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Analysis of Relationships Between Water QualityParameters and Stream Sediment with FecalBacteria in Hidden Creek, Rock Hill, SCKaitlin J. McCullochWinthrop University

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Recommended CitationMcCulloch, Kaitlin J., "Analysis of Relationships Between Water Quality Parameters and Stream Sediment with Fecal Bacteria inHidden Creek, Rock Hill, SC" (2015). Graduate Theses. 14.https://digitalcommons.winthrop.edu/graduatetheses/14

ANALYSIS OF RELATIONSHIPS BETWEEN WATER QUALITY

PARAMETERS AND STREAM SEDIMENT WITH FECAL BACTERIA

IN HIDDEN CREEK, ROCK HILL, SC.

A Thesis

Presented to the Faculty

Of the

College of Arts and Sciences

In Partial Fulfillment

Of the

Requirement for the Degree

Of

Master of Science

In Biology

Winthrop University

April, 2015

By

Kaitlin J. McCulloch

ii

ABSTRACT

Hidden Creek, in Rock Hill, SC is in need of a watershed management

program to decrease fecal coliform counts by 19% because currently, water

samples measured for fecal bacteria exceed the Total Maximum Daily Load

(TMDL). One objective of this study was to locate the area/s along Hidden Creek

with the highest fecal bacteria counts. The other objectives were to determine

whether relationships existed between fecal bacteria and water quality

parameters and whether the stream sediment was serving as a reservoir for fecal

bacteria. Five sampling sites were established and samples were taken every 1-

2 weeks from June – September 2014 (the summer season). At each site,

chemical and physical parameters such as pH, dissolved oxygen, turbidity, and

water temperature, were measured using a Eureka Manta Probe. Undisturbed

and disturbed water samples from each site were analyzed for total fecal coliform

and E. coli. The results showed that disturbed water samples had significantly

higher total fecal coliform and E. coli counts than the samples taken from the

overlying water column. Multiple regression analyses were performed to

examine if DO, water temperature, and turbidity could be used to predict E. coli

or total fecal coliform in Hidden Creek. There were no significant relationships

between any of the factors. The variability in the number of E. coli and total fecal

coliform colonies for each site were large which made it difficult to pinpoint a

specific site of interest. The only sites that had significantly different E. coli

counts were Riverview and Lexington. All other sites were not significantly

iii

different from each other for both E. coli and total fecal coliform. Across all of the

sites combined, E. coli exceeded 235 cfus/ 100 mL and total fecal coliform

exceeded 400 cfus/100 mL greater than 25% of time for both undisturbed and

disturbed samples (the standards set by the Clean Water Act and EPA). This is

a concern because Hidden Creek flows into the Catawba River which provides

recreational uses and drinking water supply for surrounding areas. Overall, this

study concluded that by monitoring bacteria in sediment, as well as the overlying

water column, a more accurate depiction of water quality could be completed.

This would save companies and municipalities time, money, and effort when

creating an effective management program.

iv

ACKNOWLEDGMENTS

I would like to thank Winthrop Research Council for providing the funding to

complete this project. Thanks to Dr. Peter Phillips for being my thesis advisor

and for letting me use his lab and equipment for sampling and analyses. Thanks

to my committee members Dr. Matt Heard and Dr. Bill Rogers for giving me

helpful review comments and suggestions on my project. Special thanks to the

City of Rock Hill Stormwater Division: David Dickson, Renee Burt, and Angela

Jackson for providing me with the information and maps for Hidden Creek and for

sampling with me throughout the summer.

v

TABLE OF CONTENTS

ABSTRACT ii

ACKNOWLEDGEMENTS iv

LIST OF FIGURES vii

LIST OF TABLES viii

INTRODUCTION 1

Clean Water Act 1

Human Impacts on the Stream Environment 3

Effects of Urbanization on Streams 4

The Impact of Urban Streams on Water Quality 6

Fecal Coliform and Urbanization 8

Type and Size of Sediment 10

Taking into Account Resuspension of Sediment and Fecal Bacteria 11

Temperature, Sunlight, and Seasonality 12

Flow of Stream 14

Relationships between Water Quality Parameters

and Fecal Bacteria 15

Site of Interest 19

Objectives 21

Research Questions 22

Hypotheses and Rationale 23

METHODS 25

Watershed Description 25

Description of Sites 25

vi

Sampling Period 28

Field Sampling Methods 28

Laboratory Analysis of Samples 29

Statistical Analysis of Data 31

RESULTS 32

Effect of Site and Disturbance Level on E. coli Colonies 32

Effect of Site and Disturbance Level on Total Fecal Coliform 35

Effect of Water Temperature, DO, and Turbidity on E. coli 37

Effect of Water Temperature, DO, and Turbidity on Total Fecal Coliform 37

Percent Exceedance Calculations 38

Percent Exceedance of E.coli per Site 38 Percent Exceedance of Total Fecal Coliform per Site 40 Percent Exceedance of E.coli and Total Fecal Coliform for Hidden Creek 42

DISCUSSION 43

CONCLUSION 49

LITERATURE CITED 50

vii

LIST OF FIGURES

Figure No. Page

1 The effect of individual factors and their interactions on E. coli survival in fresh water streams 17

2 Map of 16 preliminary sampling points located on 26 Hidden Creek (CW-221) in Rock Hill, SC.

3 Map of the 5 sites along Hidden Creek (CW-221) 27

4 Examples of results obtained from using 3M™ Petrifilm™ E.coli/ Coliform Count Plates 30

5 Mean (± SE) of E. coli colonies between undisturbed and disturbed samples 34

6 Mean (± SE) of total fecal coliform colonies between undisturbed and disturbed samples 36

viii

LIST OF TABLES

Table No. Page

1 Latitudinal and Longitudinal GPS coordinates for the 5 sampling sites on Hidden Creek 27

2 Mean ± SE of E. coli coliform counts for both undisturbed and disturbed samples from all 5 sites 33

3 Mean ± SE of Total Fecal coliform colonies for both undisturbed and disturbed samples from all 5 sites 35

4a Summary statistics for undisturbed E. coli (cfus/100 mL) concentrations 39

4b Summary statistics for disturbed E. coli (cfus/100 mL) concentrations 39

5a Summary statistics for undisturbed total fecal coliform (cfus/100 mL) concentrations 41

5b Summary statistics for disturbed total fecal coliform (cfus/100 mL) concentrations 41

6 Percent Exceedance of fecal bacteria for all of the sites combined 42

7 Mean ± SE of water temperature (ºC), turbidity 45 (NTUs), and DO (mg/L)

1

INTRODUCTION

Rivers and streams are essential to humans because they provide us with

water for drinking, farming, industry, wildlife and recreational purposes, as well as

beauty to our environment. However, as society is becoming more urbanized,

we are causing negative changes to natural water sources. There is plentiful

research demonstrating the negative effects humans are having on watersheds

around the world (Paul & Meyer 2001, Walsh et al. 2005). Due to these negative

impacts, laws and regulations have been established keep water safe and clean

for humans and other animals.

Clean Water Act

The Industrial Revolution was the beginning of a steep cline in population

growth and urbanization in the United States. Around the 1970s, the United

States government began to realize that human population growth and

urbanization were becoming detrimental to the watersheds (EPA 2012). This led

to a series of laws being passed to help clean up the environment. In 1972, the

Clean Water Act (CWA) was passed to attempt to protect and restore waters that

are used by humans (EPA 2012). Since then, many sections have been added

to the CWA to further the goal of keeping water supplies clean. These include

section 319 and section 304 (EPA 2012). Section 319 was established to help

control nonpoint source pollutants, while section 304 pushed states to develop

water quality criteria (WQC) for rivers and streams (EPA 2012). The CWA has

2

been able to regulate point source pollutants by limiting the amount and type of

discharge that can be released into the water, but controlling nonpoint source

pollutants is a more difficult task (EPA 2012).

In addition, section 303(d) of the CWA required each state to provide the EPA

with a list of impaired waters (SCDHEC 2012). If a water source is listed on the

303(d) list, then the water quality of that stream is heavily polluted or degraded

and does not meet the required standards for the CWA (EPA n.d.a.). The EPA

helps states develop water quality criteria by establishing regulations and

guidelines the states are required to follow (Barbour et al. 1999).

As a state begins to implement these regulations and guidelines, it must

develop a Total Maximum Daily Load (TMDL) for the impaired water source (EPA

n.d.a.). This is the maximum amount of a pollutant that a stream or river can

receive daily and still uphold its water quality standards (EPA n.d.a.). The TMDL

is effective because it provides states with a quantified loading capacity for their

bodies of water and helps them to begin the process of restoring the stream by

monitoring and management of the water body (Barbour et al. 1999). TMDL’s

are essentially used for controlling and mitigating stream pollution that is usually

caused by humans (EPA n.d.a.).

In South Carolina, the South Carolina Department of Health and

Environmental Control is responsible for making sure streams meet the

standards set by the EPA and CWA. SCDHEC is accountable for setting up

3

stations along a stream where monitoring by staff can be completed (SCDHEC

1999). Specifically, the standards for fecal coliform state:

Within a 30 day period, 5 consecutive samples cannot exceed a geometric

mean of 200 cfus/100 mL;

During a 30 day period, no more than 10% of the total samples should

exceed 400 cfus/100 mL (SCDHEC 2012).

Samples from a stream station are usually collected over a five year period. If

greater than 10% of the samples over the 5 year period exceed 400 cfus/100 mL,

the stream is considered impaired and is listed on the 303 (d) list (SCDHEC

1999).

Human Impacts on the Stream Environment

Humans, as well as their activities, are the reasons for these water bodies

becoming impaired and degraded. We destroy the natural habitats of these

streams because we use the areas around them for constructing buildings,

farming land, factories, and for creating roads or highways. Specifically, humans

are increasing the urbanization of land which is changing the composition and

health of the environment.

Today, the United States has a population growth rate of 0.77% (Central

Intelligence Agency 2014). As population increases, urbanization also increases

because more and more people need resources, such as a place to live. In the

4

United States, 82.4% of the total population is considered an urban population

and the current rate of urbanization is about 1.14% annual rate of change

(Central Intelligence Agency 2015). To be defined as an ‘urban cluster’ there

must be at least 2,500 people and an ‘urbanized area’ is one with 50,000 people

or more (Census Bureau 2010). In addition to the number of people, the overall

landscape of an urban area is characterized by many man-made structures and

unnatural surfaces such as roads or highways (Wenger et al. 2009). The focus

for my thesis will be on how urbanization affects water quality in streams.

Effects of Urbanization on Streams

Urbanization can affect streams in many ways such as hydrologic

alteration, geomorphologic alteration, and changes in stream flow (Wenger et al.

2009). The overall effects on an urban stream have been coined “urban stream

syndrome” (Walsh et al. 2005).

Hydrologic alteration focuses on how impervious surfaces associated with

urban areas affect streams. These surfaces include parking lots, roads,

sidewalks, and rooftops (Allan 2004). Such impervious surfaces do not allow

water to infiltrate into the ground, which in turn increases runoff from these

surfaces and causes more nonpoint source pollutants to enter into waterways

nearby (Carpenter et al. 1998; Brabec et al. 2002; Wenger et al. 2009). Nonpoint

source pollutants are a difficult problem to control because they do not come

from a single source, such as a drainage pipe (Carpenter et al. 1998). Nonpoint

source pollutants can encompass a variety of materials such as sediments, fecal

5

matter, fertilizers, heavy metals, and petroleum-derived hydrocarbons (Carpenter

et al. 1998; Marti et al. 2004; SCDHEC 2012).

In addition to hydrologic alteration, geomorphologic alterations affect

urban streams. Stream modifications include widening, straightening,

deepening, piping, or filling the stream channel (Booth et al. 2002; Allan 2004;

Walsh et al. 2005). Changing the stream shape reduces bank stability which in

turn can lead to erosion of soil and trees into the stream and, in some cases, can

initiate big landslides (Booth 1991). Bank erosion causes increased

sedimentation in the streams, which, like nonpoint source pollutants, can lead to

changes in available habitats for aquatic organisms (Booth 1991).

Geomorphologic alterations also include riparian zone clearing which increases

sunlight exposure of the stream and decreases the stability of stream banks

(Allan 2004).

Urban streams are also prone to increased flow which can be attributed to

many changes in the geomorphology of the stream (Walsh et al. 2005). For

example, piping or changing the channel shape causes increased stream flow

(Walsh et al. 2005). Urban streams tend be labeled as ‘flashy streams’ because

they often have large flow events during storms followed by slow flow events

(Walsh et al. 2005). Because of these large flow events, many urban streams

flood during storms (Booth 1991). In addition, this increased flow leads to

smaller sediments or rocks being carried downstream, while coarser, bigger

rocks remain (Schoonover et al. 2005). Changes in flow to an urban stream lead

6

to changes in the diversity and abundance of organisms that can live in this sort

of habitat (Booth 1991).

The Impact of Urban Streams on Water Quality

Although the above factors are the main ways that urbanization affects the

overall structure of a stream, it is important to look in more detail at the actual

water quality and the communities of aquatic organisms in the stream to assess

the true human impact. The effects of urbanization on water quality and aquatic

organisms in a stream have been extensively studied for many years. Each

organism or water quality parameter is affected differently depending on the state

of the impaired stream and the major pollutant in the stream. Important water

quality parameters include conductivity, total suspended solids, temperature, pH,

dissolved oxygen, and nutrient concentration (SCDHEC 2012).

“Urban stream syndrome” leads to increased levels of total suspended

solids (TSS), as well as increased temperatures in warmer months (Porcella &

Sorensen 1980). Total suspended solids (TSS) is measured by examining the

number of particles in water that will not pass through a filter with a pore size

between 0.45 -1.5 µm (Barbour et al. 1999). For example, particles such as sand,

silt, and algae could affect TSS levels (Barbour et al. 1999). Another way to

measure TSS is by testing turbidity levels. Turbidity is a measure of water clarity

that determines the degree to which light can travel through the water column

(Barbour et al. 1999).

7

When considering temperature, studies have found a five to eight degree

Celsius increase in the summer and a one and a half to three degree Celsius

decrease in the winter in urban streams when compared to forested streams

(Pluhowski 1970; Leblanc et al. 1997). A review by Allan (2004), argued that

reduction or clearing of riparian barriers in urban areas is a main contributor to

increasing temperatures in water, as well as to runoff and point source pollutants.

Increase in temperature has also been shown to lead to decreased dissolved

oxygen (DO) in the water because warmer water holds less oxygen than cooler

water (Allan 2004). Furthermore, these decreases in DO are negatively

correlated with biological oxygen demand (BOD) levels (Barbour et al. 1999).

The EPA defines BOD as the amount of oxygen that is consumed by

microorganisms for decomposition of organic matter (Barbour et al. 1999).

Higher BOD levels lead to a rapid depletion of oxygen in the stream which can

cause aquatic life, such as fish or crayfish, to become stressed and suffocate

(EPA n.d.b). Sources of high levels of BOD include leaves, dead plants and

animals, and animal manure (EPA n.d.b.).

As mentioned before, urban streams are greatly affected by nonpoint

source pollutants in runoff, especially during storms. This leads to changes in

the levels of nutrients, metals, and ion concentrations in urban streams (Porcella

& Sorensen 1980; Ometo et al. 2000; Winter & Duthie 2000). It is hard to

pinpoint where these ions and nutrients are originating because they are mostly

found in storm water runoff (Carpenter et al. 1998). It also has been found that

8

these nutrients and ions can get into the stream via ground water (Booth 1991).

Certain nutrients, especially nitrates and phosphates, when increased, can lead

to excessive algal growth or blooms in streams (Carpenter et al. 1998). When

algae die, their decomposition consumes valuable oxygen which contributes to a

decline of oxygen available for aquatic organisms (Carpenter et al. 1998).

Fecal Coliform and Urbanization

“Fecal coliform” refers to a pathogen community in streams that is

composed of bacterial colonies that are usually present in the fecal matter of

animals (EPA n.d.c). The EPA (2012) states that fecal coliform is a good

indicator of potential for human illness from recreational water. Escherichia coli is

a species of bacteria found in fecal matter of humans and other endotherms

which makes it a good indicator of potential human illnesses from the water (EPA

2012). Obtaining data on fecal coliform and E. coli concentrations in streams is a

key component to understanding the water quality of an area. Both fecal coliform

counts and E. coli counts are acceptable for measuring fecal contamination in

water (EPA 2012). The specific method depends on the state’s preference (EPA

2012).

Fecal matter can enter the stream directly, via organisms defecating in the

water, or indirectly, via runoff from the surface (Paul & Meyer 2001; Walsh et al.

2005). Acceptable levels for total fecal coliform in ambient water conditions are

400 colony forming units (cfu) per 100 mL of water and 235 cfus/ 100 mL for E.

9

coli (EPA 2012). Studies have shown that urban streams tend to have fecal

coliform counts that are higher than most rural and forested streams (Young &

Thackston 1999; Schoonover 2005). In addition, research has shown that fecal

bacteria levels are related to density of housing, population, development,

impervious surfaces, and domestic animal density (Young & Thackston 1999;

Mallin et al. 2000). Mallin et al. (2000) observed that the most significant factor

influencing fecal bacteria loads was the percentage of impervious surfaces

around the stream site. Therefore, the more impervious surfaces there are, the

higher the fecal counts (Mallin et al. 2000).

The survival of fecal bacteria in streams depends on the stream properties

and water quality. One of the biggest factors that can lead to extended survival

of E.coli in streams is the characteristics of the stream bed. Stream beds can

become a reservoir for bacteria depending on the type and size of substrate

material (Burton et al. 1987; Sherer et al. 1992; Kim et al. 2010). Many studies

have found that bacteria levels are associated with sediment in streams and the

sediment can lead to increased survival of E.coli (Matson et al. 1978;

Stephenson & Rychert 1982; Gannon et al. 1983; Sherer et al. 1992; Jamieson

et al. 2005; Garzio-Hadzick et al. 2010). This association between sediment and

E.coli creates a very complicated issue when trying to assess water quality of an

area (Jamieson et al. 2005). Methods for collecting and examining water

samples for E.coli do not account for the E. coli stored in sediment because the

10

associations between sediments and E. coli are not fully understood or

characterized (Howell et al. 1996; Jamieson et al. 2005).

Bacteria typically are attracted to and persist in certain types and sizes of

sediments. Both bacteria and most sediment in the aquatic environment are

negatively charged and Jamieson et al. (2005) hypothesized that bacteria are

drawn to the solid surfaces of the sediment by London-van der Waals forces

initially. Once the bacteria become associated with the sediment by van der

Waals forces, the bond between the bacteria and sediment can become stronger

and more permanent over time (Jamieson et al. 2005).

The depth within sediment that E. coli can survive is another important

variable. Most E. coli prefer the top layer of the sediment, specifically the upper

centimeter (Garzio-Hadzick et al. 2010; Kim et al. 2010). A study by Garzio-

Hadzick et al. (2010) observed that the concentration of E. coli decreased by

about 1 order of magnitude per 2 centimeters of sampling depth probably due to

the availability and accessibility to nutrients in the top most layers (Garzio-

Hadzick et al. 2010).

Type and Size of Sediment

Often, E. coli is found in sediments that are fine (less than 60

micrometers) and have certain organic contents (Gannon et al. 1983; Garzio-

Hadzick et al. 2010). For example, E.coli was observed to have a lower mortality

rate in clay-sized sediments (about 2 micrometers or less in diameter) than in

11

coarser sediments (Howell et al. 1996). Burton et al. (1987), monitored fecal

bacteria over 14 days in different sediment types and observed that the survival

of E. coli was greatest in sediments with at least 25% clay (Burton et al. 1987).

Most researchers agree that the smaller the particle size, the greater the

survival of fecal coliform (Gannon et al. 1983; Howell et al. 1996; Garzio-Hadzick

et al. 2010). However, one study contradicts this conclusion. Peter Cinotto

(2005) recognized that E. coli in the West Branch Brandywine Creek in

Pennsylvania survived longer in sediment with larger particle sizes ranging from

125-150 micrometers. He hypothesized that this could be due to the increased

porosity, permeability, and nutrient availability created by the larger particles

sizes (Cinotto 2005). These contradictory studies can be attributed to the fact

that not everything about the association between fecal bacteria and sediment

has been fully understood.

The length of E. coli survival is also dependent on sediment. For instance,

a study by Sherer et al. (1992) discovered that when incubated with 500 g of fine

sediment and 100 g of water, E. coli had a half-life of about 11 to 30 days which

is higher than the survival rate for E. coli in the water column.

Taking into Account Resuspension of Sediments and Fecal Bacteria

Disturbances within the stream bed can cause E. coli in the sediment to

become resuspended in the water column. Events such as precipitation, floods,

and disturbances by animals and humans are the main causes of sediment

12

resuspension (Stephenson & Rychert 1982; Vidon et al. 2008). Resuspension

permits the transportation of E. coli to new areas of the stream (Jamieson et al.

2005). During precipitation events, Jamieson et al. (2003) discovered that the

downstream sites in their study stream had higher levels of total suspended

solids and bacteria concentrations demonstrating that E. coli can, and often do,

travel with sediment to downstream sites.

The effect of rainfall events and floods on E. coli concentrations has also

been studied. Craig et al. (2004) observed that rainfall events caused an

increase in fecal coliform concentration in coastal water. In addition, they

observed that it took 7 days after the storm event for water quality guidelines to

be met in the stream sediment, compared to only 5 days for the overlying water

(Craig et al. 2004). Muirhead et al. (2004) used artificial flooding to observe how

the stream will be affected and discovered that E. coli concentrations increased

by two orders of magnitude during and after the flooding.

It is very difficult to predict and model the resuspension of the bacteria

(Jamieson et al. 2005; Kim et al. 2010). Questions that are difficult to answer for

streams include the concentration of bacteria that will be resuspended and how

far will the bacteria travel (Pandey et al. 2012).

Temperature, Sunlight, and Seasonality

Other abiotic factors that can determine the survival of E. coli over time

include temperature, sunlight, and the season. Water temperature can play a

13

role in the mortality rates of E. coli. Most studies have found an inverse

relationship between temperature and survival (McFeters & Stuart 1972; Faust

1982; Howell et al. 1996; Craig et al. 2004). Flint (1987) examined E. coli

survival at four different temperatures and concluded that survival decreased as

the temperature increased and the slowest bacterial decay rate of E. coli was at

4°C (Flint 1987). In fact, Garzio-Hadzick et al. (2010) observed that 4°C had the

highest persistence of E. coli. At 24°C, they observed the highest mortality rate

of E. coli (no E. coli survived) after one week (Garzio-Hadzick et al. 2010).

E. coli in coastal sediment was found to persist for more than 28 days when

incubated at 10° C (Craig et al. 2004). In sum, lower temperature increases

survival of E.coli while higher temperature decreases survival.

There is also a seasonal component that affects fecal coliform counts.

Fecal coliform have been observed to be higher in the months of April through

September compared to the other months (Clark & Norris 2000; Young &

Thackston 1999). Summer months are associated with more intense

thunderstorms than the winter, which leads to greater accumulation of fecal

bacteria into stream runoff (SCDHEC 1999). The winter is characterized by slow

rainfall events that usually do not cause huge spikes in fecal bacteria in a stream

(SCDHEC 1999). Therefore, when examining water samples it is important to

recognize the relationship between seasonality and temperature. Increased

water temperature leads to decreased survival of E. coli but, overall, there is

more fecal matter in streams during summer and spring.

14

Besides temperature and other seasonal effects, the amount of sunlight

that directly or indirectly impinges on the stream can affect E. coli survival

(Davies & Evison 1991; Sinton et al. 2002). The greater the intensity and time

that fecal bacteria are exposed to sunlight, the faster the decay rate (Davies &

Evison 1991; Sinton et al. 2002). The studies by Davies and Evison(1991) and

Sinton et al. (2002) were not able to confirm what role UV rays played in the

decay rate of fecal bacteria; however, they did conclude that more direct sunlight

on a stream leads to increased water temperature in that area. As noted above,

increased water temperature decreases the survival of E. coli.

Flow of Stream

The intensity of flow in a stream can affect the survival and transport of E.

coli (Matson et al. 1978; Jamieson et al. 2003; Kim et al. 2010). As mentioned

previously, disturbance events that cause changes in stream flow can lead to

resuspension of E. coli from sediments (Craig et al. 2004; Muirhead et al. 2004).

When mean velocity of streams is higher, fecal coliform is also higher at that site

(Matson et al. 1978; Vidon et al. 2008). The faster velocity of water causes an

increase in the amount of sediment and associated E. coli being resuspended in

the water column (Craig et al. 2004; Muirhead et al. 2004). However, some

studies contradict this observation. Kim et al. (2010) found that when the

velocity is low, E. coli concentrations are at their highest because the E. coli

accumulates in one area and are only slowly transported downstream.

Therefore, if samples were taken in an area of slow flow, E. coli numbers could

15

be very high because many colonies have accumulated in that area. These

contradictory data demonstrate that E. coli varies in the way that it responds to

conditions and the environment (Wickham et al. 2006). By not separating base

flow and high flow events in models, important patterns of the system might not

be revealed (Wickham et al. 2006).

Relationships between Water Quality Parameters and Fecal Bacteria

Variables that affect the persistence of E. coli are not independent of each

other (Figure 1). More than one abiotic or biotic condition may need to be met in

order for E. coli to persist (McFeters & Stuart 1972). Chemical and physical

parameters such as pH, nutrient concentration, dissolved oxygen, turbidity, and

conductivity must all be within a certain range to allow for bacteria survival

(Wickham et al. 2006). For example, McFeters and Stuart (1972) observed that

pH levels between 5.5 and 7.5 are optimal for survival of bacteria and anything

below or above that range caused a rapid decline in E. coli levels.

In addition, turbidity interacts with other factors. As turbidity increases, the

particles are able to absorb more heat which leads to increased water

temperature (Paaijmans et al. 2008). Increased levels of suspended solids also

reduce the amount of sunlight that can penetrate the water (Craig et al. 2004).

During storm events, turbidity was found to be 8 - 10 times greater than in dry

weather samples because storm events lead to increased sediment runoff and

16

increased bank erosion, as well as resuspension of stream bottom sediment

(Lawrence 2008).

Dissolved oxygen may also affect fecal coliform in streams but studies are

contradictory. Researchers found no correlation between fecal coliform and

dissolved oxygen (Mallin et al. 2000). On the other hand, Nevers & Whitman

(2005) and David & Haggard (2011) found a significant negative correlation

between fecal coliform and dissolved oxygen. Based on these studies, it is

apparent that there is not enough data to confirm whether significant correlations

exist between fecal coliform and dissolved oxygen. More research is needed on

this subject to help understand the relationships in streams.

17

E. coli

Survival

Sediment

type & sizeResuspension

of sedimentsFlow

Water

Temperature

Physical and

Chemical

Parameters

Air temp.,

sunlight,

seasonality

Figure 1. The effect of individual factors and their interactions on E. coli survival in fresh water streams. The dark blue arrows indicate that each factor has an effect on E. coli survival. The light blue arrows indicate the interactions that are occurring between the factors.

18

Understanding interactions and relationships between fecal bacteria and

the variables associated with a stream bring to light the difficulty that comes

when creating a model for predicting E. coli levels. This is strongly influenced by

the uncertainty involved in predicting resuspension of sediments (Pandey et al.

2012). Even researchers who have attempted to model resuspension admit that

resuspension mechanisms and how they affect E. coli are not fully understood

(Cho et al. 2010; Kim et al. 2010; Pandey et al. 2012). Complex processes

influence the water quality, the sediment type, the flow, and the resuspension of

sediments within a stream (Wilkinson et al. 1995).

Research on this area needs to become a more collaborative process.

Ideas and knowledge that are already present need to be understood in the

context of other factors. A stream system is not created by independent

variables; it is a group of interacting and interrelated influences that create

conditions and an environment affecting survival of E. coli. Studying the

relationships between fecal bacteria and several water quality parameters

simultaneously in individual streams, can help bring light to the bigger picture. It

is important to examine individual streams because every stream is exposed to

different conditions. Those different conditions could help reveal factors and

interactions that can further the research in this field.

19

Site of Interest

This project focuses on Hidden Creek (station # CW-221) in Rock Hill,

South Carolina in the Catawba River Basin. Hidden Creek is currently listed on

the 303 (d) list because during the 5 year sampling period (1994 –1998) by

SCDHEC, 43% of the samples exceeded 400 cfus/100 mL. In 1999, a TMDL for

fecal coliform was developed to determine the maximum amount of fecal coliform

that Hidden Creek can receive and still meet the water quality standards of the

state (SCDHEC 2012). Due to high fecal coliform numbers, recreational activities

are ‘not supported’ by SCDHEC in Hidden Creek (SCDHEC 2012). The term ‘not

supported’ implies that greater than 25% of water samples from this creek

contain more than the 400 cfus/100 mL standard for swimmable water set by the

Clean Water Act (SCDHEC 2012).

The watershed that Hidden Creek is located in has moderate to high

population growth potential (SCDHEC 2012). All properties along the stream are

considered to be in an urban area (SCDHEC 2012). Because there are no point

source pollutants that lead into this stream, SCDHEC has hypothesized that the

high fecal levels are from nonpoint sources (SCDHEC 2012). SCDHEC (2012)

hypothesized that the fecal bacteria could be originating from storm water runoff,

sewage leakage, overflow of sanitary sewers, failing septic tanks, and domestic

or non-domestic animals’ feces.

In order for Hidden Creek to meet standards, a 19% reduction in fecal

20

coliform loading is necessary (SCDHEC 2012). Due to the need for research on

this stream, I was asked by the City of Rock Hill to study this stream and help

them monitor and locate the areas of concern. The city will use my results to

develop a plan to mitigate and control the level of fecal coliform in the stream.

21

Objectives

The overall goal of my project was to perform an in-depth water quality

analysis on Hidden Creek with a specific focus on the number of fecal coliform

and E. coli colonies. My research will provide information to the city of Rock Hill

that can be used to benefit Hidden Creek’s overall stream health and eventually,

remove it from the EPA’s 303(d) impaired streams list. My study will also be

valuable to the field of freshwater biology because it will increase the knowledge

of how fecal bacteria persist in stream sediment and relate to abiotic factors in an

urban stream. This is important because these freshwater streams and rivers

are where much of the area’s drinking water is obtained.

The specific objectives were to:

• Collect chemical and physical water quality parameters for each site on

Hidden Creek;

-ex. temperature, dissolved oxygen, nutrients, and turbidity

• To assess whether the sediment serves as a reservoir for fecal bacteria;

• To assess what relationships exist between fecal bacteria and water

quality parameters in the stream;

• To locate the source or area that has the highest fecal coliform counts.

22

Research Questions

1. Is there a difference in E. coli numbers between the overlying water column

and the disturbed sediment?

2. Is there a difference in total coliform numbers between the overlying water

column and the disturbed sediment?

3. Is there a relationship between E. coli and DO, turbidity, and/or water

temperature?

a. If so, can we use DO, turbidity, and/or water temperature to predict

the presence of E. coli in a stream?

4. Is there a relationship between total coliform and DO, turbidity, and/or water

temperature?

a. If so, can we use DO, turbidity, and/or water temperature to predict

the presence of total coliform in a stream?

23

Hypotheses and Rationale

I hypothesized that both E. coli and total coliform colonies would be

greater in the water samples that were disturbed. This is because the

disturbance of the bottom sediment initiates a resuspension event of fecal

bacteria from the sediment and into the water column.

Second, I hypothesized that there would be a negative correlation

between DO and fecal coliform which would be consistent with the findings of

Nevers & Whitman (2005) and David & Haggard (2011). Low dissolved oxygen

is often found in streams with slow stream flow. Therefore, I hypothesized the

areas with low dissolved oxygen would have higher fecal coliform counts

because low flow areas allow for coliform to persist longer (Kim et al. 2010).

As the temperature of the water increases, I hypothesized that there would

be lower fecal coliform counts (McFeters & Stuart 1972; Faust 1982; Howell et al.

1996; Craig et al. 2004). Although fecal bacteria are more abundant in the

summer (due to increased rainfall and runoff), they survive longer and seem to

persist more where the water temperature is cooler.

Finally, I hypothesized that turbidity and fecal coliform would be positively

correlated. High turbidity levels can be attributed to increased runoff of sediment

into the stream which could cause the fecal coliform associated with the

sediment to enter the stream (SDHEC 2012).

24

The major goal of my project is to help the city of Rock Hill with one of its

most impaired streams. Because Hidden Creek empties into the Catawba River,

understanding its overall water quality and developing mitigation techniques for

pollutants in the stream can contribute to the health of the Catawba River. The

Catawba River supports many recreational activities such as fishing, kayaking,

and boating and is an important water supply for downstream communities.

Therefore, it is essential to maintain the water quality of the river so that

recreational purposes can continue to be supported.

25

METHODS

Watershed Description

The stream of interest, Hidden Creek, is located in the Catawba River

Basin Watershed. This watershed consists mainly of the Catawba River and its

tributaries and it is located in the Piedmont region of South Carolina within York,

Chester, and Lancaster counties (SCDHEC 2012). The watershed covers about

61.3% forested land, 17.8% urban land, 15.3% agricultural land, and the

remainder 6% falling under water, swamp, or barren land (SCDHEC 2012).

Hidden Creek covers about 182 hectares of land and all of the land is considered

to be in an urban area (SCDHEC 1999).

Descriptions of Sites

The City of Rock Hill chose 16 sites that could be potential causes for high

fecal bacteria in Hidden Creek. All sites were chosen because they were directly

downstream from a sewer line (Figure 2). Of the 16 sites, only 6 were part of the

open stream channel, while the remaining 10 could only be accessed by

manholes. One of the 6 open stream sites was very steep and inaccessible for

sampling. Therefore, only 5 sites were sampled (Figure 3). The site names

(listed from downstream to upstream) were: Paces River, Lexington, Riverview,

Stoney Point 1, and Stoney Point 2 (Table 1).

26

Figure 2. Map of 16 preliminary sampling points located on Hidden Creek (CW-221) in Rock Hill, SC.

27

Figure 3. Map of the 5 sites along Hidden Creek (CW-221) chosen to be examined for fecal bacteria and other chemical parameters (red hexagons). Green lines indicate the boundary of the Hidden Creek Watershed.

Table 1. Latitudinal and longitudinal GPS coordinates for the 5 sampling sites on

Hidden Creek in Rock Hill, SC listed from downstream (closest to the Catawba

River) to upstream.

Site Name Longitude Latitude

Paces River 80°59'28.939"W 34°59'9.564"N

Lexington 80°59'32.577"W 34°58'52.792"N

Riverview 80°59'40.942"W 34°58'44.132"N

Stoney 1 80°59'54.833"W 34°58'29.141"N

Stoney 2 80°59'54.483"W 34°58'30.242"N

28

Sampling Period

Sites were sampled during the summer season of 2014 which began June

21 and ended September 21. The sites were all sampled on the same day every

1-2 weeks depending on the weather for a total of 10 sampling dates. Sampling

could only be completed if less than 0.1 in (2.54 mm) of rain occurred within 24

hours of the proposed sampling date. This is recognized as ‘dry conditions’ for

water sampling methods (Hauer & Lamberti 1996). ‘Dry’ weather sampling

creates a baseline of water quality data that can be used to compare against

future conditions. It is also used to target illegal discharge from business or

isolate sewage/septic system leakages. For example, it can be used to target

potential businesses that are illegally discharging liquid into a sewer system.

Field Sampling Methods

While at each site, the water was analyzed for temperature, dissolved

oxygen, turbidity, and pH using a Eureka Manta Probe. Water temperature was

measured in degrees Celsius, dissolved oxygen was measured in mg/L, and

turbidity was measured in NTUs (Nephelometric Turbidity Units). Simultaneously,

grab samples were collected using methods proposed by the EPA and SDHEC

(Barbour et al. 1999; SDHEC 2012). Two types of grab samples were taken from

each site; one was taken from undisturbed overlying water, while the other was

taken after disturbing the bottom sediment of the stream for 10-15 seconds.

29

The first grab sample was obtained by standing on the edge of the bank

and filling a 50 mL plastic, sterile container with the undisturbed overlying stream

water. The second grab samples were obtained by using a course brush to

scrape the bottom sediment and rocks, causing resuspension of materials into

the water column. Both grab samples were then sealed and placed on ice until

they were transported to the lab.

Laboratory Analysis of Samples

At the lab, both the undisturbed and disturbed samples were analyzed for

fecal bacteria and nutrients within 8 hours of collection (Hach 1997). To

determine total fecal coliform and E. coli counts, 3M™ Petrifilm™ E.coli/

Coliform Count Plates were used (3M™ Petrifilm™ EC Plates). From each

sample, 1 mL of water was placed on the center of the film and the gel was

allowed to solidify. The samples were then placed in an incubator for 24 hours

at 44.5 ± 0.2°C (APHA et al. 1999). The samples were analyzed according to the

Interpretation Guide provided by 3M™ Petrifilm™ (examples in Figure 4). Blue

colonies with gas bubbles were recognized as E. coli. Total fecal coliform was

calculated by adding total red colonies with gas bubbles and total E. coli

colonies. Results were recorded using cfus / 100mL units.

30

Figure 4. Examples of results obtained from using 3M™ Petrifilm™ E.coli/ Coliform Count Plates to determine E. coli and total fecal coliform counts. A. The results for A would be 100 cfus/100 mL E. coli and 200 cfus/100 mL total coliform. B. The results for B would be 400 cfus/100 mL E. coli and 500 cfus/100 mL total fecal coliform.

Additionally, undisturbed and disturbed water samples were analyzed for

total nitrogen, total phosphorus, and organic phosphate concentrations using a

Hach DR 4000 spectrophotometer. The Persulfate Digestion method was used to

determine total nitrogen in the samples (Hach 10071). The Molybdovanadate

method was used to measure the concentration of phosphate (PO43-) in the water

sample (Hach 8114). The Hach method used for measuring Total Phosphorus

was the PhosVer3 with Acid Persulfate Digestion Method (Hach 8190).

31

Statistical Analysis of Data

The data collected from Hidden Creek were first analyzed using a 2-way

ANOVA. The first analysis examined whether there was a difference between E.

coli counts between sites and between undisturbed and disturbed samples. The

same analysis was then completed again using total fecal coliform in place of E.

coli.

The data were then analyzed by using a multiple regression to examine

whether there were relationships between number of E. coli colonies and DO,

water temperature, and turbidity. In the multiple regression, E. coli was the

dependent variable and DO, water temperature, and turbidity were the

independent variables. The same analysis was repeated except using total fecal

coliform as the dependent variable.

Due to a sewage pipe break, the data obtained on September 19, 2014 for

Paces River were excluded from all of the analyses.

32

RESULTS

Effect of Site and Disturbance Level on E. coli Colonies

The first analysis examined whether the specific site and disturbance level

(undisturbed or disturbed sample) affected the number of E. coli colonies in a

sample. A 2-way ANOVA was performed using the square root of E. coli colony

counts. Data were transformed so that homogeneity of variances could be

assumed. Assumptions of normality could not be met for the data, but ANOVAs

can deal with non-normal data. The results showed that the model was

significant (F = 2.826; P = 0.006; n = 98). There was a significant effect of site

on the number of E. coli colonies in a sample (F = 3.067; P = 0.02). Using the

Gabriel Post Hoc test and Pairwise comparisons, a significant difference was

found between Riverview and Stoney Point 2 (Table 2). The Gabriel Post Hoc

test was used because samples sizes were slightly different between the sites.

All other sites were not significantly different from each other (Table 2).

33

Table 2. Mean ± SE of E. coli colonies (cfus/ 100 mL) for both undisturbed and

disturbed samples for all 5 sites located on Hidden Creek taken from June 30 –

September 18, 2015 (total n = 98; all sites had n = 10 for undisturbed and

disturbed samples, except Paces River which had n = 9). Superscript letters

indicate results from the Gabriel Post Hoc test. Sites with the same letters are

not significantly different from each other; sites with different letters are

significantly different from each other.

E.coli (cfus/ 100 mL)

Undisturbed Disturbed

Paces River

511.11 ± 172.76 C 533.33 ± 113.04 C

Lexington 450.00 ± 131.87 C 800.00 ± 278.89 C

Riverview 400.00 ± 100.00 A, C

510.00 ± 65.74 A, C

Stoney #1 510.00 ± 91.23 C

830.00 ± 176.42 C

Stoney # 2 640.00 ± 131.83 B, C

1570.00 ± 534.38 B, C

34

The model also showed that there was a significant effect of disturbance

level on the number of E. coli colonies in a sample (F = 9.262; P = 0.003;

Figure 5). Samples that were taken after stream sediment was disturbed

(855.10 ± 136.90 cfus/ 100 mL; mean ± SE) were significantly higher in E. coli

colony counts than undisturbed samples (502.04 ± 55.52 cfus/ 100 mL). There

was no significant interaction between site and disturbance levels on the model

(F = 0.896; P = 0.470).

Figure 5. Mean (± SE) E. coli colonies (cfus/ 100 mL) between undisturbed and

disturbed samples across all 5 sites located on Hidden Creek taken from June 30

– September 18, 2015 (n = 49 for undisturbed and disturbed samples).

0

200

400

600

800

1000

1200

Undisturbed Disturbed

E.

co

li (

cfu

/100m

L)

Disturbance Level

35

Effect of Site and Disturbance Level on Total Fecal Coliform

The second analysis examined whether the specific site and disturbance

level (undisturbed or disturbed sample) affected total fecal coliform colonies in a

sample. A square root transformation for total fecal coliform was performed to

normalize the data and allow for equal variances. The results of the 2-way

ANOVA showed that the model was not significant (F = 1.942; P = 0.056; n = 98).

There was no significant effect of site on the number total fecal coliform colonies

in a sample (F = 1.759; P = 0.144; Table 3).

Table 3. Mean ± SE of Total Fecal coliform colonies (cfus/ 100 mL) for both

undisturbed and disturbed samples for all 5 sites located on Hidden Creek taken

from June 30 – September 18, 2015 (total n = 98; all sites had n = 10 for

undisturbed and disturbed samples, except Paces River which had n = 9).

Total Fecal Coliform (cfus/ 100 mL)

Undisturbed Disturbed

Paces River

788.89 ± 319.05 833.33 ± 184.84

Lexington 820.00 ± 232.76 1410.00 ± 409.73

Riverview 820.00 ± 189.62 1010.00 ± 179.78

Stoney #1 850.00 ± 141.62 1250.00 ± 236.76

Stoney # 2 990.00 ± 192.325 1970.00 ± 529.58

36

There was significant effect of disturbance level on total fecal coliform in a

sample (F = 7.842; P = 0.006; Figure 6). Samples that were taken after stream

sediment was disturbed (1304.08 ± 157.30 cfus/ 100 mL; mean ± SE) had

significantly higher total fecal coliform colonies than undisturbed samples (855.10

± 94.09 cfus/ 100 mL). There was no significant interaction between site and

disturbance levels on the model (F = 0.589; P = 0.671).

Figure 6. Mean (± SE) total fecal coliform colonies (cfus/ 100 mL)between

undisturbed and disturbed samples across all 5 sites located on Hidden Creek

taken from June 30 – September 18, 2015 (n = 49 for undisturbed and disturbed

samples).

0

200

400

600

800

1000

1200

1400

1600

Undisturbed Disturbed

To

tal F

ecal

Co

lifo

rm (

cfu

/100m

L)

Disturbance Level

37

Effect of Water Temperature, DO, and Turbidity on E. coli

A multiple regression analysis was performed using the square root values

of E. coli colonies as the outcome variable and water temperature, DO, and

turbidity as the predictor variables. The results of the multiple regression analysis

indicated that there was no significant model when using water temperature, DO,

and turbidity to predict E. coli in Hidden Creek (F = .400; P = 0.754; n = 49). The

R2 value is 0.026 which means only 2.6% of the variation in E. coli colonies is

explained by this model. There were no significant correlations between the

number of E. coli colonies and the three independent variables.

Effect of Water Temperature, DO, and Turbidity on Total Fecal Coliform

A multiple regression analysis was performed using the square root values

of total fecal coliform colonies as the outcome variable and water temperature,

DO, and turbidity as the predictor variables. The results of the multiple regression

analysis indicated that there was no significant model when using water

temperature, DO, and turbidity to predict total fecal coliform colonies in Hidden

Creek (F = 1.493; P = 0.229; n = 49). The R2 value is 0.091 which means only

9.1% of the variation in total fecal coliform colonies is explained by this model.

There were no significant correlations between the total fecal coliform and the

three independent variables.

38

Percent Exceedance Calculations

Percent Exceedance of E.coli per Site

Undisturbed samples measured for E. coli illustrated that all individual

sites had greater than 25% exceedance of 235 cfus/100mL, which is above the

standard set forth by the EPA (Table 4A; SCDHEC 2012). The lowest percent

exceedance was 60% at Riverview, while the highest percent exceedance was

80% at Stoney 1 and Stoney 2. Throughout the sampling period, the minimum

number of E. coli colonies at any individual site was 0 cfus/100 mL which was

observed at Lexington, while the maximum was 1800 cfus/100 mL which was at

Paces River. The minimum mean ± SE for undisturbed E. coli samples was

400.00 ± 100.00 cfus/100mL at Riverview and the maximum mean ± SE was

640.00 ± 131.83 cfus/100mL at Stoney 2.

Disturbed samples measured for E. coli also showed that all individual

sites had greater than 25% exceedance of 235 cfus/100mL (Table 4B). The

lowest percent exceedance was 66.7% at Paces River, while the highest percent

exceedance was 100% at Stoney 2 and Riverview. Throughout the sampling

period, the minimum number of disturbed E. coli colonies was 100 cfus/100mL at

Paces River, while the maximum was 6200 cfus/100mL at Stoney 2. The

minimum mean ± SE observed was 510.00 ± 65.74 cfus/100mL at Riverview and

the maximum mean ± SE observed was 1570.00 ± 534.38 cfus/100mL at Stoney

2.

39

Table 4. Summary statistics for E. coli (cfus/ 100 mL) undisturbed and disturbed

sample concentrations for each sampling site on Hidden Creek where n = 10 for

all sites, except Paces River (case on September 19, 2014 excluded from

analysis). % Exceedance is the percentage of samples for each site that exceed

235 cfus/ 100 mL (standard set by EPA).

A. Undisturbed E.coli

Site Name Mean ± SE Min Max % Exceedance

Paces River 511.11 ± 172.76 100

1800 77.8%

Lexington 450.00 ± 131.87 0 1400 70%

Riverview 400.00 ± 100.00 100 1000 60%

Stoney 1 510.00 ± 91.23 100 900 80%

Stoney 2 640.00 ± 131.83 100 1200 80%

B. Disturbed E. coli

Site Name Mean ± SE Min Max % Exceedance

Paces River 533.33 ± 113.04 100 1100 66.7%

Lexington 800.00 ± 278.89 200 3200 90%

Riverview 510.00 ± 65.74 300 900 100%

Stoney 1 830.00 ± 176.42 200 2100 90%

Stoney 2 1570.00 ± 534.38 400 6200 100%

40

Percent Exceedance of Total Fecal Coliform per Site

All individual sites had greater than 25% exceedance of 400 cfus/100mL

for undisturbed samples measured for total fecal coliform (Table 5A). The lowest

percent exceedance of 400 cfus/100mL was 55.6% at Paces River, while the

highest percent exceedance was 90% at Stoney 1. Throughout the sampling

period, the minimum total fecal coliform colonies between all individual sites was

0 cfus/100 mL which was observed at Lexington, while the maximum was 3300

cfus/100 mL which was at Paces River. The minimum mean ± SE for

undisturbed total fecal coliform samples was 820.00 ± 189.62 cfus/100mL at

Riverview and 820.00 ± 232.76 at Lexington. The maximum mean ± SE was

990.00 ± 192.33 cfus/100mL at Stoney 2.

Disturbed samples measured for total fecal coliform also had greater than

25% exceedance of 400 cfus/100mL for all individual sites (Table 5B). Stoney 2

had all disturbed samples (100%) exceed 400 cfus/100mL of total fecal coliform

colonies. The minimum total fecal coliform between all individual sites was 200

cfus/100 mL which was observed at Paces River and Stoney 1. The maximum

was 6400 cfus/100 mL which was observed at Stoney 2. The minimum mean ±

SE for disturbed total fecal coliform samples was 833.33 ± 184.84 cfus/100mL

found at Paces River, while the maximum mean ± SE was 1970.00 ± 529.58

cfus/100mL at Stoney 2.

41

Table 5. Summary statistics for total fecal coliform (cfus/ 100 mL) concentrations

for each sampling site on Hidden Creek where n = 10 for all sites, except Paces

River (case on September 19, 2014 excluded from analysis). % Exceedance is

the percentage of samples for each site that exceed 400 cfus/ 100 mL (standard

set by Clean Water Act).

A. Undisturbed Total Fecal Coliform

Site Name Mean ± SE Min Max % Exceedance

Paces River 788.89 ± 319.05 200

3300 55.6%

Lexington 820.00 ± 232.76 0 2200 50%

Riverview 820.00 ± 189.62 100 2000 80%

Stoney 1 850.00 ± 141.62 100 1700 90%

Stoney 2 990.00 ± 192.325 200 2300 80%

B. Disturbed Total Fecal Coliform

Site Name Mean Min Max % Exceedance

Paces River 833.33 ± 184.84 200 2000 66.7%

Lexington 1410.00 ± 409.73 400 4900 90%

Riverview 1010.00 ± 179.78 300 1700 90%

Stoney 1 1250.00 ± 236.76 200 2700 90%

Stoney 2 1970.00 ± 529.58 700 6400 100%

42

Percent Exceedance of E.coli and Total Fecal Coliform for Hidden Creek

The final calculation performed looked at all of the sites together (Table 6).

The data showed that, in Hidden Creek, E. coli and total fecal coliform, for both

undisturbed and disturbed samples, had greater than 25% of the samples

exceed the standards. The lowest percent exceedance was 71.4% (undisturbed

total fecal coliform) and the highest percent exceedance was 89.8% (disturbed E.

coli).

Table 6. % Exceedance of fecal bacteria for all of the sites combined (n =49 for

each category). % Exceedance for E. coli is the percentage of samples across all

sites that exceed 235 cfus/ 100 mL. % Exceedance for total fecal coliform is the

percentage of samples across all sites that exceed 400 cfus/ 100 mL (standard

set by Clean Water Act).

Undisturbed E. coli

Disturbed E. coli Undisturbed Total Fecal Coliform

Disturbed Total Fecal Coliform

% Exceedance

73.5%

89.8%

71.4%

87.8%

43

DISCUSSION

The relationships between fecal coliform and abiotic factors in the water

are still not fully understood. This study demonstrated that during the summer

season, dissolved oxygen, water temperature, and turbidity were not able to

predict E. coli or total fecal coliform colonies in Hidden Creek. However, there

have been studies that have demonstrated a relationship between these abiotic

factors and fecal coliform levels. Faust et al. (1975) used a regression analysis

and observed that 75.6% of the variance in E. coli survival can be explained by

water temperature, DO, and salinity in an estuarine environment. In that model,

temperature was the most important factor and it accounted for 71.7% of the

variation in E. coli alone (Faust et al. 1975). Survival of the fecal bacteria was

negatively correlated with increasing water temperature and is in accordance

with later studies that also demonstrated an inverse relationship between

decreasing temperatures and increased survival of fecal bacteria (Faust et al.

1975; Faust 1982; Flint 1987; Garzio-Hadzick et al. 2010). When dissolved

oxygen was added to the model, the variation that could be explained by the

model increased by only 3.1% (Faust et al. 1975).

Another study on the Chattahoochee River near Atlanta, Georgia

discovered that 76% of the variability in E. coli colony numbers could be

explained by the variation in turbidity, water temperature, and stream flow events

(Lawrence 2008).

44

Both of those studies demonstrated that the three factors used in this

thesis, water temperature, DO, and turbidity, could be predictors for fecal

bacteria. However, the designs of these studies were different than this thesis.

Lawrence (2008) collected and analyzed over 8 years of data, while this study

was only concerned with one season. On the other hand, Faust (1975) collected

their data on E. coli survival using controlled laboratory experiments. I

hypothesize that my study was did not find a significant relationship between

fecal bacteria with water temperature, DO, and turbidity because the data were

taken over a short period and the natural variation within the three predictor

factors was too small (Table 7) . For example, over the whole summer season,

the range in water temperatures was 5.7 °C and the range in turbidity was 16.7

NTUs. This small variation makes it difficult to use turbidity, water temperature,

and DO to predict E. coli and total fecal coliform colonies. In addition, the 10

samples from each site may have been too few to show a cause-effect

relationship. Collecting more data on Hidden Creek over years could allow for a

significant model to be developed.

45

Table 7. Mean ± SE of water temperature (ºC), turbidity (NTUs), and DO (mg/L) for Hidden Creek taken from June 30 – September 18, 2014.

Water Quality Parameter Mean ± SE

Water Temperature (ºC) 24.68 ± 0.24

DO (mg/L) 5.11 ± 0.14

Turbidity (NTUs) 12.86 ± 0.53

Many studies have been able to determine that fecal bacteria can persist

and survive in stream sediments, but the basis for the persistence is not fully

understood. In stream sediment, E. coli concentrations are much higher than in

the overlying water column (Matson et al. 1978; Stephenson & Rychert 1982;

Benjamin et al. 2013). Stephenson and Rychert (1982) showed that E. coli

concentrations in stream sediment can range from 2 to 760 times greater than in

the overlying water and Matson et al. (1978) discovered that fecal bacteria

counts in sediment were 2,500 times greater than overlying water. Sediment

seems to present a more favorable chemical and biological environment for fecal

bacteria to persist and it is a more stable environment than the water column.

Relevant factors making it a more hospitable environment for fecal bacteria

include access to nutrients and organic matter, protection from UV light, and

protection from predators (Burton et al. 1987; Sherer et al. 1992; Davies et al.

1995; Jamieson et al. 2005; Pachepsky & Shelton 2011).

The amount of nutrients and organic matter seems to be the most

46

important factor contributing to fecal bacteria survival (Haller et al. 2009; Pote et

al. 2009). An experiment by Haller et al. (2009) used microcosms containing lake

water and different sediment types to determine what environments E. coli

survive in best. They discovered that in sediments with high organic matter and

nutrients and small grain size, E. coli had higher growth and lower decay rates

(Haller et al. 2009). The specific organic components and nutrients that are the

most important to E. coli survival have not been identified

From the data obtained, Riverview and Stoney Point 2 were the only sites

that had significantly different E. coli colony counts (Table 2). For both

undisturbed and disturbed samples, Riverview had the lowest mean ± SE for E.

coli colony counts (undisturbed: 411.11 ± 111.11; disturbed: 466.67 ± 55.28),

while Stoney 2 had the highest mean ± SE for E. coli colony counts (undisturbed:

844.44 ± 158.21; disturbed: 1088.89 ± 193.97). There was no significant

difference in total fecal coliform colonies across the sites. Therefore, it is not

possible to choose a particular site that is the main source or area of concern for

Hidden Creek; all of the sites had high levels of fecal bacteria at some point.

This thesis study suggests that typical grab samples from the water

column do not give an accurate depiction of the fecal situation in the stream. The

stream sediment can easily be disturbed by animals, storms, or humans which

can lead to an elevated number of fecal bacteria in that area. Some studies have

attempted to predict resuspension, but the systems prove to be too variable to

47

follow the models (Kim et al. 2010; Pandey et al. 2012; Piorkowski et al. 2013).

For example, storm events are a big issue because they can cause increased

resuspension of fecal bacteria in streams and also increased input of fecal

bacteria from stream bank sediment which is why nonpoint source pollutants for

fecal bacteria are so difficult to locate and control (Sadeghi & Arnold 2004). For

urban areas, such as Hidden Creek, most inputs of fecal bacteria come from

pets, wildlife, urban stormwater runoff, and leaky sewer pipes (Sadeghi & Arnold

2004).

The data collected for Hidden Creek indicate that the sediment is creating

a hospitable, stable environment where fecal bacteria can persist. Therefore,

simply taking grab samples may give a biased view of the stream and affect the

monitoring program that is developed. In addition, the data were collected during

dry sampling conditions. This is alarming because the data already exceed the

standards for undisturbed E. coli and total fecal coliform samples by a large

percentage (Table 6). It has been demonstrated that after rainfall events, a

dramatic increase in fecal coliform can be observed (Jamieson et al. 2003; Craig

et al. 2004; Piorkowski et al. 2013).

Another important point to be aware of is that the E. coli and total fecal

coliform numbers were variable throughout the season for each site (Tables 4 &

5). Sampling one day may not give the full picture of what is happening at the

site. For example, the Lexington site had one day when the total fecal coliform

48

colony count was 0 cfus/100mL, but another day it was 2,200 cfus/100mL. This

is a huge range which makes it very difficult to decide how to create a

management plan for this area. Due to few resources and limited time, most

companies and municipalities sample one time and use those data to develop a

management plan. This may cause these companies and municipalities to waste

time and money on a plan that can’t solve the problem because inadequate

preliminary data were collected.

49

CONCLUSION

This study highlights the importance of understanding fecal bacteria

persistence and variability when developing watershed management programs

for a stream. Specifically for Hidden Creek, it emphasizes the seriousness of this

stream’s fecal bacteria problem. Hidden Creek flows directly into the Catawba

River where many recreational activities such as kayaking, swimming, and

fishing occur. These high fecal bacteria numbers create an increased risk of

illness for humans using the water. The data obtained from this study indicated

that there was no specific site that can be pinpointed as the source of fecal

bacteria. Because of this, the city will be required to continue monitoring on

Hidden Creek to determine any areas of illicit discharge or if additional sampling

points are required. Once an area or areas has been determined as a main

source of fecal bacteria, the city will have to develop a management plan to

decrease the fecal bacteria by 19%.

This study contributes to the understanding of the persistence of fecal

bacteria in stream sediment and how fecal bacteria relate to other abiotic factors

in the water. It emphasizes how variable fecal bacteria are over one season and

how variable they are between sites on the same stream. It highlights the

importance of proper sampling techniques when trying to get an accurate

depiction of fecal bacteria in a stream.

50

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