Floodplain storage of sediment contaminated by mercury and

Geomorphology 206 (2014) 122–132
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Geomorphology
journal homepage: www.elsevier.com/locate/geomorph
Floodplain storage of sediment contaminated by mercury and copper from
historic gold mining at Gold Hill, North Carolina, USA
Scott A. Lecce a,⁎, Robert T. Pavlowsky b
a
b
Department of Geography, Planning, and Environment, East Carolina University, NC 27858, USA
Department of Geography, Geology, and Planning, Missouri State University, Springfield, MO 65804, USA
a r t i c l e
i n f o
Article history:
Received 31 July 2012
Received in revised form 16 August 2013
Accepted 2 October 2013
Available online 10 October 2013
Keywords:
Mercury
Gold mining
Floodplains
Trace metals
North Carolina
a b s t r a c t
Previous research on the environmental consequences of mining has shown that metal contaminants can have
long-lasting impacts on water quality and aquatic ecosystems because of the remobilization of sedimentassociated contaminants that have been stored in floodplains. We examined the magnitude and distribution of
mercury (Hg) and copper (Cu) contamination of floodplain deposits associated with nineteenth century gold
(Au) mining activities in the Gold Hill mining district, North Carolina. A comparison of post-mining metal
concentrations in overbank deposits with sediment quality guidelines indicates that overall about 21% are
contaminated above the probable effect concentration (PEC; above which adverse effects are expected to occur
more often than not) for both Cu and Hg. The highest contamination occurs upstream near the mine source
where 51% of the samples exceed the PEC for Hg and 57% exceed the PEC for Cu. Of the three different methods
used to estimate metal mass storage, the most reliable estimate suggests that about 6.8 Mg of Hg and 619 Mg of
Cu currently reside in floodplain deposits within this watershed. Although overbank sediment storage increases
downstream and with valley width, about 75% of the Hg and Cu mass are stored in the upstream portion of the
watershed. Hg mass storage displays a strong negative relationship with cross-sectional stream power, but the
relationship between Cu mass storage and stream power is insignificant. We used vertical changes in overbank
metal concentrations and the mining history to estimate a mean sedimentation rate of 2.7 cm y−1 during the
most intensive period of Au mining at Gold Hill (1842–1856) that is three times the long-term (1842–2007)
rate of 0.9cmy−1. Long-term average rates at Gold Hill are comparable to those reported elsewhere in the eastern
Piedmont. The downstream increase in long-term rates may indicate a spatial and temporal lag effect where the
locus of deposition shifts downstream with time.
© 2013 Elsevier B.V. All rights reserved.
1. Introduction
Contamination of sediment by trace metals poses significant threats
to water quality, aquatic ecosystems, and human health. Numerous
studies have shown that floodplains in historically mined watersheds
can contain large quantities of sediment contaminated by trace metals
(e.g., Knox, 1987; Lewin and Macklin, 1987; Bradley and Cox, 1990;
Marron, 1992; Miller, 1997; Ettler et al., 2006; Macklin et al., 2006;
Hũrkamp et al., 2009; Žák et al., 2009; Bird et al., 2010; Gosar and
Žibret, 2011; Castro-Larragoitia et al., 2013). Although floodplains can
store contaminants for hundreds to thousands of years, these sediment
sinks remain dynamic because metals can be remobilized from storage
through processes such as bank erosion and redistributed downstream
to be returned to storage in floodplain and channel deposits (Rang and
Schouten, 1989; Bradley and Cox, 1990; Macklin and Klimek, 1992;
Lecce and Pavlowsky, 1997, 2001; Bird et al., 2010; Łokas et al., 2010).
Consequently, past mining activities can cause trace metal contamination
problems in river systems that persist long after mining activities have
⁎ Corresponding author. Tel.: +1 252 328 1047; fax: +1 252 328 6054.
E-mail address: [email protected] (S.A. Lecce).
0169-555X/$ – see front matter © 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.geomorph.2013.10.004
ceased (e.g., Miller, 1997; Walling et al., 2003; Macklin et al., 2006),
with present-day contamination primarily controlled by the physical
release of metal-contaminated sediments by bank erosion and mass
wasting. This paper investigates the magnitude and distribution of
metal contamination of floodplain deposits associated with nineteenth
century Au-mining activities in the Gold Hill mining district, North
Carolina. Previous studies have documented the overall geographic
extent and sediment-metal relationships in fluvial sediments within
the Dutch Buffalo watershed (Lecce et al., 2008; Pavlowsky et al.,
2010). This contribution focuses on understanding the downstream
variations of the mass storage of sediment and trace metals in floodplain
sinks since the early 1800's and the potential for current and future
legacy contamination.
Mercury (Hg) has been used to amalgamate gold (Au) for thousands
of years (Nriagu, 1994; Malm, 1998). Even today, Au mining is responsible
for about 10% of global anthropogenic Hg emissions, and most of the Hg
released by Au and silver (Ag) mining during the last 500 years may still
be available to the global Hg cycle through the remobilization from
abandoned tailings and contaminated deposits (Lacerda, 1997). The
environmental impacts of Hg use in Au-mining operations have been
well documented in the literature (e.g., Leigh, 1994, 1997; Malm, 1998;
S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132
Miller et al., 1998, 2003). During mineral ore grinding and amalgamation
processing at mill sites, Hg is released to the environment where it can be
sorbed to tailings particles or sediments that act as the primary reservoir
of Hg in freshwater systems (Ullrich et al., 2001). This Hg can be transformed by microbial processes in aquatic sediments into its more toxic
organic form, methylmercury (MeHg), which can bioaccumulate and
biomagnify in organisms near the top of the food chain (Gilmour et al.,
1992; Holmes and Lean, 2006; Guentzel et al., 2007). Understanding the
behavior of Hg in environmental systems is of great interest because
this element is toxic at concentrations that are commonly one or two
orders of magnitude lower than other toxic metals (MacDonald et al.,
2000). The main source of human exposure to this potent neurotoxin is
the consumption of Hg-contaminated fish, which has led to worldwide
advisories for fish consumption (NAS, 2000; Clarkson, 2002; Guentzel
et al., 2007).
Spatial variations of sediment and contaminant storage have been
explained by a variety of factors such as particle size, valley width,
distance from the source, lateral distance from the channel, flood
flow hydraulics, the frequency of floodplain inundation, floodplain
topography, and stream power (Lecce and Pavlowsky, 1997; Walling
and He, 1998; Owens et al., 2001; Walling et al., 2003; Miller and
Orbock Miller, 2007; Wyżga and Ciszewski, 2010). A variety of methods
have been used to determine how storage changes through time. Geochemical profiles in floodplain cores can be used as stratigraphic tracers
to document historical sedimentation trends and pollution histories
(Knox, 1987; Macklin and Klimek, 1992; Lecce and Pavlowsky, 2001;
Bain and Brush, 2005; Knox, 2006). Geochemical and isotopic tracers
have also been used in a fingerprinting approach to determine sources
and dispersal pathways for contaminated sediment (Hudson-Edwards
et al., 1999; Miller et al., 2002, 2005; Villarroel et al., 2006; Miller and
Orbock Miller, 2007).
Estimating metal storage in watersheds is important for several
reasons. Floodplain cores in contaminated watersheds can be useful
for documenting the history of pollution, estimating sediment delivery
ratios for polluted sediment, and evaluating the long-term storage and
ultimate fate of contaminants (Ongley, 1987; Bradley, 1989). These
contaminant profiles can also be used as tracers to understand patterns
of sediment transport and deposition (Knox, 1987; Miller, 1997; Lecce
and Pavlowsky, 2001; Miller and Orbock Miller, 2007). Metal storage
estimates can further be used to evaluate the effectiveness of alluvial
storage at attenuating pollutant transport to downstream areas.
Understanding the magnitude and spatial distribution of metal pollutants
within alluvial deposits, and where they exceed toxic limits, can assist
managers in assessing how long contaminants reside in watersheds,
identifying hot spots, and focusing future monitoring efforts (Dennis
et al., 2009).
Gold was first discovered in the USA in 1799 in the Piedmont of
North Carolina, USA. Although exploration spread slowly throughout
the southern Piedmont in a belt that stretched from Virginia to Alabama,
North Carolina produced more Au than any other state in the South and
led the nation in Au production until 1848 (Knapp and Glass, 1999).
Although the exact date that Hg amalgamation was first used in North
Carolina is uncertain, historical accounts suggest that large amounts
were being used at Gold Hill in southern Rowan County (northeast of
Charlotte, NC) by the mid-1840's (Knapp and Glass, 1999). In a
reconnaissance study of Hg contamination in the Dutch Buffalo Creek
watershed, Lecce et al. (2008) showed that a strong Cu and Hg mining
signal can be detected in overbank sediments at the basin outlet 25 km
downstream from the mines at Gold Hill. Pavlowsky et al. (2010) also
found high Hg concentrations in active channel sediments even though
terrestrial tailings sources at mining sites have been removed, which
suggests that metals are being remobilized from overbank storage. In
another study of potential Hg contamination in the nearby Cid mining
district (located about 30 km to the northeast of Gold Hill), Lecce et al.
(2011) found that, although Hg was certainly used to amalgamate Au,
Hg concentrations are considerably lower than those found at Gold Hill.
123
Thus, given that mining operations at Gold Hill were more intense than
anywhere else in North Carolina (Knapp and Glass, 1999), the floodplains
downstream from Gold Hill will likely contain the highest mining-related
Hg concentrations in the state. The purpose of this paper is to focus on the
magnitude and distribution of metal contamination of historical overbank
deposits in the Gold Hill mining district. The primary objectives are
(i) to evaluate the degree of contamination by comparing metal
concentrations to sediment toxicity guidelines, (ii) to determine the
distribution of overbank metal storage and assess geomorphic controls
on that distribution, and (iii) to use particle-bound metals as tracers to
estimate historical rates of floodplain sedimentation.
2. Study area
The Gold Hill mining district is located in the gently rolling
topography (140–270 m amsl) of the Piedmont of North Carolina in
southern Rowan and northern Cabarrus Counties (Fig. 1). This area
was first settled by Europeans between 1730 and 1740 (Merrens,
1964; Powell and Lefler, 1973). When the intensive Au-mining activities
began in 1842, this area was occupied primarily by subsistence farmers
(Knapp and Glass, 1999). Carpenter (1976) mapped the locations of 13
mines in the Gold Hill district (Fig. 1), all in close proximity to the
headwaters of Little Buffalo Creek (39 km2), which is tributary to
Dutch Buffalo Creek (254 km2). The upper portion of Dutch Buffalo
Creek (above the confluence with Little Buffalo Creek) does not contain
any mines. Little Buffalo Creek and Dutch Buffalo Creek are low sinuosity
meandering streams with pool-riffle morphology and channel beds
consisting primarily of sand, gravel and cobbles. Bedrock is frequently
exposed in the channel in the west to east-flowing section of Dutch
Buffalo Creek upstream from the confluence with Little Buffalo Creek.
The main valleys vary between about 50 m and 450 m in width.
Floodplain deposits typically consist of massive, unweathered brown
to yellowish-brown vertical accretion sediments (sands and silts) that
overlie channel bed gravels. Floodplain surfaces range from 1.5–3 m
above the channel bed along Little Buffalo Creek and 3–4 m above the
channel bed along Dutch Buffalo Creek.
The watershed is underlain by two Au-rich units separated by the
SW–NE trending Gold Hill fault, which trends approximately along
Little Buffalo Creek and the lower portion of Dutch Buffalo Creek. The
igneous intrusive rocks of the Charlotte belt lie to the west, with the
metavolcanic and metasedimentary rocks of the Carolina slate belt to
the east (Pardee and Park, 1948; Carpenter, 1976; Knapp and Glass,
1999). Most of the Au-bearing ore deposits occur in chlorite–sericite
phyllite to the east of the fault line (Carpenter, 1976). Early mining
practices were quite crude and focused on excavations above the
water table. After the discovery of the primary Au veins in 1842 (Nitze
and Wilkins, 1897), mercury was used in Chilean mills and log rockers
to amalgamate the Au (Knapp and Glass, 1999). By the mid-1850's the
larger mining companies were using steam to power their Cornish
pumps and Chilean mills (Knapp and Glass, 1999). Mining in the district
continued until about 1915, with peak production for Au in 1856. In
addition to Au, the Gold Hill district produced a significant amount of
copper (Cu) (Pardee and Park, 1948). Ores rich in Cu-sulfides were
encountered as the mines became deeper. These ores had generally
been ignored as too difficult to process by amalgamation, but as Au
production declined in the 1880's interest in Cu associated with sulfide
ores increased (Laney, 1910; Knapp and Glass, 1999). The Union Copper
Mine was opened in 1899 and produced large amounts of Cu until its
closure in 1906. See Lecce et al. (2008) for a more complete description
of the geological setting and mining history of the study area.
3. Methods
Background metal concentrations were assessed by collecting soil
and sediment samples at 63 surface (i.e., top 10 cm) and subsurface
(i.e., B horizons at soil exposures) locations not affected by mining or
124
S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132
Fig. 1. Map of the Dutch Buffalo Creek watershed showing the location of the mines and floodplain transect sites.
ore processing activities in the watershed. A total of 42 floodplain cores
were collected to depths ranging from 78 to 517 cm at nine sites (Fig. 1)
using either a 2-cm diameter Oakfield soil probe or a 5-cm diameter,
40-cm long AMS split barrel corer. We collected 783 composite samples
from the cores in depth intervals that ranged from 5 cm to 20 cm in
thickness, depending on the stratigraphy. These floodplain sites were
selected where roads provided access to the floodplain.
Sample preparation and physical analyses were performed at the
Water and Soil Laboratory of the Ozarks Environmental and Water
Resources Institute at Missouri State University. Sediment samples
were oven-dried at 60 °C, disaggregated with mortar and pestle, and
passed through a 2-mm sieve. Although many studies focus on the
b63 μm size fraction, we selected the b2 mm fraction because in
mined watersheds sand-sized ore particles and Fe–Mn coatings on
sand grains may contain significant amounts of metal (Horowitz,
1991). Geochemical analyses were conducted on the b 2 mm fraction
by a commercial laboratory (ALS Chemex, Sparks, NV, USA) where the
samples were prepared using an aqua-regia extraction (hot 3:1 nitric–
hydrochloric acids) (Mudroch et al., 1997). Mercury levels were
determined using cold vapor atomic absorption spectroscopy (AA),
while Cu (as part of a 33 element package) was analyzed by inductively
coupled plasma–atomic emission spectroscopy (ICP–AES). Although
several elements are elevated above background concentrations
(i.e., Pb, Zn), we focused on Hg and Cu because they showed the highest
contamination levels and were specifically targeted for extraction by
the mining operations. Lower detection limits are 0.01 mg/kg for Hg
and 1 mg/kg for Cu. ALS Chemex follows quality control procedures
involving both batch check standards and duplicate analyses on every
10–20 samples. Duplicate sample analyses are typically within 10%.
We have also submitted triplicate geochemical standard materials
available from the U.S. Geological Survey to ALS Chemex and found
that values are within 15%.
A number of different methods have been used to calculate metal
budgets in fluvial systems (Dennis et al., 2009). Each of the methods
employed to estimate metal storage in floodplains necessarily involves
a number of simplifying assumptions that affect the accuracy of the
S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132
estimates. Because we did not have access to the entire width of the
floodplain at most of the sites, we did not collect cores across the entire
cross-valley transect. Therefore, we calculated the mass of contaminated
deposits as:
Σ
i¼1
Di 1n
Σ
i¼1
Ci
ð1Þ
where M is the metal mass at each site (kg), Afp is the floodplain area
between each adjacent site (km2), ρb is the average bulk density of
floodplain soils in the watershed (1.5 g cm−3, from McCachren and
Woody, 2004), n is the number of cores at each transect, D is the depth
that each core is contaminated above background concentrations (cm),
and C is the vertically weighted mean metal concentration of each core
(mg kg−1). Floodplain area was obtained from the soil surveys for
Rowan and Cabarrus Counties (Stephens, 1988; McCachren and Woody,
2004). The area associated with each site was determined by dividing
the distance between sites in half and including the area closest to the
site in the calculation. This approach assumes that the distal portions of
the floodplain (which were seldom cored) contain similar metal
concentrations and contaminated depths as the means obtained from
the cored portion of each transect. Because our data at sites with several
cores across the floodplain suggest that distal floodplain deposits that
were not cored are likely thinner and have lower metal concentrations,
we believe that Eq. (1) probably overestimates the true metal mass to
some degree. Therefore, we also calculated minimum and maximum
estimates of metal mass storage to provide some context for the estimates
obtained from Eq. (1):
n
Mmin ¼ L Axs ρb 1n
Σ Ci
i¼1
ð2Þ
where Mmin is the minimum estimate of metal mass at each site (kg), L is
the longitudinal distance between each site (km), and Axs is the crosssectional area of the transect that was contaminated above background
concentrations (kg). The longitudinal distance associated with each site
was determined by bisecting the distance between sites. Eq. (2) provides
an estimate of storage only for that portion of the floodplain where we
obtained metal concentrations from cores, which ignored the distal
portions of each transect lacking core data. Therefore, because these distal
areas are likely to contain contaminated sediments, Eq. (2) produced a
minimum estimate.
We also calculated a maximum estimate of metal storage using the
depth of contamination of the one core at each site with the most well
defined and abrupt initial peak metal concentration:
Mmax ¼ D Afp ρb C
ð3Þ
where Mmax is the maximum estimate of metal mass at each site (kg).
We believe that Eq. (3) produces a maximum estimate of metal storage
because the core selected at each site tended to be relatively deep, close
to the channel, and more highly contaminated. Nevertheless, this
represents the type of sampling that is often used where it is only
feasible to collect a single core at each transect location.
We used estimates of valley width and stream power to quantify
geomorphic controls on sedimentation and metal storage (Lecce, 1997).
Valley width was obtained from floodplain soils mapped on the Cabarrus
County soil survey. Because the width of floodplain soils was quite
variable at the reach scale and therefore sensitive to the exact location
of the transects, we estimated an average width over a downvalley
distance of 500 m centered on each transit. Cross-sectional stream
power has been used as an index of the sediment-transporting capacity
of the stream flow (Rhoads, 1987):
Ω ¼ γQS
slope. The bankfull discharge was estimated using Harman et al.'s
(1999) equation developed for North Carolina Piedmont streams.
4. Results
n
n
M ¼ Afp ρb 1
n
125
ð4Þ
where Ω is power per unit length (W/m), γ is the specific weight of
water (9810 N m−3), Q is discharge (m3 s−1), and S is the energy
gradient (m m−1) approximated by field surveys of the channel bed
4.1. Background concentrations
Table 1 summarizes metal concentrations for soils in the Gold Hill
district that were not contaminated by mining activities. The mean
background concentration is 0.06 mg kg−1 for Hg and 46 mg kg−1 for
Cu. The mean Hg concentrations is similar to background levels found
globally in soils (Reimann and Caritat, 1998), in the National Geochemical
Survey for Cabarrus County (USGS, 2004), and by Lecce et al. (2011) in
the Cid mining district in nearby Davidson County. The mean background
Cu concentration is 12–24 mg kg−1 higher than these other reference
levels due to the presence of ore bodies in this watershed that are
enriched in Cu sulfides. Concentrations of Hg in surface soils are slightly
higher than subsurface soils, probably because of recent airborne inputs.
We also used the iterative 2σ technique (Matschullat et al., 2000) to
provide the likely upper limit on background values. This procedure
essentially eliminates extreme values to obtain a normal distribution
around the modal value of the original data set (Zglobicki et al., 2011),
producing values of 69 mg kg−1 for Cu and 0.09 mg kg−1 for Hg.
4.2. Downstream contamination trends
A large proportion of the post-mining overbank sediment samples
we collected had Hg and Cu concentrations above our measured
background concentrations in Table 1 (91% for Hg; 80% for Cu) and
above thresholds identified using the iterative 2σ technique (88% for
Hg; 44% for Cu). Weighted mean concentrations of Cu and Hg decrease
downstream from the primary mine sources in the headwaters of Little
Buffalo Creek (Fig. 2). This trend is typical of rivers contaminated by
point sources of contamination (Miller and Orbock Miller, 2007).
Because the upper portion of the Dutch Buffalo Creek watershed was
unmined and has a drainage area about three times as large as Little
Buffalo Creek, it contributes a significant amount of uncontaminated
sediment below the confluence with Little Buffalo Creek. This is reflected
in metal concentrations in the lower portion of Dutch Buffalo Creek
where mean Hg concentrations are about six times lower and Cu three
times lower than those upstream along Little Buffalo Creek.
Table 1
Background concentrations.
Cu (mg kg−1)
Hg (mg kg−1)
All (n = 63)
Mean
Median
SD
Range
46
40
31
3–144
0.06
0.05
0.04
0.01–0.23
Surface Soils (n = 34)
Mean
Median
SD
Range
43
38
31
8–144
0.07
0.06
0.04
0.03–0.23
Subsurface Soils (n = 29)
Mean
Median
SD
Range
Mean, Davidson Countya
Mean, global for soilb
Mean, Cid Districtc
51
50
31
3–125
22
25
34
0.05
0.05
0.03
0.01–0.13
0.04
0.05
0.05
a
Mean value (N = 9) reported in the National Geochemical Survey for Cabarrus County,
North Carolina (USGS, 2004).
b
Reimann and Caritat (1998).
c
Lecce et al. (2011).
We assessed the overall magnitude of the contamination by comparing our metal concentrations in post-mining floodplain deposits to
environmental contamination standards. In order to provide an accurate basis for predicting the presence or absence of sediment toxicity,
MacDonald et al. (2000) developed two numerical sediment quality
guidelines for freshwater ecosystems: the threshold effect concentration
(TEC; below which adverse effects are not expected to occur) and the
probable effect concentration (PEC; above which adverse effects
are expected to occur more often than not). These guidelines are
consensus-based values derived from multiple previously conducted
studies. The guidelines we report are the consensus-based value along
with the range of values from which it was derived. The PEC for Hg is
1.06 mg kg−1 (0.486–2 mg kg−1), and the PEC for Cu is 149 mg kg−1
(86–197 mg kg−1); while the TEC for Hg is 0.18 mg kg−1
(0.15–0.2 mg kg−1), and the TEC for Cu is 32 mg kg−1 (16–70 mg kg−1).
Note that the TEC for Cu is lower than the mean background Cu
concentration (46 mg kg−1) in the Dutch Buffalo Creek watershed.
About 21% of our samples from floodplain sediments deposited since
the onset of mining in the early 1840's at Gold Hill are contaminated
above the PEC for both Cu and Hg. Although only 5% of the samples
are below the TEC for Cu, 38% of the samples are below the TEC for
Hg. The decay of metal concentrations with distance from the mine
source causes the percentage of samples above the PEC and below the
TEC to change downstream (Fig. 3). The highest contamination occurs
along Little Buffalo Creek where 51% of the samples exceed the PEC for
Hg and 57% of the samples exceed the PEC for Cu. Only 9% of the samples
in the upstream reach had concentrations below the TEC for Hg, while
none were below the TEC for Cu. A much lower percentage of the
samples below the confluence with Dutch Buffalo Creek exceed the PEC
for Hg (6%) and Cu (3%), while 54% of the samples were below the TEC
for Hg. This is likely explained by dilution because of inputs of
uncontaminated sediment from the upper portion of Dutch Buffalo
Creek. In contrast, only 7% of the samples are below the TEC for Cu
because background concentrations for Cu in this mineralized watershed
are above the TEC for Cu.
4.3. Vertical metal trends
Fig. 4 provides examples of vertical trends in Hg, Cu, and the sand
content in core profiles in overbank deposits at each of the transect
100
Mercury Concentrations
100
Above PEC
Below TEC
80
60
40
20
0
0
5
15
20
25
30
Copper Concentrations
100
Above PEC
Below TEC
80
60
40
20
0
0
5
10
15
20
25
Distance downstream (km)
30
Fig. 3. Downstream changes in the percentage of post-mining overbank sediment samples
with concentrations above the PEC and below the TEC for Hg and Cu. The arrow indicates
the location of the confluence of Little Buffalo Creek and Dutch Buffalo Creek.
sites. The down-core trends in sand and comparison of sand-normalized
concentrations (Horowitz, 1991) with total element concentrations
indicated that grain size exerts a minimal influence on metal trends in
the cores. Using metal concentrations as stratigraphic markers assumes
that there has been little post-depositional mobilization of the metals.
We checked for this by comparing down-core trends in several different
metals. Hudson-Edwards (1999) showed that the amount of down-core
metal migration was metal specific, with the more strongly bound metals
demonstrating the least mobility. We see similar down-core trends in
15
Max = 12.736e-0.09x
R² = 0.61; p = 0.013
10
Mean = 3.08e-0.119x
R² = 0.71; p = 0.005
Maximum
Mean
10
1
5
Mean = 25.04x-0.891
R² = 0.91; p = 0.001
0.1
10
100
0
0
1,000
1000
5
10
15
20
25
30
600
Max = 763.9e-0.077x
R² = 0.69; p = 0.006
Mean = 317.5e-0.137x
R² = 0.67; p = 0.007
500
Cu (mg/kg)
10
20
Max = 67.78x -0.695
R² = 0.83; p = 0.001
Hg (mg/kg)
Percentage of post-mining
samples
S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132
Percentage of post-mining
samples
126
100
400
300
Max = 2488x-0.538
R² = 0.76; p = 0.002
Mean = 2830x-0.977
R² = 0.77; p = 0.002
10
1
10
100
Drainage area (km2)
200
100
0
1,000
0
5
10
15
20
25
30
Distance downstream (km)
Fig. 2. Downstream trends in mean and maximum concentrations of Hg and Cu. The mean values were obtained by taking the weighted mean of each core at a given transect and then
calculating the mean of the weighted means for each site. The maximum represents the sample with the highest concentration in any core at each site. The arrow indicates the location of
the confluence of Little Buffalo Creek and Dutch Buffalo Creek.
S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132
0
Site LB5-35
Site LB3-13.4
Hg (mg/kg)
Hg (mg/kg)
2
4
6
8
0
10
2
4
6
8
Site LB6B
Hg (mg/kg)
10 12 14 16
0
0
127
0.0
0.5
1.0
1.5
2.0
2.5
0
50
50
50
Depth (cm)
100
150
100
100
200
150
150
Hg
Cu
% Sand
250
200
300
0
100
200
300
400
200
500
0
100
Cu (mg/kg)
0
20
40
200
60
80
100
0
20
40
3
4
0
50
100
60
80
0
100
20
6
0.0
7
0
50
0.1
0.2
200
250
300
40
60
80
100
0.4
0.5
Sand (%)
Site DB7B
Hg (mg/kg)
5
150
Cu (mg/kg)
Site DB4-52.3
Hg (mg/kg)
2
500
Sand (%)
Site LB2-48
1
400
Cu (mg/kg)
Sand (%)
0
300
Hg (mg/kg)
0.3
0.4
0.5
0.0
0
0
100
100
200
200
300
300
400
400
0.1
0.2
0.3
Depth (cm)
100
150
200
250
500
300
0
100
200
300
400
500
500
0
600
20
Cu (mg/kg)
0
20
40
40
60
80
0
100
20
0
50
40
60
80
100
0
20
1.6
0
50
0.0
0.5
1.0
1.5
150
200
40
60
80
100
2.0
2.5
Sand (%)
Site DB1-55
Hg (mg/kg)
Hg (mg/kg)
1.2
100
Cu (mg/kg)
Site DB5-24.5
Hg (mg/kg)
0.8
100
Sand (%)
Site DB2-50
0.4
80
Cu (mg/kg)
Sand (%)
0.0
60
2.0
0.0
2.5
0
0
100
100
200
200
300
300
400
400
0.5
1.0
1.5
Depth (cm)
100
150
200
250
500
300
0
20
40
60
80
100
500
0
20
Cu (mg/kg)
0
20
40
60
Sand (%)
40
60
80 100 120 140
0
20 40 60 80 100 120 140 160
Cu (mg/kg)
80
100
0
20
40
60
Cu (mg/kg)
80
100
0
Sand (%)
Fig. 4. Vertical trends in Cu and Hg at each of the floodplain transect sites.
20
40
60
Sand (%)
80
100
S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132
concentrations of Pb, Zn, Cu, and Hg, which suggests that these are
primary depositional signatures. Furthermore, vertical trends in geochemistry are similar both at a site and among sites, indicating the overall
control of physical transport on spatial and temporal variations for Hg and
Cu in the cores.
In most of the cores the initial upcore increase in the concentration
above background levels is abrupt for both Cu and Hg. Concentrations
of both Hg and Cu stabilize upcore at levels that are usually at least
several times greater than background concentrations. Although these
more recently deposited sediments are not nearly as contaminated as
those deposited earlier during the peak of mining activity, contamination
levels above background suggests that erosion of channel banks is
remobilizing metals that had been previously stored in floodplain
deposits. At the sites located upstream from the confluence with Dutch
Buffalo Creek, most of the cores show an initial increase in Cu at core
depths of 30–60 cm before the initial increase in Hg. This suggests that
early Au-mining activities and agricultural land disturbance of the
Au–Cu-mineralized landscape in the immediate vicinity of the Gold
Hill mining area was supplying Cu contaminated sediment to streams
prior to the widespread use of Hg amalgamation. The early increase in
Cu does not appear in sediment cores from lower Dutch Buffalo Creek
probably due to the combined influence of locally high storage rates in
floodplain deposits along Little Buffalo Creek and dilution by uncontaminated sediment loads from the larger drainage area of the
upper Dutch Buffalo Creek. The release of Cu continued as a by-product
of Au-mining activities that incorporated the use of Hg. In the remainder
of the cores, the initial peak in Hg generally occurs at the same depth
(1–3m) as the initial Cu peak, before decreasing rapidly upcore. Although
Fig. 4 suggests that the depth to peak Hg concentrations increases
downstream, the trend in all 42 cores is quite variable and depends on
factors such as floodplain elevation and distance from the channel. Most
of the cores display a second, more recent peak in Cu concentrations as
the focus of metal extraction shifted away from Au during a period of
Cu-mining at the Union Copper Mine (1899–1907) (Lecce et al., 2008).
4.4. Metal and sediment storage
The results of our sediment storage and Hg mass calculations show
that Eq. (1), which used multiple cores at each transect to estimate
metal storage, produces a Hg mass storage of 6.8 Mg over a floodplain
area of 4.9 km2. This value is bracketed on the low side using Eq. (2),
which ignores that portion of each floodplain transect lacking core
data, to produce a minimum estimate of floodplain Hg storage of
2.0 Mg. Eq. (3), where metal storage is estimated using a single nearchannel core at each site, produces a maximum estimate of floodplain
Hg mass storage of 11.7 Mg. Eq. (1) produced a Cu mass storage value
of 618Mg, bracketed by a minimum estimate of 173Mg and a maximum
estimate of 934Mg. Note that because background metal concentrations
were subtracted out, these calculations represent metal mass storage
attributed to mining activities.
We do not have any historical data on Cu production, but we can
compare our estimates of floodplain Hg mass storage to estimates of
Hg lost during the amalgamation process in order to assess the
importance of floodplains as a sink for Hg. A common rule of thumb
used to estimate Hg release is that 1.5kg of Hg was lost for every kilogram
of Ag or Au produced (Nriagu, 1994; Lacerda, 1997). However, this ratio
can vary from as low as 0.85 for impoverished ores to as high as 4.1 for
very rich ores (Nriagu, 1994). Although estimates of the amount of Hg
released to the environment can be difficult to obtain, Lacerda (1997)
showed that most emission factors (i.e., the amount of Hg emitted to
produce 1 kg of Au) fall between 1.0 and 2.0. We use these ratios
recognizing that the amalgamation methods from which Lacerda
(1997) obtained these values may have been different than those used
at Gold Hill. Based on Pardee and Park's (1948) estimates of the total
production of Au from the Gold Hill deposits between 1842 and 1935,
about 5 Mg of Au was produced. Assuming an emission factor of 1–2
(Lacerda, 1997), then about 5–10 Mg of Hg was lost to the environment.
In calculating annual Hg emissions from gold mining, Lacerda (1997)
also assumed that approximately 65% of the total Hg emissions go to
the atmosphere. This would reduce the Hg available to be stored in
floodplains to 1.8–3.5 Mg. Because estimates of Au production in North
Carolina were based only on Au received at the mint, and therefore
underestimate the true Au production by some unknown amount,
this offsets to some degree the amount lost to the atmosphere.
Notwithstanding the unknowns discussed above, these estimates
based on emission factors and historical accounts of Au production
compare favorably with our 6.8 Mg estimate based on core data from
floodplains at Gold Hill. Note that our core estimates do not account
for channel storage or transport out of the watershed.
Spatial variations in Hg and Cu mass storage reflect the interaction of
longitudinal trends of both metal concentrations and sediment mass
storage. The storage of overbank sediment on floodplains generally
increases downstream (Fig. 5), while the highest metal concentrations
occur close to the mines along Little Buffalo Creek (Fig. 2). Fig. 6
shows the downstream distribution of Hg and Cu storage. About 77%
of the 6.8 Mg of Hg mass storage occurs close to the mines along Little
Buffalo Creek. Average Hg mass values in this upstream reach are 3.5
times the average in lower Dutch Buffalo Creek. Similarly, about 75%
of the 618 Mg of mining-related Cu is stored in the upstream reach.
Thus, while only 32% of the historical sediment is stored along Little
Buffalo Creek, this upstream reach contains about 75% of the Hg and
Cu storage.
Just as most of the mining-related Hg and Cu are stored along Little
Buffalo Creek, comparison of metal concentrations to the sediment
toxicity guidelines of MacDonald et al. (2000) suggests that the threat
of harmful biological effects is also limited to the headwater portion of
the watershed closest to the mines. Although we have not sampled
downstream from site DB1, there is little reason to believe that elevated
Hg or Cu concentrations would be detectable. Dutch Buffalo Creek flows
into the much larger Rocky River b1 km downstream from DB1;
therefore, further dilution by uncontaminated sediment would likely
reduce concentrations to levels indistinguishable from background.
Thus, even though the most intensive Au mining in North Carolina took
place in the Gold Hill district, potentially harmful metal concentrations
are limited to a reasonably small area. Nevertheless, these deposits may
be remobilized through bank erosion processes and redistributed
downstream. Previous work on active channel sediments in this system
suggests that 9% are contaminated to levels that exceed the PEC for Hg,
and 30% are contaminated above the PEC for Cu (Pavlowsky et al., 2010).
Previous research suggests that it is reasonable to expect that
watershed-scale geomorphic factors exert some influence on sediment
storage, and therefore, metal storage. Sediment storage tends to increase
in wide valleys that favor deposition by decreasing flood depth, flow
velocity, and flood power, and decrease where cross-sectional stream
Overbank sediment storage
(Mg/m)
128
1,000
y = 71.59x 0.393
R² = 0.72; p = 0.004
100
10
100
1,000
Drainage area (km2)
Fig. 5. Downstream changes in post-mining floodplain sediment storage in Mg m−1 of
reach length. The arrow indicates the location of the confluence of Little Buffalo Creek
and Dutch Buffalo Creek.
S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132
Overbank Sediment Storage
(Mg/m)
Hg storage (kg/m)
10
y = 1.793x-0.498
R² = 0.62; p = 0.012
1
0.1
0.01
10
100
1,000
129
1,000
a
y = 2.98x 0.927
R² = 0.50
p = 0.033
Little Buffalo Cr.
Dutch Buffalo Cr.
100
100
1000
Valley Width (m)
100
10
1
10
100
1,000
Drainage area (km2)
Fig. 6. Downstream changes in post-mining floodplain Hg and Cu mass storage. Values
represent the total Hg and Cu mass storage along valley reaches centered on crossvalley transect locations. The arrow indicates the location of the confluence of Little Buffalo
Creek and Dutch Buffalo Creek.
power is high (Graf, 1983; Magilligan, 1985, 1992; Lecce, 1997). The
results shown in Figs. 7 and 8 are mixed. Although valley width explains
50% of the variance in overbank sediment storage, it is clear that much of
the unexplained variance is due to differences between the upper and
lower portions of the watershed where higher storage per unit valley
width is found downstream along Dutch Buffalo Creek (Fig. 7a).
While using data from the entire watershed would suggest a positive
relationship between stream power and sediment storage, Fig. 7b
shows the expected negative relationship (e.g., Lecce, 1997) along
Dutch Buffalo Creek, but an unexpected positive relationship in Little
Buffalo Creek. This positive relationship may be due to a combination
of factors that include basin shape and valley width. The two most
upstream sites have narrow floodplains and low values for stream
power. Low stream power values would typically tend to favor
deposition, but these narrow valleys lack the space to store sediment.
The resulting low storage values produce a positive relationship between
stream power and sediment storage in Little Buffalo Creek. Basin shape
also affects the difference observed between Little Buffalo Creek and
Dutch Buffalo Creek. The basin drained by Little Buffalo Creek is long
and narrow so that drainage area and stream power increase
dramatically at the confluence with Dutch Buffalo Creek. In addition to
the large influx of sediment from upper Dutch Buffalo Creek, the higher
discharges produce higher values for stream power along lower Dutch
Buffalo Creek.
Using stream power as a variable to predict metal storage also
produced mixed results. While cross-sectional stream power explains
72% of the variance Hg mass storage in the overbank deposits, the
relationship with Cu storage is not statistically significant (Fig. 8). This
result may be due to the difference between the timing and magnitude
of Hg and Cu contamination. Most of the Hg contamination was
associated with the active period of Au mining from 1842 to 1860.
Early Cu contamination was also associated with the active Au mining
period, but there was also a late period of Cu mining at the Union Copper
Mine from 1899 to 1906. This may make the dispersal and ultimate
Overbank Sediment Storage
(Mg/m)
y = 128.69x-0.586
R² = 0.35; p = 0.076
1,000
b
y = 37219x-0.61
R² = 0.72; p = 0.071
Little Buffalo Cr.
Dutch Buffalo Cr.
y = 11.702x0.5467
R² = 0.91; p = 0.046
100
50
500
Cross-sectional Stream Power (W/m)
Fig. 7. Relationship between overbank sediment storage and (a) valley width and
(b) stream power.
distribution of Cu more erratic, and therefore difficult to predict. Stream
power can also be an inadequate predictor variable for several reasons.
Although we implicitly assume that the modern values for power that we
calculated are representative of the magnitude and spatial distribution of
power throughout the historical period, stream power can change
through time. We also assume that bankfull stream power is an adequate
surrogate for overbank flows that deposit contaminated sediment, and
yet historical changes in channel/floodplain morphology can complicate
this relationship. Despite these difficulties, the relationship between
stream power and Hg mass storage is surprising strong.
4.5. Sedimentation rates
Sediment-borne contaminants such as Hg and Cu can be used to
provide information on historical rates of floodplain sedimentation
that may be otherwise difficult to measure in environments that often
1000
100
Hg or Cu (Mg)
Cu storage (kg/m)
1,000
Cu = 590.07x- 0.347
R² = 0.21; p = 0.235
10
1
Hg = 63.936x- 0.724
R² = 0.74; p = 0.003
0.1
Hg
Cu
0.01
50
500
Cross-sectional stream power (W/m)
Fig. 8. Relationship between cross-sectional stream power and Cu and Hg storage. The
solid symbols represent sites along Little Buffalo Creek and the open symbols represent
site along Dutch Buffalo Creek.
S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132
Table 2
Summary statistics for sedimentation rates.
Sedimentation rate (cm y−1)
Mean:
SD:
Range:
N:
1842–1856
1856–1899
1899–2007
1842–2007
2.7
1.3
1.1–7.2
30
0.9
0.5
0.5–1.6
7
1.0
0.4
0.4–1.6
7
0.9
0.4
0.4–1.7
30
Sedimentation rate (cm)
10
y = 2.796x-0.032
R² = 0.01
p = 0.674
1
y = 0.356x0.191
R² = 0.29
p = 0.002
1842-2007
1842-1856
0.1
1
10
100
1,000
Drainage area (km2)
Fig. 9. Downstream changes in sedimentation rates.
by drainage area (Ad). Fig. 10 shows that sedimentation rates decrease
with both distance from the channel and elevation above the channel
bed. In a multiple regression analysis Dc is a significant independent
variable (p = 0.001) while Ec is not significant (p = 0.159). The
significance of Dc, which explains 56% of the variance in the 1842–2007
sedimentation rates, supports much previous work showing decreased
vertical accretion with distance from the channel due to sediment
transfer by particle diffusion (e.g., Pizzuto, 1987; Walling and He, 1998;
Lecce and Pavlowsky, 2004; Wyżga and Ciszewski, 2010).
The average sedimentation rates in Table 2 are generally consistent
with those reported in the literature (Table 3). Although some studies
have reported that Holocene sedimentation rates may vary over a large
range (e.g., 0.02–0.65 cm y−1, from Ferring, 1986), Holocene rates are
frequently reported to be on the order of tenths of a millimeter per
year (e.g., Knox, 1987). Leigh and Webb (2006), for example, reported
Holocene rates of 0.03–0.08 cm y−1 in the nearby, but steeper terrain
1842-2007 sedimentation rate (cm)
lack datable material or buried soils (e.g., Leigh, 1994; Lecce et al., 2011).
Our reconnaissance study (Lecce et al., 2008) reported rates of vertical
accretion from near-channel and distal floodplain cores at the DB1 site
located at the basin outlet. The mean calculated sedimentation rate
during the most intense period of mining at Gold Hill (1842–1856) was
2.5 cm y−1, with a long-term (1842–2006) mean rate of 1.3 cm y−1.
Additional coring as part of this study provided the opportunity to further
refine rates of vertical accretion where a total of 30 cores had adequate
sampling intervals and metal signals to calculate sedimentation rates.
Using historical evidence (e.g., Knapp and Glass, 1999) we calculated
these rates assuming (i) that the initial rapid increase in Hg concentrations occurred in 1842, (ii) that peak Hg levels occurred in 1856
with the Au production peak, and (iii) that the second rapid increase in
Cu concentrations was associated with the opening of the Union Copper
Mine in 1899. We acknowledge that the 5–20 cm sampling interval
introduces error in the sedimentation rate calculations, but note that
the largest intervals of 20 cm were usually used on the deepest cores
(e.g., DB1-55) below where the initial contamination occurred.
Sedimentation rates during the period of most intense mining
(1842–1856) can vary considerably from core to core with a range of
1.1–7.2 cm y−1 (Table 2 and Fig. 9). The rates during this period of
intense mining are less reliable because of the short time period.
Nevertheless, the mean rate of 2.7 cm y−1 during this early period is
three times the long-term (1842–2007) rate of 0.9 cm y−1, suggesting
a higher level of landscape disturbance during this period. Rates can
also be less reliable where sampling intervals are large. For example,
at site LB6B (Fig. 2) the more recent Cu peak associated with the
operation of the Union Copper mine (1899–1907) is barely detectable;
thus we did not use it in our sedimentation rate calculations. However,
not only is it likely that a smaller sampling interval would have
produced a more distinct peak, the depth at which it occurred could
be located anywhere in the 10–37 cm depth range. Depending upon
where the peak actually occurred in this depth-integrated sample, the
1856–1899 sedimentation rate would vary from 0.5–1.2 cm y−1.
The long-term sedimentation rates are more consistent, as indicated
by the much smaller standard deviation (0.4 cm y−1), and increase
downstream as a power function of drainage area (Fig. 9). The downstream increase in long-term rates compared to the rates during the
mining period show a spatial and temporal lag effect where the locus of
deposition shifts downstream with time (e.g., Knox, 1987; Lecce and
Pavlowsky, 2001). This may suggest that upstream channels have become
enlarged and are now capable of containing and routing larger magnitude
flows downstream to lower capacity channels, leading to higher rates of
overbank sedimentation.
Variations in the sedimentation rates noted above may be influenced by factors such as inundation frequency, distance from the
channel, overbank flow patterns, floodplain topography, and vegetation
characteristics on the floodplain (Miller and Orbock Miller, 2007). We
assessed two of these factors, distance from the channel (Dc) and the
elevation above the channel bed (Eb). Because valley width increases
downstream, the locations of our cores tended to increase in distance
from the channel bank in the lower portions of the watershed. Likewise,
floodplain elevations relative the channel bed also increase systematically
downstream. In order to control for these trends, we by divided Dc and Eb
1842-2007 sedimentation rate (cm)
130
10
a
y = 0.512x-0.247
R² = 0.57
p = 0.001
1
0.1
0.01
0.1
1
10
10
y = 0.385x-0.226
R² = 0.26
p = 0.005
b
1
0.1
0.01
0.1
Eb/Ad (m/km2)
Fig. 10. Relationships between long-term sedimentation rates (1842–2007) and (a) crossvalley distance from the channel (Dc/Ad) and (b) elevation above the channel bed (Eb/Ad).
S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132
of the southern Blue Ridge Mountains of western North Carolina. In
contrast, historical rates are typically one to several orders of magnitude
greater than Holocene rates (e.g., 15 cm y−1; from Trimble and Lund,
1982). Long-term rates at Gold Hill are about twice those in the nearby
Cid district (Lecce et al., 2011) and southern Blue Ridge Mountains
(Leigh and Webb, 2006). Mean rates during the most intensive period
of mining at Gold Hill are similar to those in the Dahlonega Au belt of
northern Georgia, USA, where hydraulic mining was practiced (Leigh,
1994). Elsewhere in the eastern Piedmont, rates of about 1 cm y−1
(Bain and Brush, 2005; Jackson et al., 2005) are comparable to longterm average rates at Gold Hill.
5. Conclusions
Nineteenth century mining activities at Gold Hill introduced
substantial quantities of Au and Cu into the Little Buffalo Creek/Dutch
Buffalo Creek system. A large proportion (91% for Hg; 80% for Cu)
of the post-mining floodplain samples we collected had Hg and Cu
concentrations above our measured background concentrations.
Comparison of our post-mining floodplain sample concentrations with
sediment quality guidelines showed that about 21% are contaminated
above the PEC for Cu and for Hg. The highest contamination occurs
upstream along Little Buffalo Creek where 51% of the samples exceed
the PEC for Hg and 57% of the samples exceed the PEC for Cu, whereas
below the confluence with Dutch Buffalo Creek only 6% exceed the
PEC for Hg and 3% exceed the PEC for Cu.
Our most reliable estimates of mining-related metal mass storage
indicate that about 6.8 Mg of Hg and 619 Mg of Cu currently reside in
floodplain deposits within this watershed. Our estimate of 6.8 Mg of
Hg storage compares favorably with a 5–10 Mg estimate based on
emission factors and historical accounts of Au production. Although
the storage of post-mining overbank sediment on floodplains increases
downstream, the high metal concentrations in upstream reaches close
to the mines causes both Hg and Cu storage to decrease downstream.
About 77% of the 6.8Mg of Hg and 75% of the 618 Mg of Cu mass storage
occurs upstream from the confluence with Dutch Buffalo Creek where
average Hg mass values are 3.5 times the average in lower Dutch Buffalo
Creek. Efforts to explain sediment and metal storage using geomorphic
variables were only moderately successful. Valley width explained 50%
of the variance in overbank sediment storage and cross-sectional stream
power explained 74% of the variance in Hg mass storage. However,
the relationship between cross-sectional stream power and Cu storage
was not statistically significant. This result may be due to the more
complicated history of Cu dispersal and inadequacies with stream
power as an index that accurately reflects spatial patterns of deposition.
Basin-wide estimates of rates of floodplain sedimentation compare
favorably with a previous estimate at the outlet of the watershed
(Lecce et al., 2008). Long-term average rates of about 1 cm y−1 at Gold
Hill are comparable to those reported elsewhere in the eastern Piedmont
(Bain and Brush, 2005; Jackson et al., 2005). The mean rate of 2.7 cm y−1
Table 3
Regional sedimentation rates.
Location
Period
Sedimentation
rate (cm y−1)
Source
Gold Hill, NC
1842–2006
This study
Gold Hill, NC
1842–1856
Cid District, NC
1832–1880 to 2007
Southern Blue Ridge
Mtns., NC
Eastern Piedmont, ME
Historical
0.4–1.7
(mean = 0.9)
1.1–7.2
(mean = 2.7)
0.3–0.9
(mean = 0.5)
0.58–0.65
1820–1880
0.45–1.19
Dahlonega, GA
Piedmont, GA
1829–1880 to 1994
1820–2005
1–3
0.87
This study
Lecce et al. (2011)
Leigh and Webb
(2006)
Bain and Brush
(2005)
Leigh (1994)
Jackson et al. (2005)
131
during the most intensive period of Au mining at Gold Hill (1842–1856)
is similar to rates in the Dahlonega Au belt of north Georgia, USA, where
hydraulic mining was used (Leigh, 1994). Furthermore, rates during the
peak mining period at Gold Hill are three times the long-term
(1842–2007) rate of 0.9 cm y−1, suggesting that during this period the
combination of disruptive mining activities, the growth of the mining
population, and land-use change associated with agriculture significantly
increased the level of landscape disturbance and sediment production.
Long-term sedimentation rates decrease with distance from the channel
and elevation above the channel bed. The downstream increase in longterm rates may be indicative of a spatial and temporal lag effect where
the locus of deposition shifts downstream with time due to the
enlargement of upstream channels that route sediment downstream to
lower capacity channels that experience more frequent overbank flows
(e.g., Knox, 1987; Lecce and Pavlowsky, 2001).
Although recently deposited sediments at the floodplain surface
contain metal concentrations that are much lower than those deposited
during the mining period, these sediments remain contaminated above
background concentrations. This study shows that the likely source of
these contaminants is the upstream part of the watershed that contains
floodplain sediments with high Cu and Hg concentrations that were
stored during the period of active mining. These legacy deposits are
remobilized primarily by bank erosion. As such, contamination from
this period of Au mining is a continuing and dynamic process.
Acknowledgments
This research was funded through a grant from the National
Geographic Society (# 8175–07), a Research Development Grant at
East Carolina University, and the Ozarks Environmental and Water
Resources Institute. We wish to thank Marc Owen, Derek Martin,
Gwenda Bassett, Stacey Armstrong, Mark Gossard, David Shaeffer, Erin
Hutchinson, Bailey Pearson, Tim Nipper, Matt Peters, Johnny Odell,
Jesse Brass, and Rebecca Dodd for their help in the field and lab.
Comments from four anonymous reviewers greatly improved the
manuscript.
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