INAYETH MUSTAPHA - FINAL THESIS

A Water Quality Assessment of the Mthinzima River
using the miniSASS Method
By Inayeth Mustapha
Supervisor: Dr J. Finch
University of KwaZulu-Natal

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Preface
I hereby declare that this dissertation, submitted in partial fulfilment of the requirements for
the degree of Bachelors of Science (Honours) in Environmental Science, to the University of
KwaZulu-Natal, is a result of my own research and investigation and that it has not been
previously submitted by me for a degree at this, or any other institution or university.
………………….... …………………..
Inayeth Mustapha Date

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Abstract
The Mthinzima River, which flows into the Midmar Dam, is a great source of pollution to the
dam. This is because the township of Mpophomeni, situated alongside the river, has no proper
sewage system, and so all sewage flows directly into the river. The aim of this study was to
assess water quality of the Mthinzima River using the miniSASS method, in order to
determine whether the township is the source of pollution of the river. The miniSASS method
is a biomonitoring technique, which involves identifying macroinvertebrate groups that
inhabit the river. Three sites were chosen for assessment, the first was located upstream
before the possible pollution source, the second was located at the possible pollution source
(the township), and then another was located at the dam entrance. It was found that site
upstream had excellent water quality and river health, as there were no anthropogenic
influences. At the township, the water quality was very poor, with the river in a critical
condition. This was because of the raw sewage, which made it uninhabitable for most of the
macroinvertebrate groups. The dam entrance also had poor water quality, although it was
slightly improved over that of the stream near the township. This was due to the river flowing
through a small wetland before reaching the dam entrance. However, the wetland was not
sufficient to remove all the pollution and only a small percentage was removed. Statistical
analysis was done to compare water quality between each of the three sites. The water quality
at each site was found to be significantly different from each other. This study serves as
crucial evidence that warrants the need for improved sewage systems in the area, especially
for any upcoming housing projects.

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Acknowledgements
The success of this dissertation was largely influenced by the assistance and support that I
have received from members of the University of KwaZulu-Natal (UKZN) staff, family and
friends throughout the duration of my project. It is with great appreciation that I would like to
acknowledge the following people:
Dr J. Finch, my supervisor and lecturer. I am extremely indebted to you for your valuable
insight and continuous monitoring of my progress, throughout the duration of the study. Your
guidance, critique and suggestions are greatly appreciated.
My girlfriend, Yasmin Rajak, for all her assistance with my fieldwork and laboratory work,
as well as her constant support and encouragement throughout the duration of the study.
My parents and immediate family, for their constant support and belief in me.
Zayd Goolam Hoosen, Sofiah Joosab, Kamleshan Pillay, Sarushen Pillay, Ahmed
Ameen, Viratha Hariram, Ashlyn Padayachee and Xiandrea Krizante Joseph for their
encouragement, love and support, especially during times of stress.
Finally, without the blessings of The Almighty, the success of this research would not have
been achieved.

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TABLE OF CONTENTS
TITLE PAGE i
PREFACE ii
ABSTRACT iii
ACKNOWLEDGEMENTS iv
TABLE OF CONTENTS v
LIST OF ACRONYMS vi
LIST OF TABLES vii
LIST OF FIGURES viii
CHAPTER ONE: INTRODUCTION AND PROBLEM CONTEXTUALISATION
1.1 PREAMBLE 1
1.2 AIMS AND OBJECTIVES 4
1.3 THESIS STRUCTURE 4
1.4 CONCLUSION 5
CHAPTER TWO: LITERATURE REVIEW
2.1 INTRODUCTION 6
2.2 DEFINING WATER QUALITY 6
2.3 MEASURING WATER QUALITY 8
2.3.1 BIOLOGICAL COMPONENTS 8
2.4 CLASSIFICATION OF WATER SYSTEMS 9
2.4.1 CHARACTERISTICS OF A GENERAL RIVER SYSTEM 9
2.4.1.1 THE HEADWATER ZONE 9
2.4.1.2 THE MIDDLE ZONE 10
2.4.1.3 THE LOWER ZONE 10
2.4.2 BIOTOPES IN A RIVER SYSTEM 10
2.4.2.1 RIFFLES AND RUNS 11
2.4.2.2 POOLS 11
2.4.2.3 AQUATIC AND MARGINAL VEGETATION 11
2.4.2.4 ALGAE 11
2.5 FRESHWATER INVERTEBRATES AS INDICATORS 11
2.5.1 ORDER: EPHEMEROPTERA (MAYFLIES) 13

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2.5.2 ORDER: TRICHOPTERA (CADDISFLIES) 13
2.5.3 ORDER: COLEOPTERA (BEETLES) 14
2.5.4 ORDER: HEMIPTERA (TRUE BUGS) 14
2.5.5 ORDER: ODONATA (DRAGONFLIES/DAMSELFLIES) 14
2.5.6 ORDER: DIPTERA (FLIES, MOSQUITOES, MIDGES) 15
2.5.7 ORDER: PLECOPTERA (STONEFLIES) 15
2.5.8 ORDER: LEPIDOPTERA (AQUATIC CATERPILLARS) 15
2.5.9 ORDER: MEGALOPTERA (DOBSONFLIES) 16
2.5.10 TAXON HYDRACARINA (WATER MITES) 16
2.5.11 CLASS: TURBELLARIA (FLATWORMS) 16
2.5.12 ORDER: AMPHIPODA (SCUDS) 16
2.5.13 ORDER: DECAPODA (CRABS, SHRIMPS) 17
2.5.14 PHYLUM: ANNELIDA 17
2.5.15 PHYLUM: PORIFERA (SPONGES) 18
2.5.16 PHYLUM: MOLLUSCA 18
2.6 THE miniSASS METHOD AS A TOOL FOR BIOMONITORING 19
2.7 CONCLUSION 24
CHAPTER THREE: STUDY AREA AND METHODOLOGY
3.1 INTRODUCTION 25
3.2 STUDY AREA 25
3.3 METHODOLOGY 26
3.3.1 SAMPLING DESIGN 26
3.3.1.1 SAMPLE COLLECTION 26
3.3.2 MACROINVERTEBRATE IDENTIFICATION 26
3.3.3 INTERPRETATION OF THE miniSASS SCORE 27
3.3.4 STATISTICAL ANALYSIS 28
3.4 CONCLUSION 28
CHAPTER FOUR: RESULTS
4.1 INTRODUCTION 29
4.2 SCORING OF MACROINVERTEBRATE GROUPS 29
4.2.1 SITE 1 – UPSTREAM 29
4.2.2 SITE 2 – TOWNSHIP 30

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4.2.3 SITE 3 – DAM ENTRANCE 31
4.3 STATISTICAL ANALYSIS – COMPARISON 32
4.3.1 SITE 1 AND 2 32
4.3.2 SITE 1 AND 3 33
4.3.3 SITE 2 AND 3 34
4.3.4 ERROR BAR PLOT 35
4.4 CONCLUSION 35
CHAPTER FIVE: DISCUSSION
5.1 INTRODUCTION 36
5.2 INTERPRETATION OF MACROINVERTEBRATE GROUP SCORES 36
5.3 INTERPRETATION OF SENSITIVITY SCORES 39
5.4 STATISTICAL ANALYSIS 40
5.5 CONCLUSION 40
CHAPTER SIX: CONCLUSION
6.1 INTRODUCTION 42
6.2 REVIEW OF AIMS AND OBJECTIVES 42
6.3 RECOMMENDATIONS 43
6.2 CONCLUSION 44
REFERENCES 45
APPENDICES
APPENDIX A – miniSASS SCORESHEETS I
APPENDIX B – RIVER HEALTH INTERPRETATION TABLES IV

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LIST OF ACRONYMS
ASPT – Average Score Per Taxon
CSIR – Council for Scientific and Industrial Research
DWAF – Department of Water Affairs and Forestry
EIA – Environmental Impact Assessment
IFR – Instream Flow Requirement
RoD – Record of Decision
SASS – South African Scoring System
SPSS – Statistical Package for the Social Sciences

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LIST OF TABLES
Table 4.1 Ranking of datasets for Site 1 and Site 2 32
Table 4.2 Test Statistics for Site 1 and Site 2 33
Table 5.1 Average Sensitivity Scores for each site 39

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LIST OF FIGURES
Figure 3.1 Image of the Mthinzima River 25
Figure 3.2 miniSASS macroinvertebrate group sensitivity scores table 27
Figure 4.1 Graph of Sensitivity Scores for Macroinvertebrate Groups found at Site 1 29
Figure 4.4 Graph of Number of Groups of Macroinvertebrates found at each site 32
Figure 4.5 Error Bar Plot showing significant differences between average
sensitivity scores 35

1
CHAPTER ONE
1. INTRODUCTION AND PROBLEM CONTEXTUALISATION
1.1 PREAMBLE
South Africa is classified as being a dry country. Despite a few areas having a higher rainfall
than others, the country’s annual average rainfall of 450 mm is way below the global average
of 860 mm per year (CSIR, 2011). As a consequence, South Africa has relatively very little
water that is available and several factors, such as water pollution, climate change, and
international obligations, further limit the amount of water that we have at our disposal
(CSIR, 2011). When compared to neighbouring countries, South Africa’s available water per
capita of 1 000 m3
/person/year, emphasises the problem we are faced with. Neighbouring
countries have a larger amount of available water per capita, as their water is drawn from
areas that have higher rainfall and/or lower populations (CSIR, 2011).The freshwater
resources in South Africa, which includes rivers, groundwater and man-made lakes, have
almost all been fully allocated and their water quality has steadily declined as a result of
increased pollution due to industry, afforestation, agriculture, urbanization, mining, and power
generation (Oberholsten and Ashton, 2008). This has placed an increasing stress on these
water resources exacerbated by an increasing population and an expanding community
(Oberholsten and Ashton, 2008). Poor quality water does not just place a limit on its
utilisation value; but also places additional economic pressure on society through both the
primary treatment costs and the secondary impacts on the economy (CSIR, 2011). As the
degree of pollution of a water resource increases, so does the treatment costs (CSIR, 2011).
Human health is directly impacted by poor water quality as it gives rise to waterborne
diseases such as cholera, bacterial infections, endocrine disrupting substances and heavy
metal accumulation (CSIR, 2011). This in turn affects economic activity (CSIR, 2011). The
limited availability of further supplies of water coupled with heavy utilisation of the nation’s
water resources require far more efficient water resource utilization by all sectors to ensure
sustainable use of water (CSIR, 2011). With the increase in pollution of our water resources,
the cost of treating water for human consumption increases as well (CSIR, 2011). The
challenge for South Africa lies in the efficient and balanced use of water, together with other
natural resources, to create an environment conducive to social and economic well-being
(CSIR, 2011).

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This study focuses on water quality of the Mthinzima River, which flows into one of
KwaZulu-Natal’s most important water resources, the Midmar Dam.The Midmar Dam is
situated in the KwaZulu-Natal Midlands and is a source of water to Pietermaritzburg and
Durban (Tarboton & Schulze, 1991). The dam’s capacity is approximately 374 million
kilolitres and provides water to 4.8 million people annually (Tarboton & Schulze, 1991). The
township of Mpophomeni has a poor record of environmental management, and there is
ongoing faecal and nutrient pollution into Midmar Dam (Beaver, 2011). The Mpophomeni
waste treatment plant had been decommissioned during the apartheid era, as effluent from
whites and blacks was not allowed to mix, therefore townships had to have their own water
works (Naidoo, 2008). The Howick plant served both areas (Naidoo, 2008). When the
transitional council had been developed, it was realised that this was a huge waste of water
and resources as both the Howick and Mpophomeni waterworks were operating at half-
capacity (Naidoo, 2008). With ongoing developments in the Howick and Merrivale area, it
prompted the need to re-open the Mpophomeni plant (Naidoo, 2008). A R30 million project
was scheduled to be launched by the end of 2008 to solve Mpophomeni’s sewage problems
and to cope with the increased amount of effluent from the new housing developments in the
Howick/Merrivale area (Naidoo, 2008). This was to be done by opening and upgrading the
Mpophomeni waste water treatment works (Naidoo, 2008).
However, it is evident that this proposed project was never put into action. Sewer manholes in
certain areas of the township have been spilling regularly for about four years now, and it is
still happening (Denny-Dimitriou, 2009). There are numerous blocked drains, unattended
broken sewerage pipes, along with rubbish been thrown into manholes (Beaver, 2011).This is
directly attributed to poor maintenance by the uMngeni Municipality (Beaver, 2011). The
township, which is located two kilometres away from Midmar, is serviced by sewer lines that
run parallel to two streams which flow into the wetlands situated just below the town (Denny-
Dimitriou, 2009, Beaver 2011). In a few areas, such as Ecabazini, Ebumnandini and B5,
sewage discharge from manholes runs into the Mthinzima stream, which flows through the
wetland and then into Midmar Dam (Denny-Dimitriou, 2009). The health state of Mthinzima
stream and its impact on the wetland system has been monitored (Denny-Dimitriou, 2009).
Testing of the water in the stream leaving the township by an environmental education
practitioner on two occasions, had found an E.coli level of 300 000 per 100 millilitres (Denny-
Dimitriou, 2009). This is way above the safe level for human contact with water, which is 185
per 100 millilitres (Denny-Dimitriou, 2009). The bacterial count of the polluted water once it

3
has flowed through the wetland is much lower, meaning that the small wetland below
Mpophomeni is working under extreme pressure to purify the water and reduce sewage
contamination levels (Denny-Dimitriou, 2009).
In 2011, plans for a new low-cost housing development, called Khayelisha, had begun
(Beaver, 2011).The uMgungundlovu Municipality had started breaking the ground to lay
foundations for the low-cost houses, located just 300 metres from the shore of the Midmar
Dam and only 20 metres away from the nearby wetland (Guy Nicholson Consulting, 2006,
Beaver, 2011). The Khayelisha site was reportedly chosen because the municipality wished to
utilise and extend the existing sewerage infrastructure (Beaver, 2011). An environmental
impact assessment (EIA) was reportedly done as part of the process (Beaver, 2011). The
Department of Water Affairs (DWA) had granted approval, but based on very stringent
conditions imposed, which were put into the Record of Decision (RoD) (Beaver, 2011).
However, construction work began on site, without these RoD issues being met (Beaver,
2011).
The Department of Water Affairs and a number of environmental experts have expressed
serious concerns about the future of the wetlands surrounding the dam (Beaver, 2011). They
believe that the development could lead to the poisoning of the main water supply, impacting
towns downstream like Howick and the greater Pietermaritzburg area (Beaver, 2011).
Research has shown that approximately 51% of the E. coli loads which are present in Midmar
Dam are from Mpophomeni (Beaver, 2011). The sewage also causes an increase in
phosphates concentration in the dam, leading to the development of blue-green algae (Beaver,
2011). A consequence of this is eutrophication of water, in which blue-green algae multiplies,
producing a serious toxin known as cyanotoxin (Beaver, 2011). This toxin has been linked to
neurological disorders (Beaver, 2011). Elevated phosphate loads were detected in
Mpophomeni, using satellite imagery on Google Earth in 2006 (Beaver, 2011).
The miniSASS (South African Scoring System) method will be used to assess water quality at
the Mthinzima River in order to determine whether the water quality has degraded as well as
the extent of the water quality degradation. This is a simplified method of biomonitoring
based on the SASS technique (Graham, et al, 2004). The advantage of using the miniSASS
method for this study is that it involves reducing the taxonomic complexity of SASS to a few
aquatic invertebrate ‘groupings’ which act as surrogates for the complete suite of SASS data
(Graham, et al, 2004). This technique is efficient because the aquatic invertebrate groups are

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easily identifiable, the method is robust and produces results comparable to that of the full
SASS technique (Graham, et al, 2004). Therefore, miniSASS can be used with confidence,
producing data which differs very slightly from SASS, but is still sufficiently accurate to be
valuable to all stakeholders interested in river health (Graham, et al, 2004).
1.2 AIM AND OBJECTIVES
The aim of the study is to assess the water quality at the Mthinzima River using the
miniSASS method, in order to determine impacts of improper sewerage systems of the low
cost housing developments. Specific objectives are as follows:
(i) To compare macroinvertebrate assemblages above and below the point pollution
source of the Mthinzima River.
(ii) To provide evidence on the usefulness of miniSASS.
(iii) Using the miniSASS method to assess water quality at a number of points along
the Mthinzima River.
(iv) Determine if there is any pollution, and if so, the extent of the pollution.
1.3 THESIS STRUCTURE
This thesis will first introduce the study, by providing a background on South Africa’s water
resources, and more specifically the Mthinzima River and Midmar Dam. The issue of water
pollution in the Mthinzima River will then be discussed, as well as a brief review of the
miniSASS method, which will be used to assess the water quality at the river. Aims and
objectives will then be stated.The literature on water quality, classification of river systems,
macroinvertebrates, biomonitoring, and the miniSASS method will then be reviewed. This
will be followed by an introduction to the study site, and a description of the methodology
involved in sample collection and data analysis. Results will then be presented, followed by a
discussion of these results. The thesis will then be concluded, together with a review of aims
and objectives, as well as recommendations for future studies.

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1.4 CONLUSION
This study may serve as scientific evidence to show that the low cost housing developments at
Mpophomeni, with its lack of a proper sewerage systems may have detrimental effects on the
quality of water in the Mthinzima River and in turn the Midmar Dam. Results from the study
may help inform and educate the local municipality when developing future low cost housing
projects in the region and aid with decision making in the future. It would make management
aware of the need for proper sanitation and sewerage systems. This would prevent further
negative impacts on the river, hence increasing the economic value of the river, dam and
surrounding areas.

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CHAPTER TWO
2. LITERATURE REVIEW
2.1 INTRODUCTION
Water is essential for all life to exist, however this precious natural resource is becoming
increasingly under threat by humans as their populations grow and with that a growing
demand for more water of high quality for economic activities and domestic use (Karr, 1999).
Abstraction of water for domestic use, industrial production, agricultural production, mining,
power generation, and forestry practices could cause deterioration in water quality and
quantity that impact the aquatic ecosystem, as well as the availability of safe water for human
consumption (Karr, 1999). Water quality and quantity are closely linked although rarely
measured concurrently (Carr & Neary, 2006). Water quantity is frequently measured through
remote hydrological monitoring stations that record water level, discharge, and velocity.
Monitoring of water quantity can, to a certain degree, be undertakenwith a minimum amount
of human intervention, once a monitoring station has been set up (Carr & Neary, 2006). On
the contrary, water quality is typically determined by analysing water samples collected by
teams of personnel that visit monitoring stations at regular intervals (Carr & Neary, 2006).
The associated costs with monitoring the numerous parameters that affect water quality, as
compared to those associated with monitoring just a small number of water quantity variables,
usually means that water quality monitoring is not undertaken as regularly as water quantity
monitoring (Karr, 1999). Nevertheless, the results of water quality monitoring are
fundamental to being able to track both spatial and temporal trends in surface and ground
waters (Karr, 1999).
2.2 DEFINING WATER QUALITY
Water quality is defined as being the quality of any body of groundwater or surface water
being a function of either or both natural influences and human activities (Carr & Neary,
2006). If it were not for the influence of humans, water quality would be determined by the
atmospheric processes of evapotranspiration, the weathering of bedrock materials, the
deposition of dust and salt by wind, the natural leaching of organic matter and nutrients from
the soil, hydrological factors which lead to runoff, and by biological processes in the aquatic
environment itself which can alter the physical and chemical composition of the water (Karr,
1999). A direct consequence of this is that water occurring naturally in the environment

7
contains numerous dissolved substances and non-dissolved particulate matter. Dissolved
minerals and salts are essential components of good quality water as they aid with
maintaining the health and vitality of the organisms that depend on this ecosystem service
(Carr & Neary, 2006).
Water can also consist of substances that are detrimental to life (Metcalfe, 1989). These
include metals such as lead, mercury and cadmium, organic toxins, pesticides and radioactive
contaminants (Carr & Neary, 2006). Water from natural sources nearly always contains living
organisms that are essential components of the biogeochemical cycles in aquatic ecosystems.
Some of these organisms, such as bacteria, fungi, protists, parasitic worms, and viruses, can
be harmful to humans if present in drinking water (Carr & Neary, 2006). The ability of
aquatic environments to sustain healthy ecosystems depends on the availability of water and
its physical, chemical, and biological composition (Karr, 1999). This results in the
degradation of water quality and quantity, which leads to ecosystems services being lost and
so organism suffer. The criteria used to assess water quality differ for each human use
because the quality of water varies according to each water use category (Carr & Neary,
2006). The quality of water required for maintenance of proper ecosystem health is dependent
on the natural background conditions (Metcalfe, 1989). Certain aquatic ecosystems have the
ability to resist large changes in water quality without displaying any noticeable effects on
ecosystem function and composition (Carr & Neary, 2006). Other ecosystems however, are
sensitive to subtle changes in the chemical and physical composition of a water body, which
could lead to loss of biological diversity and degradation of ecosystems services (Metcalfe,
1989). Water quality degradation due to humans is often gradual, and slight adaptations of
aquatic ecosystems to these changes are not always detected until such time that a great
change in ecosystem condition occurs (Carr & Neary, 2006). Frequent monitoring of the
physical, biological and chemical constituents of aquatic ecosystems help to detect extreme
situations, where an ecosystems ability to return to its normal state is compromised beyond
reproach (Karr, 1999).
Water quality is assessed by comparing the chemical and physical properties of water with
water quality standards and guidelines (Karr, 1999). For the provision of clean safe water for
human consumption, drinking water quality guidelines and standards are usually based on
scientifically assessed acceptable levels of toxicity to either humans or aquatic organisms
(Carr & Neary, 2006).Since aquatic ecosystems vary enormously in their composition both
spatially and temporally, it is more difficult to set guidelines for the protection of aquatic life

8
(Metcalfe, 1989). Another reason is that ecosystem boundaries rarely coincide with territorial
ones (Carr & Neary, 2006).Those guidelines which are designed to ensure satisfactory quality
for agricultural, recreational, or industrial activities, have limits set for the chemical, physical,
and biological composition of water essential to safely carry out different activities (Carr &
Neary, 2006).
2.3 MEASURING WATER QUALITY
Water quality varies through time and space, routine monitoring is required to detect spatial
patterns and changes over time (Karr, 1993). It is neither a static condition of a system, nor
can it be defined by measuring just one parameter (Karr, 1993). There is a multitude of
physical, chemical, and biological components affecting water quality and numerous variables
which could be analysed and measured (Karr, 1993). Certain variables present a general
indication of water pollution, while others make it possible to track the source of pollution
directly (Carr & Neary, 2006).
2.3.1 BIOLOGICAL COMPONENTS
Organisms, populations, and communities composed of different species define the biological
diversity of aquatic ecosystems (WFD, 2011). Aquatic organisms, often considered
‘engineers’ of aquatic ecosystems, not only react to physical and chemical changes in their
environment, but can also drive such changes and play important roles in detoxifying and
cleansing their environment (Carr & Neary, 2006).
In aquatic food webs, several species and trophic levels may perform similar functions of self-
purification of a body of water (duplication of function) (Karr, 1991). For instance, both
bacteria and fungi are involved in the chemical breakdown of pollutants in aquatic
environments, and filtering of water is carried out by invertebrates living in both benthic and
pelagic environments of a system (WFD, 2011). Given the significance of biological
communities to water quality, water pollution should be considered as a biological issue
because it hinders the ability of resident and non-resident organisms to utilise resources that
the ecosystem provides and to maintain ecological services (WFD, 2011). Changes in the
chemical composition of water and physical loss of habitat can hamper a species’ ability to
grow, reproduce, and interact with other species in the ecosystem (Carr & Neary, 2006).

9
The assessment of biological communities present in an aquatic environment is a reflection of
the ecosystem’s quality (Karr, 1991). Biomonitoring is a tool used for assessing
environmental quality as biological communities integrate the effects of different stressors
and hence a broad measure of their aggregate impact is provided (Carr & Neary, 2006).
Extensive use of biomonitoring techniques have been in part due to public interest in the
status of individual species and cost effectiveness of sampling regimes (Carr & Neary, 2006).
2.4 CLASSIFICATION OF WATER SYSTEMS
Water resources are classified into two general groups, namely lentic systems and lotic
systems (Christiansen & Hamblin, 2008). Lentic systems can be defined as standing water
bodies and include: lakes, ponds, farm dams, coastal lakes, estuaries and some wetlands. Lotic
systems are water bodies characterised by flowing water, and include: rivers, streams and
floodplains (Christiansen & Hamblin, 2008). In South Africa rainfall is predominantly erratic,
therefore floodplains and other wetlands can shift from being lentic to lotic and vice versa or,
under certain circumstances, can dry up until the next rainy season. These different systems
are inhabited by different species of organisms (Gerber & Gabriel, 2002).
2.4.1 CHARACTERISTICS OF A GENERAL RIVER SYSTEM
A general river ecosystem can be classified into different zones: the headwater zone -
mountain stream, the middle zone, and the lower zone (Chapman, 1996). However, there are
quite a few exceptions to this e.g. some of the rivers of the southern Cape or where a
rejuvenation zone occurs. A rejuvenation zone is defined as where the characteristics of a
river change once again to resemble a head water zone or middle zone (Hunsaker & Levine,
1995).
2.4.1.1 THE HEADWATER ZONE
A mountain stream is typically characterized by: clear, fast flowing water that is well
oxygenated, steep gradients which cause swift currents, and a stream bed which consists of
stones and boulders with very little loose soil (Chapman, 1996). Plants growing on and near
the river bank form the riparian vegetation (Gerber & Gabriel, 2002). Some rivers arise in
high altitude wetlands, called sponges (Gerber & Gabriel, 2002). These rivers have different
characteristics which include: stream bed composed of sand, mud or clay or a mixture of

10
these; no overhanging tree canopy; and riparian vegetation dominated by reeds and grasses
(Gerber & Gabriel, 2002).
2.4.1.2 THE MIDDLE ZONE
This part of the river occurs in the lower altitudes of mountains (foothills), and is
characterised by: the stream being wider as a result of contributions of tributaries (other
streams); the speed of the current being slower due to the gentler slope; less turbulent water
flow, hence a smoother stream bed; water quality being less pure than that of the mountain
stream because of abiotic processes; the water being more turbid – this depends on the
geology and the contribution of the tributaries; higher water temperatures compared to the
head waters, as there is no closed canopy, a lower altitude, and a decreased flow rate
(Christiansen & Hamblin, 2008).
2.4.1.3 THE LOWER ZONE
This zone occurs where the river flows towards the coastal plain, in the lower reaches above
the estuary (Chapman, 1996). Here the channel continues to widen and current flow velocity
decreases further (Chapman, 1996). The stream bed is made up of predominantly sand or silt
(Chapman, 1996). The concentration of oxygen is often significantly less than those of upper
zones as a result of higher temperatures and more biologically active material in the water
(Gerber & Gabriel, 2002). There is a decrease in water quality due to leaching & weathering
of rocks (Hunsaker & Levine, 1995). This zone is also characterized by being rich in nutrients
due to contributions of its tributaries, and there is increased sunlight penetration (Gerber &
Gabriel, 2002).
2.4.2 BIOTOPES IN A RIVER SYSTEM
The composition of the stream bed is one of the most important physical factors that control
the structure of a freshwater invertebrate community (Chapman, 1996). The stream bed can be
further described by biotopes (Harvey, et al, 2008). A biotope in a river ecosystem can be
defined as the environment of a community of intimately related organisms (Harvey, et al,
2008). The different biotopes include: riffles and runs; pools; aquatic vegetation; marginal
vegetation; and algae (Harvey, et al, 2008).

11
2.4.2.1 RIFFLES AND RUNS
Riffles occur in the shallow, fast-flowing portions of a river where broken water is observed
on the surface due to water flowing over cobbles and gravel, which causes turbulent flow
(Harvey, et al, 2008). A run displays no broken water on the surface, has tranquil flow, and
has a greater depth than riffles (Gerber & Gabriel, 2002).
2.4.2.2 POOLS
A pool is the deep area of a stream where the water flow velocity is lower than in other parts
of the river (Harvey, et al, 2008). It can also be a collection of water that does not form part of
the main stream of the water flow e.g. in hollows formed in the bedrock (Harvey, et al, 2008).
2.4.2.3 AQUATIC AND MARGINAL VEGETATION
Aquatic vegetation is made up of plants living in the stream channel and they may be partly or
fully submerged (Harvey, et al, 2008). Marginal vegetation is that vegetation that occurs on
the water's edge, for example grasses, sedges and reeds (Gerber & Gabriel, 2002).
2.4.2.4 ALGAE
Freshwater algae are simple plants that are characterized by unicellular, filamentous or
colonial forms (Harvey, et al, 2008). Photosynthesis is the primary mode of nutrition for algae
(Harvey, et al, 2008). Algae can often be seen during the warmer times of the year anchored
to rocks and stones or floating as clumps (Gerber & Gabriel, 2002). Nutrient enrichment
results from nutrient rich agricultural runoff, industrial and domestic effluent that enters a
river can lead to rapid algal growth resulting in algal blooms (Gerber & Gabriel, 2002).
2.5 FRESHWATER INVERTEBRATES AS INDICATORS
Rivers, streams, lakes and wetlands are inhabited by many small animals called
macroinvertebrates (Resh, et al, 1995). These animals usually comprise of insects, arachnids,
annelids, crustaceans and mollusks (Resh, et al, 1995). Macroinvertebrates are those animals
that have no backbone and are visible to the naked eye (WRC, 2001). Some aquatic
macroinvertebrates are relatively large, like freshwater crayfish, although most are
predominantly small (WRC, 2001). Invertebrates that are retained on a 0.25mm mesh net are
generally termed macroinvertebrates (WRC, 2001).Each animal is restricted to that part of the

12
river where chemical and physical conditions are favorable (Gerber & Gabriel, 2002).
Therefore, different freshwater invertebrates would be found in different parts of the river, as
the river flows from its source to the sea (Resh, et al, 1995). Land use activities such as
grazing, roading, recreation, sewage discharge, and timber harvest have an effect on chemical
water quality characteristics and hydrology (Oberholster, et al, 2008). Channel morphology
characteristics, such as pool/riffle ratio and width/depth ratios, as well as sediment size,
within the water column may be altered by the hydrologic and chemical changes that occur
(Oberholster, et al, 2008). Physical and chemical characteristics of the water column itself
such as suspended sediment, bacteria, nutrients, temperature, and stream flow may also be
altered (Oberholster, et al, 2008). A result of these changes in the stream may lead to a change
in the amount and quality of habitat which is available, directly influencing biological systems
of the stream, particularly including macroinvertebrates (Oberholster, et al, 2008). This is due
to macroinvertebrates having very specific requirements relating to temperature, substrate
types, dissolved oxygen, etc. (Oberholster, et al, 2008). A change in habitat may have a
profound impact on the macroinvertebrates being able to occupy a particular stream or
wetland (Oberholster, et al, 2008). Different conditions favour different types of
macroinvertebrates and changes in relative abundance and types of organisms could be used
to indicate a disturbance (Oberholster, et al, 2008). Some will be adapted to the slower current
of the lower part of the river, others to the fast flowing waters of the mountains, while yet
others can be found all along the length of the river, as they are adaptable (Resh, et al, 1995).
They form an important component within the food chain as they are a food source to larger
animals such as birds and fish (WRC, 2001). They are also involved in the breakdown of
organic matter and nutrients (WRC, 2001).The community that inhabits a water body is
representative of the “ecological memory” of the habitat (Wogram & Liess, 2001). Therefore,
the composition of aquatic communities can be used to monitor various stressors (Wogram &
Liess, 2001).Due to variation in characteristics between rivers, it follows that there will also
be differences in the invertebrate communities in the respective rivers (WRC, 2001). Because
many of the invertebrates that will be collected are in the larval and nymphal stages, most of
the descriptions are based on those stages (Gerber & Gabriel, 2002).
Macroinvertebrates are responsive to varying physical and chemical conditions (WRC, 2001).
If the quality of water changes, probably as a result of a pollutant entering the water, or a flow
pattern change downstream of a dam, then the macroinvertebrate assemblage could change as
well (WRC, 2001). Therefore, the richness of macroinvertebrate community composition in a

13
river can be used to give an accurate assessment of river health (WRC, 2001). Benthic aquatic
macroinvertebrates are ideal candidates for biomonitoring for a variety of reasons: they are
relatively sedentary, allowing for spatial impacts of pollution to be detected; they are
ubiquitous in aquatic systems; they are usually easy to collect and identify; their different
taxonomic groups have different sensitivities to pollution; and they act as continuous water
quality monitors (Gerber & Gabriel, 2002). There are a number of advantages of using
macroinvertebrates as indicators of water quality (Wenn, 2008). For example, it is a diverse
group, therefore there is a high probability that some members will have a response to
pollution (Wenn, 2008). Some members have long life histories, which facilitates the
observation of temporal changes within communities and the associated pollution which they
are responding to (Wenn, 2008). Precise information about contamination via chemical
analysis of water samples is often difficult to obtain and laborious (Wogram & Liess, 2001).
Therefore assessing macroinvertebrate assemblages is more favourable.
2.5.1 ORDER: EPHEMEROPTERA (MAYFLIES)
Mayfly nymphs are very well suited to the environments that they inhabit. They can be
broadly grouped into two categories: climbers, burrowers and bottom sprawlers that favour
calm waters of ponds or backwaters of streams; and clingers which can be found clinging to
rocks or any other submerged substrate found in fast riffles (Wang & McCafferty, 1996). The
Flat-headed mayflies and the Brushlegged mayfly have pollution sensitivity scores of 13 and
15 respectively, which place them in the category of having a very low tolerance to pollution
(Dickens & Graham, 2001). Their presence indicates very good water quality. Prongills and
Stout crawlers both have a pollution sensitivity score of 9. This classifies them as being
moderately tolerant to pollution (Dickens & Graham, 2001). Their presence is also an
indication of good water quality.
2.5.2 ORDER: TRICHOPTERA (CADDISFLIES)
Caddisfly larvae can be grouped into two categories, which are the portable case-building
type (cased caddisflies) and the type that construct non-portable shelters (case-less or free
living caddisflies) (Holzenthal, et al, 2007). Caddisflies spend a major part of their life cycle
in the water, firstly in the larval stage, followed by the pupal stage, which lasts for about two
weeks (Holzenthal, et al, 2007). Caddisflies have very high pollution tolerance values, usually

14
around 12, which places them in the class of very low tolerance to pollution (Dickens &
Graham, 2001). This is a strong indicator of excellent water quality and river health.
2.5.3 ORDER: COLEOPTERA (BEETLES)
Coleoptera is the largest order of insects, with the majority being terrestrial except a few
families that are aquatic from the larval to adult stage (Rainio & Niemelä, 2003). They occupy
nearly every available freshwater habitat, ranging from mountain streams to temporary pools,
or the sand and mud found at the edges of ponds (Rainio & Niemelä, 2003). Most adult
aquatic beetles require atmospheric oxygen to survive, and carry a supply in the form of air
bubbles or a thin film around the body (Rainio & Niemelä, 2003). In some families the adult
beetles have the ability to fly which enables them to move to different water bodies (Rainio &
Niemelä, 2003). Beetles generally pollution tolerance values of around 5 to 10, which places
them in the moderately tolerant to pollution class (Dickens & Graham, 2001).
2.5.4 ORDER: HEMIPTERA (TRUE BUGS)
Hemiptera can be regarded as the order with the largest variety of body shapes, but only a few
families are adapted to aquatic habitats (Anderson, 1979). Some families (such as Nepidae
and Belostomatidae) have the ability to remain under water but have to be in contact with the
water surface film (Anderson, 1979). Other families like Veliidae, Gerridae and
Hydrometridae float or run on the surface (Anderson, 1979). Those that stay on the water’s
surface have the respiratory characteristics of terrestrial insects, while the ones living below
the surface renew their air supply by coming up to the surface at intervals (Anderson, 1979).
True bugs have pollution tolerance values which range from about 3 to 6 (Dickens & Graham,
2001). Therefore they can be classified as highly tolerant to pollution (Dickens & Graham,
2001).
2.5.5 ORDER: ODONATA (DRAGONFLIES/DAMSELFLIES)
Odonata is divided into two sub-orders, which are Anisoptera (true dragonflies) and
Zygoptera (damselflies) (Remsburg & Turner, 2009). When in the adult stages they are easy
to distinguish as true dragonflies hold their wings horizontal when at rest, while damselflies
fold their wings parallel with the abdomen when at rest (Johnson, 1991). The damselfly
nymphs have gills which vary greatly in shape and size, this is used for identification
purposes (Johnson, 1991). Damselflies have a pollution sensitivity score of 10, which falls in

15
the category of moderately tolerant to pollution (Dickens & Graham, 2001). Although their
sensitivity score places it in the “moderate” category, the score is still high, and very close to
the very low tolerance to pollution range. This is another indication of good river health and
water quality. Dragonflies have a pollution sensitivity score of 8 (Dickens & Graham, 2001).
Hence, the category of moderately tolerant to pollution will apply to this group (Dickens &
Graham, 2001). This is therefore another indicator of good river health.
2.5.6 ORDER: DIPTERA (FLIES, MOSQUITOES, MIDGES)
Only a few families of Diptera have aquatic larval or pupal stages. Diptera larvae can be
found in almost every aquatic habitat (Medvedev, et al, 2007). Some families such as
Culicidae and Syrphidae cannot obtain their oxygen from the water, and use siphons which
are pushed through the surface film (Medvedev, et al, 2007). Most Diptera pupae are inactive
and float around or tightly fastened to rocks or other solid substrate (Medvedev, et al, 2007).
Mosquito and Midge pupae are the only ones that have the ability to move around by
twitching the body (Medvedev, et al, 2007). Most Diptera have low pollution tolerance
values, ranging from 1 to 5 (Dickens & Graham, 2001). Therefore they generally are strong
indicators of poor water quality.
2.5.7 ORDER: PLECOPTERA (STONEFLIES)
Stoneflies are common around unpolluted rivers, and their nymphs are strictly aquatic and can
be found under stones in every type of unpolluted stream with an abundance of oxygen
(Roque, et al, 2008). They can also be observed in algae, debris or masses of leaves (Roque,
et al, 2008). A characteristic feature of Perlidae nymphs are the tufts of gills located on the
side of the body, and gills between the two tails (Roque, et al, 2008). Stoneflies have a
pollution sensitivity score of 14 (Dickens & Graham, 2001). This classifies it as having a very
low tolerance to pollution (Dickens & Graham, 2001). Therefore, their presence is a strong
indication of pristine water quality, with very little or no pollution.
2.5.8 ORDER: LEPIDOPTERA (AQUATIC CATERPILLARS)
Lepidoptera are characteristically terrestrial, with only one family having a few species with
truly aquatic larvae (Common, 1975). These larvae have the characteristic caterpillar
morphology as well as the legs and prolegs of terrestrial species (Common, 1975). The larvae
can be found either in silken nets on rocks in rapid streams or in cases attached to floating

16
vegetation (Common, 1975). The presence of aquatic caterpillars is a strong indicator of good
water quality, as they have a pollution tolerance value of 13, which means they have a very
low tolerance to pollution (Dickens & Graham, 2001).
2.5.9 ORDER: MEGALOPTERA (DOBSONFLIES)
Megaloptera larvae are all aquatic and are the largest of aquatic insects (Yakovlev, 2009).
They are easily identifiable by the long cylindrical body shape which resembles that of
centipedes (Yakovlev, 2009). The larvae crawl out of the water just before pupation in order
to pupate under stones or in the soil (Yakovlev, 2009). Dobsonflies are moderately tolerant to
pollution, as indicated by their pollution tolerance value of 8 (Dickens & Graham, 2001).
2.5.10 TAXON HYDRACARINA (WATER MITES)
Hydracarina resemble minute spiders, but differ from them in the way the head and body
segmentation is absent (Sabatino, et al, 2000). All body segments are fused into a single
structure (Sabatino, et al, 2000). Despite their small size, they are easy to spot due to their
bright coloration which varies from green to yellow or red (Sabatino, et al, 2000). Dark
markings are caused by the digestive tract being visible through the skin (Sabatino, et al,
2000). They are found in abundance in freshwater habitats clinging to submerged vegetation
or hanging around in quiet pools (Sabatino, et al, 2000). Water mites have a pollution
tolerance value of 8, indicating that they are moderately tolerant to pollution (Dickens &
Graham, 2001).
2.5.11 CLASS: TURBELLARIA (FLATWORMS)
Freshwater Turbellaria are generally elongated, cylindrical or spindle-shaped worms (Baguñà
& Riutort, 2004). They have very flat bodies with one end widened forming an arrow shaped
head (Baguñà & Riutort, 2004). All Turbellaria have a high sensitivity to light, therefore they
are more abundant in shaded areas or areas where they can hide and offer a good food supply
(Baguñà & Riutort, 2004). Flatworms have a pollution tolerance value of 3, placing them in
the highly tolerant to pollution category (Dickens & Graham, 2001).
2.5.12 ORDER: AMPHIPODA (SCUDS)
Freshwater Amphipoda occur in unpolluted rivers, caves and in boreholes (Last & Whitman,
1999). Amphipoda are mainly nocturnal and stay hidden during the day, usually amongst

17
vegetation, under stones, or buried beneath the top layers of soft bottom substrate (Last &
Whitman, 1999). Amphiboda have a pollution tolerance value of 13, which classes them as
being highly sensitive to pollution (Dickens & Graham, 2001). They are therefore excellent
indicators of good water quality.
2.5.13 ORDER: DECAPODA (CRABS, SHRIMPS)
Decapoda are all animals with bodies and legs that have been hardened to form a tough shell
(Gerber & Gabriel, 2002). The upper body is fused together with the head, with the abdomen
having clear segmentation (Gerber & Gabriel, 2002). Crabs have a slightly different structure
where the abdomen is reduced and tucked away under the body (Gerber & Gabriel, 2002).
Crabs are highly tolerant to pollution, as indicated by their pollution tolerance value of 3
(Dickens & Graham, 2001). Shrimp have a pollution tolerance value which ranges from 8 to
10. This places them in the class of being moderately tolerant to pollution (Dickens &
Graham, 2001).
2.5.14 PHYLUM: ANNELIDA
CLASS: OLIGOCHAETA (AQUATIC EARTHWORMS)
Annelida (segmented worms) are worm-like animals with soft muscular bodies (Brinkhurst,
1982). Aquatic Oligochaeta are similar in structure to the common garden earthworms, with a
tube-like body, no definite head, no tentacles or legs (Brinkhurst, 1982). Oligochaeta can be
found coiled up or probing around in the mud and bottom substrate of stagnant pools,
digesting the substrate (Brinkhurst, 1982). They are able to survive in very low oxygen levels
(Brinkhurst, 1982). These worms are extremely tolerant to pollution, and have a pollution
tolerance value of just 1 (Dickens & Graham, 2001). Therefore they are strong indicators of
poor water quality.
CLASS: HIRUDINAE (LEECHES)
Hirudinae are referred to as “bloodsuckers” although only a few species take blood from
warm-blooded animals (Brinkhurst, 1982). They vary in size, ranging from being minute to
giant species that reach up to 45cm when extended (Brinkhurst, 1982). Leeches generally hide
under stones or among plants or indetritus, in order to avoid exposure to light (Brinkhurst,
1982). A few parasitic species of leeches feed on blood and tissue fluids of fish and
crustaceans (Brinkhurst, 1982). Leeches have a pollution tolerance value of 3, placing them in

18
the category of being highly tolerant to pollution (Dickens & Graham, 2001). They are strong
indicators of poor water quality.
2.5.15 PHYLUM: PORIFERA (SPONGES)
Porifera are morphologically different from other freshwater invertebrates, and are sessile,
inconspicuous animals that only inhabit clear ponds or slow streams (Gerber & Gabriel,
2002). They resemble crusty, mat-like patches on any stable substrates, such as rocks, logs,
pebbles or twigs underwater (Gerber & Gabriel, 2002). Uninhibited growth of sponges can
result in them covering large areas of substrate, including upper and lower surfaces, as well as
the sides (Gerber & Gabriel, 2002). Porifera are highly tolerant to pollution, as indicated by
their pollution tolerance value of 5 (Dickens & Graham, 2001).
2.5.16 PHYLUM: MOLLUSCA
CLASS: GASTROPODA (SNAILS, LIMPETS)
Snails have soft, un-segmented bodies and live inside a shell (Strong, et al, 2008). Most of the
freshwater Gastropoda have spiral shells while just a few limpet genera have flatter, conical
shells (Strong, et al, 2008). Snails are slow moving organisms that glide on a large muscular
foot, leaving behind a distinctive slime track (Strong, et al, 2008). Snails have a pollution
tolerance value of 5, which classifies it as being highly tolerant to pollution (Dickens &
Graham, 2001). However they also occur in unpolluted waters, so it cannot be regarded as an
indicator of pollution.
CLASS: BIVALVIA (PELECYPODA)
This class comprises clams and mussels that vary in shape, are elongated, oval or anything in-
between (Haszprunar & Wanninger, 2012). The shell is made up of two halves joined at a
hinge by an elastic filament (Haszprunar & Wanninger, 2012). These organisms are able to
bury themselves deep into the substrate or sand (Haszprunar & Wanninger, 2012). This class
has a pollution tolerance value of 5, which classifies it as being highly tolerant to pollution
(Dickens & Graham, 2001).

19
2.6 THE miniSASS METHOD AS A TOOL FOR BIOMONITORING
Reliable indicators of water quality are often difficult to acquire and expensive to derive
(Graham, et al, 2004). When water samples are taken to a laboratory for analysis it increases
the users distance from the source, therefore posing a threat of contamination of the water
sample (Graham, et al, 2004). There was a need for scientists to develop a low technology,
yet scientifically reliable and robust technique for the monitoring of water quality and river
health in rivers and streams (Graham, et al, 2004). Modern science has provided reliable tools
for understanding the impacts of, and providing solutions to water pollution (Graham, et al,
2004). The complication arises when communicating these solutions to the public, to inform
them of the problems, and thus enabling the public to take action (Graham, et al, 2004). A
number of carefully-orchestrated campaigns have tried to communicate the message via
formal and informal educational programmes and the media, however these attempts have
largely failed to provide the desired effect they were intended to have (Graham, et al, 2004).
This is because social change does not occur merely through people receiving clearly
communicated messages (Graham, et al, 2004). Change cannot be achieved simply by one-
way or top-down methods of communication, rather, it will only occur when the public is able
to relate with and share in a similar enquiry to the one that enables the scientists to come to
their understanding (Graham, et al, 2004).
MiniSASS has been developed in the social and environmental context that requires the
involvement of people and ‘giving the tools of science away’ and in interactions with the
findings of scientific enquiry (Graham, et al, 2004). It can be described as being an
environmental educational tool for use by communities to monitor the water quality of their
rivers and streams (Graham, et al, 2004). There are a number of countries which are presently
applying toxicity tests for water pollution assessment (Oberholster, et al, 2008). The demand
for biological tests for water toxicity testing is on the increase in South Africa, as domestic
and industrial sewage effluents are becoming a growing problem (Oberholster, et al, 2008).
The use of biological organisms as an indication of ecosystem health has been around for
quite some time (Mandaville, 2002). Recently, this science is now being referred to as
biomonitoring or bioassessment (Mandaville, 2002). Biomonitoring can be described as being
the systematic use of living organisms and/or their responses to assess the quality of the
environment (Mandaville, 2002). Biological monitoring differs from chemical monitoring in
that chemical monitoring can only provide an indication of water quality for that specific time
whereas biological monitoring provides information about past and/or episodic pollution

20
(Graham, et al, 2004). Chemical monitoring does not take into consideration the array of
human-induced perturbations including habitat degradation and destruction, and flow
alterations (Oberholster, et al, 2008). Such perturbations have the potential to harm biological
health (Oberholster, et al, 2008). Chemical measurements can be described as taking
snapshots of the ecosystem, while biological measurements are like making a videotape
(Mandaville, 2002).
The ‘water slide’ was one of the first biomonitoring techniques to be used in South Africa. It
involved being able to identify eleven aquatic invertebrate taxa. A range of five possible water
quality classes (from ‘clean water’ to ‘serious pollution’) was indicated by the presence or
absence of these invertebrates (Graham, et al, 2004). A shortcoming of this method was that
there was no quantitative index which could be derived from this system for use in ongoing
monitoring (Graham, et al, 2004). It also does not provide clear sampling techniques,
therefore comparative sampling would be unreliable. There was also no direct relationship
between the results from this method and those from more scientifically rigorous systems of
biomonitoring (Graham, et al, 2004).
The South African Scoring System (SASS) is a more rigorous method of biomonitoring and
was developed in the year 1998 by Chutter (Graham, et al, 2004). Although SASS is a fairly
simple technique for a trained practitioner, it is beyond reach for the layman as it requires the
identification of up to 90 different aquatic invertebrate families that are the foundation of this
technique (Graham, et al, 2004). Therefore a high degree of skills and training is required by
non-invertebrate taxonomists, which restricts the use of this technique to a handful of
‘specialists’ who are capable of identifying the taxa (Graham, et al, 2004).
The low quality of the data provided by the water slide technique, and the relatively
sophisticated identification skills needed by the SASS system, warranted an intermediate level
of biomonitoring which provided reliable water quality data and could be applied by non-
specialists (Graham, et al, 2004). The technique had to be based on another technique of
known pedigree. It was then decided that a simplified method of biomonitoring was to be
developed from the SASS technique (Graham, et al, 2004). This was achieved by reducing the
taxonomic complexity of SASS down to a few aquatic invertebrate ‘groupings’ that would act
as surrogates for the complete suite of SASS data (Graham, et al, 2004). The efficiency of the
technique depended on it being able to satisfy the following requirements:

21
1. The number of aquatic invertebrate groupings necessary to perform miniSASS had to
be minimized.
2. Aquatic invertebrate groups should be easily identifiable.
3. It should be a robust method and produce results comparable to those of the full SASS
technique, and
4. It should be geographically widely applicable.
SASS has been used extensively throughout South Africa, by institutions such as Umgeni
Water, Umlaas Irrigation Board, CSIR, DWAF, and numerous universities and consultants
(Graham, et al, 2004). The technique has been used in various applications such as: a routine
biomonitoring tool around known impacts; in Instream Flow Requirement (IFR) studies; the
following up on specific Environmental Impact Assessment (EIA) type problems or pollution
cases; State of Environment Reporting; a key indicator in State of the Rivers reporting
throughout South Africa; a tool for environmental management and compliance monitoring
by the major commercial timber growers in the country; one of the principle biomonitoring
tools for the National River Health Programme (Graham, et al, 2004).
The SASS system is now on version 5 after being revised and modified many times over the
years. The core of the method involves allocating a quality score to specific and easily
identifiable aquatic invertebrate taxa (Graham, et al, 2004). This score indicates the taxon’s
sensitivity to pollution. Samples of aquatic invertebrates which are collected from the river
using standardised methods are immediately examined on the riverbank, and then the sample
is ‘scored’, according to prescribed scores allocated to each taxon (Graham, et al, 2004). The
scores of the taxa found are summed to derive a Sample Score, after a fixed identification
period. The total number of SASS taxa identified is then counted and an Average Score Per
Taxon (ASPT) is calculated by dividing the Sample Score by the total number of taxa
(Graham, et al, 2004). Each of these three measures, or indices, provides valuable information
of the biological state of the river. In general, the higher the Sample Scores, Number of Taxa
and ASPT, the better the biological condition or health of that river (Graham, et al, 2004).
Due to the wide geographic spread and relative abundance of SASS Version 4 data available
in South Africa, this version provided the foundation which allowed development of the
miniSASS method. Initial indications are that SASS5 and SASS4 ASPT scores are close
enough to mean that SASS5 and miniSASS ASPT scores are likely to be closely related
(Graham, et al, 2004).

22
A pilot study was carried out to establish whether the proposed miniSASS approach was a
viable project (Graham, et al, 2004). This study had 2 main objectives: to create a reduced list
of easily identifiable groups of aquatic invertebrate taxa (from the full SASS list); and assign
“best fit” quality scores to these new groups (Graham, et al, 2004). Certain taxa, such as
Porifera, Hydra sp., Hydrachnellae, Corydalidae, and Nymphulidae, were considered to be too
cryptic, rarely encountered or difficult/confusing to identify and were eliminated from
potential incorporation into the simplified miniSASS, but their contributions to actual scores
were accounted for when testing of miniSASS against “real” SASS4 data was done (Graham,
et al, 2004). A subset of fairly easily identifiable aquatic invertebrates (damselflies,
dragonflies, flies, water bugs, etc.) were then derived and modelled against a relatively small
set (n=21) of real SASS4 data in order to determine the best quality scores to assign to the
respective new groups (Graham, et al, 2004). The objective was to minimise the differences in
ASPT scores that can be attained with the miniSASS when compared to that of a full SASS4
analysis (Graham, et al, 2004). The sites that were chosen displayed a range of water quality
conditions on the Mkomaas, Mgeni, Karkloof, Mhlatuzana, Mbilo, Mvoti, Dorpspruit Rivers,
and the Mthinzima Stream (all in KwaZulu- Natal), and had a relatively broad geographical
spread, covered near pristine water quality to highly polluted waters, and represented both
large and small rivers and streams (Graham, et al, 2004).
The results showed relatively low absolute differences between ASPT scores achieved by
miniSASS and a full SASS4 analysis, which suggested that miniSASS warranted further
development. All choices of different aquatic invertebrate groupings had plausibly good
agreement between miniSASS and SASS (Graham, et al, 2004). To test the performance of
miniSASS over a wider water quality and geographical range, it was decided to test it on as
much data as was reasonably available, and in much the same way as occurred in the pilot
study (Graham, et al, 2004). Three principal geographical sources of SASS4 data had been
tested against miniSASS, the eastern seaboard, the Western Cape, and the Mpumalanga
region. The robustness of miniSASS was tested against a total of 2127 discrete SASS records
(Graham, et al, 2004). The quality scores assigned to the miniSASS aquatic invertebrate
groupings required optimisation to minimise the differences obtained between full SASS and
miniSASS ASPT results (Graham, et al, 2004). Linear programming had to be employed
within a spreadsheet environment to solve the minimisation objective, due to the relatively
large size of the data set, and range of possible scores that could be assigned to the 13 groups
(Graham, et al, 2004). This process aimed to minimise the difference in ASPT scores

23
obtained between miniSASS and SASS analyses respectively, but was constrained to keep
some of the scores assigned to various groupings within reason e.g. positive and not too high
or too low (based on biological and previous SASS experience) (Graham, et al, 2004). Within
these constraints, the derivation of “best fit” quality scores was possible, and assigned to the
new miniSASS aquatic invertebrate groups.
It was then found that the “best fit” quality scores assigned to most aquatic invertebrate
groups were relatively stable across geographical regions (Graham, et al, 2004). Although the
combined dataset showed a respectable mean difference between SASS and miniSASS
ASPTs, when these quality scores were applied to the Western Cape data set alone, the mean
difference for this region rose significantly (Graham, et al, 2004). To counter this problem
further fine tunings of miniSASS quality scores were made. The Stoneflies and Caddisflies
quality scores were increased over those used for the rest of the country (Graham, et al, 2004).
It was then possible to maintain the Western Cape mean difference between SASS and
miniSASS ASPT the same, whilst the difference for the rest of the country was reduced
slightly (Graham, et al, 2004). This implies that within the limitations of the available data, a
miniSASS analysis is able to predict the SASS ASPT score to within one ASPT unit, meaning
that miniSASS showed itself to be a good predictor of biological water quality (Graham, et al,
2004).
There was no statistically significant difference between ASPT results obtained using
miniSASS and those obtained using the full SASS analysis for the entire data set covering
both regions (Graham, et al, 2004). Every school, environmental/community group or NGO
in the country could potentially become a monitoring cell, and miniSASS could be used as a
“red flag” tool for the identification of aquatic pollution sources in their immediate
environment (Graham, et al, 2004). MiniSASS does produce data that varies slightly from
SASS, but can still be used with some confidence, as it is sufficiently close to be of immense
value to all stakeholders interested in river health. MiniSASS makes it possible for live, real-
time monitoring and investigation of pollution sources in river systems and presents the
opportunity for improved environmental management (Graham, et al, 2004).

24
2.7 CONCLUSION
The use of biomonitoring to assess river health is very useful, especially for those who do not
have the equipment and knowledge to carry out chemical analysis of the water. The use of
macroinvertebrates in this application is advantageous in that macroinvertebrates serve as
continuous indicators of water quality. Macroinvertebrates are also easily identifiable,
because of their relatively sedentary lifestyles. The miniSASS method is a robust method and
produces results comparable to those of the full SASS technique, and it is geographically
widely applicable. The Mthinzima River is in a very poor state, with no action being taken to
rehabilitate it. The township of Mpophomeni seems the most likely source of pollution to the
Mthinzima River. This study provides an excellent opportunity to assess the water quality of
the river and determine whether the township is the cause of the pollution, thereby prompting
relevant management to repair and improve sewage systems in the area, in order to restore the
health of the river, and in turn improve water quality of the Midmar Dam.

25
CHAPTER THREE
3. STUDY AREA AND METHODOLOGY
3.1 INTRODUCTION
This chapter will introduce the study area, followed by a description of the methodology used
in sample collection and data analysis.
3.2 STUDY AREA
Figure 3.1 Image of the Mthinzima River (Source: Google Earth)

26
The Mthinzima River is located alongside the township of Mpophomeni, which is part of the
uMngeni Municipality (Figure 3.1). uMngeni Municipality falls under the uMgungundlovu
District, which is referred to as the heart of the KwaZulu-Natal Midlands (uMngeni IDP
Report, 2010). The Mgeni catchment is located on the east coast of South Africa, and covers
an area of 4387 km2
and is a source of water to 3.6 million people (Tarboton & Schulze,
1991).
3.3 METHODOLOGY
3.3.1 SAMPLING DESIGN
Three different site locations were chosen so as to allow investigating whether the township is
the source of pollution in the Mthinzima River. Site 1 was located upstream of the potential
pollution source (the township of Mpophomeni). Site 2 was located at the potential pollution
source, where two streams carrying wastewater from the township entered the river. Site 3
was located at the entrance to Midmar Dam. Samples were collected on four different days, to
ensure variation and reliability.
3.3.1.1 SAMPLE COLLECTION
The miniSASS kit was used to collect samples. The miniSASS net was placed in the water,
against the flow, whilst wading through the stream. Vegetation and rocks were disturbed
using feet and hands; this was done to ensure sampling across all different fluvial
microhabitats. This process was done for a period of five minutes for each sample taken.
The contents of the net were then rinsed out into the white dish, and any debris and vegetation
was removed. Care was taken to ensure that no organisms were discarded together with the
vegetation and debris that was removed. The contents were then put into plastic containers
and preserved with ethanol, which was diluted with distilled water from a concentration of
99.9% to a concentration of 50%, to allow for the preservation of samples.
3.3.2 MACROINVERTEBRATE IDENTIFICATION
Identification of macroinvertebrates was done in the laboratory, using the miniSASS
dichotomous key and the field guide for Aquatic Invertebrates of South African Rivers
(Gerber & Gabriel, 2002). A magnifying glass was used to help identify and distinguish

27
between different macroinvertebrate groups. In some instances, a pair of tweezers and a
pipette was used to pick up very small organisms.
3.3.3 INTERPRETATION OF THE miniSASS SCORE
The miniSASS score sheet is made up of the macroinvertebrate group sensitivity scores table
and the river health interpretation table. Macroinvertebrate groups were scored using the
group sensitivity scores table (Figure 3.2). Each macroinvertebrate group has a different
sensitivity score, based on their tolerance to pollution levels in the water. Groups with a
higher score indicate a lower tolerance to pollution, meaning that the water is relatively
unpolluted. Groups with a low sensitivity score are therefore found in more polluted waters,
as they are more resistant to the effects of water pollution. The average sensitivity score for
each site was calculated by adding all the sensitivity scores for the macroinvertebrate groups
that were found, and then dividing that by the number of groups found.
GROUPS
SENSITIVITY
SCORE
Flat worms 3
Worms 2
Leeches 2
Crabs or shrimps 6
Stoneflies 17
Minnow mayflies 5
Other mayflies 11
Damselflies 4
Dragonflies 6
Bugs or beetles 5
Caddisflies (cased &
uncased) 9
True flies 2
Snails 4
TOTAL SCORE
NUMBER OF GROUPS
AVERAGE SCORE
Figure 3.2 miniSASS macroinvertebrate group sensitivity scores table
The study sites were classified as being in the category of a rocky type river. River health was
determined using the river health interpretation table (Appendix B). Sensitivity scores were

28
compared to categories of sensitivities, with lower categories indicating poorer river health
conditions, and high categories representing good river health conditions.A sensitivity score
of < 5.1 will fall into the category of “seriously/critically modified (very poor condition)”; 5.1
- 6.1 indicating “largely modified (poor condition)”; 6.1 - 6.8 representing “moderately
modified (fair condition)”; 6.8 - 7.9 showing “largely natural/few modifications (good
condition)”; and > 7.9 indicative of “unmodified (natural condition)”.
3.3.4 STATISTICAL ANALYSIS
Since sampling was done on four days, the average sensitivity scores at each site for the four
days were added and a mean value was calculated for each site accordingly. These mean
values were then compared to each other using the Mann-Whitney U test, in order to test
whether there is a significant difference between them,and was done using SPSS. As the
dataset is relatively simplistic, with a small number of values,the dataset could not be
normalized either by increasing sample size or through transformations of the original data
(Mackey, 2008). Therefore a non-parametric test was required for analysis. The Mann-
Whitney U test is the most powerful non-parametric test that is relevant to two-sample
comparisons (Mackey, 2008). Average sensitivity scores for each site over the four days were
added together and the total divided by 4 to give an average. These averages were then ranked
and compared to each other to test for significant differences between them. The Mann-
Whitney U test does not compare the means of two distributions, but rather the ranks of the
measurements (Mackey, 2008). Ranking was done from highest to lowest, with the greatest
mean value in either of thetwo sites (i.e. within the whole dataset) given the ranking of 1, the
second greatestmean value given rank 2, and so on. Thereafter, an error bar plot was
formulated, to graphically represent the comparison of the scores at each site. In this case, the
error bar plot indicates a measure of central tendency, with some measure of uncertainty or
variability (as depicted by the error bars), with the error bars representing 95% confidence
intervals (Mackey, 2008).
3.4 CONCLUSION
In this chapter the methods used for sample collection, analysis and statistical analysis of
results have been discussed. The data acquired through the application of these processes are
further discussed in the following chapters.

29
CHAPTER FOUR
4. RESULTS
4.1 INTRODUCTION
Macroinvertebrate groups were scored using the miniSASS score sheet. Average sensitivity
scores were calculated and then interpreted using the miniSASS river health interpretation
table, to determine water quality of the river at the three study sites. A comparison between
the number of macroinvertebrate groups found in each Site over the four sampling days was
done. Statistical analysis was then done, using the Mann-Whitney U test and Error Bar Plot.
4.2 SCORING OF MACROINVERTEBRATE GROUPS
4.2.1 SITE 1 – UPSTREAM
Figure 4.1 Graph of Sensitivity Scores for Macroinvertebrate Groups found at Site 1
The macroinvertebrate groups that were found on all four days of sampling, and their
corresponding sensitivity scores were Caddisflies (cased & uncased) (9); Bugs or beetles (5);
0 2 4 6 8 10 12 14 16 18
Worms
Crabs or Shrimps
Stoneflies
Minnow Mayflies
Other Mayflies
Damselflies
Dragonflies
Bugs or Beetles
Caddisflies (cased & uncased)
Snails
Sensitivity Scores
MacroinvertebrateGroups
Day 4
Day 3
Day 2
Day 1

30
Other Mayflies (11); Stoneflies (17); and Crabs or shrimps (6). Dragonflies have a sensitivity
score of 6, and were found on days 2, 3 and 4. Damselflies have a sensitivity score of 4, and
were found on days 1, 2 and 4. Minnow mayflies have a sensitivity score of 5, and were found
on days 1, 2 and 3. Worms have a sensitivity score of 2, and were found on days 2 and 4.
Snails have a sensitivity score of 4, and were found on day 1 only (Figure 4.1).
The total score for day 1 was 61, and the number of groups found was 8. The average
sensitivity score was then calculated by dividing the total score by the number of groups
found, giving a value of 7.625. The total score for day 2 was 65, and the number of groups
found was 9. The calculated average sensitivity score was 7.2. The total score for day 3 was
59, and the number of groups found was 7. The calculated average sensitivity score was 8.43.
The total score for day 4 was 60, and the number of groups found was 8. The calculated
average sensitivity score was 7.5.
4.2.2 SITE 2 – TOWNSHIP
The only macroinvertebrate group found on all four days of sampling and itscorresponding
sensitivity score was that of True flies (2) (Figure 4.2). The total score was 2, with only one
group found. The average sensitivity score was then calculated by dividing the total score by
the number of groups found, giving a value of 2.
0 1 2 3
True Flies
Sensitivity Scores
Macroinvertebrate
Groups
Day 4
Day 3
Day 2
Day 1

31
4.2.3 SITE 3 – DAM ENTRANCE
The macroinvertebrate group of Crabs or shrimps was found on all four days of sampling and
its corresponding sensitivity score was 6. Snails were found on day 1 only, and they have a
sensitivity score of 4. Bugs or beetles were found on day 2 only, and they have a sensitivity
score of 5 (Figure 4.3).
The total score for day 1 was 10, with 2 groups found. The average sensitivity score was then
calculated by dividing the total score by the number of groups found, giving a value of 5. The
total score for day 2 was 11, and the number of groups found was 2. The average sensitivity
score was then calculated by dividing the total score by the number of groups found, giving a
value of 5.5. The total score for day 2 and day 3 was 6, with just one group found. The
average sensitivity score was then calculated by dividing the total score by the number of
groups found, giving a value of 6.
0 1 2 3 4 5 6 7
Crabs or Shrimps
Bugs or Beetles
Snails
Sensitivity Scores
MacroinvertebrateGroups
Day 4
Day 3
Day 2
Day 1

32
Figure 4.4 Graph of Number of Groups of Macroinvertebrates found at each site
The number of macroinvertebrate groups found at Site 1 on day 1, 2, 3 and 4 were 8, 9, 7 and
8 respectively. At Site 2 just one macroinvertebrate group was found on every day. At Site 3,
two groups were found on day 1 and 2, and one group was found on day 3 and 4.
4.3 STATISTICAL ANALYSIS – COMPARISON
4.3.1. SITE 1 AND 2
Table 4.1 Ranking of datasets for Site 1 and Site 2
Ranks
SITE N Mean Rank Sum of Ranks
AVG_SCORE 1 4 6.50 26.00
2 4 2.50 10.00
Total 8
The datasets for Site 1 and Site 2 were ranked from highest to lowest. The sum of ranks for
Site 1 was 26.00 and the mean rank was 6.50. The sum of ranks for Site 2 was 10.00 and the
mean rank was 2.50 (Table 4.2). These mean ranks were then compared to each other.
0
1
2
3
4
5
6
7
8
9
1 - Upstream 2 - Township 3 - Entrance
NumberofGroups
Site
Day 1
Day 2
Day 3
Day 4

33
Table 4.2 Test Statistics for Site 1 and Site 2
Test Statistics
b
AVG_SCORE
Mann-Whitney U .000
Wilcoxon W 10.000
Z -2.460
Asymp. Sig. (2-tailed) .014
Exact Sig. [2*(1-tailed Sig.)] .029
a
Since p = 0.014 (Table 4.3) which is less than 0.05 (5% confidence interval used), this
indicates that there is a significant difference between the sensitivity scores of Site 1 and Site
2. Therefore, this indicates that there is a significant difference in water quality between Site 1
and Site 2.
4.3.2 SITE 1 AND 3
Ranks
AVG_SCORE 1 4 6.50 26.00
3 4 2.50 10.00
Total 8
Test Statistics
b
AVG_SCORE
Mann-Whitney U .000
Wilcoxon W 10.000
Z -2.323
a

34
and Site 3.
4.3.3 SITE 2 AND 3
Ranks
AVG_SCORE 2 4 2.50 10.00
3 4 6.50 26.00
Total 8
Test Statistics
b
AVG_SCORE
Mann-Whitney U .000
Wilcoxon W 10.000
Z -2.477
a
and Site 3.

35
4.3.4 ERROR BAR PLOT
Figure 4.5 Error Bar Plot showing significant differences between average sensitivity scores
The error bar plot illustrates that there is a significant difference in the average sensitivity
scores between the three sample sites (Figure 4.5). There is no overlap between any two of the
three sites. Therefore the water quality at each site is significantly different from each other.
The largest difference can be seen between Site 1 and Site 2, where the bars are furthest away
from each other.
4.4 CONCLUSION
In this chapter the data obtained after the miniSASS method was used to assess water quality
at the three sites of the stream has been presented. The average sensitivity scores were stated
and interpreted. Statistical analysis of these scores was done and the results presented. The
results acquired through the application of these processes are discussed in the following
chapter.

36
CHAPTER FIVE
5. DISCUSSION
5.1 INTRODUCTION
Each macroinvertebrate group has a different tolerance level to pollution in the water body
that it inhabits. This tolerance level is based on a tolerance scale, derived from the SASS5
scoring system (Dickens & Graham, 2001). The scale is divided into three categories: highly
tolerant to pollution (sensitivity range of 1 – 5), moderately tolerant to pollution (sensitivity
range of 6 – 10), and very low tolerance to pollution (sensitivity range of 11 – 15) (Dickens &
Graham, 2001).
5.2 INTERPRETATION OF MACROINVERTEBRATE GROUP SCORES
Stoneflies were found at this Site on all four days. Stoneflies have a high requirement for
dissolved oxygen, and are regarded as being extremely sensitive to organic pollution (Wenn,
2008). They are a common sight in unpolluted rivers, with cool clean water, and an
abundance of oxygen (Wenn, 2008, WEP, 2003). Stoneflies have a pollution sensitivity score
of 14 (Dickens & Graham, 2001). This classifies it as having a very low tolerance to pollution
(Dickens & Graham, 2001). Therefore, their presence is a strong indication of pristine water
quality, with very little or no pollution.
Mayflies were found on all four sampling days. Mayflies are considered sensitive to
environmental stress (Wenn, 2008). The families found were Heptageniidae (Flat-headed
mayflies), Leptophlebiidae (Prongills), Oligoneuridae (Brushlegged mayfly) and
Tricorythidae (Stout crawlers) (Gerber and Gabriel, 2002). The Flat-headed mayflies and the
Brushlegged mayfly have pollution sensitivity scores of 13 and 15 respectively, which place
them in the category of having a very low tolerance to pollution (Dickens & Graham, 2001).
Their presence indicates very good water quality. Prongills and Stout crawlers both have a
pollution sensitivity score of 9. This classifies them as being moderately tolerant to pollution
(Dickens & Graham, 2001). Their presence is also an indication of good water quality.
Damselflies were found on three of the four days at this site. They have a pollution sensitivity
score of 10, which falls in the category of moderately tolerant to pollution (Dickens &

37
Graham, 2001). Although their sensitivity score places it in the “moderate” category, the
score is still high, and very close to the very low tolerance to pollution range. This is another
indication of good river health and water quality.
Dragonflies were also found on three of the four sampling days. This group of
macroinvertebrates has a pollution sensitivity score of 8 (Dickens & Graham, 2001). Hence,
the category of moderately tolerant to pollution will apply to this group (Dickens & Graham,
2001). This is therefore another indicator of good river health.
Caddisflies were found on all four days at this site. More specifically, the Caddisfly belonging
to the Family Polycentropodidae was found (Gerber and Gabriel, 2002). This family has a
pollution tolerance value of 12, which puts it in the class of very low tolerance to pollution
(Dickens & Graham, 2001). This is a strong indicator of excellent water quality and river
health.
Macroinvertebrates that were part of the Bugs or Beetles group was also found on all four
days that sampling took place. The Family Gyrinidae was found, which has a pollution
tolerance value of 5 (Dickens & Graham, 2001). This makes it highly tolerant to pollution, but
this value is closer to the moderately tolerant to pollution category. Nevertheless, this does not
indicate a poor state of river health in this case, as these bugs occur in a variety of habitats
with ranging conditions (Gerber & Gabriel, 2002).
Snails were also found on day 1 at this site. They have a pollution tolerance value of 5, which
classifies it as being highly tolerant to pollution (Dickens & Graham, 2001). However they
also occur in unpolluted waters, so it cannot be regarded as an indicator of pollution.
Worms were found on two of the days that sampling was done. They generally have a very
high tolerance to pollution, but can also be found in unpolluted waters.
The only macroinvertebrate group that was found at Site 2 over the four days of sampling was
that of True Fly larvae, more specifically that of the family Chironomidae (Midges). They are
found living in the bottom sediments of lakes, streams, or ponds, with their remains forming
an organic ooze (USDA, no date). There are two groups of Midges, which are distinguished
by the method applied to obtain and store oxygen (USDA, no date). The Midges that were
found at this site are termed Blood Midges, due to their red colouration (USDA, no date).

38
Blood Midges have the ability to store oxygen in their body fluid by means of a compound
much similar to haemoglobin (USDA, no date). This endows the Blood Midges with their
distinctive red colour (USDA, no date). Blood midges are able to thrive in very low levels of
dissolved oxygen (USDA, no date). They were present in large numbers, which could be an
indication of organic enrichment and highly polluted water (OCDWEP, 2003). Blood Midges
are capable of reproducing at a very rapid rate, and have been known to swiftly invade sites
that become favourable from organic runoff (Wenn, 2008). They have a high tolerance
towards pollution, hence they have a pollution tolerance value of 2 (Dickens & Graham,
2001). This means that Blood Midges are very resistant to pollution and low dissolved oxygen
levels in the water. Therefore, their presence at the Site is a clear indication of heavily
polluted water.
The absence of any other macroinvertebrate group at Site 2 can be attributed to the high levels
of pollution, and low dissolved oxygen concentrations. Therefore this habitat and its
associated high level of pollution is unable to support these macroinvertebrates.
The macroinvertebrate group found at Site 3 over all four sampling days was that of Crabs
and Shrimp, more specifically in this case Shrimp was found. They have a pollution tolerance
value of approximately 8 (Dickens & Graham, 2001). This places it in the category of being
moderately tolerant to pollution (Dickens & Graham, 2001). Their presence here gives an
indication that the stream is polluted but has been purified to a certain extent as it flowed
through the wetland, before reaching the dam entrance.
The other macroinvertebrate group that was found at this Site only on day 1 was that of
Snails. Their presence here is a clear indication of water that is heavily polluted, as it has a
pollution sensitivity score of 3 (Dickens & Graham, 2001). This classifies it as being highly
tolerant to pollution.
The macroinvertebrate group of Bugs and Beetles was found at this Site only on day 2 of
sampling. The beetle that was found belonged to the family Naucoridae (Creeping water
bugs). Their preferred habitat is that of dense vegetation, on the edges of streams (Gerber &
Gabriel, 2002). This is representative of the habitat found at Site 3. They have a pollution
sensitivity score of 6, which places it in the class of being moderately tolerant to pollution
(Dickens & Graham, 2001). This could be an indication that the river was in a state of a

39
slightly lower level of pollution than at the time of the other sampling days. This probably
could be attributed to a rainfall event which washed away or dissolved some of the pollutants.
It also is a result of the water being filtered by the wetland before reaching the dam entrance.
There is a notable absence of any other macroinvertebrate groups at this site. This illustrates
the high degree of pollution in the water at this site, which leads to unfavourable conditions.
Therefore these other macroinvertebrate groups are unable to survive here, as they do not
have the ability to tolerate the level of pollution of the water.
5.3 INTERPRETATION OF SENSITIVITY SCORES
Table 5.1 Average Sensitivity Scores for each site
Days
1 2 3 4 Average
Site 1 7.625 7.22 8.43 7.5 7.69375
Site 2 2 2 2 2 2
Site 3 5 5.5 6 6 5.625
The average sensitivity scores that were calculated for the four sampling days were
interpreted using the miniSASS river health interpretation table. The values for days 1, 2 and
4 were 7.625; 7.22; and 7.5 respectively (Table 5.1). These values lie in the range of 6.8 – 7.9,
placing it in the category of “largely natural/few modifications (good condition)” (refer to
Appendix B). The value for day 3 was 8.43, which falls within the range of > 7.9. This gives
it the category of “unmodified (natural condition)”.
The average sensitivity scores calculated over the four days of sampling were exactly the
same for each day.The value was 2, which lies in the range of < 5.1, placing it in the category
of “Seriously/critically modified (very poor condition)”.

40
The average sensitivity score calculated for day 1 was 5, which lies in the range of < 5.1,
placing it in the category of “Seriously/critically modified (very poor condition)”. The
calculated scores for days 2, 3 and 4 were 5.5; 6; and 6 respectively. All of these values lie in
the range of 5.1 – 6.1, placing it in the category of “Largely modified (poor condition)”.
5.4 STATISTICAL ANALYSIS
The Mann-Whitney U Test indicated a significant difference in water quality between Site 1
and Site 2. This was expected because Site 1was located upstream, a large distance away from
Site 2, which was the possible pollution source (the township). Since there were no
anthropogenic activities taking place at or near the stream at Site 1, the stream was largely in
a natural state. At Site 2 however, the stream was heavily influenced by anthropogenic
sources of pollution, predominantly raw sewage which flowed into the stream from the
township. Hence the stream was in a critical condition, with very poor water quality.
Water quality at Site 1 and Site 3 were also found to be significantly different from each
other. Although Site 3 was located a fair distance away from Site 2, it still displayed poor
water quality. However, the water quality was not as bad as that of Site 2, as the stream flows
through a small wetland before reaching Site 3. The wetland serves as a natural filter, by
removing a small percentage of pollutants from the water. This mechanism is not able to cope
with the large concentration of pollution in the stream, which is why the water quality is still
poor when entering the dam.
Similarly, Site 2 and Site 3 have a significant difference in water quality as well. This is due
to the detrimental condition of the stream at Site 2, which is faced with the heavy input of raw
sewage from the township, while Site 3 was located after the stream flowed through the
wetland, which purified the water to a certain extent.
5.5 CONCLUSION
Site 1 was found to have the best water quality, with very little or no pollution at all. There
were no anthropogenic influences on the stream at this site. The largest number of
macroinvertebrate groups was found at this site. Most of the macroinvertebrates found are
very sensitive to pollution and can only survive in unpolluted waters. This is attributed to the
excellent water quality. Site 2 had the worst level of water quality, as it was very heavily

41
polluted by raw sewage and other pollutants from the nearby township. Only one
macroinvertebrate group was found which is highly tolerant to pollution. Site 3 was also
found to have a high level of pollution, though not as bad as that of Site 2. This was because
of the stream flowing through a wetland before reaching the site (dam entrance). The Mann-
Whitney U Test indicated significant differences in water quality between all three sites. The
results clearly show that the township, with its lack of a proper sewage system, is having
detrimental impacts on the water quality of the stream.

42
CHAPTER SIX
6. CONCLUSION
6.1 INTRODUCTION
This study was successful in proving that the miniSASS method is a viable method for
biomonitoring of river health. The technique proved to be very simplistic and practical, yet
also being cost efficient at the same time. No expensive equipment was required.
Macroinvertebrate groups were easy to identify and distinguish. miniSASS yielded results
that were robust and still comparable to that of the full SASS5 technique.
6.2 REVIEW OF AIMS AND OBJECTIVES
(i) To compare macroinvertebrate assemblages above and below the point pollution
source of the Mthinzima River.
The miniSASS method provided a simplistic and low tech means of identifying
macroinvertebrate groups at points above and below the point pollution source, as well as at
the point pollution source of the Mthinzima River. Site 1, located above the point pollution
source, had the highest number of macroinvertebrate groups identified, and therefore the
largest macroinvertebrate assemblage. Most of the macroinvertebrate groups found here had a
very low tolerance to pollution. Site 2, located at the point source of pollution, had just one
macroinvertebrate group identified, hence a very poor macroinvertebrate assemblage. This
group had a very high tolerance to pollution. Site 3 also had a poor macroinvertebrate
assemblage, with two groups found on two days and just one group found on the other two
days. However, these groups were less tolerant to pollution than those at Site 2.
(ii) To provide evidence on the usefulness of miniSASS.
Although being a relatively newly developed method of biomonitoring, miniSASS proved to
be extremely useful and reliable when assessing water quality and river health. The easy to
follow method and simple, inexpensive equipment, together with the need for just basic
knowledge of biology was much lauded.miniSASS also yielded a very clear-cut dataset which
was very easy to analyse, interpret and understand.

43
(iii) Using the miniSASS method to assess water quality at a number of points along the
Mthinzima River.
The miniSASS method was used successfully to determine the quality of water at the different
study sites along the Mthinzima River. River health was assessed by analysing the results
produced by the miniSASS method. This was done by making use of the river health
interpretation tables, which were very straightforward and easy to understand.
(iv) Determine if there is any pollution, and if so, the extent of the pollution.
After results were analysed, it was found that the Mthinzima River was polluted. However,
the level of pollution varied along different points in the river. There was no evidence of any
pollution in the river upstream from the point pollution source. The river was most polluted at
the point pollution source, with it categorised as being in a very poor condition. The river was
also heavily polluted downstream of the point pollution source, which was at the dam
entrance. The degree of pollution was not as high as that at the point pollution source, but still
in a poor condition.
6.3 RECOMMENDATIONS
Although this study was largely a success, there are still areas that can be refined and
improved on for future studies. For example, there could have been more sample points
assessed using miniSASS along the river. This would help to ensure any variations along the
river were taken into account. This would have also yielded a more continuous dataset, with
more gradual changes probably displayed in water quality between sites, rather than the very
drastic changes noted in this study. Sampling could have also been done over more days, and
at different times of the year. This would have ensured that variables such as climate
(changing seasons, temperature, rainfall events) were taken into account.
The quality of samples and the river assessment can be done using several additional
measurements. Diversity of the sample can be measured by using an index that takes both
richness and evenness into account regarding the stream populations. Evenness of a
population can also be measured. This could be used to indicate whether different species
appear in relatively similar proportions. Another possible measurement that can be used is the
abundance of organisms from the orders Ephemeroptera, Plecopteraand Trichoptera, as they
are very highly sensitive to pollution. Lastly, the full SASS5 technique can be used to provide

44
a much more detailed account of macroinvertebrate assemblages in water bodies, thereby
yielding more conclusive results.
6.4 CONCLUSION
The Mthinzima River provided an excellent opportunity as a candidate for water quality
assessment using biomonitoring. This is because of the heavy input of sewage and other
pollutants from the nearby township of Mpophomeni which flows into the river. The results
clearly show the river displaying excellent water quality and river health upstream of the
township. Once the river passes the township, the results show a drastic decline in water
quality, with just one macroinvertebrate group found at the site, which was highly tolerant to
pollution. Water quality at the dam entrance was also found to be very poor, though not as bad
as that at the township. This was attributed to the river flowing through a small wetland. The
wetland acts as a filter, and removes a certain percentage of the pollutants from the water.
However this is not sufficient to purify the water to acceptable quality levels, as reflected in
the results. Statistically, it was also found that water quality at each of the three sites were
significantly different from each other.
This study could serve as important scientific evidence to inform the local municipality of the
area of the problem at hand, and the extent of the problem that they are faced with. A decision
needs to be taken to improve and repair the inferior sewage systems that are currently
servicing the area. The Midmar Dam must be taken into consideration, as ultimately it is
being impacted by the pollution of the Mthinzima River. As a result, the water quality of all
surrounding areas is affected, as the dam is the main source of water. South Africa is a water
scarce country, so care should be taken to conserve the resources we have.

45
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