Variation in Trace Metal Concentrations in a Fluvial Environment, Ottawa River, Toledo, Ohio

VARIATION IN TRACE METAL CONCENTRATIONS IN A FLUVIAL ENVIRONMENT, OTTAWA RIVER, TOLEDO, OHIO.

Mitra B. Khadka

A Thesis

Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the requirements for the degree of

MASTER OF SCIENCE

December 2010

Committee:

Sheila J. Roberts, Advisor

James E. Evans

Enrique Gomezdelcampo

ABSTRACT

Sheila J Roberts, Advisor

Surface sediment samples were collected from a 1000 m meander reach of the Ottawa

River, Ohio and analyzed for trace metals (Zn, Pb, Sr, Mn, Cu, Cr, Co, Ba, Ti, Cd, and Hg) by

Inductively Coupled Plasma Optical Emission Spectrometer (ICP-OES) to determine the local variability in their concentrations between geomorphic features. Eight metals (Zn, Pb, Sr, Mn,

Cu, Cr, Co, and Ba) show significantly different concentrations between five fluvial geomorphic features namely, flood plains, point bars, lateral bars, pools, and riffles. Among the features, flood plains and lateral bars are places where flows are decelerated due to surface roughness and vegetation cover, promoting the deposition of fine-grained sediments and organic matter. Thus, flood plains and lateral bars consistently exhibit the highest metal concentrations. The lowest metal concentrations in point bars are attributed to relatively coarse-grained sediment and low organic matter content. The difference in mean metal concentration between flood plains and point bars ranges from 5 times for Co to 12.5 times for Pb. It was found that Zn, Sr, Mn, Cu, Cr,

Co, and Ba are influenced by similar transport and interaction processes, and possibly have common sources, while Pb shows a weak to non-significant association with other metals indicating either a different mode of transport or separate anthropogenic sources. The concentrations of all metals in 89% of the samples are below the Threshold Effect Level (TEL) and the Probable Effect Level (PEL), and thus sediment contamination issues are to be minor concern in this section of the river. The finding that flood plains serve as sediment-associated metals sink can have important implications for monitoring and regulation, impact assessment, iii

and remediation of contaminated sediments in metal contaminated section of the Ottawa River or for other rivers having similar geomorphological, hydrological, and sedmentological characters.

Dedicated to my loving parents. v

ACKNOWLEDGMENTS

This thesis work is a product of innumerable help and support kindly provided to me by various people to whom I am highly indebted.

At first, I would like to express my deep sense of gratitude to my loving parents for their continuous support and inspiration. I would like to express my profound gratitude to my advisor,

Dr. Sheila J. Roberts for her worthy suggestions, encouragement, kind supervision, and generous co-operation, without which this thesis would not have taken its form. I am very much thankful to my thesis committee member, Dr. James E. Evans who provided helpful suggestions and great assistance during my field work and lab work. Similarly, I would like to thanks Dr. Enrique

Gomezdelcampo for taking time to be on my committee and encouraging me at various occasions.

I am also grateful to Dr. Shridhar for providing me a great help and support during trace metal analysis. My special thanks goes to Department of Geology, Bowling Green State

University for providing financial support and to Wildwood Metropark, Toledo who gave me permission to conduct research and collect the sediment samples within the park area. Last but not least, I am very much obliged to my dear friends Hari, Senthil, Asako, and Bharat who helped me right from the beginning to the end of the work in various ways.

TABLE OF CONTENTS

Page

INTRODUCTION...... …...... 1

Previous Study ...... 3

STUDY AREA AND METHODOLOGY ...... 8

Background…...... 8

Bedrock Geology and Structure ...... 8

Glacial Geology ...... 10

Soils…...... 14

Ottawa River ...... 16

Drainage Basin Characteristics ...... 16

Hydrometeorology ...... 18

Study Site ...... 20

Previous Studies ...... 22

Methods……...... 25

Geomorphic Features ...... 25

Sediment Sampling ...... 29

Grain Size Analysis and Organic Matter ...... 30

Trace Metal Analysis ...... 32

Quality Assurance ...... 35

Statistical Analysis ...... 36

RESULTS……………...... 38

Role of Fluvial Geomorphology ...... 38

Metal Variability between Geomorphic Features ...... 38 vii

Pair-wise Comparison ...... 44

Upstream-Downstream Variation ...... 45

Grain Size Distribution and Organic Matter ...... 46

Correlation Analysis ...... 48

Principal Component Analysis...... 52

Sediment Quality Assessment ...... 55

DISSCUSSION……...... 58

Variation in Trace Metal Concentrations ...... 58

Metal Association and Possible Sources ...... 62

Sediment Pollution ...... 64

Implication…...... 65

SUMMARY AND CONCLUSIONS ...... 66

REFERENCES ...... 68

APPENDICES ...... 77

APPENDIX A. SAMPLE LOCATION ...... 78

APPENDIX B. TRACE METAL CONCENTRATION FOR ALL SAMPLES ...... 79

APPENDIX C. SAND, MUD, AND ORGANIC MATTER CONTENT ...... 80

APPENDIX D. INDIVIDUAL SAMPLE GRAINSIZE STATISTICS ...... 81

APPENDIX E. GEOMORPHIC FEATURES GRAINSIZE STATISTICS ...... 97

viii

LIST OF FIGURES

Figure Page

1 Increase in metal concentration with decreasing sediments

size in the River Ems ...... 5

2 Bed rock geology of Toledo area showing the study area ...... 9

3 Figure showing the location and general extent of glacial lakes

in the Lake Erie Basin from 16 Ka through 5 Ka ...... 13

4 General soil map of Lucas County, Ohio showing location of the study area ...... 15

5 Ottawa River and Ten Mile Creek watershed ...... 17

6 Mean Monthly Rainfall recorded at the Toledo Regional Airport ...... 19

7 Mean daily discharge and precipitation hydrograph of the Ottawa River...... 19

8 Areal view of the study section of the Ottawa River with sediment

sample locations ...... 21

9 Trace metal concentration in Ottawa River sediments measured

in river length from 0 to 14 km ...... 23

10 Photographs depicting the spatial distribution of different geomorphic features ...... 27

11 Schematic geomorphological map of about 150 m long study reach

illustrating the spatial distribution of different types of geomorphic features ...... 28

12 Box plot of metal concentrations in different geomorphic features ...... 42

13 Cumulative grain size distribution ...... 47

14 Matrix plot of the eight trace metals showing the relation between them ...... 50

15 Matrix plot illustrating the relationship between trace metal

concentrations, organic matter and silt + clay content in sediments ...... 51

16 Scatter plot showing the relation between silt + clay and organic ix

matter in fluvial sediments ...... 52

17 Loading and score plots of first two principal components ...... 54

18 Dendrogram of the cluster analysis of the trace metal concentrations ...... 55

19 Trace metal concentrations at different sampling points with their

associated sediment quality guidelines ...... 56

LIST OF TABLES

Table Page

1 Bed rock stratigraphy of the study area ...... 9

2 Glacial Lakes in the Erie Basin ...... 12

3 Sediment contamination on the Ottawa River near RK 18 ...... 24

4 Total number of sediment samples collected from each

type of geomorphic feature ...... 30

5 Formulas for calculating grain-size statistical parameters by graphical methods ...... 32

6 Quality assurance data for blank, replicate, and soil standard samples ...... 36

7 Descriptive statistics of trace metals in each geomorphic feature ...... 39

8 Results of Kruskal-Wallis Test ...... 41

9 Pair-wise differences in concentration between geomorphic

features based on Mann-Whitney test ...... 45

10 Mean sediment characteristics and average organic matter

content within a particular geomorphic feature ...... 48

11 Correlation coefficient and p-values for trace metal

concentrations, silt + clay, and organic matter ...... 49

12 Principal component loadings from PCA on all trace metal data set ...... 53

INTRODUCTION

The contamination of river sediments with trace metals and the dispersal of these

sediment-related contaminants are critical issues, particularly in streams draining highly

populated urban and industrialized areas. The environmental consequences of trace metal

contamination are well-documented and include loss and degradation of habitat as well as

damage to plants and animal life in and around streams (Luoma, 1983; Dickson et al., 1987).

Moreover, because trace metals become increasingly concentrated as they are transferred from

one tropic level to another in the food chain (Marcus, 1991), the impact of contamination can

extend beyond the stream environment. Previous studies have shown that in aquatic systems

many heavy metals are intimately associated with particulate matter. Almost 90-99 % of the total metal load in streams is transported in the particulate phase, depending on the geochemical behavior of the metal and the nature of the physical and chemical environment (Trefrey and

Presley, 1976; Gibbs, 1977; Martin and Maybeck, 1979; Salomons and Forster, 1984).

Therefore, understanding of transport and redistribution of trace metals in stream sediments is of great importance because of their impact on the ecosystem.

To understand the extent of metal pollution in an aquatic system, it is important to know the content of metals in sediments and the distribution patterns and processes controlling their dispersion and disposal; once a contaminant is bound to particulate material, knowledge of particle dynamic is very important in determining its fate (Dyer, 1989; Balls, 1990). The dispersal mechanisms of the trace metals in the river sediments are complex processes which are controlled by various factors. Sediment size, sediment texture, mineralogy, hydrology, climate, and geomorphology are important parameters affecting the metal mobility and dispersion in estuarine and fluvial systems (Horowitz, 1991; Eyre and McConchie, 1993; Gallart et al., 1999; 2

Ansari et al., 2000). Some studies mention that the distributions of trace metals in sediments are

strongly related to the spatial aspects of fluvial processes (Graf et al., 1991; Miller, 1997;

Ciszewski, 1998). However, the roles of fluvial geomorphic processes on the spatial variability

of trace metals in river sediments are yet to be fully understood. Even less is known about the

spatial distribution of contaminants in stream sediments at reach scales.

Most of the research on trace metal distribution in fluvial systems have focused on describing downstream dispersion over large scale to assess watershed-wide response to contaminant input (Lewin et al., 1977; Graf, 1985; Higgis et al., 1987; Marcus, 1987; Rhoads and Cahill, 1999; Ansari et al., 2000 etc.). Studies have shown that, at large scales, concentrations of heavy metals along an individual river generally decrease with distance from a point source (Wolfenden and Lewin, 1978; Marcus, 1987; Axtmann and Luoma, 1991). Some reduction in variability can be due to dilution with clean uncontaminated sediments caused by tributary additions (Wofenden and Lewin, 1978; Marcus, 1987; Axtmann et al., 1997) or from changes in geochemical factors that put metals in solution (Moore et al., 1991). However, Taylor

(2007) found that the spatial distribution of sediment-associated metals downstream of the Rum

Jungle Mine site does not display a simple distance–metal concentration decay pattern. He suggests that the non-uniform spatial distribution of sediment-associated metals is a function of local, reach-scale variations in channel geometry and geomorphology, which control sediment storage and transfer patterns. At the individual reach scales, the variability in metal concentration can be substantial. A variety of distinct fluvial environments exist at the reach scale with considerably varying hydrodynamic condition not only between, but also within, each of these reach-scale environments, and produce considerable sorting of mobile bed sediments over short distances depending on their density and size, the turbulence and velocity of current flow, and 3

the bed morphology (Parker and Andrews, 1985; Bubb et al., 1991; Ashworth et al., 1992;

Rhoads, 1996). Therefore, at the local scale, a sound understanding of the geomorphic features of

the stream environment becomes crucial for determining spatial variation in contaminant

concentrations (Marcus, 1989)

Documenting and defining the relation between local variation in trace metal

concentrations and sediment sorting associated with reach scale geomorphologic variability is

crucial for understanding the local variation in environmental impacts and consequently for

implementing and monitoring the remedial action. The goal of this study is to determine the role

of channel morphology and fluvial processes as a factor controlling the accumulation and

distribution of trace metals in river sediments at the local scale in the Ottawa River, Ohio. The

lateral and vertical variability in metal concentrations is more strongly related to physical

processes of sedimentation than to chemical mobility (Graf et al., 1991; Ciszewski, 1998,

Rhoads and Cahill, 1999), and also, hydraulic factors are largely responsible to determine the

channel geomorphology (Schumm and Lichty, 1965). So, the hydraulic controls on the spatial

distribution of trace metal can be well reflected through hydraulically related channel

geomorphology and processes. The main objectives of this study are: (1) to determine the

variation of trace metal concentration in the river sediments between a set of local geomorphic

features, and (2) to determine the extent of sediment contamination by certain trace metals in a reach of the Ottawa River.

Previous Studies

The concentration and distribution of metals in stream sediments vary spatially as a

function of grain size, chemistry, hydraulics and geomorphic features. 4

Sediment grain size is the most significant factor controlling both suspended and

deposited sediments capacity for concentrating and retaining trace metals in aquatic system

(Gibbs, 1977; Forstner and Wittmann, 1981). Studies show that increasing trace metal

concentration has been associated with decreasing grain size (Figure 1) because smaller

sediments have a greater surface area per volume of sediments to which metals can bond

(Whitney, 1975; Gibbs, 1977; de Groot et al., 1982; Salomons and Forstner, 1984) and have a

higher cation exchange capacity than larger grains (Horowitz, 1991). However, some studies

have indicated that coarser particles show similar or even higher heavy metal concentrations than

finer ones (Graf et al., 1991; Tessier et al., 1982; Singh et al., 1999). The longer residence time at

any particular site, presence of clay rims on sand-sized quartz grains, and limited transport of coarser particles are possibly responsible for higher metal content in the coarser size fractions

(Tessier et al., 1982; Singh et al., 1999). Alternatively, this can also be due to the presence of higher concentration of metal scavenging iron and manganese oxides in these fractions

(Whitney, 1975; Moore et al., 1989). On the other hand, a study by Zhu et al. (2006) shows no gradual decrease or increase in the metal concentrations from finer to coarser sediments in lake sediments. These findings demonstrate that density may be an important factor influencing metal concentrations among different grain size sediment fractions. Conversely, Graf et al. (1991) found that in coarse-grained deposits in an ephemeral stream, the concentrations of some metals

increased with decreasing sediment size due to the fine-grained nature of the tailing metal

source. Therefore, no consistent relationship has been established between grain size and metal

concentration in a stream. 5

Figure 1: Increase in metal concentration with decreasing grain size in the River Ems (From de Groot et al., 1982).

Previous studies looking at variation in metal concentration in the fluvial environment have mainly documented geochemically controlled changes immediately downstream of acid

seeps, where rapid changes in pH, total dissolved solids, and dissolved oxygen content may

occur (Forstner and Wittmann, 1981; Salomons and Forstner, 1984; Rampe and Runnels, 1989).

However, studies suggest that in most natural systems, local variability of the metal

concentration in sediments is controlled more by hydraulics in which sediments are transported

and deposited than by proximal-distal geochemical gradients (Moriarty et al., 1982; Graf et al.,

1991; Marcus, 1996). 6

Due to variable hydrodynamic conditions, local variability in metal concentration in river sediments occurs both within and between individual types of the fluvial depositional environments (Moriarty et al., 1982; Moore et al., 1991; Ciszewski, 1998; Rhoads and Cahill,

1999). Local-scale variability in metal concentration has been documented between bars, sloughs, and main channel deposits in ephemeral streams (Graf et al., 1991), between riffle environments (Moriarty et al., 1982), and between topographic features such as point bars, channel fills, ridges and swales (Wolfenden and Lewin, 1977). A study by Ciszewski (1998) shows that the most intensive accumulation of sediments contaminated with heavy metals occurs in places where flow is decelerated due to considerable friction such as flood plains and channel bars, and the smallest variation in metals concentration occurs in active channel. On the contrary, in channels of ephemeral rivers, the maximal concentration occurs in the zone of the most frequent flow and much smaller concentrations are in the near bank deposits (Graf et al., 1991).

These local differences in metal concentration are caused by the variability in tractive forces which selectively sort sediments by size and density (Day and Fletcher, 1989; Marcus, 1996).

Specifically, the accumulation of heavy metals at unit and reach-scales in rivers is due to a variation in tractive forces resulting from changes in particle roughness, bed forms, and channel geometry (Slingerland and Smith, 1986).

Ladd et al. (1998) found significant differences in metal concentration between some morphologic units in sediments <2 mm in diameter. Their study shows that eddy-drop zones and attached bars consistently have the highest metal concentrations, while low-gradient riffles, high- gradient riffles, and glides typically have the lowest metal concentrations. However, their study did not find the significant lateral and longitudinal variation of metals within geomorphic units.

Further, a study by Rhoads and Cahill (1999) shows that the highest concentration of metals 7 occurs in two types of fluvial geomorphic environments: (1) regions of low velocity or flow recirculation such as on point bar surfaces or at stagnation zone in confluences, places that promote the accumulation of fine-grained sediment or particulate organic material, which facilitate adsorption of metals, and (2) regions of intermediate velocity that concentrate sand-size heavy minerals and metal particulates (placers). Fletcher et al. (1987) documented greater variations in Sn concentration occurring in higher energy environments. Hakanson (1984) also noted increased variability in metal concentration within transportational and erosional environments when compared to depositional settings. In contrast, Moriarty et al. (1982) found that metal concentration within individual riffles did not vary significantly. Likewise, Day and

Fletcher (1989) noted little difference in Au concentration between bar-head gravel deposits.

Though the aforementioned previous studies have examined the role of the river geomorphology on the concentration and the distribution of trace metals in the river sediments, lack of systematic studies to identify the local variation in the concentration of trace metals among the geomorphic features at a small scale has not been widely realized, particularly in a river flowing through urbanized areas.

STUDY AREA AND METHODOLOGY

Background

Bedrock Geology and Structure

The bedrocks underlying Lucas County belong to the Silurian, Devonian, and

Mississippian sedimentary rocks. The bedrock in the Ottawa River watershed is comprised of

Silurian to Late Devonian sedimentary rock units that are predominately argillaceous dolostones

and limestones (Table 1). The Ottawa River incises stratigraphically down-section from the

Antrim Shale near its headwaters, into the Tenmile Creek Dolostone, the Silica Formation, the

Dundee Limestone, the Lucas Dolostone, and finally the Tymochtee Dolomite (Figure 2). The

Devonian shales are interpreted to have been deposited in shallow, muddy, anoxic marine

settings as evidenced by the abundance of organic material, fine-grained siliciclastics, and

tempestite deposits, while carbonates formed in warm peritidal settings with a low siliciclastic input (Coogan, 1996).

The Bowling Green fault-Lucas County monocline is a major geological structure in the region (Forsyth, 1966; Onasch and Kahle, 1991; Ramsey and Onasch, 1999). The fault begins in southeastern Hancock County and trends to the north, passing 8 km west of Bowling Green, and

continuing into Lucas County. Rocks on the western side of the fault are displaced downward

with the maximum displacement of 70 meters occurring just west of Bowling Green (Onasch and

Kahle, 1991; Ramsey and Onasch, 1999). The study site is underlain by the Tymochtee Dolomite

(Figure 2). Within the study area the local bedrock is buried by glacial deposits that range in

thickness from 31 to 43 meters (Forsyth, 1968).

Table 1: Bed rock stratigraphy of the study area (From Forsyth, 1968) Period Series Group Formation Seneca Antrim Shale Traverse Ten Mile Creek Fm Erian Group Silica Fm Devonian Dundee Limestone Anderson Fm Ulsterian Lucas Dolostone Amherstburg Fm Sylvania Sandstone Rasin River Dolostone Bass Island Silurian Cayugan Put-In-Bay Dolostone Group Tymochtee Dolostone

Figure 2: Bed rock geology of Toledo area showing the study area (After Forsyth, 1968).

Glacial Geology

The evolution of Lake Erie basin during the Late Cenozoic had great impact on the origin of the present drainage pattern of Ottawa River watershed. The origin of the Lake Erie watershed is the product of multiple glaciations during the Late Cenozoic, and subsequent redirected drainages and isostatic uplift, particularly during the last glacial event which produced multiple ancestral lake phases within the watershed (Larson and Schaetzl, 2001).

Lake Erie went through a series of evolutionary stages induced by multiple readvancement and retreat of Wisconsin ice sheet in the past 18 Ka (Leverett and Taylor, 1915;

Forsyth, 1973; Calkin and Feenstra, 1985; Eschman and Karrow, 1985; Larson and Schaetzl,

2001; Table 2 and Figure 3). The first documented proglacial lake was Lake Leverett formed about 16 Ka when the ice margin retreated northward into the watershed. With subsequent

Wisconsin ice sheet retreat after the ice readvancement of about 15.5 Ka, another proglacial lake,

Lake Maumee, was developed (Figure 3B). The varying outlet location within the basin led to several lake phases known as Maumee I, II, and III (Leverett and Taylor, 1915). The lake at its highest stage, Lake Maumee I, extended southwest into the Washbash Valley via an outlet near

Fort Wayne, Indiana and thence to the Ohio River (Leverett and Taylor, 1915; Eschman and

Karrow, 1985). Expansion of the lake into the southern part of the Huron basin following the northward retreat of the ice margin gave rise to Lake Maumee II, which drained north and then west via unidentified outlet across the “thumb” of Michigan (Leverett and Taylor, 1915;

Eschman and Karrow, 1985). Following a minor readvancement of ice sheet, Lake Maumee III drained via the Imlay Channel (Leverett and Taylor, 1915; Eschman and Karrow, 1985).

In about 13.6 Ka, the glacial Lake Maumee was replaced by glacial Lake Arkona when the Wisconsin ice sheet retreated (Figure 3C). Lake Arkona expanded northward and eastward 11 against the retreating ice margin. As a result, glacial Lake Arkona drained and was replaced by glacial Lake Ypsilanti (Kunkle, 1963). The readvancement of about 13 Ka to the Port Huron moraine closed off the Trent lowland outlet and produced glacial Lake Whittlesey (Figure 3E).

Lake Whittlesey drained north and then west across the “thumb” of Michigan into glacial Lake

Saginaw in the Huron basin via Ubly channel. Subsequent retreat of the Wisconsin ice sheet resulted in the connection of the Huron and Erie Basins forming glacial Lake Warren. As the ice margin continued to retreat, a lower outlet just south of Buffalo caused lower water levels creating glacial Lake Wayne which drained east into the Mohawk River valley. Following the exposure of new outlet near Buffalo, the establishment of glacial Lake Grassmere and later glacial Lake Lundy occurred (Calkin and Feenstra, 1985; Eschman and Karrow, 1985).

At approximately 10 Ka, water levels in the Erie basin dropped resulting in Early Lake

Erie which drained north via the Niagara River (Figure 3G). At that time, the modern outlet at the Niagara River was as much as 46 meters lower than at present because of depression under the weight of glacial ice (Forsyth, 1973). That produced the initial low-level stage of Lake Erie which probably occupied only the eastern end of the basin, but retreat of the Wisconsin ice sheet in the region resulted in isostatic uplift of the outlet causing the waters to deepen resulting in the lake’s westward expansion (Coakley and Lewis, 1985; Calkin and Feenstra, 1985; Figure 3H).

Following deglacial unloading, the Niagara outlet gradually rebound and the level of Lake Erie correspondingly rose. This final lake level rise has been responsible for an increased lake level of approximately 3.5 m in the western end of Lake Erie. The rise of water level has thus produced estuaries at the mouth of all streams that empty into Lake Erie at Maumee Bay (Ver Steeg and

Yunck, 1935). 12

Gallagher (1978) examined soil of the region and documented evidences for a number of these ancestral lake levels within the Ottawa River watershed. His findings reveal discrete belts of sand ridges resting on top of glacial lacustrine silt and clay deposits. These discrete belts of sand are interpreted to represent former shorelines of different ancestral Lake Erie stages.

Table 2: Glacial Lakes in the Erie Basin (After Calkin and Feenstra 1985; Eschman and Karrow 1985). Lake Age (ka) Elevation (m) Outlet Modern Lake 5 174 Niagara Erie Early Lake Erie 10 128 Niagara

Lundy 11.8 189 East

Grassmere 12 195 East

Warren III 12 203 Grand River

Wayne 12 201 Mohawk Valley

Warren II 12 206 Grand River

Warren I 12 210 Grand River

Whittlesey 13 225 Ubly

Ypsilanti 13.2 166 Niagara

Arkona 13.6 216-212 Grand River

Maumee III 15.5 – 14 238 Imlay Unidentified outlet Maumee II 15.5 – 14 232 across "thumb" of Michigan Maumee I 15.5 – 14 244 Ft. Wayne 13

Figure 3: Figure showing the location and general extent of glacial lakes in the Lake Erie Basin from 16 Ka through 5 Ka (From Larson and Schaeztl, 2001). 14

Soils

The soils in Lucas County are postglacial in origin (Stone et al., 1980). Glacial deposits

in the area are mainly Wisconsianan ground moraines that are composed of clay-rich till. It is

capped in much of the area by lacustrine deposits, which consist of lake-bottom clays and sand

ridge beaches (Stone et al., 1980). Until the eighteenth century, the area was covered by a wet

and marshy swamp, famously known as the Black Swamp (Kaatz, 1955). The swamp was

formed when the clay-rich glacial till capped the underlying bedrock forming a relatively

impermeable layer and consequently, collecting the runoff from melting glaciers (Kaatz, 1955).

This swamp was drained out digging of a series of open ditches and later, using wooden troughs,

clay tiles, and pipes laid underground in the late-1820s and early-1830s (Kaatz, 1955).

Soils in Lucas County consist of nine associations that have a distinctive pattern of soils, relief, and drainage (Figure 4; USDA, 2009). Soils around the Wildwood Metroparks and in the study area have been categorized under Granby-Ottokee-Tedrow association (Figure 4; USDA,

2009). The soils in this association are widely distributed in Lucas County covering about 29 % of the area. The soils are level to gently sloping and are very poorly drained, somewhat poorly drained, or moderately well drained. They formed in sandy material of former lake beaches and are on broad irregular flats that have slight ridges and knolls (USDA, 2009). On the basis of textural pattern, the soils in this association are classified as loamy fine sand. According to

USDA (2008) soil taxonomy and classification system, the Granby series is defined as sandy, mixed, mesic Typic Endoaquolls. Similarly, Ottokee and Tedrow series are classified as mixed, mesic Aquic Udipsamments.

Figure 4: General soil map of Lucas County, Ohio showing location of the study area (modified from USDA, 2009). 16

Ottawa River

Drainage Basin Characteristics: The Ottawa River drains a 446 km2 low-gradient basin in

Northwestern Ohio and discharges into the Maumee Bay in the western basin of Lake Erie near

Toledo, Ohio (Figure 5). Average gradient of the river is 7.6 x 10-4 (Forsyth, 1968).

The upper portions of the watershed are largely agricultural with more suburban areas downstream. Below river kilometer 10, the river flows through highly urbanized area and has a long history of contamination from various industrial sites (Hull Assc., 2004; Parametrix Inc.,

2001). The soils of the watershed vary from level to gently sloping loamy and clayey soils on lake plains to level to gently sloping sandy soils on old beach ridges (Lawrence et al., 2007). The upstream Ten Mile Creek reach, from the headwaters to the City of Sylvania, has a fairly stable channel. The banks are low (5 to 8 meters) with indistinct valleys and floodplains (Lawrence et al., 2007). The headwaters of the North Branch of Ten Mile Creek also flow through flat terrain with indistinct floodplains. Both headwater areas are located primarily within agricultural land.

In addition to agricultural land, there is residential land use within this reach of the river, including the City of Sylvania. The gradient here is 8.1 x 10-4. Prairie Ditch, a major tributary to

Ten Mile Creek in this reach, flows through Secor Metropark.

The middle reach (between river kilometers 32 and 8) is characterized by high and

unstable banks intermixed with distinct floodplains. The land use in this reach is residential,

commercial, and industrial. The Ottawa River then continues through the Camp Miakonda Boy

Scout Reservation, Wildwood Metropark, the Village of Ottawa Hills, and enters into the lower

reach from Stickney Avenue. This segment along the river is more in industrial and residential

use. 17

Figure 5: Ottawa River and Ten Mile Creek watershed showing the present study area within Wildwood Preserve Metro Park, Toledo (modified from Horvat, 2004). 18

Hydrometeorology: The annual average precipitation in the study area is 85 cm. Rainfall is fairly

evenly distributed throughout the year. The wettest month of the year is June and the driest

month is February with an average rainfall of 9.7 and 4.8 cm respectively (Figure 6). A single

USGS stream gauging station (# 04177000) at river kilometer 17.3, about 8 km downstream

from the study site, exists on the Ottawa River on the Stadium Road Bridge on the Campus of

the University of Toledo. At base flow the river discharge is negligible; during high stage

condition, particularly during the spring, however, discharge commonly exceeds 40 m3/sec and

may exceed 90 m3/sec (Gottgens et al., 2004). The discharge data from 1977 to 2007 estimates that 10, 25, 50, and 100 year floods to be 91, 127, 170, and 219 m3/sec respectively (Harris,

2007). Harris (2007) also plotted the flood response of the Ottawa River with the precipitation data during the period of November-December 2007 and January-February 2008 and concluded that the Ottawa River is characterized by relatively low base flow and high peak flows (Figure

7). Thus, the Ottawa Rivers response to precipitation events is described as flashy (Harris 2007).

Baker et al. (2004) described a similar response of rivers in northwest Ohio and attributed it to land use changes through time. In this case, the Ottawa River is also strongly affected by urban storm drainage. 19

10.0

8.0

6.0

4.0

Precipitation (cm) Precipitation 2.0

0.0

Month

Figure 6: Mean Monthly Rainfall recorded at the Toledo Regional Airport from 1971 to 2000 (Data Source: National Climatic Data Center, USA)

Figure 7: Mean daily discharge and precipitation hydrograph of the Ottawa River from 11/01/07 to 2/27/08 (from Harris, 2007).

Study Site

On the basis of aerial photographs, topographic maps, and a field reconnaissance survey, a 1000 m reach of the river (Figure 8) was selected for sampling. The area was selected for the following reasons: (1) It poses a number of different and distinct geomorphic elements (e.g. bars, riffles, floodplain), (2) It is a preserved area and has experienced less anthropogenic interferences and physical modification of the river, and (3) The study area is far enough from any point sources of contaminants while maintaining a relatively constant geochemical environment along the length of the stream.

The study site lies in a temperate deciduous forest within the Wildwood Preserve

Metropark, Ohio. The average elevation of the study area is 180 m. The width of the river ranges from 8 m to 12 m. The study portion of the river represents meandering stream with a bed load consisting of unconsolidated sand and silt. The area is characterized by floodplain, terrace, and bar deposits. However, fallen trees and wood debris are present at some places in the channel altering the sediment deposition patterns and morphology of the channel. A small, unnamed ditch accompanies the main channel in the area (Figure 8). This ephemeral ditch collects surface run-off from the nearby Interstate Highway (I-475) and settlement area, contributing water and sediments to the main channel seasonally.

Figure 8: Areal view of the study section of the Ottawa River with sediment sample locations

(Photo Source: OGRIP, 2009). Area within box represents Figure 11. 22

Previous Study

The downstream lower section of Ottawa River in the City of Toledo has been the focus

of many efforts and research studies to address industrial discharges, contaminated sediments,

and river dredging. The upstream and middle reaches also have many significant issues that

require management attention, including storm water runoff, aquatic habitat impacts, wetland

loss, and water quality concerns. However, these areas have received little planning focus.

Recently, Parametrix Inc. (2001) conducted an ecological risk assessment of the lower

portion of the river and identified the stretch between River Kilometer (RK)-7.8 and RK-10.4 as having elevated concentrations of polychlorinated biphenyls (PCBs) and lead (Pb) based on sediment, surface water, and fish tissue data collected in 2000. Similarly, another study by Hull

and Associates (2004) collected the surface and core sediment samples and found lead

concentration in most sediment samples exceeding the Sediment Quality Guidelines (Figure 9).

The highest lead concentrations occur in Reach 2 (RK 5.1-RK 7.8) and Reach 3 (RK 7.8-RK

10.4), with maximum concentration of 470 ppm and 680 ppm respectively (Hull and Assc.,

2004). Beside Pb, other heavy metals (Cd, Cr, Cu, Ni, and Zn) have been recorded in the river sediments that exceed the Threshold Effect Levels (TEL) and Probable Effect Levels (PEL)

(OEPA, 2000; Figure 9). These studies show that the level of aforementioned heavy metals poses high potential ecological and human health risks.

Numerous potential sources of chemical contaminants have been identified in the Ottawa

River. These sources include old industrial sites, landfill, urban runoff, and agricultural runoff

(OEPA, 2000). Trace metals are adsorbed by the sediment particles and may enter aquatic food 23 chains, causing fish toxicity problems. Furthermore, the presence of metals may impair recreational uses and degrade drinking water sources.

However, the river sediments assessed near Ottawa Hills suggest potential sediment contamination with the selected eight trace metals (Table 3) to be minor (Roberts et al., 2007).

Out of the eight trace metals examined from multiple sites at river reach RK 18.3, only As, Cd, occasionally Ni and rarely Pb exceeded the TEL values, while As (one out of 15 surface samples) and Cd (1 out of 15 subsurface samples) exceeded PEL values, although these samples barely surmounted the PEL threshold (Roberts et al., 2007; Table 3). On the other hand, 7% of surface samples exceed TEL for PCB’s and all surface and subsurface samples exceeded PEL for

PAH indicating hydrocarbon contamination for this portion of river (Roberts et al., 2007).

600

500

400

300

200

Concentration (ppm) Concentration 100

0 As Cd Cr Cu Pb Hg Ni Zn (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) KM 0-5.1 7.09 1.541 41.33 65.92 79.24 0.137 34.48 194.5 KM 5.1-7.8 6 1.5 45.55 55.767 145.517 0.126 31.8 224 KM 7.8-10.4 10.84 2.3 75.98 80.18 132.45 0.44 34.74 275.4 KM 10.4-14.0 3.5 0.685 29.75 41.05 95.25 0.175 13 189 TEL (ppm) 11 0.58 36 28 37 0.174 20 98 PEL (ppm) 48 3.2 120 100 82 0.486 33 540

Figure 9: Trace metal concentration in Ottawa River sediments measured in river length from 0 to 14 km and Sediment Quality Guidelines (TEL and PEL) as given by Ingersoll et al., (1996; Data Source: OEPA, 2000). 24

Table 3: Sediment contamination on the Ottawa River near RK 18 (from Roberts et al.,

2007)

Ottawa River Metal and Standards organic % exceedance for TEL % exceedance for PEL contaminants surface subsurface surface subsurface TEL PEL Al (mg/g) 0 0 0 0 26 60 As (µ/g) 33 67 7 0 5.9 17 Cd (µ/g) 93 93 0 7 0.596 3.53 Cu (µ/g) 0 0 0 0 35.7 197 Fe (µ/g) 0 0 0 0 190 250 Ni (µ/g) 13 13 0 0 18 36 Pb (µ/g) 0 7 0 0 35 91.3 Zn (µ/g) 0 0 0 0 123 315 PAH (µ/kg) 100 100 100 100 260 3400 PCB (µ/kg) 7 7 0 0 34.1 277

Methods

Several methods including the field work, lab work, and statistical analysis, were used to

determine the distribution pattern and concentration of the selected trace metals in the river

sediments. Field work was carried out in August 2009 to identify and locate the fluvial

geomorphic units within the study area which was followed by representative sediment sample

collection. Trace metal analysis in the sediments and grain size analysis were performed in the

laboratory at the Department of Geology, Bowling Green State University, Ohio. Finally, the

data obtained from trace metal analysis and grain size analysis were statistically analyzed using

Minitab to accomplish the objectives of this study.

Geomorphic Features

Air photos were acquired from the Ohio Geographically Referenced Information Program

(OGRIP, 2009) to obtain complete coverage of the study area. These photos were used as a base

map in the field survey. In the course of field work, five types of distinctive geomorphic features

were identified and sampled in the study reach: pools, riffles, point bars, lateral bars, and the

flood plain. The extent of each feature was measured by using metric tape and their location was

noted with the help of Garmin GPS. Even though wooden debris has blocked the flow of river in

some places, altering some geomorphic features, it is relatively easy to visually identify the

features on the basis of their position, shape, size, sediment contents, and flow pattern. Examples

of morphologic elements are illustrated in Figure 10. Likewise, a schematic geomorphological

map of a section of the study area was produced to show the spatial relationship between

morphologic features (Figure 11).

Pools and riffles are recognized on the basis of water depth, velocity, and substrate composition (Jowett, 1993). In the field, riffles were identified by their shallow water depth, 26

coarse-grained sand composition, and location in the crossovers between one meander to the next

on the opposite margin of the stream (Figure 10 and 11). In contrast, pools were located in

deeper and calmer areas, comprised of fine-grained sand and silt sediments and were formed by bank scour due to flow that was diverted against the outer bank of the meander (Figure 10 and

11). However, riffle-pool sequences are not well developed in all meander sections of the Ottawa

River.

Two other depositional features identified in the study area are point bars and lateral bars

(Figure 10 and 11). Point bars occur along the concave side of the meander loop, opposite to the

outer bend of the meander. Eroded sediments from the outer bend are deposited downstream on

the inner bend of the meander due to lower-energy flow condition. Small point bars in the

Ottawa River, ranging up to few meters in length, are composed of mainly medium-grained sand.

These point bars are continuous, gently sloping features from normal stage height to the base of

the channel. Absence of point bar deposits on the inner bend of one meander can be seen in the

study area (Figure 11). The old point bar might have been detached due to the formation of

recent chute cut-off across the bar. Eventually, the detached bar was eroded away or migrated

downstream during high flow stage. Lateral bars are located on the side of the channel between

the meanders (Figure 10 and 11). These bars are composed of fine-grained sand to silt and range

from 5-35m in length.

Flood plains are present on either side of the river adjacent to the main channel (Figure

11). Nearly flat lands with the common occurrence of flood debris and freshly deposited

sediments suggest that these areas experience episodic flooding. 27

Figure 10: Photographs depicting the spatial distribution of different geomorphic features in the study area.

Figure 11: Schematic geomorphological map of the about 150 m long study reach, illustrating the spatial distribution of different types of geomorphic features.

Sediment Sampling

The field sampling program was designed in a way, so that it could provide a

geomorphologically-based characterization of trace metals concentration in the Ottawa River

sediments. A random sampling strategy was followed in which one or more sampling sites were

established within five types of geomorphic elements scattered throughout the study section of

the river. The sampling sites represent a variety of geomorphological environments, including

pools, riffles, point bars, lateral bars, and flood plains. Two additional samples were collected

from the channel bed of a small ditch within the study area to check if it has any contribution in

the supply of contaminated sediments into the main channel. The number of sampling sites in

each feature was determined on the basis of its dimension and lateral extension. The number of

samples was limited due to the difficulty in sample collection from the deeper water (e.g. pool)

and budget restrictions. The number of each type of geomorphic feature that was sampled, and

the total number of samples collected from these features are presented in Table 4. More details

on each sample location are given in Appendix A.

All sediment sampling was conducted under low flow condition within a week in the

month of August 2009 to avoid major temporal changes in sediment chemistry. Samples were

collected using 6 cm diameter decontaminated PVC pipes. Each pipe was pushed to the depth

approximately 15-20 cm and sediments were retrieved carefully. This method is effective to

collect the bed sediments because it minimizes the loss of fine-grained sediments. The sample

depth was determined taking into consideration the depth of the biologically-active zone and the possibility of exposing and scouring of the sediments during flood events. Push cores were

brought to the lab and sediments were transferred into resealable polythene bags. The sediments 30

were then homogenized thoroughly and 200-300 gm sub-samples were taken for grain size

analysis. Samples were stored at room temperature until further analyses were performed.

Table 4: Total number of sediment samples collected from each type of geomorphic feature in Ottawa River Number of each Total number of Geomorphic feature selected for samples from each type Feature sampling of feature Point bar 4 5 Lateral bar 3 5 Pool 5 5 Riffle 3 4 Flood plain 2 6 Ditch 1 2 Total 18 27

Grain Size Analysis and Organic Matter

ASTM protocols D-421 and D-422 (ASTM, 1986) were followed for grain size analysis.

About 200 gm of each sediment sample was taken and oven-dried overnight at 90oC. The dried

samples were disaggregated using mortar and pestle. A rubber head pestle was used for the

purpose to avoid any mechanical break up of the particles. The sample was placed in a plastic

sieve of +4Ø (0.0625 mm) mesh size and wet sieving was conducted to separate sand and mud

fractions. Prior to the wet sieving, large plant materials were removed with the help of a forceps,

and presence of organic materials in each sample was tested by adding a few drops of 30%

hydrogen peroxide (H2O2) in a small amount of sample. The samples, which produced vigorous

reaction, were further treated with the 30 % H2O2 solution until the vigorous reaction ceased. 31

The particles retained in the +4Ø sieve (sand fraction) were oven-dried overnight again and weight of each dry sample was recorded. Sediments greater than +4Ø were further sieved through a stack of sieves, assembled in whole Ø increments (e.g. -1Ø, 0Ø, +1Ø, +2Ø, +3Ø, +4Ø, and pan) to determine grain size distribution for the various sand-sized particles. For the sediments finer than +4Ø, the Spectrex PC-2300 Laser Particle Analyzer was used to determine the distribution of silt and clay-sized particles.

For laser particle analyzer, approximately 1 ml of sediment suspension was taken in a clean bottle, and added to a 100 ml diluted solution which consisted of 90 ml distilled water and

10 ml sodium hexametaphosphate [Na6(PO3)6] solution. The sample was left for 24 hrs after

adding Na6(PO3)6 solution to allow enough time for dispersion, so that clay particles would not flocculate. The sample was mixed thoroughly by adding magnetic stirrer bar and stirring for several minutes and was placed immediately into the laser particle analyzer. The data were then recorded for essential statistical analysis.

The composite grain size data for each sample was obtained by combining the sand-sized

fraction (sieving data) and with the mud-sized fraction (laser particle analyzer data). The

cumulative weight percentage was plotted on arithmetic graph paper (Appendix D & E) and certain size fractions were obtained. The equations of Folk and Ward (1957; Table 5) were followed to calculate grain-size statistical parameters including the mode, mean, median, standard deviation, and skewness. The required percentiles for the equations (Table 5) were determined from the graphs. Histogram for each sample was also constructed for visual inspection of the skewness of the data (Appendix D & E).

Table 5: Formulas for calculating grain-size statistical parameters by graphical methods (From Folk and Ward, 1957).

Graphic Mean Mz = (Φ16+Φ50+Φ84)/3 Inclusive graphic σ = {(Φ84-Φ16)/4 + {Φ95-Φ5)/6.6} standard deviation i

Inclusive graphic SKt = {(Φ84+Φ16-2Φ50)/(2(Φ84-Φ16))} + {(Φ95 + Φ5 - 2Φ50)/(2(Φ95- skewness Φ5))}

Graphic kurtosis KG = (Φ95-Φ5)/2.44(Φ75-Φ25)

Loss on ignition (LOI) method was used to calculate the percentage of organic matter

present in each sediment sample. It is a common, inexpensive, and widely used method. A

systematic procedure for the method can be found elsewhere (e.g. Dean, 1974; Heiri et al.,

2001).

Trace Metal Analysis

Trace metal analysis of the collected 27 river sediment samples was conducted in

Department of Geology, Bowling Green State University, Ohio. The chemical analysis for 11

trace elements was performed with Inductively Coupled Plasma Optical Emission Spectrometer

(ICP-OES) after acid digestion by following the guidelines as provided by USEPA Method

3051A (USEPA, 2007). All the reagents used for the investigation were certified and of

analytical grade chemicals and the water was Milli-Q ultra pure water. Prior and after the analysis, all digestion vessels and volumetric glassware were thoroughly acid washed and rinsed with Milli-Q water.

The USEPA Method 3051A uses nitric acid (HNO3) and hydrochloric acid (HCl) in the ratio 3:1 for a partial extraction of elements from particles. Thus, the results obtained from this 33

method reflect the elemental composition for fine-grained particles and outer surfaces of larger

particles, which are the most critical to the environment in terms of their mobility and bio-

availability. According to the provided procedures, well homogenized, bulk sediment samples

were oven-dried for 12 hours at 60oC. A 0.5 g dry sample was taken in a clean microwave XP-

1500 Teflon vessel and was treated with trace-metal grade acid, 9 ml nitric acid and 3 ml

hydrochloric acid solution. The vessels were left uncapped under the fume hood until reaction

ceased. All vessels were assembled, following the procedures as described by USEPA (2007)

and heated in the microwave unit for digestion. Method EPA 3051_7TO12V_OMNI/XP1500

was selected for the microwave digestion. The digestion process completed in two stages, each

running for 10 minutes. After the completion of second stage, the microwave entered cool down

mode for 30 minutes. The vessels were allowed to cool in microwave until vessels were below

100oC and pressure was below 50 psi. The turntable was then removed from the microwave and

placed under the operating hood for 2 hours allowing the vessels to completely depressurize. The

weight of each vessel was recorded before and after digestion to evaluate seal integrity. For each

vessel, weight loss was less than 1% of the original mass which suggests the vessels maintained

their seal throughout the digestion. The samples were then filtered into acid-washed 50 ml

volumetric flasks and diluted to 50 ml with Milli-Q water. The diluted samples were placed in

acid-washed polypropylene bottles and stored in refrigerator until the samples were analyzed for

trace metals with ICP-OES.

Three calibration standard solutions (High, Medium, and Low) of 11 trace metals (Zn, Ti,

Sr, Pb, Mn, Hg, Cu, Cr, Co, Cd, and Ba) were prepared, and a small amount of each sample was

taken in a polypropylene tube for ICP-OES analysis. In Optical Emission Spectroscopy (OES), a sample solution is introduced into the core of inductively coupled argon plasma (ICP), which 34 generates temperature of approximately ranging from 5000°C to 8000oC. At this temperature all elements become thermally excited and emit light at their characteristic wavelengths. This light is collected by the spectrometer and passes through a diffraction grating that serves to resolve the light into a spectrum of its constituent wavelengths. Within the spectrometer, this diffracted light is then collected by wavelength and amplified to yield an intensity measurement that can be converted to an elemental concentration by comparison with calibration standards (Olesik,

1991). The ICP-OES measures three replicates of each sample. The average value of these three replicates for each element was reported as instrumental metal concentration in the sediments.

The extract concentration obtained from the instrument in ppm in solution was converted into ppm in dry-weight of sample by:

Sample Concentration (ppm) = C x V x D/W x S

Where,

C = Concentration in extract (ppm)

D = Dilution factor

S = Solid dry-weight fraction for sample, g/g

V = Volume of extract, ml x 0.001

W = Weight of undried sample extracted, g x 0.001

Quality Assurance

The digestion and analytical measurement quality were tested by running calibration blanks, standards, and replicate samples. Precision was monitored by analyzing a replicate of any one real sample in each batch of 12 samples. Analysis of replicate samples indicates that the average variability between replicates differs with metal, and is less than 15% for most of the metals except Pb, Ti, and Cd (Table 6). Ti and Cd are not used for this study because their concentrations in sediments were below detection limit. To assess any possible contamination during sediment digestion, calibration blanks were processed following the same procedures. All metals, measured after the blank digestion, were below the detection limit (Table 6). Accuracy was assured through the use of certified soil standard (RM 8704) acquired from National

Institute of Standards and Technology (NIST), USA. Percentage of recovery of elements is in high range (27-78%, Table 6). Some metals (Zn, Pb, and Cr) have very low recovery percentage.

The explanation for low recovery was found to be that the partial digestion method was followed in this study, but the values given by NIST were obtained from complete dissolution of the sediments (NIST, 2000). All metal concentrations obtained from the lab are below the given values. Therefore, results are within the range of acceptable precision and accuracy.

Table 6: Quality assurance data for blank, replicate, and soil standard samples Reported Mean Soil Trace Blank Detection Limit Difference (%) Standard Metals (ppm) (ppm) for replicates recovery % Zn 5 0.87 2 45 Ti 5 -2.44 53 BDL Sr 0.5 -1.1 2 NA Pb 5 -0.4 20 27 Mn 0.75 -0.07 5 78 Hg 0.1 -0.275 14 NA Cu 1 -0.04 2 NA Cr 7.5 -0.085 9 54 Co 0.75 -0.215 9 76 Cd 0.5 -0.135 90 BDL Ba 0.5 0.165 12 72 BDL - Below Detection Limit, NA – Not analyzed in standard soil

Statistical Analysis

The statistical analysis of the concentration of trace metals in the river sediments was performed to check the variability in metal concentration in the different fluvial geomorphic features. The Kruskal-Wallis Test was used to quantify if metal concentrations vary significantly

among fluvial geomorphic elements. This test doesn’t assume normality or equal variances. The

test compares sample means of ranks of the two datasets to determine a p-value (Coreder and

Foreman, 2009). For pair-wise comparison of geomorphic features, a Mann-Whitney Test was used, which is similar to Kruskal-Wallis test but is used for only two groups. Smaller p-values indicate the greater probability of difference in mean and variance of the two datasets. A significance level of 0.05 at the 95% confidence limit was used for the test. 37

Further, the relationship among trace metal concentrations was determined by calculating

Pearson correlation coefficients of possible non-reciprocal metal pairs and by Principle

Component Analysis (PCA). PCA is primarily a data-analytic technique for pattern recognition

that attempts to explain the variance of a large set of inter-correlated variables. It indicates association between variables, thus, reducing the dimensionality of the data set (Facchinelli et al., 2000). PCA extracts the eigenvalues and eigenvectors from the covariance matrix of original variables. The principal components (PCs) are the uncorrelated (orthogonal) variables, obtained by multiplying the original correlated variables with the eigenvector (loadings). PCA was conducted to relate the possible sources of different heavy metals (Borůvka et al., 2005). PCA was performed on the combined data set (flood plain, point bar, lateral bar, pool, and riffle).

Prior to analysis, the data was log 10 transformed to account for multi-variate non-normality.

Finally, the correlations between mud content (silt + clay) or organic matter content, and the different trace metals were carried out to determine the influence of grain size fraction and organic matter on trace metal distribution. All mathematical and statistical analyses were made using Microsoft Excel 07 and MINITAB 15.

RESULTS

A total of 27 sediment samples, collected from five different types of geomorphic features and a tributary, were analyzed for eleven trace metals namely, Zn, Ti, Sr, Pb, Mn, Hg,

Cu, Cr, Co, Cd, and Ba. Among these metals, only eight metals (Zn, Sr, Pb, Mn, Cu, Cr, Co, and

Ba) were selected for further statistical analyses because the concentration of the remaining three metals (Ti, Hg, and Cd) could not exceed the detection limit of the ICP-OES. Trace metal data for each feature, including mean concentration, standard deviation, range, and coefficient of variation are summarized in Table 7.

Role of Fluvial Geomorphology

Metal Variability between Geomorphic Features

The results explicitly show large variation in the trace metal concentration in the bulk sediment samples among the fluvial geomorphic features (Table 7 and Figure 12). Box and

Whisker plots (Figure 12) for each metal concentration shows that the flood plain consistently has the highest concentration of all the metals. Similarly, the lateral bars contain the second highest amount of metals after flood plain. Conversely, point bar is a place where the lowest amount of metals are concentrated. Pools and riffles do not show much variation in metal concentrations, however pools have higher metal content except Sr and Pb, which are slightly higher in riffle. In general, the trace metal concentration in fluvial environment can be expressed as: FP>LB>PL>RF>PB.

Table 7: Descriptive statistics of trace metals in each geomorphic feature. Trace Geomorphic Mean StDev CoefVar Minimum Median Maximum Metals Features Zn (ppm) FP 91.4 38.1 41.7 48.4 91.1 148.5 LB 42.8 26.2 61.18 11.8 54.6 65.4 PB 10.735 1.324 12.33 9.635 10.57 12.924 PL 18.24 4.13 22.64 12.43 18.26 22.95 RF 14.7 4.46 30.34 8.07 16.63 17.48 Sr (ppm) FP 84.5 47.3 55.94 41.9 66.8 145.9 LB 56.6 36.6 64.6 18.6 63.6 99.8 PB 11.65 3.5 30.09 7.36 12.46 16.48 PL 30.04 11.8 39.27 18.64 27.74 48.6 RF 30.56 11.15 36.49 16.52 32.8 40.09 Pb (ppm) FP 35.53 21.41 60.24 16.1 29.35 76.46 LB 12.74 6.59 51.75 2.93 14.95 18.65 PB 2.838 0.644 22.69 2.059 2.922 3.476 PL 3.589 1.04 28.98 1.925 3.763 4.778 RF 3.901 1.533 39.29 1.733 4.266 5.34 Mn (ppm) FP 290.7 104.6 35.99 138.3 289.5 427.8 LB 169.6 119.6 70.49 60.9 146.6 342.6 PB 44.26 3.15 7.11 41.24 43.51 49.23 PL 100.3 48.1 47.9 63.8 71 171.9 RF 89.3 32.4 36.32 51.7 93.9 117.8 Cu (ppm) FP 26.57 10.05 37.81 15.03 25.12 41.91 LB 11.58 6.02 51.99 2.78 12.49 18.37 PB 2.423 0.596 24.58 1.607 2.407 3.149 PL 5.5387 0.2051 3.7 5.3208 5.5832 5.7397 RF 3.177 1.089 34.27 1.624 3.459 4.169 Cr (ppm) FP 20.32 4.99 24.55 12.51 22.92 24.48 LB 11.04 7.33 66.39 2.44 11.42 18.89 PB 2.751 0.469 17.03 2.279 2.567 3.45 PL 6.088 1.748 28.71 3.083 6.618 7.662 RF 4.472 1.81 40.46 1.98 4.944 6.02 Co (ppm) FP 7.31 2.51 34.35 3.82 8.05 10.5 LB 3.362 2.059 61.22 1.298 3.003 6.24 PB 1.1811 0.0844 7.15 1.0496 1.2106 1.26 PL 2.88 0.679 23.59 1.955 3.095 3.677 RF 1.891 0.686 36.26 1.069 1.909 2.677 Ba (ppm) FP 81.1 25.1 30.96 45.5 94.5 103.1 LB 38.5 25.6 66.33 9.4 33.4 72.1 PB 7.823 1.205 15.4 6.34 8.173 9.164 PL 22.63 8.06 35.6 12.46 21.13 33.22 RF 14.37 6.19 43.08 6.11 15.15 21.06 FP: Flood plain, LB: Lateral bar, PB: Point bar, PL: Pool, RF: Riffle 40

The degree of variation between the geomorphic features for average concentration of an

individual metal is also significantly high and varies with metals (Figure 12). Pb, Cu, and Ba are

the metals having the greatest average concentration differences between features and are

followed by Zn, Cr, Sr, Mn, and Co (Figure 12). For instance, average concentration of Pb in

flood plain is 12.5 times higher than in point bar, while the difference in the highest and lowest

average metal concentration for Co is five times, which is still significant. The range and

coefficient of variation of metal concentration also varies with metals and geomorphic features

(Figure 12). In general, the greatest range of concentration occurs in the flood plain and is

followed by the lateral bars, pools, riffles, and point bars. The exceptions are Ba with the greatest

range of concentration in the lateral bar, and Cu with the smallest range of concentration in

pools. The highest coefficient of variation occurs in lateral bar for all metals, except Pb which

has highest in the flood plain, where as the point bar displays the smallest coefficient of variation

except for Cu, which has smallest in pools.

The statistical significances of the metal variation between the geomorphic features were

tested by using Kruskal-Wallis Test. Geochemical and environmental data rarely show normal

distribution (Reimann and Filzmoser, 1999). Since Kruskal-Wallis is a non-parametric method, it doesn’t assume a normal distribution and can be used for small sample size. Table 8 summarizes the Kruskal-Wallis Test result for each metal. The results show that all analyzed metals have statistically significant differences in concentration between morphologic features.

Table 8: Results of Kruskal-Wallis Test, testing for significant differences in metal concentration between the geomorphic features (Level of significance, p = 0.05, Degree of freedom = 4, and n = 25 for each metal) Metal p-value

Zn 0.002 Sr 0.003 Pb 0.002 Mn 0.002 Cu 0.001 Cr 0.003 Co 0.001 Ba 0.001

Zinc Strontium 160 160

140 140

120 120

100 100

80 80 ( p m ) ( p m ) S r Z n 60 60

40 40

20 20

0 0 FP LB PB PL RF FP LB PB PL RF Geomorphic Features Geomorphic Features

Barium Cobalt

100 10

80 8

60 6 ( p m ) ( p m )

B a 40 C o 4

20 2

0 0 FP LB PB PL RF FP LB PB PL RF Geomorphic Features Geomorphic Features

Fig 12: Box plot of metal concentration in different geomorphic features. Solid black line and encircled plus indicate median and mean concentration respectively. FP: Flood plain, LB: Lateral bar, PB: Point bar, PL: Pool, RF: Riffle. 43

Chromium Copper 25 40

20 30 15 ( p m ) ( p m ) 20 C r

10 C u

10 5

0 0 FP LB PB PL RF FP LB PB PL RF Geomorphic Features Geomorphic Features

Lead Manganese 80 400 70

60 300 50

40 ( p m ) ( p m ) 200 b M n P 30

20 100 10

0 0 FP LB PB PL RF FP LB PB PL RF Geomorphic Features Geomorphic Features

Pair-wise Comparison

Kruskal-Wallis Test shows statistically significant variations in mean metal concentration

between geomorphic features, however the test does not indicates which specific features are

different from each other for a particular metal concentration. A Mann-Whitney test was used for

this. The Mann-Whitney test is a non-parametric test which compares the sums of the ranks of

two independent groups (Coreder and Foreman, 2009).

The test considers a total of 10 possible pairings (e.g., flood plain and lateral bar, pool

and point bar etc.) of the five geomorphic feature types for each metal (thereby giving 80 pairs of

8 different trace metals). Table 9 summarizes the p-value for each pair. The Cu shows maximum

(i.e.7) pair-wise variations between features, while Co displays minimum (i.e.4) pair-wise differences (Table 9).

It is evident that the concentrations of all metals in the flood plain are statistically different than in point bars, pools, and riffles. On the other hand, the flood plain and lateral bar tend to be statistically similar in terms of metal concentration, except for Pb, Cu, and Co. Lateral bars have statistically significant higher metal concentration than point bars, whereas there is no significant difference in metal concentrations between lateral bars, pools and riffles. Similarly, pools contain a statistically higher metal concentration than point bars, except for Pb which shows no difference in concentration. However, besides Sr and Mn, point bars and riffles do not show any differences in metal concentration. Likewise, pools and riffles relatively concentrate same amount of metals, except Cu which has higher concentration in the former.

Table 9: Pair-wise differences in concentration between geomorphic features based on Mann- Whitney test.

Pair Zn Sr Pb Mn Cu Cr Co Ba Flood plain

Lateral bar 0.121 0.523 0.023 0.171 0.036 0.055 0.036 0.055 Point bar 0.008 0.008 0.008 0.008 0.008 0.008 0.008 0.008 Pool 0.008 0.023 0.008 0.014 0.008 0.008 0.008 0.008 Riffle 0.014 0.014 0.014 0.014 0.014 0.014 0.014 0.014 Lateral bar

Point bar 0.022 0.012 0.047 0.012 0.028 0.095 0.016 0.012 Pool 0.403 0.463 0.095 0.676 0.141 0.530 1.000 0.296 Riffle 0.178 0.391 0.111 0.391 0.111 0.270 0.325 0.111 Point bar

Pool 0.021 0.012 0.144 0.012 0.012 0.021 0.012 0.012 Riffle 0.270 0.027 0.270 0.020 0.219 0.270 0.138 0.270 Pool

Riffle 0.270 1.000 0.540 0.903 0.019 0.110 0.111 0.219 Number of pair-wise 5 6 5 6 7 4 6 5 differences Bold values represent significantly different pairs at p<0.05.

Upstream-Downstream Variation

Two bed load sediment samples, one collected at the mouth of a ditch and another 75 m upstream from the former point along the ditch (Figure 8), showed relatively high concentration of Pb (112.1 and 16.9 ppm) and Zn (42.8 and 39.1 ppm) in comparison to many samples collected from main channel. Therefore, a Mann-Whitney Test was run on all the metals to test if there are any significant differences in metal concentrations between upstream and downstream reaches. Here, the study area above and below the tributary are regarded as upstream and downstream respectively (Figure 8). Equal number of samples from each type of features, except 46

flood plain, and consequently same number of samples from each reach (14 for each metal) were

used for analysis. The flood plain samples were not included because all the flood plain samples

were collected from the downstream reach, which might alter the result. The test gives the p-

values ranging from 0.3711 to 1.000. Because these values exceed the test parameter (p≤0.05), this suggests that there are no statitistically siginificant differences in metal concentrations between upstream and downstream reaches.

Grain Size Distribution and Organic Matter

Grain-size distribution and organic matter analyses were performed on all sediment samples to understand their capability for capturing the trace metals within fluvial environment.

Cumulative arithmetic curves, histograms showing the grain-size distribution, and grain-size statistics including mean, median, mode, standard deviation, and skewness for each individual sample are presented in Appendix D. The organic matter contents are given in Appendix C along with sand and mud fractions for each sample. Furthermore, grain-size data of individual sample collected from a same type of geomorphic feature were combined to determine the average grain-size distribution and statistical parameters within a particular feature (Figure 13 and

Appendix E).

The collected fluvial sediments display a remarkable spatial variations in terms of their grain-size and organic matter content. According to Folk and Ward (1957) scale, mean grain size ranges from medium sand (1.5Ø) to medium silt (5.8Ø). Most of the sediment samples (89%) are poorly sorted either with an abundance of fine particles (41% positively skewed) or with an abundance of coarse particles (33% negatively skewed). The rest (26%) are normally distributed

(Appendix D). The organic matter content in the sediments ranges from 0.7 to 13.2% on a weight 47

basis, and silt + clay content ranges from 2.5 to 76.6%, showing great variability among samples

(Appendix C).

The combined data sets show that the sediments from the flood plain, pools, and lateral

bar-top are essentially finer than those of riffle and point bar. The flood plain contains largest

proportion of silt, while the pools and lateral bars contain nearly equal proportions of sand and

silt, and the riffles (with 7.1% granules) and point bars contain highest proportion of sand

(Figure 13). Further, Table 10 summarizes the description of the sediments and their average

organic matter content within a particular feature. The mean grain size ranges from medium-

grained silt in flood plain to fine-grained sand in riffles and point bars. The distribution of

organic matter shows that flood plain sediments are enriched with organic matter. Additionally,

the bed load sediments from the ditch contain medium-grained sand with 1.2 % organic matter.

100

Grain Size (Ø) 80 +7 to +8 +6 to +7 +5 to +6 +4 to +5 ) 60 +3 to +4 ( %

+2 to +3 n

o +1 to +2 i t

c 0 to +1

r a -1 to 0

F 40 -2 to -1

0 FP PB PL RF LB Geomorphic Features

Figure 13: Cumulative grain-size distribution. For particular features average values are presented. FP = Flood plain, PB = Point bar, PL = Pool, RF = Riffle, LB = Lateral bar. 48

Table 10: Mean sediment characteristics and average organic matter content within a particular geomorphic feature. Geomorphic Organic Matter Sediment Description Features % Strongly negative skewed, Flood plain poorly sorted, medium-grained 10.4 silt Positive skewed, poorly sorted, Lateral bar 5.1 very fine-grained sand Nearly symmetrical, poorly Point bar 1.4 sorted, fine-grained sand Positive skewed, poorly sorted, Pool 2.7 very fine-grained sand Positive skewed, poorly sorted, Riffle 1.9 fine-grained sand Positive skewed, moderately Tributary 1.2 sorted, medium-grained sand

Correlation Analysis

The Pearson correlation analyses between the eight trace metals, the silt + clay fraction

(grain size fraction larger than 4Ø), and organic matter (OM) content of the sediments were performed and the resulted correlation coefficients with their p-value are presented in Table 11.

The calculated correlation coefficient (r) values explain that all the trace metals except Pb, are

highly and positively correlated (r > 0.7) with each other, and their correlations are statistically

significant at p = 0.05 (Table 11 and Figure 14). The Pb shows weak and positive association (r

= 0.3 to 0.7) with Zn, Cr, Co, Cu, and Ba, while it has no association (r < 0.3) with Sr and Mn.

Table 11: Correlation coefficient (upper figure) and p-values (lower figure) for trace metal concentrations, silt + clay ( %) and organic matter (%) in Ottawa River sediments. Zn Sr Pb Mn Cu Cr Co Ba Silt + clay Sr 0.729

0.000

Pb 0.627 0.236

0.000 0.236*

Mn 0.806 0.939 0.298

0.000 0.000 0.131*

Cu 0.975 0.721 0.539 0.826

0.000 0.000 0.004 0.000

Cr 0.922 0.889 0.448 0.946 0.934

0.000 0.000 0.019 0.000 0.000

Co 0.907 0.812 0.387 0.920 0.940 0.957

0.000 0.000 0.046 0.000 0.000 0.000

Ba 0.924 0.868 0.423 0.941 0.948 0.987 0.984

0.000 0.000 0.028 0.000 0.000 0.000 0.000

Silt + Clay 0.754 0.797 0.240 0.842 0.816 0.891 0.898 0.900

0.000 0.000 0.228* 0.000 0.000 0.000 0.000 0.000

OM 0.935 0.823 0.411 0.891 0.954 0.962 0.959 0.976 0.878 0.000 0.000 0.033 0.000 0.000 0.000 0.000 0.000 0.000 * Non-significant at p< 0.05, OM = Organic matter

Further, the correlation analysis shows that metals are strongly and positively correlated with the mud (silt + clay) or organic matter content of the sediments (Table 11 and Figure 15).

Once again, the Pb is exception, which shows weak and positive correlation with organic matter and no significant correlation with silt + clay content. Figure 16 illustrates that also a strong and positive association exits between the silt + clay and organic matter in the sediments.

Figure 14: Matrix plot of the eight trace metals showing the relationship between them. 51

Figure 15: Matrix plot illustrating the relationship between trace metal concentrations, organic matter and silt + clay content in sediments. 52

Figure 16: Scatter plot showing the relation between silt + clay and organic matter in fluvial sediments.

Principal Component Analysis Principal Component analysis (PCA) was performed on the correlation matrix of 27 log- normalized dataset reducing the observed variables into a number of principal components to identify the variance of dataset. The PCA results including loadings, eigenvalues, and variances related to each PC are presented in Table 12. In this analysis, two first principal components

(PC1 and PC2) were retained on the basis of the Eigenvalue-One Criterion (Kaiser, 1960) combined with the Scree Test (Cattell, 1966). Even though PC2 does not have eigenvalue >1

(The value is 0.95) as mentioned by the eigenvalue-one criterion, scree plot of the loadings shows a clear break after PC2 making it a meaningful component. The retained first two principal components explain 94.7% of the total variance of the data set. PC2 explaining 11.9% 53 of total variance has strong positive loading (>0.70) on Pb, while PC1 explaining 82.8% of the total variance does not show significant loadings (<0.50) on any metals.

Table 12: Principal component loadings from PCA on all trace metal data set of Ottawa River Variables PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 Zn 0.370 0.229 0.245 -0.519 0.239 -0.388 0.510 0.118 Sr 0.339 -0.340 -0.620 -0.431 -0.245 -0.226 -0.260 -0.140 Pb 0.196 0.850 -0.432 0.202 -0.063 -0.009 -0.083 -0.020 Mn 0.365 -0.266 -0.280 0.424 0.724 -0.006 0.052 0.107 Cu 0.372 0.140 0.433 -0.234 0.243 0.229 -0.679 -0.165 Cr 0.385 -0.067 -0.031 -0.035 -0.190 0.721 0.440 -0.310 Co 0.377 -0.090 0.299 0.495 -0.341 -0.465 0.032 -0.427 Ba 0.385 -0.081 0.118 0.151 -0.379 0.096 -0.082 0.806 Eigenvalue 6.625 0.953 0.285 0.074 0.025 0.022 0.013 0.004 Variance 0.828 0.119 0.036 0.009 0.003 0.003 0.002 0.000 Cumulative 0.828 0.947 0.983 0.992 0.995 0.998 1.000 1.000

The loadings plot for the first two components (Figure 17a) helps to divide the analyzed 8 metals into two distinct groups. One group of the metals includes Zn, Sr, Mn, Cu, Cr, Co, and Ba suggesting mutual relationship between them. In contrary, Pb forms another group indicating some different pattern than the rest of the metals. The PCs score plot (Figure 17b) describes the characteristics of the samples and helps to understand their spatial distribution. From the plots

(Figure 17), it is evident that the samples distributed in upper right quadrant are more enriched with Pd, Zn, and Cu, while those in lower right quadrant with Cr, Ba, Co, Mn, and Sr. The samples in upper left and lower left quadrants are enriched with metals to lesser extent. 54

Figure 17: Plots of the first two principal components (PC1 and PC2) showing (a) Loadings, and (b) scores for the trace metal concentration in the Ottawa River sediments. 55

The cluster analysis of the trace metal concentration also shows the result similar to the

PCA. The dendrogram (Figure 18) depicts that the metals Zn, Sr, Mn, Cu, Cr, Co, and Ba are very closely associated to each other with the percentage of similarity more than 93 and to a somewhat lesser extent to Pb with 62% similarity.

Figure 18: Dendrogram of the cluster analysis of the trace metal concentrations in Ottawa River Sediments.

Sediment Quality Assessment

A number of numerical sediment quality guidelines for freshwater ecosystems have been developed using a variety of approaches and criteria ( e.g. Persaud et al., 1993; Ingersoll et al.,

1996; Smith et al., 1996; MacDonald, 2000a etc.). The most commonly used and consensus- based freshwater sediment quality guidelines include the Threshold Effect Level (TEL) 56

representing the level below which adverse effects on the majority of sediment-dwelling organisms are expected to occur rarely, and the Probable Effect Level (PEL) representing the level above which adverse effects to aquatic biota are predicted to occur frequently (Smith et al.

1996). In this context, both TEL and PEL are calculated and evaluated on the basis of adverse effects as those associated with toxicity for benthic invertebrate communities such as the amphipod Hyalella azteca and the midge Chironomus riparius (Ingersoll et al., 1996).

The sediment quality assessment results for selected metals (Zn, Pb, Cu, Cr, and Mn) at each sampling sites are presented in Figure 19. The results show that the TEL is exceeded by a sediment sample OR-04 collected from a flood plain for Cu, Zn, and Pb, and slightly exceeded by another flood plain sample OR-22 for Pb. In the case of PEL, the exceedance is limited to only one out of twenty seven samples for Pb. The sample was collected from a small ditch.

However, in none of the samples, the concentration of Cr and Mn could go beyond their TEL

and PEL values.

Zinc Lead 350 120 300 PEL 100 250 PEL 80 200 60 150 TEL 100 40 TEL 50 20 Concentration (ppm) Concentration Concentration (ppm) Concentration 0 0

Samples Samples

Figure 19: Trace metal concentrations at different sampling points with their associated sediment quality guidelines. 57

Chromium Copper 100 220 PEL 80 200 PEL

180 60

40 TEL 40 TEL 20

20 (ppm) Concentration

Concentration (ppm) Concentration 0 0

Samples Samples

Manganese 1200 1100 PEL

500 TEL 400 300 200 100 Concentration (ppm) Concentration 0

Samples

Figure 19: Trace metal concentrations at different sampling points with their associated sediment quality guidelines (Continued). TEL and PEL guidelines adapted from Smith et al. (1996), except for Mn adapted from Persaud et al. (1993).

DISCUSSION

Variation in Trace Metal Concentrations

The results show a substantial local variability in trace metal concentrations between fluvial geomorphic features within a 1000 m study site (Table 7 and Figure 12). Possible causes of local variability in the distribution of sediment-associated metals are functions of local, reach-

scale variations in channel geometry and geomorphology, which control sediment storage and

transfer patterns (Rhoads and Cahill, 1999; Ansari et al. 2000; Taylor, 2007). Diverse

geomorphic features within the study site such as pools, riffles, point bars, lateral bars and near

channel flood plains produce local hydrodynamic environments which differentially may erode,

transport, and deposit sediments and associated metals, selectively concentrating metals in

certain types of geomorphic features. Moreover, the local hydrodynamic variability is

responsible for producing reach-scale spatial variation in the textural properties and organic

content of river sediments, which seems primary factors in controlling the metal concentrations.

Previous studies related to the contaminated sediments in fluvial environments have

revealed a range of metal distribution patterns. In sub-arid climate, a channel of episodic flow

has highest concentration of metals occur in most active channel and alluvium, whereas much

lower concentration of metals are found in near bank and over bank deposits regardless of grain

size (Graf et al. 1991). Similarly, a study by Taylor (2007) in Finniss River, Australia shows that

the principal storage areas for metals are within channel zone. In contrary, the trace metal

analysis of Ottawa River sediments indicates that near channel features, such as the active flood

plain are more favorable sites for the accumulation of the sediment-associated metals than

instream features such as pools, riffles, point bars, and lateral bars. The findings suggest the

possible redistribution of contaminated channel sediments over the flood plain. Within the 59

channel zone, the highest concentration exists in lateral bars. The results are in accord with other

studies by Ciszewski (1998), Rhodes and Cahill (1999), Taylor and Hudson-Edwards (2008),

who explain that high flow stages and seasonal floodings cause the resuspension and dispersal of

the contaminated sediments, accumulating them in places where flow is decelerated due to

considerable friction. Such condition occurs in the flood plains and channel bars, and

consequently, they experience the deposition of fine-grained sediments as flow recedes.

The variation of the trace metals between geomorphic features in a local scale can be

explained by differences in fine sediments (silt and clay) and organic matter content in the fluvial

sediments of the study reach. The percentage of fine-grained sediments (>4Ø) and organic matter are positively associated with the metal content (Figure 15), suggesting their influence to adsorb the metals. Similar connection between these factors has been recorded by Gottgens et al. (2004) in the downstream sediments from the present study site. Thus, flood plains and lateral bars likely have the highest concentration of metals because they have high proportion of silt and clay, and are enriched with organic matter (Figure 13 and Table 10). The relation between particle size, organic matter, and trace-element concentrations is well documented (Horowitz,

1991). The grain size and the surface area per unit weight of sediments have inverse relationship.

As grain size decreases, the surface area per unit weight of sediment increases, resulting in increasing potential for adsorption of trace elements. Similarly, organic matters have high capacity of bonding metals due to their large surface area, high cation exchange capacity, and high negative surface charge.

The river, in the study area, has a relatively low bank with indistinct natural levees. The overbank flooding generally occurs in the month of May and June when the river discharge is higher as evidenced by flood debris and freshly deposited sediments, and the area usually 60

receives flood water concentrated with suspended-load sediment. A significant portion (>90%) of the total metal load is transported in suspension phase (Gibbs, 1977) and these sediment- bound metals are deposited in flood plains as turbulent mixing and flow velocity decline due to surface roughness and vegetation present in the site. It can be postulated that the organic matter originate from the decay of leaves and twigs, abundant in the channel along the forested river reach, in-situ or are transported in suspension during medium- and higher-river discharge and are accumulated in places with slow flow velocity, mainly in the flood plain and on the tops of lateral bars. The organic matter and reworked fine sediments are intermixed, and the metals are adsorbed into these materials.

In the channel environment, the lowest concentration of all metals was found in the point bars. Generally, less contamination has been found in the coarser sediments stored in more dynamic environments (Salomons and Forstner, 1984; Miller, 1997; Rhodes and Cahill, 1999).

The point bars in the Ottawa River, are the least favorable site for metal accumulation because of the low content of fine sediments (silt and clay) and organic matter (Figure 13 and Table 10). It is possible in the case of Ottawa River that fine-grained sediments from the point bars are being winnowed by river flow even during average discharge due to their typically small size, gently riverward slope, no vegetation cover, and elevation close to flow level. Further, the erosion and transport of sediment-associated metals from point bars might have been enhanced by their low organic matter content because it has been noted that an increase in organic matter causes sediments to behave in a more cohesive manner (Lick, 2009).

The pair-wise comparison on metal concentration shows that the lateral bar deposits are not statistically different than the flood plain deposits in terms of their metal content (except for

Pb, Cu, and Co). These two features also consist of relatively fine-grained sediments and are 61

enriched with organic matter (Figure 13, Table 10). These results can be related to the history of

lateral bars formation in the Ottawa River. It was noticed in the field that some old point bars

were detached due the formation of recent chute cut-offs across them. Possibly, these detached

point bars and/or mid-channel bars gradually migrated downstream and attached to the channel

margin, forming the stabilized lateral bars. During the higher-flow stage, these bars received the

suspended sediments and organic matter, thus creating a bar-top sub-environment.

The results of the metal content in the flood plain may not essentially represent the entire

flood plain in the Ottawa River because this study analyzes only the proximal flood plain

sediments. The concentration of metal can vary between the proximal and distal flood plain as

indicated by Zhao et al. (1999) and Wyzga and Ciszewski (2010). The concentrations of metal on flood plain show an increase with distance from the channel because coarser particles are

deposited near the river bank and metals are more likely related to the finer particles deposited in

distal localities (Zhao et al., 1999). On contrary, Wyzga and Ciszewski (2010) states that the

concentrations of heavy metals are highest close to the channel margins and decrease with

increasing distance from the channel due to the rapid lateral thinning of the flood deposits.

Therefore, further study is needed to understand the lateral extent of the metals on the flood

plain.

The metal variations between the fluvial geomorphic features do not always follow a

consistent pattern in terms of their dependency on fine-grained sediments and organic matter. For instance, the highest concentration of Pb was recorded from a sample with mean grain size of

2.1Ø (fine-grained sand having only 6.3% silt and clay), and the lowest organic matter (1.2%).

The higher residence time and/or primary sources of the contaminated sediments are possibly responsible for higher metal content in the coarser size fractions (Graf, 1991; Ladd, 1995; Sing, 62

1999). The sample was collected from the mouth of a ditch, which generally receives storm

runoff from nearby road networks and settlement area. It is thus possible that road dust

containing Pb (Nriagu and Pacyna, 1988) were brought to the ditch and were accumulated

because of decrease in flow velocity, where they might remain as clay rims or interstitial clays

on coarse-sized sediments for a long time. However, on the basis of a single sample, it would not be appropriate to make a final conclusion. It is possible that the sample may have experienced

analytical error and/or contamination. Therefore, more bed load sediment samples should be

collected and analyzed from the ditch before reaching to any conclusion, which requires further

research. Nonetheless, it is clear that the ditch does not input significant amount of metal contaminated sediment into the main channel, because there is no statistically significant difference in metal concentration between the upstream and downstream reaches.

Metal Association and Possible Sources

The statistical analyses including correlation, principal component, and cluster analysis were performed between trace metals, fine sediment and organic matter content to identify, quantify and interpret complex relationships between them.

The correlation analysis indicates that metal concentrations in the sediments are highly influenced by the fine grained and organic matter content (Table 11 Figure 15). Strongly positive correlations of all the metal values (except Pb) with organic matter, and silt-clay content imply that these constituents provide active sites for the metals sorption. The plausible causes about their positive correlation have been already mentioned above. However, Pb is an exception whose concentration does not statistically suggest its dependency on silt and clay content. Hence, it can be inferred that the high values of Pb are not essentially caused by adsorption on silt and 63 clay enriched sediments. The concentration of Pb might have been more influenced my organic matter. Dissolved and particulate organic matter in the water column act as scavenger for metals, and scavenged metals may then be incorporated in bottom sediments. Pb is known to form strong complexes with humic substances (Mantoura et al., 1978). It was also found that high silt and clay contents are linked with high organic matter content in the fluvial sediments, showing positive correlation between them (Figure 16).

The intermetallic relationships are assessed on the basis of the results obtained from correlation, principal component, and cluster analysis. The degree and nature of intermetallic relationship indicates specific physico-chemical processes responsible in transfer and adsorption of the metals (Ansari et al. 2000). As per the principal component analysis, it can be assumed that Zn, Sr, Mn, Cu, Cr, Co, and Ba are affected by similar transport and interaction processes, thus indicating a similar pattern of distribution, whereas Pb shows a different pattern than other metals (Figure 17a). More specifically, it can be postulated that Zn, Sr, Mn, Cu, Cr, Co, and Ba experience more or less similar fate during transport, which suggest they have at least a common source. The results obtained from PCA are confirmed by cluster analysis giving two distinct clusters of metal concentration (Figure 18).

The cluster 1 includes the metals (Zn, Sr, Mn, Cu, Cr, Co, and Ba) that contribute mainly to the PC1. The association of these metals reveals common sources and transport. The majority of these metals are well known industrial metals (e.g., Zn, Cu, Mn, Co, Cr), therefore their derivation is probably related to the industrial effluent. Though large industries are not known in the upstream area of the study site, few small industries related to the steel and auto parts are located around the Sylvania Township area. These industries might contribute these metals to the

Ottawa River. Other sources may include domestic waste, sewage effluents, and agricultural 64

runoffs. The cluster 2 includes only Pb suggesting its separate sources than the rest of the metals.

The influence of anthropogenic inputs is clearly shown by high positive loadings for Pb in PC1.

Due to the proximity of the study site to the urban area and major road network (I-475), traffic exhaust could be a major source of the Pb. Even though, lead additives to gasoline and paint were phased out in 1980s, they can still be accumulated in sediments due to their extensive use in the past (Nriagu, 1990). Thorson (2004) has listed an asphalt company in Sylvania as a hazardous source to the Ottawa River. Coal and oil burning for the production of asphalt can also be important emission source of Pb (Nriagu and Pacyna, 1988). Atmospheric fallout can be other sources of the Pb in the study area because Ritson et al. (1994) recorded atmospheric deposition as a most prominent source of Pb in Lake Erie. However, it is difficult to quantify the influence of anthropogenic activities on the metal inputs to the river because of unavailability of metal background values in the Ottawa River sediments.

Sediment Pollution

According to data from OEPA (2000), the concentration of the heavy metals ( Pb, Cd, Cr,

Cu, Ni, and Zn) in the lower part of the Ottawa River sediments exceed the Threshold Effect

Level (TEL) and Probable Effect Level (PEL) indicating the lower portion of the river as a highly polluted area with respect to these metals (Figure 9). However, the comparison of the concentration of some selected metals (Pb, Cr, Zn, and Cu) with the TEL and PEL values (Smith et al. 1996) in this study reveals that the sediment toxicity is not a major issue for the upper portion of the Ottawa River (Figure 19). The results of the study suggest that major pollution sources to the Ottawa River are located around the lower reach of the river, however presence of some Pb sources in the upper portion cannot be completely denied. 65

Implications

Formulation of management strategies for regulating the river sediments contaminated with metals requires the knowledge about their dispersal, storage, and remobilization patterns in the fluvial environment (Marcus, 1989; Macklin et al., 2006). The outcomes of this study that the concentration of trace metals varies between fluvial geomorphic features have a wide implication for monitoring and regulation, impact assessment, and remediation of contaminated sediments in the fluvial environment. Prior recognition of the possible hotspots would be helpful to focus on certain geomorphic units for detail sampling and study minimizing the time, efforts, and fund. In the Ottawa River, for example, focused sampling and study of flood plains and lateral bars would reveal the level of metal toxicity in the sediments. The study also gives the general ideas about sampling size in each type of the feature. For instance, greater coefficient of variance in the flood plain than point bars (Table 7) indicates that many more samples should be collected from the flood plain with relative to the point bars to attain the same level of confidence for mean metal concentration.

Remediation of contaminated sediments in the lower reach of Ottawa River is mainly focused on the dredging of instream sediments (Hull Associate, 2004). However, the results of this study show that the contaminated sediments are not only limited to the channel, but are also redistributed in the flood plain, and possibly adjacent areas. If only instream sediments were dealt with remediation measures, metal concentrations at some of the most highly contaminated sites would be grossly underestimated. The approach described in this study is a first step towards identifying the hot spots in the river having similar geomorphical, hydrological, and sedimentological characters to the Ottawa River. Further research should be conducted to determine the lateral and vertical extent of the metals in the flood plain sediments. 66

SUMMARY AND CONCLUSIONS

Channel geometry and geomorphology control the dispersal, storage, and remobilization of sediment-associated metals which consequently result the variability of trace metal concentration in fluvial environment. This hypothesis was tested in a 1000 m reach of Ottawa

River located in Wildwood Metropark, Toledo. Surface sediment samples were collected from five different types of geomorphic features namely, the flood plain, point bars, lateral bars, pools, and riffles, and were analyzed by Inductively Coupled Plasma Optical Emission Spectrometer

(ICP-OES) for 11 metals including Zn, Ti, Sr, Pb, Mn, Hg, Cu, Cr, Co, Cd, and Ba. Grain size analysis and organic matter content were used to define the relationship between metal concentration, grain size, and organic matter, and further statistical analyses were conducted to test the statistical significances of the results.

There are statistically significant variations in metal concentration between geomorphic features in fluvial environment. Among the five features studied in Ottawa River, the flood plain exhibits consistently highest concentration of the trace metals suggesting the dispersal of contaminated sediments in near channel environment from the main channel. Lateral bars contain the second highest, while point bars have lowest concentration of the metals. Pb, Cu, and

Ba are the metals having the greatest average concentration differences between features followed by Zn, Cr, Sr, Mn, and Co. In a pair-wise comparison, there is no statistically significant difference between lateral bars, pools, and riffles in terms of metal concentrations.

Fine-grained sediment and higher organic matter content are primary metal carriers as indicated by their positive association with metal concentrations. It is postulated that resuspension and dispersal of contaminated sediments occur primarily during high flow stages and seasonal 67

flooding accumulating them in the flood plain and lateral bars where flow is decelerated due to considerable friction.

The results of this study illustrate that Zn, Sr, Mn, Cu, Cr, Co, and Ba are related to each other which suggest that these metals were affected by similar transport and interaction

processes, and possibly were derived from common sources. On the other side, Pb shows a weak

association with other metals and different distribution pattern indicating separate anthropogenic

sources than the rest of the metals. Even though sediments in the study section of the river are

not a major contamination concern, it is evident that the flood plain serves as a sediment-

associated metals sink. Thus, this approach can be implied in contaminated section of the Ottawa

River or other rivers having similar geomorphological, hydrological, and sedmentological

characters for monitoring and regulation, impact assessment, and remediation of contaminated

sediments.

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APPENDICES

APPENDIX A: SAMPLE LOCATION Location Sample Geomorphic Unit Northing Easting OR01 278055 4618182 Point bar OR02 278078 4618173 Point bar OR03 278075 4618186 Flood plain OR04 278114 4618181 Flood plain OR05 278054 4618220 Riffle OR06 278081 4618165 Pool OR07 278063 4618132 Lateral bar OR08 278042 4618117 Lateral bar OR09 278036 4618113 Lateral bar OR10 278053 4618117 Riffle OR11 278016 4618072 Riffle OR12 277945 4618278 Lateral bar OR13 277964 4618254 Pool OR14 277978 4618262 Point bar OR15 278023 4618304 Riffle OR16 278027 4618343 Lateral bar OR17 278111 4618410 Ditch OR18 278087 4618347 Ditch OR19 278056 4618355 Point bar OR20 278067 4618360 Pool OR21 278027 4618206 Flood plain OR22 278183 4618244 Flood plain OR23 277996 4618046 Flood plain OR24 278006 4617991 Pool OR25 278104 4618027 Flood plain OR26 278125 4618023 Point bar OR27 278125 4618031 Pool

APPENDIX B: TRACE METAL CONCENTRATION FOR ALL SAMPLES Zn Ti Sr Pb Mn Hg Cu Cr Co Cd Ba Sample (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) (ppm) OR01 12.9 BDL 16.5 3.5 42.2 BDL 2.4 2.6 1.1 BDL 8.6 OR02 9.6 BDL 12.5 2.3 49.2 BDL 1.6 3.0 1.2 BDL 8.2 OR03 96.7 BDL 145.9 30.4 369.3 BDL 26.1 24.5 7.8 0.2 96.1 OR04 148.5 BDL 72.9 76.5 251.9 BDL 41.9 23.1 8.6 0.6 93.8 OR05 16.1 BDL 40.1 4.2 117.8 BDL 3.5 5.6 2.2 BDL 15.8 OR06 21.2 BDL 48.6 4.8 171.9 BDL 5.6 6.6 3.2 BDL 27.7 OR07 17.4 BDL 18.6 9.3 60.9 BDL 9.0 4.9 1.7 BDL 21.4 OR08 64.9 BDL 81.7 18.6 232.8 BDL 15.3 17.5 4.6 0.1 56.5 OR09 54.6 BDL 63.6 15.0 146.6 BDL 12.5 11.4 3.0 BDL 33.4 OR10 8.1 BDL 16.5 1.7 51.7 BDL 1.6 2.0 1.1 BDL 6.1 OR11 17.5 BDL 26.7 5.3 72.8 BDL 3.4 4.3 1.7 BDL 14.5 OR12 11.8 BDL 19.6 2.9 65.1 BDL 2.8 2.4 1.3 BDL 9.4 OR13 18.3 BDL 27.7 3.6 128.4 BDL 5.7 6.4 3.1 BDL 18.6 OR14 10.6 BDL 12.7 2.9 43.5 BDL 2.1 2.5 1.2 BDL 9.2 OR15 17.2 BDL 38.9 4.3 115.0 BDL 4.2 6.0 2.7 BDL 21.1 OR16 65.4 BDL 99.8 17.9 342.6 BDL 18.4 18.9 6.2 0.1 72.1 OR17 39.1 BDL 12.2 16.9 59.7 BDL 4.4 3.4 1.1 BDL 8.7 OR18 42.8 BDL 13.6 112.1 49.8 BDL 5.7 5.7 1.2 BDL 13.5 OR19 9.7 BDL 9.3 2.1 45.2 BDL 3.1 2.3 1.0 BDL 6.8 OR20 22.9 BDL 33.3 3.9 71.0 BDL 5.3 7.7 3.7 BDL 33.2 OR21 85.5 BDL 141.5 23.3 427.8 BDL 24.2 23.5 8.3 0.3 95.3 OR22 116.2 BDL 60.7 38.7 327.2 BDL 34.2 22.7 10.5 0.5 103.1 OR23 53.0 BDL 41.9 16.1 138.3 BDL 15.0 12.5 3.8 0.1 45.5 OR24 16.4 BDL 21.9 3.8 63.8 BDL 5.7 6.6 2.4 BDL 21.1 OR25 48.4 BDL 44.1 28.3 229.9 BDL 18.1 15.6 4.8 0.1 52.7 OR26 10.8 BDL 7.4 3.4 41.2 BDL 2.8 3.5 1.3 BDL 6.3 OR27 12.4 BDL 40.6 2.9 106.5 BDL 5.3 3.1 2.0 BDL 12.5 BDL – Below Detection Limit

APPEDIX C: SAND, MUD, AND ORGANIC MATTER CONTENT IN OTTAWA RIVER SEDIMENTS. Sample Sand (%) Mud (Silt + Clay, %) Organic Matter (%) OR01 87.8 10.2 2.1 OR02 93.7 5.6 0.7 OR03 11.8 76.7 11.5 OR04 17.6 69.2 13.2 OR05 86.2 12.1 1.7 OR06 67.3 30.5 2.1 OR07 77.0 21.1 1.9 OR08 43.1 49.9 7.0 OR09 62.1 32.8 5.1 OR10 86.3 12.0 1.6 OR11 77.3 20.5 2.2 OR12 85.0 13.2 1.8 OR13 62.1 35.6 2.3 OR14 88.1 10.4 1.5 OR15 80.7 17.3 2.0 OR16 25.7 64.5 9.8 OR17 96.3 2.5 1.2 OR18 92.4 6.3 1.2 OR19 86.5 12.0 1.5 OR20 35.1 61.0 4.0 OR21 12.4 76.4 11.2 OR22 17.5 69.5 13.0 OR23 50.8 40.8 8.3 OR24 50.2 47.4 2.4 OR25 38.8 55.8 5.4 OR26 82.7 16.1 1.2 OR27 56.7 40.4 2.8

APPENDIX D

INDIVIDUAL SAMPLE GRAINSIZE STATISTICS

Sample – OR01

Cumulative Arithmetic Curve Histogram 100 35

80 30 25 60 20

15 40 10 Weight (%) Weight 20 5

0 0 Cumulative Percentage -1 0 1 2 3 4 5 -1 0 1 2 3 4 5 6 Grain Size (Ø) Grain Size (Ø)

Sample – OR02

Cumulative Arithmetic Curve Histogram 100 40

80 30

60 20 40

Weight (%) Weight 10 20 Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 -1 0 1 2 3 4 5 Grain Size (Ø) Grain Size (Ø) 83

Sample – OR03

Cumulative Arithmetic Curve Histogram 50 100

80 40

60 30

40 20 Weight (%) Weight 20 10

Cumulative Weight (%) Weight Cumulative 0 0 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 Grain Size (Ø) Grain Size (Ø)

Sample – OR04

Cumulative Arithmetic Curve Histogram 100 60

80 50

60 40 30 40 20 Weight (%) Weight 20 10

0 Cumulative Weight (%) Weight Cumulative 0 0 2 4 6 8 1 2 3 4 5 6 7 Grain Size (Ø) Grain Size (Ø) 84

Sample – OR05

Cumulative Arithmetic Curve Histogram 100 60

80 50

60 40

30 40 20 Weight (%) Weight 20 10 Cumulative Weight (%) Weight Cumulative 0 0 -1 1 3 5 7 -1 0 1 2 3 4 5 6 Grain Size (Ø) Grain Size (Ø)

Sample – OR06

Cumulative Arithmetic Curve Histogram 100 40

80 30 60

20 40

Weight (%) Weight 10 20

Cumulative Weight (%) Weight Cumulative 0 0 -1 1 3 5 7 -1 0 1 2 3 4 5 6 7 8 Grain Size (Ø) Grain Size (Ø) 85

Sample – OR07

Cumulative Arithmetic Curve Histogram

100 60

50 80 40 60 30 40 20 Weight (%) Weight 20 10

Cumulative Weight (%) Weight Cumulative 0 0 0 1 2 3 4 5 6 0 1 2 3 4 5 6 Grain Size (Ø) Grain Size (Ø)

Sample – OR08

Cumulative Arithmetic Curve Histogram

100 40

80 30

60 20 40

10 20 (%) Weight

Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 7 -1 0 1 2 3 4 5 6 7 Grain Size (Ø) Grain Size (Ø) 86

Sample – OR09

Cumulative Arithmetic Curve Histogram

100 60

50 80 40 60 30 40 20 Weight (%) Weight 20 10

Cumulative Weight (%) Weight Cumulative 0 0 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 Grain Size (Ø) Grain Size (Ø)

Sample – OR10

Cumulative Arithmetic Curve Histogram 100 50

80 40

60 30

40 20

20 10 Cumulative Weight (%) Weight Cumulative 0 (%) Weight Cumulative 0 -2 0 2 4 6 -2 -1 0 1 2 3 4 5 Grain Size (Ø) Grain Size (Ø) 87

Sample – OR11

Cumulative Arithmetic Curve Histogram

100 50

80 40

60 30

20 40 Weight (%) Weight 10 20

Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 -1 0 1 2 3 4 5 6 Grain Size (Ø) Grain Size (Ø)

Sample – OR12

Cumulative Arithmetic Curve Histogram

100 50

80 40

60 30

40 20 Weight (%) Weight

20 10

0 Cumulative Weight (%) Weight Cumulative 0 -1 0 1 2 3 4 5 6 7 -1 0 1 2 3 4 5 6 7 Grain Size (Ø) Grain Size (Ø)

Sample – OR13

Cumulative Arithmetic Curve Histogram

100 40

80 30

60 20 40

Weight (%) Weight 10 20

Cumulative Weight (%) Weight Cumulative 0 0 -1 1 3 5 7 -1 0 1 2 3 4 5 6 7 8 Grain Size (Ø) Grain Size (Ø)

Sample – OR14

Cumulative Arithmetic Curve Histogram

100 40

80 30

60 20 40 Weight (%) Weight 10 20

0 0 Cumulative Weight (%) Weight Cumulative -1 0 1 2 3 4 5 6 -1 0 1 2 3 4 5 Grain Size Ø) Grain Size (Ø)

Sample – 15

Cumulative Arithmetic Curve Histogram 100 50

80 40

60 30

40 20 Weight (%) Weight 20 10

Cumulative Weight (%) Weight Cumulative 0 0 -1 1 3 5 7 -1 0 1 2 3 4 5 6 Grain Size (Ø) Grain Size (Ø)

Sample – 16

Cumulative Arithmetic Curve Histogram

100 70 60 80 50

60 40

30 40

Weight (%) Weight 20 20 10

0 0 Cumulative Weight (%) Weight Cumulative -1 0 1 2 3 4 5 6 -1 0 1 2 3 4 5 6 Grain Size (Ø) Grain Size (Ø)

Sample – OR17

Cumulative Arithmetic Curve Histogram 100 80 70 80 60 60 50 40 40 30 Weight (%) Weight 20 20 10 Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 -1 0 1 2 3 4 5 6 Grain Size (Ø) Grain Size (Ø)

Sample – OR18

Cumulative Arithmetic Curve Histogram 100 50

80 40

60 30

40 20 Weight (%) Weight

20 10 Cumulative Weight (%) Weight Cumulative

0 0 -1 0 1 2 3 4 5 -1 0 1 2 3 4 5 Grain Size (Ø) Grain Size (Ø) 91

Sample – OR19

Cumulative Arithmetic Curve Histogram

100 40

80 30

60 20 40 Weight (%) Weight 10 20 Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 -1 0 1 2 3 4 5 Grain Size (Ø) Grain Size (Ø)

Sample – OR20

Cumulative Arithmetic Curve Histogram

100 40

80 30

60 20 40 Weight (%) Weight 10 20

Cumulative Weight (%) Weight Cumulative 0 0 0 2 4 6 8 1 2 3 4 5 6 7 Grain Size (Ø) Grain Size (Ø)

Sample – OR21

Cumulative Arithmetic Curve Histogram

100 70

60 80 50

60 40

40 30

Weight (%) Weight 20 20 10

Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 7 8 -1 0 1 2 3 4 5 6 7 Grain Size (Ø) Grain Size (Ø)

Sample – OR22

Cumulative Arithmetic Curve Histogram

100 50

80 40

60 30

40 20 Weight (%) Weight 20 10

Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 7 8 9 -1 0 1 2 3 4 5 6 7 8 Grain Size (Ø) Grain Size (Ø)

Sample – OR23

Cumulative Arithmetic Curve Histogram

100 40

80 30

60 20 40

Weight (%) Weight 10 20

Cumulative Weight (%) Weight Cumulative 0 0 0 1 2 3 4 5 6 7 0 1 2 3 4 5 6 Grain Size (Ø) Grain Size (Ø)

Sample – OR24

Cumulative Arithmetic Curve Histogram 100 40

80 30

60 20 40 Weight (%) Weight 10 20

Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 7 -1 0 1 2 3 4 5 6 7 Grain Size (Ø) Grain Size (Ø) 94

Sample – OR25

Cumulative Arithmetic Curve Histogram

100 50

80 40

60 30

40 20 Weight (%) Weight 20 10

Cmulative Weight (%) Weight Cmulative 0 0 -1 0 1 2 3 4 5 6 7 8 -1 0 1 2 3 4 5 6 7 Grain Size (Ø) Grain Size (Ø)

Sample – OR26

Cumulative Arithmetic Curve Histogram

100 40

80 30

60 20 40

Weight (%) Weight 10 20

Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 -1 0 1 2 3 4 5 Grain Size (Ø) Grain Size (Ø)

Sample – OR27

Cumulative Arithematic Curve Histogram

100 40

80 30

60 20 40 Weight (%) Weight 10 20 Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 7 8 -1 0 1 2 3 4 5 6 7 Grain Size (Ø) Grain Size ((Ø)

Grain size statistics

Standard Sample Mode (Ø) Median (Ø) Mean (Ø) Skewness Deviation OR01 2 1.9 2.0 1.33 0.06 OR02 2 1.9 2.0 1.22 0.02 OR03 7 6.15 5.8 1.52 -0.52 OR04 7 6 5.1 1.82 -0.68 OR05 2 1.65 1.8 1.17 0.27 OR06 3 2.7 3.0 1.74 0.14 OR07 3 2.45 2.8 1.23 0.36 OR08 5 4.15 3.9 1.44 -0.30 OR09 3 2.7 3.3 1.31 0.55 OR10 2 1.6 1.5 1.53 -0.05 OR11 2 1.9 2.4 1.30 0.49 OR12 2 2.2 2.2 1.02 0.14 OR13 3 2.8 3.1 1.52 0.29 OR14 2 2 2.1 1.21 0.07 OR15 2 1.9 2.4 1.39 0.50 OR16 5 4.4 3.9 1.17 -0.50 OR17 2 1.5 1.5 0.70 -0.03 OR18 3 2.1 2.1 0.67 -0.01 OR19 2 2 2.1 1.40 0.14 OR20 5 4.4 4.1 1.41 -0.26 OR21 6 5.5 5.4 1.07 -0.37 OR22 7 6.3 5.2 2.29 -0.64 OR23 6 3.65 3.8 1.55 0.05 OR24 5 3.95 3.7 1.25 -0.19 OR25 6 5.2 4.6 1.48 -0.53 OR26 2 2.1 2.4 1.40 0.23 OR27 5 3 3.1 1.35 0.11

APPENDIX E

GEOMORPHIC FEATURES GRAINSIZE STATISTICS

Geomorphic Unit – Flood Plain

Cumulative Arithmetic Curve Histogram 100 40 80 30 60 20 40

20 (%) Weight 10

Cumulative Weight (%) Weight Cumulative 0 -1 0 1 2 3 4 5 6 7 8 -1 0 1 2 3 4 5 6 7 8 Grain Size (Ø) Grain Size (Ø)

Geomorphic Unit – Point Bar

Cumulative Arithmetic Curve Histogram 100 40 80 30 60

20 40 Weight (%) Weight 20 10

Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 -1 0 1 2 3 4 5 Grain Size (Ø) Grain Size (Ø)

Geomorphic Feature – Lateral Bar

Cumulative Arithmetic Curve Histogram 100 50

80 40

60 30

40 20 Weight (%) Weight 20 10

0 0 Cumulative Weight (%) Weight Cumulative -1 0 1 2 3 4 5 6 7 -1 0 1 2 3 4 5 6 7

Grain Size (Ø) Grain Size (Ø)

Geomorphic Feature – Pool

Cumulative Arithmetic Curve Histogram

100 30

80 20 60

40 10 Weight (%) Weight 20 Cumulative Weight (%) Weight Cumulative 0 0 -1 1 3 5 7 -1 0 1 2 3 4 5 6 7 8 Grain Size (Ø) Grain Size (Ø) 100

Geomorphic Feature – Riffle

Cumulative Arithmetic Curve Histogram 100 50

80 40

60 30

40 20 Weight (%) Weight 20 10

Cumulative Weight (%) Weight Cumulative 0 0 -2 -1 0 1 2 3 4 5 6 -2 -1 0 1 2 3 4 5 6

Grain Size (Ø) Grain Size (Ø)

Geomorphic Feature – Ditch

Cumulative Weight (%) Histogram 100 60

80 50

40 60 30 40 20 Weight (%) Weight 20 10 Cumulative Weight (%) Weight Cumulative 0 0 -1 0 1 2 3 4 5 6 -1 0 1 2 3 4 5 6 Grain Size (Ø) Grain Size (Ø) 101

Grain size statistics

Mode Median Mean Standard Geomorphic Feature Skewness (Ø) (Ø) (Ø) Deviation Flood Plain 6.00 5.40 5.07 1.87 -0.42 Point Bar 2.00 2.00 2.07 1.30 0.07 Lateral Bar 3.00 2.80 3.07 1.42 0.25 Pool 5.00 3.20 3.35 1.49 0.14 Riffle 2.00 1.75 2.12 1.71 0.16 Ditch 2.00 1.75 1.83 0.66 0.17