Baseline Monitoring of Metal Concentrations over
a one-year period in the Daly River near Nauiyu
Peter Novak* and Julia Schult
Report 18/2015D
July 2015
1
Bibliographic Reference
Novak, P. A. and Schult, J. 2015. Baseline monitoring of metal concentrations over a one
year period in the Daly River near Nauiyu. Report No. 18/2015D, Northern Territory
Department of Land Resource Management, Palmerston.
ISBN
978-1-74350-067-5
Baseline monitoring of metal concentrations
over a one year period in the Daly River near
Nauiyu
Contacts:
Peter Novak
*Peter Novak Consulting, Unit 6/70 Kurrajong Cres, Nightcliff NT 0810
Julia Schult
Department of Land Resource Management
PO Box 496, Palmerston, NT, 0831
Australia
Acknowledgements
Special thanks goes to Kerry Draper from the Banyan Farm Caravan Park. She supported
the project throughout and not only allowed us to use their facilities and pontoon but
volunteered a lot of her time to collect samples for the project. You are a champion!
We would also like to thank Andrew Gould and Matt Majid who organised the field work for
this project and got to know the Daly River Road very well over the year; and the Larrakia
Rangers for providing field support. Last, but not least, Simon Townsend provided helpful
comments and feedback on drafts of this report.
Copyright and Disclaimer
© 2015 Northern Territory Government
Copyright resides with the Northern Territory Government, 2015. Information contained in
this publication may be copied or reproduced for study, research, information, or educational
purposes, subject to inclusion of an acknowledgment of the source.
2
Contents
List of Tables ......................................................................................................................................... 4
List of Figures ....................................................................................................................................... 4
1.
Summary ................................................................................................................................... 5
2.
Introduction................................................................................................................................ 6
3.
4.
1.1
Background ....................................................................................................................... 6
1.2
Aim ...................................................................................................................................... 7
Methods ..................................................................................................................................... 8
2.1
Catchment and site description ...................................................................................... 8
2.2
Sampling method.............................................................................................................. 9
3.3
Analysis ............................................................................................................................ 10
Results ..................................................................................................................................... 14
3.1
General patterns ............................................................................................................. 14
3.2
Seasonal description...................................................................................................... 19
3.3
ANZECC metals ............................................................................................................. 23
5.
Discussion ............................................................................................................................... 24
6.
Conclusion ............................................................................................................................... 27
Literature cited ................................................................................................................................ 36
Appendix .......................................................................................................................................... 38
3
List of Tables
Table 1. Mean variation (%) in replicate samples of all metals. .................................................. 12
Table 2. Start dates for 2013/14 hydrological seasons ................................................................ 14
Table 3. All sampled metals/non-metals placed in respective categories. Metals that were
not detected are in bold font. ............................................................................................................ 16
Table 4. Summary regression output from the analysis of all variables(log) regressed to
discharge(log). Values shown are means (SE) for each category. The full regression
estimates for individual metals are included as Appendix. .......................................................... 17
Table 5. Regression output from analysis of pH, conductivity(log), alkalinity(log) and total
suspended solids(log) (TSS) to discharge(log). ............................................................................ 17
Table 6. Summary output of regression analysis of all metals(log) with pH. Values shown
are means (SE) for each category. The regression estimates for each metal are included as
Appendix. ............................................................................................................................................. 18
Table 7. Summary output of regression analysis of all metals(log) with conductivity(log).
Values shown are means (SE) for each category. The regression estimates for each metal
are included as Appendix.................................................................................................................. 18
Table 8. Summary output of regression analysis of all metals(log) with alkalinity(log). Values
shown are means (SE) for each category. The full regression estimates for each metal are
included as Appendix. ....................................................................................................................... 18
Table 9. Summary output of regression analysis of all metals(log) with total suspended
solids(log) (TSS). Values shown are means (SE) for each category. The full regression
estimates for each metal is included as Appendix. ....................................................................... 19
Table 10. Mean metal concentrations and standard errors during the: dry-to-wet season
transition (DS-WS), wet season (WS), wet-to-dry season transition (WS-DS), dry season
(DS) and for the year (Annual). The number of decimal places presented represents the
accuracy of the detection limits for each metal in the analytical procedure. ............................. 20
Table 11. ANZECC trigger values and number of exceedances during the study period ...... 24
List of Figures
Figure 1. Map of the study region ...................................................................................................... 9
Figure 2. Daly River discharge at Mt Nancar gauge station. Dots indicate when samples
were taken and vertical lines show start of hydrological seasons. ............................................. 10
Figure 3. Typical relationship between category 1, 2 and 3 metal concentrations (µg/L, log
transformed) with discharge (m3/s, log transformed) with regression line and R2. .................. 15
Figure 4. Category 1 metals (µg/L) and river discharge (m3/s). ANZECC metals are identified
with *. Guideline limits are presented on the figure as either a line or in the chart title........... 28
Figure 5. Category 2 metals (µg/L) and river discharge (m3/s). ANZECC metals are identified
with *. Guideline limits are presented on the figure as either a line or in the chart title........... 32
Figure 6. Category 3 metals (µg/L) and river discharge (m3/s). .................................................. 34
Figure 7. Water quality variables and river discharge .................................................................. 35
4
1. Summary
During the 2013/14 hydrologic year water samples were collected from a site on the lower
Daly River on average every 9 days over a one-year period to determine baseline metal
concentrations. The relationships between metal concentration and (1) discharge, (2) water
chemistry, and (3) season are examined, and concentrations compared with ANZECC water
quality guidelines.
Metals were classified into 3 categories, based on their relationship with discharge.
Category 1 metal concentrations rose with increasing discharge, category 2 metal
concentrations declined with increasing discharge and category 3 metals showed no
discernible relationship with discharge at all.
Strong correlations were found between metals of different categories and water quality
parameters (pH, conductivity, alkalinity and total suspended solids), however, these need to
be viewed with caution due to a high level of covariance between water quality variables and
discharge.
Seasonal differences were related to hydrology and the change in water source from surface
water runoff during the wet to predominantly groundwater in the dry.
Only 3 metals in category 1 exceeded the ANZECC guideline values for water quality during
the study period. Aluminium exceeded guidelines in 64% (29) of samples, Chromium in 2%
(1) of samples and Copper in 9% (4). The high aluminium concentrations are a natural
occurrence caused by weathering of aluminium-rich soils and rocks in the catchment, as
there is no evidence of aluminium pollution.
Metal concentrations in the lower Daly River are currently very low compared to rivers
around the world. Considering anthropogenic land use is predicted to increase in the coming
years, this study can be used to provide a baseline for metal concentrations at this site, and
be a guide for Daly River waters further upstream.
5
2. Introduction
1.1
Background
The Daly River is one of the largest perennial rivers in Northern Australia. It harbours
significant biodiversity, and indigenous and non-indigenous values (Jackson, Finn &
Featherston, 2012; Schult & Townsend, 2012). It drains a catchment area of approximately
53 000 km2, of which approximately 5.6% has been cleared for agriculture, mining and other
uses (Schult & Townsend, 2012). The river system has no significant modifications to its
flow regime and a recent river health survey has found very low levels of anthropogenic
disturbances (Schult & Townsend, 2012). Considering the high biodiversity and human
values of the river and its biota, it has been identified that there is a need for further data on
water quality parameters, notably metals (Erskine et al., 2003; Schult & Townsend, 2012).
Metals found in surface waters can be derived from a variety of sources and elevated
concentrations of metals can be toxic to aquatic life. Surface water runoff during the wet
season carries high sediment loads containing metals from erosion and weathering of the
surrounding soils and rock. These metal concentrations are generally low and reflect the
geology of the catchment.
Elevated concentrations can occur when other sources contribute to the metal load. Point
sources like mining operations pose a high risk because they mobilise many metals that
were previously contained deep underground and closely bound in the rocks. Chemical
processing of ore and the exposure of previously buried soils and rocks to oxygen can
release these metals and there is a risk that they enter the stream environment through
regulated and unregulated discharges from mine sites. Acidic conditions contribute further to
their mobilisation and increase the toxicity of metals. Other sources of metals include
industrial operations and urban development. Sewage discharge can also contain elevated
metal concentrations.
There are few major point sources of contaminants within the catchment, the wastewater
treatment plant discharges from the townships of Nauiyu and Katherine, and the Mount Todd
and Pine Creek mine sites. Agricultural runoff from horticulture may provide a diffuse source
of contamination. The Mount Todd mine and Katherine waste water treatment plant are both
licenced to discharge waste water into the river during high wet season flows. The Pine
Creek mine has been rehabilitated.
6
The Daly River and its catchment are often the focus of discussion surrounding future
agricultural developments (see - Australian Government White Paper on Northern
Development). If such developments occur it is likely the risk to water quality will increase.
Metals can enter the river through discrete pollution events. During the wet season of
2011/12 there were two notable contaminant events within the Edith River in the Daly
catchment. The derailment of a freight train released a large quantity of copper concentrate
directly to the river and an unregulated discharge of acid mine water occurred from the
Mount Todd mine site. In light of these contaminant concerns there has been considerable
interest in determining the baseline metal concentrations of the Daly River.
To assess the impact of such contaminant events and increasing development within the
catchment, it is important to determine baseline metal concentrations. During the dry season
the Daly River maintains a high base flow from significant groundwater inputs via springs
within the bank or stream bed (Tickell, 2002). These groundwater inputs have elevated
alkalinity, conductivity and concentrations of magnesium and calcium ions compared to wet
season flows (Tickell, 2002). In the wet season, surface water run-off from rainfall and
resulting elevated stream discharge can be substantial and often carry a significant sediment
load. This surface water run-off is likely to contain metals from catchment sources which will
reflect the surface geology. This project seeks to determine the baseline total metal
concentrations in the Daly River over a 12-month period and describe the seasonal trends in
metal concentrations. In addition, water chemistry can influence the toxicity and
bioavailability of metals thus pH, conductivity, alkalinity and total suspended solids are also
presented. This information is valuable to assessing changes to river water quality in the
future.
1.2
Aim
The aim of this report was to characterise the metal concentrations in the Daly River close to
the community of Nauiyu, approximately 60 km upstream of the river’s mouth, over a
12-month period. The relationships between metal concentration and (1) discharge, (2) ionic
chemistry, and (3) season are examined, and concentrations compared with ANZECC water
quality guidelines.
7
3. Methods
2.1
Catchment and site description
The wet-dry tropical climate of the region and underlying geology determines the annual flow
regime and water quality of the Daly River. During the wet season months, flows in the Daly
River are dominated by surface water runoff, caused by monsoonal rains and cyclones that
deliver large quantities of water and cause regular annual flooding. In the dry season, flows
are sustained by groundwater inputs from underlying karst aquifers.
On average, more than 90% of annual rainfall occurs during the wet season. The water
quality of the river during this time characteristically shows high turbidity and nutrient
concentrations relative to the dry season (DLRM unpublished data) which are associated
with sediment inputs from runoff; and low electrical conductivity and alkalinity reflecting the
rainwater source of flows.
During the transition from wet to dry season, flows slowly recede after the rains stop and the
water becomes progressively clearer. Conductivity increases as the groundwater influence
on water quality grows.
During the dry season months from approximately May to October, virtually no rainfall occurs
in most years. Groundwater inputs from the underlying karst aquifers become the principal
source of dry season flows in the Daly River. By the end of the dry season, as flow continues
to recede, conductivity rises to more than 700 µS/cm reflecting the limestone geology of the
aquifers.
The transition from dry to wet season begins when convective storms associated with heavy
rains cause the first runoff events after the prolonged dry season drought. These first runoff
events of the dry-to wet transition wash large quantities of accumulated debris into the
streams leading to the highest turbidities and concentrations of suspended solids and
nutrients of the year (DLRM unpublished data).
The study site is located in the lower reaches of the Daly River (Figure 1) close to the
Aboriginal community of Nauiyu at the Banyan Farm Tourist Park (13.714468 S,
130.669179 E). There is some tidal influence on water levels at this site during very high
tides, caused by the restriction of outward flow towards the estuary; however, water quality is
not affected by saltwater even during the highest tides. The metals sampling program was
part of a larger program of seasonal water quality monitoring conducted at this site in
2013/14.
8
Figure 1. Map of the study region
2.2
Sampling method
Metals samples were collected between November 2013 and December 2014. A total of
45 samples were collected during the study period at intervals of 4 - 28 days (Figure 2,
average interval: 9 days). During the first four weeks of sampling (07/11/2013-27/11/2014),
two replicate samples were collected at each occasion for to provide an indication of
variability.
Whenever possible, samples were taken from the middle of the river at a depth of
approximately 0.2 m using a sampling pole extended from a boat. During very high flow
periods, when boat access to the river was considered too dangerous, samples were
collected using the same pole extended from the water’s edge.
Samples were placed on ice immediately after collection and frozen on return to the
laboratory, usually within 3-4 hours. Total metals were determined by the Environmental
Chemistry and Microbiology Unit of Charles Darwin University. Prior to analysis samples
were acidified with concentrated HNO3 to 0.013% and placed in an oven overnight at 60 °C.
An semi-quantitative scan of elemental composition (APHA 2005, method 3125 B) was
carried out on a Agilent 7500 series ICPMS .
9
Additional water samples were collected for analysis of alkalinity (APHA 2320 B) and
suspended solids (APHA 2540 D). Field measurements of temperature, pH, dissolved
oxygen and electrical conductivity were taken at the time of sampling using a Quanta
(Hydrolab) multi-parameter probe.
Figure 2. Daly River discharge at Mt Nancar gauge station. Dots indicate when samples were taken
and vertical lines show start of hydrological seasons.
3.3
Analysis
Variability
Replicate samples were taken on the first four weeks of sampling to gain an insight into the
variability of river concentrations on a sample occasion. The variation between replicates
was calculated for each variable and used to derive the mean variation for each sampling
occasion. In all subsequent analysis the mean concentration of the replicates was used.
The mean variation across all metal sampled is presented in
10
Table 1. Variation ranged from 14.2% to 9.9%
11
Table 1. Mean variation (%) in replicate samples of all metals.
Mean
SE
7/11/13
14.2
2.8
19/11/13
11.5
1.7
26/11/13
9.9
1.6
27/11/13
10.8
1.6
Environmental effects
All statistical analyses were carried out using Statistica 12.0 (Statsoft) software. To
determine the relationships between discharge, conductivity, pH, alkalinity and total
suspended solids (TSS), and metal concentrations a correlation matrix was constructed.
There was a high level of co-variance between the environmental descriptors. While the R2
is presented and is useful in determining the strength of the relationship, caution must be
used in its interpretation. We would caution against using the regression parameters from
the water quality variables to predict metal concentrations. All water quality variables were
strongly predicted by discharge and accordingly discharge should be primarily used in any
predictive capacity. Discharge, metal concentration and water quality variables, except pH
(already log scale), were log transformed to improve the assumptions of normality and
homogeneity of variance. Summarised regression tables are presented in text and the full
regression tables are presented in the Appendix (Tables A1.1 to A5.3)
Seasonal trends
The annual hydrograph was divided into 4 “seasons”. The very first runoff event after the dry
season indicates the beginning of the dry-to-wet transition period. This point is defined by
the annual minimum flow, i.e. the point immediately before the event. The wet season proper
is defined as starting with the first major runoff event after which there is no immediate return
to base flow. Because of the gradual recession of flows, the start dates of the wet-to-dry
transition and the dry season were defined by the rise in conductivity rather than using
hydrographic criteria. The chosen conductivity cut-off points of 200 and 450 µS/cm represent
the approximate point when groundwater constitutes 25% and 65% of total flow respectively.
Dates for the seasons as they apply for the 2013/2014 hydrological year are shown in
12
Table 2.
13
Table 2. Start dates for 2013/14 hydrological seasons
Season
Start date
Defined by
dry-wet transition 2013/14
30/10/2013 minimum flow
wet 2014
15/01/2014 first major runoff event
wet-dry transition 2014
13/03/2014 200 <EC ≤450 µS/cm
dry season
17/04/2014 EC > 450 µS/cm
dry-wet transition 2014/15
3/11/2014 minimum flow
4. Results
3.1
General patterns
There were 69 metals/non-metals sampled in this study, five were not detected at all and a
further 13 were detected on eight or less occasions. The next most infrequently detected
variable was detected 18 times. Therefore the variables detected 8 times or less during the
study, including the non-detections, are referred to as uncommon throughout the report.
Discharge and metal concentration
Three major patterns were observed in all variables in relation to hydrology. Firstly, an
increase in concentration during the wet season (category 1); secondly, a decrease in metal
concentration during the wet season (category 2) and; thirdly, a highly variable relationship
to discharge (category 3). Figure 3 shows typical patterns for the relationship of discharge
and metal concentrations in each category.
There were 32 metals classed as category 1, 17 metals classed as category 2, and 2 metals
classed as category 3 (Table 3).
Category 1 metals showed a weak to moderate positive correlation to discharge (Mean
standardised slope est = 0.74 ± 0.02SE) (Table 4). Discharge explained a mean 55%
(2% SE) of the variation in concentration of category 1 metals and for every metal the
relationship was significant (p < 0.001). The concentration of category 2 metals was
negatively correlated with discharge (Standardised slope est = -0.86 ± 0.02SE) (Table 4).
Discharge explained a mean of 75% (4%SE) of the variation in the category 2 metal
concentrations, and for every metal the slope was significant (p < 0.001). Category 3 metals
remained variable throughout the sampling period and no significant relationship with
discharge was observed (Table 4).
14
Category 1 contained all of the lanthanoid metals. The majority of the other category 1
elements were the transition metals except for caesium (Cs), beryllium (Be), and phosphorus
(P). All of the elements in category 2 were alkali metals, alkaline earth metals or non-metals.
Palladium (Pd) and uranium (U) were the only exceptions to this. The majority of the nondetects were heavier elements from niobium onwards. There was no pattern in the category
3 metals which consisted of thorium (Th) - an actinoid, and thallium (Tl)- a metalloid.
Figure 3. Typical relationship between category 1, 2 and 3 metal concentrations (µg/L, log
3
2
transformed) with discharge (m /s, log transformed) with regression line and R .
15
Table 3. All sampled metals/non-metals placed in respective categories. Metals that were not
detected are in bold font.
Category 1
Category 2
Category 3
Uncommon
Aluminium
Arsenic
Thallium
Antimony
Beryllium
Boron
Thorium
Bismuth
Caesium
Barium
Cadmium
Cerium
Bromide
Gold
Chromium
Calcium
Hafnium
Cobalt
Iodine
Iridium
Copper
Lithium
Mercury
Dysprosium
Magnesium
Molybdenum
Erbium
Palladium
Niobium
Europium
Potassium
Osmium
Gadolinium
Rubidium
Platinum
Gallium
Scandium
Ruthenium
Germanium
Silicon
Selenium
Holmium
Sodium
Silver
Iron
Strontium
Tantalum
Lanthanum
Sulphur
Tellurium
Lead
Uranium
Tin
Lutetium
Tungsten
Manganese
Neodymium
Nickel
Phosphorus
Praseodymium
Samarium
Terbium
Thulium
Titanium
Vanadium
Ytterbium
Yttrium
Zinc
Zirconium
16
Table 4. Summary regression output from the analysis of all variables(log) regressed to
discharge(log). Values shown are means (SE) for each category. The full regression estimates for
individual metals are included as Appendix.
Slope
R²
Standardised slope
Intercept
Estimate
SE
p
Estimate
SE
Estimate
SE
p
Category 1
(n=32)
0.55
(0.02)
0.58
(0.04)
0.07
(0.00)
<0.001
0.74
(0.02)
0.10
(0.00)
-4.48
(0.57)
0.34
(0.02)
0.016
(0.010)
Category 2
(n=17)
0.75
(0.04)
-0.27
(0.03)
0.02
(0.00)
<0.001
-0.86
(0.02)
0.07
(0.01)
-5.45
(1.08)
0.10
(0.01)
≤0.005
Category 3
(n=2)
0.02
(0.02)
0.05
(0.08)
0.08
(0.02)
0.385
(0.208)
-0.06
(0.14)
0.15
(0.15)
-3.09
(0.27)
0.35
(0.11)
<0.001
Discharge and water quality variables
Alkalinity and conductivity exhibited very similar patterns to the category 2 metals and
decreased during the wet season (Figure 7). Conductivity and alkalinity dropped sharply
(more than 60%) on the first flush in the early wet season. They remained low throughout the
wet season and increased steadily throughout the dry season as groundwater influence
increased. The temporal variation in pH was less pronounced than conductivity and alkalinity
but followed the same general trend. These three variables showed a strong negative
relationship with discharge (Table 5). There was significant covariance of all three water
quality variables with discharge (Table 5). The relationship between total suspended solids
and discharge was very similar to that exhibited by category 1 metals.
Table 5. Regression output from analysis of pH, conductivity(log), alkalinity(log) and total suspended
solids(log) (TSS) to discharge(log).
Variable
R²
Slope
Estimate
SE
p
Standardised
slope
Intercept
Estimate
Estimate
SE
SE
p
pH
0.63
-0.24
0.03
<0.001
-0.80
0.09
8.88
0.13
<0.001
Conductivity
0.90
-0.45
0.02
<0.001
-0.95
0.05
7.94
0.11
<0.001
Alkalinity
0.89
-0.48
0.03
<0.001
-0.94
0.05
7.36
0.12
<0.001
TSS
0.56
0.66
0.09
<0.001
0.75
0.10
-0.97
0.42
0.026
Water quality variables and metal concentration
The water quality variables were strongly affected by discharge and show similar patterns to
the metals. As such, caution should be used in using R2 to interpret the relationship. The
17
standardised slopes estimate provides a very close approximation of Pearson’s correlation
co-efficient and can be used to interpret the strength of the relationship. Category 1 metals
had a strong negative correlation with both alkalinity and conductivity, while the correlation
with pH was weak (Table 6 to Table 8). Total suspended solids (TSS) and category 1 metals
however, exhibited a strong positive relationship (Table 9). Category 2 metals showed a very
strong positive correlation with both alkalinity and conductivity, pH showed a weaker
relationship, and the relationship with TSS was strongly negative (Table 6 to Table 9).
Category 3 metals showed no covariance with any water quality variable (Table 6 to Table
9).
Table 6. Summary output of regression analysis of all metals(log) with pH. Values shown are means
(SE) for each category. The regression estimates for each metal are included as Appendix.
Slope
pH
R²
Standardised slope
Intercept
Estimate
SE
p
Estimate
SE
Estimate
SE
p
Category 1
(n=32)
0.35
(0.02)
-1.52
(0.11)
0.32
(0.02)
≤0.005
-0.58
(0.01)
0.13
(0.00)
9.99
(0.86)
2.47
(0.15)
0.031
(0.017)
Category 2
(n=17)
0.53
(0.03)
0.77
(0.09)
0.11
(0.01)
<0.001
0.72
(0.02)
0.11
(0.00)
-1.77
(1.02)
0.84
(0.08)
0.187
(0.074)
Category 3
(n=2)
0.03
(0.03)
0.25
(0.25)
0.25
(0.07)
0.563
(0.434)
0.12
(0.12)
0.15
(0.00)
-5.29
(2.60)
1.95
(0.56)
0.032
(0.029)
Table 7. Summary output of regression analysis of all metals(log) with conductivity(log). Values
shown are means (SE) for each category. The regression estimates for each metal are included as
Appendix.
Slope
Conductivity
R²
Standardised slope
Intercept
Estimate
SE
p
Estimate
SE
Estimate
SE
p
Category 1
(n=32)
0.66
(0.02)
-1.33
(0.09)
0.14
(0.01)
<0.001
-0.81
(0.01)
0.09
(0.00)
6.01
(0.60)
0.85
(0.05)
0.065
(0.042)
Category 2
(n=17)
0.80
(0.04)
0.61
(0.08)
0.03
(0.00)
<0.001
0.89
(0.03)
0.06
(0.01)
0.62
(0.93)
0.20
(0.03)
0.106
(0.063)
Category 3
(n=2)
0.02
(0.00)
0.04
(0.15)
0.16
(0.05)
0.368
(0.011)
0.00
(0.14)
0.15
(0.00)
-3.59
(1.52)
0.96
(0.29)
0.002
(0.002)
Table 8. Summary output of regression analysis of all metals(log) with alkalinity(log). Values shown
are means (SE) for each category. The full regression estimates for each metal are included as
Appendix.
Slope
Alkalinity
R²
Standardised slope
Intercept
Estimate
SE
p
Estimate
SE
Estimate
SE
p
Category 1
(n=32)
0.66
(0.02)
-1.24
(0.08)
0.13
(0.01)
<0.001
-0.81
(0.01)
0.09
(0.00)
4.56
(0.54)
0.70
(0.04)
0.037
(0.020)
Category 2
(n=17)
0.79
(0.04)
0.56
(0.07)
0.03
(0.00)
<0.001
0.88
(0.03)
0.06
(0.01)
1.28
(0.93)
0.17
(0.02)
0.007
(0.006)
Category 3
(n=2)
0.02
(0.00)
0.03
(0.13)
0.15
(0.05)
0.377
(0.046)
-0.01
(0.14)
0.15
(0.00)
-3.49
(1.33)
0.79
(0.24)
<0.001
18
Table 9. Summary output of regression analysis of all metals(log) with total suspended solids(log)
(TSS). Values shown are means (SE) for each category. The full regression estimates for each metal
is included as Appendix.
Slope
TSS
R²
Standardised slope
Intercept
Estimate
SE
p
Estimate
SE
Estimate
SE
p
Category 1
(n=32)
0.77
(0.03)
0.77
(0.05)
0.06
(0.00)
<0.001
0.87
(0.02)
0.07
(0.00)
-3.43
(0.52)
0.13
(0.01)
0.006
(0.006)
Category 2
(n=17)
0.50
(0.05)
-0.25
(0.03)
0.04
(0.00)
<0.001
-0.69
(0.04)
0.11
(0.00)
4.73
(1.04)
0.08
(0.01)
0.049
(0.047)
Category 3
(n=2)
0.01
(0.01)
0.03
(0.02)
0.09
(0.03)
0.644
(0.276)
0.08
(0.06)
0.15
(0.00)
-3.39
(0.59)
0.20
(0.06)
<0.001
Uncommon metals
18 metals were detected on 8 or fewer occasions throughout the sampling period. Out of
these, five were not detected at all during this study (see Table 3).
Niobium (Nb), molybdenum (Mo), tin (Sn), tellurium (Te), hafnium (Hf), tantalum (Ta),
tungsten (W), osmium (Os), bismuth (Bi), cadmium (Cd), silver (Ag) and selenium (Se) and
mercury (Hg) were rarely detected during this study. There were some apparent patterns in
these rarely detected metals. Nb, Hf, Ta, W, Bi, Te, Ag and Cd were detected only during
periods of elevated discharge including the dry-to-wet season transition, wet season and
wet-to-dry season transition and thus are classed at category 1 metals. Sn and Mo were
detected during the low flow period from the wet-to-dry season transition and the dry season
and thus could be classed as category 2 metals. Os was detected on two occasions once
during the dry-to-wet transition and once during the dry season.
Mercury (Hg) was detected on three occasions. The detections occurred twice in the dry
season and once in the dry-to-wet season transition. The pattern of these detections during
low flow periods suggest that mercury may be a category 2 metal.
There was no discernible pattern in the detection of Se, with detections in the dry, dry-to-wet
and wet seasons. Os and Se may be category 3 metals but there are too few detections for
this classification to be reliable. Ruthenium (Ru), antimony (Sb), iridium (Ir), platinum (Pt),
and gold (Au) were not detected at all during this study.
3.2
Seasonal description
The analysis in this section refers to Figure 4 to Figure 6.
Category 3 metals are not discussed in this section as regression analysis found the
relationship to discharge was negligible (highly variable) and insignificant (Table 4)
19
Dry-to-wet season transition
The first discharge event of the dry-to-wet transition began on the 3rd December 2013 and
had a major effect on the concentrations of virtually all variables sampled in this study.
Between the 23rd November 2013 and 3rd December 2013 discharge increased from
45.9 – 230.8 m3/s, and resulted in an average increase in the category 1 metals of 843%
and an average decrease in category 2 metals of 42%.
Category 1 metals and discharge remained elevated by the next sampling occasion, after
which metal concentration declined as discharge decreased. Category 2 metals remained
low during the period of elevated discharge but increased as discharge declined.
The same pattern is evident in the dry-to-wet season transition during late 2014. A very
small increase in discharge, from 30-54 m3/s led to a 65% increase in category 1 metal
concentration and 19% decrease in category 2 metals.
Mean concentrations of category 1 and 2 metals during this period were the second highest
observed (Table 10).
Table 10. Mean metal concentrations and standard errors during the: dry-to-wet season transition
(DS-WS), wet season (WS), wet-to-dry season transition (WS-DS), dry season (DS) and for the year
(Annual). The number of decimal places presented represents the accuracy of the detection limits for
each metal in the analytical procedure.
Metal
Symbol
Cat 1
DS-WS
(µg/L)
WS (µg/L)
WS-DS
(µg/L)
DS (µg/L)
Annual
(µg/L)
Aluminium
Al
172.3
(63.1)
507.7 (189.1)
132.6 (47.6)
43.9 (6.3)
155.5 (38.0)
Beryllium
Be
0.03 (0.01)
0.09 (0.03)
0.03 (0.01)
0.00 (0.00)
0.02 (0.01)
Caesium
Cs
0.04 (0.01)
0.06 (0.01)
0.02 (0.01)
0.02 (0.00)
0.03 (0.00)
Cerium
Ce
1.10 (0.45)
3.90 (1.49)
1.08 (0.48)
0.31 (0.03)
1.12 (0.29)
Chromium
Cr
0.36 (0.06)
0.064 (0.20)
0.31 (0.05)
0.18 (0.01)
0.31 (0.04)
Cobalt
Co
0.41 (0.15)
1.21 (0.51)
0.38 (0.12)
0.18 (0.01)
0.41 (0.09)
Copper
Cu
0.79 (0.19)
1.79 (0.53)
0.67 (0.13)
0.40 (0.01)
0.74 (0.11)
Dysprosium
Dy
0.07 (0.03)
0.26 (0.10)
0.07 (0.28)
0.02 (0.00)
0.07 (0.02)
Erbium
Er
0.04 (0.02)
0.14 (0.05)
0.04 (0.02)
0.01 (0.00)
0.04 (0.01)
Europium
Eu
0.04 (0.01)
0.09 (0.03)
0.04 (0.01)
0.04 (0.00)
0.04 (0.01)
Gadolinium
Gd
0.11 (0.04)
0.38 (0.15)
0.12 (0.05)
0.03 (0.00)
0.11 (0.03)
Gallium
Ga
0.05 (0.01)
0.13 (0.04)
0.04 (0.01)
0.02 (0.00)
0.04 (0.01)
Germanium
Ge
0.09 (0.02)
0.16 (0.04)
0.09 (0.02)
0.05 (0.00)
0.08 (0.01)
Ho
0.012
(0.005)
0.047 (0.019)
0.015
(0.005)
0.003
(0.001)
0.013 (0.004)
Iron
Fe
311 (107)
1000 (249)
321 (123)
112 (9)
316 (63)
Lanthanum
La
0.47 (0.19)
1.74 (0.65)
0.48 (0.21)
0.13 (0.01)
0.05 (0.13)
Lead
Pb
0.30 (0.12)
1.06 (0.42)
0.28 (0.12)
0.08 (0.01)
0.30 (0.08)
Holmium
20
Metal
Symbol
WS (µg/L)
WS-DS
(µg/L)
Annual
(µg/L)
0.004
(0.001)
0.014 (0.006)
0.004
(0.002)
0.002
(0.000)
0.004 (0.001)
Mn
31.79
(9.13)
68.45 (18.64)
32.00 (8.00)
24.94
(1.59)
33.70 (4.30)
Neodymium
Nd
0.54 (0.23)
1.96 (0.74)
0.55 (0.24)
0.14 (0.02)
0.55 (0.15)
Nickel
Ni
0.70 (0.09)
1.12 (0.27)
0.63 (0.06)
0.57 (0.03)
0.69 (0.05)
Phosphorus
P
3 (1)
8 (1)
3 (1)
0 (0)
2 (1)
Praseodymiu
m
Pr
0.13 (0.05)
0.47 (0.18)
0.13 (0.05)
0.04 (0.00)
0.13 (0.04)
Samarium
Sm
0.11 (0.04)
0.38 (0.15)
0.11 (0.04)
0.03 (0.00)
0.11 (0.03)
Tb
0.013
(0.006)
0.047 (0.018)
0.014
(0.005)
0.004
(0.001)
0.013 (0.004)
Tm
0.005
(0.002)
0.016 (0.006)
0.006
(0.002)
0.002
(0.000)
0.005 (0.001)
Titanium
Ti
2.32 (0.48)
3.60 (0.56)
1.71 (0.12)
1.76 (0.17)
2.17 (0.20)
Vanadium
V
2.88 (0.34)
4.02 (0.95)
3.16 (0.21)
2.09 (0.07)
2.71 (0.20)
Ytterbium
Yb
0.03 (0.01)
0.10 (0.04)
0.03 (0.01)
0.01 (0.00)
0.03 (0.01)
Yttrium
Y
0.36 (0.14)
1.19 (0.47)
0.37 (0.12)
0.01 (0.02)
0.37 (0.09)
Zinc
Zn
0.72 (0.26)
2.44 (0.80)
0.57 (0.22)
0.74 (0.38)
0.94 (0.23)
Zirconium
Zr
0.16 (0.12)
0.11 (0.01)
0.05 (0.01)
0.02 (0.00)
0.08 0.04)
Arsenic
As
0.66 (0.07)
0.28 (0.04)
0.46 (0.04)
0.58 (0.03)
0.55 (0.03)
Boron
B
21.0 (1.1)
10.8 (0.6)
18.6 (0.6)
23.0 (0.3)
20.3 (0.7)
Ba
91.01
(4.68)
52.67 (4.46)
100.00
(5.00)
105.00
(1.00)
93.18 (3.08)
Br
62.0 (5.3)
26.7 (0.4)
57.3 (3.8)
70.6 (1.2)
60.6 (2.7)
Ca
41014
(3147)
10725 (1384)
39700
(3839)
53930
(793)
42570 (2371)
Manganese
Terbium
Thulium
Barium
Bromide
Calcium
Iodine
I
8.7 (0.4)
6.0 (0.9)
9.7 (0.3)
9.2 (0.1)
8.7 (0.2)
Lithium
Li
6.58 (0.71)
1.59 (0.157)
4.38 (0.41)
7.69 (0.33)
6.17 (0.41)
Mg
29107
(3451)
5738 (913)
22500
(2354)
36050
(789)
28343 (1877)
Palladium
Pd
0.05 (0.00)
0.03 (0.01)
0.05 (0.00)
0.07 (0.00)
0.06 (0.002)
Potassium
K
2631 (132)
1590 (44)
1992 (92)
2528 (47)
2376 (71)
Rubidium
Rb
4.99 (0.22)
3.50 (0.27)
3.95 (0.09)
4.88 (0.13)
4.63 (012)
Scandium
Sc
2.0 (0.1)
1.5 (0.1)
1.8 (0.1)
2.0 (0.1)
1.9 (0.1)
Si
9443 (401)
6940 (409)
9672 (186)
10716
(267)
9701 (256)
Sodium
Na
8240 (696)
2900 (315)
7528 (569)
9771 (189)
8130 (409)
Strontium
Sr
55.4 (5.2)
16.4 (2.3)
49.3 (4.3)
69.0 (1.0)
55.7 (3.1)
Sulphur
S
4527 (307)
1213 (213)
4170 (422)
5210 (174)
4349 (234)
Uranium
U
0.58 (0.03)
0.29 (0.05)
0.75 (0.06)
0.68 (0.01)
0.60 (0.02)
Thallium
Tl
0.02 (0.01)
0.01 (0.00)
0.01 (0.00)
0.04 (0.01)
0.03 (0.01)
Thorium
Th
0.07 (0.01)
0.08 (0.01)
0.08 (0.02)
0.07 (0.01)
0.07 (0.01)
Magnesium
Silicon
Cat 3
DS (µg/L)
Lu
Lutetium
Cat 2
DS-WS
(µg/L)
21
Wet season
Category 1
The first flow event of the wet season began on the 15th January 2014, discharge increased
from 66 m3/s to 1,240 m3/s on the 20th January 2014 (Figure 2). Sampling occurred on the
21st and a large increase in the metal concentrations from the previous sampling occasion
was observed. Between sampling on the 21st and 28th Jan 2014, discharged declined from
1,184 m3/s on the 21st January 2014, to 591 m3/s on the 25th January, 2014. A second flow
event, which became the largest for the wet season reaching a peak discharge of
4,004 m3/s, began on the 26th January 2014.Sampling occurred on the 28th on the early
rising limb of the flow event. This sampling event captured the highest metal concentrations
for the majority of the variables for the sampling program. Discharge continued to increase
rapidly and by the next sampling period (14th February 2014) discharge had reached
3,132 m3/s, the metal concentrations however, had decreased by an average of 74%.
Throughout the remainder of this flow event metal concentrations remained low. Another
small flow event between the 2nd and 12th March 2014 resulted in a slight increase in metal
concentrations.
Mean metal concentrations were highest during the wet season (Table 10).
Category 2
Category 2 metal concentrations generally remained low during the wet season. Mean metal
concentrations were lowest during this time (Table 10).
Wet-to-dry season
Category 1
There was a small flow event that resulted in increased metal concentrations at the start of
this time period and is discussed in the previous section. Category 1 metals reached
baseline dry season concentrations rapidly and remained at this level throughout this period.
Mean concentrations for category 1 metals at this time were approximate to the annual
mean (Table 10).
Category 2
Metal concentrations started to increase during this time. There was a range in the rate of
increase. Eleven category 2 metals increased rapidly during the early stages of this period
and reached dry season baseline levels by the end of this period. The remainder increased
22
slowly throughout the period and continued to do so during the dry season. Mean
concentrations for category 2 metals at this time were approximate to the annual mean
(Table 10).
Dry season
Category 1
The majority of the metals in this category were at levels during this period and did not
change until the first flush event of the dry-to-wet transition. Mean metal concentrations were
lowest during the dry season (Table 10).
Category 2
The concentrations of many elements were relatively stable during this time. Some, in
particular, Magnesium (Mg), Lithium (Li), Rubidium (Rb), Sodium (Na), and Potassium (K)
continued to increase slowly throughout the dry season. Mean metal concentrations were
highest during the dry season (Table 10).
3.3
ANZECC metals
The ANZECC water quality guidelines (ANZECC 2000) provide trigger values for a number
of metals and non-metals that can be potentially toxic to aquatic life. Concentrations below
these trigger values are not considered to be harmful, however, when trigger values are
exceeded, further investigation and management action may be warranted.
Patterns
Four metals listed under the ANZECC guidelines were rarely detected during the study
period and did not exceed the guidelines (Table 11). Seven of the ANZECC-listed metals
were from category 1 and two metals were from category 2 (Table 11).
Summary statistics of metals listed in the ANZECC guidelines for each season are provided
in the Appendix (Appendix, Table 6.1).
Exceedances
Three metals, copper, chromium and aluminium, exceeded the ANZECC guidelines
throughout this study (Table 9). Chromium exceeded the trigger value during the wet season
and copper exceeded the guideline during the wet-to-dry season transition and the wet
23
season (Figure 1). Chromium was 1.54 times higher than the trigger value and the maximum
exceedance of the copper value was 3.03 times greater. Aluminium was consistently higher
than the trigger value throughout the wet-to-dry transition, wet season and dry-to-wet
transition (Figure 1). The trigger value was less commonly exceeded during the dry season.
The exceedance increased markedly during the wet season and ranged from 1.2 to 24.7
times trigger value. All three metals where exceedances were observed were category 1
metals.
Table 11. ANZECC trigger values and number of exceedances during the study period
Metal
ANZECC trigger
values (µg/L) for
95% species
protection
# times
exceeded
% time
guideline
exceeded
55.00
29
64%
24.00
0
0
370.00
0
0
0.20
0
0
1.00
1
2%
1.40
4
9%
3.40
0
0
1,900.00
0
0
0.06
0
0
Nickel
11.00
0
0
Selenium*
5.00
0
0
Silver*
0.05
0
0
8.00
0
0
Aluminium1
Arsenic
Boron
2
2
Cadmium*
Chromium
Copper
Lead
1
1
1
Manganese
1
Mercury*
1
Zinc
1
1
Category 1 metals
2
Category 2 metals
* uncommon metals
5. Discussion
Metal concentrations in the Daly River were strongly influenced by hydrology. Two major
patterns in the relationship between metal concentrations and discharge were observed.
Firstly, those that increased in concentration with increasing discharge (category 1) and
secondly, those that decreased in concentration with increasing discharge (category 2).
24
Similar relationships have been observed elsewhere (Markich & Brown, 1998) and reflect
different catchment sources.
Elements in category 1 had a positive relationship to discharge and increased significantly
during the onset of the wet season. The elements in this category included all the heavier,
more inert elements and “heavy metals” with the exception of cadmium which was not
detected in this study. These metals are usually bound within soils and are transported to
streams with sediments in surface runoff. For example, metals such as aluminium and iron
are most likely derived from the old laterite geologic formations within the catchment (Wright,
1963; Day, 1982). The first flush of the wet season and the rising limb of the largest flow
events produced the highest concentrations of these metals. Subsequent samples during
elevated, but declining discharge, revealed a large drop in metal concentrations. This pattern
corresponds with the pattern observed for suspended sediments that is evident in the Daly
River. Sediment concentrations are highest during the first flushes after the dry and an
exhaustion effect occurs during very high flow events, with concentrations starting to decline
before peak flow is reached (Robson et al. 2009).
Category 2 metals included alkali metals and alkali earth metals, as well as a number of nonmetal elements. These elements readily form ions and are therefore easily dissolved and
transported in groundwater. For instance, the concentrations of the major cations sodium,
potassium, magnesium and calcium decreased substantially during the first flush of the dryto-wet transition and remained low throughout the period of elevated discharge. Once the
wet season flows subsided the concentrations began to increase. The catchment source of
these elements is primarily the significant karst groundwater springs which provide a
substantial proportion of the dry season discharge (Tickell, 2002). These springs can
discharge up to 1200 L/s and generally have elevated alkalinity, conductivity and sometimes
temperature (Tickell, 2002).
ANZECC metals
The ANZECC guidelines provide trigger values for a number of heavy metals and elements
that can be toxic to aquatic organisms. There were few exceedances of the ANZECC
guidelines during this study, only aluminium, copper and chromium exceeded the trigger
values on occasion.
Aluminium had the greatest number of exceedances which occurred primarily during periods
of elevated discharge. Given the occurrence of weathered laterites within the catchment
(Wright, 1963), and the lack of evidence of anthropogenic aluminium pollution, the high
concentrations of aluminium probably reflect natural catchment sources of the metal.
25
Laterites can contribute significant inputs of iron and aluminium (McFarlane, 1976, Day,
1982) and some soils in the catchment are known to contain high levels of aluminium (e.g.
Edmeades 2011a &b). High aluminium concentrations in surface waters are widespread in
streams of the Top End with approximately 60% of historical samples held in the Water
Resources Division database exceeding guideline values (WRD, unpublished data from
Hydstra database).
The risk presented by the high aluminium concentrations to biota is limited as aluminium is
toxic to fish only under acidic conditions (pH<6) (Gensemer & Playle, 1999) which do not
occur in the Daly River (lowest pH in this study = 6.85, average was 7.8). Furthermore, high
levels of silica are known to buffer aluminium toxicity and at a molar ratio above 2.6:1 (Si :
Al) aluminium toxicity has been negated (Camilleri et al., 2003). In this study the Si:Al molar
ratio ranged from 5.8:1 in the wet season to over 1318:1 in the dry season.
As a result, the high natural aluminium levels found in the Daly River combined with low
toxicity to biota, are not considered a threat to the ecosystem.
Copper exceeded the guidelines on three occasions during the wet season. The response of
biota to copper is varied, in some instances found to be toxic at low pH and others found it to
be toxic at higher pH (see Franklin et al., 2000 and references therein). Previous work in
Northern Australia has found copper toxicity to increase with pH, and values reported in this
study may cause harmful effects in algae, fish or mollusc (Markich & Camilleri, 1997;
Franklin et al., 2000). However caution must also be used as the toxic effects of copper can
vary greatly depending on the complexes formed with other materials within the water
column (Markich & Camilleri, 1997; Franklin et al., 2000; Hyne et al., 2005). If continued
exceedances of the copper ANZECC limit are detected then further work will be required to
investigate the copper complexes and possible toxic effects (ANZECC & ARMCANZ, 2000).
Chromium exceeded the guidelines once during the rising hydrograph in the wet season.
This was a negligible exceedance (1.54 µg/L) and well below concentrations that have
previously been found to have a negative effect on biota (>50 µg/L) (see review by Chandra
& Kulshreshtha, 2004). Chromium toxicity varies significantly depending on the chromium
species present. In well aerated freshwater hexavalent chromium is the common species
and is highly toxic. However, in this instance, due to the single low level exceedance it is
unlikely there is a significant risk to biota. If further work is to be completed on chromium the
dry-to-wet transition and the rising limb of large flood events would be periods to target for
sampling. If further exceedances are detected then further work will be required to
understand the chromium species present and toxic effects.
26
There are currently very few point sources of pollution within the Daly River catchment.
Wastewater discharge licences exist for the sewage treatment plants of the townships of
Katherine and Nauiyu, and mining operations at Mount Todd on the Edith River and the Pine
Creek Mine Site discharging to Copperfield Creek. Agriculture is a dominant land use,
primarily low density pastoralism that is unlikely to have a significant effect on metals
concentrations. Horticulture occurs in isolated pockets throughout the catchment and at the
current scale is unlikely to cause a significant increase in metal concentrations. Furthermore,
in freshwaters where the dominant anion is calcium (i.e the Daly River) this can have a
strong ameliorative effect on the toxicity of trace metals such as manganese, lead, cobalt
and cadmium (Markich and Jeffery, 1994).
A comparison of metal concentrations recorded in rivers around the world indicated that
those found in the Daly River are currently very low (Markich & Brown, 1998). The lack of
anthropogenic disturbance within the catchment is the most likely reason for such low metal
concentrations.
6. Conclusion
This report provides a baseline of metal concentrations in the Daly River. The seasonal
patterns of metal concentrations are clearly driven by hydrology and potential catchment
sources. We have identified two major patterns in the metal concentration to hydrology
relationship which likely reflect catchment sources such as groundwater and surface water.
Metal concentrations in the Daly River are compared to those in other rivers globally.
However exceedances of the ANZECC guidelines were observed for three metals and this
may require further monitoring and research in the future (ANZECC & ARMCANZ, 2000).
Since these three metals were classed as category 1 metals, any further investigation should
be targeted towards early runoff events and periods of high flow. Considering anthropogenic
land use is predicted to increase in the coming years, these values can be used to provide
baseline water quality data to assess whether river water is polluted by metals at this site,
and be a guide for Daly River waters further upstream.
27
3
Figure 4. Category 1 metals (µg/L) and river discharge (m /s). ANZECC metals are identified with *.
Guideline limits are presented on the figure as either a line or in the chart title.
28
Figure 3 (continued). Category 1 metals (µg/L) and river discharge (m3/s).
29
Figure 3 (continued). Category 1 metals (µg/L) and river discharge (m3/s).
30
Figure 3 (continued). Category 1 metals (µg/L) and river discharge (m3/s).
31
3
Figure 5. Category 2 metals (µg/L) and river discharge (m /s). ANZECC metals are identified with *.
Guideline limits are presented on the figure as either a line or in the chart title.
32
3
Figure 4 (continued). Category 2 metals (µg/L) and river discharge (m /s).
33
3
Figure 4 (continued). Category 2 metals (µg/L) and river discharge (m /s).
Figure 6. Category 3 metals (µg/L) and river discharge (m3/s).
34
Figure 7. Water quality variables and river discharge
35
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Marine Water Quality. Canberra.
APHA (2005). Standard Methods for the Examination of Water and Wastewater. 21st Edition.
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Public Health Association.
Camilleri C., Markich S.J., Noller B.N., Turley C.J., Parker G. & van Dam R.A. (2003) Silica
reduces the toxicity of aluminium to a tropical freshwater fish (Mogurnda mogurnda).
Chemosphere, 50, 355-364.
Chandra P. & Kulshreshtha K. (2004) Chromium accumulation and toxicity in aquatic
vascular plants. The Botanical Review, 70, 313-327.
Day K. (1982) Fertility studies on some soils of the Adelaide & Daly basins, Northern
Territory. Conservation Commission of the Northern Territory.
Edmeades, B. F. (2011a). Properties of selected soils in the Douglas Area and Mary River
Floodplain. Report 05/2011. Department of Natural Resources, Environment, The
Arts and Sport, Natural Resources Division, Palmerston.
Edmeades, B. F. (2011b). Properties of selected soils in the Douglas and Katherine
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and Sport, Natural Resources Division, Palmerston.
Erskine W.D., Begg G., Jolly P., Georges A., O’Grady A., Eamus D., et al. (2003)
Recommended environmental water requirements for the Daly River, Northern
Territory, based on ecological, hydological and biological principles. Environmental
Research Institute of the Supervising Scientist Darwin (NT).
Franklin N.M., Stauber J.L., Markich S.J. & Lim R.P. (2000) pH-dependent toxicity of copper
and uranium to a tropical freshwater alga (Chlorella sp.). Aquatic toxicology, 48, 275289.
Gensemer R.W. & Playle R.C. (1999) The bioavailability and toxicity of aluminum in aquatic
environments. Critical Reviews in Environmental Science and Technology, 29, 315450.
Hyne R.V., Pablo F., Julli M. & Markich S.J. (2005) Influence of water chemistry on the acute
toxicity of copper and zinc to the cladoceran Ceriodaphnia cf dubia. Environmental
Toxicology and Chemistry, 24, 1667-1675.
Jackson S., Finn M. & Featherston P. (2012) Aquatic resource use by Indigenous
Australians in two tropical river catchments: The Fitzroy River and Daly River. Human
Ecology, 40, 893-908.
Markich S.J. & Jeffree, R.A. (1994). Absorption of divalent trace metals as analogues of
calcium by Australian freshwater bivalves: an explanantion of how water hardness
reduces metal toxicity. Aquatic Toxicology 29, 257-290.
Markich S.J. & Camilleri C. (1997) Investigation of metal toxicity to tropical biota.
Recommendations for revision of Australian water quality guidelines. Canberra.
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influences on the fresh surface water chemistry of the Hawkesbury–Nepean River,
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37
Appendix
NOTE: Regression outputs are for log transformed variables.
Table A1.1 Regression outputs for all category 1 metals with discharge
Metal
Symbol
R²
Standardised
slope
Slope
Estimate
SE
p
Estimate
Intercept
SE
Estimate
SE
p
Aluminium
Al
0.58
0.75
0.10
<0.001
0.76
0.10
0.92
0.45
0.047
Beryllium
Be
0.63
0.74
0.09
<0.001
0.80
0.09
-7.71
0.40
<0.001
Caesium
Cs
0.30
0.21
0.05
<0.001
0.55
0.13
-4.50
0.22
<0.001
Cerium
Ce
0.67
0.77
0.08
<0.001
0.82
0.09
-4.11
0.38
<0.001
Chromium
Cr
0.43
0.28
0.05
<0.001
0.66
0.11
-2.59
0.23
<0.001
Cobalt
Co
0.59
0.49
0.06
<0.001
0.77
0.10
-3.52
0.29
<0.001
Copper
Cu
0.61
0.39
0.05
<0.001
0.78
0.10
-2.30
0.22
<0.001
Dysprosium
Dy
0.67
0.76
0.08
<0.001
0.82
0.09
-6.77
0.37
<0.001
Erbium
Er
0.67
0.72
0.08
<0.001
0.82
0.09
-7.18
0.36
<0.001
Europium
Eu
0.40
0.22
0.04
<0.001
0.63
0.12
-4.19
0.19
<0.001
Gadolinium
Gd
0.64
0.81
0.09
<0.001
0.80
0.09
-6.64
0.42
<0.001
Gallium
Ga
0.61
0.61
0.07
<0.001
0.78
0.09
-6.32
0.34
<0.001
Germanium
Ge
0.39
0.31
0.06
<0.001
0.63
0.12
-4.10
0.27
<0.001
Holmium
Ho
0.57
1.00
0.13
<0.001
0.76
0.10
-9.86
0.60
<0.001
Iron
Fe
0.71
0.67
0.07
<0.001
0.84
0.08
2.20
0.30
<0.001
Lanthanum
La
0.68
0.80
0.08
<0.001
0.83
0.09
-5.06
0.38
<0.001
Lead
Pb
0.68
0.73
0.08
<0.001
0.82
0.09
-5.21
0.35
<0.001
Lutetium
Lu
0.47
0.57
0.09
<0.001
0.69
0.11
-8.55
0.42
<0.001
Manganese
Mn
0.41
0.31
0.06
<0.001
0.64
0.12
1.95
0.26
<0.001
Neodymium
Nd
0.68
0.81
0.08
<0.001
0.83
0.09
-5.05
0.39
<0.001
Nickel
Ni
0.40
0.17
0.03
<0.001
0.63
0.12
-1.20
0.15
<0.001
Phosphorus
P
0.64
0.57
0.07
<0.001
0.80
0.09
-1.86
0.30
<0.001
Praseodymium
Pr
0.67
0.78
0.08
<0.001
0.82
0.09
-6.27
0.38
<0.001
Samarium
Sm
0.63
0.79
0.09
<0.001
0.79
0.09
-6.50
0.42
<0.001
Terbium
Tb
0.64
0.82
0.09
<0.001
0.80
0.09
-8.79
0.43
<0.001
Thulium
Tm
0.50
0.67
0.10
<0.001
0.71
0.11
-8.95
0.47
<0.001
Titanium
Ti
0.35
0.21
0.04
<0.001
0.59
0.12
-0.25
0.20
0.215
Vanadium
V
0.38
0.17
0.03
<0.001
0.61
0.12
0.18
0.15
0.235
Ytterbium
Yb
0.68
0.72
0.08
<0.001
0.83
0.09
-7.42
0.34
<0.001
Yttrium
Y
0.64
0.71
0.08
<0.001
0.80
0.09
-4.86
0.38
<0.001
Zinc
Zn
0.43
0.52
0.09
<0.001
0.66
0.11
-3.02
0.42
<0.001
Zirconium
Zr
0.36
0.52
0.11
<0.001
0.60
0.12
-5.83
0.49
<0.001
38
Table A1.2. Regression output for all Category 2 metals with discharge
Metal
Symbo
l
R²
Slope
Estimate
SE
p
Standardised
slope
Intercept
Estimate
Estimate
SE
SE
p
Arsenic
As
0.56
-0.26
0.04
<0.001
-0.75
0.10
0.49
0.17
0.005
Barium
Ba
0.74
-0.19
0.02
<0.001
-0.86
0.08
5.33
0.08
<0.001
Boron
B
0.93
-0.21
0.01
<0.001
-0.96
0.04
3.92
0.04
<0.001
Bromide
Br
0.82
-0.29
0.02
<0.001
-0.90
0.06
5.34
0.10
<0.001
Calcium
Ca
0.87
-0.43
0.03
<0.001
-0.93
0.06
12.45
0.12
<0.001
Iodine
I
0.47
-0.13
0.02
<0.001
-0.69
0.11
2.70
0.09
<0.001
Lithium
Li
0.94
-0.47
0.02
<0.001
-0.97
0.04
3.77
0.08
<0.001
Magnesium
Mg
0.89
-0.54
0.03
<0.001
-0.94
0.05
12.45
0.13
<0.001
Palladium
Pd
0.69
-0.26
0.03
<0.001
-0.83
0.08
-1.80
0.12
<0.001
Potassium
K
0.76
-0.15
0.01
<0.001
-0.87
0.07
8.41
0.06
<0.001
Rubidium
Rb
0.66
-0.12
0.01
<0.001
-0.81
0.09
2.05
0.06
<0.001
Scandium
Sc
0.53
-0.10
0.02
<0.001
-0.72
0.11
1.10
0.07
<0.001
Silicon
Si
0.61
-0.12
0.01
<0.001
-0.78
0.09
9.69
0.07
<0.001
Sodium
Na
0.89
-0.35
0.02
<0.001
-0.94
0.05
10.47
0.09
<0.001
Strontium
Sr
0.87
-0.41
0.02
<0.001
-0.93
0.06
5.73
0.11
<0.001
Sulphur
S
0.80
-0.38
0.03
<0.001
-0.89
0.07
9.97
0.14
<0.001
Uranium
U
0.66
-0.24
0.03
<0.001
-0.81
0.09
0.51
0.12
<0.001
Table A1.3. Regression output for all Category 3 metals with discharge
Metal
Symbo
l
R²
Standardised
slope
Slope
Estimate
SE
p
Estimate
Intercept
SE
Estimate
SE
p
Thallium
Tl
0.04
-0.14
0.10
0.177
-0.20
0.15
-3.36
0.45
<0.001
Thorium
Th
0.01
0.03
0.05
0.594
0.08
0.15
-2.81
0.24
<0.001
Table A2.1 Regression table for Category 1 metals with pH
Metal
Symbol
R²
Standardised
slope
Slope
Estimate
SE
p
Estimate
Intercept
SE
Estimate
SE
p
Aluminium
Al
0.38
-2.02
0.40
<0.001
-0.62
0.12
20.03
3.14
<0.001
Beryllium
Be
0.50
-2.17
0.34
<0.001
-0.70
0.11
12.50
2.67
<0.001
Caesium
Cs
0.30
-0.69
0.16
<0.001
-0.55
0.13
1.81
1.28
0.165
Cerium
Ce
0.41
-2.02
0.38
<0.001
-0.64
0.12
15.05
2.94
<0.001
Chromium
Cr
0.36
-0.86
0.18
<0.001
-0.60
0.12
5.38
1.40
<0.001
Cobalt
Co
0.36
-1.29
0.27
<0.001
-0.60
0.12
8.74
2.08
<0.001
Copper
Cu
0.40
-1.05
0.20
<0.001
-0.63
0.12
7.63
1.58
<0.001
Dysprosium
Dy
0.38
-1.90
0.38
<0.001
-0.61
0.12
11.43
2.98
<0.001
Erbium
Er
0.38
-1.82
0.36
<0.001
-0.61
0.12
10.20
2.85
0.001
39
Europium
Eu
0.20
-0.52
0.16
0.002
-0.45
0.14
0.81
1.25
0.518
Gadolinium
Gd
0.40
-2.12
0.40
<0.001
-0.63
0.12
13.43
3.17
<0.001
Gallium
Ga
0.39
-1.61
0.31
<0.001
-0.62
0.12
8.95
2.46
0.001
Germanium
Ge
0.29
-0.91
0.22
<0.001
-0.54
0.13
4.38
1.72
0.015
Holmium
Ho
0.32
-2.49
0.56
<0.001
-0.57
0.13
14.00
4.39
0.003
Iron
Fe
0.44
-1.78
0.31
<0.001
-0.66
0.12
19.05
2.44
<0.001
Lanthanum
La
0.41
-2.05
0.39
<0.001
-0.64
0.12
14.48
3.02
<0.001
Lead
Pb
0.44
-1.97
0.35
<0.001
-0.67
0.12
13.43
2.70
<0.001
Lutetium
Lu
0.23
-1.33
0.38
0.001
-0.48
0.14
4.38
2.99
0.150
Manganese
Mn
0.18
-0.67
0.22
0.005
-0.42
0.14
8.54
1.76
<0.001
Neodymium
Nd
0.41
-2.10
0.39
<0.001
-0.64
0.12
14.92
3.08
<0.001
Nickel
Ni
0.22
-0.42
0.12
0.001
-0.47
0.14
2.86
0.97
0.005
Phosphorus
P
0.54
-1.71
0.25
<0.001
-0.74
0.11
14.00
1.92
<0.001
Praseodymium
Pr
0.42
-2.06
0.38
<0.001
-0.65
0.12
13.23
2.95
<0.001
Samarium
Sm
0.38
-2.04
0.41
<0.001
-0.62
0.12
12.87
3.17
<0.001
Terbium
Tb
0.41
-2.18
0.41
<0.001
-0.64
0.12
11.87
3.22
0.001
Thulium
Tm
0.23
-1.51
0.43
0.001
-0.48
0.14
5.83
3.38
0.092
Titanium
Ti
0.23
-0.56
0.16
0.001
-0.48
0.14
5.01
1.25
<0.001
Vanadium
V
0.19
-0.40
0.13
0.003
-0.44
0.14
4.05
1.00
<0.001
Ytterbium
Yb
0.44
-1.94
0.34
<0.001
-0.67
0.12
10.90
2.65
<0.001
Yttrium
Y
0.36
-1.79
0.37
<0.001
-0.60
0.13
12.23
2.93
<0.001
Zinc
Zn
0.29
-1.44
0.35
<0.001
-0.54
0.13
10.52
2.73
<0.001
Zirconium
Zr
0.23
-1.38
0.40
0.001
-0.48
0.14
7.27
3.11
0.024
Table A2.2 Regression table for Category 2 metals with pH
Metal
Symbo
l
R²
Slope
Estimate
SE
p
Standardised
slope
Intercept
Estimat
e
Estimate
SE
SE
p
Arsenic
As
0.29
0.64
0.15
<0.001
0.54
0.13
-5.68
1.21
<0.001
Barium
Ba
0.63
0.58
0.07
<0.001
0.80
0.09
-0.03
0.54
0.955
Boron
B
0.64
0.59
0.07
<0.001
0.80
0.09
-1.66
0.54
0.004
Bromide
Br
0.54
0.80
0.11
<0.001
0.74
0.11
-2.21
0.90
0.018
Calcium
Ca
0.67
1.27
0.14
<0.001
0.82
0.09
0.64
1.09
0.560
Iodine
I
0.37
0.37
0.08
<0.001
0.60
0.12
-0.77
0.60
0.206
Lithium
Li
0.61
1.28
0.16
<0.001
0.78
0.10
-8.30
1.25
<0.001
Magnesium
Mg
0.61
1.48
0.19
<0.001
0.78
0.10
-1.51
1.46
0.306
Palladium
Pd
0.58
0.79
0.10
<0.001
0.76
0.10
-9.07
0.81
<0.001
Potassium
K
0.45
0.37
0.06
<0.001
0.67
0.12
4.83
0.50
<0.001
Rubidium
Rb
0.35
0.29
0.06
<0.001
0.59
0.13
-0.77
0.49
0.121
Scandium
Sc
0.29
0.25
0.06
<0.001
0.54
0.13
-1.29
0.47
0.009
40
Silicon
Si
0.45
0.33
0.06
<0.001
0.67
0.12
6.57
0.45
<0.001
Sodium
Na
0.63
0.98
0.12
<0.001
0.79
0.09
1.22
0.92
0.192
Strontium
Sr
0.61
1.16
0.14
<0.001
0.78
0.10
-5.14
1.12
<0.001
Sulphur
S
0.58
1.09
0.15
<0.001
0.76
0.10
-0.27
1.14
0.813
Uranium
U
0.62
0.78
0.10
<0.001
0.79
0.10
-6.66
0.74
<0.001
Table A2.3 Regression table for Category 3 metals with pH
Metal
Symbo
l
R²
Standardised
slope
Slope
Estimate
SE
p
Estimate
Intercept
SE
Estimate
SE
p
Thallium
Tl
0.06
0.50
0.32
0.129
0.24
0.15
-7.89
2.52
0.003
Thorium
Th
0.00
0.00
0.18
0.997
0.00
0.16
-2.69
1.39
0.061
Table A3.1 Regression table for Category 1 metals with conductivity
Metal
Symbol
R²
Slope
Estimat
e
SE
p
Standardised
slope
Intercept
Estimate
Estimate
SE
SE
p
Aluminium
Al
0.68
-1.72
0.18
<0.001
-0.82
0.09
14.45
1.11
<0.001
Beryllium
Be
0.82
-1.78
0.13
<0.001
-0.90
0.07
6.14
0.78
<0.001
Caesium
Cs
0.58
-0.61
0.08
<0.001
-0.76
0.10
0.04
0.49
0.935
Cerium
Ce
0.75
-1.74
0.16
<0.001
-0.87
0.08
9.63
0.93
<0.001
Chromium
Cr
0.62
-0.72
0.09
<0.001
-0.79
0.10
2.95
0.52
<0.001
Cobalt
Co
0.73
-1.17
0.11
<0.001
-0.85
0.08
5.59
0.66
<0.001
Copper
Cu
0.84
-0.97
0.07
<0.001
-0.91
0.06
5.21
0.40
<0.001
Dysprosium
Dy
0.75
-1.71
0.15
<0.001
-0.87
0.08
6.75
0.93
<0.001
Erbium
Er
0.76
-1.65
0.14
<0.001
-0.87
0.08
5.81
0.86
<0.001
Europium
Eu
0.55
-0.54
0.08
<0.001
-0.74
0.11
0.00
0.46
0.994
Gadolinium
Gd
0.71
-1.80
0.18
<0.001
-0.84
0.08
7.63
1.07
<0.001
Gallium
Ga
0.71
-1.39
0.14
<0.001
-0.84
0.08
4.63
0.83
<0.001
Germanium
Ge
0.50
-0.76
0.12
<0.001
-0.71
0.11
1.79
0.70
0.015
Holmium
Ho
0.59
-2.14
0.28
<0.001
-0.77
0.10
7.23
1.68
<0.001
Iron
Fe
0.79
-1.51
0.12
<0.001
-0.89
0.07
14.17
0.74
<0.001
Lanthanum
La
0.76
-1.78
0.16
<0.001
-0.87
0.08
9.04
0.95
<0.001
Lead
Pb
0.79
-1.68
0.14
<0.001
-0.89
0.07
8.01
0.81
<0.001
Lutetium
Lu
0.56
-1.33
0.18
<0.001
-0.75
0.10
1.88
1.11
0.098
Manganese
Mn
0.47
-0.69
0.12
<0.001
-0.68
0.11
7.43
0.69
<0.001
Neodymium
Nd
0.76
-1.82
0.16
<0.001
-0.87
0.08
9.36
0.96
<0.001
Nickel
Ni
0.58
-0.44
0.06
<0.001
-0.76
0.10
2.16
0.35
<0.001
Phosphorus
P
0.82
-1.34
0.10
<0.001
-0.90
0.07
8.61
0.59
<0.001
Praseodymiu
m
Pr
0.75
-1.76
0.16
<0.001
-0.87
0.08
7.63
0.94
<0.001
41
Samarium
Sm
0.71
-1.77
0.18
<0.001
-0.84
0.08
7.47
1.07
<0.001
Terbium
Tb
0.67
-1.79
0.19
<0.001
-0.82
0.09
5.50
1.17
<0.001
Thulium
Tm
0.58
-1.53
0.20
<0.001
-0.76
0.10
3.14
1.22
0.014
Titanium
Ti
0.41
-0.47
0.09
<0.001
-0.64
0.12
3.49
0.53
<0.001
Vanadium
V
0.53
-0.42
0.06
<0.001
-0.73
0.11
3.45
0.37
<0.001
Ytterbium
Yb
0.78
-1.63
0.14
<0.001
-0.88
0.07
5.48
0.82
<0.001
Yttrium
Y
0.70
-1.59
0.16
<0.001
-0.83
0.09
7.75
0.98
<0.001
Zinc
Zn
0.52
-1.22
0.18
<0.001
-0.72
0.11
6.58
1.10
<0.001
Zirconium
Zr
0.40
-1.17
0.22
<0.001
-0.63
0.12
3.41
1.34
0.015
Table A3.2 Regression table for Category 2 metals with conductivity
Metal
Symbo
l
R²
Slope
Estimate
SE
p
Standardised
slope
Intercept
Estimate
Estimate
SE
SE
p
Arsenic
As
0.64
0.60
0.07
<0.001
0.80
0.09
-4.28
0.42
<0.001
Barium
Ba
0.86
0.43
0.03
<0.001
0.92
0.06
1.94
0.17
<0.001
Boron
B
0.96
0.46
0.01
<0.001
0.98
0.03
0.22
0.09
0.017
Bromide
Br
0.96
0.68
0.02
<0.001
0.98
0.03
0.00
0.13
0.992
Calcium
Ca
0.98
0.98
0.02
<0.001
0.99
0.02
4.71
0.14
<0.001
Iodine
I
0.71
0.33
0.03
<0.001
0.84
0.08
0.17
0.20
0.388
Lithium
Li
0.92
1.00
0.05
<0.001
0.96
0.05
-4.27
0.28
<0.001
Magnesium
Mg
0.99
1.21
0.02
<0.001
1.00
0.02
2.87
0.11
<0.001
Palladium
Pd
0.59
0.50
0.07
<0.001
0.77
0.10
-5.93
0.39
<0.001
Potassium
K
0.73
0.30
0.03
<0.001
0.86
0.08
5.94
0.17
<0.001
Rubidium
Rb
0.50
0.22
0.04
<0.001
0.70
0.11
0.19
0.21
0.381
Scandium
Sc
0.43
0.19
0.03
<0.001
0.66
0.12
-0.51
0.20
0.016
Silicon
Si
0.73
0.27
0.03
<0.001
0.86
0.08
7.55
0.15
<0.001
Sodium
Na
0.98
0.78
0.02
<0.001
0.99
0.02
4.25
0.10
<0.001
Strontium
Sr
0.99
0.94
0.01
<0.001
1.00
0.01
-1.69
0.07
<0.001
Sulphur
S
0.87
0.85
0.05
<0.001
0.93
0.06
3.18
0.31
<0.001
Uranium
U
0.72
0.53
0.05
<0.001
0.85
0.08
-3.75
0.31
<0.001
Table A3.3 Regression table for Category 3 metals with conductivity
Metal
Symbo
l
R²
Estimate
Thallium
Thorium
Standardised
slope
Slope
SE
p
Estimate
Intercept
SE
Estimate
SE
p
Tl
0.02
0.19
0.21
0.379
0.14
0.15
-5.11
1.25
<0.00
1
Th
0.02
-0.10
0.11
0.357
-0.14
0.15
-2.07
0.67
0.004
42
Table A4.1 Regression table for Category 1 metals with alkalinity
Metal
Symbol
R²
Slope
Estimat
e
SE
p
Standardised
slope
Intercept
Estimat
e
Estimate
SE
SE
p
Aluminium
Al
0.67
-1.59
0.17
<0.001
-0.82
0.09
12.56
0.91
<0.001
Beryllium
Be
0.82
-1.65
0.12
<0.001
-0.90
0.07
4.20
0.64
<0.001
Caesium
Cs
0.58
-0.57
0.08
<0.001
-0.76
0.10
-0.61
0.40
0.132
Cerium
Ce
0.75
-1.61
0.15
<0.001
-0.87
0.08
7.74
0.77
<0.001
Chromium
Cr
0.62
-0.67
0.08
<0.001
-0.79
0.10
2.17
0.43
<0.001
Cobalt
Co
0.73
-1.09
0.10
<0.001
-0.86
0.08
4.34
0.54
<0.001
Copper
Cu
0.84
-0.91
0.06
<0.001
-0.92
0.06
4.18
0.32
<0.001
Dysprosium
Dy
0.75
-1.59
0.14
<0.001
-0.86
0.08
4.88
0.76
<0.001
Erbium
Er
0.76
-1.53
0.13
<0.001
-0.87
0.08
4.02
0.71
<0.001
Europium
Eu
0.56
-0.51
0.07
<0.001
-0.75
0.10
-0.55
0.37
0.142
Gadolinium
Gd
0.71
-1.67
0.17
<0.001
-0.84
0.08
5.64
0.89
<0.001
Gallium
Ga
0.71
-1.29
0.13
<0.001
-0.84
0.08
3.11
0.68
<0.001
Germanium
Ge
0.50
-0.70
0.11
<0.001
-0.71
0.11
0.97
0.58
0.100
Holmium
Ho
0.58
-1.97
0.26
<0.001
-0.76
0.10
4.84
1.39
0.001
Iron
Fe
0.78
-1.41
0.12
<0.001
-0.89
0.07
12.53
0.61
<0.001
Lanthanum
La
0.75
-1.65
0.15
<0.001
-0.87
0.08
7.09
0.78
<0.001
Lead
Pb
0.79
-1.56
0.13
<0.001
-0.89
0.07
6.20
0.66
<0.001
Lutetium
Lu
0.56
-1.24
0.17
<0.001
-0.75
0.10
0.47
0.90
0.607
Manganese
Mn
0.47
-0.65
0.11
<0.001
-0.69
0.11
6.70
0.56
<0.001
Neodymium
Nd
0.75
-1.69
0.15
<0.001
-0.87
0.08
7.37
0.79
<0.001
Nickel
Ni
0.59
-0.41
0.05
<0.001
-0.77
0.10
1.70
0.28
<0.001
Phosphorus
P
0.81
-1.24
0.09
<0.001
-0.90
0.07
7.14
0.49
<0.001
Praseodymium
Pr
0.75
-1.63
0.15
<0.001
-0.87
0.08
5.71
0.77
<0.001
Samarium
Sm
0.70
-1.64
0.17
<0.001
-0.84
0.09
5.52
0.88
<0.001
Terbium
Tb
0.67
-1.67
0.18
<0.001
-0.82
0.09
3.55
0.96
0.001
Thulium
Tm
0.58
-1.43
0.19
<0.001
-0.76
0.10
1.46
1.00
0.150
Titanium
Ti
0.42
-0.45
0.08
<0.001
-0.64
0.12
2.99
0.44
<0.001
Vanadium
V
0.54
-0.40
0.06
<0.001
-0.74
0.11
3.01
0.30
<0.001
Ytterbium
Yb
0.77
-1.52
0.13
<0.001
-0.88
0.07
3.70
0.68
<0.001
Yttrium
Y
0.70
-1.48
0.15
<0.001
-0.83
0.09
6.02
0.81
<0.001
Zinc
Zn
0.52
-1.14
0.17
<0.001
-0.72
0.11
5.25
0.90
<0.001
Zirconium
Zr
0.40
-1.09
0.21
<0.001
-0.63
0.12
2.15
1.09
0.056
43
Table A4.2 Regression table for Category 2 metals with alkalinity
Metal
Symbol
R²
Slope
Estimate
SE
p
Standardised
slope
Intercept
Estimate
Estimate
SE
SE
p
Arsenic
As
0.64
0.56
0.07
<0.001
0.80
0.09
-3.63
0.35
<0.001
Barium
Ba
0.85
0.40
0.03
<0.001
0.92
0.06
2.42
0.14
<0.001
Boron
B
0.95
0.43
0.01
<0.001
0.98
0.03
0.73
0.08
<0.001
Bromide
Br
0.96
0.63
0.02
<0.001
0.98
0.03
0.74
0.11
<0.001
Calcium
Ca
0.98
0.91
0.02
<0.001
0.99
0.02
5.77
0.12
<0.001
Iodine
I
0.72
0.31
0.03
<0.001
0.85
0.08
0.52
0.16
0.002
Lithium
Li
0.91
0.93
0.05
<0.001
0.95
0.05
-3.17
0.24
<0.001
Magnesium
Mg
0.99
1.12
0.02
<0.001
0.99
0.02
4.19
0.09
<0.001
Palladium
Pd
0.58
0.46
0.06
<0.001
0.76
0.10
-5.36
0.33
<0.001
Potassium
K
0.73
0.28
0.03
<0.001
0.85
0.08
6.27
0.14
<0.001
Rubidium
Rb
0.48
0.20
0.03
<0.001
0.70
0.11
0.44
0.17
0.015
Scandium
Sc
0.42
0.18
0.03
<0.001
0.65
0.12
-0.29
0.17
0.093
Silicon
Si
0.73
0.25
0.02
<0.001
0.85
0.08
7.85
0.13
<0.001
Sodium
Na
0.98
0.73
0.02
<0.001
0.99
0.02
5.10
0.09
<0.001
Strontium
Sr
0.99
0.87
0.01
<0.001
1.00
0.01
-0.67
0.06
<0.001
Sulphur
S
0.87
0.79
0.05
<0.001
0.93
0.06
4.11
0.26
<0.001
Uranium
U
0.71
0.50
0.05
<0.001
0.84
0.08
-3.16
0.26
<0.001
Table A4.3 Regression table for Category 3 metals with alkalinity
Slope
Metal
Symbol
Standardised slope
Intercept
Estimate
Estimate
R²
Estimate
SE
p
SE
SE
p
Thallium
Tl
0.02
0.16
0.19
0.424
0.13
0.15
-4.82
1.03
<0.001
Thorium
Th
0.02
-0.10
0.10
0.331
-0.15
0.15
-2.15
0.55
<0.001
Table A5.1 Regression table for Category 1 metals with total suspended solids
R²
Standardised
slope
Slope
Intercept
Metal
Symbol
Aluminium
Al
0.91
1.07
0.05
<0.001
0.95
0.05
2.12
0.12
<0.001
Beryllium
Be
0.78
0.94
0.08
<0.001
0.88
0.07
-6.27
0.17
<0.001
Caesium
Cs
0.49
0.30
0.05
<0.001
0.70
0.11
-4.19
0.11
<0.001
Cerium
Ce
0.92
1.03
0.05
<0.001
0.96
0.04
-2.73
0.11
<0.001
Chromium
Cr
0.73
0.42
0.04
<0.001
0.86
0.08
-2.17
0.09
<0.001
Cobalt
Co
0.90
0.69
0.04
<0.001
0.95
0.05
-2.72
0.08
<0.001
Copper
Cu
0.81
0.52
0.04
<0.001
0.90
0.07
-1.59
0.09
<0.001
Dysprosium
Dy
0.90
1.00
0.05
<0.001
0.95
0.05
-5.39
0.11
<0.001
Erbium
Er
0.84
0.92
0.06
<0.001
0.92
0.06
-5.81
0.14
<0.001
Estimate
SE
p
Estimate
SE
Estimate
SE
p
44
Europium
Eu
0.72
0.33
0.03
<0.001
0.85
0.08
-3.89
0.07
<0.001
Gadolinium
Gd
0.88
1.07
0.06
<0.001
0.94
0.05
-5.20
0.14
<0.001
Gallium
Ga
0.90
0.84
0.04
<0.001
0.95
0.05
-5.29
0.10
<0.001
Germanium
Ge
0.48
0.40
0.06
<0.001
0.70
0.11
-3.50
0.14
<0.001
Holmium
Ho
0.76
1.31
0.11
<0.001
0.87
0.07
-8.05
0.25
<0.001
Iron
Fe
0.91
0.87
0.04
<0.001
0.96
0.04
3.45
0.09
<0.001
Lanthanum
La
0.92
1.05
0.05
<0.001
0.96
0.04
-3.63
0.11
<0.001
Lead
Pb
0.91
0.97
0.05
<0.001
0.95
0.05
-3.88
0.10
<0.001
Lutetium
Lu
0.62
0.75
0.09
<0.001
0.79
0.09
-7.50
0.20
<0.001
Manganese
Mn
0.75
0.47
0.04
<0.001
0.87
0.08
2.37
0.09
<0.001
Neodymium
Nd
0.91
1.07
0.05
<0.001
0.95
0.05
-3.57
0.12
<0.001
Nickel
Ni
0.73
0.26
0.02
<0.001
0.86
0.08
-0.96
0.05
<0.001
Phosphorus
P
0.69
0.67
0.07
<0.001
0.83
0.08
-0.68
0.16
<0.001
Praseodymium
Pr
0.88
1.02
0.06
<0.001
0.94
0.05
-4.84
0.13
<0.001
Samarium
Sm
0.87
1.05
0.06
<0.001
0.93
0.06
-5.11
0.14
<0.001
Terbium
Tb
0.89
1.10
0.06
<0.001
0.94
0.05
-7.34
0.14
<0.001
Thulium
Tm
0.57
0.81
0.11
<0.001
0.75
0.10
-7.59
0.24
<0.001
Titanium
Ti
0.45
0.27
0.05
<0.001
0.67
0.11
0.13
0.10
<0.001
Vanadium
V
0.75
0.27
0.02
<0.001
0.86
0.08
0.39
0.05
<0.001
Ytterbium
Yb
0.82
0.90
0.06
<0.001
0.90
0.07
-6.01
0.15
<0.001
Yttrium
Y
0.90
0.97
0.05
<0.001
0.95
0.05
-3.62
0.11
<0.001
Zinc
Zn
0.57
0.68
0.09
<0.001
0.75
0.10
-2.05
0.21
<0.001
Zirconium
Zr
0.40
0.63
0.12
<0.001
0.63
0.12
-4.78
0.26
<0.001
Table A5.2 Regression table for Category 2 metals with total suspended solids
Metal
Symbo
l
R²
Slope
Estimate
SE
p
Standardised
slope
Intercept
Estimat
e
Estimate
SE
SE
p
Arsenic
As
0.77
-0.35
0.03
<0.001
-0.88
0.07
0.02
0.07
0.805
Barium
Ba
0.40
-0.16
0.03
<0.001
-0.63
0.12
4.81
0.07
<0.001
Boron
B
0.61
-0.20
0.02
<0.001
-0.78
0.10
3.36
0.05
<0.001
Bromide
Br
0.73
-0.32
0.03
<0.001
-0.85
0.08
4.66
0.07
<0.001
Calcium
Ca
0.53
-0.39
0.06
<0.001
-0.73
0.10
11.30
0.12
<0.001
Iodine
I
0.55
-0.16
0.02
<0.001
-0.74
0.10
2.45
0.05
<0.001
Lithium
Li
0.64
-0.44
0.05
<0.001
-0.80
0.09
2.55
0.12
<0.001
Magnesium
Mg
0.68
-0.54
0.06
<0.001
-0.83
0.09
11.13
0.13
<0.001
Palladium
Pd
0.19
-0.15
0.05
<0.001
-0.43
0.14
-2.63
0.11
<0.001
Potassium
K
0.51
-0.14
0.02
<0.001
-0.71
0.11
8.03
0.05
<0.001
Rubidium
Rb
0.41
-0.11
0.02
<0.001
-0.64
0.12
1.73
0.05
<0.001
Scandium
Sc
0.17
-0.07
0.02
<0.001
-0.41
0.14
0.77
0.05
<0.001
45
Silicon
Si
0.37
-0.11
0.02
<0.001
-0.61
0.12
9.37
0.05
<0.001
Sodium
Na
0.65
-0.34
0.04
<0.001
-0.81
0.09
9.59
0.09
<0.001
Strontium
Sr
0.67
-0.41
0.04
<0.001
-0.82
0.09
4.72
0.10
<0.001
Sulphur
S
0.34
-0.28
0.06
<0.001
-0.58
0.12
8.83
0.14
<0.001
Uranium
U
0.23
-0.16
0.05
<0.001
-0.48
0.13
-0.24
0.10
0.024
Table A5.3 Regression table for Category 3 metals with total suspended solids
Metal
Symbo
l
R²
Standardised
slope
Slope
Estimate
SE
p
Estimate
Intercept
SE
Estimate
SE
p
Thallium
Tl
0.00
0.01
0.11
0.920
0.02
0.15
-3.98
0.26
<0.001
Thorium
Th
0.02
0.05
0.06
0.367
0.14
0.15
-2.79
0.13
<0.001
46
Dry (n = 20)
0.45
0.10
24.4
0.04
0.57
0.51
n/a
0.35
Min
7.4
0.16
9.6
0.02
0.14
0.31
0.03
9.9
0.03
0.46
0.51
<0.005
0.11
Max
1360
1.09
25.0
0.03
1.54
4.25
3.09
151.0
0.05
2.39
0.63
0.01
7.96
20th percentile
18
0.35
16.1
n/a
0.17
0.36
0.06
18.3
n/a
0.53
n/a
n/a
0.26
80th percentile
197
0.77
23.9
n/a
0.35
1.00
0.44
32.7
n/a
0.73
n/a
n/a
1.02
Median
65
0.69
22.7
n/a
0.30
0.43
0.09
20.1
n/a
0.57
n/a
n/a
0.30
Min
12
0.21
14.5
<0.02
0.15
0.33
0.03
9.9
<0.02
0.46
<0.3
<0.005
0.18
Max
808.
1.09
25.0
<0.02
0.95
2.50
1.39
124
0.03
1.53
0.6
<0.005
3.19
20th percentile
16
0.32
15.3
n/a
0.20
0.34
0.05
12.9
n/a
0.50
n/a
n/a
0.20
80th percentile
253
0.92
24.0
n/a
0.41
1.10
0.43
24.4
n/a
0.73
n/a
n/a
0.78
Median
283
0.29
10.5
n/a
0.41
1.15
0.59
65.9
n/a
0.81
n/a
n/a
1.88
Min
182
0.16
9.6
<0.02
0.31
0.89
0.44
21.5
<0.02
0.67
<0.3
<0.005
0.96
Max
1360
0.41
13.4
0.02
1.54
4.25
3.09
151
<0.02
2.39
0.5
<0.005
6.18
20th percentile
212
0.16
9.8
n/a
0.32
1.10
0.46
32
n/a
0.73
n/a
n/a
1.06
80th percentile
727
0.36
11.1
n/a
0.83
2.20
1.20
74
n/a
1.32
n/a
n/a
2.70
Median
87
0.47
18.7
n/a
0.28
0.55
0.17
26.4
n/a
0.57
n/a
n/a
0.37
Min
80
0.32
16.8
<0.02
0.23
0.51
0.14
18.8
<0.02
0.53
<0.3
<0.005
0.30
Max
323
0.59
19.9
0.04
0.48
1.20
0.78
63.5
<0.02
0.86
<0.3
0.008
1.45
20th percentile
80
0.38
17.4
n/a
0.24
0.53
0.14
20.8
n/a
0.55
n/a
n/a
0.31
80th percentile
208
0.54
19.9
n/a
0.39
0.88
0.48
47.1
n/a
0.75
n/a
n/a
0.93
Zinc
1
Lead
Copper
1
0.24
Silver*
n/a
1
22.2
Mercury*
0.50
1
63
1
Selenium*
Manganese
1
Median
2
Nickel
Wet -Dry (n = 5)
Chromium
Wet (n = 6)
Cadmium*
Dry-Wet (n = 14)
Boron2
Full study period
(n = 45)
Arsenic
Aluminium
1
Table A6.1. Seasonal summary statistics for ANZECC metals. All concentrations are in µg/L.
Median
49
0.51
22.7
n/a
0.18
0.39
0.09
27.2
n/a
0.55
n/a
n/a
0.32
Min
7.4
0.42
20.8
<0.02
0.14
0.31
0.03
11.9
0.04
0.48
<0.3
<0.005
0.11
Max
123
0.83
24.8
<0.02
0.28
0.51
0.14
34.2
0.05
1.01
0.5
<0.005
7.96
20th percentile
14.4
0.48
21.8
n/a
0.16
0.36
0.04
15.85
n/a
0.50
n/a
n/a
0.22
80th percentile
62.7
0.78
23.9
n/a
0.21
0.46
0.11
31.20
n/a
0.59
n/a
n/a
0.47
47
48