Tungsten in Washington State surface waters

Chemosphere 242 (2020) 125151

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Tungsten in Washington State surface waters Philip Steenstra*, Nikolay Strigul, John Harrison School of Environmental Science, 14204 NE Salmon Creek Avenue, Washington State University Vancouver, Vancouver, WA, 98686, USA

h i g h l i g h t s
Tungsten is present in WA surface waters at low concentrations.
Tungsten concentrations in WA surface waters are below current levels of concern.
The derived tungsten concentration model can help identify tungsten hotspots.

a r t i c l e i n f o

a b s t r a c t

Article history: Received 27 June 2019 Received in revised form 11 October 2019 Accepted 18 October 2019 Available online 25 October 2019

At high concentrations, tungsten can be toxic to humans, animals, and the environment, though little is known about natural, aqueous tungsten in surface waters. To improve understanding and develop a model predicting tungsten concentrations, we collected water and sediment from 77 water bodies in 20 watersheds in Washington State, USA. We found aqueous tungsten concentrations spanning two orders of magnitude (10.3 ng L1 - 2.05 mg L1) with average tungsten concentrations in both water and sediments more than two-fold higher in watersheds with tungsten-bearing underlying rock types (average: 0.217 mg L1, 0.669 mg kg1; range: 0.010e2.05 mg L1, 0.0713e4.691 mg kg1 for surface waters and sediments, respectively) than in watersheds without such underlying geology (average: 0.068 mg L1, 0.352 mg kg1; range: 0.010e0.211 mg L1, 0.0349e2.399 mg kg1 for surface waters and sediments, respectively). Aqueous concentrations of tungsten significantly correlated with beryllium (Be) and copper (Cu) (R2 ¼ 0.31, 0.41, respectively) and a multiple linear regression model using Be and Cu explained 65% of the variance in measured aqueous tungsten concentrations. Applying this model to existing Be and Cu data from 19 sites across the Pacific Northwest resulted in predicted tungsten concentrations ranging from 0.116 to 0.458 mg L1. These predicted concentrations along with our measured concentrations indicate none of these sites were close to the drinking water standard for tungsten set by the former Soviet Uniondthe only country so far to set limits for tungsten in drinking water (50 mg L1). © 2019 Elsevier Ltd. All rights reserved.

Handling Editor: Patryk Oleszczuk Keywords: Scheelite Wolframite W Heavy metal

1. Introduction Tungsten (W) is a transition metal with high density, high strength, and the highest melting point of any metal making it exceedingly useful in industrial, defense, and day-to-day applications (Andrews, 1955; USGS, 2014). Although previous studies have shown that tungsten has the potential to leach into surface waters where it can negatively affect human health and aquatic ecosystems (Strigul et al. 2005; Hamilton, 2016), little is known about the geographic distribution of tungsten in surface waters or its geochemical controls. Tungsten does not occur naturally in its elemental form, but

* Corresponding author. E-mail address: [email protected] (P. Steenstra). https://doi.org/10.1016/j.chemosphere.2019.125151 0045-6535/© 2019 Elsevier Ltd. All rights reserved.

occurs instead as compounds with calcium, iron, manganese, or an iron/manganese complex; these are called Scheelite, Ferberite, Hubnerite, and Wolframite, respectively (US Dept. of Commerce, 1956). In the United States, tungsten-bearing minerals have been surveyed in granite and quartz formations across the western half of the country with high concentrations occurring in Washington, Oregon, Idaho, Montana, California, and Nevada (USGS, 2011). Historically, tungsten has been considered to be insoluble (Lide et al., 2003; Koutsospyros et al., 2006), which promoted its use in various applications within industry and in daily life. However, research has shown that metallic tungsten and its alloys can be quite soluble in water under environmentally relevant conditions (Koutsospyros et al., 2006). Studies have also reported that tungsten is difficult to detect due to its high ionization energy and thereby requires the use of specialized equipment such as an

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P. Steenstra et al. / Chemosphere 242 (2020) 125151

Inductively Coupled Plasma Mass Spectrometer (ICP-MS) or Inductively Coupled Plasma Atomic Emission Spectroscopy (ICPAES): the ICP-MS being preferred due to its better accuracy compared to the ICP-AES (Wang et al., 1999; US DHHS 2005). Unfortunately, these types of equipment are expensive to purchase and as a consequence (and in part because it is not a regulated compound), tungsten is frequently not included in standard monitoring protocols of heavy metals and trace elements in surface and drinking water. Hence, predicting tungsten concentration as a function of more commonly measured solutes might help us to understand historic and current water quality in regions where tungsten concentration has not been measured. Predicting tungsten concentration in this manner would also allow researchers to identify potential tungsten hotspots which may require additional attention. While tungsten has historically been portrayed as environmentally benign (Koutsospyros et al., 2006; Clausen and Korte, 2009), there is a growing body of evidence indicating tungsten may accumulate in and be toxic to plants, animals, and even microbes (Strigul et al. 2005; Inouye et al., 2006; Strigul, 2010; Kumar and Aery, 2011). Additional studies have reported correlations between tungsten concentrations in air and drinking water and the prevalence of childhood leukemia, stroke, diabetes, and peripheral arterial disease (Koutsospyros et al., 2006; Sheppard et al., 2012; Tyrrell et al., 2013; Menke et al., 2015). Due to the growing body of evidence fleshing out tungsten’s toxicity profile, the US EPA has designated tungsten as an “Emerging Contaminant” (US EPA, 2015). This designation means that tungsten warrants further study but is not yet regulated or designated as “toxic” under either the Clean Water Act or the Safe Drinking Water Act. Those potentially affected by tungsten were in many cases exposed to both naturally occurring and anthropogenic sourcesdconfounding the data as to whether one, the other, or both are to blame for these ailments (Hamilton, 2016). Regardless of exposure source, there is evidence that people living in the United States have significant concentrations of tungsten in their urine with those in the American West potentially having higher exposure. In Washington State, those tested (n ¼ 1,419) displayed creatinine corrected levels of tungsten 130e150% that of the national average (WEBS, 2014). Previous studies related to tungsten environmental toxicity have focused on anthropogenic sources of pollution including metallic tungsten and its alloys, their movement through the environment, and the resulting toxic effects (Strigul et al. 2005; Koutsospyros et al., 2006; Clausen and Korte, 2009). However, naturally occurring tungsten minerals have received far less scrutiny from environmental scientists, so it is unknown whether naturally occurring compounds behave in the same manner as metallic tungsten. The little data available concerning the environmental effects of naturally occurring tungsten indicates that tungsten can make it into soil and water (Lin et al., 2014). Another study, similar to one by Dermatas (Dermatas et al., 2004), indicates that pH may predict tungsten concentrations in groundwater (Johannesson et al., 2013). While valuable, these studies leave significant knowledge gaps concerning the presence, concentration, and potential controls on naturally occurring tungsten in the environment. With these knowledge gaps in mind, we addressed the following research questions: 1) What environmental factors predict the presence and concentration of tungsten in surface waters and sediments? 2) What is the concentration of tungsten in the surface waters and sediments of Washington State?

3) To what extent can we predict tungsten concentrations in water using concentrations of other elements as proxies? 4) What do such predictions tell us about likely distribution of tungsten loading in Pacific Northwest surface waters? This study represents the first synoptic sampling of naturally occurring tungsten in the Pacific Northwest and thereby addresses these knowledge gaps by providing a regional snapshot of naturally occurring tungsten concentrations in Washington State surface waters. 2. Methods 2.1. Overview In this study, we hypothesized that tungsten can leach out of rocks and soil and persist in the water and sediments overlying naturally occurring tungsten deposits. We used USGS databases to identify tungsten deposits across Washington State, categorized them according to their underlying rock types, and identified specific sampling areas by delineating watersheds. Locations with and without tungsten were paired according to similar geologic and geographic features with the only difference being underlying rock type and tungsten presence. Water collected from each of these sites was analyzed for pH, tungsten concentration, as well as concentrations of Ag, Ba, Be, Ca, Cr, Cu, Fe, Mg, Mo, Mn, U, V, and Zn. Sediments were also collected from each site and analyzed for tungsten content. 2.2. Site selection Overlaying USGS watersheds (USGS, 2017) onto rock types (USGS, 2014; Horton, 2017), we identified watersheds that were entirely underlaid by tungsten bearing rock types as well as watersheds that were entirely devoid of tungsten bearing rock. These watershed characteristics were compared with mining claims (USGS, 2011) to further assess the presence of tungsten for selected sites. Finally, using the USGS National Hydrography Dataset (USGS, 2012), we identified waterbodies that occur within the aforementioned selected watersheds and eliminated those locations which did not allow for sampling (i.e. a watershed without water to sample was excluded). These sites were assigned to three different categories: “Tungsten” sites (8), locations with surveyed tungsten deposits; “Hypothesized” sites (4), locations with underlying rock types that are potentially tungsten-bearing; and “No Tungsten” sites (9), locations devoid of tungsten-bearing rock types. In addition, “Tungsten” and “No Tungsten” sites were matched to the extent possible using rainfall, elevation, exposure, and slope [Table 1]. Further winnowing watersheds by accessibility resulted in the identification of 21 study sites [Fig. 1]. 2.3. Field sampling Water was collected from up to five different first-order waterbodies (the smallest tributaries within a watershed) at each site. Waterbodies were categorized into four groups to assess differences in tungsten concentration between water sources: 1) groundwater seepage, 2) pond/lake, 3) fast stream (flow rate > > 1 m s1), and 4) slow stream (flow rate < < 1 m s1). All water samples were collected in two trips from 18 to 22 June and 15e18 July 2018, filtered on site using 0.45 mm syringe filters, and stored in acid washed 60 ml HDPE centrifuge tubes until analysis. Bed sediment samples were collected from within the sampled water source when possible. When no aquatic sediment was present, we sampled dry sediment from directly adjacent to

P. Steenstra et al. / Chemosphere 242 (2020) 125151

3

Table 1 Site characteristics for comparison. Type

Name

Lat

Long

RockType1

RockType2

Rainfall 24h2yr (Inches) Elevation (m) Exposure (WH/m2) Slope (DEG)

Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten Tungsten No Tungsten Tungsten No Tungsten Tungsten Hypothesized No Tungsten Tungsten Tungsten No Tungsten Hypothesized Hypothesized Hypothesized Tungsten Tungsten

Blue Grouse Mine Cascade Chief China Bar Placer Dandy Claims Duhl Uranium Mine Farrar Place Germania Mine Great Excelsior Mine Kelly Camp Last Chance Lone Star Mine Norway Mine Palmer Summit Paymaster Red Bird Silver Creek Placer Skagit Talc Products Skoal Talc Mine Sunday Morning Tinex Prospect Wolframite Tungsten

48.10068 47.283436 47.834298 48.78315 47.95024 48.744886 48.00818 48.8987 48.80373 48.66651 48.57761 46.30314 48.85956 48.100982 46.79399 46.9312 48.611589 48.52044 48.21343 47.412345 48.97677

117.5097 120.648422 118.325561 118.47333 117.169951 120.779289 118.10054 121.80596 118.79781 118.75114 119.767 122.11119 119.56395 120.055137 121.35701 121.4773 121.350578 121.25169 120.58425 120.651273 120.13313

Argilite Arkose Tholeiite Orthogneiss Granite Arkose Granite Andesite Granite Quartz Monzonite Granite Granodiorite Metasedimentary Biotite Gneiss Granodiorite Andesite Schist Schist Biotite Gneiss Serpentinite Granite

Siltstone Shale Andesite Paragneis s Granodiorite Black Shale Granodiorite Dacite Granodiorite Granite Granodiorite Granite Sochkist Orthogneiss Granite Dacite Gneiss Gneiss Orthogneiss Peridotite Granodiorite

1.5 2 1 1.5 2 3 1.5 3.5 1.5 1.5 1.5 5 1.5 1.5 3.5 3.5 4 4 2 2 2

1120 994 521 1437 918 1047 1071 568 1365 948 943 1130 902 692 1576 1387 262 629 337 958 2059

343533 536635 476799 455145 509916 305733 471460 367981 527984 516451 397996 510674 519777 427125 590870 463038 345099 509613 519976 445250 618590

20 17 12 16 9 28 9 18 17 21 20 23 15 19 28 9 26 22 24 21 15

Fig. 1. Selected field sampling sites across Washington State for the survey of tungsten in surface water. Major cities and political boundaries are also shown.

the sampled water body. We successfully obtained sediment and water samples from twenty sites, but due to some watersheds having fewer than five separate, suitable waterbodies, we obtained 77 samples total [Table 2].

2.4. Water and sediment analysis Analysis of the samples included pH measurement and inductively coupled plasma mass spectrometer (ICP-MS) analysis of common and trace metals. The pH for all samples was determined within four days of initial collection using a Fisher Scientific Accumet AR20. Tungsten concentrations were determined using an Agilent

7900 ICP-MS. Water and sediment samples were analyzed within ten days and six months of collection, respectively. Due to the difficulty of detecting low concentrations of tungsten, the ICP-MS was operated in “Low Matrix Mode” during analysis and included a 90-s rinse time to minimize the potential for carry-over between samples. All other elements detected during this study were run using factory settings (General Purpose Mode) with a 45-s rinse time (Agilent Technologies, 2016). The elements measured in each water sample in addition to tungsten were: Ag, Ba, Be, Ca, Cr, Cu, Fe, Mg, Mo, Mn, U, V, and Zn. The use of the ICP-MS to detect trace elements is described by USEPA Method 6020A, which was later verified for use in the accurate detection of tungsten (Bednar et al., 2010). The sediments collected alongside water samples were

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P. Steenstra et al. / Chemosphere 242 (2020) 125151

Table 2 Sample Sites with data and corresponding underlying rock types from USGS database. Type

Name

Rep

Lat

Long

Notes

pH

W (ug/l)

W (mg/kg)

Rock Type

Tungsten Tungsten Tungsten Tungsten Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten Tungsten Tungsten Tungsten No Tungsten No Tungsten No Tungsten No Tungsten

Blue Grouse Mine Blue Grouse Mine Blue Grouse Mine Blue Grouse Mine Blue Grouse Mine Cascade Chief Cascade Chief Cascade Chief Cascade Chief Dandy Claims Dandy Claims Dandy Claims Dandy Claims Dandy Claims Duhl Uranium Min Duhl Uranium Min Duhl Uranium Min Duhl Uranium Min Duhl Uranium Min Farrar Place Farrar Place Farrar Place Farrar Place Germania Min e Germania Mine Germania Mine Great Excelsior Mir Great Excelsior Mir Great Excelsior Mir Great Excelsior Min

1 2 3 4 5 1 2 3 4 1 2 3 4 5 1 2 3 4 5 1 2 3 4 1 2 3 1 2 3 4

48.10973 48.10899 48.10654 48.04094 47.96218 47.24733 47.25303 47.25971 47.27533 48.65052 48.69696 48.70941 48.71037 48.7119 47.95255 47.95264 47.9442 47.9628 47.96138 48.65615 48.66633 48.71293 48.71806 48.01652 48.01569 48.01573 48.90412 48.90915 48.90751 48.89973

117.51295 117.51144 117.52969 117.50198 117.48298 120.69399 120.66942 120.65363 120.64707 118.44051 118.43805 118.44473 118.45092 118.45726 117.18228 117.18221 117.1779 117.19387 117.1813 120.57866 120.59166 120.65388 120.66144 118.13779 118.13475 118.13478 121.8747 121.81955 121.80442 121.80267

Slow moving stream Pond Large stream Culvery under road fur Stagnant lake at bottom Small creek, slow movir Small stream running th Small stream running b Sluggish stream in the Roadside groundwater Hillside groundwater p Pooling off of hillside Flowing from hillside Flowing from hillside Quickly flowing stream Quickly flowing stream Quickly flowing stream Slow moving stream Slow moving stream Stream on the side of a Larger stream coming f Downhill stream flowin Closest pool to Hart’s I Stream flowing down a Stream coming out of t Stream from different fl Stream flowing out of a Stream quickly flowing Trickling stream next to Slow, wide stream. Loo

7.09 7.09 7.26 7.8 7.88 8.15 8.02 7.91 8.03 7.78 7.03 7.32 7.32 6.83 7.52 7.09 7.25 7.13 6.8 7.95 8.1 7.59 7.28 8.04 8.08 8.34 7.1 7.53 7.2 7.3

0.911485066 0.932638013 0.339425746 0.10047499 0.088544848 0.0156 0.01608 0.01374 0.01332 0.21122046 0.065386258 0.087085226 0.062856389 0.061610621 0.100416613 0.097302778 0.091269697 0.081208013 0.087980731 0.104639808 0.106527724 0.099209958 0.090646814 0.074571897 0.262939469 2.051115868 0.0425 0.0352 0.034 0.02166

0.99435859 0.778924546 1.247170683 0.236229831 0.7477205222 0.102392375 0.132732323 0.098920607 0.150042134 0.154314071 0.115444472 0.228531015 0.270970393 0.038713832 0.389945162 0.187478328 0.316630203 1.586526093 0.872410949 0.459999045 0.53562642 0.306191098 0.171770967 0.691027771 0.910281115 0.412897239 0.186532639 0.170804871 0.07827547 0.034971128

granite granite argillite glacial drift glacial drift granite granite granite granite granite granite granite granite schist schist schist glacial drift alluvium andesite granodiorite granodiorite andesite granodiorite alluvium phyllite serpentinite serpentinite granite granite alluvium

Type

Name

Rep

Lat

Long

Notes

pH

W (ug/1)

W (mg/kg)

Rock Type

Tungsten Tungsten Tungsten Tungsten Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten Tungsten Tungsten Tungsten Tungsten Tungsten Tungsten Tungsten Hypothesized Hypothesized Hypothesized Hypothesized Hypothesized No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten

Kelly Camp Kelly Camp Kelly Camp Kelly Camp Mudget Lake Last Chance Last Chance Last Chance Last Chance Last Chance Lone Star Mine Lone Star Mine Lone Star Mine Lone Star Mine Paymaster Wolframite Tungst Wolframite Tungst Norway Mine Norway Mine Norway Mine Norway Mine Norway Mine Palmer Summit Palmer Summit Palmer Summit Palmer Summit Palmer Summit

1 2 3 4 1 1 2 3 4 5 1 2 3 4 1 1 2 1 2 3 4 5 1 2 3 4 5

48.76749 48.78723 48.79964 48.80388 47.9622 48.66106 48.67018 48.67163 48.6554 48.64878 48.58322 48.58727 48.61701 48.56091 48.09019 48.87229 48.87419 46.32964 46.29593 46.28826 46.27743 46.28104 48.81521 48.82384 48.83647 48.85396 48.84616

118.74009 118.76477 118.78228 118.79616 117.4802 118.75041 118.75045 118.76435 118.7439 118.75997 119.75983 119.76394 119.79346 119.74469 120.01902 120.00342 120.01239 121.96987 122.08133 122.09204 122.11034 122.11791 119.52312 119.58265 119.56416 119.56925 119.56653

Small stream running d Flowing from hills ide Flowing down a hillside Flowing out of a hills id 0 Creek by road Small ditch with ground Mud lake at TOP of wa Other fork of stream in Lower form of Granite Quickly flowing stream Fast-flowing river North Fork Salmon Cret Conconully Lake. Earth Fast, medium sized stre Fast-flowing stream abc Fast-flowing small river Large stream flowing q Meta Lake. Ashy, still l Stream flowing down a Trickle of water down t Spirit Lake. Scummy wa Spectacle Lake. Next to Stream flowing quickly Stream flowing through Pond in a copse. Cows Stagnant stream freque

7.94 7.52 7.55 7.84 8.33 7.96 8.1 8.06 8.01 8.17 8.25 8.01 7.86 8.37 8.04 7.59 7.06 7.33 7.14 7.04 7.11 6.62 8.5 8.3 8.36 8.14 8.06

0.075389177 0.070017899 0.0961545 0.128403185 0.07935921 0.077568589 0.118905423 0.085431208 0.078191667 0.070134458 0.11394259 0.095181548 0.117524026 0.137394817 0.216417385 0.097672499 0.103666661 0.007622 0.01004 0.009976 0.0191 0.01200 0.080606339 0.09292391 0.085216964 0.084380225 0.14482992

0.153080583 1.1108235 0.204163699 0.077355136 0.082117059 0.199603487 0.203705076 0.223807731 0.181400857 0.491254593 0.287269643 0.435100065 4.690820637 0.3742588 0.146236481 0.195343337 0.16419326 0.182644864 0.165673027 0.098569164 0.081275458 0.530100225 0.420789653 0.223826907 0.284181958 0.538334815 0.923459311

arkose alluvium alluvium wacke granite orthogneiss orthogneiss orthogneiss granite granite granite granite glacial drift arkose arkose arkose arkose argillite andesite andesite andesite quartz monzonite andesite andesite conglomerate arkose glacial drift

Type

Name

Rep

Lat

Long

Notes

pH

W (ug/1)

W (mg/kg)

Rock Type

Tungsten Tungsten Tungsten Tungsten Tungsten No Tungsten No Tungsten No Tungsten No Tungsten No Tungsten Hypothesized Hypothesized Hypothesized

Red Bird Red Bird Red Bird Red Bird Red Bird Silver Creek Placer Silver Creek Placer Silver Creek Placer Silver Creek Placer Silver Creek Placer Skagit Talc Produc Skoal Talc Mine Skoal Talc Mine

1 2 3 4 5 1 2 3 4 5 2 1 2

46.82501 46.81329 46.8134 46.803 46.78514 46.97279 46.97251 46.94231 46.93532 46.91805 48.61953 48.52295 48.5325

121.30733 121.30584 121.30621 121.31979 121.33627 121.49564 121.49514 121.47356 121.47153 121.48346 121.35011 121.37944 121.32149

Stream in a road ditch. Quickly flowing stream Puddle formed by grou Stream which looks pro Low water cros sing wit Large stream. Appears Quickly flwoing, rocky Stream flowing from oti Small trickle of water co Lake high on mountain Only flowing water in lo Stagnant water in a ditc Medium-sized stream f

6.96 7.44 6.83 7.07 7 43 7.79 7.75 7.42 7.2 7.11 7.38 7.72 7.72

0.01446 0.02622 0.00866 0.01266 0.02816 0.01266 0.01566 0.01438 0.01178 0.01028 0.02686 0.02038 0.03294

0.214438158 0.1761063 0.235278961 0.265764484 0.112237174 0.068057127 0.086751099 0.07969809 0.095346098 2.399435374 0.290866099 0.190204842 0.116743248

meta sedimentary meta sedimentary meta sedimentary meta sedimentary andesite andesite andesite andesite andesite andesite andesite andesite andesite

rock rock rock rock

P. Steenstra et al. / Chemosphere 242 (2020) 125151

5

Table 2 (continued ) Type

Name

Rep

Lat

Long

Notes

pH

W (ug/1)

W (mg/kg)

Rock Type

Hypothesized Hypothesized Hypothesized Tungsten Tungsten Tungsten Tungsten

Skoal Talc Mine Skoal Talc Mine Skoal Talc Mine Tinex Prospect Tinex Prospect Tinex Prospect Tinex Prospect

3 4 5 1 2 3 4

48.53742 48.52201 48.52247 47.46361 47.44146 47.41028 47.41028

121.29682 121.2553 121.25448 120.66075 120.66252 120.65825 120.65825

Quick, medium, very ro Stream flowing down th Small stream flowing out Large stream with clear Smaller stream further u Even smaller tributary i Small pipe draining gro

7.58 7.66 7.81 8.16 8.29 8.21 8.22

0.01376 0.01246 0 01324 0.048 0.0358 0.02914 0.00972

0.53670903 0.006022048 15.73521013 0.240251445 0.072241293 0.0713189 4.064807772

andesite alluvium glacial drift glacial drift glacial drift schist schist

Table 3 Map key for sampled site names. Name

Code

Blue Grouse Mine Cascade Chief Dandy Claims Duhl Uranium Mine Farrar Place Germania Mine Great Excelsior Mine Kelly Camp Last Chance Lone Star Mine Mudget Lake Norway Mine Palmer Summit Paymaster Red Bird Silver Creek Placer Skagit Talc Products Skoal Talc Mine Sunday Morning Tinex Prospect Wolframite Tungsten Claim

BMG CC DC DUM FP GM GEM KC LC LSM ML NM PS PM RB SCP STP STM SM TP WTC

prepared for analysis using a modified version of USEPA Method 3050b as detailed in Bednar et al. (2010). This modification increases the percent recovery of tungsten relative to the original EPA method, making it more applicable and accurate for this study when compared to the unmodified version. The ICP-MS used in this study was experimentally determined to have a limit of detection (LOD) of 10 ng L1 using ANOVA and Tukey’s HSD based on repeated measures of standards at concentrations ranging by order of magnitude from 0.1 ng L1 to 100 ng L1. Measurements at 10 ng L1 ranged from 9.77 to 10.26 ng L1. 2.5. Data analysis and management All data obtained from the ICP-MS, field work, GPS, and other sources were consolidated using Microsoft Excel and imported into RStudio (version 1.0.153) for statistical analysis. All mapwork was accomplished using Esri ArcGIS (version 10.3.1). We used data from the USGS Water Quality Network (WQN, 1995) database for predictive modeling input data, and from the USGS for soils, hydrology, elevation, and mineral data used in site selection (USGS, 2011; 2012, 2017; Horton, 2017). 2.6. Statistical analysis ANOVA was used to test for significant differences in tungsten concentrations between site types with reference to underlying rock type and mining claims (“Tungsten,” “No Tungsten,” or “Hypothesized”). The non-parametric Kruskal-Wallis Rank Sum Test was used to determine significant differences in tungsten concentration between water source types and between sediments collected from watersheds with and without tungsten-bearing

minerals as these data were non-normally distributed. For model development, the tungsten water concentration data were log10 transformed to obtain normal distribution. To predict log10-transformed concentrations of tungsten, we performed a stepwise regression using the other elements detected in each sample. The best model was determined using Akaike’s Information Criterion and then modified according to whether other elements are known to co-occur with tungsten. Elements not known to co-occur with tungsten were eliminated from use in the model as potential independent variables. Nash-Sutcliffe Efficiency (NSE) was used to evaluate model fit to the observations. The NSE operates on the scale of -∞ to 1, with 1 being a perfect model fit and 0 meaning that the model predictions are as accurate as the mean of the data. 3. Results 3.1. Surface water Sampled tungsten concentrations (77 water samples from 20 sites [Fig. 2a]) spanned two orders of magnitude (range: 0.010e2.05 mg L1 [Fig. 2b]). The tungsten concentrations in surface water were, on average, significantly higher in watersheds with known tungsten deposits (“Tungsten” Sites; range: 0.010e2.05 mg L1) than they were in watersheds without known tungsten deposits or tungsten-bearing rock types (“No Tungsten” Sites; range: 0.010e0.211 mg L1). Mean tungsten concentrations were more than three times higher for “Tungsten” sites than for “No Tungsten” sites (0.217 mg L1 and 0.068 mg L1, respectively; f ¼ 4.64, p ¼ 0.035 by ANOVA). With reference to the outliers in the “Tungsten” data, a test of medians did not show a significant difference between the “Tungsten” and “No Tungsten” sites (0.095 mg L1 and 0.078 mg L1, respectively; z ¼ 0.738, p ¼ 0.46 by Mood’s Median Test). There was no significant difference in tungsten concentration by water source type (Slow Stream, Fast Stream, Pond/Lake, and Groundwater Seepage) (c2 ¼ 2.122, df ¼ 3, p ¼ 0.5475) by Kruskal-Wallis Rank Sum Test. 3.2. Tungsten in sediments Tungsten concentration in sediments (77 sediment samples from 20 sites [Fig. 2a]) spanned three orders of magnitude (range: 0.0349e15.7 mg kg1 [Fig. 3]). The tungsten concentrations in sediments collected from the “Tungsten” sites (range: 0.0713e4.691 mg kg1) were significantly higher than those collected from “No Tungsten” sites (range: 0.0349e2.399 mg kg1) (c2 ¼ 10.369, df ¼ 2, p ¼ 0.0056 by Kruskal-Wallis Test). These concentrations were, on average, almost double at 0.669 mg kg1 and 0.352 mg kg1 for “Tungsten” and “No Tungsten” sites respectively. A comparison of medians, 0.240 mg kg1 and 0.200 mg kg1 for “Tungsten” and “No Tungsten” sites respectively, does not indicate a significant difference between the sites using this test (Z ¼ 1.23, p ¼ 0.219 by Mood’s Median Test). These data when analyzed using the Kruskal-Wallis Test support our hypothesis that

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Fig. 2. (a) Sites in Washington State sampled for tungsten. Reference Map Key [Table 3] for full site names. (b) Tungsten concentrations in Washington State by sampling location. Each location consisted of 1e5 samples depending on the number of first-order streams present.

the underlying rock type (i.e. tungsten-bearing rocks) predict the presence of tungsten in the sediments. While sediments from sites with tungsten-bearing underlying lithology had higher tungsten concentrations than “No Tungsten”

sites, the concentration of tungsten in the sampled water had no significant correlation with the amount of tungsten detected in the sediment (f ¼ 0.0119, df ¼ 75, p ¼ 0.9134 by regression). This disparity is illustrated by a number of instances within the data

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Fig. 3. Sediment tungsten concentration (mg kg1) by sample site.

where high tungsten concentrations in sediment do not correspond with high tungsten concentrations in water and vice versa. Outliers were identified as data points exceeding the mean by an order of magnitude or by visually being separate from the main group, for soil and water samples respectively. Removing these outliers from both sediment and water tungsten concentrations (n ¼ 7) and performing a regression yields a significant equation for predicting water tungsten concentrations from sediment tungsten concentrations, though it only describes 20% of the observed variance in the data (f ¼ 19.24, df ¼ 68, p < 0.001; R2 ¼ 0.2091). This equation is [W] ¼ 0.0417 þ 0.0958*[W-Soil] with tungsten concentration in mg L1 and soil-tungsten concentration in mg kg1.

3.3. Tungsten modeling Elemental tungsten does not occur as such through natural processes, rather tungsten co-occurs with other elements (Andrews, 1955). Using tungsten’s chemical properties, we hypothesized that any attempt to model tungsten concentration using co-occurring elements would involve either the elements with which tungsten occurs in common minerals (Ca, Fe, and Mn) or elements in the same or adjacent column on the periodic table (i.e. can substitute for each other ionically) and thereby usually occur in the same mineral veins (Cr, Mo, V) (Allaby, 2008). Specifically, Cr and V are normally found in granitic rock and Mo has been reported to co-occur with wolframite in Washington State (Hess, 1917; Salminen, 2018). The data collected in this study on the cooccurrence of tungsten with other elements allowed us to develop a statistical model for tungsten concentration in Washington State surface waterbodies. Using Cr, Mo, and V to estimate tungsten concentrations resulted in a poor model describing only 4% of the observed variance (R2 ¼ 0.04, p ¼ 0.1087). Similar results with Ca, Fe, and Mn (R2 ¼ 0.14, p ¼ 0.0029) indicated the need for a new model. Utilizing Akaike’s Information Criterion resulted in a

multiple linear regression model with Ba, Be, Cu, V, and Zn as independent variables (R2 ¼ 0.70, p < 0.001). The elements selected for this initial model were compared to USGS records which reported that tungsten usually co-occurs with Be, Cu, V, and Zndindicating that the initial inclusion of Ba could be a statistical artifact (USGS, 2011). Further analysis of the components of this model indicated that Be and Cu independently described the most variance with R2 ¼ 0.31 and 0.41, respectively. Having Be and Cu as good predictors of tungsten is reasonable as both have been shown to co-occur with tungsten in concentrated mineral veins (Jahns and Glass, 1944; Jung, 2008). The derived model equation ½log10 ðW184Þ ¼ 1:8915 þ2759:9567 *Be þ19:78486 *Cu uses mg L1 inputs for [Be] and [Cu] then outputs mg L1 for tungsten concentration. This model with just Be and Cu described 65% of the variance in tungsten concentration data (R2 ¼ 0.65, p < 0.001) and achieved a NSE of 0.18, with NSE rising to 0.64 after removing the highest measured tungsten concentration value [Fig. 4]. These data in Fig. 4 cluster into three groups. The first group at the bottom left of the plot is comprised of exceedingly low concentrations of tungsten detected in both the Tungsten and No Tungsten sites. These samples were all very close to the detection limit of 10 ng L1 of tungsten. The second group is comprised of samples where the concentration of tungsten was well above the detection limit, but not necessarily above what can be considered the background level of tungsten for the state of Washington. The third grouping is the higher tungsten concentrations detected indicating a significant presence of tungsten in those samples. Except for the highest detected sample, these higher concentrations are reasonably predicted using the derived model. Presenting the sample data spatially [Fig. 5] gives an indication of the distribution of tungsten concentration in the surface waters of Washington State. By using the USGS WQN data as inputs for the BeeCu model, we can predict tungsten concentration in surface waters at locations not covered by our sampling efforts [Fig. 5].

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Fig. 4. Surface water tungsten concentrations compared to concentrations predicted by the BeeCu model.

Fig. 5. Tungsten concentrations in mg L1 of sites sampled in this study (circles) and predicted concentrations using the derived BeeCu model and WQN data (triangles).

These tungsten predictions indicate the possibility of hotspots across the state, such as those near Arial, WA (Southwest corner of map; 0.45 mg L1) and nearby Mallot, WA (Northern-central section of map; 0.32 mg L1), though these values are not particularly high when compared to the Soviet Union safety standards.

4. Discussion 4.1. Presence of tungsten The range of tungsten concentrations we found was quite low and always far less than what is considered harmful to humans (approximately 50 mg L1; Strigul et al., 2009). Average tungsten

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concentration for the “No Tungsten” sites, 68.3 ng L1 (SD: 44 ng L1), are consistent with those reported by other studies, which have reported mean or background concentrations of tungsten at 45.6 ng L1 and 100 ng L1 in a Texas aquifer (Johannesson et al., 2013) and in Australia, respectively (Pyatt and Pyatt, 2004). The average tungsten detected in water for watersheds containing tungsten-bearing rock types was higher than these baselines, reaching 217 ng L1. Even though compared to the “No Tungsten” sites, tungsten concentrations are significantly higher in the areas with known tungsten deposits and tungstenbearing rock types, the highest concentrations we observed are still much lower than some of the highest reported concentrations for tungsten-contaminated sites identified around the world. For example, contaminated groundwater in India registered an average of 634 ng L1 (Mohajerin et al., 2014); ground water in Fallon, Nevada had a median concentration of 13.4 mg L1 (Seiler et al., 2005); tungsten concentrations in a contaminated lake in an abandoned mine in Montana ranged from 14.1 to 25.6 mg L1 (Gammons et al., 2013); and the average for drinking water throughout Nevada is 30 mg L1 (Walker and Fosbury, 2009). Although the average concentrations were higher in “Tungsten” than in “No Tungsten” sites, there was a significant overlap in concentrations between the two types of watersheds (as indicated with the comparison of medians) meaning that even with a tungsten-rich lithology, there may not be a high concentration of tungsten in the water at locations with tungsten-rich lithology. The presence of tungsten in the underlying rock types is only an indicator as to the possibility of tungsten being present in surface waters and does not definitively indicate that it will be present in surface waters in every tungsten-rich location. Experimentally derived maximum concentrations of tungsten (from scheelite) are 2.98 mg L1 and 4 mg L1 for 0.01 m NaCl Solution and distilled water, respectively (Atademir et al., 1979; Montgomery, 2013). This indicates that there is a far greater potential for tungsten to dissolve in water than was observed during this study, indicating that other factors are preventing tungsten from dissolving at the experimentally derived maximum concentrations by many orders of magnitude.

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sediment tungsten content and water tungsten concentrations, but this could be due to the variability in sediment composition within each sample site. Additionally, there was much overlap between the concentrations of tungsten detected in sediments at “Tungsten” and “No Tungsten” sites (as indicated in the comparison of medians), indicating that some of the sites with tungsten-bearing rock-types have tungsten levels near the calculated background level for the state of Washington. This result indicates that the presence of underlying tungsten-bearing rock types does not necessarily result in high tungsten concentrations in the overlaying sediments or surface waters, only that it is possible for tungsten concentrations to be elevated in those locations. Our calculated background level for tungsten in sediments for Washington State (0.352 mg kg1) is consistent with the background level range of 0e2 mg kg1 reported in Iowa, but is low compared to ranges published by the European Union of 0.5e83 mg kg1 (Koutsospyros et al., 2006). The presence of tungsten in underlying rock types predicts the presence of tungsten in the water, but the lack of correlation between water and sediment tungsten concentrations indicate that there are additional factors controlling the amount of tungsten dissolving into water from these natural tungsten deposits.

4.4. Implications for health and water quality The US EPA does not regulate tungsten concentrations under either the Clean Water Act or the Safe Drinking Water act. In fact, there are very few regulations worldwide which dictate safe levels of tungsten exposure through water, none of which are recognized by the United States (US DHHS 2005). Before its collapse, the Soviet Union did have regulations limiting exposure to tungsten in drinking water at 50 mg L1, surface reservoirs at 50 mg L1, and fisheries at 0.8e1.1 mg L1 (Strigul et al., 2009). Using these limits as guidelines, no samples taken during this study would be classified as “undrinkable,” but three out of 77 would be excluded for use as fishing reservoirs.

4.2. Significance of pH on tungsten concentration 4.5. Modeling Based on prior work (e.g. Atademir et al., 1979), we expected pH to greatly affect the amount of tungsten dissolved in surface waters; however, pH was not significantly correlated with tungsten concentration in this study. The pH range in this study was 6.6e8.5 which was consistent with the state-wide soil pH range measured by the USDA of 5.5e8.5 (USDA, 2017). The lack of correlation between pH and tungsten concentration in our study was surprising as pH plays a crucial role in tungsten solubility in industrial processes, during deposit formation (Horner, 1979), and even predicted tungsten concentration in a costal aquifer in Maryland (Johannesson et al., 2013). It is possible that other environmental factors affected the water pH after the tungsten had dissolved. The tungsten could have dissolved when in contact with water, flowed away from the tungsten-bearing rock, and then mixed with other waterdthereby obscuring any link between concentration and pH. This is plausible considering that most of this water is expected to have some groundwater-surface water mixing occurring. The lack of correlation between pH and tungsten concentration in this study indicates that there are additional factors dictating tungsten concentrations in surface waters, an insight meriting further study. 4.3. Sediment link to water-tungsten concentration We did not observe any significant relationship between local

The BeeCu model derived during this study describes 65% of the variance observed in the sample data (R2 ¼ 0.65; Normalized Root Squared Mean Error ¼ 0.12). Be and Cu themselves range from below the limit of detection to 0.577 mg L1 and 3.87e59.6 mg L1, respectively. Although there is substantial uncertainty associated with model predictions, this model can be used to identify potential tungsten hotspots which may merit closer scrutiny or an additional sampling effort. Using data collected and stored in the USGS National Stream Water-Quality Monitoring Networks datasets to model tungsten concentrations, we did not identify any areas for concern in the Pacific Northwest. Modeling outside of the original sampling area is possible, keeping in mind the number of assumptions this would require concerning factors such as rock types, soil composition, and temperature (to name a few)dall of which may influence tungsten concentration in surface water (Bednar et al., 2009). Performing a sensitivity analysis on the model by varying the coefficients by 5% each indicated that the model is far more sensitive to small changes in Be concentration than it is to changes in Cu concentrations. This sensitivity is expected considering the model coefficients but should also be taken as a caveat for those looking to use this model to predict tungsten concentrations as the accuracy of a lab’s instruments may come into play when measuring such minute concentrations of Be from field samples.

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4.6. Future studies Future studies are required to flesh out the controls on tungsten concentrations. Previous research has indicated a strong link between pH and tungsten concentration and cannot be ruled out by the results of this study without further investigation into other factors potentially controlling pH. A future study might use geochemical tracers (e.g. deuterium and tritium) to estimate the fraction of sampled surface water originating as groundwater to see whether there is any link between tungsten concentration, pH, and percentage of water volume originating as groundwater (i.e. groundwater-surface water interactions). Additional research into the elements accompanying tungsten in surface water could also help to explain which elements could be used as proxies for tungsten concentrations and thereby improve the model. Incorporating additional variables to create a more comprehensive model (e.g. soil data and climate) might also improve accuracy in predicting tungsten concentrations. Expanding the study over multiple years and multiple times during different seasons would also improve the model by helping to account for differences in weather and natural variations in flow. Utilizing this tungsten prediction model along with human or environmental health data could provide valuable input to epidemiologists working to determine whether there are significant clusters of disease or environmental damage correlating with higher concentrations of tungsten. This work would help researchers to understand whether naturally occurring tungsten is contributing to human illness and potentially inform efforts to mitigate such impacts. Despite a clear need for additional research, this study represents an important first step in quantifying the distribution of naturally occurring tungsten in surface waters and provides some initial insights into the controls on tungsten’s distribution. 5. Conclusions Naturally occurring tungsten is present in surface water in the state of Washington, though generally in low concentrations, and is subject to some control by underlying geological conditions. The amounts detected were below the range currently identified as being harmful to humans and mostly below the range considered harmful to the environment, although these “safety” levels may change as our understanding of tungsten toxicity and environmental effects continues to develop. The tungsten concentration model presented here can be improved through follow-on research but currently can be used to identify likely tungsten hotspots that merit additional attention. Funding This work was supported through a seed grant from the State of Washington Water Research Center. Acknowledgements We would like to thank Jeff Vervoort for his expertise, guidance, and support throughout this research. References Agilent Technologies, 2016. Agilent ICP-MS 7800/7900 MassHunter Workstation User Guide (Version A). Agilent Technologies. Allaby, Michael, 2008. A Dictionary of Earth Sciences, third ed. Oxford University Press. Andrews, Mildred Gwin, 1955. Tungsten the Story of an Indispensable Metal. The Tungsten Institute.

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