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A Comparative Water and Sediment Quality Assessment of the Nyl River System, Limpopo, South Africa

Simone Dahms

MINOR DISSERTATION

Submitted in Fulfilment of the Requirements for the Degree

MAGISTER SCIENTIAE

Aquatic Health

In the

FACULTY OF SCIENCE

At the

UNIVERSITY OF JOHANNESBURG

Supervisor: Dr. R Greenfield

November 2015

The financial assistance of the National Research Foundation (NRF) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at are those of the author and are not necessarily to be attributed to the NRF

CONTENTS

LIST OF TABLES…………………………………...……………………………………...iii

LIST OF FIGURES……………………………………………………………………….....v

LIST OF ABBREVIATIONS……….…………………………………………………….vii

ACKNOWLEDGEMENTS…………….…………………………………………………..x

SUMMARY…………..……………………………………………………………………..xi

CHAPTER 1: INTRODUCTION...…………..…………………………………………….1

1.1 General Introduction………………………………………………………...….2 1.2 Hypotheses, Aims and Objectives…………………………………………….4 1.2.1 Hypotheses……………………………………………………………4 1.2.2 Aims……………………………………………………………………4 1.2.3 Objectives……………………………………………………………..4 1.3 Chapter Outline…………………………………………………………………5

CHAPTER 2: LITERATURE REVIEW……………………………………………………6

2.1 Study Background………………………………………………………………7 2.2 Study Sites………………………………………………………………………8 2.3 Experimental Design....………………………...……………………...……..13 2.4 Conclusion……………………………………………………………………..16

CHAPTER 3: WATER AND ARTIFICIAL MUSSELS...... ……………………………..17

3.1 Introduction………………………………………………………………….....18 3.2 Materials and Methods………………………………………………………..19 3.2.1 In situ water quality parameters…………………………………...19 3.2.2 Water…………………………………………………………………19 3.2.3 Artificial Mussels…………………………………………………….23 3.2.4 Statistical Analysis……………………………………………...…..26 3.3 Results………………………………………………………………………….26 3.3.1 In situ water quality parameters…………………………………...26

3.3.2 Water…………………………………………………………………28

3.3.3 Artificial Mussels…………………………………………………….36 3.3.4 Water and AMs……………………………………………………...40 3.4 Discussion……………………………………………………………………...42 3.4.1 In situ water quality parameters……………………………………42 3.4.2 Water and AMs………………………………………………………42 3.5 Conclusion……………………………………………………………………...48

CHAPTER 4: SEDIMENT AND ECOLOGICAL RISK ASSESSMENT……………....50

4.1 Introduction……………………………………………………………………..51 4.2 Materials and Methods………………………………………………………..52 4.2.1 Site Selection……………………………………………………….. 52 4.2.2 Sample Collection and Preparation…………………………...…..52 4.2.3 ICP-OES Analysis…………………………………………………...53 4.2.4 Statistical Analysis…………………………………………………..54 4.2.5 Ecological Risk Assessment……………………………………….54 4.3 Results………………………………………………………………………….58 4.3.1 Metal Concentrations in Sediment………………………………...58 4.3.2 Ecological Risk Assessment……………………………………….62 4.4 Discussion……………………………………………………………………...68 4.5 Conclusion……………………………………………………………………...72

CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS………………………74

5.1 Concluding Remarks………………………………………………………….75 5.2 Recommendations…………………………………………………………….76

CHAPTER 6: REFERENCES……………………………………………………………...78

APPENDIX A………………………………………………………………………………87

APPENDIX B…………………………………...………………………………………….88

LIST OF TABLES:

Table 2.1: Locations, abbreviations and GPS coordinates of study sites

Table 2.2: A review of the history of AMs (Adapted from Claassens et al. 2016)

Table 3.1: Sampling dates of water, AMs and In situ parameters for determining metal concentrations in the Nyl River system.

Table 3.2: Recoveries of CRMs from ICP-MS analyses including Dogfish Liver Tissue (DOLT-4), Lake Sediment (LKSD-3) and Freshwater Sediment (FWSD) analysed for QA/QC purposes.

Table 3.3: ICP-MS LODs for water sample analysis. LOD numbers are expressed in µg/l.

Table 3.4: ICP-MS LODs for AM analysis analysed for QA/QC purposes. LODs are expressed in µg/l.

Table 3.5: Total Hardness of water samples from the Nyl River system, Limpopo,

South Africa. Measurements given in mg/L CaCO3. Water Hardness is determined according to the DWAF guidelines for aquatic ecosystems (DWAF, 1996), <60mg/L is Soft, 60-119mg/L is Medium, 120-180mg/L is Hard and >180mg/L is Very Hard.

Table 4.1: The detection limits as calculated by the ICP-OES for each of the metals expressed in parts per million (ppm) as well as CRM Recovery percentages for Lake Sediment-3 and Freshwater Sediment.

Table 4.2: Description of Contamination Factor (CF) values as described by Thomilson et al. (1980).

Table 4.3: Descriptions of the Geo-Accumulation Index (Igeo) values and classes (0- 6) as described by Muller (1979).

Table 4.4: The Enrichment Factor (EF) Index description of values as described by Li et al. (2013).

iii

Table 4.5: Contamination Factor (CF), Geo-accumulation Index (Igeo) and Enrichment Factor for High Flow (HF) February-April 2014 and Low Flow (LF) July-August 2014 for Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn for seven sites along the upper Nyl River.

Table 4.6: Results of the Pollution Load Index for High Flow (HF) (February-April 2014) and Low Flow (LF) (July-August 2014) periods for seven sites along the Nyl River.

LIST OF FIGURES:

Figure 2.1: A Map of the Nyl River, Limpopo Province, South Africa with the sampling sites indicated. Sampling sites include the Klein Nyl Oog (KNO), Donkerpoort Dam (DPD), Golf Course (GC), Sewage Treatment Works (STW), Jasper (JAS), Nylsvley Nature Reserve (NYL) and the Moorddrift Dam (MDD).

Figure 2.2: Sampling sites along the Nyl River, Limpopo, South Africa. A: Klein Nyl Oog (KNO), B: Donkerpoort Dam (DPD), C: Golf Course (GC), D: Sewage Treatment Works (STW), E: Jasper (JAS), F: Nylsvley Nature Reserve (NYL), G: Moorddrift Dam (MDD)

Figure 3.1: Recoveries of internal standards during ICP-MS analysis for Rh and Lu expressed in % recovery of each element.

Figure 3.2: A constructed Artificial Mussel as designed by Wu et al. (2007).

Figure 3.3: A flow diagram representation of the process of constructing an AM as described by Hossain et al. (2015).

Figure 3.4: In situ water quality parameters including Conductivity, pH and Oxygen Saturation measured in the Nyl River system for 27/02/2014 and 02/04/2014 in the wet season and 27/07/2014 and 22/08/2014 in the dry season. The acid spill sampling took place on 06/06/2015.

Figure 3.5: Metal concentrations from water sample ICP-MS analysis for Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn, from seven sites along the Nyl River in Limpopo, South Africa, expressed in µg/l. Sampling periods were from 27/02/2014-02/04/2014 for the wet season and 27/07/2014-22/08/2014 for the dry season. The acid spill sampling took place on 06/06/2015. Common superscripts denounce significant differences (p<0.05).

Figure 3.6: A Canonical Discriminant Function Analysis (DFA) showing grouping of water samples for seven sites along the Nyl River system, Limpopo. The acid spill sampling (AS) took place on 06/06/2015. Sampling periods were from 27/02/2014- 02/04/2014 for the wet season (HFW) and 27/07/2014-22/08/2014 for the dry season (LFW).

Figure 3.7: A Canonical Discriminant Function Analysis (DFA) showing grouping of water samples for seven sites along the Nyl River system, Limpopo. Sampling periods were from 27/02/2014-02/04/2014 for the wet season (HFW) and 27/07/2014- 22/08/2014 for the dry season (LFW).

Figure 3.8: Metal concentrations from AM sample ICP-MS analysis for Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn, from five sites along the Nyl River in Limpopo, South Africa, expressed in µg/l. Sampling periods were from 27/02/2014-02/04/2014 for the wet season and 27/07/2014-22/08/2014 for the dry season. Common superscripts denounce significant differences (p<0.05).

Figure 3.9: A Canonical Discriminant Function Analysis (DFA) showing grouping of AM samples for five sites along the Nyl River system, Limpopo. Sampling periods were from 27/02/2014-02/04/2014 for the wet season (HF AM) and 27/07/2014-22/08/2014 for the dry season (LF AM).

Figure 3.10: A Canonical Discriminant Function Analysis (DFA) showing grouping of water and AM samples for seven sites along the Nyl River system, Limpopo. Sampling periods were from 27/02/2014-02/04/2014 for the wet season (HF) and 27/07/2014- 22/08/2014 for the dry season (LF). Dry season AMs are labelled LFAM, wet season AMs HFAM, dry season water LFW, wet season water HFW.

Figure 4.1: Metal levels from seven sites along the Nyl River collected for February (HF1), April (HF2), July (LF1) and August (LF2) for 2014. Red lines indicate SQGs (CCME, 2002) for Cd, Cu, Cr and Zn and the guidelines set out by MacDonald et al (2000) for Ni. Concentrations were determined by means of ICP-OES and are expressed in µg/g dry weight.

Figure 4.2: A Canonical Discriminant Function Analysis showing variation of Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn in sediment samples from February, April, July and August 2014. Concentrations were determined by ICP-MS from seven sites along the upper Nyl River.

LIST OF ABBREVIATIONS:

Al - Aluminium

AM - Artificial Mussel

ANOVA – Analysis of Variance

As – Arsenic

AW – Acid Spill Water

CaCO3 – Calcium Carbonate

CCB – Continuing Calibration Blank

CCV – Calibration Verification Standard

Cd – Cadmium

CF – Contamination Factor

Co – Cobalt

Cr – Chromium

Cu – Copper

DAM – Dry season Artificial Mussel

DFA - Discriminant Function Analysis

DOLT-4 – Dogfish Liver Tissue

DPD – Donkerpoort Dam

DW – Dry season Water

DWA – Department of Water Affairs

DWAF – Department of Water Affairs and Forestry

DWS – Department of Water Affairs and Sanitation

EF – Enrichment Factor

EIA – Environmental Impact Assessment

vii

Fe – Iron

FWSD – Freshwater Sediment

GC – Golf Course

HF – High Flow

Hg – Mercury

ICP-OES – Inductively Coupled Plasma Optical Emission Spectrometry

ICP-MS – Inductively Coupled Plasma Mass Spectrometry

ICVS – Internal Calibration Verification Standard

Igeo – Geo-accumulation Index

JAS - Jasper

KNO – Klein Nyl Oog

LF – Low Flow

LKSD-3 – Lake Sediment

LOD – Limit of Detection

Lu – Lutetium

MDD – Moorddrift Dam

Mn – Manganese

Ni – Nickel

NYL – Nylsvley Nature Reserve

Pb – Lead

PEL – Probable Effect Level

PLI – Pollution Load Index

Ppb – Parts per billion

Ppm – Parts per Million

viii

QA/QC – Quality Control/ Quality Assurance

Rh – Rhodium

SeQI – Sediment Quality Guidelines

STW – Sewage Treatment Works

TMEDA – N,N,N,N-Tetramethylethylenediamene

TWQR – Target Water Quality Range for Aquatic Ecosystems

V - Vanadium

WAM – Wet season Artificial Mussel

WRC – Water Research Commission

WW – Wet season Water

U – Uranium

Zn – Zinc

ACKNOWLEDGEMENTS

A sincere word of thanks to the following organisations and people:

The University of Johannesburg and National Research Foundation for your financial support without which this project would not have been possible.

Mrs E. Kroukamp at Spectrum Analytical Facility of the University of Johannesburg I value your training and assistance in completion of the ICP analyses.

Dr Ruan Gerber for guiding me through my stats and teaching me the patience that statistics requires.

Dr Richard Greenfield for mentoring me from start to finish and always going the extra mile to help.

Mr. Ryaz Musa, Mr. Nathan Baker, Ms. Kelly Dyamond and Mr. Gregg van Rensburg for assistance in fieldwork and laboratory analysis, support and friendship.

Mr. Beric Gilbert for methodological assistance and guidance.

Ms. Claire Edwards for editorial assistance and boundless friendship and support.

My colleagues in the Ecotoxicology Laboratory for all of your advice, friendship and for keeping me sane.

Family and Friends for being so understanding and for motivating me to always do my best. A special thanks to my parents, without you this wouldn’t have been possible.

Mr. Carinus Verster for your encouragement to follow my dreams and for sharing me with my MSc for two years.

This dissertation is dedicated to the late Mr C.E. Verster who was always in my corner and always supported my decisions.

SUMMARY

Metal pollution in aquatic systems is considered a serious environmental issue globally due to their non-biodegradable nature and they therefore accumulate in aquatic environments. Wetlands are vulnerable to this pollution as they are known to trap toxins, removing them from the water. The Nyl River floodplain is one of the largest and most ecologically significant wetlands in South Africa and has a Ramsar classification. The aims of this study were to determine metal contamination along the Nyl River system by means of artificial mussels (AM), sediment and water ICP-OES and ICP-MS analysis. The concentrations of Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn were determined at seven sites including Klein Nyl Oog (KNO), Donkerpoort Dam (DPD), Golf Course (GC), Sewage treatment works (STW), Jasper (JAS), Nylsvley (NYL) and Moorddrift Dam (MDD). The AMs accumulated higher levels of Al, Cr, Cu and Pb during the dry season compared to levels accumulated in the wet season. The levels of Co, Fe, Mn and Ni accumulated by the AMs were highest at KNO decreasing downstream. The concentrations determined from the spot water samples indicated that levels of Cr, Cu, Mn, Pb and Zn exceeded the target water quality range. For Cr, Cu and Zn, the levels of metals in the water were below levels recorded in previous studies. The levels of metals determined after a 28000L sulphuric acid spill in May 2015 into the Nyl River showed that the levels of Al, Co, Fe, Mn, Ni and Zn were higher after the acid spill. After the acid spill, levels of Cr and Pb decreased and levels of Cu and Cd remained similar. The results of the sediment metal analysis showed that KNO and DPD were nearly pristine according to the Contamination Factor (CF), Geo-accumulation Index (Igeo) and Enrichment Factor (EF). There was a gradual increase in metal concentrations moving downstream from DPD. The STW showed relatively increased levels of metals in the wet season but had some of the lowest metal concentrations in the dry season. JAS was found to have good sediment quality as it is located directly downstream of a wetland. The Nylsvley Nature Reserve indicated increased levels of metals for the wet and dry season leading to a CF classification of Considerable or Moderate contamination for most metals and an EF classification of Moderate and Moderately Severe contamination for most metals. The MDD had decreased sediment quality regarding metal concentrations. It is recommended that constant monitoring be done on the Nyl River Floodplain to ensure that it remains in a good condition in the future.

Chapter 1

1.1. General Introduction

The importance of wetlands in South Africa is highlighted by the increase of water pollution over the past decade, defying the efforts made by the Department of Water Affairs and Sanitation (DWS) to improve the quality of our natural water resources. South Africa is home to a unique set of pollution sources that can lead to the demise of our water resources in the near future. Among these are agriculture, mining, urban runoff, industrial runoff and high levels of raw or partially treated sewage. Wetlands serve many ecological functions to the environment including filtering, removal of toxins and flood attenuation and their value can therefore be seen as intrinsic (Vlok et al. 2006).

South Africa is home to thousands of ecologically significant wetlands of which the Nyl River Floodplain is one (Friends of Nylsvley, 2014). The Nyl River Floodplain located near Mookgophong, Limpopo is the largest ephemeral floodplain wetland in South Africa, covering more than 16000 ha when fully inundated (Friends of Nylsvley, 2014). The floodplain is home to multiple threatened and endangered species including the Roan antelope (Hippotragus equinus) and waterfowl including the critically endangered Bittern and the Striped Crake which is known to breed only in the Nylsvley Nature Reserve (NCC Environmental Services, 2015). The floodplain was awarded Ramsar status in 1997 due to its sheer size and ecological significance. The threat of two proposed opencast platinum mines looms over the upper Nyl River Floodplain, which is already experiencing the pressures of increasing urbanization, agriculture and industrialization (EScience Associates, 2013).

Metal contamination has been a popular topic of research in ecotoxicology studies due to the fact that most metals have potentially disastrous effects in concentrations above threshold levels. Metal pollution has occurred since the benefits of the first metal ores were discovered. The first recorded case of metal toxicity was by Hippocrates approximately 300BC (Bateman, 1999). Metals are released into the environment by various sources including: fossil fuels, waste incineration, mining processes and many manufacturing processes (Krishnan et al. 1993). There has been a multitude of debates about the use of the term ‘heavy metal’ as it is a term that has no proper

definition (Bately, 2012; Chapman, 2012). Therefore for the purposes of this study the term shall be avoided and the metals contained in this study will be referred to as metals.

The levels of metals in a system can be influenced by many factors such as pH and total hardness of water as well as the synergistic interactions of different metals when in contact with one another (DWAF, 1996). Metals occur in different forms according to the chemical make-up of the water. Some forms of metals such as aluminium occur in harmless concentrations under neutral conditions, but become soluble and toxic in acidic conditions (DWAF, 1996). Certain metals are naturally occurring in the system and are necessary to sustain life, such as iron and zinc. These metals start negatively influencing species when present in water in concentrations exceeding threshold levels (DWAF, 1996).

Artificial Mussels (AMs) are passive sampling devices developed for replacing biological indicators in the determination of metal concentrations in water resources. AMs have been extensively validated for use in marine environments and has also recently been used in determining metal concentrations in freshwater systems (Kibria et al. 2010). Artificial mussels consist of a Perspex tube containing Chelex-100 beads and Milli-Q water enclosed by two semi-permeable polyacrylamide gel plugs (Wu et al. 2007). The artificial mussels function by means of osmosis through the semi- permeable membranes. The bioavailable fraction of metals are then bound to the chelating resin beads, which become saturated within 4-6 weeks of being submerged in the water body in question (Degger et al. 2011).

Though metals are commonly tested in water, they also accumulate in sediments (Greenfield et al. 2007). Wetland sediment studies are crucial in the protection of these ecologically significant aquatic ecosystems. The determination of metal contamination in wetland soils is important due to the fact that contaminants can be imbedded deep into wetland soils during flooding events. Metals in sediments can either be naturally occurring or anthropogenically introduced. The determination of metal contamination in sediments is crucial as metals trapped in wetland soils can be re-suspended during flooding events potentially releasing high levels of toxic metals into the aquatic system (Greenfield et al. 2007).

1.2. Hypotheses, Aims and Objectives 1.2.1. Hypotheses During the study four working hypotheses were tested. These hypotheses were: i. The water quality of the Nyl River system has declined over the past decade. ii. The Nyl River floodplain is threatened by the inflow of polluted water. iii. The sewage treatment plant is the main contributor of metal pollution in the Nyl River system. iv. Artificial mussels can be used to monitor bioavailable metals within freshwater ecosystems.

1.2.2. Aims In order to accept or reject the hypotheses the following aims were set: i. The determination of spatial and temporal differences in metal contaminants in water and sediment from the origin of the Klein Nyl River (Modimolle) to the Moorddrift Dam (Mokopane). ii. Compare water and sediment quality conditions in the system now to those found 10 years ago in a study by Vlok et al. (2006).

1.2.3. Objectives In order to achieve the aims the following objectives were set: i. Determine the bioavailable metal levels in the water via artificial mussel technology and ICP-MS. ii. Evaluate artificial mussels for use in freshwater aquatic systems by comparing accumulated metal concentrations in the artificial mussels and filtered water samples from the different sampling sites. iii. Measure the ecological risk the sediment poses by means of Aqua Regia acid extraction and ICP-OES analysis and the application of different contamination indices. iv. Determining the full scope of metals in water for each of the sampling periods by means of ICP-MS.

v. Comparing the levels of metals determined in this study to levels from 10 years ago.

1.3. Chapter Outline

Chapter 1: General Introduction

This chapter outlines the rationale of the study with focus on the general problem statement, including the hypotheses, aims and objectives of the study.

Chapter 2: Literature Review

A literature review of the study area including maps. Full descriptions of the study sites including photographs of the sites. This chapter includes an overview of the methods used in the study.

Chapter 3: Water and Artificial Mussels

This chapter gives a brief introduction of water and artificial mussels. It then follows with a comprehensive description of the materials and methods followed along with quality assurance measures. The results of the study and a comprehensive discussion is then included.

Chapter 4: Sediment and Ecological Risk Assessment

This chapter discusses the sediment and ecological risk assessment section of the study. A brief introduction is followed by a comprehensive discussion of the materials and methods used in the study. Thereafter the results of the ICP-OES analysis are included in which the concentrations are represented followed by the risk assessment data. Both sections are discussed in detail.

Chapter 5: Conclusions and Recommendations

This section contains concluding remarks on the study in its entirety along with some recommendations to ensure the future of the floodplain.

Chapter 6: References

This chapter includes the literature cited throughout this study.

Chapter 2

2.1. Study Background

The Nyl River originates near Modimolle, Limpopo and flows through the towns of Modimolle and Mookgophong (Greenfield et al. 2007). It continues through the town of Mokopane from where it is renamed the Mogalakwena River. From Mokopane, the Mogalakwena River flows North East towards the Botswana border where it meets the Limpopo River. The Nyl River is dammed upstream of Modimolle to form the Donkerpoort Dam and just upstream of Mokopane to form Moorddrift Dam. The Nyl River floodplain is one of the largest floodplain wetlands in South Africa (McCarthy et al. 2011). It acts as habitat to multiple threatened and endangered species and acts as breeding grounds to many bird species. The abundance of avian species in the area was one of the leading factors leading to its Ramsar accreditation (Haskins and Kruger, 1997).

The importance of the Nyl River floodplain is highlighted by an increase in flooding events in the area which can indicate that the wetland is not performing optimally (Vlok et al. 2006). The Nyl River and Nyl River floodplain are contained in the Waterberg area which is a registered Biosphere Reserve. This fact further increases the local and international importance of this river and the floodplain which it forms part of.

The Nyl River has been studied in recent years comprehensively by the Water Research Commission (WRC) (Vlok et al. 2006) and by environmental consultants for the purposes of Environmental Impact Assessments (EIA) (AED, 2012). The interest in this system is due to its sheer size and high biodiversity.

In the WRC study focus was placed on the contamination of water and sediments with metals (Vlok et al. 2006). In the WRC study it was determined that the levels of metals in the river were of little concern at the time of sampling (2001/2002). The report determined that the metal levels generally remained below the Guidelines for Aquatic Ecosystems (DWAF, 1996). At the time of the study the water was determined to be soft to medium regarding hardness and the pH of the water was mostly neutral throughout the study and system (Vlok et al. 2006). The concentrations of metals

measured were also found to be high throughout the system which indicated that metal concentrations in the water and sediment were naturally high (Greenfield, 2004).

A specialist study on the surface water of the Nyl River system was conducted by the consulting firm AED for an Environmental Impact Assessment (EIA) for a Platinum mine that had been scheduled to open (AED, 2012). It was determined that the Nyl Floodplain was threatened by the increase in anthropogenic impacts which could alter the balance in the inflow and release of water (AED, 2012). A metal assessment in water conducted by AED (2012) revealed that the quality of the water was exceptionally good for sites downstream of the Nyl Floodplain. Unfortunately the study by AED only included sites downstream of the Nyl Floodplain and therefore direct comparisons are not possible.

2.2. Study Sites

Sampling sites for the study were located in the upper reaches of the Nyl River system located from Modimolle to Mokopane, Limpopo (Fig 2.1). The sites selected include seven sites chosen for their positions relative to possible sources of pollution. These sites also correlate to the sites used in the WRC study conducted in 2006 for the purposes of comparison (Vlok et al. 2006). The names, abbreviations and GPS coordinates of each site can be found in Table 2.1.

Site 1 or Klein Nyl Oog (KNO) (Fig 2.2: A) was located approximately 2 km below the origin of the Klein Nyl River. The sampling was conducted just upstream of a weir on a cattle farm. The site had noticeable sedimentation and an abundance of aquatic macrophytes such as Carex austro-africana, and Cladium marsiscus (Gerber et al. 2004) as can be seen in Fig. 2.2. At this point in the river there are limited impacts and the site is expected to have relatively low metal contamination.

Site 2 or Donkerpoort Dam (DPD) (Fig 2.2: B) was located just downstream of the Donkerpoort Dam on the Klein Nyl River. The water was clear and there was a clear presence of Carex austro-africana at this site. The DPD weir is the main alteration and possible impact at this point.

Site 3 or Golf Course (GC) was located downstream of the Koro Creek golf course. The river flows through the golf course and then into the town of Modimolle. This site had little flowing water throughout the study period with an abundance of Phragmites

mauritianum and can be classified as a wetland site rather than a riverine site (Fig 2.2: C). The golf course could be altering nutrient levels in the water through the addition of fertilizers to the course.

Site 4 or Sewage Treatment Works (STW) is located directly next to the Modimolle sewage treatment facility effluent discharge point (Fig 2.2: D). During the sampling period the STW wasn’t functioning optimally with only certain parts of the system in working condition. The STW had a properly functioning solid waste removal system at the time of sampling, however the flocculation procedures were not taking place and the partially treated sewage was released into the river. During high flow periods, the incoming sewage exceeded the capacity of the facility causing raw sewage to spill directly into the Nyl River. This riverine site had a low density of aquatic macrophytes and clear evidence of erosion. Water turbidity had visibly increased compared to sites upstream of the STW.

Site 5 or Jasper (JAS) is around 2 km downstream of the STW (Fig 2.2: E). The Nyl River flows through a wetland just before JAS which could have a purifying effect on the water from the STW if the wetland is functioning properly. This site has an abundance of aquatic macrophyte species including Cyperus sexangularis, and Phragmites mauritianum. The turbidity of the water is visibly reduced compared to the water at the STW site.

Site 6 or Nylsvley Nature Reserve (NYL) is located in the Nylsvley Nature Reserve at the Jacana bird hide (Fig 2.2: F). This site is located in the floodplain section of the wetland. The site had the highest abundance and diversity of aquatic plants such as Phragmites mauritianum, Nymphaea mexicana, and Veronica anagallis-aquatica. The two latter species are both weeds and were removed from the site after the first sampling trip. The site also had diverse bird species especially waterfowl utilising the water resource. The substrate at this site consisted mainly of dense floating organic material.

Site 7 or Moorddrift Dam (MDD) is located approximately 50km downstream of NYL. Samples were taken just upstream of the weir (Fig 2.2: G). Water levels in the dam remained high throughout the wet and dry sampling periods. The dam is located on the Moorddrift Dairy Farm which no longer houses any cattle but acts as a game reserve and lodge. Water from the Moorddrift Dam is used to supplement water supply to neighbouring municipalities.

Table 2.1: Locations, abbreviations and GPS coordinates of study sites

Site Site Name Abbreviation Southern Eastern

Number Co-ordinate Co-ordinate 1 Klein Nyl Oog KNO 24° 42’ 967” 28° 14’ 542”

2 Donkerpoort Dam DPD 24° 40’ 542” 28° 20’ 019” 3 Golf Course GC 24° 41’ 697” 28° 25’ 074” 4 Sewage Treatment STW 24° 42’ 335” 28° 25’ 747”

Works

5 Jasper JAS 24° 42’ 536” 28° 28’ 786” 6 Nylsvley NYL 24° 38’ 960” 28° 41’ 445” 7 Moorddrift MDD 24° 15’ 175” 28° 58’ 521”

Figure 2.1: Map of the Nyl River, Limpopo Province, South Africa with the sampling sites indicated. Sampling sites include the Klein Nyl Oog (KNO), Donkerpoort Dam (DPD), Golf Course (GC), Sewage Treatment Works (STW), Jasper (JAS), Nylsvley Nature Reserve (NYL) and the Moorddrift Dam (MOOR). Triangles indicate the towns of Modimolle and Mookgophong.

2.3. Experimental Design

When undertaking scientific studies to determine the extent of human impacts on an aquatic environment, the experimental design must encompass an assessment of multiple factors in the system. According to Sanders (1997), metals occur in four main reservoirs in an aquatic ecosystem. These include: suspended particles, surface water, interstitial water and sediment. A thorough study of more than one of these metal reservoirs is required to form a clear picture of the integrity of the system. This study included an assessment of the in situ water parameters and samples were taken for laboratory assessments to determine the metal content in the surface water and sediment.

Water

When considering environmental pollution holistically, it is evident that inland water systems such as rivers, lakes and wetlands receive pollution directly by industry and municipalities releasing wastewater directly into rivers, but that they are also subject to pollutants from the air and sediment through various natural or man-made processes (Echols et al. 2009). Among these pollutants are metals that end up in water systems through these various processes (Sanders, 1997). It is because of the toxicity of metals in high concentrations that certain metals were placed on the US EPA Priority Pollutant List (Echols et al. 2009). Among the metals on the list are: Cd, Cr, Cu, Ni, Pb and Zn (Echols et al. 2009).

When studying the metal content of water it is important to note that the results acquired will only be an indication of the contaminants in the water at the precise moment of sampling (Prosi, 1979). Conditions may differ hourly, daily and due to individual pollution events which makes the use of spot water sampling for metal analysis problematic (Phillips, 1977).

Artificial Mussels

Artificial Mussels (AMs) are passive water sampling devices that can accumulate toxic metals from the environment (Hossain et al. 2015). The AM is successful at accumulating metals in environmentally relevant concentrations in freshwater, brackish and marine environments (Hossain et al. 2015). AM technology determines

the bioavailable metals in water in a time integrated fashion making spatial and temporal comparison a possibility (Wu et al. 2007).

The AM technology has been validated for use in water systems from Australia (Kibria et al. 2010), Iceland (Leung et al. 2008), Hong Kong (Wu et al. 2007), Portugal (Gonzalez-Rey et al. 2011), Scotland (Leung et al. 2008) and South Africa (Degger et al. 2011). A summarised table of some of the previous literature can be seen in Table 2.2. It is evident that AMs are successful at accumulating Cd, Cr, Co, Cu, Fe, Pb, Ni, Mn, Hg, U and Zn (Hossain et al. 2015). These studies form part of the ‘Global AM Watch Programme’ which uses this standardised method of monitoring metal pollution in water (Hossain et al. 2015).

Sediment

Wetland sediment studies considering metal contamination are crucial in determining the condition of an aquatic ecosystem. Metals and sediment interact in different ways: firstly sediment traps metals physically, especially in wetland sediment by slowing the water and allowing impurities to settle out (Sanders, 1997). Contaminant particles such as metals can also interact with sediment by chemically binding to sediment particles (Buykx et al. 2002). This results in metals not being detected in water metal analysis.

Due to the deposition of metal-bound suspended matter in sediment over time, core samples can reveal the historical metal pollution in a river system at different depths (Wittman and Forstner, 1976). The analysis of sediments for metal contamination can reveal sites with extensive metal contamination which require constant monitoring and can aid in identifying the sources of metal pollution in a system by determining spatial differences in metal contamination (Wittman and Forstner, 1976). Sediment studies can also give information on the enrichment of the sediment with metals that is relative to the specific river by including reference metal concentrations (Sanders, 1997).

When determining metal contamination in sediments there are multiple techniques that effectively determine metal concentrations, each method having its own benefits. Metal concentrations can be determined by an acid extraction of the full scope of metals from a sample (Sanders, 1997). This method doesn’t distinguish between the background levels and the anthropogenically introduced metals (Phillips, 1977).

Table 2.2: A review of the history of AMs (Adapted from Claassens et al. 2016)

Study Metals Objectives Findings Developed a metal pollution monitoring device that regulates uptake and release. AM indicative of spatial and temporal metal Wu et al. Cd, Cr, Cu, Determined uptake and release rates of levels and accumulate at levels relevant to 2007. Pb, Zn metals. Undertake field evaluation of the the environment. device. Compare differences both spatial and Sample sizes must be greater than 5 per Leung et Cd, Cr, Cu, temporal in the accumulation of metals in site. AMs successfully accumulated metals al. 2008. Pb, Zn AMs and transplanted M. edulis. from the environment. Determining whether AMs are useful in Under South African conditions AMs were Degger et Cd, Cr, Cu, determining metal concentrations in successful. AMs and mussels accumulate al. 2011. Pb, Zn. South African marine conditions. of metals in a similar pattern. Compare the accumulation metals in AMs useful when mussels aren’t available, Gonzalez- Cd, Cr, Cu, AMs to metals accumulated in M. in case of significant intra and interspecific Rey et al. Pb, Zn. galloprovincialis along the Portuguese discrepancies, changes in physico- 2011. coast. chemical parameters. Evaluating AMs in to determining hot Kibria et Cd, Cu, AMs were successful at determining spots of pollution in Golburn Murray al. 2012. Hg, Pb, Zn. pollution hot spots. Waterway. Hossain AMs can be used as a global monitoring Cd, Cr, Cu, Develop a guide to producing AMs in a et al. tool for metal pollution in aquatic systems: Pb, Zn. standardised manner 2015. freshwater and marine. As, Cd, Cr, Claassens Co, Cu, To evaluate AMs for use in freshwater AMs successful in freshwater systems. et al. 2016 Pb, Mn, Ni, systems. in the Koekemoer Spruit AMs accumulate As at significant levels. U, V, Zn

There are however different sediment indices that incorporate background levels and give a relative contamination factor (Muller 1979; Thomilson et al. 1980; Li et al. 2013). Another method of determining metal pollution in sediment entails the sequential extraction of metals from sediment samples (Sanders, 1997). During this process the natural and anthropogenic metal levels in the sediment can be determined. A notable downfall of this method is that sample preparation can lead to variation in the results (Tessier and Campbell, 1990).

Analytical Techniques

In Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) analysis, samples are subjected to temperatures high enough to cause the atomization and also ionization of the sample (Boss and Fredeen, 2004). Once in their excited state, ions decay and release energy in the form of photons. The intensity and wavelength of the light emitted is measured and these factors then determine which elements are present at which concentrations (Boss and Fredeen, 2004). One advantage of using the ICP-OES technique is that it uses a range of energy levels which enables the instrument to analyse for multiple elements simultaneously. This is unfortunately also

the main downfall of the ICP-OES. Due to the high energy range and the multitude of elements being analysed concurrently, there can be interferences from other elements which could alter the measurements of the element in question (Boss and Fredeen, 2004).

Water samples were taken at each of the sites for metal analysis on the ICP-MS (Greenfield, 2004). ICP-MS differs to ICP-OES in the sense that the ICP-MS uses the mass to charge ratio of the atoms present to distinguish between elements and as such determines concentrations present instead of using the radiation released from exciting the different elements in the sample (Boss and Fredeen, 2004).

2.4. Conclusion

Metal contamination is a major environmental issue that requires scientific attention due to the fact that metals are not biodegradable and therefore constantly accumulate in a system. There are many methods of determining metal contamination including water, sediment and bio-indicator studies, as well as many analytical procedures to analyse samples including ICP-OES and ICP-MS. Each method has its own positive and negative attributes and cannot be used as a standalone method of determining metal pollution. Essentially it is necessary to incorporate various methods to acquire a full understanding of the metal pollution in a certain aquatic ecosystem.

Chapter 3

3.1. Introduction

The use of live organisms as biological indicators (bio-indicators) in monitoring environmental pollution has become standard practice over the last few decades due to their ability to bioaccumulate pollutants from aquatic environments. This practice gives an indication of pollutant concentrations on a temporal scale seeing as other methods, such as spot water sampling, give only a snap shot of the conditions in the water body (Hellawell, 1986). The use of bio-indicators can however be problematic due to variations in physiological regulation, organism availability, inter-species differences and ethical considerations (Markert et al. 2003).

These factors have led to the development of a passive sampling device called the Artificial Mussel (AM) by Wu et al. in 2007. Artificial mussels contain Chelex-100, a chelating resin that is the ingredient which passively accumulates the bioavailable fraction of metals from the water over time. AM technology was originally developed and evaluated under marine conditions, however recent studies have used AMs to evaluate metal contamination in freshwater systems (Kibria et al. 2010; Hossain et al. 2015; Claassens et al. 2016). According to previous studies, AMs are less affected by salinity and temperature than bio-indicator species and can as such be compared between systems (Wu et al. 2007).

Extensive research on AMs has proven that they are able to accumulate environmentally relevant concentrations of Cd, Cr, Cu, Pb and Zn. Wu et al. (2007) determined that the resin contained in the AMs accumulates and releases metals as the levels in the water rise and fall. A recent study has also shown that AMs are effective at accumulating anionic arsenic from the water (Claassens et al, 2016).

On the 16th of May 2015 the Nyl River was exposed to a spill of 28000 L of sulphuric acid (SABC, 2015). A truck carrying the acid overturned where the river flows through the town of Modimolle. Emergency services used hoses to wash the acid from the road into the Nyl River. As a result of the spill, many fish and other aquatic species died and some farmers’ boreholes had decreased pH levels which affected their crops and cattle (ENCA, 2015).

The present study aims to further evaluate the use of AMs in determining metal contamination in freshwater environments through comparisons between the full scope of metals present in both the water and AMS as well as determine the effect that the acid spill had on metal concentrations in the water of the Nyl River.

3.2. Materials and Methods

Sampling of water took place twice for the wet season and twice for the dry season. The exact sampling dates can be seen in Table 3.1. The water samples were taken a month apart for the high and low flow seasons as to coincide with the deployment and retrieval of the AMs.

Table 3.1: Sampling dates of water, AMs and In situ parameters for determining metal concentrations in the Nyl River system.

Sampling Dates Water AMs In situ 27-29 February 2014 Collected Deployment Not measured 1-2 April 2014 Collected Retrieval Measured 27-28 July 2014 Collected Deployment Measured 21-22 August 2014 Collected Retrieval Measured 6 July 2015 Collected n/a Measured

3.2.1. In situ water quality parameters

The physico-chemical parameters of the water were determined during each sampling trip using a Eutech multi-probe water quality meter following similar procedures to Fisher (2011). The parameters measured include pH, temperature (ºC), oxygen saturation (% and mg/L) and electrical conductivity (µS/cm). The instrument consisted of three different probes that each measured a specific parameter. Each probe was calibrated beforehand according to the supplied manual (Greenfield, 2004).

3.2.2. Water

Water sample collection

Water samples were collected in the field for each site on each sampling trip using clean acid washed (HCl) polyethylene bottles (Fisher, 2011). Water samples were

immediately frozen for transportation back to the University of Johannesburg Spectrum Analytical Facility for preparation and analysis.

Water sample preparation and analysis

Water samples were filtered using a vacuum filtration system and 0.45 µm pore size cellulose-nitrate filter paper. Samples were acidified to 1% nitric acid using 65% Suprapur nitric acid (Martin et al. 1994). The prepared water samples were then analysed for Al, Cd, Cr, Co, Fe, Mn, Ni, Pb and Zn using Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The total hardness of the water was also determined by means of a Spectroquant Pharo 100 spectrophotometer using a standardised Merck test kit.

Analytical Standards and Quality Assurance

The ICP-OES analysis was conducted using the Perkin Elmer Spectro Arcos FSH 12 ICP-OES. The analysis included calibration standard concentrations of 5 ppb, 50 ppb, 100 ppb, 500 ppb, 1 ppm, 10 ppm, 20 ppm and 40 ppm. After calibration, the peaks and background levels of each wavelength and each metal were manually corrected to avoid interference by other elements (Boss and Fredeen, 2004). Outliers were removed to insure an accurate standard curve. Certified reference materials were added to the analysis for the purposes of Quality Assurance/Quality Control (QA/QC). Dogfish Liver Tissue (DOLT-4) (National Research Council Canada); Lake Sediment (LKSD-3) (CANMET) and Freshwater Sediment (FWSD) (ACLASS) were analysed with the samples (Table 3.2). An internal calibration verification standard (ICVS) was also added to the analysis for QA/QC purposes. Four wavelengths for each metal were analysed with the ICP-OES (Boss and Fredeen, 2004). Metal concentrations were then also determined by means of ICP-MS.

The ICP-MS analysis was conducted using the Perkin Elmer Nexion X-series ICP-MS (Fisher, 2011). The ICP-MS analysis included calibration standards of 0.5 ppb, 1 ppb, 5 ppb, 10 ppb, 20 ppb, 40 ppb, 60 ppb and 80 ppb. It also included an ICVS, Continuing Calibration Verification Standard (CCV), Continuing Calibration Blank (CCB) and two internal standards namely rhodium (Rh) and lutetium (Lu). The detection limits (LODs) of the analysis are indicated in Table 3.3. LODs were determined using the following equation:

= (3 × . )/

The recoveries of the internal standards𝐿𝐿𝐿𝐿𝐿𝐿 ranged𝑆𝑆𝑆𝑆 from𝑥𝑥 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 97.5%-114.4% for Rh and 98%- 113.6% for Lu. The recoveries of these elements throughout the analysis are indicated in Fig. 3.1.

Table 3.2: Recoveries of CRMs from ICP-MS analyses including Dogfish Liver Tissue (DOLT-4), Lake Sediment (LKSD-3) and Freshwater Sediment (FWSD) analysed for QA/QC purposes.

Metal DOLT - 4 LKSD - 3 FWSD Obtained Certified Recovery Obtained Certified Recovery Obtained Certified Recovery values values (%) values values (%) values values (%)

Cd 24.42 24.30 100.51 n/a n/a n/a n/a n/a n/a

Cu 34.03 31.20 109.05 35.99 35.00 102.82 14.86 16.10 92.28

Fe 1697.94 1833.00 92.63 n/a n/a n/a 18283.61 17100.0 106.92

Mn n/a n/a n/a 1507.28 1440.00 104.67 171.81 183.00 93.88

Ni 1.04 0.97 107.28 n/a n/a n/a 14.97 17.50 85.56

Zn 146.21 116.00 126.04 152.98 152.00 100.64 46.48 69.90 66.50

Table 3.3: ICP-MS LODs for water sample analysis. LOD numbers are expressed in µg/L.

Sy.x Slope LOD Half LOD Al 4.767 0.987 14.491 7.245 Cr 0.463 1.007 1.379 0.690

Fe 0.551 1.003 1.647 0.823 Mn 0.447 1.001 1.339 0.670 Co 0.381 1.006 1.136 0.568

Ni 0.331 1 0.993 0.497 Cu 0.419 1.008 1.246 0.623 Zn 0.692 0.997 2.082 1.041 Cd 0.261 1 0.782 0.391 Pb 0.973 0.997 2.930 1.465

Table 3.4: ICP-MS LODs for AM analysis analysed for QA/QC purposes. LODs are expressed in µg/L.

Sy.x Slope LOD Half LOD

Al 1.770 0.993 5.348 2.674 Cr 0.521 1 1.563 0.782

Fe 0.184 1 0.551 0.275 Mn 0.311 0.999 0.935 0.467

Co 0.393 1.01 1.166 0.583 Ni 0.455 1.013 1.347 0.674 Cu 0.618 1.016 1.824 0.912

Zn 0.831 1.008 2.472 1.236 Cd 0.313 1 0.939 0.470 Pb 0.881 1.008 2.622 1.311

Internal Standards 20.0%

15.0%

10.0% Rh 103 % Recovery 5.0% Lu 175

0.0% 1 3 5 7 9 12 14 16 18 20 22 24 26 28 30 32 34

Row Index Number

Figure 3.1: Recoveries of internal standards during ICP-MS analysis for Rhodium and Lutetium expressed in % recovery of each element. Row Index Numbers indicate the positions of the samples in the instrument.

3.2.3. Artificial Mussels Artificial mussels (AMs) are semi-permeable passive sampling devices used for determining bioavailable metal levels in a water body over time. Figure 3.2 indicates the structure of a constructed AM as developed by Wu et al. (2007) for use in marine environments. The AM construction has been adapted for freshwater environments by using Milli-Q water as a replacement for artificial seawater. AMs consist of a 25 mm Perspex tubing containing 0.2 g Chelex-100 resin beads and a 1 cm Perspex spacer suspended in Milli-Q water. These elements are enclosed between two semi- permeable polyacrylamide gel plugs. The plugs consist of a mix of acrylamide to N,N- methylene-bis-acrylamide, with ammonium peroxodisulfate and N,N,N’,N’- tetramethylethylenediamine (TMEDA). The construction of the AM is set out graphically in Fig. 3.3.

Once the AMs were constructed, they were placed in Milli-Q water until deployment into the aquatic environment (Hossain, 2015). Ten AMs were deployed at each site by fastening them to the sides of plastic baskets using cable ties. The baskets were tied together and then tied to a permanent structure to protect the AMs and keep the basket in place. The AMs were submerged in the waterbody and left for four weeks for the AMs to reach their saturation point (Hossain, 2015). Sampling was conducted for one wet season (February to April 2014) and one dry season (July to August 2014).

The saturated AMs were collected from the waterbody and placed in buckets containing water from the site for transportation back to the laboratory. The AMs were removed from the buckets and the biofilm was removed as thoroughly as possible. The contents of each AM was then removed and the Chelex-100 beads were filtered from the rest of the contents using a sintered glass vacuum filtration system and 0.45 µm pore size cellulose nitrate filter paper. The Chelex-100 beads from each individual AM from each site were placed in glass conical flasks in 20 ml 6 M nitric acid. The conical flasks were closed using Parafilm to prevent evaporation and volatilization of metals from the flasks. The flasks were placed on a ‘shaker’ for 24 hours to aid the detachment of metals from the Chelex-100 beads (Hossain et al. 2015).

After the detachment process each individual sample was filtered again to separate the Chelex resin beads from the acid solution. The acid solution was diluted to 50 ml volumetrically and stored in 50 ml Falcon tubes in the refrigerator until analysis. The

AM samples were analysed for metals on the ICP-OES and ICP-MS instruments following the same protocol as discussed in section 3.2.2.

Figure 3.2: A constructed Artificial Mussel as designed by Wu et al. (2007) for marine metal pollution studies. The AMs used in this study have been adapted for freshwater use by replacing artificial seawater with Milli-Q water.

Perspex tubing covered on one end with Parafilm.

Gel Construction

Solution of 1:30 Dissolve 10 g Ammonium acrylamide to N,N- Wait 5minutes for gel to Add spacer and 0.2 g of peroxodisulfate in 100ml methylene-bis-acrylamide polymerize Chelex-100 Milli-Q water with Milli-Q water

Add Milli-Q water until 4 ml Pippetted into Place gels in milli-Q water Add 160 µl to the tubing spacer and beads are perspex tubing to swell to size (1hr) submerged

Push gel from another Add 40 µl TMEDA to the tube into tube containing tubing spacer and chelex-100

Keep constrcted AM in Milli-Q water until use

Figure 3.3: A flow diagram representation of the process of constructing an AM as described by Hossain et al. (2015).

3.2.4. Statistical analysis

Raw data was sorted and the respective method blanks were subtracted from each reading. The AM data was converted to µg/g units by using the following formula (Fisher, 2011):

[ ] / = ([ ]( . ) ) × ( / ) /1000 −1 Water𝑀𝑀 data𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 w𝜇𝜇𝜇𝜇as 𝑔𝑔expressed𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 in𝑟𝑟 µg/𝜇𝜇𝜇𝜇L 𝐿𝐿after− analysis,𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 therefore𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 blanks𝑑𝑑𝑑𝑑𝑑𝑑 𝑚𝑚 were𝑚𝑚𝑚𝑚𝑚𝑚 subtracted from the concentration given and no further changes were required. Once data was converted, descriptive statistics were determined using IBM SPSS v. 22.

All data was normalised by log transformation and One-Way ANOVA with Tukey Post Hoc Test were performed to determine significant differences (p<0.05) between sites for AMs (van Emden, 2008). A Student t-test was performed to determine significant differences between water samples per site and season (van Emden, 2008). A bivariate correlation with Spearman’s Correlation test was performed to identify similar trends in metal concentrations between AMs and water samples (van Emden, 2008).

Discriminant Function Analyses (DFA) were performed on the data to determine whether AMs and water from the same sites and seasons showed similar metal concentrations or similar trends (Gerber, 2015).

3.3. Results

3.3.1. In situ water quality parameters

Water quality parameters were measured for all sampling trips except the first low flow sampling trip. The data in Fig. 3.4 shows a clear increasing trend in the conductivity for each of the sites except for KNO which stayed relatively constant throughout the sampling period. The conductivity for KNO ranged from 27.2-51.9 µS/cm which was the lowest of all sites. STW had the highest conductivity ranging from 144.1-817 µS/cm. Sites downstream of where the acid spill occurred, except for NYL, have increased levels of conductivity compared to previous sampling seasons.

The pH levels recorded in the system ranged from 6.2-8.4 and were lower during the dry seasons compared to levels recorded in the wet season. The acidity levels recorded after the acid spill were not severely affected due to the addition of lime as a rectifying measure.

The lowest measurements were taken at MDD in the wet season (1.9 mg/L). The highest O2 levels were measured at JAS in August 2014 (15.8 mg/L).

Conductivity pH 1000 10 3.14 3.14 800 7.14 8 7.14

S/cm) 8.14 8.14 µ 600 6.15 6 6.15 pH 400 4

200 2 Conductivity(

0 0

GC GC KNO DPD STW JAS NYL MDD KNO DPD STW JAS NYL MDD Site Site

Oxygen Saturation 20 3.14 7.14 15 8.14 6.15 10 (mg/L) 2 O 5

GC KNO DPD STW JAS NYL MDD Site

Figure 3.4: In situ water quality parameters including conductivity, pH and oxygen Saturation measured in the Nyl River system for 27/02/2014 and 02/04/2014 in the wet season and 27/07/2014 and 22/08/2014 in the dry season. The acid spill sampling took place on 06/06/2015.

3.3.2. Water

The levels of metals detected in water samples are represented graphically in Fig. 3.5. Statistically significant differences identified by means of One-way ANOVA analysis are indicated on the graphs where common superscripts denounce significant differences (p<0.05).

The levels of Al ranged from 25.1495-367.4903 µg/L with the highest levels occurring at MDD after the acid spill took place. The lowest levels of Al for the wet and dry seasons were from JAS and DPD for 2014 and JAS for 2015. Levels of Al were notably higher in the dry season of 2015 for most sites including those upstream from where the spill occurred, however there is a general increasing trend for Al for sites downstream of the acid spill location. Al levels at GC were significantly higher than levels at DPD for the wet season of 2014. Al levels were found to be significantly higher at NYL compared to MDD in the dry season of 2014.

Cadmium concentrations ranged from 0-0.7551 µg/L and were generally below detection in the dry seasons except for MDD. Levels of Cd were also below detection for GC and STW for the 2014 wet season. No significant differences were found between sites and seasons for this metal.

The levels of Cr ranged from 0.1202-9.5208 µg/L and were generally lower for the 2015 dry season than the previous years. Levels of Cr showed an increasing trend from the origin of the river moving downstream for the dry season of 2014. The trend of Cr for the 2015 dry season showed levels lower than the previous sampling trips but in the same trend as the 2014 wet season. Significant differences were identified between the wet and dry seasons for STW. STW was also found to have significantly higher concentrations than DPD, JAS, and MDD for the dry season of 2014.

Cobalt concentrations ranged from 0-9.0999 µg/L with many of the sites having levels below detection. The highest concentrations were detected at STW after the acid spill for the 2015 dry season. The levels of Co were below detection for all sites except STW and GC in the dry season of 2014 and were below detection for all sites except GC, STW and MDD for the 2015 dry season. Cobalt levels for STW after the acid spill were notably higher than any other site or any other season.

Copper levels had a range of 0.6065-11.0087 µg/L with DPD from the 2015 dry season having the lowest concentration and GC from the 2014 wet season having the highest concentration. Levels of Cu after the acid spill were lower than previous seasons except for STW and MDD. Student t-tests determined that DPD was significantly higher than JAS of the 2014 wet season. It was also determined that STW was significantly higher than DPD and significantly lower than JAS from the 2014 dry season.

Levels ranged from 34.8587-5006.985 µg/L. The lowest concentrations were found at KNO which is located close to the origin of the river. The highest levels were discovered at STW after the acid spill for the 2015 dry season which were notably higher than any other site or season. STW was discovered to contain significantly higher levels of Fe than KNO and DPD for the 2014 wet season.

The concentrations of Mn ranged from 0.1643-660.7442 µg/L and the lowest levels were from the 2014 dry season for NYL. The highest levels were detected at STW after the acid spill in the 2015 dry season. Manganese levels from KNO for the 2014 dry season were found to be significantly higher than MDD, NYL, JAS and GC from that season. KNO wet season 2014 was significantly lower than KNO dry season 2014. MDD had significantly higher concentrations of Mn than KNO for the 2014 wet season. GC differed significantly between the wet and dry seasons of 2014 and also had significantly lower levels of Mn than MDD for the 2014 wet season. Though not statistically significant, the levels of Mn found at STW for both dry seasons were notably higher than other sites and seasons.

Nickel levels had a range of 0.4795-10.2498 µg/L with DPD for the 2014 dry season having the lowest levels and NYL for the 2014 wet season having the highest levels. The levels of NYL for 2014 dry season were discovered to be significantly higher than KNO, DPD, GC, STW, JAS and MDD for that season. Nickel levels for STW were significantly higher than DPD for the 2014 dry season. Though not statistically significant, the levels of Ni were increased for the 2014 wet season for NYL and for the 2015 dry season for STW.

The concentrations of Pb detected by ICP-MS ranged from 0.1777-11.2702 µg/L with DPD from Dry’15 containing the lowest levels and MDD from Dry’14 containing the highest levels. The levels of Pb were generally lower than concentrations from

previous sampling trips. Significant differences were detected between DPD and GC for the 2014 wet season. DPD from this season was also significantly higher than NYL. NYL was however significantly higher than GC for the wet season of 2014.

Zinc levels had a range of 14.6161-229.6959 µg/L. DPD from the 2014 dry season had the lowest levels of Zn whereas STW from the 2015 dry season, after the acid spill, had the highest levels of Zn. Zn levels were increased for the 2015 sampling season even in sites not affected by the acid spill. It was determined that KNO and STW had significantly lower levels of Zn than DPD for the 2014 wet season. The levels of Zn for DPD also differed significantly between seasons. GC had significantly higher concentrations of Zn than DPD for the 2014 dry season.

A Canonical Discriminant Function Analysis (DFA) was applied to ICP-MS data on metal accumulation in water for each site and season (Fig. 3.6). The DFA results show that 98.7% of variation is explained on the first two functions. There was a clear separation of the acid spill sites for NYL and MDD with a clear Mn gradient (1.111) on the first axis explaining 96.6% of variation and Al (1.154) explaining a further 2.1% of variation on the second axis. The metal accumulation data was able to reclassify 77.1% of the samples into predefined groups.

A DFA of only the ICP-MS data on metal accumulation in water from the first 4 sampling trips without the acid spill data can be seen in Fig. 3.7. It determined that 57.8% of variation is explained on the first function based on a strong Ni gradient (- 1.115). A further 14.7% of variation in explained on the second function based on Pb as the driving factor. Therefore the DFA indicates that 72.5% of variation is explained on function 1 and 2. Based on these statistics there was a clear separation of NYL in the HF and LF periods based on Ni differences as well as MDD in the LF season and KNO in the HF season based on Pb differences.

Cd Al 0.8 500 Wet'14 Wet'14 Dry'14 g/L)

Dry'14 µ 0.6 g/L) 400 Dry'15 µ Dry'15 300 0.4

200 0.2 a b Concentration ( 100 a b Concentration ( 0.0 0 KNO DPD GC STW JAS NYL MDD KNO DPD GC STW JAS NYL MDD Sites Sites

Cr Co 10 10 Wet'14 9 Wet'14 Dry'14 8 Dry'14 g/L) g/L) 8 µ µ d Dry'15 7 Dry'15 6 c 6 b 5 a 1.0 4 0.8 c 0.6 2 a b d 0.4 Concentration ( Concentration ( 0.2 0 0.0 KNO DPD GC STW JAS NYL MDD KNO DPD GC STW JAS NYL MDD Sites Sites

Cu Fe 15 6000 Wet'14 Wet'14 Dry'14 4000 Dry'14 g/L) g/L) µ µ c Dry'15 Dry'15 10 2000 c b 5 a 600 a 400 b b Concentration ( Concentration ( 200 a b a 0 0 KNO DPD GC STW JAS NYL MDD KNO DPD GC STW JAS NYL MDD Sites Sites

Figure 3.5: Metal concentrations from water sample ICP-MS analysis for Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn, from seven sites along the Nyl River in Limpopo, South Africa, expressed in µg/L. Sampling periods were from 27/02/2014-02/04/2014 for the wet season and 27/07/2014-22/08/2014 for the dry season. The acid spill sampling took place on 06/06/2015. Common superscripts denounce significant differences (p<0.05).

Mn Ni 800 Wet'14 700 15 Dry'14 Wet'14 g/L) 600 µ g Dry'14 Dry'15 g/L) 500 µ f Dry'15 400 10 e 20 f d e b 15 d a 10 c 5 ba g h e Concentration ( h 5 a gf d bc c d c g e Concentration ( a 0 b f KNO DPD GC STW JAS NYL MDD 0 Sites KNO DPD GC STW JAS NYL MDD Sites

Pb Zn 250 14 Wet'14 Wet'14 12 Dry'14 Dry'14 g/L) 10 g/L) 200 µ µ 8 Dry'15 Dry'15 6 150 4 100 3 c 2 b c d a c 50 bb d c Concentration ( 1 a b Concentration ( a a 0 0 KNO DPD GC STW JAS NYL MDD KNO DPD GC STW JAS NYL MDD Sites Sites

Figure 3.5 Cont.: Metal concentrations from water sample ICP-MS analysis for Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn, from seven sites along the Nyl River in Limpopo, South Africa, expressed in µg/L. Sampling periods were from 27/02/2014-02/04/2014 for the wet season and 27/07/2014-22/08/2014 for the dry season. The acid spill sampling took place on 06/06/2015. Common superscripts denounce significant differences (p<0.05).

Table 3.5: Total hardness of water samples from the Nyl River system, Limpopo, South

Africa. Measurements given in mg/L CaCO3. Water hardness is determined according to the DWAF guidelines for aquatic ecosystems (DWAF, 1996), <60 mg/L is soft, 60- 119 mg/L is medium, 120-180 mg/L is hard and >180 mg/L is very hard.

Sites Season CaCO3 (mg/L) Hardness of Water KNO HF 14.01 Soft LF 9.51 Soft DPD HF 12.01 Soft LF 20.02 Soft GC HF 27.52 Soft LF 23.52 Soft STW HF 42.54 Soft LF 62.05 Soft JAS HF 24.52 Soft LF 42.53 Soft NYL HF 33.53 Soft LF 29.03 Soft MDD HF 41.54 Soft LF 51.04 Soft

3.3.3. Artificial Mussels

The levels of metals accumulated by the AMs are represented in Fig. 3.7. AMs were only collected for GC for the Wet season and for STW for the Dry season. Significant differences were determined by means of one-way ANOVA analysis (p<0.05) and are indicated by similar superscripts (Fig. 3.7).

Aluminium levels ranged from 51.2978-359.935 µg/g and were lower for the wet season, remaining relatively constant as one moves downstream. The levels of Al for the dry season increased moving downstream to STW but were lower at JAS. STW had the highest levels of Al and GC had the lowest. The levels of Al at STW were significantly higher than those at KNO and JAS for the Dry season.

The levels of Cd accumulated by the AMs were all below the detection limits of the ICP-MS.

The AMs accumulated Cr in the range of 0.2441-4.6158 µg/g. The levels of Cr for KNO stayed constant for the wet and dry seasons which were also the lowest levels in the system. The highest levels were detected at STW in the dry season. Levels of Cr for DPD and JAS were higher for the dry season compared to the wet season. No significant differences were found for Cr levels.

Considering Co concentrations, only KNO and DPD for the wet seasons had levels above detection. Levels ranged from 5.1404-1.3634 µg/g. No significant differences were detected.

Copper concentrations ranged from 1.785 to 12.0092 µg/g with KNO having the lowest levels in the wet season and the highest levels in the dry season. KNO and DPD had higher levels of Cu in the dry seasons. JAS had similar levels during the wet and dry seasons. No significant differences were detected.

The levels of Fe ranged from 7.7395-8573.464 µg/g with KNO having the highest concentrations during the wet season and JAS having the lowest concentrations during the dry season. Fe concentrations in the wet season showed a decreasing trend moving downstream but his trend is not carried through in the dry season. Levels of Cu from KNO for the wet season were significantly higher than all other sites and seasons.

Manganese concentrations ranged from 2.2103-107.7395 µg/g with KNO having the highest concentration during the wet season and JAS having the lowest levels in the dry season. The wet season shows a decreasing trend moving downstream. A significant difference was found between the wet and dry season for KNO, DPD and JAS with all three indicating higher levels in the wet season. Considering the wet season, KNO was significantly higher than DPD, GC and JAS. DPD was significantly higher than JAS in the wet season. Considering the dry season, KNO and DPD were significantly higher than JAS

The AMs accumulated Ni in a range of 0.2712-1.6357 µg/g. KNO had the lowest levels in the dry season and the highest levels in the wet season. Significant differences were detected between the wet and dry season for KNO. It was also determined that STW had significantly higher levels of Ni than KNO for the dry season.

Lead concentrations ranged from 1.064-2.9439 µg/g with the lowest levels found at GC during the wet season and the highest found at STW in the dry season. The levels for the wet season stay relatively constant moving downstream whereas the dry season shows an increasing trend to STW and a decrease at JAS. No significant differences were found for Pb.

The AMs accumulated levels of Zn ranging from 24.646-55.2314 µg/g. The lowest levels were from JAS in the dry season and the highest were also from JAS for the wet season. Significant differences were detected for JAS between the wet and dry seasons. Considering the dry season, STW was found to be significantly higher than JAS. All the other levels are relatively constant throughout the system.

A DFA was conducted on the ICP-MS data on metal accumulation in AMs which explains a classification of 70.7% of the data on the first two functions. The DFA shows a separation of KNO for the wet season explained by a gradient of Mn (-0.563) on the first axis which accounts for 45.9% of the variation and Ni (0.715) accounting for a further 24.8% of variation on the second axis indicating a separation of STW in the low flow period. The DFA was able to reclassify 65.4% of the data into prospective groups.

Cr Al 8 500 Wet b Wet Dry a g/g)

Dry µ g/g) 400 6 µ

300 b 4 200 a 2

100 Concentration ( Concentration ( 0 0 KNO DPD GC STW JAS KNO DPD GC STW JAS Sites Sites

Co Cu 8 25 Wet Wet Dry g/g) Dry g/g) 20

µ 6 µ

15 4 10 2 5 Concentration ( Concentration ( 0 0 KNO DPD GC STW JAS KNO DPD GC STW JAS Sites Sites

f Mn Fe e a 150 b 12000 Wet 11000 Wet a g 10000 Dry Dry g/g) d g/g) 9000 µ

µ b 8000 100 1600 1400 1200 1000 800 50 h i d i 300 g e c

200 Concentration ( f a h Concentration ( 100 c 0 0 KNO DPD GC STW JAS KNO DPD GC STW JAS Sites Sites

Figure 3.8: Metal concentrations from AM sample ICP-MS analysis for Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn, from five sites along the Nyl River in Limpopo, South Africa, expressed in µg/g. Sampling periods were from 27/02/2014-02/04/2014 for the wet season and 27/07/2014-22/08/2014 for the dry season. Common superscripts denounce significant differences (p<0.05). Values are not available for GC for the dry season or for STW for the wet season due to AMs being stolen or lost.

Ni Pb 2.0 4 a Wet Wet Dry Dry g/g) g/g)

µ 1.5 b µ 3

1.0 2

b 0.5 a 1 Concentration ( Concentration ( 0.0 0 KNO DPD GC STW JAS KNO DPD GC STW JAS Sites Sites

Zn 80 Wet a Dry g/g)

µ 60 b 40 b a

20 Concentration ( 0 KNO DPD GC STW JAS Sites

Figure 3.8 cont.: Metal concentrations from AM sample ICP-MS analysis for Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn, from five sites along the Nyl River in Limpopo, South Africa, expressed in µg/g. Sampling periods were from 27/02/2014-02/04/2014 for the wet season and 27/07/2014-22/08/2014 for the dry season. Common superscripts denounce significant differences (p<0.05). Values are not available for GC for the dry season or for STW for the wet season due to AMs being stolen or lost.

3.3.4. Water and AMs

Spearman rank order indicated no significant correlations (p<0.05) between AMs and water from the same sites and seasons.

A DFA was conducted on the AMs and Water ICP-MS data of only sites where AMs were retreived which explains 55.6% of variation along function 1 and funtion 2. The grouping was driven by a strong Ni gradient (-0.778) explaining 29.1% of variation on the first axis and Mn (-0.843) explaining an additional 26.5% of variation on the second function. In this analysis, 56.4% of samples were reclassified into groups.

3.4. Discussion

3.4.1. In situ water quality parameters

The conductivity of the water at sites near the origin of the river remained very low throughout the study period with levels as low as 28µS/cm. Moving downstream it gets progressively higher and reaches a peak at the STW that was releasing partially treated sewage intermittently during the study period. After the acid spill occurred there was a major increase in the conductivity at sites downstream of where the spill occurred which could be due to the influx of acid and the resultant increase in dissolved and reactive constituents in the water (Sanders, 1997). Another possible reason could be the influx of untreated sewage from the STW (Daniel et al. 2002).

The pH of water is an indirect measure of the concentration of free hydrogen ions in the water. The pH of deionised water at room temperature is completely neutral with a pH of 7 (DWAF, 1996). Though pH levels fluctuate from approximately 6 to approximately 8 in this study, it still falls within the range of historical data for this system dating from the 1980’s (Greenfield, 2004).

Oxygen saturation varied notably possibly due to the presence of high volumes of aquatic macrophytes and algae. The variation could also be explained by the differences in the times of sampling between sites and seasons. When considering the percentage values of the oxygen saturation, the readings ranged from 21-150.4%. The Target Water Quality Range (TWQR) proposed by the Department of Water Affairs and Forestry (DWAF) is 80-120% saturated (DWAF, 1996). Considering the wet season, STW and MDD had levels below the TWQR. Measurements from the dry season indicated that STW, JAS, and NYL had levels below the TWQR and KNO, DPD, GC, JAS and NYL were supersaturated and exceeded the TWQR. The fact that some sites had levels both above and below the TWQR could indicate that there is hyperoxia and hypoxia occurring at these sites respectively, however due to the variation in the sampling times and water temperature, this cannot be confirmed

3.4.2. Water and AMs

Aluminium is commonly found in high concentrations in aquatic ecosystems as it forms part of the natural make up of sediment. It can however be introduced by human activity such as fossil fuel combustion and multiple industries (Kempster et al. 1980).

Aluminium is highly reactive to changes in pH. A lowering of the pH of water in an aquatic ecosystem can lead to an increase in the concentration of dissolved Al (Munk and Faure, 2004). A decrease in pH also leads to changes in the form of Al available in the water from harmless Al ions to hexahydrate species that are highly toxic to aquatic biota (DWAF, 1996). Under alkaline conditions Al normally occurs as biologically unavailable forms. The effect of the pH on the levels of Al is reflected in the results as Al levels were increased and pH levels reduced after the acid spill occurred.

The AMs accumulated relatively similar levels of Al than those detected in the spot water tests. The AMs showed significantly higher levels at the STW for the 2014 dry season indicating that the inefficiency of the facility was affecting the water quality over the entire accumulation period. At sites where data was available for both seasons, levels of Al were higher for the dry seasons for each instance. This result is expected because of the dilution factor during the wet season caused by major flooding and heavy rainfall that occurred in that month (Gouws and Du Toit, 2014).

Cadmium is a trace element that is non-essential to life. It does occur naturally in aquatic ecosystems but can be introduced by various human activities such as mining, manufacturing processes, agriculture especially fertilizer and pesticides and the breakdown of cadmium plated containers (DWAF, 1996) The levels and species of Cd available in an aquatic system is highly dependent on the pH of the water. The results of this study showed that Cd was generally below the limit of detection for most of the sites and seasons. This is also reflected in historical data in a previous WRC study (Vlok et al. 2006). The levels of Cd detected at the reference site were notably lower than levels found by Vlok et al. (2006) and AED (2011). No Cd levels were above the South African TWQR. Considering the accumulation of Cd by the AMs, all sites and seasons had levels below detection indicating that Cd pollution is of little to no concern in this system.

Chromium is a metal that is not commonly found in high concentrations in aquatic ecosystems (Greenfield, 2004). It is normally found in three forms depending on their level of oxidation (Fisher, 2011). Some forms of Cr are highly toxic and soluble regardless of the pH of the water (DWAF, 1996). The forms of Cr are influenced by the amount of organic material in the water which could change the Cr molecules to

less toxic forms (Greenfield, 2004). Though Cr occurs in different forms it is difficult to distinguish between the forms present in the water. The levels of Cr were below the TWQR of 7 µg/L for all sites and seasons except MDD in the dry season but were well below the levels detected by Greenfield (2004). STW in the wet season showed significantly higher concentrations than most of the other sites for that season. This could potentially be due to the inefficient functioning of the STW during that time causing untreated urban runoff and sewage to enter the Nyl River. The exact extent of the ineffectiveness of the STW could not be determined.

The AMs accumulated Cr in a similar range to levels detected in the water. With Cr it is also evident that the AMs accumulated higher levels in the dry season compared to the wet season. Unfortunately data for AM accumulation for MDD during the period where levels were elevated above the TWQR are not available due to the AMs being lost or removed from the water body.

There is no TWQR data available for South Africa concerning Co concentrations in aquatic ecosystems as this metal is rarely found in levels above detection in waterbodies. Levels of Co were below detection for most sites but were highly elevated after the acid spill occurred at the sites just downstream from the spill location. Since Co is generally below detection, and found in sediment in higher concentrations, it can be speculated that the influx of acid aided in leaching Co from the sediment which lead to an increase in the concentrations available in the water (Xu et al. 2015).

The accumulation pattern of Co by the AMs support this theory as Co levels for GC, STW and JAS were below detection for the wet and dry seasons of 2014. The AMs did accumulate Co from KNO and DPD that are located in close proximity to the origin of the river.

Copper is a metal that occurs naturally in aquatic ecosystems in three different oxidation states. When this element occurs in the environment under certain

conditions such as water containing high levels of CaCO3, it becomes highly toxic (Greenfield, 2004). The TWQR for Cu under soft water conditions is 0.3µg/l therefore the levels of Cu were well above the TWQR for all sites and seasons in 2014. High levels of Cu were detected close to the source of the river.

The 2015 dry season shows elevated levels of Cu at STW where the acid spill occurred, possibly due to leaching. Copper concentrations were also elevated for MDD

compared to the levels at the reference sites for that season. Sources of Cu pollution include STW effluents, algicides and pesticides, corrosion of copper pipes, mining, smelting and metal refineries as well as iron and steel industries (DWAF, 1996) The town of Modimolle receives water from the Roodeplaat Dam which has had water quality issues in the past (DWA, 2011).

The AMs accumulated Cu in levels notably lower than levels detected in water for the wet season. This could indicate that the influx of Cu at the time of water sampling was an isolated event. The increased levels of Cu found in the water could be due to agricultural activities in the upper region of the river and the Cu contained in their pesticides (DWAF, 1996). The AMs did however accumulate levels of Cu closer to those found in the water for the 2014 dry season.

Iron is a naturally occurring metal that is found in varying quantities in aquatic ecosystems (Vlok et al. 2006). The levels of Fe found in aquatic systems is subject to the geology of the area in which the system resides. Though Fe occurs naturally and is naturally leached from iron ores, it can also be introduced into the system by various industries and can be found in household chemical products, fungicides and fertilisers. The levels of Fe at the STW after the acid spill were significantly increased compared to previous seasons and other sites. The levels of Fe from the site just downstream of STW also had highly elevated levels of Fe. When compared to results from Greenfield (2004), it is evident that although there is a major difference in the levels of Fe after the acid spill, these levels are still within the range found by Greenfield (2004). These levels also fall within the 500 – 50 000 µg/L range for Fe in aquatic ecosystems determined by Galvin (1996).

The AMs accumulated significantly higher levels of Fe for KNO for the 2014 wet season. These levels seem to decrease moving downstream from the source. The KNO AMs for the wet season contained high volumes of biofouling which could have influenced the results for this metal (Hossain et al. 2015). The increased Fe concentrations could also be due to the high concentrations of Fe normally found in some fertilisers (DWAF, 1996).

Manganese is an essential element to most living organisms, however in high concentrations it can have negative effects on the environment (DWAF, 1996). Manganese is mostly found in high concentrations in sediment and is used in many

industrial processes. Anthropogenic sources of Mn that can lead to elevated Mn concentrations in the water include the steel, chemical and fertilizer industries (DWAF, 1996). When considering the results for this study it is important to note that Mn levels are closely associated with Fe concentrations and follow the same spatial trend. Fe and Mn levels for the 2015 dry season were elevated well above other levels found in the study. Besides the elevated levels at STW for the dry season of 2015, all other sites and seasons showed levels below the TWQR of 180 µg/L (DWAF, 1996).

The AMs accumulated Mn in the same spatial pattern as Fe in the 2014 wet season, with significantly higher levels at the reference site, KNO. This could be due to fertilisers containing Mn ending up in the stream via agricultural runoff as the lands surrounding this site are predominantly agricultural (DWAF, 1996). Levels decreased moving downstream as they did with the Fe levels. There are also major differences in the accumulation of Mn between the wet and dry seasons. The dry season samples showed notably lower concentrations.

Nickel is used in many industrial and commercial processes. This element is essential in sustaining life and when absent can have detrimental effects on the organisms inhabiting the environment (Cempel and Nikel, 2005). The levels of Ni determined in the current study were similar to levels found by AED (2012) for the same area.

The accumulation of Ni by the AMs had levels 10 times lower than those detected in the water samples indicating that the levels in the water at the time of sampling wasn’t a true reflection of the conditions in the system. For KNO and JAS, the AMs accumulated higher levels of Ni in the wet season compared to the dry season.

Lead exists in the aquatic environment in four oxidation states with divalent lead (Pb2+) the most toxic form, being bioavailable to aquatic species (Fisher, 2011). Lead is introduced into the aquatic environment through fossil fuel combustion, industrial processes and urban runoff. The bioavailability is known to decrease as pH decreases. The toxicity of Pb also decreases as water hardness increases which is of concern as water total hardness tests for all sites and seasons revealed that the water was soft. It can increase in areas with high oxygen saturation which is important as the water was supersaturated for some of the sites and seasons (Fig. 3.4). Most of the results of this study showed levels that exceed the TWQR for South Africa but were below the acute effect value (DWAF, 1996).

The AMs accumulated Pb in a narrower range than the water samples. It also showed notably lower levels of Pb at KNO for the wet season. The AMs have proven to accumulate significantly similar concentrations of Pb when compared to multiple bio- indicator species and could therefore be an indication of the Pb levels also being isolated pollution events instead of constant pollution on a temporal scale (Kibria et al. 2010; Claassens et al. 2016).

Zn is an essential metal found in aquatic ecosystems in two forms, the metal and the divalent cation Zn2+ (DWAF, 1996). In an aquatic ecosystem it is the divalent cation form which is potentially toxic to biota. Zinc naturally occurs in rocks and can enter the environment by natural weathering, erosion and leaching from substrates (Fisher, 2011). It is reactive to changes in pH which could explain the sharp increase in Zn at STW where the acid spill occurred (DWAF, 1996). All sites and seasons contained concentrations of Zn that exceeded the TWQR. Zn levels were lower for KNO, JAS and NYL than levels found in 2001/2002 by Vlok et al. (2006). The levels of Zn were higher for DPD, STW and MDD compared to levels detected by Vlok et al. (2006).

The accumulation of Zn by the AMs was generally lower than levels found in the water. There was less variation in Zn on a spatial scale in the AM accumulation indicating that the levels are naturally high in the system, but that there was an input of Zn from other sources at the time of sampling seeing that levels in spot water samples were higher and that it is not corroborated in the AM accumulation.

The DFA containing all of the ICP-MS water data (Fig 3.7) shows that the acid spill data except for JAS separated from the main grouping. NYL, MDD, KNO and DPD separated from the main group based on differences in Mn concentrations. The STW separated completely from both aforementioned groups based on Al concentrations. For a better idea of the water metal contamination during 2014 the acid spill data was removed and another DFA was conducted (Fig. 3.8) due to the influence of the acid spill on the driving factors in the DFA. When considering the water metal concentrations from 2014, it is evident that the samples from NYL separated from the other sites based on higher Ni concentrations. The NYL LFW exhibited a higher degree of separation than the NYL HFW, this could possibly be due to the dilution factor brought on by heavy rainfall during the HF period (Gouws and Du Toit, 2014).

There was also a slight separation of MDD in the LF period and KNO in the HF period based on the fact that these sites had higher Pb concentrations than the other sites.

The AM DFA (Fig. 3.9) indicates a pattern in the accumulation for wet and dry seasons. All of the HF season AMs are located to the left of the LF season AMs. KNO HF AM and STW LF AM separated from the main grouping. The separation of KNO for the HF season was based on differences in Mn and Al on the first function, and Ni on the second function. KNO had high levels of Mn and Ni whereas STW had high levels of Al and low levels of Mn which explains why they separated so definitively from each other. On the second function, both sites had high Ni concentrations which was the driving factor behind the separation on the Y-axis.

The combined DFA containing the AM data with the respective water data from those sites and seasons, indicated that there was no clear pattern of water and AMs from the same sites grouping together or that there were separations between seasons. This was not expected due to the fact that spot water sampling reveals what is in the water at the exact time of sampling (Sanders, 1997) only whereas the AMs accumulate the bioavailable fraction of metals over a 4-6 week period (Wu et al. 2007). The data did however generally group together indicating that the AMs perform their function of accumulating metals in concentrations relevant to the environment.

3.5. Conclusion

When considering the results as a whole, it is clear that the STW is not the largest contributor of metal pollution in the system. The results indicate that the acid spill had multiple influences on the metal levels and on the in situ water quality parameters. Some of the metals that are more reactive to changes in pH had increased levels of metals after the acid spill (Al, Co, Fe, Mn, Ni and Zn). Some of the other metals showed no change (Cu and Cd), and some metal concentrations decreased (Cr and Pb).

The AM concentrations showed that the metal levels were higher for the dry season than the wet season for Al, Cr, Cu and Pb. This can be attributed to the lack of dilution factor in the dry season. However a decreasing trend is evident with AM accumulated levels of Co, Fe, Ni and Mn possibly due to fertilisers washed into the river by heavy rainfall and flooding for the wet season and either decrease by the settling of these metals from the water body or because of dilution of the effluent moving downstream.

With regard to the water metal concentrations, the differences between wet and dry seasons were not as clear. This is because the water samples give only a snapshot of the conditions at the time of sampling. The water data showed that many of the metals were present in concentrations that exceeded the TWQR (Cr, Cu, Mn, Pb and Zn), but that the levels were in some cases lower than historical data (Cr), or consistently high throughout the entire system (Cu and Zn).

Chapter 4 4.1. Introduction

The aquatic environment is reliant on the health of the sediments which is a vital component of the ecosystem (Pejman et al. 2015). Sediment acts as a natural pollution sink which assists the aquatic environment in maintaining its health. Toxic chemicals i.e. metals, are accumulated in sediments and have the potential to be harmful when released (Wang et al. 2007). These toxic metals can accumulate in biota especially benthic organisms that have constant contact with sediment (Wang et al. 2012). Metals that are trapped and bound to the sediment layer in an aquatic ecosystem, can be released through changes in pH and resuspension of sediment particles into the waterbody (Soares et al. 1999).

Once metals have entered the aquatic environment, they remain there for a long period of time due to their resistance to degradation. These metals can also spread throughout trophic levels through the processes of bioaccumulation and biomagnification (Maceda-Veiga et al. 2012). Some metals are essential to certain life forms and only become toxic under certain conditions such as altered pH and when they are present in high concentrations (Newman, 2009). Metals enter the aquatic ecosystem naturally through weathering, anthropogenically through mining, and by urban or agricultural runoff.

Metals contained in sediments pose a risk to aquatic systems if the metals contained in the sediment-store are released. Multiple studies have formulated methods of determining the degree of risk the metal pollution in sediment poses (Iqbal et al. 2012; Banu et al. 2013; Li et al. 2013; Cheng et al. 2015; Low et al. 2015). Sediment ecological risk studies are crucial in the Nyl River system, which is home to the 16 000 ha floodplain wetland with Ramsar accreditation. The Nyl floodplain is known for its variety of threatened and endangered waterfowl as well as being home to the endangered Roan Antelope (Hippotragus equinus) (Friends of Nylsvley, 2014).

The aim of this study was to determine the concentrations of metals contained in sediment from the Nyl River system by means of full acid extraction as adapted by Greenfield et al. (2007) from USEPA (1996) and analysis of metal content with Inductively Coupled Plasma Optical Emission Spectrometry (ICP-OES). This study

also aims to determine the potential risk the sediment poses to the ecologically significant Nyl River system and Nyl floodplain wetland by means of four sediment risk assessment indices namely: Contamination Factor, Geo-accumulation Index, Pollution Load Index and Enrichment Factor. These indices indicate the degree of metal pollution in the sampling area.

4.2. Materials and Methods 4.2.1. Site selection

Sediment was sampled in the Nyl River system, Limpopo Province, South Africa. Sediment samples were collected from 7 sites in the upper reaches of the Nyl River system including sites from the Klein Nyl before the confluence with the Groot Nyl, as well as sites from the Nyl River after the confluence. The sites included in the study ranged from the origin of the Klein Nyl outside of Modimolle to the Moorddrift Dam near Mokopane. The sites chosen are Klein Nyl Oog (KNO), Donkerpoort Dam (DPD), Golf Course (GC), Sewage Treatment Works (STW), Jasper (JAS), Nylsvley Nature Reserve (NYL) and Moorddrift Dam (MDD).

4.2.2. Sample collection and preparation

Sampling trips were conducted twice for the wet season (February 2014 and March 2014) and twice for the dry season (July 2014 and August 2014) spaced one month apart and are consistent with water sampling dates discussed in Chapter 3. The top 15 cm of sediment was sampled, placed into acid cleaned polyethylene bottles and frozen for transportation back to the laboratory. In the laboratory, samples were thawed and placed in the oven at 60 ºC to dry. Once samples were completely dry, larger particles were removed manually from the samples for consistency between samples.

Metals were extracted from the sediment samples using high temperature and high pressure microwave digestion procedures (Greenfield et al. 2007). The digestions were conducted using a Mars 6 Microwave Digester. 0.5g of sample was measured and added to separate digestion chambers. 9 ml of Suprapur 30 % hydrochloric acid and 3 ml of Suprapur 65 % nitric acid were added to each of the vessels. The vessels were closed and placed in the microwave digester to digest at 200 ºC for an hour.

Once the vessels were cooled, they were emptied into falcon tubes and filtered using 0.45 µm pore size Whatmann filter paper and a vacuum filter. After filtration samples were diluted to 50 ml volumetrically using ultrapure Milli-Q water.

4.2.3. ICP-OES Analysis

Sediment samples were analysed for metal content using the Perkin Elmer Spectro Arcos FSH 12 ICP-OES. The samples were analysed for 10 metals including Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn. The analysis included several quality control/ quality assurance (QC/QA) measures namely certified reference materials (CRMs), internal calibration verification standard (ICVS) and continuous calibration blank (CCB). The analysis included calibration standards of the following concentrations: 5 ppb, 50 ppb, 100 ppb, 500 ppb, 1 ppm, 5 ppm, 10 ppm, 20 ppm, and 40 ppm. After the initial calibration of the instrument, the outliers were removed, curve peaks and backgrounds were corrected and the regressions were recalculated for accurate standard curves. The LODs of each metal can be found in Table 4.1.

Table 4.1: The detection limits as calculated by the ICP-OES for each of the metals expressed in parts per million (ppm) as well as CRM Recovery percentages for Lake Sediment-3 (LKSD-3) and Freshwater Sediment (FWSD). Metals that were out of the 80-120% range are indicated by OOR.

Metals LOD (ppm) CRM Recovery % CRM Recovery % LKSD-3 FWSD

Al 0.0309 N/A OOR

Cd 0.0025 N/A N/A

Cr 0.0118 OOR OOR

Co 0.0038 N/A N/A

Cu 0.0018 102.82 92.28

Fe 0.0188 N/A 106.92

Mn 0.0017 104.67 93.88

Ni 0.0077 OOR 85.56

Pb 0.0134 OOR OOR

Zn 0.0212 104.67 OOR

4.2.4. Statistical Analysis

Statistical analysis was performed on the data using IBM SPSS v 22 and GraphPad Prism v.5. The raw data was transformed from ppm to µg/g dry mass using the following equation:

[ ] / = ([ ]( . ) ) × ( / ) /1000 −1 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝜇𝜇𝜇𝜇 𝑔𝑔 𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝜇𝜇𝜇𝜇 𝐿𝐿 − 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝑑𝑑𝑑𝑑𝑑𝑑 𝑚𝑚𝑎𝑎𝑎𝑎𝑎𝑎 This formula includes the subtraction of method blanks from the original readings, incorporates the dilution which in this case was 50 ml which was divided by the dry mass of each sample. This was then divided by 1000 to account for the change from ppm to µg/g.

Canonical Discriminant Function Analysis (DFA) was conducted to determine the degree of similarities and differences between sites and seasons and to identify spatial and temporal trends.

4.2.5. Ecological Risk Assessment

To determine the environmental risk the sediment poses to the ecosystem several pollution indices were utilised. These include Contamination Factor (CF) proposed by Thomilson et al. (1980), the Geo-Accumulation Index (Igeo) proposed by Muller (1979) as well as Enrichment Factor (EF) (Li et al. 2013).

Contamination Factor (CF)

In this study the contamination factor is used to determine the status of sediment contamination from the various sites discussed previously. The CF (Thomilson et al. 1980) is calculated using the following equation:

( ) = ( ) 𝐶𝐶 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝐶𝐶𝐶𝐶 𝐶𝐶 𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏𝑏 In this equation C (metal) is the concentration of the selected metal measured at a particular site. C (background) is the level of the selected metal measured at the reference site in this study: KNO. KNO is seen as a reference site due to its location

being in close proximity to the origin of the river and the limited impacts to the river at that point.

The description of values for the CF index can be seen in Table 4.2. A CF value less than one describes an area with low contamination (Thomilson et al. 1980). When the CF value ranges from one to three it is indicative of moderate contamination. A CF value that falls between three and six shows considerable contamination. When a CF value exceeds 6 the system is likely highly contaminated (Thomilson et al. 1980).

Pollution Load Index (PLI)

The PLI is used in conjunction with the CF index. The PLI of a river system is determined by the following formula:

( ) = ( 1 × 2 × 3 × … .× ) 1 𝑛𝑛 𝑃𝑃𝑃𝑃𝑃𝑃 𝐶𝐶𝐹𝐹 𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶 Where CF relates to the Contamination Factor determined as seen above. The PLI is determined by multiplying the CF for each of the metals determined and then determining the (1/n) exponent of the product of CF values (Wang et al. 2015a). This produces an overall status of Polluted or Unpolluted for the site considering all metals included in the study.

Geo-accumulation Index (Igeo)

The Igeo is a sediment pollution criterion that is often used in aquatic pollution studies (Muller, 1979; Banu et al. 2013; Saleem et al. 2015). This index is a comparison between the levels of metal pollution found in a system to pre-industrial levels. The following formula was used to determine the Igeo values:

= 2 1.5 𝐶𝐶𝐶𝐶 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑙𝑙𝑙𝑙𝑙𝑙 � � Where Cn is the concentration of the metal in question𝐵𝐵𝐵𝐵 measured at a certain site and Bn is the geochemical background metal levels (Muller, 1979). In this study the levels of the metal in question will be taken from the reference site KNO. The descriptions of Igeo values and classes can be seen in Table 4.3.

Enrichment Factor (EF)

The EF of sediment is used in ecological risk assessments to determine the degree of enrichment in sediments by incorporating the concentrations of metals measured and background levels of metals (Li et al. 2013; Saleem et al. 2015; Wang et al. 2015b). It also integrates concentrations of a predetermined reference metal of both measured and background levels. The EF can be calculated using this formula:

𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 = (� �) 𝐹𝐹𝐹𝐹 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝐸𝐸𝐸𝐸 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 In this formula Msample is the level of a𝐹𝐹𝐹𝐹 certain𝑏𝑏𝑏𝑏𝑚𝑚𝑆𝑆𝑆𝑆𝑟𝑟𝑟𝑟𝑆𝑆 metal measured. Fe sample is the level of a predetermined reference metal (Li et al. 2013). Fe was chosen as reference metal because it was determined that Fe was a suitable baseline measure as its distribution in an aquatic ecosystem is unrelated to other metals (Abrahim and Parker, 2008). M baseline is the level of the metal in question measured at the reference site, in this case KNO. Fe baseline is the level of the reference metal measured at the reference site (Li et al. 2013). The description of EF values can be seen in Table 4.4.

Table 4.2: Description of Contamination Factor (CF) values as described by Thomilson et al (1980).

CF Value Cd Description

CF<1 Cd<6 Low contamination

CF>6 Cd≥24 Highly contaminated

Table 4.3: Descriptions of the Geo-Accumulation Index (Igeo) values and classes (0- 6) as described by Muller (1979).

Class Sediment Quality Igeo Value 0 Unpolluted ≤0 1 Unpolluted to moderately polluted 0-1 2 Moderately polluted 1-2 3 Moderately to strongly polluted 2-3 4 Strongly polluted 3-4 5 Strongly to extremely polluted 4-5

6 Extremely polluted >6

Table 4.4: The Enrichment Factor (EF) Index description of values as described by Li et al. (2013).

Description EF Value No enrichment EF<1 Minor 150

4.3. Results 4.3.1. Metal concentrations in sediment

Metal concentrations determined in the Nyl River for the wet and dry seasons of 2014 can be seen in Fig. 4.1.

Aluminium

The lowest Al concentrations were recorded for STW in LF1 (2.571 µg/g) and the highest for NYL for the LF2 season (61507.54 µg/g). The levels were generally low for KNO, DPD and JAS and were greatly elevated for NYL and MDD.

Cadmium

Cadmium concentrations were relatively constant throughout the system except for KNO in HF1 (4.045 µg/g) and NYL for LF2 (18.944 µg/g). Throughout the system the levels of Cd stayed relatively constant but had elevated concentrations for KNO HF1 and NYL LF2.

Chromium

The levels of Cr recorded in the Nyl River ranged from 8.383 µg/g to 116.55 µg/g. The highest levels were recorded at MDD for LF2 and the lowest at KNO for HF2. There was a general increasing trend in Cr concentrations moving downstream from the source of the river

Copper

The highest Cu levels were found at NYL during HF1 (35.406 µg/g) and the lowest at JAS during HF1 (5.592 µg/g). The levels of Cu were all below Sediment Quality Guidelines (SQG) as determined by the Canadian Council of Ministers of the Environment (CCME) for all sites and seasons (CCME, 2002).

Iron

The levels of Fe ranged from 3600.804 µg/g to 55815.84 µg/g with STW having the lowest levels in LF2 and NYL having the highest levels in LF2. No clear pattern was

observable between sites and seasons. No guidelines or historical levels are available for this metal.

Manganese

Manganese concentrations varied from 13.772 µg/g to 4324.05 µg/g. The highest levels were recorded at NYL in the LF2 period. The lowest levels were found for STW in the LF2 season. This seems to follow a similar pattern to Fe as seen above. Levels were elevated for KNO in HF1 and LF2 as well as GC for HF1. The Mn concentrations at NYL for LF2 were notably higher than any other site or season.

Nickel

The levels of Ni ranged from 0.437 µg/g to 58.227 µg/g in the Nyl River system. The lowest concentrations were recorded at DPD in the rainy season. The highest levels were found at NYL in the dry season.

Zinc

The highest levels of Zn were recorded at NYL in the LF2 season (149.4 µg/g). The lowest levels were found at JAS in the HF1 season (7.772 µg/g). The levels were elevated for GC, NYL and MDD.

Discriminant Function Analysis (DFA)

A canonical DFA was conducted on the metal readings from all 7 sampling sites and is shown in Fig. 4.2. The DFA shows 98.2% of variation is explained on function 1 and function 2 collectively. There is a clear grouping of some of the less polluted sites and a separation of GC Wet, STW Wet, NYL Wet and Dry and MDD Dry. These separations occurred based on a clear Zn gradient (10.639) on the first axis which explains 93.5% of the variation. On the second axis a Ni gradient (3.918) explains a further 4.7% of the variation. The DFA was able to reclassify 100% of data into predefined groups.

Cd Al 20 80000 19 HF1 HF1 18 HF2 g/g)

HF2 µ g/g) 17 LF1

µ 60000 LF1 16 LF2 15 LF2 5 40000 4 3 20000 2 Concentration( 1 Concentration( 0 0 KNO DPD GC STW JAS NYL MDD KNO DPD GC STW JAS NYL MDD Sites Sites

Cr Cu 150 40 HF1 HF1 HF2 HF2 g/g) g/g)

µ 30 µ LF1 100 LF1 LF2 LF2 20

50 10 Concentration( Concentration( 0 0 KNO DPD GC STW JAS NYL MDD KNO DPD GC STW JAS NYL MDD Sites Sites

Fe Mn 60000 5000 HF1 HF1 4500 HF2 HF2 g/g) g/g) µ µ 4000 LF1 40000 LF1 LF2 LF2 3500 1500

20000 1000

500 Concentration( Concentration( 0 0 KNO DPD GC STW JAS NYL MDD KNO DPD GC STW JAS NYL MDD Sites Sites

Ni Zn 80 200 HF1 HF1 HF2 g/g) HF2 g/g)

µ 60

µ 150 LF1 LF1 LF2 LF2 40 100

20 50 Concentration( Concentration( 0 0 KNO DPD GC STW JAS NYL MDD KNO DPD GC STW JAS NYL MDD Sites Sites

Figure 4.1: Metal levels from seven sites along the Nyl River collected for February (HF1), April (HF2), July (LF1) and August (LF2) for 2014. Red lines indicate SQGSQGs (CCME, 2002) for Cd, Cu, Cr and Zn and the guidelines set out by MacDonald et al. (2000) for Ni. Concentrations were determined by means of ICP- OES and are expressed in µg/g dry mass.

4.3.2. Ecological Risk Assessment

Aluminium

The results of the CF index are represented in Table 4.5. The levels of Al remained low to moderate for sites in the upper reaches of the river. Al levels for NYL had a high CF score for both the high (6.371) and low (6.857) flow periods. Moorddrift Dam (MDD) had a CF score of 2.085 in the HF season, giving it a moderately contaminated classification. This score was nearly doubled for the dry season (4.065) changing the classification to considerably contaminated.

Results for the geo-accumulation index are represented in Table 4.5 where it is evident that Al levels for all sites and seasons fell under class 1 which classifies the system in the Unpolluted to Moderately Polluted category.

The enrichment factor results for all sites and seasons can be seen in Table 4.5. The results show no enrichment for the reference site (KNO) (0.444; 0.911) as well as the site just downstream of the reference site, DPD (0.298; 0.336). Downstream of the golf course (1.972; 1.733) and at the STW (2.506; 1.727) there was minor sediment enrichment. The site just downstream of the STW had no enrichment (0.982; 0.578). Sediment samples from Nylsvley Nature Reserve indicated moderate to severe enrichment of sediment for the wet season (6.755) but only minor enrichment for the dry season (1.790).

Cadmium

Cadmium concentrations were generally low throughout the system according to the CF values calculated. KNO had elevated levels of Cd in the HF season, causing it to acquire a classification of moderate contamination in the CF index (1.247), a minor enrichment factor (1.142) and an Igeo classification of unpolluted to moderately polluted (0.359). In the dry season, both DPD (1.065) and GC (1.709) had elevated levels of Cd which lead to a classification of minor enrichment. The STW had low CF values (0.515; 0.593) and fell in class 1 under the Igeo index (0.0355-0.0998), however, the STW showed moderate enrichment from pre-industrial levels in the dry

season (3.145). NYL showed considerable contamination (4.766) of Cd and minor enrichment (1.732) whereas the Igeo indicated that the site was unpolluted to moderately polluted (0.6828) in the dry season.

Chromium

The levels of Cr contamination were low for KNO in the HF season (0.556), for STW in the LF season (0.218), and for MDD in the HF season (0.918). MDD had a CF value of 3.315 placing it in the Considerable Contamination category. All other sites and seasons were classified in the Moderate Contamination category. All Cr levels were classified into the Unpolluted to Moderately Polluted category regarding the Igeo Index. The EF index indicated that KNO (0.503) and MDD (0.904) had no enrichment in the HF season. The GC had moderate enrichment (3.07) of Cr. All other sites and seasons had minor Cr enrichment.

Copper

Copper levels showed a low CF value for KNO (0.839) and DPD (0.595) for the HF season as well as GC (0.714) and STW (0.606) for the LF season. The CF factor was elevated for the dry season for KNO (1.161) and for the wet seasons for GC, and (1.786) STW (2.658). NYL had the highest CF value for Cu and was classified as having considerable contamination for the HF season (3.295) and moderate contamination for the LF season (2.228). For both seasons, MDD had moderate Cu contamination (1.237; 2.135). All sites were classified as being unpolluted to moderately polluted considering Cu concentrations for the Igeo index.

The EF index showed no enrichment for KNO (0.942) or DPD (0.668) for the HF season. Both of these sites had minor enrichment in the LF season. GC showed minor enrichment for both seasons (1.717; 2.112). The STW (3.212) and NYL (3.494) showed moderate enrichment of the sediment with Cu. MDD had minor enrichment for both seasons (1.21; 1.348) considering Cu concentrations.

Manganese

The levels of manganese were low for DPD (0.152; 0.12), STW (0.532; 0.026), JAS (0.145; 0.327), and MDD (0.372; 0.442) for both seasons. KNO (1.151) and GC

(1.679) had elevated CF values for the HF season but both were low for the LF season. The levels of Mn for NYL were elevated and showed considerable Mn contamination for the LF season (4.659) but were low for the HF season (0.905). This pattern is also shown in the EF index with the elevated levels being reflected as minor enrichment. The Igeo classifies all sites and seasons as being unpolluted to moderately polluted with Mn.

Nickel

Considering Ni concentrations, both DPD and JAS had low CF scores for both the HF and LF periods. KNO had elevated levels of Ni in the dry season and concurrently had a moderate CF classification (1.119). GC (2) and STW (3.772) both had increased CF values for the HF period and were classified as having moderate contamination of Ni. Both GC (0.328) and STW (0.116) had low levels of Ni contamination for the LF period. Nylsvley Nature Reserve had considerably high Ni contamination for both wet (5.613) and dry seasons (5.805). MDD downstream of NYL had a low CF classification for the wet season (0.954) but had considerably high contamination in the LF period (3.645).

The Igeo classified DPD as being unpolluted for both seasons (-0.019) and JAS for the HF season (-0.001). The rest of the sites and seasons were placed in class 1 (Unpolluted to Moderately Polluted) of the Igeo index.

The EF showed the same trend as the CF considering Ni concentrations for KNO, DPD and GC. STW was found to have moderate Ni enrichment in the HF season (3.638). NYL was found to have moderately severe Ni enrichment for the HF season (5.951) but levels were lower for the LF season (2.907) which classified it as having minor Ni enrichment.

Zinc

The contamination factor index showed variation in contamination levels in the river. Both JAS (0.541; 0.616) and DPD (0.551; 0.413) had a low CF value for both seasons. For the reference site (KNO), CF was low for the wet season (0.942) but moderate for the dry season (1.058) considering Zn levels. GC had considerably high levels of Zn contamination in the wet season (5.291) and moderate levels in the dry season (1.308). STW had moderate contamination of Zn in the wet season (2.532) but

improved to low levels in the dry season (0.611). NYL had considerably high Zn contamination for both seasons (5.82; 5.161). The CF values of MDD were moderate considering Zn concentrations (2.765; 2.371). The Igeo index classified all sites and season in the Unpolluted to moderately polluted category for Zn.

The EF for Zn reflects the same trend for KNO, DPD, GC, JAS, and MDD. For NYL, the EF classified the LF season as having minor Zn enrichment (2.619) whereas the CF classified it as having considerable Zn contamination.

The Pollution Load Index results can be found in Table 4.6. Values below 1 indicate areas that are not polluted whereas sites with PLI values exceeding 1 are most likely polluted. According to the PLI, the sites that are not polluted are KNO (0.137-0.808), DPD (0.001; 0.0001) and JAS (0.003; 0.008). The sites that are polluted are GC (4.586) and STW (2.617) in the HF season as well as NYL (79.842; 303878.77) and MDD (1.903; 9.256) for both seasons.

Site Season CF Description Igeo Igeo Igeo Description EF EF Metal value Value Class value description KNO HF 0.876 Low 0.0016 1 Unpolluted to moderate 0.444 None Al LF 1.124 Moderate 0.0011 1 Unpolluted to moderate 0.911 None DPD HF 0.526 Low 0.001 1 Unpolluted to moderate 0.298 None LF 0.346 Low 0.0009 1 Unpolluted to moderate 0.336 None GC HF 2.048 Moderate 0.0012 1 Unpolluted to moderate 1.972 Minor LF 0.586 Low 0.001 1 Unpolluted to moderate 1.733 Minor STW HF 2.598 Moderate 0.0012 1 Unpolluted to moderate 2.506 Minor LF 0.326 Low 0.001 1 Unpolluted to moderate 1.727 Minor JAS HF 0.534 Low 0.001 1 Unpolluted to moderate 0.982 None LF 0.589 Low 0.001 1 Unpolluted to moderate 0.578 None NYL HF 6.371 High 0.0013 1 Unpolluted to moderate 6.755 Moderate to severe LF 6.857 High 0.0013 1 Unpolluted to moderate 2.909 Minor MDD HF 2.085 Moderate 0.0011 1 Unpolluted to moderate 1.79 Minor LF 4.065 Considerable 0.0013 1 Unpolluted to moderate 2.567 Minor KNO HF 1.247 Moderate 0.359 1 Unpolluted to moderate 1.142 Minor Cd LF 0.753 Low 0.1934 1 Unpolluted to moderate 0.995 None DPD HF 0.712 Low 0.1722 1 Unpolluted to moderate 0.75 None LF 0.558 Low 0.0715 1 Unpolluted to moderate 1.065 Minor GC HF 0.514 Low 0.0343 1 Unpolluted to moderate 0.49 None LF 0.578 Low 0.0879 1 Unpolluted to moderate 1.709 Minor STW HF 0.515 Low 0.0355 1 Unpolluted to moderate 0.497 None LF 0.593 Low 0.0998 1 Unpolluted to moderate 3.145 Moderate JAS HF 0.552 Low 0.1649 1 Unpolluted to moderate 1.411 Minor LF 0.752 Low 0.1882 1 Unpolluted to moderate 0.633 None NYL HF 0.532 Low 0.0502 1 Unpolluted to moderate 0.564 None LF 4.766 Considerable 0.6828 1 Unpolluted to moderate 1.732 Minor MDD HF 0.517 Low 0.0348 1 Unpolluted to moderate 0.727 None LF 0.463 Low -0.0133 0 Unpolluted 0.292 None KNO HF 0.556 Low 0.077 1 Unpolluted to moderate 0.503 None Cr LF 1.444 Moderate 0.107 1 Unpolluted to moderate 1.795 Minor DPD HF 1.306 Moderate 0.104 1 Unpolluted to moderate 1.295 Minor LF 1.072 Moderate 0.1 1 Unpolluted to moderate 1.943 Minor GC HF 1.451 Moderate 0.109 1 Unpolluted to moderate 1.369 Minor LF 1.014 Moderate 0.096 1 Unpolluted to moderate 3.07 Moderate STW HF 1.331 Moderate 0.107 1 Unpolluted to moderate 1.284 Minor LF 0.218 Low 0.063 1 Unpolluted to moderate 1.477 Minor JAS HF 1.606 Moderate 0.12 1 Unpolluted to moderate 2.229 Minor LF 1.721 Moderate 0.114 1 Unpolluted to moderate 1.685 Minor NYL HF 2.446 Moderate 0.124 1 Unpolluted to moderate 2.593 Minor LF 2.816 Moderate 0.128 1 Unpolluted to moderate 1.476 Minor MDD HF 0.918 Low 0.092 1 Unpolluted to moderate 0.904 None LF 3.315 Considerable 0.133 1 Unpolluted to moderate 2.095 Minor KNO HF 0.839 Low 0.193 1 Unpolluted to moderate 0.942 None Cu LF 1.161 Moderate 0.224 1 Unpolluted to moderate 1.831 Minor DPD HF 0.595 Low 0.166 1 Unpolluted to moderate 0.668 None LF 0.569 Low 0.162 1 Unpolluted to moderate 1.073 Minor GC HF 1.786 Moderate 0.264 1 Unpolluted to moderate 1.717 Minor LF 0.714 Low 0.182 1 Unpolluted to moderate 2.112 Minor STW HF 2.658 Moderate 0.3 1 Unpolluted to moderate 2.563 Minor LF 0.606 Low 0.168 1 Unpolluted to moderate 3.212 Moderate JAS HF 0.648 Low 0.172 1 Unpolluted to moderate 1.29 Minor LF 0.705 Low 0.181 1 Unpolluted to moderate 0.686 None NYL HF 3.295 Considerable 0.319 1 Unpolluted to moderate 3.494 Moderate LF 2.228 Moderate 0.283 1 Unpolluted to moderate 1.154 Minor MDD HF 1.237 Moderate 0.218 1 Unpolluted to moderate 1.21 Minor LF 2.135 Moderate 0.279 1 Unpolluted to moderate 1.348 Minor

Table 4.5 cont.: Contamination Factor (CF), Geo-accumulation Index (Igeo) and Enrichment Factor for High Flow (HF) February-April 2014 and Low Flow (LF) July-August 2014 for Al, Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn for seven sites along the upper Nyl River.

Site Season CF Description Igeo Igeo Igeo Description EF EF Metal value Value Class value description KNO HF 1.151 Moderate 0.011 1 Unpolluted to moderate 1.031 Minor Mn LF 0.849 Low 0.011 1 Unpolluted to moderate 0.931 None DPD HF 0.152 Low 0.008 1 Unpolluted to moderate 0.169 None LF 0.12 Low 0.007 1 Unpolluted to moderate 0.211 None GC HF 1.679 Moderate 0.012 1 Unpolluted to moderate 1.559 Minor LF 0.196 Low 0.008 1 Unpolluted to moderate 0.57 None STW HF 0.532 Low 0.01 1 Unpolluted to moderate 0.513 None LF 0.026 Low 0.005 1 Unpolluted to moderate 0.138 None JAS HF 0.145 Low 0.008 1 Unpolluted to moderate 0.226 None LF 0.327 Low 0.009 1 Unpolluted to moderate 0.285 None NYL HF 0.905 Low 0.011 1 Unpolluted to moderate 0.959 None LF 4.659 Considerable 0.013 1 Unpolluted to moderate 1.834 Minor MDD HF 0.372 Low 0.009 1 Unpolluted to moderate 0.318 None LF 0.442 Low 0.009 1 Unpolluted to moderate 0.279 None KNO HF 0.881 Low 0.169 1 Unpolluted to moderate 0.629 None Ni LF 1.119 Moderate 0.26 1 Unpolluted to moderate 1.617 Minor DPD HF 0.128 Low -0.019 0 Unpolluted 0.117 None LF 0.104 Low -0.019 0 Unpolluted 0.195 None GC HF 2 Moderate 0.325 1 Unpolluted to moderate 1.932 Minor LF 0.328 Low 0.115 1 Unpolluted to moderate 0.961 None STW HF 3.772 Moderate 0.403 1 Unpolluted to moderate 3.638 Moderate LF 0.166 Low 0.036 1 Unpolluted to moderate 0.881 None JAS HF 0.155 Low -0.001 0 Unpolluted 0.206 None LF 0.275 Low 0.075 1 Unpolluted to moderate 0.242 None NYL HF 5.613 Considerable 0.449 1 Unpolluted to moderate 5.951 Moderately severe LF 5.805 Considerable 0.45 1 Unpolluted to moderate 2.907 Minor MDD HF 0.954 Low 0.143 1 Unpolluted to moderate 0.672 None LF 3.645 Considerable 0.398 1 Unpolluted to moderate 2.302 Minor KNO HF 0.942 Low 0.12 1 Unpolluted to moderate 0.96 None Zn LF 1.058 Moderate 0.127 1 Unpolluted to moderate 1.638 Minor DPD HF 0.551 Low 0.103 1 Unpolluted to moderate 0.614 None LF 0.413 Low 0.091 1 Unpolluted to moderate 0.773 None GC HF 5.291 Considerable 0.192 1 Unpolluted to moderate 5.072 Moderately Severe LF 1.308 Moderate 0.137 1 Unpolluted to moderate 3.85 Moderate STW HF 2.532 Moderate 0.163 1 Unpolluted to moderate 2.442 Minor LF 0.611 Low 0.107 1 Unpolluted to moderate 3.24 Moderate JAS HF 0.541 Low 0.098 1 Unpolluted to moderate 0.901 None LF 0.616 Low 0.107 1 Unpolluted to moderate 0.599 None NYL HF 5.82 Considerable 0.196 1 Unpolluted to moderate 6.171 Moderately Severe LF 5.161 Considerable 0.19 1 Unpolluted to moderate 2.619 Minor MDD HF 2.765 Moderate 0.153 1 Unpolluted to moderate 2.352 Minor LF 2.371 Moderate 0.16 1 Unpolluted to moderate 1.497 Minor

Table 4.6: Results of the Pollution Load Index for High Flow (HF) (February-April 2014) and Low Flow (LF) (July-August 2014) periods for seven sites along the Nyl River.

Season PLI Value Description Sites HF 0.808172577 Unpolluted KNO LF 0.13743781 Unpolluted HF 0.000762457 Unpolluted DPD LF 0.000055189 Unpolluted HF 4.586396908 Polluted GC LF 0.000626515 Unpolluted HF 2.61670396 Polluted STW LF 0.000001694 Unpolluted

HF 0.003164574 Unpolluted JAS LF 0.008207386 Unpolluted HF 79.84215051 Polluted NYL LF 30378.76794 Polluted HF 1.903134002 Polluted MDD LF 9.256019851 Polluted

4.4. Discussion

In aquatic health studies it is imperative to study the quality of sediments. Sediments in aquatic ecosystems act as a medium where impurities are stored. This makes it an important component in the study of the overall health of an aquatic system. Wetland sediments are of particular interest due to the ecological services they provide. These include retaining water for slower release, filtering out toxins physically and biologically removing toxins by means of plants accumulating toxins from the water (Lisk, 1972).

Metal Concentrations and Discriminant Function Analysis

Aluminium is an element naturally found in sediment in various concentrations. This element can become toxic when exposed to changes in pH which changes it to a more toxic and bioavailable form (Munk and Faure, 2004). Though no sediment quality guidelines are available for Al, the levels of Al show a similar pattern to that found by Greenfield (2004) where levels of Al were highest for NYL (25 983.12 µg/g) and MDD

(24 757.47 µg/g). The levels detected in this study were however notably higher for NYL (61 507.54 µg/g) than were found by Greenfield (2004). The levels of Al from KNO and DPD in the current study were similar to levels in previous studies (Greenfield et al. 2007; Vlok et al. 2006).

Cadmium concentrations at all sites and seasons exceeded the Canadian Sediment Quality Guideline value for aquatic ecosystems of 0.6 µg/g (CCME, 2002). . Though relatively consistent throughout the system, the levels of Cd recorded in this study were generally higher than levels recorded by Greenfield et al. (2007).

The SQGs set the guideline concentration of Cr at 37.3 µg/g and the probable effect level (PEL) at 90 µg/g. During LF1 the levels of Cr were below SQG levels for KNO, DPD, GC and STW. JAS, NYL and MDD exceeded these levels and MDD exceeded the PEL. The levels of Cr exceed the SQGs for all sites in LF2 but only exceed the PEL for NYL and MDD. During the HF2 period the Cr levels remained below SQGs for all sites except for GC, STW and NYL but stayed below the PEL for all sites. For HF2 levels were only below the SQGs for KNO though none of the sites exceeded the PEL. The levels of Cr found in this study were similar in range to levels found by Greenfield (2007) (0 – 244.51 µg/g).

When compared to Cu levels in previous studies it can be concluded that the levels found in this study were generally similar to levels found by Greenfield et al (2007) except for KNO where Cu concentrations exceeded 750 µg/g in the study by Greenfield et al. (2007).

Compared to previous studies, the levels of Mn in JAS were notably lower in the current study and the levels of Mn in NYL (4324.05 µg/g) were notably higher than in previous studies (2237.90 µg/g) (Greenfield, 2004). No SQGs were available for this metal.

Unfortunately no historical information is available on Ni. There is also no SQG concentration for this metal but according to the proposed guidelines set out by MacDonald et al. (2000) Ni levels at STW and NYL in the HF1 season and NYL and MDD for both dry seasons, exceeded the guideline levels determined by MacDonald et al. (2000). The concentrations were elevated for STW in the wet season and for

NYL for all seasons. The Ni levels in MDD showed an increasing trend over the seasons.

The levels of Zn exceed SQGs (123 µg/g) at GC in the HF2 season and NYL in the HF1 and LF2 seasons. Though some of the sites exceed the guidelines, there have been improvements from the study by Greenfield et al. (2007) where the following sites have decreased Zn concentrations in the current study: KNO, DPD, STW, JAS, NYL and MDD.

The Canonical DFA determined that there was a grouping together of all sites except for STW in the wet season, MDD in the dry season and NYL for both seasons. This separation was mainly due to differences in Ni and Zn concentrations.

Ecological Risk Assessment

The Ecological Risk Assessment indices indicate that KNO had a low ecological risk for the wet season but Mn and Cd contamination was moderate in this season. This is not of major concern as the contamination of these two metals at KNO were classified as having a low pollution factor and no enrichment for the dry season whereas the rest of the sites showed an increase in the rest of the metals from low to moderate. This site has limited impacts and is located close to the source of the river making it one of the more pristine sites in the study.

Donkerpoort Dam is located close to KNO and also has limited pollution sources and therefore it is expected to have good water and sediment quality. The CF indicated that DPD had low contamination of Al, Cd, Cu, Mn, Ni and Zn but had moderate contamination of Cr. The EF contradicted the CF because it determined that DPD had minor enrichment of Cd and Cu in the dry season whereas the CF determined these metals had low contamination factors. Apart from the Cr concentrations, the sediment poses low ecological risk to the aquatic system.

The GC site is located where the river exits the Koro Creek Golf Course in Modimolle. Before this point in the system there are few pollution sources. This site had higher CF classifications for the wet seasons than the dry seasons except for Cd and Cr which had similar classifications for both seasons. For GC in the wet season, Al, Cd, Cu, Mn and Ni had moderate contamination. The contamination and enrichment of Zn

is of importance at this site as most sites had levels of concern. The CF index classified GC as having considerable contamination of Zn in the wet season and moderate contamination in the dry season. The EF classifies GC as having moderately severe enrichment for the wet season and moderate enrichment in the dry season.

The STW sampling point is located just downstream of the STW outlet. During the sampling period there were times when the STW was not functioning optimally and times when it was not functioning at all. It was expected that the sediment quality of the STW would be extremely poor. However, the STW had some of the lowest concentrations of some metals in the dry season. The CF labelled the STW as having low contamination of Al, Cd, Cr, Cu, Mn, Ni and Zn for the dry season and for Cd and Mn for the wet seasons as well. The levels of these metals in the wet season were placed in the Moderate Contamination class. The EF index contradicted some of the CF classifications for instance; Cd for the dry season had a low CF value but was classified as having a moderate enrichment factor. The same is evident or Cu from the dry season and Zn from the dry season.

The sediment quality of JAS was relatively good for all metals except Cr which had a moderate contamination factor and minor enrichment factor for both wet and dry seasons. The EF index indicated that JAS had no enrichment of Al, Cu, Mn, Ni and Zn in both seasons as well as Cd in the high flow season. Though this site is located just downstream of the STW, the levels were all low. This could be due to the wetland situated between the STW and JAS which filters out some of the metal particles.

The general quality of the sediments in the NYL was worse than any of the other sites. The NYL sediment had high contamination levels of Al for both seasons. It also had considerable contamination levels of Cd and Mn in the dry season, Cu in the wet season, and Ni and Zn for both seasons. Moderate contamination was found at NYL for Cr for both seasons and for Cu in the dry season. The metals in NYL that showed low levels of contamination were Cd and Mn in the wet season. Some of the highest concentrations recorded in this study were from inside the Nylsvley Nature Reserve for the second low flow period. This could be attributed to the purifying function that a wetland performs by filtering out and binding toxic particles.

The MDD is located near Mokopane, approximately 50km downstream from NYL. At this point in the river there are more impacts: both point source and non-point source. The MDD had considerable levels of contamination in the dry season for Al, Cr, and Ni. MDD had moderate contamination of Al in the HF season. MDD had moderate contamination of Cu and Zn for both seasons. This indicated that closer attention should be payed to this aquatic ecosystem.

It is peculiar that the STW had the lowest concentrations of some metals in the second dry season seeing that the site is located directly next to the STW outlet. It is possible that this anomaly can be attributed to metals leaching from sediments due to a lowering of the pH in the system before sampling (Saleem et al. 2015).

The PLI indicated that four of the seven sites had polluted sediments. For both sampling seasons KNO and DPD were unpolluted. The GC and STW were both polluted during the wet season but unpolluted for the dry seasons which could indicate that there was some improvement in sediment quality for these two sites. For both seasons the NYL and MDD sites were determined to be polluted according to the PLI. These results are supported by the DFA, CF and EF.

From this study it is also evident that the Igeo index is not as sensitive in determining the level of contaminants in the system as the other indices were. There were minor discrepancies between the different indices but these indices reach the same conclusions in the end.

4.5. Conclusion

In this study it was determined that studying wetland sediments gives insight into the health of an ecosystem that water quality studies fail to attain. The study of metals in sediment gives a time integrated assessment of what had been accumulating in the top layer of sediment in the past. In the case of the Nyl River system, it was determined that the sites closest to the origin of the river had lower levels of metals than the other sites. Moving downstream from the origin the metal levels generally increase for the golf course and sewage treatment works sites. The STW delivered peculiar results with the sediment samples taken during the high flow period having high metal concentrations and the low flow period having some of the lowest levels of metals in

the sediment. This anomaly could be attributed to changes in pH leading to metals leaching from sediments (DWAF, 1996). The site directly downstream of the STW, JAS had overall good sediment quality with low contamination. This could be due to a wetland situated between the two sites that is performing its ecological function (Vlok et al. 2006).

The high metal contamination found in the Nylsvley Nature Reserve is of major concern. This indicates that the Nyl floodplain is accumulating the pollution levels from the town of Modimolle and the surrounding mining and agriculture. This also indicates that the Nyl floodplain is performing its ecological function well by trapping and removing toxins from the water. Sediment quality from the MDD had once again deteriorated as the MDD receives water that is impacted by mining and the town of Mookgophong.

The sediment contamination indices differ in their sensitivity and there are discrepancies between some of the classifications made by the indices. Overall the sediment quality in the Nyl River system starts out pristine, then decreases as one moves downstream due to an increase in sources of contamination.

Chapter 5

5.1. Concluding remarks

The Nyl River is an ecologically important river located in the Limpopo Province of South Africa. It is also one of the largest floodplain wetlands in South Africa and is Ramsar accredited (Friends of Nylsvley, 2014). The water and sediment quality of the Nyl River is of importance as it acts as habitat for multiple ecologically important species which could be vulnerable to metal pollution (Friends of Nylsvley, 2014). This river system is exposed to various pollution sources, especially mining, which could alter the water and sediment quality (Vlok et al. 2006).

Metal pollution is a research field which has garnered great attention in the last few decades. The reason for the increasing interest in metal studies in aquatic environments is mainly because metals are not degradable and accumulate in biota and sediments (Sanders, 1997). Each metal has its own unique negative effects on individual organisms and are released into the environment by various industrial processes (DWAF, 1996).

The water metal data indicated that the STW contributed notably to the pollution in the system. The water data from the acid spill showed that some metal levels increased after the spill and some decreased again highlighting the complexity of water chemistry. The AM accumulation data showed that the levels of bioavailable metals were generally higher in the dry season than in the wet season for Al, Cr, Cu and Pb. There were also elevated levels of Co, Fe, Mn and Ni at the reference site which decreases moving downstream indicating that the STW is not the main metal pollution contributor. The study of the water metal levels also revealed that the concentrations that were found in this study are similar if not lower than levels found by Vlok et al. (2006).

The sediment results revealed that the most polluted sites were the Nyl Floodplain and the Moorddrift Dam. It also shows that the two uppermost sites in the system are close to pristine. There is a gradual increase in metal concentrations moving downstream from the source. The sediment quality of the STW site were peculiar as the wet season revealed high metal concentrations and the dry season revealing very low metal concentrations possibly due to leaching of metals from the sediment as higher levels

of metals were found in the water for that period (Sanders, 1997). The high metal levels in the Nyl Floodplain is indicative that the wetland is performing its ecological function but also places the biota at risk.

The first hypothesis of the study was that the water quality of the Nyl River has deteriorated since the last available study which was released in 2006. This hypothesis is rejected as many metal concentrations have decreased and it can therefore be deduced that the water quality has improved since the last study.

The second hypothesis was that the Nyl Floodplain is threatened by the inflow of polluted water. This hypothesis is accepted due to the high amounts of most metals detected in the Nyl floodplain sediment which indicates that the wetland is accumulating high levels of metals from the water that flows into the wetland.

The third hypothesis was that the sewage treatment plant was the main contributor of metal pollution in the system. This hypothesis is rejected based on the high accumulation of certain metals by the AMs upstream of the STW. The rejection of this hypothesis is also supported by low concentrations of metals found at STW during the Low Flow period.

The last hypothesis of this study was that AMs can be used to monitor metal contamination in freshwater systems. This hypothesis is accepted as the AMs accumulated metals in concentrations relevant to concentrations detected in the environment. The AMs do only accumulate the bioavailable metals and so the concentrations between AMs and water samples differed. Artificial Mussels are good indicators of bioavailable metal pollution in the water as they accumulate metals in concentrations relevant to the environment (Wu et al. 2007). They also provide a standardised monitoring method which allows for future comparison in the same river or in a different river system (Hossain et al. 2015). The use of AMs in this study also provided a few challenges especially theft, loss of AMs in flooding events and a lack of sites that were protected from the public.

5.2. Recommendations

It is recommended that monitoring programs continue in the Nyl River system on a seasonal basis as there is a threat of multiple new mines due to start in the area as well as the constant increase of urban, industrial and agricultural runoff (EScience

Associates, 2013). This rings true especially for the Nyl floodplain as it is the most vulnerable to metal pollution at this stage.

It is also recommended that a study be conducted on the entire system from the origin of the Groot and Klein Nyl Rivers to the confluence of the Mogalakwena River and the Limpopo River. This is recommended due to South Africa’s international obligations in the protection of this water resource, as the Limpopo River acts as a border between South Africa and its neighbouring countries: Botswana, Zimbabwe and Mozambique. The National Water Act makes provisions for Transboundary Rivers and regulations for the supply of water (quantity and quality) to our neighbouring countries exist (National Water Act 36 of 1998).

The system could benefit from studies on fish and invertebrate assemblages as well as the accumulation of metals in fish as it has been determined that some of the levels of metals exceeded the TWQR and can be negatively influencing the biota (DWAF, 1996). The effect of metals in the water on the fish in the system by means of biomarker analysis could also be beneficial and could help to protect this remarkable ecosystem for future generations.

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APPENDIX A

Table 1: The levels of metals detected in water samples from the Nyl River, Limpopo, South Africa for the period of February (HF) to August (LF) 2014 as well as spot water sampling after an acid spill (AS). Levels of metals are expressed in µg/l. Levels below detection are labelled BD. Al Cd Co Cr Cu Fe Mn Ni Pb Zn KNO HF 77.719 0.144 0.058 3.256 10.321 34.876 0.526 0.961 7.341 21.452 LF 54.309 BD BD 1.457 7.289 36.604 4.686 1.427 1.106 89.673 AS 210.459 BD BD 0.545 0.985 407.134 2.66 1.198 0.895 105.775 DPD HF 54.269 0.082 0.236 1.213 4.656 41.01 4.876 3.91 1.029 37.003 LF 40.745 BD BD 3.537 2.104 73.884 2.146 0.48 0.51 14.616 AS 161.163 BD BD 0.226 0.607 312.773 0.346 0.572 0.178 42.324 STW HF 69.189 BD 0.503 4.08 11.009 59.814 0.821 1.901 1.462 27.334 LF 82.326 BD 0.541 1.586 5.776 205.413 16.699 2.071 3.811 39.497 AS 362.965 BD 9.1 0.886 8.801 5006.985 660.744 9.893 0.774 229.696 GC HF 106.191 BD 0.13 1.383 4.06 175.403 0.185 1.539 0.577 45.125 LF 73.32 BD 0.066 2.07 4.307 46.518 0.825 1.687 0.943 31.277 AS 77.004 BD 0.077 0.138 1.146 141.523 0.543 2.336 0.233 97.805 JAS HF 46.384 0.027 0.046 1.897 2.31 915.652 1.613 1.816 1.358 27.07 LF 74.659 BD BD 1.158 10.503 50.65 0.1 1.077 0.603 86.923 AS 25.149 BD BD 0.203 0.921 227.576 0.376 1.12 0.359 53.771 NYL HF 103.419 0.036 0.71 1.7 6.597 314.21 1.942 10.25 0.84 23.466 LF 115.589 BD BD 5.739 8.323 327.618 0.164 5.963 0.502 24.659 AS 402.976 BD BD 0.12 0.934 722.385 6.74 0.76 0.293 73.532 MDD HF 93.41 0.001 0.149 1.474 3.164 52.646 2.191 2.091 2.206 79.727 LF 60.891 BD BD 9.521 6.007 365.75 2.242 1.437 11.27 33.186 AS 367.490 BD 0.067 0.508 7.056 137.686 3.691 4.244 0.649 63.203

Table 2: The levels of metals accumulated from the Nyl River, Limpopo, South Africa by AMs for the period of February (HF) to August (LF) 2014. Levels of metals are expressed in µg/g Chelex-100 resin. Levels below detection are labelled BD. Season Al Cd Co Cr Cu Fe Mn Ni Pb Zn KNO HF 52.612 BD 5.14 0.266 1.785 8573.46 107.739 1.636 1.121 47.666 LF 159.075 BD BD 0.244 12.009 157.618 12.641 0.271 1.797 43.958 DPD HF 81.391 BD 0.942 0.878 2.845 137.942 70.797 0.614 1.17 52.828 LF 222.919 BD BD 2.165 6.142 96.959 14.234 0.805 2.247 51.182 STW LF 359.934 BD BD 4.616 7.033 186.198 6.006 1.171 2.944 56.308 GC HF 51.298 BD BD 0.281 2.271 123.079 16.032 0.462 1.064 42.002 JAS HF 52.185 BD BD 1.414 2.694 65.91 7.319 1.148 1.121 69.039 LF 70.946 BD BD 2.001 2.423 69.046 2.21 0.338 1.399 30.808

APPENDIX B

Table 3: Levels of metals determined from sediment samples by ICP-OES analysis for the Nyl River system in Limpopo, South Africa from February-August 2014. Measurements are expressed in µg/g dry weight of sediment sample. Levels below detection are labelled BD. Al Cd Co Cr Cu Fe Mn Ni Pb Zn KNO HF 6918.519 2.614 BD 19.111 9.015 21286.85 611.1 7.22 BD 22.893 LF 8877.119 1.578 BD 49.655 12.473 15153.97 450.6 9.175 BD 25.735 DPD HF 4152.144 1.492 BD 44.89 6.398 18915.44 80.818 1.053 BD 13.396 LF 2730.432 1.169 BD 36.846 6.117 10321.75 63.842 0.852 BD 10.053 GC HF 16178.29 1.078 BD 49.879 19.192 20129.87 891.1 16.398 BD 128.65 LF 4627.657 1.211 BD 34.851 7.676 6479.57 104.116 2.687 BD 31.799 STW HF 20517.57 1.08 BD 45.774 28.555 19800.64 282.55 30.919 BD 61.552 LF 2571.719 1.243 BD 9.571 6.507 3600.804 13.772 1.362 BD 14.855 JAS HF 4219.082 1.453 BD 55.205 6.961 15028.84 76.985 1.274 BD 13.149 LF 4648.119 1.577 BD 59.171 7.576 20120.44 173.364 2.254 BD 14.983 NYL HF 48558.04 1.076 BD 85.221 28.006 21728.34 551.775 41.48 BD 121.54 LF 61507.54 18.944 BD 107.304 27.267 55815.84 4324.05 58.227 BD 149.4 MDD HF 16469.37 1.083 BD 31.554 13.288 19722.94 197.684 7.816 BD 67.237 LF 32103.99 0.971 BD 113.988 22.941 30215.75 234.475 29.883 BD 57.638