Quantifying Metal Contributions from Multiple Sources to the Clark Fork River, Montana, U.S.A.

Journal of Environmental Forensics (2000) 1, 55±62 doi:10.1006/enfo.2000.0008, available online at http://www.idealibrary.com on

Quantifying Metal Contributions from Multiple Sources to the Clark Fork River, Montana, U.S.A. Steven O. Helgen and Andy Davis* Geomega, 2995 Baseline Road, Suite 202, Boulder, CO 80303, U.S.A. (Received 23 December 1999, revised manuscript accepted 15 March 2000) Identifying and quantifying the contributions of multiple sources of trace elements to stream sediments in a basin containing several possible inputs presents a unique problem related to the investigation of rivers impacted by industrial activity. A multi-source dilution±mixing model was developed and applied to determine the relative contributions to As, Cu and Pb burdens in the Clark Fork River, Montana, a recipient of historical mine wastes as a result of over a century of mining and milling operations. The results identi®ed the Flint Creek drainage as a major source of anthropogenic As (47%) and Pb (35%) to sediments of the Clark Fork River and the Milltown Reservoir, in addition to the major sources associated with mining operations in Butte, MT. The Little Blackfoot River also contributes anthropogenic As (3%) and Pb (4%) to the Clark Fork River, while minor inputs of Cu (1%) and Pb (2%) emanate from the Blackfoot River. The model allows source quanti®cation, and an understanding of the fate and transport of mine wastes in a basin, # 2000 AEHS allowing identi®cation and eventual prioritization of sites destined for remediation. Keywords: tailings, metals, sediment, quanti®cation, multiple sources.

Introduction

tion. These areas may then become the focus of subsequent sampling e€orts to re®ne the global picture of metal loading to the mainstem watercourse. The original dilution±mixing model was developed to determine the size of a soil metal anomaly overlying an ore deposit based on stream sediment data collected downstream (Hawkes, 1976; Marcus, 1987). Subsequently, the basic equation was modi®ed and used to determine the relative magnitude of a single anthropogenic source of trace elements to stream sediments (Helgen and Moore, 1996). This contribution describes development and application of a multi-source dilution±mixing model that allows the calculation of the relative contributions from several sources to the sediments of the mainstem. In this example, the multi-source dilution±mixing model was applied to the Clark Fork River basin in western Montana, where a number of mining districts contribute trace elements to the sediment load of the Clark Fork River (CFR), resulting in elevated trace element concentrations, particularly upstream from Milltown Reservoir (Figure 1). Trace element data from all major tributaries were screened to identify possible secondary sources, which were then sampled in more detail.

Discrimination between the sources of historical mine impacts to ¯uvial surface waters and sediments is frequently complicated by the similar chemical signatures that emanate from mine and mill sites along the water course, making it dicult to discriminate between contributions and determine the relative impacts of each site on downstream resources. Frequently, such interpretations are important in identifying ownership of costs related to remedial actions associated with historical releases. In the western United States, a long history of mining has resulted in a legacy of thousands of abandoned mine sites. Many of these are small prospects, but some, such as the Butte and Philipsburg Districts in western Montana, were the sites of large mining and milling operations. While the mines and mills located in the headwaters of the Clark Fork River at Butte and Anaconda have been the focus of numerous investigations in the Clark Fork basin (Multitech, 1987; Titan, 1995), there has been only minimal investigation of additional tributary sources, such as the Philipsburg District, that also contributed mine wastes to the lower Clark Fork system (Essig and Moore, 1992; Axtmann and Luoma, 1991). In order to facilitate the investigation and quanti®cation of additional sources, we have developed a model that expands on existing principles of dilution± mixing (Hawkes, 1976), using the distribution of trace element concentrations in stream sediments to quantify the relative contribution of each source. The model and principles described in this paper can also be used as a screening tool to identify previously unsuspected contributions with a minimal amount of data collec-

History of Mining in the CFR Basin The Clark Fork River basin (Figure 1), located in western Montana, has been the site of mining, milling, and smelting for over a century. The mining era in Butte began with the discovery of placer gold in 1864, which quickly gave way to silver and copper mining (Freeman, 1900). By 1900, the silver mills had closed due to the removal of government price support for silver, and copper ore dominated mining production, continuing to the present. Prior to transfer of the

*Author for correspondence. E-mail: [email protected]

55 1527-5922/00/020055+08 $35.00/00

# 2000 AEHS

56 S. O. Helgen and A. Davis r

ive

Missoula

tR foo

ck

Bla

Milltown Dam (Mile 165)

Montana

Milltown Reservoir Map Area

Mile 147 Mile 112

Fork River

k Cr ee ck

CFR Start Mile 45.6

reek

Silver Bow Cr.

Mile 14.5

. Cr

miles

ll

20

ka

10

Butte

ac

0

Warm Springs Ponds

Bl

S

10

Butte was the Site of: 11 Copper Mills 1881−1924 10 Silver Mills 1876−1900 2 Zinc Mills 1912−1930 1 Manganese Mill 1927−1945

Basin C r.

E

k Cree

Willow

Anaconda

N

Zosell District

Opportunity Ponds

Warm Springs C

Anaconda was the site of: the Old Works 1884−1901 the New Works 1902−1980

Little Blackfoot District

Deer Lodge

Site of ~20 mills 1867−1970

er

Mile 84

Fl in t

Ro

Dunkleberg District Philipsburg District

W

e Blackfoot Riv

ttl

Li

Cr ee k

Clark

Mile 0.0

Figure 1. Map of study area. Distances given in miles (1 mile ˆ 1.6 km).

major milling and smelting operations from Butte to Anaconda, early copper and silver mills in Butte disposed of highly enriched tailings along Silver Bow Creek (SBC). Disposal in the SBC corridor led to downstream transport and deposition of wastes by ¯uvial processes, especially during ¯ood events, including the largest event on record in 1908 (Nimick and Moore, 1991). Mining in the Philipsburg District in the Flint Creek valley also began in the 1860s, continuing to the present. Milling of silver ore was initiated in the 1880s, with the mills later converted to process the rich manganese ore in the district (Emmons and Calkins, 1913; Goddard, 1940). Over a century of mining and milling in the Philipsburg District has resulted in elevated trace element concentrations in the sediments of Flint Creek, extending to the con¯uence with the CFR (Axtmann and Luoma, 1991; Helgen, 1995; Lambing, 1991). Numerous other hard rock mining districts occupy the upper CFR basin, including the Little Blackfoot District, the Zosell or Emery District, the Dunkleberg District, and others (Figure 1). These smaller districts contain a variety of mine and mill sites on a much smaller scale than the operations at Butte and Philipsburg. The Milltown Reservoir, formed at the con¯uence of the Blackfoot and Clark Fork Rivers, is the farthest downstream repository of signi®cant quantities of

tailings in the CFR basin (Figure 1). The reservoir began trapping sediments shortly after completion in 1907, and is presently close to capacity (ARCO, 1992). In this paper, the multi-source dilution±mixing model was applied to the CFR basin above the Milltown Reservoir to determine the relative contributions of trace elements from sources identi®ed in the basin to the sediments of the Clark Fork River at Milltown Reservoir.

Numerical Method Model development Hawkes (1976) demonstrated that the trace element concentration of stream sediments in a mineralized region could be related to the area of the drainage basin above the sample location, the background concentration of the element, and the area and concentration of trace elements in a soil anomaly overlying an ore deposit, according to the equation, ‰MeŠkm ˆ Cb

At ÿ Ao Ao ‡ Co At At

…1†

where [Me]km is the trace element concentration at a particular 1-kilometer stretch of river, Cb is the average background concentration in the basin, At is the total drainage basin area above river kilometer km, Ao is the area of a mineralized anomaly in the

Quantifying Metal Contributions from Multiple Sources 57

drainage basin containing elevated metal concentrations equal to Co. This equation has been applied to drainage in a variety of climates and geological regions (Marcus, 1987). The dilution±mixing model relates the trace element concentration at an arbitrary point downstream from a source to the area and the concentration of the anomaly and the area and concentration of the entire drainage basin above the sample location (Figure 2). The model assumes that the quantity of sediment contributed to a river over a period of time is directly related to the area of the basin, i.e. there is an average erosion rate that applies in the basin over a large temporal and spatial scale. It is recognized that individual storm events greatly a€ect the instantaneous load of suspended sediments, however, data input to the mixing model represent bed sediments collected in depositional areas, not suspended sediments, and therefore represents a time-integrated sample. Data from bed sediments deposited over a period of months integrate deposition from several storm events and represent the average condition in the river. The model also assumes that the trace element being modeled behaves in a net conservative manner within the stream sediments, as would be the case in a pH circum-neutral stream with consistent redox conditions. In this environment the relative mass of nonreactive elements sorbed to the particulate fraction overwhelms the soluble fraction, remaining at a consistent ratio throughout the length of the watercourse. It is also assumed that the concentration of trace elements in the ®ne fraction is proportional to the bulk concentration of trace elements in the watercourse (Horowitz and Elrick, 1987; Chao and Theobald, 1976). These assumptions apply to circum-neutral drainages with an adequate gradient and ¯ow to actively transport sediment. Elements such as lead and copper have been demonstrated to meet this criterion. Arsenic also meets the criterion, although at higher pH values that may occur seasonally in late summer it becomes more mobile. More reactive elements, such as zinc (Zn), deviate from model predictions, while highly reactive elements, such as manganese, cannot be modeled using this method (Hawkes, 1976; Helgen, 1995). In addition, the model does not apply in highly acidic reaches, where the solute fraction increases relative to the particulate (Helgen and Moore, 1996), or in extreme losing streams that eventually run dry and therefore have a greatly reduced capacity to transport sediment with increasing distance downstream. Equation 1 determines the trend of trace elements downstream from an undisturbed ore deposit, but does not account for anthropogenic inputs of sediment and trace elements caused by the release of mill tailings and weathering of freshly exposed waste rock. A coecient (S) must be added to the source area term in order to incorporate the higher ¯ux of metals and sediment following mining because sediment yield has been assumed to be directly proportional to area. The coecient re¯ects a combination of both a physical increase in the source area caused by mining (i.e. waste rock piles and tailings deposits), and also an increase in the erosion rate of anomalous materials (i.e. bank

Ore Deposits Area of Ore Deposit = Ao Original Concentration = Co Post-Mining Source Coefficient = S Concentration = Cx

Drainage Basin Area = At Background Concentration = Cb

[Me]km = Concentration of metal in sediment Figure 2. Illustration of terms used in Equations 1±3.

erosion or direct disposal of tailings into streams). Therefore, if a tailings pile adjacent to a stream has an area twice as large as the original anomaly and is eroding at a rate ®ve times greater than the surrounding area, the source coecient would be equal to ten. Since the erosion rate is dicult to quantify, the source coecient (S) is ®t to observed trace element data for a given source concentration (Cx). The anthropogenic source terms S and Cx were incorporated into Equation 1 (Helgen and Moore, 1996), i.e.: ‰MeŠkm ˆ Cb

At ÿ Ao SAo ‡ Cx At ‡ Ao …S-1† At ‡ Ao …S-1†

…2†

To expand this model further to include multiple contributions and allow relative quanti®cation of sources, we modi®ed Equation 2 to include m total sources and n total tributaries, i.e.: ‰MeŠkm ˆ

n X

Cbk

kˆ1

Atribk m X At ‡ Aoj …Sj -1† jˆ1

At ÿ ‡ Cb-ave

n X

Atribk ÿ

kˆ1 m X

A1 ‡

jˆ1

‡

n X kˆ1

Cx k

m X jˆ1

Ao j …3†

Aoj …Sj ÿ 1†

Sk Aok m X At ‡ Aoj …Sj ÿ 1† jˆ1

where [Me]km is the concentration of a trace element at a point km kilometers from the headwaters of the river, Cb is the average background concentration of the trace element in a tributary basin, Cb-ave is the average background concentration over the entire basin, Atrib is the area of the tributary basin at concentration Cb, At is

58 S. O. Helgen and A. Davis 10,000

Butte WSP

Arsenic (mg/kg)

the total basin area above the kilometer in question, Ao is the area of the surface anomaly of a source prior to mining, S is the source coecient following mining, and Cx is the average concentration of the post-mining source material (Figure 2). The ®rst term of the equation sums the relative background contribution of each tributary, the second term adds the relative background contribution of the basin not included in the speci®c tributaries, and the third term adds the sum of the relative contributions of each of the sources. Equation 3 was incorporated into a program written in Visual Basic 4.02 which allows intricate model structures, including automatic area calculations for any stream point based on measured basin areas, use of speci®c background concentrations for each tributary basin, and inclusion in the model of any number of sources and any number of trace elements for each source.

Little Blackfoot

1000

Flint Creek

Milltown Reservoir

100

10

1

0

25

50

75

100

125

150

175

Mile downstream

Figure 3. Clark Fork River arsenic data from 563 mm sediments (Essig and Moore, 1992) compared to 52 mm arsenic data from freeze core samples (Davis, 1995). (1 mile ˆ 1.6 km.)

Model application Source quanti®cation The product of the post-mining source terms SAo and Cx is proportional to the load of the element in the modeled particle size fraction, because SAo is proportional to the quantity of anthropogenically derived sediment, and Cx represents the concentration of an element in the sediment. The relative contribution of each source can then be calculated by taking the ratio of the product of the source terms over the sum of the products of all the source terms i.e.: %Sourcen ˆ

Sn Aon Cxn m X Si Aoi Cxi

…4†

iˆ1

Thus, the percentage contributions of each source returned by Equation 4 is a proportional representation of the total masses of trace elements entering the receiving stream from each anthropogenic source, resulting in a mass-based apportionment of various sources in a drainage basin on an element by element basis.

Data compilation Data sets from the Clark Fork River, the Little Blackfoot, Flint Creek, and the Blackfoot River (Essig and Moore, 1992; Helgen, 1995; Moore et al., 1991) were used to model the inputs of arsenic, copper, and lead to the sediments of the Clark Fork River above Milltown Reservoir. Sediment samples of the ®ne-grained fraction (563 mm) were used because they provide a large, internally consistent, reproducible data set. Data with a coarser cuto€ could also be used, but tend to be more variable. For example, comparison of the 563 mm data with 2 mm data from channel bed samples (Figure 3) illustrates the tendency for trace elements to partition into the ®ne fraction (Davis, 1995), and the higher variability of the coarser fractions. Both size fractions show a similar decline in concentration with distance downstream, but at di€erent orders of magnitude of concentration.

Multi-source basin analysis requires identi®cation of the primary and secondary sources, and source and background metal concentrations. Subsequently, area parameters are determined for each secondary source using Equation 2. Once these source terms are known, the primary source terms are calculated in the mainstem model from the measured mainstem source term Cx, using a log-least squares ®tting routine to compute a unique value for S. In the Clark Fork model, source coecients were ®t to the observed trace element trends in the Little Blackfoot River, Flint Creek, and the Blackfoot River using Equation 2, then source coecients for arsenic, copper and lead were ®t to the data set from the Clark Fork River using Equation 3 (Figure 4). All tributaries not identi®ed as a source were included in the background terms of Equation 3. All parameters in the model with the exception of S were measured directly or estimated from historic information. Nonmineralized background values in the CFR basin were taken from an average of regional rivers with little or no impact from mining (Essig and Moore, 1992), with basin areas determined from digitized USGS maps.

Results and Discussion Sources above the con¯uence of the Little Blackfoot and Clark Fork Rivers were found to contribute the largest portions of anthropogenic arsenic (50%), copper (96%), and lead (59%) to the bed sediments of the CFR above Milltown Reservoir. Flint Creek was also identi®ed as a major secondary contributor of anthropogenic arsenic (47%), copper (2%) and lead (35%), and the Little Blackfoot was identi®ed as a minor secondary contributor of arsenic (3%), copper (1%) and lead (4%). The Blackfoot River enters the reservoir close to the dam, and contributes only 2% of the lead and 1% of the copper loads to the reservoir sediments. The Little Blackfoot River The Little Blackfoot was identi®ed as a source by a few anomalously high samples collected near the con¯uence with the Clark Fork (Essig and Moore, 1992;

Quantifying Metal Contributions from Multiple Sources 59 (a)

LBF

Milltown

100

25

50

75 100 125 Mile downstream

150

WSP LBF

Flint Creek Milltown

1000

10,000

25

(c)

50

75 100 125 Mile downstream

150

Ramsay Flats

1000

WSP

LBF Flint Creek

25

50

75

100

125

40

CFR

50

0

10

20 Mile downstream

30

40

(c)

200 CFR 100

0

0

30

Milltown

100

0

20 Mile downstream

(b)

300

175

10

100

0

0

0

150

Ramsay Flats

(b)

10,000

100

CFR 100

175

Copper (mg/kg)

0

(a)

200

0

100,000 Copper (mg/kg)

Flint Creek

Arsenic (mg/kg)

WSP

1000

10

Lead (mg/kg)

300

Ramsay Flats

Lead (mg/kg)

Arsenic (mg/kg)

10,000

150

175

Mile downstream Figure 4. Clark Fork River model ®ts to 563 mm sediment data for (a) arsenic, (b) copper, and (c) lead. Solid lines indicate model predictions, open symbols indicate sediment data. (1 mile ˆ 1.6 km.)

Axtmann and Luoma, 1991) and subsequently sampled farther upstream to quantify the dispersion train (Figure 5). Based on these data, model results demonstrate that the Little Blackfoot contributes a small portion of the arsenic (3%), copper (51%), and lead (4%) to the CFR. Although the Little Blackfoot contributions are minor, they are still appreciable considering the magnitude of the major sources. Flint Creek Remnant tailings materials produced by at least twenty di€erent milling operations in the Philipsburg District contain arsenic, copper, manganese, lead and zinc (Pioneer, 1995). Tailings containing up to 2.6% arsenic are actively eroding into Fred Burr Creek (Figure 1) downstream from the ruins of the Rumsey Mill. Sediments eroded from tailings deposits move through the Flint Creek system (Figure 6) and cause an increase in the concentrations of arsenic and lead in the CFR below the con¯uence with Flint Creek. The combin-

0

10

20 Mile downstream

30

40

Figure 5. Little Blackfoot River model ®ts to 563 mm sediment data for (a) arsenic, (b) copper, and (c) lead. Solid lines indicate model predictions, open symbols indicate sediment data. (1 mile ˆ 1.6 km.)

ation of higher arsenic and lead concentrations (2±3 times over CFR), copper above background, and the quantity of sediment contributed to the CFR (15±30% of the load at the con¯uence) results in a contribution of 47% of the arsenic, 36% of the lead, and 2% of the copper to the anthropogenic trace element load to the CFR above Milltown Reservoir. Model predictions indicate a rise in arsenic and lead concentrations in CFR sediments below the con¯uence with Flint Creek, decreasing gradually downstream to Milltown Reservoir. The predicted trend is not clearly observed in the data of Essig and Moore (1992), which represent individual samples, (Figure 7(a)), but is clear in the Axtmann et al. (1997) data set, which represents the averages of samples collected at the same locations between 1986±1990 (Figure 7(b)). The higher variability observed in the Essig and Moore data, collected in October and November of 1991, is attributed to greater variability observed in the individual samples and to the time of year the samples were collected. During the low ¯ow conditions of fall, erosion and transport of source sediments is at a minimum. The Axtmann data set, averaged over a number of years of sampling, agrees much better with model predictions due to a smoothing of seasonal and year-to-year variability.

60 S. O. Helgen and A. Davis

400

300

(a) Philipsburg District

Lead (mg/kg)

Arsenic (mg/kg)

600

200

(a)

200

Milltown Reservoir

100

CFR 0

10

20

30

0

40

Little Blackfoot

50

75

Mile downstream

Lead (mg/kg)

800

300

(b) Philipsburg District

CFR

600 400

30

40

Mile downstream

Lead (mg/kg)

800

Milltown Reservoir

Philipsburg District

75

100

125

150

175

Mile downstream

400

12,000

(a)

10,000

200 0 10

50

Flint Creek

Figure 7. Clark Fork River model ®ts to (a) 563 mm lead data collected fall 1991 (Essig and Moore, 1992) and (b) 563 mm lead data compiled from 1986, 1987, 1988, and 1990 sampling (Axtmann et al., 1997). Solid lines indicate model predictions, open symbols indicate 563 mm sediment data, solid symbol indicates concentration in Flint Creek at con¯uence. (1 mile ˆ 1.6 km.)

(c)

600

175

100

0

20

150

200

Little Blackfoot

10

125

(b)

200 0

100

Mile downstream

CFR 20

30

40

Mile downstream Figure 6. Flint Creek model ®ts to 563 mm sediment data for (a) arsenic, (b) copper, and (c) lead. Solid lines indicate model predictions, open symbols indicate sediment data, solid symbol indicates concentration in CFR at con¯uence. (1 mile ˆ 1.6 km.)

Copper (mg/kg)

Copper (mg/kg)

1000

Flint Creek

8000 6000 Milltown Reservoir

4000 2000 0

0

20

40

The Blackfoot River 5000

120

140

(b)

4000 Lead (mg/kg)

Historic mining operations in the headwaters of the Blackfoot provide copper (Figure 8(a)) and lead (Figure 8(b)) inputs to the stream sediments that result in a well-de®ned dispersion train (Moore et al., 1991) to which good model ®ts were obtained. Based on these data, the Blackfoot contributes 0.7% of the anthropogenic copper load, and 2.2% of the anthropogenic lead load to the Clark Fork River at Milltown Reservoir. Arsenic was not analyzed.

60 80 100 Mile downstream

3000 Milltown Reservoir

2000 1000

Tributary screening Potential source tributaries can be quickly screened using basin area and con¯uence trace element concentrations. For example, the Flint Creek Basin spatially represents 20% of the CFR Basin at the con¯uence,

0

0

20

40

60 80 100 Mile downstream

120

140

Figure 8. Blackfoot River model ®ts to 563 mm (a) copper and (b) lead data (Moore et al., 1991). Solid lines indicate model predictions, open symbols indicate sediment data. (1 mile ˆ 1.6 km.)

Quantifying Metal Contributions from Multiple Sources 61

and therefore approximately 20% of the sediment load, a value consistent with the reported range of 15±30% (Axtmann and Luoma, 1991; Lambing, 1991). Using a 20/80 sediment contribution and arsenic concentrations of 224 mg/kg from Flint Creek, and 66 mg/ kg from the CFR above the con¯uence, the relative inputs of arsenic to the sediments below the con¯uence are: 0.8  66 mg/kg ˆ 52.8 mg/kg representing the aggregate CFR contribution, and 0.2  224 mg/kg ˆ 44.8 mg/kg for the FC contribution, or a contribution of 46% of the arsenic out of Flint Creek in 20% of the sediment, in good agreement with the model result of 47%. Con¯uence concentration and basin area calculations provide a screening level tool that may be used to identify which tributaries contribute anomalous metal loads to the mainstem. However, it is important to note that because the metal load of a secondary source tributary is controlled by both the concentration and quantity of sediment; there may be no observed change in concentration below the con¯uence as a signi®cant secondary source enters the mainstem. For example, if both a source tributary and the mainstem have the same concentration of a trace element and the tributary contributes 40% of the sediment load, then 40% of the trace element load would be contributed by the tributary. In this case, the mainstem would show no change in concentration below the con¯uence with the additional of a signi®cant metal load, but data collected in the tributary would indicate concentrations above background. Error/Sensitivity analysis The three main sources of error are related to the assumption that sediment discharge is a function of basin area, how representative the sediment chemistry data are, and the selection of the model parameters Cx, Ao, and Cb. Sediment discharge as a function of basin area. The relationship of basin area to sediment yield was investigated in the Clark Fork Basin using United States Geological Survey (USGS) 1985±1990 (Lambing, 1991) suspended sediment data set. For this analysis, the total annual input of suspended sediment to Milltown Reservoir was compared to the annual suspended sediment load in the Little Blackfoot River, Flint Creek, Rock Creek, and the Blackfoot River to determine the relative inputs of each of these tributaries. These results were then compared to the relative percentage of the basin represented by each tributary (Table 2). The relative load of sediment to Milltown Reservoir estimated from the USGS data averaged 75% lower than the area-calculated data, consistent with the lower (70±80% of normal) ¯ow observed in water years 1985±1990 (Lambing, 1991). The agreement between these two alternative methods was good except for the Little Blackfoot

Table 1. Model parameters and relative inputs of arsenic, copper and lead to the CFR above Milltown Reservoir

Trace element Arsenic Cb (mg/kg) Co (mg/kg) Ao (km2) S Cx (mg/kg) SAoCx (km2mg/kg) Contribution (%) Copper Cb (mg/kg) Co (mg/kg) Ao (km2) S Cx (mg/kg) AxCx (km2mg/kg) Contribution (%) Lead Cb (mg/kg) Co (mg/kg) Ao (km2) S Cx (mg/kg) SAoCx (km2mg/kg) Contribution (%)

Clark Fork Little River above Blackfoot LBF River

Flint Creek

Blackfoot River

7 400 10 2.87 10 000 287 000 50

6 500 0.256 75 1000 19 200 3

25 28 000 0.026 511.5 20 000 266 000 47

Ð Ð Ð Ð Ð Ð

20 2000 10 13.57 26 000 3 528 000 96

20 1000 0.256 10 18 000 46 000 1

20 6400 0.026 396 6400 66 000 2

25 10 000 0.0036 711 10 000 25 600 1

15 300 20 1.075 18 000 387 000 59

15 500 0.256 219 500 28 000 4

57 28 000 0.026 320 28 000 233 000 35

18 71 400 0.0036 55.6 71 400 14 300 2

Table 2. Comparison of estimated annual suspended sediment loads (Lambing, 1991) to relative basin area Tributary

Susp. Sediment (tons/year; 1985±1990) Susp. Sediment to Milltown Reservoir Input to Milltown (%) Normal Flow (1985±1990/ All recorded) (%) Basin Area (km2) Area above Milltown (km2) Area above Milltown (%)

Little Flint Blackfoot Creek River 1977 75 446 2.6 70 1075 15 117 7.1

4709

Rock Blackfoot Creek River 8466

75 446 75 446 6.2 NA

11.2 77

1149 2286 15 117 15 117 7.6 15.1

32 273 75 446 42.8 80 5990 15 117 39.6

NA: not analyzed. tons: 2000 lbs.

River in which the estimated suspended sediment yield was 37% of that predicted by the area calculations (Table 2). This di€erence is attributed to variability in the 27 suspended sediment samples collected over the study period that exhibited a range of suspended sediment discharge of 0.17±7920 tons per day. The range of daily discharge compared to the annual average discharge of 4700 tons per year with a standard error of 116%, demonstrates the diculty of calculating annual sediment discharge given a highly variable data set. In cases such as this, using basin area as an estimate of the average relative sediment discharge to the receiving stream minimizes the impact of data anomalies. Data representativeness. The trace element data used to ®t the model should be representative of long-term average conditions in the river. For example,

62 S. O. Helgen and A. Davis Table 3. Sensitivity of calculated source magnitudes to estimated model parameters Original Value

‡50%

ÿ50%

Cx Change in SAoCx (%)

26 000 Ð

39 000 ÿ1.5

13 000 ‡4

20 Ð

30 ‡1.5

10 ÿ1.5

Ao Change in SAoCx (%)

10 Ð

15 ÿ0.2

5 0.0

Parameter

Cb Change in SAoCx (%)

Table 4. Little Blackfoot River Sample Model Input/Output (using Equation 2 and Parameters from Table 1) River Kilometer 9.6 12.8 16 19.2 24 32 40 48 56 60

mass loading, and provides a mass-based apportionment of the trace element loads from di€erent sources to the receiving stream that augments the use and interpretation of a small body of data. The model is generic in nature and can be applied to any river system where the basic assumptions are met, and there are multiple sources of trace elements. Application of the model in systems such as the Clark Fork resulted in allocation between the upper CFR, the Little Blackfoot River, Flint Creek, and the Blackfoot River for arsenic, copper and lead, and identi®ed Flint Creek as a major secondary source of arsenic (47%) and Pb (35%) to the CFR.

References

Drainage Basin Area (km2)

Model As (mg/kg)

50.7 81.9 11.8 161.0 233.6 377.2 547.2 741.5 958.7 1075.5

280 195 145 112 82 54 40 31 26 23

di€erences in the trace element trend below Flint Creek in the Essig and Moore (1992) data set compared to those of Axtmann et al. (1997) indicate both seasonal and annual variability, although there is also a central trend around which the variability occurs. In order to minimize the e€ects of localized spatial variability, sediment samples should be collected from the top few centimeters of recently deposited sediments in lower energy areas of the active channel where ®ne sediments accumulate between ¯ood events, and be homogenized from 10±20 locations along a 100 m reach at each point. Homogenizing samples both spatially and temporally, if possible, minimizes local and seasonal variations in the trace element trends, enhancing the accuracy of long-term metal loading estimates. Parameter selection. Because S is a unique ®tting parameter and the drainage basin area is a measured quantity, only the terms Ao, Cb, and Cx have signi®cant uncertainty that could a€ect the resulting source quanti®cation. A sensitivity analysis demonstrates that changing each parameter +50% results in only a 0±4% variability in the calculated source magnitude (Table 3), indicating the model is insensitive to the selection of Ao, Cb, and Cx. Sample input and output are included for the Little Blackfoot arsenic model in Table 4.

Conclusion The diculty of quantifying and understanding the interactions of multiple trace element sources in a river basin underscores the potential usefulness of the dilution±mixing model as an analytical tool. The multi-source model allows quanti®cation of the relative inputs of each source in terms of concentration and

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