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Graduate Theses & Non-Theses Student Scholarship
Fall 2019
CHEMICAL SPECIATION IN SILVER BOW AND BLACKTAIL CREEKS: IMPLICATIONS FOR BIOAVAILABILITY AND RESTORATION
Johnathan Feldman
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Part of the Chemistry Commons
CHEMICAL SPECIATION IN SILVER BOW AND BLACKTAIL CREEKS: IMPLICATIONS FOR BIOAVAILABILITY AND RESTORATION
by Johnathan Robert Feldman
A thesis submitted in partial fulfillment of the requirements for the degree of
Master of Science in Geoscience: Geochemistry option
Montana Tech 2019
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Abstract
Silver Bow and Blacktail Creeks, contaminated with toxic elements from mining, present a need for remediation and restoration. Trace elements are present in elevated concentrations, particularly copper. Determining element speciation will allow informed consideration of effective restoration strategies, by providing a foundation for assessing bioavailability and toxicity. The three goals are: determine how speciation varies between seasons and sites in four impacted sites from the greater Butte Area One, an impacted downstream site known as Santa, and a control site on Upper Blacktail Creek known as Blacktail; how these variations influence bioavailability and toxicity; and what causes these variations. Total concentration measurements of these elements exist for every season since November 2015, whereas speciation calculations are lacking. The complete aqueous chemistry needed for speciation calculations is considered: pH, dissolved oxygen, conductivity, temperature, major cations, major anions, dissolved organic carbon, dissolved inorganic carbon, and trace elements. The chemical speciation program EQ3 produced all speciation data for As, Cu, Fe, Ba, Zn, and Mn, all contaminants released by mining activities, for five seasonal sampling trips. Variations in pH values and contributions of tailings- contaminated waters conceivably influenced seasonal and geographical changes in bioavailability and toxicity. Photosynthetic activity acted as the primary influence on seasonal variations in bioavailability for all elements except barium and arsenic. Barium’s bioavailability and toxicity stayed relatively consistent between seasons and sites, whereas competition with phosphate plausibly created seasonal variations in arsenic toxicity. Copper carbonate complexes predominated through the year at most sites, and as a result, copper had a low bioavailability there. Inflow of tailings-contaminated groundwater drove most spatial variations in bioavailability and toxicity for zinc, copper, and iron during all months except May 2016 and August 2016. During August 2016, phosphate differences between sites acted as a major influence on geographical variations in bioavailability and toxicity for all speciated elements. Overall, this study has provided a foundation for restoration projects by discovering and explaining geographic and seasonal variations in chemical speciation.
Keywords: Northside Tailings, Diggings East Tailings, Remediation, Toxicity, Barium, Arsenic, Zinc, Copper, Iron, Manganese
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Acknowledgements
I would like to thank the Laboratory Exploring Geobiochemical Engineering and Natural Dynamics (LEGEND) team for collecting samples. The LEGEND team members involved in sample collection: Nathan Carpenter, Cynthia Cree, Georgia Dahlquist, McKenzie Dillard, James Foltz, Jordan Foster, Shanna Law, Kyle Nacey, Mallory Nelson, Isaiah Robertson, and Renee Schmidt. I would like to thanks my advisor Dr. Alysia Cox for providing guidance and support. Thanks to Jackie Timmer and Ashley Huft from the Montana Bureau of Mines and Geology for providing sample analysis. I would like to thank writing tutors John Della, Hiroshi O., Tori B., Julie Kim, Phillip L., Robert W., Rae K., and Kenneth A. for their assistance in editing this thesis. I would like to thank Shanna Law for aiding in using EQ3 and Sigma Plot. I would like to thank Dr. Rick Rossi for assisting me with statistical analyses. I would like to thank Nathan Carpenter for preparing GIS maps showing spatial variations in environmental chemical parameters such as pH and conductivity. I would like to thank Rika Lashley for providing helpful information on the Waste Water Treatment Plant. I would like to thank my thesis committee members: Dr. Chris Gammons, Dr. William Gleason, and Dr. Jerry Downey for supporting me and offering advice. Funding for this research mainly came from the Butte Natural Resource Damage Restoration Council and the Montana Natural Resource Damage Program. Funding from the Montana Water Center, Montana Tech Faculty Seed Grant and Faculty Development Initiatives, and the Montana Institute on Ecosystems provided additional research support.
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Table of Contents
ABSTRACT ...... II
ACKNOWLEDGEMENTS ...... III
LIST OF TABLES ...... VIII
LIST OF FIGURES ...... IX
GLOSSARY OF TERMS ...... XII
1. INTRODUCTION ...... 1
1.1. Historical Background ...... 2
1.2. Silver Bow and Blacktail Creek Geochemistry ...... 3
1.3. Speciation’s Effect on Bioavailability ...... 6
1.3.1.1. Complexation ...... 10
1.3.1.2. Oxidation State ...... 14
2. METHODS ...... 18
2.1. Sampling Sites ...... 18
2.2. Geochemical Field Analysis ...... 21
2.3. Sample Collection Procedures ...... 23
2.4. Cleaning Procedures ...... 24
2.4.1. Trace Metal Bottle Cleaning ...... 24
2.4.2. DIC and DOC ...... 25
2.5. Sample Storage Procedures ...... 25
2.6. Laboratory Analysis ...... 26
2.6.1. δD and δ18O ...... 26
2.6.2. Major Cations, Anions, and Trace Elements ...... 26
2.6.3. DIC and DOC ...... 27
2.7. Chemical Speciation Calculations ...... 28 v
2.8. Data Analysis Methods ...... 33
3. RESULTS ...... 35
3.1. Aqueous Chemistry...... 35
3.1.1. pH ...... 35
3.1.2. Temperature ...... 37
3.1.3. Dissolved Oxygen ...... 38
3.1.4. Specific Conductivity ...... 39
3.1.5. Dissolved Organic Carbon ...... 41
3.1.6. Dissolved Inorganic Carbon ...... 43
3.1.7. Major Anions ...... 44
3.1.7.1. Phosphate ...... 44
3.1.7.2. Sulfate ...... 47
3.2. Field Spectrophotometry ...... 48
3.2.1. Total Dissolved Silica ...... 49
3.3. δD and δ18O ...... 50
3.4. Trace Elements ...... 51
3.4.1. Barium ...... 53
3.4.1. Arsenic ...... 56
3.4.1. Zinc ...... 59
3.4.2. Copper ...... 62
3.4.3. Iron ...... 65
3.4.4. Manganese ...... 68
3.5. Speciation Calculated Using EQ3 ...... 70
3.5.1. Barium ...... 70
3.5.2. Arsenic ...... 72
3.5.1. Zinc ...... 74
3.5.2. Copper ...... 76
3.5.3. Iron ...... 79
3.5.4. Manganese ...... 81 vi
4. DISCUSSION ...... 83
4.1. Spatial Trends ...... 83
4.1.1. Urban Phosphate Discharge ...... 84
4.1.1.1. Sources of Aqueous Phosphate ...... 84
4.1.1.2. Phosphate and Photosynthesis ...... 85
4.1.1.3. Phosphate as a Competing Ion ...... 89
4.1.2. Tailings Impacted Water Influx ...... 91
4.1.3. Dissolved Organic Carbon ...... 98
4.1.4. Minor Influences on Bioavailability and Toxicity ...... 102
4.2. Seasonal Trends ...... 103
4.2.1. Seasonal Variations in Photosynthesis Impact Speciation ...... 104
4.2.2. Phosphate as a Competing Ion for Arsenic ...... 107
4.3. Limitations...... 110
4.4. Future Research ...... 113
5. CONCLUSIONS ...... 114
5.1. Recommendations Based on Speciation Data ...... 117
6. REFERENCES CITED ...... 118
7. APPENDIX A: TABLES ...... 132
7.1. Raw Water Chemistry Data ...... 132
7.2. Dissolved Organic Carbon and Dissolved Inorganic Carbon ...... 133
7.3. Major Anion Concentrations ...... 134
7.4. Major Cation Concentrations ...... 138
7.5. Field Spectrophotometry ...... 140
7.6. Raw Data Tables for Element Concentrations ...... 141
7.6.1. B, Al, Ti, V, and Cr ...... 141
7.6.2. Mn, Fe, Ni, Cu, and Zn ...... 143
7.6.3. Ga, As, Se, Rb, and Sr ...... 145
7.6.4. Zr, Nb, Mo, Pd, Ag, and Cd ...... 147 vii
7.6.5. Sn, Sb, Cs, Ba, La, and Ce ...... 149
7.6.6. W and U ...... 151
7.7. EQ3 Calculated Speciation ...... 153
7.8. 18O and D in Water ...... 157
7.9. GPS ...... 158
8. APPENDIX B: FIELD PICTURES ...... 159
8.1. November 2015 ...... 159
8.2. February 2016 ...... 160
8.3. May 2016 ...... 161
8.4. August 2016 ...... 162
8.5. November 2016 ...... 163
9. APPENDIX C: SPATIAL VARIATIONS IN SPECIATION ...... 164
10. APPENDIX D: PCA EXTRAS ...... 170
11. APPENDIX E: GEOGRAPHICAL BIOAVAILABILITY VARIATIONS ...... 173
12. APPENDIX F: SAMPLE EQ3 INPUT FILE ...... 174
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List of Tables
Table I: pH, dissolved oxygen, conductivity, and temperature ...... 132
Table II: Dissolved inorganic carbon and dissolved organic carbon concentrations ...... 133
Table III: Major anion concentrations for chlorine, fluorine, bromine, and sulfate ...... 134
Table IV: Major anion concentrations for bicarbonate, phosphate, nitrate, and nitrite... 136
Table V: Major cation concentrations ...... 138
Table VI: Field spectrophotometry ...... 140
Table VII: B, Al, Ti, V, and Cr concentrations ...... 141
Table VIII: Mn, Fe, Ni, Cu, and Zn concentrations ...... 143
Table IX: Ga, As, Se, Rb, and Sr concentrations ...... 145
Table X: Zr, Nb, Mo, Pd, Ag, and Cd concentrations ...... 147
Table XI: Sb, Cs, Ba, and Ce concentrations ...... 149
Table XII: W and U concentrations ...... 151
Table XIII: Speciation results for arsenic and barium ...... 153
Table XIV: Speciation results for copper ...... 154
Table XV: Speciation results for iron and manganese ...... 155
Table XVI: Speciation results for zinc ...... 156
Table XVII: 18O and D ...... 157
Table XVIII: GPS coordinates ...... 158
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List of Figures
Figure 1: Sampling site location map ...... 18
Figure 2: pH vs temperature ...... 37
Figure 3: Dissolved oxygen concentrations vs temperature...... 39
Figure 4: Specific conductivity vs pH ...... 40
Figure 5: Dissolved organic carbon vs pH ...... 42
Figure 6: Dissolved inorganic carbon vs pH ...... 44
Figure 7: Total phosphate vs total arsenic concentrations ...... 46
Figure 8: Total aqueous nitrogen vs total dissolved phosphate ...... 47
Figure 9: Total sulfate vs pH ...... 48
Figure 10: Si vs pH ...... 50
Figure 11: D vs 18O ...... 51
Figure 12: Average concentrations for Ba, As, Zn, Cu, Fe, and Mn ...... 52
Figure 13: Plot of concentration vs time for Ba, As, Zn, Cu, Fe, and Mn ...... 53
Figure 14: Barium concentration vs time ...... 55
Figure 15: Barium concentration vs pH ...... 56
Figure 16: Arsenic concentration vs time ...... 58
Figure 17: Arsenic concentration vs pH...... 59
Figure 18: Zinc concentration vs time ...... 61
Figure 19: Zinc concentration vs pH ...... 62
Figure 20: Copper concentration vs time ...... 64
Figure 21: Copper concentration vs pH ...... 65
Figure 22: Iron concentration vs time ...... 67 x
Figure 23: Iron concentration vs pH ...... 68
Figure 24: Manganese concentration vs time ...... 69
Figure 25: Manganese concentration vs pH ...... 70
Figure 26: Year-round speciation results for barium ...... 71
Figure 27: Year-round speciation results for arsenic ...... 73
Figure 28: Year-round speciation results for zinc ...... 75
Figure 29: Year-round speciation results for copper ...... 78
Figure 30: Year-round speciation results for iron ...... 80
Figure 31: Year-round speciation results for manganese ...... 82
Figure 32: ZnOH+ speciation vs phosphate ...... 87
- Figure 33: FeO2 vs phosphate ...... 88
Figure 34: FeO+ vs pH ...... 89
Figure 35: Principal component analysis plot ...... 96
Figure 36: Graph of predicted Cu2+ relative abundance vs DOC ...... 101
Figure 37: Relative abundance of MnSO4° vs sulfate concentrations ...... 103
Figure 38: USGS stream gauge data ...... 103
Figure 39: Summary of seasonal bioavailability changes ...... 110
Figure 40: November 2015 photos ...... 159
Figure 41: February 2016 photos ...... 160
Figure 42: May 2016 photos ...... 161
Figure 43: August 2016 photos ...... 162
Figure 44: November 2016 photos ...... 163
Figure 45: Geographical variations in barium speciation ...... 164 xi
Figure 46: Geographical variations in arsenic speciation ...... 165
Figure 47: Geographical variations in zinc speciation ...... 166
Figure 48: Geographical variations in copper speciation ...... 167
Figure 49: Geographical variations in iron speciation ...... 168
Figure 50: Geographical variations in manganese speciation ...... 169
Figure 51: Scree plot showing contributions of principal components to the variance ...170
Figure 52: Contribution of each variable to the two main principal components ...... 171
Figure 53: PCA plot showing relationships of sites to principal components ...... 172
Figure 54: Summary of geographical bioavailability variations ...... 173
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Glossary of Terms
Term Definition DEQ Department of Environmental Quality
DIC Dissolved inorganic carbon
DOC Dissolved organic carbon
EPA Environmental Protection Agency
MBMG Montana Bureau of Mines and Geology
PCA Principal component analysis
SBC Silver Bow Creek
USBC Upper Silver Bow Creek
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1. Introduction
In the 1800’s, mining activities contaminated Silver Bow and Blacktail Creeks with metals and metalloids including barium, arsenic, zinc, copper, iron, and manganese. Since then, remediation and restoration efforts have been conducted to ameliorate these problems. Various studies have been conducted to determine the effects of metals on wildlife, and to better guide restoration and remediation efforts. To date, this is the first study focusing primarily on chemical speciation in Blacktail and Silver Bow Creeks. Chemical speciation refers to the specific form an element is present in the environment, which can include oxidation state and complexation.
Metal and semimetal speciation states can exert a significant influence on their bioavailability and toxicity (Rajaković et al., 2013). As a result, regulatory agencies such as the
Environmental Protection Agency (EPA) have recognized that total concentrations alone are inadequate for assessing bioavailability and toxicity (Rajaković et al., 2013). Bioavailability refers to how readily contaminants enter the systems of living organisms (Elder, 1989).
Ascertaining the dominant influences on both seasonal and spatial speciation variations will assist remediation and restoration professionals in designing plans to reduce metal and semimetal bioavailability and toxicity to reintroduced organisms. For instance, developing an awareness of how copper toxicity varies seasonally allows restoration professionals to determine the ideal time of the year for reintroducing cutthroat trout, as survival rates are almost certainly higher during lower toxicity months.
The concentrations and bioavailability of barium, arsenic, zinc, copper, iron, and manganese to humans who may use Silver Bow Creek for recreational purposes is of concern to restoration professionals. For instance, contaminants may affect children and pets who play in the water. Fishermen may ingest contaminants from fish they consume. 2
This introduction provides a historical background for this work and explains how the creek became contaminated. Specifically, the historical background will explain the role of historical mining operations in creating sources of surface water contamination. A review of previously collected water chemistry data vital to understanding speciation is also provided, as well as an overview of chemical speciation and its effects on bioavailability and toxicity.
1.1. Historical Background
Silver Bow Creek is a 42 km long stream in Silver Bow County in Montana which flows east to west and forms the headwaters of the Clark Fork River. Uptown Butte is north of Silver
Bow Creek. Blacktail Creek flows from southeast to northwest, and confluences with Upper
Silver Bow Creek at South Montana Street near the Diggings East Tailings. The Upper Clark
Fork basin encompasses the towns of Rocker, Butte, Anaconda, Deer Lodge, and Bonner-
Milltown. Prospectors discovered valuable mineral deposits in the Upper Clark Fork Basin during the 19th century (Weed, 1912). Miners discovered gold placer deposits in 1864 (Weed,
1912). In 1882, prospectors discovered copper in Butte, Montana (Weed, 1912). Shortly after copper discovery, large-scale copper mining began. Butte became a major copper producer, producing 25% of the world’s copper in 1882 (Weed, 1912). The copper mining boom lasted well into the 20th century.
During the mining boom of the 19th and 20th centuries, Silver Bow Creek and the Clark
Fork River served as waste disposal sites for mining and mineral processing operations mostly for the Anaconda Mining Company (MTDOJ, 2011). Mining operations created mine waste dumps along the banks of Silver Bow Creek. Such dumps include the Diggings East Tailings,
Northside Tailings, Slag Wall Canyon, and the Parrot Tailings (EPA, 2018) 3
Flooding in 1908 spread heavy metals and arsenic from mine tailings sites across the flood plain, leading to significant metal and arsenic contamination of surface water and groundwater in the Silver Bow and lower Blacktail creeks (Gammons & Metesh, 2006). As a result, the EPA designated the Upper Clark Fork basin as a superfund site in 1983 (Gammons &
Metesh, 2006). The Upper Clark Fork basin is one of America’s largest superfund sites
(Gammons & Metesh, 2006).
The Montana Department of Environmental Quality (DEQ) and private environmental contractors have conducted several remediation and restoration efforts in the Upper Clark Fork
Basin. Restoration projects have involved reintroducing native species such as cutthroat trout
(Peters, 1985). Restoration and remediation projects are still ongoing in the Upper Clark Fork
Basin. Such projects include the removal of the Parrot Tailings (EPA, 2018). As a result, there is an ongoing need for scientific research that quantifies the bioavailability and toxicity of dissolved metals to aquatic organisms and humans.
1.2. Silver Bow and Blacktail Creek Geochemistry
Most previous studies have not measured the entire aqueous chemistry needed to calculate speciation; however, multiple studies established a foundation for future speciation research by collecting data on chemical parameters vital to calculating speciation. This study measures all geochemical parameters required to calculate speciation. Previous studies have characterized the hydrology of Silver Bow Creek and Upper Clark Fork.
Earlier studies have used field measurements to characterize three chemically distinct hydrological zones of Silver Bow Creek. These zones are the hyporheic zone (pH 6.2-7.9, average dissolved oxygen 188 μmol/L (6 mg/L)), the groundwater (pH 4.4-4.9, average dissolved oxygen 31.3 μmol/L (1 mg/L)), and the surface water (pH 7.8-9.1, average dissolved 4 oxygen 228 μmol/L (7.3 mg/L)) (Benner et al., 1995). The hyporheic zone is an interface between the groundwater and the surface water characterized by mixing of ground and surface waters (Benner et al., 1995; Nagorski & Moore, 1998). For comparison, typical stream pH values in the United States range from 6.50-8.50, and typical United States stream dissolved oxygen values range from 188 μmol/L to 313 μmol/L (6.00-10.00 mg/L) (CSU, 2010).
Previous studies have revealed surface water zinc, manganese, iron, and copper concentrations exceed those found in typical US creeks (Benner et al., 1995). Typical manganese concentrations in creeks and streams are 0.437 μmol/kg (24 μg/L) in the US (Smith et al., 1987).
Rivers and streams typically contain 0.276-0.552 μmol/kg (5-10 μg/L) zinc (Baker et al., 2011).
Typical copper levels in streams are 0.0910 μmol/kg (4 μg/L) (Baker et al., 2011). According to the World Health Organization (WHO), median iron levels in rivers worldwide are 13.7 μmol/kg
(0.7 mg/L) (Fawell, 2000). Typical baseline arsenic levels for US streams and rivers are 0.011
μmol/L (0.83 μg/l) (Ayers et al., 2017). Previously measured arsenic concentrations in surface waters of these two creeks match typical US values (Benner et al., 1995).
Relative amounts of As(III)/As(V) in all three hydrological zones revealed speciation differences between the hyporheic zone and the groundwater that dissolved oxygen differences alone cannot explain. Generally, As(III) becomes more abundant as dissolved oxygen concentrations decrease (Crecelius, 1977; Schmöger, 2000; Rajaković et al., 2013; Can et al.,
2014). On average, As(III) occupied 15% of the total arsenic in the groundwater and 78% of all arsenic in the hyporheic zone (Nagorski & Moore, 1999). Sulfidic anoxic conditions caused iron- sulfide minerals such as pyrite to bind up much of the As(III) in the groundwater zone (Nagorski
& Moore, 1999). In the hyporheic zone, reductive dissolution caused As(III) to desorb from iron hydroxide minerals and enter the surface water (Nagorski & Moore, 1999). 5
If As(III) enters surface water from lower hydrological zones, it is possible that similar processes influenced speciation for other elements. Further studies indicated that similar desorption reactions in the hyporheic zone caused phosphate to desorb from iron oxide minerals in pore water and enter the surface water (Rader, 2019). Phosphate concentrations can influence the speciation and bioavailability of elements such as arsenic and manganese.
In addition to directly transporting chemical species, water inflow from other zones into the surface water can influence speciation by altering key aspects of the surface water chemistry.
Previous studies suggest that acidic tailings-contaminated groundwater could be entering Silver
Bow Creek’s surface water during precipitation events at gaining reaches and in areas closest to the Diggings East Tailings (Tucci & Icopini, 2012; Runkel et al., 2016). This groundwater influx may lower surface water pH, thereby influencing speciation.
Hydrological tracer studies have determined if streams are net gaining or losing, and determined inflow sources (groundwater, adjacent wetlands, surface water) into gaining portions
(Tucci, 2014). These tracer tests focused on a reach of stream located in Butte Area One (Tucci,
2014). That reach spanned from Pine Avenue to Montana Street (Tucci, 2014). Tracer tests did not categorize the portion of Silver Bow Creek flowing by the Slag Wall Canyon as a non- gaining or gaining stream (Tucci, 2014). This portion of the creek receives inflow from gaining streams that receive inflow from tailings-contaminated groundwater (Tucci, 2014). Inadequate discharge prevented tracer tests from being conducted at Upper Silver Bow Creek (USBC)
(Tucci, 2014). The portion of Blacktail Creek flowing past the Kampgrounds of America (KOA) area near the Blacktail trail is a non-gaining area which is immediately downstream from a gaining portion (Tucci, 2014). That gaining portion receives 39% of its inflow directly from the groundwater and another 61% from wetlands which receive an influx of groundwater (Tucci, 6
2014). Because of the input from gaining reaches, it is possible there are some reduced species present in the stream surface water near the KOA area (Tucci, 2014).
1.3. Introduction of Bioavailability and Toxicity
Toxicity refers to a substance’s ability to cause harm to living organisms. An element’s toxicity is heavily influenced by its concentration. Regulatory limits for maximum allowable total concentrations have been established for most of the elements in this study. The Montana
DEQ standards for metals in freshwaters are based on total recoverable metals (MT DEQ, 2019).
The EPA standards for metals in water are based on dissolved concentrations only. The EPA defines “dissolved metals” as smaller than 0.45 m when discussing freshwater metals standards
(EPA, 2017). Both the EPA and the Montana DEQ have separate standards for drinking waters, groundwater, and surface water. The EPA and Montana DEQ further subdivide surface waters into human health and aquatic life standards. Aquatic life standards are primarily concerned with protecting aquatic wildlife such as fish, whereas human health standards and drinking water standards are mainly tasked with protecting human life.
Regulatory limits for aquatic life are often lower than drinking water, and human health standards and are typically subdivided into acute and chronic concentrations. Acute concentrations produce immediate toxic effects to life, whereas chronic concentrations produce toxic effects to life after prolonged periods of exposure. However, no EPA or Montana DEQ aquatic life standards exist for barium. The barium 48-hour LC50 value for daphnia magna, a common zooplankton species eaten by freshwater fish is 94.0 mol/l (14.5 mg/L) (Biesinger &
Christensen, 1972). There is little current research on barium LC50 values for fish, however one study has demonstrated a 96-hour LC50 of 324 mol/l (500 mg/L) for the sheepshead minnow 7
(Heitmuller et al., 1981). Barium has a MTDEQ freshwater human health standard of 64.8
mol/l (1000 g/l) (MT DEQ, 2019).
Unlike barium, arsenic has EPA and Montana DEQ aquatic life limits in addition to drinking water and human health standards. The USEPA’s limits for arsenic in freshwater are
2.00 mol/l (150 g/l) for chronic exposure and 4.54 mol/l (340 g/l) for acute toxicity (EPA,
2017). The EPA sets a maximum contaminant level (MCL) drinking water standard of 0.133
mol/L (10 g/l) for arsenic. The Montana DEQ sets a chronic toxicity limit of 2.00 mol/l (150
g/l) and an acute toxicity limit of 4.54 mol/l (340 g/l) for arsenic (MT DEQ, 2019). The
Montana DEQ sets a human health standard of 0.133 mol/L (10 g/l) arsenic for freshwaters.
Like the EPA and the Montana DEQ, the World Health Organization has aquatic life criteria for arsenic toxicity to fish, amphibians, invertebrates, and algae (WHO, 2004). The onset of sublethal toxic effects to algae can occur at concentrations between 0.0133 to 0.133 mmol/L
(1 and 10 mg/L) whereas chronic sublethal toxic effects to fish often begin to occur around 0.133 mmol/L (10 mg/L) (WHO, 2004). Lethal toxic effects in freshwater amphibians and invertebrates start to occur at concentrations between 0.133 to 1.33 mmol/L (100 to 1000 mg/L)
(WHO, 2004). Lethal toxic effects in freshwater fish start to occur at concentrations between
1.33 to 13.3 mmol/L (1000 to 10000 mg/L) (WHO, 2004).
Neither the USEPA nor the Montana DEQ have separate acute and chronic zinc exposure limits for zinc. The EPA sets an aquatic life limit of 6.62 mol/l (120 g/l) for dissolved zinc.
Montana DEQ aquatic life zinc standards are adjusted for hardness (MT DEQ, 2019). Zinc has a
Montana DEQ aquatic life limit of 2.04 mol/l (37 g/l) at a hardness level of 25 mg/L (MT
DEQ, 2019). The Montana DEQ zinc human health standard is 408 mol/l (7400 g/l) and there 8 is no enforceable zinc drinking water standard. The USEPA has a secondary zinc standard of 276
mol/L (5000 mg/L) based on taste and aesthetic considerations for drinking waters.
Like zinc, aquatic life limits of copper are also hardness dependent. The Montana DEQ sets acute and chronic copper limits of 0.0448 mol/l (2.85 g/L) and 0.0596 mol/l (3.79 g/L) copper respectively, at a hardness of 25 mg/L (MT DEQ, 2019). The Montana DEQ sets a human health standard of 15.7 mol/l (1000 g/l) copper for surface waters (MT DEQ, 2019).
The USEPA and the World Health Organization (WHO) set copper drinking water standards of
29.5 mol/L (1300 g/L) and 45.5 mol/L (2000 g/L) respectively. The USEPA uses the Biotic
Ligand Model to determine maximum allowable concentrations of copper in freshwater. Biotic
Ligand Model calculations utilize total dissolved organic carbon concentrations, alkalinity, and hardness to predict copper toxicity to aquatic life. Toxicological studies have evaluated the toxicity of copper to phytoplankton using the species Closterium acerosum, Pediastrum simplex,
Chlorella vulgaris and Scenedesmus quadricauda (Bilgrami & Kumar, 1997). Total concentrations exceeding 0.230 mol/L (10 g/L) caused toxic effects in algae such as inhibited growth (Bilgrami & Kumar, 1997). The 96 hour LD50 value for the freshwater fish Oreochromis niloticus is 181 mol/L (7940 g/L) (Alkobaby & Abd El-Wahed, 2017).
Unlike copper, iron does not have separate Montana DEQ chronic and acute limits (MT
DEQ, 2019). The Montana DEQ maintains an aquatic life standard of 17.9 mol/l (1000 g/l) for iron (MT DEQ, 2019). The USEPA has an aquatic life standard of 17.9 mol/l (1000 g/l) of total dissolved iron (MT DEQ, 2019).
There are also no enforceable Montana DEQ or EPA aquatic life, human health, or drinking water standards for manganese; however, guidelines exist which can help evaluate manganese toxicity. The EPA has recommended drinking water health advisory standards for 9 manganese. The lifetime health advisory standard is 5.46 mol/l (300 g/l). It is likely that the lifetime health advisory standard protects humans against neurological effects associated with manganese poisoning. The Secondary Maximum Contaminant Limit (SMCL) is 0.05 mg/l (0.91
mol/l). The SMCL is mainly based on taste and aesthetic considerations such as discoloration or staining of laundry rather than health risks. Toxicology studies have determined LC50 values for two water flea species. These species are C. dubia and H. Azteca (Lasier et al., 2000). These two water flea species are often used in toxicity analyses of wastewater effluents (Lasier et al.,
2000). For H. Azteca, LC50 values are 54.6 to 157 to 249 mol/L (3.0 to 8.6 to 13.7 mg/L) for soft, moderately hard, and hard waters respectively (Lasier et al., 2000). For C. dubia, LC50 values averaged 71.0, 155, and 209 mol/L (3.9, 8.5 and 11.5 mg/L) for soft, moderately hard, and hard waters (Lasier et al., 2000).
Bioavailability refers to how easily a substance enters the system of living organisms, and it can be just as influential on an element’s toxicity as total concentrations (Smith & Huyck,
1999). Thus, in order to exhibit toxicity, an element must be speciated in a bioavailable form
(Smith & Huyck, 1999). For instance, barium sulfate is relatively non-toxic because it is not bioavailable due to its insolubility in water (Smith & Huyck, 1999).
Informed predictions of how speciation can affect bioavailability necessitate both reliable, geochemical modeling results, and a thorough review of existing literature. Existing literature defines two main forms of chemical speciation which influence bioavailability and toxicity. The first is complexation, which is further subdivided into ion pairing and organic complexation. The second is oxidation state. 10
1.3.1.1. Complexation
Chemical elements can either exist as free ions or can complex with other constituents, influencing their bioavailability (Elder, 1999; Chakoumakos et al., 2002). For barium, copper, iron, manganese, and zinc the monatomic free ion always has the greatest bioavailability and therefore toxicity to plants and freshwater fish such as the cutthroat trout (Elder, 1999;
Chakoumakos et al., 2002). Arsenic does not exist as a monatomic free ion in natural waters, instead speciating as various polyatomic anions. The bioavailability and toxicity of arsenic oxyanions usually decreases when complexation occurs. Every element in this study can form both ion pairs and organo-metallic complexes.
Ion pairing can occur through a variety of processes and is thought to lower elemental bioavailability and toxicity (Elder, 1989; Chakoumakos et al., 2002; Karel et al., 2002; Buck et al. 2007; Cooper et al., 2013). Several elements form various polyatomic oxyanion species.
Cations pair with anions. Many cation species can also complex with water.
Arsenic, copper, manganese, and zinc can form hydroxyl complex species when complexing with water. Arsenic and manganese readily form hydroxyl complexes in conditions more basic and reducing than natural surface waters (Boytsova et al., 2015; Libera et al., 2017).
+ + 2+ By contrast, the zinc and copper hydroxyl species ZnOH , CuOH , and Cu2(OH)2 commonly exist in natural surface waters (Elder, 1989; Chakoumakos et al., 2002). Whereas likely less toxic than the free ions, zinc and copper hydroxyl ions are still highly toxic to life (Elder, 1989;
Karel et al., 2002).
Barium, copper, arsenic, zinc, and manganese can form ion pairs with the carbonic acid system (Andrew et al., 1977; Elder, 1989; Smedley & Kinniburgh, 2002; Wilkin et al., 2003;
+ + Tudorache et al., 2010). Examples of such species include BaHCO3 , CuHCO3 , CuCO3°,
+ ZnCO3°, MnHCO3 , and MnCO3°. More carbonate complexation occurs when elevated 11 dissolved inorganic carbon levels exist in the environment (Elder, 1989; Tudorache et al., 2010).
+ CuHCO3 and CuCO3° are essentially non-toxic to cutthroat trout, and have a low bioavailability and toxicity to aquatic life overall (Andrew et al., 1977; Elder, 1989; Chakoumakos et al., 2002;
+ Buck et al. 2007; Cooper et al., 2013); however, BaHCO3 is almost as toxic and bioavailable as free Ba2+ to freshwater fish and mammals (Elder, 1989). Although no published studies prove
+ 2+ that MnHCO3 and MnCO3° are less bioavailable than the free Mn ion, this is likely the case because carbonate complexation lowers bioavailability for other important elements such as copper.
Sulfate can bind with arsenic, barium, copper, and iron, all of which can precipitate as solid minerals (Elder, 1989; Chakoumakos et al., 2002; Ritchie, 2004). Once precipitated, barium sulfate is essentially insoluble and nontoxic (Elder, 1989; Chakoumakos et al., 2002).
Copper sulfate is still relatively toxic and soluble. Sulfate binds with manganese, forming a stable MnSO4° ion pair which is still bioavailable and toxic, but less so than the free ion.
Iron, copper, and arsenic can all form oxyanions in addition to binding with sulfate.
3- Inorganic As(V) exists as the arsenate ion (AsO4 ) in highly basic conditions and H3AsO4 in
- 2- acidic conditions (Inam et al., 2018). The species H2AsO4 and HAsO4 are usually predominant in most natural surface waters (Inam et al., 2018). Iron(III) is commonly present as the species
+ - FeO , HFeO2°, and FeO2 in surface water. No existing studies explicitly state any difference in bioavailability or toxicity for the different ion pairs of As(V), As(III), Fe(III), or Fe(II).
Copper forms an oxyanion CuO (aq), which may be less bioavailable and toxic than
CuOH+. While existing research does not show a difference in bioavailability between these species, CuO is most stable as a solid species (Ritchie, 2004). Solid metal particles are less 12 bioavailable and toxic than dissolved metals (EPA, 1993). Because CuO (aq) more readily becomes a solid particle, it could have a lower bioavailability.
Organic complexation exerts a strong influence on elemental toxicity. For barium, arsenate, zinc, copper, iron, and manganese organic complexes are thought to be less bioavailable and toxic to animals such as mussels and fish than free ions and ion pairs (Jain &
Ali, 2000; Naidu, 2013; Rajaković et al., 2013; Jablonska-Czapla, 2015). Known exceptions to this rule are the organo-arsenite species monomethylarsonic acid (MMAA(III)) and dimethylarsinic acid (DMAA(III)). They have a higher toxicity than the inorganic forms of both
As(V) and As(III) (Wang et al., 2006). Organo-metallic complex stability influences the effective bioavailability and toxicity. More stable complexes do not break down as readily.
Organo-barium and organo-manganese complexes are very unstable in natural waters (Naidu,
2013; Jablonska-Czapla, 2015).
The chemical characteristics of dissolved organic carbon ligands are influential in determining the stability and abundance of organic complexes for copper and iron (Stijn et al.,
2011). For instance, aromatic humic substances are more likely than other dissolved organic compounds to form stable organo-copper complexes (Stijn et al., 2011). For iron, the molar mass of organic matter and the functional groups present influence organo-iron complex stability
(Eusterhues et al., 2003; Hassler et al., 2011).
Organo-copper complex abundance and stability also increases as a result of elevated dissolved organic carbon concentrations, particularly in fresh waters (Meador, 1990; Cooper et al., 2013). The EPA’s current freshwater copper toxicity standards consider total dissolved organic carbon concentrations. Standards permit less total copper for areas with low dissolved organic carbon concentrations (EPA, 2018). 13
Existing research does not show dissolved organic carbon concentrations influencing the stability or abundance of organic complexes for arsenic, iron, or zinc; instead a variety of other factors influence this. Organo-zinc complex stability increases as pH becomes more alkaline
(Florence & Batley, 1977). For arsenic and iron, ion pair characteristics likely act as the primary influence on organic complex stability. Such characteristics include total charge, polarity, oxidation state, and number of hydrogen bond donor and acceptor sites. Total charge is probably the most important influence on organo-metallic complex stability for iron and arsenic.
Charge magnitude on the metal ions and ligands is the primary influence on organic complex stability (Greenwood & Earnshaw, 2005). Most natural organic matter is negatively charged (Davis, 1981; CUCE, 2007; Stuckey, 2018). Humic and fulvic substances, which can be constituents of dissolved organic matter, have negatively charged carboxyl groups (Schnitzer,
1965). As a result, cations with high positive charges tend to form more stable organic complexes than anions or ions with lower positive charges. Based on this interpretation, it is
2- - more difficult for HAsO4 to form stable organometallic complexes than H2AsO4 . Based on total charges alone, FeO+ is the most likely to form stable complexes with organic matter, and
- FeO2 is the least likely.
The plausibility of hydrogen bond formation acts as the next most important influence on organic complex stability for iron and arsenic after total charge. Hydrogen bonding is one of the strongest types of intermolecular forces (Ritchie, 2004). Hydrogen bonds often stabilize bonds between polar organic compounds and other compounds such as metal and semi-metal ions
(Wada et al., 2004; Gellrich et al., 2011; Pace et al., 2014; Bhadra et al., 2018). Such polar organic compounds include polar proteins and nucleic acids, both key constituents of dissolved organic carbon (Pace et al., 2014). 14
Species with a higher number of hydrogen bond donor or acceptor sites form more stable
- hydrogen bonds. Therefore, these species form more stable organo-metallic complexes. H2AsO4
2- has two hydrogen bond donor sites, whereas HAsO4 has one hydrogen bond donor site (NCBI,
- 2019). For that reason, H2AsO4 more readily forms stable complexes with dissolved organic
2- compounds than HAsO4 . HFeO2° has two hydrogen bond acceptor sites and one hydrogen bond
- donor site (NCBI, 2019). FeO2 has two hydrogen bond acceptor sites and no hydrogen bond
- donor sites (NCBI, 2019). Because HFeO2° is more likely to form hydrogen bonds than FeO2 , it probably forms organic complexes with a lower stability.
Like hydrogen bonding, dipole-dipole forces can act to increase organic complex stability. Polarity can influence the likelihood that dipole-dipole intermolecular forces occur between inorganic ions and organic ligands. Molecular geometry influences the polarity of inorganic arsenic ion pairs. All four forms of inorganic As(V) have tetrahedral molecular geometry. As a result, the polarity of all arsenic compounds are likely the same. FeO+ has the greatest net dipole of all three species (due to its linear molecular geometry and strongly polar intramolecular bond) (NCBI, 2019). As such, it is the most likely to form stable bonds with
- dissolved organic carbon compounds via dipole-dipole forces. HFeO2° and FeO2 have smaller net dipoles than FeO+. They are both bent molecules with oxygen atoms located on both sides of the oxygen (NCBI, 2019). Because of their lower net dipole, it is likely more difficult for them to form stable dipole-dipole bonds with dissolved organic carbon compounds.
1.3.1.2. Oxidation State
Redox inactive elements such as Ba and Zn are present in only one oxidation state, in this case 2+, whereas redox active elements exist in a variety of oxidation states. As, Fe, and Mn can exist in multiple oxidation states with different bioavailabilities and toxicities. Iron occurs in the 15
2+ and 3+ oxidation states; the 3+ state is the most stable in natural surface waters, and the least bioavailable (Singh & Hider, 1994; Hassler et al., 2009; Xing & Liu, 2011). Arsenic exists in the 3+ and 5+ oxidation states. Manganese exists in the 2+, 3+, and 7+ oxidation states as a dissolved metal.
Environmental conditions control which oxidation states of manganese occur (Pearson &
Greenway, 2005). Mn4+ and Mn3+ are more stable in the solid phase in most natural surface waters; whereas Mn(II) species predominate in the aqueous phase (Pearson & Greenway, 2005;
4+ 3+ Rumsby et al., 2014). Mn is found in the mineral pyrolusite (MnO2) and Mn is found in the
2+ mineral hausmannite (Mn3O4) alongside Mn (Fritsch et al., 1998). Manganese oxidation state is strongly influenced by pH and dissolved oxygen levels (Jaudon et al., 1989; LaZerte &
Burling, 1990; Rumsby et al., 2014). Above pH 8.30, Mn(IV) and Mn(VII) species increase in abundance due to oxidation and sorption reactions (Rumsby et al., 2014). Biologically unavailable solid colloid species such as MnO2 are eventually formed, because the +4 and +7 oxidation states are largely unstable in natural waters (Rumsby et al., 2014). The oxidation of
Mn2+ to Mn4+ and Mn7+ species is most likely to occur in oxic environments (Jaudon et al., 1989;
LaZerte & Burling, 1990; Rumsby et al., 2014). Mn(VII) is relatively unstable in natural surface waters, instead requiring extreme oxidizing conditions for stability (Jaudon et al., 1989; LaZerte
& Burling, 1990; Rumsby et al., 2014).
Important differences in bioavailability exist between the four oxidation states of manganese. Manganese species with 3+ or 2+ oxidation states are more bioavailable to humans and other mammals than Mn(IV) species, as Mn(IV) readily forms insoluble compounds
- (Rumsby et al., 2014; Jablonska-Czapla, 2015). Mn(VII) ions exist as permanganate (MnO4 ) in aqueous solutions. Permanganate ions can exhibit toxicity to humans and animal life by causing 16 oxidative injury to organs and skin as they readily oxidize organic materials (Rumsby et al.,
2014; Jablonska-Czapla, 2015). Permanganate ions in solution exhibit sensitivity to light,
4+ reducing to Mn and eventually forming the insoluble MnO2 compound (Sundar Rao, 1937).
Inferring from this information, permanganate ions readily reduce to MnO2 in natural waters as ample light is present during the day, and as a result have a low bioavailability in natural waters
(Rumsby et al., 2014; Jablonska-Czapla, 2015). Of the two lowest manganese oxidation states,
Mn(III) is three times less toxic to humans and other mammals than Mn(II) (Jablonska-Czapla,
2015). The ceruloplasmin protein converts Mn(II) species to Mn(III) inside the body in mammals via oxidation (Rumsby et al., 2014).
Arsenic’s two oxidation states differ from each other in bioavailability and toxicity. The most common oxidation state in surface waters is the pentavalent form (arsenate), the trivalent form (arsenite) which is typically 25-60 times more toxic and bioavailable than arsenate mainly occurs in anoxic waters (Crecelius, 1977; Schmöger, 2000; Rajaković et al., 2013; Can et al.,
2014). The difference in bioavailability and toxicity between the two oxidation states is a result of distinct chemical mechanisms. Arsenate replaces phosphate in cells whereas arsenite species bind to sulfhydryl groups of lipoic acids (Schmöger, 2000). Arsenite species also conjugate with glutathione, causing oxidative stress in cells (Suzuki et al., 2002).
Competing ions, particularly phosphate, have a stronger effect on the effective toxicity of
As(V) than on As(III) species in natural freshwaters (Ullrich-Eberius et al., 1989; Rahman et al.,
2014). The effective bioavailability and toxicity of As(V) species to phytoplankton and algae declines as aqueous phosphate ions increase in concentration (Rahman et al., 2014). The effective toxicity (expressed as the EC20) of As(V) to the common fresh water algae species
Chlorella sp. and Monoraphidium arcuatum decreased 20 fold once the phosphate to arsenic 17 ratio exceeded 6.47 µmol phosphate:1 µmol As(V) (Rahman et al., 2014). Accumulation of
As(V) in algae due to lower phosphate concentrations can bioaccumulate up the food chain.
Larger organisms such as cutthroat trout eat zooplankton, which in turn feed on algae and phytoplankton. During periods of lower phosphate concentrations, trout take up more As(V) by eating plankton containing elevated As(V) levels.
Nitrate concentrations have a much lesser influence on arsenic bioavailability and toxicity than phosphate in natural freshwaters (Rahman et al., 2014). A very small, but statistically significant positive relationship existed between As(V) toxicity and aqueous nitrate
- concentrations (Rahman et al., 2014). As(V) toxicity decreased slightly when the ratio of NO3
3- :PO4 fell below 15:1. Nitrate competed with As(V) for sorption sites, which slightly increased the effective toxicity of arsenic (Rahman et al., 2014).
Studies of aquifers in Bangladesh suggest silica can influence pentavalent arsenic’s effective toxicity (Davis et al., 2002; Gao et al., 2011). Dissolved silica competes more strongly with pentavalent arsenic for sorption sites on iron oxide and clay minerals once dissolved silica concentrations exceed 600 µmol/kg (Davis et al., 2002; Gao et al., 2011). This increases pentavalent arsenic’s effective toxicity to swimming animals and humans (Davis et al., 2002;
Gao et al., 2011). As more dissolved silica adsorbs to minerals, more dissolved arsenate enters the surface water.
The study seeks to determine how and why speciation varies in Silver Bow and Blacktail creeks between different seasons and locations for Ba, As, Zn, Cu, Fe, and Mn, and discuss how speciation can impact bioavailability and toxicity. EQ3 speciation calculations utilized water chemistry data to calculate speciation for all elements. The original hypothesis predicts that all elements are speciated in their least bioavailable and toxic forms during the spring and summer 18 as a result of photosynthesis-induced pH increases, and speciate in their most bioavailable and toxic forms at sites closest to the Diggings East Tailings.
2. Methods
2.1. Sampling Sites
Blacktail Creek (Blacktail), Quality Drain, Upper Silver Bow Creek (USBC), Blacktail Creek at the Kampgrounds of America (KOA), Silver Bow Creek (SBC) at Slag Canyon, and SBC at
Santa are vital locations for assessing risks posed to wildlife and humans by contaminants
(Figure 1). Blacktail is the control site for the study (Figure 1). It is located 10 kilometers upstream of the other sites (Figure 1). Activities such as mining and fertilizer application have had a minimal impact at Blacktail, so it serves as a useful comparison to the other sites.
Figure 1: Sampling site map. The map also shows mines and tailings which may influence spatial variations in speciation. The creek is outlined in blue. GPS coordinates for sampling sites are shown in Appendix A:Table XVIII and photos of the sampling sites are shown in Appendix B.
Quality Drain, a storm drain near Quality Inn provides a measurement of metal inputs into
Blacktail Creek from urban storm water. Calculating element speciation at Quality Drain will 19 give experts an idea of the seasonal risk posed to wildlife by storm water contaminants. In this study, field sampling only occurred during months in which visible flow emerged from the drain.
USBC is important because it is the closest site to the Diggings East Tailings, an unlined landfill containing high levels of copper, zinc, arsenic, and barium (MT DOJ, 2014). These contaminants are known to enter both the surrounding groundwater and surface water in Butte
Area One (Tucci & Icopini, 2012). USBC is lined, so groundwater does not enter it (NDRP,
2007). The EPA and the DEQ plan to remove the Diggings East Tailings and perform environmental restoration afterwards (MT DOJ, 2014).
KOA, a site located near Lexington Avenue and the Blacktail Trail, is important in assessing the risk posed by contaminants because campers use the stream at KOA for recreational purposes such as fishing. Because KOA receives inflow from gaining reaches, it is possible for groundwater impacted by the Diggings East Tailings to enter KOA (Tucci, 2014). Mine impacted surface runoff can also enter this recreational area during storm events.
Slag Canyon refers to the portion of Silver Bow Creek near the Slag Canyon wall (MT DOJ,
2014). The Slag Canyon wall is a massive wall of mine waste (slag), a potential source of surface water metal contamination (particularly copper and manganese) to Silver Bow Creek (MT DOJ,
2014). Laboratory tests utilizing Synthetic Precipitation Leaching Procedure (SPLP) and
Toxicity Characteristic Leaching Procedure (TCLP) methods have evaluated the leachability of
Slag Wall Tailings material, determining relatively low leachability for most metals and metalloids from the Slag Wall Tailings into the creek, except for As, W, and possibly Pb and Cu
(Kaplan, 2016). The SPLP leachate samples contained elevated tungsten and arsenic levels, suggesting that these metals can leach into the creek water from the Slag Wall Tailings (Kaplan, 20
2016). These experiments yielded mixed results for copper and lead leachability, possibly a result of slag heterogeneity (Kaplan, 2016).
The site Santa resides in the lower reaches of Silver Bow Creek near Whiskey Gulch. Santa is the only site in this study located downstream from the Waste Water Treatment Plant (Figure
1). The Waste Water Treatment Plant (WWTP) consists of the Sewage Treatment Plant and the
Butte Treatment Lagoons (J. Griffin, pers. comm). The Butte Treatment Lagoon treats stormwater and mine tailings-contaminated groundwater (J. Griffin, pers. comm). The Sewage
Treatment Plant primarily treats municipal sewage water but receives some stormwater during high precipitation periods (J. Griffin, pers. comm). The treated water from both facilities is discharged approximately 4.5 kilometers upstream of Santa (MT DOJ, 2014). The State of
Montana is looking to turn Whiskey Gulch and the nearby portions of the creek into a recreational area suitable for fishing (MT DOJ, 2014). The DEQ seeks to determine how contaminants from the discharge affect the populations of cutthroat trout and brook trout (MT
DOJ, 2014).
The Outer and West Camp mines are inactive mines located near Santa (Figure 1).
Sampling occurred at these mines during May and March 2016 (Schmidt, 2017). The West Camp
Extraction well catches most groundwater flowing towards Santa from the West Camp mines.
All sites were sampled in the same sequence during every trip to minimize the impact of diel cycles on seasonal comparisons of data. Diel cycles are water chemistry changes that occur over a 24-hour period (Gammons et al., 2015). For instance, pH is usually more basic in the middle of the day than at night due to photosynthetic activity (Gammons et al., 2015).
Concentration and speciation for aqueous metals can also vary between different times of the day 21
(Gammons et al., 2015). Sampling the sites in a consistent order ensures that sites are sampled at approximately the same time of day during every sampling trip.
2.2. Geochemical Field Analysis
Dissolved oxygen, pH, and conductivity were all measured using handheld meters. For all three types of measurements, the probes were placed directly into the stream. Field workers waited for meter readings to stabilize, and recorded measurements in field notebooks. Any fluctuations in field measurements were recorded as errors.
A WTW 3110 ProfiLine meter with Sentix 41-3 probe was used to collect pH measurements (manufacturer error = ±0.01). Three pH standards (4,7, and 10) were used to calibrate the pH meters each sampling day. For sites where pH errors were not recorded, errors were estimated as ±0.05 following Cox et al., 2011, which is five times standard manufacturer error (±0.01).
Dissolved oxygen measurements were collected using a DS5 Hydrometer (Schmidt,
2017). Standard manufacturer error is 0.5%. Dissolved oxygen measurements were used to calculate pO2 (Fernández-Prini et al., 2003). pO2 was the measurement of oxidation potential used in this study.
A YSI 30 meter was used to collect conductivity measurements. The error is assumed to be five times the manufacturer error of ±0.5% for sites with missing errors (Schmidt, 2017).
Conductivity probes were calibrated once every year.
A HACH DR/2010 spectrophotometer with wet chemical tests were used to test for Fe2+, total dissolved sulfide, and dissolved silica in the field. For silica analysis, Silicomolybdate EPA
Method 8185 was used. All silica field spectrophotometry results were reported in micromoles per liter (μmol/L). For this method, the lower detection limit for silica was 17 μmol/L, and the 22 upper detection limit was 1600 μmol/L silica. Silica testing samples were filtered through a 0.2
μm membrane before analysis (Schmidt, 2017; Law, 2018). High sulfide or iron concentrations can interfere with silica (Schmidt, 2017; Law, 2018).
The ferrozine method was used for spectrophotometric Fe2+ testing during the period
November 2015-May 2016 (Schmidt, 2017; Law, 2018). Prior to analysis, the samples were filtered to 0.2 μm. The pH of the samples was adjusted to a value within the range 3-5 with nitric acid and ammonium hydroxide prior to adding the ferrozine reagent (Schmidt, 2017; Law, 2018).
Ferric iron (Fe3+) precipitates at a pH of 3.5 (Balintova & Petrilakova, 2011). A 0.5 mL aliquot of ferrozine reagent was added to each sample, which formed a colored complex with Fe2+
(Schmidt, 2017; Law, 2018). The absorbance of the sample at 562 nm was quantified and used to determine Fe2+ concentration (Schmidt, 2017; Law, 2018). The lower detection limit for Fe2+ was 0.04 μmol/L and the upper detection limit was 18 μmol/L (Schmidt, 2017; Law, 2018).
For August 2016-November 2016, a different Fe2+ analysis method was adopted. The new method involved using 1,10-phenanthroline to form colored complexes with Fe2+. The absorbance of the sample at 510 nm was quantified and used to determine Fe2+ concentration
(Law, 2018). The lower detection limit for Fe2+ was 0.35 μmol/L (0.02 mg/l), and the upper detection limit was 44.8 μmol/L (2.50 mg/L) (St. Clair., 2017; Law, 2018; St. Clair et al., 2019)
- For total sulfide spec testing (H2S and HS ), the Hach method based on the Cline method was used. The lower detection limit was 0.31 μmol/L, and the maximum detection limit was 18
μmol/L (Cox et al., 2011; Schmidt, 2017; Law, 2018). At the beginning of the procedure, 25 mL of the unfiltered sample was added to the cuvette (Schmidt, 2017; Law, 2018). After adding the sample to the cuvette, 1 mL of sulfuric acid was added. After the addition of sulfuric acid, 1 mL of methylene blue (HACH Sulfide Reagent 2) was added to the sample, five minutes before 23 zeroing the instrument (Schmidt, 2017; Law, 2018). The same procedure was used for regular samples and blanks (Schmidt, 2017; Law, 2018).
2.3. Sample Collection Procedures
Dissolved inorganic carbon (DIC), dissolved organic carbon (DOC), hydrogen and oxygen isotopes found in water (δD and δ18O), major cations, major anions, and trace metals were analyzed in the MBMG laboratory. However, correct field sampling procedures were necessary in order to ensure accurate laboratory results for each of these parameters. The same sample collection procedures were used for all six sample types.
A polyethylene scoop was used to collect water samples from each location. Water samples were collected at the exact spot where meter readings were taken. The scoop was then rinsed three times with water from the sample location. A trace metal clean HDPE 1L bottle was used to hold the sample during water filtration (Schmidt, 2017; Law, 2018). The 1L bottle was rinsed three times with water from the sampling site prior to use (Schmidt, 2017; Law, 2018).
After the rinsing was complete, water was poured from the scoop into the 1L bottle for filtration
(Schmidt, 2017; Law, 2018).Water samples for dissolved organic carbon, dissolved inorganic carbon, δ18O and δD, cation, anion, and trace element analysis were filtered in the field using a trace metal clean 140 mL polypropylene syringe (Schmidt, 2017; Law, 2018). Trace metal clean,
DEHP-free XL-60 TygonTM tubing was used to transfer the water sample from the 1L bottle to the 140 mL syringe. The 140 mL syringe was also rinsed three times with water from the sample location prior to each use (Schmidt, 2017; Law, 2018). All sampling equipment used for cations, anions, and trace metal sample collection (1 L sampling bottle, syringe, tubing, stopcock, and scoop) were trace metal cleaned prior to field sampling (Schmidt, 2017; Law, 2018). 24
Two different 25mm diameter PALL® Acrodisc® Supor®membrane (hydrophilic polyethersulfone) sterile syringe filters were used in the filtration process: a 1.2 μm and 0.8/0.2
μm filter (Schmidt, 2017; Law, 2018). Both filters were attached to the syringe by a three-way nylon stopcock. Water was filtered into separate trace metal cleaned HDPE NalgeneTM Narrow
Mouth 30 mL unacidified bottles for cation and anion analysis and immediately frozen (Schmidt,
2017; Law, 2018). Water was filtered into trace metal cleaned pre-acidified HDPE NalgeneTM
Narrow Mouth 30 mL bottles for trace element analysis; the bottles were pre-acidified with 300
L of trace metal grade concentrated (15.8 M HNO3) nitric acid (Fischer Scientific) for a final concentration of 1% by volume nitric acid (Schmidt, 2017; Law, 2018). Water was filtered into
40 mL muffled amber glass bottles (Fischer Scientific) for both dissolved inorganic carbon and dissolved organic carbon analysis (Schmidt, 2017; Law, 2018). Vials used for DIC samples were unacidified, whereas DOC vials were preacidified with an 85 L aliquot of 85% by volume ACS reagent grade phosphoric acid (Arcos Organics) to a final concentration of 0.21% by volume phosphoric acid (Schmidt, 2017; Law, 2018). The procedure for cleaning glass vials used for dissolved inorganic carbon and dissolved organic carbon was intended to remove as much carbon residue as possible (Schmidt, 2017; Law, 2018). All cleaning procedures are described in the next section.
2.4. Cleaning Procedures
2.4.1. Trace Metal Bottle Cleaning
All equipment was rinsed seven times with Q-water, then filled with 1% CitranoxTM acid soap and left to soak for three days (Schmidt, 2017; Law, 2018). Afterward, bottles and other equipment were rotated and soaked for another three days (Schmidt, 2017; Law, 2018). After soaking in citranox was complete, all equipment was again rinsed seven times with Q-water 25
(Schmidt, 2017; Law, 2018). Then, equipment was soaked with 1.2M TraceMetal grade HCl
(Fischer Scientific, J.T. Baker) for a period of three days (Schmidt, 2017; Law, 2018). The bottles were rotated, then soaked for another three days (Schmidt, 2017; Law, 2018). Equipment was rinsed seven times with Q-water after this step was over (Schmidt, 2017; Law, 2018). All equipment was soaked in TraceMetal grade pH 2 HCl for three days, then rotated and soaked again for another three (Schmidt, 2017; Law, 2018). Bottles were rinsed seven times with Q- water after the pH 2 step and stored in clean plastic bags (Schmidt, 2017; Law, 2018). Trace metal cleaning procedures were also used for cation and anion analysis bottles. Cation and anion analysis bottles were filled with Q-water immediately after cleaning.
2.4.2. Dissolved Inorganic Carbon and Dissolved Organic Carbon
The same septa cap materials (PFTE-lined silicone) and cleaning procedures were used for both DIC and DOC containers. The bottles were soaked in a 1.2 M HCl bath for three days.
They were rinsed seven times with Q water before and after the acid bath. Afterward, the glassware was muffled in tin foil at 450°C for 4 hours. Lids were soaked in pH 2 HCl. They were rinsed seven times with Q water before and after soaking. After the lids were cleaned, they were dried in the HEPA hood.
2.5. Sample Storage Procedures
In order to ensure analytical result accuracy, specific sample storage procedures were followed. The anion samples were placed on ice and stored at -20°C until analysis within one week to six months. The acidified trace element samples were stored at room temperature (for one week to six months) until analysis. Major cations were analyzed from the same bottles as for trace element analysis. DIC and DOC samples were stored at 4°C until analysis within one week to six months. 26
2.6. Laboratory Analysis
All filtered water samples were analyzed on Montana Bureau of Mines and Geology
(MBMG) equipment. LEGEND students preformed the DIC and DOC analyses, mainly Renee
Schmidt. MBMG chemists preformed analyses for cations, anions, trace elements, and the stable isotopes of hydrogen and oxygen in water (δD and δ18O).
2.6.1. δD and δ18O
Stable hydrogen and oxygen isotopes found in water were analyzed with a Picarro
Isotopic Water Analyzer L2130-i. Results were reported as δD and δ18O comparative to the
Vienna Standard Mean Ocean Water (VSMOW) standard (Schmidt, 2017; Law, 2018). All δD and δ18O results were reported in units of per mille (‰) (Schmidt, 2017; Law, 2018). MBMG chemists reported analytical errors of ± 1‰ for δD and ± 0.1‰ for δ18O (Schmidt, 2017; Law,
2018).
2.6.2. Major Cations, Anions, and Trace Elements
A Thermo Scientific iCAP 6000 Series inductively coupled plasma optical emission spectrometer (ICP-OES) was used to quantify cation concentrations in water samples following
EPA method 200.7 (Schmidt, 2017; Law, 2018). Anion data was obtained with an ion chromatograph (Metrohm Compact IC Plus) using EPA method 300.1 (Schmidt, 2017; Law,
2018). A Thermo Scientific iCAP Q ICP-MS (inductively coupled plasma-mass spectrometer) was used to measure trace elements using EPA method 200.8 (Schmidt, 2017; Law, 2018). All instruments were calibrated prior to use (Schmidt, 2017; Law, 2018).
After calibrating the instruments, a 5 ppm standard (Initial Calibration Verification) was analyzed in order to verify machine accuracy (Schmidt, 2017; Law, 2018). During analysis of cation, anion and trace element samples, a 5 ppm standard (Continuous Calibration Verification) 27 was analyzed every 10 samples in order to quantify the amount of error in the analysis due to instrument drift (Schmidt, 2017; Law, 2018).
A continuous calibration blank (CCB) was also analyzed every 10 samples for cations, anions, and trace metals (Schmidt, 2017; Law, 2018). The CCB was a blank composed of deionized water. Its purpose was to both monitor levels of contamination in the water and assess the extent of instrument drift (Schmidt, 2017; Law, 2018).
2.6.3. Dissolved Inorganic Carbon and Dissolved Organic Carbon
An Aurora 1030W Total Carbon Analyzer with Autosampler 1088 was used to determine total dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC) (Schmidt, 2017;
Law, 2018). For DIC analysis, one blank was collected in the field for each site (Schmidt, 2017;
Law, 2018). Standards were prepared from lithium bicarbonate and sodium bicarbonate for five concentrations (1 ppm, 5 ppm, 10 ppm, 30 ppm, 50 ppm) – 10 standards total were prepared for every analytical run (Schmidt, 2017; Law, 2018). The standards were added to the auto sampler along with the field samples and blanks (Schmidt, 2017; Law, 2018). The data for the standards was used to construct a calibration curve, and standards were analyzed at the beginning and end of the analytical run to examine instrumental drift (Schmidt, 2017; Law, 2018). An 8.00 mL aliquot from each sample and each standard was reacted with 1.50 mL of ~5% phosphoric acid inside the auto sampler at 70°C for 1.5 minutes in order to convert dissolved inorganic carbon to gaseous carbon dioxide (Schmidt, 2017; Law, 2018). A detector inside the machine measured the gaseous carbon dioxide. Each sample was analyzed three times using an auto sampler (Schmidt,
2017; Law, 2018). 10.00 mL of deionized water was used to rinse the instrument after every sample or standard was analyzed (Schmidt, 2017; Law, 2018). 28
Most of the DOC analysis procedure was identical to the DIC procedure (Schmidt, 2017;
Law, 2018). Samples and standards were sparged with nitrogen gas prior to analysis to remove carbon dioxide. Another key difference was that DOC samples were reacted with 1.00 mL
(rather than 1.50 mL) of 5% phosphoric acid (Schmidt, 2017; Law, 2018). After this step, DOC samples were incubated for three minutes at 70°C. Samples were oxidized with 1.50 mL of 10% by volume sodium persulfate solution at 70°C for three minutes (Schmidt, 2017; Law, 2018).
Sodium persulfate was added to convert dissolved organic carbon into gaseous carbon dioxide
(Schmidt, 2017; Law, 2018). Nitrogen gas was used to sparge the sample an additional time to remove dissolved carbon dioxide (Schmidt, 2017; Law, 2018).
The raw DIC and DOC data from the instruments was present as integrated areas of signal peaks (Schmidt, 2017; Law, 2018). This raw data was converted to parts per million
(ppm), using calibration curve results as a reference (Schmidt, 2017; Law, 2018). These results were then converted to molality from ppm assuming that the density of the solution was 1 g/cm3
(Schmidt, 2017; Law, 2018). The standard deviation of each three replicate analysis was used as a measure of analytical error for each sample (Schmidt, 2017; Law, 2018).
2.7. Chemical Speciation Calculations
In EQ3, thermodynamic equilibrium calculations incorporating activity coefficients, ion pairing, and complexation were used to predict the speciation states (and relative abundances) for trace elements (Wolery, 1992). The calculations use aqueous chemistry such as pH, temperature, dissolved oxygen, major cations, major anions, and concentrations of trace elements to determine speciation. EQ3 is based on FORTRAN 77 code (Wolery, 1992).
The thermodynamic database used for EQ3 calculations is based on the SUPCRT92 database designed by Drs. James Johnson (Lawrence Livermore National Laboratory), Harold 29
Helgeson (University of California, Berkeley), and Eric Oelkers (University of California,
Berkeley) (Johnson et al., 1992). The database is valid for temperatures between 0°C to 350°C and pressures of 1 bar to 165.21 bars (Johnson et al., 1992). The database is maintained by Dr.
Everett Shock and others from the Group Exploring Organic Processes in Geobiochemistry
(GEOPIG) at Arizona State University (St. Clair et al., 2019). Dr. Brian St. Clair updated this database in 2016 using methods derived from Sverjensky et al. (1997). The most recent version of this database was the version used to produce the results shown in this thesis. Data such as stability constants used in the updated database were derived from a variety of sources (Kreis,
1921; Nakayama, 1971; Gardiner, 1974; Bilinski et al., 1976; Smith & Martell, 1976; Mattigod
& Sposito, 1977; Siebert & Hostetler, 1977; Ryan & Bauman, 1978; Sunda & Hanson, 1979;
Helgeson et al., 1981; Bilinski & Schindler, 1982; Plummer & Busenberg, 1982; Busenberg et al., 1984; Fouillac & Criaud, 1984; Byrne & Miller, 1985; Koslov, 1985; Wimberley et al.,
1985; Kubota et al., 1988; Stanley & Byrne, 1990; Néher-Neumann, 1992; Haas et al., 1995;
Hinder et al., 1999a; Hinder et al., 1999b; Prapaipong et al., 1999; Preis & Gamsjäger, 2002;
Powell et al., 2007; Vinson et al., 2007; Powell et al., 2009; Powell et al., 2011; Powell et al.,
2013; Mori et al., 2015; Canovas & Shock, 2016). Speciation analysis results were plotted in the graphing program Sigma Plot to illustrate speciation variations over seasonal cycles and between sites.
Settings for EQ3 were carefully chosen to create an accurate model. The same settings were used for all samples. The B-Dot model was chosen to calculate activity coefficients for aqueous species because it is effective at salinity levels found in natural freshwaters (Appendix
F; Wolery, 1992). The B-Dot model is a form of the extended Debye-Hückel equation (Wolery,
1992). The Pitzer model is best suited for calculating activity coefficients in concentrated 30 solutions such as brines (Wolery, 1992). The NBS pH scale was used as the pH scale because it forms the basis for all modern pH measurements (Appendix F; Wolery, 1992). The NBS scale uses the Bates-Guggenheim equation (Wolery, 1992). LogfO2 was chosen as the redox constraint because oxygen is the dominant redox couple in natural freshwaters such as streams and lakes
(Wolery, 1992). For pressure “data file reference curve” was selected because water pressure can vary slightly due to environmental conditions such as temperature (Appendix F).
An electrical balancing setting exists which can correct charge imbalances by balancing on an ion. Several ions such as chloride can be used for this setting. Cl- ions are added or taken away from the model to correct for charge imbalances when this setting is used to balance on Cl-.
This setting was not used for any samples.
Prior to making EQ3 input files with measured aqueous chemistry for chemical speciation calculations, the charge balance error was calculated for each site in Microsoft Excel.
EQ3 also was used to calculate charge balance error for each site. The charge balance errors calculated from Microsoft Excel and EQ3 agreed with each other, and all charge balance errors were below 10%.
Concentrations of cations, anions, dissolved inorganic carbon, dissolved organic carbon, and trace elements were not adjusted for blank concentrations in final modeling results. Field blank samples generally had concentrations which are at least one order of magnitude lower than the actual samples, so it is unlikely that contamination would affect modeling results. All field blanks for every major cation had concentrations at least one order of magnitude lower than the lowest field sample result for the day in which the blank was collected. Similarly, nearly all field blank concentrations for every major anion fell at least one order of magnitude below the lowest actual sample result for all elements during each month. The only exception is phosphate during 31 the months of February 2016 and November 2015. During those months, phosphate field blank results had the same order of magnitude (10-7) as most of the regular sample results. Phosphate results for the blanks decreased in the following months because a more efficient deionized water system was installed in the laboratory. Even for this exception, adjusting for blank concentrations did not appear to influence speciation results so the original data were used. For
DIC (measured as bicarbonate), all field blanks either fell below the detection limits or had concentrations at least one order of magnitude lower than that of the regular samples during every month in the study. For DOC, all blank concentrations measured below the detection limit.
Trace element blanks generally had concentrations below the detection limit, or at least one order of magnitude lower than that of the regular samples during every month of the study.
There were two exceptions: zinc and aluminum. The field blank results for zinc and aluminum always had a concentration on the same order of magnitude as that of the lowest measuring samples in each month. As with phosphate, adjusting zinc and aluminum concentrations did not influence speciation results so the original data were used.
Bicarbonate data was missing for some samples and had to be estimated by substituting data from other months. Missing bicarbonate values were not estimated from alkalinity values using equilibrium calculations. While some of the sites with missing values had alkalinity measurements, most of them did not. It is best to be as consistent as possible when estimating concentrations of chemical data. Also, other ions can affect alkalinity aside from carbonates and bicarbonates, which can introduce error into calculations. Data derived from equilibrium calculations using acid dissociation constants were not used as final estimates of missing data because they resulted in large charge imbalances (>10%). Different months were tried as substitutes for the missing bicarbonate data, and charge balance errors were compared for each 32 time. Charge balance errors were used to evaluate the accuracy of the estimate. If the charge balance error is high, it is likely that the substituted value is drastically different from the actual number. An attempt was made to use values from the same season for bicarbonate. For missing
August samples, June 2017 and September 2017 data were used first as estimates for DIC, they produced unacceptable charge balances (>10%). May 2016 values produced the best charge balance errors for all missing summer data except Quality Drain, so they were used as the final estimates. For Quality Drain February 2016 and KOA November 2016, November 2015 data was used. For Quality Drain August 2016, the average for the months November 2017 and June
2017 was used as the estimate. Individually, the data for both months yielded unacceptable charge balance errors.
Overall, errors in modeling analyses are minimal. Errors in modeling analysis are most likely a result of fluctuations in pH measurements. pH is the most influential parameter on speciation, and therefore likely has the greatest impact on speciation errors. Temperature errors may also cause slight errors in modeling results, particularly for zinc ion pairing predictions. The maximum pH or temperature fluctuations fail to introduce uncertainty of more than a tenth of a percent for any species. Errors in total concentrations likely do not affect modeling results at all, as differences in total trace element concentrations don’t appear to significantly influence speciation. Errors in dissolved inorganic carbon concentrations may impact barium speciation:
+ numbers which are high may result in larger relative abundances being reported for BaHCO3 .
Errors in dissolved inorganic carbon concentrations did not appear to influence speciation results for other elements. 33
2.8. Data Analysis Methods
Multiple linear regression was used in Sigma Plot and in MINITAB to determine correlations between geochemical parameters and speciation. Principal component analysis was also used to look for correlations in the data using the program R (version 3.5.2). Principal component analysis results were used to ascertain causes of geographical variations in speciation.
Principal component analysis is utilized to study the interrelationships in a large set of variables. Principal component analysis is useful in cases when multicollinearity exists in data sets (Descalu & Rozma, 2009; Byrne et al., 2017). Principal component analysis creates a smaller set of variables, known as principal components, out of the existing data (Descalu &
Rozma, 2009; Byrne et al., 2017). These new variables are closely related to the old variables
(Descalu & Rozma, 2009; Byrne et al., 2017). The principal components are not linearly correlated with each other (Descalu & Rozma, 2009; Byrne et al., 2017). Loadings are correlations between the variables originally from the study and unit scaled components which were created (principal components) (Descalu & Rozma, 2009; Byrne et al., 2017). For instance, if a high loading exists between pH and a principal component, it can be assumed that the component approximates pH well. For example, if Zn2+ correlates with that component, it is likely that pH changes had a strong influence on zinc speciation.
Principal component analysis results are often represented graphically. The two principal components which best explain the variation in the entire data set (PC1 and PC2) are represented as the graph’s x and y axes. Variables are often represented as vectors.
Loadings are represented graphically by the length of vectors and their distance from the axes. The length of the vector indicates how strongly a variable relates to the principal components. The vector’s direction indicates which principal component it relates to most 34 strongly. If the vector is closest to the y-axis and the y-axis represents principal component 1, the variable relates more strongly to principal component 1.
In addition to representing relationships between variables and principal components, these graphs represent relationships between sample data sets and principal components as well as similarities between the data sets of distinct samples. Distinct samples are represented as data points. The amount of scatter between points in a PCA plot demonstrates the degree of overall similarity between the data sets of distinct samples. If two data points are close together, their data sets are relatively similar. The distance between a data point and an axis represents the relationship between that samples data set and that principal component. If a data point is close to the axis representing principal component 1, there is a close relationship between principal component 1 and that data point.
35
3. Results
The results section presents data for chemical parameters necessary for performing speciation calculations or bioavailability and toxicity assessments, as well as total concentration and speciation data for Ba, As, Zn, Cu, Fe, and Mn. Such necessary parameters include pH, temperature, dissolved oxygen, conductivity, dissolved organic carbon, dissolved inorganic carbon, major cations, and major anions. Cation and anion concentrations which affect the bioavailability and toxicity of Ba, As, Zn, Cu, Fe, and Mn are also described. The results section describes data for stable hydrogen and oxygen isotopes in water. The end of this section reports both total dissolved concentration and speciation data for Ba, As, Zn, Cu, Fe, and Mn.
3.1. Aqueous Chemistry
The aqueous chemistry section describes chemical parameters necessary for calculating speciation and assessing bioavailability. Such parameters include pH, temperature, dissolved oxygen, conductivity, dissolved organic carbon, dissolved inorganic carbon, major cations, and major ions. Both seasonal and geographical variations are depicted.
3.1.1. pH
The overall range of pH values spans from 6.33 to 8.41 ± 0.05. The most acidic pH values for all sites occurred during the month of November 2015 and the most basic pH values generally occurred in August 2016. All sites had more basic pH values in February 2016 and May 2016 than in November 2015. November 2016 pH values are generally more basic than November
2015 but are less basic than August 2016. 36
The pH values for November 2015 ranged from 6.33 to 7.24 ± 0.05 (Figure 2). All November
2015 sites had near neutral pH values except USBC, which is the only value below 7 in the entire study (6.33 ± 0.05) (Figure 2). Santa had the most basic pH value for all sites in November 2015
(7.24 ± 0.05) (Figure 2).
Values for pH in February 2016 are all near neutral, ranging from 7.23 to 7.53 ± 0.05 (Figure
2). As in November 2015, USBC had the least basic pH (7.23 ± 0.05) of all sites during the month of February 2016. In May 2016, very little variation in pH existed between sites (Figure
2). Values ranged from 7.86-7.98 with errors of ± 0.01 to ± 0.05 (Figure 2).
In August 2016, pH values ranged from 7.75 to 8.41 with errors of ± 0.03 to ± 0.05 (Figure
2). Slag Canyon and Blacktail are the only sites where the most basic pH values did not occur in
August, but instead occurred in May 2016 at 7.91 ± 0.05 and 7.94 ± 0.01 respectively (Figure 2).
In August 2016, Santa and Quality Drain had the most basic pH values at 8.37 ± 0.05 and 8.41 ±
0.05 respectively (Figure 2). KOA had the least basic pH value of all sites in August 2016 (7.90
± 0.04) (Figure 2).
The pH values for November 2016 are all near neutral. Santa had the most basic pH of all sites in November 2016 (7.99 ± 0.05) (Figure 2). KOA had the least basic value (7.55 ± 0.05)
(Figure 2). No flow at USBC during November 2016 prevented sampling. 37
Figure 2: pH vs temperature. Bars represent meter reading error for pH (±0.01 to ±0.05) and temperature (±0.1°C). All temperature error bars fell within the symbols. EPA freshwater criteria for pH is 6.5-9.0. 3.1.2. Temperature
Temperatures ranged from 2.2 to 19.8°C overall. The highest values occurred during
August 2016, the month which also had the highest pH values. The lowest overall values occurred in February and November, the months with the least basic pH values.
Temperatures ranged from 12.4 -19.8°C with errors of ± 0.1 to ± 0.5°C during August
2016. The highest recorded temperature measurement for all sites (19.8 ± 0.1°C) occurred at
Quality Drain in August 2016 (Figure 2). May 2016, the month with the second highest pH values, also had the second highest temperature values. In May 2016, temperature values ranged from 8.1 to 11.3 ± 0.5°C (Figure 2). In November 2016, temperatures ranged from 2.2 to 6.0°C ± 38
0.5°C (Figure 2). November 2015, the month with the lowest overall pH values, had the second lowest overall temperatures. In November 2015, temperatures ranged from 3.2 to 9.2 ± 0.5°C
(Figure 2). The lowest temperature values occurred during February 2016, which also had the second lowest pH values (Figure 2). Temperatures ranged from 1.4 to 5.7 ± 0.5°C in February
2016 (Figure 2).
3.1.3. Dissolved Oxygen
Dissolved oxygen had oxic values at all sites, ranging from 226 to 335 ± 1 to 3 mol/kg
(Figure 3). Concentrations between 6.25 and 62.5 mol/kg are suboxic (NOAA, 2016). Anoxic waters have dissolved oxygen values lower than 6.25 mol/kg (Ma & Love, 2001; NOAA,
2016).
Like temperature, dissolved oxygen varied seasonally. Dissolved oxygen concentrations peaked during the two warmest seasons, spring (273 to 334 ± 1 mol/kg) and summer (248 to
335 ± 1 to 3 mol/kg) (Figure 3). The lowest dissolved oxygen concentrations occurred during the two coolest seasons, winter (262 to 340 ± 3 mol/kg) and fall (226 to 324 ± 2 to 3mol/kg)
(Figure 3). Even though winter generally had lower dissolved oxygen values than summer, the highest dissolved oxygen value for the entire study occurred during February 2016 at Quality
Drain (340 ± 3 mol/kg) (Figure 3). 39
Figure 3: Dissolved oxygen (DO) concentrations vs temperature. Bars represent instrumental errors for meter readings of DO (± 0.63 to ± 60 mol/kg) and temperature (±0.1°C). Error bars resided within the symbol. 3.1.4. Specific Conductivity
Specific conductivity values ranged from 144 S/cm to 1157 S/cm. The lowest specific conductivity values occurred in the spring and summer months. The highest specific conductivity values occurred during November 2015 and February 2016, the two months with the least basic pH values. 40
Figure 4: Specific conductivity vs pH. Bars represent instrumental errors for pH (±0.01 to 0.05) and conductivity (± 0.4 to 3 S/cm) measured by meters. Error bars residing within the symbol are omitted.
Overall, May 2016 and August 2016 had lower specific conductivity values than those of the other three months. During May 2016, specific conductivities ranged from 144 to 323 S/cm, with errors ranging from ± 0.7 to 2 S/cm (Figure 4). The lowest specific conductivity for the entire study (144 ± 0.7 S/cm) occurred at Blacktail during May 2016 (Figure 4). In August
2016, specific conductivities spanned from 233 to 572 S/cm, with errors ranging from ± 1 to 3 uS/cm (Figure 4). These lower overall seasonal specific conductivity values in May and August
2016 coincided with greater overall seasonal pH values. USBC had a much lower specific conductivity value in May 2016 (160 ± 0.8 S/cm) than in November 2015 and February 2016. 41
It did not have a higher specific conductivity than other sites in May 2016. Instead, Santa had the highest specific conductivity for all sites during May 2016 (323 ± 2 S/cm).
Specific conductivity values for November 2016 generally are higher than those occurring in the more basic month of May 2016. In November 2016, 224 ± 1 S/cm is the lowest value, with the highest value measured at 474 ± 2 S/cm (Figure 4). Errors for November 2016 spanned from 1 to 2 S/cm (Figure 4).
During November 2015, specific conductivities ranged from 262.2 to 1115 S/cm, with errors of ± 1 to 6 S/cm (Figure 4). During February 2016, specific conductivities ranged from
341.4 to 1157 S/cm, with errors ranging from ± 2 to 6 S/cm (Figure 4). The highest recorded specific conductivity measurement in this entire data set occurred at USBC during February
2016 (1157 ± 6 S/cm) (Figure 4). The second highest recorded specific conductivity measurement for the entire study (1115 ± 6 S/cm) occurred at USBC during November 2015.
This conductivity measurement coincided with the lowest recorded pH value (6.33 ± 0.05)
(Figure 4).
3.1.5. Dissolved Organic Carbon
For dissolved organic carbon, values varied seasonally and geographically with values ranging from 1.9 to 8.2 ppmC. Instrumental error equaled ± 3% for all samples during every month. As an overall trend, November 2015 generally had lower DOC than other months.
During November 2015, the month with the least basic pH values, dissolved organic carbon concentrations ranged from 2.6 to 4.4 ppmC (Figure 5). In February 2016, a wider range of values existed, from 1.9 to 7.2 ppmC (Figure 5). The minimum concentration for the entire study, 1.9 ppmC, occurred during February 2016 at Slag Canyon (Figure 5). Seasonal changes in dissolved organic carbon demonstrated a relationship with seasonal changes in pH (Figure 5). 42
Dissolved organic carbon values from May 2016 generally exceeded most values from the least basic months November 2015 and February 2016 (Figure 5). The maximum concentration for the entire data set, 8.2 ppmC, occurred at Blacktail Creek in May 2016, the month with the second lowest pH values (Figure 5). Limited DOC data for August and
November 2016 show Santa having the second greatest value for the whole data set (7.6 ppmC) during November 2016; Blacktail in August 2016 had a concentration of 3.3 ppmC (Figure 5).
Figure 5: Dissolved organic carbon vs pH. Bars represent analytical errors of ± 3% for dissolved organic carbon measured using Aurora 1030W Total Carbon Analyzer and ± 0.01 to 0.05 for pH data collected from meters. Some error bars fell within the symbol. 43
3.1.6. Dissolved Inorganic Carbon
Dissolved inorganic carbon occurred primarily as the major anion bicarbonate and varied seasonally with values ranging from 100 to 2270 mol/kg with an error of ± 3% (Figure 6).
Overall, bicarbonate concentrations for the lower pH months of November 2015 and February
2016 exceeded those of other months (Figure 6). Overall, November 2015 had the highest bicarbonate concentrations, ranging from 600 to 2010 mol/kg (Figure 6). Quality Drain in
November 2015 is an exception to this trend as it had the lowest bicarbonate value measured overall (600mol/kg). During February, bicarbonate concentrations ranged from 1130 to
2270mol/kg (Figure 6). February 2016 Santa had the most bicarbonate of all sites overall measuring 2270 mol/kg (Figure 6). During May, bicarbonate concentrations fell relative to the two preceding less-basic months. May bicarbonate concentrations fell within the range of 100 to
1700 mol/kg (Figure 6). Estimated bicarbonate data used for August ranged from 100 to 1910
mol/kg (Figure 6). November 2016 had bicarbonate concentrations spanning from 1040 to 1990
mol/kg. 44
Figure 6: DIC (expressed as bicarbonate) vs pH for all study sites with DIC data. Bars represent analytical error for DIC values measured by Aurora 1030W Total Carbon Analyzer (±3%) and pH values determined by meters (±0.01 to 0.05). 3.1.7. Major Anions
This section describes anions necessary to calculate speciation and evaluate bioavailability, including phosphate and sulfate. Values for total concentrations are provided and trends in data sets are described. Trends include seasonal and geographic concentration variations and relationships between major anion concentrations and other variables such as pH.
3.1.7.1. Phosphate
Phosphate concentrations varied between both sampling months and sites, with values ranging from 0.35 to 3.3 mol/kg ± 1.5 to 7.8%. Overall, the month with the highest pH values and temperatures (August 2016) had the highest phosphate values, whereas the lowest phosphate concentrations generally occurred in the winter. August 2016 phosphate concentrations ranged from 0.42 to 3.3mol/kg ± 7.8% (Figure 7). The overall highest phosphate value occurred 45 during August 2016 at Santa (mol/kg) (Figures 7, 8). May 2016 phosphate results generally fell below summer values, spanning from 0.36mol/kg to 1.7mol/kg ± 6.8% (Figure 7). Santa usually had higher levels of both phosphate and nitrate than the other sites during every month of sampling (Figure 8). November 2015, phosphate concentrations occurred within a range of 0.48 to 2.4 mol/kg ± 5.0% (Figure 7). For all sites except Santa, phosphate values fell from their
August 2016 peaks during November 2016. This decrease in phosphate coincided with a decrease in pH, as November 2016 phosphate results occupied a lower range 0.369 to
3.23mol/kg ± 1.5% (Figure 7). For February 2016, phosphate concentrations occurred within a lower range of 0.33 and 1.34 mol/kg ± 3.4% (Figure 7). 46
3- Figure 7: Total phosphate vs arsenic concentrations. 6.47 PO4 :1 As μmolal ratio shown for comparison (Rahman et al., 2014). Bars represent analytical ion chromatography errors of ± 1.5 to 7.8% for phosphate, and ICP-MS errors of ± 1.3 to 10.9% for arsenic. Error bars within symbol not shown. 47
- - Figure 8: Total dissolved nitrogen (NO3 + NO2 ) vs total dissolved phosphate. Bars represent analytical ion chromatograph errors of ±1.5 to 7.8% for phosphate, and ±0.2 to 1.1% for nitrate and nitrite. All error bars for nitrate + nitrite fell within the symbols. 3.1.7.2. Sulfate
Sulfate concentrations varied between months and sites and did not appear to increase with pH or temperature, values ranged from 80.1 to 1410 mol/kg ± 0.23 to 0.90%. During
November 2015, concentrations ranged from 92.4 to 771 mol/kg ± 0.80% (Figure 9). For the month of February 2016, concentrations spanned the range of 81.7 to 907 mol/kg ± 0.62%
(Figure 9). For the month of May 2016, concentrations spanned the range of 80.1 to 506 mol/kg
± 0.23% (Figure 9). Sulfate concentrations for August 2016 generally exceeded those of other months. In August 2016, concentrations spanned the range of 104 to 1410 mol/kg ± 0.60% 48
(Figure 9). The maximum value for all sites 1410 mol/kg occurred during August 2016 at Santa
(Figure 9). Santa had the highest sulfate concentrations for all sites during each month (Figure
9). During November 2016, values for sulfate ranged from 212 to 898 mol/kg ± 0.93% (Figure
9).
2- Figure 9: SO4 vs pH. Bars represent analytical ion chromatograph errors for sulfate (±0.23 to 0.93%) and pH (±0.01 to 0.05). All error bars for sulfate (and some pH bars) fell within the symbol. 3.2. Field Spectrophotometry
Total dissolved silica, ferrous iron, and sulfide measurements performed in the field yielded values for silica at all sites, values for ferrous iron at about half, and sulfide at a few sites. It is likely that most ferrous iron and sulfide values are the result of analytical interference. 49
Ferrous iron typically does not typically exist at pH values above 8. At near neutral oxidized conditions typical of Silver Bow and Blacktail creeks, ferrous iron is typically fleeting. Sulfide typically does not exist in oxidizing conditions. This section will report total concentrations for these three analytes and patterns in their data sets.
3.2.1. Total Dissolved Silica
Silica speciated primarily as SiO2, AQ, and its concentrations varied widely between both sites and seasons, with a low of 90 and a high of 514 mol/L (Figure 10). All sites had an error of ± 17 mol/L for silica (Schmidt, 2017; Law, 2018). Silica did not appear to have a strong relationship with pH (Figure 10). May 2016 had the greatest range of silica values, spanning from 514 at KOA to 120 mol/L at USBC (Figure 10). February 2016 and November 2015 are tied for having the second greatest range of silica concentrations. Silica concentrations ranged from 120 at USBC to 430 mol/L at Slag Canyon during February 2016 and ranged from 90 at
Quality Drain to 400 mol/L at KOA during November 2015 (Figure 10). August 2016 had a smaller range of silica values than November 2015 and February 2016, spanning from 280 at
Quality Drain to 450 mol/L at KOA (Figure 10). November 2016 had the smallest range of silica values, spanning from 370 at Santa to 450 mol/L at Blacktail (Figure 10). 50
Figure 10: Dissolved SiO2 vs pH. Bars represent analytical error for silica (±17 mol/L) and pH (±0.01 to ±0.05). Some errors for pH fell within the symbol. Dissolved silica data measured using wet spectrophotometry in the field. 3.3. δD and δ18O
δD and δ18O for the creek are compared to the Global Meteoric Water Line (GMWL) and the Butte Meteoric Water Line (Butte MWL) (Figure 11; Craig, 1961; Gammons et al., 2006b).
δD and δ18O are stable isotopes of hydrogen and oxygen respectively. Instrumental errors are ±
1‰ for δD and ± 0.1‰ for δ18O for all samples (Schmidt, 2017; Law, 2018). The trend line produced for the samples in this study resided above the GWML and matched the Butte MWL closely (Figure 11). The less basic, higher conductivity months November 2015 and February
2016 had the lightest overall δD and δ18O values (Figure 11). Quality Drain November 2015 is 51 the sample with the lightest δD and δ18O results (D=-175, 18O=-22.5) followed closely by
USBC November 2015 (D=-161, 18O=-20.9) (Figure 11). The two months with the most basic overall pH values, May and August, had the heaviest overall δD and δ18O values.
Figure 11: D vs 18O. Errors are ± 1‰ for δD and ± 0.1‰ for δ18O and fell within the symbol. 3.4. Trace Elements
This section presents Ba, As, Zn, Cu, Fe, and Mn concentrations. The subsections will present seasonal concentration ranges for each of these trace elements. The subsections will also describe trends in trace element concentrations and their relationships to other variables such as 52 pH. Both seasonal and geographical trends existed in trace element concentrations, and barium generally had higher concentrations than other trace elements (Figure 12,13).
Figure 12: Average of all concentrations in the study for Ba, As, Zn, Cu, Fe, Mn. Vertical bars represent standard deviations for ICP-MS sample results of ± 0.98, ± 0.019, ± 1.9, ± 0.26, ± 1.6, and ± 0.76 mol/kg for barium, arsenic, zinc, copper, iron, and manganese respectively. Detection limits are 0.0648 mol/kg, 0.0267 mol/kg, 0.0552 mol/kg, 0.0227 mol/kg, 0.0976 mol/kg, and 0.0364 mol/kg for barium, arsenic, zinc, copper, iron, and manganese respectively. 53
Figure 13: Plot of concentration vs time for Ba, As, Zn, Cu, Fe, and Mn. X axis represents days elapsed since the first day of creek sampling (November 14, 2015). Vertical bars represent ICP-MS instrumental errors of ± 1.5 to 15.8%, ± 1.3 to 10.9%, ± 1.9 to 13.0%, ± 1.2 to 12.3%, ± 4 to 18%, and ± 1.8 to 15.4% for barium, arsenic, zinc, copper, iron, and manganese respectively. The error for each sample is used, rather than the standard deviation for all errors of that element. Some error bars within symbol. Detection limits are 0.0648 mol/kg, 0.0267 mol/kg, 0.0552 mol/kg, 0.0227 mol/kg, 0.0976 mol/kg, and 0.0364 mol/kg for barium, arsenic, zinc, copper, iron, and manganese respectively. 3.4.1. Barium
Barium concentrations varied throughout the year and between sites, ranging from 1.03 to
5.0 mol/kg ± 1.5 to 15.8% (Figure 14). Concentrations for November demonstrated a relationship with pH. Sites with the least basic pH values also had the most barium for
November 2015 (Figure 15). For instance, USBC November 2015 had more barium than all other sites in the entire study (5.0 mol/kg ± 1.6%). In November 2015, barium concentrations 54 varied between 1.43and 5.0mol/kg ± 1.6% (Figure 14). In February 2016, barium concentrations ranged from 1.54 to 3.2mol/kg ± 5.6% (Figure 15). The concentrations for May
2016 span a smaller range than other months from 1.14 to 1.8 mol/kg ± 8.1% (Figure 15). In
November 2016 and August 2016, sites which had the least basic pH values had more barium
(Figure 15). August 2016 concentrations spanned a range of 1.07 to 3.24 mol/kg ± 1.5%
(Figure 15). The lowest concentration measured for the entire study existed at Blacktail in
August 2016 (1.07mol/kg ± 1.5%) (Figure 15). In November 2016 concentrations ranged from
1.3 to 4.0 mol/kg ± 15.8% (Figure 15). 55
Figure 14: Barium concentration vs time. X axis represents days elapsed since the first day of creek sampling (November 14, 2015). (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. Bars represent instrumental ICP-MS errors of ± 1.5 to 15.8%. Some error bars within symbol. Detection limit for barium is 0.0648 mol/kg. NC = not collected. 56
Figure 15: Barium concentration vs pH. Horizontal bars represent analytical error for pH (±0.01 to 0.05). Vertical bars represent ICP-MS instrumental errors of ± 1.5 to 15.8% for barium. Some error bars within symbol for both barium concentration and pH. Detection limit for barium is 0.0648 mol/kg. 3.4.1. Arsenic
Arsenic concentrations fluctuated seasonally, with concentrations ranging from 0.0213 to
0.046 mol/kg ± 1.3 to 10.9% (Figures 16, 17). The colder, less basic, months of November
2015 and February 2016 showed generally lower concentrations than May; exhibiting ranges of
0.0331to 0.0739mol/kg ± 1.3% and 0.027 to 0.09mol/kg ± 4.7%, respectively (Figures 16,
17). In May, concentrations spanned a range of 0.045to 0.080 mol/kg ± 3.4% (Figures 16, 17).
In the other high pH month, August 2016, arsenic concentrations exceeded those of February
2016 and November 2015, though not as much as May 2016 (Figures 16, 17). In August, 57 concentrations ranged from 0.0213 to 0.046 mol/kg ± 2.4% (Figures 16, 17). Overall arsenic concentrations in November 2016 declined alongside pH. In November 2016, concentrations ranged from 0.024 to 0.045 mol/kg ± 10.9% (Figure 17).
Arsenic concentrations also varied between sites. Typically, the sites Santa, USBC, and
Quality Drain had the highest arsenic concentrations, whereas Blacktail had the lowest. The highest arsenic concentration measured overall occurred at USBC during February 2016
(0.09mol/kg ± 4.7%) (Figure 16). The lowest arsenic concentration for all sites occurred at
Blacktail during August 2016 (0.0213mol/kg ± 2.4%) (Figure 16). Variations in total arsenic concentrations between sites did not seem to show a strong relationship with pH. 58
Figure 16: Arsenic concentration vs time. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. X axis represents days elapsed since the first day of creek sampling (November 14, 2015). Bars represent ICP-MS instrumental errors of ± 1.3 to 10.9%. Some error bars fell within the symbol. The detection limit is 0.00267 mol/kg As. NC = not collected. 59
Figure 17: Arsenic concentration vs pH. Horizontal bars represent analytical error for pH (± 0.01 to 0.05) and vertical bars represent ICP-MS analytical errors of ± 1.3 to 10.9% for arsenic. Some error bars fell within the symbol for arsenic and pH. The detection limit is 0.00267 mol/kg for arsenic. 3.4.1. Zinc
Zinc concentrations varied between months and sites, ranging from 0.148 to 8.9 mol/kg
± 1.3 to 5.3% (Figures 18, 19). The greatest zinc concentrations typically occurred in November
2015 and February 2016. In both November 2015 and February 2016, greater zinc concentrations occurred at locations which had less basic pH values (Figure 19). USBC November 2015 had the least basic pH value in the entire study and the greatest zinc concentration of all sites,
8.9mol/kg (Figure 18). In November 2015, zinc concentrations spanned a range of 0.26 to
8.9mol/kg ± 4.0% (Figure 19). February 2016, zinc concentrations ranged from 0.27 to 3.9 60
mol/kg ± 6.8% (Figure 19). Generally, seasonal decreases in total zinc concentrations occurred simultaneously with pH increases (Figure 19). Samples from May and August 2016 generally had lower total zinc concentrations than samples from November 2015. May 2016 had concentrations spanning from 0.170 to 2.0mol/kg ± 5.3% (Figure 18). In August 2016, zinc concentrations ranged from 0.148 to 1.33mol/kg ± 1.9% (Figure 18). In November 2016, concentrations fell within a range of 0.16 to 3.5mol/kg ± 13.0% (Figure 18). 61
Figure 18: Zinc over time. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. X axis represents days elapsed since the first day of creek sampling (November 14, 2015). Error bars represent ICP-MS instrumental errors of ± 1.9 to 13.0%. Error bars fell within the symbol for some months. BDL = below the detection limit of 0.0552 mol/kg. NC = not collected. 62
Figure 19: Zinc vs pH. Vertical error bars represent ICP-MS instrumental errors of ± 1.9 to 13.0%. Horizontal error bars represent analytical error for pH (± 0.01 to 0.05). Error bars fell within the symbol for some months. BDL = below the detection limit of 0.0552 mol/kg. 3.4.2. Copper
Copper concentrations changed seasonally and differed between sites, ranging from
0.0230 to 1.10 mol/l (Figures 20, 21). Generally, the highest copper concentrations occurred in
November 2015 and the lowest concentrations occurred in the summer. Copper displayed a relationship with pH for both November 2015 and February 2016 (Figure 21). Sites which had higher copper concentrations within those months also had lower pH values (Figure 21). For instance, USBC November 2015 had both the greatest copper concentration in this study (1.10
mol/kg) and the only pH value below 7 (Figure 20). May 2016 had copper concentrations 63 spanning a range of 0.084 to 0.34 mol/kg ± 5.3% (Figure 20). In November 2015, concentrations spanned a range of 0.084 to 1.10 mol/kg ± 1.2% (Figure 20). In February 2016, copper concentrations ranged from 0.031 to 0.64 mol/kg ± 5.9% (Figure 20). USBC had a much lower copper concentration during May 2016 (0.121 mol/kg) compared to November
2015. Copper concentrations in August 2016 spanned a range of 0.0230 to 0.270 mol/kg ±
2.3% (Figure 21). The lowest detectable copper concentrations for the entire study occurred in
August 2016 at the site Slag Canyon (0.0230 mol/kg) (Figure 21). In November 2016, concentrations spanned a range of 0.0280 to 0.1 mol/kg ± 12.3% (Figure 21). 64
Figure 20: Copper concentration vs time. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. X axis represents days elapsed since the first day of creek sampling (November 14, 2015). Bars represent instrumental ICP-MS errors of ± 1.2 to 12.3%. Error bars fell within the symbol for some months. BDL = below the detection limit of 0.0227 mol/kg Cu. NC = not collected. 65
Figure 21: Copper concentration vs pH. Horizontal bars represent analytical error for pH (± 0.01 to 0.05). Vertical bars represent ICP-MS errors of ± 1.2 to 12.3% for total copper. Some error bars for both pH and copper fell within the symbol. BDL = below the detection limit of 0.0227 mol/kg Cu. 3.4.3. Iron
Iron concentrations ranged from 0.26 to 5.5 mol/kg ± 4 to 18% (Figure 22). Total dissolved iron concentrations had seasonal variations which did not seem to display a strong relationship with pH (Figures 22, 23). The highest iron concentrations generally occurred in
November 2016 and May 2016. February and August generally had the lowest total dissolved iron concentrations. In November 2015, iron concentrations ranged from 0.64 to 3.2 mol/kg ±
4.5% (Figure 22). February 2016 had iron concentrations ranging from 0.26 to 1 mol/kg ±
13.9% (Figure 22). May 2016 had iron concentrations ranging from 0.32to 5.5 mol/kg ± 4.0% 66
(Figure 22). August 2016 had lower iron concentrations than May 2016, with concentrations between 0.500 and 1.15 mol/kg ± 4.0% (Figure 22). Iron concentrations in November 2016 generally exceeded those of August 2016, ranging from 1.2 to 5.3 mol/kg ± 18.0% (Figure 22).
Variations between sites also exist for iron concentrations. For two out of three months with existing data, Blacktail had higher iron concentrations than all other samples. Typically,
USBC had higher iron concentrations than other sites (Figure 22). The highest iron concentration overall occurred at Blacktail during May 2016 (5.5 mol/kg ± 4.0%). The lowest iron concentration occurred at KOA during February 2016 (0.26 mol/kg ± 13.9%). 67
Figure 22: Total dissolved iron over time. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. X axis represents days elapsed since the first day of creek sampling (November 14, 2015). Error bars represent ICP-MS instrumental errors of ± 4 to 18%. Error bars within the symbol are not included. The detection limit for iron is 0.0976 mol/kg Fe. NC = not collected. 68
Figure 23: Total dissolved iron vs pH. Vertical error bars represent ICP-MS instrumental errors of ± 4 to 18% for iron. Horizontal error bars represent analytical error for pH (± 0.01 to 0.05). Error bars within the symbol are not included. The detection limit for total dissolved iron is 0.0976 mol/L 3.4.4. Manganese
Total dissolved manganese concentrations varied throughout the year and between sites, ranging from 0.311 to 3.6 mol/kg ± 1.8 to 15.4% (Figure 24). The samples with the highest total manganese concentrations typically have pH values between 7 and 7.4 (Figure 24).
Manganese concentrations ranged from 0.311 to 2.04 mol/kg ± 3.1% in November 2015
(Figure 24). For February 2016, manganese concentrations range from 1.01 to 3.6 mol/kg ±
6.2% (Figure 24). Santa February 2016 had the highest total manganese concentration of all sites in the study, 3.6 mol/kg ± 6.2% (Figure 24). May 2016 generally had lower concentrations of 69 manganese than November 2015 and February 2016; ranging from 0.413 to 1.06 mol/kg ±
7.3%. In August 2016, manganese concentrations ranged from 0.500 to 1.15 mol/kg ± 1.8% and generally fell below November 2015 and February 2016 concentrations (Figure 25). For
November 2016, manganese concentrations ranged from 0.8 to 1.8 mol/kg ± 18.0% (Figure
25).
Figure 24: Total manganese concentrations over time. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. X axis represents days elapsed since the first day of creek sampling (November 14, 2015). Bars represent ICP-MS instrumental errors of ± 1.8 to 15.4%. Error bars which fell within the symbol not shown. NC = not collected. BDL = below the detection limit of 0.0364 mol/kg. 70
Figure 25: Total dissolved manganese vs pH. Vertical bars represent ICP-MS instrumental errors of ± 1.8 to 15.4% for manganese. Horizontal error bars represent analytical error for pH (± 0.01 to 0.05). Error bars within the symbol not shown. The detection limit for total dissolved manganese is 0.0364 mol/kg. 3.5. Speciation Calculated Using EQ3
3.5.1. Barium
For barium, calculations showed two main species: the free ion and a barium-bicarbonate
+ + ion pair (BaHCO3 ) (Figure 26). BaHCO3 only occurred during the months August 2016 and
November 2016 and never exceeded more than 2% of the total barium (Figure 26). Calculations did not predict the barium-bicarbonate ion pair for any creek samples during the months
November 2015, February 2016, and May 2016. 71
Figure 26: Barium speciation for November 2015-November 2016. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. NC = sample not collected.
72
3.5.2. Arsenic
- 2- Speciation calculations revealed two main species, H2AsO4 and HAsO4 , the abundances of
2- which varied on both a seasonal and geographical basis. HAsO4 ranged from 31 to 98% and
- 2- H2AsO4 ranged from 2 to 69% over the course of the study. HAsO4 exhibited lower
2- abundances in November 2015 and February 2016 than in other months. For HAsO4 , relative abundances ranged between 31% at USBC and 76% at Santa in November 2015 (Figure 27). In
- November 2015, H2AsO4 had relative abundances between 24% at Santa and 69% at USBC.
2- USBC November 2015 had the lowest relative abundance of HAsO4 for any site in this study
- (31%). In February 2016, relative abundances for H2AsO4 ranged from 14 to 23% and relative
2- abundances for HAsO4 ranged from 77% at USBC to 86% at Quality Drain (Figure 27).
2- Relative abundances of HAsO4 increased as pH increased. May and August had the most
2- 2- HAsO4 of all months. In May 2016, HAsO4 ranged from 93 - 94% and correspondingly
- 2- H2AsO4 ranged from 6 - 7%. In the month of August 2016, HAsO4 spanned from 91 - 98% and
- 2- H2AsO4 ranged from 2 - 9% (Figure 27). The highest relative abundances of HAsO4 (98%) occurred during August 2016 at Santa and Quality Drain, the two sites with the most basic pH
2- values. Both pH and HAsO4 decreased in November 2016 relative to August 2016 and May
2- 2016. In November 2016, HAsO4 relative abundance values ranged between 86% at KOA and
- 95% at Santa and H2AsO4 ranged from 5 - 14% (Figure 27). 73
100 a) 2- b) HAsO4 - 80 H2AsO4
60
40
20
NC NC c) d)
80
60
40
20
Relative Abundance (%) Abundance Relative NC NC e) Nov 15 Feb 16 May 16 Aug 16 Nov 16 f) 80
60
40
20
NC NC 0 Nov 15 Feb 16 May 16 Aug 16 Nov 16 Nov 15 Feb 16 May 16 Aug 16 Nov 16
Figure 27: Arsenic speciation from November 2015-November 2016. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. NC = not collected.
74
3.5.1. Zinc
2+ + + Calculations predicted four species of zinc for all sites: Zn , ZnOH , ZnHCO3 , and
ZnCO3 (aq), the relative abundances of these species varied between seasons alongside pH.
Overall, ZnOH+ ranged from 0-45% and Zn2+ ranged from 30-99% (Figure 28). Relative
+ abundances ranged from 0 to 25% for ZnCO3 (aq) and 0 to 5% for ZnHCO3 . In November
2015, Zn2+ had a relative abundance of 91% at Santa to 99% at USBC. ZnOH+ had a relative abundance of 0% at USBC to 2.33% at Santa (Figure 28). ZnCO3 (aq) relative abundances
+ spanned from 0% at USBC to 3% at Santa (Figure 28). ZnHCO3 had relative abundances of
<1% at USBC to 4% at Santa. February 2016 had relative abundances of 89-95% for Zn2+ and 1-
+ + 3% for ZnOH (Figure 28). Relative abundances of 1-5% and 1-4% existed for ZnHCO3 and
ZnCO3 (aq) respectively (Figure 28). As pH values increased in the spring, free ion relative abundances decreased and ion-pairs became more abundant. May 2016 had relative abundances
2+ + + of 72-77% for Zn , 12-16% for ZnOH , 2-4% for ZnHCO3 , and 8-13% for ZnCO3 (aq) (Figure
28). As pH rose further in August, free ion abundances declined. Free zinc ion values ranged
+ from 30 to 67%; whereas the ion pair ZnOH rose to 19 to 45% (Figure 28). ZnCO3 (aq) and
+ ZnHCO3 ranged from 10-25% and 0-3% respectively. The lowest zinc free ion relative abundance value for the entire study occurred during the month of August 2016 at the site
Quality Drain, 30% (Figure 28). As pH decreased in November 2016, free ion relative abundances increased again. Zn2+ relative abundances ranged from 71 to 85% (Figure 28).
+ + ZnOH relative abundances ranged from 5 to 10% in November 2016. ZnHCO3 and ZnCO3 (aq) values fell within ranges of 3-4% and 6-15%. 75
Figure 28: Zinc speciation from November 2015 to November 2016. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. NC = not collected. BDL = below detection limit of 0.0552 mol/kg.
76
3.5.2. Copper
2+ + Predicted speciations showed six copper species for all sites: Cu , CuO (aq), CuOH , CuCO3
+ 2- (aq), CuHCO3 , and Cu(CO3)2 whose abundances varied year-round. Ranges of relative
2+ + + 2- abundances for Cu , CuO (aq), CuOH , CuCO3 (aq), CuHCO3 , and Cu(CO3)2 are 0 to 89%, 0 to 27%, 0 to 2%, 9-88%, 0-2% and 0-9% respectively. According to predictions, copper carbonate ion pairs predominated at most sites in the study (Figure 29) In November 2015,
CuCO3 (aq) predominated at 3/5 sites and exhibited relative abundances ranging from 9% at
2- USBC to 77% at Santa. Cu(CO3)2 did not have a relative abundance above 1% at any site during November 2015. As pH values rose in February 2016, CuCO3 (aq) became predominant
2- at all sites. Values spanned from 63-83% for CuCO3 (aq) and no sites had Cu(CO3)2 abundances exceeding 1%. In May 2016, copper carbonate relative abundances further increased alongside pH. All sites had relative abundances between 82-86% and 1-3% for CuCO3 (aq) and
2- Cu(CO3)2 respectively. In August 2016, copper carbonate species continued to predominate at
2- all sites. Relative abundances of Cu(CO3)2 increased, occupying a range of 2-9%. CuCO3 (aq)
2- ranged from 63-75%. In November 2016, Cu(CO3)2 decreased in abundance and displayed a range of 1-5% while CuCO3 (aq) ranged from 86-88%.
CuO (aq) exhibited several different relative abundance values throughout time. Seasonal cycles in CuO (aq) speciation coincide with pH changes (Figures 29, 2). August 2016 had the highest CuO (aq) of all months in the study, with values ranging from 16 to 28%. The most basic site in August 2016, Quality Drain (pH = 8.41 ± 0.05), had the highest relative abundance of
CuO (aq) of all sites in the study (28%). By contrast, CuO (aq) existed as a minor species in
November 2015, having relative abundances of 0-1% (Figure 29). For February 2016, 0-2% of the total copper existed as CuO (aq); with the lowest values occurring at USBC. During May 77
2016, 0-9% of all copper speciated as CuO (aq). November 2016 had CuO (aq) relative abundances ranging from 0 to 4%.
Cu2+ exhibited pH related seasonal trends opposite to those of CuO (aq) and the copper carbonate species. According to predictions, Cu2+ had its highest relative abundances in
November 2015 and February 2016 (Figure 29). In November 2015, Cu2+ displayed a range of relative abundances of 19% at Santa to 89% at USBC. Relative abundances of Cu2+ ranged from
15% at Santa to 34% at USBC for February 2016 (Figure 29). Seasonal declines in the relative abundance of the free ion coincided with pH increases. In May 2016, relative abundances of
Cu2+ ranged from 0 to 6% (Figure 29). For August 2016, Cu2+ occupied 0% at Quality Drain to
4% at Slag Canyon of the total copper while pH values exceeded 8.00 for nearly all sites (Figure
29). Cu2+ rose to a range of relative abundances from 3 to 10% in November 2016 (Figure 29).
+ + + The species CuOH and CuHCO3 never had relative abundances exceeding 2%. CuHCO3 only occurred in November 2015 and February 2016. 78
Figure 29: Copper speciation from November 2015 to November 2016. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. NC = not collected. BDL = below the copper detection limit of 0.0227 mol/kg.
79
3.5.3. Iron
According to iron speciation calculations, all iron species including HFeO2° varied in
- abundance both throughout the year and geographically. The species FeO2 exhibited relative abundances ranging from 0 to 4% (Figure 30). For the entire study, FeO+ values ranged from 2% to 88%, and HFeO2° relative abundance values ranged from 11 to 91% (Figure 30). During
November 2015, HFeO2° occupied 11% of total iron at USBC and 55% of all iron at Santa
(Figure 30). November 2015 USBC had the highest FeO+ for the entire study, 88%. During
February 2016, HFeO2° occupied 45 - 63% of the total iron; with the lowest percentage occurring at USBC (Figure 30).
The decrease in FeO+ and the corresponding increases for other species during the May and August months coincided with an increase in pH values (Figure 2, 30). During May 2016,
HFeO2° predominated occupying 88 to 90% of the total iron, in stark contrast to less basic months (Figure 30). During May 2016, February 2016, and November 2015 all the remaining
+ - iron speciated as FeO ; in August 2016 a small percentage of FeO2 also appeared alongside
+ - + FeO . In August 2016, FeO2 occupied 1 - 4% of all iron, FeO occupied 2-18% of the total. In
August 2016, 81-94% of all iron speciated as HFeO2°. Quality Drain August 2016, the most
- basic site in the entire study, had the highest relative abundances for HFeO2° and FeO2 (94 and
4% respectively). HFeO2° occupied 73 to 87% of total iron in November 2016. In the same month, FeO+ represented 12 to 26% of total iron (Figure 30). 80
100 a) b) HFeO2 80 FeO+ - FeO2 60
40
20
NC NC c) d)
80
60
40
20
Relative Abundance (%) Relative Abundance NC NC e) f)
80
60
40
20
0 NC NC Nov 15 Feb 16 May 16 Aug 16 Nov 16 Nov 15 Feb 16 May 16 Aug 16 Nov 16
Figure 30: Year-round iron speciation. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. NC = not collected. BDL = below the detection limit of 0.0976 mol/kg.
81
3.5.4. Manganese
According to modeling predictions, Mn2+ acted as the predominant species year-round at nearly all sites, ranging from 47 to 97% of the total manganese (Figure 31). According to
- calculations, septivalent manganese (in the form MnO4 ) only occurred during the month August
2016 at three select sites (KOA, Santa, Slag Canyon) and at two sites in May 2016 (Blacktail and
- - KOA) (Figure 31). MnO4 appeared as pH became more basic. The relative abundance of MnO4
- ranged from 0 to 20% during the month of August. MnO4 relative abundances exceeding 1% did not occur in the month of May, and the species only occurred at pH values above 7.90 (Figure
- 31). Speciation calculations suggested that MnO4 reached its highest abundance at Santa (20%) during August 2016 where the pH exceeded 8.30 (Figure 31, Figure 2). Mn7+ species did not exceed 3.6% of the total species during any time in this study for sites other than Santa.
Predictions revealed that manganese formed ion pairs with sulfate during every month of this study at Santa. These ion pairs never occupied more than 3% of the total manganese. Manganese
+ also formed ion pairs with carbonate and bicarbonate. MnHCO3 ranged in abundance from <1% to 9% of total manganese and did not exhibit any major seasonal or geographical variations.
MnCO3 (aq) ranged in abundance from 0% to 12% of total manganese, and increased alongside pH during the summer and spring months. MnCO3 (aq) exhibited ranges of 7-12% and 4-24% in
May 2016 and August 2016 respectively. Santa August 2016 had the highest relative abundance of MnCO3 (aq) in the entire study. In November 2015 and February 2016, MnCO3 (aq) never had a relative abundance exceeding 5%. In November 2016, MnCO3 (aq) relative abundances ranged from 8-13%. 82
Figure 31: Manganese speciation results over time. (a) Blacktail, (b) KOA, (c) Quality Drain, (d) Slag Canyon, (e) USBC, and (f) Santa. NC = not collected. BDL = below the detection limit of 0.0364 mol/kg.
83
4. Discussion
4.1. Spatial Trends
The two most important influences on geographical variations in speciation, bioavailability, and toxicity, are distance from the Diggings East Tailings and proximity to urban phosphate discharge sources. Discharge of phosphate from point and non-point urban sources served as an influence on the bioavailability and toxicity of Ba, As, Zn, Cu, Fe, and Mn. Tailings impacted water influenced total Zn and Cu concentrations, and therefore their toxicity. Influx of water contaminated by the Diggings East Tailings served as a major influence on the speciation, and therefore bioavailability of Cu, Zn, and Fe. Runoff from the Diggings East Tailings did not appear to influence manganese speciation. Because more than 95% of the total barium speciated as free ions at all sites in the study, barium’s speciation state likely did not affect its bioavailability. Because all barium concentrations fell far below the EPA human health standard
(64.8 mol/L) and the LC50 value for the zooplankton daphnia magna (94.0 mol/L), differences in barium concentrations between sites did not cause significant differences in toxicity to humans or wildlife.
Several minor influences on element bioavailability and toxicity also existed. Differences in dissolved organic carbon concentrations between sites had the potential to influence speciation and bioavailability for copper alone. Temperature may have played a role in causing zinc speciation differences. Dissolved oxygen did not influence speciation differences because all sites had oxic values. 84
4.1.1. Urban Phosphate Discharge
4.1.1.1. Sources of Aqueous Phosphate
Land use influenced variations in phosphate concentrations between sites. There are two main sources of phosphate discharge into the creek, surface water runoff and discharge from the
Waste Water Treatment Plant. The following portion of this section discusses sources of phosphate in surface water runoff and Waste Water Treatment Plant discharge.
A variety of activities can cause phosphate laden surface water runoff to enter the creek.
Watering farms and lawns people have treated with phosphate containing fertilizers can produce runoff. Fire hydrant testing also can increase the amount of phosphate laden runoff entering streams, as it causes large amounts of water to flow over lawns and into storm drains. The runoff can enter storm drains such as Quality Drain (Toor et al., 2019). According to the State of
Montana Department of Transportation’s Butte storm drain map, manholes which eventually lead to Quality Drain exist near several parks such as JFK Park which may use phosphate fertilizers (MT DOT, 2014). Areas of the creek receiving water from storm drains are susceptible to the effects of phosphate runoff.
Sites which receive sewage treatment plant discharge can also have increased phosphate concentrations. Municipal sewage waters can contain phosphates from pharmaceutical products, tile cleaners, and dishwasher detergents (Toor et al., 2019). The Sewage Treatment Plant at the
Waste Water Treatment Plant has been known to discharge large amounts of phosphate into the creek, even after the upgrades began in 2016 (J. Griffin, pers. comm; Rader, 2019; Lashley,
2019a; Lashley, 2019b; R. Lashley, pers. comm). The new treatment system had not been fully operational in the years 2015 and 2016 (R. Lashley, pers. comm; Lashley, 2019a; Lashley,
2019b). As a result, phosphate concentrations in Sewage Treatment Plant Effluent often exceeded those in creek samples by 1-2 orders of magnitude. Phosphate averaged about 21.1 85
mol/L (2 mg/l) in Sewage Treatment Plant effluent during August 2016 and reached a peak of
31.6 mol/L (3 mg/l) on August 24th (Lashley, 2019a; Lashley, 2019b). During the years 2018-
2019 phosphate concentrations in Sewage Treatment Plant effluent fell dramatically, reaching average concentrations of 2 mol/L (0.2 mg/l) during the months June-September for the years
2018 and 2019 as a result of the upgrades being completed (Lashley, 2019b). Discharge from the
Waste Water Treatment Plant likely caused Santa to have higher phosphate concentrations than any other point in the creek at every time of the year. The control site, Blacktail, upstream of any urban input and having the lowest phosphate concentrations of all sites during every month further buttresses this analysis.
It is probable that phosphate discharge influenced the bioavailability and toxicity of elements through two mechanisms, ion-competition and indirectly influencing water pH.
Phosphate acted as a competing ion for arsenic. For other elements, phosphate did not act as a competing ion, suggesting another process at work. Previous research suggests that there is a high likelihood that waters in Santa and Butte Area One are phosphate limited (Robertson,
2019). Inferring from this information, differences in phosphate concentrations between sites can create differences in biological activity between sites. Differences in photosynthetic productivity between sites lead to pH differences between sites. These pH differences can potentially cause geographical variations in chemical speciation, and therefore bioavailability and toxicity, for all elements.
4.1.1.2. Phosphate and Photosynthesis
Geographical differences in phosphate concentrations had a pronounced effect on pH differences between sites only during the summer months. This interpretation is based on temperature values falling below the ideal range for photosynthetic activity (10 to 30°C) during 86 all other seasons (Adam et al., 2017). Above 10°C, chemical processes driving photosynthesis become more efficient for aquatic plants (Adam et al., 2017). Also, the growth rate of phytoplankton roughly doubles with every 10°C increase in water temperature between 0 and
40°C (Eppley, 1992). As the biomass of photosynthetic organisms increases, so does the amount of photosynthetic activity (Eppley, 1992). Also, in seasons where insufficient light exists (winter and fall), phosphate concentrations cannot influence photosynthetic processes. Santa and Quality
Drain had more phosphate than other sites during the summer months. The ample supply of nutrients at these sites likely increased photosynthetic activity, which may have raised their pH values above those of other sites.
Higher pH values caused speciation for Cu, Fe, Mn, and Zn at Santa and Quality Drain to differ from other sites. Much less free zinc ions existed at Quality Drain and Santa than elsewhere due to higher pH values (Figures 2, 28, 32). This could have lowered zinc’s bioavailability at those sites (Figures 2, 28, 32). Assuming Florence & Batley, 1977 is correct, higher pH values encouraged zinc to complex with organic matter at Santa and Quality Drain.
This analysis suggests that zinc had a lower bioavailability at the phosphate-rich sites compared to others in August 2016. Santa’s pH values exceeding 8.30 caused it to have the highest relative
- abundances of MnO4 (20%) and MnCO3 (aq) (25%) of all sites during August 2016 (Figure 31).
As a result, Santa had the lowest manganese bioavailability of all sites with measurable manganese concentrations in August 2016. While copper speciation differences existed between sites, they are too fleeting to have caused differences in copper bioavailability in August 2016.
The most toxic form of copper, the free ion, had a much smaller range of relative abundances between sites (0-4%) than the free zinc ion (30-67%) (Figure 29). CuCO3 (aq) predominated at 87 all sites in August 2016, suggesting a very low copper bioavailability to fish and planktonic life everywhere (Figure 29).
In contrast to copper, manganese, and zinc, speciation results suggest that pH values increased the bioavailability of iron at Santa and Quality Drain relative to other sites. Elevated
- ° pH values caused more FeO2 and HFeO2 to form at Santa and Quality Drain than at other sites
- ° (Figures 30, 33, 34). As more FeO2 and HFeO2 formed at those sites than at others, organo-iron complexes probably had a lower stability at Santa and Quality Drain. Iron’s bioavailability increases as stability of organo-iron complexes decreases.
+ + Figure 32: Zinc ion pair (ZnCO3 (aq) + ZnHCO3 + ZnOH ) speciation vs phosphate in August 2016 compared to zinc ion pair speciation vs phosphate in November 2015 and February 2016. The graph demonstrates a relationship between zinc speciation and phosphate. An R2 coefficient of 0.69 is calculated for all August sites. The second R2 coefficient (0.99) is calculated for all August sites except Quality Drain. Bars represent ion chromatograph analytical errors of ±1.5 to 7.8% for phosphate. 88
- Figure 33: FeO2 vs phosphate. Bars represent analytical ion chromatograph errors of ± 1.5 to 7.8% for phosphate. 89
Figure 34: FeO+ vs pH. Error bars represent instrumental error for pH field measurements. pH errors range from ± 0.01 to 0.05. Some error bars within symbol. 4.1.1.3. Phosphate as a Competing Ion
All arsenic occurred as As(V), which caused competition between phosphate and arsenic to act as the main influence on arsenic bioavailability. Other competing ions such as silica could in theory reduce the beneficial effects of phosphate competition with As(V) (Davis et al., 2002;
Gao et al., 2011; Rahman et al., 2014). Silica can have an important effect on arsenic bioavailability, and therefore toxicity when its concentrations exceed 600 mol/kg (Davis et al.,
2002; Gao et al., 2011). No sites had silica concentrations exceeding 600 mol/kg, therefore, silica did not exert an important influence on arsenic bioavailability. Unlike phosphate, nitrate has not been shown to have a large effect on As(V) bioavailability (Rahman et al., 2014). 90
Differences in phosphate concentrations between sites are also more reliable in forming predictions about how arsenic bioavailability differs between sites than differences in inorganic arsenic speciation that may influence complexation. Existing literature demonstrates a relationship between aqueous phosphate concentrations and As(V) bioavailability to phytoplankton, especially when ratios of arsenic to phosphate exceed 6.47:1 mol/L (Rahman et al., 2014). All sites had micro molar ratios exceeding this value (Figure 7), however phosphate can act to decrease arsenic’s bioavailability even when this ratio is exceeded (Rahman et al.,
- 2014). While it is possible that H2AsO4 forms more stable complexes with organic matter than
2- HAsO4 due to having more hydrogen bonding sites, no published sources confirm.
Based on phosphate concentrations, Santa and Quality Drain had the lowest arsenic bioavailability of all sites. Due to higher phosphate concentrations, more competition existed between As(V) and phosphate at these sites. Based on phosphate concentrations, USBC also likely had a lower arsenic bioavailability than most sites during every month where data exists.
Variations between sites, in both bioavailability and total dissolved arsenic concentrations, caused geographical differences in arsenic toxicity to algae. As all sites had total dissolved arsenic concentrations exceeding 0.0133 mmol/kg, it is possible that arsenic exhibited some significant degree of sublethal toxicity to algae at most or every site in the study. Santa likely had the lowest arsenic toxicity to algae of all sites during every month of the study, due in part to high phosphate concentrations lowering arsenic’s bioavailability there. Differences in total, dissolved arsenic concentrations between USBC and other sites likely acted as the main cause of differences in arsenic toxicity between USBC and other sites in November 2015. USBC had dissolved arsenic concentrations roughly two times greater than those of the other sites during the month of November 2015, while its phosphate concentrations exceeded those of the 91 other Butte Area One sites by about 20%. USBC likely did not have a higher toxicity to algae in other sites during May 2016, as arsenic likely had a much lower effective bioavailability there than at most other sites. USBC’s phosphate concentrations being nearly three times higher than those of other sites likely counteracted the effects of higher arsenic concentrations on toxicity.
During May 2016, the sites Blacktail, KOA, and Slag Canyon had the highest arsenic toxicity of all sites, as they had a lower effective arsenic bioavailability due to lower phosphate while having similar total arsenic concentrations to Santa. Slag Canyon had the highest arsenic toxicity to algae during August 2016 because it had the third highest total concentration of arsenic
(having a concentration only 0.08 mmol/kg lower than Santa).
While arsenic probably exhibited varying degrees of algae toxicity, it did not exhibit significant levels of toxicity to fish or invertebrates as all arsenic concentrations fell below 0.133 mmol/kg. All total dissolved arsenic concentrations fell below the EPA’s human health standard.
Inferring from this information, arsenic did not exhibit significant levels of toxicity to human life at any site.
4.1.2. Tailings Impacted Water Influx
Evidence suggests that inflow of water contaminated by the Diggings East and Northside
Tailings drove pH variations and differences in total zinc and copper concentrations between sites during the months November 2015 and 2016, and to a lesser extent February 2016. Previous research suggests that meteoric water can interact with the Diggings East and Northside Tailings, thereby producing both acidic groundwater and metal-rich surface runoff (NDRP, 2007; Tucci &
Icopini, 2012). Both types of contaminated water influence groundwater quality parameters such as pH and total zinc concentrations in Butte Area One surface waters (NDRP, 2007; Tucci &
Icopini, 2012). It is important to distinguish that only runoff, often mining-impacted, can enter 92 the surface water at USBC, due to the liner preventing groundwater from entering (Tucci &
Icopini, 2012). These pH differences caused speciation changes between sites in Butte Area One.
Precipitation events during these months (and in the preceding months) increased the amount of mining contaminated water entering the creek.
Heavy precipitation occurred during the month November 2015. Multiple precipitation events occurred during that time period. The total precipitation that occurred in November 2015 exceeded that of the average November value for the last 12 years by a factor of two (USDA,
2019). δD and δ18O results for November 2015 are lighter than other seasons, further providing evidence for substantial precipitation.
As in November 2015, the total monthly precipitation values for November 2016 exceeded its respective monthly average by roughly a factor of two, thereby enhancing tailings runoff (USDA, 2019). November 2016 had 3.08 cm (1.20 in) of precipitation, compared to the
November average for the last 12 years of 1.45 cm (0.57 in) (USDA, 2019). The two months preceding November 2016 had precipitation values similar to their monthly averages for the last twelve years (USDA, 2019).
By and large, both day and night air temperatures exceeded the freezing point during
November 2015 and November 2016 (NOAA, 2019). Most daytime temperatures for the two months preceding November 2015 and November 2016 also exceeded the freezing point
(NOAA, 2019). As a result, most precipitation fell as rain (NOAA, 2019). Any snow or ice present melted. These consequences resulted in more runoff and infiltration occurring at tailings areas, thus creating geographical pH variations.
Interpreting from meteorological data, less runoff from tailings piles entered the creek in
February 2016 than in November 2016 and 2015. More precipitation fell as snow during 93
February 2016 than in other months. Lower daytime temperatures occurred in February 2016 than in November 2015 and 2016, which meant that less of the snow melted in February 2016 compared to the other two months (NOAA, 2019). In February 2016 and the preceding month, less precipitation fell than in November 2016 and 2015, thus causing smaller geographic differences in pH. In February 2016, total monthly precipitation fell below the average February value for the last 12 years (USDA, 2019). Of the two preceding months, January 2016 had a total monthly precipitation value roughly 1.2 times greater than the average January value for the last
12 years (USDA, 2019). December 2015 had a total monthly precipitation value roughly 1.5 times greater than the average December value for the past 12 years (USDA, 2019).
In contrast to February and both November months, May 2016 and August 2016 did not experience elevated precipitation levels. May 2016 had precipitation levels well below the average for the last 12 years (USDA, 2019). The preceding month April 2016 had precipitation levels slightly below the monthly April average for the last 12 years (USDA, 2019). Compared to the average August value for the last 12 years, little precipitation occurred in August 2016
(USDA, 2019). As a result, little infiltration and runoff from the tailings piles occurred during those months. Based on this information, runoff of tailings-contaminated water did not influence speciation in the months of May and August.
Precipitation could have contributed to some of the pH differences between sites observed in this study. As precipitation increased, the volume of both groundwater and surface water interacting with Northside and Diggings East Tailings increased. As USBC is lined, groundwater does not flow into it (NDRP, 2007). While there is no groundwater input, rainwater interacted with exposed tailings sediment, thereby contributing acidic, contaminated surface water to USBC. 94
Because USBC is closest to the Diggings East and Northside Tailings, water influenced its chemistry more than other sites during these three months. 18O and D results suggest that precipitation-derived runoff had a stronger influence on the chemistry of USBC than other sites; as its results are much lighter than most other sites during November 2015 and February 2016
(Figure 11). USBC did not have lighter 18O and D values than other sites for May 2016, when precipitation was low and warmer air temperatures existed. Quality Drain, another site primarily sourced from precipitation, also had much lighter 18O and D ratios than most other sites in
November 2015 and February 2016. In November 2015 and February 2016, USBC had a lower pH and higher concentrations of copper, arsenic, zinc, and barium than other sites; suggesting that acidic tailings-contaminated water impacted it more heavily than other sites. USBC having a higher conductivity than other sites in November 2015 and February 2016 bolsters the case for mine-impacted surface runoff lowering its pH (Figure 4). KOA and Slag Canyon had higher pH values and lower specific conductivity values than USBC in November 2015 because they reside further away from the tailings.
Geochemical data suggests that tailings impacted water influenced the pH at Slag Canyon and KOA during the three high precipitation months. Slag Canyon and KOA both had higher conductivities and higher zinc concentrations than the control site in November 2016, suggesting that tailings-impacted water is entering these sites. Principal component analysis suggests that relationships exist between tailings-impacted water and geochemical data influencing speciation
(Figure 35).
Principal component 1 best represents water-rock interaction with minerals in the
Diggings East Tailings and the Northside Tailings (Figure 35). Principal component 1 is represented by the vertical axis (Figure 35). At USBC, stagnant surface water can become more 95 copper rich and acidic by interacting with minerals in contaminated sediment. Groundwater can also undergo water-rock interaction with the tailings, becoming copper and zinc rich in the process. Northside Tailings groundwater and soil are heavily contaminated with zinc.
Concentrations of zinc rose as high as 5660 mol/L (249,000 g/L) in groundwater passing through the Northside Tailings and 2890 mol/L (127,000 g/L) in groundwater passing through the Diggings East Tailings (Tucci & Icopini, 2012). For the Northside Tailings, groundwater concentrations of copper reached as high as 764 mol/L (33,600 g/L) (Tucci & Icopini, 2012).
Copper and zinc, elements abundant in the Northside and Diggings East Tailings piles, had the strongest relationship with principal component 1 (Figure 35). Boron and lithium, both indicators of water-rock interaction, correlated closely with principal component 1 (Figure 35). These relationships lend further credence to the idea that principal component 1 represented water-rock interaction with minerals in the Northside Tailings and Diggings East Tailings.
Overall, relationships between each site and the first principal component bolster the idea that tailings-influenced water drove spatial variations in element speciation during November
2015, February 2016, and November 2016. Of all sites, USBC November 2015 and USBC
February 2016 demonstrated the strongest relationship with principal component 1 (Figure 35).
Slag Canyon exhibited the weakest relationship with principal component 1 for all sites. Slag
Canyon is the second farthest away from the Northside Tailings and the Diggings East Tailings of all down gradient sites (Figure 35). The data points for all sites in November 2015, February
2016, and November 2016 correlate more closely with principal component 1 than other months
(Figure 35). 96
Figure 35: PCA plot made in R showing the relationship of each site to both the two main principal components and the vectors. Speciation results suggest that these tailings water-induced pH changes caused differences in bioavailability and toxicity between sites for copper, zinc, and iron during the highest precipitation months (November 2015, February 2016, and November 2016). As all pH values fell below 7.60 in November 2015, only minor differences in zinc speciation existed between sites in that month. Zinc speciation does not change appreciably below pH 7.60 (Powell et al., 2015). Because the tailings water lowered the pH at USBC 0.50-1.00 units relative to other sites, more free copper and zinc ions formed there compared to other sites, with free copper ions
(88%) predominating over copper ion pairs during November 2015. As a result, their 97 bioavailability at USBC increased during those months, especially during November 2015. It is fairly certain that copper exhibited a low bioavailability to fish and planktonic species at Santa,
Slag Canyon, and KOA during November 2015 because copper carbonate ion pair species predominated there. Also, the lower pH made it more difficult for zinc to complex with organic matter at USBC during those months. The lower pH at USBC caused iron to speciate as FeO+, likely causing iron to form more stable organic complexes. Based on this interpretation, organo- iron complexes did not dissociate into free ions as readily at USBC as at other sites. As a result,
USBC likely had lower levels of un-complexed iron. Because organo-iron complexes are generally less bioavailable than free iron, the greater relative abundance of FeO+ acted to lower iron bioavailability at USBC.
While iron bioavailability had large variations between sites as a result of tailings impacted runoff, toxicity did not. All sites in this study had concentrations well below the EPA aquatic life limit for iron (17.9 mol/L), even those closest to the tailings. Concentrations suggest that iron did not exhibit toxicity to aquatic life at any site.
In contrast to iron, zinc likely exhibited variations in toxicity between sites which resulted from tailings impacted runoff. USBC likely had the highest zinc toxicity of all sites in
November 2015. Total concentration data bolsters this argument because USBC exceeded the
EPA’s aquatic life standard while the others did not in November 2015. Having slightly more of the higher-bioavailability Zn2+ species may have also raised the toxicity at USBC during
November 2015. Even though USBC had higher zinc concentrations than other sites in February
2016, major differences in toxicity could not have existed between sites. All the sites had values well below the EPA aquatic life limit for zinc, so it is unlikely that significant zinc toxicity 98 existed at any of the sites in February. For November 2016, zinc toxicity did not exist at any site as all sites fell well below the EPA aquatic life limit.
Interpolating from speciation data, influx of tailings-contaminated water into the creek did not affect bioavailability for manganese or barium to humans or aquatic life such as fish.
Sites closest to the tailings (such as USBC) had nearly the same relative abundance of Mn2+, and
+ only slightly fewer MnCO3 (aq) and MnHCO3 species, as sites located farther away during
November 2015, February 2016, and November 2016. Lowering the pH does not have a large effect on manganese speciation if the pH is below 8.30.
Also, tailings-contaminated water did not act to increase the toxicity of manganese or barium. Sites closest to the tailings such as USBC did not have higher total dissolved concentrations of manganese or barium than sites further away. Indeed, all manganese total dissolved concentrations in the study fell below the LC50 values for C. dubia and H. Azteca and the recommended EPA lifetime human health advisory limit of 5.46 mol/L (300 g/L), which suggests that manganese had a low toxicity to humans and aquatic life at both sites closest to the tailings and those further away.
4.1.3. Dissolved Organic Carbon
Unlike in November 2015, it is plausible that major differences in dissolved organic carbon concentrations between sites acted as the main cause of geographical variations in copper bioavailability during February and May 2016. In November 2015, little difference in dissolved organic carbon existed between sites, which allowed ion-pairing variations to assume a major role. By contrast, copper ion pairing did not cause geographical differences in bioavailability between sites in May or February 2016. Geographical ion pairing differences are small compared to dissolved organic carbon differences in May 2016. Copper probably had a low bioavailability 99 to aquatic life everywhere during May 2016 and February 2016 because copper carbonate dominated inorganic speciation at all sites.
Inferring from dissolved organic carbon concentrations, copper had a lower bioavailability at Quality Drain and USBC than at the other sites during February 2016. USBC and Quality Drain likely had more organo-copper complexes than the other sites due to having higher dissolved organic carbon concentrations. Organo-copper complexes are generally less bioavailable to fish and other animals than inorganic species. As Santa, Slag Canyon, and KOA had nearly identical levels of dissolved organic carbon, they had identical amounts of organo- copper complexes. Inferring from this, copper had similar bioavailability to fish and other animals between these four sites.
Based on higher dissolved organic carbon concentrations, copper had a lower bioavailability at Blacktail than at all the other sites during May 2016. Blacktail likely had more organo-copper complexes than other sites because of the higher dissolved organic carbon (Figure
36). The bioavailability of copper did not vary between sites other than Blacktail as similar dissolved organic carbon concentrations could have caused similar organic copper complexation.
Both total dissolved copper concentrations and bioavailability variations caused by speciation influenced differences in copper toxicity between sites. No sites exceeded World
Health Organization drinking water or EPA human health limits, so copper did not exhibit significant levels of toxicity to human life. Because no sites exceeded the 90 hour LC 50 value for copper toxicity for the freshwater fish Oreochromis niloticus, acute toxic effects to fish did not occur at any site. Because little difference in dissolved organic carbon concentrations existed between sites in November 2015, total dissolved copper concentrations and relative abundances of Cu2+ acted as the main influences on toxicity. Because USBC had the highest dissolved 100 copper concentration and predominantly free copper ions, in all probability it had the highest copper toxicity in November 2015. Indeed, USBC is the only site which exceeded 0.230 mmol/kg in November 2015, suggesting that toxic effects to phytoplankton occurred there. In
February 2016, high concentrations of organic carbon suggest that organic complexation mitigated the increases in copper toxicity at USBC caused by elevated total copper concentrations and higher relative abundances of free ions. Even though USBC had higher relative abundances of free ions and more total dissolved copper (exceeding the algae toxicity limit from Bilgrami & Kumar), copious amounts of organic matter could have lowered copper phytoplankton toxicity to levels similar to or lower than other sites in February 2016. Because total dissolved organic carbon concentrations and copper ion relative abundances are similar between sites in May, it is likely that organic carbon acted as the main influence on phytoplankton toxicity. In May 2016, Blacktail had the lowest copper phytoplankton toxicity because it had much higher dissolved organic carbon concentrations than the other sites (Figure
36). Copper exhibited a similar phytoplankton toxicity between the other sites because they had similar concentrations of dissolved organic carbon and total dissolved copper. Although it is fairly certain that Blacktail had the lowest toxicity, copper had a low toxicity to aquatic life at all sites in May 2016 due to the predominance of the low-bioavailability species CuCO3 (aq). Due to incomplete dissolved organic carbon data, toxicity to phytoplankton must be estimated solely based on total copper concentrations and ion pair speciation results in August and November
2016. As with May 2016, copper had a low toxicity to aquatic life at all sites in August and
November 2016 as a result of CuCO3 (aq) predominating everywhere. Copper had the lowest toxicity to all life at KOA and Blacktail during August 2016 as concentrations fell below the detection limit at both locations. Copper did not exhibit toxicity to aquatic life at Santa or Slag 101
Canyon as neither concentration exceeded 0.230mol/kg. Only Quality Drain exceeded 0.230
mol/kg during August 2016. Despite this, copper’s speciation state rendered it non-toxic at
Quality Drain. Nearly all copper speciated as the low-bioavailability species CuCO3 (aq) and
CuO (aq), and no free ions existed. In addition, it is probable that Quality Drain’s high dissolved organic carbon concentrations encouraged organic complexation. In November 2016, Santa may have had a slightly lower copper toxicity than the other sites due to having a slightly lower copper bioavailability. All sites in November 2016 had similar total dissolved copper concentrations, but Santa had the highest relative abundance of ion-pair species.
Figure 36: Calculated Cu2+relative abundance vs measured DOC concentrations. Analytical errors for DOC are ± 3% for all samples. Error bars within symbols not included.
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4.1.4. Minor Influences on Bioavailability and Toxicity
Minor influences on speciation include temperature, desorption of phosphate from stream sediments, and discharge of water from the Butte Treatment Lagoon and the Pole Treatment
Plant into Santa. Temperature differences caused variations in copper and zinc speciation for
August 2016 which photosynthetically driven pH changes alone could not explain. Even though they had similar pH values, modeling results revealed more ion pairs at Quality Drain than at
Santa. Increased temperatures cause ion-pair formation to become more favorable for copper and zinc (Ritchie, 2004). Quality Drain having a temperature 5.5°C higher than Santa for August
2016 could have caused more ion pairs to form there. Relatively shallow water in Quality Drain likely caused a temperature 5.9°C higher than the average temperature for all sites sampled in
August 2016.
Temperature could have accounted for minor differences in copper speciation which occurred in May 2016. For instance, even though Slag Canyon had a slightly more basic pH than
USBC, it had more Cu2+ due to being 3°C cooler. Slag Canyon had a greater water depth and more shade than USBC, which lowered its temperature. Temperature-related spatial differences in speciation resulted in copper being slightly more bioavailable at Santa and Slag Canyon than at USBC during May 2016, assuming that Cu2+ is more bioavailable than copper ion pairs.
Sulfate rich water from the Butte Treatment Lagoon and the Montana Pole and Treatment
Plant is discharged into Silver Bow Creek (Plumb, 2009). This water eventually reaches Santa, thereby causing more MnSO4° to form there than at the other sampling sites (Figure 37). Santa is the only sampling site downstream from the Butte Treatment Lagoon and the Montana Pole and
Treatment plant (Figure 1). The greater abundance of MnSO4° may have slightly lowered the bioavailability of manganese at Santa. 103
Figure 37: Calculated relative abundance of MnSO4° vs measured sulfate. Figure shows a close relationship between sulfate concentrations and MnSO4° speciation. Analytical ion chromatography errors for sulfate range from (±0.23 to 0.93%). Error bars fell within symbol. 4.2. Seasonal Trends
In addition to varying between sites, the predicted bioavailability and toxicity of metals and semimetals also varied seasonally. Seasonal cycles in photosynthetic activity created variations in pH that influenced speciation, and by extension bioavailability and toxicity, for all elements speciated except barium. Seasonal variations in phosphate concentrations directly influenced seasonal cycles in predicted arsenic bioavailability and toxicity by acting as a competing ion. 104
4.2.1. Seasonal Variations in Photosynthesis Impact Speciation
Ideal conditions for photosynthesis existed during the summer, which allowed photosynthesis to increase the pH above that of other months at nearly all sites. Photosynthetic organisms are exposed to more light during the summer than in the fall and winter. All sites had ideal water temperatures for photosynthetic activities during the summer, but not during other seasons. Concentrations of phosphate, an essential nutrient for algae and phytoplankton growth, peaked during the summer months for all sites further fueling plant growth and photosynthetic activity. Phosphate concentrations increase during the summer as a result of increased irrigation and fertilizer application.
Although not as conducive as the summer months, conditions for photosynthesis remained relatively hospitable during the spring months. As a result of these conditions, the second most basic pH values occurred during the spring for most sites. Ample light existed to drive photosynthetic processes during the spring. While most temperatures did not fall within the ideal range for photosynthesis during the spring, all far exceeded the freezing point of water.
The fall and winter had much less photosynthetic activity than the spring and summer.
Photosynthetic processes slowed down due to scarce light and temperatures falling well below the ideal range for photosynthesis. As a result of low photosynthetic activity, nearly all sites had less basic pH values during the fall and winter.
Seasonal variations in pH resultant from photosynthetic activity influenced many aspects of speciation. For Cu, Zn, and Mn photosynthesis-induced fluctuations in pH caused seasonal increases in ion pairing during the spring and summer months. For the site Santa, seasonal pH variations caused changes in manganese oxidation states during the summer months.
Photosynthetic activity caused seasonal pH differences, which may have increased organic 105 complexation for zinc and decreased complexation for iron during the summer and spring months.
These seasonal differences in speciation potentially caused the bioavailability of the elements Cu, Fe, Mn, and Zn to vary between seasons. Speciation differences almost certainly caused the bioavailability of Cu and Zn to vary seasonally at all sites with pH. Speciation differences only caused major seasonal bioavailability variations at Santa for manganese.
Speciation probably caused iron to have opposite seasonal bioavailability trends from Cu and Zn.
Iron’s speciation increased its bioavailability during the summer whereas the opposite case is true for copper and zinc.
Seasonal pH related variations in ion pair abundance almost certainly caused major variations in bioavailability for copper and zinc, but not for manganese and barium. For barium, ion pair relative abundances always fell below 2%; and therefore, did not influence its bioavailability. The increase in pH observed during the summer months caused zinc and copper ion pairs to reach their highest abundance which likely acted to decrease the bioavailability of copper and zinc to freshwater fish, plants, and humans. The less basic pH values present in the fall and winter months brought on by lower levels of photosynthesis acted to decrease the relative abundances of copper and zinc ion pairs relative to the more bioavailable free ion.
Seasonal pH fluctuations caused MnCO3 (aq) to exhibit somewhat higher abundances in the summer months than in November 2015. However, manganese exhibited smaller seasonal changes in ion pairing than copper and zinc: MnCO3 (aq) relative abundances increased 10% moving from November 2015 to August 2016 whereas CuCO3 (aq) increased about 30% on average during that time period. Because of this, ion pairing had a smaller influence on manganese bioavailability. 106
In addition to influencing ion pairing, seasonal pH changes had the potential to influence organic complex stability for Zn and Fe. These differences in organic complexation probably impacted their bioavailability. As pH increased, organo-metallic complex stability nearly certainly increased for zinc and decreased for iron.
For iron, seasonal differences in ion pairing driven by pH could have decreased the stability of organo-iron complexes during the summer relative to other seasons. FeO+ the species which, judging from its chemical properties, forms the most stable bonds with organic matter, had the lowest relative abundance during the summer at all sites (Figure 34). The appearance of
- FeO2 during the summer also had a high probability of increasing the stability of organo-iron complexes, thereby raising iron bioavailability. As the highest relative abundances for FeO+ occurred during the winter and fall months at all sites, organo-iron complexes had a higher stability during the winter and fall. As a result, iron speciation assumed its least bioavailable form at all sites during the winter and fall.
Based on organic complexation, zinc assumed its least bioavailable form during the summer and spring. Organo-zinc complexes are relatively certain to have had a higher stability in the summer and spring than in the winter and fall. The summer and spring months having a more alkaline pH than the fall and winter months caused predicted differences in stability.
Speciation influenced differences in bioavailability could have acted in concert with seasonal differences in total concentrations to cause zinc and copper toxicity to vary seasonally.
Zinc and copper generally had lower aqueous concentrations during the summer and spring than in the winter and fall due to higher pH values. As pH values became more basic as a result of increased photosynthetic activity, zinc and copper became less soluble. Interpreting from data,
Zinc had a lower toxicity to aquatic life at USBC during May 2016 and February 2016 than in 107
November 2015. As zinc bioavailability decreased and total zinc concentrations fell below the
EPA aquatic life limit during May 2016 and February 2016, toxicity lowered as a result. Zinc concentrations did not exhibit meaningful levels of toxicity to freshwater life such as fish and algae at sites other than USBC November 2015, as no others had concentrations exceeding the
EPA aquatic life limit. Zinc never exhibited significant toxicity to humans at any other site as all concentrations fell below the human health limit. Seasonal variations in copper toxicity likely only occurred at USBC. For every part of the year, copper concentrations at other sites fell below the limit where toxic effects to algae are likely to begin. Thus, it is not likely that copper exhibited significant toxicity to algae or other phytoplankton at other sites during any time of the year. Because copper had a higher total concentration and bioavailability during November 2015 at USBC, copper had a higher toxicity in November 2015 at USBC than in other months.
Barium, arsenic, iron, and manganese did not display seasonal fluctuations in toxicity to humans or aquatic life as these elements likely did not have high enough concentrations during any time of the year to exhibit significant levels of toxicity.
4.2.2. Phosphate as a Competing Ion for Arsenic
Seasonal land use patterns caused seasonal variations in phosphate concentrations at
Santa and all Butte Area One sites. Phosphate fertilizer use on lawns and parks is most common during the summer, which caused phosphate concentrations to reach their peak values during the summer at all sites. Widespread irrigation also occurs on lawns and parks during the summer, creating more phosphate rich runoff that enters streams. Data from Blacktail further supports the conclusion that human activities are the main influence on seasonal fluctuations in phosphate concentrations. In contrast to the sites heavily affected by human activities, spring and summer concentrations at Blacktail are roughly the same as the fall concentrations. 108
Because all arsenic existed as As(V), phosphate concentrations likely caused seasonal differences in effective arsenic bioavailability. All sites had their highest phosphate concentrations in August 2016, as a result less phytoplankton uptake of arsenic occurred.
Interpolating from this information, arsenic had the lowest effective bioavailability to phytoplankton during August 2016 at all sites. The sites Santa and Slag Canyon followed the same seasonal phosphate cycles, with the winter month February having the lowest phosphate concentrations. Phosphate concentrations suggest that arsenic had the highest effective bioavailability during the winter at Santa and Slag Canyon. Inferring from phosphate concentrations, As(V) had the highest bioavailability during the fall for KOA, Quality Drain, and
Upper Silver Bow Creek.
Inferring from both bioavailability and total dissolved arsenic concentrations, arsenic toxicity varied through the year. The summer had similar dissolved arsenic concentrations to other months but had lower predicted arsenic bioavailability due to higher phosphate concentrations. Inferring from this, arsenic had a lower toxicity to phytoplankton during the summer than in other months. Arsenic had the highest toxicity to phytoplankton in May 2016.
Arsenic had high total concentrations in May as a result of conditions becoming more alkaline compared to the winter and fall months. Arsenic becomes more soluble and mobile as pH increases (Eby, 2004). May 2016 had higher measured stream discharge than the summer months as a result of spring thaw (Figure 38; USGS, 2019). Higher stream flow means that streams picked up more arsenic in May 2016 than in August 2016. Arsenic toxicity is likely the same for winter and fall as dissolved arsenic concentrations and bioavailability are similar for November
2015, February 2016, and November 2016. 109
Figure 38: Stream gauge showing USGS discharge data at Blacktail Creek, station USGS 12323240 (USGS, 2019). Lines indicate sampling dates. 110
Figure 39: Spatial and seasonal bioavailability differences for Ba, As, Zn, Cu, Fe, and Mn. NC = not collected. BDL= below detection limit. 4.3. Limitations
One limitation is the assumption that pO2 is the main constraint on redox conditions.
Literature suggests that pO2 is nearly always the main constraint on redox conditions in relatively clean freshwaters (Boyd, 2000). However, in highly contaminated environments (such as the
Berkeley pit), metal oxidants such as Fe3+ can assume this role (Madison et al., 2003). Because
USBC had particularly elevated levels of dissolved metals in November 2015, there may have been other important redox constraints 111
Missing data acts as a limitation on redox estimates for November 2016 because dissolved oxygen measurements are missing. Modeling efforts estimated missing numbers by utilizing data from November 2015 to approximate these missing values. Using estimates rather than actual field measurements introduced more uncertainty into calculations of redox potential for
November 2016 sampling sites. Even considering these flaws, it is certain that all sites in this study had oxidizing waters.
Although all sites had oxidizing waters, trace amounts of As(III) may have existed. Indeed, earlier research has demonstrated that small amounts of As(III) can exist in the surface waters of the creek as a result of transport from groundwater or hyporheic zone water (Nagorski & Moore,
1999). In instances where As(III) species are present in surface waters, concentrations are generally very low because they readily oxidize to As(V) (Wilke & Hering, 1998).
Similarly, organoarsenic and organometallic species existed in the creek but did not appear in the modeling results because good thermodynamic data does not exist upon which to base those calculations. Instead, predictions of organic speciation utilized environmental chemical data and chemical properties of calculated ion-pairs to form predictions about complex stabilities and abundances. The current lack of published research explicitly proving differences in organic complexation between different arsenic ion pairs or iron(III) ion pairs acts as an additional limitation.
Similarly, incomplete data hampered accurate comparisons of copper toxicity to EPA standards. The lack of alkalinity data for most sites in the study prevented the Biotic Ligand
Model from being accurately used. The lack of dissolved organic carbon data at some sites also impeded Biotic Ligand Model calculations. Instead, copper values from toxicological studies and
World Health Organization standards served as comparisons to copper concentrations. 112
Chemical characteristics of organic matter, such as functional groups, can influence the amount and stability of organo-metallic and organo-arsenic complexes. The instrumentation used in this study could not predict specific chemical characteristics of dissolved organic matter. If data existed for dissolved organic carbon characteristics, predictions about the stability of organo-arsenic complexes could be more accurate.
Microbial life can also alter the speciation state of metal and semimetal contaminants; however current knowledge of creek microbial communities is in progress. For instance, algae may act to create reduced species. Algal cells may attract metal and metalloid anions (such as the
2- metalloid arsenic species HAsO4 ) causing them to absorb into the biofilm (Gammons et al.,
2015). Anoxic conditions may exist inside biofilms, especially at night, which can potentially reduce As(V), Fe(III), and Mn(IV) (Gammons et al., 2015). Bacteria can also alter speciation by oxidizing elements. For instance, bacteria can play a key role in oxidizing As(III) in stream waters (Wilkie et al., 1998). Also, communities of bacteria such as Leptospirillum ferrooxidans can oxidize Fe(II) species to Fe(III) species in mine-impacted sites (Bruneel et al., 2006; Gandy et al., 2007). Identifying microbes in the creek is in progress therefore, there is no way of knowing if certain species of microbes are causing geographical or seasonal variations in chemical speciation.
This study did not produce a detailed assessment of how the mineralogy of the stream bed could be affecting chemical speciation. Like microorganisms, streambed sediment mineralogy can affect the speciation of aqueous metals (Ehlert et al., 2014). For instance, vernadite, birnessite, and ferrihydrite can influence the speciation of iron and arsenic (Ehlert et al., 2014;
Hochella et al., 2015). While surface water undergoes less water-rock interaction than hyporheic zone water, mineralogy may still influence surface water chemical speciation. 113
Inorganic carbon concentrations are not available for all samples in the study.
Calculations used data from other months to estimate the missing values. This limitation likely had a very minimal impact on speciation results, as pH acts as the predominant influence on ion pairing for the elements in this study.
4.4. Future Research
Future research will look for correlations between chemical speciation and both microorganisms and minerals. DNA metagenome information exists for Butte Area One. Such projects will use metagenome information to identify microbial species living in the creek and their metabolic potential. Future studies will produce a detailed characterization of the stream bed’s mineralogy and use EQ6 to model water-rock interactions which may influence metal and semi-metal speciation.
These research endeavors will use additional chemical analysis techniques to improve speciation predictions. Direct arsenic speciation analysis will test the model’s prediction that
As(III) does not exist in any surface waters. Typically, field speciation analysis for arsenic uses spectrophotometric methods (D’amore et al., 2005). Future research will conduct arsenic speciation testing in accordance with the protocol described in Hu et al., 2011. Future research activities could involve determining the chemical properties of dissolved organic matter.
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5. Conclusions
Bioavailability varied seasonally and spatially for As, Zn, Cu, Fe, and Mn as a result of speciation. Differences in both speciation and total concentrations caused spatial variations in toxicity for copper and zinc. All barium concentrations were below the EPA’s human health standard and the LC50 values for the zooplankton daphnia magna. Therefore, barium almost certainly exhibited low toxicity to humans and aquatic life at all sites. Barium’s speciation state did not change between seasons or sites, which suggests speciation did not influence its bioavailability. Speciation varied spatially due to the inflow of tailings impacted runoff during the fall and winter months. Spatial variations in speciation during summer months are indirectly influenced by differences in phosphate concentrations between sites caused by land use activities. Competition between phosphate and arsenic acted to create spatial and seasonal differences in arsenic bioavailability and toxicity to phytoplankton. Seasonal differences in photosynthetic activity acted as the primary influence on seasonal speciation variations, and these variations likely caused differences in bioavailability for Zn, Cu, Fe, and Mn.
Precipitation events increased the influx of acidic tailings-contaminated water into the creek during the months November 2015, February 2016, and November 2016, thereby influencing speciation and total dissolved concentrations for copper, zinc, and iron. Inflow of tailings contaminated runoff increased copper and zinc toxicity at USBC by increasing their total dissolved concentrations. Indeed, copper concentrations exceeded the sublethal toxicity limit for algae from Bilgrami & Kumar, 1997 at USBC during November 2015 and February 2016. Zinc concentrations exceeded EPA aquatic life standards in USBC during November 2015 and
February 2016. Influx of acidic water to the creek probably altered speciation, which caused zinc and copper to have a higher bioavailability at sites closest to the Diggings East and Northside 115
Tailings such as USBC. It is anticipated that copper had a high bioavailability at USBC during the month of November 2015 as free ions dominated speciation. Copper likely had a low bioavailability, and therefore toxicity, to aquatic life year-round at the other 5 sites in the study due to copper carbonate ion pairs predominating. Inflow of acidic water decreased iron bioavailability at sites close to the tailings by encouraging the formation of FeO+.
Geographical differences in phosphate concentrations could have caused differences in speciation which influenced bioavailability between sites during the summer months for Zn, Cu,
Fe, and Mn. Phosphate containing discharge from the Sewage Treatment Plant likely acted to increase photosynthetic activity at Santa, thereby altering the pH which potentially influenced speciation. As Santa and Quality Drain had a higher pH than other sites, more zinc and copper ion pairing and organo-zinc complexation could have occurred there. As a result, the bioavailability of zinc and copper decreased at those sites. The increase in pH caused more
- HFeO2 and FeO2 to form, decreasing organo-iron complexation, and therefore increasing iron bioavailability at Santa and Quality Drain relative to other sites. At Santa, increased phosphate concentrations caused the pH to rise above 8.3, thereby causing 20% of the manganese to speciate in the 7+ state and 25% of manganese to speciate as MnCO3 (aq). Year-round at all other sites, more than 70% of all manganese speciated as free ions in the 2+ state. Of the three divalent manganese species, the free ion Mn2+ nearly always predominated. The ion pair
MnSO4° never had a major influence on manganese bioavailability or toxicity as it never occupied more than 4% of the total manganese.
As all arsenic speciated as As(V), variations in phosphate concentrations caused differences in arsenic bioavailability between sites during all months. Santa had elevated phosphate due to Wastewater Treatment Plant discharge. As a result, more competition for 116 phytoplankton uptake occurred between arsenic and phosphate at Santa compared to other sites.
This lowered the bioavailability and therefore toxicity of arsenic to algae there. Although phosphate rich runoff acted to lower arsenic bioavailability and toxicity at Quality Drain and
USBC, high total arsenic concentrations counteracted the beneficial effects of phosphate on arsenic toxicity there.
Variations in phosphate concentrations also influenced arsenic bioavailability and toxicity differences between seasons. As a result of intensified land use activities, phosphate concentrations are higher in the summer than in other months, which could have acted to lower arsenic bioavailability and toxicity to phytoplankton in the summer. It is almost certain that May
2016 had the highest arsenic toxicity to phytoplankton because it had the highest total arsenic concentrations.
Seasonal fluctuations in photosynthetic activities caused copper and zinc to form species which are less bioavailable during the summer and spring. Zinc and copper ion pairs had higher relative abundances during the summer. Inferring from elevated pH values, zinc formed more organic complexes during the summer and spring, further lowering bioavailability. Zinc and copper toxicity decreased during the summer and spring as a result of both lower bioavailability and lower total concentrations. The increase in pH during the spring and summer could have caused zinc and copper to become less mobile, thereby lowering their total dissolved concentrations.
Like copper and zinc, the bioavailability of iron also feasibly varied on a seasonal basis.
- Iron’s bioavailability increased during the summer. The high summer pH caused FeO2 and
+ HFeO2° to predominate over FeO , thereby decreasing the stability of organo-iron complexes. 117
5.1. Recommendations Based on Concentration and Speciation Data
Spatial and seasonal speciation results guide recommendations for future remediation and restoration projects. Based on seasonal speciation results, summer is the optimum time to reintroduce native fish such as cutthroat trout to the creek. Because copper, zinc, and arsenic species have their lowest bioavailability during the summer months, survival rates for reintroduced fish are conceivably higher. The Diggings East Tailings and the Northside Tailings should be removed because runoff from the tailings acts to increase copper and zinc toxicity during the high precipitation months. Runoff increased toxicity by increasing copper and zinc concentrations, particularly at sites closest to the tailings. Runoff also resulted in the formation of more bioavailable copper and zinc species.
118
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7. Appendix A: Tables
7.1. Raw Water Chemistry Data
Table I: pH, dissolved oxygen, conductivity, and temperature. Specific Dissolved Temperature Date Site pH Conductivity Oxygen (µmol/l) (°C) (µS/cm) Orphan Boy 151029B 7.34 ± 0.05 NA 22.4 ± 0.1 1605 ± 8 Tap* Orphan Girl 151029C 7.70 ± 0.05 NA 26.4 ± 0.1 1617 ± 8 Shaft* 151114B Santa 7.24 ± 0.05 292 ± 3 4.5 ± 0.5 430 ± 2 151114C Slag Canyon 7.10 ± 0.05 313 ± 3 3.2 ± 0.5 275 ± 1 151114D USBC 6.33 ± 0.05 226 ± 2 3.4 ± 0.5 1115 ± 6 151114E KOA 7.15 ± 0.05 324 ± 3 4.8 ± 0.5 262 ± 1 151114F Quality Drain 7.07 ± 0.05 268 ± 3 9.2 ± 0.5 505 ± 3 160215B Santa 7.33 ± 0.05 262 ± 3 4.7 ± 0.5 557 ± 3 160215C Slag Canyon 7.35 ± 0.05 291 ± 3 4.5 ± 0.5 351 ± 2 160215D KOA 7.34 ± 0.05 330 ± 3 5.7 ± 0.5 341 ± 2 160215E USBC 7.23 ± 0.05 268 ± 3 1.4 ± 0.5 1157 ± 6 160215F Quality Drain 7.53 ± 0.05 340 ± 3 1.5 ± 0.5 577 ± 3 160316B Travona* 6.38 ± 0.05 95 ± 1 12.3 ± 0.1 1296 ± 6 160316C Ophir* 6.55 ± 0.05 9 ± 1 11.5 ± 0.1 511 ± 3 Orphan Boy 160523C 7.07 ± 0.05 BDL 22.4 ± 0.1 1990 ± 10 Shaft* Orphan Girl 160523D 7.87 ± 0.05 BDL 26.6 ± 0.1 1990 ± 10 Shaft* 160523H Emma* 6.34 ± 0.05 BDL 17.1 ± 0.1 1432 ± 7 160522H Santa 7.88 ± 0.05 326 ± 3 9.0 ± 0.5 323 ± 2 160522I Slag Canyon 7.91 ± 0.05 331 ± 3 8.4 ± 0.5 222 ± 1 160522J KOA 7.98 ± 0.02 334 ± 3 8.1 ± 0.5 217 ± 1 160522K USBC 7.86 ± 0.01 273 ± 3 11.3 ± 0.5 160 ± 0.8 160522L Blacktail 7.94 ± 0.01 322 ± 3 9.2 ± 0.5 144 ± 0.7 160828K Santa 8.37 ± 0.05 335 ± 3 14.3 ± 0.5 572 ± 3 160828L Slag Canyon 8.08 ± 0.05 301 ± 3 12.4 ± 0.1 364 ± 2 160828M KOA 7.90 ± 0.04 316 ± 1 13.7 ± 0.1 369 ± 2 160828N Quality Drain 8.41 ± 0.05 248 ± 1 19.8 ± 0.1 233 ± 1 160828O Blacktail 7.75 ± 0.03 232 ± 2 14.3 ± 0.1 238 ± 1 161112K Santa 7.99 ± 0.05 303 ± 3 5.9 ± 0.5 474 ± 2 161112L Slag Canyon 7.90 ± 0.05 310 ± 3 4.9 ± 0.5 329 ± 2 161112M KOA 7.55 ± 0.05 302 ± 3 6.0 ± 0.5 325 ± 2 161112N Blacktail 7.90 ± 0.05 231 ± 2 2.2 ± 0.5 224 ± 1 BDL=Below the detection limit of 5 mol/L (Schmidt, 2017). *Data from Schmidt, 2017. All measurements collected using handheld field meters. Meter errors are a result of manufacturer error (±0.5%). The manufacturer error is always greater than recorded field fluctuations. 133
7.2. Dissolved Organic Carbon and Dissolved Inorganic Carbon
Table II: Dissolved inorganic carbon and dissolved organic carbon concentrations. DOC DIC δ13C DIC Average DOC Average DIC DOC δ13C Date Site VPDB Deviation Deviation (ppmC) (ppmC) VPDB (‰) (ppmC) (ppmC) (‰) 151114A Field Blank 1.10 -19.5 0.1 0.03 NA 0.0 151114B Santa 24.2 -11.8 0.4 3.5 -26.2 0.1 151114C Slag Canyon 21.7 -10.7 0.1 2.7 -26.6 0.0 151114D USBC 11.2 -10.2 0.2 4.4 -28.3 0.1 151114E KOA 20.7 -10.5 0.1 2.6 -26.5 0.0 151114F Quality Drain 7.2 -12.4 0.0 3.9 -27.3 0.1 160215A Field Blank BDL NA NA BDL NA 0.0 160215B Santa 27.3 -13.1 0.2 2.8 -26.4 0.1 160215C Slag Canyon 24.6 -12.8 0.1 1.9 -26.4 0.0 160215D KOA 23.9 -12.7 0.6 2.1 -26.5 0.1 160215E USBC 13.5 -13.7 0.4 7.2 -30.2 0.2 160215F Quality Drain (7.2†) NA (0.0†) 6.4 -27.6 0.1 160521A Field Blank BDL BDL NA BDL NA NA 160522H Santa 20.5 -11.3 0.0 4.9 -27.0 0.1 160522I Slag Canyon 11.9 -9.2 0.0 5.7 -27.3 0.1 160522J KOA 18.6 -10.9 0.1 5.6 -27.2 0.1 160522K USBC 15.3 -12.3 0.1 5.8 -26.6 0.2 160522L Blacktail 12.4 -7.4 0.1 8.2 -27.0 0.2 160827A Field Blank NA NA NA NA NA NA 160828K Santa (20.5*) NA (0.0*) NA NA NA 160828L Slag Canyon (11.9*) NA (0.0*) NA NA NA 160828M KOA (18.6*) NA (0.1*) NA NA NA 160828O Blacktail (12.4*) NA (0.1*) NA NA NA 160828N Quality Drain (22.9☨) NA (0.8☨) NA NA NA 161112I Field Blank BDL NA NA BDL NA NA 161112K Santa 23.9 -12.4 0.4 7.6 -26.8 0.1 161112L Slag Canyon 23.6 -12.2 0.5 NA NA NA 161112N Blacktail 17.2 -8.1 0.5 3.3 NA 0.0 161112M KOA (20.7☩) NA (0.1☩) NA NA NA An Aurora 1030W Total Carbon Analyzer with Autosampler 1088 was used to determine total dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC). Analytical error for both DOC and DIC concentrations is ± 3%. The analytical error of ± 3% is produced from analyzing standards. The average deviations are produced by analyzing each sample two times. The instrumental error for both dissolved inorganic carbon and dissolved organic carbon isotopes is ± 3%. NA=missing data Data for DIC isotopes and total DIC concentrations missing for all sites in August 2016, Quality Drain for February 2016 and August 2016, and KOA in November 2016. *Estimates used May 2016 data to approximate missing values for all sites except Quality Drain August 2016, Quality Drain February 2016, and KOA November 2016. ☨The average of the dissolved inorganic carbon data for the months November 2017 (13.11 ppmC) and June 2017 (32.73 ppmC) serves as the estimate for the missing August Quality Drain value. ☩ - The values for November 2015 serve as the estimate for KOA November 2016. † November 2015 data for Quality Drain serves as the estimated value for the missing February 2016 Quality Drain data. DOC data is also missing for the dates with missing dissolved inorganic carbon data. DIC detection limit: 1 mg/L (Schmidt, 2017) DOC detection limit: 0.1 mg/L (Schmidt, 2017) 134
7.3. Major Anion Concentrations
Table III: Major anion concentrations for chlorine, fluorine, bromine, and sulfate. - - - 2- Date Site F (mol/kg) Cl (mol/kg) Br (mol/kg) SO4 (mol/kg) Orphan Boy 151029B^ 3.4E-05 5.19E-04 1.1E-06 2.08E-03 Tap Orphan Girl 151029C^ 3.4E-05 5.19E-04 1.1E-06 2.04E-03 Shaft Percent Error 5.6 0.8 10 0.8 151114A Field Blank BDL 2.30E-05 BDL BDL 151114B Santa 1.69E-05 8.43E-04 1.28E-06 7.71E-04 151114C Slag Canyon 1.34E-05 4.37E-04 8.3E-07 3.20E-04 151114D USBC 2.81E-05 9.7E-03 2.87E-05 3.93E-04 151114E KOA 9.1E-06 3.86E-04 7.5E-07 3.20E-04 151114F Quality Drain 3.01E-05 3.53E-03 8.7E-06 9.24E-05 Percent Error 2.50 1.10 1.60 0.80 160215A Field Blank BDL 6.91E-06 BDL BDL 160215B Santa 1.76E-05 1.59E-03 2.04E-06 9.07E-04 160215C Slag Canyon 1.37E-05 9.93E-04 1.36E-06 3.35E-04 160215D KOA 1.27E-05 8.41E-04 1.06E-06 3.49E-04 160215E USBC 5.16E-05 9.87E-03 2.03E-05 2.38E-04 160215F Quality Drain 3.02E-05 4.60E-03 7.35E-06 8.17E-05 Percent Error 0.93 0.49 0.68 0.62 160314AA^ Lab Blank BDL 1.06E-05 BDL 1.10E-05 160314A^ Field Blank BDL BDL BDL BDL 160315AA^ Lab Blank BDL 9.84E-06 BDL 1.21E-05 160315A^ Field Blank BDL 8.80E-06 BDL 1.11E-05 160316AA^ Lab Blank 5.3E-07 1.09E-05 1.3E-07 1.27E-05 160316A^ Field Blank BDL 8.60E-06 BDL 1.08E-05 160316B^ Travona 1.73E-05 2.74E-03 4.6E-06 3.80E-03 160316C^ Ophir 2.9E-05 4.99E-04 1.2E-06 8.56E-04 Percent Error 5.6 0.8 10 0.8
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Table III: Major anion concentrations, for chlorine, fluorine, bromine, and sulfate, cont. - - - 2- Date Site F (mol/kg) Cl (mol/kg) Br (mol/kg) SO4 (mol/kg) 160522H Santa 1.3E-05 4.94E-04 BDL 5.06E-04 160522I Slag Canyon 9.4E-06 2.82E-04 BDL 1.92E-04 160522J KOA 9.1E-06 2.72E-04 BDL 1.88E-04 160522K USBC 4.5E-05 2.28E-04 BDL 8.89E-05 160522L Blacktail 8.3E-06 2.12E-04 BDL 8.01E-05 160521A Field Blank BDL 1.99E-04 BDL 5.20E-06 160523A^ Lab Blank BDL 1.01E-04 BDL 4.80E-05 160523B^ Field Blank BDL 4.43E-06 BDL 5.20E-06 160523C^ Orphan Boy 3.8E-05 5.05E-04 6.38E-07 1.95E-03 Shaft 160523D^ Orphan Girl Shaft 3.8E-05 5.11E-04 6.51E-07 1.93E-03 160524E^ Lab Blank BDL 7.59E-06 BDL 5.20E-06 160524F^ Field Blank BDL 3.36E-04 BDL 5.20E-06 160523H^ Emma 2.1E-05 2.02E-03 2.42E-06 3.98E-03 Percent Error 8.70 0.32 0.49 0.23 160828I Field Blank BDL BDL BDL BDL 160828K Santa 2.49E-05 1.21E-03 1.35E-06 1.41E-03 160828L Slag Canyon 1.68E-05 5.87E-04 1.25E-06 4.14E-04 160828M KOA 1.60E-05 6.04E-04 1.31E-06 4.18E-04 160828N Quality Drain 3.70E-05 1.50E-04 1.25E-07 2.86E-04 160828O Blacktail 1.86E-05 3.13E-04 1.25E-07 1.04E-04 Percent Error 2.22 1.1 1.2 0.6 161112I Field Blank BDL BDL BDL BDL 161112K Santa 1.54E-05 8.41E-04 1.25E-07 8.98E-04 161112L Slag Canyon 1.25E-05 4.68E-04 1.25E-07 3.69E-04 161112M KOA 1.04E-05 4.37E-04 1.25E-07 2.12E-04 161112N Blacktail 1.08E-05 3.44E-04 1.25E-07 2.12E-04 Percent Error 1.4 0.96 0.96 0.93 Detection Limits 5.26E-07 2.82E-06 1.25E-07 5.20E-06 All major ion concentrations except bicarbonate are measured with ion chromatography. All analytical errors for major anions are produced from standards. BDL=Below the detection limit. ^Data from Schmidt, 2017.
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Table IV: Major anion concentrations for bicarbonate, phosphate, nitrate, and nitrite. PO 3- NO - Date Site 4 NO - (mol/kg) 3 HCO - (mol/kg) (mol/kg) 2 (mol/kg) 3 Orphan Boy 151029A^ 8.5E-07 BDL BDL 1.61E-02 Tap Orphan Girl 151029C^ 7.8E-07 BDL BDL 1.74E-02 Shaft Percent Error 10 10 10 3 151114A Field Blank 2.7E-07 BDL BDL 9.2E-05 151114B Santa 2.4E-06 2.61E-07 1.61E-05 2.01E-03 151114C Slag Canyon 4.8E-07 BDL 1.29E-05 1.81E-03 151114D USBC 6.0E-07 BDL 8.97E-06 9.3E-04 151114E KOA 4.8E-07 BDL 1.35E-05 1.72E-03 151114F Quality Drain 8.5E-07 BDL 2.82E-06 6.0E-04 Percent Error 5 1.70 1.10 3 160215A Field Blank 2.11E-07 BDL BDL BDL 160215B Santa 8.8E-07 1.37E-06 2.19E-05 2.27E-03 160215C Slag Canyon 3.3E-07 BDL 1.95E-05 2.05E-03 160215D USBC 5.1E-07 BDL 1.98E-05 1.13E-03 160215E KOA 8.7E-07 BDL 4.56E-06 1.99E-03 160215F Quality Drain 1.34E-06 1.20E-06 5.26E-06 (6.0E-04†) Percent Error 3.4 1.90 0.51 3 160314AA^ Lab Blank BDL BDL 7.7E-07 BDL 160314A^ Field Blank BDL BDL BDL BDL 160315AA^ Lab Blank BDL BDL 7.9E-07 BDL 160315A^ Field Blank BDL BDL 7.7E-07 BDL 160316AA^ Lab Blank BDL BDL 8.2E-07 BDL 160316A^ Field Blank BDL BDL 7.6E-07 BDL 160316B^ Travona BDL BDL BDL 6.6E-03 160316C^ Ophir BDL BDL BDL 3.7E-03 Percent Error 10 10 10 3
137
Table IV: Major anion concentrations for bicarbonate, phosphate, nitrate, and nitrite, cont. 3- - - - Date Site PO4 (mol/kg) NO2 (mol/kg) NO3 (mol/kg) HCO3 (mol/kg) 160522H Santa 1.7E-06 BDL 7.81E-06 1.70E-03 160522I Slag Canyon 4.4E-07 BDL 4.73E-06 1.0E-04 160522J KOA 4.7E-07 BDL 4.57E-06 1.55E-03 160522K USBC 1.32E-06 BDL 1.16E-06 1.27E-03 160522L Blacktail 3.6E-07 BDL BDL 1.04E-03 160521A Field Blank BDL BDL BDL BDL 160523A^ Lab Blank BDL BDL BDL BDL 160523B^ Field Blank BDL BDL BDL BDL 160523C^ Orphan Boy Shaft 1.03E-06 BDL BDL 1.88E-02 160523D^ Orphan Girl Shaft 1.01E-06 BDL BDL 1.95E-02 160524E^ Lab Blank BDL BDL 5.64E-07 BDL 160524F^ Field Blank BDL BDL BDL BDL ^ 160523H Emma BDL BDL 5.81E-07 1.04E-02 Percent Error 6.80 0.64 0.34 3.00 160828I Blank BDL BDL BDL NA 160828K Santa 3.3E-06 2.17E-07 1.87E-05 (1.70E-03*) 160828L Slag Canyon 7.2E-07 2.17E-07 2.08E-05 (1.0E-04*) 160828M KOA 6.3E-07 2.17E-07 2.32E-05 (1.55E-03*) 160828N Quality Drain 2.1E-06 2.17E-07 1.35E-05 (1.91E-03☨) 160828O Blacktail 4.2E-07 2.17E-07 1.61E-07 (1.04E-03*) Percent Error 7.80 0.85 0.2 3.00 161112I Blank BDL BDL BDL 2.65E-05 161112K Santa 3.23E-06 2.17E-07 2.82E-05 1.99E-03 161112L Slag Canyon 5.69E-07 2.17E-07 1.76E-05 1.96E-03 161112M KOA 3.69E-07 2.17E-07 1.90E-05 (1.72E-03☩) 161112N Blacktail 3.69E-07 2.17E-07 8.71E-07 1.04E-03 Percent Error 1.50 1.30 1.00 3.00 Detection Limits 2.11E-07 2.17E-07 1.61E-07 8.33E-05 All major ion data except bicarbonate are collected with Ion Chromatography. All analytical errors for major anions are produced from standards. Bicarbonate concentrations are calculated assuming 100% of dissolved inorganic carbon exists in the bicarbonate form. This is an accurate assumption at pH 6-8.5. The ppmC values (from the Aurora 1030W total carbon analyzer) are converted to molesC, which is then converted to moles of bicarbonate. Data for dissolved inorganic carbon is missing for all sites in August 2016, Quality Drain for February 2016 and August 2016, and KOA in November 2016. *Estimates used May 2016 data to approximate missing values for all sites except Quality Drain August 2016, Quality Drain February 2016 and KOA November 2016. ☨The average of the dissolved inorganic carbon data for the months November 2017 (13.11 ppmC) and June 2017 (32.73 ppmC) serves as the estimate for the missing August Quality Drain value. ☩ - The values for November 2015 serve as the estimate for KOA November 2016. † November 2015 data for Quality Drain serves as the estimated value for the missing February 2016 Quality Drain data. BDL=Below detection limit. ^Data from Schmidt, 2017
138
7.4. Major Cation Concentrations
Table V: Major cation concentrations. Li+ Na+ K+ Mg2+ Ca2+ Date Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) 151029A Field Blank BDL BDL BDL BDL 1.4E-07 Orphan Girl 151029C^ 2.0E-05 4.7E-03 2.2E-04 2.0E-03 5.7E-04 Shaft Percent Error 10 10 10 10 10 151114A Field Blank BDL BDL 4.99E-07 4.73E-07 4.02E-07 151114B Santa 4.54E-06 1.02E-03 1.25E-04 5.2E-04 6.3E-04 151114C Slag Canyon BDL 5.4E-04 7.65E-05 3.56E-04 4.44E-04 151114D USBC 1.12E-04 1.20E-03 9.3E-05 3.32E-03 7.9E-04 151114E* KOA 2.18E-06 4.5E-04 7.06E-05 3.16E-04 3.35E-04 151114F Quality Drain 4.75E-05 6.9E-04 4.48E-05 1.35E-03 2.10E-04 Percent Error 2.10 5.30 1.10 2.10 1.70 160215A Field Blank 2.87E-06 6.2E-07 1.30E-06 2.80E-07 3.60E-07 160215B Santa 9.57E-06 1.42E-03 1.27E-04 7.2E-04 7.4E-04 160215C Slag Canyon 5.66E-06 7.7E-04 8.08E-05 5.0E-04 5.24E-04 160215D KOA 3.90E-06 7.4E-04 7.57E-05 4.53E-04 5.19E-04 160215E USBC 1.03E-04 3.0E-03 1.18E-04 3.08E-03 4.04E-04 160215F Quality Drain 3.89E-05 1.86E-03 8.39E-05 1.18E-03 2.61E-04 Percent Error 2.10 5.30 1.10 2.10 1.70 160314AA^ Lab Blank NA NA NA NA NA 160314A^ Field Blank BDL BDL 5.7E-07 3.5E-07 BDL 160315AA^ Lab Blank NA NA NA NA NA 160315A^ Field Blank BDL BDL BDL BDL BDL 160316AA^ Lab Blank BDL BDL BDL BDL 9.9E-07 160316A^ Field Blank NA NA NA NA NA 160316B^ Travona 4.9E-06 1.8E-03 1.8E-04 2.3E-03 2.7E-03 160316C^ Ophir BDL 3.2E-05 1.8E-05 8.1E-05 3.3E-05 Percent Error 10.00 10.00 10.00 10.00 10.00
139
Table V: Major cation concentrations, cont. Na+ Mg2+ Ca2+ Date Site Li+ (mol/kg) K+ (mol/kg) (mol/kg) (mol/kg) (mol/kg) 160522H Santa BDL 7.10E-04 9.40E-05 3.64E-04 4.89E-04 160522I Slag Canyon BDL 4.50E-04 7.11E-05 2.88E-04 3.90E-04 160522J* KOA 2.18E-06 3.90E-04 8.13E-05 2.56E-04 3.35E-04 160522K USBC 5.60E-06 2.50E-04 5.06E-05 2.74E-04 2.31E-04 160522L* Blacktail 1.33E-06 3.70E-04 5.42E-05 1.67E-04 2.47E-04 160521A Field Blank BDL 1.90E-05 6.09E-06 2.88E-06 3.97E-06 160523A^ Lab Blank NA NA NA NA NA 160523B^ Field Blank NA NA NA NA NA Orphan Boy 160523C^ 2.35E-05 5.20E-03 2.35E-04 2.09E-03 2.63E-03 Shaft Orphan Girl 160523D^ 2.42E-05 5.40E-03 2.38E-04 2.11E-03 2.67E-03 Shaft 160524E^ Lab Blank BDL BDL 7.65E-07 1.51E-06 8.40E-07 160524F^ Field Blank BDL BDL BDL BDL BDL 160523H^ Emma 6.30E-06 2.10E-03 2.05E-04 2.54E-03 2.86E-03 Percent Error 2.1 5.3 1.1 2.1 1.7 160828I Blank BDL BDL BDL BDL BDL 160828K Santa 5.30E-06 1.44E-03 1.57E-04 6.30E-04 8.40E-04 160828L Slag Canyon BDL 6.70E-04 8.49E-05 4.24E-04 6.00E-04 160828M KOA BDL 6.40E-04 8.34E-05 4.24E-04 6.00E-04 160828N Quality Drain BDL 4.80E-04 1.40E-04 3.55E-04 4.77E-04 160828O Blacktail 3.14E-06 3.30E-04 5.29E-05 2.72E-04 4.01E-04 Percent Error 2.1 5.3 1.1 2.1 1.7 161112I Blank BDL BDL BDL BDL BDL 161112K* Santa 3.69E-06 1.20E-03 1.34E-04 5.40E-04 7.30E-04 161112L* Slag Canyon BDL 5.00E-04 6.45E-05 3.18E-04 4.37E-04 161112M KOA BDL 6.40E-04 8.85E-05 4.28E-04 5.77E-04 161112N Blacktail BDL 4.20E-04 5.78E-05 2.77E-04 4.00E-04 Percent Error 2.1 5.3 1.1 2.1 1.7 Detection 1.33E-06 1.91E-07 1.10E-06 1.93E-07 3.60E-07 Limits *The major cation concentrations for the sites 151114E (KOA), 161112L (Slag Canyon), and 160522J (KOA) all had different percent errors than the others. All three of these samples had percent errors of: 2.7% (Li+), 6.1% (Na+), 1.1% (K+), 1.5% (Mg2+), and 1.3% (Ca2+). The samples 160522L (Blacktail) and 161112K (Santa) also had unique percent errors. These two samples had percent errors of 0.96% (Li+), 9.2% (Na+), 3.1% (K+), 0.7% (Mg2+), and 0.73% (Ca2+). Cation concentrations are measured using ICP-OES. Errors are determined from standards. BDL=Below Detection Limit NA=No measurement performed. ^Data from Schmidt, 2017.
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7.5. Field Spectrophotometry
Table VI: Field spectrophotometry measurements. 2+ 2- Date Site Fe (M) S (M) SiO2 (M) 151029C^ Orphan Girl Shaft 7.0E-07 NA 1.5E-04 151114B Santa 4.5E-07 BDL 3.7E-04 151114C Slag Canyon 3.4E-07 BDL 3.8E-04 151114D USBC 4E-08 BDL 1.8E-04 151114E KOA BDL BDL 4.0E-04 151114F Quality Drain 2.3E-07 1.5E-06 9E-05 160215B Santa 1.23E-04 BDL 3.8E-04 160215C Slag Canyon 4.44E-06 BDL 4.3E-04 160215D KOA BDL BDL 4.0E-04 160215E USBC 3.3E-07 2.6E-06 1.2E-04 160215F Quality Drain 5.41E-06 3.6E-06 1.2E-04 160316AA^ Travona 6.80E-06 7.1E-06 3.0E-04 160316A^ Ophir 4.71E-05 3.2E-06 3.4E-04 160522H Santa 4.5E-07 BDL 3.9E-04 160522I Slag Canyon 3.3E-07 BDL 4.0E-04 160522J KOA 2.1E-07 BDL 5.1E-04 160522K USBC 2.14E-06 1.2E-06 1.2E-04 160522L Blacktail Creek 9.28E-06 BDL 4.0E-04 160523C^ Orphan Boy Shaft BDL 1.3E-04 NA 160523D^ Orphan Girl Shaft BDL 1.5E-04 2.3E-04 160523H^ Emma 1.16E-04 1.2E-05 NA 160828K Santa 3.6E-07 BDL 4.0E-04 160828L Slag Canyon BDL BDL NA 160828M KOA BDL BDL 4.5E-04 160828N Quality Drain BDL BDL 2.8E-04 160828O Blacktail BDL BDL 4.3E-04 161112K Santa BDL BDL 3.7E-04 161112L Slag Canyon BDL BDL 4.2E-04 161112M KOA BDL BDL 4.4E-04 161112N Blacktail 5.4E-07 BDL 4.5E-04 Silica detection limit: 1.70x10-5 M (Schmidt, 2017; Law, 2018) Fe2+ lower detection limit (August 2016-November 2016): 3.5x10-7 M (Law, 2018) Fe2+ lower detection limit (November 2015-May 2016): 4.0x10-8 M (Schmidt, 2017) Sulfide detection limit: 3.1x10-7 M (Cox et al., 2011) Silica error: 1.70x10-5 M (Schmidt, 2017; Law, 2018) Iron error: 2.5x10-7 M (Schmidt, 2017; Law, 2018) Iron field spectrophotometry values are generally lower than ICP- MS values. Sulfide error: 9x10-8 M (Cox et al., 2011) Int: matrix interference. ^=From Schmidt, 2017 NA=Data not available. BDL: below detection limit. 141
7.6. Raw Data Tables for Element Concentrations
7.6.1. B, Al, Ti, V, and Cr
Table VII: B, Al, Ti, V, and Cr concentrations. B Al Ti V Cr Date Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) 151029A^ Field Blank 4.7E-07 BDL BDL BDL BDL Orphan Girl 151029C^ 4.0E-06 BDL 4.6E-05 6.2E-08 3.16E-08 Shaft Percent Error 4 5.6 3.8 4.4 2 151114A Field Blank 2.3E-08 4.7E-07 BDL BDL BDL 151114B Santa 5.1E-06 4.0E-07 1.3E-05 3.4E-08 BDL 151114C Slag Canyon 1.53E-06 3.3E-07 8.5E-06 3.7E-08 BDL 151114D USBC 7.9E-05 1.00E-06 1.7E-05 1.20E-08 1.15E-08 151114E KOA 1.42E-06 3.5E-07 8.9E-06 3.7E-08 BDL 151114F Quality Drain 4.4E-05 5.1E-07 5.3E-06 2.3E-08 6.5E-09 Percent Error 4.5 6.9 8 6.7 4.8 160215A Field Blank 1.45E-07 1.5E-07 BDL BDL BDL 160215B Santa 9.6E-06 2.0E-07 1.39E-05 3.1E-08 BDL 160215C Slag Canyon 4.7E-06 1.4E-07 9.2E-06 3.9E-08 BDL 160215D KOA 3.5E-06 6.6E-08 9.0E-06 4.2E-08 3.5E-06 160215E USBC 6.2E-05 3.5E-07 7.9E-06 2.6E-08 6.2E-05 160215F Quality Drain 2.29E-05 2.5E-07 4.5E-06 2.3E-08 6.7E-09 Percent Error 3.8 8.9 6.8 6.8 7 160314AA^ Lab Blank 4.7E-07 9.1E-08 BDL BDL BDL 160314A^ Field Blank 3.2E-07 7.2E-08 BDL BDL BDL 160315AA^ Lab Blank 1.81E-07 5.2E-08 BDL BDL BDL 160315A^ Field Blank 2.08E-07 1.1E-07 BDL BDL BDL Percent Error 4.0 5.6 3.8 4.4 2 Detection Limits 2.31E-08 1.85E-08 1.93E-07 9.84E-09 4.59E-09
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Table VII: B, Al, Ti, Cr concentrations, cont. Date B Al Ti V Cr Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) 160316AA^ Lab Blank 2.17E-07 2.9E-08 BDL BDL BDL 160316A^ Field Blank 1.53E-07 8.2E-08 BDL BDL BDL 160316C^ Ophir 4.2E-06 2.9E-07 1.19E-05 BDL BDL Percent Error 4 5.6 3.8 4.4 2 160522H Santa 3.07E-06 2.82E-07 1.05E-05 2.74E-08 BDL 160522I Slag Canyon 1.03E-06 1.31E-07 6.57E-06 2.11E-08 BDL 160522J KOA 1.05E-06 2.90E-07 6.8E-06 2.32E-08 BDL 160522K USBC 6.6E-06 3.37E-07 5.68E-06 3.54E-08 BDL 160522L Blacktail 7.1E-07 8.6E-07 5.75E-06 2.07E-08 BDL 160521A Field Blank 2.31E-08 1.71E-07 BDL BDL BDL 160523A^ Lab Blank 5.3E-08 1.74E-07 BDL BDL BDL 160523B^ Field Blank 2.31E-08 8.6E-08 BDL BDL BDL Orphan Boy 160523C^ 3.71E-06 7.7E-08 5.23E-05 4.42E-08 BDL Shaft 160523D^ Orphan Girl Shaft 5.5E-06 4.6E-08 5.74E-05 4.47E-08 BDL 160524E^ Lab Blank 2.31E-08 1.85E-08 BDL BDL BDL 160524F^ Field Blank 2.31E-08 2.70E-07 BDL BDL BDL 160523H^ Emma 1.46E-05 8.7E-08 6.8E-05 BDL BDL Percent Error 2.6 2.2 1.5 1.9 2 160828I Blank 9.2E-08 1.85E-08 BDL BDL BDL 160828K Santa 8.3E-06 8.8E-08 8.0E-07 2.92E-08 BDL 160828L Slag Canyon 2.17E-06 4.26E-08 2.44E-07 3.62E-08 BDL 160828M KOA 2.15E-06 BDL 2.17E-07 4.25E-08 BDL 160828N Quality Drain 1.31E-06 1.36E-07 BDL 3.88E-08 BDL 160828O Blacktail 5.2E-07 BDL BDL BDL 7.4E-09 Percent Error 2.6 2.2 1.5 1.9 2 161112I Blank 2.3E-08 1.1E-07 BDL 4.3E-09 BDL 161112K Santa 7E-06 5E-07 8E-07 3.9E-08 6E-09 161112L Slag Canyon 2.6E-06 3.4E-08 4.1E-07 4.8E-08 BDL 161112M KOA 2.5E-06 5E-08 4.1E-07 5.4E-08 6E-09 161112N Blacktail 5.4E-07 1.8E-07 3.1E-07 1.3E-08 4.6E-09 Percent Error 16.1 19.9 17.8 17.9 17.6 Detection Limits 2.31E-08 1.85E-08 1.93E-07 9.84E-09 4.59E-09 Be is BDL for all locations. The detection limit for Be is 5.58*E-08 mol/kg. BDL = Below Detection Limit. ICP-MS produced all data. All percent errors are determined from standards. ^=From Schmidt, 2017.
143
7.6.2. Mn, Fe, Ni, Cu, and Zn
Table VIII: Mn, Fe, Ni, Cu, and Zn concentrations. Mn Fe Ni Cu Zn Date Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) 151029A^ Field Blank BDL 7.4E-07 BDL 4.2E-08 1.57E-07 151029C^ Orphan Girl Shaft 5.9E-05 2.2E-06 BDL 1.35E-07 8.1E-08 Percent Error 5 5.4 4.1 4.3 4.8 151114A Field Blank 3.4E-07 4.3E-08 BDL BDL 3.6E-07 151114B Santa 2.04E-06 3.2E-06 BDL 8.4E-08 1.85E-06 151114C Slag Canyon 1.62E-06 1.53E-06 BDL 2.98E-08 2.6E-07 151114D USBC 3.11E-07 2.9E-06 BDL 1.10E-06 8.9E-06 151114E KOA 1.37E-06 9.1E-07 BDL 3.85E-08 3.1E-07 151114F Quality Drain 4.2E-07 6.4E-07 3.89E-08 2.28E-07 1.10E-06 Percent Error 3.1 4.5 2.50 1.2 4 160215A Field Blank BDL BDL BDL BDL 2.7E-07 160215B Santa 3.6E-06 3.9E-07 BDL 1.0E-07 2.8E-06 160215C Slag Canyon 1.7E-06 1E-06 BDL 4.8E-08 5.9E-07 160215D KOA 1.23E-06 2.6E-07 BDL 3.1E-08 3.7E-07 160215E USBC 2.7E-06 7.0E-07 BDL 6.4E-07 3.9E-06 160215F Quality Drain 1.01E-06 3.1E-07 BDL 3.7E-07 9.7E-07 Percent Error 6.2 13.9 5.1 5.9 6.8 160314AA^ Lab Blank BDL BDL 5.2E-08 BDL 1.42E-07 160314A^ Field Blank BDL 1.32E-07 BDL BDL 5.5E-08 160315AA^ Lab Blank 8.3E-08 1.0E-06 9.05E-08 BDL 8.3E-07 160315A^ Field Blank BDL 1.83E-07 BDL BDL 5.5E-08 Percent Error 5.0 5.4 4.1 4.3 4.8
144
Table VIII: Mn, Fe, Ni, Cu, and Zn, concentrations cont.
Date Site Mn Fe Ni Cu Zn (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) 160316AA^ Lab Blank BDL 1.51E-07 BDL BDL 2.08E-07 160316A^ Field Blank BDL 1.38E-07 BDL BDL 1.60E-07 160316B^ Travona 6.3E-05 7.0E-06 BDL BDL BDL 160316C^ Ophir 8.8E-05 3.2E-05 9.3E-08 BDL 1.03E-07 Percent Error 5.0 5.4 4.1 4.3 4.8 160522H Santa 1.06E-06 3.3E-06 BDL 1.21E-07 1.58E-06 160522I Slag Canyon 6.3E-07 3.7E-06 BDL 8.4E-08 2.8E-07 160522J KOA 5.8E-07 3.8E-06 BDL 9.1E-08 3.1E-07 160522K USBC 6.9E-07 3.2E-07 BDL 3.4E-07 2.0E-06 160522L Blacktail 4.13E-07 5.5E-06 BDL 1.69E-07 1.70E-07 160521A Field Blank 3.64E-08 9.8E-08 BDL BDL 3.2E-07 160523A^ Lab Blank 3.64E-08 9.8E-08 BDL BDL 2.0E-06 160523B^ Field Blank 3.64E-08 9.8E-08 BDL BDL 1.46E-07 160523C^ Orphan Boy Shaft 7.1E-05 2.6E-07 BDL BDL BDL 160523D^ Orphan Girl Shaft 7.4E-05 3.3E-07 BDL BDL BDL 160524E^ Lab Blank 1.26E-07 9.8E-08 4.6E-07 5.7E-07 5.6E-07 160524F^ Field Blank 3.83E-08 3.4E-07 BDL BDL 1.375E-06 160523H^ Emma 1.34E-04 9.3E-06 1.577E-07 BDL BDL Percent Error 7.3 4.0 5.9 5.2 5.3 160828I Blank BDL BDL BDL BDL 2.79E-07 160828K Santa 1.15E-06 9.2E-07 BDL 7.4E-08 1.33E-06 160828L Slag Canyon 1.10E-06 7.0E-07 BDL 2.30E-08 2.65E-07 160828M KOA 6.6E-07 4.7E-07 BDL BDL 2.94E-07 160828N Quality Drain BDL 2.05E-07 BDL 2.70E-07 1.48E-07 160828O Blacktail 5.00E-07 2.48E-07 BDL BDL BDL Percent Error 1.8 4.0 1.8 2.3 1.9 161112I Blank BDL BDL BDL BDL 4.1E-07 161112K Santa 1.8E-06 1.2E-06 BDL 1E-07 3.5E-06 161112L Slag Canyon 1.2E-06 1.4E-06 BDL 2.8E-08 4.2E-07 161112M KOA 8E-07 1.3E-06 BDL 2.8E-08 4.9E-07 161112N Blacktail 1.2E-06 5.3E-06 BDL 3.3E-08 1.6E-07 Percent Error 15.4 18 12.8 12.3 13 Detection Limits: 3.64E-08 9.76E-08 3.25E-08 2.27E-08 5.52E-08 *Cobalt concentrations all BDL. Detection limit for cobalt is 8.48E-09 mol/kg. BDL = Below Detection Limit. ICP-MS produced all trace element data. All percent errors are determined from standards. ^ From Schmidt, 2017
145
7.6.3. Ga, As, Se, Rb, and Sr
Table IX: Ga, As, Se, Rb, and Sr concentrations Ga As Se Rb Sr Date Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) 151029A^ Field Blank BDL BDL BDL BDL BDL 151029C^ Orphan Girl Shaft 1.3E-07 2.06E-08 BDL 1.84E-07 5.9E-05 Percent Error 10 3.8 4.6 3 3.8 151114A Field Blank BDL 3.71E-09 BDL BDL 1.87E-08 151114B Santa 5.3E-08 4.20E-08 7.9E-08 3.00E-08 4.08E-06 151114C Slag Canyon 5.7E-08 3.65E-08 BDL 1.02E-08 2.70E-06 151114D USBC 1.09E-07 7.39E-08 1.60E-07 2.43E-08 3.12E-06 151114E KOA 5.5E-08 3.31E-08 BDL 1.06E-08 2.55E-06 151114F Quality Drain 3.01E-08 5.33E-08 7.07E-08 1.16E-08 7.8E-07 Percent Error 2.4 1.3 3.8 1.7 2.2 160215A Field Blank BDL BDL BDL BDL BDL 160215B Santa 6.3E-08 3.7E-08 7.1E-08 3.7E-08 4.2E-06 160215C Slag Canyon 7.4E-08 3.0E-08 6.5E-08 BDL 2.6E-06 160215D KOA 7.0E-08 2.7E-08 BDL BDL 2.6E-06 160215E USBC 5.7E-08 9.0E-08 BDL 2.9E-08 1.38E-06 160215F Quality Drain 3.5E-08 4.7E-08 BDL 1.80E-08 8.7E-07 Percent Error 7 4.7 4.7 4.4 6.7 160314AA^ Lab Blank BDL BDL BDL BDL BDL 160314A^ Field Blank BDL BDL BDL 8.1E-09 BDL 160315AA^ Lab Blank BDL 9.9E-09 BDL BDL BDL 160315A^ Field Blank BDL 3.0E-09 BDL BDL BDL Percent Error 10 3.8 4.6 3 3.8
146
Table IX: Ga, As, Se, Rb, and Sr concentrations, cont. Ga As Se Rb Sr Date Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) 160316AA^ Lab Blank BDL 8.3E-09 BDL BDL BDL 160316A^ Field Blank BDL 3.1E-09 BDL BDL BDL 160316B^ Travona 4.5E-08 7.9E-07 BDL 1.41E-07 1.40E-05 160316C^ Ophir BDL 5.8E-08 BDL 6.8E-08 2.7E-06 Percent Error 10 3.8 4.6 3 3.8 160522H Santa 3.6E-08 5.9E-08 BDL 2.59E-08 3.0E-06 160522I Slag Canyon 3.4E-08 5.2E-08 BDL 1.41E-08 1.91E-06 160522J KOA 3.3E-08 5.2E-08 BDL 1.52E-08 1.96E-06 160522K USBC 2.4E-08 8.0E-08 BDL 1.30E-08 8.0E-07 160522L Blacktail 2.2E-08 4.5E-08 BDL 1.65E-08 1.42E-06 160521A Field Blank BDL BDL BDL BDL 1.38E-08 160523A^ Lab Blank BDL BDL BDL BDL 1.38E-08 160523B^ Field Blank BDL BDL BDL BDL 1.38E-08 Orphan Boy 1.33E-07 4.5E-08 7.3E-08 2.19E-07 6.7E-05 160523C^ Shaft 160523D^ Orphan Girl Shaft 1.49E-07 2.03E-08 7.3E-08 2.28E-07 7.1E-05 160524E^ Lab Blank BDL BDL BDL BDL 1.38E-08 160524F^ Field Blank BDL BDL BDL BDL 1.38E-08 160523H^ Emma 4.5E-08 4.1E-08 7.3E-08 1.89E-07 2.3E-05 Percent Error 5.3 3.4 4 3.2 4.7 160828I Blank BDL BDL BDL BDL BDL 160828K Santa 4.8E-08 4.6E-08 BDL 3.95E-08 4.94E-06 160828L Slag Canyon 7.3E-08 3.80E-08 BDL BDL 3.03E-06 160828M KOA 7.3E-08 2.35E-08 BDL BDL 2.99E-06 160828N Quality Drain 3.56E-08 8.5E-08 BDL 1.62E-08 1.18E-06 160828O Blacktail 2.44E-08 2.13E-08 BDL 1.14E-08 1.58E-06 Percent Error 2.7 2.4 2 1.6 1.4 161112I Blank 1.8E-08 BDL BDL BDL BDL 161112K Santa 7.4E-08 4.5E-08 6.7E-08 3.9E-08 4.8E-06 161112L Slag Canyon 9.5E-08 3.8E-08 BDL 9.0E-09 3.5E-06 161112M KOA 1.0E-07 3.0E-08 BDL 9.2E-09 3.6E-06 161112N Blacktail 3.6E-08 2.4E-08 BDL 1.3E-08 1.9E-06 Percent Error 13.2 10.9 10.6 8.7 12.1 Detection Limits 1.80E-08 2.67E-09 2.90E-08 8.11E-09 1.38E-08 BDL = Below Detection Limit. ICP-MS produced all trace element data. All percent errors are determined from standards. ^ From Schmidt, 2017.
147
7.6.4. Zr, Nb, Mo, Pd, Ag, and Cd
Table X: Zr, Nb, Mo, Pd, Ag, and Cd concentrations. Zr Nb Mo Pd Ag Cd Date Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) 151029A^ Field Blank BDL BDL 3.5E-08 BDL BDL BDL Orphan Girl 151029C^ BDL BDL BDL 1.6E-07 BDL BDL Shaft Percent Error 10 10 3.9 10 10 4 151114A Field Blank BDL BDL BDL BDL BDL BDL 151114B Santa BDL BDL 1.56E-07 BDL BDL BDL 151114C Slag Canyon BDL BDL 1.57E-07 BDL BDL BDL 151114D USBC BDL BDL BDL BDL BDL 4.6E-08 151114E KOA BDL BDL 1.51E-07 BDL BDL BDL 151114F Quality Drain BDL BDL BDL BDL BDL BDL Percent Error 2.8 2.4 3.3 4 4.2 6.6 160215A Field Blank BDL BDL BDL BDL BDL BDL 160215B Santa BDL BDL 2.2E-07 BDL BDL BDL 160215C Slag Canyon BDL BDL 2.5E-07 BDL BDL BDL 160215D KOA BDL BDL 2.2E-07 BDL BDL BDL 160215E USBC BDL 1.35E-08 2.7E-07 BDL BDL BDL 160215F Quality Drain BDL BDL 8.0E-08 BDL BDL BDL Percent Error 5.8 5.2 5.9 6.6 4.5 7.2 160314AA^ Lab Blank BDL BDL 3.5E-08 BDL BDL BDL 160314A^ Field Blank BDL BDL 2.16E-08 BDL BDL BDL 160315AA^ Lab Blank BDL BDL BDL BDL BDL BDL 160315A^ Field Blank BDL BDL BDL BDL BDL BDL Percent Error 10 10 3.9 10 10 4
148
Table X: Zr, Nb, Mo, Pd, Ag, and Cd concentrations, cont. Date Zr Nb Mo Pd Ag Cd Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) (mol/kg) 160316AA^ Lab Blank BDL BDL BDL BDL BDL BDL 160316A^ Field Blank BDL BDL BDL BDL BDL BDL 160316B^ Travona BDL BDL BDL BDL BDL BDL 160316C^ Ophir BDL BDL 4.0E-08 BDL BDL BDL Percent Error 10 10 3.9 10 10 4 160522H Santa BDL BDL 1.8E-07 BDL BDL BDL 160522I Slag Canyon BDL BDL 1.60E-07 BDL BDL BDL 160522J KOA BDL BDL 1.7E-07 BDL BDL BDL 160522K USBC BDL BDL 2.2E-07 BDL BDL BDL 160522L Blacktail BDL BDL 2.1E-07 BDL BDL BDL 160521A Field Blank 3.5E-08 BDL BDL BDL BDL BDL 160523A^ Lab Blank BDL BDL BDL BDL BDL BDL 160523B^ Field Blank BDL BDL BDL BDL BDL BDL 160523C^ Orphan Boy Shaft 2.7E-08 BDL 5.3E-08 1.14E-07 8.9E-09 3.5E-08 160523D^ Orphan Girl Shaft 2.7E-08 BDL 5.3E-08 1.22E-07 8.9E-09 3.5E-08 160524E^ Lab Blank 1.07E-08 BDL 2.1E-08 2.10E-08 3.6E-09 1.4E-08 160524F^ Field Blank 1.07E-08 BDL 2.1E-08 2.10E-08 3.6E-09 1.4E-08 160523H^ Emma 2.7E-08 BDL 5.3E-08 5.3E-08 8.9E-09 3.5E-08 Percent Error 5.6 6 6.1 3.9 5.1 7.8 160828I Blank BDL BDL BDL BDL BDL BDL 160828K Santa BDL BDL 2.2E-07 BDL BDL BDL 160828L Slag Canyon BDL BDL 2.6E-07 BDL BDL BDL 160828M KOA BDL BDL 2.49E-07 BDL BDL BDL 160828N Quality Drain BDL BDL 1.18E-07 BDL BDL BDL 160828O Blacktail BDL BDL 3.1E-07 BDL BDL BDL Percent Error 2.1 1.6 3.9 0.7 1.1 1.5 161112I Blank BDL BDL BDL BDL BDL BDL 161112K Santa BDL BDL 2.9E-07 BDL BDL BDL 161112L Slag Canyon BDL BDL 2.9E-07 BDL BDL BDL 161112M KOA BDL BDL 3.1E-07 BDL BDL BDL 161112N Blacktail BDL BDL 3.0E-07 BDL BDL BDL Percent Error 12.2 13.3 12.1 13.0 12.1 16.4 Detection Limits 1.07E-08 5.38E-09 2.14E-08 2.10E-08 3.58E-09 1.39E-08 BDL = Below Detection Limit. ICP-MS produced all trace element data. All percent errors are determined from standards. ^ From Schmidt, 2017
149
7.6.5. Sn, Sb, Cs, Ba, La, and Ce
Table XI: Sb, Cs, Ba, and Ce concentrations. Sb Cs Ba Ce Date Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) 151029A^ Field Blank BDL BDL BDL BDL 151029C^ Orphan Girl Shaft BDL BDL 6.72E-06 BDL Percent Error 10.0 3.0 3.8 10.0 151114A Field Blank BDL BDL BDL BDL 151114B Santa 4.6E-09 BDL 2.4E-06 BDL 151114C Slag Canyon 3.4E-09 BDL 2.7E-06 BDL 151114D USBC 2.2E-08 BDL 5.0E-06 BDL 151114E KOA 3.2E-09 BDL 2.4E-06 BDL 151114F Quality Drain 1.01E-08 BDL 1.43E-06 BDL Percent Error 6.1 3.8 4.6 4.5 160215A Field Blank BDL BDL BDL BDL 160215B Santa 4.0E-09 BDL 3.0E-06 BDL 160215C Slag Canyon BDL BDL 3.2E-06 BDL 160215D KOA BDL BDL 3.1E-06 BDL 160215E USBC 2.1E-08 BDL 2.8E-06 BDL 160215F Quality Drain 9.0E-09 BDL 1.54E-06 BDL Percent Error 6.7 4.5 5.9 6.4 160314AA^ Lab Blank BDL BDL BDL BDL 160314A^ Field Blank BDL BDL BDL BDL 160315AA^ Lab Blank BDL BDL BDL BDL 160315A^ Field Blank BDL BDL BDL BDL Percent Error 10.0 3.0 3.8 10.0
150
Table XI: Sb, Cs, Ba, and Ce concentrations, cont. Sb Cs Ba Ce Date Site (mol/kg) (mol/kg) (mol/kg) (mol/kg) 160316AA^ Lab Blank BDL BDL BDL BDL 160316A^ Field Blank BDL BDL BDL BDL 160316B^ Travona BDL 1.72E-08 1.67E-06 4.0E-09 160316C^ Ophir BDL 1.27E-08 5.9E-07 1.7E-09 Percent Error 10.0 3.0 3.8 10.0 160522H Santa BDL BDL 1.8E-06 BDL 160522I Slag Canyon BDL BDL 1.7E-06 BDL 160522J KOA BDL BDL 1.7E-06 BDL 160522K USBC 1.01E-08 BDL 1.14E-06 BDL 160522L Blacktail 2.9E-09 BDL 1.16E-06 BDL 160521A Field Blank 2.9E-09 BDL BDL BDL 160523A^ Lab Blank 2.9E-09 BDL BDL BDL 160523B^ Field Blank 2.9E-09 BDL BDL BDL Orphan Boy 160523C^ 7.2E-09 1.34E-08 7.0E-06 BDL Shaft 160523D^ Orphan Girl Shaft 7.2E-09 1.33E-08 7.9E-06 BDL 160524E^ Lab Blank 2.9E-09 3.8E-09 BDL BDL 160524F^ Field Blank 2.9E-09 3.8E-09 BDL BDL 160523H^ Emma 1.8E-08 1.6E-08 2.5E-06 BDL Percent Error 9.6 6.9 8.1 8.4 160828I Blank BDL BDL BDL BDL 160828K Santa 4.08E-09 BDL 2.28E-06 BDL 160828L Slag Canyon BDL BDL 3.14E-06 BDL 160828M KOA BDL BDL 3.24E-06 BDL 160828N Quality Drain 4.68E-09 BDL 1.51E-06 BDL 160828O Blacktail BDL BDL 1.07E-06 BDL Percent Error 1.7 1.2 1.5 1.2 161112I Blank BDL BDL BDL BDL 161112K Santa 4.5E-09 BDL 2.8E-06 BDL 161112L Slag Canyon 2.9E-09 BDL 3.6E-06 BDL 161112M KOA 2.9E-09 BDL 4.0E-06 BDL 161112N Blacktail 2.9E-09 BDL 1.3E-06 BDL Percent Error 16.8 13.1 15.8 17.6 Detection Limits 2.87E-09 3.76E-09 6.48E-08 1.61E-09 Sn and La had BDL concentrations with detection limits of 4.35E-08 and 1.44E-09 respectively. ^=From Schmidt, 2017.
151
7.6.6. W and U
Table XII: W and U concentrations. Date Site W (mol/kg) U (mol/kg) 151029A^ Field Blank BDL BDL 151029C^ Orphan Girl Shaft 1.43E-07 1.02E-07 Percent Error 3.20 4.10 151114A Field Blank BDL BDL 151114B Santa 6.5E-09 1.8E-08 151114C Slag Canyon BDL 1.37E-08 151114D USBC BDL 3.3E-09 151114E KOA BDL 1.28E-08 151114F Quality Drain 7.0E-09 3.6E-09 Percent Error 5.50 5.90 160215A Field Blank BDL BDL 160215B Santa 1.14E-08 2.6E-08 160215C Slag Canyon 9.2E-09 1.9E-08 160215D KOA 7.4E-09 1.9E-08 160215E USBC 2.4E-08 8.5E-09 160215F Quality Drain 1.28E-08 5.0E-09 Percent Error 7.40 8.30 160314AA^ Lab Blank BDL BDL 160314A^ Field Blank BDL BDL 160315AA^ Lab Blank BDL BDL 160315A^ Field Blank BDL BDL Percent Error 14.80 18.20
152
Table XII: W and U concentrations, cont. Date Site W (mol/kg) U (mol/kg) 160316AA^ Lab Blank BDL BDL 160316A^ Field Blank BDL BDL 160316B^ Travona 1.40E-08 6.9E-08 160316C^ Ophir BDL 1.26E-08 Percent Error 3.2 4.1 160522H Santa 9.3E-09 1.10E-08 160522I Slag Canyon 5.9E-09 8.0E-09 160522J KOA 5.3E-09 7.9E-09 160522K USBC 2.2E-08 4.0E-09 160522L Blacktail 4.1E-09 3.7E-09 160521A Field Blank BDL BDL 160523A^ Lab Blank BDL BDL 160523B^ Field Blank BDL BDL Orphan Boy 160523C^ 1.4E-07 1.20E-07 Shaft 160523D^ Orphan Girl Shaft 1.6E-07 1.18E-07 160524E^ Lab Blank 4.1E-09 BDL 160524F^ Field Blank 4.1E-09 BDL 160523H^ Emma 3.9E-08 8.3E-08 Percent Error 8 7.8 160828I Blank BDL BDL 160828K Santa 1.61E-08 2.06E-08 160828L Slag Canyon 1.10E-08 1.77E-08 160828M KOA 6.72E-09 1.96E-08 160828N Quality Drain 9.94E-09 8.9E-09 160828O Blacktail BDL 3.38E-09 Percent Error 0.6 2.7 161112I Blank BDL BDL 161112K Santa 1.1E-08 2.1E-08 161112L Slag Canyon 7E-09 1.9E-08 161112M KOA 5.8E-09 2.0E-08 161112N Blacktail 4.1E-09 4.9E-09 Percent Error 14.8 18.2 Detection Limits 4.11E-09 8.46E-10 Pr, Nd, Tl, and Pb are all BDL and had detection limits of 1.42E-09 mol/kg, 8.07E-09 mol/kg, 3.32E-09 mol/kg, and 1.84E-09 mol/kg respectively. BDL = Below Detection Limit. ICP-MS produced all trace element data. All percent errors are determined from standards. ^ From Schmidt, 2017
153
7.7. EQ3 Calculated Speciation
Table XIII: Speciation results for arsenic and barium. Arsenic Barium 2- - 2+ + Date Site HAsO4 H2AsO4 Ba BaHCO3 151114B Santa 76 24 99 0 151114C Slag Canyon 69 31 99 0 151114D USBC 31 69 100 0 151114E KOA 71 29 99 0 151114F Quality Drain 69 31 100 0 160215B Santa 80 20 99 0 160215C Slag Canyon 80 20 99 0 160215D KOA 80 20 99 0 160215E USBC 77 23 100 0 160215F Quality Drain 86 14 100 0 160522I Slag Canyon 94 6 99 0 160522H Santa 93 7 99 0 160522J KOA 94 6 99 0 160522K USBC 93 7 99 0 160522L Blacktail 94 6 99 0 160828K Santa 98 2 99 0 160828L Slag Canyon 96 4 99 0 160828M KOA BDL BDL 99 <1 160828N Quality Drain 98 2 98 2 160828O Blacktail 91 9 99 0 161112K Santa 95 5 99 <1 161112L Slag Canyon 93 7 99 0 161112M KOA 86 14 100 0 161112N Blacktail 93 7 99 0 These numbers are relative abundances. Relative abundances represent the percent of the total element concentration that each chemical species occupies.
154
Table XIV: Speciation results for copper. Copper 2+ + + 2- Date Site Cu CuO CuOH CuCO3 CuHCO3 Cu(CO3)2 151114B Santa 19 0 <1 77 1 <1 151114C Slag Canyon 25 0 <1 72 2 0 151114D USBC 89 0 0 9 1 0 151114E KOA 23 0 <1 74 2 0 151114F Quality Drain 51 1 2 45 1 0 160215B Santa 15 0 <1 81 1 1 160215C Slag Canyon 13 <1 0 83 1 1 160215D KOA 14 <1 0 82 1 <1 160215E USBC 34 0 1 63 1 0 160215F Quality Drain 26 2 2 68 <1 0 160522I Slag Canyon 6 8 2 82 0 2 160522H Santa 5 8 2 83 0 2 160522J KOA 4 6 1 86 0 3 160522K USBC 5 8 2 83 0 2 160522L Blacktail 5 9 2 82 0 2 160828K Santa 1 18 1 71 0 9 160828L Slag Canyon 4 16 2 75 0 2 160828M KOA BDL BDL BDL BDL BDL BDL 160828N Quality Drain 0 28 1 63 0 7 160828O Blacktail BDL BDL BDL BDL BDL BDL 161112K Santa 3 4 <1 87 0 5 161112L Slag Canyon 4 3 <1 88 0 4 161112M KOA 10 2 <1 86 0 1 161112N Blacktail 5 3 <1 88 0 3
155
Table XV: Speciation results for iron and manganese. Iron Manganese + - 2+ - + Date Site HFeO2° FeO FeO2 Mn MnSO4° MnO4 MnHCO3 MnCO3 151114B Santa 54 46 0 87 2 0 8 0 151114C Slag Canyon 43 57 0 89 1 0 8 1 151114D USBC 11 88 0 97 <1 0 2 0 151114E KOA 50 50 0 89 0 0 8 2 151114F Quality 55 45 0 96 0 0 3 1 Drain 160215B Santa 59 41 0 86 3 0 9 3 160215C Slag Canyon 60 40 0 86 1 0 9 3 160215D KOA 62 38 0 86 1 0 9 3 160215E USBC 45 55 0 94 0 0 4 1 160215F Quality 63 37 0 95 0 0 3 1 Drain 160522I Slag Canyon 88 11 0 86 <1 0 5 7 160522H Santa 88 11 0 79 2 0 8 10 160522J KOA 89 10 0 79 0 <1 8 12 160522K USBC 90 9 0 83 0 0 7 9 160522L Blacktail 90 10 0 84 0 <1 6 9 160828K Santa 94 2 3 47 2 20 6 25 160828L Slag Canyon 93 6 1 80 1 3 5 11 160828M KOA 92 7 1 78 1 1 8 11 160828N Quality 94 2 4 BDL BDL BDL BDL BDL Drain 160828O Blacktail 81 18 0 88 0 0 8 4 161112K Santa 87 12 0 76 2 0 9 13 161112L Slag Canyon 84 15 0 79 1 0 9 11 161112M KOA 73 26 0 79 1 0 9 11 161112N Blacktail 81 19 0 84 0 0 7 8 These numbers are relative abundances. Relative abundances represent the percent of the total element concentration that each chemical species occupies.
156
Table XVI: Speciation results for zinc. Zinc + 2+ + Date Site ZnOH Zn ZnHCO3 ZnCO3 151114B Santa 2 91 4 3 151114C Slag Canyon 4 3 1 88 151114D USBC 0 99 <1 0 151114E KOA 2 92 4 2 151114F Quality Drain 2 96 1 0 160215B Santa 2 90 4 4 160215C Slag Canyon 2 89 5 4 160215D KOA 3 89 5 4 160215E USBC 1 95 2 1 160215F Quality Drain 3 94 1 2 160522I Slag Canyon 13 77 2 8 160522H Santa 12 73 4 12 160522J KOA 13 71 3 13 160522K USBC 15 72 3 10 160522L Blacktail 15 74 2 9 160828K Santa 33 39 2 25 160828L Slag Canyon 24 64 2 10 160828M KOA 19 67 3 11 160828N Quality Drain 45 30 2 23 160828O Blacktail BDL BDL BDL BDL 161112K Santa 10 71 4 15 161112L Slag Canyon 7 75 4 13 161112M KOA 6 80 3 10 161112N Blacktail 4 86 4 6
157
7.8. 18O and D in Water
Table XVII: 18O and D. Date Site δ18O (‰) δD (‰) 151114B Santa -17.3 -136 151114C Slag Canyon -17.2 -134 151114D USBC -20.9 -161 151114E KOA -17.1 -134 151114F Quality Drain -22.5 -175 160215B Santa -17.3 -137 160215C Slag Canyon -17.4 -137 160215D KOA -17.4 -137 160215E USBC -19.6 -155 160215F Quality Drain -18.9 -154 160522H Santa -15.9 -128 160522I Slag Canyon -16.5 -129 160522J KOA -16.6 -130 160522K USBC -14.6 -113 160522L Blacktail -16.6 -128 160828K Santa -16.6 -133 160828L Slag Canyon -16.9 -135 160828M KOA -17.1 -136 160828N Quality Drain -15.2 -124 160828O Blacktail -17.3 -135 161112K Santa -16.9 -134 161112L Slag Canyon -17.0 -134 161112M KOA -17.0 -135 161112N Blacktail -17.2 -134 Error for δ18O: ± 0.1% (Schmidt, 2017). Error for δD: ± 1% (Schmidt, 2017). 18O and D values measured using a Picarro Isotopic Water Analyzer L2130-i.
158
7.9. GPS
Table XVIII: GPS coordinates. Latitude x Longitude x Error (+/-) Site Elevation m (12T) (12T) m Quality Drain 45.9856 112.5073 3 1681 Santa 45.9992 112.5779 3 1651 Slag Canyon 45.9958 112.5393 3 1656 KOA 45.9922 112.5301 4 1671 USBC 45.9974 112.5245 NC 1658 Blacktail 45.9049 112.4661 NC 1710 Orphan Boy 46.0098 112.5661 NC 1735 Orphan Girl 46.0113 112.5672 NC 1735 Travona 46.0049 112.5455 NC 1704 Ophir 46.0031 112.5372 NC 1687 Emma 46.0097 112.5372 NC 1718 WWTP* 45.9906 112.5907 NC 1658 Northside Tailings* 45.9978 112.5243 NC 1665 *Values for WWTP and Northside Tailings are from Google Earth. Field GPS measurements produced all other measurements in this data set.
159
8. Appendix B: Field Pictures
8.1. November 2015
No Photo
Figure 40: November 2015 photos. USBC (a and b), KOA (c and d), Slag Canyon (e), and Santa (f and g). Pictures do not exist for Quality Drain for this month. Photo credits: Dr. Alysia Cox and Renee Schmidt. 160
8.2. February 2016
Figure 41: February sampling photos. Quality Drain (a and b), USBC (c and d), KOA (e and f), Slag Canyon (g and h), and Santa (i and j). Photo credits: Dr. Alysia Cox.
161
8.3. May 2016
Figure 42: May sampling pictures. Blacktail (a and b), USBC (c and d), KOA (e and f), Slag Canyon (g and h), and Santa (i and j). Photo credits: Dr. Alysia Cox. 162
8.4. August 2016
Figure 43: August sampling photos. Blacktail (a and b), Quality Drain (c and d), KOA (e and f), Slag Canyon (g and h), and Santa (i and j). Photo credits: Dr. Alysia Cox. 163
8.5. November 2016
Figure 44: November 2016 pictures. Blacktail (a and b), KOA (c and d), Slag Canyon (e and f), and Santa (g and h). Photo credits: Dr. Alysia Cox. 164
9. Appendix C: Spatial Variations in Speciation
Figure 45: Geographical variations in barium speciation. Barium graphs for November 2015 – November 2016. (a) November 2015, (b) February 2016, (c) May 2016, (d) August 2016, and (e) November 2016. NC = not collected. 165
Figure 46: Arsenic spatial speciation variations in November 2015-2016. (a) November 2015, (b) February 2016, (c) May 2016, (d) August 2016, and (e) November 2016. NC = not collected. 166
Figure 47: Spatial variations in zinc speciation for November 2015-November 2016. (a) November 2015, (b) February 2016, (c) May 2016, (d) August 2016, and (e) November 2016. NC = not collected. BDL = below detection limit of 0.0552 mol/kg.
167
Figure 48: Spatial variations in copper speciation during all 5 months. (a) November 2015, (b) February 2016, (c) May 2016, (d) August 2016, and (e) November 2016. BDL = below the detection limit of 0.0227 mol/kg. NC = not collected.
168
Figure 49: Iron spatial variations for each month between November 2015 and November 2016. (a) November 2015, (b) February 2016, (c) May 2016, (d) August 2016, and (e) November 2016. NC = not collected.
169
Figure 50: Manganese spatial variations for each month between November 2015 and November 2016. (a) November 2015, (b) February 2016, (c) May 2016, (d) August 2016, and (e) November 2016. NC = not collected. BDL = below the detection limit of 0.0364 mol/kg. 170
10. Appendix D: PCA Extras
Figure 51: Scree plot showing contributions of principal components to the variance.
171
Figure 52: Plot showing the contribution of each variable to the two main principal components.
172
Figure 53: PCA plot showing relationships of sites to principal components. 173
11. Appendix E: Geographical Bioavailability Variations
Figure 54: Geographical variations in predicted bioavailability. Bioavailability predictions are primarily based on calculated speciation states.
174
12. Appendix F: Sample EQ3 input file
|------| | Title | (utitl(n)) | |------| |EQ3NR input file name= KOA15114E_acidified_for_final.3i | |Version level= 8.0 | |Created 190908 Creator= jrf | | | |written for use with jus.R60 or higher database from the GEOPIG Lab ASU | | | | | |------| |Special Basis Switches (for model definition only) | (nsbswt) | |------| |Replace |None | (usbsw(1,n)) | | with |None | (usbsw(2,n)) | |------| |Temperature (C) | 4.81000E+00| (tempc) | |------| |Pressure option (jpres3): | | [x] ( 0) Data file reference curve value | | [ ] ( 1) 1.013-bar/steam-saturation curve value | | [ ] ( 2) Value (bars) | 0.00000E+00| (press) | |------| |Density (g/cm3) | 0.99950E+00| (rho) | |------| |Total dissolved solutes option (itdsf3): | | [x] ( 0) Value (mg/kg.sol) | 0.00000E+00| (tdspkg) | | [ ] ( 1) Value (mg/L) | 0.00000E+00| (tdspl) | |------| |Electrical balancing option (iebal3): | | [x] ( 0) No balancing is done | | [ ] ( 1) Balance on species |Cl- | (uebal) | |------| |Default redox constraint (irdxc3): | | [ ] (-3) Use O2(g) line in the aqueous basis species block | | [ ] (-2) pe (pe units) | 0.00000E+00| (pei) | | [ ] (-1) Eh (volts) | 0.00000E+00| (ehi) | | [x] ( 0) Log fO2 (log bars) |-7.50000E-01| (fo2lgi) | | [ ] ( 1) Couple (aux. sp.) |None | (uredox) | |------| |Aqueous Basis Species/Constraint Species |Conc., etc. |Units/Constraint| | (uspeci(n)/ucospi(n)) | (covali(n))|(ujf3(jflgi(n)))| |------| |H+ |-7.15000E+00|Log activity | |SiO2,AQ | 2.41000E+01|mg/L | |HCO3- | 105.111E+00|mg/L | |Cl- | 3.8600E-04|Molality | |Br- | 7.5090E-07|Molality | |SO4-2 | 3.1958E-04|Molality | |NO3- | 1.3451E-05|Molality | |Na+ | 4.9200E-04|Molality | |K+ | 7.6000E-05|Molality | |Mg+2 | 3.4400E-04|Molality | |Ca+2 | 4.4000E-04|Molality | |HS- | 1.2475E-07|Molality | |F- | 9.1060E-06|Molality | |Al+3 | 3.4849E-07|Molality | |VO+2 | 3.7112E-08|Molality | |Mn+2 | 1.3710E-06|Molality | 175
|Fe+2 | 9.13249E-07|Molality | |Cu+2 | 3.85324E-08|Molality | |MoO4-2 | 1.51086E-07|Molality | |Sr+2 | 2.55337E-06|Molality | |HPO4-2 | 4.79092E-07|Molality | |H2AsO4- | 3.30681E-08|Molality | |UO2+2 | 1.27792E-08|Molality | |SbO2- | 3.21599E-09|Molality | |Ti+4 | 8.87000E-06|Molality | |Zn+2 | 3.06000E-07|Molality | |Ba+2 | 2.41000E-06|Molality | |------| * Valid jflag strings (ujf3(jflgi(n))) are: * * Suppressed Molality Molarity * * mg/L mg/kg.sol Alk., eq/kg.H2O * * Alk., eq/L Alk., eq/kg.sol Alk., mg/L CaCO3 * * Alk., mg/L HCO3- Log activity Log act combo * * Log mean act pX pH * * pHCl pmH pmX * * Hetero. equil. Homo. equil. Make non-basis * *------* |Create Ion Exchangers | (net) | |------| |Advisory: no exchanger creation blocks follow on this file. | |Option: on further processing (writing a PICKUP file or running XCON3 on the | |present file), force the inclusion of at least one such block (qgexsh): | | [ ] (.true.) | |------| |Ion Exchanger Compositions | (neti) | |------| |Exchanger phase |None | (ugexpi(n)) | |------| |->|Moles/kg.H2O | 0.0000 | (cgexpi(n)) | |------| |->|Exchange site |None | (ugexji(j,n)) | |------| |--->|Exchange species |Eq. frac. | (this is a table header) | |------| |--->|None | 0.00000E+00| (ugexsi(i,j,n), egexsi(i,j,n)) | |------| |Solid Solution Compositions | (nxti) | |------| |Solid Solution |None | (usoli(n)) | |------| |->|Component |Mole frac. | (this is a table header) | |------| |->|None | 0.00000E+00| (umemi(i,n), xbari(i,n)) | |------| |Alter/Suppress Options | (nxmod) | |------| |Species |Option |Alter value | | (uxmod(n)) |(ukxm(kxmod(n)))| (xlkmod(n))| |------| |S2-2 |Suppress | 0.00000E+00| |S2O3-2 |Suppress | 0.00000E+00| |CN- |Suppress | 0.00000E+00| |SCN- |Suppress | 0.00000E+00| |OCN- |Suppress | 0.00000E+00| |N2,AQ |Suppress | 0.00000E+00| |N2H5+ |Suppress | 0.00000E+00| |N2O2-2 |Suppress | 0.00000E+00| |HN2O2- |Suppress | 0.00000E+00| |HCN,AQ |Suppress | 0.00000E+00| 176
|------| * Valid alter/suppress strings (ukxm(kxmod(n))) are: * * Suppress Replace AugmentLogK * * AugmentG * *------* |Iopt Model Option Switches ("( 0)" marks default choices) | |------| |iopt(4) - Solid Solutions: | | [x] ( 0) Ignore | | [ ] ( 1) Permit | |------| |iopt(11) - Auto Basis Switching in pre-N-R Optimization: | | [x] ( 0) Turn off | | [ ] ( 1) Turn on | |------| |iopt(17) - PICKUP File Options: | | [ ] (-1) Don't write a PICKUP file | | [x] ( 0) Write a PICKUP file | |------| |iopt(19) - Advanced EQ3NR PICKUP File Options: | | [x] ( 0) Write a normal EQ3NR PICKUP file | | [ ] ( 1) Write an EQ6 INPUT file with Quartz dissolving, relative rate law | | [ ] ( 2) Write an EQ6 INPUT file with Albite dissolving, TST rate law | | [ ] ( 3) Write an EQ6 INPUT file with Fluid 1 set up for fluid mixing | |------| |Iopg Activity Coefficient Option Switches ("( 0)" marks default choices) | |------| |iopg(1) - Aqueous Species Activity Coefficient Model: | | [ ] (-1) The Davies equation | | [x] ( 0) The B-dot equation | | [ ] ( 1) Pitzer's equations | | [ ] ( 2) HC + DH equations | |------| |iopg(2) - Choice of pH Scale (Rescales Activity Coefficients): | | [ ] (-1) "Internal" pH scale (no rescaling) | | [x] ( 0) NBS pH scale (uses the Bates-Guggenheim equation) | | [ ] ( 1) Mesmer pH scale (numerically, pH = -log m(H+)) | |------| |Iopr Print Option Switches ("( 0)" marks default choices) | |------| |iopr(1) - Print All Species Read from the Data File: | | [x] ( 0) Don't print | | [ ] ( 1) Print | |------| |iopr(2) - Print All Reactions: | | [x] ( 0) Don't print | | [ ] ( 1) Print the reactions | | [ ] ( 2) Print the reactions and log K values | | [ ] ( 3) Print the reactions, log K values, and associated data | |------| |iopr(3) - Print the Aqueous Species Hard Core Diameters: | | [x] ( 0) Don't print | | [ ] ( 1) Print | |------| |iopr(4) - Print a Table of Aqueous Species Concentrations, Activities, etc.: | | [ ] (-3) Omit species with molalities < 1.e-8 | | [ ] (-2) Omit species with molalities < 1.e-12 | | [ ] (-1) Omit species with molalities < 1.e-20 | | [x] ( 0) Omit species with molalities < 1.e-100 | | [ ] ( 1) Include all species | |------| |iopr(5) - Print a Table of Aqueous Species/H+ Activity Ratios: | | [x] ( 0) Don't print | 177
| [ ] ( 1) Print cation/H+ activity ratios only | | [ ] ( 2) Print cation/H+ and anion/H+ activity ratios | | [ ] ( 3) Print ion/H+ activity ratios and neutral species activities | |------| |iopr(6) - Print a Table of Aqueous Mass Balance Percentages: | | [ ] (-1) Don't print | | [x] ( 0) Print those species comprising at least 99% of each mass balance | | [ ] ( 1) Print all contributing species | |------| |iopr(7) - Print Tables of Saturation Indices and Affinities: | | [ ] (-1) Don't print | | [x] ( 0) Print, omitting those phases undersaturated by more than 10 kcal | | [ ] ( 1) Print for all phases | |------| |iopr(8) - Print a Table of Fugacities: | | [ ] (-1) Don't print | | [x] ( 0) Print | |------| |iopr(9) - Print a Table of Mean Molal Activity Coefficients: | | [x] ( 0) Don't print | | [ ] ( 1) Print | |------| |iopr(10) - Print a Tabulation of the Pitzer Interaction Coefficients: | | [x] ( 0) Don't print | | [ ] ( 1) Print a summary tabulation | | [ ] ( 2) Print a more detailed tabulation | |------| |iopr(17) - PICKUP file format ("W" or "D"): | | [x] ( 0) Use the format of the INPUT file | | [ ] ( 1) Use "W" format | | [ ] ( 2) Use "D" format | |------| |Iodb Debugging Print Option Switches ("( 0)" marks default choices) | |------| |iodb(1) - Print General Diagnostic Messages: | | [x] ( 0) Don't print | | [ ] ( 1) Print Level 1 diagnostic messages | | [ ] ( 2) Print Level 1 and Level 2 diagnostic messages | |------| |iodb(3) - Print Pre-Newton-Raphson Optimization Information: | | [x] ( 0) Don't print | | [ ] ( 1) Print summary information | | [ ] ( 2) Print detailed information (including the beta and del vectors) | | [ ] ( 3) Print more detailed information (including matrix equations) | | [ ] ( 4) Print most detailed information (including activity coefficients) | |------| |iodb(4) - Print Newton-Raphson Iteration Information: | | [x] ( 0) Don't print | | [ ] ( 1) Print summary information | | [ ] ( 2) Print detailed information (including the beta and del vectors) | | [ ] ( 3) Print more detailed information (including the Jacobian) | | [ ] ( 4) Print most detailed information (including activity coefficients) | |------| |iodb(6) - Print Details of Hypothetical Affinity Calculations: | | [x] ( 0) Don't print | | [ ] ( 1) Print summary information | | [ ] ( 2) Print detailed information | |------| |Numerical Parameters | |------| | Beta convergence tolerance | 0.00000E+00| (tolbt) | | Del convergence tolerance | 0.00000E+00| (toldl) | | Max. Number of N-R Iterations | 0 | (itermx) | 178
|------| |Ordinary Basis Switches (for numerical purposes only) | (nobswt) | |------| |Replace |None | (uobsw(1,n)) | | with |None | (uobsw(2,n)) | |------| |Sat. flag tolerance | 0.00000E+00| (tolspf) | |------| |Aq. Phase Scale Factor | 1.00000E+00| (scamas) | |------| |End of problem | |------|