A B

C D

A. Geese at Mosquito Lake, Cortland, Ohio. (Photo by Ted Smith, Trumbull County Health Department)

B. USGS scientist digging holes for collection of swash-zone materials at Edgewater Park, Cleveland, Ohio.

C. Rain gage at Huntington Reservation, Bay Village, Ohio.

D. Sample locations 3 and 6 feet inland from the outer edge of the swash zone.
U.S. Department of the Interior

U.S. Geological Survey

Escherichia coli at Ohio Bathing Beaches— 

Distribution, Sources, Wastewater Indicators, 

and Predictive Modeling

By Donna S. Francy, Amie M. Gifford, and Robert A. Darner

Water-Resources Investigations Report 02-4285

In cooperation with the Ohio Water Development Authority, Northeast Ohio Regional Sewer District, Ohio 

Lake Erie Office, Cuyahoga County Board of Health, Cuyahoga County Sanitary Engineers, and Cuyahoga River Community Planning Organization
U.S. Department of the Interior 

GALE A. NORTON, Secretary 

U.S. Geological Survey 

Charles G. Groat, Director 

Any use of trade, product, or firm names is for 

descriptive purposes only and does not imply

endorsement by the U.S. Government.

For additional information write to

District Chief

U.S. Geological Survey

6480 Doubletree Avenue

Columbus, OH 43229-1111 

Copies of this report can be purchased from

U.S.Geological Survey

Information Services

Box 25286

Denver, CO 80225-0286

Columbus, Ohio

2003
Contents III

CONTENTS

Abstract ................................................................................................................................................................................. 1

Introduction .......................................................................................................................................................................... 2

Purpose and scope ....................................................................................................................................................... 2

Related studies ............................................................................................................................................................ 3

Distribution of fecal-indicator bacteria in sediment.......................................................................................... 3

Wastewater indicators........................................................................................................................................ 3

Predictive models .............................................................................................................................................. 4

Site descriptions ......................................................................................................................................................... 5

Acknowledgments ....................................................................................................................................................... 5

Methods of study .................................................................................................................................................................. 7

Sampling frequency and locations .............................................................................................................................. 10

Distribution, source, and spatial studies ........................................................................................................... 10

Routine studies .................................................................................................................................................. 10

Sampling methods ....................................................................................................................................................... 10

Established sampling methods for all studies.................................................................................................... 10

Special sampling methods for samples from near the swash zone.................................................................... 12

Special sampling methods for discrete vertical sampling ................................................................................. 12

Sampling methods for chemical constituents and suspended sediment ............................................................ 13

Analysis of water and sediment samples .................................................................................................................... 13

Distribution, source, and spatial studies ............................................................................................................ 13

Routine studies ................................................................................................................................................. 14

Collection and compilation of ancillary information .................................................................................................. 14

Statistical methods ....................................................................................................................................................... 16

Quality-assurance and quality-control practices.......................................................................................................... 17

Distribution, sources, and wasterwater indicators for Escherichia coli at bathing beaches.................................................. 17

Distribution studies ..................................................................................................................................................... 18

Distribution of Escherichia coli within and outside the bathing area ............................................................... 18

Concentrations of Escherichia coli near the swash zone .................................................................................. 23

Source studies at Mosquito Lake ................................................................................................................................ 26

Spatial studies at Edgewater ....................................................................................................................................... 26

Wastewater indicators as surrogates for Escherichia coli .......................................................................................... 30

Predictive models for Escherichia coli ................................................................................................................................. 32

Relations between Escherichia coli concentrations and environmental or water-quality variables ........................... 32

Development and evaluation of beach-specific models ............................................................................................. 37

The 1997 model for Edgewater Park and Villa Angela and model evaluation steps ........................................ 37

Edgewater Park—new models to improve predictions of water quality ........................................................... 40

Villa Angela— new models to improve predictions of water quality............................................................... 42

Fairport Harbor and Mentor Headlands—predictive models............................................................................ 43

Huntington Reservation—predictive models .................................................................................................... 43

Mosquito Lake State Park—predictive models................................................................................................. 43

Summary and conclusions .................................................................................................................................................... 43

References ............................................................................................................................................................................ 45

Appendix A —Quality-control data.

A1. Quality-control data for percent dry weight of sediment in samples collected during 

distribution, source, and spatial studies, 2000 and 2001 ............................................................................... 48 

A2. Quality-control data for concentrations of Escherichia coli in water samples collected 

during routine studies, 2000 and 2001........................................................................................................... 49
IV Contents

A3. Quality-control data for concentrations of Escherichia coli in water and 

sediment samples collected during distribution, source, and spatial studies, 2000 and 2001 ...................... 51

A4. Quality-control data for caffeine concentrations in water samples collected 

during distribution, source, and spatial studies in 2000 and routine studies during 2001 ............................ 53

Appendix B – Water samples—physical properties, Escherichia coli, water quality, and 

environmental data collected during distribution, source, and spatial studies, 2000 and 2001

B1. Water samples from Edgewater Park.............................................................................................................. 55

B2. Water samples from Huntington Reservation ................................................................................................. 69

B3. Water samples fromVilla Angela .................................................................................................................... 73

B4. Water samples from Mosquito Lake State Park.............................................................................................. 77

Appendix C – Sediment samples—physical properties, Escherichia coli, sediment quality, and 

environmental data collected at three Lake Erie beaches and one inland lake during distribution,

source, and spatial studies, 2000 and 2001. .............................................................................................................. 91

Appendix D – Results of analyses for wastewater indicators in samples collected at three Lake Erie 

beaches and one inland lake during distribution, source, and spatial studies, 2000. ............................................... 109

Appendix E – Results of caffeine analysis for water samples collected at three Lake Erie beaches and 

one inland lake during distribution and source studies in 2000 and routine studies in 2001.................................... 115

Appendix F – Selected model equations used to predict Escherichia coli concentrations at three Lake Erie 

urban beaches and Mosquito Lake............................................................................................................................ 119

ILLUSTRATIONS

1-5. Maps showing:

1.Locations of Lake Erie urban beaches–Huntington Reservation, Edgewater Park, and 

Villa Angela–in the Cleveland, Ohio, metropolitan area, 2000 and 2001 .................................................... 6

2. Locations of Lake Erie beaches in a less populated area–Mentor Headlands and 

Fairport Harbor–in Lake County, Ohio, 2000............................................................................................... 7

3. Location of inland beach–Mosquito Lake State Park–Cortland, Ohio, 2000 and 2001 .................................. 8

4. Sampling areas at Lake Erie urban beaches: (A) Edgewater Park, (B) Villa Angela, and 

(C) Huntington Reservation in the Cleveland, Ohio, metropolitan area, 2000 and 2001............................. 9

5. Sampling areas at Mosquito Lake State Park Beach in Cortland, Ohio, 2000 and 2001................................. 10

6. Photo showing sampling of interstitial water from subsample holes, 3 feet and 6 feet inland 

from the swash zone.......................................................................................................................................................... 13

7-11. Graphs showing:

7. Distribution of Escherichia coli concentrations in water samples collected within the 

bathing area and comparison to samples collected outside the bathing area for (A)

 Lake Erie beaches and (B) Mosquito Lake State Park................................................................................. 19

8. Distribution of Escherichia coli concentrations in sediment samples collected within 

the bathing area and comparison to samples collected outside the bathing area for 

 (A) Lake Erie beaches and (B) Mosquito Lake State Park .......................................................................... 20

9. Comparison of Escherichia coli concentrations in bathing water to Escherichia coli concentrations 

in lake-bottom sediments at (A) Edgewater Park in 2000 and 2001, (B) Huntington Reservation 

in 2000, (C) Villa Angela in 2000, and (D) Mosquito Lake State Park in 2000 and 2001. ......................... 21

10. Concentrations of Escherichia coli in interstitial waters collected 3 feet and 6 feet inland from near 

the swash zone during 2000 and 2001 at (A) Edgewater Park and (B) Mosquito Lake State Park.............. 24

 11. Concentrations of Escherichia coli in subsurface sediments collected 3 feet and 6 feet inland 

from near the swash zone during 2000 and 2001 at (A) Edgewater Park and 

(B) Mosquito Lake State Park....................................................................................................................... 25

12. Average concentrations of Escherichia coli in (S) shoreline, (N) nearshore, and (O) offshore 

water samples collected at Mosquito Lake State Park, June-August 2000 and August 2001 .................................. 27

13. Concentrations and detections of Escherichia coli in (S) shoreline, (N) nearshore, and 

(O) offshore sediment samples collected at Mosquito Lake State Park, June-August 2000 

and August 2001 ....................................................................................................................................................... 28
Contents V

14. Distribution of Escherichia coli concentrations in water by current direction, 2000 and 2001, 

at (A) Edgewater Park, (B) Villa Angela, and (C) Huntington Reservation ......................................................... 38

15. Establishment of the threshold probability based on the single-sample bathing water standard 

of 235 colonies per 100 milliliters and the 1997 model for Villa Angela, Cleveland, Ohio ................................... 41

16. Establishment of the threshold probability based on the single-sample bathing water standard 

of 235 colonies per 100 milliliters and the 1997 model for Edgewater Park, Cleveland, Ohio ............................... 41

TABLES

1. Dates of sampling and locations of water and sediment samples collected at Ohio beaches for 

distribution, source, and spatial studies, 2000 and 2001 .......................................................................................... 11

 2. Beach study sites and sampling information for routine studies at Ohio beaches, May through 

August 2000 and 2001 .............................................................................................................................................. 12

3. Constituents and properties determined on water and sediment samples collected at 

Ohio beaches during distribution, source, spatial, and routine studies, 2000 and 2001 ........................................... 15

4. Summary of particle-size analysis of lake-bottom and subsurface sediments collected at 

Ohio beaches, 2000 and 2001 ................................................................................................................................... 18

5. Concentrations of Escherichia coli in interstitial water and subsurface sediment samples 

collected from near the swash zone at Ohio beaches, 2000 and 2001 ..................................................................... 23

 6. Correlations between log10 Escherichia coli concentrations in bathing waters and those in 

interstitial water or subsurface-sediment samples collected from near the swash zone at Ohio 

beaches, 2000 and 2001 ........................................................................................................................................... 26

7. Summary of the range of Escherichia coli concentrations and the maximum differences for

physical water-quality properties in spatial studies at Edgewater Park, 2000 and 2001 .......................................... 29

8. Detections of wastewater indicators in samples collected at Lake Erie beaches and

 Mosquito Lake, 2000, and their relations to log10 Escherichia coli concentrations .............................................. 31

9. Summary statistics of Escherichia coli concentrations in water collected at five Lake Erie 

beaches and one inland lake during routine studies, 2000 and 2001 ....................................................................... 33

10-12. Summary of Pearson's r correlations between log10 Escherichia coli concentrations in 

water and environmental or water-quality factors at:

10. Three Lake Erie urban beaches, 2000 and 2001.............................................................................................. 34

11. Mosquito Lake State Park, 2000 and 2001 ...................................................................................................... 35

12. Beaches in a less populated area, 2000............................................................................................................ 36

13. Variables, regression statistics, and threshold probablilities for beach models, 1997, 2000, and 2001 ........................ 39

14. Comparison of the numbers of correct and false negative and positive predictions of 

recreational water-quality conditions using regression models with indicated threshold 

probabilities and antecedent Escherchia coli (E. coli) concentrations ..................................................................... 42
VI Contents

CONVERSION FACTORS AND ABBREVIATED WATER- AND SEDIMENT-QUALITY UNITS

Temperature: Temperature is given in degrees Celsius (°C) which can be converted to degrees Fahrenheit (°F) by use of the following 

equation:

°F=1.8(°C) + 32

Abbreviated water- and sediment-quality units used in this report: Chemical concentrations in water are reported in milligrams per 

liter (mg/L) and micrograms per liter (μg/L). Both units express the concentration of chemical constituents as weight (milligrams or 

micrograms) of chemical per unit volume (liter) of water. Turbidity is reported in Nephelometric Turbidity Units (NTU).

Concentrations of bacteria in water are reported in colonies per 100 milliliters (col/100 mL).

Concentrations of bacteria in sediment are reported in colonies per gram of (dry weight) sediment (col/gdw).

Multiply By To obtain

micrometer (μm) 0.00003937 inch

millimeter (mm) 0.03937 inch

centimeter (cm) 0.3937 inch

foot (ft) 0.3048 meter

mile (mi) 1.609 kilometer

milliliter (mL) 0.06102 cubic inch

liter (L) 0.03531 cubic foot

gram (g) 0.03527 ounce
Abstract 1

Escherichia coli at Ohio bathing beaches—Distribution, 

sources, wastewater indicators, and predictive modeling

By Donna S. Francy, Amie M. Gifford, and Robert A. Darner

Abstract

Results of studies during the recreational seasons of 2000 

and 2001 strengthen the science that supports monitoring of 

our Nation’s beaches. Water and sediment samples were 

collected and analyzed for concentrations of Escherichia 

coli (E. coli). Ancillary water-quality and environmental 

data were collected or compiled to determine their relation 

to E. coli concentrations. Data were collected at three Lake 

Erie urban beaches (Edgewater, Villa Angela, and Huntington), two Lake Erie beaches in a less populated area (Mentor Headlands and Fairport Harbor), and one inland-lake 

beach (Mosquito Lake). 

The distribution of E. coli in water and sediments 

within the bathing area, outside the bathing area, and near 

the swash zone was investigated at the three Lake Erie urban 

beaches and at Mosquito Lake. (The swash zone is the zone 

that is alternately covered and exposed by waves.) Lake-bottom sediments from outside the bathing area were not significant deposition areas for E. coli. In contrast, interstitial 

water and subsurface sediments from near the swash zone 

were enriched with E. coli. For example, E. coli concentrations were as high as 100,000 colonies per 100 milliliters in 

some interstitial waters. Although there are no standards for 

E. coli in swash-zone materials, the high concentrations 

found at some locations warrant concern for public health.

Studies were done at Mosquito Lake to identify 

sources of fecal contamination to the lake and bathing 

beach. Escherichia coli concentrations decreased with distance from a suspected source of fecal contamination that is 

north of the beach but increased at the bathing beach. This 

evidence indicated that elevated E. coli concentrations at the 

bathing beach are of local origin rather than from transport 

of bacteria from sites to the north. 

Samples collected from the three Lake Erie urban 

beaches and Mosquito Lake were analyzed to determine 

whether wastewater indicators could be used as surrogates 

for E. coli at bathing beaches. None of the concentrations of 

wastewater indicators of fecal contamination, including 3βcoprostanol and cholesterol, were significantly correlated 

(α=0.05) to concentrations of E. coli. Concentrations of the 

two compounds that were significantly correlated to E. coli

were components of coal tar and asphalt, which are not necessarily indicative of fecal contamination.

Data were collected to build on an earlier 1997 study 

to develop and test multiple-linear-regression models to predict E. coli concentrations using water-quality and environmental variables as explanatory variables. The probability 

of exceeding the single-sample bathing-water standard for 

E. coli (235 colonies per 100 milliliters) was used as the 

model output variable. Threshold probabilities for each 

model were established. Computed probabilities that are 

less than a threshold probability indicate that bacterial water 

quality is most likely acceptable. Computed probabilities 

equal to or above the threshold probability indicate that the 

water quality is most likely not acceptable and that a waterquality advisory may be needed. 

Models were developed at each beach, whenever 

possible, using combinations of 1997, 2000, and (or) 2001 

data. The models developed and tested in this study were 

shown to be beach specific; that is, different explanatory 

variables were used to predict the probability of exceeding 

the standard at each beach. At Mentor Headlands and Fairport Harbor, models were not developed because water 

quality was generally good. At the three Lake Erie urban 

beaches, models were developed with variable lists that 

included the number of birds on the beach at the time of 

sampling, lake-current direction, wave height, turbidity, 

streamflow of a nearby river, and rainfall. The models for 

Huntington explained a larger percentage of the variability 

in E. coli concentrations than the models for Edgewater and 

Villa Angela. At Mosquito Lake, a model based on 2000 

and 2001 data contained the explanatory variables rainfall, 
2 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

number of dry days preceding a rainfall, date, wind direction, wind speed, and turbidity. Additional research could 

include testing the threshold probabilities assigned for these 

models in subsequent years and comparing the models’ 

ability to predict recreational water quality to results from 

the current method—using antecedent E. coli concentrations. Each year the model is tested, new data can be added 

and model variables can be recalculated to determine 

whether the predictive ability improves with additional data. 

Introduction

Water-resource managers and the scientific community have 

long recognized the need for improved monitoring methods 

to adequately protect public health at our Nation’s beaches. 

Across the country, a total of 11,270 closings and advisories 

were issued during 2000 (Natural Resources Defense Council, 2001). Of these, 85 percent were based on monitoring 

that detected fecal-indicator bacteria levels exceeding 

water-quality standards. Even as more states begin to monitor their beaches, not all states have routine monitoring programs or have adopted the U.S. Environmental Protection 

Agency (USEPA) recommended fecal indicators for beach 

monitoring (Natural Resources Defense Council, 2001) 

—Escherichia coli (E. coli) or enterococci for fresh waters 

and enterococci for marine waters. 

The USEPA, recognizing the problem and the inconsistency of monitoring methods, initiated the Beaches Environmental Assessment, Closure, and Health Program 

(BEACH), the goal of which is to reduce the risk of infection to users of the Nation’s recreational waters (U.S. Environmental Protection Agency, 1998). As part of the BEACH 

program, USEPA published the “Beach Action Plan” to 

enable consistent management of recreational-water-quality programs and strengthen the science that supports such 

monitoring programs (U.S. Environmental Protection 

Agency, 1999a). The Beach Action Plan addresses three 

areas of scientific research: (1) water-quality indicators, (2) 

modeling and monitoring research, and (3) exposure and 

health-effects research. 

A topic mentioned for exposure and health-effects 

research under the Beach Action Plan is to determine 

whether the swash zone may be conducive to the growth of 

bacterial pathogens and indicators. The swash zone is the 

zone that is alternately covered and exposed by waves and is 

an area where children commonly play in the sand. The 

water that occupies the spaces between the sand particles 

near or in this zone is often referred to as “interstitial water.” 

Little is known about the concentrations of pathogens and 

indicators in interstitial waters or subsurface sediments collected from in or near the swash zones and potential effects 

on children and other sensitive populations.

Also mentioned in the Beach Action Plan is the goal 

to carry out research on the development of mathematical 

models to determine or predict recreational water quality. 

Current methods for assessing the recreational water quality 

are based on measured concentrations of fecal indicators. 

These methods take at least 24 hours to complete—too long 

a lapse between sampling and analytical results to be relevant to water-resource managers and the public. Water-quality conditions can change overnight, so a water-quality 

advisory may be issued when the recreational-water-quality 

standard is met or may not be issued when the standard is 

exceeded. Some beach managers post the beach with a 

water-quality advisory whereas other beach managers close 

the beach to swimming. Mathematical models based on 

water-quality and environmental surrogates or hydrodynamic processes may be able to provide an assessment of 

recreational water quality within a few hours. 

The Beach Action Plan mentions that another way to 

identify risk before exposure takes place is to develop realtime or near-real-time (less than 2 hours) analytical methods. One possible method is the use of wastewater indicators of fecal contamination. These include such compounds 

as caffeine and coprostanol, which are present in human 

wastes and may be a suitable surrogate for the presence of 

E. coli and pathogens. 

The U.S. Geological Survey (USGS), in cooperation 

with the Ohio Water Development Authority, Northeast 

Ohio Regional Sewer District, Ohio Lake Erie Office, Cuyahoga County Board of Health, Cuyahoga County Sanitary 

Engineers, and the Cuyahoga River Community Planning 

Organization, studied the use of predictive models and 

wastewater indicators as surrogates for E. coli. The USGS 

collaborated with The Ohio State University (OSU), Great 

Lakes Forecasting System (GLFS) in predictive model 

development. In addition, data were gathered on concentrations of E. coli in lake-bottom sediments and subsurface 

sediments collected near the swash zone and on fecal contaminant sources in an inland lake. This project addresses 

several of the USEPA research priorities described in the 

Beach Action Plan. 

Purpose and scope

This report describes field studies done throughout 

the recreational seasons (May through August) of 2000 and 

2001 at six public bathing beaches in Ohio. Four types of 

studies were done—distribution, source, spatial, and routine 

studies. During all studies, water and (or) sediment samples 

were analyzed for E. coli concentrations. Additional water 

samples were collected to determine whether wastewater 

indicators could be used as surrogates for E. coli concentrations at bathing beaches.

For the distribution, source, and spatial studies, the 

USGS collected water and sediment samples and analyzed 

them for E. coli during 1- to 4-day studies at three Lake Erie 

beaches and one inland beach. Ancillary data were collected 

to help understand patterns of E. coli concentrations. These 
Introduction 3

data included particle-size distributions and organic carbon 

concentrations of sediments; and turbidity, specific conductance, and temperature of interstitial and bathing waters. 

Distribution studies were done at four beaches to compare 

E. coli concentrations in water and sediments from the bathing area to concentrations near the swash zone and outside 

the bathing area. Source studies were done to determine a 

possible source of E. coli contamination to the bathing 

beach at the inland lake. Spatial studies were done to aid the 

GLFS in the development of a hydrodynamic predictive 

model with two objectives: (1) collect data on the vertical 

distribution of E. coli in the water column outside the bathing area, and (2) provide a detailed characterization of the 

spatial and temporal distribution of E. coli in water and sediments within the bathing area during dry weather and after 

a significant rainfall.

Routine studies were designed to collect data with 

which to test an existing predictive E. coli model and to 

develop new models. For the routine studies, water samples 

were collected by local agencies at five Lake Erie beaches 

and at one inland lake 4 or 5 days a week throughout the 

season. Statistical methods were used to evaluate the relations between E. coli and environmental and water-quality 

variables. These variables were used to develop multiplelinear-regression models for predicting E. coli concentrations.

Related studies

Distribution of fecal-indicator bacteria in sediment. 

In an earlier study (Francy and Darner, 1998), the distribution of E. coli in lake-bottom sediments was investigated at 

three beaches in the Cleveland, Ohio, metropolitan area. 

Concentration patterns of E. coli indicated that short-term 

storage (less than 1 week) of E. coli in sediments may have 

occurred, although no evidence for long-term storage was 

found during the sampling period. The authors hypothesized 

that an increase in E. coli in bathing waters in the absence of 

rainfall may have been due to resuspension of 

E. coli from lake-bottom sediments in the bathing area or 

from deeper sediments outside the area.

In a study done to determine sources of enterococci 

contamination to California beaches (Grant and others, 

2001), bottom-sediment samples were collected from marsh 

and surf zones. Nineteen percent of the sediment samples 

from the marsh were positive for enterococci compared to 

2 percent from the surf zone, indicating that the marsh was a 

significant source of enterococci. In the marsh sediments, 

bacteria were concentrated in the top 1 cm of the sediment 

cores. In another study (Schiff and Kinney, 2000), sediment samples were collected at 20 shoreline monitoring 

sites in Mission Bay near San Diego, Calif. Concentrations 

of fecal coliforms and enterococci in bottom sediments 

were low during dry weather, increased substantially 1 day 

after a storm, and were only slightly higher than dryweather levels 2 weeks after a storm. From these data, the 

authors concluded that levels of fecal-indicator bacteria in 

sediments did not appear to represent a long-lasting source 

of contamination to bay waters. 

It is well known and has been repeatedly demonstrated that bacteria survive longer and are present in higher 

numbers in sediments than in water (Burton and others, 

1987; LaLiberte and Grimes, 1982; Matson and others, 

1978; Sherer and others, 1992). Bacteria that are attached to 

sediments may be protected from attack by predators and 

bactericidal factors such as sunlight. Fecal-indicator bacteria survival has been investigated in stream-bottom sediments, lake-bottom sediments, storm-drain sediments, and 

ocean-outfall sediments; however, there is a paucity of 

information on the presence and survival of pathogens or 

fecal-indicator bacteria in sediments along the shorelines of 

bathing beaches (swash zone). 

Wastewater indicators. The use of wastewater indicators as surrogates for E. coli has received considerable 

attention. These compounds are typically analyzed by gas 

chromatography/mass spectrometry (GS/MS), and portable 

GS/MS instruments would facilitate onsite, rapid analysis 

(Mark Sandstrom, U.S. Geological Survey, oral commun., 

1998).

Sterols, such as coprostanol and cholesterol, are 

present in municipal wastewaters (Nguyen and others, 

1995) and have been used to determine the spatial distribution of contamination from sewage sludge (Kelly and 

Campbell, 1995) and to differentiate human and animal 

sources of fecal contamination (Leeming and others, 1996). 

Coprostanol is produced in the digestive tract of humans 

and other higher animals by the microbiological degradation 

of cholesterol. Leeming and Nichols (1996) found that 

water samples from an Australian estuary contaminated by 

sewage contained between 0.007 and 0.954 mg/L of coprostanol. A statistically significant relation was found between 

enterococci or fecal coliforms and coprostanol, although 

several outliers confounded the relation between fecal 

coliforms and coprostanol. In a study of the Mississippi 

River (Pereira and others, 1995), investigators found the 

highest concentrations of coprostanol in sediments near 

municipal-sewage outfalls. In another study of streams in 

the Puget Sound Basin, coprostanol or cholesterol were 

detected at the three sites with the highest fecal-indicator 

bacteria concentrations among the sites tested (Embrey, 

2001). 

Caffeine is a potential indicator of domestic wastewater because it is solely of anthropogenic origin (Seiler 

and others, 1999). Caffeine is ubiquitous in the human diet, 

is one of the most widely consumed drugs in the world, and 

is present in raw and treated sewage (Ogunseitan, 1996). 

Caffeine was shown to be a good tracer of domestic wastewater in a study of the fate of organic contaminants in the 

Mississippi River (Pereira and others, 1995). In the Missis-
4 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

sippi River study, caffeine concentrations ranged from 0.010 

to 0.070 μg/L, and elevated concentrations were found 

downstream from major metropolitan areas. In contrast, in 

the Puget Sound Basin study, caffeine was detected at sites 

with and without elevated concentrations of fecal-indicator 

bacteria (Embrey, 2001). 

Other compounds are found in wastewaters. Surfactants are major ingredients of soaps and detergents and may 

be indicators of domestic wastewater. In the Mississippi 

River study, anionic detergent concentrations had peaks in 

the vicinity of major cities and decreased rapidly downstream (Pereira and others, 1995). In the Puget Sound study 

(Embrey, 2001), polynuclear aromatic hydrocarbons (PAHs) 

such as naphthalene, fluoranthene, acenaphthalene, and 

anthracene were found at sites with elevated fecal-indicator 

bacteria concentrations. Two chemicals known to be used as 

fumigants and deodorizers in lavatories, 1,3-dichlorobenzene and 1,4-dichlorobenzene, were detected in a sample 

from an urban stream (Embrey, 2001). 

Predictive models. A survey done by the USEPA in 

1998 revealed that few local agencies were using predictive 

models for assessing recreational water quality (U.S. Environmental Protection Agency, 1999b). Of the agencies using 

models, two approaches to the development of models to 

predict recreational water quality were used. One approach 

is a statistics-based model that was used to predict E. coli

concentrations by means of simple or multiple-linearregression analysis and environmental and water-quality 

variables measured onsite as predictors. The second 

approach was the use of deterministic or mechanics-based 

models based on complex modeling of the dominant mixing 

and transport processes (U.S. Environmental Protection 

Agency, 1999b). 

The city of Stamford, Conn., and the state of Delaware are currently using statistics-based models in their 

beach programs. These models were developed in studies 

that established site-specific relations between rainfall and 

concentrations of fecal-indicator bacteria. The city of Stamford developed beach-closure guidelines on the basis of 

rainfall and enterococci data collected for 8 years at four 

estuary beaches on Long Island Sound (Joseph E. Kuntz, 

City of Stamford, Conn., Health Department, written commun., 2001). Investigators found that rainfall greater than 

1 in. usually resulted in enterococci concentrations greater 

than the established standard of 61 colonies per 100 milliliters (col/100 mL), except during two relatively dry summers 

wherein the threshold was lower. At one of four beaches, 

however, high enterococci counts were found without any 

antecedent rainfall; the cause of contamination was found to 

be the presence of a marina nearby where boat operators 

were not properly disposing of wastes (Joseph E. Kuntz, 

City of Stamford, Conn., Health Department, oral commun., 

2001). The state of Delaware developed rainfall thresholds 

to establish beach-closure guidance on the basis of exceedance of the enterococci standard at seven freshwater ponds. 

Thresholds were based on the linear relations between 

enterococci concentrations and 12-, 24-, and 48-hour antecedent rainfall amounts (Jack Pingree, State of Delaware, 

oral commun., 2001). After the first 2 years of threshold 

development, they have successfully applied pond-specific 

thresholds for 10 years, having correctly issued advisories 

based on thresholds 50-85 percent of the time, depending on 

the year.

 In the Cleveland, Ohio, area during summer 1997, 

water-quality and environmental variables were measured 

and evaluated for possible inclusion in statistics-based predictive models for three Lake Erie beaches (Francy and 

Darner, 1998). Turbidity, antecedent rainfall, volumes of 

wastewater-treatment plant overflows and metered outfalls, 

a sediment resuspension index, and wave heights were 

found to be related to E. coli concentrations. Wind speed, 

wind direction, water temperature, and the presence of 

swimmers were shown to be statistically unrelated to E. coli

concentrations. A single variable, turbidity, was found to be 

a poor predictor of E. coli and, as a result, investigators used 

multiple linear regression (MLR) to better explain the variability in E. coli concentrations. An MLR model that most 

reasonably represented the system and accounted for 

58 percent of the variability in E. coli concentrations was 

chosen for further testing. The model included weighted 

categorical rainfall, beach-specific turbidity, and wave 

height as explanatory variables. Because 90-percent prediction intervals for E. coli concentrations were fairly wide, the 

model did a poor job in providing an accurate numerical 

estimate of E. coli. Instead, the model was used to determine the probability, given a set of input variables, that 

E. coli concentrations will be greater than 235 col/100 

mL—the single-sample bathing-water standard in Ohio. 

Using this method, the model provided an assessment of 

recreational water quality as well as, and in some cases better than, the current method (that is, use of previous day’s 

E. coli concentration). 

In a similar study, work is ongoing to develop and 

test predictive equations for E. coli concentrations at two 

Milwaukee, Wis., beaches based on variables such as rainfall, combined-sewer-overflow volumes, wind vectors, 

water temperature, and turbidity. The investigators believe 

that more accurate predictions can be made when real-time 

measurements of hydrometeorological and water-quality 

parameters are used in the regression equations (Greg 

Olyphant, Indiana University, written commun., 2001). 

The second approach to modeling recreational water 

quality is to develop deterministic prediction models. Those 

models are physics oriented, require accurate meteorological information as initial and boundary conditions, and 

involve intensive computations. Virginia, New York-New 

Jersey, Rhode Island, and Washington are using deterministic models in their beach programs (U.S. Environmental 

Protection Agency, 1999b). A preliminary combined-sewer 

overflow (CSO) study of fecal-coliform distributions near 
Introduction 5

Cleveland was done for the Northeast Ohio Regional Sewer 

District (NEORSD) (Podber and others, 1994) using a 

deterministic prediction model. The GLFS at OSU has been 

making real-time predictions of Lake Erie water levels, 

wave heights, and three-dimensional temperature and current fields based on a three-dimensional coastal ocean circulation model since 1994 (Schwab and Bedford, 1994). 

Development of a high-resolution three-dimensional model 

to predict E. coli concentrations at one Lake Erie beach also 

is underway at OSU. 

Site descriptions 

Six Ohio bathing beaches were selected for this investigation: three Lake Erie urban beaches—Edgewater Park, Villa 

Angela, and Huntington Reservation (fig. 1), two Lake Erie 

beaches a in less populated area—Mentor Headlands and 

Fairport Harbor (fig. 2), and one inland beach—Mosquito 

Lake State Park (fig. 3). 

Edgewater Park (Edgewater), in Cleveland, Ohio, 

operated by the Ohio Department of Natural Resources, is 

used extensively during the recreational season. The bathing area is open to the lake, and the breakwall (east of the 

beach) impedes flowthrough of longshore currents (fig. 4A). 

The beach was divided into six sampling areas for this 

investigation based on the locations of markers (lifeguard 

stations and trees) on the beach. Sources of fecal contamination to the beach are stormwater runoff and combined-sewer 

overflows (CSOs) from the Edgewater outfall (fig. 4A), the 

117th Street outfall, and the Rocky River (fig. 1). Potential 

sources from the east are the Westerly Wastewater Treatment Plant Combined-Sewer Overflow Treatment Facility 

(CSOTF) and the Cuyahoga River. The CSOTF discharges 

primary treated CSO after heavy rains into the inner harbor 

area east of the Edgewater Park breakwall. Wastewater from 

the Westerly Wastewater Treatment Plant is an unlikely 

source of fecal contamination because the effluent is disinfected throughout the recreational season. 

Villa Angela, in Cleveland, Ohio, is another popular 

bathing beach operated by the Ohio Department of Natural 

Resources. At Villa Angela, four breakwalls were built to 

stabilize the beach area. The locations of these breakwalls 

were used to divide the beach into four sampling areas; two 

sampling areas are in front of breakwalls and two are 

between breakwalls (fig. 4B). Sources of fecal contamination include the East 156th Street outfall, Ninemile Creek, 

Dugway Brook, Doan Brook, and the Cuyahoga River to the 

west and Euclid Creek to the east (fig. 1). All of these outfalls and streams receive inputs from stormwater runoff and 

CSOs, and some receive inputs from sanitary sewer overflows (SSOs). The Euclid Creek Pump Station pumps sanitary sewage to a higher elevation and then to the wastewater 

treatment plant. The overflow from the Euclid Creek Pump 

Station is a minor source of fecal contamination. The Easterly Wastewater Treatment Plant discharges disinfected 

effluent into Lake Erie and thus is an unlikely source of 

fecal contamination to Villa Angela. 

Huntington Reservation (Huntington) is in a suburb 

of Cleveland—Bay Village, Ohio—and is operated by the 

City of Cleveland Metroparks (fig. 1). Water quality in 

Cahoon and Porter Creeks, directly to the east, is generally 

good, and these streams are not major sources of fecal contamination to Huntington (Don Killinger, Cuyahoga County 

Board of Health, oral commun., 2001). The City of Bay Village operates the Rocky River Wastewater Treatment Plant, 

which discharges treated sewage effluent into Lake Erie to 

the east of Huntington. Two outfalls discharge storm runoff 

from the parking lot into Huntington. The sources of fecal 

contamination to Huntington are largely unidentified. Huntington was divided into four sampling areas for this investigation based on the locations of piers (fig. 4C). 

Fairport Harbor and Mentor Headlands are in Lake 

County to the east of Cleveland in a less-populated area (fig. 

2). Fairport Harbor is a small, popular beach operated by 

Lake Metroparks. The beach area is protected by breakwalls 

and consists of a shallow swimming area with little wave 

action. Sources of fecal contamination include septic systems, wastewater-treatment-plant effluent, and stormwater 

runoff. The Grand River drains into Lake Erie directly west 

of Fairport Harbor. Mentor Headlands is a 1-mi-long, 

heavily used beach operated by Ohio Department of Natural 

Resources. Sources of fecal contamination to Mentor Headlands are similar to those at Fairport Harbor. At Fairport 

Harbor, one sample was collected from the center of the 

beach area for routine studies (fig. 2). At Mentor Headlands, 

two samples were collected for routine studies—one at the 

east end and one at the west end the beach (fig. 2). 

Mosquito Lake State Park (Mosquito Lake), operated 

by Ohio Department of Natural Resources, is the only 

inland beach site in this study. The beach is on the southwest corner of an 11-mi-long reservoir from north to south 

that is 1 mi wide at most points (fig. 3). The dam, operated 

by the U.S. Army Corps of Engineers, is less than one-half 

mile southeast of the beach. The beach was divided into 

three areas for this investigation based on the locations of 

lifeguard stations (fig. 5). Ancillary sampling sites were 

established to investigate possible sources of fecal contamination to the lake (fig. 3). Possible sources of fecal contamination include discharges from septic systems from a 

subdivision north of the beach, runoff from parking lots and 

wooded areas, birds, and recreational users. 

Acknowledgments

Individuals from many agencies helped to ensure the successful completion of this investigation. The authors thank 

Eva Hatvani, Ildiko Kubiak, and Oljeg Jamnicky, of the 

Northeast Ohio Regional Sewer District; Jill Lis of the 

Cuyahoga County Board of Health; Ann McCready-Gliha 

and Bob Martz of the Cuyahoga County Sanitary Engineers; 
6 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

DB

LAKE ERIE

Cuyahoga

River

Rocky

River

Cahoon

Creek

Creek

Porter

HUNTINGTONRESERVATION

EDGEWATER

PARK

117th Street

outfall Westerly Wastewater

Treatment

Plant

Eas

Bay

Villiage

Cleveland

Rocky River

Wastewater

Treatment 

Plant

State Route 2

41o30'

41o35'

81o50' 81o45' 81o40' 

Base map modified from U.S. Geological SurveyCleveland North 1:24,000, 1963, photorevised1973; Cleveland South, E. Cleveland, Lakewood1:24,000, 1963, photorevised 1979; and NorthOlmsted 1:24,000, 1963, photorevised 1985

81o55'

0

0

1

1

2 3

2 3 4 MILES

4 KILOMETERS

Figure 1. Locations of Lake Erie urban beaches–Huntington Reservation, Edgewater Park, and Villa Angela–in the Cleveland, Ohio, 

metropolitan area, 2000 and 2001.
Methods of study 7

State Route 44

LAKE ERIE

MENTOR

HEADLANDS 

Grand River

FAIRPORT

HARBOR

breakwall

breakwall

East 

sampling 

location

West

sampling

location

LAKE ERIE

Sampling

41 location O45'45"

41O45'

81O17'30" 81O16'15" 81O15"

Base map modified from U.S. Geological Survey

Mentor 1963, revised 1992

0

0 1 KILOMETER

1 MILE

Study Area

OHIO

and Dr. Adam Leff and Marc Crissman of Kent State University, Trumbull Campus, for their assistance with project 

planning and sample collection and analysis. The authors 

also acknowledge the assistance of others in the planning 

and implementation of this project— Lester Stumpe, John 

Graves, and Dan Hudson of the Northeast Ohio Regional 

Sewer District; Wayne Holmes and Thomas Filbert of the 

Ohio Department of Natural Resources; Donald Killinger of 

the Cuyahoga County Health Department; Ted Smith of the 

Trumbull County Health Department; and Edward Binic of 

the Lake County Health Department. Thanks are also 

extended to Paul Anderson and Ted Conlin, at Ohio Environmental Protection Agency, and Robert Powers at Mosquito Lake State Park, for the operation and use of their 

boats in collection of samples. Special thanks are extended 

to Armah de la Cruz, at the U.S. Environmental Protection 

Agency, for analyzing many samples for caffeine, and to 

Gary Tasker, a retired USGS employee, for providing essential help on the statistical modeling. 

Methods of study

Concentrations of E. coli were used to assess recreational 

water quality. This follows the recommendation in the 

Beach Action Plan, wherein USEPA stated that it intends to 

promulgate the exclusive use of E. coli or enterococci criteria by all states for analysis of freshwater samples by 2003 

(U.S. Environmental Protection Agency, 1999a). The 

USEPA added that current monitoring approaches that use 

the geometric mean of five samples collected over a 30-day 

period are outdated and there is interest in developing monitoring requirements that use the results of single samples. 

For this report, therefore, the single-sample bathing-water 

standard is used as a point of reference to evaluate recreational water quality. For Ohio, the single-sample bathing 

water standard of 235 col/100 mL cannot be exceeded in 

more than 10 percent of samples collected during any 30-

day period (Ohio Environmental Protection Agency, 2002). 

Figure 2. Locations of Lake Erie beaches in a less populated area–Mentor Headlands and Fairport Harbor–in Lake County, Ohio, 

2000.
8 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

State Route 305

Mosquito

Creek

Dam

MOSQUITO

LAKE

Campground

Upper Campground

Lower Campground

Docks

Beach

South Lakeshore

North Lakeshore

Upstream

MOSQUITO LAKE

STATE PARK

X

Base map modified from U.S. Geological Survey

Champion 1960, photorevised 1984

OHIO

Study Area

80o 80 45' o

46'15"

41o

20'

41o

18'45"

41o

17'30"

0

0 1 KILOMETER

1 MILE

80o47'30"

Ancillary sampling sites

N

EXPLANATION

Figure 3. Location of inland beach–Mosquito Lake State Park–Cortland, Ohio, 2000 and 2001.
Methods of study 9

0 100 200 300 400 FEET

AREA 1 AREA 2 AREA 3

AREA 4

C

HUNTINGTON RESERVATION 0 100 METERS

pier

LAKE ERIE

B

VILLA ANGELA

AREA 4

AREA 3 AREA 2 AREA 1 Euclid Beach

0 50 METERS

0 50 100 150 200 FEET

breakwall

LAKE ERIE

A

EDGEWATER PARK

AREA 6

Edgewater

Outfall

West Beach

East Beach

AREA 2 AREA 4

AREA 1

AREA 3

AREA 5

0 100 200 300 400 FEET

0 100 METERS

breakwall

5

5

3

4

5

6

7

5

9

Spatial study sampling location

(number is depth in feet)

5

LAKE ERIE

Figure 4. Sampling areas at Lake Erie urban beaches: (A) Edgewater Park, (B) Villa Angela, and (C) Huntington Reservation in the 

Cleveland, Ohio, metropolitan area, 2000 and 2001.
10 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

Sampling frequency and locations

Distribution, source, and spatial studies. During 

distribution, source, and spatial studies, water and sediment 

samples were collected between 7 and 11 a.m. at Edgewater, 

Villa Angela, Huntington, and Mosquito Lake by the USGS 

on selected days in 2000 and at Edgewater and Mosquito 

Lake in 2001 (table 1). For distribution studies, samples 

were collected from two or three areas at each beach (figs. 4 

and 5). From each area, samples were collected from within 

the bathing area, outside the bathing area (about 100 ft offshore from buoys that demarcate the boundary of the bathing area), and (or) near the swash zone. For source studies, 

samples were collected at six ancillary sites at Mosquito 

Lake and at the beach (fig. 3). Samples were collected from 

ancillary sites at three locations: shoreline samples were 

collected less than 50 ft from the shore, nearshore samples 

were collected approximately 200–400 ft from the shore, 

and offshore samples were collected approximately onefourth mile offshore in open water. For spatial studies during 2000, sampling was done from a boat at two randomly 

selected areas at Edgewater outside the bathing area. Water 

depths were from 7 to 10 ft. During 2001 spatial studies, 

samples were collected at Edgewater at 5-ft water depths 

from areas 1, 4, 5, and 6 and at six depths, ranging from 3 to 

9 ft, from area 3 (fig. 4A). 

Routine studies. During routine studies, water samples were collected 4 or 5 days a week by local agencies at 

all six beach study sites throughout summer 2000 and at 

four beaches (excluding Fairport Harbor and Mentor Headlands) during 2001. Sampling frequencies, times, and areas 

sampled were different among the six beach study sites 

(table 2). At Huntington and Mentor Headlands during 

2000, two samples were collected daily because of the 

extended length of these beaches. During 2001, it was 

decided to collect two samples daily at all four beaches to 

reduce sampling and analytical variability. An average of

the two values was used for data analysis.

Sampling methods

Field personnel used established sampling methods, 

described below, to collect water and (or) sediment samples 

for E. coli during source and routine studies and during 

most distribution and spatial studies. Special sampling 

methods were employed to collect some samples during distribution and spatial studies. For chemical constituents and 

suspended-sediment analyses, separate sample bottles and 

different sampling techniques were required, as described 

below. 

Established sampling methods for all studies. 

Lake-water samples were collected by means of a grab-sampling technique that minimized contamination of sterile 

sampling containers (Myers and Sylvester, 1997). After 

wading or swimming to the area and depth designated, a 

sterile polypropylene bottle was opened about 18 in. below 

the water surface and filled. For spatial studies during 2001, 

at some locations and depths, an additional bottle was 

opened at about 18 in. above the lake bottom and filled. 

Lake-bottom sediments were collected into autoclaved wide-mouth 250-mL polypropylene jars. A diver 

swam out (or was transported by boat) to the designated 

sampling point, secured the lid on the sampling jar, opened 

the lid upon reaching the lake bottom, and scooped the bottom sediments to obtain a sample. The diver closed the lid 

of the jar before surfacing to minimize contamination by the 

overlying water. Because of spatial heterogeneity of bacteria 

concentrations in sediment, three sediment jars were collected from each sampling point and composited before 

analysis.

MOSQUITO LAKE

STATE PARK

BEACH

0 50 METERS

0 50 100 150 200 FEET

AREA 2

AREA 1

AREA 3

MOSQUITO

LAKE

Figure 5. Sampling areas at Mosquito Lake State Park Beach in 

Cortland, Ohio, 2000 and 2001. (Beach area in relation to rest of 

lake is shown in fig.3.)
Methods of study 11

Table 1. Dates of sampling and locations of water and sediment samples collected at Ohio beaches for 

distribution, source, and spatial studies, 2000 and 2001

[Samples from within the bathing area were collected on all dates.]

Type of study Samples collected

Beach Date(s)

Distribution

Source

Spatial

Outside bathing

Near swash zone

Shoreline

Nearshore

Offshore

Edgewater June 19 & 21, 2000 x x

(Lake Erie) June 20 & 22, 2000 x x x

July 26, 2000 x x

August 7, 2000 x x

August 8, 2000 x

August 17, 2000 x x

August 28, 2000 x

August 29, 2000 x x x

February 27, 2001 x x

June 27 - 28, 2001 x x x x

August 4 - 6, 2001 x x x x

Huntington June 19 & 21, 2000 x x

(Lake Erie) June 20 & 22, 2000 x x

July 26, 2000 x x

August 7, 2000 x x

August 8, 2000 x

August 17, 2000 x x

August 28, 2000 x

Mosquito Lake June 5 & 7, 2000 x x x x

June 6 & 8, 2000 x x x x

July 31 & August 2, 2000 x x x x

August 1 and 3, 2000 x x x x

August 30, 2000 x x x x x

February 27, 2001 x x

June 12 - 13, 2001 x x

August 28, 2001 x x x x

Villa Angela June 19 & 21, 2000 x x

(Lake Erie) June 20 & 22, 2000 x x

July 26, 2000 x x

August 7, 2000 x x

August 8, 2000 x

August 17, 2000 x x

August 28, 2000 x

August 29, 2000 x x
12 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

Table 2. Beach study sites and sampling information for routine studies at Ohio beaches, May through August 2000 and 2001

Beach Year and area(s) sampled Sampling frequency / time Collecting agency / 

analyzing agencya

a

 Collecting and analyzing agency are the same unless indicated otherwise.

Edgewater 2000 - Area 3 Monday to Friday, 8 - 9 a.m. Northeast Ohio Regional Sewer District

2001 - Areas 2 and 3

Villa Angela 2000 - Areas 3 Monday to Friday, 7 - 8 a.m. Northeast Ohio Regional Sewer District

2001 - Areas 2 and 3

Huntington 2000 - Areas 1 and 2 Monday to Thursday, 7 - 9 a.m. Cuyahoga County Board of Health/

2001 - Areas 1 and 2 Cuyahoga County Sanitary Engineers

Fairport Harbor 2000 - Central Monday and Wednesday, 8 - 10 a.m. Lake County General Health District

Tuesday and Thursday, 9 a.m. - 1 p.m. Ohio Department of Health/

Cuyahoga County Sanitary Engineers

Mentor Headlands 2000 - West and East Monday and Wednesday, 8 - 11 a.m. Lake County General Health District

Tuesday and Thursday, 

11 a.m. - 12 p.m.

Ohio Department of Health/

Cuyahoga County Sanitary Engineers

Mosquito Lake 2000 - Area 2 Monday to Thursday, 8 - 9 a.m. U.S. Geological Survey contractor

2001 - Areas 2 and 3

Special sampling methods for samples from near the 

swash zone. Interstitial-water and sediment samples from 

near the swash zone were collected and analyzed for E. coli

during distribution studies. Sample sites were determined by 

first locating the landward edge of the swash zone, which is 

the zone alternatively covered and exposed by waves. Sample locations were chosen 3 and 6 ft inland from the outer 

edge of the swash zone. Three subsample points were then 

marked in a row, each 1.5 ft apart, parallel to the shore at 

both the 3 and 6 ft inland locations. The depth of dry sand 

was measured and removed with a shovel from each subsample point. Using a sterile post-hole digger for the subsample holes, a field technician made a 6-in.-diameter hole 

in the moist sand. The post-hole digger was sterilized by 

applying household bleach for 2 minutes and rinsing it with 

sterile sodium thiosulfate and sterile deionized water. The 

hole was dug deep enough to allow free interstitial water to 

accumulate. Once the interstitial water entered the hole, a 

sterilized well casing was inserted to prevent the hole from 

collapsing. During initial studies, ethanol was used to sterilize the well casings; however, ethanol disinfection was 

found to be inadequate, so bleach was used in later studies. 

In order to protect E. coli from the effects of ultraviolet radiation that they are not normally exposed to, a tarp was 

placed over the holes as they filled with interstitial water. 

Once stabilized, the interstitial water was collected with a 

sterile 25-mL pipet (fig. 6). Approximately 100 mL of interstitial water was collected from each of the three subsample 

holes and composited into a sterile 1-L polypropylene bottle. After collecting water, the casings were removed, and 

the distances from the top of moist sand to the interstitial 

water line (depth to the water table) were measured with a 

tape measure; an average depth of three holes was calculated. Subsurface sediment was removed from each subsample hole with a sterile spatula and was placed in a 125-mL 

sterile plastic jar. The sediments collected from the three 

holes were composited before analysis. 

 Special sampling methods for discrete vertical sampling. For spatial studies during 2000, discrete vertical 

water samples for E. coli were collected outside the bathing 

area where water depths ranged from 7 to 9 ft. Samples 

were collected from a boat by means of a subsurface grab 

sampler (Wheaton Science Products, Millville, N.J.); the 

pole of the sampler was marked to the nearest 0.5 ft. The 

approximate depth of the water at the sampling point was 

measured with a sonar depth finder. Taking into account 

depth, the sampler operator identified three evenly spaced 

vertical locations for sampling, starting 1 ft from the bottom 

and ending at least 2 ft below the surface. The operator 

screwed a sterile bottle onto the end of the sampler, lowered 
Methods of study 13

the sampler through the water column to the designated 

depth, and pulled a rope through a pulley to expose a sampling port. After the bottle was almost full, the operator 

closed the sampling port using a spring-operated cap and 

then retrieved, capped, and placed the sample on ice. The 

sampling port was sterilized and cleaned between samples 

with dilute bleach, sodium thiosulfate, and sterile deionized 

water.

Sampling methods for chemical constituents and 

suspended sediment. For some studies, samples were collected in separate bottles for analyses of chemical constituents and suspended sediment. For distribution and source 

studies during 2000 and for routine studies during 2001, 

water samples were collected and analyzed for wastewater 

indicators and (or) caffeine concentrations. These samples 

were collected in the same manner that the general samples 

were collected. The 1-L glass amber bottles used were previously baked at 450°C to remove all organic contaminants. 

During distribution studies, interstitial-water samples for 

total nitrogen concentrations were removed by use of 25 mL 

pipets, and approximately 30 mL of water from each hole 

was composited into 125-mL brown polypropylene bottles. 

After collection, the total nitrogen samples were preserved 

with 1 mL of 4.5 N sulfuric acid. For spatial studies, two 

glass 500-mL bottles were filled with water to determine 

suspended-sediment concentrations. For spatial studies during 2000, the discrete vertical sampling technique was used 

to fill the bottles. For spatial studies during 2001, the grab 

sampling technique was used.

Analysis of water and sediment samples

All water and sediment samples for E. coli were placed on 

ice and transported to the laboratory. In the laboratory, samples were analyzed for E. coli and processed for other constituents within 6 hours for water and 24 hours for sediment 

samples. Samples collected for wastewater indicators, caffeine, and total nitrogen and suspended-sediment concentrations were sent to the appropriate analyzing laboratory 

within 3 days of sampling. 

Distribution, source, and spatial studies. For distribution, source, and spatial studies, USGS personnel analyzed the water samples at a local laboratory (the Cuyahoga 

County Sanitary Engineers Laboratory in Valley View, 

Ohio, or Kent State University, Trumbull Campus, in Warren, Ohio). Sediment samples were sent for analysis to the 

USGS Ohio District Microbiology Laboratory in Columbus, 

Ohio, by overnight mail or courier. The constituents 

included and frequencies of analysis were different among 

the types of studies; standard methods were followed for 

most tests (table 3).

All water and sediment samples were analyzed for 

concentrations of E. coli by use of the mTEC membrane-filtration method (U.S. Environmental Protection Agency, 

1985). Approximately 10 percent of sediment samples collected during source studies were also analyzed by use of 

the most-probable-number Colilert Quantitray method 

(Idexx Laboratories, Westbrook, Maine). This was done 

because of sediment interferences in the growth of bacteria 

on some agar plates; however, poor recoveries of E. coli on 

Colilert precluded use of Colilert as a substitute for membrane filtration for sediment samples. For sediment samples, 

additional sample-processing steps, developed during an 

earlier study (Francy and Darner, 1998), were required 

before plating. Briefly, 50 g of sediment was aseptically 

removed from each of three replicate sample jars and composited into a sterile 1-L jar. Twenty grams of the mixed 

sediment was then placed into a bottle containing 200 mL of 

saline buffer; a second aliquot of mixed sediment was 

removed to determine percent dry weight. The analyst 

placed the bottle on a shaker for 45 minutes, removed the 

bottle, allowed suspended materials to settle for 30 seconds, 

and decanted the liquid phase for plating. Calculations were 

made as described in Francy and Darner (1998) to convert 

colony counts to colonies per gram of dry weight sediment 

(col/gDW). 

After processing water and sediment samples for 

E. coli, some additional analyses were done. Turbidity was 

determined in all water samples (Hach Company, 1989). 

Sediment samples were randomly selected for particle-size 

distributions and total organic carbon concentrations. For 

these analyses, after E. coli samples were processed, the 

remaining sediment in the three jars was composited. For 

particle-size analysis, the composited sediment was dried 

for several days at room temperature and processed through 

a series of sieves to weigh the percentage of sediment finer 

than 63, 250, and 1,000 μm (Guy, 1969). For total organic 

carbon analysis, the composited sediment was processed 

through a 2-mm sieve, and the < 2-mm fraction was analyzed at the USGS National Water Quality Laboratory 

(NWQL) in Lakewood, Colo. 

Figure 6. Sampling of interstitial water from subsample holes,

3 feet and 6 feet inland from the swash zone.
14 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

Lake-water samples were collected for wastewater 

indicators and sent to two laboratories. The USGS NWQL 

analyzed samples for a wastewater indicator method that 

was under development at the time of the study (Michael 

Schroeder, U.S. Geological Survey, written commun., 

2000). Water samples were extracted with methylene chloride by means of continuous liquid-liquid extraction under 

acidic and then basic conditions. The extracts were concentrated and analyzed by selected ion monitor gas chromatography/mass spectrometry (SIM GC/MS) for the 

determination of 45 compounds. The compounds included, 

but were not limited to, plasticizers, fumigants, detergents, 

polyaromatic hydrocarbons, an analgesic, fecal indicators, 

and a stimulant (caffeine), many of which are common in 

the environment. The USEPA in Cincinnati, Ohio, analyzed 

samples for caffeine using a method of solid-phase extraction and high-performance liquid chromatography with fluoresence detection (Piocos and de la Cruz, 2000). During 

2000, sample volumes of 100 mL were subjected to the preconcentration and extraction step; however, this proved to 

be too small a volume to detect caffeine. During 2001, the 

analytical volume was increased to 200 mL to improve the 

sensitivity of the analysis (Armah de la Cruz, U.S. Environmental Protection Agency, oral commun., 2001).

Other water-quality properties and constituents were 

measured. Field measurements of specific conductance, pH, 

temperature, and (or) concentrations of dissolved oxygen in 

lake and interstitial waters were measured according to standard USGS methods (Wilde and Radtke, 1998). For interstitial waters, measurements of specific conductance and 

temperature were made in each hole; measurements from 

the three holes were averaged. Interstitial waters were sent 

to the USGS NWQL for total nitrogen analysis, and lakewater samples were sent to Heidelberg Water Quality Laboratory, Tiffin, Ohio, for determination of suspended-sediment concentrations. 

Routine studies. The methods of analyses during 

routine studies, when applicable, were the same methods as 

those described above and used for distribution, source, and 

spatial studies (table 3). During routine studies, field personnel measured water temperature and specific conductance onsite; all water samples were analyzed for turbidity 

and concentrations of E. coli in local laboratories. (See table 

2 for collecting and analyzing agencies.)

Collection and compilation of ancillary information

Ancillary environmental data were collected by field crews 

or compiled from a variety of sources. These data were used 

to develop predictive models. At all beaches, personnel 

from the USGS or cooperating agencies estimated the number of birds and wave heights at the time of sample collection. Data from four USGS-operated gaging stations were 

used to estimate streamflow—Cuyahoga River at Independence (04020800) and Rocky River near Berea (04201500) 

for three Lake Erie urban beaches, Grand River near Painesville (04212100) for two Lake Erie beaches in a less populated area, and Eagle Creek at Phalanx Station (03093000) 

for Mosquito Lake (Shindel and others, 2001 and 2002). 

Wind speed and direction were measured at a weather station at the Youngstown Regional Airport (National Oceanic 

and Atmospheric Administration, 2001), 6 mi. southeast of 

Mosquito Lake. Data on the average elevation of the lake 

pool and the average flow through the dam outlet at Mosquito Lake were obtained from the U.S. Army Corps of 

Engineers (George Kusko, U.S. Army Corps of Engineers, 

written commun., 2000 and 2001). The ultraviolet index 

(UV) is a measure of the intensity of UV radiation. Daily 

UV index data were measured at noon during 2000 (Craig 

Long, National Oceanic and Atmospheric Administration, 

written commun., 2000). Data for 2001 were an average of 

two daily values (Accuweather, 2001); one measurement 

obtained between 11 a.m. to 1 p.m. and the second from 4 to 

6 p.m. Calculated wave heights and current directions were 

obtained from the GLFS at OSU for three Lake Erie locations—the Rocky River (used for Huntington), Edgewater 

Park (used for Edgewater), and the Cuyahoga River (used 

for Villa Angela) (Yifei Philip Chu, The Ohio State University, written commun., 2000 and 2001). Personnel at the 

GLFS used a three-dimensional hydrodynamic prediction 

model based on surface meteorological observations and 

forecasts from numerical weather prediction models to forecast currents and waves (Schwab and Bedford, 1994). 

 Daily rainfall amounts were compiled from several 

agencies. The Northeast Ohio Regional Sewer District 

(NEORSD) operates a network of rain gages in the Cleveland, Ohio, area. Data from two of these gages (John 

Graves, Northeast Ohio Regional Sewer District, written 

commun., 1998) were used to develop models in 1997 for 

Edgewater and Villa Angela during an earlier study (Francy 

and Darner, 1998) and are used in this report to test the 1997 

model. These NEORSD data were not used in new model 

development because the data were not readily available and 

often required interpretation and qualification. Instead, rain 

data used in new model development were obtained from a 

gage at Hopkins International Airport (National Oceanic 

and Atmospheric Administration, 2001) for Edgewater, 

Villa Angela, and Huntington; a gage at the Painesville 

Municipal Water Plant for Mentor Headlands and Fairport 

Harbor (Ed Binic, Lake County Health Department, written 

commun., 2000); and a gage at the Youngstown Airport 

(National Oceanic and Atmospheric Administration, 2001) 

for Mosquito Lake. These gages represent rainfall amounts 

in nearby watersheds. To obtain rainfall amounts for a specific beach area, the USGS installed a rain gage on the roof 

of a structure at Huntington Beach during 2000.
Methods of study 15

Table 3. Constituents and properties determined on water and sediment samples collected at Ohio beaches during distribution, 

source, spatial, and routine studies, 2000 and 2001

[mL, milliliters; gDW, gram per dry weight sediment; MPN, most-probable number; NTU, nephelometric turbidity unit; g/kg, grams per kilogram; 

μS/cm, microsiemens per centimeter; μg/L, micrograms per liter; mg/ L, milligrams per liter; USEPA, U.S. Environmental Protection Agency]

Constituent or 

property

Type of Study

Frequency or 

number of 

samples

Method (reference) Detection limit

Distribution

Source

Spatial

Routine

Escherichia coli in water x x x x Every sample mTEC method 

(USEPA, 1985) 1 colony / 100mL

Escherichia coli in sediment x x x Every sample mTEC method 

(modified from 

USEPA, 1985)

1 colony / gDW

Escherichia coli in water x Every samplea Colilert (Idexx 

Laboratories, 

Westbrook, Maine)

1 MPN/100 mL

Escherichia coli in sediment x 10 percent of 

samples

Colilert (Idexx 

Laboratories, 

Westbrook, Maine)

1 MPN/100 mL

Percent dry weight of 

sediment

x x x Every sample (American Society of 

Agronomy, 1982, 

p. 790-791

Not applicable

Turbidity of water x x x Every sample Nephelometric method 

(Hach Company, 

1989)

0.01 NTU

Particle size of sediment x

x

x 70 samples, 2000

41 samples, 2001

(Guy, 1969, p. 47-51) Not applicable

Total organic carbon of 

sediment

x 6 subsurface samples 

from near the 

swash zone, 2000

USGS O-5101-83

(Wershaw and 

others, 1987; Brandt 

and others, 1990)

0.1 g/kg

Wastewater indicators in water x x x 13 samples, 2000 (LeRoy Schroeder, 

U.S. Geological 

Survey, 2000,written 

commun.)

0.03 - 6.4 μg/L

Caffeine in water x x 55 samples, 2000 (Piocos and de la Cruz, 

2000) 0.008 μg/L

x 84 samples, 2001

Temperature of water x x Every sample Thermistor 

thermometer

(Wilde and Radtke, 

1998, chapter 6.1)

Not applicable

Specific conductance of water

x x

x

x

Every sample, 2000b

Every sample, 2001c

Every sample

Conductivity sensor

(Wilde and Radtke, 

1998, chapter 6.3)

1 μS/cm
16 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

Dissolved oxygen of water x Every sample Amperometric method

(Wilde and Radtke, 

1998, chapter 6.2) 1 mg/L

pH of water x Every sample Hydrogen ion 

electrode

(Wilde and Radtke, 

1998, chapter 6.4)

Not applicable

Total nitrogen in water x 12 subsurface 

samples from near 

the swash zone, 

2001

(Patton and Truitt, 

2000) 0.08 mg/L

Suspended sediment in water x Every sample (Guy, 1969, p. 11-13) 1.0 mg/L

a

 Fairport Harbor and Mentor Headlands only.

b Mosquito Lake only.

c

 Edgewater Park and Villa Angela only.

Table 3. Constituents and properties determined on water and sediment samples collected at Ohio beaches during distribution, 

source, spatial, and routine studies, 2000 and 2001 —Continued

[mL, milliliters; gDW, gram per dry weight sediment; MPN, most-probable number; NTU, nephelometric turbidity unit; g/kg, grams per kilogram; 

μS/cm, microsiemens per centimeter; μg/L, micrograms per liter; mg/ L, milligrams per liter; USEPA, U.S. Environmental Protection Agency]

Constituent or 

property

Type of Study

Frequency or 

number of 

samples

Method (reference) Detection limit

Distribution

Source

Spatial

Routine

Statistical methods

Correlation analysis and x/y scatterplots were used as 

exploratory tools to examine the relations between E. coli

concentrations and environmental or water-quality variables. This is the first step in identifying explanatory variables that are related to E. coli and could be used in 

regression models. Pearson’s r is a correlation coefficient 

that measures the linear association between two variables. 

Spearman’s rho, another correlation coefficient, measures 

the monotonic relation (nonlinear or linear) between two 

variables. Because the Pearson’s r values were similar to the 

Spearman’s rho values for most relations in this study, only 

the Pearson’s r values were used to identify variables related 

to E. coli concentrations. For Pearson’s r, if the data lie 

exactly along a straight line with positive slope, then the 

correlation coefficient is equal to 1 (Helsel and Hirsch, 

1992, p. 209-218). The more the correlation coefficient 

deviates from 1 or –1 and approaches zero, the weaker the 

relation. The level of significance was set at α=0.05, unless 

specified otherwise.

The rank transform test, a nonparametric analysis of 

variance (ANOVA), was used to compare more than two 

groups of data. In the rank transform test, all data are combined and ranked from lowest to highest value, and an 

ANOVA is computed on the ranks. This test determines 

whether the median differs between any of the groups. If 

the rank transform test showed differences among groups, 

the Tukey-Kramer multiple comparison test was used to 

determine which groups differed from each other (Helsel 

and Hirsch, 1992, p. 198-200).

Multiple linear regression was used to develop models to predict E. coli concentrations from explanatory variables. Models were chosen among all possible variable 

combinations to maximize the coefficient of determination 

(R2) and minimize the Mallows’ Cp (Mallows, 1973). The 

R2

 of the model is the fraction of the variation in the E. coli

concentration that can be explained by a combination of 

explanatory variables. The adjusted R2 was used and 

reported for all statistical analyses; the adjusted R2

 differs 

from the R2 in that the former has been adjusted for degrees 

of freedom. The Cp statistic is a measure of the error variance and the bias introduced by not including important 

variables in a model. When several models had nearly equal 

R2 and Cp values, a set of models was chosen to reduce 

multi-collinearity (where at least one explanatory variable is 

related to one or more other explanatory variables). Further 

evaluation of these models was based on their having significant parameter estimates and acceptable partial residual 
Distribution, sources, and wastewater indicators 17

plots. In a partial residual plot, log E. coli is regressed 

against all explanatory variables except for one, and the 

residuals are plotted against the omitted explanatory variable. These plots provide information on how much influence the omitted variable has on the regression by 

eliminating the effects from other variables; they are also 

useful in evaluating whether the relation between E. coli and 

explanatory variables is specified correctly. After the partial 

plots were examined, a beach model (or more than one 

model) was selected on the basis of having a set of explanatory variables that seemed reasonable and having data that 

were easy to collect or compile. 

Even with the best models, prediction intervals were 

shown to be too wide to offer an accurate prediction of 

E. coli concentrations (Francy and Darner, 1998). Consequently, the probability of exceeding the single-sample 

bathing-water standard of 235 col/100 mL was used as the 

model output variable. This variable was calculated as the 

probability that Student's t with n-p degrees of freedom is 

greater than or equal to X, where X= (log(235) - y)/s and y is 

the regression estimate of the log of E. coli, s is the standard 

error of prediction of y, n is the number of observations used 

in the regression, and p is the number of regression coefficients estimated in the regression equation. For example, a 

set of explanatory variables may result in an output value of 

45 percent—this means that, given this set of explanatory 

variables, there is a 45 percent chance that the E. coli concentration will exceed 235 col/100 mL. Threshold probabilities were set by taking the data set used to develop the 

model and finding the probability that provided the highest 

number of correct responses and lowest number of false 

negative responses. Output values below the threshold 

probability would result in the assumption by the beach 

manager that water quality is acceptable; output values 

above the threshold probability would result in the posting 

of the beach with a water-quality advisory. 

Quality-assurance and quality-control practices

Quality-assurance and quality-control (QA/QC) practices 

are considered an integral part of all data-collection activities. Field and laboratory protocols for all studies were 

written and distributed to ensure that procedures were followed correctly and consistently by USGS and cooperative 

agencies. Detailed QA/QC practices for collection of waterquality data at the USGS, Ohio District, are described in 

Francy and others (1998). For the USGS Ohio District 

Microbiology Laboratory, procedures for laboratory operation and equipment maintenance are described in Francy 

and others (2001). 

Quality-control samples were collected to measure 

sampling and analytical variability or contamination and to 

ensure that data satisfied project objectives. To ensure that 

membrane-filtration equipment was clean and sterile and 

that reagents were uncontaminated, a filter blank—a 50-mL 

aliquot of sterile buffered water plated before the sample—was included with each new bottle of buffer. All filter 

blanks were negative. To measure sampling and analytical 

variability, duplicate samples were collected and analyzed 

for several constituents. For turbidity, duplicate aliquots 

were measured from the same bottle, and measurements that 

did not agree within 10 percent were repeated; an average 

turbidity of two aliquots that agreed within 10 percent was 

reported for each sample. For percent dry weight, duplicate 

jars were analyzed for approximately 10 percent of the samples; for some samples, split samples were also analyzed 

(Appendix A1). Splits are subsamples taken from the same 

jar or bottle. For E. coli analysis, approximately 4 percent 

of the samples from routine studies (Appendix A2), as well 

as distribution, source, and spatial studies (Appendix A3), 

were collected in duplicate and (or) analyzed as split samples. For caffeine, approximately 10 percent of the samples 

collected were quality-control samples—replicates, spikes, 

and blanks (Appendix A4). Results of quality-control samples for percent dry weights, E. coli, and caffeine were 

examined qualitatively to ensure that procedures were correctly followed. For suspended-sediment analysis, two 

duplicate bottles from each location were collected. Results 

from both bottles were reported because a comparison of 

suspended-sediment concentrations between duplicate bottles found large differences in some cases (Appendix B). 

Distribution, sources, and wastewater indicators

for Escherichia coli at bathing beaches

Data collected during distribution, source, and spatial studies at three Lake Erie beaches and one inland lake for water, 

sediment, and concentrations of wastewater indicators are 

listed in Appendixes B, C, and D, respectively. Data collected during routine studies for concentrations of caffeine 

in water samples are listed in Appendix E. Samples were 

collected during extended dry periods and after rainfalls 

ranging from trace amounts to 1.5 in. 

To characterize the size class of sediment at each 

beach and aid in data interpretation, particle-size analyses 

were done on a subset of lake-bottom sediments and subsurface sediments from near the swash zone (Appendix C). 

More detailed summary statistical information is shown 

(table 4) for the percent finer than 250 μm, because these 

values showed the greatest variability among sites and sediment types. For sediment samples collected within the 

bathing area, the greatest percentage of sediments coarser 

than 1,000 μm and finer than 63 μm were found at Mosquito Lake. Among Lake Erie beaches, the coarsest sediments were found at Villa Angela in all three types of 

sediment. At Edgewater and Huntington, the subsurface 

sediments from near the swash zone varied considerably in 
18 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

Table 4. Summary of particle-size analysis of lake-bottom and subsurface sediments collected at Ohio beaches, 2000 and 2001

[Bathing, samples collected within the bathing area; Outside, samples collected outside the bathing area; Swash zone, subsurface sediments collected

from near the swash zone]

Beach Type of 

sediment

Number of 

samples

Percent finer than (micrometers)

1000, 250 63,

average Average Minimum Maximum average

Edgewater Bathing 19 99.3 94.0 78.9 98.0 5.4

Outside 8 97.5 95.3 89.2 98.6 2.0

Swash zone 20 96.1 75.2 24.9 93.9 2.9

Huntington Bathing 2 95.8 59.2 48.1 70.2 0.7

Outside 1 99.9a 98.6a

a

 Not an average value because only one sample was collected.

1.1a

Swash zone 6 92.7 45.7 13.3 71.7 1.3

Villa Angela Bathing 2 64.6 3.2 0.2 6.1 0.1

Outside 4 94.9 59.4 33.6 81.9 1.2

Swash zone 6 67.6 7.6 0.1 24.4 0.2

Mosquito Bathing 12 50.9 28.7 11.1 83.8 11.3

Outside 1 43.6a 23.1a 3.4a

Swash zone 14 52.9 19.1 13.2 32.1 1.6

particle size and were generally classified as fine to coarse 

sands (63 to 1,000 μm). At Villa Angela and Mosquito 

Lake, most subsurface sediments from near the swash zone 

were classified as medium sand to gravel (250 to >1,000 

μm). 

Distribution studies

Distribution of Escherichia coli within and outside 

the bathing area. The distributions of E. coli concentrations in water (fig. 7) and sediments (fig. 8) within the bathing area were compared to concentrations outside the 

bathing area to determine whether sediments outside the 

bathing area were a significant deposition area for E. coli. 

Results from samples collected from within the bathing area 

are shown in boxplots, and results from samples collected 

outside the bathing area are superimposed on the boxplots. 

This provides a comparison between the two groups of data 

based on the 5th to 95th percentiles. For Lake Erie beaches 

(Edgewater, Villa Angela, and Huntington), samples were 

collected within the bathing areas at water depths of 3–5 ft 

and outside the bathing areas at water depths of 8–11 ft. At 

Mosquito Lake, samples within the bathing area were collected at 3- to 6-ft water depths, and samples outside the 

bathing area were collected at depths ranging from 3 to 

11 ft. 

For Lake Erie water samples (fig. 7A), concentrations of E. coli outside the bathing area followed a similar 

distribution or were less than those within the bathing area. 

This same pattern was seen in water samples collected at 

Mosquito Lake except on June 2000 (fig. 7B). For Lake 

Erie and Mosquito Lake sediment samples (fig. 8), concentrations of E. coli outside the bathing area were generally in 

the same range or were less than those within the bathing 

area except in June 2000. The two highest sediment E. coli

concentrations (820 and 3,000 col/gDW) were found in June 

2000 at Edgewater within the bathing area of area 1; this 

area is the eastern section of the beach closest to the break 

wall (fig. 4A). For most time periods, therefore, concentrations of E. coli in water and sediments outside the bathing 

area were in the same range or less than concentrations 

within the bathing area. 

Concentrations of E. coli in water were compared to 

their concentrations in associated lake-bottom sediments by 

means of correlation analysis. A combined data set from 

within and outside the bathing area was used for this analysis (fig. 9). Statistically significant correlations (α= 0.05) 

were found between water E. coli concentrations and sediment E. coli concentrations at Edgewater during 2000 but 

not at Edgewater during 2001 (fig. 9A). The relation 

between concentrations of E. coli in water and sediment was 

significant at Huntington for 2000. This may be because 

E. coli concentrations at Huntington were above 50 col/100 

mL in the water samples collected (fig. 9B). This was not 

the case at the other beaches and may be why, for example, 

the correlation was poor at Edgewater in 2001. At Villa 

Angela, the coarseness of the sediments from the bathing 
Distribution, sources, and wastewater indicators 19

ESCHERICHIA COLI, IN COLONIES PER 100 MILLILITERS

A. Lake Erie beaches

B. Mosquito Lake State Park

JUNE 00 AUGUST 00 JUNE 01 AUGUST 01

JUNE 00 AUGUST 00 AUGUST 01

10,000

1,000

100

1

10

10,000

1,000

100

10

1

EXPLANATION

Outlier

95th percentile

75th percentile

25th percentile

5th percentile

Median

Outside

bathing

area sample

0.1

0.1

Figure 7. Distribution of Escherichia coli concentrations in water samples collected within the bathing area and comparison 

to samples collected outside the bathing area for (A) Lake Erie beaches and (B) Mosquito Lake State Park.
20 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

JUNE 00 AUGUST 00 AUGUST 01

JUNE 00 AUGUST 00 JUNE 01 AUGUST 01

A. Lake Erie beaches

B. Mosquito Lake State Park

10,000

1,000

100

1

10

10,000

1,000

100

10

1

ESCHERICHIA COLI, IN COLONIES PER GRAM DRY WEIGHT OF SEDIMENT

EXPLANATION

Outlier

95th percentile

75th percentile

25th percentile

5th percentile

Median

Outside

bathing

area sample

0.1

0.1

Figure 8. Distribution of Escherichia coli concentrations in sediment samples collected within the bathing area and 

comparison to samples collected outside the bathing area for (A) Lake Erie beaches and (B) Mosquito Lake State Park.

area may be why the relation was weak (fig. 9C and table 

4); coarse sediments have less surface area for bacterial

attachment than fine sediments do. At Mosquito Lake, a 

statistically significant relation was found between water 

and sediment E. coli concentrations when 2000 and 2001 

data were combined (r=0.346, p=0.036), but not when each 

year was examined separately (fig. 9D). Overall, the data 

indicate that water and sediment E. coli concentrations 

were related at Huntington to a greater extent than the other 

beaches. 
Distribution, sources, and wastewater indicators 21

2000

 r = 0.507

 p = 0.0082

2001

 r = 0.030

 p = 0.8812

A. Edgewater Park–2000 and 2001

B. Huntington Reservation–2000

10,000

1,000

100

10

1

10,000

1,000

100

10

1

1 10 100 1,000 10,000 100,000

1 10 100 1,000 10,000 100,000 Lower detection limit Lower detection limit

Lower detection limit

Lower detection limit

ESCHERICHIA COLI, IN COLONIES PER GRAM DRY WEIGHT OF SEDIMENT

ESCHERICHIA COLI, IN COLONIES PER 100 MILLILITERS

r = 0.729

p = 0.0009

Figure 9. Comparison of Escherichia coli concentrations in bathing water to Escherichia coli concentrations in lake-bottom 

sediments at (A) Edgewater Park in 2000 and 2001, (B) Huntington Reservation in 2000, (C) Villa Angela in 2000, and (D) Mosquito Lake State Park in 2000 and 2001. (r is the Pearson’s correlation coefficient, and p is the significance of the correlation; 

samples were collected within and outside the bathing area.)
22 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling Lower detection limit

Lower detection limit

ESCHERICHIA COLI, IN COLONIES PER 100 MILLILITERS

ESCHERICHIA COLI, IN COLONIES PER GRAM DRY WEIGHT OF SEDIMENT

Lower detection limit

Lower detection limit

C. Villa Angela–2000

D. Mosquito Lake State Park–2000 and 2001

10,000

1,000

100

10

1

10,000

1,000

100

10

1

1 10 100 1,000 10,000 100,000

1 10 100 1,000 10,000 100,000

r = 0.382

p = 0.0873

2000

 r = 0.278

 p = 0.1992

2001

 r = 0.104

 p = 0.7221

Figure 9. Comparison of Escherichia coli concentrations in bathing water to Escherichia coli concentrations in lakebottom sediments at (A) Edgewater Park in 2000 and 2001, (B) Huntington Reservation in 2000, (C) Villa Angela in 2000, 

and (D) Mosquito Lake State Park in 2000 and 2001. (r is the Pearson’s correlation coefficient, and p is the significance of 

the correlation; samples were collected within and outside the bathing area.)
Distribution, sources, and wastewater indicators 23

Concentrations of Escherichia coli near the swash 

zone. Concentrations of E. coli in interstitial water and 

subsurface sediment samples collected from near the swash 

zone for each beach by year are summarized in table 5. The 

number of water-sample results is less than the number of 

sediment-sample results because results for water samples 

collected from June 19 through August 16, 2000, were discarded because of contamination from casings. Interstitial 

water comes in contact with the casings, whereas subsurface 

sediments do not. In interstitial water samples, the highest 

concentrations of E. coli were found at Mosquito Lake during 2000 and at Edgewater during 2001 (table 5). The high 

concentrations of E. coli in interstitial water samples from 

Edgewater during 2001 were not reflected in sediment samples; sediment E. coli concentrations at Edgewater were in 

the same range during 2000 and 2001. During 2000 when 

three Lake Erie beaches were studied, median concentrations of E. coli in interstitial water and subsurface sediments 

were lowest at Huntington and highest at Villa Angela. 

Although there are no standards for E. coli in interstitial 

waters, the high concentrations found in sediment and water 

samples alike at some locations warrant some concern for 

public health. 

At Edgewater and Mosquito Lake, swash-zone samples were collected during 2000 and 2001 to facilitate a 

more in-depth spatial and temporal analysis. Concentrations of E. coli in interstitial water and subsurface sediments 

for individual dates are shown in figures 10 and 11, respectively. At Edgewater, concentrations of E. coli in interstitial 

waters (fig. 10A) collected on the same date differed among

areas by about 1 to more than 2 orders of magnitude. Interstitial-water samples collected 6 ft inland showed a spatial 

pattern on August 4, 5, and 6 at Edgewater; concentrations 

were highest in area 3 and lower in areas 1 and 4 on all three 

dates. This pattern was not seen in the samples collected 

3 ft inland on these dates. At Mosquito Lake, concentrations of E. coli in interstitial waters (fig. 10B) were very 

high during 2000, remained somewhat elevated in February 

2001, and dropped off significantly in June 2001. Concentrations of E. coli in interstitial waters at Mosquito Lake collected on the same date differed among areas by only 1 to 

1.5 orders of magnitude or less. No spatial patterns of E. 

coli concentrations were observed in the subsurface sediments at Edgewater (fig. 11A). Concentrations of E. coli in 

 

Table 5. Concentrations of Escherichia coli in interstitial water and subsurface sediment samples collected from near the swash 

zone, 2000 and 2001

Beach Year Number of 

samples Minimum Maximum Geometric mean

Interstitial water a

a

 Colonies per 100 milliliters. 

Edgewater 2000 4 <33 4,100 580

2001 34 <3 110,000 1,800

Villa Angela 2000 4 290 3,500 1,500

Huntington 2000 4 67 670 270

Mosquito Lake 2000 7 4,800 400,000 38,000

2001 15 <3 19,000 33

Subsurface sediment b

b

 Colonies per gram dry weight of sediment.

Edgewater 2000 22 <1 220 19

2001 34 3 300 30

Villa Angela 2000 12 6 2,100 26

Huntington 2000 14 <1 90 5

Mosquito Lake 2000 23 22 30,000 1,600

2001 15 2 9,200 23
24 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

1

3

1

3

1

3

1 1

3

1

1,3

2

1,3

2

1

2

3

1

2

3

6/5 8/30 2/27 6/12 6/3

1

10

100

1,000

10,000

100,000

1,000,000

1

4

1

4 1

3

1

3

1

3

4

1

3

4

1

3

4

1

3

4

4

1

3

3

4

1

4

1

3

3

4

1

4

1

3

3

1

4

8/17 2/27 6/27 6/28 8/4 8/5 8/6

1

10

100

1,000

10,000

100,000

1,000,000

A. Edgewater Park

B. Mosquito Lake State Park

2000 2001

2000 2001

ESCHERICHIA COLI, IN COLONIES PER 100 MILLILITERS

3 feet

6 feet

Lower detection limit

Numbers indicate sampling area

 (refer to fig. 4a and 5)

Figure 10. Concentrations of Escherichia coli in interstitial waters collected 3 feet and 6 feet inland 

from near the swach zone during 2000 and 2001 at (A) Edgewater Park and (B) Mosquito Lake State 

Park.
Distribution, sources, and wastewater indicators 25

1

3

1

3

1

3

1

3

1,3

2

1

2

3

1

2

3

1

2

3

1

3 1

1

3

1 1

2

3

1

2

3

1

2

3

1,2

3

6/5 6/7 7/31 8/2 8/30 2/27 6/12 6/13

0.1

1

10

100

1,000

10,000

100,000

1

1

2

3 3

1

2

3,4

1

1

2

1

2

1,4

1

4 3,1

1

3

3,1

4

1

3

4 1

3

4

1

3

4

1

3

4

1

3,4

1,3

4

1

3

4 1

3

4

6/19 6/21 7/26 8/7 8/17 2/27 6/27 6/28 8/4 8/5 8/6

0.1

1

10

100

1,000

10,000

100,000

2000 2001

2000 2001

A. Edgewater Park

B. Mosquito Lake State Park

ESCHERICHIA COLI, IN COLONIES PER GRAM DRY WEIGHT OF SEDIMENT

2

4

2,3

4

3,1

3 feet

6 feet

Lower detection limit

Numbers indicate sampling area

 (refer to fig. 4a and 5)

Figure 11. Concentrations of Escherichia coli in subsurface sediments collected 3 feet and 6 feet 

inland from near the swash zone during 2000 and 2001 at (A) Edgewater Park and (B) Mosquito 

Lake State Park.
26 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

subsurface sediments at Mosquito Lake (fig. 11B) were 

more variable between areas on the same date than those 

found at Edgewater. At Mosquito Lake, subsurface sediment samples collected 3 ft inland were generally higher 

than those collected concurrently at 6 ft inland; the highest 

concentrations were usually found in samples from area 1. 

For Edgewater and Mosquito Lake during 2000 and 

2001, the relations between E. coli concentrations found in 

bathing waters to those found in interstitial waters or subsurface sediments collected from near the swash zone were 

examined (table 6). At Edgewater, no significant correlations were found. At Mosquito Lake, in contrast, significant 

correlations were found between bathing waters and interstitial waters collected 3 ft inland; the relation between bathing waters and interstitial waters collected 6 ft inland was 

not significant, owing to the influence of an outlier sample 

collected during February 2001. 

Source studies at Mosquito Lake

Source studies were done at Mosquito Lake to investigate 

possible sources of fecal contamination to the lake and the 

bathing beach. Water and sediment samples were collected 

at six sites north of the bathing beach (fig. 3), referred to as 

“ancillary sites,” and at the three areas at the bathing beach 

(fig. 5). Samples collected outside the bathing area at areas 

1, 2, and 3, were considered offshore samples for this data 

analysis. 

Because shoreline, nearshore, and offshore samples 

were not all sampled on one date (table 1), data analysis is 

complicated; however, by combining the data one can gain 

insight into the patterns of E. coli concentrations in samples 

collected from the southwest part of Mosquito Lake. Concentrations of E. coli were elevated in shoreline water samples collected from North Lakeshore and South Lakeshore 

but were considerably lower in the nearshore and offshore 

water samples from these sites (fig. 12). In fact, E. coli concentrations showed a decreasing pattern as one moved further away from the North Lakeshore shoreline until 

reaching the bathing areas. Concentrations of E. coli in 

water in shoreline, nearshore, and offshore bathing area 

sites were higher than those found at the docks, lower campground, and upper campground sites. 

Lake-bottom sediment samples were not collected 

from shoreline ancillary locations. The predominance of 

near-detection and below-detection values found in samples 

from nearshore and offshore ancillary locations suggests 

that E. coli is not accumulating in the bottom sediments at 

these sites (fig. 13). In contrast, at the bathing beach, E. coli

was detected in sediments collected from the shoreline and 

nearshore areas, indicating some storage of E. coli in lakebottom sediments. Concentrations of E. coli in the offshore 

bathing-area sites were highly variable. 

Spatial studies at Edgewater

Spatial studies were done during 2000 and 2001 to aid the 

GLFS in development of deterministic models to predict 

E. coli concentrations. Data on the spatial and temporal 

variability of E. coli concentrations and water-quality measurements were made at Edgewater. Comparisons among 

suspended-sediment concentrations could not be made 

because sample variability (as measured by duplicate samples) was larger than the vertical and spatial differences that 

were being measured (Appendix B).

Table 6. Correlations between log10 Escherichia coli concentrations in bathing waters and those in 

interstitial water or subsurface-sediment samples collected from near the swash zone at Ohio 

beaches, 2000 and 2001

[Pearson's r correlations significant at a=0.05 are indicated in bold]

Bathing water 

samples

Samples collected from near the swash zone

Interstitial water Subsurface sediment

3 feet inland 6 feet inland 3 feet inland 6 feet inland

Edgewater 0.184 -0.255 0.243 0.179

Mosquito Lake 0.632 0.433 -0.303 -0.162
Distribution, sources, and wastewater indicators 27

State Route 305

MOSQUITO

LAKE

Mosquito

Creek

Dam

Campground

 Upper Campground

Lower Campground

Docks

Beach

South

Lakeshore

North

Lakeshore

Upstream

MOSQUITO LAKE

STATE PARK

X

S N O

120 6

<11 8

4

6 14

9 7

15 7

130 75

180

150

Numbers are an average of 2-7 values

Only one sample in this category

No samples

150

(1)

140

11(1)

140

54

590

240

33

34

16

41(1)

Base map modified from U.S. Geological Survey

Champion 1960, photorevised 1984

OHIO

Study Area

80o 80 45' o

46'15"

41o

20'

41o

18'45"

41o

17'30"

0

0 1 KILOMETER

1 MILE

80o47'30"

EXPLANATION

Figure 12. Average concentrations of Escherichia coli in (S) shoreline, (N) nearshore, and (O) offshore water samples 

collected at Mosquito Lake State Park, June-August 2000 and August 2001. (Concentrations are in colonies per 100 

milliliters.)
28 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

State Route 305

MOSQUITO

LAKE

Mosquito

Creek

Dam

Campground

 Upper Campground

Lower Campground

Docks

Beach

South

Lakeshore

North

Lakeshore

Upstream

MOSQUITO LAKE

STATE PARK

X

S N O

< <

< N <

<

< <,N

<,N N

<,N <

220A <,N

N,350A

200A

Less than detection

Near detection–a total of <6 colonies on all plates

Average of 2 or more values

Number of values in this category

No samples

<

N

A

(1)

EXPLANATION

 <

150A

42(1)

<,N(2),120(1)

N(2),170(1)

Base map modified from U.S. Geological Survey

Champion 1960, photorevised 1984

OHIO

Study Area

80o 80 45' o

46'15"

41o

20'

41o

18'45"

41o

17'30"

0

0 1 KILOMETER

1 MILE

80o47'30"

Figure 13. Concentrations and detections of Escherichia coli in (S) shoreline, (N) nearshore, and (O) offshore sediment 

samples collected at Mosquito Lake State Park, June-August 2000 and August 2001. (Concentrations are in colonies per 

gram dry weight of sediment.)
Distribution, sources, and wastewater indicators 29

During 2000, spatial studies consisted of vertical profiles of measurements at locations outside the bathing area 

(table 7). Measurements were made and samples were collected at 3-, 6-, and 9-ft points when water depths were 

10 to11 ft in June and at 2-, 4-, and 7-ft points when water 

depths were 8 to 9 ft in August. The range of values for 

E. coli concentrations and the maximum differences among 

values in each profile for temperature, specific conductance, 

turbidity, and dissolved oxygen were fairly small and precluded any comparisons among vertical points (table 7).

During 2001, spatial studies were done within the 

bathing area during dry weather and after a significant rainfall. Water and lake-bottom sediment samples were collected on June 27 and 28 after 5 days of dry weather and on 

August 4 through 6 after 1.4 in. of rainfall fell on August 3. 

Sampling locations were at 5-ft water depths from areas 1, 

4, 5, and 6 and at six different water depths, ranging from 3 

to 9 ft, from area 3 (fig. 4A). To gain information on vertical distributions, some of the deeper sampling locations 

included two sampling points—one approximately 18 in. 

* Difference of only two samples.

Table 7. Summary of the range of Escherichia coli concentrations and the maximum differences for physical water-quality properties in 

spatial studies at Edgewater Park, 2000 and 2001

[ft, feet; col/100 mL, colonies per 100 millimeters; °C, degrees Celsius; μS/cm, microsiemens per centimeter at 25°C; NTU, nephelometric turbidity units; 

mg/L, milligrams per liter; nd, not determined; --, not applicable]

Date Area

Sampling/ 

measurement 

depths (ft)

Escherichia coli 

concentrations, 

range (col/100 mL)

Maximum difference

Water 

temperature 

(°C)

Specific 

conductance 

(μS/cm)

pH 

(standard 

units)

Turbidity 

(NTU)

Oxygen, 

dissolved 

(mg/L)

Vertical profiles - water samples, 2000

20-Jun-00 1 3, 6, 9 150-240 0.3 4 nd 4 nd

20-Jun-00 4 3, 6, 9 180-220 0.2 7 nd 5 nd

22-Jun-00 1 3, 6, 9 26-38 0 1 nd 1 nd

22-Jun-00 3 3, 6, 9 20-30 0 0 nd 0 nd

29-Aug-00 1 2, 4, 7 3-9 0.9 7 0.1 0.7 0.7

29-Aug-00 4 2, 4, 7 4-7 0.5 0 0.1 0.8 0.6

Spatial and temporal distributions - water samples, 2001

27-Jun-01 1,3,4,5,6 3-9 3-21 0.8 27 0.2 2.4 1.8

28-Jun-01 1,3,4,5,6 3-9 1-13 1.5 5 0.1 0.4 1.7

4-Aug-01 1,3,4,5,6 3-7 30-320 0* 50* nd 9.1 nd

5-Aug-01 1,3,4,5,6 3-9 15-62 1.4 10 0.6 3.2 1.0

6-Aug-01 1,3,4,5,6 3-9 20-56 0.4 6 0.1 0.4 1.0

Spatial and temporal distributions - sediment samples, 2001

27-Jun-01 1, 3, 4, 6 3-7 11-40 -- -- -- -- --

28-Jun-01 1, 3, 4, 6 3-9 3-75 -- -- -- -- --

4-Aug-01 1, 3, 4, 5, 6 3-7 3-59 -- -- -- -- --

5-Aug-01 1, 3, 4, 5, 6 3-9 2-57 -- -- -- -- --

6-Aug-01 1, 3, 4, 5, 6 3-9 2-140 -- -- -- -- --
30 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

above the lake bottom (AB) and one 18 in. below the water 

surface (BW). 

The range of E. coli concentrations in water found on 

any given sampling day were fairly narrow except on 

August 4, 2001, after significant rainfall (table 7). On 

August 4, the highest E. coli concentrations were found in 

water samples collected from areas 5 and 6 (320 and 280 

col/100 mL, respectively), followed by the sample collected 

from area 1 (160 col/100 mL) (Appendix B). On August 4, 

5, and 6, 2001, the highest E. coli concentrations in sediment were found in area 1 (Appendix C). As for vertical 

distributions, E. coli concentrations were fairly similar in 

those water samples collected BW and AB at each location 

(Appendix B). Values for water temperature, specific conductance, pH, and dissolved oxygen showed little spatial 

variation on the days where data were available. Because 

of meter problems, the spatial variation of water-quality 

measurements could not be assessed on August 4, although 

for the two specific-conductance measurements made on 

that date, the difference was the largest found in this study 

(50 μS/cm). The highest turbidity value (12 NTU) was 

found at area 1 on August 4. 

Wastewater indicators as surrogates for Escherichia 

coli

During 2000, the USGS collected 13 water samples for a 

suite of wastewater indicators from a variety of locations 

and beaches (Appendix D). To serve as positive controls, 

two lake-water samples were spiked with primary-treated 

wastewater collected from the Westerly Wastewater Treatment Plant in Cleveland, Ohio.

Of the 45 target compounds in the wastewater indicator analysis, 16 were not detected in any of the samples 

(Appendix D). Information on the other 29 wastewater indicators, including the number of detections for each compound, is listed in table 8. Of the 29 compounds detected in 

environmental samples, 13 were also detected in both of the 

spiked samples. These were 3β-coprostanol; caffeine; cholesterol; cotinine; ethanol, 2-butoxy-phosphate; fluoranthene; N,N-diethyltoluamide; NPEO1; phenol; paranonylphenol; pyrene; tri (2-chloroethyl) phosphate; and triclosan. 

If a compound was detected in four or more samples, 

correlation analysis was done to determine the relation 

between the compound and E. coli concentrations (table 8). 

Two fecal indicators, 3β-coprostanol and cholesterol, were 

found in one sample or had no relation to E. coli concentrations, respectively. Some other general indicators of wastewater contamination—caffeine, NPEO1, and 1,4,-

Dichlorobenzene—were poorly correlated to E. coli or were 

not detected enough to evaluate the relation. The concentrations of two compounds, however, showed a significant 

relation to E. coli concentrations at α=0.1— fluoranthene 

and pyrene. (An α = 0.1 was used as the level of significance for this analysis, because no concentrations of any 

wastewater indicators were related to concentrations of 

E. coli at a significance level of α = 0.05.) Fluoranthene 

and pyrene are components of coal tar and asphalt and not 

necessarily indicative of fecal contamination. Most likely, 

these compounds are delivered to storm sewers through runoff across asphalt roads and parkings lots. It seems reasonable, therefore, that the delivery of these compounds would 

correlate well with the delivery of bacteria from CSOs and 

storm sewers. 

For many wastewater compounds, poor or variable 

method performance was indicated; hence, detected values 

did not have the precision that one might expect for other 

compounds. In addition, at the time of sampling and analysis, this method was under development, and improved techniques or new wastewater indicators have since been 

incorporated into the method. During this study, samples 

were collected from a variety of study sites having different 

contaminant sources; this variety makes establishing a significant relation between E. coli and wastewater indicators 

difficult. 

 In addition to the samples for the wastewater indicators described above, investigators collected samples that 

were specifically analyzed for caffeine at the USEPA laboratory. The USGS collected 55 samples for caffeine during 

2000 distribution and source studies at a variety of locations, including ancillary sites at Mosquito Lake (Appendix 

E). Local cooperators collected 84 samples for caffeine 

during 2001 routine studies at bathing-water locations 

(Appendix E).

An examination of the data from quality-control 

samples—replicates and blanks—places limitations on the 

interpretation of caffeine results (Appendix A4). The quality-control data were unacceptable because of two reasons. 

For one, the range of differences between bottles of replicate samples was 0.004 to 0.431 μg/L of caffeine with an 

average difference of 0.082 μg/L and an average percent 

difference of 33.6 percent. Some of these differences were 

rather large and were the same value as concentrations 

found in many samples. A second problem involved detections of caffeine in two of the blank samples, indicating the 

potential for contamination during collection and handling 

of samples. Because the samples were extracted and frozen 

for several months before analysis, quality-control data 

were not available to take corrective actions while sampling 

was in progress.

A large number of nondetections of caffeine in samples collected during 2000 (Appendix E) necessitated 

changes to the analytical method to increase sensitivity for 

samples collected during 2001. As a result, caffeine was 

detected in only 25 percent of the samples collected in 2000 

but in 99 percent of the samples collected in 2001. Of the 

14 detections in 2000, the five highest concentrations of caffeine were found in samples collected at Mosquito Lake, 

and four of these were from North or South Lakeshore
Distribution, sources, and wastewater indicators 31

Table 8. Detections of wastewater indicators in samples collected at Lake Erie beaches and Mosquito Lake, 2000, and their relations to log10

Escherichia coli concentrations

[μg/L, micrograms per liter of sample; nd, not determined; correlations that are significant at α = 0.1 are in bold text.]

Analytea

Number of detections for

Pearson's rb Method reporting 

limit (μg/L) Possible uses or sourcesc

Samples (n=13) Spikes (n=2)

1,4-Dichlorobenzene 2 0 nd 0.03 Moth repellant; lavatory fumigant; 

deodorant

3β-Coprostanol 1 2 nd 0.60 Found in feces of man and 

carniverous animals

Bis(2-ethylhexyl) adipate 3 1 nd 2.0 Plasticizer

Bis(2-ethylhexyl) phthalate 2 0 nd 2.5 Plasticizer

Bisphenol A 6 1 0.322 0.09 Manufacturer of epoxy resins and 

polycarbonates; fungicide; 

plasticizer; flame retardant

Caffeine 8 2 0.349 0.08 Stimulant and diuretic

Chloropyrifos 1 0 nd 0.02 Domestic pest and termite control

Cholesterol 12 2 0.019 1.50 Found in all body tissues and in 

animal fats and oils

Codeine 2 0 nd 0.10 Narcotic

Cotinine 6 2 0.306 0.04 Nicotine metabolite

Diazinon 3 0 nd 0.03 pesticide; >40 percent nonagricultural 

usage

Diethyphthalate 3 1 nd 0.25 Plasticizer

Ethanol,2-butoxy-,phosphate 4 2 0.396 0.2 Plasticizer

Fluoranthene 6 2 0.482 0.03 Coal tar and asphalt

N,N-Diethyltoluamide 11 2 0.268 0.04 Insect repellent; poisonous

Naphthalene 4 1 - 0.225 0.02 From coal tar; toxic to humans; used 

in manufucture of synthetic resins, 

celluloid, lubricants, and motor fuels; 

moth repellent and insecticide; 

antiseptic

NPEO1-total 5 2 -0.090 1.00 General indicators of wastewater 

contamination; directly associated 

with nonionic detergent use

OPEO1 7 1 0.036 0.10 Nonionic detergent metabolite

OPEO2 2 0 nd 0.20 Nonionic detergent metabolite

Para-cresol 3 1 nd 0.04 Wood preservative

Para-Nonylphenol-total 3 2 nd 0.50 In preparation of lubricating-oil 

additives, resins, surface active 

agents, and plasticizers

Phenol 7 2 0.136 0.25 From coal tar; general disinfectant for 

toilets,stables, cesspools, floors, 

drains, etc.; antiseptic

Phthalic anhydride 4 1 0.168 0.25 Prepared from naphthalene; used in 

the manufacture of synthetic indigo, 

artificial resins

Pyrene 6 2 0.494 0.03 Occurs in coal tar; fuel combustion

Tetrachloroethylene 3 0 nd 0.03 Solvent; degreaser; veterinary 

anthelmintic

Tri (dichloroisopropyl) phosphate 2 1 nd 0.10 Flame retardant
32 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

Tri(2-chloroethyl)phosphate 4 2 0.057 0.04 Hypnotic; sedative; plasticizer; fire 

retardant

Triclosan 8 2 0.265 0.04 Bacteriostat and preservative for 

cosmetic and detergent preparations; 

disinfectant

Triphenyl phosphate 3 0 nd 0.10 Plasticizer; resin; wax; finish; roofing 

paper

a

 Analytes were included if one or more detections were found.

b Correlations were done if four or more detections were found. c

 Zaugg and others, 2001.

Table 8. Detections of wastewater indicators in samples collected at Lake Erie beaches and Mosquito Lake, 2000, and their relations to log10

Escherichia coli concentrations —Continued

[μg/L, micrograms per liter of sample; nd, not determined; correlations that are significant at α = 0.1 are in bold text.]

Analytea

Number of detections for

Pearson's rb Method reporting 

limit (μg/L) Possible uses or sourcesc

Samples (n=13) Spikes (n=2)

ancillary locations. Samples for caffeine analyses were not 

collected at Mosquito Lake during 2001. Because sample 

variability was large and contamination potential was 

unknown, statistical analysis cannot be done on the caffeine 

data. 

Predictive models for Escherichia coli

Summary statistics of E. coli concentrations in water collected during the recreational seasons of 2000 and 2001 during routine studies are shown in table 9. Concentrations of 

E. coli were lowest at Fairport Harbor, Mentor Headlands, 

and during 2001 at Mosquito Lake. Bacterial water quality 

was generally good at these beaches, exceeding the singlesample bathing-water standard 10 percent or less of the days 

sampled. At Edgewater, Huntington, and Mosquito Lake, 

higher E. coli concentrations were found in 2000 than in 

2001. Villa Angela had the poorest water quality with 

respect to E. coli concentrations among the beaches sampled, exceeding the single-sample bathing-water standard in 

about 40 percent of the samples collected during 2000 and 

2001. 

Relations between Escherichia coli concentrations 

and environmental or water-quality variables

Statistical tests were done to evaluate quantitatively the relations between environmental or water-quality variables and 

E. coli concentrations in water. This is the first step in 

development of predictive models; that is, identifying those 

factors that show a relation to E. coli and may be used in the 

models. Continuous variables are listed for the three Lake 

Erie urban beaches (table 10), Mosquito Lake (table 11), 

and the two Lake Erie beaches in a less populated area 

(table 12). 

 Because of the importance of rainfall as a predictor 

of E. coli concentrations, several rainfall variables were 

developed. For Edgewater, Villa Angela, Huntington, and 

Mosquito Lake, “rainfall 24” was defined as the amount of 

rain, in inches, that fell in the 24-hour period preceding the 

9 a.m. sampling. “Rainfall 48” and “rainfall 72” were the 

amounts of rain that fell in the 24-hour periods 2 days and 3 

days, respectively, before the 9 a.m. sampling. These two 

variables were used to determine whether there was a lag 

between rainfall in the watershed and elevated E. coli concentrations. The variables “rainfall weighted 72” and “rainfall local weighted 72” (USGS-installed rain gage) were 

computed from the rainfall amounts in the 72-hour period 

preceding the 9:00 a.m. sampling, with the most recent rainfall receiving the highest weight. These variables were 

computed as the sum of following three values: 

3 × rainfall amount for the 0- to 24-hour antecedent 

period

2 × rainfall amount for the greater than 24- to 48-hour 

antecedent period

1 × rainfall amount for the greater than 48- to 72-hour 

antecedent period

“Rainfall weighted 72 × dry days” was a variable developed 

for Mosquito Lake (table 11). It is the “rainfall weighted 

72” times the number of preceding dry days; dry days are 

days in which the daily rainfall was less than or equal to 0.2 

in. For Fairport Harbor and Mentor Headlands, the beaches 

in a less populated area, rainfall was measured at 5 p.m. 

daily. The rainfall variables (table 12) were defined as rainfall in the 24-hour period up to 5 p.m. on the previous day 

(“rainfall antecedent”) and rainfall in the 72-hour period up
Predictive models 33

Table 9. Summary statistics of Escherichia coli concentrations in water collected at five Lake Erie beaches and one inland lake during 

routine studies, 2000 and 2001

[Concentrations are in colonies per 100 mililiters]

Beach Number of 

samples

Escherichia coli concentrations Number (percent) of days 

bathing-water standarda

was exceeded

a

 Numbers of days the concentration of Escherichia coli in water exceeded the single-sample bathing-water standard of 235 colonies per

100 milliliters.

Median Minimum Maximum

Edgewater

2000 72 140 4 12,000 24 (33.3)

2001b

b

 The daily concentrations were determined by calculating the mean of two water samples

70 73 10 4,000 15 (21.4)

Villa Angela

2000 72 170 1 4,500 29 (40.3)

2001b 69 130 6 6,400 28 (40.6)

Huntington

2000b 53 130 8 6,600 14 (26.4)

2001b 50 43 3 1,200 10 (20.0)

Fairport Harbor

2000 49 16 2 390 3 (6.1)

Mentor Headlands

2000b 50 14 2 950 5 (10.0)

Mosquito Lake

2000 53 110 6 5,400 15 (28.3)

2001b 52 25 3 320 2 (3.8)

to 5 p.m. on the day of sampling, with the most recent rainfall receiving the highest weight (“rainfall weighted 72”). 

Other variables need further descriptions. “Date” is 

based on the chronological day of year and the hypothesis 

that, in some situations, E. coli may accumulate over the 

course of the summer. “Escherichia coli previous day” is 

the concentration of E. coli determined by analyzing a water 

sample collected on the previous day. “Streamflow 7am” is 

the instantaneous streamflow of a stream in a nearby watershed at 7 a.m. on the day of sampling. This variable was 

developed to provide a measure of streamflow that couldeasily be obtained by beach managers for the Lake Erie 

urban beaches (table 10). At Mosquito Lake, “streamflow 

previous day” was the daily mean streamflow for the day 

before the day of sampling (table 11). For Mosquito Lake, 

the “wind direction” was the instantaneous wind direction at 

9 a.m. on the day of sampling; “wind speed” was similarly 

determined. The cosine and sine of the wind direction at 9 

a.m. on the day of sampling also were used as variables. By 

using the cosine and sine of the wind direction, one is able 

to assign weighted measures on wind direction; for example, an easterly wind direction would have a sine of 1 and 

westerly wind direction a sine of –1 with all other directions 

having intermediate values. This places more weight on 

winds from an easterly direction. 
34 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

Table 10. Summary of Pearson's r correlations between log10 Escherichia coli concentrations in water and environmental or water-quality 

factors at three Lake Erie urban beaches, 2000 and 2001

[NS, not statistically significant at α = 0.05; all other realations were statistically significant; GLFS, Great Lakes Forecasting System at The Ohio State University; 

--, no data]

Factor

Edgewater Villa Angela Huntington 

2000 2001 Combined 2000 2001 Combined 2000 2001 Combined

Birds, number on beach at 

time of sampling

0.293 0.376 0.354 NS NS NS -0.280 NS NS

Date NS 0.273 NS NS NS NS -0.374 0.361 NS

Escherichia coli previous 

day

NS NS 0.266 0.342 NS 0.199 NS 0.395 0.370

Rainfall 24 a

a Rainfall 24 was the amount, in inches, at Hopkins Airport, Cleveland, Ohio, in the 24-hour period preceding the 9 a.m. sampling.

0.471 NS 0.282 0.466 0.329 0.390 0.468 0.276 0.399

Rainfall 48 b

b Rainfall 48 was the amount, in inches, at Hopkins Airport, Cleveland, Ohio, in the 24-hour period two days before the 9 a.m. sampling.

NS NS NS NS NS NS 0.277 NS 0.236

Rainfall 72 c

c Rainfall 72 was the amount, in inches, at Hopkins Airport, Cleveland, Ohio, in the 24-hour period three days before the 9 a.m. sampling.

NS NS NS NS NS NS NS NS NS

Rainfall weighted 72 d

d Rainfall weighted 72 was the amount, in inches, at Hopkins Airport, Cleveland, Ohio, in the 72-hour period preceding the 9 a.m. sampling,

with the most recent rainfall receiving the greatest weight.

0.395 NS 0.256 0.343 0.298 0.320 0.459 NS 0.394

Rainfall local weighted 72 

e

e

 Rainfall local weighted 72 was the amount, in inches, at the USGS rainfall gage at Huntington Beach in the 72-hour period preceding the

9 a.m. sampling, with the most recent rainfall receiving the greatest weight.

-- -- -- -- -- -- NS -- --

Streamflow 7 a.m. f

f Streamflow 7 a.m. was the streamflow at the Cuyahoga River at Independence, Ohio, at 7 a.m. on the date of sampling.

0.269 NS 0.306 0.296 NS 0.234 0.386 NS 0.331

Turbidity NS NS NS 0.516 0.278 0.408 0.673 0.600 0.500

Turbidity, log NS NS NS 0.497 0.381 0.439 0.627 0.511 0.443

Ultraviolet intensity, 

previous 24 hours

NS NS NS NS NS NS NS NS 0.253

Water temperature NS NS NS NS NS NS -0.356 0.543 0.212

Wave height, predicted by 

GLFS

0.451 NS 0.274 0.573 NS 0.366 0.570 0.349 0.430

One may want to place more weight on winds from an easterly direction, for example, if the source of contamination is 

suspected to be from a source to the east. “Wind direction, 

sum sine” and “wind direction, sum cosine” were the sums 

of the sines and cosines, respectively, of the wind direction 

at 9 a.m. on the day of sampling, on the previous day, and 2 

days prior. 

Pearson’s r correlation coefficients were computed to assess 

the linear relation between E. coli concentrations and the 

continuous variables described above. “E. coli previous 

day,” “rainfall 24,” “rainfall weighted 72,” “streamflow 

7am,” and “wave height” were statistically related to E. coli

concentrations for at least one data set at each of the three 

Lake Erie urban beaches (table 10). “Turbidity” was 
Predictive models 35

Table 11. Summary of Pearson's r correlations between log10 Escherichia coli concentrations in water and 

environmental or water-quality factors at Mosquito Lake State Park, 2000 and 2001

[NS, not statistically significant at α = 0.05; all other relations were statistically significant; --, no data]

Factor 2000 2001 2000-2001

Birds, number on beach at time of sampling NS NS 0.250

Date 0.542 0.535 0.448

Escherichia coli previous day 0.368 0.550 0.610

Flow (average) through dam outlet, previous NS 0.298 NS

Pool elevation (average), previous day -0.518 -0.528 NS

Rainfall 24 a

a Rainfall 24 was the amount, in inches, at Youngstown Regional Airport, Youngstown, Ohio,

in the 24-hour period preceding the 9 a.m. sampling.

NS 0.397 0.203

Rainfall 48 b

b Rainfall 48 was the amount, in inches, at Youngstown Regional Airport, Youngstown, Ohio,

in the 24-hour period two days before the 9 a.m. sampling.

NS NS 0.188

Rainfall 72 c

c Rainfall 72 was the amount, in inches, at Youngstown Regional Airport, Youngstown, Ohio,

in the 24-hour period three days before the 9 a.m. sampling.

NS NS NS

Rainfall weighted 72 d

d Rainfall weighted 72 was the amount, in inches, at Youngstown Regional Airport, Youngstown, Ohio, in the 72-hour period preceding the 9 a.m. sampling, with the most recent rainfall receiving the greatest weight.

NS 0.406 0.282

Rainfall weighted 72 × dry dayse

e Rainfall weighted 72 × dry days was the "rainfall weighted 72" times the number of preceding dry days; dry days are days in which the daily rainfall was < 0.2 inch.

NS 0.402 0.232

Specific conductance NS -- --

Streamflow previous dayf

f Daily mean streamflow at Eagle Creek at Phalanx Station, Ohio, the day before sampling.

NS NS NS

Turbidity 0.495 0.527 0.471

Turbidity, log 0.482 0.576 0.502

Water temperature NS NS NS

Wind direction 9 a.m. g

g Wind direction was instantaneous wind direction at 9 a.m. the day of sampling, measured at

Youngstown Regional Airport, Youngstown, Ohio.

NS NS NS

Wind direction 9 a.m., cosine 0.307 NS NS

Wind direction 9 a.m., sine NS NS NS

Wind direction, sum cosine h

h Wind direction, sum cosine was the sums of the cosines of the instantaneous wind direction

at 9 a.m. on the day of sampling and on the two previous days.

0.313 NS 0.174

Wind direction, sum sine i

i Wind direction, sum sine was the sums of the sines of the instantaneous wind direction at 9

a.m. on the day of sampling and on the two previous days.

0.358 NS 0.235

Wind speed 9 a.m. NS NS 0.231
36 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

Table 12. Summary of Pearson's r correlations between log10 Escherichia coli

concentrations in water and environmental or water-quality factors at Ohio

beaches in a less populated area, 2000

[NS, not statistically significant at α = 0.05; all other relations were statistically 

significant]

Factor Mentor Headlands Fairport Harbor

Birds, time of sampling NS NS

Date NS NS

Escherichia coli previous day NS NS

Rainfall antecedent a

a Rainfall antecedent was the amount, in inches, that accumulated in 

the 24-hour period up to 5 p.m. on the previous day at Painesville Municipal Water Plant, Painesville, Ohio.

NS NS

Rainfall weighted 72 b

b Rainfall weighted 72 was the amount, in inches, that accumulated 

in the 72-hour period up to 5 p.m. on the previous day at Painesville Municipal Water Plant, Painesville, Ohio, with the most 

recent rainfall receiving the highest weight.

NS NS

Streamflow 7 a.m.c

c Streamflow, 7 a.m. was the streamflow measured at the USGS 

gaging station, Grand River near Painesville, Ohio, at 7 a.m. on 

the date of sampling.

NS NS

Streamflow antecedent d

d Streamflow, antecedent was the daily mean streamflow for the 

previous day at the Grand River near Painesville, Ohio.

NS NS

Turbidity 0.327 NS

Turbidity, log 0.490 NS

Water temperature NS NS

significantly related to E. coli at Villa Angela and Huntington, but not at Edgewater. “Rainfall 48” and “rainfall 72” 

were not significantly related to E. coli concentrations 

except for a weak correlation for “rainfall 48” at Huntington. At Huntington, the USGS-installed rain gage at the 

beach (“rainfall local weighted 72”) was shown to provide 

rainfall data that were not related to E. coli concentrations, 

and this gage was discontinued after 2000. At Mosquito 

Lake (table 11), the factors “date,” “E. coli previous day,” 

and “turbidity” were related to E. coli concentrations for 

2000, 2001, and the combined years. The sum of the sines 

or cosines of the wind directions for the previous 3 days 

were found to be related to E. coli concentrations for 2000 

and the combined years, but not for 2001. At Mentor Headlands, only turbidity was related to E. coli concentrations 

(table 12). No statistically significant relations were found 

between E. coli concentrations and any variables at Fairport 

Harbor (table 12). 

Analysis of variance and Tukey’s test were used to 

assess the relations between categorical environmental variables (current direction and estimated wave height) and 

E. coli concentrations in water. Data on log10 E. coli concentrations were placed into groups based on the direction 

of currents the previous day, as predicted by the GLFS (fig. 

14). If a current-direction category had less than five observations, categories were combined on the basis of the most 

reasonable combination of similar wind directions (for 

example, west and northwest). At Edgewater during 2000 

and 2001, significantly higher E. coli concentrations were 

found for southeast currents than for northeast or east currents (fig. 14A). This seems reasonable, because southeast 

currents would bring contamination into the beach area 
Predictive models 37

where it would accumulate behind the Edgewater breakwall 

that lies to the east of the beach. In contrast, northeast or 

east currents would divert contamination away from the 

beach and into the open lake. (See fig. 4.) For modeling at 

Edgewater, currents were placed into three categories in 

ascending order: (1) currents to the east and northeast, (2) 

currents to the west, northwest, southwest, and north, and 

(3) currents to the southeast. At Villa Angela, no statistically significant differences among any of the current-direction categories were found (fig. 14B). Current direction was 

not used as a variable for modeling at Villa Angela. At Huntington, significantly higher E. coli concentrations were 

found for southwest currents than for east and southeast currents and west currents (fig. 14C). For modeling at Huntington, currents were placed into categories in ascending 

order: (1) west, northeast, east, and southeast currents and 

(2) southwest currents.

Wave heights were placed into four categories based 

on minimum and maximum heights in each wave train: 

(1) 0 to 2 ft, (2) 1 to 3 ft, (3) 2 to 4 ft, and (4) 3 to 5 ft or 

greater. During 2000 and 2001, as was found in the earlier 

study (Francy and Darner, 1998), median E. coli concentrations generally increased with increasing wave height, 

except for category 4 at Huntington (data not shown). Statistically significant differences of E. coli concentrations 

were found among some of the wave-height categories (data 

not shown); similar results were also found in the earlier 

study. As in the earlier study, wave height was found to be 

important variable affecting E. coli concentrations in 2000 

and 2001. 

Development and evaluation of beach-specific 

models 

Environmental and water-quality variables that were related 

to E. coli concentrations were used to develop statistical 

models by use of MLR techniques. For each beach, models 

based on data collected during 1997, 2000, and (or) 2001 

(table 13) were developed. The probability of exceeding the 

single-sample bathing-water standard of 235 col/100 mL 

was used as the model output variable (hereinafter “probability”). Of the models listed in table 13, those that were 

determined to be useful to beach managers (in terms of predictive ability and ease of use) were evaluated for their abilities to correctly predict a probability above or below a 

threshold probability. Some models were tested in subsequent years, if data were available.

 The 1997 model for Edgewater Park and Villa Angela 

and model evaluation steps. A model developed based on 

1997 data for Edgewater and Villa Angela during a previous 

study (Francy and Darner, 1998) was evaluated on 2000 

data collected during routine studies. The 1997 model contained weighted categorical rainfall, beach-specific turbidity, and wave height and explained 58 percent of the 

variability in E. coli concentrations at Edgewater and Villa 

Angela (table 13, lines 1 and 11) and a third beach not evaluated during the current study.

The first step in model evaluation is to determine a 

threshold probability—that is, the lowest (most conservative) probability that produces the most correct responses 

and (or) fewest false negative responses. This concept can 

best be explained by examining the plot for the 1997 model 

at Villa Angela (fig. 15). Using the 1997 data, a thresholdprobability of 44 percent was established for Villa Angela. 

The plot is divided into four quadrants by drawing a vertical 

line through 2.37 on the x-axis (represents the log10 of 

235 colonies/100 mL) and a horizontal line through the 

threshold probability of 44. The four quadrants in figure 15 

are 

1. Correct nonexceedance. E. coli concentration met 

the standard (was less than 235 col/100 mL), and 

the predicted probability of the exceedance was 

below the threshold.

2. False positive. E. coli concentration met the standard, but the predicted probability of the exceedance was above the threshold.

3. Correct exceedance. Actual E. coli concentration 

exceeded the standard (was greater than 235 col/

100 mL), and the predicted probability of exceedance was above the threshold.

4. False negative. Actual E. coli concentration 

exceeded the standard, but the predicted probability of the exceedance was below the threshold.

The threshold probability of 44 gives a total of 33 correct 

responses (quadrants 1 and 3), 3 false positives (quadrant 2), 

and 5 false negatives (quadrant 4). These numbers are summarized along with model information in table 13, line 11. 

Moving the threshold to 43 would result in fewer correct 

responses; moving the threshold to 45 would not improve 

on the number of correct responses (fig. 15). 

A threshold probability was determined for the 1997 

model at Edgewater (fig. 16 and table 13, line 1) with different results and a more complicated determination process. 

A threshold of 49 would produce the highest number of correct responses but would also produce a high number of 

false negatives and no false positives. False negative 

responses are especially troubling because the recreational 

water-quality is determined to be acceptable when in fact 

the standard was exceeded. The threshold at 49 would overemphasize the influence of four outlier values and establish 

a threshold that is too high to be realistic. Instead, establishing a threshold of 26 would still provide a reasonably high 

number of correct responses, minimize false negative 

responses, and better represent the system. 
38 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

W SW NE E+SE

0

1

2

3

4

5

39

26

31

LOG10 OF

ESCHERICHIA COLI CONCENTRATION 

CURRENT DIRECTION IN LAKE ERIE

NEAR THE MOUTH OF ROCKY RIVER,

PREVIOUS 24 HOURS

A C ABC AB

C. Huntington Reservation

W+NW+N SW SE NE E

0

1

2

3

4

68

30

20

7

16

CURRENT DIRECTION IN LAKE ERIE

NEAR EDGEWATER BEACH, 

PREVIOUS 24 HOURS

A. Edgewater Park

BC BC AC B B

30

EXPLANATION

CURRENT DIRECTION IN LAKE ERIE

NEAR THE MOUTH OF CUYAHOGA RIVER,

PREVIOUS 24 HOURS

SW NE E+SE

B. Villa Angela

47

70 23

A A A

LOG10 OF

ESCHERICHIA COLI CONCENTRATION 

Number of observations

Outlier data values less than or equal to

 3 and more than 1.5 times the interquartile

 range outside the quartile

Data value less than or equal to 1.5 times the

 interquartile range outside the quartile

75th percentile

Median

25th percentile

Mean

Figure 14. Distribution of Escherichia coli concentrations in water by current direction, 2000 and 2001, at (A) Edgewater Park, (B) Villa Angela, and (C) Huntington Reservation. (Results of Tukey’s test are presented as letters, and concentrations with at least one letter in common do not differ significantly.)
Predictive models 39

Table 13. Variables, regression statistics, and threshold probablilities for beach models, 1997, 2000, and 2001

[E. coli, Escherichia coli; ND, not determined; R2, fraction of the variation in the dependent variable that is explained by the model; threshold probability is 

based on meeting and exceeding the single-sample bathing standard for Escherichia coli]

Beach 

Time period of 

data for model 

development

Variables in model R2 of 

model

Number of model responses by category

Threshold 

probability

Correct 

(percent)

False 

positive 

(percent)

False 

negative

(percent)

1 Edgewater 1997 Wave height, rainfall 

weighted 72a

, 

turbidity

0.58 26 26 (78.8) 5 (15.1) 2 (6.1)

2 Edgewater 1997 and 2000 Wave height, rainfall 

weighted 72 0.17 ND ND ND ND

3 Edgewater 2000 and 2001 Number of birdsb, 

current directionc

, 

wave height, rainfall 

24d

0.32 45 109 (79.5) 8 (5.8) 20 (14.6)

4 Huntington 2000 Wave height, 

rainfall weighted 72, 

turbidity

0.58 43 46 (90.2) 3 (5.9) 2 (3.9)

5 Huntington 2000 and 2001 Wave height, rainfall 24, 

turbidity, 

current direction

0.41 31 88 (88.9) 7 (7.1) 4 (4.0)

6 Huntington 2000 and 2001 Wave height, streamflow 

7 a.m.e

, turbidity, 

number of birds

0.40 30 83 (83.8) 7 (7.1) 9 (9.1)

7 Mentor 

Headlands

2000 Wave height, 

rainfall weighted 72, 

log turbidity

0.26 ND ND ND ND

8 Mosquito Lake 2000 Date, sum sine wind 

directionf

, 

turbidity, rainfall 

weighted 72

0.44 47 42 (80.8) 4 (7.7) 6 (11.5)

9 Mosquito Lake 2000 and 2001 Date, sum sine wind 

direction, rainfall 

weighted 72, wind 

speed

0.39 29 90 (90.9) 4 (4) 5 (5.1)

10 Mosquito Lake 2000 and 2001 Rainfall weighted 72 x 

dry daysg, previous 

day's E. coli, log 

turbidity

0.48 22 60 (81.1) 7 (9.5) 7 (9.5)

11 Villa Angela 1997 Wave height, rainfall 

weighted 72, 

turbidity

0.58 44 33 (80.5) 3 (7.3) 5 (12.2)

12 Villa Angela 1997 and 2000 Wave height, 

rainfall weighted 72, 

log turbidity

0.42 43 78 (74.3) 13 (12.4) 14 (13.3)

13 Villa Angela 2000 and 2001 Wave height, rainfall 24, 

turbidity 0.29 34 93 (73.2) 18 (14.2) 16 (12.6)

14 Villa Angela 2000 and 2001 Wave height, rainfall 24, 

turbidity, previous 

day's E. coli

0.31 ND ND ND ND
40 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

15 Villa Angela 1997, 2000, and 

2001

Wave height, rainfall 

weighted 72, log 

turbidity

0.32 39 119 (71.2) 24 (14.4) 24 (14.4)

a Rainfall weighted 72 was the amount, in inches, at a nearby rain gage in the 72-hour period preceding the 9 a.m. sampling, with

the most recent rainfall receiving the greatest weight. b Number of birds on the beach at the time of sampling. c Lake Erie current directions, as predicted by The Ohio State University, Great Lakes Forecasting System. d Rainfall 24 was the amount, in inches, at Hopkins International Airport, Cleveland, Ohio, in the 24-hour period preceding the

9 a.m. sampling.

e Streamflow 7 a.m. was the streamflow at the Cuyahoga River at Independence, Ohio, at 7 a.m. on the date of sampling. f Wind direction, sum sine was the sum of the sines of the instantaneous wind direction at 9 a.m. on the day of sampling and on

the two previous days.

g Rainfall weighted 72 × dry days was the "rainfall weighted 72" times the number of preceding dry days; dry days are days in

which the daily rainfall was < 0.2 inch.

Table 13. Variables, regression statistics, and threshold probablilities for beach models, 1997, 2000, and 2001 —Continued

[E. coli, Escherichia coli; ND, not determined; R2, fraction of the variation in the dependent variable that is explained by the model; threshold probability is 

based on meeting and exceeding the single-sample bathing standard for Escherichia coli]

Beach 

Time period of 

data for model 

development

Variables in model R2 of 

model

Number of model responses by category

Threshold 

probability

Correct 

(percent)

False 

positive 

(percent)

False 

negative

(percent)

Models perform fairly well in predicting responses 

on the data sets used for model development. A true test of 

a model is to test responses that result during a subsequent 

year. The 1997 model was tested on the data collected during routine studies in 2000 by applying the threshold probabilities to the 2000 data sets. Analyzing the 2000 data with 

the 1997 model produced similar model responses at Edgewater and Villa Angela (table 14, lines 1 and 7). The false 

negative percentages of 27.1 and 25.0 at Edgewater and 

Villa Angela, respectively, were high and would need to be 

reduced for the predictive ability of the model to offer an 

acceptable level of protection to the public. 

As a further test of the model, the predictive ability 

of the 1997 model can be compared to the current method 

used to assess recreational water quality. A commonly used 

method for determining whether to post a beach is to examine the E. coli concentration determined from samples collected on the previous day (antecedent E. coli). If 

antecedent E. coli is greater than the single-sample bathingwater standard of 235 col/100 mL, the beach is posted with 

a water-quality advisory; if it is less than the standard, the 

beach is not posted. The accuracy of predicting current recreational water-quality conditions by use of antecedent 

E. coli concentrations was compared to the accuracy of predicting water quality with the threshold probability and 

1997 model. For Edgewater (table 14, lines 1 and 2) and 

Villa Angela (table 14, lines 7 and 8) during 2000, the 1997 

model provided a higher percentage of correct responses 

and lower percentage of false positive responses than use of 

antecedent E. coli concentrations, but the model erred to a 

greater degree in the percentage of false negative responses. 

Edgewater Park—new models to improve predictions of water quality. Because of the high numbers of false 

negatives obtained using the 1997 model at Edgewater during 2000, new beach-specific models were developed using 

2 years of data and (or) additional variables. First, data for 

1997 and 2000 were combined into a new model; this model 

is worth developing because it can be tested on data available from a subsequent year—2001. However, the 1997 and 

2001 model for Edgewater did not improve the predictive 

ability over the 1997 model; in fact, the R2 was so low that 

the model was not considered for further use and testing 

(table 13, line 2). 

The next step was to attempt to develop an improved 

model for Edgewater using data for 2000 and 2001. 

Because the 1997 data lacked some important variables 

measured in 2000 and 2001 (current direction and number 

of birds), data from 1997 were not used in further model 

development. On the basis of data collected during 2000 

and 2001, a model was chosen for Edgewater for future useand testing (table 13, line 3 and Appendix F). The model 

variables included the number of birds, current direction, 

wave height, and rainfall in the previous 24 hours. 
0

10

20

30

40

50

60

70

80

90

100

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

LOG10 ESCHERICHIA COLI

0

10

20

30

40

50

60

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5

Villa Angela 1997 model

Bathing-water standard

44-percent threshold

PROBABILITY OF EXCEEDING

235 COLONIES PER 100 MILLILITERS

2. False positive

1. Correct 

 nonexceedance

3. Correct 

 exceedance

4. False negative

Probability Total correct False+ False42

43

44

45

46

31

32

33

33

33

5

4

3

3

3

5

5

5

5

5

44-percent threshold

Figure 15. Establishment of the threshold probability based on the single-sample bathing water standard of 235 colonies per 100 milliliters

and the 1997 model for Villa Angela, Cleveland, Ohio. (Samples were collected from May through August 1997.) PROBABILITY OF EXCEEDING 235 COLONIES PER 100 MILLILITERS

LOG10 ESCHERICHIA COLI

2. False positive

1. Correct 

 nonexceedance

3. Correct 

 exceedance

4. False negative

Edgewater Park 1997 model

Bathing-water standard

26-percent threshold

Probability Total correct False+ False25

26

27

49

50

25

26

26

28

28

6

5

5

0

0

2

2

2

5

5

26-percent threshold

Figure 16. Establishment of the threshold probability based on the single-sample bathing water standard of 235 colonies per 100 milliliters

and the 1997 model for Edgewater Park, Cleveland, Ohio. (Samples were collected from May through August 1997.)

Predictive models 41
42 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

 

Table 14. Comparison of the numbers of correct and false negative and positive predictions of recreational water-quality conditions using 

regression models with indicated threshold probabilities and antecedent Escherchia coli (E. coli) concentrations

Beach Year 

tested

Model 

developed

Threshold 

probability

Model response for year tested

Number of 

samples

Correct 

predictions 

(percent)

False negative 

predictions 

(percent)

False positive 

predictions 

(percent)

1 Edgewater 2000 1997 26 59 41 (69.5) 16 (27.1) 2 (3.4)

2 Antecedent E. 

coli 57 29 (50.9) 13 (22.8) 15 (26.3)

3 Huntington 2001 2000 43 49 40 (81.6) 7 (14.3) 2 (4.1)

4 Antecedent E. 

coli 35 28 (80.0) 5 (14.3) 2 (5.7)

5 Mosquito 

Lake 2001 2000 47 51 41 (80.4) 0 (0.0) 10 (19.6)

6 Antecedent E. 

coli 38 36 (94.7) 1 (2.6) 1 (2.6)

7 Villa Angela 2000 1997 44 56 39 (69.6) 14 (25.0) 3 (5.4)

8 Antecedent E. 

coli 56 37 (66.1) 9 (16.1) 10 (17.8)

9 Villa Angela 2001 1997 and 2000 43 62 39 (62.9) 17 (27.4) 6 (9.7)

10 Antecedent E. 

coli 51 31 (60.8) 11 (21.6) 9 (17.6)

The threshold probability of 45 resulted in correct 

responses 79.5 percent of the time, but it produced false 

negative responses 14.6 percent of the time. The model 

explained only 32 percent of the variability in E. coli concentrations, leaving a large percentage unexplained. Indeed, 

there were several days in 2000 and 2001 when elevated E. 

coli concentrations were not explained by expected changes 

in environmental conditions. Perhaps a source of fecal contamination at Edgewater is from localized activity that cannot be measured or predicted, such as the discharge of 

wastes from boats. This was found to be true in a study in 

Connecticut where the beach was located near a marina, as 

is Edgewater.

 Villa Angela— new models to improve predictions 

of water quality. As was done for Edgewater, new models 

were developed at Villa Angela. At Villa Angela, the combination of 1997 and 2000 data produced a model that 

explained 42 percent of the variability in E. coli concentrations (table 13, line 12). The 1997 and 2000 model tested 

on 2001 data (table 14, line 9) did not increase the number 

of correct responses or reduce the false negative percentage 

as compared to the 1997 model (table 14, line 7) or as compared to use of antecedent E. coli concentrations (table 14, 

line 10). 

Steps were taken to improve the predictive ability by 

developing models for Villa Angela using additional variables measured in 2000 and 2001. Additional variables, 

however, were not found to be useful at Villa Angela. The 

two models based on 2000 and 2001 data for Villa Angela 

contained the same variable list as the 1997 model—wave 

height, rainfall, and turbidity (table 13, lines 13 and 14). 

Additional variables were not significant and were not 

included in the 2000 and 2001 models, except for the previous day’s E. coli. Adding previous day’s E. coli to the variable list improved the R2 slightly (0.31) over not including 

previous day’s E. coli (0.29). Because previous day’s 

E. coli is not an easily measured variable and E. coli results 

are not usually available in the morning, the model with previous day’s E. coli seems to be the least desirable from an 

operational standpoint and was not further evaluated. Fur-
Summary and conclusions 43

thermore, because the same variables were used in all the 

Villa Angela models, 3 years of data were combined into a 

new model (table 13, line 15 and Appendix F). The regression statistics and threshold information indicate that the 

2-year and 3-year models were fairly similar in their abilities to assess recreational-water quality (table 13, lines 13 

and 15). The 2-year and 3-year models both provided a 

similar percentage of correct (73.2 and 71.2 percent) and 

false negative (14.2 and 14.4 percent) responses. Both models left a large amount of the variability in E. coli concentrations as unexplained (R2

=0.29 and 0.32), as was found in 

the Edgewater 2000 and 2001 model.

Fairport Harbor and Mentor Headlands—predictive 

models. Using the only data available (2000 data), model 

development steps were taken for Fairport Harbor and Mentor Headlands. At Fairport Harbor, a model was not developed because no variables were found to be significantly 

related to E. coli, and the single-sample bathing-water standard was exceeded on only 3 days during 2000. At Mentor 

Headlands during 2000, because the standard was exceeded 

on only 5 days and the best model accounted for only 

26 percent of the variability in E. coli concentrations, probability thresholds were not developed, and the model was not 

tested any further (table 13, line 7). 

Huntington Reservation—predictive models. Models were developed for Huntington using 2000 or 2000 and 

2001 data. The 2000 model contained wave height, rainfall, 

and turbidity as explanatory variables and established a 

threshold probability of 43 (table 13, line 4). An R2 of 0.58 

was the highest among any of the 2000 and (or) 2001 models. The 2000 model and associated threshold probabilities 

were then tested on the 2001 data. During 2001, the 2000 

model for Huntington did a good job of predicting exceedance of the E. coli standard based on probabilities, and it 

provided the same percentage of correct responses as using 

antecedent E. coli concentrations (table 14, lines 3 and 4). 

This is an improvement over current methods, because the 

2000 model for Huntington provided a greater number of 

predictions than by using antecedent E. coli. Data on antecedent E. coli concentrations are not always available.

Two models were developed and threshold probabilities were determined at Huntington on the basis of 2000 and 

2001 data combined. Both models (Appendix F) contained 

wave height and turbidity with two additional variables—rainfall and currents (table 13, line 5) or streamflow 

and birds (table 13, line 6). Both models had moderate R2

values (0.41 and 0.40), set similar threshold values (32 and 

30), and produced less than 10 percent false negatives. The 

decision of which model to use can be based on the preferences of the beach manager; that is, whether the beach manager prefers to use rainfall and currents or use stream flow 

and birds to predict recreational water quality. Testing these 

models in subsequent years may reveal the superiority of 

one of the models in predicting the probability of exceeding 

the single-sample bathing water standard.

Mosquito Lake State Park—predictive models. 

Models were developed for Mosquito Lake using 2000 or 

2000 and 2001 data. First, a model was developed using 

2000 data only. Rainfall and turbidity were the only two 

variables common to models for Lake Erie beaches and 

Mosquito Lake. Two additional variables—sum of the sine 

of the wind direction and date—were also found to help 

describe the variability in E. coli concentrations at Mosquito 

Lake (table 13, line 8). Mosquito Lake is an inland lake 

with a long north-to-south fetch length, and the beach is 

along the southwest shoreline of the lake. It seems reasonable that a wind coming from the east would confine E. coli

to the beach area. The date variable is less easily explained. 

It may be possible that in a more closed system such as a 

reservoir, E. coli concentrations tend to build up over the 

course of the summer. On the other hand, swimmer and 

bird use of the beach area may increase throughout the summer, leading to increased sources of contamination. Unfortunately, the model could not be adequately tested, because 

the standard was exceeded on only 2 days during 2001 

(table 14, lines 5 and 6). 

Threshold probabilities were determined for two 

models at Mosquito Lake based on 2000 and 2001 data. 

The variables included in these models were quite different 

than the variables used to develop models at the Lake Erie 

beaches. The first model was based on variables that are 

easily obtained—date, wind direction, rainfall, and wind 

speed (table 13, line 9 and Appendix F). This model 

explained 39 percent of the variability in E. coli concentrations. Adding previous day’s E. coli to the second model 

(table 13, line 10) improved the predictive ability of the 

model, explaining 48 percent of the variability in E. coli

concentrations. The second model was different from the 

first model in other ways; it contained log turbidity (instead 

of date), did not include variables for wind direction or 

speed, and included a variable with the number of antecedent dry days. Previous day’s E. coli, however, is a variable 

that is not easily obtained nor is it always available. Furthermore, the 2000 and 2001 model without previous day’s 

E. coli provided a higher threshold probability, a higher percentage of correct responses, and lower percentages of false 

negative and false positive responses than the model with 

previous day’s E. coli. 

Summary and conclusions

Four types of studies were done to strengthen the science 

that supports monitoring of our Nation’s beaches—distribution, source, spatial, and routine studies. Data were collected during the recreational seasons of 2000 and 2001 at 

six public bathing beaches in Ohio—three urban Lake Erie 

beaches (Edgewater, Villa Angela, and Huntington), two 

Lake Erie beaches in a less populated area (Mentor Headlands and Fairport Harbor), and one inland-lake beach 
44 Escherichia coli at Ohio bathing beaches—Distribution, sources, wastewater indicators, and predictive modeling

(Mosquito Lake). For distribution, source, and spatial studies, the USGS collected water and sediment samples during 

1- to 4-day studies at the three urban Lake Erie beaches and 

Mosquito Lake. For routine studies, local cooperators collected water samples 4 or 5 days a week at six beaches. During all studies, water and (or) sediment samples were 

analyzed for concentrations of E. coli, and water-quality and 

environmental data were measured or compiled. 

Distribution studies were done to determine whether 

lake-bottom sediments outside the bathing area were significant deposition areas for E. coli and whether sediment and 

water E. coli concentrations were related. Generally, sediments outside the bathing area were not more bacteriologically enriched than those within the bathing area. 

Concentrations of E. coli in water were compared to their 

concentrations in associated sediments by means of correlation analysis. Sediment and water E. coli concentrations 

were related at Huntington to a greater extent than at Edgewater, Villa Angela, or Mosquito Lake. 

Distribution studies were also done to determine concentrations of E. coli in interstitial water and subsurface 

sediments collected from near the swash zone. Concentrations of E. coli in interstitial waters ranged from <3 to 

400,000 col/100 mL; in subsurface sediments, concentrations ranged from <1 to 30,000 col/gDW sediment. 

Although there are no standards for E. coli in interstitial 

waters and subsurface sediments collected from near the 

swash zone, the high concentrations found in this study 

could be of some concern for public health. A spatial and 

temporal analysis of E. coli concentrations at Edgewater and 

Mosquito Lake indicated that the concentrations in water 

and sediment samples collected on the same date from different areas of the beach could vary by as much as 2.5 

orders of magnitude. Further, concentrations of E. coli during February 2001 were in the same range as many samples 

collected during the spring or summer months. This finding 

indicates that there was a continuous source of E. coli during the winter and (or) an overwintering of E. coli in interstitial waters and subsurface sediments.

 Source studies were done at Mosquito Lake to 

investigate possible sources of fecal contamination to the 

lake and bathing beach. It was suspected that fecal contamination from the north was affecting water quality at the 

bathing beach; however, investigators found decreasing 

E. coli concentrations with distance from the north shoreline 

and then an increase in E. coli concentrations at the bathing 

beach. The source of fecal contamination at the bathing 

beach is therefore, more likely of local origin. The large 

population of birds that reside on the beach and are fed by 

beachgoers has been suggested as a possible source of 

E. coli. 

Spatial studies were done to aid The Ohio State University Great Lakes Forecasting System (GLFS) in development of deterministic models to predict E. coli

concentrations at Edgewater. The data, however, were not 

very useful to the GLFS because values for the measured 

properties were fairly homogeneous and the resolution was 

too small to match the needs of deterministic modeling. 

Nevertheless, the greatest spatial variations among E. coli

concentrations, specific conductance, and turbidity in water 

appeared to occur after a significant rainfall, although more 

work would be required to verify this finding.

 The use of wastewater indicators as surrogates for 

E. coli was tested and evaluated during distribution, source, 

and routine studies. In a limited study in which 13 samples 

were collected from a variety of beach locations by the 

USGS during 2000, none of the compounds commonly 

found in domestic wastewater—such as 3β-coprostanol, 

cholesterol, caffeine, NPEO1, and p-nonylphenol—were 

related to concentrations of E. coli. The two compounds 

that were statistically related to E. coli—fluoranthene and 

pyrene—are components of coal tar and asphalt and not 

indicative of fecal contamination. Most likely, these compounds are delivered to storm sewers through runoff across 

asphalt roads and parkings lots. It seems reasonable, therefore, that the delivery of these compounds would correlate 

well with the delivery of bacteria from combined-sewer 

overflows and storm sewers. To add evidence to this delivery mechanism, future research could be done with 

improved analytical methods and a focus on the collection 

of samples at one beach or several beaches with a common 

fecal source, and not from a variety of sites, as was done in 

this study.

Routine studies were done to include research on the 

development of statistics-based models to predict concentrations of E. coli. The first step was to identify variables 

that showed a relation to E. coli and thus were possible 

explanatory variables in the multiple-linear regression 

(MLR) models. For continuous variables, correlation analysis was used; for categorical variables, analysis of variance 

was used. Variables based on the previous day’s E. coli concentrations, rainfall in the last 24 hours, weighted rainfall in 

the last 72 hours, streamflow, and wave height were statistically related to E. coli concentrations at each of the three 

Lake Erie urban beaches. Turbidity, an important variable 

in an earlier study (Francy and Darner, 1998) was significantly related to E. coli at Villa Angela and Huntington, but 

not at Edgewater. At Mosquito Lake, previous day’s E. coli, 

date, and turbidity showed the strongest relations to E. coli

concentrations. Measurements of the wind speed and direction were significantly related to E. coli concentrations for 

some data sets at Mosquito Lake. Two Lake Erie beaches in 

a less populated area also were investigated. At Mentor 

Headlands, only rainfall and turbidity were related to E. coli

concentrations; and at Fairport Harbor, no statistically significant relations were found between E. coli concentrations 

and any variables. Use of analysis of variance revealed significant differences for E. coli concentrations among current-direction categories at Edgewater and Huntington, but 

not at Villa Angela. As in the earlier study (Francy and 
References 45

Darner, 1998), wave height was found to be important variable affecting E. coli concentrations in 2000 and 2001 at 

Lake Erie urban beaches. 

Before any new models were developed, a model 

based on 1997 data for Edgewater and Villa Angela was 

tested on 2000 data. The 1997 model contained weighted 

categorical rainfall, beach-specific turbidity, and wave 

height. The model output variable is the probability that the 

single-sample bathing-water standard (235 col/100 mL) is 

exceeded. Threshold probabilities for the 1997 model were 

developed and tested on 2000 data. During 2000, the 1997 

model provided approximately 70 percent correct responses 

at Edgewater and Villa Angela. This means that, for 2000, 

the 1997 model correctly predicted that the E. coli concentration would meet or exceed the bathing-water standard 

70 percent of the time. 

Work continued to develop improved predictive 

models for Edgewater. Combining the 2000 and 2001 data 

resulted in an improved model with a different variable 

list—number of birds, current direction, wave height, and 

rainfall in the previous 24 hours. This model explained 

32 percent of the variability in E. coli concentrations, leaving a large percentage unexplained. The source of the unexplained fecal contamination at Edgewater may be from the 

discharge of wastes from boats en route to a nearby marina. 

Attempts to develop new improved models at Villa 

Angela met with the same limited success as was found at 

Edgewater. At Villa Angela, unlike Edgewater, expanding 

the variable list did not prove to be useful. Investigators 

were left with the same list of explanatory variables for 

2000 and 2001 data (wave height, rainfall, and turbidity) as 

was used for the 1997 model. It seemed reasonable, therefore, to combine 3 years of data—1997, 2000, and 

2001—into a Villa Angela model. The 3-year model for 

Villa Angela explained 32 percent of the variability in 

E. coli concentrations, leaving a large percentage unexplained, as was found in the Edgewater 2000 and 2001 

model. Three years of data for model development may be 

better than 2 years, because 3 years of data would provide a 

wider range of representative environmental and water-quality conditions. The true test of the three-year model will be 

its ability to provide accurate assessments of water quality 

in subsequent years.

Model development at Huntington using 2000 and 

(or) 2001 data was more successful than at Edgewater and 

Villa Angela. A model based on 2000 data and containing 

the variables, wave height, rainfall, and turbidity was tested 

on 2001 data. During 2001, the 2000 model for Huntington 

did a good job of predicting exceedance of the E. coli standard based on probabilities, and it resulted in the same number of correct and false negative responses as was found 

using antecedent E. coli concentrations. In addition, the 

2000 model for Huntington provided a greater number of 

predictions than by using antecedent E. coli. Two models 

were developed at Huntington on the basis of 2000 and 

2001 data. Both models contained wave height, turbidity, 

and two additional variables and explained approximately 

40 percent of the variability in E. coli concentrations. The 

decision of which model to use can be based on the preferences of the beach manager and the performance of the 

models in predicting recreational water quality in subsequent years.

Mosquito Lake is an inland lake; therefore, the 

hydrologic processes affecting the beach are different from 

those affecting the Lake Erie beaches. It is not surprising 

that the variables included in two 2000 and 2001 models for 

Mosquito Lake were somewhat different from the variables 

used to develop models at the Lake Erie beaches. The two 

models for Mosquito Lake contained various combinations 

of easily obtained variables—rainfall, number of dry days 

preceding a rainfall, date, wind direction, wind speed, and 

turbidity. The first model did not include the previous day’s 

E. coli, whereas the second one did; R2 values were 0.39 

and 0.49, respectively. In addition, adding previous day’s 

E. coli, a hard to measure variable, did not improve the ability of the model to correctly predict probabilities. Both 

models need to be tested on data collected in subsequent 

years.

The variables that best explained the variability in

E. coli concentrations have been identified for the beaches 

investigated during this study. The models are beach specific; that is, different variable lists were used to predict the 

probability of exceeding the standard at each beach. Additional research could include testing the models already 

developed for Edgewater, Villa Angela, Huntington, and 

Mosquito Lake in subsequent years and comparing each 

model’s ability to predict recreational water quality with 

results obtained by use of antecedent E. coli concentrations. 

Each year the model is tested, new data can be added and 

model variables can be recalculated to determine whether 

the predictive ability improves with an added year of data. 

If the model is able to predict recreational water quality as 

well as or better than use of antecedent E. coli for several 

years in a row, beach managers may consider using the 

models to aid or direct decisions on posting beach advisories. 

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