Water Quality Report

MUDGEERABA
CATCHMENT: A
STUDY INTO
RIVERINE
WATER
QUALITY
IMPACTED BY
URBANIZATION
Daniel Hawkins
[Course Title]
[Teacher’s Name]

1
CONTENTS
CHAPTER 1: OVERVIEW AND OPERATIONAL STUDY
1. INTRODUCTION
2. METHODOLOGY
3. OPERATIONAL RESULTS AND DISCUSSION
CHAPTER 2: INVESTIGATIVE STUDY
1. INTRODUCTION
2. INVESTIGATIVE RESULTS
CHAPTER 3: RELATIONSHIPS, ANTHROPOGENIC INPUTS AND
FUTURE RECOMMENDATIONS
CHAPTER 4: APPENDIX

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1.0.0 INTRODUCTION
1.1.0 PREMISE AND AIMS
1.1.1 Premise
Water quality is of utmost importance of human health in all civilisations. Before modern water quality
testing and treatment practices came into use during the 19th
century, many fatal illnesses and death
were observed world over. The advent of modern day water quality controls have cut such occurrences
down to a minimum. (fig 1.1)
Fig 1.1: death rates of typhoid fever in the USA, showing the effects chlorination and water quality
monitoring have had on the incidence of typhoid fever. (Sourced from: US Centers for Disease Control
and Prevention, Summary of Notifiable Diseases, 1997.)
However, there have still been incidences of high fatality and illness caused by a lapse of water quality in
countries where water quality has not been maintained. An example of this is where high levels of
arsenic poisoning in drinking water in Bangladesh, India, have been found to be responsible for high
levels of cancer incidence and health problems (Uddin & Huda, 2011). In fact, regular surveillance
monitoring constantly detects occurrences where there has been a lapse in water quality within gold
coast itself, the subject of the study. Where in April of 2014, water testing exposed faecal contamination
levels within the hope island marina to be in excess of 4 times of guideline limits. (Ardern L., 2014).
In light of recent incidences, a surveillance water quality monitoring program has been approved for the
Mudgeeraba catchment area, within the Gold Coast region of Queensland, Australia. This report outlines

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and reports the results of the sampling plan. Several sampling locations have been defined, occurring at
several points where the creek is exposed to possible urban and suburban pollution. 6 sampling areas
were chosen for the study, with 4 sampling events occurring over a period of three months (August-
October).
1.1.2 Aims
The first two sampling events were in the form of a pilot study, where several broad parameters were
sampled. These sampling parameters were then compared against current water quality guidelines. Any
sampling parameter that was found to be in excess of the allowable guideline value would warrant
further testing.
Therefore the aims of the study is to identify, through event sampling, an idea as to the quality of the
catchment, along with identifying any “areas for improvement” regarding the health and safety of the
Mudgeeraba catchment.
BROAD SITE CATCHMENT OVERVIEW 1.2.0
This section attempts to provide background information on the Mudgeeraba catchment by outlining
the geology, recent geomorphology, climate and riparian vegetation of the catchment.
1.2.1 Geology of Mudgeeraba creek catchment
Considering the scale of geological formations, it is impossible to describe the geological history of
Mudgeeraba without including Gold Coast in its entirety. The last 300-400 million years before present
has seen Gold Coast undergo 5 stages of geological development into the current state as seen today.
(Gold Coast City Council Research unit, 1997) This includes a period of volcanic activity, approx. 400
MYBP, followed by an uplift in the eastern crust around Australia to give rise to a stable sedimentary
period (300 MYBP). A second volcanic age of activity followed this (225 MYBP), followed by the current
stable sedimentary period (Gold Coast City Council Research unit, 1997) As a result of this geological
history, a large proportion of Gold Coasts rocks are constituted of igneous rocks. This has had a large
influence on the types of soils seen around the gold coast region. The figure (1.2) displays the soil types
around gold coast, identifying that the area around Springbrook is largely composed of “highly fertile”
red volcanic soils (Queensland Department of primary Industries, 1996).

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Fig 1.2: the geological soil constituents of gold coast, circled is the Mudgeeraba/Springbrook area
sourced from (Queensland Department of primary Industries, 1996).
1.2.2 Recent Geomorphology of Mudgeeraba creek catchment
By “recent”, this section describes the short 20 years in which the geological condition of the catchment
and its surroundings have been shaped as a consequence of rapid urbanisation. The report
“Mudgeeraba & Worongary Creek Catchment Management Study” (Tomlinson et al. 2006), details that
over the past 2 decades, approximately 28% of the natural resources of the area have been cleared.
Whilst this number is by no means significant when compared with other rapidly urbanised locations,
the result is still significant.
As detailed in the study, the majority of the catchment is of a short and steep nature. Thusly, removal of
natural resources mostly likely has resulted in an increase in landslide and debris flow events.
Accelerated erosion of soil is a large problem as 60% of all erosion ends up in waterways, causing
significant ecological damage (Pimentel et al.1995).

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1.2.3 Climate of Mudgeeraba catchment
Mudgeeraba, being a subsidiary of gold coast, is also subject to the climate conditions of the region.
Being considered a “sub-tropical” and humid environment. With severe thunderstorms and rainfall in
the summer months (Hall P., 1990). This can be observed in the mean rainfall of the winter months in
table 1.1. Where a significant lull in rainfall can be observed. This, in conjunction with the erosional
conditions, as described in the geomorphology section of the report, suggests that sedimentation and
turbidity may exhibit higher values in the summer months.
Table 1.1: average rainfall, max temp and min temp for Mudgeeraba creek catchment.
1.2.4 Vegetation of Mudgeeraba creek
The vegetation climate of the Mudgeeraba creek has been covered in the report “Mudgeeraba &
Worongary Creek Catchment Management Study” (Tomlinson et al. 2006)). The report used the grading
screen shown in table (1.1) to grade the creek catchments of Bonogin and Mudgeeraba creek. This
report will adapt the vegetation report taken from that study, and use it as a basis for the vegetation
grading used for the sites.

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Table 1.2 : riparian vegetation quality rating for the study
The study has broadly identified mudgereeba creek catchment to be in poor condition. Stating that
throughout the valley of springbrook road, vegetation has been subject to heavy clearing. According to
the report, vegetation along the alluvial side have been heavily cleared, where the remaining riparian
vegetation has been described as discontinuous, typically narrow and weedy. This is exhibited in the
MUD site 5 and site 6 locations, where along most stretches of the creek, mid story and canopy cover
vegetation have been removed, leaving only small patches of grass. The end effect is riverine habitat
with an absent riparian zone, of which is critical to inland water body health (Waters and Rivers
commission, 2000).
In the site descriptions for the following section (section 1.3.2); the site vegetation quality will be
described as according to the table (1.2)
1.3.0 SAMPLING DESIGN
1.3.1 Sampling design overview
As described, the sampling program is that of a surveillance monitoring program, where the effects of
pollution/contamination will be observered in the 6 sampling sites along the mudgereeba creek. The
size of the study site is in a linear fashion and would therefore not be suitably described in a KM2
format. It is more fitting to exclaim that the sampling area encompasses a distance of approximately 14
Km. starting from close to the boomerang golf course on Gold coast-springbrook road, nerangwood, to
LOT 503, Robina Parkway, Robina (fig 1.3). Grab samples were taken at each location, as grab samples of
flowing waterways have shown to be more than adequate, due to constant water mixing.
(Environmental Protection Authority, 2007).

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Fig 1.3: the full monitoring study scope

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1.3.2 Sampling locations
- Mud 1
Fig 1.4 Mudgeeraba site 1. (Left: displaying sampling location), (right: displaying address).
Where circled in red is the location of the site in relation to roads, address being 33-41 Staghorn drive,
Austinville, QLD, 4213. Where circled in green, is the location of the sampling site, on the side of the
road.

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Fig 1.5: (Left: side of creek on north of road), (right: side of creek facing south of road)
As shown in the above photos, the site exhibits a fair amount of riparian cover, however, there are still
large gaps in the canopy close to the road. As distancing from the road, the riparian vegetation is shown
to increase, with a full canopy cover, midstory and understory observed High levels of erosion can be
observed, some emergent weeds can be observed, though no smothering vines. For this reason, the
sample site is given a rating of “Fair”.
- MUD 2
Fig 1.6: displaying the sampling location and address.

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The green circles in the above figure (1.6) indicates the sampling area. With one photo indicating the
location to the side of the road, and the figure below it showing the sampling site in relation to the
height of the road. The red circle indicates the address of the sampling location. Known as 610-614
Springbrook road, Mudgeeraba, QLD, 4213.
Fig 1.7: Mudgeeraba site 2, (Left: west of the road. Right: east of road)
As shown in the above figure (1.7), the sampling location included a rocky region, facing a steep
rockface. This “bedrock” type cliff-face of the creek exihibits a well defined canopy cover, understory
and midstory. However, the alluvial side appears to consist of mostly understory, some midstory, and
some canopy cover. The part of the creek to the west of the road appears to have a dominant
understory close to the road, but quickly develops into full riparian vegetation within 20-30 Meters of
the road. For these reasons, the riparian vegetation is given a rating of “good”.

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- MUD 3
Figure 1.8: sampling location and address of mud 3
As shown in the above figure (1.8), the green circle indicates the sampling site. The sampling location is
just south of the road, (5-10 meters). The red circle indicates the sampling location. The address is 4
Berrigans road, Mudgeeraba, QLD, 4213.
Figure 1.9: the sampling site riparian vegetation. (Left: section of creek north of road. Right: section of
creek south of road).
As shown in the above figure (1.9), the riparian vegetation of this creek sampling site has been subject
to reasonable vegetation clearance. To the right of the creek section on the north of the road, the
riparian zone has been almost completely cleared, with only a few sections of canopy cover left. The left
side of the creek section facing the north of the road has also been subject to heavy disturbance, where
vines and weeds dominate the understory. The section of the creek on the south facing side exhibits

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similar characteristics. Where clearance and heavy disturbance has severely damaged the riparian one.
For these reasons, the vegetation rating for this site is “poor”.
- MUD 4
Shown in the above figure is the address and the exact location of sampling. In order to reach the site,
one would travel approximately 20 meters east from the road, the sampling location is located on the
southern bank. The address of the sampling location, shown in the red circle on the map, is 39A
Somerset Drive, Mudgeeraba, QLD, 4213.

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Figure 1.11: a display of the riparian vegetation surrounding Mud 4
Unfortunately, as of yet, proper sampling photos are yet to be taken. However, as can be seen from the
photos and on-site observations (Fig 1.11), the site has been heavily impacted by urbanization. With
only approximately 5-10 meters of riparian vegetation on either side. Additionally, there is little
understory and heavy erosion on the banks of the creek. For these reasons, the riparian vegetation
rating is described as “poor”.
- MUD 5
Fig 1.12: the sampling location is displayed on the right as a birds-eye view, whilst the picture on the left
shows the eye-level view of the sampling location.

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Shown in the above fig (1.12), the picture of the site on the left is the exact location of where sampling
occurred. Located 10-15 meters west of peach drive, on the eastern bank of the river. The address of the
sampling site is circled in red on the figure. The address is 1 Peach drive, Robina, QLD, 4226.
Fig 1.13: a photo displaying the Riparian vegetation quality of Mud 5
As shown in the above fig (1.14), the riparian zone is almost non-existent. The understory and midstory
do not exist, whilst canopy cover is at a minimum. As can be seen in the figure, it is possible that the
presence of the Lilly pads could be indicative of a high nutrient content within the river. Due to the
extent of riparian vegetation clearance, the riparian vegetation of the site is described as “very poor”.

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- Mud 6
Fig 1.14: the sampling location and address of the site
As seen in fig 1.14, the sampling site is close to the road. Just under the bridge. The address of the
sampling location is on Robina parkway, Robina,QLD, 4226.
As can be seen from the above photo, along with observations garnered from on site sample. The
riparian vegetation has been heavily disturbed, and in some cases, has been completely cleared and
replaced with waterfront urban residential properties. For this reason, the riparean vegetationg of the
site is classed as “very poor”.

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1.3.3 Study parameters
The pilot study parameters for the program involves:
- Faecal contamination
- Nutrient levels (Nitrate + phosphate)
- Dissolved metals
- Turbidity and total suspended solids
Faecal contamination
Faecal contamination is the most significant pathway for pathogenic viruses to enter inland
water systems. Where improper handling of faecal contamination and waste occurs, high levels
of water-borne diseases occur, i.e. typhoid, cholera etc. (Sack et al., 2004)
This is outlined in previous publications (Finegold et al. 1983) where faecal matter has been
shown to contain bacterial densities of 1012
-1014
organisms per gram. Whereas not all of these
bacteria are pathogenic, the high populations can indicate the possibility of pathogenic bacteria
being present.
As the study sampling locations of this study are centred around the mudgeeraba creek. Trigger
values surrounding faecal contamination varies highly depending on usage. The official
document “Nerang River environmental values and water quality objectives: Basin No. 146
(part), including all tributaries of Nerang River” (Water quality and Ecosystem Health Policy Unit,
Department of Environment and Resource management, 2010) shows that rather than a specific
“trigger value”, there are expected values for faecal coliforms after rainfall events. These values
were chosen as they were most locally relevant.
Table 1.3: trigger values of total faecal coliforms for recreational purposes. Primary contact involves
swimming. Sourced from (Australian and New Zealand Environment and Conservation Council, 2000)

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Nutrient levels (NOx and Phosphate)
Nutrient levels particularly that of nitrogen and phosphate species, are incredibly important to
measure within water bodies. Essentially as these are the 2 limiting nutrients preventing algal
blooms in all water bodies. Of particular interest is that of phosphorus species concentrations,
the more limiting nutrient of the two for inland water bodies. As described in (Carpenter et al.
1998), high levels of nutrient input can facilitate eutrophic and hypertrophic conditions within
water ecosystems. This accelerates algal growth in the water body, causing algal blooms,
typically that of cyanobacteria. In several studies cyanobacterial (fig 1.15) blooms have been
attributed to a wide range of human and animal health complications. This is due to the wide
range of toxins excreted by said cyanobacteria, including neurotoxins, dermatoxins, cytotoxins
and Hepatotoxins. (Van Apeldoorn et al. 2007)
Fig 1.15: Cyanobacterial bloom, as can be seen, this can also severely decrease water clarity. (Sourced
from USGS webisite: http://wfrc.usgs.gov/fieldstations/klamath/wq.html)
Other problems include an increase in heterotrophic bacterial blooms after the cyanotoxin blooms die,
resulting in anoxic conditions within the water body. Resulting in a high level of fish kills, and complete
local ecosystem collapse. (Del Giorgio et al. 2005).
On the health aspects of the spectrum, high levels of nitrate and nitrite has been shown to be
potentially toxic to humans and animals in high concentrations. Nitrite has been shown to be much
more toxic to animals than nitrate. Ruminants (i.e. cows) are shown to be incredibly sensitive to nitrate
and nitrite.
Symptoms of nitrate and nitrate poisoning include; increased urination, restlessness and cyanosis, which
leads to vomiting, convulsions, and death, (ANZECC, 2000).

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Table 1.4: guideline values for NOx, FRP pH and DO (%saturation). (sourced from: Nerang river
environmental values and water quality objectives (Water quality and Ecosystem Health Policy Unit,
DERM, 2010)
Dissolved metals (Al, Cu, Co, Fe, Mn, Ni, Pb & Zn)
Many metals naturally occur in the environment, and are essential for the growth of many micro-
organisms, there is a small range in which essential metal nutrient concentrations can become toxic
metal concentrations. (Sánchez M.L., (2008).
Although metal contamination can prove a risk to drinking water supplies, the study area does not
incorporate drinking sources. Therefore, the ecological effects of metal contamination are to be the
main focus regarding dissolved metal concentrations within this study.
Previous studies have shown to have bio accumulative effects on organisms. The study (Khaled A.,
2004) has shown Zinc, copper and iron tend to accumulate in the gills and live of fish, whilst lead
chromium and cadmium tend to accumulate most in the bones and muscles of aquatic organisms.
(Khaled A., 2004) although the study also outlined that the metals Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn
were found in concentrations within the muscles, bones, gills and livers of the fish studied.
On the contrary the same study found that metal concentration levels were not found to be of a
high level of concern for the fish specimens. In addition to a separate study that found lead
biomagnification to only effect marine invertebrate (Rubio-Franchini I., Rico-Martínez R., 2011). The
study also only found lead to bio-concentrate in one top end consumer, also being an invertebrate.
However high metal concentrations affecting invertebrates can lead to troubling effects, where
elimination of invertebrate populations in aquatic ecosystems may lead to trophic cascade
(Terborgh & Estes, 2010).

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Table 1.5: dissolved metal guidelines for Freshwater and marine water
- Turbidity and total suspended solids
Turbidity and TSS have both been shown in several studies (Holliday et al. (2003)) to be at least partially
correlated with each other. Although study outcomes regarding said correlations have been variable
Where in the laboratory experiment, the ratio between TSS and NTU (Nephelometric Turbidity Unit) was
found to be 1:1 for silt and clay fractions. Where turbidity could be used to estimate the levels of TSS in
the water. However, the study also found that an underestimation of TSS in the water occurred when
sand-size particle were present. Nevertheless, TSS and turbidity are instrumental monitoring parameters
to all water quality studies. Due to the wide range of constituents within suspended solids (Huey, 2010).
Turbidity along with TSS are key factors of water quality (Huey G.M., 2010) due to the optical properties
of turbidity, which makes it a relatively “easy” compared to other diagnostic techniques. Where before
even measuring the turbidity levels, one can ascertain as to whether a water body is highly turbid. (Fig
1.16)
Fig 1.16: visual representation of
optical difference between high
and low turbidity. (sourced:
http://cals.arizona.edu/watershe
dsteward/resources/module/Soil
/soils-watermgmt-pg3.htm)

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Turbidity is commonly caused through a large amount of particulates within the water that scatter and
prevent the penetration of light. This causes water to appear opaque and/or discoloured. High levels of
turbidity and TSS usually indicate high levels of disturbance or contamination/pollution. This can be
shown in table 1.6 taken from the ANZECC water quality guidelines. According to the guidelines most
lowland riverine ecosystems are expected to have relatively low turbidity where vegetation is present.
(ANZECC, 2000)
Table 1.6: guidelines regarding turbidity. Note: most sampling sites are lowland rivers. (Sourced from
ANZECC, 2000)
TSS as described previously, is related to the amount of “solids” suspended in the water body.
Suspended solids can include a wide range of constituents, including but not limited to; organic matter,
plastics, glass,
1.3.4 In-situ measurements
Additionally, several other parameter have been recorded over the course of the study. This includes:
- DO % saturation and mg/L
- pH
- Temperature
- Conductivity
- Stream depth
- DO % saturation and mg/L
This property is measured for supplementing evidence of water quality. Studies have shown that
“healthy” waterways tend to have a high level of DO saturation. Additionally, low dissolved oxygen
saturation level could have been influenced by BOD (Biochemical oxygen demand) levels in the water.
(Manivanan et al., 2013), High BOD levels are associated with high heterotrophic bacterial populations.
High bacterial populations can present a severe issues to the water system. As high bacterial populations
may indicate possible Eutrophic conditions, high organic matter concentrations, and/or a possibility of
the presence of pathogenic bacteria sheltered within their biofilms (Chowdhury S., (2012).
Refer to table 1.4 in the previous part for guideline trigger values regarding DO
- pH
pH is an important measurement to make when water sampling. As the pH in the water changes, metals
can precipitate. Therefore it is important to recognise the pH levels of the water body, as this will
influence the speciation of the metals. Which in turn, affects the toxicity of the metal present, as some
metal species are more bioavailable than others. (Cunningham T.M., Koehl J.L., Summers J.S., Haydel

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S.E., (2010) The pH measurement itself is also subject to change when sampling. This is due to carbon
dioxide content changing as the air in the sample is progressively dissolved. Therefore it is most
common to either measure pH on site, or within 2 hours.
Refer to table 1.4 in the previous part for guideline trigger values regarding pH
- Temperature
For obvious reasons, temperature must be measured on site. As soon as the water sample is removed
from the site, the temperature changes. It is therefore necessary to measure the water temp of the
sample location in situ. Temperature has many different effects on the water body and properties of the
sample. One parameter is that of Dissolved oxygen levels, of which the maximum oxygen saturation
level changes with temperature, as cold water has a higher oxygen saturation level than warm water.
Additionally, certain constituents may volatize with increasing temperatures, or metal speciation may
change. Therefore it is important to be able to allow for these possible variations and compensate
within laboratory testing (Imran, 2005).
- Conductivity (Salinity)
Salinity is also one of the sampling parameters to be measured on site. This is measured through
electrical conductivity. This method measures the levels of salt within the sample by passing an electrical
current through it.This sample parameter is important, as the sample sites, being inland creeks, are
expected to be freshwater. Therefore, any high level of salinity found within the sampling locations may
be alarming, as fresh water organisms may die and the ecosystem may undergo high stress when
exposed to high levels of salinity. Other environmental outcomes may involve if the creek water is used
for irrigation, to where plants and soils may experience stress when the salinity of the soil is altered.
(Läuchli & Lüttge, 2002)
For the study, the salinity with be measured through conductivity, to where the measurement will be
converted to salinity.
Table 1.8: salinity guidelines for lowland streams.
Stream depth (cm)
For the purposes of the experiment and extra information as to the riverine profile, the stream depth
was measured.

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2.0.0 METHODOLOGY
2.1.1 Sampling Procedure
The procedure for collection was very similar for all site parameters. There were a few differences
however. Therefore the differing methods have been tabulated and can be read in the below table
(Table 2.1).
Study parameter Sampling method
Phosphate (FRP) + NOx (NO3
-
) Use extendable pole sampler with HDPE container
to “grab” water sample. Water sample is then
poured into the appropriate container, to where the
container is then marked and placed in the esky for
preservation until testing
TSS + Turbidity Use extendable pole sampler with HDPE container
poured into the appropriate container. This was
repeated until the 1L bottle was filled. The container
was then marked and placed in the esky for
preservation until testing
Dissolved Metals Use extendable pole sampler with HDPE container
poured into the appropriate container, to where the
preservation until testing. Initially filtration was
supposed to take place. However, problems with the
filtration equipment prevented this from occurring.
Total Coliforms Use extendable pole sampler with glass container to
“grab” water sample. Water sample is then poured
into the appropriate container, to where the
preservation until testing.
Table 2.1: sampling methods for the analytes within the study.
Collection of the samples involved the use of the extension pole, an illustration of this method is
included in figure 2.1
Fig 2.1: sampling
through the use of an
extendable pole
sampler

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2.1.2 Lab Measurement methods of site parameters
For the monitoring program, 6 parameters were chosen, this includes;
- NOX (Nitrate [NO3
-
] and Nitrite [NO2
-
]),
- Phosphorus,
- TSS,
- Turbidity,
- Dissolved Metals,
- Faecal contamination (Total coliforms).1
NOx was measured using the 4500-N F Nitrate (NOx) Automated Cadmium Reduction Method. Details as
to this method can be viewed in the (American Public Health Association, 1995) handbook.
Phosphorus levels were measured in the formed of Filterable Reactive Phosphorus (FRP), using the
4500- P E Phosphorus absorbic acid method. This method is used by itself due to time constraints. FRP
can be considered an acceptable method of fast phosphate contamination detection, as this method
measures levels of orthophosphate in the water bodies, the most bioavailable form of phosphate
contamination (Zhang & Oldham, 2001).
Total suspended solids were measured by filtration and drying at 103-105⁰C, the 2540 D Total
Suspended Solids method. Turbidity was measured in the lab using the Nephelometric the 2130 B
Turbidity method rather than using the common secchi disk method, which is more subject to human
error.
Dissolved metals were measured using the inductively coupled plasma optical emission spectroscopy
method, known as the 3120 B Metals by ICPOES. This method has been shown in several previous
papers to be more effective at metal identification compared to alternative methods of spectroscopy.
Mainly due to its lower limit of detection (Marcos et al., 2011). Unfortunately, due to equipment
difficulties, dissolved metals were not filtered on field. Dissolved metal samples were filtered in the lab.
This acquaints to 2 hours of a time interval from sample collection to sample filtration.
Total coliforms was chosen, albeit with some changes to the method. Described in section 2.1.3.
These parameters were chosen as the first stage of broad testing. Further specialisation into specific
testing depended on the results at hand. i.e. High coliform levels as an indication of faecal
contamination may then result in additional testing into coliform counts. (i.e. E.coli and Enterococci
plate counts).
2.1.3 Changes to the lab method
- Total coliforms, the 9222 B Total coliforms method
For week 1: High levels of heterotrophic bacterial colonies resulted in a retardation of coliform
colony development. Therefore for the second round of testing the range of volumes to be run
through the filtration procedure was changed from 100 mL, 50 mL, 20 mL and 10 mL to 20 mL,
10mL, 1mL and 0.1 mL. Further explanation into the reasoning behind this change, along with data
representation is included in the results section.

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2.1.4 In situ analysis for properties of water bodies in the study
- DO (% and mg/L)
- pH
- Temperature
- Redox Potential
- Conductivity (Salinity)
These were all measured using a YSI Professional Plus Probe.
Fig 2.1: The equipment used with and including the probe used in the study.
2.1.5 Apparatus
Apparatus used is displayed in table 2.2
Parameter Volume
required (mL)
Bottle type Number
of
bottles
needed
Processing
and
preservation
Cleaning
requirements
DM 250 HDPE 18 Filter on site,
decrease pH
of sample to
<2, add nitric
acid (HNO3)
Acid
TSS + Turb 1000 HDPE 18 Refrigerate Detergent
FRP + NOx 250 HDPE 18 Refrigerate Water
TC 100 Glass 18 Refrigerate
or within 1
hour
Sterile
Containers
Table 2.2: The volume, type, preservation and cleaning bottle requirements for the analytes.
Filterable reactive phosphorus and NOx(Nitrate) were included in the same bottles. The sample was
thereafter decanted into 2 separate 125 mL beakers for the respective lab procedures. This made up the
nutrient analysis for the water body. There were no qualms in relation to volumes need for the analysis.
Ample sample was available for the experiment. This same approach was conducted for the TSS and
turbidity. Both parameters were analysed in the lab separately.
Dissolved metals were collected separately in the field to the other parameters requiring HDPE
containers. This was due to the difference in pre-cleaning procedures along with processing and
preservation requirements of the aforementioned dissolved metals sample.

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The total coliform collected sample required its own separate container due to the separate
requirements of an inorganic container needed for collection, along with the requirements of a sterile
container.
Fig 2.1: the apparatus used for the experiment
Sampling equipment apart from the containers used included:
- Extendable pole samplers
This enabled sampling of water further from the side of the river and more closer to the centre of
the water bodies. This enabled a more representative sample of the water body to be attained.
- Box of sterile surgical gloves
This ensured the validation of necessary QA methods, preventing contamination from the hands of
the sampling personal. Which would compromise the integrity of the sample.
- Large esky for carrying samples, filled with ice
This eases the labour intensive method of carrying containers and equipment between sites. The
esky is also filled with ice to adhere to the sampling policy requirements of sample preservation, as
shown in table 2.1
- Water probe (for in situ measurement parameters)
This enabled the measurement of in situ properties of the water body that may effect the subject
parameters of the study. Such parameters measured are displayed in the previous section (1.3.4).

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Fig 2.2: the probe in use.
- Plastic bags for needed separation of equipment
This was more of a luxury item, rather than a necessity. Plastic bags were used to separate the different
containers used for each subject parameter of the study.
- Clipboard (for carrying maps, documentation, etc.)
This too was a luxury item, ensuring that the risk assessment, chain of custody and experiment APHA
methods all stayed in one place
- Field log book
This was used for recording on site observations over the course of the study period. Information
regarding the field log book and its purpose is elaborated in the QC/QA section of the report (2.1.6).
2.1.6 QA/QC methods
Methods of QA/QC incorporated into the study are described as below:
- Field log book
Several observations have been recorded in the field log book. The information contained encompasses
time, temperature, date, weather, weather over the past week before the sampling period and several
other factors. The full contents of the field log book are accessible in the appendix located at the back of
the report.
- Calibration and ensuring proper measurement with field probes
At the beginning of the day to on samples were taken, the field probe was calibrated before taking it out
into the field. Additionally, when recording the DO % and mg/L reading, the probe was moved around in
order to ensure an accurate reading. As the oxygen receptor within the probe consumes oxygen as it
measures. Additionally, the probe was left in the water for a period of a few minutes before recording

27
the time. This ensured that the probe readings were stabilized and representative when recording the
readings taken.
- “Clean hands, Dirty hands” procedure
In order to prevent contamination of the water samples, one group member was designated as “clean
hands”. This group member would only come into contact with the sample and sterilized equipment.
The elected “dirty hands” member was responsible for preparing the “dirty equipment, along with
removing the packaging to the sterilized equipment, without touching the equipment. This is displayed
in fig 2.4
Fig 2.4: in this case if the container being placed in the bag is fully sterile, then the “dirty hands”
elected member holds open the bag, whilst the “clean hands” elected member places the sterile
sample container in the bag.
- Field Blanks/Lab controls
Quality assurance methods included the use of lab and field test blanks in the study. This ensures the
integrity of the sampe. For each of the sampling parameters a field blank was taken on site. This entails
taking DI water out into the field, incorporating the sampling of the DI water by the same methods as to
which the sampling method was taken. This allowed us to assess the integrity of our field testing
methods.
A lab control was incorporated into the lab experiments in a similar fashion. This enabled us to examine
the integrity of the lab testing equipment.
- Chain of Custody form
A chain of custody form was utilized for the study. This documents who is responsible for the samples at
what given time. This form give documentation as to provide evidence that the chain of custody has
been maintain throughout the study period.
Quality assurance methods included the use of lab and field test blanks in the study. This ensures the
integrity of the sampling equipment.
Other methods include the use of a chain of custody form.

28
- Repetitions
Defined as a repeated measurement of the sample. The use of repetitions are the most powerful
method in testing the repeatability of the experiment. This method was employed in the group analysis
for Phosphate only. However in the individual analysis, Dissolved metals and NOx will also be tested. As
these 2 methods are also subject to extensive sample processing before measurement.
2.1.7 Statistical QA/QC measurements
- Sample Mean
As repetitions will be conducted for some samples, the calculation of the sample mean was necessary.
This blends the repetitions of a sample together, thereby allowing for an “average” value for the
measurement, along with the ability for SD and RSD calculation.
Fig 2.6: sample mean calculation
- RSD and SD
Measurements for precision include the Relative Standard deviation and standard deviation. Where the
standard deviation is defined as the “total deviation of sample measurements around the sample
mean”, the relative standard deviation is a percentage of this value. This method was employed for the
quantitative analysis regarding all experiments where repetitions were used.
- LOQ and LOD
Calculating the LOD (Limit of detection) and LOQ (Limit of quantification for the blanks
Another calculation in the analysis was conducted. This was for the blanks only and is known as the limit
of detection. The LOD is defined as the lowest quantity of a substance that can be distinguished from
the absence of that substance (a blank value). Additionally, the Limit of Quantification is calculated, this
fulfils the same purpose of the LOD, but shows a much higher confidence level (99%) in determining if
the sample is significantly different from the blank. (McNaught & International Union of Pure and
Applied Chemistry, 1997)
Fig 2.7: Calculation of Standard
Deviation (left) and calculation of
Relative Standard deviation
(Right).

29
The limit of detection is calculated as such
𝐿𝑂𝐷 = 3 ∗ 𝑆𝑏
Where:
𝐿𝑂𝐷 = 𝑙𝑖𝑚𝑖𝑡 𝑜𝑓 𝑑𝑒𝑡𝑒𝑐𝑡𝑖𝑜𝑛, 𝑆𝑏 = 𝑆𝑡𝑎𝑛𝑑𝑎𝑟𝑑 𝑑𝑒𝑣𝑖𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑡ℎ𝑒 𝑏𝑙𝑎𝑛𝑘𝑠
The limit of quantification is very similar in calculation, however, for LOQ the standard deviation of the
blanks is multiplied by 10 rather than 3.
- QC RECOVERY
This measured the accuracy of the reading. This was calculated through running a known concentration
in addition to the calibration curve. The known QC recovery would then be calculated as if it was a
known value. This is the measured value. The recovery would then be calculated as such;
𝑄𝐶 𝑅𝑒𝑐𝑜𝑣𝑒𝑟𝑦 (%) =
𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑑 𝑉𝑎𝑙𝑢𝑒
𝑇𝑟𝑢𝑒 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛
∗ 100
This recovery value describes the accuracy of the measurements, along with identifying if
overestimations or underestimations have been made. (McNaught & International Union of Pure and
Applied Chemistry, 1997)

30
3.0.0 OPERATIONAL RESULTS AND DISCUSSION
It is important to note that only 3 of the sampling parameters will be displayed in this section. All
other parameters were carried onto the investigational study. Therefore to aid the “flow” of this
study, only operational parameters that were omitted from the investigative report will be included in
chapter 1
3.1 INDIVIDUAL RESULTS AND DISCUSSION
3.1.1 Total Coliforms
Day 1
In the samples extremely high heterotrophic bacterial counts were found. Therefore Coliform
development was heavily impacted. This prevented measurement of the coliforms, as space limitations
and heavy competition from other heterotrophic populations prevented the coliforms from developing
fully or surviving within the agar plate. Furthermore, where there were small populations of bacteria, no
coliforms were present (i.e. MUD 6).
The filtration volumes performed in this MUD 6 (shown in fig 4) were 100mL, 50 mL, 20mL and 10 mL..
In order to combat the issue shown in the experiment, the second day involved filtrations performed at
lower volumes. The second day filtration volumes were 20mL, 10mL, 1mL and 0.1mL. Results for the
second sampling day is shown below.
For pictures of the agar plates on day 1, please refer to the appendix of the report
TC sampling day 2
Fig 5: Total coliform measurements for day 2. Red line indicates primary contact trigger value.
0
150
300
450
600
750
900
1050
1200
1350
1500
1650
1800
1950
MUD 1 MUD 2 MUD 3 MUD 4 MUD 5 MUD 6
Faecalcoliforms(CFU/100mL
Total Coliforms day 2 of sampling period

31
As shown in fig 5, MUD 1, 3, 4 and 5 are all above the swimming trigger values. MUD 5 appears to be
much higher than the other values, and are more than ten-fold over the guideline limit for primary
contact.
Of interest is the very low CFU displayed in the MUD 6 sampling site. This corresponds with the first day
of total coliform detection, where even at low filtration volumes there is a small amount of CFU in the
site. This indicates much lower levels of faecal contamination of the sampling site.
CFU calculation:
𝑪𝑭𝑼 𝟏𝟎𝟎𝒎𝑳 =⁄
(𝑪𝒐𝒍𝒐𝒏𝒊𝒆𝒔 𝒐𝒏 𝒑𝒍𝒂𝒕𝒆 ∗ 𝟏𝟎𝟎)
𝑽𝒐𝒍𝒖𝒎𝒆 𝒇𝒊𝒍𝒕𝒆𝒓𝒆𝒅 (𝒎𝑳)
e.g. for sample day 2, MUD 2 sample, 10 mL filtered (14 colonies on plate)
𝑪𝑭𝑼 𝟏𝟎𝟎𝒎𝑳 =⁄
(𝟏𝟒 ∗ 𝟏𝟎𝟎)
𝟏𝟎 𝒎𝑳
𝑪𝑭𝑼 𝟏𝟎𝟎𝒎𝑳 =⁄ 𝟏𝟒𝟎

32
3.2 GROUP RESULTS AND DISCUSSION
3.2.1: Phosphate
Figure: the phosphate results for the first 2 weeks of the study period. LOD and LOQ have not been
labelled as all measurements are below the LOD.
As shown, all measurements were well below the Limit of detection. Of which in this case, was very
high. It can therefore be assumed that not only are the measurements able to be significantly
differentiated from the blanks, but also that there are high levels of imprecision between the 2 days in
which data was collected. This is expected, as the study sites are inland streams (i.e. phosphate is the
limiting nutrient).
The issue with the imprecision for this parameters is that the LOD is higher than the trigger value itself.
This problem means that even if one of the measurements if higher than the trigger value, it still cannot
be differentiated from the blank. Therefore to a large extent, the phosphate measurements for this
study cannot be considered valid for the sampling sites.
This source of error could possibly come from either constructing an incorrect calibration curve, errors
in the instrument, or contamination whilst sampling. Due to the high R2
value, along with the inclusion
of a 0 value in a calibration curve, this may be unlikely. However, QC measurements for phosphate were
not conducted. Therefore a measurement of the accuracy of the calibration curve cannot be measured.
Without a QC, the accuracy of the instrument cannot be measured either.
Another source of error may arise from contamination. This Is possibly the cause for the high LOD and
RSD of the blank values. Phosphate contamination may arise from improperly washed glassware or
instruments. Additionally, the failure to fully follow the standard operating procedure for FRP analysis
may also result in contamination. Special attention must be paid towards avoiding glassware
contamination and following the SOP, as even small miscalculations my result in high levels of
contamination. (Jarvie et al., 2002)
Day 1 Day 2
Average corrected conc (µg/L)
Conc SD
(µg/L)
Conc
RSD
Average corrected conc
(µg/L) Conc SD
Conc
RSD
MUD 1 -7.67 0.12 0.46 5.00 0.00 0.00
MUD 2 -26.67 0.12 1.79 3.67 1.65 -21.52
MUD 3
-11.42 0.24 1.08 9.50 3.54
-
192.85
MUD 4 10 0.35 0.82 12.92 1.77 111.65
MUD 5 -3 0.12 0.39 1.25 0.59 -5.84
MUD 6 -0.17 0.12 0.36 0.33 0.47 -4.29
Blank average (µg/L)
Blank SD
(µg/L)
Blank
RSD LOD (µg/L)
LOQ
(µg/L)
10.96 31.53 287.68 94.58 315.25

33
Phosphate was calculated from a calibration curve. Therefore:
An example will be used, this pertains to using the Mud 1 day one absorbance (0.0221) measurement of
the sample against the phosphate calibration curve
Using the formula from the calibration curve:
𝑦 = 𝑚𝑥 + 𝑐
Where:
𝑦 = 𝐴𝑏𝑠𝑜𝑟𝑏𝑎𝑛𝑐𝑒 , 𝑚 = 𝑠𝑙𝑜𝑝𝑒 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑎𝑙𝑖𝑏𝑟𝑎𝑡𝑖𝑜𝑛 𝑐𝑢𝑟𝑣𝑒, 𝑥 = 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 &
𝑐 = 𝑦 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡 𝑜𝑓 𝑡ℎ𝑒 𝑐𝑎𝑙𝑖𝑏𝑟𝑎𝑡𝑖𝑜𝑛 𝑐𝑢𝑟𝑣𝑒.
Therefore, the function is reorganised to find the value of x
𝑥 =
𝑦 − 𝑐
𝑚
Therefore, using the calibration curve for phosphate (𝑦 = 0.0006𝑥 − 0.0052), and Mud 1 sample
replicate 1 (y = 0.0221):
𝑥 =
0.0221 − 0.0068
0.0006
The x value is found to be:
𝑥 = 25.5
This value was then averaged with the second duplicate (0.0222). The averaged concentration was then
corrected by the blank.

34
Nitrate (NOX)
Figure: Nitrogen measurements for the study; The red line is the trigger value for oxidised nitrogen in
lowland freshwater streams.
As shown, the concentration of nitrogen on day 2 was much higher than day 1. This drastic increase will
be talked about in the third chapter of the report. Additionally, as the study of this parameter occurred
for only 2 sampling trips of the study, an LOD and LOQ was not generated for the study. However, the
deviation between the 2 blanks of the study were both incredibly small (both approx. 4µg/L
respectively). So it should be noted that it is likely that there may possible be low levels of
contamination of the samples, if any. The full raw data is displayed in the appendix, for the reader’s
perusal.
Nox calculation
For the Nox, concentration was already calculated, hence no calculations were needed.
0
20
40
60
80
100
120
140
160
180
200
Mud 1 Mud 2 Mud 3 Mud 4 Mud 5 Mud 6
Concentration(μ/L)
Nox concentrations for Study period
Day 1
Day 2

35
CHAPTER 2:
INVESTIGATIVE STUDY

36
1.0 Introduction
Premise and reason for expansion
The second half of the monitoring program involved delving into the water clarity portion of water
quality regarding the catchment. As high turbidity and TSS values were observed, along with a
underdevelopment in riparian vegetation, it was found that water clarity could be one of the more
direct and persistent issues surrounding the catchment.
Previous studies into the matter have identified poor clarity. As a major issue, along with
recommendations into further improving these parameters. (Robertson et al. 2006) One of the
associated issues was that of the extensive construction and urban development in the region. The
second part of this study attempts to associate parameters affecting water clarity.
Turbidity and TSS aren’t the only factors that can affect water clarity. Algal concentrations and staining
from metals can also affect this parameter. For this reason, these parameters will be assessed as a side
study to the main focus (sedimentation).
2.0 Methodology
As a consequence of this investigational path taken, some parameters were discontiniued, whilst new
sampling parameters were adopted. This is displayed in table blah
Parameter Omitted/adopted/maintained
Total Coliforms Omitted Not directly related to water
clarity
Nox Omitted Not directly related to water
clarity*
FRP Omitted Not directly related to water
clarity*
Chlorophyll-a Adopted Could possibly influence water
clarity*
Light Adopted Amount of light can affect light
in water column (i.e. more
light=more light available to
penetrate water column)
Particle size Adopted Could possibly affect TSS
levels, thereby affecting Water
clarity
Dissolved metals
(Fe,Mn,Al,Ni,Cu,Co,Pb,Zn)
Maintained Can be related to water clarity,
some metals cause staining
TSS Maintained Directly related to water
clarity
Turbidity Maintained Directly related to water
clarity
*Both Nox and Phosphate affect algal levels in the aquatic ecosystem, therefore the concentration of
Chlorophyll-a (and therefore photosynthetic organisms) was measured rather than the two parameters
themselves. This was conducted due to time constraints.

37
The adopted sites all included specific methods of lab or in situ measurements. This is displayed in the
following table:
Parameter Method of measurement
Light In situ, using Luxometer
Chlorophyll-a 10200 H Plankton (Chlorophyll)
Particle Size Particle Sieve analysis
Whereas Particle size only required preservation and transport using a resealable plastic bag, and light
was measured In situ, Chlorophyll-a required special transport and preservation methods. This Is
outlined below.
Parameter Sampling Transport and preservation
Chlorophyll-a 1L HDPE bottle attached to
extendable pole in order to
gain representative sample.
This also minimised stream
disturbance
Kept in the dark, Stored on Ice
at 4⁰C to ensure chlorophyll
was properly preserved. Kept
in dark once collected as light
could affect the future
measurement.
Site Descriptions
In addition, two extra sites were added to the study, the reasoning behind this is that Worongary,
Bonogin and mudgeeraba creeks are all separate tributaries of the Nerang river. Through additional
sampling at the Bonogin 3 site and Worongary 1 site, a comparative study between the three sites can
be conducted. Additionally, a more “Overall” outlook of the tributaries of the Nerang catchment can be
overseen.

38
Woronagary 1
The Worongary creek sampling site was located at the All Saints Anglican school, on Highfield Drive,
Merrimac road. Queensland 4226. The creek was about 200 metres south of the entrance to the school.
As can be seen in the photo, along with observations garnered whilst sampling, there is a complete
absence of any understory and mid-story Riparian vegetation. There is “some” canopy cover. Though
this is only 2-10 metres thick. The banks of the stream are at a steep angle (approx. 45⁰), and loose soil
appears to be abundant on the banks and in the water body itself.
For these reasons, the riparian vegetation along the site is classed as “very poor”.

39
Bonogin 3:
As seen in fig, the site is located at 154 Glenmore Drive, Gold Coast, Queensland, 4213. The site is
opposite Gaw terrace and about 50 metres into the bushland.
As can be seen, the site has an incredibly well developed Riparian zone. An understory, midstory and
canopy cover can be observed. The canopy cover also covers large sections of the creek. This could

40
provide advantageous to the creek health, as the decrease in light could limit algal blooms even in
eutrophic conditions. For this reason the creek is given the highest rating: “Very good”.
2.0 INVESTIGATIVE RESULTS
In this section, all parameters that were maintained through the operational study are included here.
This is done so as to provide more data for the investigation and to help improve the flow of the report.
INDIVIDUAL RESULTS AND DISCUSSION
TSS days 1+4
TSS Day 1
Day 1 BEFORE AFTER DIFFERENCE
MUD 1 1.4218 0.5087 -0.9131
MUD 2 1.4232 0.5837 -0.8395
MUD 3 1.424 0.518 -0.906
MUD 4 1.3959 0.4994 -0.8965
MUD 5 1.4077 0.5127 -0.895
MUD 6 1.4088 0.5132 -0.8956
BLANK 1.4022 0.4811 -0.9211
As shown in the results, there was a large amount of error in the first day of TSS. The after weight is
much lower than the before weight. This was calculated to be due to a difference in method of the
measurement of the TSS filtration papers. As the weighing of before filtration and after filtration was
conducted by different members of the group, an error in communication and the following of the
standard procedure took place. The before measurement involved measuring the filtration paper in the
tin foil cup, which was used for drying the filter paper after filtration. The next day, the filtration paper
was taken out of the tin foil cup and placed in a plastic weighing boat. Additionally, the filtration paper
in the tin foil cup was measured as a whole, whereas in the after measurement, the plastic weighing
boat was zeroed before placing the filtration paper on it.
Because of this, the difference in weighing took place. Therefore, the difficulties expressed in the
measurement for this day is attributed to an error in team communication, along with errors in
following the correct procedure.
TSS Day 4
This error was corrected for the second TSS measurement to be conducted. This time round, the
weighing and preparation was prepared by the same person. Thusly, accurate results were achieved.
The results for the day 4 TSS is displayed below:

41
Figure: TSS day 4 data, Trigger value Is displayed as the red line, whilst yellow is the LOQ.
As shown in the results, only MUD 1 (2.7mg) was below the LOQ (2.8mg). Therefore, it can be stated
with 99% confidence that all samples, excluding MUD 1, are significantly different from the blank values.
With MUD 1, it can be stated with 95% confidence that the sample is significantly different from the
blank value.
Calculating the TSS:
For the TSS, the after weight of the filter was subtracted from the before weight of the filter. This gave
the mg weight of suspended solids. The blank suspended soling weight was then subtracted from the mg
weight of suspended solid, this gave the corrected weight.
The blank values were averaged, this was used to calculate the LOD and LOQ.
Turbidity Day 3
The third sampling trip included Worongary and Bonogin 3 sites.
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
MUD 1 MUD 2 MUD 3 MUD 4 MUD 5 MUD 6 WOR 1 BON 3
Weight(mg) TSS Day 4

42
Figure: Turbidity measurements for day 4. LOD is displayed as the red line, whilst the yellow line
represents the LOQ.
Shown in figure, all sites were above the limit of detection value. With all sites except Mud 2,3 and 6
exceeding the limit of quantification. It can therefore be stated with 95% confidence that the samples
are significantly different from the blank values.
Turbidity Day 4
Shown in these results are the turbidity results for turbidity day 4. There are slight differences between
the day 4 TSS data and day 4 turbidity data. This will be further explored within the third chapter of the
report. However, all measurements are above the LOD, hence they are all significantly different from
the blank (95% confidence level).
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
30
32
34
36
38
40
42
44
MUD 1 MUD 2 MUD 3 MUD 4 MUD 5 MUD 6 Wor 1 BON 3
Turbidity(NTU) Turbidity day 3

43
Figure: Turbidity measurements for day 4. LOD is displayed as the red line, whilst the yellow line
represents the LOQ.
For both days, it was expected that the values would mostly exceed LOQ and LOD values. This is due to
the lack of sample processing within this measurement. Any measurements below the LOQ value were
likely just “low” in turbidity.
Calculating the Turbidity
No calculations were needed for the turbidity measurements. However, the corrected turbidity was
calculated using the blanks. The blank weights were then used to calculate LOD, LOQ, SD and RSD.
0
2
4
6
8
10
12
14
16
18
20
22
24
MUD 1 MUD 2 MUD 3 MUD 4 MUD 5 MUD 6 Wor 1 BON 3
Turbidity(NTU) Turbidity day 4

44
DM week 3 + 4
Iron (Fe)
Day 3
Figure: Day 3 iron concentrations. Yellow line represents the LOQ, whilst the LOD is represented by the
red line
As shown, only sampling sites Mud 5 and 6 were at such low concentrations that they were below the
LOD. All other sites are much higher and exceed the LOQ value. The recovery for this day was 111.8%.
Therefore there was some slight inaccuracy in the data. Therefore there is slight overestimation in the
data. The high values exhibited in all sites (except Mud 5+6) however show that this may not have a
significant effect on the results however.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1.1
Mud 1 Mud 2 Mud 3 Mud 4 Mud 5 Mud 6 Wor 1 Bon 3
IronConcentration(mg/L)
Iron measurements for Study Period

45
Day 4
The results for this day differed slightly. Although the overall concentrations were lower, only mud 6
was below the limit of detection. Therefore it can be deemed that mud 6 has very low iron
concentrations during day 3 and 4. Qc recovery for this day was much more accurate (100.77%).
Therefore the measurement for this day was much more accurate than day 3. Showing a possible
improvement in technical accuracy.
Aluminium
For the aluminium, all values in sampling trip 3 and 4 were in the minuses and thusly, the LOD value.
This can be explained however by the detection limit of the ICP-OES. Of which, this spectrometer has a
detection limit of 10µg/L. As the concentrations were likely all below this value, the ICP-OES is unable to
reliably measure the concentration of this data (Evans Analytical Group, 2014). This is reinforce by the
high amount of QC recovery for the day. Which is at a concentration of 10 mg/L. QC recovery values
were 111.6% on sampling trip 3 and 100.5% on sampling days three and four respectfully. Therefore, it is
unlikely that technical accuracy may have had an effect on the result encountered.
Cobalt (Co)
For Cobalt, similar results were encountered. All results were below the LOD for not only weeks 3, but
for the entirety of the study. The results and calibration curve was checked against the QC recovery. Of
which the calculations and experimental methods were sound (104.7% for trip 3 and 100.2% for trip 4).
Levels of cobalt contamination and concentration were therefore likely below instrumental detection
limits values at all sites for the study, according to the results.
Copper (Cu)
Results for copper were also all below the 0 values. However the QC recovery value show that there was
a large overestimation of the concentrations (recovery = 119.3%), day 4 contained a much more
accurate recovery value (100.5%). It can therefore be postulated that analytical techniques improved
from trip 3 to trip 4. There was a relatively high RSD value of the blanks (160%). This could therefore
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Iron measurements for Sampling Trip 4

46
raise the LOD value. It is most likely that these variations are closely due to the low concentrations
present however, as the QC recoveries were 0.5mg/L for trip 3 and 10mg/L for trip 4.
Manganese (Mn)
Day 3
Figure: concentrations of Manganese for day 3. Yellow line represents the LOQ whilst the Red line
represents LOD.
As shown in the above figure, successful results have been sequestered from the manganese. Therefore
the concentrations are at a level where they can be detected by the ICP-OES. Additionally, high recovery
values were taken for this sampling trip (105.7%) however, this is unlikely to change the overall LOD
results much. As shown both Mud 1 and Bonogin 3 were found to be under the detection limit.
However, due to high accuracy, it is theorised that this could possibly be attributed to the extremely low
concentrations of manganese in the samples.
0
0.025
0.05
0.075
0.1
0.125
0.15
0.175
0.2
0.225
0.25
0.275
Concentration(mg/L)
Manganese Concentrations for Day 3

47
Day 4
Figure: day 4 Mn measurements. Yellow line represents the LOQ whilst the Red line represents LOD.
As shown, for this trip, Mud 6 was slightly under the LOD value. QC recovery for this day was calculated
to be 99.7%, so it is possible that with correction the mud 6 could actually be present above the LOD
value. Measurements for this day were slightly lower, which could be attributed to actual factors, or due
to slight underestimation of the data. It is imperative to note that underestimation is only 0.3%
however. So there is still a strong amount of accuracy in the result.
Nickel (Ni)
For the Nickel measurement, all values were found to be below the LOD value for both trip 3 and 4.
Therefore nickel concentration levels in all samples could not be distinguished from the blank samples.
The calibration curve for the nickel measurements had an R2
of 1, whilst the QC Measurement displayed
an overestimation in accuracy for trip 3 (108.6%) and a slight underestimation for trip 4 (99.9%). RSD
was calculated to be around 138%. Hence, the inability for the study to provide any measurements
above the LOD value could possibly be attributed to a slight imprecision in the experiment.
Lead (Pb)
Once again for Lead, all measurements were below the LOD. QC measurements found that
measurements could have been affected by an overestimation on day 3 (QC recovery = 104.3%) and a
slight amount of overestimation on day 4 (QC recovery = 100.6%). Thereby, this variation in accuracy
may have caused some imprecision in the data over the study (RSD of the blanks was 185.9%). Which in
turn could have been the cause of the inability of the measurements to exceed the LOD value. Another
postulation is that according to the literature (Evans Analytical Group, 2014,), the measurements are all
below the detection limit for the ICP-OES. This is most likely the main contributing factor for the
negative values expressed in the sample measurements.
0
0.025
0.05
0.075
0.1
0.125
0.15
0.175
0.2
0.225
Concentration(mg/L)
Manganese Concentrations for Trip 4

48
Zinc (Zn)
For the Zinc measurement; all sites were unable to exceed the LOD value. As there were minimal
negative values in the measurements, it is most likely that varying degrees of contamination of the
blanks may have been the leading cause. This is can be common with measurements regarding zinc, as
many pieces of glassware and laboratory equipment have varying degrees of zinc (Vanclay E., 2012). QC
recovery for trip 4 displayed a larger amount of overestimation (QC recovery = 109.4%). However, for
trip 4 QC recovery was much more accurate (QC recovery =100.3%). This could possibly be the leading
cause for the high LOD. As blank RSD recorded were the highest expressed in the study (RSD = 538%).
The metals were calculated through using the calibration curve function. The full calculation of
concentrations from calibration curves is outlined in chapter 1 section 3.2.1.
Chlorophyll/Pheophytin week 3
Figure: the Chlorophyll data for day 3. The red line represents the trigger value.
0
10
20
30
40
50
60
70
80
Concentration(µg/L)
Trip 3 Chlorophyll-a measurements

49
As shown in the above figure, not all sites were shown. This is because several sites exhibited
chlorophyll concentrations below the 0 value. As only 1 blank was used for chlorophyll in the study and
no QC recovery used. It is impossible to test the precision nor accuracy of the experiment. As 3 blanks
are typically required to form an LOD measurement.
This similar method was conducted for pheophytin. Additionally, as no LOD and QC recovery could be
calculated, the precision and accuracy of the experiment cannot be adequately assessed.
Calculation of μg/L Chlorophyll-a is calculated as such:
𝐶ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑎 𝜇𝑔 𝐿⁄ = 𝐶ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑙𝑎 𝑚𝑔 𝑚3⁄ =
(26.1 ∗ (664 𝑏 − 665 𝑎)) ∗ 𝑉1
𝑉2 ∗ 𝐿
Where: C1 = volume of extract (L), V2 = Volume of sample (m3). L=cell path length (cm)
Therefore, for the Mud one trip 3 sample, the measurement was 664b=0.0049, 665a=-0.0065:
Te 664 and 665 measurements need to be corrected by their respective 750nm measurements.
Therefore
𝐶ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑎 𝜇𝑔 𝐿⁄ =
(26.1 ∗ (〈0.0049 + 0.0084〉 − 〈0.0065 − 0.0086〉)) ∗ 0.01
0.0005 ∗ 1
Therefore, for the mud 1 trip 3 measurement:
𝐶ℎ𝑙𝑜𝑟𝑜𝑝ℎ𝑦𝑙𝑎 𝜇𝑔 𝐿⁄ = 15.1656
The full unedited graphs are available for perusal in the appendix.
0
20
40
60
80
100
120
Concentration(µg/L)
Trip 3 Pheophytin measurements

50
Group results and discussion
Dissolved Metals
Iron
As the water quality guidelines for iron do not exist for ecological conditions, the recreational guidelines
for iron are to be used
Figure blah: the iron measurements for the study period. The red line represents the trigger value.
As shown in the above figure, iron contamination was highest on the second sampling trip. For sites such
as mud 5, this provides and interesting result, where day 2 expressed a huge “spike” in iron
concentration. Whereas sites such as Mud 4 sustained high levels of metal contamination throughout
almost the entire period of the study. Interestingly, mud 6 expressed extremely low concentrations of
iron for the whole study, despite possibly being one of the most urbanised locations. Bonogin had
relatively high levels of iron contamination for day 3 of the study, whereas Worogan contained very low
levels also.
Day 2 expressed the highest consecutive concentration of iron in all sampling sites. Therefore there is
the possibility of a relationship with the weather during the study. This possibility will be further
explained in the third chapterof the report.
The blank overall measurements for the study showed RSD values of around 65%. This shows slight
imprecision within the data, however, with most measurements being above the LOD and LOQ, the
most important measurements for the data (i.e. above the trigger value) are most likely not
compromised by contamination within the data.
0
0.2
0.4
0.6
0.8
1
1.2
Iron measurements for Study Period
Trip 1
Trip 2
Trip 3
Trip 4

51
Manganese
Figure blah: Manganese concentrations over the study period. The red line represents the trigger value.
As shown in the data, sites mud 4 and 5 both experienced the highest concentrations of manganese
over the period of the study. Day 1 also appeared to contain certain “spikes” in manganese
concentrations in sites Mud 1 & 2.These concentrations diluted down for the rest of the study. Unlike
the iron concentrations, these results were quite low compared to the trigger values. Hence it is unlikely
that any pollution took place.
For the experiment, blank concentrations appeared to deviate highly, with RSD values reaching
approximately 172%. This shows that there is some relative imprecision in the data. However, a strong
recovery proves that the data is still accurate regardless.
For all other metals, the measurements were all under their respective LOD values. Hence the results for
these metals are not included in the report. The results for these metals are included in the appendix
however. So that they may be perused if needed.
0
0.05
0.1
0.15
0.2
0.25
0.3
Concentration(mg/L)
Manganese Concentrations for Study
Trip 1
Trip 2
Trip 3
Trip 4

52
Total Suspended Solids
Due to the clear measurement errors conduct in day 1 of the Total suspended solids, the first day for TSS
is omitted from the report. However, data regarding this day can still be found in the appendix section
of the report.
Figure: TSS measurements for the sample period. Red line represents the Trigger value for TSS.
As shown above, for day 2, 5 out of 8 sites are all in breach of the trigger value. However, it is day 3 that
displays the most interesting data. Day 3, which incorporates all sites, shows TSS levels which are many-
fold above the trigger value in some sites. The consequences of this will be explored in the third chapter
of the report, along with potential reasons as to why this may have occurred.
The RSD of the blanks, being approx. 11%, is comparatively much lower when compared to other results
in the study. Therefore, there is much more precision in the study. This is all when removing the first
sampling day however.
0
5
10
15
20
25
30
35
40
45
50
TotalSuspendedSolids
TSS Measurements for the Study
Trip 2
Trip 3
Trip 4

53
Turbidity
Figure: Turbidity measurements for the study. Black line represents, red line represents the trigger value
Once again, like the TSS measurement, all measurements are above their respective LOD values, and
thus are all significantly different from the blank values. Site like Mud 5, Wor 1 and Bon 3 all have
extremely high spikes in turbidity. However, Mud 5 does also appear to have some ability at
recuperating from highly turbid conditions. The Wor 1 site specifically appears to have a much lower
ability to “bounce back”, considering that the Worogin sampling site appears to have a high turbidity
value for the fourth sampling trip.
Mud 6 appears to have the strongest “clarity” compared to the other sites. Reasons for this may also be
further explained within the third chapter of the report.
RSD values for the blanks involving this experiment were relatively high, being approximately 57%. This
could also indicte a certain level of imprecision to the data. Sources to the imprecision when recording
turbidity may be attributed to; not keeping the bottles and water samples in a disturbed state whilst and
before measuring, and due to different members of the group taking the measurements on separate
days.
0
5
10
15
20
25
30
35
40
45
Turbidity(NTU)
Turbidity Measurements for the Study
Trip 1
Trip 2
Trip 3
Trip 4

54
Light meter
As shown, the differences in light varied from site to site. For example, in Mud 1, the light affecting the
creek was higher during sampling trip 4, whilst Mud 6 had a higher amount of light affecting the site in
sampling trip 3.
This is most likely due to the differing times in which sampling for each site occurred. As there was
heavy delays in the first trip, measurements at sites such as Mud 6 were recorded much later on day 3
compared to day 4 of the study. Additionally, these measurements were highly subject to where they
were taken. I.e. MUD 2, where the sampling location didn’t contain a vegetation canopy, although the
majority of the creek at mud 2 was under a canopy cover.
Calculation of light
No calculations were involved with light
0
100
200
300
400
500
600
700
800
900
1000
Trip 3Trip 4Trip 3Trip 4Trip 3Trip 4Trip 3Trip 4Trip 3Trip 4Trip 3Trip 4Trip 3Trip 4Trip 3Trip 4
Light(Lux@50,000)
Luxometer reading for the study

55
Particle size
The particle sizes calculated are all non-significantly different from each other. However, it is important
to note that the actual abundance of sediment at some locations were different from each other. For
mud 6, collecting a sediment sample was incredibly difficult. As most of the stream bed in mud 6 was
either large rocks or pebbles.
These relationships will be expanded on further in the third chapter of the report.
0
10
20
30
40
50
60
70
MUD 1 MUD 2 MUD 3 MUD 4 MUD 5 MUD 6 BON 3 WOR 1
Weight(%)
Particle sizes for the sample sites
>4.75mm
<4.75mm

56
Chlorophyll-a
Shown above, concentrations not included in the graph were negative values. . In order to preserve the
visual integrity of the graph, values below the LOD were omitted, though the full graph and data can be
perused in the appendix section of the report. As there were only 2 blanks for the study, an LOD and
LOQ value could not be generated. Furthermore, as there was no supply of known chlorophyll
concentrations, a QC regarding the experimental method used could not be generated. Additionally, as
duplicates were not run for the samples, SD and RSD measurements could not be calculated for the
sample.
As shown in the figure, only Mud 1 was in breach of the trigger value. The possible reasons and
consequences for this will be described in the third chapter of the report. However, it is important to
note, that as the SD, RSD and QC recovery measurements were not taken for this study, it is difficult to
claim that these results are accurate and precise. However, a relative deviation between the 2 blanks of
0% largely imply that there may be strong precision within the measurement.
0
2
4
6
8
10
12
14
16
Chlorophyll-aconcentration
(µg/L) Chlorophyll-a measurements for study

57
In-situ parameters
DO (%)
As shown in the above figure, DO(%) values for all sites barring Mud 6 and Mud 1 experienced a peak
during the second sampling trip. As shown, only three sampling trips are displayed. This is due to the use
of an older probe in the fourth sampling trip. Said probe did not contain a measurement of DO%. This
would not be a problem, as percentage DO concentrations can be calculated using the DO mg/L value
and temperature value. However, temperature is not the only parameter that can affect the maximum
saturation level of water, suspended solids and salts can all affect the solubility of gas in water.
0
20
40
60
80
100
120
DissolvedOxygen(%)
Do Saturation measurements over study period
Trip 1
Trip 2
Trip 3

58
DO (mg/L)
The results for this set of data is different from the previous set. Mainly as the probe for day 4 did
contain a DO(mg/L) sensor.
As water solubility is directly proportional to temperature, on warmer days, the DO (mg/L) will decrease
on warmer days, compared to colder days. This explains why there is no trigger value for DO (mg/L).
However, it is also important to use this on a comparison basis with the DO(%) measurement.
Conductivity (salinity)
0
5
10
15
20
25
30
35
40
DissolvedOxygen(mg/L)
DO concentration measurements over study
periodTrip 1
Trip 2
Trip 3
Trip 4

59
As shown in the graph, MUD 5 and 6 appear to be saltwater systems. It is important also to note their
high variance in concentration when compared to the freshwater systems. This is because of the higher
concentration of salt. Therefore these two sites are more prone to saltwater dilution after rainfall
events. Whereas the freshwater sites appear to vary little due to the fact that salt in these sites is only
present as a trace mineral.
Temperature
0
1000
2000
3000
4000
5000
6000
Conductivity(µS/cm) Conductivity measurements over study period
Day 1
Day 2
Day 3
Day 4

60
For the majority of the study, the water temp remained very similar. Although there does appear to be a
slowly increasing trend in temperature. This can be described due to the time of year. Considering that
the months September-October is the progression of winter to summer.
Pearson correlations did detect a relationship between temperature and conductivity. However this was
related to the fact that sites high in conductivity (i.e. Mud 5 and Mud 6) were recorded much later in the
day, compared to the freshwater sites. Therefore, temperature data collected in this report failed to
provide any significant correlations along with any reasons as to how temperature may have
significantly impacted any other factors other than Dissolved oxygen concentrations.
pH
0
5
10
15
20
25
30
WaterTemperature(⁰C) Water Temperature measurements over study
periodTrip 1
Trip 2
Trip 3
Trip 4

61
As shown, the pH levels in the study varied highly between 6.4 and 7.6. As shown, on two instances, the
minimum pH trigger value (6.5) was slightly exceeded. The second day for Mud 4 (6.46) and first day for
Mud 2 (6.49). It is possible that there are several variables affecting this result, extending to the types of
rock on the river bed, amount of organic matter in the aquatic system and metal concentrations in the
water, these will be explained in the Third chapter of the report.
5.8
6
6.2
6.4
6.6
6.8
7
7.2
7.4
7.6
7.8
pH pH measurements over study period
Trip 1
Trip 2
Trip 3
Trip 4

62
CHAPTER 3: RELATIONSHIPS,
ANTHROPOGENIC INPUTS AND
FUTURE RECOMMENDATIONS

63
Environmental and anthropogenic relationships:
In this section, parts of the results that indicate possible relationships between the data will be
discussed, along with any indications of possible breaches to the water quality guidelines. Possible
reasons and consequences will be explored.
Turbidity and Total suspended solids
One of the hypothesises of the study involved the comparison between turbidity and TSS. It was
predicted that there would be a significant relationship between the two parameters. This was an
excellent way to test the experimental methods of the study, as many studies in the past have indicated
a correlation between these two variables.
This hypothesis was therefore tested against the statistics program SPSS. Scatter Graphs plotted show a
loose relationship between the parameters (fig). Where an increase in turbidity can be observed as total
suspended solids increased.
Although there was a “loose” relationship shown (R2
=0.510. Therefore, further statistical analysis was
conducted with the use of a Pearson correlation graph(fig). This statistically proved a strong correlation
(p<0.001) between TSS and Turbidity.

64
It can therefore be concluded that there is a significant correlation between TSS and Turbidity.
There were additional correlations found in the study between the turbidity and tss value, however,
these were not statistically tested against a pearson correlation.
Relationship between riparian vegetation and turbidity.
As shown in sites Worongary 1 and mud 5, the turbidity levels are incredibly high and in some cases
breach the guideline values. This is possibly attributed to riparian vegetation. Both sites were given a
“very poor” rating for vegetation (figure blah). Thusly it is possible that riparian vegetation has a
significant effect on TSS and turbidity levels in the respective water body it surrounds.
Several previous studies have assessed the importance of riparian vegetation to their respective streams
(Micheli E.R. and J. W. Kirchne J.W., 2002),( Sepúlveda-Lozada et al 2009). These studies was able

65
to showcase that erosion of the stream banks and consequent fluvial sedimentation were affected by
the species composition of the riparian zones. It was found that certain species of plants were more
effective at stabilising the soil on stream banks than others.
As related to another previous study(Pimentel et al.1995), 60% of all land based erosion was found to
end up in surrounding waterways. Therefore, as riparian zones can affect erosion processes, and a large
portion of sediment erosion ends up in surrounding waterways, a link can be made between suspended
sediment particles in the waterways and their respective water bodies.
It is recommended that the riparian zones be rehabilitated in order to improve the turbidity and
suspended sediment concentrations in MUD 5 and Worongary 1. Conversely, there have been little
studies into the effects of reintroduced riparian vegetation to stream banks. Nevertheless the following
study (GORRICK, S. and RODRÍGUEZ, J.F., 2012) was able to detail that reintroduced riparian
vegetation had significant effects to downstream flow dynamics and sedimentation. Therefore it is
important to note that restoration of riparian vegetation may have further reaching effects than local
bank protection. Henceforth careful planning is required if such a project is to be conducted.
Relationship between turbidity and conductivity
In this study, the relationship between turbidity and conductivity was not found. However, previous
studies have indicated a relationship between these two variables. Literature has identified a
relationship between salinity levels and the settlement velocity of suspended solids in the aquatic
environment. (Ha˚kanson L., 2006)
The study details that salinity concentration increases the rates of aggregation and flocculation of
sediment particles in the water column. By increasing the rates of flocculation, the settlement velocity is
thusly increased. Through this mechanism, the clarity of the water column is improved, due to the
decrease in suspended solids(fig 1).( Ha˚kanson L., 2006)
The study was able to come to this conclusion through the correlation in secchi depth measurements
and conductivity measurements. It is thereby possible that this study overlooked this relationship due to
the absence of a secchi depth measurement.
Figure meow (source
http://www.fondriest.com/envir
onmental-
measurements/parameters/water
-quality/turbidity-total-
suspended-solids-water-clarity/)

66
Mud 6 exhibit the lowest turbidity and TSS values were observed, but the riparian was completely
underdeveloped and in most cases non-existent. Consequently, it is possible that conductivity may be a
cause of the low levels of turbidity expressed in Mud 6. As Mud 6 has a higher conductivity than Mud 5,
it may be possible that the flocculation of sediment particles in this site has a much more significant
effect than that of the Mudgeeraba 5 site.
Relationship between turbidity and particle size
Additionally, although at Mud 6 the relative percentages of particle size ratios were the same, the actual
abundance of sediment at this site was much lower. In the first event sampling day where particle size
was measured, it was a struggle to collect enough sediment in order to test the relative sizes. This is due
to the bed of the mud 6 site being comprised of both rocks and pebbles, rather than purely sediment.
This hypothesis was checked against previous literature. It is found in previous literature that larger
particulates require a larger flow speed in order to be resuspended in the water column. Therefore In
order to further attribute the correlation between sediment size and turbidity, it is recommended that
the stream flow be analysed in future studies. (Osmund et. Al., 1995).
Previous studies regarding particulate size has found correlations between particulate size and rates of
sediment Resuspension in the water body. However, as the actual percentage composition of all sites
are relatively similar.
Stream flow
As described prior, stream flow may give a possible reasoning as some discrepancies were detected in
the results. In previous studies, high incidences of rainfall has been linked to increases in turbidity and
sediment suspension in the column. (Osmund et. Al., 1995)
When looking at the overall rainfall for each previous week, against the TSS/Turb data, the expected
results don’t appear to line up. One would expect the turbidity and TSS data to spike in the second
sampling event. As is usual in heavy rainfall events (White, T. 1994) This does not occur however. The

67
highest turbidity and TSS values appear to occur on the third sampling week, to where relatively low
occurrences of rainfall were absorbed. (fig blah)
Figure blah: comparison between rainfall and turbidity. Note, day 3 has highest rate of turbidity, but day
3 has highest volume of rainfall.
Additionally, high stream flow, along with erosion events, have been linked as a consequence to heavy
rainfall events. Erosion and runoff events are typically responsible for increases in turbidity by washing
sediment into the water column. Stream flow, as described earlier has been linked in some prior studies
0
10
20
30
40
50
60
70
80
1 (5-12/8) 2 (19-26/8) 3 (9-16/9) 4 (16-23/9)
Rainfallto9am(mm)
Sampling trip (Week leading up to sampling day)
Rainfall over monitoring program
0
5
10
15
20
25
30
35
40
45
Turbidity(NTU)
Turbidity Measurements for the Study
Trip 1
Trip 2
Trip 3
Trip 4

68
as being a causing turbidity spikes (White, T. 1994). Although this has proven to be only a factor in some
water bodies.
Although, generally the rainfall must be of sufficient volume in order to increase stream flow enough to
cause sediment Resuspension (White, T. 1994). It is therefore recommended in future studies within the
Nerang catchment for stream flow to be monitored. Through this, the relationship between rainfall
events can be measured with stream flow. Thereby describing as to whether stream flow may be a
factor for sites such as Bonogin 3, along with establishing a link between weather and turbidity.
Land use
Whilst sampling at the Worongary site 1, a strong insight as to how raised TSS and turbidity levels were
present. As shown in the below figure, not only is the riparian vegetation almost absent, but commercial
moving activities was observed to directly “spill” shredding organic grass matter into the waterway.
Fig blah: worongary site 1: direct irresponsible land use resulting in organic matter being directly
dumped into stream. (Source: Daniel Hawkins)
Additionally, amongst almost all sites road works were being conducted. It is additionally hypothesised
that incorrect construction and commercial activity could be responsible for the high levels exhibited at
the sample sites.
This issue has been addressed in the previous monitoring program regarding the Nerang catchment.
(Robertson et al. 2006) It was suggested in this program that better urban planning and development

69
practices be put into practice in order to reduce inputs. It appears that these recommendations have
not been adhered to.
Relationships between Iron, pH and conductivity
A relationship between the concentration of iron in the study and pH was also detected. This was
validated significantly to the P< 0.05 level. Therefore a correlation between these two parameters can
be confirmed with 95% confidence.
A correlation between the iron and pH is described in the following study. (Kenshi, K. (2014) The
solubility of iron can be affected by pH. Although within the ranges of pH 5-8, there is little change in the
oxidation state. Henceforth although the pH is significantly correlated with iron concentrations in the
water column, it can be hardly stated that the pH levels themselves directly cause a large change in iron
solubility between the ranges of pH5-8 (Correlation=/=Causation).
Further statistical analysis detected a significant correlation between conductivity and pH. The Pearson
correlation detected a strong positive correlation (0.561). Therefore, as conductivity increases, so to
does pH. This was then further plotted in a scatter plot, thereby visually representing the increase in pH
and conductivity. As shown in the graph, at low salinity (conductivity) a large range of pH values are
observed. However, at high conductivity, the general trend of the pH is in an increasing fashion. (figure
blah)
The relationship is explained by the very definition of conductivity, which is the capacity of a solution to
carry and electrical current. Electrical conductivity in a solution is carried by Ions. This includes the
presence of positive (H+
, Na+
etc.) and negative (OH-
, Cl-
etc.) Ions. In these solutions, the Hydrogen Ion
itself becomes less directly relevant as the concentration of other ions also increase. This explains as to
why in higher conductivity environments there are higher concentrations of H+
Ions (due to the
additional additive effects of H+
and non pH conductive ions). Whereas The simple fact that the H+
ion
alone isn’t responsible for conductivity explains as to why the pH can still be on the upper end of the
scale whilst conductivity is low.
6.4
6.6
6.8
7
7.2
7.4
7.6
7.8
0 1000 2000 3000 4000 5000 6000
pH
Conductivity (µS/cm)
Conductivity vs pH

70
Conductivity itself was also correlated with dissolved iron concentrations. Thereby explaining the
significant correlation between pH and iron solubility. Further investigation into previous studies further
reinforces that conductivity may be one of the driving non-anthropogenic factors of varying iron
concentrations between sites. (Des W. Connell, 2005)
There are several mechanisms by which salinity affects the solubility of the iron species. The salinity
affects iron solubility through affecting the speciation in which the iron exists in the environment. Iron
oxide (Fe(III)) is highly insoluble in water and tends to form colloidal species and/or precipitate.
However, when iron is present in an organic complex, it can become soluble in much higher
concentrations. Therefore, the species in which iron exists in the aqueous environment affects the
solubility of the metal (Kenshi, 2014).
The differing metal speciation of iron in fresh and saline environments are attributed to several factors.
These factors include; 1) differing ionic strengths, 2) the lower content of adsorbing surfaces in
seawater, 3) the differing concentrations of trace metals, 4) the differing concentration of major cations
and anions, and 5) the higher abundance of organic ligands in freshwater systems.( Des W. Connell,
2005)

71
Possible Anthropogenic influences
Although several iron related relationships have been proposed, these relationships do not fully
describe the factors that may be responsible for the breach in recreational guidelines. As shown in the
below figure, water stained by iron appears to be concentrated around an effluent output in Mud 4.
Figure blah: the source of what appears to be iron pollution at site 4. (Source: Nicholas Buss).
This red staining observed around the effluent correlates with the observation that the Mud 4 site
maintained a consistent higher concentration than the opposing sites. The most disturbing factor is that
this anthropogenic input was not described in the previous monitoring report. Therefore in the past 8
years, high iron levels have become prevalent in some sections of the catchment due to anthropogenic
output.

72
Figure blah: mud 4 had consistently high concentrations of iron.
It is therefore recommended that future investigations possibly focus on trying to find and cease this
anthropogenic point source.
Manganese
As seen in the results section, manganese was the only other trace metal detected above the LOD in the
study. When running the manganese against all other factors in a Pearson correlation, no correlations
were detected. Additionally, manganese concentrations were well below the trigger value. Therefore it
can also be concluded that manganese exists in the catchment at relatively “safe” concentrations.
The previous study details that manganese tends to precipitate highly when the pH of a solution exceeds
7.5, and when the mg/L of dissolved oxygen exceeds 5mg/L. Unfortunately, this relationship was unable
to be mapped. Most likely as there are several other factors that may also affect manganese solubility
and concentration. (Casey T.J., 2009)
Manganese is very similar to iron in oxidation state and behaviour in waterways. This metal species is
subject to almost the same factors as iron solubility. Manganese, much like iron, will oxidise into its Mn4+
oxidation state and can form colloidal precipitate (Casey T.J., 2009). Unlike iron, metal complexation
does not play a large role in manganese solubility. This is because Manganese only weakly binds to
organic carbon in natural waters (L'her Roux et al. 2003). However, the previous study ((L'her Roux et al.
2003) was also able to correlate a decrease in manganese solubility with increasing solubility. This may
be likely attributed to the factors as previously suggested (ionic strength, trace metal concentrations,
adsorption surfaces etc.).
Unfortunately, all other metal measurements below the LOD. Rather than low concentrations being the
cause of such a result, it is more likely that monitoring and laboratory analytical skills may be the root
0
0.2
0.4
0.6
0.8
1
1.2
IronConcentration(mg/L) Iron measurements for Study Period
Day 1
Day 2
Day 3
Day 4

73
cause. This conclusion was drawn due to previous studies in the area that have detected higher metal
concentrations (specifically Cu) in the past. (Robertson et al. 2006)
Both iron and manganese oxidation states are largely attributed to the eH-pH relationship in the
aqueous environment. Although this factor is mostly a driving factor in soils and sediment. It may be
recommended in future studies that sediment samples be taken, in addition, there is more “room” to
study possible microbial interactions with both the iron and manganese. As both the redox potential and
microbial interactions have been shown in prior studies to affect both the concentration and speciation
of metals in aquatic and soil environments. (Altomare C.,Norvell W.A., Björkman T., Harman G.E., 1999)
Chlorophyll and factors affecting its concentrations
In the study, Chlorophyll measurements weren’t correlated with any other measurements. As
chlorophyll is a measurement of photosynthetic organism populations in the aqueous environment, and
is typically a consequence of environmental factors (i.e. nutrients), it is almost impossible to analyse the
reasons behind increased algal concentrations in the Mudgeeraba creek catchment with this study. It is
not impossible to postulate on reasons behind the results obtained in this study however.
Chlorophyll has in previous studies been linked to factors such as Dissolved oxygen, Nutrients (Morgan
et. Al., 2006) and even metal concentrations (Maniosa T., Stentifordb E.I., Millnerc P.A., 2003).
Additionally, the absence of any correlations could also possibly be attributed to incorrect measurement
protocol execution. The incorrect measurement hypothesis is reinforced as previous studies in the creek
catchment were able to identify factors behind high algal concentrations.
A correlation between TSS, turbidity and light on chlorophyll levels has been attempted to been
established by several studies (Morgan et.al. 2006) (Carrick et al. 1994). Most of these studies have had
contradicting and conflicting results. With some studies establishing a positive correlation between
turbidity/TSS and chlorophyll (Morgan et.al. 2006)), whilst others have indicated negative correlations
(Robertson et. Al. 2006). The difficulty in associating this data with turbidity and light penetration
related parameters (light, TSS) is that other factors have a much stronger effect on chlorophyll and algal
concentrations (Carrick et al. 1994). Additionally, the factors affecting chlorophyll concentrations are
much more complex than availability of light.
Studies have shown however that it is only where light is the limiting factor, that photosynthetic
organisms can become heavily affected by light penetration. This was showcased in the study (Morgan
et. Al., 2006) where at one of the sites, nutrient concentrations were high, but there was an almost
complete absence of periphyton. It was at this site, that periphyton concentrations were negatively
correlated with depth, which was hypothesised to occur due to light attenuation in the water column.
This study is unable to clarify that light penetration and water clarity had a significant effect on
photosynthetic organism abundance. Therefore, it is concluded that light and water clarity were not the
limiting factors in the Nerang catchment regarding periphyton and photosynthetic populations.
Metal concentrations have only been weakly correlated with chlorophyll-a concentrations. The following
study (Maniosa T., Stentifordb E.I., Millnerc P.A., 2003) detected that chlorophyll-a concentrations
may have possibly increased in the leaves due to increases in metal concentrations of Cd, Cu, Ni, Pb and
Zn. Conversely, significant toxicity was not established in aquatic photosynthetic organisms due to
increases in these metal concentrations

74
Studies related to the Nerang catchment specifically have been more focused on correlating chlorophyll
concentration with nutrient levels. (Robertson et al. 2006). Additionally, such expected positive
correlations between chlorophyll and nutrients have been well established in the scientific community
(Dodds et al. 2011)( Biggs B.J.F., 2000). Unfortunately, this study did not include nutrient
measurements in the investigation section of the report. It is therefore postulated that any increase
above guideline values of chlorophyll in the sampling period might be attributed towards nutrient
concentrations. The study (Robertson et al. 2006) associated a relationship between the high nutrient
concentrations along with high algal concentrations in the catchment. It was outlined in this study that
high nutrient levels were a direct cause of anthropogenic inputs from agricultural activities throughout
the area, along with the prevalence of on-site wastewater treatment systems on acreage properties.
Nutrient concentrations and correlations
Phosphate
Low levels of phosphate was an expected result of the study. Although LOD was relatively high, the
concentrations themselves are still present in limiting concentrations. This is a healthy indicator
regarding eutrophication. As phosphate has been commonly identified as a limiting nutrient within
inland water bodies preventing algal blooms (Correll D.L., 1999). These results give an indication that the
Nerang creek catchment is not yet under direct threat of eutrophication. Thereby presenting the
possibility that nutrient concentrations regarding phosphate may have improved since previous
monitoring programs within the area (Robertson et al. 2006)
Unfortunately, due to the high LOD and therefore high amount of imprecision in the study, we were
unable to find any direct correlations between Phosphate and other pieces of data. One possible
correlation that studies in the past (Bravo et al., 2003) have indicated is that of phosphate and faecal
contamination. As only one day of faecal coliforms were taken however, we were unable to establish
any trends between these two variables.

75
NOX
As nitrogen typically isn’t a limiting nutrient within inland streams, high levels of nitrogen was expected.
However, on the second day, nitrogen levels spiked heavily. This immediately indicated a possible
correlation with rainfall. As rainfall drastically increased from sampling day 1 to sampling day 2.
This was then run in a Pearson correlation. The hypothesis was proven correct as the Pearson
correlation indicated a very strong positive correlation (0.866). This was then plotted into an error bar
plot using SPSS in order to visually represent the data (figure blah).
Therefore, it is most likely possible that sources of high nitrogen concentrations in the catchment
originates from rural and industrial diffuse sources. As rainfall occurs, these diffuse sources increase in
magnitude, thusly causing high levels of nitrogen to enter the aquatic ecosystem (Carpenter et al.1998)
This result has also been detected in the previous study regarding the catchment. (Robertson et al.
2006) The previous monitoring program was able to correlate these increased levels in nitrogen input
from possible leakage from on-site wastewater treatment systems. It is imperative to understand that
full effects of these diffuse sources can sometimes only be witnessed during high rainwater events.
Total Coliforms could be correlated with phosphates, but studies regarding these two factors must be
expanded on if a link in the catchment is to be found
As described in the first chapter individual results of the report, only the second day showed a
conclusive result. Weather regarding the second sampling day could also be a possible factor. The week
prior to the second sampling event saw the highest amount of rainfall over the entire study period.
Therefore, the coliform data can be compared with the typical stormwater expected CFU/100mL as
detailed by the Queensland Water quality guidelines (fig blah). The fifth sampling site was the location
Figure blah: standard
error plot of Nox vs
Rainfall.

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