1407
Sediment-bound heavy metals as indicators of human influence
and biological risk in coastal water bodies
Gavin F. Birch and Marco A. Olmos
Birch, G. F., and Olmos, M. A. 2008. Sediment-bound heavy metals as indicators of human influence and biological risk in coastal water
bodies. – ICES Journal of Marine Science, 65: 1407– 1413.
Currently, many institutions are conducting or planning large, regional-scale ecosystem assessments of estuarine health. A full, integrated assessment of these environments requires a large suite of biological, physical, and chemical indicators, including sedimentary
chemistry, ecotoxicology, benthic community structure, and bioaccumulation. This commitment is beyond the capacity of most
organizations, and a simpler approach is required to accommodate limited financial resources. A case is made for the use of sedimentary heavy metals as an easy and inexpensive indicator. The advantages are that sediments identify the “pristine” condition and give
baseline information against which future management strategies may be benchmarked, and that they differentiate solely humaninduced change from natural variation. Sediment indicators in depositional environments are also less dynamic than those associated
with water and biota. Our objective is to demonstrate that sediment-bound heavy metals data provide the spatial extent and magnitude of chemical change, as well as the risk of biological stress attributable to contamination in estuarine ecosystems. An assessment of
this scheme involving seven New South Wales (Australia) estuaries suggests that sedimentary heavy-metal indicators used in a weightof-evidence approach, with data collected during the recent Australian National Land and Water Resources Audit, enhances estuarine
condition assessment.
Keywords: estuarine health, heavy metals, indicators, sediment quality.
Received 7 January 2008; accepted 2 June 2008; advance access publication 15 September 2008.
G. F. Birch and M. A. Olmos: Environmental Geology Group, School of Geosciences, University of Sydney, Sydney NSW 2006, Australia.
Correspondence to G. F. Birch: tel: þ61 2 9351 2921; fax: þ61 2 9036 6588; e-mail: [email protected].
Introduction
The increased effort of achieving sustainable use of the marine
environment, in particular of coastal zones, has resulted in a
search for reliable indicators of “healthy” ecosystems. Ecosystem
health has been defined as the ability of an environment to maintain biodiversity and integrity (ANZECC/ARMCANZ, 2000). For
the system to be healthy, it should also be temporally stable,
resilient to change, and lack distress signals (Rapport, 1995).
The Intergovernmental Oceanographic Commission (IOC) of
the United Nations Conference on Environment and Development (UNCED) initiated several programmes to address the
sustainable use of the marine environment (Magni, 2003; Magni
et al., 2004). The Health of the Ocean (HOTO) module of the
Global Ocean Observation System (GOOS), formed under the
auspices of the Global Investigation of Pollution in the Marine
Environment (GIPME), is encouraging the adoption of a
common, integrated strategy for assessing the effect of anthropogenic activities on the marine environment.
The UNESCO-sponsored, ad hoc Benthic Indicator Group,
formed in 1999 by the IOC, is assessing options for determining
ecosystem health that are globally applicable, as well as being
easy and inexpensive to measure (AHBIG, 2000). This group
recognizes that, for such a purpose, a wide range of tools,
methods, and models would be preferable to a single indicator.
A weight-of-evidence approach is being promoted, whereby
information from multiple indicators (chemical, biogeochemical,
# 2008
toxicological, physical, and hydrologic) is assessed within a
decision framework.
A hierarchical approach is also being applied to the assessment
of estuaries in Australia, so that limited resources can be distributed most effectively. An initial qualitative assessment of the
condition of Australia’s 970 estuaries was completed recently by
the National Land and Water Resources Audit (NLWRA, 2002).
Four conditions were distinguished: near-pristine, largely unmodified, modified, and extensively modified, using criteria including
catchment or watershed land cover, land use, catchment hydrology, estuary use, and ecology. The second stage, assessing the
extent of change for modified estuaries, proved difficult, mainly
because of limited availability and inconsistency of data. A more
detailed, second-generation assessment will require the use of a
suite of environmental indicators.
An additional motivation for establishing a scheme to assess estuarine condition has come from the development of Catchment
Action Plans (CAPs), which have been mandated in Australia
through a State/Federal Government Bilateral Agreement, under
which targets are set for key natural resources, including estuarine/coastal/marine environments.
Some indicators of ecosystem health are compromised by being
spatially and temporally variable and by being influenced by
natural stress (Birch et al., 2001). Such confounding makes it difficult to identify and quantify the human-induced component of
change (Hogg and Norris, 1991). Natural stress during droughts,
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1408
storms, and floods may expose or erode marine substratum or
smother benthic floral and faunal communities. Although seagrass
distribution is a common and valuable indicator of ecosystem
health (Dennison, 1999; Dennison and Abal, 1999), large
(natural) run-off events may result in widespread decrease in seagrass distribution in some regions (Dennison and Abal, 1999).
Considerable increase in the area of mangrove stands in some
east coast estuaries comes at the expense of saltmarsh coverage.
Turbidity, another commonly used indicator, varies temporally
by a factor of .100 in response to changes in rainfall, wind,
tide, and temperature (Schoellhamer, 1996; Taylor, 2000).
Variation in turbidity affects other ecosystem indicators associated
with suspended particulate matter in the water column, such as
nutrients, contaminants, and dissolved oxygen. Thus, indicator
variance may be the result of natural processes, making it difficult
to determine the “non-disturbed” (pristine) state under variable
ambient conditions.
Assessment of estuarine condition requires baseline information from undisturbed areas or historical-trend data to detect
anthropogenic influence and to determine the magnitude of
change. However, on high-population seaboards, the entire
environment being assessed is altered, and commonly pristine
control sites are unavailable and historical contaminant information is limited (Birch et al., 1999; Maher et al., 1999).
For the second phase audit of Australian estuaries, a detailed
scheme incorporating a full range of chemical, biological, and
ecotoxicological analyses, as undertaken in the Environmental
Monitoring and Assessment Programme (EMAP) and the
National Status and Trends Programme (NS&T) in the USA
(Hyland et al., 1999, 2000), will not be financially feasible.
Instead, a limited set of key indicators will have to be chosen.
The list of potential indicators of benthic condition compiled by
the Benthic Indicator Group discriminated non-disturbed from
heavily disturbed environments (Magni, 2003; Magni et al.,
2004). The list included biological- (community composition,
structure, biomass, and functional species) and sedimentassociated abiotic factors (salinity, pore-water chemistry, sediment
organic matter, and contaminants). Sediment quality guidelines
(SQGs) for assessing individual chemicals as well as mixtures of
contaminants (Hyland et al., 2000) were also listed.
Our objective is to demonstrate that heavy metals in sediments
provide an inexpensive approach to measuring human-induced
change and act as an indicator of contaminant-related biological
stress. Use of the scheme is demonstrated using Brisbane Water,
an estuary 30 km north of Sydney, as a case study, and the
ability of the approach to differentiate estuaries at various stages
of degradation is illustrated using seven estuaries on the
Australian east coast (Figure 1). These assessments are compared
against estuarine health as determined by the National Audit.
The coastal monitoring and assessment
programme
General approach
The objective of the coastal monitoring and assessment programme at Sydney University is to provide a cost-effective, integrated, and regionally consistent assessment of estuarine health.
Further, the programme aims to provide baseline data, against
which future temporal trends can be assessed and against which
success of management strategies can be judged. Currently, 34 of
the 130 estuaries in New South Wales (NSW) are being
G. F. Birch and M. A. Olmos
Figure 1. Location of the seven selected estuaries in New South
Wales.
investigated as part of a coastal monitoring and assessment programme. Sediments are used as indicators of estuarine health to
determine the magnitude and spatial extent of human-induced
change and to assess sediment quality, i.e. the ability of sediment
to sustain a healthy biological community. The seven estuaries
used to demonstrate the approach are Durras Lake, Burril Lake,
Myall Lake, Saint Georges Basin, Pittwater, Brisbane Water, and
Port Jackson (Figure 1). The procedure for collection and analysis
of surficial sediment samples has been described in detail by Birch
and Taylor (1999, 2004).
Preferably, unconsolidated sediment should be characterized
chemically using a wide range of analytes, including heavy
metals as well as the whole range of organic contaminants, as
was carried out in the USA by EMAP and NS&T. A similar investigation was made in Port Jackson (Birch and Taylor, 1999, 2000;
McCready et al., 2006a, b). However, a lack of resources prohibited
its incorporation in the current regional monitoring programme;
it is expected that scarce resources will have the same effect on any
future, large-scale national assessment programme. A less expensive approach is required to estimate the risk of adverse biological
effects caused by sedimentary contaminants. A suite of nine
elements that are ubiquitous in estuarine environments and are
easily and inexpensively analysed (Cd, Co, Cr, Cu, Fe, Mn, Ni,
Pb, and Zn) was selected to determine possible biological stress
resulting from contamination. To investigate the extent to which
heavy metals reflect the distribution of other priority contaminants of concern [organochloride pesticides (OCs), polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons
(PAHs)] in estuarine sediments, a regression analysis was made
of multi-chemical data available from studies in Australia and
the US (Long and Sloane, 2005; McCready et al., 2006b, c).
Because an initial investigation of the data for four estuaries
revealed that three metals (Cu, Pb, and Zn) closely correlated
with the full suite of nine metals (Port Jackson: r 2 future ¼ 0.92;
Hawkesbury River: r 2 ¼ 0.87; New York: r 2 ¼ 0.71; Tampa Bay:
r 2 ¼ 0.68), only these metals were used in the assessment, to
simplify the process further.
The correlation between the three (Cu, Pb, and Zn) metals and
other analytes, including OCs, PCBs, and PAHs, from 103 sites in
Port Jackson and nearby estuaries (McCready et al., 2006b, c) was
Sediment-bound heavy metals as indicators of human influence and biological risk
significant (r 2 ¼ 0.63, p , 0.05). The correlation between the
same three metals and a suite of nine OCs and hexachlorobenzene
was also significant (r 2 ¼ 0.75, p , 0.05) in published data from
the Hawkesbury River (Birch et al., 1999). The same was true for
the three metals and PCBs in data from New York (r 2 ¼ 0.77,
p , 0.05), but less so (r 2 ¼ 0.43) in data from San Diego
sediments (Long et al., 1995b; Wolfe et al., 1996).
Contaminants in estuarine sediments will not always co-vary
spatially, but are more likely to do so in areas dominated by stormwater discharge, because the chemical mix of urban stormwater
appears reasonably consistent in both US (NURP, 1983; US
EPA, 1983) and Australian studies (Fletcher et al., 2004). This welldocumented relationship is also the basis of urban stormwater
run-off models (Fletcher et al., 2004). However, in areas receiving
elevated, chemical-specific industrial discharge, contaminants
are less likely to co-vary spatially.
Determining the extent of human-induced change
We determined the impact of human influence on the estuarine
environment using grain-size-normalized (,62.5 mm) sedimentary heavy-metal data obtained from piston and push cores
taken in undisturbed areas of deposition (Forstner and
Wittmann, 1979; Birch, 2003). Cores were subsampled at 2-cm
intervals to 10-cm depth, then over 2 cm every 10 cm to the
bottom of the core.
The magnitude and spatial extent of human-induced change
was determined by expressing current metal concentrations as
enrichment over pre-anthropogenic or background levels observed
at greater depths in the sediment (Figure 2). Typically, the preanthropogenic, or pristine, condition for the environment being
assessed is expressed as consistently low metal concentrations
towards the bottom of the core (Carballeira et al., 2000). This
section of the metal profile has been demonstrated, using dated
cores, to be pre-European in 12 cores from Sydney Harbour
(Birch, 2007), and historical trends of trace metal accumulation
have demonstrated this section of the core to be pre-anthropogenic in other estuaries (Zwolsman et al., 1993; Deely and
Fergusson, 1994). A continuous historical record of humaninduced change, and therefore the date of onset of contamination,
may be obtained from core material using 210Pb and 137Cs isotopic
Figure 2. Vertical profiles of normalized Cu, Pb, and Zn
concentrations from a sediment core in Port Jackson
(from Taylor et al., 2004).
1409
data (Taylor et al., 2004; Birch, 2007). Core data should be interpreted with caution because subsurface sediments may be affected
by bioturbation (Luoma and Phillips, 1988; Geyh and Schleicher,
1990) and post-depositional mobilization (Ridgway and Price,
1987; Benoit and Hemond, 1991). The depth of bioturbation
averages 20 cm in Port Jackson (Taylor et al., 2004), and oxic/
sub-oxic chemical conditions, leading to mobilization in reducing
organic-rich sediment and preferential sorption by
particle-reactive chemical species (Koide et al., 1973; Mayer,
1994), is restricted to the upper few centimetres in this area
(Simpson et al., 2002).
An enrichment factor (EF ¼ concentration in top layer/background concentration) of .1.5 was considered indicative of
human influence and (arbitrarily) an EF of 1.5– 3, 3 –5, 5 –10,
and .10 was considered evidence of minor, moderate, severe,
and very severe modification, respectively. A mean enrichment
quotient (MEQ) for the three metals was used to estimate the magnitude of human-induced change in each estuary by summing EFs
for Cu, Pb, and Zn and dividing by three. The spatial extent of
heavy-metal distributions was determined by ordinary kriging
interpolation, using the geostatistical analyst tool in ArcGIS.
Assessing possible biological stress
The activity, diversity, and functionality of a biological community
may be determined by measuring its structure and abundance.
However, these measurements are time-consuming and expensive,
and the interpretation of the results can be problematic if a pristine
control site is lacking. The relationship between the chemical composition of sediment and adverse biological effect is now well
established in many parts of the world, including the US,
Canada, Australia, the Netherlands, and Hong Kong (Long and
MacDonald, 1998; Birch and Taylor, 2002a, b).
The SQGs adopted by Australia and New Zealand (ANZECC/
ARMCANZ, 2000) are based largely on a scheme developed in
North America (Long and Morgan, 1990; Long et al., 1995a;
Long and MacDonald, 1998). The US scheme provides two
values for each particular chemical: effects range low (ERL) and
effects range median (ERM), which delineate three concentration
ranges. Concentrations below ERL values (65, 50, and 200 mg g21
for Cu, Pb, and Zn, respectively) identify conditions where adverse
biological effects would be observed rarely; concentrations equal to
or greater than ERL but below ERM (270, 220, and 410 mg g21,
respectively) represent a range within which biological effects
occur occasionally; concentrations at or above ERM values represent a range above which adverse biological effects are frequent.
The ANZECC scheme provides interim sediment quality
guidelines-low (ISQG-L) and -high (ISQG-H) values, which are
broadly equivalent to the ERL and ERM values, respectively, and
are exactly the same for Cu, Pb, and Zn (ANZECC/ARMCANZ,
2000; Simpson et al., 2005).
Contaminants rarely occur in sediment as single chemicals. To
assess the adverse effects of mixtures of chemicals, mean ERM
quotients (MERMQs) have been developed by normalizing the
concentration of each substance for its ERM value, summing
the quotients for each substance, and dividing the sum by the
number of chemicals used (Long et al., 2000, 2006; Ingersoll
et al., 2005). The MERMQ was derived from field-collected sediment data for mixtures of metallic and organic toxicants, which
act together to cause adverse biological effects that were measured,
either in the benthos or in laboratory animals. When potentially
toxic chemicals are found in sediments alone, concentrations
1410
G. F. Birch and M. A. Olmos
that induce toxicity may differ from those that are part of a
contaminant mixture (Berry et al., 2004; Becker et al., 2006). The
use of MERMQs for only three metals, therefore, will not predict
adverse effect accurately. However, increasing MERMQs indicate
increasing risk of detrimental biological effects posed by sediment.
Brisbane Water estuary: a case study
Brisbane Water is a shallow (6– 8 m), wave-dominated barrier
estuary (Figure 1). Catchment (168 km2) land use comprises
mainly residential (27%), agricultural (33%), and parkland
(35%), with commercial and industrial centres in the northwest.
Pre-anthropogenic or background metal (Cu, Pb, and Zn) concentrations were established for estuarine sediments from five cores
that penetrated the full post-glacial section (Table 1). The MEQ in
surficial samples (n ¼ 83) over most of the estuary was ,3 but
increased to values .6 towards the source of contamination in
the industrialized north of the estuary, and was intermediate
(3 , MEQ , 6) in the west, next to a sewage treatment plant
(Figure 3a). Surficial sediment was unlikely to have had an
adverse effect on benthic animals over most of the waterway,
because individual metal concentrations were ,ISQG-L, and
MERMQ for combined metals was ,0.5 (Figure 3b).
Comparison of seven NSW estuaries
The estuaries selected for the regional comparison covered the full
range of four estuarine conditions established by NLWRA (2002):
near-pristine (Durras Lake); largely unmodified (Burril Lake,
Myall Lake); modified (Saint Georges Basin, Pittwater); and extensively modified (Brisbane Water and Port Jackson). Sediment
in Port Jackson contained the highest mean concentrations
Table 1. Background concentrations compared with both normalized and total mean, maximum, and minimum concentrations (mg g21)
of Cu, Pb, and Zn in surficial sediment for seven selected New South Wales estuaries.
Estuary
Normalized
Total
n
Cu
Pb
Zn
n
Cu
Pb
Zn
Background
26
19
107
–
–
–
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Mean
10
29
24
123
10
14
14
67
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
s.d.
8
2
17
11
12
53
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Maximum
46
28
144
26
27
123
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Minimum
21
20
94
0
0
1
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Burril Lake
Background
75
17
63
–
–
–
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Mean
21
76
33
100
21
50
20
55
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
s.d.
16
10
54
32
14
33
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Maximum
119
55
299
89
46
103
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Minimum
41
18
57
0
1
2
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Myall Lakes
Background
3
16
44
–
–
–
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Mean
22
5
19
49
22
10
17
54
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
s.d.
2
6
20
5
8
27
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Maximum
9
28
82
17
27
97
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Minimum
2
5
11
1
2
6
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
St Georges Basin
Background
32
14
40
–
–
–
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Mean
36
75
28
100
39
11
15
42
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
s.d.
66
8
37
8
10
32
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Maximum
318
55
256
25
29
81
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Minimum
10
17
54
0
0
0
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Pittwater
Background
10
33
47
–
–
–
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Mean
75
87
65
134
73
16
13
24
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
s.d.
76
32
40
34
25
41
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Maximum
596
174
272
184
127
187
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Minimum
25
20
20
0
0
0
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Brisbane Water
Background
9
22
59
–
–
–
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Mean
83
30
57
157
83
18
36
98
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
s.d.
15
38
77
11
24
58
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Maximum
153
362
775
47
144
269
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Minimum
13
26
76
18
1
5
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Port Jackson
Background
12
23
53
–
–
–
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Mean
528
181
274
578
593
132
203
446
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
s.d.
114
130
245
116
157
354
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Maximum
727
1 302
1 925
730
1 130
3 660
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . .
Minimum
31
49
86
0
0
0
Durras Lake
1411
Sediment-bound heavy metals as indicators of human influence and biological risk
Figure 3. Evaluation of three heavy metals in surficial sediments in
Brisbane Water estuary: (a) MEQ; (b) MERMQs.
(total as well as normalized) for the three metals, whereas the mean
concentrations were lowest in Myall Lake (Table 1).
Background concentrations of heavy metals in sediments
depend on the geology of the catchment area and the local physiochemical conditions, and may vary considerably (Olmos and
Birch, 2008). Therefore, the background Cu concentrations vary
from 75 mg g21 in Burril Lake, attributable to Cu-rich porphyry
rocks in the catchment (Gillis and Birch, 2006), to 3 mg g21 in
Myall Lake, which is mantled in organic-rich sediments.
Port Jackson had the highest MEQ, followed by Pittwater,
although the values for the other five estuaries were considerably
lower (Table 2). Ranking based on MEQ was: Durras and Myall
Lakes , Burril Lake , St Georges Basin , Brisbane Water ,
Pittwater , Port Jackson. The ISQG-L value for total sediment
was exceeded for Cu in Burril Lake and Pittwater, for Pb in
Brisbane Water and Pittwater, and for Zn in Brisbane Water.
The only area to exceed ISQG-H values was in Port Jackson for
all three metals (Table 1).
The recently completed National Land and Water Resources
Audit (NLWRA, 2002) of Australia’s 970 estuaries made extensive
use of two important criteria, i.e. catchment land cover and
catchment land use. Port Jackson has the largest proportion of
residential, commercial, and industrial land use, followed by
Pittwater, whereas Durras and Myall lakes have almost undeveloped catchments (Table 2). Land use was correlated with the magnitude of human-induced change and the risk of adverse biological
effects attributable to chemical contamination to assess the extent
to which these parameters are related, using Pearson correlation
coefficient analysis. A strong positive correlation between the
three factors was found, particularly between land use and MEQ
(0.9), and also between MERMQ and MEQ (0.98).
The impact of a highly urbanized catchment on the adjacent
receiving basin was substantial. Sediment in estuaries with more
than 85% of the land use dedicated to parkland, agriculture, and
educational facilities (St Georges Basin, Myall, Burril, and
Durras lakes) did not exceed sediment quality guidelines (Birch
and Taylor, 1999, 2004). Sediment in Pittwater and Brisbane
Water estuaries, where 45 and 29%, respectively, of the land use
is dedicated to residential, commercial, and industrial activities,
exceeded ISQG-L for total sediment, whereas Port Jackson
(74%) was the most affected of all estuaries explored, with heavymetal enrichment of .50 times background in some areas.
The parameters considered, i.e. land use, MEQ, and MERMQs,
for the seven estuaries related broadly to the NLWRA assessment
of estuarine condition, except for Brisbane Water, which appears
to be less affected than Pittwater estuary, based on heavy-metal
concentrations, and Myall Lake, which appears to be near-pristine.
NLWRA (2002) recognized that, owing to lack of data, the
Audit was unable to define benchmarks to establish the extent of
Table 2. Comparison of seven New South Wales estuaries: mean enrichment factors (EF) for three heavy metals and MEQ (maximum
values in parenthesis); and NRLWA condition typology (NP: near-pristine; LU: largely unmodified; M: modified; EM: extensively modified),
land use proportion for industrial, commercial, and residential activities (including transport), and percentage areas above specific
thresholds for MEQ and MERMQ.
Estuary
Durras Lake
Burril Lake
Myall Lake
St Georges Basin
Pittwater
Brisbane Water
Port Jackson
EF-Cu
1.1
(1.8)
1.0
(1.6)
1.4
(2.5)
2.3
(9.9)
8.7
(59.6)
3.3
(16.9)
15.1 (60.6)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .
EF-Pb
1.3
(1.5
1.9
(3.2)
1.2
(1.7)
2.0
(4.0)
2.0
(5.3)
2.6
(16.6)
11.9 (56.6)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .
EF-Zn
1.1
(1.3)
1.6
(4.7)
1.1
(1.9)
2.5
(6.5)
2.8
(5.8)
2.6
(13.0)
10.9 (36.3)
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .
MEQ
1.2 (1.5)
1.5 (3.2)
1.2 (2.0
2.3 (6.8)
4.5 (23.6)
2.9 (15.5)
12.6 (51.2)
NLWRA condition
NP
LU
LU
M
M
EM
EM
Land use (%)
1
8
1
15
45
29
74
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .
MEQ
.3%
0
0
0
2
19
9
100
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .
MERMQ .0.5%
0
0
0
0
0
0
54
1412
change for modified estuaries. Our results suggest that the use of
sediment MERMQs and MEQs, based on three heavy-metal concentrations (Cu, Pb, and Zn) and catchment land use, would be
valid indicators of ecosystem health and should be considered in
future assessment of estuarine health. These indicators provide
additional lines-of-evidence in assessments of estuary condition
relative to pristine conditions and provide added value to allow
a greater differentiation of estuarine ecosystem health (Birch and
Davies, 2003; Davies and Birch, 2003).
Conclusions
Sedimentary heavy metals provide an inexpensive approach to
measuring human-induced change and act as an indicator of
contaminant-related biological stress. The data required for this
type of assessment is acquired relatively easily and inexpensively,
and provides the contaminant framework on which more detailed
and focused multidisciplinary investigations can be based for large
regional or national estuarine monitoring and trend programmes.
Acknowledgements
We thank Ed Long for use of the New York and San Diego
chemical data, Ed Long and J. de Boer for constructive reviews,
and guest editor Niels Daan for his suggestions for improvement.
References
AHBIG. 2000. Ad Hoc Benthic Indicator Group, Results of Final
Planning Meeting. UNESCO. IOC Technical Series, 57.
ANZECC/ARMCANZ. 2000. Australian and New Zealand Guidelines
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