Science of the Total Environment 622-623 (2018) 1668-1679
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Organic matter control on the distribution of arsenic in lake sediments
impacted by ~ 65 years of gold ore processing in subarctic Canada
Jennifer M. Galloway a '*, Graeme T. Swindles b , Heather E. Jamieson c , Michael Palmer d , Michael B. Parsons e ,
Hamed Sanei a , Andrew L. Macumber f * l , R. Timothy Patterson f , Hendrik Falck §
a Natural Resources Canada/Ressources n aturelles Canada Geological Survey of Canada/Commission geologique du Canada, 3303 33rd Street N.W., Calgary, Ab, T2L 2A7, Canada
b School of Geography, University of Leeds, Leeds, LS2 9JT, United Kingdom
c Department of Geological Sciences and Geological Engineering, Queen's University, Kingston, ON, KL7 3N6, Canada
d NWT Cumulative Impact Monitoring Program, Government of the Northwest Territories, Yellowknife, NT, XIA 2R3, Canada
e Natural Resources Canada/Ressources naturelles Canada Geological Survey of Canada/Commission geologique du Canada, I Challenger Drive, Dartmouth, NS, B2Y 4A2, Canada
1 Department of Earth Sciences, Carleton University, Ottawa, ON, K1S 5B6, Canada
g Northwest Territories Geological Survey, Yellowknife, NT, XIA 2R3, Canada
Controls on As in the hydrosphere of a
contaminated environment were stud¬
Distance and direction from a historic
mine control sedimentary As concentra¬
Organic matter mediates diagenesis of
anthropogenically-derived As in sedi¬
Climate change is expected to profound¬
ly affect biogeochemical cycling
Received 9June 2017
Received in revised form 2 October 2017
Accepted 6 October 2017
Available online 28 October 2017
Editor: F.M. Tack
Climate change is profoundly affecting seasonality, biological productivity, and hydrology in high northern lati¬
tudes. In sensitive subarctic environments exploitation of mineral resources led to contamination and it is not
known how cumulative effects of resource extraction and climate warming will impact ecosystems. Gold
mines near Yellowknife, Northwest Territories, subarctic Canada, operated from 1938 to 2004 and released >
20,0001 of arsenic trioxide (As 2 0 3 ) to the environment through stack emissions. This release resulted in elevated
arsenic concentrations in lake surface waters and sediments relative to Canadian drinking water standards and
guidelines for the protection of aquatic life. A meta-analytical approach is used to better understand controls
on As distribution in lake sediments within a 30-km radius of historic mineral processing activities. Arsenic con¬
centrations in the near-surface sediments range from 5 mg-kg” 1 to over 10,000 mg-kg” 1 (median 81 mg-kg” 1 ;
n = 105). Distance and direction from the historic roaster stack are significantly (p < 0.05) related to sedimentary
As concentration, with highest As concentrations in sediments within 11 km and lakes located downwind.
Synchrotron-based pXRF and pXRD confirm the persistence of As 2 0 3 in near surface sediments of two lakes. La¬
bile organic matter (SI) is significantly (p < 0.05) related to As and S concentrations in sediments and this rela¬
tionship is greatest in lakes within 11 km from the mine. These relations are interpreted to reflect labile organic
* Corresponding author.
E-mail address: Jennifer.Galfirstname.lastname@example.org (J.M. Galloway).
1 Current address: School of Natural and Built Environments, Queen's University, Belfast, BT7 INN, United Kingdom.
0048-9697/0 2017 Elsevier B.V. All rights reserved.
J.M. Calloway etal. / Science of the Total Environment 622-623 (2018) 1668-1679
matter acting as a substrate for microbial growth and mediation of authigenic precipitation of As-sulphides in
lakes close to the historic mine where As concentrations are highest. Continued climate warming is expected
to lead to increased biological productivity and changes in organic geochemistry of lake sediments that are likely
to play an important role in the mobility and fate of As in aquatic ecosystems.
© 2017 Elsevier B.V. All rights reserved.
Lakes and wetlands play an important role in the storage and mobi¬
lization of arsenic (As) (La Force et al„ 2000; Gurung et al„ 2005;
MacDonald et al., 2005; Du Laing et al„ 2009). The mobility and bioavail¬
ability of As in the environment is strongly controlled by Fe and Mn ox¬
ides and (oxy)hydroxides, sulphides, and organic matter (OM) (La Force
et al., 2000; Du Laing et al., 2009; Langner et al., 2012). Interactions be¬
tween As and these solid phases are in turn mediated by pH and redox
conditions (Smedley and Kinniburgh, 2002; Du Laing et al., 2009).
Redox conditions in lacustrine settings are influenced by basin mor¬
phometry, temperature, OM production and decomposition, and
microbial-mediated redox processes within the sediment column
(Toevs et al., 2006). Twentieth and twenty-first century global warming
has, and is predicted to, result in profound changes to the biogeochem¬
ical environment in high northern latitudes through changing hydrolo¬
gy, permafrost, and the length of the ice free season (MacDonald et al.,
2005; Spence et al., 2015). These changes may result in increased bio¬
logical productivity and OM transport to aquatic environments and in¬
fluence loading, cycling, and stability of metal(loids) (Schindler et al.,
1997; Hejzlar et al., 2003; Vonk et al., 2013). The complexity of potential
biogeochemical interactions warrants detailed evaluation of the inter¬
action between As and OM in lacustrine settings. Organic matter is a
heterogeneous mixture of organic compounds with varying structural
and functional properties that influence reactivity in natural environ¬
ments (Gu et al., 1995; Chen et al., 2002, 2003). These compounds are
redox reactive and can mediate the release and redox transformation
of solid-phase As(V) at depth in the sediment column to As(III), which
can diffuse upward to be released to overlying waters or re-precipitate
in oxic sediments (Lovley et al., 1996; Redman et al., 2002; van Geen
et al., 2004) and result in substantial surface sediment enrichment of
As (Martin and Pedersen, 2002). Interactions between As and OM also
include competitive adsorption (Grafe et al., 2001; Redman et al.,
2002), stabilization and physical coating of As-bearing colloids
(Neubauer et al., 2013), OM and dissolved OM-Fe complexation with
As (Langner et al„ 2012, 2014) and carbon-limited microbial-mediated
precipitation of As-bearing minerals (Kirk et al., 2004). Dissolved OM
(e.g., OM < 0.45 or 0.22 pm) plays a critical role in controlling As mobility
in soils (Kalbitz and Wennrich, 1998; Grafe et al., 2001; Redman et al.,
2002; Arai et al., 2006; Dobran and Zagury, 2006), aquifer sediments
(Lawson et al., 2016), and stream and wetland sediments (La Force
et al., 2000; Beauchemin and Kwong, 2006; Langner et al., 2012, 2014;
Al-Sid-Cheikh et al., 2015) but comparatively little is known about the
role of kerogen (sedimentary OM > 0.45 or 0.22 tun that is solvent-
insoluble; Durand, 1980) in element mobility in general (Langner
et al., 2012) and in lake sediments in particular (Sanei and Goodarzi,
The Yellowknife region in subarctic Northwest Territories, Canada,
contains geogenic As from hydrothermal gold mineralization in Yellow¬
knife Supergroup rocks and anthropogenic As from historic gold ore
processing activities that resulted in a release of over 20,0001 of arsenic
trioxide (As 2 0 3 ) to the environment (Suppl. 1; Hocking et al., 1978).
Historical release of As 2 0 3 caused elevated concentrations of As in lake
waters and sediments within -20 km of the largest historic mine in
the area relative to lakes outside of this range (Galloway et al„ 2015;
Palmer et al., 2015; Houben et al., 2016). To provide insight into the
physical and chemical parameters affecting the mobility of As and to
better understand the cumulative effects of past anthropogenic
activities and current and forecasted climate change possible physical
(distance and direction from historic mining activity, lake connectivity,
lake order, lake size) and chemical (organic matter, other elements)
controls on the distribution of As in lake sediments within a 30 km radi¬
us of a historic mine roaster stack are assessed.
2. Study area
The City of Yellowknife and surrounding area is located in the south¬
western Slave Geological Province, District of Mackenzie (Fig. 1). Eleva¬
tion in the region rises gradually from 157 m above sea level (MASL)
near Great Slave Lake to approximately 400 MASL north of 63° latitude.
The Yellowknife River is the main drainage for the area and its southern
outlet flows into Yellowknife Bay, Great Slave Lake. Many lakes east of
Yellowknife lie within the Cameron River-Prelude Lake watershed.
The study area lies south of the treeline and spans the Great Slave
Lake Lowland and Great Slave Lake Upland ecoregions of the Taiga
Shield Ecozone (Ecosystem Classification Group, 2007). The climate
has a mean summer temperature of 11 °C and a mean winter tempera¬
ture of — 21.5 °C (mean annual temperature ranges from — 3.5 to — 9
°C). Mean annual precipitation ranges between 200 and 375 mm. Vege¬
tation is composed of a mosaic of closed stands of trembling aspen, bal¬
sam poplar, paper birch, jack pine, and white and black spruce Poorly
drained fens and bogs are common and often covered with open stands
of larch and black spruce.
Detailed information on the main bedrock elements of the Slave
Geological Province and their structural evolution are summarized in
Villeneuve et al. (1997), Villeneuve and Relf (1998), Yamashita and
Creaser (1999), Yamashita et al. (1999), Bleeker and Davis (1999),
Cousens (2000), Kjarsgaard et al. (2002), and Cousens et al. (2002).
Major gold deposits of the area are hosted in Yellowknife Supergroup
rocks dominated by 2.71-2.65 Ga mafic meta-volcanics that trend
north-south. East of the City of Yellowknife Archean meta¬
sedimentary rocks dominate and consist of greywacke, slate, schist,
and phyllite. West of Yellowknife, granitoid intrusions, consisting of
granite, granodiorite, and tonalite, compose the majority of the bedrock.
The region is crosscut by early Proterozoic diabase and gabbro dykes
and several major faults, such as the Kam Lake Fault and the West Bay
Fault that run through the City of Yellowknife, separating the volcanic
rocks from younger granitoids (Yamashita and Creaser, 1999;
Yamashita et al., 1999; Cousens, 2000; Cousens et al., 2002). Arsenic
concentrations in local bedrock are comparable to global crustal aver¬
ages for granitoid, meta-sedimentary, and basic and ultrabasic igneous
rocks (Turekian and Wedepohl, 1961; Koljonen, 1992; Smedley and
Kinniburgh, 2002); ranging from -2 mg-kg^ 1 for granitoids to
33 mg-kg -1 in meta-volcanics and up to 90 mg-kg^ 1 in mineralized
rocks (Boyle, 1960; Yamashita and Creaser, 1999; Yamashita et al.,
1999; Cousens, 2000; Cousens et al., 2002; Ootes, 2004; Ootes et al.,
2006; Kerr and Wilson, 2000). The surficial geology of the Yellowknife
region is dominated by a mosaic of Glacial Lake McConnell sediments
and glacial tills that infill the topographic lows of the abundant bedrock
outcrops (Dyke and Prest, 1987; Smith, 1994; Kerr and Wilson, 2000;
Wolfe et al., 2014). Accumulations of Holocene-aged peat also occur in
the study area (Kerr and Wilson, 2000). Tills in the Yellowknife region
can contain As concentrations up to 1560 mg-kg^ 1 within in situ
weathered material over mineralized zones, although typically As con¬
centrations are between 5 and 30 mg-kg -1 (Kerr, 2006). The As
J.M. Calloway et at. / Science of the Total Environment 622-623 (2018) 1668-1679
concentrations in glaciofluvial, glaciolacustrine, and peat deposits in the
region are not published.
To assess the spatial distribution of arsenic in near-surface lake sed¬
iments in the Yellowknife area 105 near-surface sediment samples were
collected from 100 lakes within a 30 km radius of Yellowknife (Fig. 1).
Sites were accessed during summer and fall between 2009 and 2014
by canoe and helicopter. To test the influence of physical and hydrolog¬
ical properties of the lakes on near-surface sediment geochemistry,
sampled lakes span a range of sizes and connectivity (Suppl. 2). Lake
area and order were calculated using the digital 1:50,000 National To¬
pographic Database (NTDB) in ArcMap (v.10). Lake connectivity was
assessed using a combination of the 1:50,000 NTDB, imagery available
from Google Earth™, and field observations. Sixty-eight lakes are located
in catchments predominantly underlain by granitoid bedrock, the majority
of which belong to the Defeat Plutonic Suite undifferentiated granitoids that
are located W and SE of the City of Yellowknife. Twenty-nine lakes occur on
metasedimentary bedrock of the Burwash Formation that lies west of
Yellowknife, and 8 lakes occur on volcanic bedrock (Suppl. 2).
Near-surface sediment samples were collected using an Ekman Grab
sampler. The top 2 to 5 cm of sediment was sub-sampled for analyses.
Samples were kept cool in the field and during shipping to Carleton Uni¬
versity where they were kept cold at 4 °C until analyses. Surface water
chemistry of 98 of the lakes sampled are published in Palmer et al.
3.1. Sediment textural, organic, and elemental geochemical
Sedimentaiy grain size was determined using a Beckman Coulter LS
13320 laser diffraction particle size analyzer fitted with a universal liq¬
uid module and a measurement range between 0.37 and 2000 pm. Hy¬
drogen peroxide (30%) was added to sub-samples in an 80 °C water
bath to oxidize organic matter prior to analysis (Murray, 2002; van
J.M. Calloway etal. / Science of the Total Environment 622-623 (2018) 1668-1679
Hengstum et al., 2007). The samples were loaded into the instrument
until an obscuration level of 10 ± 3% was attained. Summary statistics
were compiled using GRADISTAT (Version 8; Blott and Pye, 2001).
Two reference materials were used: an accuracy standard provided by
Beckman Coulter (Garnetl5: mean diameter 15 pm) run once per
month and an in-house mud sample (Cushendun Mud) as a precision
control run at the beginning of every session.
Rock-Eval® 6 pyrolysis was used to analyze organic constituents of
the sediments (Vinci Technologies, Rueil-Malmaison, France; Lafargue
et al., 1998). The Rock-Eval® 6 instrument pyrolyses organic matter
under an inert (N 2 ) atmosphere and oxidizes organic matter by pro¬
grammed temperature heating of bulk sediments (-20 mg; heating
rate of 25 °C/min). Rock-Eval® 6 pyrolysis measures the quantity of la¬
bile, readily degradable hydrocarbon devolatilized at 300 °C (SI, mg hy-
drocarbon/g), the hydrogen-rich, higher molecular weight kerogen-
derived hydrocarbon released by thermal cracking of organic matter
at 650 °C (S2, mg hydrocarbon/g), the amount of carbon dioxide re¬
leased during pyrolysis of kerogen (S3, mg hydrocarbon/g), and refrac¬
tory, residual carbon (RC wt%) measured by automated transferal to an
oxidation oven and heated from 400 °C to 850 °C. Total Organic Carbon
(TOC; wt%) represents the quantity of all organic matter released during
pyrolysis and oxidation heating. SI, S2, and S3 were converted to weight
% by multiplying by 0.083 (Sanei and Goodarzi, 2006). Analyses of stan¬
dard reference materials (IFP 160000, Institut Franqais du Petrole and
internal 9107 shale standard, Geological Survey of Canada, Calgary;
Ardakani et al., 2016) was run every 5th sample and shows accuracy
and precision to be better than 5% relative standard deviation.
In near-surface sediments, the SI fraction mainly consists of readily
degradable geolipids and pigments predominantly derived from au¬
tochthonous OM (e.g., algal-derived lipids; Carrie et al., 2012). Opera¬
tional definition of organic lipids is the fraction of organic matter
isolated from biological material by extraction with organic solvents
(Meyers and Ishiwatari, 1993). Geolipids are diagenetically derived
from biological lipids that undergo degradative alteration as the algae
sinks to the bottom of lakes and after sedimentation when molecular
composition is modified to various degrees depending on the composi¬
tion of the parent lipid (Meyers and Ishiwatari, 1993). S2 compounds in
near-surface sediment are derived from the highly aliphatic
biomacromolecule structure of algal cell walls and other aquatic biolog¬
ical matter (Sanei et al., 2005; Carrie et al., 2012). The S3 portion of or¬
ganic matter is dominated by carbohydrates, lignins, and terrigenous
plant materials (Carrie et al., 2012). Humic and fulvic acids are also rep¬
resented in the S3 fraction (Albrecht et al., 2015).
Sediment sub-samples were submitted to Acme Analytical Laborato¬
ries (Bureau Veritas), Vancouver, for geochemical analyses. Sub¬
samples were freeze dried and screened to <180 pm ( — 80 mesh
ASTM) at the laboratory. Concentrations of elements in sediment sam¬
ples were determined by inductively coupled plasma-mass spectrome¬
try (ICP-MS 1F/AQ250 package) following digestion by a modified aqua
regia treatment (0.50 g of sample digested in a solution of 2.0 mL HC1,
2.0 mL HN0 3 and 2.0 mL H 2 0 at 95 °C for one hour) with the exception
of phosphorus, which was extracted using NaHC0 3 . Partial digestion
with aqua regia was used to extract metal(loid)s that could become bio-
available and because complete digestion methods that involve high-
temperature fuming can volatilize As and Sb, both contaminants of po¬
tential concern in this study (Parsons et al., 2012). Three pulp duplicates
were analyzed to assess analytical precision. Relative Percent Difference
(RPD) ranges from 1.5% to 4.3% for As. Standard reference materials
(STD OREAS45EA n = 11; STD D10 n = 2; STD DS9 n = 9) were used
assess analytical accuracy. For STD OREAS45EA mean measured As con¬
centration is 9.7 mg-kg -1 ± 1.16 (n = 11) vs. an expected concentra¬
tion of 10.3 mg-kg -1 for As following aqua regia digestion. Mean RPD
between As concentrations measured in STD OREAS45EA vs. the expect¬
ed value is 6.9% ± 11.9. STD DS10 had a mean measured As concentra¬
tion of 45.6 mg-kg _1 ± 0.1 (n = 2) vs. an expected concentration of
46.2 mg-kg -1 (mean RPD of 1.3% ± 0.3). STD DS9 had a mean
measured As concentration of 27.4 mg-kg -1 ± 1.42 (n = 9)
vs. an expected concentration of 25.5 mg-kg -1 (mean RPD of 7.8%
± 4.0). Eleven laboratory methods blanks were analyzed. Arsenic is
undetectable (<0.1 mg-kg~') in n = 9 laboratory blanks. Two
blanks had measured concentrations of As of 0.2 mg-kg -1 and
0.1 mg-kg -1 .
3.2. Arsenic mineralogy
Several mineral forms of As are expected to be present in near¬
surface lake sediments of the Yellowknife area. These are arsenopyrite
(FeAsS) containing up to 46 wt% As, arsenic sulphides (e.g., realgar
(As 4 S 4 ) and arsenian pyrite (FeS 2 )) that contain up to 70 wt% As, and
iron oxyhydroxides (e.g., goethite, ferrihydrite) containing up to 4 wt%
As (Walker et al., 2005). These minerals are geogenic or authigenic in
origin. Iron oxides (hematite, magnetite, maghemite) containing up to
7 wt% As (Schuh et al., 2017) and As 2 0 3 containing up to 76 wt% As
are anthropogenic in origin and emitted directly from the roaster
stack (Bromstad et al., 2017). Arsenopyrite in sediments of lakes away
from tailings and waste rock is expected to be geogenic and unrelated
to mining and mineral processing. The iron oxyhydroxides, realgar,
and some pyrite, particularly framboidal pyrite, likely form in situ in
sediments and can be therefore described as authigenic although the
As, and possibly S, may originate from the deposition of stack emissions
of As 2 0 3 and SO x (Schuh et al., 2017).
Near-surface lake sediment samples (L14S3, L19S2, BC-02, BC-13,
BC-17, BC-19, BC-32, BC-47) within -20 km of the historic Giant Mine
roaster were selected based on total As concentration (>100 mgkg -1 )
for identification of mineral forms of As using Scanning Electron Micros¬
copy (SEM) (Gallowayetal.,2012,2015; Howell,2014; Fig. 1; Suppl.2).
Three additional near-surface lake sediment samples were analyzed as
controls; one from a lake 15.6 km west of the historic Giant Mine roaster
(L16S3; 62.6905°N, — 114.6642°W) and two from lakes located distal to
Giant Mine along the Tibbitt to Contwoyto Winter Road (R11-14-11,
65.0642°N, — 109.9141°W, -372.4 km NE of the historic roaster; Rll-
15-05, 63.1354°N, -113.2303°W, -109.5 km NE of the historic roaster;
Macumber et al., 2011; Galloway et al., 2012, 2015).
Sediment sub-samples were dried and doubly-polished thin sec¬
tions, 35-50 pm thick, were prepared by Vancouver Petrographies. Sam¬
ples were designed to be “liftable” so that synchrotron-based pXRD
would be possible. Two samples with high As concentrations (BC-13
and BC-17; 740.7 ppm and 4778.2 ppm, respectively, Suppl. 2) and
one sample with a lower concentration of As (L16S3; 155 pm by ICP-
OES and aqua regia digestion; Galloway et al., 2012) were carbon coated
for Mineral Liberation Analysis (MLA). Mineral Liberation Analysis al¬
lows for automated scanning of thousands of particles to more efficient¬
ly locate and analyze rare As-bearing minerals (Sylvester, 2012; Van
Den Berghe, 2016). Thin sections were examined using the MLA 650
FEG ESEM (Environmental Scanning Electron Microscope) at Queen's
University, Kingston, Ontario, to observe As-bearing minerals. Samples
were analyzed using a voltage of 25 kV, chamber pressure of 0.6 Torr,
and a spot size of 5.00-5.78 pm. Operating conditions used during
MLA analysis were set to 25 kV for the accelerating voltage and 5.78
pm for the spot size. Mineral Liberation Analysis (MLA) was used to lo¬
cate rare As-oxide phases in two of the samples (BC-13, BC-17; Howell,
Samples BC-13 and BC-32 were selected for synchrotron-based mi¬
croanalysis due to the presence of As-oxide in BC-13 as determined
using MLA, and because of relatively high As concentrations in sample
BC-32 (955.1 ppm; Suppl. 2). The thin sections used for synchrotron-
based microanalysis were soaked in HPLC-grade acetone to dissolve
the cyanoacrylate holding the polished section to the glass slide. Once
detached, the polished sections were placed on polyimide (Kapton)
tape. Synchrotron-based pXRF and pXRD were performed at the x 26-
A beamline at the National Synchrotron Light Source, Brookhaven Na¬
tional Laboratories, New York. A beam energy of 13.5 KeV was used
J.M. Calloway et al. / Science of the Total Environment 622-623 (2018) 1668-1679
for pXRF to excite elements of interest (K- and L-edge emissions). Beam
spot size was approximately 6x9 pm. pXRF maps were produced with a
step (pixel) size of 3 to 7 pm and a dwell time of 0.1 s/pixel. pXRD anal¬
yses were done at 17.479 KeV to enable a suitable 2-theta range to iden¬
tify most minerals. Background diffraction patterns from analyses of the
polyimide tape were subtracted, significant bright spots from macro¬
crystallinity were masked out, and the final 2-D diffraction pattern of
the targeted minerals was integrated and converted to 1-D spectra
using the computer program Fit2D™ (Hammersley, 2004). The spectra
were then compared to mineralogical phases using the peak-matching
software X-Pert HighScore Plus (PANalytical). Five As oxide grains locat¬
ed in BC-13 and BC-32 were analyzed using synchrotron-based pXRF to
produce an elemental map to identify targets for pXRD. Two grains, 1
from each sample, were suitable for synchrotron-based pXRD
3.3. Statistical analyses
Elements with concentration below detection in 35% or more of the
samples were removed from statistical analyses (B,Te, Ge, In, Re, Pd, Pt).
One half of the method detection limit (MDL) was used for element con¬
centrations below the MDL (W, Hg, Se, Hf, Sn had 5, 2, 2,16, and 10%
non-detects, respectively). While substituting Vi of the MDL for non-
detects can result in loss of information (e.g., Helsel, 2006), this effect
is minimized if the proportion of non-detects is low (e.g., 10-15%;
e.g., Lubin et al„ 2004) and is thus a commonly used method
(e.g., RCRA, 1992, 2002). Where element concentration exceeded
MDL, we used the upper MDL in statistical analyses. This case only oc¬
curred for As in sample BC-19 (As MDL = 10,000 mg-kg -1 ).
Statistical analyses are conducted on raw data. Geochemical data are
not normalized because grain size variation is low (e.g., CV si i t = 7.87%;
Reimann and de Caritat, 2005) and is not related to As concentration
(e.g., clay; Suppl. 3).
Principal Components Analysis was used to explore the chemical
and ordinal dataset following log-transformation of numerical data. Po¬
tential control variables (grain size, Rock Eval pyrolysis parameters, lake
area, and distance from the historic roaster) were fitted to the solution
post-hoc using the Envfit procedure with 999 permutations. Permuta-
tional Multivariate Analysis of Variance (PERMANOVA) was used to
test the homogeneity of multivariate dispersions within groups and
thus evaluate which possible controls are important for explaining dif¬
ferences in the multivariate dataset. Samples were tested for normality
using the Anderson-Darling normality test alongside plotting on a nor¬
mal probability plot. Arsenic concentrations are highly non-normally
distributed. Spearman's rank correlation analysis was used to explore
the relationship between sedimentary As concentration and other vari¬
ables. Distance from the historic mine has one of the strongest relation¬
ships with sedimentary As concentration (r s = —0.57, p < 0.05, n =
105) and was further evaluated using log-transformed linear regression
modelling. To remove the influence of distance and explore the relation¬
ship of the other variables with As concentration, two sub-populations
of samples were determined using distance-constrained paired group
hierarchical cluster analysis based on sedimentary As concentration.
The two sub-populations, those within 11 km from the historic roaster
stack and those beyond this distance have non-identical As concentra¬
tions (Kruskal-Wallis test H = 7.29, p < 0.05, n = 105). Spearman's
rank correlation analysis was again performed on the two sub¬
populations to explore the relationship of chemical and other ordinal
variables with sedimentary As concentration. Direction from the histor¬
ic roaster stack (circular data) cannot be analyzed by standard statistical
methods. These data were binned into eight categories (0-45, 46-90,
91-135,136-180,181-225, 226-270, 271-315, 316-360°). Median As
concentrations in each category were compared using the Kruskal-
Wallis test and box plots. All analyses were performed in R v.3.1.2 (R
Core Team, 2014) and PAST v. 3.11 (Hammer et al., 2001). The vegan
package in R was also used for multivariate analysis (Oksanen et al.,
The area of each of the 100 lakes sampled ranges between 0.3 and
3561.0 ha (median 30.3 ha, n = 105). Median sample site distance
from the historic Giant Mine roaster stack is 10.3 km (range 1.0 to
31.4 km, n = 105). Surface waters are alkaline (median pH = 7.9,
range 6.6-9.0, n = 104) and well-oxygenated at the time of sampling
(median dissolved oxygen surface 11.2 mg/L, range 1.7-14.2 mg/L,
n = 103). Only one site had surface water oxygen <3.0 mg/L. Bottom
waters range from dysoxic to oxic (median dissolved oxygen
10.4 mg/L, range 0.1-13.9 mg/L, n = 73) and seven lakes are dysoxic
(bottom water oxygen <3.0 mg/L) during the open water season.
Surface water conductivity ranges from 31.3-626.0 pS/cm (median
124.8 pS/cm, n = 103) and bottom water conductivity ranges from
31.3-626.0 pS/cm (median 91.1 pS/cm, n = 73). Median water depth
at sampling locations was 1.6 m (range 0.3-13.3 m, n = 102; Suppl. 2).
4.1. Sediment characteristics
Lake sediment samples are dominated by silt sized particles (<63
pm; median 74.77%, range 4.92% to 90.32%, n = 105). Median clay (<4
pm) content of samples is 13.13% (range 1.40% to 35.55%) and median
sand (>63 pm) content of samples is 9.98% (range 0.00% to 93.68%)
The samples have total organic carbon (TOC) content typical of lake
sediments (median24.86%,rangel.l5%to33.39%,n = 105). The major¬
ity of organic matter in sediment samples is S2 kerogen (median
7.38 wt%, range 0.20-11.26 wt%). S3 kerogen ranges from 0.17-
4.68 wt% (median 2.91 wt%) and SI ranges from 0.03-5.52 wt% (median
2.33 wt%) (Suppl. 2).
4.2. Arsenic concentration
Arsenic concentration in the lake sediment samples is highly var¬
iable, ranging from 5.0 mg-kg -1 to >10,000 mg-kg -1 (median
81.2 mg-kg -1 , n = 105; Suppl. 2). Median As concentration in the
sediments is above the Canadian Council of the Ministers of the Envi¬
ronment (CCME) Probable Effects Level (PEL) of 17 mg-kg -1 (CCME,
2002) and regional background concentrations of -25 mg-kg -1
for As in lake sediments of the Yellowknife area (Galloway et al.,
4.3. Assessing controls on the distribution of arsenic in lake sediments
Principal Components Analysis reveals an association of As with
both Au and Sb in the lake sediments (Fig. 2). PERMANOVA analysis
shows that the lithology of the catchment bedrock is important for
explaining differences in the overall multivariate chemical dataset (p <
The relationship of As to other elements, bedrock type, sedimentaiy
particle size, organic matter, and physical characteristics (e.g., lake area,
connectivity) was explored using Spearman's Rank correlation analysis
to determine the association and potential influence of these variables
on the concentration of As in the lake sediments. In order of decreasing
importance, these are SI, bedrock type, S3, S2, silt, and TOC (p < 0.05, n
= 105; Suppl. 3). Arsenic is highly positively (r s > 0.50) and significantly
(p < 0.05) correlated to other elements enriched in the ore mined at
Giant Mine, including Sb, Au, Cd, Mo, and S. The relationship between
As and all of the other ordinal variables, including lake order, hydrology,
area, connectivity, and Strahler stream order and catchment type are
non-significant (Suppl. 3).
Ordinary least squares regression on log-transformed data was used
to model the relationship between the concentrations of sedimentaiy
J.M. Galloway et al. / Science of the Total Environment 622-623 (2018) 1668-1679
Bedrock type PC 1 (Eigenvalue 7.49)
Defeat Plutonic Suite undifferentiated
Plutonic Suite undifferentiated
Defeat Plutonic Suite with inclusions of
Yellowknife Supergroup rocks
+ Burwash Fm, medium metamorphic grade
O Burwash Fm, low metamorphic grade
V Jackson Lake Fm
O Ingraham Fm
O Townsite Fm
□ Felsic volcanic rocks
□ Crestaurum Fm
O Chan Fm
Fig. 2. Principal Components Analysis of log-transformed data. Potential control variables (grain size, Rock Eval parameters, lake area, and distance from the historic roaster) were fitted to
the solution post-hoc using the Envfit procedure with 999 permutations.
As and distance from the historic Giant Mine roaster and SI, the two
non-element geochemical variables with the highest relationship to
sedimentary As concentration, for all lakes. Sedimentary As
concentration is significantly negatively related to distance from the
historic mine (r 2 = 0.35, p < 0.001, n = 105) and positively related to
SI (r 2 = 0.25, p < 0.001, n = 105; Suppl. 3, 4).
J.M. Calloway et al. / Science of the Total Environment 622-623 (2018) 1668-1679
Sedimentary As concentrations decline with increasing distance
from the historic mine (Suppl. 4). To remove the influence of distance
on sedimentary As concentration and explore other relationships,
distance-constrained paired group hierarchical cluster analysis was
used to delineate two sub-populations of lakes based on sedimentary
As concentration (Suppl. 5). We selected 11 km as a cut-off based on
cluster analysis results and sample size consideration in sub¬
populations for further statistical analyses. Arsenic concentrations of
sediment samples from lakes within 11 km of the historic mine are sig¬
nificantly greater (median 160.5 mg kg -1 ,5.0-10,000 mgkg -1 , n = 54)
than those in samples from lakes beyond this distance (39.6 mgkg -1 ,
5.0-5.2 mgkg -1 , n = 51; Kruskal-Wallis test H = 7.29, p < 0.05, n =
105; Fig. 3).
Spearman rank correlation analysis on the two sub-populations
show that similar to the whole dataset, Au and Sb remain correlated
(p < 0.05) to As concentration in sediments from lakes within 11 km
from the historic roaster and in lakes beyond this distance. SI and As
are also significantly (p < 0.05) correlated in both sub-populations but
the relationship is strongest in the within 11 km sub-population (r s =
0.71 vs. r s = 0.38; Suppl. 3).
Direction from the historic roaster also appears to be a control on
sedimentary As concentrations because there is a significant difference
between category medians (Kruskal-Wallis H = 42.78; p < 0.05, n =
105, 8 groups). Median As concentrations are higher in sediments of
lakes to the N and NW of the historic roaster (Fig. 4).
4.4.1. Scanning electron microscopy and mineral liberation analysis (SEM-
Iron-oxides, As-sulphides, As-oxides, rare arsenopyrite (FeAsS), and
pyrite (FeS 2 ) were observed and identified using SEM and MLA analysis
of sediments. Fe-oxides were observed in many of the samples and
were common in samples R11-14-11 and BC-2, where Fe-oxides ap¬
peared to be Fe-Mn-oxides and did not exhibit the texture associated
with roaster-generated Fe oxides. Pyrite was present in every sample
except R11-14-11 and was particularly abundant in samples and
L19S2, BC-32, and BC-47. Where present, pyrite was often framboidal
and As was present in trace amounts. SEM-MLA was used to identify ar¬
senopyrite, As-sulphides, and traces of As-oxides with a distinct spongy
texture in BC-13 and BC-17.
Lakes < 11 km
Lakes > 11 km
Fig. 3. Box and whisker plot of sedimentary As concentration in samples from lakes within
11 km from the historic roaster and lakes beyond this distance.
0 >1 >5 • >12*>19• >28 »>38 >50»>61 km/h
Fig. 4. Top - wind rose diagram for the Yellowknife A climate station (62.46°N, 114.44°W
205.7 m asl) showing how many hours per year the wind blows in the indicated direction.
Data from 1970 to 2010 available at http://climate.weather.gc.ca/climate_normals/
results_e.html?stnID=1706; figure from https://www.meteoblue.com/en/weather/
forecast/modelclimate/yellowknife-airport_canada_6296340). Bottom - Box and
whisker plot of sedimentary log As concentration in samples from lakes at different
directions (degrees) from the historic roaster.
4.4.2. Synchrotron-based pXRF and pXRD
Five As-bearing grains in two selected samples (BC-17, BC-32) were
targeted for pXRF and pXRD analysis. Two grains (one from each sam¬
ple) could be reliably located on pXRF images and subsequently provid¬
ed adequate diffraction patterns for integration and identification. The
grain from sample BC-32, which was obtained from sediments of a
lake 9.2 km from the historic Giant Mine Roaster at 273° (NNW and
down-wind from the roaster), gave the clearest diffraction pattern
with the most distinct peaks (Suppl. 6). The mineral phase arsenolite
(As 2 0 3 ) provided the closest match to the sample's integrated diffrac¬
tion spectra. The As-oxide grain from sample BC-17 (3.2 km and 249°
(NW) from the historic Giant Mine roaster) had a less distinct pattern;
however, the main peaks still provided a close match to arsenolite.
A single As- and S-rich grain on the MLA map from sample BC-17
was selected for pXRD. Diffraction from this grain proved to be relatively
poor and there was difficulty in reliably matching the integrated spectra
to a known mineral phase. Peaks matching both realgar and arsenolite
suggest this may be a mixture.
J.M. Calloway etal. / Science of the Total Environment 622-623 (2018) 1668-1679
Basin bathymetry was not known for Yellowknife study lakes and
Zmax could not be targeted. As a result, As and other element concentra¬
tions of Yellowknife area lakes reported here may, if zones of erosion or
transportation (sensu Blais and Klaff, 1995) were sampled, be substan¬
tially lower than those in the zone of accumulation in the study lakes. A
lack of grain size variation (CV silt = 7.87%) and lack of relationship be¬
tween clay and As (p < 0.05; Suppl. 3) suggests that sediment size, ex¬
pected to be related to sample location, is not a dominant control on
As concentration in Yellowknife area lake sediment samples. Approxi¬
mately 86% of the As 2 0 3 released as stack emissions from Giant Mine oc¬
curred prior to 1963 (Wrye, 2008). Consequently, maximum As
concentration in some lake sediment profiles occurs below the
sediment-water interface in sediments dating to the late 1940's
(Schuh et al., 2017), but in other lakes maxima occur in younger sedi¬
ments (Andrade et ah, 2010) or sediments near the sediment-water in¬
terface (Schuh et ah, 2017) likely controlled by post-depositional
remobilization of arsenic via reductive dissolution and upward
5.1. Legacy mineral processing released arsenic to surrounding
Arsenic concentrations in the Yellowknife area lake sediment sam¬
ples are significantly negatively related to the distance from the historic
Giant Mine roaster (r s = — 0.57, p < 0.05, n = 105, Suppl. 3; ordinary
linear squares regression r = —0.60, r 2 = 0.35, p < 0.001, n = 105;
Suppl. 4). Palmer et ah (2015) show that the concentration of As in
Yellowknife area lake surface water within a 17.5 km radius of Giant
Mine and downwind from historic mining activity are elevated relative
to more distal lakes and upwind sites. Houben et ah (2016), in their
study of As concentration of surface waters of 25 small (median
2.9 ha) and shallow (median 1.2 m) lakes within a 25 km radius of
Giant Mine, also show that As concentrations in surface waters are
highest in lakes closest to the mine, a pattern they interpret to be the re¬
sult of relatively proximal deposition of atmospherically emitted roaster
stack combustion products. Roasting of gold ore associated with arseno-
pyrite released S0 2 along with metal(loid)s, including Sb, to the atmo¬
sphere (Hocking et ah, 1978; Hutchinson et ah, 1982). Stibnite (Sb 2 S 3 )
and Sb-bearing sulfosalts were present in the ore roasted at Giant
Mine, resulting in generation of a gaseous Sb-phase that was incorporat¬
ed in the structure of As 2 0 3 during its crystallization (Riveros et ah,
2000; Fawcett and Jamieson, 2011 ) and Sb oxide was the third largest
oxide concentration in baghouse dust collections from Giant Mine
(SRK, 2002). Antimony also declines with distance from the roaster
stack in Yellowknife area lake surface waters (Houben et ah, 2016). Sed¬
imentary Sb is highly correlated to As and Au in Yellowknife area lake
sediments (r s = 0.92 and r s = 0.84, respectively, p < 0.05, n = 105)
and declines with distance from the historic roaster stack (r s =
— 0.58, p < 0.05, n = 105; Suppl. 3). While these spatial observations
and high positive element correlations between As, Au, and Sb are sug¬
gestive of point source emission (e.g., Bonham-Carter, 2005; Houben
et ah, 2016), the Giant Mine is also located on mineralized bedrock ele¬
vated in these elements relative to average upper crustal composition
(As = 4.4-4.8 mg-kg _1 ; Au = 1.2—1.8 ng-g^ 1 ; Sb = 0.4 mg-kg -1 ;
Rudnick and Gao, 2004). This bedrock and locally derived surficial ma¬
terials represent a geogenic source of As and other elements to lake sed¬
iments. Our analysis show that bedrock formation is related to the As
concentration of lake sediments (r s = —0.35, p < 0.05, n = 105,
Suppl. 3; PERMANOVAp = 0.04; Fig. 2). The concentration of metal(-
loid)s associated with gold ore and its mineral processing, including
Au, Sb, and Hg are also significantly related to bedrock type (r s =
— 0.35, r s = — 0.48, respectively, p < 0.05, n = 105), with highest con¬
centrations in sediments of lakes occurring on granitoid bedrock, ex¬
pected to provide little geogenic input of these elements (Suppl. 3).
Sedimentary As concentrations are significantly related to direction
from the historic roaster (Fig. 4). Higher concentrations occur in sedi¬
ments of lakes to the N and NW underlain by granitoid bedrock where
prevailing winds would have dispersed emitted As 2 0 3 and other roaster
emissions (Figs. 1, 4; Galloway et ah, 2012). We therefore interpret
these element relations with bedrock to reflect emission from the his¬
toric roaster, transport to the NW with prevailing winds and airborne
deposition into these lakes and their watersheds (Galloway et ah,
2012). The meta-analysis ofHoubenet ah (2016) on a smaller number
of sample lakes show that while bedrock composition has an influence
on the As concentration of regional surface waters, geogenic sources
are not an important factor controlling elevated As in waters of lakes
near the mine.
To explore the hypothesis that mineral processing has influenced
lake sediment geochemistry further, SEM and MhA analyses of selected
sediment samples from lakes within 20 km of Giant Mine were used to
demonstrate the presence of As oxide in sediments of two of the five
lake sediment samples analyzed (BC-17, BC-32; Howell, 2014).
Synchrotron-based pXRF was used to target two As oxide grains in sed¬
iment samples from lakes BC-13 and BC-32 and pXRD was used to iden¬
tify the As oxide phases as arsenolite (As 2 0 3 ). These lakes are located
3.2 km and 9.2 km away from the Giant Mine historic roaster, respec¬
tively, and both are located downwind of the historic roaster and under¬
lain by granitoid bedrock (Suppl. 3). To our knowledge, arsenolite has
never been found to naturally occur in lake sediments; its presence
therefore provides convincing evidence that roasting of gold ore in the
Yellowknife region resulted in atmospheric dispersion of this mineral
to the landscape near the Giant Mine historic roaster stack. Previous
studies demonstrated the persistence of As 2 0 3 in the immediate envi¬
ronment surrounding the historic Giant Mine roaster in thin soils on
rocky outcrops (Bromstad et al„ 2017). Recent studies document
As 2 0 3 in the sediments of five other lakes within five km of the historic
roaster (BC-20, Handle Lake/YK-42, Lower Martin Lake/BC-15, Long
Lake, Martin Lake/BC-13; Van Den Berghe, 2016; Schuh et al., 2017).
5.2. Controls on sedimentary arsenic in Yellowknife area lakes
Several interrelated processes control As cycling in freshwater sedi¬
ments. Arsenic that enters surface waters as detrital minerals may be di¬
rectly deposited into lake sediments with little or no alteration of the
original As-bearing phases. The ore roasting product As 2 0 3 is present
in Yellowknife area lake sediments, indicating that deposition and pres¬
ervation of even this highly soluble mineral form is possible (Stavinga,
2014; Van Den Berghe, 2016; Schuh et al., 2017). In oxic and circum-
neutral settings, oxidation and dissolution of As-bearing sulphide min¬
erals may release As into waters where dissolved As(V) has a strong af¬
finity for mineral surfaces, particularly Fe/Mn(hydr)oxides, and may be
removed from solution through adsorption or co-precipitation (Bowell,
1994; Smedley and Kinniburgh, 2002). Arsenic sorbed to mineral sur¬
faces may then be accumulated in the sediments and this can be an ef¬
fective means of sequestration (Bowell, 1994; Smedley and Kinniburgh,
2002; Langner et al., 2013), so long as redox conditions remain consis¬
tent. In Yellowknife area lake sediments, As is negatively correlated to
Al (Suppl. 3) although the partial digestion method used makes this dif¬
ficult to interpret. Arsenic is non-significantly correlated to Mn, regard¬
less of distance from the historic mine, and displays a significant
relationship with Fe in samples from lakes beyond 11 km from the his¬
toric mine but not in those within 11 km, despite the fact that Fe and Mn
are significantly related to each other (Fig. 5). These relationships sug¬
gest that in lakes close to the historic roaster stack, Fe/Mn(hydr)oxide
sequestration of As is not a dominant process controlling elevated sed¬
imentary As concentration.
Using X-ray Absorption Near Edge Spectroscopy (XANES), Van Den
Berghe (2016) documents As(V) and As(III) associated with ferric ox¬
ides in the upper 4 cm of Handle Lake (YK-42), Lake BC-20, and Lower
Martin Lake (BC-15), but not as a major host of As. Most of the As is
J.M. Galloway et al. / Science of the Total Environment 622-623 (2018) 1668-1679
r s =-0.22, p<0.05, n=105
r =0.49, p<0.05, n=105
10 100 1000 10000
Concentration of Fe (mg kg')
1000 10000 100000
Concentration of S (mg kg ’)
Fig. 5. Scatterplots of selected variables. Note changes in scale. Spearman rank correlation coefficients from Suppl. 3.
hosted in As-sulphide minerals, and more As is hosted in As 2 0 3 than in
Fe oxides. Van Den Berghe (2016) hypothesizes that dissolution of
As 2 0 3 and reductive dissolution of Fe/Mn(hydr)oxides is releasing solu¬
ble As to porewaters, most of which diffuses upward in the sediment,
while the remaining As is authigenically reprecipitated as As-sulphide.
In Yellowknife study lakes, sediment As concentration is correlated
with S (r s = 0.49, p < 0.05, n = 105) but negatively correlated with Fe
(r s = — 0.22, p < 0.05, n = 105; Fig. 5), suggesting that formation of sec¬
ondary As-sulphide minerals is an important process throughout the re¬
gion. In deep water sediments from Long Lake enriched in As 2 0 3 , the
presence of As-bearing sulphides suggests that partial dissolution of
As 2 0 3 in the presence of reduced S has attenuated more bioaccessible
As 2 0 3 from stack emissions to a less accessible sulphide phase (Schuh
et al., 2017). Iron free As-sulphide is not associated with mineralization
(Coleman, 1957) or any tails at Giant (Walker et al., 2005; Fawcett and
Jamieson, 2011), and is therefore interpreted to be an authigenic amor¬
phous, realgar-like preciptitate (Schuh et al., 2017). Authigenic precipi¬
tation of As-bearing sulphides is likely to be mediated by OM through its
influence on pore water redox gradient and microbial activity. Precipita¬
tion of As-bearing sulphide minerals such as realgar, pararealgar, or or-
piment is often microbial-mediated (Newman et al., 1997; Smedley and
Kinniburgh, 2002; O'Day et al., 2004; Root et al., 2009; Drahota et al.,
2013). Organic carbon is a substrate for microbial growth (Campbell
and Nordstrom, 2014), and in particular, the labile geolipids that repre¬
sent the SI fraction of TOC, are readily biodegradable (Sanei et al.,
2005). Promotion of microbial-mediated authigenic precipitation of
As-sulphides by OM may explain the observed relationship between
the highly bioavailable and labile form of OM (SI) and the concentra¬
tion of As in Yellowknife area lake sediments (As:S r s = 0.55, p < 0.05,
n = 105; Fig. 5). SI and As are also both correlated to S (r s = 0.63, p <
0.05; r s = 0.49, p < 0.05, respectively, n = 105; Fig. 5).
In addition to promoting and mediating sulphide formation in sedi¬
ments, OM, and in particular the SI fraction, can also coat surface sedi¬
ment particles providing an organic substrate with a large surface area
for metal(loid)-OM complexation (Sanei et al., 2005; Campbell and
Nordstrom, 2014). Organic carbon is also capable of directly storing
adsorbed As (Sadiq, 1997; Wrye, 2008; Meunieret al., 2011). For exam¬
ple, As(lll) can be sequestered through passive complexation with sulf-
hydryl groups on OM that appear to occur under conditions unfavorable
for As-sulphide precipitation, such as where the quantity of dissolved S
was too low to support precipitation of As-sulphide minerals (Langner
et al., 2013). Breakdown of low molecular weight OM, such as sugars
(related to the SI fraction; Carrie et al., 2012), can release organic
acids that comprise a portion of dissolved OM (DOM; Martinez et al.,
2003). Dissolved OM can affect the mobility of As through direct com¬
plexation with aqueous As(IlI) and As(V) via positively charged amino
groups in DOM (Saada et al., 2003), metal cation bridges (Redman
et al., 2002), or through mediation of processes at mineral surfaces (pre¬
cipitation, dissolution, ad- and de-sorption). Dissolved OM (e.g., fulvic
and humic acids) can form stable complexes with mineral surfaces
that block As adsorption (Kaiser et al., 1997; Grafe et al„ 2001, 2002;
Bauer and Blodau, 2006; Dobran and Zagury, 2006). Organic anions
and DOM have been found to enhance As leaching from soil material
(Lin et al., 2002; Dobran and Zagury, 2006) where As is associated
with the metal oxide fraction (Lombi et al., 2000). Arsenic desorption
from Fe oxides in the presence of DOM (Redman et al., 2002; Bauer
and Blodau, 2006) and fulvic or humic acids (Grafe et al., 2001, 2002)
may also be microbial-mediated whereby DOM serves as a labile sub¬
strate for microbial growth (Harvey and Swartz, 2002; Mladenov
et al., 2009; Campbell and Nordstrom, 2014). Redox active functional
groups associated with DOM can also act as an electron shuttle between
micro-organisms and Fe and thus enhance microbial iron reduction and
release of sorbed As (Schwarzenbach et al., 1990; Lovley et al., 1996;
Mladenov et al., 2009).
The relationship between SI and As in Yellowknife area lake sedi¬
ments may reflect a complex set of mechanisms by which both solid
OM and DOM can influence As mobility, and are likely to become
more important under a warming climate with enhanced OM flux
from thawing permafrost (e.g., Vonk et al., 2013) among other mecha¬
nisms, resulting in potential for increased As concentrations in the
water column of Yellowknife area lakes over time. Additional research
(e.g., Carrie et al., 2012) is required to better characterize solid organic
matter fractions as determined by Rock-Eval pyrolysis to better under¬
stand the nature of SI and As interaction. Additional research character¬
izing bacterial assemblages and their metabolic activities would be key
for understanding OM and metal redox geochemistry in the lake
Lake sediment As concentrations are significantly related to distance
and direction from the former Giant Mine, with increased concentra¬
tions in lakes close to and downwind from the historic roaster. Ordina¬
tion shows that lakes with the highest concentration of As in sediments
occur on granitoid bedrock; a bedrock type containing average As con¬
centrations near 2 mg-kg -1 . We interpret this relationship to reflect
J.M. Calloway etal. / Science of the Total Environment 622-623 (2018) 1668-1679
aerial emission and transport direction of As predominantly to the NW
by winds and deposition in lakes and catchments located on granitoid
bedrock. Arsenic trioxide (As 2 0 3 ) is documented in the sediments of
two lakes studied using synchrotron-based pXRF and pXRD, providing
direct evidence of historic roaster impacts and persistence of this miner¬
al in lake sediments.
Labile organic matter (SI as determined by Rock Eval pyrolysis) is
significantly related to sedimentary As and S concentrations in Yellow¬
knife area lake sediments. SI may be a substrate for microbial growth
and mediate authigenic precipitation of As-sulphides. Other possibilities
include physical coating of particles by SI, creating a large and reactive
surface for As complexation, coating and encapsulation of pre-existing
solid-phase As; and, soluble organic anion competition with As for sorp¬
tion sites on mineral surfaces. Increased biological production, release of
OM from melting permafrost, and changes in transportation pathways
though changing hydrological regimes may thus lead to changes in As
biogeochemical cycling. The type and source of OM is an important con¬
sideration for characterization of the mobility and fate of As and other
Supplementary data to this article can be found online at https://doi.
This project was carried out with financial support from Polar
Knowledge Canada (Project# 1519-149 toJMG and RTP), Natural Sci¬
ences and Engineering Research Council (NSERC) of Canada (to RTP
RGPIN 41665-2012 and HEJ RGPIN-2016-03736 and a Visiting Fellow¬
ship in a Canadian Government Laboratory toJMG), the Cumulative Im¬
pact Monitoring Program of the Government of the Northwest
Territories (to MP CIMP Project# 151), Northwest Territories Geological
Survey, the Geological Survey of Canada (Environmental Geoscience
Program), Queen's University, and Carleton University. We are grateful
to Nawaf Nasser, Lisa Neville, Great Slave Helicopters, and the staff of
the Tibbitt to Contwotyo Winter Road for assistance in sample collec¬
tion. We are grateful to Douglas Lemay (CSC) for drafting assistance.
We thank Omid Ardakani for an internal CSC review of this manuscript
and we are thankful for the helpful comments of Martin Van Den Berghe
and Christopher Schuh. We are grateful for the helpful comments of an
anonymous reviewer and the Associate Editor. This contribution repre¬
sents NRCan Contribution Number 20170227.
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