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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 eorts 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 dicult 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
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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 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, Mekm 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 aect 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 coecient (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 coecient 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 coecient would be equal to ten. Since the erosion rate is dicult to quantify, the source coecient (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.: Mekm 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.: Mekm
n X
Cbk
k1
Atribk m X At Aoj Sj -1 j1
At ÿ Cb-ave
n X
Atribk ÿ
k1 m X
A1
j1
n X k1
Cx k
m X j1
Ao j 3
Aoj Sj ÿ 1
Sk Aok m X At Aoj Sj ÿ 1 j1
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 coecient 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 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
Sn Aon Cxn m X Si Aoi Cxi
4
i1
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 dierent 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 coecients were ®t to the observed trace element trends in the Little Blackfoot River, Flint Creek, and the Blackfoot River using Equation 2, then source coecients 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 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 dierent 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 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 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 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 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 52.8 mg/kg representing the aggregate CFR contribution, and 0.2 224 mg/kg 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 dierence 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 diculty 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 dierent 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
dierences 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 eects 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 aect 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 diculty 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
ARCO. 1992. Milltown Reservoir Superfund site remedial investigation draft report. Prepared by ARCO, Anaconda, MT. Axtmann, E.V., Cain, D.J. and Luoma, S.N. 1997. Eect of tributary in¯ows on the distributions of trace metals in ®ne-grained bed sediments and benthic insects of the Clark Fork River, Montana. Environ. Sci. Technol. 31, 750±758. Axtmann, E.V. and Luoma, S.N. 1991. Large-scale distribution of metal contamination in the ®ne-grained sediments of the Clark Fork River, Montana, U.S.A. Appl. Geochem. 6, 75±88. Chao, T.T. and Theobald, P.K. 1976. The signi®cance of secondary iron and Mn oxides in geochemical exploration. Econ. Geol. 71, 1560±1569. Davis, A. 1995. Expert Report of Andy Davis, Ph.D. State of Montana vs. Atlantic Rich®eld Co. No. CV-83-317-HLN-PGH. Emmons, W.H. and Calkins, F.C. 1913. Geology and ore deposits of the Philipsburg quadrangle Montana. U.S. Geol. Surv. Prof. Paper 78. Essig, D.A. and Moore, J.N. 1992. Clark Fork damage assessment bed sediment sampling and chemical analysis report. Prepared for Natural Resource Damage Program. State of Montana, University of Montana. Freeman, H.C. 1900. A brief history of Butte, Montana, pp 123. Chicago, IL, Henry O. Shepard Co.. Goddard, E.N. 1940. Mn deposits at Philipsburg Granite County, Montana. U.S. Geol. Surv. Bull. 922-G. Hawkes, H.E. 1976. The downstream dilution of stream sediment anomalies. J. Geochem. Explor. 6, 345±358. Helgen, S.O. and Moore, J.N. 1996. Natural background determination and impact quanti®cation in trace metal-contaminated river sediments. Environ. Sci. Technol. 30, 129±135. Helgen, S.O. 1995. Natural background determination and impact quanti®cation in trace metal contaminated river sediments. Masters Thesis, Univ. of MT. Horowitz, A.J. and Elrick, K.A. 1987. The relationship of stream sediment surface area, grain size, and composition to trace element chemistry. Appl. Geochem. 2, 437±451. Lambing, J.H. 1991. Water-quality and transport characteristics of suspended sediment and trace elements in stream¯ow of the Upper Clark Fork Basin from Galen to Missoula, Montana. U.S. Geological Survey Water Resources Investigation. 91±4139. Marcus, W.A. 1987. Cu dispersion in ephemeral stream sediments. Earth Surf. Proc. 12, 217±228. Moore, J.N., Luoma, S.N. and Peters, D. 1991. Downstream eects of mine euent on an intermontane riparian system. Can. J. Fisheries Aquatic Sci. 48, 222±232. Multitech. 1987. Silver Bow Creek remedial investigation draft ®nal report. Prepared for Solid and hazardous waste bureau, Montana department of health and environmental sciences, by Multitech, Butte, MT. Nimick, D.A. and Moore, J.N. 1991. Predictions of water-soluble concentrations in ¯uvially deposited tailings sediments, Upper Clark Fork Valley, Montana, U.S.A. Appl. Geochem. 6, 635±646. Pioneer. 1995. Abandoned hardrock mine priority sites 1995 summary report. Prepared for MT Dept. of State Lands, Abandoned Mine Reclamation Bureau, Helena, MT by Pioneer Technical Services Inc. Titan. 1995. Silver Bow Creek/Butte Area NPL site streamside tailings operable unit RI/FS. Prepared for ARCO, Anaconda Montana by Titan, Bozeman, MT.
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 eorts 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 dicult 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
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ka
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Butte
ac
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Warm Springs Ponds
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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
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Cr ee k
Clark
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Figure 1. Map of study area. Distances given in miles (1 mile 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, Mekm 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 aect 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 coecient (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 coecient 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 coecient would be equal to ten. Since the erosion rate is dicult to quantify, the source coecient (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.: Mekm 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.: Mekm
n X
Cbk
k1
Atribk m X At Aoj Sj -1 j1
At ÿ Cb-ave
n X
Atribk ÿ
k1 m X
A1
j1
n X k1
Cx k
m X j1
Ao j 3
Aoj Sj ÿ 1
Sk Aok m X At Aoj Sj ÿ 1 j1
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 coecient 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 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
Sn Aon Cxn m X Si Aoi Cxi
4
i1
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 dierent 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 coecients were ®t to the observed trace element trends in the Little Blackfoot River, Flint Creek, and the Blackfoot River using Equation 2, then source coecients 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
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WSP LBF
Flint Creek Milltown
1000
10,000
25
(c)
50
75 100 125 Mile downstream
150
Ramsay Flats
1000
WSP
LBF Flint Creek
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40
CFR
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Milltown
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20 Mile downstream
(b)
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(b)
10,000
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CFR 100
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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 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 dierent 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 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
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(a)
200
Milltown Reservoir
100
CFR 0
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Little Blackfoot
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Mile downstream
Lead (mg/kg)
800
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(b) Philipsburg District
CFR
600 400
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Mile downstream
Lead (mg/kg)
800
Milltown Reservoir
Philipsburg District
75
100
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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 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 1.6 km.)
Copper (mg/kg)
Copper (mg/kg)
1000
Flint Creek
8000 6000 Milltown Reservoir
4000 2000 0
0
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40
The Blackfoot River 5000
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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 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 52.8 mg/kg representing the aggregate CFR contribution, and 0.2 224 mg/kg 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 dierence 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 diculty 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 dierent 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
dierences 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 eects 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 aect 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 diculty 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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