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ASSESSMENT OF SEDIMENT QUALITY AND SOURCES
IN THE NORTH SASKATCHEWAN RIVER
AND ITS TRIBUTARIES
Prepared for
Alberta Environment and Water
March 23, 2012
by
Dr Micheal Stone
Department of Geography and Environmental Management
University of Waterloo, 200 University Avenue West,
Waterloo, Ontario
N2L 3G1
Dr Adrian Collins
Principal Scientist
Head of Water Quality Science
ADAS UK Ltd.
Environment Group
Woodthorne
Wergs Road
Wolverhampton WV6 8TQ
UK
i
Executive Summary
Alberta Environment has identified water quality in the North Saskatchewan River (NSR) as a
major issue to be addressed in the North Saskatchewan Regional Plan (NSRP). Knowledge of
the source, transport and fate of sediment-associated contaminants in this watershed is
fundamental to understanding and managing anthropogenic impacts on water quality and
related ecosystem services. This report presents the results of a sediment quality and source
assessment study conducted for Alberta Environment and Water to provide information
regarding the following two research questions;
1) What are the physical (grain size distribution), geochemical (mineralogy, major
element composition) and contaminant (trace metals, PAH) characteristics of
sediment in the North Saskatchewan River and its tributaries?
2) What are the key spatial sources of sediment in the NSR?
Summary of findings:
1. NSR and tributary sediments consist of varying concentrations of silicates (quartz),
feldspars (albite, microcline), micaceous phyllo-silicates (chlorite, muscovite), carbonates
(dolomite, calcite), clay minerals (smectite) and amorphous groups.
2. NSR sediments consist mainly of SiO2, Al2O3, CaO and Fe2O3. The relative proportions of
mineralogical properties and major element composition vary as a function of the regional
and local geology, predominant soil types and differenetial weathering rates in the
sediment source areas.
3. Chromium and nickel exceeded the consensus based threshold effect concentration (TEC)
by 28% and 20% of the NSR and tributary samples analyzed, respectively. Contrary to
the results of previous studies on the distribution of metals in the NSR metal which report
that metal concentrations increase downstream, no downstream increases in metal levels
were observed in the present study. Metal speciation data indicate that the majority of Cr
is bound to the largely non-bioavailable silicate phase and may represent a natural
geological source.
4. PAHs are present in the NSR sediment but at concentrations well below the consensus
based threshold effect condition for the congeners evaluated in this study. There was no
downstream increase in PAH levels. With the exception of samples at the Drayton Valley
Bridge and the Baptiste River near the mouth, the data suggests that PAHs are
predominantly of pyrolytic rather than petrogenic origin.
ii
5. Sediment pressures continue to represent cause for concern with respect to the ecological
vitality and amenity value of riverine systems, including those in Canada. Given that the
sources of fine-grained sediment are typically diffuse in nature, it is essential to adopt a
catchment-wide perspective to corresponding management strategies and sediment source
tracing procedures have proved useful in assisting such planning. Against this context,
the work in the NSR provided an opportunity for further application and testing of a
recently refined statistical procedure for sediment source discrimination with composite
fingerprints. The revised statistical verification of composite signatures was combined
with numerical mass balance modeling using recent refinements including a range of
tracer weightings, both local and GA optimization and diagnostic uncertainty analysis.
Comparison of the local and GA optimization outputs increased confidence in the latter
and the goodness-of-fit for the predicted spatial source contributions using the optimum
composite signatures selected from the revised statistical testing ranged from 0.95 – 0.97.
Overall relative frequency-weighted average median spatial source contributions were
estimated to be 11% (Vermilion River), 19% (Sturgeon River), 6% (Brazeau River), 12%
(Baptiste River), 11% (Nordegg River), 14% (Clearwater River), 15% (Ram River), 4%
(Bighorn River), 4% (Cline River) and 4% (Siffleur River). The study provides further
evidence of the utility of sediment tracing using composite geochemical signatures for
elucidating spatial sediment provenance in river systems.
Table of Content
EXECUTIVE SUMMARY ...................................................................................................... I
1. INTRODUCTION .......................................................................................................... 1
2. METHODS ...................................................................................................................... 2
2.1. STUDY APPROACH.................................................................................................... 2
2.2. STUDY AREA DESCRIPTION ..................................................................................... 4
2.3. SAMPLE LOCATIONS................................................................................................. 4
2.4. METHODS PART 1: ANALYTICAL PROCEDURES ....................................................... 6
2.4.1. Major elements: ................................................................................................... 6
2.4.2. Mineralogy: ......................................................................................................... 6
2.4.3. Trace elements: .................................................................................................... 6
2.4.4. Hg analysis: ......................................................................................................... 7
2.4.5. Metal Fractionation: ........................................................................................... 7
2.4.6. PAHs: ................................................................................................................... 8
2.4.7. Particle size analysis, TC and TN: ...................................................................... 8
2.4.8. Sediment Quality Guidelines for Freshwater Ecosystems ................................... 9
2.5. METHODS PART 2: STATISTICAL DISCRIMINATION OF POTENTIAL TRIBUTARY SUB-
CATCHMENT SPATIAL SEDIMENT SOURCES ON THE NSR .................................................... 10
2.5.1. Numerical mass balance modeling of spatial sediment source contributions on
the NSR 17
3. RESULTS AND DISCUSSION ................................................................................... 20
3.1. PART 1 – SEDIMENT QUALITY ASSESSMENT .............................................. 20
3.1.1. Mineralogy ......................................................................................................... 20
3.1.2. Particle Size ....................................................................................................... 21
3.1.3. Major Element Composition .............................................................................. 25
3.1.4. Trace Elements .................................................................................................. 27
3.1.4.1. Total Metals .............................................................................................. 27
3.1.4.2. Metal Speciation in Sediment ................................................................... 32
3.1.5. PAHs .................................................................................................................. 34
3.1.5.1. Total PAHs ................................................................................................ 34
3.2. PART 2 – SEDIMENT SOURCE APPORTIONMENT ......................................... 39
4. CONCLUSIONS ........................................................................................................... 44
5. REFERENCES ............................................................................................................. 45
List of Tables Table 1: Sediment sampling locations on the NSR and its tributaries ........................... 5
Table 2: The results of the Lilliefors test for Normality. ............................................. 12 Table 3: Geochemical fingerprint properties passing the mass conservation test. ...... 14 Table 4: Ranked KW test results. ................................................................................ 15 Table 5: Ranked property loadings provided by the outputs of the PCA. ................... 16 Table 6: The optimum composite signatures selected using KW and PCA. ............... 17
Table 7: Mineralogy of NSR and tributary sediment (% by weight) ........................... 20 Table 8: Particle size characteristics in NSR and tributary sediment. ........................ 22 Table 9: Specific surface area and textural composition of NSR and tributary sediment
...................................................................................................................................... 23
Table 10: Major element composition in NSR and tributary sediment. ...................... 25 Table 11: Total metal concentrations in NSR sediments (µg/g) .................................. 29 Table 12: Total metal concentrations in tributary sediments (µg/g) ............................ 29
Table 13: Summary of PAHs in NSR and tributary sediment (µg/kg) ........................ 34 Table 14: The goodness-of-fit (GOF) for the mixing model runs based on each
composite signature. .................................................................................................... 41 Table 15: Relative frequency-weighted average median spatial sediment source
contributions, based on each optimum composite fingerprint. .................................... 41 Table 16: Results of the diagnostic evaluation of the uncertainty associated with the
mass balance modelling for spatial sediment sources on the NSR. ............................. 42
List of Figures Figure 1: Statistical and numerical modeling components of the composite sediment
fingerprinting procedure. ............................................................................................. 11 Figure 2: Temporal downstream variation in grain size characteristics of NSR
sediments...................................................................................................................... 24
Figure 3: Downstream variation in SiO2, Fe2O3 and Al2O3. ........................................ 26 Figure 4: Downstream variation in MgO and CaO ...................................................... 27
Figure 5: Spatial and temporal variation in Co, Cr, Ni and Pb in NSR and tributary
sediments...................................................................................................................... 30 Figure 6: Inter-annual (2010 & 2011) and spatial variation in total metal concentration
of NSR sediment .......................................................................................................... 31
Figure 7: Inter-annual variation in Pb, Hg, Ni, Zn speciation in NSR sediment ......... 33 Figure 8: Spatial and temporal variation of total PAHs in NSR sediment .................. 35
Figure 9: Spatial and temporal variation of individual PAHs in NSR sediment ......... 36 Figure 10: Ratios of PHE:ANT and FLT:PYR for NSR and tributary sediments. ...... 38 Figure 11: Probability density functions (pdfs) for the predicted deviate median
relative contributions from each tributary sub-catchment spatial sediment source
identified for the NSR .................................................................................................. 40
1
1. INTRODUCTION
Knowledge of the source, transport, fate and effect of sediment-associated contaminants in
rivers is fundamental to understanding and managing anthropogenic impacts on water quality
and ecosystem health. The North Saskatchewan River (NSR) receives inputs of a variety of
sediment and associated contaminants from multiple sources that include municipal and
industrial wastewater discharges, storm and combined sewer discharges, tributary inputs,
diffuse overland sources, direct erosion from banks and riparian areas and the erosion of river
bed deposits (AECOM, 2011; Donahue, 2009). However, the chemical, physical and
biological characteristics sediment in the NSR has not been fully elucidated and additional
information is required to assess their provenance, storage (short and long term), fate and
environmental impact (bioavailability and toxicity). Accordingly, a sediment quality
assessment program is required as a first step towards identifying areas containing probable
contamination and addresses the following three questions
1) What is the nature and spatial extent of chemical contaminants in NSR sediments
relative to appropriate upstream reference conditions and
2) What sediments have sufficiently high concentrations of chemical contaminants
that present unacceptable risks to humans or aquatic biota?
3) What are the primary tributary sources of sediment to the NSR?
To address these questions and more fully quantify the physical and chemical properties of
sediment in the NSR, this report presents the results of a sediment quality and source
assessment conducted for Alberta Environment and Water to 1) evaluate the physical (grain
size distribution), geochemical (mineralogy, major element composition) and contaminant
(trace metals, PAH) characteristics of sediment in the North Saskatchewan River and its
tributaries and 2) to reconstruct longer term sediment provenance originating from key spatial
source units using a sediment fingerprinting model. The geochemical and contaminant data
are presented and discussed in the context of their spatial (gradient from headwater sites to
downstream) and temporal (inter-annual) variation. An assessment for sediment quality
conditions in the NSR is provided by comparing the contaminant data (trace elements, PAHs)
to sediment quality guidelines (SQGs) that include the consensus based Threshold Effect
Concentration (TEC) and Probable Effect concentration (PEC) reported by MacDonald et al.,
(2000).
2
Specific objectives of the study are to:
1) evaluate spatial and temporal variability of the geochemical (mineralogy, major
elements) and contaminant (trace elements, PAH) properties of archived sediment
samples collected in 2010 and 2011 at 20 sampling stations in the NSR and an
additional 10 composite samples collected at the confluence of key tributaries to the
NSR and
2) use a novel source apportionment framework combining statistical approaches for
discrimination and numerical mass balance modelling of sediment in the NSR.
2. METHODS
2.1. Study Approach
There is a paucity of data on the physical, chemical and biological properties of sediment in
the North Saskatchewan River (NSR). The approach taken in this study was to conduct a
sediment quality assessment along the main stem of the NSR and 10 of its tributaries to obtain
scientifically credible information that will allow improved description of baseline sediment
quality and its potential impact on aquatic biota in the NSR. The study was conducted in two
parts to 1) determine the geochemical and contaminant properties and 2) evaluate key spatial
sources of sediment in the NSR.
Part 1 of the study was designed to determine the geochemical and contaminant properties of
NSR and tributary sediment using archived sediment samples collected by Alberta
Environment and Water at 20 sampling stations along the main stem of the NSR in 2010 and
2011. An additional 10 composite samples were collected in 2011 at the confluence of key
tributaries to the NSR. Trace element (ICP/MS multi-element scan), mineralogy (XRD),
major element composition (XRF), PAH (16 congeners), total metals and metal speciation
(sequential leaching), total nitrogen and carbon and particle size distribution of the samples
were determined to evaluate spatial (downstream gradient) and temporal (inter-annual)
variation in the sediment parameters. The data are compared to consensus based sediment
quality guidelines for freshwater ecosystems (MacDonald et al., 2000).
Part 2 of the study was designed to provide information regarding sediment contributions
originating from key spatial source units in the NSR using a recently revised composite
fingerprinting procedure outlined in Collins et al. (2010a,b, in press). There is a growing
3
requirement for implementing improved catchment management strategies aimed at
controlling sediment mobilisation and delivery to watercourses to help support the
maintenance of good water quality and ecological status. Excess sediment can degrade the
aquatic environment since elevated turbidity levels reduce light penetration through the water
column, decreasing the depth of the photic zone and impacting on levels of primary
production (Kiffney and Bull, 2000; Devlin et al., 2008). Well-documented specific impacts
of excess sediment as a stressor on aquatic ecology include, amongst others, the siltation of
fish spawning gravels and smothering of incubating progeny, gill clogging, histological
changes, reduced resistance to disease and suppressed feeding efficiency (Wood and
Armitage, 1997; Milner et al., 2003; Greig et al., 2005, Bilotta and Brazier, 2008). Further
deleterious impacts can be associated with the importance of sediment redistribution in the
transfer, dispersal and fate of harmful excess nutrients and contaminants (Warren et al., 2003;
Kronvang et al., 2003; Chalmers et al., 2007; Horowitz, 2008; Yakutina, 2011). Sediment is
therefore increasingly identified as a priority pollutant requiring improved management and
abatement.
Since water policy requires mitigation strategies to be introduced to tackle diffuse pollution
from agriculture and other key sources, there is a need to adopt a catchment-wide perspective
in developing sediment management plans, since off-site sediment problems reflect diffuse
inputs from across contributing areas. Deploying traditional measurement and monitoring
techniques on a spatially-distributed basis faces many logistical problems and issues of cost
and as a result, sediment source tracing procedures have been increasingly used to document
key sediment sources at catchment scale (Caitcheon, 1998; Foster and Lees, 2000; Motha et
al., 2004; Walling, 2005; Foster et al., 2007; Minella et al., 2008; Davis and Fox, 2009;
Wilkinson et al., 2009; Hatfield and Maher, 2009; Pittam et al., 2009; Bird et al., 2010;
Walling et al., 2011).
Against the context of the increasing application of sediment source tracing techniques, recent
work has exploited the scope for further refinements to the methodologies involved. For
example, recent efforts have focused on revising numerical mass balance models to
incorporate weightings for within-source property variation and tracer discriminatory power,
prior information on inputs from specific sources and the inclusion of Latin Hypercube
Sampling (LHS) to improve the efficiency of repeat iterations during Monte Carlo analysis
(Collins et al., 2010a). In addition, Monte Carlo frameworks for sediment mixing models
have recently been revised to include genetic algorithm (GA) optimization alongside more
conventional local search tools, to help assess confidence in mixing model outputs with
respect to predicting measured sediment geochemistry (Collins et al., 2010b). In addition,
4
although less attention has been directed towards exploring the scope for refining the
statistical components of sediment source tracing methodologies, the need to verify
statistically, the selection and discriminatory efficiency of composite signatures remains of
paramount importance. Consequently, the recent work of Collins et al. (2012) has devised
and applied a sediment source fingerprinting procedure with a revised statistical component
for selecting and confirming the discriminatory efficiency of geochemical composite
signatures. This study extends that procedure further by including diagnostic uncertainty
analysis for the components of the numerical mass balance model.
2.2. Study Area Description
The source waters of the NSR originate from the Saskatchewan Glacier in Banff National
Park. The river flows east from the Rocky Mountains across Alberta to Saskatchewan. It has a
total length of about 1,000km and drains an area of approximately 55,000km2. Mean annual
discharge of the NSR at the Alberta Saskatchewan border is ~7 billion m3. The main
tributaries of the NSR in the headwaters are the Brazeau, Ram, and Clearwater rivers. The
Sturgeon and Vermillion rivers contribute flow downstream of Edmonton. Two dams
(Brazeau and Bighorn) were designed to regulate river discharge and are located in the upper
reaches of the NSR. During the winter, flows are low but increase dramatically in late spring
and early summer during snowmelt and rain events. Flow regulation and storage have altered
seasonal patterns and resulted in somewhat lower summer flows and higher winter flows. The
NSR basin drains a wide range of physiographic settings that differ in climate, geology, soils
and landscape, elevation and natural vegetation. The regions include the Rocky Mountain,
Foothills, Boreal Forest and Grassland regions but the majority of the drainage area is within
the Central Parkland Natural Region (AECOM, 2011; Donahue, 2009).
2.3. Sample Locations
Grab samples of fine grained river bed/bank sediment deposits were collected by an Alberta
Environment and Water team at 20 Long term river network (LTRN) monitoring sites along
the NSR from Rocky Mountain House to Lloydminister in 2010 and 2011. An additional 10
samples (fine-grained river bed/bank sediment) were collected at the confluence of 10
tributaries of the NSR to evaluate the source apportionment of sediment from key spatial
source units. A list of all sample locations and their distance downstream on the NSR is
provided in Table 1.
5
Sediment samples were frozen and kept in storage until they were transported to Activation
Labs located in Ancaster, Ontario for physical, geochemical and contaminant analysis. All
methods reported below are standard analytical procedures. The accuracy of analyses for each
method is reported by comparing the analytical results with stated reference values. QA/QC
and method detection limits are provided for each analysis.
Table 1: Sediment sampling locations on the NSR and its tributaries
Site Latitude Longitude
Distance
(km)
Cline River u/s of Abraham Lake Tributary 521016 1162849 30
Siffleur River u/s confluence with NSR Tributary 520306 1162335 46
Bighorn River u/s confluence with NSR Tributary 522040 1161700 67
Ram River u/s confluence with NSR Tributary 522205 1152430 142
NSR at Rocky Mtn House Mainstem 522712 1145911 186
Clearwater River @ Rocky Mntn House Tributary 522040 1145610 191
NSR 1 km above Bapiste River Mainstem 533746 1150246 228
Baptiste River near the mouth Tributary 523952 1150434 229
Brazeau River at Brazeau Dam Tributary 530058 1154557 266
NSR at Drayton Valley Mainstem 531233 1145611 320
NSR at Tomahawk Mainstem 531911 1144434 346
NSR at Genesee Bridge Mainstem 532240 1141642 407
NSR at Genesee Bridge Mainstem 532238 1141651 407
NSR at Devon Mainstem 532221 1134422 449
NSR at Devon Mainstem 532215 1134422 449
NSR at Anthony Henday Mainstem 532743 1133649 471
NSR u/s of Qunesnell Br Mainstem 533020 1133402 482
NSR at Walterdale Br. Mainstem 533154 1133044 491
NSR at Beverly Bridge Mainstem 533404 1132242 505
NSR at Beverly Bridge Mainstem 533403 1132230 505
NSR 0.5 km u/s Horsehills Ck Mainstem 533741 1131915 516
NSR u/s Fort Sask at Hwy 15 Br Mainstem 534143 1131515 528
Sturgeon River at Hwy 825 Tributary 534714 1131324 539
NSR at Vinca Bridge Mainstem 535243 1130002 555
NSR at Vinca Bridge Mainstem 535209 1130228 555
NSR at Waskatenau Br. Mainstem 540331 1124631 583
NSR at Pakan Mainstem 535933 1122705 611
NSR at Pakan Mainstem 535927 1122711 611
NSR at Duvernay Mainstem 534723 1114204 687
NSR at Myrnam Mainstem 534514 1111360 722
NSR at Elk Point Mainstem 535136 1105319 747
Vermilion River confluence with NSR Tributary 533920 1102020 799
NSR at Lea Park Mainstem 533934 1102014 799
NSR at Lloydminister Ferry LB Mainstem 533603 1095948 830
NSR at Lloydminister Ferry RB Mainstem 533557 1100007 830
6
2.4. Methods Part 1: Analytical Procedures
2.4.1. Major elements:
Concentrations of major elements (Al2O3, Fe2O3, MnO, MgO, CaO, Na2O, K2O, TiO2, P2O5,
Cr2O3, SiO2) and loss on ignition (LOI) were determined by X-ray fluorescence. Prior to
fusion, the loss on ignition (LOI), which includes H2O, CO2 , S and other volatiles, can be
determined from the weight loss after roasting the sample at 1050°C for 2 hours. The fusion
disk is made by mixing a 0.5 g equivalent of the roasted sample with 6.5 g of a combination
of lithium metaborate and lithium tetraborate with lithium bromide as a releasing
agent. Samples are fused in Pt crucibles using an automated crucible fluxer and automatically
poured into Pt molds for casting. Samples are analyzed on a Panalytical Axios Advanced
wavelength dispersive XRF. The intensities are then measured and the concentrations are
calculated against the standard G-16 provided by Dr. K. Norrish of CSIRO, Australia. Matrix
corrections were done by using the oxide alpha - influence coefficients provided also by Dr
K. Norrish. In general, the limit of detection is about 0.01 wt% for most of the elements.
2.4.2. Mineralogy:
Quantitative mineralogy of the NSR sediment was determined by X-ray diffraction (XRD).
Mineral identification is made by comparing diffraction patterns with a library of over 17,000
mineral patterns stored in the International Centre for Diffraction Data (ICDD). Detection
limits depend on the nature of the sample. It is estimated that the minerals present in less than
3% of the sample might not be detected. The samples for X-ray diffraction analysis are
ground or milled to a fine powder and then hand pressed into a 1cm3 sample holder.
2.4.3. Trace elements:
Concentrations of Al, As, Ba, Bi, Cd, Ce, Co, Cr, Cs, Cu, Dy, Er, Eu, Fe, Ga, Gd, Gd, Hf,
Ho, In, K, La, Li, Mg, Mn, Mo, Na, Nd, Ni, Pb, Pd, Pr, Rb, Sb, Sc, Sm, Sn, Sr, Tb, Ti, Tl, U,
V, Y, Yb, Zn, and Zr were measured using ICP-MS. A 0.5 g sample is digested in aqua regia
at 90°C in a microprocessor controlled digestion block for 2 hours. The solution is diluted and
analyzed by ICP/MS using a Perkin Elmer SCIEX ELAN 6000, 6100 or 9000 ICP/MS. One
blank is run for every 68 samples. An in-house control is run every 33 samples. Digested
standards are run every 68 samples. After every 15 samples, a digestion duplicate is analyzed
and the instrument is recalibrated every 68 samples. Certain elements (Ti, P and S) are
analyzed by ICP/OES using a Varian 735 ES. This extends the dynamic range for a number of
7
elements as well. International certified reference materials USGS GXR-1, GXR-2, GXR-4
and GXR-6 are analyzed at the beginning and end of each batch of samples. Internal control
standards are analyzed every 10 samples and a duplicate is run for every 10 samples. This
digestion is not total and will not dissolve silicates some oxides and resistant minerals (e.g.,
zircon, monazite, sphene, etc.).
2.4.4. Hg analysis:
A 0.5 g sample is digested with aqua regia at 90ºC. The Hg in the resulting solution is
oxidized to the stable divalent form. Since the concentration of Hg is determined via the
absorption of light at 253.7 nm by Hg vapour, Hg (II) is reduced to the volatile free atomic
state using stannous chloride. Argon is bubbled through the mixture of sample and reductant
solutions to liberate and to transport the Hg atoms into an absorption cell. The cell is placed in
the light path of an Atomic Absorption Spectrophotometer. The maximum amount absorbed
(peak height) is directly proportional to the concentration of mercury atoms in the light path.
Measurement can be performed manually or automatically using a flow injection technique
(FIMS). Hg analysis is performed on a Perkin Elmer FIMS 100 cold vapour Hg analyzer.
Detection limit is 5 ppb.
2.4.5. Metal Fractionation:
To gain a better understanding of biological and geochemical processes, sequential extraction
techniques can be used to obtain information about the 'solid-speciation' of metals in soils and
aquatic sediments (Martin et al., 1987; Tessier and Campbell, 1988). The technique provides
data that should be interpreted as a gradient for the physicochemical association strength
between trace elements and solid particles (Martin et al., 19870. While some authors suggest
that the biological availability of metal can be estimated using these techniques (Tessier and
Campbell, 1988), sequential extraction data are used in this study primarily as a method to
provide information about the relative distribution of metals in various geochemical phases of
sediment in the NSR and its tributaries. Detailed information about the bioavailability and
toxicity of metals in the sediment samples would require additional analytical procedures not
conducted in this study.
Sediment samples undergo a sequential leaching process; starting with the weakest leach to
the strongest leach and the solutions are analyzed on a Perkin Elmer ELAN 6000, 6100 or
9000 ICP/MS. One matrix blank is analyzed per 49 samples. Two controls are run at the
beginning and end of the group of 49 samples. Duplicate samples are leached and run every
8
10 samples. The sequential leaching method produces the following five operationally
defined metal fractions: Fraction 1: exchangeable metals are determined by leaching a 0.75 g
sample in a water matrix at 30°C for 1 hour. Two controls for every 49 samples are leached in
the same procedure; Fraction 2: exchangeable cations adsorbed by clay and elements co-
precipitated with carbonates are extracted with a sodium acetate leach at pH 5. Fraction 3:
Elements adsorbed by organic material (humic and fulvic components) are extracted with a
0.1M sodium pyrophosphate leach. Fraction 4: Amorphous and crystalline Fe oxides
and crystalline Mn oxides using a hot hydroxylamine leach and Fraction 5: a four acid
digestion is used to dissolve remnant silicate materials remaining. The sum of fractions 1 to 4
is subtracted from the total metal content to determine the residual metal content (Fraction 5).
2.4.6. PAHs:
Individual congeners for the standard list of 16 PAHs were extracted in sediment samples
(about 2 g) for 16 h with 100 ml acetone/dichloromethane/n-hexane (1:1:1, v/v/v) in Soxhlet
apparatus (U.S. EPA, 1996a). The concentrated extract was cleaned up using a florisil column
according to the EPA Standard Method 3620B (U.S. EPA, 1996b). Deuterated PAHs
[naphthalene-d8 (d8-Nap), acenaphthene-d10 (d10-Ace), phenanthrene-d10 (d10-Phe),
chrysened12 (d12-Chr) and perylene-d12 (d12-Per)] were used as internal standards for
quantification. The extracts were analyzed for PAHs using a Hewlett-Packard (HP) 6890N
gas chromatograph (GC) coupled with a HP-5973mass selective detector (MSD) and a 30
m×0.25 mm×0.25 μm DB-5 capillary column (J & W Scientific Co. Ltd., USA) using the
EPA Standard method 8270C (U.S. EPA, 1996c). Sixteen US EPA priority 2- to 6-ring PAHs
were detected by GC with mass spectrometry (GC-MS): Naphthalene (Nap), acenaphthylene
(Acy), acenaphthene (Ace), fluorine (Fl), phenanthrenes (Phe), anthracene (Ant), fluoranthene
(Flu), pyrene (Pyr), benzo(a)anthracene (BaA), chrysene (Chr), benzo(b)
fluoranthene+benzo(k)fluoranthene (B(b+k)F), benzo(a)pyrene (BaP), indeno(1,2,3-
c,d)pyrene (IcdP), dibenzo(a,h)anthracene (DBA), and benzo(g,h,i)perylene (BghiP).
Benzo(b)fluoranthene and benzo(k) fluoranthene co-eluted and therefore were quantified
together.
2.4.7. Particle size analysis, TC and TN:
Particle size distributions and specific surface area (SSA) were measured using a Malvern
Mastersizer 2000. The median diameter (D50) and the diameter for the 10th and 90
th % of each
size distribution (D10, D90) are presented.
9
Total carbon (volatile organic carbon species) was determined by heating a 0.1 g sample in a
pure oxygen environment at 380 o
C. The carbon (TC) is measured as carbon dioxide in an IR
cell.
2.4.8. Sediment Quality Guidelines for Freshwater Ecosystems
Numerical sediment quality guidelines (SQGs; including sediment quality criteria, sediment
quality objectives, and sediment quality standards) have been developed by various federal,
state and provincial agencies in North America for both freshwater and marine ecosystems.
SQGs are used in numerous applications, including designing monitoring programs,
interpreting historical data, evaluating the need for detailed sediment quality assessments,
conducting remedial investigations and ecological risk assessments, and developing sediment
quality remediation objectives (Long and MacDonald 1998). Numerical SQGs have also been
used to identify contaminants of concern in aquatic ecosystems and to rank areas of concern
on a regional or national basis (US EPA 1997a). When used in combination with other tools,
such as sediment toxicity tests, SQGs represent a useful approach to assess sediment quality
in freshwater and marine environments (Mac-Donald et al. 1992; US EPA 1992, 1996, 1997a;
Adams et al. 1992; Ingersoll et al. 1996, 1997).
In North America, SQGs have been developed using a variety of approaches. The approaches
selected by individual jurisdictions depend on the receptors that are to be considered (e.g.,
sediment-dwelling organisms, wildlife, or humans), the degree of protection that is to be
afforded, the geographic area to which the values are intended to apply (e.g., site-specific,
regional, or national), and their intended uses (e.g., screening tools, remediation objectives,
identifying toxic and not-toxic samples, bioaccumulation assessment). Guidelines for
assessing sediment quality relative to the potential for adverse effects on sediment-dwelling
organisms in freshwater systems have been derived using a combination of theoretical and
empirical approaches, primarily including the equilibrium partitioning approach (EqPA; Di
Toro et al. 1991; NYSDEC 1994; US EPA 1997a), screening level concentration approach
(SLCA; Persaud et al. 1993), effects range approach (ERA; Long and Morgan 1991; Ingersoll
et al. 1996), effects level approach (ELA; Smith et al. 1996; Ingersoll et al. 1996), and
apparent effects threshold approach (AETA; Cubbage et al. 1997). Application of these
methods has resulted in the derivation of numerical SQGs for many chemicals of potential
concern in freshwater sediments.
An evaluation of consensus-based SQGs was conducted by MacDonald et al., (2000) to
provide a basis for determining the ability of these tools to predict the presence, absence, and
10
frequency of sediment toxicity in field-collected sediments from various locations across the
United States. They conclude that consensus-based SQGs can be used to identify hot spots
with respect to sediment contamination, determine the potential for and spatial extent of
injury to sediment-dwelling organisms, evaluate the need for sediment remediation, and
support the development of monitoring programs to further assess the extent of contamination
and the effects of contaminated sediments on sediment-dwelling organisms. Accordingly,
consensus based Threshold Effect Conditions (TECs) proposed by MacDonald et al., (2000)
for metals and PAHs are often used to identify hot spots with respect to sediment
contamination and determine the potential for and spatial extent of injury to sediment-
dwelling organisms in the NSR. In this report, consensus based Threshold Effect Conditions
(TECs) are used to assess the level of metal and PAH contamination in NSR and tributary
sediments.
2.5. Methods Part 2: Statistical discrimination of potential tributary sub-
catchment spatial sediment sources on the NSR
For the purpose of this work, spatial sediment sources on the NSR were classified on the basis
of ten individual tributary sub-catchments, namely; the Vermilion River, Sturgeon River,
Brazeau River, Baptiste River, Nordegg River, Clearwater River, Ram River, Bighorn River,
Cline River and Siffleur River. Sediment sourcing therefore aimed to assess the relative
inputs from these ten spatial sources to the channel bed sediment samples collected along the
main stem of the NSR. The need to verify statistically the discriminatory power of composite
signatures remains of paramount importance for the robust application of sediment source
tracing. Use of a range of statistical techniques and tests to confirm affinities between
replicate source samples on the basis of fingerprint properties, to test the discriminatory
power of those properties and to confirm robust composite fingerprints has been reported in
the literature.
Many studies have used the two-stage procedure combining either the Mann-Whitney U-test
or Kruskal-Wallis H-test with Discriminant Function Analysis (DFA) proposed by Collins et
al. (1997) or dérivatives thereof (Walling et al., 1999; Owens et al., 1999; Bottrill et al., 2000;
Owens et al., 2000; Carter et al., 2003; Walling et al., 2006, 2008; Minella et al., 2008;
Hughes et al., 2009; Bird et al., 2010). Alternatively, meaningful combinations of properties
have been selected using analysis of variance (ANOVA) combined with DFA (Motha et al.,
2004), ANOVA coupled with cluster analysis (Walling and Woodward, 1995), principal
components analysis (PCA) followed by DFA (Foster et al., 2007), hierarchical cluster
11
analysis (de Boer and Crosby, 1995), multivariate cluster analysis using the Pattern Analysis
Package (PATN) (Yu and Oldfield, 1989), R- and Q-mode factor analysis (Jenns et al., 2002)
or the Mann-Whitney U-test on its own (Stott, 1986).
More recently, fuzzy clustering has been used as an alternative to hierarchical or k-means
analysis to demonstrate the use of fingerprint property ratio data for source discrimination
(Hatfield et al., 2008). As the above experience has continued to be disseminated by the
sediment research community, some tracing studies have used combinations of properties
selected a priori (Olley et al., 1993; Caitcheon, 1993; Walling and Amos, 1999; Oldfield et
al., 1999; Wallbrink et al., 2003; Walling et al., 2003; Wilkinson et al., 2010). Whilst a priori
selection can generally be justified on the basis of wider experience, it remains important to
demonstrate that a suite of properties reliably distinguishes the set of source samples being
used and that the individual members of a composite signature each contribute robustly to
discrimination.
Figure 1: Statistical and numerical modeling components of the composite sediment
fingerprinting procedure.
12
Figure 1 above summarizes the refined statistical procedure (Collins et al., 2012) applied
during this study on the NSR. It was considered important to test for Normality prior to
proceeding with the selection of metrics for fingerprint property parameter location and scale
(Arcones and Wang, 2006). Accordingly, the Lilliefors test (Lilliefors, 1969; Henderson,
2006) was used to assess the Normality of the fingerprint property dataset for the tributary
sub-catchment spatial sediment sources on the NSR. The Lilliefors test represents an
adaptation of the Kolmogorov-Smirnov test and provides a two-sided goodness-of-fit
procedure in situations where the fully specified null population for each fingerprint property
is unknown, thereby requiring the estimation of its parameters using the significance of
comparison at p = 0.05. During the application of the Lilliefors test, the sample mean and
standard deviation were used to represent the corresponding values for the benchmark
population against which the measured fingerprint property data were compared. Table 2
shows that the majority of the fingerprint properties used to characterize the tributary sub-
catchment spatial sediment sources failed the Lilliefors test, thereby confirming that the data
were non-uniform in distribution.
The revised statistical verification of composite signatures (Collins et al., 2012) explored the
use of genetic algorithm-driven Discriminant Function Analysis (GA-DFA), the Kruskal-
Table 2: The results of the Lilliefors test for Normality.
Property P value Property P value Property P value
Li 0.140 As 0.035* Th 0.500
Na 0.245 Rb 0.454 U 0.076
Mg 0.001* Y 0.401 SiO2 0.183
Al 0.093 Sr 0.184 Al2O3 0.103
K 0.028* Zr 0.039* Fe2O3(T) 0.430
Ca 0.001* Nb 0.004* MnO 0.500
V 0.013* Ba 0.379 MgO 0.001*
Cr 0.095 La 0.500 CaO 0.001*
Mn 0.009* Ce 0.500 Na2O 0.500
Fe 0.017* Pr 0.500 K2O 0.472
Hf 0.141 Nd 0.500 TiO2 0.038*
Ni 0.187 Sm 0.500 P2O5 0.500
Er 0.106 Gd 0.354 Cr2O3 0.001*
Be 0.008* Tb 0.001* V2O5 0.5
Ho 0.072 Dy 0.042* Zn 0.001*
Cs 0.251 Cu 0.015* Ga 0.017*
Co 0.500 Ge 0.001* Tl 0.232
Eu 0.500 Tm 0.001* Pb 0.500
Bi 0.014* Yb 0.111
Se 0.001* Re 0.001*
* statistically significant values at p = 0.05
13
On the basis of the results for the Lilliefors test, tracer parameter distributions were defined
using the measured median and robust scaling estimator nQ proposed by Rousseeuw and
Croux (1993) as an alternative to the median absolute deviation (MAD):
)(; kjin jixxdQ [1]
where d is a constant factor (1.0483), ji xx is the pairwise distances and k =
2
n
4/2
nwhere 1
2
nh is roughly half the number of the observations. The same
procedure was applied for defining the fingerprint property tracer distributions for the
sediment samples collected at the outlet of the study area on the basis of measured data. In
situations where the fingerprint property data satisfy the Lilliefors test, tracer parameter
distributions would be defined using conventional location (mean) and scale (standard
deviation) estimators (Figure 1). The ranges of the fingerprint property values (derived using
the values measured on the single composite sample from each tributary confluence, plus an
assumed 20% CV) for each tributary sub-catchment spatial source category (with corrections
described in the subsequent section on mass balance modeling) were used to define parameter
space for a mass conservation test (Figure 1; Table 3) and only those properties for which the
main stem sediment sample ranges were located in the mixing polygon were entered into the
statistical analysis for spatial sediment source discrimination (cf. Collins et al., 2010b) Wallis
H-test (KW) and Principal Components Analysis (PCA). The sample numbers available for
this project did not permit the GA-DFA to be used to identify optimum signatures, but this
particular procedure was deployed to confirm the discriminatory power of both the individual
properties and the composite signatures selected using the KW test and PCA.
14
Table 3: Geochemical fingerprint properties passing the mass conservation test.
Li Bi Ge
Na Zn Yb
Mg Ga Tl
Al As Pb
K Rb Th
Ca Y U
V Sr SiO2
Cr Zr Al2O3
Mn Ba Fe2O3(T)
Fe La MnO
Hf Ce MgO
Ni Pr CaO
Er Nd Na2O
Be Sm K2O
Ho Gd TiO2
Cs Tb P2O5
Co Dy V2O5
Eu Cu
During the application of the KW test, the Chi-square and p-value associated with each
property passing the mass conservation test was ranked (Table 4) and an optimum composite
signature identified using the highest ranked properties (Table 5). Each individual property
comprising the composite fingerprint, as well as the property set in its entirety, was passed
through the GA-DFA to calculate the tracer discriminatory weightings and the total
discriminatory efficiency of the set of properties (Table 5). On this basis, the KW selected
optimum composite fingerprint correctly classified 80% of the tributary sub-catchment spatial
source samples. The error associated with this discrimination should be borne in mind when
interpreting the results of the corresponding numerical modeling.
15
Table 4: Ranked KW test results.
Property H Value p Property H Value p
Li 25.03 0.003 Zr 24.13 0.004
Na 27.69 0.001 Ba 25.80 0.002
Mg 26.81 0.002 La 24.18 0.004
Al 25.63 0.002 Ce 23.88 0.004
K 18.53 0.029 Pr 23.85 0.005
Ca 27.63 0.001 Nd 23.61 0.005
V 25.50 0.002 Sm 23.96 0.004
Cr 26.85 0.001 Gd 23.48 0.005
Mn 21.55 0.010 Tb 22.37 0.008
Fe 23.83 0.005 Dy 23.09 0.006
Hf 26.27 0.002 Cu 25.90 0.002
Ni 23.25 0.006 Ge 25.28 0.003
Er 24.88 0.003 Yb 25.96 0.002
Be 22.97 0.006 Tl 24.31 0.004
Ho 22.53 0.007 Pb 20.54 0.015
Cs 24.57 0.003 Th 25.02 0.003
Co 18.09 0.034 U 21.99 0.009
Eu 24.90 0.003 SiO2 22.63 0.007
Bi 26.39 0.002 Al2O3 24.50 0.004
Zn 24.54 0.004 Fe2O3(T) 24.45 0.004
Ga 22.65 0.007 MnO 21.84 0.009
As 26.41 0.002 MgO 27.23 0.001
Rb 22.75 0.007 CaO 27.68 0.001
Y 24.64 0.003 Na2O 27.84 0.001
Sr 27.18 0.001 K2O 21.53 0.011
P2O5 21.60 0.010 TiO2 24.29 0.004
V2O5 25.63 0.002
An alternative robust optimum composite fingerprint was identified using PCA by selecting
the properties with the highest ranked loadings (Table 4 and Table 5). Two components were
consistently sufficient for explaining between 95.5-99.2% of the variance. For consistency,
the individual properties and the entire property set associated with this optimum signature
were passed through the GA-DFA to calculate tracer discriminatory weightings and to assess
the percentage of the spatial source samples classified into the correct category by this
alternative composite fingerprint. Table 6 illustrates that the optimum signature selected
using PCA correctly distinguished 75% of the tributary sub-catchment spatial source samples.
The error associated with this discrimination of the tributary sub-catchment samples should
be borne in mind when interpreting the results of the corresponding mass balance modelling.
16
Table 5: Ranked property loadings provided by the outputs of the PCA.
Property PC-1a Property PC-2
b
SiO2 0.9047 CaO 0.883684
CaO 0.4090 SiO2 0.419824
MgO 0.0857 MgO 0.189926
Al2O3 0.0672 Ca 0.058102
Ca 0.0235 Mg 0.039984
Al 0.0222 K 0.022739
Na2O 0.0179 Al 0.02178
Mg 0.0178 Fe 0.014483
Fe2O3(T) 0.0172 K2O 0.014138
Na 0.0159 Na2O 0.012077
K2O 0.0082 Na 0.010509
TiO2 0.0029 Fe2O3(T) 0.010341
K 0.0017 Al2O3 0.005974
Fe 0.0015 P2O5 0.001898
Ba 0.0008 MnO 0.000586
Sr 0.0002 Ba 0.000372
P2O5 0.0001 TiO2 0.000263
MnO 0.0001 Mn 0.000135
V2O5 0.0000 V2O5 0.000102
Cr 0.0000 Sr 0.0000
Zr 0.0000 Rb 0.0000
Mn 0.0000 Ce 0.0000
Rb 0.0000 Li 0.0000
Li 0.0000 Zn 0.0000
V 0.0000 Zr 0.0000
Zn 0.0000 La 0.0000
Cu 0.0000 Ni 0.0000
Ga 0.0000 Cu 0.0000
Ce 0.0000 Nd 0.0000
Ni 0.0000 V 0.0000
Y 0.0000 Y 0.0000
Pb 0.0000 Th 0.0000
Nd 0.0000 Pb 0.0000
As 0.0000 Co 0.0000
Sm 0.0000 Ga 0.0000
Dy 0.0000 Pr 0.0000
Gd 0.0000 Sm 0.0000
Be 0.0000 Cs 0.0000
Ge 0.0000 Gd 0.0000
Pr 0.0000 U 0.0000
Th 0.0000 Cr 0.0000
Hf 0.0000 Dy 0.0000
Er 0.0000 Hf 0.0000
Yb 0.0000 Er 0.0000
Cs 0.0000 Yb 0.0000
Eu 0.0000 Be 0.0000
Co 0.0000 As 0.0000
Tl 0.0000 Ho 0.0000
Tb 0.0000 Eu 0.0000
Ho 0.0000 Tl 0.0000
La 0.0000 Bi 0.0000
Bi 0.0000 Ge 0.0000
U 0.0000 Tb 0.0000
VE% 95.50 3.70
a Principal Component 1; b Principal Component 2; VE % variance explained
17
Table 6: The optimum composite signatures selected using KW and PCA.
KW PCA
Property %1 TDW
2 Property %
1 TDW
2
Ba 57 1.89 Al 40 1.20
Bi 40 1.33 Al2O3 43 1.30
Ca 60 2.00 Ba 57 1.70
CaO 67 2.22 Ca 60 1.80
Cr 47 1.56 CaO 67 2.00
Eu 37 1.22 Fe 50 1.50
Ge 40 1.33 Fe2O3T 40 1.20
Hf 33 1.11 K 47 1.40
Ho 30 1.00 K2O 37 1.10
K 47 1.56 Mg 60 1.80
Mg 60 2.00 MgO 67 2.00
MgO 67 2.22 MnO 37 1.10
MnO 37 1.22 Na 63 1.90
Na 63 2.11 Na2O 53 1.60
Na2O 53 1.78 P2O5 33 1.00
Sr 57 1.89 SiO2 47 1.40
V2O5 37 1.22 Sr 57 1.70
Yb 33 1.11 TiO2 40 1.20
Zn 57 1.89 V2O5 37 1.10
Total3 80 Total
3 75
1 % tributary sub-catchment spatial source samples classified correctly by individual properties
2 tracer discriminatory weighting used in the mass balance modeling
3 % tributary sub-catchment spatial source samples classified correctly by composite signature
2.5.1. Numerical mass balance modeling of spatial sediment source contributions
on the NSR
The relative contributions of the ten tributary sub-catchment spatial sediment sources to the channel
bed sediment samples collected along the main stem of the NSR were quantified using the mass
balance mixing model described by Collins et al. (2010a). In short, the model seeks to solve a set of
linear equations for each composite signature by minimizing the sum of squares of the weighted
relative errors:
i
n
i
isisssi
m
s
si WCSVOZSPC
2
1 1
/
[2]
where: iC = deviate median concentration of fingerprint property i in NSR main stem bed sediment
samples; sP = the optimized percentage contribution from tributary sub-catchment spatial source s ;
siS = deviate median concentration of fingerprint property i in tributary sub-catchment spatial source
s ; Z = particle size correction factor for tributary sub-catchment spatial source s ; O = organic
18
matter content correction factor for tributary sub-catchment spatial source s ; siSV = weighting
representing the potential within-source variation of fingerprint property i in tributary sub-catchment
spatial source category s ; iW = tracer discriminatory weighting; n = number of fingerprint
properties comprising the optimum composite fingerprint; m = number of tributary sub-catchment
spatial sediment sources
Corrections for particle size and organic matter content are used to take account of the role of
selective delivery and enrichment in compromising direct comparisons of sediment sample
geochemistry. The within-source variation weighting is incorporated in the mixing model to ensure
that those properties with smaller variance exert more influence on the mathematical solutions
generated. The estimation of this weighting was based on the variance of the geochemical properties
across the population of individual tributary sub-catchment spatial sediment sources. Since use of the
inverse of the standard deviation generated disproportionately large weightings for some tracers, the
inverse of the coefficient of variation was used as an alternative basis for the calculations. The tracer
discriminatory power weighting is based on the relative outputs of the GA-DFA for the individual
properties comprising each composite fingerprint (Collins et al., 2010a).
The uncertainties in characterizing the input median tracer values for the model on the basis of
relatively few tributary spatial source and NSR main stem sediment samples were quantified
explicitly using the scaling of the parameter distributions based on nQ and a Monte Carlo approach.
Stratified repeat mixing model iterations (10000 for each composite fingerprint identified for each bed
sediment sampling period) using Latin Hypercube Sampling generated deviate predicted median
relative contributions from each tributary sub-catchment spatial sediment source. The pdfs generated
on this basis were used to estimate relative frequency-weighted average median inputs (R) from the
individual spatial sediment sources, viz.:
n
i
ii FvR1
[3]
where n is the number of intervals for the predicted deviate relative contribution, scaled between 0
and 1; and v and F are the mid-value and the relative frequency for the ith interval, respectively. Use
of the frequency-weighted approach provided a convenient means of summarizing the average median
spatial sediment source contributions on the basis of a single number, whilst still taking into account
the full range of the predicted deviates generated using the Monte Carlo analysis. The convergence of
the mixing model solutions and their reproducibility was interrogated by calculating 95% confidence
limits about the average median inputs, using 10 sets of 10000 repeat iterations for the composite
signatures selected using the KW-H test and PCA.
19
The Monte Carlo framework included both local and global (genetic algorithm; GA) optimization of
the mixing model repeat solutions (Collins et al., 2010b). Genetic algorithms evolve a population of
candidate solutions to an optimization problem using iterative application of the evolutionary
processes of selection, crossover and mutation (Goldberg, 1989; Savic et al., 2011). Repeat model
iteration creates a generation of individual solutions that on average are fitter than the previous ones
as measured by the minimization of the objective function. GA-driven mass balance modeling was
initiated with the output from the non-GA (local) optimization as the starting point. An alternative
would be to initiate the GA-driven source apportionment using a random set of source proportions (cf.
Collins et al., 2010b). Non-GA and GA-driven modeling was compared using the minimization of the
objective function and the corresponding goodness-of-fit based on the relative error between predicted
and measured bed sediment tracer values for the main stem of the NSR.
In an extension to recent work using the above refined sediment tracing procedure, an uncertainty
budget was calculated for the mass balance modeling component. The analysis of the uncertainty
budget included consideration of the importance of the variability associated with the measured
tributary spatial source and NSR main stem sediment tracer properties, plus that associated with the
corrections and weightings used in the revised objective function. 5000 repeat iterations were used
during the mixing model runs to estimate the uncertainty budgets for the mass balance modeling for
each optimum composite signature (i.e. the signatures identified using KW and PCA). The outputs of
this analysis were ranked to identify the most important factors contributing to the gross uncertainty
associated with the mixing model predictions of the spatial sources of channel bed sediment collected
along the main stem of the NSR.
20
3. RESULTS AND DISCUSSION
3.1. PART 1 – SEDIMENT QUALITY ASSESSMENT
3.1.1. Mineralogy
Quantitative mineralogy was used to examine spatial and temporal patterns in mineralogical
composition of NSR and tributary sediment. Both sediment source types consist of varying
concentrations silicates (quartz), feldspars (albite, microcline), micaceous phyllosilicates (chlorite
muscovite), carbonates (dolomite, calcite), clay minerals (smectiite) and amorphous groups (Table 7).
Median concentrations of quartz, albite, dolomite and calcite were lower in NSR sediment compared
to tributary sediment. The relative mineralogical composition of the NSR sediment varies in relation
to the regional and local geology, textural composition and soil type and weathering rates in the
sediment source areas. The mineralogy is further influence by the grain size characteristics of each
sample.
Table 7: Mineralogy of NSR and tributary sediment (% by weight)
The mineralogical composition of tributary sediment was more variable (standard deviation) than
NSR sediments. This variation is due to the unique geochemical composition of tributary inputs that
results from the regional and local geology and soil type in the sediment source areas. The variation in
particle size and the dilution of geochemical signatures in the NSR is related to 1) the effect of flow
on sediment sorting based on particle size and density and 2) the type of sediment sampling method
Qu
artz
Alb
ite
Mic
rocl
ine
Mu
sco
vite
Ch
lori
te
Do
lom
ite
Cal
cite
Sme
ctit
e
Am
orp
ho
us
NSR Sites
average 33.8 11.2 4.4 10.6 2.3 12.7 7.1 3.0 16.3
median 32.7 11.3 4.5 10.7 2.2 11.8 7.0 3.0 17.6
stdev 4.5 2.3 1.1 1.8 0.5 3.5 1.3 0.9 4.4
max 47.2 15.5 6.9 15.4 3.8 20.9 10.6 5 25.9
min 27.6 4.8 2.1 6.5 1.4 4.7 4.3 2 5.6
Tributary sites
average 38.0 10.7 3.5 8.9 1.7 16.4 14.4 2.0 13.4
median 37.4 12.3 3.7 9.1 1.4 13.5 8.1 2.0 16.4
stdev 14.5 5.9 1.8 2.7 1.0 14.5 13.2 0.0 7.8
max 68.4 18.3 5.6 13.3 3.1 38.4 35.5 2 23.6
min 18.6 1 0.6 2.7 0.6 3.2 1.5 2 1.4
21
used in this study. For example, calcite levels in the Bighorn (36%), Cline (34%) and Stiffler River
(24%) sediments were elevated by a factor of ~4 to 5 compared to the calcite levels in the NSR.
Concentrations of dolomite were also elevated in the Bighorn (34%), Cline (31%) and Stiffler River
(38%). In contrast, quartz levels in sediment from these tributaries were markedly lower than in the
NSR. The data indicate that the Bighorn, Cline and Stiffler Rivers drain primarily calcareous parent
materials that are abundant at their confluence with the NSR. Because grab samples of cohesive bed
and bank deposits were used in this study, the variation in grain size is much higher than if suspended
solids samples would have been collected passively with time integrating sediment samplers.
Accordingly, the sediment sampling protocol will have a strong influence on the geochemical
properties of sediment.
Concentrations of carbon and nitrogen measured as a percent were low in the NSR and its tributaries
relative to data reported in the literature. The median TC concentration for NSR sediments was 0.81%
(± 0.28) compared to 0.44 (± 0.31) in tributary sediments. The median TN concentration for NSR
sediments was 0.07% (± 0.06) compared to 0.08 (± 0.07) in tributary sediments.
3.1.2. Particle Size
Particle size characteristics of NSR and tributary sediment are summarized in Table 8 and Table 9 and
inter-annual (2011 and 2012) variation in grain size characteristics of fine sediment deposits in the
NSR and its main tributaries are presented in Figure 2. Despite the intent of this project to sample
cohesive sediment deposits (bed and bank), the percentage of clay and silt in each sample was highly
variable and several samples consisted mainly of sand fractions. For example, tributary sediments all
consisted of > 75% sand and samples from the Duvernay Bridge and Anthony Henday Bridge were
comprised of > 85% sand. Alternative approaches to selectively sample cohesive sediment in the
water column during a range of flow events might be a useful consideration for future sediment
quality assessments in the NSR. For example, centrifuge samplers or less expensive time-integrating
samplers (Phillips et al. 2000) could be deployed in a longitudinal gradient along the river to passively
collect, composite samples of suspended solids. This type of sampler is routinely used to collect
sufficiently large sample mass for laboratory analyses in fingerprinting studies (Collins and Walling
2006; Walling et al. 2006, 2008; Collins et al. 2010b). The sampler is made of PVC pipe (98 mm
internal diameter, 1 m length) with two end caps containing a central inlet/outlet pipe (4 mm internal
diameter) as described by (Phillips et al. 2000) and provides a simple pragmatic means of capturing
the natural variation in sediment properties during snowmelt and storm events.
22
The average median diamater (D50) of NSR sediment was 90 µm compared to 228 µm for tributary
sediments. In 2010, D10 and D50 of NSR sediment was remarkably consistent downstream (Figure 2).
The D50 was relatively constant from Devon to the Pakham Bridge but inter-annual differences in
particle size properties (D10 and D90) are apparent. The data suggest inputs of coarse grained sediment
to the NSR at the Drayton Valley Bridge and Duvernay Bridge. Compared to other NSR sites, finer
sediment was observed at the Tomahwk Bridge, Beverly Bridge and Mrynam Bridge in 2010 and
these trends were more pronounced in 2011 (Figure 2). In 2011, the particle size range increased and
the D90 was larger than in 2010 samples. Sediment fining was observed at the Baptiste River
confluence, Tomahwk Bridge amd Elk Point Bridge sample locations.
Table 8: Particle size characteristics in NSR and tributary sediment.
Location D10 (μm) D50 (μm) D90 (μm)
North Saskatchewan River
average 23 90 262
median 17 86 232
min 3 17 66
max 121 273 487
stdev 20 46 115
Tributaries
average 34 228 530
median 27 235 496
min 12 62 221
max 78 360 921
stdev 21 101 219
23
Table 9: Specific surface area and textural composition of NSR and tributary sediment
SS
A (
m2/g
)
% c
lay
% s
ilt
% s
an
d
NSR 2010 Mean 0.287 3.51 36.43 63.57
Median 0.266 3.16 32.38 67.62
Max 0.596 7.87 70.31 85.59
Min 0.123 1.34 14.41 29.69
NSR 2010 Mean 0.252 3.02 33.75 66.25
Median 0.218 2.41 28.57 71.43
Max 0.908 13.41 75.76 96.03
Min 0.047 0.36 3.97 24.24
Tributaries Mean 0.161 2.01 18.98 81.02
Median 0.147 1.87 16.29 83.71
Max 0.285 3.78 47.03 94.82
Min 0.049 0.32 5.18 52.97
24
Figure 2: Temporal downstream variation in grain size characteristics of NSR sediments.
The potential of stream sediments to bind and concentrate pollutants such as metals is related to
physical (e.g. grain size, surface area, surface charge) and chemical (e.g. composition, cation
exchange capacity) properties of sediment (Horowitz and Elrick, 1987). These properties are related
and as grain size decreases, surface area and the concentration of many trace element concentrating
geochemical phases such as Fe and Mn oxides and hydroxides, organic carbon and clay minerals
typically increase (Forstner and Whitman, 1981). Surface area is an important factor controlling
sediment trace element concentrations and variability because most processes involved in sediment-
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20
10
Pa
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(μ
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Location
D10
D50
D90
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e B
rid
ge
Bev
erly
Bri
dge
Ho
rseh
ills
Cre
ek C
on
Fo
rt S
ask
atch
ewan
Bri
dge
Vin
ce B
rid
ge
Was
kat
enau
Bri
dge
Pak
an B
rid
ge
Du
ver
nay
Bri
dge
Mry
nam
Bri
dge
Elk
Po
int
Bri
dge
Lea
Par
k B
rid
ge
Llo
yd
min
ster
Fer
ry
20
11
Pa
rtic
le S
ize
(μ
m)
Location
D10
D50
D90
25
trace element interactions are governed by surface reactions or surface chemistry. Accordingly,
sediment with large surface areas have an increased number of binding sites and therefore tend to
concentrate and transport metals and other sediment-bound pollutants. Specific surface areas (m2/g) of
NSR and tributary sediments are summarized in Table 9. The median SSA of NSR sediment was
0.266 and 0.218 m2/g for 2010 and 2011, respectively. Median SSA for tributary sediment 0.147 m
2/g
and five sites had a SSA < 0.1 m2/g indicating that these samples were very coarse grained. Sampler
locations with the highest SSA were Tomahawk Bridge (0.908 m2/g), Lea Park Bridge (0.596 m
2/g)
and Baptiste River Confluence (0.528 m2/g).
3.1.3. Major Element Composition
Major element composition of NSR and tributary sediments are sumarrized in Table 10 and their
downstream inter-annual variation are illustrated in Figures 3 and 4. NSR sediments consist mainly of
SiO2, Al2O3, CaO and Fe2O3 but the geochemical composition by site was highly variable depending
upon source area geology and particle size characteristics. Compared to the NSR sediments, the
percent SiO2 was elevated in the upper five tributaries but lower in the bottom two tributaries (Figure
3). The bottom two tributaries of the NSR (Vermillion River and Sturgeon River) have the lowest the
lowest SiO2, Fe2O3 and Al2O3 concnetrations in the data set.
Table 10: Major element composition in NSR and tributary sediment.
SiO
2
Al 2
O3
Fe
2O
3
Mn
O
Mg
O
Ca
O
Na
2O
K2O
TiO
2
P2O
5
Cr 2
O3
LO
I
% % % % % % % % % % % %
North Saskatchewan River
Average 62.46 8.49 2.94 0.06 2.64 7.19 1.18 1.61 0.43 0.15 0.01 12.01
Median 62.33 8.51 2.94 0.06 2.56 7.10 1.23 1.63 0.43 0.16 0.01 12.08
Max 75.84 10.92 3.84 0.09 4.11 10.72 1.47 1.98 0.52 0.19 0.04 16.80
Min 54.60 5.72 2.02 0.04 1.34 4.43 0.64 1.21 0.25 0.11 0.01 5.46
StDev 4.40 0.92 0.35 0.01 0.53 1.24 0.20 0.12 0.05 0.02 0.01 2.31
Tributaries
Average 60.70 6.57 2.36 0.05 2.88 10.66 0.97 1.41 0.29 0.12 0.01 13.38
Median 67.17 6.72 2.38 0.05 1.81 6.22 0.93 1.31 0.31 0.12 0.01 10.04
Max 86.80 9.15 3.57 0.06 6.42 27.74 1.81 1.76 0.40 0.19 0.02 28.85
Min 29.28 3.99 1.57 0.03 0.31 0.86 0.22 1.10 0.17 0.08 0.01 2.49
StDev 19.96 2.10 0.66 0.01 2.25 10.31 0.55 0.28 0.09 0.03 0.00 10.07
26
Figure 3: Downstream variation in SiO2, Fe2O3 and Al2O3.
Spatial and inter-annual variation in MgO and CaO in NSR and tributary sediment are shown in
Figure 4. Lower concentrations of CaO and MgO are present in the upper five tributary sediments
compared to the bottom two tributaries. The major element data indicate the strong influence of local
and regional geology on the major element composition of sediment in the NSR. The data suggest that
27
the upper tributaries drain predominantly igneous and metamorphic substrates and that the lower
portions of the NSR receive higher inputs of carbonate rich materials from tributaries.
Figure 4: Downstream variation in MgO and CaO
3.1.4. Trace Elements
3.1.4.1. Total Metals
Metals enter aquatic environments from a variety of sources that include naturally occurring metals
through biogeochemical cycles (Garrett, 2000) and metals anthropogenic sources (Forstner and
Wittman, 1981). The transport behavior and bioavailability of metals is controlled by a variety of
environmental factors that govern the partitioning of metal ions between aqueous and particulate
phases (Salomons and Forstner, 1984; Horowitz, 1991). Sediments are the primary vector for metal
transport in aquatic systems (Horowitz, 1999) and once deposited (either short or long term) can
represent potential secondary sources of metal contamination in freshwater aquatic systems
(Salomons and Forstner, 1984). Changes in environmental conditions (e.g., variations in pH, redox
potential, metal concentrations in solution, and complexation) can influence the mobility and toxicity
28
of metals in sediments thus posing an environmental risk to aquatic biota and human health (Calmano
et al., 1993). Accordingly, these conditions influence the association of metals with sediments which
bind to sediment in various geochemical phases (Tessier et al., 1979).
Total metal concentrations in NSR and tributary sediments are presented in Tables 11 and 12. For
NSR sediments, Cr and Ni exceeded the consensus based threshold effect concentration (TEC) by
28% and 3% of the samples analyzed, respectively. Twenty percent of the tributary sediments
exceeded the Cr TEC (Table 12). Chromium levels were generally higher in 2011 than 2010 (Figure
5) and maximum concentrations were observed in a section of the NSR from the Waskatenau Bridge
to the Duvernay Bridge (Figure 6). Manganese levels were elevated and may be related to legacy of
wildfires in the province of Alberta. Contrary to other assessments of metals in the NSR (AECOM,
2011), no downstream increases in metal concentrations were observed on the data set.
Understanding the factors affecting the distribution of sediment-associated metals in the NSR is
complex and influenced by land use and a range of physical and biogeochemical processes that
influence metal source, mobility and fate. No longitudinal trends in metals were found in the data set.
This in part may be attributed to the variation in grain size of the sediment samples examined.
29
Table 11: Total metal concentrations in NSR sediments (µg/g)
Metal
Consensus
based TEC Mean
Med
ian
Ma
xim
um
Min
imu
m
Sta
nd
ard
Dev
iati
on
% of
samples
>TEC
As 9.79 2.79 2.55 5.50 1.50 1.00 0
Cd 0.99 0.50 0.50 0.50 0.50 0.00 0
Cr 43.4 37.05 35.55 84.00 16.90 14.95 28
Co N/A 3.85 3.78 4.80 2.37 0.47 N/A
Cu 31.6 7.42 6.87 18.71 3.34 2.46 0
Pb 35.8 5.94 6.00 6.90 4.30 0.60 0
Mn N/A 260.14 262.55 332.05 195.45 30.32 N/A
Hg 0.18 0.13 0.12 0.18 0.12 0.02 0
Ni 22.7 13.36 12.25 37.22 6.50 4.73 3
Zn 121 35.50 34.66 60.00 18.94 8.93 0
Table 12: Total metal concentrations in tributary sediments (µg/g)
Metal
Consensus
based TEC Mean
Med
ian
Ma
xim
um
Min
imu
m
Sta
nd
ard
Dev
iati
on
% of
samples
>TEC
As 9.79 3.00 2.60 6.80 1.60 1.49 0
Cd 0.99 0.50 0.50 0.50 0.50 0.00 0
Cr 43.4 30.91 29.40 54.40 13.80 14.14 20
Co N/A 3.26 3.18 4.05 2.70 0.53 N/A
Cu 31.6 6.34 6.36 10.20 3.74 1.89 0
Pb 35.8 5.03 4.75 6.60 3.80 0.95 0
Mn N/A 228.24 227.16 310.49 150.79 52.27 N/A
Hg 0.18 0.12 0.12 0.14 0.12 0.01 0
Ni 22.7 10.30 9.90 16.30 5.54 2.78 0
Zn 121 27.26 24.97 58.30 17.30 11.75 0
30
Figure 5: Spatial and temporal variation in Co, Cr, Ni and Pb in NSR and tributary sediments
31
Figure 6: Inter-annual (2010 & 2011) and spatial variation in total metal concentration of NSR
sediment
32
3.1.4.2. Metal Speciation in Sediment
In sediment assessment studies, total metal concentration in sediment is often determined and
compared to reference sites as a first synoptic step to evaluate the degree of metal contamination in an
aquatic system (Singh et al., 2005). While a variety of other more detailed approaches are used to
evaluate sediment quality and their environmental impact on aquatic biota (whole sediment toxicity
testing, spiked sediment toxicity testing, interstitial water toxicity approach, tissue residue approach,
documentation of the structure of benthic macroinvertebrate communities through the taxonomic
identification and threshold effect concentration approaches), these approaches to evaluate sediment
quality in the NSR and its environmental impact are beyond the scope of this sediment assessment
study.
A second and often used approach to evaluate sediment quality in aquatic systems is to determine the
sediment associated metal phases using sequential extraction methods (Franco et al 2007). The
advantages of sequential metal speciation over total metal extraction include: 1) assessment of the
source of a particular metal (i.e., natural or anthropogenic), 2) determination of the relative toxicities
to aquatic biota and 3) developing a better understating of metal-sediment interactions (Jain, 2004).
Since the mobility of a metal and its bioavailability also depend on its speciation, considerable
attention has been directed in sediment assessment studies towards employing a five-step sequential
metal extraction procedure to evaluate metal pollution. Metal speciation data for the NSR and
tributary sediments are presented in Figure 7.
Because Cr and Ni are the only two metals that exceeded the consensus based threshold effect
concentration (TEC), the association of only these two metals with various geochemical phases will
be discussed below. The sequential extraction data indicate that Cr is predominantly bound to silicates
(~92%, 83%) and to a lesser extent (~7%, 10%) to Fe and Mn oxides in NSR and tributary sediments,
respectively. Ni was predominantly bound to Fe and Mn oxides (63%, 43%) and silicates 31% and
21%), respectively. The data suggest a strong geological control on metal inputs to the NSR but that
metal levels likely increase when receiving inputs from industrial effluent and urban runoff.
33
Figure 7: Inter-annual variation in Pb, Hg, Ni, Zn speciation in NSR sediment
34
3.1.5. PAHs
3.1.5.1. Total PAHs
Polycyclic aromatic hydrocarbons (PAHs) are a group of more than 100 organic compounds with
fused aromatic carbon rings. They are widely distributed in the environment and because of their
carcinogenic and mutagenic properties; the USEPA has classified 16 PAHs as priority pollutants
(Gremm and Frimmel, 1994). PAHs can originate from geologic deposits (petrogenic origin, e.g., in
bitumen) but are mainly derived by processes such as combustion (pyrogenic origin) or microbial
degradation (diagenic origin). Because PAHs are hydrophobic, they preferentially bind to organic
matter and small particles in the water column and deposited sediments in aquatic systems.
A summary of PAHs and percentage of samples exceeding the TEC in NSR and tributary sediment
are presented in Table 13. The data show that PAHs are present but at concentrations well below the
consensus based threshold effect condition defined by MacDonald et al (2000). The total PAH
concentration (sum of the 15 congeners) ranged from varied from 7 to 40 µg/kg and the levels of total
PAH there was inter-annual and longitudinal variation in the data (Figure 8 and Figure 9) in PAH
congeners. For example, in 2010 the highest total PAH concentrations were measured in the
uppermost three NSR sites but these levels decreased in 2011. There was no increasing trend in PAH
concentration downstream.
Table 13: Summary of PAHs in NSR and tributary sediment (µg/kg)
Compund TEC Aromatic
Ring Mean Median Max Min StDev % of samples
>TEC
Napthalene 176 2 5.72 4.25 22.54 0.86 4.84 0
Acenaphthylene 3 0.53 0.50 1.81 0.50 0.19
Fluorene 77.4 3 0.62 0.50 3.11 0.50 0.43 0
Phenanthrene 204 3 2.34 1.59 21.34 0.50 3.67 0
Anthracene 57.2 3 0.54 0.50 2.43 0.50 0.27 0
Fluoranthene 423 4 0.74 0.50 3.82 0.50 0.57 0
Pyrene 195 4 0.81 0.50 4.42 0.50 0.67 0
Benzo(a)anthracene 108 4 0.55 0.50 2.35 0.50 0.28 0
Chrysene 166 4 0.65 0.50 3.85 0.50 0.55 0
Benzo(b)fluoranthene 5 0.82 0.50 8.83 0.50 1.22
Benzo(k)fluoranthene 5 0.55 0.50 2.86 0.50 0.33
Benzo(a)pyrene 150 5 0.67 0.50 5.09 0.50 0.73 0
Indeno(123-cd)pyrene 5 0.52 0.50 1.30 0.50 0.11
Dibenzo(ah)anthracene 33 6 0.60 0.50 3.26 0.50 0.45 0
Benzo(ghi)pyrene 6 0.68 0.50 6.36 0.50 0.88
35
Figure 8: Spatial and temporal variation of total PAHs in NSR sediment
36
Figure 9: Spatial and temporal variation of individual PAHs in NSR sediment
37
The relative abundance of PAHs to two- and three-ring hydrocarbons can be used to help distinguish
between petrogenic and pyrogenic sources (Robertson, 1998). For example, phenanthrene:anthracene
(PHE/ANT) and fluoranthene:pyrene (FLT:PYR) ratios have been widely used to distinguish between
PAHs of diverse origin (Gschwend and Hites, 1981; Sicre et al., 1987; Colombo et al., 1989;
Budzinsky et al., 1997). Simultaneous study of these two ratios can allow the definition of two
different classes of sediments: PHE:ANT and FLT:PYR for petrogenic inputs and PHE:ANT and
FLT:PYR for the dominance of pyrolitic sources (Budzinsky et al., 1997). Robertson (1998)
demonstrated that the relative abundance of two- and three-ring hydrocarbons can be used to
distinguish between petrogenic and pyrogenic sources. For example, phenanthrene:anthracene
(PHE/ANT) and fluoranthene:pyrene (FLT:PYR) ratios have been widely used to distinguish between
PAHs of diverse origin (Gschwend and Hites, 1981; Sicre et al., 1987; Colombo et al., 1989;
Budzinsky et al., 1997). Simultaneous study of these two ratios can allow the definition of two
different classes of sediments: PHE:ANT and FLT:PYR for petrogenic inputs and PHE:ANT and
FLT:PYR for the dominance of pyrolitic sources (Budzinsky et al., 1997).
Ratios of PAH compounds can be used to define different classes of sediments: PHE:ANT > 10 for
petrogenic inputs and PHE:ANT < 10 for the dominance of pyrolytic sources (Budzinsky et al., 1997).
Sicre et al., (1987) reported that PHE:ANT was less than 15 for the incomplete combustion of organic
matter such as coal or crude oils (Benner et al., 1990). Before combustion petroleum products often
have much lower ratios and Williams et al. (1986) report values range from 4 to 10 for gas-oils. For
crude oils, PHE/ANT ratios are approximately 14 (Benner et al., 1990). When FLT:PYR ratios are
greater than 1 they are considered to be of pyrolytic origin and are mainly due to mining and
combustion fossil fuels and some industrial discharges (Sicre et al., 1987).
Raoux, (1991) suggested that ratios of PHE/ANT and FLT:PYR should be examined jointly because
this approach provides information about PAH sources. Variation in PHE:ANT vs FLT:PYR are
plotted in Figure 10 for all NSR and tributary sediments. The figure demonstrates that the majority of
PAHs are of pyrolytic origin (likely due to mining and combustion of fossil fuels and some industrial
discharges). Two exceptions in the data set are Drayton Valley Bridge (PHE/ANT 42.68) and Baptiste
River near the mouth (PHE/ANT 33.02). PAHs at these two sites are likely of petrogenic origin from
sources such as petroleum, crude oil and its refined products. Some PAH natural sources include
forest fires and natural erosion from coal or bitumen seams.
38
Figure 10: Ratios of PHE:ANT and FLT:PYR for NSR and tributary sediments.
39
3.2. Part 2 – SEDIMENT SOURCE APPORTIONMENT
Local optimisation using the different optimum composite signatures consistently performed better
than the GA-driven searches, increasing confidence in the selection of the former sets of mixing
model solutions. Previous work has shown that the comparison of local and global optimisation for
sediment source fingerprinting must be undertaken on a dataset specific basis (Collins et al., 2010b, in
press). Figure 11 presents the probability density functions (pdfs) for the predicted deviate median
contributions from the individual tributary sub-catchment spatial sediment sources, using each (KW
and PCA derived) composite signature. These pdfs summarise feasible solutions generated using the
Monte Carlo repeat iterations. Since the signatures selected using both KW and PCA yielded highly
acceptable GOF estimates (Table 14), the source apportionment estimates provided by both were
taken to be equally acceptable. The 95% confidence limits about the predicted average median spatial
source proportions generated using the repeat sets of Monte Carlo analysis, indicated convergence of
the model solutions and their reproducibility within ±1%. As a means of summarising the mixing
model output pdfs, the relative frequency-weighted average median spatial sediment source
contributions provided by each of the KW and PCA optimum composite signatures, the Q1-Q3 ranges
in the corresponding output pdfs (Figure 11) and the overall average relative frequency-weighted
median inputs from each of the ten tributary sub-catchment spatial sediment sources are presented in
Table 15. Overall relative frequency-weighted average median spatial source contributions were
estimated to be 11% (Vermilion River), 19% (Sturgeon River), 6% (Brazeau River), 12% (Baptiste
River), 11% (Nordegg River), 14% (Clearwater River), 15% (Ram River), 4% (Bighorn River), 4%
(Cline River) and 4% (Siffleur River)
.
40
Figure 11: Probability density functions (pdfs) for the predicted deviate median relative contributions from each tributary sub-catchment spatial sediment source identified
for the NSR
41
Table 14: The goodness-of-fit (GOF) for the mixing model runs based on each composite signature.
Composite signature for discriminating the spatial sediment sources GOF
KW-H 0.95
PCA 0.97
Table 15: Relative frequency-weighted average median spatial sediment source contributions, based
on each optimum composite fingerprint.
Composite
signature Vermilion River
Sturgeon
River
Brazeau
River
Baptiste
River
Nordegg
River
KW 9 23 5 12 12
Q1-Q3 range 0-15 14-33 0-7 1-20 0-20
PCA 13 15 7 13 10
Q1-Q3 range 2-21 7-22 0-11 5-20 0-17
Overall average 11 19 6 12 11
Composite
signature
Clearwater
River Ram River
Bighorn
River Cline River Siffleur River
KW 14 13 4 4 5
Q1-Q3 range 6-23 5-20 0-5 0-4 0-7
PCA 15 17 4 4 4
Q1-Q3 range 5-23 12-24 0-5 0-4 0-5
Overall average 14 15 4 4 4
Table 16 presents the results of the diagnostic uncertainty analysis for the numerical mass balance
modeling. The r values quantify the correlation between the objective function solutions and the
variability associated with tracer values for tributary sub-catchment or NSR main stem sediment, or
the corrections and weightings used in the modelling. In the case of the optimum composite signature
selected using the KW test, the greatest contributions to the uncertainty budget for the objective
function solutions were generated by the NSR main stem sediment values for Mg (r = -0.38) and Ca (r
= -0.23) and the tracer property discriminatory weighting for Ca (r = 0.22). For the optimum
composite fingerprint identified using PCA, the most important contributions to the uncertainty
budget for the objective function solutions were generated by the NSR main stem sediment values for
Mg (r = -0.53) and Ca (r = -0.35) and the tracer property discriminatory weighting for Ca (r = 0.25).
42
Table 16: Results of the diagnostic evaluation of the uncertainty associated with the mass
balance modelling for spatial sediment sources on the NSR.
KW signature PCA signature
Variable r Variable r
Sed_Mg -0.38 Sed_Mg -0.53
Sed_Ca -0.23 Sed_Ca -0.35
Pdis_Ca 0.22 Pdis_Ca 0.25
Sed_Ge -0.21 Pdis_Mg 0.21
Sed_Cr -0.16 Source_Siffleur_Ca 0.16
Pdis_Mg 0.16 Source_Cline_Mg 0.15
Sed_Bi -0.13 Source_Cline_Ca 0.15
Sed_CaO -0.11 Source_Bighorn_Ca 0.13
Source_Cline_Ca 0.11 Pdis_CaO 0.12
Source_Siffleur_Ca 0.10 Sed_CaO -0.12
Source_Siffleur_Mg 0.10 Source_Siffleur_CaO 0.11
Pdis_CaO 0.10 Source_Siffleur_Mg 0.11
Source_Bighorn_Mg 0.10 Source_Bighorn_Mg 0.11
Source_Bighorn_CaO 0.08 Source_Bighorn_CaO 0.10
Sed_Hf -0.07 Source_Cline_CaO 0.09
Source_Bighorn_Ca 0.07 Pdis_MnO 0.08
Source_Brazeau_Cr 0.06 Sed_TiO2 0.07
Source_Cline_Mg 0.06 Source_Bighorn_MgO 0.07
Source_Cline_CaO 0.06 Pdis_MgO 0.06
Source_Nordegg_Cr 0.06 Sed_MgO -0.06
Pdis_Cr 0.05 Pdis_Ba 0.06
Source_Nordegg_Ca 0.05 Source_Siffleur_MgO 0.06
Source_Nordegg_CaO 0.05 Source_Nordegg_CaO 0.05
Source_Brazeau_Ca 0.05 Source_Clearwater_Mg 0.05
Source_Cline_CaO 0.05 Source_Cline_MgO 0.05
Pdis_Bi 0.05 Source_Clearwater_Ca 0.05
Sed_Sr -0.04 Source_Brazeau_CaO 0.05
Pdis_Eu 0.04 Source_Cline_K 0.05
Source_Vermilion_Ge 0.04 Source_Bighorn_P2O5 -0.05
Source_Ram_Mg 0.04 Sed_Fe2O3T 0.05
Source_Ram_Hf 0.04 Source_Vermilion_Ca 0.05
Source_Sturgeon_Eu -0.04 Source_Sturgeon_Fe2O3T 0.05
Pdis_Ge 0.04 Pdis_Sr 0.05
Source_Bighorn_MgO 0.04 Source_Brazeau_Ca 0.05
Pdis_MnO -0.04 Source_Ram_TiO2 -0.05
Source_Clearwater_Ge -0.04 Source_Sturgeon_SiO2 0.05
Sed_XX: tracer property for NSR main stem sediment where XX is the geochemical property
Source_XX_YY: tracer property for source XX indicates the tributary sub-catchment spatial source category
and YY indicates the geochemical property
Pdis_XX: tracer property discriminatory weighting factor where XX is the geochemical property
43
A number of potential limitations should be taken into consideration when interpreting the findings of
this investigation. The mixing model estimates represent relative, as opposed to absolute,
contributions. Bed sediment loadings across the NSR will vary temporally (e.g. seasonally) and the
representativeness of the sample set should therefore be borne in mind. Some previous sediment
tracing work has coupled source apportionment with measurements of sediment pressure to provide
the magnitude of the inputs from specific sources (cf. Collins et al., 2010a). Whilst the findings
suggest that sediment mitigation planning in the study area needs to target the Sturgeon River,
Baptiste River, Clearwater River and Ram River tributary sub-catchments in particular, management
strategies for combating diffuse pollution, including sediment, need to consider detrimental impacts
on aquatic ecology such as fish and macroinvertebrates. Further work could therefore be undertaken
to explore source apportionment for the sediment fractions most responsible for damaging ecology
and influencing the transfer of harmful excess nutrients and contaminants (very fine clays and
colloids), as opposed to the bulk <63 µm fraction. The practicality of such work is now greatly
enhanced by newly developing laboratory equipment for the analysis of bulk sediment samples.
Equally, the sourcing work could be expanded to include investigation of the contributions from
sediment source types (e.g. forests, agricultural land, urban areas, channel banks) in the most
important tributary sub-catchments identified by this spatial sourcing exercise. Whereas this sourcing
exercise focused primarily on the inorganic fraction of the available sediment samples from the NSR,
future work could also examine the key sources of the organic fractions harmful to freshwater ecology
on account of their influence on sediment oxygen demand in the aquatic environment.
44
4. CONCLUSIONS
1. NSR and tributary sediments consist of varying concentrations silicates (quartz),
feldspars (albite, microcline), micaceous phyllosilicates (chlorite muscovite), carbonates
(dolomite, calcite), clay minerals (smectiite) and amorphous groups). However, the
relative mineralogical composition of the NSR sediment varies in relation to the regional
and local geology, textural composition and soil type and weathering rates in the
sediment source areas. The mineralogy is further influence by the grain size
characteristics of each sample.
2. NSR sediments consist mainly of SiO2, Al2O3, CaO and Fe2O3 but the geochemical
composition by site was highly variable depending upon source area geology and
particle size characteristics. Only Cr and Ni exceeded the ISQG.
3. PAHs predominantly of pyrolytic origin were detected below ISQG levels across the NSR.
4. A recently refined geochemical composite fingerprinting procedure incorporating a new
approach for identifying statistically robust signatures has been successfully used to
apportion contemporary channel bed sediment sources in the NSR. By combining
composite signatures verified using KW and PCA, with GA-DFA, the revised statistical
component of the methodology provided a basis for extracting maximum value from the
available geochemical datasets. Future work could usefully present the spatial source
proportions using the different optimum signatures to catchment stakeholders with a
view to coupling source apportionment estimates with consensus building founded on
local knowledge and experience.
45
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