Geomorphology 206 (2014) 122–132

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Floodplain storage of sediment contaminated by mercury and copper fromhistoric gold mining at Gold Hill, North Carolina, USA

Scott A. Lecce a,⁎, Robert T. Pavlowsky b

a Department of Geography, Planning, and Environment, East Carolina University, NC 27858, USAb Department of Geography, Geology, and Planning, Missouri State University, Springfield, MO 65804, USA

⁎ Corresponding author. Tel.: +1 252 328 1047; fax: +E-mail address: lecces@ecu.edu (S.A. Lecce).

0169-555X/$ – see front matter © 2013 Elsevier B.V. All rhttp://dx.doi.org/10.1016/j.geomorph.2013.10.004

a b s t r a c t

a r t i c l e i n f o

Article history:Received 31 July 2012Received in revised form 16 August 2013Accepted 2 October 2013Available online 10 October 2013

Keywords:MercuryGold miningFloodplainsTrace metalsNorth Carolina

Previous research on the environmental consequences of mining has shown that metal contaminants can havelong-lasting impacts on water quality and aquatic ecosystems because of the remobilization of sediment-associated contaminants that have been stored in floodplains. We examined the magnitude and distribution ofmercury (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 metalconcentrations in overbank deposits with sediment quality guidelines indicates that overall about 21% arecontaminated above the probable effect concentration (PEC; above which adverse effects are expected to occurmore often than not) for both Cu and Hg. The highest contamination occurs upstream near the mine sourcewhere 51% of the samples exceed the PEC for Hg and 57% exceed the PEC for Cu. Of the three different methodsused to estimate metal mass storage, the most reliable estimate suggests that about 6.8Mg of Hg and 619Mg ofCu currently reside in floodplain deposits within this watershed. Although overbank sediment storage increasesdownstream and with valley width, about 75% of the Hg and Cu mass are stored in the upstream portion of thewatershed. Hg mass storage displays a strong negative relationship with cross-sectional stream power, but therelationship between Cu mass storage and stream power is insignificant. We used vertical changes in overbankmetal concentrations and the mining history to estimate a mean sedimentation rate of 2.7 cm y−1 during themost 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-termaverage rates at GoldHill are comparable to those reported elsewhere in the easternPiedmont. The downstream increase in long-term rates may indicate a spatial and temporal lag effect where thelocus of deposition shifts downstream with time.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Contamination of sediment by tracemetals poses significant threatsto water quality, aquatic ecosystems, and human health. Numerousstudies have shown that floodplains in historically mined watershedscan 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 andŽibret, 2011; Castro-Larragoitia et al., 2013). Although floodplains canstore contaminants for hundreds to thousands of years, these sedimentsinks remain dynamic because metals can be remobilized from storagethrough processes such as bank erosion and redistributed downstreamto be returned to storage in floodplain and channel deposits (Rang andSchouten, 1989; Bradley and Cox, 1990; Macklin and Klimek, 1992;Lecce and Pavlowsky, 1997, 2001; Bird et al., 2010; Łokas et al., 2010).Consequently, pastmining activities can cause tracemetal contaminationproblems in river systems that persist long after mining activities have

1 252 328 6054.

ights reserved.

ceased (e.g., Miller, 1997; Walling et al., 2003; Macklin et al., 2006),with present-day contamination primarily controlled by the physicalrelease of metal-contaminated sediments by bank erosion and masswasting. This paper investigates the magnitude and distribution ofmetal contamination of floodplain deposits associated with nineteenthcentury Au-mining activities in the Gold Hill mining district, NorthCarolina. Previous studies have documented the overall geographicextent and sediment-metal relationships in fluvial sediments withinthe Dutch Buffalo watershed (Lecce et al., 2008; Pavlowsky et al.,2010). This contribution focuses on understanding the downstreamvariations of themass storage of sediment and trace metals in floodplainsinks since the early 1800's and the potential for current and futurelegacy contamination.

Mercury (Hg) has been used to amalgamate gold (Au) for thousandsof years (Nriagu, 1994;Malm, 1998). Even today, Aumining is responsiblefor about 10% of global anthropogenic Hg emissions, and most of the Hgreleased by Au and silver (Ag) mining during the last 500years may stillbe available to the global Hg cycle through the remobilization fromabandoned tailings and contaminated deposits (Lacerda, 1997). Theenvironmental impacts of Hg use in Au-mining operations have beenwell documented in the literature (e.g., Leigh, 1994, 1997; Malm, 1998;

123S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132

Miller et al., 1998, 2003). During mineral ore grinding and amalgamationprocessing atmill sites, Hg is released to the environmentwhere it can besorbed to tailings particles or sediments that act as the primary reservoirof Hg in freshwater systems (Ullrich et al., 2001). This Hg can be trans-formed by microbial processes in aquatic sediments into its more toxicorganic form, methylmercury (MeHg), which can bioaccumulate andbiomagnify in organisms near the top of the food chain (Gilmour et al.,1992; Holmes and Lean, 2006; Guentzel et al., 2007). Understanding thebehavior of Hg in environmental systems is of great interest becausethis element is toxic at concentrations that are commonly one or twoorders of magnitude lower than other toxic metals (MacDonald et al.,2000). The main source of human exposure to this potent neurotoxin isthe consumption of Hg-contaminated fish, which has led to worldwideadvisories for fish consumption (NAS, 2000; Clarkson, 2002; Guentzelet al., 2007).

Spatial variations of sediment and contaminant storage have beenexplained by a variety of factors such as particle size, valley width,distance from the source, lateral distance from the channel, floodflow hydraulics, the frequency of floodplain inundation, floodplaintopography, and stream power (Lecce and Pavlowsky, 1997; Wallingand He, 1998; Owens et al., 2001; Walling et al., 2003; Miller andOrbock Miller, 2007; Wyżga and Ciszewski, 2010). A variety of methodshave been used to determine how storage changes through time. Geo-chemical profiles in floodplain cores can be used as stratigraphic tracersto 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 tracershave also been used in a fingerprinting approach to determine sourcesand dispersal pathways for contaminated sediment (Hudson-Edwardset al., 1999; Miller et al., 2002, 2005; Villarroel et al., 2006; Miller andOrbock Miller, 2007).

Estimating metal storage in watersheds is important for severalreasons. Floodplain cores in contaminated watersheds can be usefulfor documenting the history of pollution, estimating sediment deliveryratios for polluted sediment, and evaluating the long-term storage andultimate fate of contaminants (Ongley, 1987; Bradley, 1989). Thesecontaminant profiles can also be used as tracers to understand patternsof sediment transport and deposition (Knox, 1987; Miller, 1997; Lecceand Pavlowsky, 2001; Miller and Orbock Miller, 2007). Metal storageestimates can further be used to evaluate the effectiveness of alluvialstorage at attenuating pollutant transport to downstream areas.Understanding themagnitude and spatial distribution ofmetal pollutantswithin alluvial deposits, and where they exceed toxic limits, can assistmanagers in assessing how long contaminants reside in watersheds,identifying hot spots, and focusing future monitoring efforts (Denniset al., 2009).

Gold was first discovered in the USA in 1799 in the Piedmont ofNorth Carolina, USA. Although exploration spread slowly throughoutthe southern Piedmont in a belt that stretched fromVirginia toAlabama,North Carolina producedmore Au than any other state in the South andled the nation in Au production until 1848 (Knapp and Glass, 1999).Although the exact date that Hg amalgamation was first used in NorthCarolina is uncertain, historical accounts suggest that large amountswere being used at Gold Hill in southern Rowan County (northeast ofCharlotte, NC) by the mid-1840's (Knapp and Glass, 1999). In areconnaissance study of Hg contamination in the Dutch Buffalo Creekwatershed, Lecce et al. (2008) showed that a strong Cu and Hg miningsignal can be detected in overbank sediments at the basin outlet 25 kmdownstream from the mines at Gold Hill. Pavlowsky et al. (2010) alsofound high Hg concentrations in active channel sediments even thoughterrestrial tailings sources at mining sites have been removed, whichsuggests that metals are being remobilized from overbank storage. Inanother study of potential Hg contamination in the nearby Cid miningdistrict (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.

Thus, given that mining operations at Gold Hill were more intense thananywhere else in North Carolina (Knapp andGlass, 1999), thefloodplainsdownstream fromGold Hill will likely contain the highestmining-relatedHg concentrations in the state. The purpose of this paper is to focus on themagnitude anddistribution ofmetal contaminationof historical overbankdeposits in the Gold Hill mining district. The primary objectives are(i) to evaluate the degree of contamination by comparing metalconcentrations to sediment toxicity guidelines, (ii) to determine thedistribution of overbank metal storage and assess geomorphic controlson that distribution, and (iii) to use particle-bound metals as tracers toestimate historical rates of floodplain sedimentation.

2. Study area

The Gold Hill mining district is located in the gently rollingtopography (140–270 m amsl) of the Piedmont of North Carolina insouthern Rowan and northern Cabarrus Counties (Fig. 1). This areawas first settled by Europeans between 1730 and 1740 (Merrens,1964; Powell and Lefler, 1973).When the intensive Au-mining activitiesbegan in 1842, this area was occupied primarily by subsistence farmers(Knapp and Glass, 1999). Carpenter (1976) mapped the locations of 13mines in the Gold Hill district (Fig. 1), all in close proximity to theheadwaters of Little Buffalo Creek (39 km2), which is tributary toDutch Buffalo Creek (254 km2). The upper portion of Dutch BuffaloCreek (above the confluence with Little Buffalo Creek) does not containanymines. Little Buffalo Creek andDutch Buffalo Creek are low sinuositymeandering streams with pool-riffle morphology and channel bedsconsisting primarily of sand, gravel and cobbles. Bedrock is frequentlyexposed in the channel in the west to east-flowing section of DutchBuffalo 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 brownto yellowish-brown vertical accretion sediments (sands and silts) thatoverlie channel bed gravels. Floodplain surfaces range from 1.5–3 mabove the channel bed along Little Buffalo Creek and 3–4m above thechannel bed along Dutch Buffalo Creek.

The watershed is underlain by two Au-rich units separated by theSW–NE trending Gold Hill fault, which trends approximately alongLittle Buffalo Creek and the lower portion of Dutch Buffalo Creek. Theigneous intrusive rocks of the Charlotte belt lie to the west, with themetavolcanic and metasedimentary rocks of the Carolina slate belt tothe east (Pardee and Park, 1948; Carpenter, 1976; Knapp and Glass,1999). Most of the Au-bearing ore deposits occur in chlorite–sericitephyllite to the east of the fault line (Carpenter, 1976). Early miningpractices were quite crude and focused on excavations above thewater table. After the discovery of the primary Au veins in 1842 (Nitzeand Wilkins, 1897), mercury was used in Chilean mills and log rockersto amalgamate the Au (Knapp and Glass, 1999). By the mid-1850's thelarger mining companies were using steam to power their Cornishpumps and Chileanmills (Knapp andGlass, 1999).Mining in the districtcontinued until about 1915, with peak production for Au in 1856. Inaddition to Au, the Gold Hill district produced a significant amount ofcopper (Cu) (Pardee and Park, 1948). Ores rich in Cu-sulfides wereencountered as the mines became deeper. These ores had generallybeen ignored as too difficult to process by amalgamation, but as Auproduction declined in the 1880's interest in Cu associated with sulfideores increased (Laney, 1910; Knapp andGlass, 1999). The Union CopperMine was opened in 1899 and produced large amounts of Cu until itsclosure in 1906. See Lecce et al. (2008) for a more complete descriptionof the geological setting and mining history of the study area.

3. Methods

Background metal concentrations were assessed by collecting soiland 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

Fig. 1.Map of the Dutch Buffalo Creek watershed showing the location of the mines and floodplain transect sites.

124 S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132

ore processing activities in the watershed. A total of 42 floodplain coreswere collected to depths ranging from 78 to 517cm 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 samplesfrom the cores in depth intervals that ranged from 5 cm to 20 cm inthickness, depending on the stratigraphy. These floodplain sites wereselected where roads provided access to the floodplain.

Sample preparation and physical analyses were performed at theWater and Soil Laboratory of the Ozarks Environmental and WaterResources Institute at Missouri State University. Sediment sampleswere oven-dried at 60 °C, disaggregated with mortar and pestle, andpassed through a 2-mm sieve. Although many studies focus on theb63 μm size fraction, we selected the b2 mm fraction because inmined watersheds sand-sized ore particles and Fe–Mn coatings onsand grains may contain significant amounts of metal (Horowitz,1991). Geochemical analyses were conducted on the b 2 mm fractionby a commercial laboratory (ALS Chemex, Sparks, NV, USA) where thesamples were prepared using an aqua-regia extraction (hot 3:1 nitric–

hydrochloric acids) (Mudroch et al., 1997). Mercury levels weredetermined using cold vapor atomic absorption spectroscopy (AA),while Cu (as part of a 33 element package) was analyzed by inductivelycoupled plasma–atomic emission spectroscopy (ICP–AES). Althoughseveral elements are elevated above background concentrations(i.e., Pb, Zn), we focused onHg andCubecause they showed the highestcontamination levels and were specifically targeted for extraction bythe mining operations. Lower detection limits are 0.01 mg/kg for Hgand 1 mg/kg for Cu. ALS Chemex follows quality control proceduresinvolving both batch check standards and duplicate analyses on every10–20 samples. Duplicate sample analyses are typically within 10%.We have also submitted triplicate geochemical standard materialsavailable from the U.S. Geological Survey to ALS Chemex and foundthat values are within 15%.

A number of different methods have been used to calculate metalbudgets in fluvial systems (Dennis et al., 2009). Each of the methodsemployed to estimate metal storage in floodplains necessarily involvesa number of simplifying assumptions that affect the accuracy of the

Table 1Background concentrations.

Cu (mg kg−1) Hg (mg kg−1)

All (n= 63)Mean 46 0.06Median 40 0.05SD 31 0.04Range 3–144 0.01–0.23

Surface Soils (n=34)Mean 43 0.07Median 38 0.06SD 31 0.04Range 8–144 0.03–0.23

Subsurface Soils (n=29)Mean 51 0.05Median 50 0.05SD 31 0.03Range 3–125 0.01–0.13Mean, Davidson Countya 22 0.04Mean, global for soilb 25 0.05Mean, Cid Districtc 34 0.05

a Mean value (N=9) reported in theNational Geochemical Survey for Cabarrus County,North Carolina (USGS, 2004).

b Reimann and Caritat (1998).c Lecce et al. (2011).

125S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132

estimates. Because we did not have access to the entire width of thefloodplain at most of the sites, we did not collect cores across the entirecross-valley transect. Therefore, we calculated themass of contaminateddeposits as:

M ¼ Afpρb1nΣn

i¼1Di

1nΣn

i¼1Ci ð1Þ

where M is the metal mass at each site (kg), Afp is the floodplain areabetween each adjacent site (km2), ρb is the average bulk density offloodplain soils in the watershed (1.5 g cm−3, from McCachren andWoody, 2004), n is the number of cores at each transect, D is the depththat 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 forRowan and Cabarrus Counties (Stephens, 1988; McCachren and Woody,2004). The area associated with each site was determined by dividingthe distance between sites in half and including the area closest to thesite in the calculation. This approach assumes that the distal portions ofthe floodplain (which were seldom cored) contain similar metalconcentrations and contaminated depths as the means obtained fromthe cored portion of each transect. Because our data at sites with severalcores across the floodplain suggest that distal floodplain deposits thatwere not cored are likely thinner and have lower metal concentrations,we believe that Eq. (1) probably overestimates the true metal mass tosome degree. Therefore, we also calculated minimum and maximumestimates ofmetalmass storage toprovide somecontext for the estimatesobtained from Eq. (1):

Mmin ¼ L Axsρb1nΣn

i¼1Ci ð2Þ

whereMmin is the minimum estimate of metal mass at each site (kg), L isthe longitudinal distance between each site (km), and Axs is the cross-sectional area of the transect that was contaminated above backgroundconcentrations (kg). The longitudinal distance associated with each sitewas determined by bisecting the distance between sites. Eq. (2) providesan estimate of storage only for that portion of the floodplain where weobtained metal concentrations from cores, which ignored the distalportions of each transect lacking core data. Therefore, because these distalareas are likely to contain contaminated sediments, Eq. (2) produced aminimum estimate.

We also calculated a maximum estimate of metal storage using thedepth of contamination of the one core at each site with the most welldefined and abrupt initial peak metal concentration:

Mmax ¼ D AfpρbC 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 storagebecause the core selected at each site tended to be relatively deep, closeto the channel, and more highly contaminated. Nevertheless, thisrepresents the type of sampling that is often used where it is onlyfeasible to collect a single core at each transect location.

We used estimates of valley width and stream power to quantifygeomorphic controls on sedimentation and metal storage (Lecce, 1997).Valleywidthwas obtained from floodplain soils mapped on the CabarrusCounty soil survey. Because the width of floodplain soils was quitevariable at the reach scale and therefore sensitive to the exact locationof the transects, we estimated an average width over a downvalleydistance of 500 m centered on each transit. Cross-sectional streampower has been used as an index of the sediment-transporting capacityof the stream flow (Rhoads, 1987):

Ω ¼ γQS ð4Þ

where Ω is power per unit length (W/m), γ is the specific weight ofwater (9810 N m−3), Q is discharge (m3 s−1), and S is the energygradient (m m−1) approximated by field surveys of the channel bed

slope. The bankfull discharge was estimated using Harman et al.'s(1999) equation developed for North Carolina Piedmont streams.

4. Results

4.1. Background concentrations

Table 1 summarizes metal concentrations for soils in the Gold Hilldistrict that were not contaminated by mining activities. The meanbackground concentration is 0.06 mg kg−1 for Hg and 46 mg kg−1 forCu. The mean Hg concentrations is similar to background levels foundglobally in soils (Reimann andCaritat, 1998), in theNational GeochemicalSurvey for Cabarrus County (USGS, 2004), and by Lecce et al. (2011) inthe Cidmining district in nearbyDavidson County. Themean backgroundCu concentration is 12–24 mg kg−1 higher than these other referencelevels due to the presence of ore bodies in this watershed that areenriched in Cu sulfides. Concentrations of Hg in surface soils are slightlyhigher than subsurface soils, probably because of recent airborne inputs.We also used the iterative 2σ technique (Matschullat et al., 2000) toprovide the likely upper limit on background values. This procedureessentially eliminates extreme values to obtain a normal distributionaround the modal value of the original data set (Zglobicki et al., 2011),producing values of 69mgkg−1 for Cu and 0.09mgkg−1 for Hg.

4.2. Downstream contamination trends

A large proportion of the post-mining overbank sediment sampleswe collected had Hg and Cu concentrations above our measuredbackground concentrations in Table 1 (91% for Hg; 80% for Cu) andabove thresholds identified using the iterative 2σ technique (88% forHg; 44% for Cu). Weighted mean concentrations of Cu and Hg decreasedownstream from the primary mine sources in the headwaters of LittleBuffalo Creek (Fig. 2). This trend is typical of rivers contaminated bypoint sources of contamination (Miller and Orbock Miller, 2007).Because the upper portion of the Dutch Buffalo Creek watershed wasunmined and has a drainage area about three times as large as LittleBuffalo Creek, it contributes a significant amount of uncontaminatedsediment below the confluencewith Little Buffalo Creek. This is reflectedin metal concentrations in the lower portion of Dutch Buffalo Creekwhere mean Hg concentrations are about six times lower and Cu threetimes lower than those upstream along Little Buffalo Creek.

0

20

40

60

80

100

0 5 10 15 20 25 30P

erce

nta

ge

of

po

st-m

inin

g

sam

ple

sDistance downstream (km)

Copper Concentrations

Above PEC

Below TEC

0

20

40

60

80

100

0 5 10 15 20 25 30

Per

cen

tag

e o

f p

ost

-min

ing

sa

mp

les

Mercury ConcentrationsAbove PEC

Below TEC

Fig. 3.Downstreamchanges in the percentage of post-mining overbank sediment sampleswith concentrations above the PEC and below the TEC for Hg and Cu. The arrow indicatesthe location of the confluence of Little Buffalo Creek and Dutch Buffalo Creek.

126 S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132

We assessed the overall magnitude of the contamination by com-paring our metal concentrations in post-mining floodplain deposits toenvironmental contamination standards. In order to provide an ac-curate basis for predicting the presence or absence of sediment toxicity,MacDonald et al. (2000) developed two numerical sediment qualityguidelines for freshwater ecosystems: the threshold effect concentration(TEC; below which adverse effects are not expected to occur) and theprobable effect concentration (PEC; above which adverse effectsare expected to occur more often than not). These guidelines areconsensus-based values derived frommultiple previously conductedstudies. The guidelines we report are the consensus-based value alongwith the range of values from which it was derived. The PEC for Hg is1.06 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

(0.15–0.2mgkg−1), and the TEC for Cu is 32mgkg−1 (16–70mgkg−1).Note that the TEC for Cu is lower than the mean background Cuconcentration (46mgkg−1) in the Dutch Buffalo Creek watershed.

About 21% of our samples fromfloodplain sediments deposited sincethe onset of mining in the early 1840's at Gold Hill are contaminatedabove the PEC for both Cu and Hg. Although only 5% of the samplesare below the TEC for Cu, 38% of the samples are below the TEC forHg. The decay of metal concentrations with distance from the minesource causes the percentage of samples above the PEC and below theTEC to change downstream (Fig. 3). The highest contamination occursalong Little Buffalo Creek where 51% of the samples exceed the PEC forHg and57% of the samples exceed the PEC for Cu. Only 9% of the samplesin the upstream reach had concentrations below the TEC for Hg, whilenone were below the TEC for Cu. A much lower percentage of thesamples below the confluence with Dutch Buffalo Creek exceed the PECfor Hg (6%) and Cu (3%), while 54% of the samples were below the TECfor Hg. This is likely explained by dilution because of inputs ofuncontaminated sediment from the upper portion of Dutch BuffaloCreek. In contrast, only 7% of the samples are below the TEC for Cubecause background concentrations for Cu in thismineralizedwatershedare above the TEC for Cu.

4.3. Vertical metal trends

Fig. 4 provides examples of vertical trends in Hg, Cu, and the sandcontent in core profiles in overbank deposits at each of the transect

Max = 67.78x-0.695

R² = 0.83; p = 0.001

Mean = 25.04x-0.891

R² = 0.91; p = 0.0010.1

1

10

100

10 100 1,000

Hg

(m

g/k

g)

Maximum

Mean

Max = 2488x-0.538

R² = 0.76; p = 0.002Mean = 2830x-0.977

R² = 0.77; p = 0.0021

10

100

1000

10 100 1,000

Cu

(m

g/k

g)

Drainage area (km2)

Fig. 2. Downstream trends in mean and maximum concentrations of Hg and Cu. The mean valcalculating themean of the weightedmeans for each site. Themaximum represents the samplethe confluence of Little Buffalo Creek and Dutch Buffalo Creek.

sites. The down-core trends in sand and comparison of sand-normalizedconcentrations (Horowitz, 1991) with total element concentrationsindicated that grain size exerts a minimal influence on metal trends inthe cores. Using metal concentrations as stratigraphic markers assumesthat there has been little post-depositional mobilization of the metals.We checked for this by comparing down-core trends in several differentmetals. Hudson-Edwards (1999) showed that the amount of down-coremetalmigrationwasmetal specific, with themore strongly boundmetalsdemonstrating the least mobility. We see similar down-core trends in

Max = 12.736e-0.09x

R² = 0.61; p = 0.013

Mean = 3.08e-0.119x

R² = 0.71; p = 0.005

0

5

10

15

20

0 5 10 15 20 25 30

Max = 763.9e-0.077x

R² = 0.69; p = 0.006Mean = 317.5e-0.137x

R² = 0.67; p = 0.007

0

100

200

300

400

500

600

0 5 10 15 20 25 30

Distance downstream (km)

ues were obtained by taking the weighted mean of each core at a given transect and thenwith the highest concentration in any core at each site. The arrow indicates the location of

Site LB5-35Hg (mg/kg)

Dep

th (

cm)

0

50

100

150

200

Cu (mg/kg)

Sand (%)

Hg

Cu

% Sand

Site LB3-13.4Hg (mg/kg)

0

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250

300

Cu (mg/kg)

Sand (%)

Site LB6BHg (mg/kg)

0

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Cu (mg/kg)

Sand (%)

Site LB2-48Hg (mg/kg)

Dep

th (

cm)

0

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150

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250

300

Cu (mg/kg)

Sand (%)

Site DB7BHg (mg/kg)

0

100

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400

500

Cu (mg/kg)

Sand (%)

Site DB2-50Hg (mg/kg)

Dep

th (

cm)

0

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250

300

Cu (mg/kg)

Sand (%)

Site DB5-24.5Hg (mg/kg)

0

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Cu (mg/kg)

Sand (%)

Site DB1-55Hg (mg/kg)

0

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Cu (mg/kg)

Sand (%)

Site DB4-52.3Hg (mg/kg)

0

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500

Cu (mg/kg)

Sand (%)

0 2 4 6 8 10

0 100 200 300 400 500

0 20 40 60 80 100

0 2 4 6 8 10 12 14 16

0 100 200 300 400 500

0 20 40 60 80 100

0.0 0.5 1.0 1.5 2.0 2.5

0 50 100 150 200 250 300

0 20 40 60 80 100

2 3 40 1 5 6 7

0 100 200 300 400 500 600

0 20 40 60 80 100

0.0 0.1 0.2 0.3 0.4 0.5

0 50 100 150 200

0 20 40 60 80 100

0.0 0.4 0.8 1.2 1.6

0 20 40 60 80 100

0 20 40 60 80 100

0.0 0.5 1.0 1.5 2.0 2.5

0 20 40 60 80 100 120 140

0 20 40 60 80 100

0.0 0.5 1.0 1.5 2.0 2.5

0 20 40 60 80 100 120 140 160

0 20 40 60 80 100

0.0 0.1 0.2 0.3 0.4 0.5

0 20 40 60 80 100

0 20 40 60 80 100

Fig. 4. Vertical trends in Cu and Hg at each of the floodplain transect sites.

127S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132

y = 71.59x0.393

R² = 0.72; p = 0.004

100

1,000

10 100 1,000Ove

rban

k se

dim

ent

sto

rag

e (M

g/m

)

Drainage area (km2)

Fig. 5. Downstream changes in post-mining floodplain sediment storage in Mg m−1 ofreach length. The arrow indicates the location of the confluence of Little Buffalo Creekand Dutch Buffalo Creek.

128 S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132

concentrations of Pb, Zn, Cu, and Hg, which suggests that these areprimary depositional signatures. Furthermore, vertical trends in geo-chemistry are similar both at a site and among sites, indicating the overallcontrol of physical transport on spatial and temporal variations forHg andCu in the cores.

In most of the cores the initial upcore increase in the concentrationabove background levels is abrupt for both Cu and Hg. Concentrationsof both Hg and Cu stabilize upcore at levels that are usually at leastseveral times greater than background concentrations. Although thesemore recently deposited sediments are not nearly as contaminated asthose deposited earlier during the peak ofmining activity, contaminationlevels above background suggests that erosion of channel banks isremobilizing metals that had been previously stored in floodplaindeposits. At the sites located upstream from the confluence with DutchBuffalo Creek, most of the cores show an initial increase in Cu at coredepths of 30–60 cm before the initial increase in Hg. This suggests thatearly Au-mining activities and agricultural land disturbance of theAu–Cu-mineralized landscape in the immediate vicinity of the GoldHill mining area was supplying Cu contaminated sediment to streamsprior to the widespread use of Hg amalgamation. The early increase inCu does not appear in sediment cores from lower Dutch Buffalo Creekprobably due to the combined influence of locally high storage rates infloodplain deposits along Little Buffalo Creek and dilution by un-contaminated sediment loads from the larger drainage area of theupper Dutch Buffalo Creek. The release of Cu continued as a by-productof Au-mining activities that incorporated the use of Hg. In the remainderof the cores, the initial peak in Hg generally occurs at the same depth(1–3m) as the initial Cu peak, before decreasing rapidly upcore. AlthoughFig. 4 suggests that the depth to peak Hg concentrations increasesdownstream, the trend in all 42 cores is quite variable and depends onfactors such as floodplain elevation and distance from the channel. Mostof the cores display a second, more recent peak in Cu concentrations asthe focus of metal extraction shifted away from Au during a period ofCu-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 showthat Eq. (1), which used multiple cores at each transect to estimatemetal storage, produces a Hg mass storage of 6.8Mg over a floodplainarea of 4.9 km2. This value is bracketed on the low side using Eq. (2),which ignores that portion of each floodplain transect lacking coredata, to produce a minimum estimate of floodplain Hg storage of2.0 Mg. Eq. (3), where metal storage is estimated using a single near-channel core at each site, produces a maximum estimate of floodplainHg mass storage of 11.7Mg. Eq. (1) produced a Cu mass storage valueof 618Mg, bracketed by aminimumestimate of 173Mg and amaximumestimate of 934Mg. Note that because backgroundmetal concentrationswere subtracted out, these calculations represent metal mass storageattributed to mining activities.

We do not have any historical data on Cu production, but we cancompare our estimates of floodplain Hg mass storage to estimates ofHg lost during the amalgamation process in order to assess theimportance of floodplains as a sink for Hg. A common rule of thumbused to estimateHg release is that 1.5kg of Hgwas lost for every kilogramof Ag or Au produced (Nriagu, 1994; Lacerda, 1997). However, this ratiocan vary from as low as 0.85 for impoverished ores to as high as 4.1 forvery rich ores (Nriagu, 1994). Although estimates of the amount of Hgreleased to the environment can be difficult to obtain, Lacerda (1997)showed that most emission factors (i.e., the amount of Hg emitted toproduce 1 kg of Au) fall between 1.0 and 2.0. We use these ratiosrecognizing that the amalgamation methods from which Lacerda(1997) obtained these values may have been different than those usedat Gold Hill. Based on Pardee and Park's (1948) estimates of the totalproduction 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 5–10Mg 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 tothe atmosphere. This would reduce the Hg available to be stored infloodplains to 1.8–3.5Mg. Because estimates of Au production in NorthCarolina were based only on Au received at the mint, and thereforeunderestimate 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 estimatesbased on emission factors and historical accounts of Au productioncompare favorably with our 6.8Mg estimate based on core data fromfloodplains at Gold Hill. Note that our core estimates do not accountfor channel storage or transport out of the watershed.

Spatial variations in Hg andCumass storage reflect the interaction oflongitudinal trends of both metal concentrations and sediment massstorage. The storage of overbank sediment on floodplains generallyincreases downstream (Fig. 5), while the highest metal concentrationsoccur close to the mines along Little Buffalo Creek (Fig. 2). Fig. 6shows the downstream distribution of Hg and Cu storage. About 77%of the 6.8Mg of Hg mass storage occurs close to the mines along LittleBuffalo Creek. Average Hg mass values in this upstream reach are 3.5times 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 LittleBuffalo Creek, this upstream reach contains about 75% of the Hg andCu storage.

Just as most of the mining-related Hg and Cu are stored along LittleBuffalo Creek, comparison of metal concentrations to the sedimenttoxicity guidelines of MacDonald et al. (2000) suggests that the threatof harmful biological effects is also limited to the headwater portion ofthe watershed closest to the mines. Although we have not sampleddownstream from site DB1, there is little reason to believe that elevatedHg or Cu concentrationswould be detectable. Dutch Buffalo Creek flowsinto the much larger Rocky River b1 km downstream from DB1;therefore, further dilution by uncontaminated sediment would likelyreduce concentrations to levels indistinguishable from background.Thus, even though the most intensive Au mining in North Carolina tookplace in the Gold Hill district, potentially harmful metal concentrationsare limited to a reasonably small area. Nevertheless, these deposits maybe remobilized through bank erosion processes and redistributeddownstream. Previous work on active channel sediments in this systemsuggests 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 thatwatershed-scale geomorphic factors exert some influence on sedimentstorage, and therefore,metal storage. Sediment storage tends to increasein wide valleys that favor deposition by decreasing flood depth, flowvelocity, and flood power, and decrease where cross-sectional stream

y = 1.793x-0.498

R² = 0.62; p = 0.012

0.01

0.1

1

10

10 100 1,000

Hg

sto

rag

e (k

g/m

)

y = 128.69x-0.586

R² = 0.35; p = 0.076

1

10

100

1,000

10 100 1,000

Cu

sto

rag

e (k

g/m

)

Drainage area (km2)

Fig. 6. Downstream changes in post-mining floodplain Hg and Cu mass storage. Valuesrepresent the total Hg and Cu mass storage along valley reaches centered on cross-valley transect locations. The arrow indicates the location of the confluence of Little BuffaloCreek and Dutch Buffalo Creek.

y = 2.98x 0.927

R² = 0.50p = 0.033

100

1,000

100 1000

Ove

rban

k S

edim

ent

Sto

rag

e (M

g/m

)

Valley Width (m)

Little Buffalo Cr.

Dutch Buffalo Cr.

y = 11.702x0.5467

R² = 0.91; p = 0.046

y = 37219x-0.61

R² = 0.72; p = 0.071

100

1,000

50 500O

verb

ank

Sed

imen

t S

tora

ge

(Mg

/m)

Cross-sectional Stream Power (W/m)

Little Buffalo Cr.

Dutch Buffalo Cr.

b

a

Fig. 7. Relationship between overbank sediment storage and (a) valley width and(b) stream power.

Hg = 63.936x- 0.724

R² = 0.74; p = 0.003

Cu = 590.07x- 0.347

R² = 0.21; p = 0.235

0.01

0.1

1

10

100

1000

50 500

Hg

or

Cu

(M

g)

Cross-sectional stream power (W/m)

Hg Cu

Fig. 8. Relationship between cross-sectional stream power and Cu and Hg storage. Thesolid symbols represent sites along Little Buffalo Creek and the open symbols representsite along Dutch Buffalo Creek.

129S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132

power is high (Graf, 1983; Magilligan, 1985, 1992; Lecce, 1997). Theresults shown in Figs. 7 and 8 are mixed. Although valley width explains50% of the variance in overbank sediment storage, it is clear thatmuch ofthe unexplained variance is due to differences between the upper andlower portions of the watershed where higher storage per unit valleywidth is found downstream along Dutch Buffalo Creek (Fig. 7a).While using data from the entire watershed would suggest a positiverelationship between stream power and sediment storage, Fig. 7bshows the expected negative relationship (e.g., Lecce, 1997) alongDutch Buffalo Creek, but an unexpected positive relationship in LittleBuffalo Creek. This positive relationshipmay be due to a combinationof factors that include basin shape and valley width. The two mostupstream sites have narrow floodplains and low values for streampower. Low stream power values would typically tend to favordeposition, but these narrow valleys lack the space to store sediment.The resulting low storage values produce a positive relationship betweenstream power and sediment storage in Little Buffalo Creek. Basin shapealso affects the difference observed between Little Buffalo Creek andDutch Buffalo Creek. The basin drained by Little Buffalo Creek is longand narrow so that drainage area and stream power increasedramatically at the confluence with Dutch Buffalo Creek. In addition tothe large influx of sediment from upper Dutch Buffalo Creek, the higherdischarges produce higher values for stream power along lower DutchBuffalo Creek.

Using stream power as a variable to predict metal storage alsoproduced mixed results. While cross-sectional stream power explains72% of the variance Hg mass storage in the overbank deposits, therelationship with Cu storage is not statistically significant (Fig. 8). Thisresult may be due to the difference between the timing and magnitudeof Hg and Cu contamination. Most of the Hg contamination wasassociated with the active period of Au mining from 1842 to 1860.Early Cu contamination was also associated with the active Au miningperiod, but therewas also a late period of Cumining at theUnionCopperMine from 1899 to 1906. This may make the dispersal and ultimate

distribution of Cumore erratic, and therefore difficult to predict. Streampower can also be an inadequate predictor variable for several reasons.Althoughwe implicitly assume that themodern values for power thatwecalculated are representative of themagnitude and spatial distribution ofpower throughout the historical period, stream power can changethrough time.We also assume that bankfull streampower is an adequatesurrogate for overbank flows that deposit contaminated sediment, andyet historical changes in channel/floodplain morphology can complicatethis relationship. Despite these difficulties, the relationship betweenstream power and Hg mass storage is surprising strong.

4.5. Sedimentation rates

Sediment-borne contaminants such as Hg and Cu can be used toprovide information on historical rates of floodplain sedimentationthat may be otherwise difficult to measure in environments that often

y = 0.356x0.191

R² = 0.29p = 0.002

y = 2.796x-0.032

R² = 0.01p = 0.674

0.1

1

10

1 10 100 1,000

Sed

imen

tati

on

rat

e (c

m)

Drainage area (km2)

1842-2007

1842-1856

Fig. 9. Downstream changes in sedimentation rates.

y = 0.512x-0.247

R² = 0.57p = 0.001

0.1

1

10

0.01 0.1 1 101842

-200

7 se

dim

enta

tio

n r

ate

(cm

)

a

y = 0.385x-0.226

R² = 0.26p = 0.005

10

atio

n r

ate

(cm

)

b

130 S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132

lack datablematerial or buried soils (e.g., Leigh, 1994; Lecce et al., 2011).Our reconnaissance study (Lecce et al., 2008) reported rates of verticalaccretion from near-channel and distal floodplain cores at the DB1 sitelocated at the basin outlet. The mean calculated sedimentation rateduring the most intense period of mining at Gold Hill (1842–1856) was2.5 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 furtherrefine rates of vertical accretion where a total of 30 cores had adequatesampling intervals and metal signals to calculate sedimentation rates.Using historical evidence (e.g., Knapp and Glass, 1999) we calculatedthese rates assuming (i) that the initial rapid increase in Hg con-centrations occurred in 1842, (ii) that peak Hg levels occurred in 1856with the Au production peak, and (iii) that the second rapid increase inCu concentrations was associated with the opening of the Union CopperMine in 1899. We acknowledge that the 5–20 cm sampling intervalintroduces error in the sedimentation rate calculations, but note thatthe 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 of1.1–7.2 cm y−1 (Table 2 and Fig. 9). The rates during this period ofintense mining are less reliable because of the short time period.Nevertheless, the mean rate of 2.7 cm y−1 during this early period isthree times the long-term (1842–2007) rate of 0.9 cmy−1, suggestinga higher level of landscape disturbance during this period. Rates canalso be less reliable where sampling intervals are large. For example,at site LB6B (Fig. 2) the more recent Cu peak associated with theoperation 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 haveproduced a more distinct peak, the depth at which it occurred couldbe located anywhere in the 10–37 cm depth range. Depending uponwhere the peak actually occurred in this depth-integrated sample, the1856–1899 sedimentation rate would vary from 0.5–1.2 cmy−1.

The long-term sedimentation rates aremore consistent, as indicatedby the much smaller standard deviation (0.4 cm y−1), and increasedownstream as a power function of drainage area (Fig. 9). The down-stream increase in long-term rates compared to the rates during themining period show a spatial and temporal lag effect where the locus ofdeposition shifts downstream with time (e.g., Knox, 1987; Lecce andPavlowsky, 2001). Thismay suggest that upstreamchannels have becomeenlarged and are now capable of containing and routing largermagnitudeflows downstream to lower capacity channels, leading to higher rates ofoverbank sedimentation.

Variations in the sedimentation rates noted above may be in-fluenced by factors such as inundation frequency, distance from thechannel, overbank flowpatterns, floodplain topography, and vegetationcharacteristics on the floodplain (Miller and Orbock Miller, 2007). Weassessed two of these factors, distance from the channel (Dc) and theelevation above the channel bed (Eb). Because valley width increasesdownstream, the locations of our cores tended to increase in distancefrom the channel bank in the lower portions of thewatershed. Likewise,floodplain elevations relative the channel bed also increase systematicallydownstream. In order to control for these trends, we by divided Dc and Eb

Table 2Summary statistics for sedimentation rates.

Sedimentation rate (cmy−1)

1842–1856 1856–1899 1899–2007 1842–2007

Mean: 2.7 0.9 1.0 0.9SD: 1.3 0.5 0.4 0.4Range: 1.1–7.2 0.5–1.6 0.4–1.6 0.4–1.7N: 30 7 7 30

by drainage area (Ad). Fig. 10 shows that sedimentation rates decreasewith both distance from the channel and elevation above the channelbed. In a multiple regression analysis Dc is a significant independentvariable (p = 0.001) while Ec is not significant (p = 0.159). Thesignificance of Dc, which explains 56% of the variance in the 1842–2007sedimentation rates, supports much previous work showing decreasedvertical accretion with distance from the channel due to sedimenttransfer 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 consistentwith those reported in the literature (Table 3). Although some studieshave reported that Holocene sedimentation rates may vary over a largerange (e.g., 0.02–0.65 cm y−1, from Ferring, 1986), Holocene rates arefrequently reported to be on the order of tenths of a millimeter peryear (e.g., Knox, 1987). Leigh and Webb (2006), for example, reportedHolocene rates of 0.03–0.08 cm y−1 in the nearby, but steeper terrain

0.1

1

0.01 0.1

1842

-200

7 se

dim

ent

Eb/Ad (m/km2)

Fig. 10. Relationships between long-term sedimentation rates (1842–2007) and (a) cross-valley distance from the channel (Dc/Ad) and (b) elevation above the channel bed (Eb/Ad).

131S.A. Lecce, R.T. Pavlowsky / Geomorphology 206 (2014) 122–132

of the southern Blue Ridge Mountains of western North Carolina. Incontrast, historical rates are typically one to several orders of magnitudegreater 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 nearbyCid district (Lecce et al., 2011) and southern Blue Ridge Mountains(Leigh and Webb, 2006). Mean rates during the most intensive periodof mining at Gold Hill are similar to those in the Dahlonega Au belt ofnorthern 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 long-term average rates at Gold Hill.

5. Conclusions

Nineteenth century mining activities at Gold Hill introducedsubstantial quantities of Au and Cu into the Little Buffalo Creek/DutchBuffalo Creek system. A large proportion (91% for Hg; 80% for Cu)of the post-mining floodplain samples we collected had Hg and Cuconcentrations above our measured background concentrations.Comparison of our post-mining floodplain sample concentrations withsediment quality guidelines showed that about 21% are contaminatedabove the PEC for Cu and for Hg. The highest contamination occursupstream along Little Buffalo Creek where 51% of the samples exceedthe PEC for Hg and 57% of the samples exceed the PEC for Cu, whereasbelow the confluence with Dutch Buffalo Creek only 6% exceed thePEC for Hg and 3% exceed the PEC for Cu.

Our most reliable estimates of mining-related metal mass storageindicate that about 6.8Mg of Hg and 619Mg of Cu currently reside infloodplain deposits within this watershed. Our estimate of 6.8 Mg ofHg storage compares favorably with a 5–10 Mg estimate based onemission factors and historical accounts of Au production. Althoughthe storage of post-mining overbank sediment on floodplains increasesdownstream, the high metal concentrations in upstream reaches closeto the mines causes both Hg and Cu storage to decrease downstream.About 77% of the 6.8Mg of Hg and 75% of the 618Mg of Cumass storageoccurs upstream from the confluence with Dutch Buffalo Creek whereaverage Hgmass values are 3.5 times the average in lower Dutch BuffaloCreek. Efforts to explain sediment and metal storage using geomorphicvariables were only moderately successful. Valley width explained 50%of the variance in overbank sediment storage and cross-sectional streampower explained 74% of the variance in Hg mass storage. However,the relationship between cross-sectional stream power and Cu storagewas not statistically significant. This result may be due to the morecomplicated history of Cu dispersal and inadequacies with streampower as an index that accurately reflects spatial patterns of deposition.

Basin-wide estimates of rates of floodplain sedimentation comparefavorably with a previous estimate at the outlet of the watershed(Lecce et al., 2008). Long-term average rates of about 1 cmy−1 at GoldHill are comparable to those reported elsewhere in the eastern Piedmont(Bain and Brush, 2005; Jackson et al., 2005). The mean rate of 2.7cmy−1

Table 3Regional sedimentation rates.

Location Period Sedimentationrate (cm y−1)

Source

Gold Hill, NC 1842–2006 0.4–1.7(mean=0.9)

This study

Gold Hill, NC 1842–1856 1.1–7.2(mean=2.7)

This study

Cid District, NC 1832–1880 to 2007 0.3–0.9(mean=0.5)

Lecce et al. (2011)

Southern Blue RidgeMtns., NC

Historical 0.58–0.65 Leigh and Webb(2006)

Eastern Piedmont, ME 1820–1880 0.45–1.19 Bain and Brush(2005)

Dahlonega, GA 1829–1880 to 1994 1–3 Leigh (1994)Piedmont, GA 1820–2005 0.87 Jackson et al. (2005)

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, wherehydraulic mining was used (Leigh, 1994). Furthermore, rates during thepeak 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 thecombination of disruptive mining activities, the growth of the miningpopulation, and land-use change associatedwith agriculture significantlyincreased the level of landscape disturbance and sediment production.Long-term sedimentation rates decrease with distance from the channeland elevation above the channel bed. The downstream increase in long-term rates may be indicative of a spatial and temporal lag effect wherethe locus of deposition shifts downstream with time due to theenlargement of upstream channels that route sediment downstream tolower capacity channels that experience more frequent overbank flows(e.g., Knox, 1987; Lecce and Pavlowsky, 2001).

Although recently deposited sediments at the floodplain surfacecontainmetal concentrations that aremuch lower than those depositedduring the mining period, these sediments remain contaminated abovebackground concentrations. This study shows that the likely source ofthese contaminants is the upstream part of the watershed that containsfloodplain sediments with high Cu and Hg concentrations that werestored during the period of active mining. These legacy deposits areremobilized primarily by bank erosion. As such, contamination fromthis period of Au mining is a continuing and dynamic process.

Acknowledgments

This research was funded through a grant from the NationalGeographic Society (# 8175–07), a Research Development Grant atEast Carolina University, and the Ozarks Environmental and WaterResources Institute. We wish to thank Marc Owen, Derek Martin,Gwenda Bassett, Stacey Armstrong, Mark Gossard, David Shaeffer, ErinHutchinson, 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 themanuscript.

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