesetoestand van ‘n sisteem bepaal. Die toevoeging van bioakkumulasie monitering tot die fisies-chemiese monitering dra by tot die integrasie van metaal akkumulering oor tyd en ruimte. Water en sediment monsters is tydens laagvloei- (September 2005) en hoogvloeitoestande (Maart 2006) by lokaliteite binne en buite die grense van die NKW versamel. Lokaliteite buite die grense van die Park was so geselekteer dat hulle verteenwoordigend was van toestande in die bepaalde rivier voordat die rivier die NKW binne gevloei het. By elke lokaliteit is water en sediment monsters vir fisies-chemiese parameters geanaliseer. Twee visspesies (Labeobarbus marequensis and Barbus radiatus) is as metaal bioakkumulerings indikatororganismes geselekteer. Heel vismonsters is saam met water en sediment monsters geanaliseer met behulp van IGP-tegnieke om die vlakke van Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb en Zn te bepaal.

Die water en sediment analise het twee duidelike groeperinge uitgewys. Lokaliteite in die Luvuvhu en Sabieriviere het min tot geen afwykings van aanvaarbare waterkwaliteitsriglyne getoon nie. Daarteen het daar groot afwykings van diė riglyne

v Opsomming by lokaliteite in die Shingwedzi en Letabariviere voorgekom. Dit was veral opmerklik by Letaba 1a waar al die veranderlikes behalwe temperatuur bo die aanvaarbare kwaliteitsvlakke was.

Vis is as bioindikatororganimes aangewend om die biobeskikbaarheid van metale in die riviersisteme uit te wys. Die vlakke van metale in die visweefsel bied nie net ‘n aanduiding van huidige blootstelling nie maar integreer ook die biobeskikbaarheid van die metale by die lokaliteite wat ondersoek is. Alhoewel daar nie ‘n enkele sisteem was wat uitgestaan het vanweë die metaalvlakke in die water, sediment of vis nie, is daar tog drie kenmerkende tendense waargeneem. Metale was oor die algemeen hoër tydens hoogvloeitoestande en dit is aan die toename in gesuspendeerde metale, wat vanaf die opvanggebiede afkomstig was, toegeskryf. Hoë konsentrasies van Cr, Cu, Fe, Pb, Mn, Ni en Zn is by die Luvuvhu 1, Luvuvhu 2 en Shingwedzi 1 lokaliteite tydens hoogvloeitoestande gemeet. Hierdie lokaliteite het konstant hoër konsentrasies getoon. Die water, sediment en vismonsters van die Sabierivier was deurgans laer as by die ander lokaliteite.

Die metaalvlakke in die visweefsel, water en sediment is met behulp van ‘n ekwilibrium verspreidingstegniek (EV) geïntegreer om lokaliteitspesifieke kwaliteitstandaarde vir die metale te ontwikkel. Toksiese-effek kwaliteitsdata was die laagste vir Cr, Cu, Pb en Zn, terwyl produkstandaard-gebaseerde kriteria weer die laagste waardes vir Fe, Mn en Ni getoon het. Vir Cd het beide die twee kriteria soortgelyke waardes weergegee. Resultate toon dat huidige omgewingskriteria vir Al en Fe waarskynlik te streng is terwyl dié vir Ni en Mn moontlik onvoldoende is. Die EV tegniek is nog nie wyd toegepas in Suid Afrika nie en die relevansie van die lokaliteitspesifieke kriteria behoort verder ondersoek te word.

Gevolgtrekkings wat gemaak is, is dat monitering van die diverse ekosisteme in die NKW voortgesit behoort te word. Daar moet meer indringende aandag geskenk word aan die ontwikkeling van ‘n biomoniteringsprogram wat die invloed van impakte buite die grense van die Park op meer biologiesesisteme kan ondersoek. Toekomstige monitering behoort die makro-invertebraatindeks (SASS5) in te sluit om sodoende ‘n meer holistiese oorsig van die toestande in die riviere te verskaf.

vi List of Tables

LIST OF TABLES

Table 1: Major crops (X) grown within the four catchment areas ...... 17 Table 2: Physico-chemical water quality parameters measured in situ during high and low flow for the Luvuvhu, Shingwedzi, Letaba and Sabie rivers.... 29 Table 3: pH percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data...... 32 Table 4: Conductivity percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data...... 35 Table 5: Nutrient and other chemical variables measured during high and low flow for the four rivers under investigation...... 37 Table 6: Ammonium percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data...... 40 Table 7: Chloride percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data...... 43 Table 8: Phosphate percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data...... 46 Table 9: Nitrate and nitrite percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data.... 49 Table 10: Sulphate percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data...... 52 Table 11: Percentage organic content present within sediments for all localities... 53 Table 12: Mean ± standard error of Aluminium and Cadmium in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows, means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05)...... 76 Table 13: Mean ± standard error of Chromium and Copper in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows, means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05)...... 77

vii List of Tables

Table 14: Mean ± standard error of Iron and Lead in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05)...... 78 Table 15: Mean ± standard error of Manganese and Nickel in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows, means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05)...... 79 Table 16: Mean ± standard error of Zinc in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows, means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05). 80 Table 17: Data used to determine quality criteria of aluminium, cadmium, chromium, copper and iron...... 104 Table 18: Data used to determine quality criteria for lead, manganese, nickel and zinc...... 105 Table 19: Total, dissolved and sediment quality criteria concentrations for aluminium, cadmium and chromium utilising physico-chemical and biological data for the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers ...... 108 Table 20: Total, dissolved and sediment quality criteria concentrations for copper, iron and lead utilising physico-chemical and biological data for the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers ...... 109 Table 21: Total, dissolved and sediment quality criteria concentrations for manganese, nickel and zinc utilising physico-chemical and biological data for the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers...... 110

viii List of Figures

LIST OF FIGURES

Figure 1: Map illustrating the locality of the Kruger National Park and the four major rivers that were sampled during the study...... 10 Figure 2: Position of the Sabie River in relation to the Kruger National Park, black circles represent sampling sites inside and outside the Park...... 11 Figure 3: The Klein and Groot Letaba rivers joining to form the Letaba River inside the Kruger National Park, black circle represent sampling sites.. 14 Figure 4: The two sampling points (black circles) upon the Shingwedzi River in the northern Kruger National Park...... 16 Figure 5: The northern most river sampled, the Luvuvhu River, black circles represent sampling sites...... 19 Figure 6: Historic pH values for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1983 and 2005. The trend is indicated with a second-order polynomial trend line...... 30 Figure 7: Historic pH values for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and Sabie River 1 (D) between the years 1976 and 2005. The trend is indicated with a second-order polynomial trend line 31 Figure 8: Historic pH values for the Sabie River 2 between the years 1984 and 2005. The trend is indicated with a second-order polynomial trend line.32 Figure 9: Historic conductivity for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) ...... 33 Figure 10: Historic conductivity for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and Sabie River 1 (D) between the years 1976 and 2005. The trend is indicated with a second-order polynomial trend line 34 Figure 11: Historic conductivity for the Sabie River 2 between the years 1984 and 2005. The trend is indicated with a second-order polynomial trend line 35 Figure 12: Historic ammonium data for the Luvuvhu 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line...... 38

ix List of Figures

Figure 13: Historic ammonium data for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line...... 39 Figure 14: Historic ammonium data for the Sabie River 2 between the years 1983 and 2005. The trend is indicated with a second-order polynomial trend line...... 40 Figure 15: Historic chloride data for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line...... 41 Figure 16: Historic chloride data for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.42 Figure 17: Historic chloride data for the Sabie River 2 between the years 1984 to 2005. The trend is indicated with a second-order polynomial trend line.43 Figure 18: Historic phosphate data for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 to 2005. The trend is indicated with a second-order polynomial trend line...... 44 Figure 19: Historic phosphate data for the Letaba River 1a (A), 1b Letaba River (B), Letaba River 2 (C) and the Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.45 Figure 20: Historic phosphate data for the Sabie River 2 between the years 1985 to 2005. The trend is indicated with a second-order polynomial trend line.46 Figure 21: Historic nitrate and nitrite data for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 to 2005. The trend is indicated with a second- order polynomial trend line...... 47 Figure 22: Historic nitrate and nitrite data for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and the Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line...... 48

x List of Figures

Figure 23: Historic nitrate and nitrite data for the Sabie River 2 between the years 1983 to 2005. The trend is indicated with a second-order polynomial trend line...... 49 Figure 24: Historic sulphate data for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 to 2005. The trend is indicated with a second-order polynomial trend line...... 50 Figure 25: Historic sulphate data for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and the Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.51 Figure 26: Historic sulphate data for the Sabie River 2 between the years 1983 to 2005. The trend is indicated with a second-order polynomial trend line.52 Figure 27: The average percentage contribution of grain size obtained during September 2005 (low flow) for all localities, no flow recorded at the Shingwedzi 1...... 54 Figure 28: The average percentage contribution of grain size obtained during March 2006 (high flow) for all localities...... 55 Figure 29: PCA biplot with focus on sampling localities in relation to nutrient, sediment and physico-chemical variables signifying the determining factors...... 56 Figure 30: The test species sampled during the period of September 2005 to March 2006 in the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers,...... 72 Figure 31: Percentage metal concentrations within the four different sediment extractions steps. A = Al (high flow), B = Al (low flow), C = Cd (high flow), D = Cd (low flow), E = Cr (high flow) and F = Cr (low flow). ... 83 Figure 32: Percentage metal concentrations within the four different sediment extractions steps. A = Cu (high flow), B = Cu (low flow), C = Fe (high flow), D = Fe (low flow), E = Pb (high flow) and F = Pb (low flow). ... 84 Figure 33: Percentage metal concentrations within the four different sediment extractions steps. A = Mn (high flow), B = Mn (low flow), C =Ni (high flow), D = Ni (low flow), E = Zn (high flow) and F = Zn (low flow).... 85

xi Chapter 1: General Introduction

CHAPTER 1 GENERAL INTRODUCTION

1.1 Background

The Kruger National Park (KNP) found along South Africa’s eastern border is a leading conservation area and a major attraction to local as well as foreign tourists. It is responsible for maintaining ecological processes and life support systems whilst at the same time ensuring the preservation of genetic diversity and the sustainable utilization of species and ecosystems (Moore et al., 1991; Venter and Deacon, 1992). It is for these reasons that the Department of Water Affairs and Forestry (DWAF) has identified it as an important water user to which sufficient water must be allocated (Department of Water Affairs, 1986). To carry out the above conservation role the KNP needs to receive waters that are of good quality and quantity.

The KNP by virtue of its position is dependent upon seven major rivers for its water supply all of which arise outside the Western borders, draining eastwards through the park and on into Mozambique (Birkhead et al., 2000). These rivers are intimately connected to the KNP and need to be given special consideration. When management and conservation plans are drawn up the entire cathment should be considered as units rather than attention being focused on those sections within the KNP alone (Moore et al., 1991; Venter and Deacon, 1992).

Expanding agriculture, forestry and industrial development as well as the escalating human population growth in the catchment areas of these rivers has affected the quality and quantity of waters and is placing the riverine, riparian and ultimately terrestrial ecosystem under threat (Jewitt et al., 1998; Birkhead et al., 2000). As a result of this increased development, the flow regimes and quality of these eastward flowing rivers have been negatively affected and many have changed from perennial to seasonal (Everson et al., 2001).

In this regard an ecotoxicological monitoring project was undertaken in four of the major rivers that traverse the KNP, namely the Luvuvhu, Shingwedzi, Letaba and

1 Chapter 1: General Introduction

Sabie. This dissertation addresses and compares the quality of the waters inside and outside the KNP by investigating biotic (fish) and abiotic (water and sediment) components, with special regard to metal bioaccumulation in fish and metal concentrations in water and sediments.

1.2 Water quality and quantity with regard to South Africa

South Africa is an arid to semi-arid country with limited freshwater resources and a highly seasonal and variable rainfall pattern. Located at the southern tip of the African continent more than half of South Africa receives less than 500 mm of rainfall per annum, well below the world average of 800 mm per annum, this together with hydrological extremes such as long droughts and unpredictable floods result in an erratic and very limited supply of water making it one of the twenty driest countries on earth (Department of Water Affairs, 1986; Wepener et al., 1995).

Throughout South Africa permanent bodies of standing fresh water are essentially non-existent. This leaves rivers and streams as the only exploitable source of fresh water. Although maps of South Africa show an extremely large network of river systems many of these are small or only flow during the wet season, or both (Dallas and Day, 2004). Perennial River systems occur over one quarter of the Southern African landscape whilst seasonally flowing ones make up a further quarter, the remaining rivers flow rarely except after infrequent storms or severe floods (Ferrar et al., 1988).

The rivers within South Africa can be seen as highly integrated natural systems which act as drains for the landscape and bring together land, water and biota. They are major sources of fresh water and are dynamic longitudinal ecosystems which are ecologically, aesthetically and recreationally important parts of the landscape containing a variety of important biota (Day et al., 1986). They are also a critical component of private and conservational lands and thus there needs to be a high level of co-ordination and collaboration among scientists, stakeholders and managers in order to maintain the health and biodiversity of these systems world wide (Breen et al., 2000).

2 Chapter 1: General Introduction

Within South Africa there is a growing concern with regard to the deterioration of the country’s rivers. One of the major causes for concern is the demand for water from South Africa’s growing population of approximately 45 million people. This demand has resulted in many large scale abstractions along rivers as well as an extensive dam building programmes. However, 12 million people still do not have access to an adequate supply of potable water, and nearly 21 million lack basic sanitation (DWAF, 1994; King and Louw, 1999).

The large amount of industrial development that has taken place within South Africa over the past decade has had a major impact on water resources. Pollution and impoundments has changed the biodiversity in many of the river systems whilst increased agricultural development has amplified sediment loads in rivers like the Olifants River in Limpopo and the Orange River in the Free State (Dallas and Day, 1993; Bauermann et al., 1995).

Before the 1970’s water resource management in South Africa was devoted almost exclusively to the development of schemes that stored or transported water. More recently it has been recognised that aquatic ecosystems require a water allocation in order to preserve their ecological integrity and functioning (Moore et al., 1991). The South African National Water Act (Act 36 of 1998) recognises that water should be used in a sustainable manner and thus has the protection of our natural water resources as one of its main objectives (Germs et al., 2004). Currently the Department of Water Affairs and Forestry is the primary state agency responsible for the management of South Africa’s fresh water resources (DWAF, 1991).

1.3 Metal ecotoxicology

Metals can be divided into two groups, those like cobalt (Co), copper (Cu), manganese (Mn), molybdenum (Mo) and zinc (Zn) that occur naturally in trace amounts in most waters and are important plant nutrients and those like cadmium (Cd), mercury (Hg) and lead (Pb) that usually do not occur in measurable amounts, are potentially toxic in low concentrations and have become widely distributed due to various human activities (Dallas and Day, 2004)

3 Chapter 1: General Introduction

Metal concentrations are generally very low in most naturally occurring water bodies and thus any increase would result in organisms being exposed to concentrations not previously encountered. This can have detrimental effects to the river system as a whole and thus contamination by metals is of significance and should be carefully monitored and controlled (Dallas and Day, 2004).

1.3.1 Emission of metals

Within South Africa point discharges (factory effluent, waste water treatment works) and diffuse runoff (agricultural, urban and mining) are major sources of metal pollution whilst pesticides and fertilizers of agricultural origin and metals of industrial origin have become of increasing concern in recent years (Heath and Claassen, 1999). Chapter 2 of this dissertation will discuss the study areas in further detail whilst also pointing out possible sources of pollution.

Once a pollutant is released into the system a number of physical, biological and chemical factors attempt to modify or degrade the pollutant, however, due to the non- degradable nature of metals it is important to determine factors (physical and chemical) which influence the availability and toxicity of metals to fish (Wepener, 1997). Physical and chemical characteristics of water and sediment are discussed in Chapter 3 together with nutrient concentrations.

1.3.2 Biomonitoring

Pollutants found in air, land and water are often reflected in the accumulation and effects on a variety of aquatic organisms. This is due to the fact that water is often the sink for human generated pollutants (Couch, 1982). In order to obtain a completely reliable assessment of pollution within aquatic ecosystems physical and chemical monitoring should be supported by some means of biological monitoring. Aquatic biomonitoring utilizes these aquatic organisms to reflect the quality of associated waters not only at present times but as they have experienced it throughout their lives (Abel, 1989).

4 Chapter 1: General Introduction

Metal analysis was carried out upon water, sediment and biota from the four rivers in order to determine the occurrence of potentially toxic metals; these results are discussed in Chapter 4 whilst Chapter 5 deals with Equilibrium Partitioning (EP).

1.4 Main objectives

The research aim to be carried out during this two year study is to determine and integrate the physico-chemical characteristics, nutrient and metal concentrations at sites situated upstream and within the KNP thereby determining the quality of waters supplied to this extremely diverse area.

The main objectives of the research project were therefore:

• To determine physical and chemical water quality at two selected sites, one upstream and the other within the KNP. • To determine nutrient concentrations at selected sites. • To collect information upon rivers (Shingwedzi) that has little historic data. • To compare historic and collected data in order to evaluate trends in improvement/deterioration of water quality. • To determine spatial and temporal variation in selected metal concentrations of sediment, water and fish at selected sites. • To identify the presence of metals in selected tissues of freshwater fish. • To utilize Equilibrium Partitioning for the first time on these river systems. • To identify impacted areas that will possibly require further investigation.

In conclusion water quantity and quality is of extreme importance in South Africa where water is an extremely limited resource. It must be used sparingly to meet the demands of the rapidly growing population whilst at the same time still be able to supply industrial and agricultural activities. This resource must also remain unpolluted in order to supply fresh potable water to the large animal populations present in conservation areas (Grabow, 1986). The deterioration of water quality and quantity of the main rivers that flow through the KNP is thus of national concern.

5 Chapter 1: General Introduction

1.5 References

Abel, P.D. 1989. Water Pollution Biology. Ellis Horwood Limited Publishers, Chichester. 231 pp.

Bauermann, Y., Du Preez, H.H., Steyn, G.J., Harmse, J.T. and Deacon, A.R. 1995. Suspended silt concentrations in the lower Olifants River (Mpumalanga) and the impact of silt releases from the Phalaborwa Barrage on water quality and fish survival. Koedoe 38 (2): 11- 34.

Birkhead, A.L., Heritage, G.L., James, C.S., Rogers, K.H. and Van Niekerk, A.W. 2000. Geomorphological change models for the Sabie River in the Kruger National Park. WRC Report No 782/1/00. Water Research Commission, Pretoria. 132 pp.

Breen, C., Dent, M., Jaganyi, J., Madikizela, B., Ndlovu, A., O’Keeffe, J., Rogers, K Uys, M. and Venter, F. 2000. The Kruger National Park rivers research Programme. WRC Report No TT 130/00. Water Research Commission, Pretoria. 159 pp.

Couch, J.A.1982. Aquatic animals as indicators of environmental exposure. Journal of Environmental Science and Health. 17 (4): 473-476.

Dallas, H.F. and Day, J.A. 1993. The effect of water quality variables of riverine ecosystems: a review. WRC Report No TT 61/93. Water Research Commission, Pretoria. 240 pp.

Dallas, H.F. and Day, J.A. 2004. The effect of water quality variables on aquatic ecosystems: a review. WRC Report No TT 224/04. Water Research Commission, Pretoria. 221 pp.

6 Chapter 1: General Introduction

Day, J.A., Davies, B.R. and King, J.M. 1986. Riverine ecosystems. In: O’Keefe, J.H (Ed) The conservation of South African rivers. South African National Scientific Programmes Report No 131. Foundation for Research Development, Pretoria, South Africa. 117 pp.

Department of Water Affairs. 1986. Management of water resources of the Republic of South Africa. Department of Water Affairs, Pretoria. 549 pp.

Department of Water Affairs and Forestry. 1991. Water quality management policies and strategies in the RSA. Department of Water Affairs and Forestry, Pretoria. 32 pp.

Department of Water Affairs and Forestry. 1994. Water supply and sanitation. White Paper. Department of Water Affairs and Forestry, Pretoria. 38 pp.

Everson, C.S., Burger, C., Olbrich, B.W., Gush, M.B. 2001. Verification of estimates of water use from riverine vegetation on the Sabie River in the Kruger National Park. WRC Report No 877/1/01. Water Research Commission, Pretoria. 57 pp.

Ferrar, A.A., O’Keeffe, J.H. and Davies, B.R. 1988. The River Research Programme. South African National Scientific Programmes Report No 46. 28 pp.

Germs, W., Coetzee, M.S., Van Rensberg, L. and Maboeta M.S. 2004. A preliminary assessment of the chemical and microbial water quality of the Chunies River – Limpopo. Water SA 30 (2): 267-272.

Grabow, W.O.K. 1986. Water quality assessment and control in South Africa. South African Journal of Science. 82 (1): 342-346.

7 Chapter 1: General Introduction

Heath, R.G.M. and Claassen, M. 1999. An overview of the pesticide and metal levels present in populations of the larger indigenous fish species of selected South African Rivers. Division of Water, Environment and Forestry Technology, CSIR. 318 pp.

Jewitt, G.P.W., Heritage, G.L., Weeks, D.C., Mackenzie, J.A., Van Niekerk, A., Gorgens, A.H.M., O’Keeffe, J., Rogers, K. and Horn, M. 1998. Modeling abiotic-biotic links in the Sabie River. WRC Report No 777/1/98. Water Research Commission, Pretoria. 157 pp.

King, J.M. and Louw, M.D. 1999. Instream flow assessments for regulated rivers in South Africa using the building block methodology. Department Of Water Affairs and Forestry, Pretoria. 20 pp.

Moore, C.A., Van Veelen, M., Ashton, P.M. and Walmsely, R.D. 1991. Preliminary water quality guidelines for the Kruger National Parks Rivers. KNPRRP Report No xi + 91 pp.

Venter, F.J. and Deacon, A.R. 1992. Management objectives of the Kruger National Parks Board regarding water quality requirements of rivers in the Kruger National Park. Report No 3/92, Scientific Services Section, Skukuza, 13 pp.

Wepener, V., Fleischer, L. and Vivier, L. 1995. Biomonitoring: A literature survey. Coastal Research Unit of Zululand. South Africa.103 pp.

Wepener, V. 1997. Metal ecotoxicology of the Olifants River in the Kruger National Park and the effect thereof on fish haematology. Unpublished Ph.D. Thesis, Rand Afrikaans University, Johannesburg. 835 pp.

8 Chapter 2: Situation Analysis and Locality Descriptions.

CHAPTER 2 SITUATION ANALYSIS AND LOCALITY DESCRIPTIONS.

2.1 Introduction

The KNP (Figure 1) is situated in the South African Lowveld between the towering escarpment of the northern Drakensberg Mountains and the Mozambique coastal plains. The Park and various associated private reserves together constitute one of the worlds most extensive (1 948 528 ha) and valued nature conservation areas (Van Rensburg and Dent, 1997; Figure 1). From north to south it extends over almost 350 km, while its width averages a mere 60 km.

The KNP falls in the savanna biome of Southern Africa. Most of this region experiences hot wet seasons lasting four to eight months whilst the remaining months consist of a warm dry season. For the most part the landscape is classified as plains with a gentle eastward slope (Muller and Villet, 2004).

The rivers within the KNP encompass a diverse range of geology, soils and morphology. These physical characteristics no doubt have an influence upon the ecological processes within the various river systems and suggest the presence of a diverse range of biotypes which could support a variety of organisms (Muller and Villet, 2004).

There are seven major rivers that transect the KNP draining a combined area of about 88 600 km2; they are the Limpopo, Luvuvhu, Shingwedzi, Letaba, Olifants, Sabie and Crocodile. All these rivers arise outside the borders of the Park and their upper catchments are therefore not within the jurisdiction of the National Parks Board. Commercial forestry, mining, impoundments, water abstractions, irrigation, poor land-use practices and growing populations have all resulted in water quality and quantity problems for these river systems (Du Preez and Steyn, 1992). It is due to these reasons that this project was undertaken. Four of the seven major rivers were chosen for this study. There is limited information on two of the rivers, the Luvuvhu and Shingwedzi.

9 Chapter 2: Situation Analysis and Locality Descriptions.

Figure 1: Map illustrating the locality of the Kruger National Park and the four major rivers that were sampled during the study.

10 Chapter 2: Situation Analysis and Locality Descriptions.

2.2 Sabie River

2.2.1 General description

The Sabie River (Figure 2) is situated between latitudes 24°30’ and 25°20’ south and longitudes 30°05’ and 32°25’ east. It has its source in the Drakensberg escarpment on the eastern slopes of the Mauch Berg approximately 2160 m above mean sea level and flows through Mpumalanga and the Northern Province before stretching across the Lebombo Mountains and flowing on into Mozambique (Weeks et al., 1996; Jewitt et al., 1998). The Sabie River drains a catchment area of approximately 6437 km2 and forms part of the larger Incomati System, an international drainage basin that extends over several political boundaries (Weeks et al., 1996; RHP, 2001a).

• Sampling point

Figure 2: Position of the Sabie River in relation to the Kruger National Park, black circles represent sampling sites inside and outside the Park.

The Sabie River is a semi-arid system with a mean annual precipitation varying from 2000 mm on the escarpment in the west to 600 mm in the eastern lowveld. Most of this rain falls in the form of tropical storms between November and March, with peaks in January (Weeks et al., l996, RHP, 2001a). The Sabie flows throughout the year but is subject to discharge extremes. It is fed by two main important tributaries in the lowveld zone, the perennial Marite River outside the KNP and the seasonal Sand River inside the KNP (Jewitt et al., 1998). These two tributaries each provide

11 Chapter 2: Situation Analysis and Locality Descriptions. approximately 25 percent of the mean annual runoff to the Sabie River (Van Niekerk et al., 1995). The natural vegetation in the catchment consists of grassland, inland tropical forest and tropical bush and savanna (Acocks, 1988).

The Sabie catchment is underlain by the Basement Complex traversing the lower Middleveld and upper Lowveld. The Karroo sequence in the eastern part of the Lowveld and the Transvaal sequence on the mountainous western extremes (Weeks et al., 1996). There are seven main geological units under-lying this catchment area, they are Meihardskraal granite, Sand River gnies, Suurberg, Drakensberg, Lebombo, Transvaal, Rooiberg and Griqualand west. The major soil types derived from these geological units are Glenrosa, Hutton and Shortland. These soils tend generally to be resistant to erosion (Heath and Claassen, 1999).

2.2.2 Water quality

Water use along this river system is diverse however the Sabie River within the KNP has been in recent times described as the most pristine system within South Africa with much of its 110 km remaining free from any direct alteration (Moon et al., 1997). Past literature, however, indicates that this was not always so. In 1922 Stevenson-Hamilton described the river as a sterile stream whilst Mr. F.B. Jeary indicated in 1933 that micro-organisms were non-existent in the Sabie (Pienaar, 1985). It was only in the 1940’s that action was taken by the Mining Department against pollution and the river has recovered to become the most biologically diverse in South Africa, this is due to recolonisation from the tributaries which were unaffected by mining (Pienaar, 1985).

A variety of different activities affects and takes place along this river system. The upper catchment area has already been exploited as far as possible due to commercial forestry plantations of exotic tree species, especially Pinus and Eucalyptus species. Currently more than 71 100 ha (16%) of the total catchment area is afforested whilst 11 300 ha (1.8%) consists of irrigated crops. The principal crops grown particularly in the lower catchment are bananas, avocados, citrus, paw paws and vegetables (Chunnett, Fourie and Partners, 1990; Table 1)

12 Chapter 2: Situation Analysis and Locality Descriptions.

Water quality along the Sabie-Sand system is generally considered to be in a good to excellent condition with regular water chemistry samples having been analysed since 1983 at four sites within the KNP. Outside the Park regular sampling has occurred at ten sites since the late 1970’s and results show that these waters are suitable for irrigation, livestock watering and domestic consumption (Weeks et al., 1996). An analysis by Van Veelen (1991) concluded that the Sabie River is the least mineralized of the rivers in the KNP. It was also found that the pH was below 7.0 for a considerable part of the year thus causing the system to be poorly buffered. These facts coupled together with observed low total dissolved salt (TDS) concentrations make this a stable but sensitive system, should changes occur in the catchment area.

2.3 Letaba River

2.3.1 General description

The Letaba River (Figure 3) originates in the Drakensberg mountains near Haenertsburg between 1 100 and 1 800 m above mean sea level and cascades down its slopes in a north easterly direction where it flows through part of the Limpopo Province in the north east of South Africa. (RHP, 2001b). The entire Letaba catchment area covers 13 670 km2 with 60 % of the catchment area and all the major tributaries being located outside the KNP (Chutter and Heath, 1993a). This system is important in draining the plains of the Lowveld before it runs eastwards through the KNP to its confluence with the Olifants River a short distance upstream of the Mozambique border (RHP, 2001b).

The Letaba catchment is classified as a semi-arid system with a mean annual precipitation ranging between 500 and 1800 mm in the mountainous western parts falling to between 450 and 700 mm in the drier Lowveld region. Of this rainfall 85% falls during the summer months (Moon and Heritage, 2001; RHP, 2001b).The River itself has two main tributaries, namely the Klein and Groot Letaba (Heath and Claassen, 1999). Although the Letaba Rivers is described as being perennial, this stands true only in the upper reaches. As it flows eastwards towards the KNP it

13 Chapter 2: Situation Analysis and Locality Descriptions. becomes progressively more seasonal or periodic with no or little surface flow being recorded during the dry winter months for the most part of the last 20 years (Ashton et al., 2001).

• Sampling point

Figure 3: The Klein and Groot Letaba rivers joining to form the Letaba River inside the Kruger National Park, black circle represent sampling sites.

The geology in the Groot Letaba river catchment is dominated by biotite granite, migmatite and biotite gneiss. This results in the substratum on the river being dominated by sand (Vlok and Engelbrecht, 2000). The main geological units found in the catchment are Barberton, Murchison, Giyani, Beit- Bridge, Meihardskraal granite, Sand river gneiss, Suurberg, Drakensberg, Lebombo, Transvaal, Rooiberg and Griquland west. It is due to this great diversity in geological units that four major soil types are found; they are Glenrosa, Hutton, Mispah, and Shortland (Heath and Claassen, 1999).

2.3.2 Water quality

Twenty major dams and weirs have been constructed along the Groot Letaba River catchment with the Tzaneen Dam on the Groot Letaba and the Middle Letaba Dam being the two largest in the Northern Province. These are meant to divert and supply

14 Chapter 2: Situation Analysis and Locality Descriptions. water for domestic consumption in various towns as well as irrigation water, however many are silted and not designed in a way to provide irrigation head. Other than Engelhardt dam within the KNP none is provided with fish ladders (Chutter and Health, 1993a; RHP, 2001b).

Development within the catchment area is dominated by agriculture and afforestation. The Drakensberg and Soutpansberg mountains are populated by exotic plantations of pines, eucalypts and mahoganies (plantations cover 3.6% of the Letaba catchment) whilst the foothills are important for tea estates, the Klein Letaba, Molotsi and Nsama tributaries on the other hand support rural populations together with their cattle, goats and subsistence farming of various crops (Vlok and Engelbrecht, 2000; RHP, 2001b). The major crops grown in the area are citrus, bananas and mangoes (Table 1). The over utilization of water in this catchment area has resulted in the once perennial Letaba River no longer flowing in the dry winter months within the KNP (Chutter and Heath, 1993b).

Water quality studies carried out by Van Veelen (1991) on the Letaba River in the KNP showed high TDS concentrations where it flows into the Kruger; these concentrations were generally higher during the winter months. The pH varied between 7.5 and 8.0 with ammonium concentrations generally above 0.03 mg/P. These high values were limited to the summer months. The river system seems to be stable although it is moderately mineralized possibly due to agricultural activities in the catchment. Data obtained from a survey of the water quality of the Groot and Middle Letaba by Ashton et al. (2001) demonstrate that the general water quality of these two rivers and their main tributaries is generally good, this information is supported by the river health program which states that the Letaba River itself is in a natural state (RHP, 2001b).

15 Chapter 2: Situation Analysis and Locality Descriptions.

2.4 Shingwedzi River

2.4.1 General description

The Shingwedzi River (Figure 4) found between latitudes 23°00’ and 23°55’ South and longitudes 30°35’ and 32°15’ East rises in low hills at 650 m and flows through the Limpopo Province for some 165 km before reaching the Mozambique border (Walmsley et al., 1987). Its headwaters lie about 50 km outside of the borders of the KNP and along with its tributaries drain the Tsende sandveld, a landscape of altitudes varying between 300 and 450 m. Most of the catchment area of the Shingwedzi River and its more important tributaries lie within the borders of the KNP (Ashton et al., 2001).

• Sampling point

Figure 4: The two sampling points (black circles) upon the Shingwedzi River in the northern Kruger National Park.

The Shingwedzi is fed by three important tributaries, the Phugwane, Mphongolo and Shisha Rivers. All of these river systems are located in low rainfall areas receiving between 450 and 550 mm of rainfall per annum, this results in the Shingwedzi having seasonal or episodic flows. This fact along with poor water use practises have resulted

16 Chapter 2: Situation Analysis and Locality Descriptions. in flows in the lower reaches of the Shingwedzi river system being entirely seasonal with no surface flow recorded during the dry winter months (Ashton et al., 2001).

The geological features in the central and western portions of the Shingwedzi catchment consist predominantly of large areas of granitic and gneissic rocks of the crystalline Basement Complex, whilst the northern extention consists of acidic and intermediate rhyolites and lavas of the Karoo Sequence. The granitic and gneissic rocks of the Basement Complex outcrop at various points across the central area of the catchment, forming prominent hills or koppies. These exposed weathered granites erode easily, and accelerated rates of sediment production occur in this region, with large amounts of coarse and fine sediments accumulating in river channels (Ashton et al., 2001).

Soils in the Shingwedzi catchment consist of sandy colluvial soils at the foot of the hills becoming sandy-loamy in the western part of the catchment whilst shallow, brownish to greyish-brown sandy soils overlying coarsely weathered rock in the central and eastern portion of the catchment. Transported alluvial deposits of course to fine-grained sands and silts are located along drainage lines (Ashton et al., 2001).

Table 1: Major crops (X) grown within the four catchment areas Crop Sabie Letaba Shingwedzi Luvuvhu

Sugar Cane X Banana X X X Citrus X X Mangoes X X Tomatoes X Vegetables X X X Maize X X Avocados X Tobacco X Paw Paws X X X Subsistence X X X X

17 Chapter 2: Situation Analysis and Locality Descriptions.

2.4.2 Water quality

Water use within the upper reaches of the Shingwedzi catchment consists almost completely of subsistence agriculture and wildlife conservation in nature reserves.

The central portion is located entirely within the KNP and land and water use therefore consists entirely of wildlife conservation. The Mozambique or eastern portion of the catchment consists once again of small scale subsistence agriculture. No towns of any size or major crops occur in the Shingwedzi catchment (Ashton et al., 2001, Table 1)

Small farm dams are found in the headwater zones of all the tributaries of the Shingwedzi River these supply water for domestic consumption in several small settlements and mines. In the drier central and eastern portions of this catchment within the KNP, boreholes are used to supplement water supplies during the dry winter months (Ashton et al., 2001).

Water quality studies carried out by Van Veelen (1991) showed that flow within this river system is highly seasonal and no significant period of flow has been observed since 1985. It was also observed that TDS concentrations were relatively low with pH values between 6.5 and 7.0. Due to these low values this system may be highly susceptible to change should conditions be altered in the catchment area (Van Veelen, 1991).

Very little information is available on the Shingwedzi River at present. It is for this reason that a greater Shingwedzi River study was initiated in 2005 to gain a multitude of diverse information. The data obtained in this study forms part of the greater Shingwedzi River project. All results obtained will be fed into the project, thus supplying information on the water and sediment chemistry components.

18 Chapter 2: Situation Analysis and Locality Descriptions.

2.5 Luvuvhu River

2.5.1 General description

The Luvuvhu River (Figure 5) is situated in the Limpopo Province between latitudes 22°25’ and 23°20’ South and longitudes 29°50’ and 31°20’ East. It has a catchment area of around 5943 km2 of which 24% is situated in the KNP. This river rises on the southern slopes of the Soutpansberg Mountains about 20 km South of Louis Trichardt and flows immediately eastwards into the KNP where it joins the Limpopo River at Crooks corner where the borders of South Africa, Zimbabwe and Mozambique meet (Heath and Claassen, 1999; RHP, 2001b).

• Sampling point

Figure 5: The northern most river sampled, the Luvuvhu River, black circles represent sampling sites.

The Luvuvhu system is classified as a semi arid system and has a mean annual precipitation of around 731 mm. Of this most is received during the summer months with the highest rainfall occurring on the steep slopes of the Soutpansberg mountains (Heath and Claassen, 1999). The Luvuvhu River in its natural state is said to be a perennial river with cessation of flow increasing from the first record of no flow during 1965 up to present times in which the river is described as ephemeral (O’Keeffe, 1991; Venter and Deacon, 1994). The main veld types recognised in this catchment area are inland tropical forest and tropical bush and savanna (Acocks,

19 Chapter 2: Situation Analysis and Locality Descriptions.

1988). The Luvuvhu has several important sub-tributaries, namely the Dzindi, Mutshindudi, Latonyanda and Mbwedi rivers, however the Mutale represents the main tributary

A great variety of geological units contribute to the geology of the Luvuvhu catchment, the most important ones being Barberton, Murchison, Giyani, Beit bridge, Suurberg, Drakensberg, Lebombo, Waterberg, Soutpansberg, Orange river alluvium, Sand, Calcrete, Meinhardskraal granite and Sand river gnies. This great variety has lead to the origin of four main soil types; they are Glenrosa, Hutton, Mispah and Shortland (Heath and Claassen, 1999).

2.5.2 Water quality

Water use along this system is limited to small industries and mining whilst forestry covers 14 600 ha (4.2%) and causes reduced runoff in the upper reaches of the river. Crops account for 9 900 ha (2.8%) of the catchment area and are mainly subtropical fruits including bananas, mangoes, litchis and paw paws (Table 1). A number of semi-subsistence agricultural areas (cotton, maize and tobacco) are found along the Luvuvhu River as well as substantial livestock populations of cattle, sheep and goats. The development of industries in this catchment is still in the early stages (Heath and Claassen, 1999)

Several water supply impoundments, as well as flow gauging weirs, for the provision of water for agriculture, are found in the Luvuvhu River. Four large and over 180 smaller dams are found within the catchment area, these supply water mainly for domestic use and livestock watering

Land use in the upper catchment comprises mostly commercial farming and large forestry plantations with Louis Trichardt being the main industrial and service centre. In the central reaches land use is concentrated upon subsistence farming and livestock rearing around small villages, the village of Thohoyandou is rapidly expanding. The lower reaches of this river system are entirely devoted to conservation in nature reserves and the KNP.

20 Chapter 2: Situation Analysis and Locality Descriptions.

2.6 References

Acocks, J.P.H. 1988 Veld types of South Africa. Botanical Research Institute. South Africa 146 pp.

Ashton, P.J., Love, D., Mahachi, H and Dirks P.H.G.M. 2001. An overview of the impact of mining and mineral processing operations in water resources and water quality in the Zambezi, Limpopo and Olifants catchments in Southern Africa. Contract report to the mining, minerals and sustainable development project, CSIR-Environmentek, Pretoria, South Africa and Geology Department , University of Zimbabwe, Harare, Zimbabwe. Report No ENV-P-C 2001-042.

Chunnet, Fourie and Partners. 1990. Water resources: Planning of the Sabie River catchment. Report to the South African Department of Water Affairs, Pretoria.

Chutter, F.M. and Heath, R.G.M. 1993a. Relationships between low flow and the river fauna in the Letaba River. WRC Report No 293/1/93. Water Research Commission, Pretoria. 79 pp.

Chutter, F.M. and Heath, R.G.M. 1993b. Low flow fauna in the Letaba River. WRC Report No 293/2/93. Water Research Commission, Pretoria. 79 pp.

Du Preez, H.H. and Steyn, G.J. 1992. A preliminary investigation of the concentration of selected metals in the tissues and organs of the tigerfish (Hydrocynus vittatus) from the Olifants River, Kruger National Park, South Africa. Water SA 18 (2): 131-136.

Heath, R.G.M. and Claassen, M. 1999. An overview of the pesticide and metal levels present in populations of the larger indigenous fish species of selected South African Rivers. Division of Water, Environment and Forestry Technology, CSIR. 318 pp.

21 Chapter 2: Situation Analysis and Locality Descriptions.

Jewitt, G.P.W., Heritage, G.L., Weeks, D.C., Mackenzie, J.A., Van Niekerk, A., Gorgens, A.H.M., O’Keeffe, J., Rogers, K. and Horn, M. 1998. Modelling abiotic-biotic links in the Sabie River. WRC Report No 777/1/98. Water Research Commission, Pretoria. 157 pp.

Moon, B.P., Van Niekerk, G.L., Heritage, K.H. and James, C.S. 1997. A geomorphological approach to the management of rivers in the Kruger National Park: The case of the Sabie River. Transactions of the institute of British Geographers 22: 31-48.

Moon, B.P. and Heritage, G.L. 2001. The contemporary geomorphology of the Letaba River in the Kruger National Park. Koedoe 44 (1): 45-55.

Muller, W.J. and Villet, M.H. 2004. Similarities and differences between rivers of the Kruger National Park. WRC Report No 881/1/04. Water Research Commission, Pretoria. 37 pp.

O’Keeffe, J. 1991. Water requirements of the Luvuvhu River. In: Proceedings of a workshop on the flow requirements of the Kruger National Parks Rivers.

Pienaar, U.de V. 1985. Indications of progressive desiccation of the Transvaal lowveld over the past 100 years, and implications for the water stabilization programme in the Kruger National Park. Koedoe 28: 93-165.

RHP (River Health Programme). 2001a. State of the rivers report: Crocodile, Sabie-Sand and Olifants River Systems. WRC Report No TT 147/01. Water Research Commission, Pretoria. 40 pp.

RHP (River Health Programme). 2001b. State of the rivers report: Letaba and Luvuvhu River systems. WRC Report No TT 165/01. Water Research Commission, Pretoria. 45 pp.

22 Chapter 2: Situation Analysis and Locality Descriptions.

Van Niekerk, A.W., Hertage, G.L. and Moon, B.P. 1995. River classification for management: the geomorphological of the Sabie River. South African Geographical Journal. 77 (2): 68-76.

Van Rensburg, J.D.J. and Dent, M.C. 1997. Development of a water quality and quantity modelling system which will provide a common currency for communication between researchers in the Kruger National Park Rivers Research Programme. WRC Report No 654/1/97. Water Research Commission, Pretoria. 39 pp.

Van Veelen. 1991. Kruger National Park – Assessment of the current water quality status. Department of Water Affairs and Forestry, Pretoria. Report NoN 0000/00/REQ/3391. 14pp.

Venter, F.J. and Deacon, A.R. 1994. The Luvuvhu River in the Kruger National Park: Management objectives of the National Parks Board and general overview of impacts on the river. Report presented at the Luvuvhu instream flow requirements workshop. Held in Punda Maria from 26-29 July 1994. Report No 13/94. Scientific Services. Skukuza. 6 pp.

Vlok, W. and Engelbrecht, J.S. 2000. Some aspects of the ecology of the Groot Letaba River in the Northern Province, South Africa. African Journal of Aquatic Science. 25: 76-83.

Walmsley, B., Langhout, C.L. and Pullen, R.A. 1987. : Summary of the Letaba- Shingwedzi catchment. Report presented at the workshop on requirements for ecological systems. Held in Skukuza from 16 – 19 March 1987.

Weeks, D.C., O’Keeffe, J.H., Fourie, A. and Davies, B.R. 1996. A pre-impound study of the Sabie-Sand river system, Mpumalanga with special referenceto the predicted impacts on the Kruger National Park. Volume One: The ecological status of the Sabie-Sand River System. WRC Report No 294/1/96.Water Research Commission, Pretoria. 261 pp.

23 Chapter 3: Physico-Chemical and Nutrient Analysis.

CHAPTER 3 PHYSICO-CHEMICAL AND NUTRIENT ANALYSIS OF WATER AND SEDIMENT IN THE LUVUVHU, SHINGWEDZI, LETABA AND SABIE RIVERS.

3.1 Introduction

River systems can be considered as arteries of the land supplying life giving water to an abundance of organisms whilst at the same time supporting modern civilisations (King et al., 2003). The term water quality was coined with reference to the quality of water required for human use (i.e. drinking, agricultural and industrial purposes). This term which is entirely a human perspective does not hold true for all aquatic organisms or ecosystems (Dallas et al., 1994). A more modern approach is to consider water quality as the combined effect of the physical attributes and chemical constituents not only in the water but upon all aspects of the aquatic environment (King et al., 2003).

A major threat to aquatic ecosystems which can lead to severe pollution problems is nutrient enrichment. Nutrients are important building blocks for healthy aquatic ecosystems and are generally non-toxic even in high concentrations; however this can change with alterations in environmental parameters such as pH and temperature. Increased nutrient levels (especially nitrogen and phosphorus) can result in over stimulated growth of aquatic weeds and algae and can ultimately lead to oxygen depletion resulting in a eutrophic system. The occurrence of nutrients in aquatic ecosystems is closely linked to activities in the catchment, such as natural weathering, agricultural runoff and the disposal of untreated or partially treated wastes (Madikizela et al., 2001; Nhapi and Tirivarombo, 2004).

Sediments act as carriers or sinks of pollution. They are composed of numerous individual layers. Each of these layers corresponds to a distinct condition of water flow. Fine layers are composed almost entirely of suspended load deposited during weaker or no currents whilst coarse layers represent bed loads deposited by stronger currents (Van Vuren et al., 1999). Sediments have been used widely as environmental

24 Chapter 3: Physico-Chemical and Nutrient Analysis. indicators due to their ability to store contaminants as well as to act as trace contamination sources. Although most adsorbed pollutants on sediments are unavailable to aquatic organism’s changes in environmental conditions can release pollutants back into the water column thus making them available for uptake (Soares et al., 1999). Soils and sediments receive potentially toxic elements from both natural as well as anthropogenic sources. These include weathering of natural minerals, mining, industries, agriculture and waste disposal (Tokalioglu et al., 2003).

Due to South Africa's unpredictable rainfall, high evaporation rates and low conversion of rainfall to runoff, South Africa is already a water stressed country. Permanent bodies of standing fresh water are virtually non-existent thus leaving rivers as the only exploitable form of surface water (Dallas and Day, 1993). Any alterations in the concentration of chemical constituents and physical variables can and will exert a profound effect upon the aquatic environment and ecosystem as a whole (Malan et al., 2003). These facts coupled with the rising populations in the catchment areas surrounding the KNP are placing increasing demands on the rivers that supply this extremely diverse area with water.

3.2 Methods

3.2.1 Field sampling

Field surveys were carried out during the months of August 2005 (low flow) and March 2006 (high flow) within major rivers that flow through the KNP (Luvuvhu, Shingwedzi, Letaba and Sabie Rivers). Two sites were selected in each river system, excluding the Letaba which had three sites. One site was situated within the borders of the KNP and the other upstream of the border (Figure 1 through Figure 5). Each site outside the park was of sufficient distance from the border so as to be regarded as representative of water quality conditions before entering the park. For the purpose of this dissertation sites outside the borders of the KNP are indicated by the number 1 whilst sites inside the borders by the number 2.

25 Chapter 3: Physico-Chemical and Nutrient Analysis.

Water and bottom sediment samples were collected in triplicate directly (hand sampling) from the rivers into acid-prewashed polythene bottles. These were stored deep frozen until analysis. The following physico-chemical water quality parameters were analysed in situ by making use of standard measurement techniques and apparatus.

• Conductivity (µS/cm; Cyberscan con 11) • Dissolved oxygen concentration (mg/l; Ecoscan DO6) • Percentage oxygen saturation (%, Ecoscan DO6) • Temperature (°C; Ecoscan DO6) • pH (Cyberscan pH 110)

3.2.2 Laboratory techniques

3.2.2.1 Water

Water samples were thawed at room temperature so as to prevent any changes occurring in the physical and chemical composition. A variety of nutrient analysis tests were carried out, metal analysis was also undertaken and will be discussed in Chapter 4.

Nutrient analysis was undertaken by making use of Merck cell test kits for ammonium chlorides, nitrates, nitrites, sulphates, phosphates and COD. Historic data was also collected for each nutrient analysed as well as pH and conductivity via the Department of Water Affairs and Forestry (DWAF, 2001). This data was analysed by creating concentration versus time graphs utilising Microsoft Excel 2003. A polynomial trend line with the order of two was determined for each nutrient whilst the 5th, 25th, 50th, 75th and 95th percentile was calculated. No monitoring point for the Shingwedzi River outside the borders of the KNP was available and thus two stations were selected inside to provide better representation.

26 Chapter 3: Physico-Chemical and Nutrient Analysis.

3.2.2.2 Sediment

Sediment samples were thawed and dried in an oven at 60 º C for a period of 24 hours. This temperature allows for drying without altering the chemical, physical and organically properties. After cooling grain size and organic content were determined. Metal analyses which were carried out on the same samples are discussed in Chapter Four.

Grain size was determined by taking an 80 g sub-sample of sediment and placing it in an Endecott mechanical siever with sieve racks of 4000, 2000, 500, 212 and 53 µm respectively. Samples were sieved for 20 minutes and each fraction was weighed to the nearest 0.001 g. The average percentage of each fraction was calculated.

Organic content was analysed by placing a pre-weighed amount of sediment in crucibles and incinerating in a furnace at 600° C for a period of six hours. Sediment was cooled and weighed in order to determine the amount of organic material burnt off (EPA, 1991).

3.2.3 Data processing

Descriptive statistics (means, percentiles, trends) were computed using Microsoft Excel, whereas multivariate statistics (principal component analysis) was carried out using the Canoco for Windows v.4.5 software.

3.3 Results

3.3.1 Water quality and nutrient analysis

Table 2 represents the physico-chemical water quality characteristics. It can be seen on the whole that the Shingwedzi and Letaba Rivers show the most deviations with

27 Chapter 3: Physico-Chemical and Nutrient Analysis. regard to the physical water quality whilst the Luvuvhu and Sabie Rivers appear to be in a more stable condition.

The water in the Luvuvhu River seemed to be well oxygenated with dissolved oxygen concentrations ranging between 7.9 to 9.4 mg/l and percentage saturation ranging between 93.4 to 116.5 % (Table 2). The Letaba 1a, Letaba 2 and Sabie 1 sites each showed low dissolved oxygen concentrations and percentage saturations during the low flow sampling period. It can be observed that the Shingwedzi 2 site had the lowest oxygen readings with values of 3.8 mg/l or 42.7 %. One high reading of 14.1 mg/l was obtained for the Letaba 1a during the high flow sampling period (Table 2). Oxygen readings were observed to be higher during the high flow period with the exception of Luvuvhu 1.

Temperature values were generally consistent, a notable exception was 28.1 ° C measured at Letaba 2 during March 2006. A decrease in dissolved oxygen was also noted at this site (Table 2). Large fluctuations between summer and winter sampling were not evident. Basic or alkaline pH values were obtained through out the sampling. Luvuvhu 1, Shingwedzi 2, Letaba 1a, and Letaba 2 showing values > 8 this suggests that all localities had alkaline waters. Historic pH recordings (Figure 6 to Figure 8) show consistency between the values of 6.5 and 8.5 with the Shingwedzi and Letaba 1 stations showing a trend of increase over time. This correlates with an increase in conductivity. Values for these two rivers ranked in the 75th and 95th percentile (Table 3).

The highest turbidity (113 NTU) was recorded at Shingwedzi 2 and Letaba 1a during September 2005. Both sites showed high conductivity values of 1547 µS/cm and 1438 µS/cm respectively. High turbidity and conductivity readings were also obtained for Letaba 2 and Shingwedzi 2 during the March sampling period (Table 2). When historic data (Figure 9 to Figure 11) is examined it can be seen that the Shingwedzi, Letaba 1a and Letaba 2 have been experiencing drastic increases in conductivity over the past ten years. This once again correlates with high pH values and the results obtained in this study (Table 2) which rank in the 95th percentile (Table 4).

28 Chapter 3: Physico-Chemical and Nutrient Analysis.

Table 2: Physico-chemical water quality parameters measured in situ during high and low flow for the Luvuvhu, Shingwedzi, Letaba and Sabie rivers. Conductivity Dissolved Oxygen Concentration Percentage Oxygen Saturation Temperature pH Turbidity (µS/cm) (mg/l) (%) (° C) (NTU)

Luvuvhu 1 H 398 8.5 96.9 22.4 7.3 38 L 177 9.4 116.5 23.3 8.0 22 Luvuvhu 2 H 274 8.5 104.4 25.1 7.4 35 L 177 7.9 93.4 22.3 7.6 1 Shingwedzi 1 H 369 8.0 92.6 22.9 7.7 25 L N/F N/F N/F N/F N/F N/F Shingwedzi 2 H 994 8.6 108.3 26.3 8.1 50 L 1547 3.8 42.7 19.9 8.1 113 Letaba 1a H 364 14.1 174.4 25.2 8.3 26 L 1438 5.3 61.2 19.0 7.9 113 Letaba 1b H 527 8.6 106.4 25.6 7.8 24 L 213 8.2 100.4 23.5 7.7 4 Letaba 2 H 799 7.5 95.7 28.1 7.8 87 L 128 5.5 56.4 21.8 7.9 53 Sabie 1 H 201 9.1 105.2 24.3 7.0 12 L 116 3.7 43.50 20.8 7.4 7 Sabie 2 H 383 8.2 105.00 25.8 7.3 32 L 147 8.2 93 20.5 7.7 6 H = High flow, L = Low flow, N/F = No flow recorded.

29 Chapter 3: Physico-Chemical and Nutrient Analysis.

9.0 9.5

8.5 9.0

8.0 8.5 8.0 7.5 7.5 7.0 7.0 6.5 6.5 pH units pH

units pH 6.0 6.0 5.5 5.5 5.0 5.0 A B Oct-83 Oct-85 Oct-87 Oct-89 Oct-91 Oct-93 Oct-95 Oct-97 Oct-99 Oct-01 Oct-03

Dec-86 Dec-88 Dec-90 Dec-92 Dec-94 Dec-96 Dec-98 Dec-00 Dec-02 Dec-04 9.5 9.5 9.0 9.0

8.5 8.5 8.0 8.0 7.5 7.5 7.0 7.0 6.5 6.5

units pH

pH units pH 6.0 6.0 5.5 5.5 5.0 5.0 C D Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Figure 6: Historic pH values for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1983 and 2005. The trend is indicated with a second-order polynomial trend line.

30 Chapter 3: Physico-Chemical and Nutrient Analysis.

9.0 9.5 9.0 8.5 8.5 8.0 8.0 7.5 7.5 7.0

7.0 6.5 pH units pH units pH 6.0 6.5 5.5 6.0 5.0 A B Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05

Feb-76 Feb-78 Feb-80 Feb-82 Feb-84 Feb-86 Feb-88 Feb-90 Feb-92 Feb-94 Feb-96 Feb-98 Feb-00 Feb-02 Feb-04 9.5 9.0 9.0 8.5 8.0 8.5 7.5 8.0 7.0 7.5 6.5

7.0 units pH pH units pH 6.0

6.5 5.5 6.0 5.0 C D Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Oct-76 Oct-78 Oct-80 Oct-82 Oct-84 Oct-86 Oct-88 Oct-90 Oct-92 Oct-94 Oct-96 Oct-98 Oct-00 Oct-02 Oct-04 Figure 7: Historic pH values for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and Sabie River 1 (D) between the years 1976 and 2005. The trend is indicated with a second-order polynomial trend line

31 Chapter 3: Physico-Chemical and Nutrient Analysis.

9.5 9.0 8.5 8.0 7.5 7.0

pH units pH 6.5 6.0 5.5 5.0 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Figure 8: Historic pH values for the Sabie River 2 between the years 1984 and 2005. The trend is indicated with a second-order polynomial trend line.

Table 3: pH percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data. 5th 25th 50th 75th 95th Percentile Percentile Percentile Percentile Percentile

Luvuvhu 1 6.9 7.7 7.9 8.0 8.2 Luvuvhu 2 6.5 7.1 7.7 8.0 8.3 Shingwedzi 1 6.9 8.0 8.2 8.4 8.6 Shingwedzi 2 7.6 8.1 8.4 8.5 8.8 Letaba 1a 7.0 7.9 8.1 8.3 8.4 Letaba 1b 6.2 6.8 7.4 7.7 8.1 Letaba 2 7.3 8.1 8.3 8.5 8.7 Sabie 1 6.3 6.9 7.4 7.8 8.2 Sabie 2 6.6 7.3 7.7 8.0 8.2

32 Chapter 3: Physico-Chemical and Nutrient Analysis.

400 800 350 700 600 300 500 250 200 400

µS/cm 150 µS/cm 300

100 200 50 100 0 0 A B Jun-76 Jun-78 Jun-80 Jun-82 Jun-84 Jun-86 Jun-88 Jun-90 Jun-92 Jun-94 Jun-96 Jun-98 Jun-00 Jun-02 Jun-04 Oct-83 Oct-85 Oct-87 Oct-89 Oct-91 Oct-93 Oct-95 Oct-97 Oct-99 Oct-01 Oct-03

2000 2500 1800 1600 2000 1400 1200 1500 1000

µS/cm 800 1000 µS/cm 600 500 400 200 0 0 C D Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Figure 9: Historic conductivity for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1976 and 2005. The trend is indicated with a second-order polynomial trend line

33 Chapter 3: Physico-Chemical and Nutrient Analysis.

600 900 800 500 700

400 600 500 300 400 µS/cm µS/cm 300 200 200 100 100 0 0 A B Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05

Feb-76 Feb-78 Feb-80 Feb-82 Feb-84 Feb-86 Feb-88 Feb-90 Feb-92 Feb-94 Feb-96 Feb-98 Feb-00 Feb-02 Feb-04

1400 250

1200 200 1000

800 150

600 µS/cm

µS/cm 100 400 50 200 0 0 C D Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Oct-76 Oct-78 Oct-80 Oct-82 Oct-84 Oct-86 Oct-88 Oct-90 Oct-92 Oct-94 Oct-96 Oct-98 Oct-00 Oct-02 Oct-04 Figure 10: Historic conductivity for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and Sabie River 1 (D) between the years 1976 and 2005. The trend is indicated with a second-order polynomial trend line

34 Chapter 3: Physico-Chemical and Nutrient Analysis.

700

600

500

400

µS/cm 300

200

100

0 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Figure 11: Historic conductivity for the Sabie River 2 between the years 1984 and 2005. The trend is indicated with a second-order polynomial trend line

Table 4: Conductivity percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data. 5th 25th 50th 75th 95th Percentile Percentile Percentile Percentile Percentile

Luvuvhu 1 94.0 110.8 126.0 147.0 171.0 Luvuvhu 2 88.7 115.0 130.5 151.5 203.1 Shingwedzi 1 103.5 215.0 333.0 542.0 1440.0 Shingwedzi 2 145.0 259.0 377.5 514.0 1319.3 Letaba 1a 137.0 181.0 220.0 386.0 460.6 Letaba 1b 68.0 84.0 100.0 122.0 255.4 Letaba 2 200.1 300.0 397.5 660.0 1074.5 Sabie 1 53.0 80.0 102.0 120.0 152.0 Sabie 2 81.4 97.0 112.0 128.0 217.8

35 Chapter 3: Physico-Chemical and Nutrient Analysis.

High ammonium levels were observed at all sampling sites throughout the study. Extreme values which ranked in the 95th percentile were observed at the Luvuvhu 1 during September 2005 and Letaba 1a and Letaba 2 during March 2006 (Table 5). Of significant importance was a very high reading of 2.70 mg/l (2700 µg/l) which was measured at the Shingwedzi 2 site during September 2005 (Table 5), this value according to historic data was also in the 95th percentile. Historic ammonium data obtained from the Department of Water Affairs and Forestry is presented in (Figure 12 to Figure 14). These show a general trend of decreasing ammonium levels with the exception of the Sabie 1 (Figure 13D).

Nitrite levels were low throughout the sampling period whilst nitrate levels were considerably higher, this was particularly true for the March (high flow) sampling period with values ranging between 2.1 and 10.2 mg/l. Phosphate levels remained below 0.02 mg/l for all samples taken with the exception of the low flow reading for Shingwedzi 2 (Table 5). Historic nitrate/nitrite (Figure 21 to Figure 23) and phosphate data (Figure 18 to Figure 20) show a slight increase with regards to most of the rivers.

Chloride values were high particularly with regards to the Letaba system as a whole this is evident by looking at the values obtained during the study which were 75 and 36 mg/l for the Letaba 1a whilst those of the Letaba 2 climbed as high as 100 mg/l. Values for the Shingwedzi 2 were also found to be slightly high at 64 mg/l (Table 5). Historical data for all four river systems show chloride levels have been steadily increasing especially in the last ten years. Drastic increases have been experienced at the Letaba 1a, Letaba 2 and Shingwedzi 2 (Figure 15D, Figure 16A and C) which correlate with the findings of this study.

Sulphate levels were found to be slightly high for the Luvuvhu, Letaba and Sabie rivers on a whole. Values ranged between 15 and 29 mg/l however never increased above. Historic data show a trend of increasing sulphate concentrations (Figure 24 to Figure 26). Chemical oxygen demand (COD) was found to remain below detection limits. One high reading of 49 mg/l was obtained for the Luvuvhu 2 during high flow.

36 Chapter 3: Physico-Chemical and Nutrient Analysis.

Table 5: Nutrient and other chemical variables measured during high and low flow for the four rivers under investigation. Nitrates (mg/l) Nitrites (mg/l) Ammonium (mg/l) Phosphates (mg/l) Chlorides (mg/l) Sulphates (mg/l) COD (mg/l)

Blank 0 0 0 0 0 0 0 Luvuvhu 1 H 4.4 0.04 0.12 0.01 5 3 5 L <1 0.01 0.06 0.02 8 15 <4 Luvuvhu 2 H 2.1 0.02 0.02 0.01 4 5 49 L <1 0.01 0.02 <0.01 8 15 <4 Shingwedzi 1 H 8.1 0.03 0.09 0.01 16 4 7 L N/F N/F N/F N/F N/F N/F N/F Shingwedzi 2 H 3.4 0.04 0.01 0.01 19 2 0 L 1.20 0.02 2.70 0.41 64 21 13 Letaba 1a H 4.4 0.14 0.01 0.01 75 17 14 L 1.20 0.07 0.14 0.01 37 29 <4 Letaba 1b H 2.8 0.05 <0.01 <0.01 12 12 5 L <1 0.01 0.01 0.01 10 17 <4 Letaba 2 H 3.8 0.12 0.01 0.01 14 17 3 L <1 0.03 0.24 0.01 100 24 <4 Sabie 1 H 10.2 0.02 0.01 0.01 4 8 <4 L <1 0.03 0.01 0.02 <0.01 16 <4 Sabie 2 H 7.4 0.05 0.02 0.02 11 8 8 L <1 0.01 0.06 0.01 2 17 <4 H = High flow, L = Low flow, N/F = No flow recorded, < = values below detection limits.

37 Chapter 3: Physico-Chemical and Nutrient Analysis.

0.16 0.40 0.14 0.35

0.12 0.30

0.10 0.25 0.08 0.20

mg/l 0.06 0.15 mg/l 0.04 0.10 ` 0.02 0.05 0.00 0.00 A B Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Oct-83 Oct-85 Oct-87 Oct-89 Oct-91 Oct-93 Oct-95 Oct-97 Oct-99 Oct-01 Oct-03 2.00 1.80 2.50

1.60 2.00 1.40 1.20 1.50 1.00

0.80 1.00 mg/l

mg/l 0.60 0.50 0.40 0.20 0.00 0.00 C D Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Figure 12: Historic ammonium data for the Luvuvhu 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.

38 Chapter 3: Physico-Chemical and Nutrient Analysis.

0.30 0.30

0.25 0.25

0.20 0.20

0.15 0.15

mg/l mg/l 0.10 0.10

0.05 0.05

0.00 0.00

A B Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96 Jan-97 Jan-98 Jan-99 Jan-00 Jan-01 Jan-02 Jan-03 Jan-04 Jan-05 7.00 0.60

6.00 0.50 5.00 0.40 4.00 0.30 3.00

l

/ mg/l 0.20 g

m 2.00

1.00 0.10

0.00 0.00 C D Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-78 Jan-80 Jan-82 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Figure 13: Historic ammonium data for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.

39 Chapter 3: Physico-Chemical and Nutrient Analysis.

0.12

0.10

0.08

0.06 mg/l 0.04

0.02

0.00 Nov-83 Nov-85 Nov-87 Nov-89 Nov-91 Nov-93 Nov-95 Nov-97 Nov-99 Nov-01 Nov-03 Figure 14: Historic ammonium data for the Sabie River 2 between the years 1983 and 2005. The trend is indicated with a second-order polynomial trend line.

Table 6: Ammonium percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data. 5th 25th 50th 75th 95th Percentile Percentile Percentile Percentile Percentile

Luvuvhu 1 0.01 0.02 0.03 0.05 0.08 Luvuvhu 2 0.01 0.03 0.05 0.07 0.12 Shingwedzi 1 0.01 0.02 0.07 0.20 0.60 Shingwedzi 2 0.01 0.02 0.05 0.13 0.51 Letaba 1a 0.02 0.02 0.05 0.08 0.18 Letaba 1b 0.00 0.02 0.03 0.05 0.09 Letaba 2 0.02 0.05 0.09 0.23 5.28 Sabie 1 0.00 0.00 0.02 0.05 0.12 Sabie 2 0.00 0.02 0.03 0.05 0.08

40 Chapter 3: Physico-Chemical and Nutrient Analysis.

30 40 35 25 30

20 25

15 20

mg/l 15 mg/l 10 10 5 5 0 0 A B Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05

Oct-83 Oct-85 Oct-87 Oct-89 Oct-91 Oct-93 Oct-95 Oct-97 Oct-99 Oct-01 Oct-03

700 350

600 300

500 250 400 200

300 150 mg/l mg/l 200 100 100 50 0 0 C D Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06

Figure 15: Historic chloride data for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.

41 Chapter 3: Physico-Chemical and Nutrient Analysis.

45 80 40 70 35 30 60 25 50 40 20 30 15 mg/l mg/l 10 20 5 10 0 0

A B Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05

Feb-87 Feb-89 Feb-91 Feb-93 Feb-95 Feb-97 Feb-99 Feb-01 Feb-03 Feb-05

200 12 180 160 10 140 8 120 100 6 80

mg/l 4 mg/l 60 40 2 20 0 0

C D Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Figure 16: Historic chloride data for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.

42 Chapter 3: Physico-Chemical and Nutrient Analysis.

35 30

25 20 15 mg/l 10

5 0 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Figure 17: Historic chloride data for the Sabie River 2 between the years 1984 to 2005. The trend is indicated with a second-order polynomial trend line.

Table 7: Chloride percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data. 5th 25th 50th 75th 95th Percentile Percentile Percentile Percentile Percentile

Luvuvhu 1 4.6 5.6 7.5 10.1 12.4 Luvuvhu 2 5.0 8.6 10.5 12.4 17.0 Shingwedzi 1 5.7 9.4 15.8 52.4 212.3 Shingwedzi 2 5.1 7.8 14.6 34.9 200.0 Letaba 1a 5.7 7.5 11.1 27.1 34.6 Letaba 1b 0.0 4.9 7.5 11.0 28.5 Letaba 2 14.0 21.7 29.7 40.0 155.5 Sabie 1 0.0 1.7 3.4 5.0 5.8 Sabie 2 3.9 5.0 6.4 8.0 14.4

43 Chapter 3: Physico-Chemical and Nutrient Analysis.

0.12 0.12

0.10 0.10

0.08 0.08

0.06 0.06

mg/l

mg/l 0.04 0.04

0.02 0.02

0.00 0.00

A B Oct-83 Oct-85 Oct-87 Oct-89 Oct-91 Oct-93 Oct-95 Oct-97 Oct-99 Oct-01 Oct-03 Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 0.35 0.45 0.30 0.40 0.25 0.35 0.30 0.20 0.25 0.15 0.20 mg/l mg/l 0.10 0.15 0.10 0.05 0.05 0.00 0.00 C D Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Figure 18: Historic phosphate data for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 to 2005. The trend is indicated with a second-order polynomial trend line.

44 Chapter 3: Physico-Chemical and Nutrient Analysis.

0.16 0.14 0.14 0.12 0.12 0.10 0.10 0.08 0.08 0.06 0.06 mg/l mg/l 0.04 0.04 0.02 0.02 0.00 0.00 A B Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 0.12 0.12

0.10 0.10

0.08 0.08

0.06 0.06

0.04 0.04 mg/l mg/l 0.02 0.02

0.00 0.00

C Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 D Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Figure 19: Historic phosphate data for the Letaba River 1a (A), 1b Letaba River (B), Letaba River 2 (C) and the Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.

45 Chapter 3: Physico-Chemical and Nutrient Analysis.

0.07 0.06 0.05 0.04

mg/l 0.03 0.02 0.01 0.00 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Figure 20: Historic phosphate data for the Sabie River 2 between the years 1985 to 2005. The trend is indicated with a second-order polynomial trend line.

Table 8: Phosphate percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data. 5th 25th 50th 75th 95th Percentile Percentile Percentile Percentile Percentile

Luvuvhu 1 0.01 0.01 0.02 0.02 0.05 Luvuvhu 2 0.00 0.01 0.02 0.02 0.05 Shingwedzi 1 0.01 0.02 0.03 0.05 0.17 Shingwedzi 2 0.01 0.02 0.02 0.04 0.12 Letaba 1a 0.00 0.01 0.02 0.03 0.06 Letaba 1b 0.00 0.00 0.01 0.02 0.04 Letaba 2 0.01 0.02 0.02 0.03 0.06 Sabie 1 0.00 0.01 0.01 0.02 0.03 Sabie 2 0.00 0.01 0.01 0.02 0.03

46 Chapter 3: Physico-Chemical and Nutrient Analysis.

1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 mg/l mg/l 0.4 0.2 0.2 0.0 0.0 A B Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Oct-83 Oct-85 Oct-87 Oct-89 Oct-91 Oct-93 Oct-95 Oct-97 Oct-99 Oct-01 Oct-03 2.5 6.0

2.0 5.0 4.0 1.5

3.0 1.0

mg/l 2.0 mg/l 0.5 1.0

0.0 0.0

C D Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Figure 21: Historic nitrate and nitrite data for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 to 2005. The trend is indicated with a second-order polynomial trend line.

47 Chapter 3: Physico-Chemical and Nutrient Analysis.

0.9 2.5 0.8

0.7 2.0 0.6 1.5 0.5 0.4 1.0 mg/l 0.3 mg/l 0.2 0.5 0.1 0.0 0.0 A B Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05

Feb-87 Feb-89 Feb-91 Feb-93 Feb-95 Feb-97 Feb-99 Feb-01 Feb-03 Feb-05

1.4 1.4 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 mg/l 0.4 mg/l 0.4 0.2 0.2 0.0 0.0 C D Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Figure 22: Historic nitrate and nitrite data for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and the Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.

48 Chapter 3: Physico-Chemical and Nutrient Analysis.

0.8

0.7

0.6

0.5

0.4 mg/l 0.3

0.2

0.1

0.0 Nov-83 Nov-85 Nov-87 Nov-89 Nov-91 Nov-93 Nov-95 Nov-97 Nov-99 Nov-01 Nov-03

Figure 23: Historic nitrate and nitrite data for the Sabie River 2 between the years 1983 to 2005. The trend is indicated with a second-order polynomial trend line.

Table 9: Nitrate and nitrite percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data. 5th 25th 50th 75th 95th Percentile Percentile Percentile Percentile Percentile

Luvuvhu 1 0.01 0.09 0.22 0.30 0.45 Luvuvhu 2 0.01 0.04 0.12 0.32 0.73 Shingwedzi 1 0.02 0.04 0.11 0.34 1.08 Shingwedzi 2 0.02 0.04 0.15 0.39 1.14 Letaba 1a 0.01 0.02 0.08 0.18 0.51 Letaba 1b 0.00 0.05 0.25 0.44 0.79 Letaba 2 0.02 0.04 0.10 0.36 0.61 Sabie 1 0.00 0.08 0.23 0.32 0.49 Sabie 2 0.02 0.11 0.19 0.25 0.41

49 Chapter 3: Physico-Chemical and Nutrient Analysis.

25 30 25 20

20 15 15 10 mg/l mg/l 10

5 5

0 0

A B Oct-83 Oct-85 Oct-87 Oct-89 Oct-91 Oct-93 Oct-95 Oct-97 Oct-99 Oct-01 Oct-03 Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05

35 45 30 40 35

25 30 20 25 15 20 mg/l mg/l 15 10 10 5 5 0 0

C D Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Jan-98 Jan-00 Jan-02 Jan-04 Jan-06 Figure 24: Historic sulphate data for the Luvuvhu River 1 (A), Luvuvhu River 2 (B), Shingwedzi River 2 (C) and Shingwedzi River 2 (D) between the years 1977 to 2005. The trend is indicated with a second-order polynomial trend line.

50 Chapter 3: Physico-Chemical and Nutrient Analysis.

20 40 18 35 16 30 14 25 12 10 20 8 mg/ mg/l 15 6 4 10 2 5 0 0 A B Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05

35 25 30 20 25 20 15 15

mg/l 10 mg/l 10 5 5 0 0

C D Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Jan-77 Jan-79 Jan-81 Jan-83 Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01 Jan-03 Jan-05 Figure 25: Historic sulphate data for the Letaba River 1a (A), Letaba River 1b (B), Letaba River 2 (C) and the Sabie River 1 (D) between the years 1977 and 2005. The trend is indicated with a second-order polynomial trend line.

51 Chapter 3: Physico-Chemical and Nutrient Analysis.

20 18 16 14 12 10

mg/l 8 6 4 2 0 Nov-83 Nov-85 Nov-87 Nov-89 Nov-91 Nov-93 Nov-95 Nov-97 Nov-99 Nov-01 Nov-03 Figure 26: Historic sulphate data for the Sabie River 2 between the years 1983 to 2005. The trend is indicated with a second-order polynomial trend line.

Table 10: Sulphate percentile values obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie river system based upon historic data. 5th 25th 50th 75th 95th Percentile Percentile Percentile Percentile Percentile

Luvuvhu 1 0.4 2.5 4.1 6.1 9.6 Luvuvhu 2 0.0 2.0 3.6 5.8 10.3 Shingwedzi 1 0.8 5.6 7.5 13.2 19.4 Shingwedzi 2 2.0 5.5 8.0 12.2 25.5 Letaba 1a 0.6 3.4 6.1 9.4 14.9 Letaba 1b 0.0 1.2 3.4 7.3 12.3 Letaba 2 3.3 6.8 9.9 16.4 23.2 Sabie 1 0.0 0.0 2.0 6.1 13.1 Sabie 2 2.1 4.2 5.8 7.7 12.1

52 Chapter 3: Physico-Chemical and Nutrient Analysis.

3.3.2 Sediment

The average percentage of grain size is presented in Figure 27 and Figure 28. Most localities sampled consisted of fine to medium sands with values lying between 53 µm and 500 µm. The Luvuvhu 2 and Letaba 1b sites were predominantly composed of small grain sizes. Little coarse sand (4000 µm) was present throughout the sampling periods however there was a slight increase during September 2005 (low flow).

Organic content is represented in Table 11. Most localities outside the borders of the KNP with the exception of the Sabie River had lower organic content. Data for the Shingwedzi 2 showed the highest percentage with values of 6.1 % during high flow and 6.3 % during low flow. It was also noted that the Letaba River varied the most over the entire river system with values ranging from 0.8 to 4.9 %.

Table 11: Percentage organic content present within sediments for all localities. High flow (%) Low flow (%)

Luvuvhu 1 1.3 1.3 Luvuvhu 2 2.1 5.0 Shingwedzi 1 1.2 N/S Shingwedzi 2 6.1 6.3 Letaba 1a 1.7 2.9 Letaba 1b 4.9 3.6 Letaba 2 4.4 0.8 Sabie 1 1.3 0.6 Sabie 2 0.7 0.4 N/S = No sample recorded

53 Chapter 3: Physico-Chemical and Nutrient Analysis.

100%

80%

60%

40%

20%

4000 µm 0%

1 2 1 2 a b 2 1 2 i i 2000 µm u u 1 1 z z a e e h h i i d d a a b v v b b e e b b a 500 µm u u t a a a a v v w w t t e S S u u g g e e L 212 µm L L n n i i L L

h h 53 µm S S Figure 27: The average percentage contribution of grain size obtained during September 2005 (low flow) for all localities, no flow recorded at the Shingwedzi 1.

54 Chapter 3: Physico-Chemical and Nutrient Analysis.

100%

80%

60%

40%

20%

4000 µm 0%

1 2 1 2 a b 2 1 2 i i 2000 µm u u 1 1 z z a e e h h i i d d a a b v v b b e e b b a 500 µm u u t a a a a v v w w t t e S S u u g g e e L 212 µm L L n n i i L L

h h

53 µm S S Figure 28: The average percentage contribution of grain size obtained during March 2006 (high flow) for all localities.

55 Chapter 3: Physico-Chemical and Nutrient Analysis.

3.3.3 Multivariate analysis.

Figure 29 displays the first two axes of a PCA biplot where sampling sites have been analysed with regards to nutrient, sediment and water quality variables. The bi-plot explains 79.2% of the variance in the data with 66.3 % for the first and a further 2.9% explained by the second axis. All sites show some form of grouping with the exception of the Letaba 2 and Luvuvhu 1 during low flow, whilst the Letaba 1a during high flow is similar to the three main groupings. Separate analysis of both water and sediment generated biplots of similar composition with sites grouping in the same manner. For this reason one combined figure is presented.

Figure 29: PCA biplot with focus on sampling localities in relation to nutrient, sediment and physico-chemical variables signifying the determining factors.

56 Chapter 3: Physico-Chemical and Nutrient Analysis.

The first grouping displayed in grey shows that these sites are strongly influenced by the conductivity; this data strengthens the results in Table 2. The second grouping (green) was equally affected by conductivity and nitrate values, conductivity values were found to be lower in these four localities compared to the first grouping. Nitrates ranged from 2.2 to 10.2 mg/l and represent the highest values. The final grouping (red) was mostly affected by organic content and a grain size of 53µm whilst COD and nitrates had a smaller influence The three remaining sites did not group and this suggests that they are entirely different in their physical, organic and nutrient composition.

3.4 Discussion

3.4.1 Water quality and nutrient analysis.

Water quality degradation world-wide is due mainly to anthropogenic activities which release pollutants into the environment thereby having an adverse effect upon aquatic ecosystems (Fatoki et al., 2001). Water quality can be regarded as a network of variables (e.g. pH, oxygen concentration, temperature, etc) that are linked and co- linked; any changes in these physical and chemical variables can affect aquatic biota in a variety of ways (Dallas and Day, 1993)

Most aquatic organisms are dependent upon water for their survival and thus maintenance of adequate dissolved oxygen within the aquatic ecosystem is of vital importance (Dallas and Day, 2004). Low dissolved oxygen concentrations as were experienced in the Shingwedzi 2, Letaba 1a, Letaba 2 and Sabie 1 (Table 2) can impact the behaviour, emergence, blood chemistry, reproduction, growth rate and food intake of aquatic organisms and can result in death, whilst high dissolved oxygen concentrations are of equal danger leading to gas bubble disease in fish (King et al., 2003). The mean dissolved oxygen concentration of the Luvuvhu fell within the proposed guideline limits of 80 – 120 % saturation or 9.09 mg/l (DWAF, 1996; Dallas and Day, 2004). The remaining rivers each showed depletion in oxygen at some point of sampling. According to Dallas and Day (2004) the significance of depleted oxygen

57 Chapter 3: Physico-Chemical and Nutrient Analysis. depends upon the frequency, timing and duration of exposure, therefore a more rigid monitoring of the oxygen levels will supply greater accuracy. Super –saturation (> 100 %) occurred at Letaba 1a (174.4 %) during March 2006 (Table 2). It was noted that the river was flowing extremely slow with many pools of stagnant or sluggish water, due to this factor limited fish were observed in the area. Lethal effects due to super-saturation threaten mainly non or less mobile life stages (Dallas and Day, 2004).

Water temperature plays an important role in influencing the quality and ecology of streams and rivers. It affects not only the physical nature of water by changing the viscosity, density and surface tension but also the rate and types of chemical reactions that occur within. Water temperature is thus an important factor that influences the rate of all biological activities. Temperature can therefore be used as a first step in predicting the effects of mans activities on the aquatic ecosystem (Rivers-Moore et al., 2004). Since temperature has a direct relationship to dissolved oxygen any increase in temperatures will result in a decrease in the concentration of dissolved oxygen. This is evident with the high temperature (28.1 °C) experienced at Letaba 2 resulting in a decrease of the dissolved oxygen concentration to 7.5 mg/l (Table 2). Temperature changes can also affect the timing of life cycles, the development of different life stages, predator-prey interactions, genetic selection and the abundance of algae and plants (King et al., 2003). In polluted waters the effect of increased temperature is dependent upon the type as well as concentration of the pollutant. Generally increases in temperature will reduce the period of survival of fish in poison (Alabaster and Lloyd, 1980). Temperatures measured remained within the norms for the high ambient temperatures of the lowveld and thus seem to be of no great concern.

Geology and atmospheric influences determine the pH of natural waters and thus most fresh waters within South Africa are relatively well buffered with pH ranges between 6 and 8. pH affects aquatic ecosystems by determining the chemical species and thus the potential toxicity and availability in which numerous elements (trace metals, non- metallic elements and essential elements) are found in water (DWAF, 1996; Dallas and Day, 2004). A change in pH from around neutral will thus result in changes to the water quality and ultimately increase the stresses upon aquatic organisms. The pH values during the study were within the normal range and never dropped below 7.

58 Chapter 3: Physico-Chemical and Nutrient Analysis.

Four sites Luvuvhu 1 (L), Shingwedzi 2 (H and L) and Klein Letaba 1a (H) rose above a pH value of 8 (Table 2). This increased pH value can result in non toxic + ammonium ions (NH4 ) being converted to the highly toxic un-ionized ammonia

(NH3) (Dallas and Day, 2004). Increased pH levels are generally considered to be less of a threat to aquatic organisms, however high levels create favourable conditions for algal blooms and increased weed growth. This is of increasing concern in agricultural areas which undergo large amounts of nutrient enrichment (Van Vuren and Du Preez, 1997).

The natural hydrology and geomorphological processes within a catchment area are important in determining the turbidity of its surface waters. Turbidity of waters is caused naturally by suspended matter such as clay, silt, organic matter, in-organic matter, plankton and various other microscopic organism which enter the system through natural processes, however land use practises such as over-grazing, non- contour ploughing, removal of riparian vegetation and forestry can accelerate erosion thus resulting in increased turbidity levels (Van Vuren et al., 1999). Anthropogenic processes such as release of domestic sewage, industrial discharge and physical perturbations can also increase turbidity. Continuous high level inputs may have a serious consequence for aquatic biota and can lead to a complete change in the natural ecosystem. South African rivers become highly turbid and show an increase in suspended solids during the rainy season (Dallas and Day, 2004). This was evident throughout the sampling with the exceptions of the Shingwedzi 2 and Klein Letaba 1a sites (Table 2) which had very high turbidity levels during low flow, a reason for this could be due to the fact that the area was very dry resulting in little water in both river system. This would cause an increase in the total suspended solids (TSS) and thus an increase in the turbidity. It can also be noted that the Letaba 2 and Shingwedzi 2 (Table 2) showed high values compared to the other sites, this can possibly be attributed to the large rural population upstream of these two sites utilising the land for agriculture and livestock watering.

One of the major descriptions of water quality is the total amount of substances dissolved in it. The natural weathering of rocks by erosion results in the majority of ions in most natural waters, whilst anthropogenic activities such as domestic and industrial effluent may contribute to increased levels. Ions can be measured in a

59 Chapter 3: Physico-Chemical and Nutrient Analysis. variety of ways. Conductivity is the ability of a sample of water to conduct an electrical current and measures the total quantity of salts in a sample of water and therefore the total dissolved salts (TDS). TDS values vary within South Africa from values as low as 9 µS/cm to as high as 97900 µS/cm (Dallas and Day, 2004). High conductivity values of 1547 µS/cm and 1438 µS/cm were found at the Shingwedzi 2 and Letaba 1a locality, linked to this and possibly explaining the high conductivity was a high turbidity reading of 113 NTU (Table 2). Higher values were obtained during the March sampling; this was to be expected due to increased runoff from the land depositing salts and other substances in the water column. When historic data is taken into consideration it can be seen that the Shingwedzi 2, Letaba 1a and Letaba 2 (Figure 9C and D and Figure 10A and C) have experienced a drastic increase in conductivity values over the past ten years. These results were confirmed by values obtained during this study (Table 2) which ranked in the 95th percentile (Table 4). These increases along with high pH values (Figure 6 to Figure 8) are indicative of salinisation. According to Kefford et al. (2004) there is considerable uncertainty with regards to the effect that increased salinity has upon aquatic organisms especially in South African systems, however Dallas and Day (2004) state that salinity may act as an antagonist or synergist with regards to toxic pollutants often providing some form of protection against heavy metals.

+ Ammonia is a common pollutant that occurs either in the free un-ionised (NH3 ) form + or as ammonium ions (NH4 ). Both are reduced forms of inorganic nitrogen derived from the decomposition of organic material. Ammonia is commonly associated with sewage and industrial effluents and forms part of many fertilisers (DWAF, 1996; Dallas and Day, 2004). The toxicity of ammonia is directly related to concentration of the un-ionised form. The ammonium ion has very little or no toxicity (Williams et al., 1986). In surface or ground water ammonium usually occurs due to the decomposition of nitrogenous organic matter, natural waters typically contain ammonia and ammonium compounds in concentrations below 0.1 mg/l (Dallas and Day, 2004). Water temperature and pH are two important or significant factors that affect the proportion and toxicity of un-ionised ammonia, any increase in either will result in an increase in un-ionised ammonia and therefore an increase in toxicity to aquatic organisms (DWAF, 1996). Other factors such as the concentration of dissolved oxygen, carbon dioxide and the presence of toxicants also have an effect on ammonia

60 Chapter 3: Physico-Chemical and Nutrient Analysis. toxicity. It was noted that during March 2005 the Shingwedzi 2 site showed a pH value of 8.1. Linked to this value was a high ammonium reading of 2.70 mg/l (Table 2 and Table 5). As the literature states increases in pH result in an increase in the conversion of the non-toxic ammonium ions into toxic ammonia. This locality represents a possible problem area. The South African target water quality range (TWQR) for ammonia in aquatic ecosystems is 7 µg/l. Chronic effects (reduction in growth rate, morphological development, hatching success and pathological changes in gill, liver and kidney tissue) occur at 15 µg/l whilst acute effects (loss of equilibrium, increased breathing rate, increased cardiac output and oxygen uptake and in extreme case convulsion, coma and death) are present at 100 µg/l (DWAF, 1996; Morrison et al., 2001). The following localities showed high ammonium concentrations Luvuvhu 1, Shingwedzi 2, Letaba 1a and Letaba 2, these rivers could receive fertilisers, industrial and sewage effluent as well as effluent from informal settlements. When compared to historic data (Figure 12A, D and Figure 13D and C) it is evident that the Letaba 1a has been experiencing high values up to the present this is possibly due to the large agricultural activities in the area. The remaining three sites have shown fluctuations in the past. The results obtained during the study are within the 75th and 95th percentile, suggesting that values are normal for these rivers systems (Table 6).

- - Nitrate (NO3 ), nitrite (NO2 ) and ammonia (NH3) are intimately linked in natural systems. Nitrite is the intermediate in the conversion of ammonia into nitrates through the process of nitrification and denitrification by bacteria. Nitrites are toxic at certain concentrations and can cause damage to gills as well as diffusing into red blood cells where it results in the formation of methhaemoglobin. Methhaemoglobin lacks the ability to bind with oxygen thereby resulting in retarded oxygen transport. During highly active periods death can result due to anoxia (Dallas and Day, 1993). Sites sampled showed low nitrite concentrations with values never increasing above 0.14 mg/l (Table 5). When compared to historic data (Figure 21 to Figure 23) these values lie within the normal ranges. All samples baring the Letaba 1a and Letaba 2 high flows were found to occur within the 5th or 25th percentile (Table 9). Nitrates however were noted in higher concentrations throughout the sampling, values ranged from as low as 0.1 to 8.1 mg/l (Table 5). Nitrates can enter waters via fertilisers and agricultural runoff and are often found in high concentrations in ground water.

61 Chapter 3: Physico-Chemical and Nutrient Analysis.

Nitrates in high concentrations can be toxic to juveniles however generally nitrates are non-toxic (Dallas and Day, 2004). The rivers investigated are all situated in agricultural and subsistence farming areas and thus slightly higher nitrate levels were to be expected during the March sampling (high flow) due to an increase in runoff from the land.

Phosphorous is an important macronutrient and plays a major role in the structure of nucleic acids and in molecules (e.g. ATP) that are involved in the storage and use of energy in cells. In surface waters it occurs most commonly either as orthophosphates or as polyphosphates. Orthophosphate is in a form that is immediately available to aquatic biota. Phosphorous is seldom found in high concentrations in non-polluted water due to the fact that it is utilised by plants and sequestered by cells (Dallas and Day, 2004). In aquatic ecosystems large portions of phosphorous may be unavailable due to the fact that it can adsorb readily to suspensoids or bond to particles such as iron, aluminium, calcium or organic phenols. Sediments can also act as a sink to high phosphorous concentrations during low flow periods, releasing them again during high flows and/or anoxic conditions (Van Vuren et al., 1999). During this study exceedingly high levels of phosphate (0.41 mg/l) were found at Shingwedzi 2 during the winter sampling period (Table 5). Historic phosphate data (Figure 18 to Figure 20) shows an increase in phosphate levels for all four of the rivers. The Shingwedzi River (Figure 18C and D) has the most dramatic increase with values approaching 0.1 mg/l, this tie’s in with the data from the study which shows that the reading obtained lies within the 95th percentile (Table 8).

Chloride (Cl-) is a major anion of the element chlorine (Williams et al., 2003). Chloride due to its high solubility is a common constituent of water often accumulating to values as high as a few hundred mg/l (DWAF, 1996). Chloride ions are important compounds of all living systems contributing to the osmotic, ionic as well as water regulation functions within organisms. Chloride ions exhibit no toxic effects upon living systems (Dallas and Day, 1993). Historic data for chloride (Figure 15 to Figure 17) shows that most values obtained during the study are within the historic limits, an exception was the Letaba 1a site during high flow. The value of 75 mg/l was higher than historic values of 45 mg/l (Table 5) and sat within the 95th percentile (Table 7).

62 Chapter 3: Physico-Chemical and Nutrient Analysis.

Sulphur is an important building block of proteins and many other organic 2- compounds. Within water it occurs mostly as the sulphate ion (SO4 ). Sulphate is the stable form of sulphur and is non-toxic, however when occurring in excess sulphates form sulphuric acid (H2SO4). This acid can have a devastating effect upon aquatic ecosystems (Taylor et al., 1984; Nussey, 1998). Sulphate when compared to historic data (Table 5; Figure 24 to Figure 26) showed values higher than normal for the Luvuvhu, Letaba and Sabie rivers. Values were noted to be higher mostly during the dry sampling period however these never rose to drastic levels. Sulphates therefore do not seem to represent a problem at present however the continued increase within the Shingwedzi, Letaba 1a, 2 and Sabie 1 should be monitored (Figure 24 C and D; Figure 25A,C and D).

3.4.2 Sediment

Organic matter is a natural component of any aquatic ecosystem. However man contributes to the organic content through a variety of ways the most important being sewage and sewage effluents. Organic enrichment, although not toxic can have a significant change upon the biotic community as well as chemical and physical changes to the water quality (Dallas and Day, 2004). Results obtained for organic content were determined based upon the percentage organic matter within the sediment. Values < 0.5 % were classified as very low, 0.5 - 1.0 % low, 1.0 – 2.0 % moderately low, 2.0 – 4.0 % medium and > 4.0 % high (Table 11; EPA, 1991). From this information it can be deduced that the Shingwedzi 2 contained high organic content year round whilst all other systems showed seasonal fluctuations. Organic enrichment results in a decrease in dissolved oxygen concentrations; this was evident at the Shingwedzi 2 (Table 2).

Sediments temporarily bind pollutants to their surfaces and therefore can be considered as a source of pollution. Pollutants especially metals present no danger as long as they are bound to sediments. The potential danger occurs when under certain environmental conditions pollutants are released back into the water column. It follows that sediments composed mainly of small grain size will be able to bind more pollutants due to the increased surface area. Sediments of the Luvuvhu 2 and Letaba

63 Chapter 3: Physico-Chemical and Nutrient Analysis.

1b sites consist almost entirely of grain sizes in the 53 and 212 µm range, these sites would thus be expected to show higher metal concentrations which will be discussed in Chapter 4.

3.4.3 Multivariate analysis

Figure 29 shows three main groupings of sites (principal component analysis). These groupings are situated close to one another and therefore sites seem to be of similar composition with regards to physical, chemical and nutrient composition. Small differences however were observed, this type of data is expected due to the fact that these four river systems occur in a similar geographic area with sources of pollution being composed almost entirely of agriculture, subsistence farming and small scale industry. It was noted that Shingwedzi 2, Sabie 1 and Luvuvhu 2 grouped together for both flow conditions, this suggest that a change in flow does not have as much of an influence on these sites.

The red group is determined by a grain size of 53 µm and organic content, whilst COD plays a smaller part. According to Wepener and Vermeulen (2005) sediments are considered to be the ultimate sink for pollutants and thus pose the highest risk to the aquatic environment whilst the small grain size results in an increased surface area for pollutants to bind to. These sites due to their organic-rich sediments and large surface area may therefore show the highest levels of potential metal contamination.

The green grouping is determined exclusively by nitrates whilst the grey group is due to conductivity as well as nitrates. This suggests nitrate pollution is high within the Letaba 1b, Shingwedzi 1, Sabie 2 and Luvuvhu 1 areas during the high flow period, a fact which is supported by the high nitrate levels found during the study at these sites (Table 5). The areas along these rivers have large agricultural lands up to the river banks and thus nitrates due to agricultural runoff could be causing these high values.

The remaining three sites (Letaba 2, Letaba 1a and Luvuvhu 1) are all influenced by other variables. It was noted that there was a big decrease in flow when sampling occurred at the Letaba 2 site during low flow and that large amounts of animal

64 Chapter 3: Physico-Chemical and Nutrient Analysis. activity could be seen near the water, this may account for ammonium being the determining factor due to the increase in urea in the direct area. The Letaba 1a site (high flow) had experienced a large amount of rain during and before sampling, this could account for turbidity being a determining factor. Finally when the Luvuvhu 1 site (low flow) was sampled a large number of people were present in the area washing clothes, themselves as well as cars in the river. This could account for the pH, temperature and oxygen being determining factors at this site.

3.5 Conclusions

It can be concluded that physico-chemical water quality parameters, nutrients and sediment characteristics are important in determining the quality of the aquatic environment. The results above suggest that the Luvuvhu and Sabie River systems are in a more stable condition than the Letaba and Shingwedzi.

This is to be expected due to the fact that the Sabie River has received much attention in the past years with numerous studies being carried out upon it. At present it is described as the most pristine system within South Africa with much of its 110 km remaining free from any direct alteration (Moon et al., 1997). The Luvuvhu River on the other hand does not have any large industrial or agricultural areas as compared to the Letaba and Shingwedzi systems. This has resulted in fewer impacts upon the river.

Large rural populations are supported by the Letaba and Shingwedzi River systems. This places increasing demand on the quantity of water often resulting in the rivers drying up. This was experienced on the Shingwedzi 2 during the September 2005 sampling period. When all the data is considered as a whole it can also be seen that these two rivers show the most deviation from normal water quality standards.

More research is needed, especially on the Luvuvhu and Shingwedzi Rivers which have limited current data. The Letaba also needs careful monitoring to prevent further decreases in the quality and quantity of its waters.

65 Chapter 3: Physico-Chemical and Nutrient Analysis.

3.6 References

Alabaster, J.S. and Lloyd, R. 1980. Water quality criteria for freshwater fish. Butterworth and Co. London. 297 pp.

Dallas, H.F. and Day, J.A. 1993. The effect of water quality variables of riverine ecosystems: a review. WRC Report No TT 61/93. Water Research Commission, Pretoria. 240 pp.

Dallas and Day, 2004. The effect of water quality variables on aquatic ecosystems: a review. WRC Report No TT 224/04. Water Research Commission, Pretoria 222 pp.

Dallas, H.F., Day, J.A. and Reynolds, E.G. 1994. The effect of water quality variables on riverine biotas. WRC Report No 351/1/94. Water Research Commission, Pretoria, 230 pp.

Department of Water Affairs and Forestry. 1996. South African Water Quality Guidelines – Second edition. Volume 7: Aquatic Ecosystems. 145pp

Department of Water Affairs and Forestry. 2001. Water Quality on Disc. Version 1.01 (10/01/2001).

Environmental Protection Agency (EPA). 1991. Description and sampling of contaminated soils: A field pocket guide. EPA Report No 625/12-91/002. Technology transfer, U.S. Environmental Protection Agency, Washington D.C.

Fatoki, O.S., Muyima, N.Y.O. and Lujiza, N. 2001. Situation analysis of water quality in the Umtata Rive catchment. Water SA 27 (4): 467 – 473.

66 Chapter 3: Physico-Chemical and Nutrient Analysis.

Kefford, B.J., Palmer, C.G., Pakhomova, L. and Nugegoda, D. 2004. Compairing test systems to measure the salinity tolerance of freshwater invertebrates. Water SA. 30 (4): 499 – 506.

King, J.M., Scheepers, A.C.T., Fisher, R.C., Reinecke, M.K. and Smith, L.B. 2003. River rehabilitation: Literature review, case studies and emerging principles. WRC Report No 1161/1/03. Water Research Commission, Pretoria. 273 pp.

Madikizela, B.R., Dye, A.H. and O’Keeffe. 2001. Water quality and faunal studies in the Umzimvubu catchment, Eastern Cape, with particular emphasis on species as indicators of environmental change. WRC Report No 716/1/01. Water Research Commission, Pretoria. 117 pp.

Malan, H., Bath, A., Day, J. and Joubert, A. 2003. A simple flow-concentration modelling method for integrated water quality and water quantity in rivers. Water SA 29 (3): 305 – 311.

Moon, B.P., Van Niekerk, G.L., Heritage, K.H. and James, C.S. 1997. A geomorphological approach to the management of rivers in the Kruger National Park: The case of the Sabie River. Transactions of the institute of British Geographers 22: 31-48.

Morrison, G., Fatoki, O.S., Persson, L. and Ekberg, A. 2001. Assessment of the impact of point source pollution from the Keiskammahoek sewage treatment planton the Keiskamma River – pH, electrical conductivity, oxygen- demanding substances (COD) and nutrients. Water SA 27 (4): 475 – 480.

Nhapi, I. and Tirivarombo, S. 2004. Sewage discharges and nutrient levels in Marimba River, Zimbabwe. Water SA 30 (1): 107 – 113.

Nussey, G. 1998. Metal ecotoxicology of the upper Olifants River at selected localities and the effect of copper and zinc on fish blood physiology. Unpublished Ph.D. Thesis, Rand Afrikaans University. Johannesburg.

67 Chapter 3: Physico-Chemical and Nutrient Analysis.

Rivers-Moore, N.A., Jewitt, G.P.W., Weeks, D.C. and O’Keeffe, J.H. 2004. Water temperature and fish distribution in the Sabie River system: towards the development of an adaptive management tool. WRC Report NO 1065/1/04. Water Research Commission, Pretoria. 188 pp.

Soares, H.M.V.M., Boaventura, R.A.R., Machado, A.A.S.C. and Esteves Da Silva, J.C.G. 1999. Sediments as monitors of heavy metal contamination in the Ave river basin (Portugal): multivariate analysis of data. Environmental Pollution 105: 311 – 323.

Taylor, D.J., Green, N.P.O. and Stout, G.W. 1984. Biological science – Third edition. Cambridge University Press. United Kingdom. 984 pp.

Tokalioglu, S., Kartal, S. and Birol, G. 2003. Application of a three-stage sequential extraction procedure for the determination of extractable metal contents in highway soils. Turk J Chem. 27: 333 – 346.

Van Vuren, J.H.J. and Du Preez, H.H. 1997. Lethal and sublethal effects of metals on the physiology of fish: An experimental approach with monitoring support. WRC Report No 608/1/97. Water Research Commission, Pretoria. 284 pp.

Van Vuren, J.H.J., Du Preez, H.H., Wepener, V., Adendorff, A., Barnhorn, I.E.J., Coetzee, L., Kotze, P. and Nussey, G. 1999. Lethal and sublethal effects of metals on the physiology of fish: An experimental approach with monitoring support. WRC Report No 608/1/99. Water Research Commission, Pretoria. 254 pp.

Wepener, V. and Vermeulen, L.A. 2005. A note on the concentration and bioavailability of selected metals in sedimentsof Richards Bay Harbour, South Africa. Water SA. 31 (4): 589 – 595.

68 Chapter 3: Physico-Chemical and Nutrient Analysis.

Williams, K.A., Green, D.W.J. and Pascoe, D. 1986. Studies on the acute toxicity of pollutants to freshwater macroinvertebrates. Arch. Hydrobiol. 106: 61 – 70.

Williams, M.L., Palmer, C.G. and Gordon, A.K. 2003. Riverine macroinvertebrate responses to chlorine and chlorinated sewage effluents – Acute chlorine tolerances of Baetis harrisoni (Ephemeroptera) from two rivers in KwaZulu- Natal, South Africa. Water SA. 29 (4): 483 – 488.

69 Chapter 4: Metal Bioaccumulation

CHAPTER 4 METAL ACCUMULATION IN WATER, SEDIMENT AND FISH TISSUE FROM THE LUVUVHU, SHINGWEDZI, LETABA AND SABIE RIVERS.

4.1 Introduction

Accumulation of metallic contaminants in the environment is of increasing concern particularly with regards to the possible ecological effects. It is thus vitally important that heavy metals in water, sediment and biota are investigated since any changes in their concentrations above acceptable levels can result in serious environmental problems (Melaku et al., 2005).

Metals are natural constituents of the environment and occur in all ground as well as surface waters. Some are required for normal metabolism to occur and are thus essential whereas others are non-essential and play no significant biological roles (Coetzee et al., 2002). Trace metals are introduced into the environment by a wide range of natural and anthropogenic sources and in abnormally high concentrations can be toxic to aquatic organisms resulting in a reduction of species richness and diversity and ultimately a change in ecosystem composition (Dallas and Day, 1993; Wepener et al., 2001).

Many metallic contaminants adsorb to suspended sediment and are trapped in the bottom sediments thereby eliminating pollutants from the water column and reducing the toxicity to aquatic organisms. As a result sediments are considered to be a sink for pollutants and thus pose the highest risk to the aquatic environment (Salomons et al., 1987; Wepener and Vermeulen, 2005).

Metals bound in sediments have no direct danger to the system as long as they remain there. Dangers arise when changes occur in environmental conditions such as pH, salinity, temperature or redox potential. This allows bound metals to be released back into the aquatic environment (Van Vuren et al., 1994). It should be noted that pollutants bound to sediments can be assimilated by aquatic benthic organisms in

70 Chapter 4: Metal Bioaccumulation close proximity to the sediment. Some of these organisms may then be subjected to predation thus transferring pollutants through out the food web a process known as biomagnification (Andersson et al., 1990).

In order to obtain a completely reliable assessment of pollution within aquatic ecosystems physical and chemical monitoring should be supported by some means of biological monitoring, this is important because living organisms are indicative of the chemical condition of the water not only at present times but as they have experienced it throughout their lives (Abel, 1989). Fish are considered to be suitable monitoring organisms in aquatic systems because as consumers they are high in the aquatic food chain and thus reflect any environmental changes that occur over time (Heath and Claassen, 1999). Fish are often used in biological monitoring and bioaccumulation due to the fact that they are easily identifiable, can be sampled easily and quantitatively and have a cosmopolitan distribution often found throughout more than one river system. They are also known to accumulate pollutants in their organs and tissues (Hellawell, 1986).

A variety of studies have been carried out on the Olifants River in the KNP with regards to metal bioaccumulation (Grobler et al., 1994; Van Vuren et al., 1994; Seymore et al., 1994; Seymore et al., 1995; Robinson and Avenant-Oldewage, 1997; Nussey, 1998; Kotze et al., 1999; Avenant-Oldewage and Marx, 2000 and Coetzee et al., 2002). However research, especially with regards to metals is limited to the remaining major river systems (Roux et al., 1994; Claassen, 1996; Heath and Claassen, 1999) and particularly in the other KNP Rivers.

This chapter therefore deals with bioaccumulation of metals (aluminium, cadmium, chromium, copper, iron, manganese, nickel, lead and zinc) in the bioaccumulator indicator species Labeobarbus marequensis (Lowveld largescale yellowfish) and Barbus radiatus (Beira barb)) as well as the concentrations found in water and sediment. Due to the absence of biological effects and bioaccumulation data in the rivers of the KNP this chapter will be a baseline study to determine spatial and temporal variations in metal distribution.

71 Chapter 4: Metal Bioaccumulation

4.2 Methods

4.2.1 Field sampling

Fish were sampled during September 2005 (low flow) and March 2006 (high flow) on the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers by electrofishing using a Honda generator producing 230 V. Two sites were selected in each river system, excluding the Letaba which had three sites. One site was situated within the borders of the KNP and the other upstream of the border (Figure 1 through Figure 5). Sites outside the KNP were of sufficient distance from the borders so as to be representative of the environmental conditions before entering the park.

A

B

Figure 30: The test species sampled during the period of September 2005 to March 2006 in the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers, A= Barbus radiatus and B=Labeobarbus marequensis (Skelton, 1993).

72 Chapter 4: Metal Bioaccumulation

The test species (Figure 30) consisted almost entirely of L. marequensis (78 %). This species is a common fish in all the rivers of the KNP. At sites where insufficient numbers of L. marequensis were present, B. radiatus (21 %) were sampled. After capture fork length measurements were noted. Fish were then sacrificed by severing the spinal cord behind the head and dissected on a polyethylene work surface using stainless steel dissecting tools; care was taken to prevent contamination. Fish were placed directly into 50 ml falcon tubes. Samples were frozen and returned as such to the laboratory. Water and sediment samples were obtained at the time of fish sampling. It should be noted that no sample (N/S) was obtained at Shingwedzi 1 during the low flow sampling period due to the fact that the river was not flowing.

4.2.2 Laboratory techniques

4.2.2.1 Water

A known volume (100 ml) of each water sample (2 per site) was filtered through pre- weighed 0.45 µm cellulose nitrate membrane filters according to methods by Singh et al. (1988). The filtrate was retained and stored in amber bottles whilst the membrane filters were placed in a drying oven at 60° C. Dried filter papers were re-weighed before being placed in falcon tubes for acidification. Three ml nitric acid and 30 µl hydrogen peroxide was added to each tube and allowed to digest overnight at room temperature. Further digestion (if needed) was carried out in the fume cupboard by making use of an 800W microwave oven. Both filtered water and digested membranes were stored in a cool dark cupboard until undergoing metal analysis using inductively coupled plasma (ICP) methods.

4.2.2.2 Sediment

Sediment samples (2 per site) were chemically analysed by making use of a technique proposed by the BCR (Ure et al., 1993). This technique is a three step sequential extraction scheme (SES) in which the metals are divided into acid soluble, reducible

73 Chapter 4: Metal Bioaccumulation and oxidizable fractions (Gomez Ariza et al., 2000). Basically it is intended that each step exposes the sediment sample to the action of an extractant which solubilizes a specific component of the sediment along with its associated metals. This process is selective and one reagent attacks only one component without affecting the rest (Fernandes, 1997). In this process approximately one gram of sediment was subjected to extraction in 50 ml acid washed falcon tubes.

The first stage involved the addition of 40 ml of 0.11 M acetic acid. This was shaken overnight (16 hours) at room temperature, after which it was centrifuged at 3000 revolutions per minute (rpm) and the supernatant removed. The residue sediment was washed with MilliQ water for 15 minutes and centrifuged again. Both supernatants were added together and made up to 100 ml with MilliQ water. Samples were stored in amber bottles until metal analysis using ICP.

In the second stage 40 ml of 0.1M hydroxylamine hydrochloride was added to the residue, from there on the same process was followed as in the first stage. The third stage involved the addition of 10 ml of 30% hydrogen peroxide. Tubes were covered with a watch glass and digested first at room temperature for 1 hour and then at 85° C in a hot bath for a further hour. Following this the watch glasses were removed and the contents reduced by evaporation to a few millilitres. At this point 50 ml of 1M ammonium acetate was added and extracted overnight (16 hours). Once complete the tubes were centrifuged at 3000 rpm and the supernatant removed and made up to 100 ml with MilliQ water. Samples were again stored in amber bottles until metal analysis using ICP.

The final stage involved Aqua regia digestion (3:1 ratio of nitric and hydrochloric acid) of the remaining sediment. Samples were cold-digested and stored for later ICP analysis.

74 Chapter 4: Metal Bioaccumulation

4.2.2.3 Fish

Samples (10 per site) were thawed in the laboratory and dried in an oven at 60° C for a period of 48 hours. In order to determine the moisture content the dry and wet mass of each sample was recorded. Digestion processes were followed according to methods by Bervoets and Blust (2003). In this process 2 ml of concentrated nitric acid (65%) and 200 µl of concentrated hydrogen peroxide (30%) were added to each sample. These were left standing overnight prior to digestion in a microwave oven. Samples were microwaved for 5 minutes at 10, 20, 30 and 40 % power and then stored in a cool dark place until metal analysis using ICP techniques (Bervoets and Blust, 2003). Analytical accuracy was determined using certified reference material, i.e. standard for trace elements in dogfish liver (DOLT – 3). Recoveries were within 20 % of the certified values.

4.2.3 Data processing

Descriptive statistics (means, standard deviations and significant differences) were computed using SPSS 13.0. The differences in metal concentrations were tested by one-way analysis of variance (ANOVA), considering sites as variables. Data were tested for normality and homogeneity of variance using Kolmogorov-Smirnoff and Levene’s tests, respectively. When the ANOVA revealed significant differences, post-hoc multiple comparisons between sites, and between flow periods, were made using the appropriate Scheffe (parametric) or Dunnette-T3 (non-parametric) test to determine which means differed significantly. The significance of results was ascertained at P<0.05.

4.3 Results

Metal analysis results are presented in Table 12 to Table 16. Dissolved water concentrations are in µg/l whilst total sediment extraction and fish values are in µg/g

75 Chapter 4: Metal Bioaccumulation

Table 12: Mean ± standard error of Aluminium and Cadmium in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows, means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05). Aluminium Water Sediment Fish (µg/l) (µg/g) (µg/g) Luvuvhu 1 H 50.0 ± 13.0 2758.4 ± 64.9 a 127.6 ± 31.1 L 61.0 ± 3.0 2226.3 ± 36.1 bc 68.3 ± 18.1 Luvuvhu 2 H 61.5 ± 17.5 2877.3 ± 684.9 243.0 ± 47.0 a L 61.5 ± 13.5 3831.2 ± 977.9 89.9 ± 24.1 Shingwedzi 1 H 57.0 ± 12.0 1211.4 ± 24.1 bde 222.3 ± 54.1 L N/S N/S N/S Shingwedzi 2 H 39.5 ± 5.5 9843.6 ± 1105.3 284.1 ± 152.6 L 28.5 ± 14.5 9409.3 ± 252.9 fghi 66.0 ± 37.9 Letaba 1a H 52.0 ± 0.0 2351.1 ± 150.5 f 381.7 ± 91.8 L 27.5 ± 13.5 2227.8 ± 8.9 dj 13.0 ± 4.6 ab Letaba 1b H 90.0 ± 5.0 4610.6 ± 733.1 663.0 ± 252.8 L 9.5 ± 1.5 a 5379.0 ± 1043.9 151.5 ± 35.9 Letaba 2 H 67.0 ± 14.0 7349.7 ± 2724.9 604.1 ± 118.5 b L 45.5 ± 6.5 1424.4 ± 278.0 g 54.2 ± 30.4 Sabie 1 H 129.5 ± 32.5 1841.6 ± 107.5 h 184.2 ± 78.3 L 53.5 ± 4.5 2022.4 ± 409.7 70.4 ± 29.9 Sabie 2 H 97.5 ± 9.5 700.6 ± 131.7 i 155.5 ± 80.5 L 61.5 ± 1.5 a 336.3 ± 31.3 acej 44.4 ± 25.6 Cadmium Water Sediment Fish (µg/l) (µg/g) (µg/g) Luvuvhu 1 H 1.0 ± 0.0 4.7 ± 0.2 1.8 ± 0.8 L 1.5 ± 0.5 4.0 ± 0.3 0.1 ± 0.0 Luvuvhu 2 H 2.5 ± 0.5 3.6 ± 0.5 2.0 ± 0.7 L 2.0 ± 1.0 4.7 ± 1.2 0.1 ± 0.0 Shingwedzi 1 H 2.0 ± 1.0 3.3 ± 0.5 0.5 ± 0.1 L N/S N/S N/S Shingwedzi 2 H 2.5 ± 0.5 7.1 ± 0.5 0.1 ± 0.0 L 3.0 ± 2.0 7.1 ± 0.8 0.5 ± 0.0 Letaba 1a H 7.0 ± 1.0 3.7 ± 0.1 0.1 ± 0.0 L 3.0 ± 0.0 5.1 ± 1.4 0.2 ± 0.0 Letaba 1b H 7.5 ± 0.5 3.8 ± 0.2 0.2 ± 0.0 L 3.5 ± 0.5 3.0 ± 0.2 0.3 ± 0.0 Letaba 2 H 5.0 ± 0.0 6.6 ± 1.4 0.2 ± 0.0 L 1.5 ± 0.5 4.2 ± 1.1 1.2 ± 0.4 Sabie 1 H 6.5 ± 2.5 3.3 ± 0.7 0.3 ± 0.1 L 3.0 ± 1.0 4.0 ± 0.4 2.8 ± 0.9 Sabie 2 H 4.5 ± 2.5 1.0 ± 0.3 0.6 ± 0.2 L 6.0 ± 2.0 1.9 ± 1.0 0.7 ± 0.2 H = High flow, L = low flow, N/S = No sample recorded

76 Chapter 4: Metal Bioaccumulation

Table 13: Mean ± standard error of Chromium and Copper in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows, means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05). Chromium Water Sediment Fish (µg/l) (µg/g) (µg/g) Luvuvhu 1 H 57.0 ± 6.0 28.6 ± 8.7 19.9 ± 8.3 L 56.0 ± 2.0 47.9 ± 0.8 1.6 ± 1.0 Luvuvhu 2 H 60.5 ± 2.5 29.7 ± 4.5 ab 12.8 ± 4.8 L 62.5 ± 0.5 41.2 ± 7.5 0.1 ± 0.1 a Shingwedzi 1 H 62.5 ± 1.5 48.2 ± 4.5 15.0 ± 6.0 L N/S N/S N/S Shingwedzi 2 H 58.5 ± 2.5 71.6 ± 6.9 0.8 ± 0.6 b L 65.0 ± 3.0 63.5 ± 12.1 0.7 ± 0.2 c Letaba 1a H 55.5 ± 0.5 77.3 ± 6.5 2.8 ± 0.7 L 69.0 ± 1.0 85.7 ± 32.6 0.3 ± 0.1 d Letaba 1b H 53.5 ± 1.5 22.1 ± 4.8 1.2 ± 0.5 L 70.0 ± 1.0 25.4 ± 4.8 1.3 ± 0.6 Letaba 2 H 54.0 ± 0.0 47.5 ± 17.2 2.0 ± 0.4 L 70.0 ± 0.0 27.1 ± 5.1 2.6 ± 0.5 Sabie 1 H 51.0 ± 5.0 20.1 ± 1.9 0.4 ± 0.2 e L 60.5 ± 3.5 20.7 ± 0.0 4.3 ± 1.1 Sabie 2 H 54.0 ± 2.0 6.9 ± 0.5 a 2.9 ± 2.1 L 52.5 ± 8.5 5.2 ± 0.0 b 7.3 ± 1.3 abcde Copper Water Sediment Fish (µg/l) (µg/g) (µg/g) Luvuvhu 1 H 57.5 ± 1.5 20.2 ± 0.0 ab 20.8 ± 8.7 L 38.5 ± 16.5 13.5 ± 0.4 c 1.2 ± 0.6 Luvuvhu 2 H 51.0 ± 3.0 12.5 ± 4.2 13.4 ± 4.8 L 53.0 ± 4.0 16.5 ± 4.9 0.7 ± 0.1 ab Shingwedzi 1 H 51.0 ± 3.0 8.1 ± 1.3 15.7 ± 6.8 L N/S N/S N/S Shingwedzi 2 H 52.0 ± 2.0 37.3 ± 6.2 4.3 ± 1.1 L 63.5 ± 3.5 36.7 ± 0.5 cdefghi 0.4 ± 0.1 cd Letaba 1a H 53.5 ± 3.5 8.8 ± 0.6 d 2.1 ± 0.5 L 72.0 ± 0.0 9.4 ± 1.4 0.4 ± 0.1 ef Letaba 1b H 34.5 ± 5.5 14.2 ± 2.1 2.5 ± 0.4 ceg L 69.5 ± 1.5 a 13.4 ± 3.6 1.3 ± 0.3 Letaba 2 H 50.0 ± 2.0 26.3 ± 9.5 3.5 ± 0.6 adfh L 71.0 ± 2.0 3.5 ± 0.2 ae 7.2 ± 2.8 Sabie 1 H 45.0 ± 7.0 6.0 ± 0.9 f 2.2 ± 0.5 L 58.0 ± 9.0 9.1 ± 0.9 g 3.1 ± 1.0 Sabie 2 H 42.5 ± 1.5 a 1.0 ± 0.0 h 1.7 ± 0.4 L 62.0 ± 0.0 1.2 ± 0.1 bi 0.1 ± 0.0 bgh H = High flow, L = low flow, N/S = No sample recorded

77 Chapter 4: Metal Bioaccumulation

Table 14: Mean ± standard error of Iron and Lead in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05). Iron Water Sediment Fish (µg/l) (µg/g) (µg/g) Luvuvhu 1 H 68.0 ± 49.0 12709.4 ± 642.4 361.9 ± 65.2 ab L 38.0 ± 15.0 1771.1 ± 383.7 a 107.7 ± 25.2 c Luvuvhu 2 H 18.0 ± 17.0 5389.6 ± 3093.5 754.4 ± 128.9 cdefg L 42.5 ± 20.5 5929.8 ± 3154.2 133.2 ± 31.6 Shingwedzi 1 H 40.5 ± 34.5 4661.2 ± 3490.5 773.5 ± 198.9 L N/S N/S N/S Shingwedzi 2 H 19.5 ± 7.5 19965.8 ± 1456.5 438.7 ± 222.6 L 30.0 ± 20.0 20122.5 ± 692.1 abc 156.4 ± 72.7 Letaba 1a H 31.5 ± 16.5 1681.2 ± 105.9 520.1 ± 127.0 L 92.5 ± 29.5 9575.8 ± 7593.5 26.9 ± 4.7 adh Letaba 1b H 4.0 ± 3.0 2184.5 ± 200.2 760.0 ± 275.1 L 34.5 ± 0.5 2037.0 ± 144.8 173.5 ± 37.8 Letaba 2 H 58.5 ± 26.5 11365.9 ± 9125.6 631.5 ± 115.6 hijk L 46.0 ± 14.0 1271.4 ± 317.8 b 102.9 ± 26.4 e Sabie 1 H 40.0 ± 31.0 1298.4 ± 215.1 136.9 ± 24.9 l L 56.5 ± 16.5 1898.9 ± 304.8 c 63.8 ± 24.1 fj Sabie 2 H 30.0 ± 20.0 1924.7 ± 218.9 194.7 ± 86.0 L 72.0 ± 51.0 1490.4 ± 61.5 0.5 ± 0.3 bfgikl Lead Water Sediment Fish (µg/l) (µg/g) (µg/g) Luvuvhu 1 H 17.0 ± 10.0 3.3 ± 0.1 11.8 ± 5.2 L 40.5 ± 9.5 2.1 ± 0.8 0.4 ± 0.1 a Luvuvhu 2 H 47.5 ± 7.5 3.0 ± 0.7 9.5 ± 3.5 L 41.0 ± 4.0 3.5 ± 1.0 0.7 ± 0.1 Shingwedzi 1 H 24.5 ± 9.5 2.1 ± 0.3 a 5.5 ± 2.1 L N/S N/S N/S Shingwedzi 2 H 52.0 ± 18.0 7.8 ± 0.3 abcd 1.4 ± 0.2 a L 43.0 ± 6.0 7.3 ± 1.9 2.0 ± 0.4 Letaba 1a H 7.5 ± 5.5 4.4 ± 0.6 2.0 ± 0.4 L 61.0 ± 10.0 4.1 ± 0.3 0.9 ± 0.1 Letaba 1b H 76.5 ± 9.5 2.4 ± 0.3 b 1.5 ± 0.4 L 1.0 ± 0.0 a 2.4 ± 0.7 1.6 ± 0.4 Letaba 2 H 47.5 ± 0.5 a 5.8 ± 1.5 1.2 ± 0.3 L 28.0 ± 3.0 2.1 ± 0.8 24.8 ± 8.0 Sabie 1 H 81.5 ± 30.5 2.6 ± 0.8 0.8 ± 0.2 L 49.0 ± 7.0 2.5 ± 0.2 c 26.0 ± 10.3 Sabie 2 H 78.5 ± 8.5 1.9 ± 0.3 d 1.8 ± 0.7 L 74.5 ± 48.5 2.2 ± 0.4 5.1 ± 1.9 H = High flow, L = low flow, N/S = No sample recorded

78 Chapter 4: Metal Bioaccumulation

Table 15: Mean ± standard error of Manganese and Nickel in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows, means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05). Manganese Water Sediment Fish (µg/l) (µg/g) (µg/g) Luvuvhu 1 H 33.5 ± 30.5 111.4 ± 35.5 31.1 ± 11.9 L 79.0 ± 1.0 618.4 ± 64.7 26.7 ± 5.5 Luvuvhu 2 H 65.0 ± 5.0 240.1 ± 33.9 40.2 ± 6.6 abc L 81.0 ± 2.0 242.3 ± 86.5 25.3 ± 4.3 Shingwedzi 1 H 100.5 ± 25.5 49.5 ± 7.2 ab 22.3 ± 4.1 L N/S N/S N/S Shingwedzi 2 H 71.0 ± 4.0 486.9 ± 11.9 acdef 37.2 ± 17.0 L 83.0 ± 0.0 738.8 ± 167.4 7.8 ± 1.4 a Letaba 1a H 63.5 ± 4.5 172.6 ± 61.6 28.1 ± 7.6 L 82.5 ± 0.5 174.1 ± 37.2 5.2 ± 0.5 bd Letaba 1b H 75.5 ± 0.5 213.6 ± 8.8 bc 75.0 ± 26.3 L 82.0 ± 1.0 147.8 ± 13.5 d 11.8 ± 2.5 Letaba 2 H 78.0 ± 2.0 320.3 ± 95.6 35.0 ± 6.8 L 83.0 ± 0.0 111.0 ± 52.6 13.9 ± 5.6 Sabie 1 H 52.0 ± 3.0 105.0 ± 17.3 e 26.7 ± 4.2 d L 77.0 ± 2.0 493.6 ± 101.1 7.8 ± 2.8 c Sabie 2 H 75.5 ± 2.5 68.7 ± 21.3 25.1 ± 6.2 L 50.5 ± 30.5 102.8 ± 9.4 f 11.2 ± 4.2 Nickel Water Sediment Fish (µg/l) (µg/g) (µg/g) Luvuvhu 1 H 7.5 ± 6.5 11.7 ± 0.1 6.5 ± 2.1 L 20.0 ± 1.0 9.4 ± 5.7 0.1 ± 0.0 Luvuvhu 2 H 24.5 ± 5.5 13.4 ± 3.1 a 7.7 ± 2.5 L 15.0 ± 1.0 17.4 ± 3.6 0.1 ± 0.0 Shingwedzi 1 H 22.5 ± 7.5 10.8 ± 0.6 6.4 ± 2.8 L N/S N/S N/S Shingwedzi 2 H 24.0 ± 6.0 98.2 ± 8.9 0.6 ± 0.4 L 7.5 ± 2.5 80.5 ± 2.5 abcde 0.5 ± 0.2 Letaba 1a H 29.5 ± 0.5 ab 57.4 ± 13.5 1.8 ± 0.5 L 1.5 ± 0.5 ac 32.1 ± 8.2 0.1 ± 0.0 Letaba 1b H 30.0 ± 5.0 11.8 ± 1.5 b 0.7 ± 0.3 L 4.0 ± 2.0 12.0 ± 2.2 c 0.5 ± 0.2 Letaba 2 H 24.5 ± 0.5 cd 46.0 ± 15.9 2.8 ± 1.6 L 1.5 ± 0.5 bd 11.6 ± 1.5 d 5.0 ± 1.5 Sabie 1 H 37.5 ± 11.5 9.3 ± 0.6 0.1 ± 0.0 L 17.5 ± 5.5 11.0 ± 1.4 e 12.0 ± 4.0 Sabie 2 H 27.5 ± 5.5 3.9 ± 0.7 0.4 ± 0.1 L 19.0 ± 3.0 3.7 ± 0.4 3.1 ± 0.9 H = High flow, L = low flow, N/S = No sample recorded

79 Chapter 4: Metal Bioaccumulation

Table 16: Mean ± standard error of Zinc in water, sediment and biota (dry mass) at each sampling station for the period September 2005 (L) and March 2006 (H). Within rows, means with common superscript are significantly different (ANOVA, Scheffe Post Hoc multiple Comparisons, P<0.05). Zinc Water Sediment Fish (µg/l) (µg/g) (µg/g) Luvuvhu 1 H 72.5 ± 6.5 9.1 ± 0.8 67.6 ± 15.3 L 67.5 ± 1.5 8.5 ± 1.9 29.6 ± 5.7 Luvuvhu 2 H 63.0 ± 1.0 10.2 ± 4.1 93.6 ± 20.8 L 82.5 ± 6.5 10.8 ± 0.8 38.8 ± 2.7 a Shingwedzi 1 H 68.5 ± 1.5 4.7 ± 0.4 134.8 ± 29.1 L N/S N/S N/S Shingwedzi 2 H 82.0 ± 2.0 31.2 ± 5.3 20.2 ± 3.5 L 86.5 ± 1.5 29.4 ± 3.8 81.0 ± 17.9 Letaba 1a H 78.5 ± 3.5 6.6 ± 0.2 ab 43.8 ± 7.5 L 68.5 ± 18.5 7.7 ± 1.9 33.9 ± 4.4 b Letaba 1b H 79.0 ± 1.0 10.7 ± 0.1 ac 67.0 ± 12.0 L 76.5 ± 0.5 14.9 ± 0.1 bc 47.2 ± 6.5 c Letaba 2 H 81.0 ± 2.0 20.9 ± 4.9 39.3 ± 8.2 L 68.5 ± 14.5 25.6 ± 18.1 55.9 ± 9.3 Sabie 1 H 79.5 ± 0.5 6.1 ± 0.9 54.4 ± 13.3 L 82.5 ± 1.5 17.9 ± 3.0 11.0 ± 2.9 abc Sabie 2 H 75.0 ± 4.0 5.6 ± 0.4 80.8 ± 14.9 L 75.0 ± 5.0 5.4 ± 0.4 37.2 ± 12.9 H = High flow, L = low flow, N/S = No sample recorded dry weight. Metal concentrations within each of the four sediment extraction steps are depicted in Figure 31 to Figure 33.

Mean aluminium concentrations are presented in Table 12, whilst Figure 31A and B represent sediment extraction concentrations. High concentrations were found within all water samples with the exception of the Letaba 1b during low flow. Sediments showed values ranging between 300 and 10000 µg/g however only between 5 and 10 % of these concentrations are found within the first two bio-available extraction steps (Figure 31A and B). Extreme Al values of 663 and 604.1 µg/g were noted in fish on the Letaba 1b and Letaba 2 stations respectively.

Cadmium concentrations (Table 12) did not show any major fluctuations with values for water ranging between 1 and 6.5 µg/l. Sediment concentrations were noted to be slightly higher whilst concentrations in fish were the lowest, never increasing above

80 Chapter 4: Metal Bioaccumulation

2.8 µg/g (Sabie 1). Cadmium levels at all sampling stations were essentially the same and exhibited similar trends, thus no significant difference were noted. Once again the Cd levels within the sediments were restricted to the first two extraction steps, exceptions to this where the Letaba 2, Sabie 1 and Sabie 2 sites (Figure 31C and D). Correspondingly these sites showed slightly higher Cd levels.

The highest Cr concentrations (Table 13) in fish were noted at Luvuvhu 1, Luvuvhu 2 and Shingwedzi 1 over the high flow sampling period with values of 19.9, 12.8 and 15.0 respectively. At the same time Sabie 2 (low flow) was significantly different to Luvuvhu 2, Shingwedzi 2 and Letaba 1a (low flow) as well as Shingwedzi 2 and Sabie 1 (high flow) with regards to fish. Values within the water column were constant whilst Letaba 1a depicted the highest sediment concentrations (85.7 µg/g). Sediments showed the largest fluctuations with regards to Cr, with values ranging between 5.2 and 85.7 µg/g. Copper and Zn when compared to Cr exhibited similar trends, especially with regards to fish.

As stated above Cu exhibited similar characteristics as Cr. Concentrations in fish were found to be significantly higher at the Luvuvhu 1, Luvuvhu 2 and Shingwedzi 2 sampling stations during high flow (Table 13). Values were 20.8, 13.4 and 15.7 µg/g respectively, these were higher than values at all other sites which remained below 7.2 µg/g. Water concentrations fluctuated between 38 and 72 µg/l with the Letaba 1a site having the highest value. The Shingwedzi 2 site displayed the highest sediment Cu concentrations of 36.7 µg/g and this site was also significantly different from seven other sites (Table 13). Figure 32A and B clearly show that Cu within the Sabie River during both flow conditions is located in the first two bioavailable extractions, whilst the remaining rivers show Cu to be restricted to the last two non-bioavailable extractions.

Sediment Fe values range from 1490.4 µg/g on the Sabie River to as high as 20122.5 µg/g on the Shingwedzi 2 (Table 14), of these values large percentages are found within the first two extraction steps and are thus bioavailable (Figure 32C and D). Correspondingly high Fe concentrations were found occurring in fish samples with the exception of the Sabie 2 site which had a very low reading of 0.5 µg/g. It can be further noted that higher Fe concentrations were observed in fish during the high flow

81 Chapter 4: Metal Bioaccumulation sampling period. Water Fe concentrations were lowest for Luvuvhu 2 and Letaba 1a during low flow, interestingly these two sites showed the highest fish concentrations.

Lead sediment concentrations were found to be most consistent when compared to water and fish samples (Table 14). Values remained around 2 to 4 µg/g with the Shingwedzi 2 showing the two highest readings of 7.8 and 7.3 µg/g. It can be seen that Shingwedzi 2 sediment results for the high flow period was significantly different from the four other sites. A large portion of Pb in the sediments was found to be bioavailable for all sites occurring within the first and second extractions (Figure 32E and F). Water values showed the highest concentrations occurring at both Sabie localities with the Sabie 1 site during high flow depicting the highest value of 81.5 µg/l, at the same time this site had the highest concentration of Pb in fish of 26.0 µg/g during the low flow sampling period. Once again it can be noted that the Luvuvhu 1, Luvuvhu 2 and Shingwedzi 1 high flow as well as Letaba 2 and Sabie 1 low flow readings for fish were significantly higher.

Manganese values showed a great variation in concentrations with regards to water, sediment as well as fish (Table 15). Water ranged from 33.5 µg/l at Luvuvhu 1 to 100.5 µg/l at Shingwedzi 1 both during high flow while the remaining sites had values around 80 µg/l. High sediment concentrations were observed at the Shingwedzi 2 site on a whole whilst values of 618.4 and 493.6 µg/g were measured at Luvuvhu 1 and Sabie 1 respectively. Manganese was found to occur in high concentrations in the first and second sediment extraction steps (Figure 33A and B) indicated increased bioavailable Mn within the systems. High levels within fish were noted throughout the samples with the highest reading of 75.0 µg/g at Letaba 1b.

Nickel water concentrations showed higher readings during the high flow period with the exception of Luvuvhu 1. Concentrations ranged between 1.5 µg/l at the Letaba 1a and Letaba 2 sites to as high as 37.5 µg/l at the Sabie 1 site (Table 15). Again a trend of higher Ni concentrations was noted within fish at the Luvuvhu 1, Luvuvhu 2 and Shingwedzi 1 sites during high flow. Two high sediment Ni loads were observed both of which occurred at the Shingwedzi 2 site, this site was also noted to be significantly different from at least one site on all other rivers. Figure 33C and D depicts Ni to be present 80 % of the time in the last two non-bioavailable extraction steps.

82 Chapter 4: Metal Bioaccumulation

100% 100%

A 80% B 80%

60% 60%

40% 40%

20% 20%

0% 0% 4th Extraction 4th Extraction 3rd Extraction 3rd Extraction Sabie 1 Sabie 2 Sabie Sanie 1 Sanie 2 Sabie 2bd Extraction 2nd Extraction 2 Letaba Letaba 2 Letaba Letaba 1a Letaba 1b Letaba Luvuvhu 1 Luvuvhu 2 Luvuvhu Letaba 1a Letaba 1b Letaba 1st Extraction 1 Luvuvhu 2 Luvuvhu 1st Extraction Shingwedzi 1 Shingwedzi 2 Shingwedzi Shingwedzi 1 Shingwedzi 2 Shingwedzi

100% 100%

80% C 80% D

60% 60%

40% 40%

20% 20%

0% 0% 4th extraction 4th Extraction 3rd Extraction 3rd Extraction Sabie 1 Sabie 2 Sabie Sabie 1 Sabie 2 Sabie Letaba 2 Letaba 2nd Extraction 2nd Extraction 2 Letaba Luvuvhu1 Letaba 1a Letaba 1b Letaba Letaba 1a Letaba 1b Letaba Luvuvhu 2 Luvuvhu Luvuvhu 1 Luvuvhu 2 Luvuvhu 1st Extraction 1st Extraction Shingwedzi 1 Shingwedzi 2 Shingwedzi Shingwedzi 1 Shingwedzi 2 Shingwedzi

100% 100%

E F 80% 80%

60% 60%

40% 40%

20% 20%

0% 0% 4th Extraction 4th Extraction 3rd Extraction 3rd Extraction Sabie 1 Sabie 2 Sabie Sabie 1 Sabie 2 Sabie 2nd Extraction 2 Letaba 2nd Extraction Letaba 2 Letaba Letaba 1a Letaba 1b Letaba Luvuvhu 1 Luvuvhu 2 Luvuvhu Letaba 1a Letaba 1b Letaba 1st Extraction 1st Extraction 1 Luvuvhu 2 Luvuvhu Shingwedzi 1 Shingwedzi 2 Shingwedzi Shingwedzi 1 Shingwedzi 2 Shingwedzi

Figure 31: Percentage metal concentrations within the four different sediment extractions steps. A = Al (high flow), B = Al (low flow), C = Cd (high flow), D = Cd (low flow), E = Cr (high flow) and F = Cr (low flow).

83 Chapter 4: Metal Bioaccumulation

100% 100%

A 80% B 80%

60% 60%

40% 40%

20% 20%

0% 0% 4th Extraction 4th Extraction 3rd Extraction 3rd Extraction

2nd Extraction 1 Sabie 2 Sabie 2nd Extraction 1 Sabie 2 Sabie Letaba 2 Letaba 2 Letaba Letaba 1a Letaba 1b Letaba 1a Letaba 1b Letaba 1st Extraction 1 Luvuvhu 2 Luvuvhu 1st Extraction 1 Luvuvhu 2 Luvuvhu Shingwedzi 1 Shingwedzi 2 Shingwedzi 1 Shingwedzi 2 Shingwedzi

100% 100%

C 80% 80%

60% 60%

40% 40%

20% 20%

0% 0%

4th Extraction 4th Extraction 3rd Extraction 3rd Extraction Sabie 1 Sabie 2 Sabie 1 Sabie 2 Sabie

2nd Extraction 2 Letaba 2nd Extraction 2 Letaba Letaba 1a Letaba 1b Letaba 1a Letaba 1b Letaba Luvuvhu 1 Luvuvhu 2 Luvuvhu 1 Luvuvhu 2 Luvuvhu 1st Extraction 1st Extraction Shingwedzi 1 Shingwedzi 2 Shingwedzi 1 Shingwedzi 2 Shingwedzi

100% 100%

E 80% F 80%

60% 60%

40% 40%

20% 20%

0% 0% 4th Extraction 4th Extraction 3rd Extraction 3rd Extraction Sabie 1 Sabie 2 Sabie Sabie 1 Sabie 2 Sabie 2nd Extraction 2nd Extraction 2 Letaba Letaba 2 Letaba Letaba 1a Letaba 1b Letaba Luvuvhu 1 Luvuvhu 2 Luvuvhu Letaba 1a Letaba 1b Letaba 1st Extraction 1 Luvuvhu 2 Luvuvhu 1st Extraction Shingwedzi 1 Shingwedzi 2 Shingwedzi Shingwedzi 1 Shingwedzi 2 Shingwedzi

Figure 32: Percentage metal concentrations within the four different sediment extractions steps. A = Cu (high flow), B = Cu (low flow), C = Fe (high flow), D = Fe (low flow), E = Pb (high flow) and F = Pb (low flow).

84 Chapter 4: Metal Bioaccumulation

100% 100%

A 80% B 80%

60% 60%

40% 40%

20% 20%

0% 0%

4th Extraction 4th Extraction 3rd Extraction 3rd Extraction Sabie 1 Sabie 2 Sabie Sabie 1 Sabie 2 Sabie Letaba 2 Letaba 2nd Extraction 2 Letaba 2nd Extraction Letaba 1a Letaba 1b Letaba Letaba 1a Letaba 1b Letaba Luvuvhu 1 Luvuvhu 2 Luvuvhu Luvuvhu 1 Luvuvhu 2 Luvuvhu 1st Extraction 1st Extraction Shingwedzi 1 Shingwedzi 2 Shingwedzi Shingwedzi 1 Shingwedzi 2 Shingwedzi

100% 100%

80% 80% C D

60% 60%

40% 40%

20% 20%

0% 0% 4th Extraction 4th Extraction 3rd Extraction 3rd Extraction Sabie 1 Sabie 2 Sabie Sabie 1 Sabie 2 Sabie Letaba 2 Letaba 2nd Extraction 2 Letaba 2nd Extraction Letaba 1a Letaba 1b Letaba Letaba 1a Letaba 1b Letaba Luvuvhu 1 Luvuvhu 2 Luvuvhu Luvuvhu 1 Luvuvhu 2 Luvuvhu 1st Extraction 1st Extraction Shingwedzi 1 Shingwedzi 2 Shingwedzi Shingwedzi 1 Shingwedzi 2 Shingwedzi

100% 100%

F E 80% 80%

60% 60%

40% 40%

20% 20%

0% 0% 4th Extraction 4th Extraction 3rd Extraction 3rd Extraction Sabie 1 Sabie 2 Sabie Sabie 1 Sabie 2 Sabie Letaba 2 Letaba

Letaba 2 Letaba 2nd Extraction Letaba 1a Letaba 1b Letaba

2nd Extraction 1 Luvuvhu 2 Luvuvhu Letaba 1a Letaba 1b Letaba Luvuvhu 1 Luvuvhu 2 Luvuvhu 1st Extraction 1st Extraction Shingwedzi 1 Shingwedzi 2 Shingwedzi Shingwedzi 1 Shingwedzi 2 Shingwedzi

Figure 33: Percentage metal concentrations within the four different sediment extractions steps. A = Mn (high flow), B = Mn (low flow), C =Ni (high flow), D = Ni (low flow), E = Zn (high flow) and F = Zn (low flow).

85 Chapter 4: Metal Bioaccumulation

High Zn concentrations were noted once again in fish from the Luvuvhu 1, Luvuvhu 2 and Shingwedzi 1 sites (high flow). This trend has occurred with Cr, Cu, Fe, Pb, Mn, Ni and finally Zn suggesting a possible problem at these three sites (Table 16). High Zn concentrations were also noted at Shingwedzi 2 (low flow) and Sabie 2 (high flow Zinc water values did not show great fluctuations between sites. The same was true for total sediment concentrations with around 20 % being found in the first two extractions (Figure 33E and F).

4.4 Discussion

Many trace metals either of natural origin or not are found partitioning between the water, sediment and biotic components of aquatic ecosystems (Seenayya and Prahalad, 1987). Metals are persistent and tend to accumulate in the environment, ultimately becoming toxic at elevated concentrations. This results in the inability of organisms to excrete, sequester or otherwise detoxify themselves and can lead to major problems ultimately resulting in death (Coetzee et al., 2002).

A large variety of research has been carried out upon the Olifants River system and due to the fact that the four rivers under investigation occur in the same geographical area comparisons will be made to studies by (Grobler et al., 1994, Roux et al., 1994; Seymore et al., 1994; Robinson and Avenant-Oldewage, 1997 and Van Vuren et al., 1999). The Olifants rivers when compared to international systems such as the Rhine, Scheldt and Mulde (Middlekop, 2000; Vandecasteele et al., 2002 and Knittel et al., 2005) shows lower concentrations of metals. This facts suggest that the Olifants is a modified system (not yet as metal modified as international systems) and thus makes it ideal to use as a comparison. Study carried out by Heath and Claassen (1999) on the Berg, Luvuvhu, Letaba and Sabie Rivers also serve as historical data for comparative purposes in this study.

Aluminium is one of the more toxic trace metals that is physiologically non essential. At intermediate pH values Al is moderately soluble and occurs as hydroxo- and polyhydroxo- complexes whilst at alkaline pH values it is soluble but biologically unavailable. Acidic conditions result in Al becoming soluble, toxic and available

86 Chapter 4: Metal Bioaccumulation

(Dallas and Day, 2004). During this study high Al concentrations (Table 12) were found within the water, sediment and fish samples for all four of the rivers sampled. Water concentrations were well above the target water quality guidelines (TWQGs of DWAF) of 5 to 10 µg/l with the Sabie 1 high flow reading within acute effect values (AEV) (DWAF, 1996). All surface pH values recorded were above pH 7 thus rendering Al mostly biologically unavailable, however any changes to this can have drastic effects. Aluminium can be mobilised from soils and sediments by the process of natural weathering as well as accelerated acidification processes, thus resulting in detectable concentrations in surface waters (DWAF, 1996) Concentrations of Al in sediment were found to be extremely high however when Figure 31A and B are taken into consideration it can be seen that most of this (90 %) is bound up in the third and fourth process of extraction which is biologically unavailable to aquatic organisms. Fish concentrations for Letaba 1b and Letaba 2 during high flow where found to be above 600 µg/g, these values at first seem to be high however when compared to a study carried out by Heath and Claassen (1999) on the same river systems it can be seen that they fall within the same concentration range, often well below values they obtained. The same is true for studies by Grobler et al. (1994); Roux et al. (1994); Claassen (1996); Nussey (1998) and Van Vuren et al. (1999) on the Olifants, Luvuvhu, Sabie and Crocodile River systems all of which fall within the same geographical area. These data along with the high water readings suggests that the current water quality guidelines for Al are unrealistically stringent for South African rivers a fact which is supported by Heath and Claassen (1999).

Cadmium is a common environmental pollutant that is usually closely associated with Zn forming a toxic soup that often acts synergistically. Small amounts of Cd are released by natural processes such as leaching of rocks, however the levels in inland waters are often greatly increased due to anthropogenic activities such incinerators, atmospheric deposition from non-ferrous metal mines, smelters and refineries, Cd based batteries, coal combustion, runoff from agricultural soils, industrial effluents, domestic effluents and urban storm water runoff (Fatoki and Awofolu, 2003; Fatoki et al., 2004). Cadmium is reported to be one of the most toxic elements and accumulates in the liver and kidneys. High concentrations have been found to lead to chronic kidney dysfunction whilst carcinogenic, mutagenic and teratogenic effects have been reported. Cd has been found to be highly toxic to fish and various other aquatic

87 Chapter 4: Metal Bioaccumulation organisms (DWAF, 1996; Awofolu et al., 2005) with adverse effects to kidney and liver functioning. Cd also cause a variety of physiological responses such as anaemia, a reduction in oxygen diffusion capacity, malformation of erythrocytes and disruption of hormones during reproduction (Heath, 1995; Van Aard and Booysen, 2004). The concentration of cadmium in freshwater is generally between 0.1 and 10 µg/l however the South African guidelines for Cd are set at 0.15 µg/l (DWAF, 1996; Sanders et al., 1999). Water concentrations (Table 12) obtained were found to be above these guidelines however never rose over 7.5 µg/l. Fish bioaccumulation data when compared to a study by Heath and Claassen (1999) where within the same concentration ranges. These facts suggest that Cd does not present a major problem in the rivers studied at present, however continued monitoring is essential due to the high toxicity of this metal.

In low concentrations Cr is one of the least toxic of the trace metals and is essential for fat and carbohydrate metabolism in mammals. Cr occurs in a variety of oxidative states of which Cr6+ is the most toxic (Robinson and Avenant-Oldewage, 1997; Dallas and Day, 2004). Due to the sparseness of this metal in the natural environment amounts are generally low in the aquatic ecosystems however waters may receive Cr from anthropogenic sources such as industrial effluents (Galvin, 1996). Chromium exerts a toxic effect at different concentrations in different groups of aquatic organisms with fish being the most resistant (DWAF, 1996). Levels of Cr found within the water column for this study were consistent between 50 and 70 µg/l, these values however are above the water quality guidelines of 7 µg/l set out by DWAF (Table 13). Sediment values where once again restricted to the last two extraction steps and thus do not contribute to the bioavailable fraction (Figure 31 E and F). When compared to studies by Grobler et al. (1994); Seymore et al. (1994), Robinson and Avenant-Oldewage (1997) and Van Vuren et al. (1999) carried out on the Olifants River in the same geographical area it was noted that Cr levels within the water and sediment were either of the same magnitude or lower. Chromium levels in fish tissue were found to be high at Luvuvhu 1, Luvuvhu 2 and Shingwedzi 1 during the high flow sampling period. These values obtained were higher than those recorded by Heath and Claassen (1999).

88 Chapter 4: Metal Bioaccumulation

Copper is not only an essential element required for plant and animal metabolism but is also very toxic to aquatic ecosystems (Hung et al., 1992). This element occurs in the environment as Cu+, Cu2+ and Cu3+ oxidative states, toxicity is mostly attributed to Cu2+ which is only present in small quantities in fresh water (Robinson and Avenant- Oldewage, 1997). Copper input into the aquatic ecosystem is therefore due to anthropogenic activities such as mining, industry and agriculture. In alkaline waters copper precipitates and is thus not toxic whilst in acidic conditions it is soluble, mobile and toxic (Dallas and Day, 2004). Sub-lethal Cu concentrations result in a reduction in survival and reproductive rate of a variety of fish species. Chronic exposure include a variety of biochemical and physiological indicators of which reproduction and ionic regulation are the more important (Taylor et al., 2000; Mzimela et al., 2002).Copper levels obtained during the study (Table 13) indicate high levels in fish at the Luvuvhu 1, Luvuvhu 2 and Shingwedzi 2 site during the high flow condition, however these levels were found to be lower than those recorded by Heath and Claassen (1999). Further more studies on the Olifants River by Seymore et al. (1994); Robinson and Avenant-Oldewage (1997), Kotze et al. (1999) and Coetzee et al. (2002) also show higher Cu levels. Water concentrations were above the water quality guidelines for Cu. Heath and Claassen (1999) once again state that the guidelines do not present a realist range for South African rivers and that the guidelines set by Kempster et al. (1980) of 200 µg/l are more acceptable. Based on the latter standard the Cu levels would be well within the range for protection of aquatic ecosystems. Sediment Cu concentrations were found to be low for the two southern rivers (Sabie and Letaba) whilst the Luvuvhu and Shingwedzi rivers in the north had higher levels. These concentrations where however of the same magnitude as a study by Seymore et al. (1994).

Iron is the fourth most abundant element found within the earth crust and can therefore occur in waters in varying degrees depending on the geology of the area (DWAF, 1996). It commonly occurs in two oxidative states, namely ferrous (Fe2+) and ferric (Fe3+) of which the latter is essentially unavailable for uptake. Iron is regarded as an essential micronutrient and forms an important part of haeme- containing respiratory pigments. At high concentrations Fe becomes toxic, inhibiting various enzymes (Dallas and Day, 2004). Wepener et al. (1992) observed haemolitical and osmotic changes in Tilapia sparrmanii exposed to a concentration of 1570 µg/l

89 Chapter 4: Metal Bioaccumulation however more research with regards to toxic effects to fish is needed. Due to the fact that the river systems under investigation were located in a geologically Fe rich area high concentrations were to be expected (Heath and Claassen, 1999; Ashton et al., 2001). This was particularly true for total sediment concentrations where values as high as 19965.8 and 20122.5 µg/g in Shingwedzi 2 were to be found (Table 14). Iron concentrations within fish samples at first seem high with values of 760.0, 754.4 and 773.5 µg/g in the Letaba 1a, Luvuvhu 2 and Shingwedzi 1 sites, however these fish are naturally exposed to high Fe levels and are functioning optimally, this indicates an adaptation of South African fish populations to relatively high Fe concentrations. Increased levels during high flow were also noted at all stations, suggesting high Fe levels entering water systems due to run off from the land.

Lead can be regarded as a common as well as toxic trace metal which tends to accumulate in living tissue and become immobilised in the bones of vertebrates (Dallas and Day, 2004). It exists in several oxidative states all of which are environmentally important; however the divalent form is thought to be the one that aquatic organisms bioaccumulate the most (Nussey, 2000). A major problem caused by Pb is its interference with the synthesis of haeme, however it also affects membrane permeability as well as inhibiting enzymes involved in energy metabolism (Dallas and Day, 2004). The toxicity of Pb is dependent upon the life stage of the fish, water hardness and pH, as the pH of waters decrease so the toxicity of Pb increases (Jackson et al., 2005). According to DWAF (1996) the amount of Pb present within water should be around 0.2 to 1.2 µg/l. Based on these standards all sites (excluding the Letaba 1b during low flow) showed values within and above the AEV range (Table 14). When this guideline is compared to those for Australia and Canada it is seen to be within their suggested ranges, however Heath and Claassen (1999) suggest that this value is unrealistic for South African rivers. The values for this study were found to be well below those found by Seymore et al. (1995) on the Olifants River. High sediment concentrations were not noted throughout the sampling; however the Shingwedzi 2 site did display the highest values and should be monitored for further increases. Once again values were below those of the study by Seymore et al. (1995). The high concentration of Pb in fish is of concern, values were higher than the study by Heath and Claassen (1999) and data obtained for Letaba 2 and Sabie 1 (low flow) showed very high concentrations. Again the trend of high values for the Luvuvhu 1,

90 Chapter 4: Metal Bioaccumulation

Luvuvhu 2 and Shingwedzi 1 (high flow) was noted suggesting possible problems in this area.

Manganese is regarded as a metal with relatively low toxicity; however exposure of fish to high concentrations of this metal can result in anaemic conditions due to necrosis of the intestinal mucosa and kidneys (Wepener et al., 1992). In low concentrations Mn is an essential micronutrient with important functions in skeletal formation and reproduction (Dallas and Day, 2004; DWAF, 1996). The values obtained during this study (Table 15) are found to be well within the TWQGs of 180 µg/l for all sites and thus Mn in the water column does not present a problem (DWAF, 1996). Concentrations found within fish tissue showed a trend of higher concentrations during the high flow, however when these values are compared to similar studies by Robinson and Avenant-Oldewage (1997); Heath and Claassen (1999) and Coetzee et al. (2002) values are within the same range. Sediment Mn concentrations were found to occur at least 60 % or more of the time in the first two extraction steps (Figure 33A and B), thus Mn is bound within the bioavailable fraction of the sediment and any change in environmental conditions can result in the release of this metal back into the system (Van Vuren et al., 1994).

Nickel is widespread in the environment however a large percentage is released globally due to the burning of fossil fuels (DWAF, 1996). Nickel has been suggested as a micronutrient however this fact is unproven. In fairly small concentrations Ni becomes toxic resulting in the inhibition of cytochrome oxidase and various enzymes in the citric acid cycle. Nickel has also been shown to be carcinogenic to mammals (Dallas and Day, 2004). Nickel ions are soluble at pH values less than 6.5 whilst above 6.7 they mostly form insoluble nickel hydroxides (Dallas and Day, 2004). During this study Ni concentrations were found to range between 1.5 and 37.5 µg/l however no guidelines are presented by DWAF. According to Galvin (1996) dissolved Ni concentrations in aquatic ecosystems usually occur in concentrations between 5 and 10 µg/l. Kempster et al. (1980) suggests Ni should occur between 25 and 50 µg/l. This value is similar to the Australian and Canadian guidelines (Heath and Claassen, 1999) and by these standards Ni concentrations were in acceptable ranges. A laboratory study by Brand (2001) upon Ni at neutral pH however showed the LC50 to be 50 µg/l. This study also showed sublethal exposures to have negative

91 Chapter 4: Metal Bioaccumulation effects upon haematological parameters and therefore Ni in concentrations lower than 50µg/l can be considered a problem. When fish readings are compared to other studies in the area (Heath and Claassen, 1999) it can be noted that the high concentrations occurring in the Luvuvhu and Shingwedzi sites are of concern as they were above those for the Letaba River in the same area. Sediment concentrations for Ni were within acceptable limits with only 20 % occurring in the first two extractions. An exception to this was the Shingwedzi 2 site where the values of 98.2 and 80.5 µg/g obtained were high when compared to a study by Robinson and Avenant-Oldewage (1997).

Zinc is an essential and important micronutrient which forms the active site of various metalloenzymes such as DNA and RNA polymerase (Dallas and Day, 2004). It is a common pollutant in freshwaters and occurs more commonly in surface waters near industrial areas (Alabaster and Lloyd, 1980). The mechanism of toxic action of Zn is unknown, but it is speculated that it results in direct damage to the gills of fish thus inhibiting the respiratory function (Jackson et al., 2005). Van Rensburg (1989) showed that Zn had a major influence on the gills of T. sparrmanii, firstly affecting the bioconcentration of metals and secondarily the structural alterations of the gills. Values for a study by Coetzee et al. (2002) on Clarias gariepinus gill tissue from the Olifants River were found to be between 88 and 257 µg/g dry mass, whilst a study in Germiston Lake in Gauteng reported similar values for this species (Bezuidenhout et al., 1990). Similar values were obtained during this study at Shingwedzi 2 (low flow) and Luvuvhu 2, Shnigwedzi 2 and Sabie 2 (high flow). Values were generally lower than the study by Heath and Claassen (1999). Water concentrations were found to be well above the recommended 2 µg/l by DWAF (1996) but still below those in the study on the Olifants River (Seymore et al., 1994). Sediment Zn did not show high concentrations and were below those reported by Roux et al. (1994) and Seymore et al. (1994).

4.5 Conclusion

This study showed that there is considerable variation in metal concentrations and that no single river system can be pinpointed as being in a worse condition than another.

92 Chapter 4: Metal Bioaccumulation

Each system showed a high level of one of the metals at some time throughout the sampling period. It was however noted that concentrations within the Sabie system (especially within the borders of the park) were generally lower when compared to values obtained for the other three systems. This strengthens the quote by Moon et al. (1997) that the Sabie River within the KNP is the most pristine river system within South Africa. An interesting trend that was evident was the high concentrations of Cr, Cu, Fe, Pb, Mn, Ni and Zn all occurring at the Luvuvhu 1, Luvuvhu 2 and Shingwedzi 1 sites during the high flow sampling period. These values were generally to a degree higher than other sites and could suggest possible problems in this area. Further monitoring would thus be essential in order to prevent future problems.

4.6 References

Abel, P.D. 1989. Water Pollution Biology. Ellis Horwood Limited Publishers, Chichester. 231 pp.

Alabaster, J.S. and Lloyd, R. 1980. Water quality criteria for freshwater fish. Butterworth and Co. London. 297 pp.

Andersson, I., Parkman, H. and Jernelov, A. 1990. The role of sediments as sink or source for environmental contaminants – a case study of mercury and chlorinated organic compounds. Limnologica. 20(2): 347 – 359.

Ashton, P.J., Love, D., Mahachi, H and Dirks P.H.G.M. 2001. An overview of the impact of mining and mineral processing operations in water resources and water quality in the Zambezi, Limpopo and Olifants catchments in Southern Africa. Contract report to the mining, minerals and sustainable development project, CSIR-Environmentek, Pretoria, South Africa and Geology Department , University of Zimbabwe, Harare, Zimbabwe. Report No ENV-P-C 2001-042.

93 Chapter 4: Metal Bioaccumulation

Avenant-Oldewage, A. and Marx, H.M. 2000. Bioaccumulation of chromium, copper and iron in the organs and tissues of Clarias gariepinus in the Olifants River, Kruger National Park. Water SA. 26 (4): 569 – 582.

Awofolu, O.R., Mbolekwa, Z., Mtshemla, V. and Fatoki, O.S. 2005. Levels of trace metals in water and sediment from Tyume River and its effects on an irrigates farmland. Water SA 31 (1): 87 – 94.

Bervoets, L. and Blust, R. 2003. Metal concentratios in water, sediment and gudgeon (Gogio gobio) from a pollution gradient: relationships with fish condition factor. Environmental pollution 126: 9 – 19.

Bezuidenhout, L.M., Schoonbee, H.J. and de Wet, L.P.D. 1990. Heavy metal content in organs of the African sharptooth catfish Clarias gariepinus (Burchell), from a Traansvaal lake affected by mine and industrial effluents. Part 1. Zinc and Copper. Water SA. 16 (2): 125 – 129.

Brand, M.E. 2001. Bioaccumulation of metals in Labeo congoro from the Olifants River (Mpumalanga) and the effect of nickel on the haematology of fish. Unpublished M.Sc. Dissertation. Rand Afrikaans University, Johannesburg.

Claassen, M. 1996. Assessment of selected metal and biocide bioaccumulation in fish from the Berg, Luvuvhu, Olifants and Sabie Rivers, South Africa. Unpublished M.Sc. Dissertation. Rand Afrikaans University. Johannesburg.

Coetzee, L., du Preez, H.H. and van Vuren, J.H.J. 2002. Metal concentration in Clarias gariepinus and Labeo umbratus from the Olifants and Klein Olifants River, Mpumalanga, South Africa: Zinc, copper, manganese, lead, chromium, nickel, aluminium and iron. Water SA. 28 (4): 433 – 448.

Department of Water Affairs and Forestry. 1996. South African Water Quality Guidelines – Second edition. Volume 7: Aquatic Ecosystems. 145pp

94 Chapter 4: Metal Bioaccumulation

Dallas, H.F. and Day, J.A. 1993. The effect of water quality variables of riverine ecosystems: a review. WRC Report No TT 61/93. Water Research Commission. Pretoria. 240 pp.

Dallas and Day, 2004. The effect of water quality variables on aquatic ecosystems: a review. WRC Report No TT224/04. Water Research Commission. 222 pp.

Fatoki, O.S. and Awofolu, O.R. 2003. Levels of Cd, Hg and Zn in some surface waters from the Eastern Cape Province, South Africa. Water SA 29 (4): 375 – 380

Fatoki, O.S., Awofolu, O.R. and Genthe, B. 2004. Cadmiun in the Umtata River and the associated health impact of on rural communities who are primary users of water from the river. Water SA. 30 (4): 507 – 513.

Fernandes, H.M. 1997. Heavy metal distribution in sediments an ecological risk assessment: The role of diagenetic processes in reducing metal toxicity in bottom sediments. Environmental Pollution. 97 (3): 317 – 325.

Galvin, R.M. 1996. Occurrence of metals in water: An overview. Water SA. 22 (1): 7 – 18.

Gomez Ariza. J.L, Giraldez, I., Sanchez-Rodas, D. and Morales, E. 2000. Comparison of the feasibility of three extraction procedures for trace metal partitioning in sediments from South-West Spain. The Science of the Total Environment. 246: 271 – 283.

Grobler, D.F., Kempster, P.L. and van der Merwe, L. 1994. A note on the occurrence of metals in the Olifants River, Eastern Transvaal, South Africa. Water SA. 20 (3): 195 – 204.

Heath, A.G. 1995. Water pollution and fish physiology – 2nd Edition. Lewis Publishers. New York. 359 pp.

95 Chapter 4: Metal Bioaccumulation

Heath, R.G.M. and Claassen, M. 1999. An overview of the pesticide and metal levels present in populations of the larger indigenous fish species of selected South African Rivers. Division of Water, Environment and Forestry Technology, CSIR. 318 pp.

Hellawell, J.M. 1986. Biological Indicators of Freshwater Pollution and Environmental Management. Elsevier Applie Science Publishers Ltd. London. 546 pp.

Hung, T., Meng, P. and Wu. 1992. Species of copper and zinc in sediments collected from the Antartic Ocean and the Taiwan Erhjin coastal area. Environmental Pollution. 80: 223 – 230.

Jackson, R.N., Baird, D. and Els, S. 2005. The effect of the heavy metals lead (Pb2+) and zinc (Zn2+) on the brood and larval development of the burrowing crustacean, Callianassa kraussi. Water SA. 31 (1): 107 – 116.

Kempster, P.L., Hattingh, W.H.J. and Van Vliet, H.R. 1980. Summarised water quality criteria. Department of Environmental Affairs Technical report TR 108. Pretoria.

Knittel, U., Klemm, W. and Greif, A. 2005. Heavy metal pollution downstream of old mining camps as a result of flood events: an example from the Mulde River system, Eastern part of Germany. TAO. 16 (4): 919 – 931.

Kotze, P., du Preez, H.H. and van Vuren, J.H.J. 1999. Bioaccumulation of copper and zinc in Oreochromis mossambicus and Clarias gariepinus, from the Olifants River, Mpumalanga, South Africa. Water SA. 25 (1): 99 – 110.

Melaku, S., Wondimu, T., Dams, R. and Moens, L. 2005. Multi-element analysis of Tinishu Akaki River sediment, Ethiopia, by ICP-MS after microwave digestion. Canadian Journal of Analytical Sciences and Spectroscopy. 50 (1): 31 – 40.

96 Chapter 4: Metal Bioaccumulation

Middelkoop, H. 2000. Heavy metal pollution of the river Rhine and Meuase floodplains in the Netherlands. Netherlands Journal of Geoscience. 79 (4): 411 – 428.

Moon, B.P., Van Niekerk, G.L., Heritage, K.H. and James, C.S. 1997. A geomorphological approach to the management of rivers in the Kruger National Park: The case of the Sabie River. Transactions of the institute of British Geographers 22: 31-48.

Mzimela, H.M., Wepener, V. and Cyrus, D.P. 2002. The sublethal effects of copper and lead on the haematology and acid-base balance of the groovy mullet, Liza dumerilli. African Journal of Aquatic Science. 27: 39 – 46.

Nussey, G. 1998. Metal ecotoxicology of the upper Olifants River at selected localities and the effect of copper and zinc on fish blood physiology. Unpublished Ph.D. Thesis, Rand Afrikaans University. Johannesburg

Nussey, G. Van Vuren, J.H.J. and Du Preez. H.H. 2000. Bioaccumulation of chromium, manganese, nickel and lead in the tissues of the moggel, Labeo umbratus (Cyprinidae), from Witbank Dam, Mpumalanga. Water SA. 26 (2): 269 – 284.

Robinson, J. and Avenant-Oldewage, A. 1997. Chromium, copper, iron and manganese bioaccumulation in some organs and tissues of Oreochromis mossambicus from the lower Olifants River, inside the Kruger National Park. Water SA. 23 (4): 387 – 403.

Roux, D.J. Badenhorst, J.E., du Preez, H.H. and Steyn, G.J. 1994. Note on the occurrence of selected metals and organic compounds in water, sediment and biota of the Crocodile River, Eastern Transvaal, South Africa. Water SA. 20 (4): 333 – 340.

Salomons, W., DeRooij, N.M., Kerdijk, H. and Bril, J. 1987. Sediments as a source of contaminants. Hydrobiologia. 13: 30 – 38.

97 Chapter 4: Metal Bioaccumulation

Sanders, M.J., du Preez, H.H. and van Vuren, J.H.J. 1999. Monitoring cadmium and zinc contamination in freshwater systems with the use of the freshwater river crab, Potamonautes warreni. Water SA 25 (1): 91 – 98

Seenayya,G. and Prahalad, A.K. 1987. In situ compartmentation and biomagnification of chromium and manganese in industriall polluted Husainsagar lake, Hyderabad, India. Water, Air, and Soil Pollution. 35: 233 – 239.

Seymore, T., du Preez, H.H., van Vuren, H.J.H., Deacon, A. and Strydom, G. 1994. Variations in selected water quality variables and metal concentrations in the sediment of the lower Olifants and Selati rivers, South Africa. Koedoe. 37 (2): 1 – 18.

Seymore, T., du Preez, H.H. and van Vuren, J.H.J. 1995. Manganese, lead and strontium bioaccumulation in the tissues of the yellowfish, Barbus marequensis from the lower Olifants River, Eastern Transvaal. Water SA. 21 (2): 159 – 172.

Singh, K.P., Chandra, H., Modak, D.P. and Ray, P.K. 1988. Determination of chromium speciation in water. In: Kruif H.A.M., De Zwart D., Viswanathan, P.N. and Ray, P.K. (eds.) Manual on Aquatic Ecotoxicology. Kluwer Academic Publishers, Dordrecht. 279 – 283.

Skelton, P. 1993. A complete guide to the freshwater fishes of Southern Africa. Struik Publishers. Cape Town. 395 pp.

Taylor, L.N., McGeer, J.C., Wood, C.M. and McDonald, D.G. 2000. Physiological effects of chronic copper exposure to rainbow trout (Oncorhynchus mykiss) in hard and soft water: Evaluation of chronic indicators. Environmental Toxicology and Chemistry. 19 (9): 2298 – 2308.

98 Chapter 4: Metal Bioaccumulation

Ure, A.M., Quevauviller, P.H., Muntau, H. and Griepink, B. 1993. Speciation of heavy metals in soils and sediments. An account of the improvement and harmonization of extraction techniques undertaken under the auspices of the BCR of the commission of the European Communities. International Journal of Environmental Analytical Chemistry. 51: 135 – 151.

Van Aardt, W.J. and Booysen, J. 2004 Water hardness and the effects of Cd on oxygen concumption, plasma chlorides and bioaccumulation in Tilapia sparrmanii. Water SA 30 (1): 57 – 64.

Van Rensburg, E.L. 1989. Die Biokonsentrering van Artrasien, Sink en Yster in Tilapia sparrmanii (Cichlidae). Ongepubliseerde M.Sc. Verhandeling. Rand Afrikaanse Universiteid. Suid Afrika.

Van Vuren, J.H.J., du Preez, H.H. and Deacon, A.R. 1994. Effects of pollutants on the physiology of fish in the Olifants River (Eastern Traansvaal). WRC Report No 350/1/94. Water Research Commision. Pretoria. 214 pp.

Vandecasteele, B., De Vos, B. and Tack, F.M.G. 2002. Heavy metal contents in surface soils along the upper Scheldt River (Belguim) affected by historical upland disposal of dredged materials. The Science of the Total Environment. 290 (1): 1 – 14.

Wepener, V., Van Vuren, J.H.J. and Du Preez, H.H. 1992. Effect of manganese and iron at neutral and acidic pH on the hematology of the banded Tilapia. Bull. Environ. Contam. Toxicol. 49: 613 – 619.

Wepener, V., Van Vuren, J.H.J. and Du Preez, H.H. 2001. Uptake and distribution of copper, iron and zinc mixture in gill, liver and plasma of a fresh water teleost, Tilapia sparrmarnii. Water SA. 27 (1): 99 – 108.

Wepener, V. and Vermeulen, L.A. 2005. A note on the concentration and bioavailability of selected metals in sedimentsof Richards Bay Harbour, South Africa. Water SA. 31 (4): 589 – 595

99 Chapter 5: Equilibrium partitioning

CHAPTER 5 SETTING QUALITY CRITERIA FOR NINE METALS IN THE LUVUVHU, SHINGWEDZI, LETABA AND SABIE RIVERS BY APPLYING THE EQUILIBRIUM PARTITIONING METHOD.

5.1 Introduction

Effective environmental decision making and natural resource management have become of increasing importance especially with regard to the aquatic ecosystem (Van Eeden, 2003). Water resources and water quality management within South Africa were in the past based on the need to protect human health however over time it has been realised that the entire ecosystem was suffering resulting in a broader philosophy of integrated ecosystem management (Gyedu-Ababio and Van Wyk, 2004).

South African water resources are under serious threat particularly due to metal effluents released from industries, mines, sewage purification works, suburban areas and agricultural practices (Van Eeden, 2003). Metals are persistent and tend to accrue in the aquatic environment; this is especially true for sediments which act as a sink for pollution (Coetzee et al., 2002; Wepener and Vermeulen, 2005).

According to Botes and Van Staden (2005) South African sediments as indicators of pollution are not that intensively investigated in comparison to the water resource component. Wepener et al. (2000) further states that present water quality guidelines are based upon overly simplistic and entrenched philosophies with emphasis on dissolved chemicals and the water column with very little attention being paid to developing criteria for sediment bound chemicals.

Equilibrium partitioning (EP) is a simple approach for calculating the distribution of metals in the water column and that substance such as the sediments that are in contact with the water. This method assumes that the concentrations of chemicals among the environmental compartments are stable at equilibrium, the partitioning of metals can be predicted based on partitioning coefficients, the sensitivity of species in the two phases are similar, the level of protection by water quality criteria are appropriate for benthic

100 Chapter 5: Equilibrium partitioning organisms and exposures are similar regardless of habitat or feeding type (USEPA, 1993; Jansen et al., 1997a).

The EP approach has been utilised far more extensively with regards to developing sediment quality criteria for organic compounds (Di Toro et al., 1991; Driscoll and Landrum, 1997), however internationally Cd, Co, Ni, Cu and Zn have been looked at (Van der Kooij et al., 1991; Janssen et al., 1997a; Janssen et al., 1997b). Little work, especially with regards to the South African context has been carried out upon metals. A study by Wepener et al. (2000) determined sediment quality criteria for Cu and Zn in the Olifants River. This chapter therefore investigates the use of the EP method to derive quality criteria for all nine metals by utilising data obtained from the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers.

5.2 Methods

The determination of metal quality criteria was carried out by making use of two types of data sets, namely toxic effect data and product standards. Water and sediment as well whole fish samples were collected and processed for metals analysis as discussed in Chapter 4.

Toxic effects data

The South African water quality guidelines for the protection of aquatic ecosystems were utilised in setting the quality criteria (DWAF, 1996). These guidelines are derived from toxicity tests. For the purpose of this assessment the chronic effects value (CEV) obtained from the guidelines were selected as quality criterion for the chemical in the 1 dissolved phase (Cr w) due to the fact that they provide protection to most aquatic organisms during chronic exposure (Wepener et al., 2000).

Product standards

Product standards are based upon the assumption that any concentration of a chemical deemed acceptable for human consumption is acceptable to aquatic organisms. These

101 Chapter 5: Equilibrium partitioning concentrations were translated into critical concentrations by making use of the following bioconcentration factor (BCF),

BCF = Corg/Cw (1) where: BCF = bioconcentration factor (l/kg)

Corg = content of chemical in organism (µg/kg)

Cw = concentration of chemical in water (µg/l)

Solids-water partitioning coefficient

The solids-water partitioning coefficients (Ksw) are used to translate BCF’s and toxic effects data into critical concentrations in solids. Solids water coefficients were derived from field measurements in water before and after filtration. Ksw values were calculated by making use of the following equation:

Ksw = (Ctotal – Cwater) / (SM x Cwater) (2) where:

Ctotal = total concentration in the water before filtration (µg/l)

Cwater = concentration in water phase after filtration (µg/l) SM = concentration in suspended matter (g/l)

The following equations were utilised from equations proposed by Van der Kooij et al. (1991) in order to determine the quality criteria.

5.2.1 Toxic effects data

1 The CEV in the dissolved phase Cr w are obtained from DWAF (1996), therefore:

1 Cr w = CEV (3)

1 The CEV-related criterion for total metal concentration in water (Cr tot) was calculated as follows:

102 Chapter 5: Equilibrium partitioning

1 1 Cr tot = Cr w x (1 + Ksw x SM) (4)

1 The CEV-related criterion for metals in the sediment (Cr sed) was calculated as follows:

1 1 Cr sed = (Ksw x Cr w) / r (5) where r represents the ratio of suspended matter to sediment.

5.2.2 Product standard data

2 The product standard criterion for metals in the dissolved phase (Cr w) was calculated as follows:

2 Cr w = Corg / BCF (6)

2 The product standard criterion for total metals in the water (Cr tot) was calculated as follows:

2 Cr tot = Corg x (1 + Ksw x SM) / BCF (7)

2 The product standard criterion for metals in the sediment (Cr sed) was calculated as follows:

2 Cr sed = (Ksw x CORG) / (r x BCF) (8)

Both toxic effect data and product standards allow for the calculation of quality criteria, thus yielding two sets of criteria for each chemical. The lowest criteria are deemed the more decisive and is thus utilised.

5.3 Results

The metal concentrations in the different phases (e.g. dissolved, suspended matter, fish, etc.) are not presented in this chapter and only the values for BCFs, Ksw, SM and

103 Chapter 5: Equilibrium partitioning

Table 17: Data used to determine quality criteria of aluminium, cadmium, chromium, copper and iron. Aluminium Luvuvhu Shingwedzi Letaba Sabie BCF (l/kg) 2259.8 4575.5 6403.3 1328.7 SM (g/l) 0.01 0.008 0.008 0.011

Ksw (l/g) 207.9 150.9 134.5 70.5 CEV (µg/l) 20.0 20.0 20.0 20.0 r 24.1 24.1 24.1 24.1 Cadmium Luvuvhu Shingwedzi Letaba Sabie BCF (l/kg) 571.4 148.0 80.8 220.0 SM (g/l) 0.0007 0.001 0.0009 0.0006

Ksw (l/g) 4139.1 2539.6 1527.6 1410.7 CEV (µg/l) 2.0 * 2.0 * 2.0 * 2.0 * r 5.3 5.3 5.3 5.3 Chromium Luvuvhu Shingwedzi Letaba Sabie BCF (l/kg) 145.8 88.7 27.4 85.5 SM (g/l) 0.01 0.008 0.008 0.011

Ksw (l/g) 100.4 95.3 78.7 77.0 CEV (µg/l) 14.0 14.0 14.0 14.0 r 4.5 4.5 4.5 4.5 Copper Luvuvhu Shingwedzi Letaba Sabie BCF (l/kg) 180.5 122.5 48.4 34.3 SM (g/l) 0.011 0.009 0.01 0.012

Ksw (l/g) 113.6 107.0 103.5 107.9 CEV (µg/l) 0.8 0.8 0.8 0.8 r 1.6 1.6 1.6 1.6 Iron Luvuvhu Shingwedzi Letaba Sabie BCF (l/kg) 5249.9 5377.8 4768.1 1552.5 SM (g/l) 0.5 0.4 0.5 0.4

Ksw (l/g) 88.2 72.1 80.5 90.0 CEV (µg/l) 1000.0 * 1000.0 * 1000.0 * 1000.0 * r 0.6 0.6 0.6 0.6 * = No South African CEV available therefore values used from Australian guidelines.

104 Chapter 5: Equilibrium partitioning

Table 18: Data used to determine quality criteria for lead, manganese, nickel and zinc. Lead Luvuvhu Shingwedzi Letaba Sabie BCF (l/kg) 153.4 74.6 144.4 118.9 SM (g/l) 0.008 0.01 0.008 0.005

Ksw (l/g) 160.4 145.2 144.3 82.5 CEV (µg/l) 1.0 1.0 1.0 1.0 r 0.5 0.5 0.5 0.5 Manganese Luvuvhu Shingwedzi Letaba Sabie BCF (l/kg) 47.7 264.4 363.9 277.6 SM (g/l) 0.04 0.06 0.05 0.02

Ksw (l/g) 89.4 71.3 78.3 91.5 CEV (µg/l) 370.0 370.0 370.0 370.0 r 8.1 8.1 8.1 8.1 Nickel Luvuvhu Shingwedzi Letaba Sabie BCF (l/kg) 214.9 138.9 120.0 153.7 SM (g/l) 0.003 0.005 0.003 0.002

Ksw (l/g) 339.4 323.8 393.8 194.4 CEV (µg/l) 150.0 * 150.0 * 150.0 * 150.0 * r 7.6 7.6 7.6 7.6 Zinc Luvuvhu Shingwedzi Letaba Sabie BCF (l/kg) 804.1 995.8 635.5 587.8 SM (g/l) 0.01 0.008 0.01 0.01

Ksw (l/g) 79.9 85.4 81.5 75.3 CEV (µg/l) 3.6 3.6 3.6 3.6 r 1.3 1.3 1.3 1.3 * = No South African CEV available therefore values used from Australian guidelines. r- values, as described in methods section above are presented in Tables 17 and 18. The water quality guidelines for all nine metals are also presented in Table 17 and 18. These values were obtained from DWAF (1996) and represent the CEV values. Where values were not available the Australian guidelines from the Department of Environment and Heritage (DEH) for the protection of aquatic ecosystems were utilised (ANZECC, 2000). R-values were calculated by determining the concentration ratio of suspended matter to sediment. Metal concentrations on which calculations in this study

105 Chapter 5: Equilibrium partitioning were based on are presented in Chapter 4 whilst all physico-chemical conditions during each sampling survey can be found in Chapter 3. In order to illustrate the above approaches a working example is presented below for aluminium on the Luvuvhu River. Calculated criterion values are presented in Table 19 to Table 21.

Working Example: Aluminium (Luvuvhu River)

Direct toxic effect data

The water quality guideline value obtained from DWAF (1996) is utilised as the quality criterion in the dissolved phase, therefore:

1 Cr w = CEV = 20 µg/l

By making use of Equation 4 the total aluminium concentration criterion in the water is calculated as follows:

1 1 Cr tot = Cr w x (1 + Ksw x SM) = 20 µg/l x (1 + 207.9 l/g x 0.118 g/l) = 510.6 µg/l

Equation 5 is utilised in determining the total aluminium concentration criterion in sediment:

1 1 Cr sed = (Ksw x Cr w) / r = (207.9 l/g x 20 µg/l) / 24.09 = 172.6 µg/g

106 Chapter 5: Equilibrium partitioning

Product standards

The dissolved aluminium can be calculated utilising Equation 6:

2 Cr w = Corg / BCF = 132200 µg/kg / 2259.8 l/kg = 58.5 µg/l

The total concentration of aluminium in water can be calculated with Equation 7:

2 Cr tot = Corg x (1 + Ksw x SM) / BCF = 132200 µg/kg x (1 + 207.9 l/g x 0.118 g/l) / 2259.8 l/kg = 1493.6 µg/l

Lastly the value for aluminium in the sediments has been obtained utilising Equation 8:

2 Cr sed = (Ksw x CORG) / (r x BCF) = (207.9 l/g x 132200 µg/kg) / (24.09 x 2259.8 l/kg) = 504.9 µg/g

5.4 Discussion

Due to their widespread release and persistent nature many metals are commonly elevated within aquatic ecosystems, often within the sediment phase (Ankley et al., 1996). Predicting the fate and effects of these metals in order to derive quality criteria continues to be an important element in ecological and human based risk assessment (Driscoll and Landrum, 1997).

The data obtained in this chapter results in two different types of effects to derive quality criteria, however only one criterion can be utilised and according to Van der Kooij et al. (1991) the lowest value takes preference. The results from this study indicate that toxic effect-derived quality data is the lowest for Al, Cr, Cu, Pb and Zn and thus take preference whilst product standard-based criteria produce the lowest values

107 Chapter 5: Equilibrium partitioning

Table 19: Total, dissolved and sediment quality criteria concentrations for aluminium, cadmium and chromium utilising physico-chemical and biological data for the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers Aluminium Luvuvhu Shingwedzi Letaba Sabie Cr1 Cr2 Cr1 Cr2 Cr1 Cr2 Cr1 Cr2

Cr w (µg/l) 20.0 58.5 20.0 41.7 20.0 48.6 20.0 85.5

Cr tot (µg/l) 510.6 1493.6 557.2 1161.7 469.3 1140.3 194.9 833.0

Cr sed (µg/g) 172.6 504.9 125.2 261.1 111.6 272.2 58.5 250.1 Cadmium Luvuvhu Shingwedzi Letaba Sabie Cr1 Cr2 Cr1 Cr2 Cr1 Cr2 Cr1 Cr2

Cr w (µg/l) 2.0 1.8 2.0 2.5 2.0 4.6 2.0 5.0

Cr tot (µg/l) 7.7 6.8 7.1 8.8 4.7 10.7 3.8 9.5

Cr sed (µg/g) 1971.7 1399.7 1181.2 1197.9 710.5 1320.1 656.1 1330.8 Chromium Luvuvhu Shingwedzi Letaba Sabie Cr1 Cr2 Cr1 Cr2 Cr1 Cr2 Cr1 Cr2

Cr w (µg/l) 14.0 59.0 14.0 62.0 14.0 62.0 14.0 55.0

Cr tot (µg/l) 27.3 115.3 24.5 108.7 23.0 102.0 26.1 102.4

Cr sed (µg/g) 312.2 1315.8 296.6 1313.6 244.8 1084.0 239.5 940.7

Cr 1 Toxic effect-derived quality data Cr 2 Product standard-derived quality data

Cr w Concentration in the dissolved phase

Cr tot Total concentration

Cr sed Concentration in the sediment

108 Chapter 5: Equilibrium partitioning

Table 20: Total, dissolved and sediment quality criteria concentrations for copper, iron and lead utilising physico-chemical and biological data for the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers Copper Luvuvhu Shingwedzi Letaba Sabie Cr1 Cr2 Cr1 Cr2 Cr1 Cr2 Cr1 Cr2

Cr w (µg/l) 0.8 50.0 0.8 55.5 0.8 58.4 0.8 51.9

Cr tot (µg/l) 1.8 112.5 1.5 107.2 1.6 116.8 1.9 120.6

Cr sed (µg/g) 56.8 3551.1 53.5 3711.9 51.7 3777.9 54.0 3499.2 Iron Luvuvhu Shingwedzi Letaba Sabie Cr1 Cr2 Cr1 Cr2 Cr1 Cr2 Cr1 Cr2

Cr w (µg/l) 1000.0 64.6 1000.0 84.8 1000.0 77.4 1000.0 63.8

Cr tot (µg/l) 40842.2 2639.6 32239.0 2734.8 39039.3 3022.8 36592.2 2332.9

Cr sed (µg/g) 137849.2 8909.2 112633.5 9554.7 125785.9 9739.7 140549.8 8960.5 Lead Luvuvhu Shingwedzi Letaba Sabie Cr1 Cr2 Cr1 Cr2 Cr1 Cr2 Cr1 Cr2

Cr w (µg/l) 1.0 36.5 1.0 14.0 1.0 37.0 1.0 70.9

Cr tot (µg/l) 2.3 82.8 2.5 10.2 2.2 79.4 1.4 97.1

Cr sed (µg/g) 341.3 12456.2 309.0 1230.9 306.9 11331.5 175.5 12439.0

Cr 1 Toxic effect-derived quality data Cr 2 Product standard-derived quality data

Cr w Concentration in the dissolved phase

Cr tot Total concentration

Cr sed Concentration in the sediment

109 Chapter 5: Equilibrium partitioning

Table 21: Total, dissolved and sediment quality criteria concentrations for manganese, nickel and zinc utilising physico-chemical and biological data for the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers Manganese Luvuvhu Shingwedzi Letaba Sabie Cr1 Cr2 Cr1 Cr2 Cr1 Cr2 Cr1 Cr2

Cr w (µg/l) 370.0 64.6 370.0 84.8 370.0 77.4 370.0 63.8

Cr tot (µg/l) 1600.9 279.6 1903.9 436.5 1716.1 359.1 885.1 152.5

Cr sed (µg/g) 4081.6 713.0 3300.8 756.8 3574.7 748.0 4181.0 720.4 Nickel Luvuvhu Shingwedzi Letaba Sabie Cr1 Cr2 Cr1 Cr2 Cr1 Cr2 Cr1 Cr2

Cr w (µg/l) 150.0 16.7 150.0 18.0 150.0 15.2 150.0 25.4

Cr tot (µg/l) 306.7 34.3 385.8 46.3 331.2 33.5 201.7 34.1

Cr sed (µg/g) 6690.5 747.1 6383.1 766.0 7763.0 785.1 3832.8 648.5 Zinc Luvuvhu Shingwedzi Letaba Sabie Cr1 Cr2 Cr1 Cr2 Cr1 Cr2 Cr1 Cr2

Cr w (µg/l) 3.6 71.4 3.6 79.0 3.6 75.3 3.6 78.0

Cr tot (µg/l) 7.0 138.9 6.1 134.0 6.7 139.5 6.9 149.3

Cr sed (µg/g) 221.4 4389.1 236.4 5188.0 225.8 4722.9 208.5 4518.4

Cr 1 Toxic effect-derived quality data Cr 2 Product standard-derived quality data

Cr w Concentration in the dissolved phase

Cr tot Total concentration

Cr sed Concentration in the sediment

110 Chapter 5: Equilibrium partitioning for Fe, Mn and Ni. Results for Cd were extremely close and thus both criteria could be assumed correct.

Analysis of Al suggests that values obtained for toxic effect-derived quality data were over protective when compared to dissolved water quality criteria for other countries. Values for Australia are reported to be between 55 and 100 µg/l (ANZECC, 2000). It is thus suggested that product standard quality criteria be utilised. If this were the case then the values of 58.5, 41.7, 48.6 and 85.5 µg/l obtained for the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers respectively (Table 19) would thus result in Al concentrations during the study (Table 12) being within acceptable water quality limits.

Data obtained for Fe (Table 20) suggests that amounts ranging between 60 and 90 µg/l are appropriate levels for the rivers under investigation. Although theses values seem on the low side they are found to tie in with data collected during the study (Table 14). It should be noted that no CEV value was available for South African rivers and thus the Australian value was utilised (Heath and Claassen, 1999), it therefore seems that South African rivers require lower Fe guidelines, however further research upon this metal in South African systems is essential.

Mn and Ni suggest the use of product standard-derived quality data in setting quality guidelines. This suggests that the current CEV values reported by DWAF (1996) for Mn and ANZECC (2000) for Ni are considered to lenient for the systems under investigation. Further research on these metals is thus essential.

When results For Cu and Zn are compared to those found during a study by Wepener et al. (2000) on the Olifants River it can be seen that values are similar with the data obtained. Their suggested values for water were slightly lower than those calculated here, however not by drastic measures. Sediment quality criteria showed greater variation, but due to the fact that these are different systems values were not of that great difference. Values for all systems were noted to be with the same range.

111 Chapter 5: Equilibrium partitioning

5.5 Conclusion

This chapter has showed that it is possible to utilise two different types of criteria in order to develop quality criteria for water and sediment. Although this seems a simple enough process it should be borne in mind that data, especially for South African rivers is limited. It should also be noted that this was the first attempt to utilise the EP method on the Luvuvhu, Shingwedzi, Letaba and Sabie rivers and further research upon these systems utilising this method is important in order to validate results. It must be kept in mind that EP derived criteria values may only be valid for these river systems and further research on other rivers within South Africa is essential in order to generate a country based criteria data set.

5.6 References

Ankley, G.T., Di Toro, D.M., Hansen, D.J. and Berry, W.J. 1996. Technical basis and proposal for deriving sediment quality criteria for metals. Environmental Toxicology and Chemistry. 15 (12): 2056 – 2066.

Australian and New Zealand Environment and Conservation Council 2000. Australian and New Zealand Guidelines for Fresh and Marine Water Quality: Volume 1. Available from: http://www.deh.gov.au/water/quality/nwqms/index.html. (Accessed: October 2006)

Botes, P.J. and Van Staden, J.F. 2005. Investigation of trace element mobility in river sediments using ICP-OES. Water SA 31 (2): 183 – 191.

Coetzee, L., Du Preez, H.H. and Van Vuren, J.H.J. 2002. Metal concentration in Clarias gariepinus and Labeo umbratus from the Olifants and Klein Olifants River, Mpumalanga, South Africa: Zinc, copper, manganese, lead, chromium, nickel, aluminium and iron. Water SA. 28 (4): 433 – 448.

112 Chapter 5: Equilibrium partitioning

Department of Water Affairs and Forestry. 1996. South African Water Quality Guidelines – Second edition. Volume 5: Aquatic Ecosystems. 145pp

Di Toro, D.M., Zarba, C., Hansen, D.J., Swartz, R.C., Cowan, C.E., Allen, H.E., Thomas, N.A., Paquin, P.R. ad Berry, W.J. 1991. Technical basis for establishing sediment quality criteria for non-ionic organic chemicals using equilibrium partitioning. Environmental Toxicology and Chemistry. 10: 1541 – 1583.

Driscoll, S.K. and Landrum, P.F. 1997. A comparison of equilibrium partitioning and critical body residue approaches for predicting toxicity of sediment- associated fluoranthene to freshwater amphipods. Environmental Toxicology and Chemistry. 16 (10): 2179 – 2186.

Gyedu-Ababio, T..K. and Van Wyk, F. 2004. Effects of human activities on the Waterval River, Vaal River catchment, South Africa. African Journal of Aquatic Science. 29 (1): 75 – 81.

Heath, R.G.M. and Claassen, M. 1999. An overview of the pesticide and metal levels present in populations of the larger indigenous fish species of selected South African Rivers. Division of Water, Environment and Forestry Technology, CSIR. 318 pp.

Jansen, R.P.T., Peijnenburg, W.J.G.M., Posthuma, L. and Van den Hoop, M.A.G.T. 1997a. Equilibrium partitioning of heavy metals in dutch soils. I. Relationships between metal partition coefficients ans soil characteristics. Environmental Toxicology and Chemistry. 16 (12): 2470 – 2478.

Janssen, R.P.T., Posthma, L., Baerselman, R., Den Hollander, H.A., Van Veen, R.P.M. and Peijnenburg, W.J.G.M. 1997b. Equilibrium partitioning of heavy metals in Dutch fields of soil. II. Prediction of metal accumulation in earthworms. Environmental Toxicology and Chemistry. 16 (12): 2479 – 2488.

113 Chapter 5: Equilibrium partitioning

USEPA (United States Environmental Protection Agency). 1993. Sediment quality criteria for the protection of benthic organisms: Acenaphthene. EPA- 822- R-93-013. Washington D.C.

Van der Kooij, L.A., Van de Meent, D., Van Leeuwen, C.J. and Bruggeman, W.A. 1991. Deriving quality criteria for water and sediment from the results of aquatic toxicity tests and product standards: Application of the equilibrium partitioning method. Water Res. 25 (6): 679 – 705.

Van Eeden, P.H. 2003. Metal concentrations in selected organs and tissues of five Red-knobbed Coot (Fulica cristata) populations. Water SA. 29 (3): 313 – 322.

Wepener, V., Van Vuren, J.H.J. and Du Preez, H.H. 2000. Application of the equilibrium partitioning method to derive copper and zinc quality criteria for water and sediment: A South African perspective. Water SA. 26 (1): 97 – 104.

Wepener, V. and Vermeulen, L.A. 2005. A note on the concentration and bioavailability of selected metals in sedimentsof Richards Bay Harbour, South Africa. Water SA. 31 (4): 589 – 595.

114 Chapter 6: Conclusions and Recommendations

CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS

6.1 Conclusions

Within South Africa and any developing country for that matter, the optimal and sustainable use of natural resources is vital. Aquatic ecosystems fall within this category and if managed appropriately can supply waters of good quality and quantity not only to the countries population but also its fauna and flora (Palmer et al., 2005).

South Africa can be considered as a water scarce country with limited fresh water supplies. Due to the growth in population and development over the past years there has been a large increase in municipal and industrial pollution (Fatoki et al., 2001). This is particularly true in the catchment areas of the rivers that supply the Kruger National Park with water and has resulted in a decrease in the quality and quantity (Jewitt et al., 1998; Birkhead et al., 2000).

The present study was undertaken in order to determine the physico-chemical, nutrient and metal concentrations within four major river systems that transect the KNP, namely the Luvuvhu, Shingwedzi, Letaba and Sabie Rivers. Due to the fact that little background information is available on these systems, these data were considered to contribute towards the baseline information. Data obtained for the Shingwedzi River will form part of a greater Shingwedzi River project.

Contamination of aquatic ecosystems can be determined by investigating physico- chemical properties as well as nutrient and metal concentrations occurring in the water, sediment and fish. Analysis of these three biotic and abiotic components will not only determine the current state of the ecosystem but will also be able to interpret the water quality as organisms have experienced it over their life histories (Abel, 1989).

Physico-chemical characteristics (Table 2 and Table 5) showed that the Luvuvhu and Sabie rivers appeared to be in better condition than the Shingwedzi and Letaba.

115 Chapter 6: Conclusions and Recommendations

Physical and chemical characteristics were generally within guideline limits for the protection of the aquatic environment (DWAF, 1996). Exceptions were found for nitrates and sulphates and high values may be attributed to the large amount of agriculture and subsistence farming within the area. Values for the Shingwedzi and Letaba on the other hand were found to be above acceptable limits. This was particularly true for the Letaba 1a site, which showed deviations for all variables investigated baring COD and temperature. Abnormal values may be attributed to the reduction in flow experienced as well as the growing rural populations along the banks of these systems. Decrease in flow seems to be a major problem with the rivers often drying up as was experienced in the Shingwedzi during the September 2005 sampling survey where no water could be found until well within the borders of the KNP.

Sediments showed a great variation with regards to physical properties. Organic content was highest for the Shingwedzi 2 site whilst lowest for the Sabie 2 (Table 11). Grain size contribution varied extensively between the flow periods. It was noted that Luvuvhu 1, Luvuvhu 2 and Shingwedzi 1 were composed extensively of smaller grain sizes during the high flow period (Figure 27 and Figure 28) and this could attribute to the higher metal concentrations found at these sites.

In order to obtain a completely reliable assessment of pollution within aquatic ecosystems physical and chemical monitoring should be supported by some means of biological monitoring. This is important because living organisms are indicative of the chemical condition of the water not only at present times but as they have experienced it throughout their lives (Abel, 1989). Fish are important indicator organisms that are known to accumulate metals within their tissues, however it is further noted that they show a high degree of metal regulation and are able to move great distances especially away from unsuitable areas.

Metal analysis was carried out for Al, Cd, Cr, Cu, Fe, Pb, Mn, Ni and Zn (Table 12 to Table 16). No one river system can be regarded as being more polluted with regards to metals due to the fact that each river showed high levels of one metal at some or another stage. It was noted however that concentrations within the Sabie system (especially within the borders of the park) were generally lower when compared to

116 Chapter 6: Conclusions and Recommendations values obtained for the other three systems. This strengthens the observation by Moon et al. (1997) that the Sabie River within the KNP has been in recent times described as the most pristine system within South Africa. A trend of high concentrations of Cr, Cu, Fe, Pb, Mn, Ni and Zn, all occurring at the Luvuvhu 1, Luvuvhu 2 and Shingwedzi 1 sites during the high flow sampling was observed. These values were higher than other sites and could suggest possible problems in this area. Further monitoring would thus be essential in order to prevent future problems.

The EP method was utilised to set ecotoxicological quality criteria for water and sediment. Results obtained from this study are varied, however, this method has merit in future studies. It should be noted that further studies on these as well as other South African rivers are essential to validate results. Only then can this method be utilised to its full capacity in determining quality criteria for South African systems.

6.2 Recommendations

This study was undertaken to gain knowledge and information often on rivers that had little previous water quality history such as was the case with the Shingwedzi River. The following recommendations are put forward.

• Future monitoring programs on these diverse river systems are essential and it is suggested that each system be looked at separately. This should include a full evaluation of the physical and chemical attributes and will allow for more intense studies to be conducted.

• A detailed field biomonitoring programme should be implemented at the same time in order to determine the impacts upon the biological components and aquatic ecosystem.

• It is suggested that the number of sampling stations are kept to one outside the western boundary and one within the borders of the Kruger. This allows for increased frequency of water and sediment monitoring (on a

117 Chapter 6: Conclusions and Recommendations

monthly/fortnightly basis) whilst at the same time keeping costs at a minimum.

• The number of water and sediment samples obtained per site should be increased. Although samples were collected in triplicate an increase in this number will result in a decrease in variation whilst at the same time increasing the statistical probability of assessing potential problems.

• A metal bioaccumulation program in these four rivers as well as the remaining three major rivers of the KNP should be implemented. This has already been suggested by Wepener (1997) in which he suggested that the Olifants River be used as the pilot river and that sampling should occur on a bi-annual basis in order to account for seasonal variability. Up to date no such programme has been implemented and it is recognised that this could be due to the availability of money, time and equipment which are often in short supply. It is therefore suggested that aquatic macroinvertebrate monitoring be looked at not as the solution but rather as a temporary alternative.

• Fish are mobile organisms that can regulate metal concentrations and move away from unsuitable areas, it is therefore recommended that future metal bioaccumulation studies look at using alternative indicator organisms to supply different types of results

• Aquatic organisms are constantly active within their home ranges and are sensitive to all alterations of the water body. They can thus be seen as indicators of water and sediment quality not only at present times but as they have experienced it through out their life histories. It is recommended that future studies incorporate the use of aquatic macroinvertebrates (SASS5) in order to supply a better assessment of the health of the system.

• The use of the equilibrium partitioning method is suggested as a handy means of determining water and sediment quality criteria, however more extensive research is needed in South African systems with regards to this tool.

118 Chapter 6: Conclusions and Recommendations

6.3 References

Abel, P.D. 1989. Water Pollution Biology. Ellis Horwood Limited Publishers, Chichester. 231 pp.

Birkhead, A.L., Heritage, G.L., James, C.S., Rogers, K.H. and Van Niekerk, A.W. 2000. Geomorphological change models for the Sabie River in the Kruger National Park. WRC Report No 782/1/00. Water Research Commission, Pretoria. 132 pp.

Department of Water Affairs and Forestry. 1996. South African Water Quality Guidelines – Second edition. Volume 5: Aquatic Ecosystems. 145pp

Fatoki, O.S., Muyima, N.Y.O. and Lujiza, N. 2001. Situation analysis of water quality in the Umtata River catchment. Water SA. 27 (4): 467 – 473.

Jewitt, G.P.W., Heritage, G.L., Weeks, D.C., Mackenzie, J.A., Van Niekerk, A., Gorgens, A.H.M., O’Keeffe, J., Rogers, K. and Horn, M. 1998. Modeling abiotic-biotic links in the Sabie River. WRC Report No 777/1/98. Water Research Commission, Pretoria. 157 pp.

Moon, B.P., Van Niekerk, G.L., Heritage, K.H. and James, C.S. 1997. A geomorphological approach to the management of rivers in the Kruger National Park: The case of the Sabie River. Transactions of the institute of British Geographers 22: 31-48.

Palmer, C.G., Rossouw, N., Muller, W.J. and Scherman, P-A. 2005. The development of water quality methods within ecological reserve assessments, and links to environmental flows. Water SA. 31 (2): 161 – 170.

Wepener, V. 1997. Metal ecotoxicology of the Olifants River in the Kruger National Park and the effect thereof on fish haematology. Unpublished PhD. Thesis, Rand Afrikaans University, Johannesburg.

119