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 [email protected]
Dr Adrian Collins Principal Scientist Head of Water Quality Science ADAS UK Ltd. Environment Group Woodthorne Wergs Road Wolverhampton WV6 8TQ UK [email protected]
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.
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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.
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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. 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).
1 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
2 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,
3 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.
4 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
Distance Site Latitude Longitude (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
5 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
6 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
7 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 th th Mastersizer 2000. The median diameter (D50) and the diameter for the 10 and 90 % of each size distribution (D10, D90) are presented.
8 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
9 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
10 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.
11 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
12 On the basis of the results for the Lilliefors test, tracer parameter distributions were defined using the measured median and robust scaling estimator Qn proposed by Rousseeuw and Croux (1993) as an alternative to the median absolute deviation (MAD):
Qn d xi x j ;i j (k) [1]
n where d is a constant factor (1.0483), xi x j is the pairwise distances and k = 2 n n / 4 where h 1 is roughly half the number of the observations. The same 2 2 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.
13 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.
14 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.
15 Table 5: Ranked property loadings provided by the outputs of the PCA.
Property PC-1a Property PC-2b 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 16 Table 6: The optimum composite signatures selected using KW and PCA.
KW PCA Property %1 TDW2 Property %1 TDW2 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 Total3 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:
2 n m si Ci Ps S siZ sOs SV / Ci Wi i1 s1 [2] where: Ci = deviate median concentration of fingerprint property iin NSR main stem bed sediment samples; Ps = the optimized percentage contribution from tributary sub-catchment spatial source s;
S si = deviate median concentration of fingerprint property iin tributary sub-catchment spatial source s; Z = particle size correction factor for tributary sub-catchment spatial source ; O = organic
17 matter content correction factor for tributary sub-catchment spatial source s; SVsi = weighting representing the potential within-source variation of fingerprint property iin tributary sub-catchment spatial source category ; Wi = 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 Qn 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 R vi Fi [3] i1 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.
18 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.
19 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)
Quartz Albite Microcline Muscovite Chlorite Dolomite Calcite Smectite Amorphous 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
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
20 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.
21 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
22
Table 9: Specific surface area and textural composition of NSR and tributary sediment
/g)
2
SSA (m SSA clay % silt % sand % 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
23 500.00
400.00
300.00
200.00
100.00 D10 0.00 2010 2010 Particle Size(μm) D50
Devon D90
Vince Bridge
PakanBridge
BaptisteRiver…
Beverly Bridge
GenesseBridge
MrynamBridge
Quesnell Bridge
Lea ParkLea Bridge
Elk BridgePoint
Duvernay Bridge
TomahawkBridge
FortSaskatchewan…
Walter DaleBridge
LloydminsterFerry
WaskatenauBridge
HorsehillsCreek Con
Drayton ValleyBridge
Rocky MountainHouse Anthony HendayBridge Location
500.00
400.00
300.00
200.00
100.00 D10 2011 2011 Particle Size(μm) 0.00 D50
Devon D90
Vince Bridge
PakanBridge
Beverly Bridge
GenesseBridge
MrynamBridge
Quesnell Bridge
Lea ParkLea Bridge
Elk BridgePoint
Duvernay Bridge
TomahawkBridge
Walter BridgeWalter Dale
LloydminsterFerry
WaskatenauBridge
HorsehillsCreek Con
Drayton ValleyBridge
Rocky MountainHouse
Anthony HendayBridge
BaptisteRiver Confluence FortSaskatchewan Bridge Location
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-
24 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 m2/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 m2/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.
3 3
3
2 5
2
O
O O
O
2
O 2 2
2
O
2
2
SiO Al Fe MnO MgO CaO Na K TiO P Cr LOI
% % % % % % % % % % % % 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
25
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
26 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
27 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.
28
Table 11: Total metal concentrations in NSR sediments (µg/g)
% of
Consensus samples
Median
Standard
Deviation Minimum Metal based TEC Mean Maximum >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)
d d r % of
Consensus samples
Median
Standa
Deviation Minimum Metal based TEC Mean Maximum >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
29
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 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.
32
Figure 7: Inter-annual variation in Pb, Hg, Ni, Zn speciation in NSR sediment
33 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)
Aromatic % of samples Compund TEC Ring Mean Median Max Min StDev >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
34
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 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.
37
Figure 10: Ratios of PHE:ANT and FLT:PYR for NSR and tributary sediments.
38 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) .
39
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 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 Sturgeon Brazeau Baptiste Nordegg signature Vermilion River River River River 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 Clearwater Bighorn signature River Ram River 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).
41 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
42 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.
43 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.
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