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Use of predictive models and rapid

methods to nowcast bacteria levels at

coastal beaches

Donna S. Francy a

a

 U.S. Geological Survey, Ohio Water Science Center, 6480

Doubletree Avenue, Columbus, OH, 43229

Available online: 14 May 2009

To cite this article: Donna S. Francy (2009): Use of predictive models and rapid methods to nowcast

bacteria levels at coastal beaches, Aquatic Ecosystem Health & Management, 12:2, 177-182

To link to this article: http://dx.doi.org/10.1080/14634980902905767

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Use of predictive models and rapid methods to nowcast

bacteria levels at coastal beaches

By Donna S. Francy∗

U.S. Geological Survey, Ohio Water Science Center

6480 Doubletree Avenue, Columbus, OH 43229

∗Corresponding author: dsfrancy@usgs.gov

The need for rapid assessments of recreational water quality to better protect public health is well

accepted throughout the research and regulatory communities. Rapid analytical methods, such as quantitative polymerase chain reaction (qPCR) and immunomagnetic separation/adenosine triphosphate (ATP)

analysis, are being tested but are not yet ready for widespread use.

Another solution is the use of predictive models, wherein variable(s) that are easily and quickly measured

are surrogates for concentrations of fecal-indicator bacteria. Rainfall-based alerts, the simplest type

of model, have been used by several communities for a number of years. Deterministic models use

mathematical representations of the processes that affect bacteria concentrations; this type of model is

being used for beach-closure decisions at one location in the USA. Multivariable statistical models are

being developed and tested in many areas of the USA; however, they are only used in three areas of the Great

Lakes to aid in notifications of beach advisories or closings. These “operational” statistical models can

result in more accurate assessments of recreational water quality than use of the previous day’s Escherichia

coli (E. coli) concentration as determined by traditional culture methods. The Ohio Nowcast, at Huntington

Beach, Bay Village, Ohio, is described in this paper as an example of an operational statistical model.

Because predictive modeling is a dynamic process, water-resource managers continue to collect additional

data to improve the predictive ability of the nowcast and expand the nowcast to other Ohio beaches and

a recreational river. Although predictive models have been shown to work well at some beaches and are

becoming more widely accepted, implementation in many areas is limited by funding, lack of coordinated

technical leadership, and lack of supporting epidemiological data.

Keywords: bacteria models, E. coli, recreational water, beach water quality, statistical models, multiple

linear regression

Introduction

The need for rapid assessments of recreational

water quality to better protect public health is well

accepted throughout the research and regulatory

communities. In the USA, concentrations of fecalindicator bacteria–Escherichia coli (E. coli) or enThis article is not subject to U.S. Copyright laws.

terococci for fresh waters and enterococci for marine waters—are used to issue beach advisories or

closings. The widely acknowledged shortcoming of

using this approach is that standard culture methods

for these bacteria take at least 18−24 hrs before results are available. The beach is posted with an advisory or closing or is determined to be acceptable for

swimming on the basis of the previous day’s concentration of E. coli or enterococci. Sanitary conditions

177

Aquatic Ecosystem Health & Management, 12(2):177–182, 2009.

DOI: 10.1080/14634980902905767

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178 Francy / Aquatic Ecosystem Health and Management 12 (2009) 177–182

may change overnight and even throughout the day

(Boehm et al., 2002), so beach managers may issue

water-quality advisories based on outdated information of current public-health risk. Because of this

time-lag issue, water-resource managers are seeking solutions that provide near-real-time estimates

of recreational water quality.

Rapid analytical methods

One obvious solution is the use of a rapid

analytical method. For fecal-indicator bacteria,

three types of rapid methods, described in detail

by Noble and Weisberg (2005), are undergoing

development and testing: (1) surface and whole-cell

recognition methods, (2) molecular methods,

and (3) enzyme/substrate methods. The first two

types are being tested by the U.S. Geological

Survey (USGS) and others—immunomagnetic

separation/adenosine triphosphate (IMS/ATP) and

real-time quantitative polymerase-chain reaction

(QPCR).

Lee and Deininger (2004) originally developed the IMS/ATP method, a surface recognition

method. IMS/ATP involves mixing the sample with

antibody-coated magnetic beads specific to cellsurface antigens of the bacteria species of interest,

separating the tagged bacteria from the remainder of

the sample in a magnetic field, and using a bioluminescent assay to obtain a concentration of bacteria

based on the amount of ATP. (ATP is the energy

molecule present in all cells and is a measure of cell

viability.) The results are reported in relative light

units (RLUs). In a study in a recreational river, investigators found significant relations (r = 0.62 and

r = 0.77) between RLUs and the standard culture

method for E. coli (n = 228) and enterococci (n =

35), respectively (Bushon et al., 2009).

Quantitative-PCR is a molecular method that targets DNA specific to an organism, such asE. coli and

enterococci. In QCPR, preprocessing steps involve

lysing of cells, extraction of DNA, and removal of

inhibitory substances. Results may be reported in

cell equivalents (Haugland et al., 2005) or other

units. By using known amounts (serial dilutions of

bacteria cells) of DNA of an organism of interest,

one first must create a standard curve to obtain the

cell equivalents in your sample. Water quality measured by QPCR for enterococci was shown to predict

swimming-associated health effects at two Great

Lakes beaches (Wade et al., 2006) and be significantly related to results obtained by use of standard

culture methods (Haugland et al., 2005).

Although rapid analytical methods are a promising solution, they are not yet ready for widespread

use. IMS/ATP and QPCR, along with other rapid

analytical methods, are currently being tested at

southern California beaches (J.F. Griffith, Southern

California Coastal Water Research Project, Costa

Mesa, CA, pers. comm.). For QPCR, current limitations include the high cost of equipment and the

lack of portability. Research is needed to establish

uniform DNA standards, cell suspensions for standard curves, and interpretation procedures, as well

as procedures to account for DNA-extraction efficiency and inhibition. The IMS/ATP method has

been tested in a limited geographic area only, and

the relation between RLUs and the standard culture

method may need to be established for each location or type of beach. For both methods, studies are

needed to establish the link between rapid analytical

results with health risk.

Predictive models

While waiting for rapid analytical methods to

be available for routine use, some beach managers

have adopted predictive models. Predictive models

use variable(s) that are easily and quickly measured

as surrogates for concentrations of fecal-indicator

bacteria. The models currently in use at bathing

beaches include rainfall based alerts, deterministic

models, and multivariable statistical models.

Rainfall-based alerts have been used by several

communities for a number of years. In this type

of system, the beach manager establishes a rainfall threshold by examining the relation between

fecal-indicator bacteria concentrations and rainfall

in historical data, either qualitatively or statistically. The beach is posted if the rainfall amount

exceeds a beach-specific threshold. Officials with

the City of Stamford, CT, have been using rainfallbased alerts for beaches on Long Island Sound

for almost 20 years (Kuntz, 2006). Beaches are

closed with >25 mm of rainfall under normal or

above-normal rainfall conditions or >12.5 mm under drought conditions; beaches are also closed

if floatables are observed in the water. In southern California, investigators found an increase in

coastal marine bacteria concentrations associated

with almost all rainfalls >6 mm and with every

rainfall >25 mm (Ackerman and Weisberg, 2003).

Officials in southern California issue warnings for

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Francy / Aquatic Ecosystem Health and Management 12 (2009) 177–182 179

the public to avoid recreational water contact for 3

days after rainfall events; San Diego County uses

a rainfall threshold of >5 mm (http://sdcounty.

ca.gov/deh/lwq/beachbay/index.html). Other communities in the USA that use rainfall advisories include Door County and Milwaukee, WI; Delaware

(U.S. Environmental Protection Agency (USEPA),

1999); Myrtle Beach, SC; and Boston, MA—there

are probably more.

Deterministic models use mathematical representations of the processes that affect bacteria concentrations. These processes include transport by

currents; dieoff from sunlight, predation, etc.; and

deposition and resuspension. Sources of bacterial

contamination need to be known and quantified.

The only deterministic model in use in the USA

for beach-closure decisions that this author is aware

of is the Regional Bypass Model (RBM) in the

New York-New Jersey Harbor. The model simulates a discharge of a known quantity of bacteria

from a sewage spill and calculates the water-quality

response at beaches and shellfish harvesting areas

(HydroQual, Inc., 1998; USEPA, 1999). Although

not yet used for beach-closure decisions, hydrodynamic models are being developed for beaches

at two locations in Lake Michigan. At both locations, bacterial water quality is known to be

affected by the drainage from a nearby stream

or ditch. Investigators are now able to track the

movement of stream water and forecast lake circulation (http://www.glerl.noaa.gov/res/glcfs/bd/);

adding the bacterial component is the next step.

Techniques such as multiple linear regression

(MLR) are used to develop multivariable statistical models (hereinafter “statistical models”) on

the basis of relations between fecal-indicator bacteria concentrations and variables known or suspected to affect their concentrations at a particular

beach. Unlike deterministic models, the sources of

fecal contamination do not need to be identified–

unknown contamination sources are common situations at many beaches. Statistical models are

being developed and tested in many areas of the

USA; however, they are used for beach closure

or advisory decisions at only three locations, all

at Great Lakes beaches. These model-based advisories are the Swim Advisory Forecast Estimate (Project SAFE), SwimCast, and the Ohio

Nowcast. For Project SAFE, a predictive model

is used to forecast bacterial concentrations at

five Lake Michigan beaches in northwest Indiana

(http://www.glsc.usgs.gov/projectSAFE.php). The

model has been incorporated into local management programs since 2005, and includes the variables wave height, turbidity in a nearby ditch and in

the lake, rainfall, and chlorophyll concentrations.

For SwimCast (M. Pfister, Lake County Health

Dept., Waukegan, IL, pers. comm.), automated meteorological and water-quality sensors are installed

in or near the surf zone at four Lake Michigan beaches in Lake County, IL (http://www.co.

lake.il.us/health/ehs/SwimCastDataAP.asp). Models may include the variables air and water

temperature, wind speed and direction, rainfall, relative humidity, wave height, lake level, and insolation (light energy). The specific variables that

are used in SwimCast are most strongly associated with the fecal-indicator bacteria. Two models

(Forest Park Beach and Illinois Beach State Park)

have been used since 2005 for beach-closure decisions after model creation in 2004. Two additional models were developed in 2006 and 2007

(Rosewood Beach and Waukegan South Beach), respectively; the Rosewood Beach model was operational in 2007. Wave height is a variable that has

been associated with the fecal-indicator bacteria at

all four beach locations, whereas 24 and (or) 36-

hour rainfall is associated with fecal-indicator bacteria at only two beach locations because of differences in drainage inflows. For the SwimCast,

beach water quality is monitored daily in the morning and in the afternoon, five days per week, and

the models can distinguish between morning and

afternoon conditions. Additionally, predictions can

be made on an hourly basis, a beneficial feature

because the fecal-indicator bacteria concentrations

and hydrometeorological conditions change at these

beaches throughout the day. For the Ohio Nowcast, scientists and local agencies have been working to develop predictive models for five Lake Erie

beaches; currently, beach advisories on the nowcast

are based on a predictive model for one of those

beaches (http://www.ohionowcast.info/index.asp).

Details of the nowcast are presented below as an

example of an operational beach statistical model.

The Ohio Nowcast

The Ohio Nowcast has been operational since

2006 for Huntington, Bay Village, OH, a suburb of

Cleveland. A model based on data from 2000−2006

was developed for use in the nowcast in 2007. The

model included the variables log turbidity, wave

height, rainfall (radar and airport), and day of the

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180 Francy / Aquatic Ecosystem Health and Management 12 (2009) 177–182

year as predictors for E. coli concentrations. The

output from the model was the probability that the

Ohio single-sample bathing-water standard for E.

coli, 235 colony-forming units per 100 ml (CFU

100 ml−1), would be exceeded. A threshold probability of 30% was established for Huntington on

the basis of historical data. The threshold was the

probability associated with too great a risk to allow swimming—a probability that would warrant

the issuance of a bathing-water advisory.

From May 21 through September 3, 2007, the

daily nowcast for Huntington was operated by the

Cuyahoga County Board of Health (CCHB) with

assistance from the USGS. Samples were collected

by the CCBH at Huntington by 9:00 a.m., seven

days week. The CCBH compiled data on rainfall

from the National Weather Service over the Internet, measured wave height and turbidity at the beach,

and entered data into a Fortran program that computed the output from the model. The probability of

exceeding the bathing-water standard was posted on

the Ohio Nowcast web site by 9:30 each morning,

along with current water-quality conditions. During

2007, the Huntington model resulted in a similar

percentage of correct responses (82.7%) than that

found by use of the previous day’s E. coli concentrations (80.2%) (current method); this percent difference was smaller than expected and not as great

as that found during 2006 (http://www.ohionowcast.

info/ohionowcastfindoutmore.htm). The model did,

however, result in a greater sensitivity (53%) than

that found by use of the current method (31%) but

with similar specificity. (The sensitivity is the percentage of events where the model correctly predicted exceedance of the bathing-water standard

among days where the standard was exceeded. The

specificity is the percentage of events where the

model correctly predicted non-exceedance of the

standard.) Investigators will continue to work on

improving the predictive ability of the Huntington

model in 2008. For example, the 2000−2007 data

were split into two or three segments by date. A

two-segment split resulted with data placed into two

groups: (1) May 21–July 23 and (2) July 24–Sept 3.

After examining the grouped data, it was noted that

the percentages of false negatives decreased over

time and the percentages of false positives increased

over time (Francy and Darner, 2008). A false negative occurs when the model predicts that the standard

will not be exceeded, but cultural results indicate

that the E. coli concentration was actually greater

than the standard. In contrast, a false positive occurs

when the model predicts that the standard was exceeded, but bacteria-culture results indicate that the

concentration was less than the standard. The pattern

of false negatives and positives at Huntington would

lead one to believe that a bi-phase seasonal model

may be suitable for use in the nowcast in 2008.

From May 21 through Sept 10, 2007, data were

collected by the Northeast Ohio Regional Sewer

District (NEORSD) for testing of a predictive

model for possible future inclusion in the Ohio

Nowcast. Samples were collected at two beaches

in Cleveland, OH—Edgewater and Villa Angela.

The Edgewater model included the variables log

turbidity, wave height, radar rainfall, and lake level,

with a threshold probability of 29%. The Villa

Angela model included the variables log turbidity,

airport rainfall, and water temperature, with a

threshold probability of 32%. During 2007, the

Edgewater model resulted in a greater percentage

of correct responses (82.1%) than that found by

use of the current method (66.7%); the model also

resulted in both higher sensitivity and specificity

than the current method. These results show that

predictions based on the Edgewater model can be

used to issue advisories through the Ohio Nowcast

system in 2008. In contrast, at Villa Angela, the

model resulted in correct responses only 61.3% of

the days monitored; this percentage was lower than

that achieved by use of the current method (74.6%).

At Villa Angela, other options will be examined,

such as the use of a rapid analytical method for E.

coli for issuing beach advisories.

At Lakeshore Beach in northeast Ohio (Ashtabula, OH), model results were similar to those at Villa

Angela (E.E. Bertke, USGS, Columbus, OH, pers.

comm.). At both Villa Angela and Lakeshore, the

bathing-water standard was exceeded on the majority of days sampled. It appears, therefore, that

predictive models do not work as well at beaches

where the standard is exceeded on the majority of

days. In fact, models do not work well at relatively

clean beaches either (for example, <5 exceedances

per season), because of the lack of data on days when

the standard is exceeded. Models seem to work best

at moderately contaminated beaches. Regardless,

predictive modeling is a dynamic process wherein

water-resource managers must continue to collect

additional data to improve the predictive ability of

the models from year to year.

Although recent attention has been focused on

coastal beaches, it is important to keep in mind

that many recreational activities take place in

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Francy / Aquatic Ecosystem Health and Management 12 (2009) 177–182 181

recreational rivers. The Cuyahoga River is a valuable recreational resource in northeast Ohio, especially within the Cuyahoga Valley National Park

(CVNP). Recreational water quality within CVNP

has been a concern to park managers since the inception of the park (Brady, 2007). Management would

like to encourage use of the river by canoeists and

waders when the water quality, based on E. coli concentrations, is within acceptable limits. To reach this

goal, scientists and park employees have been testing the use of rapid assessments of recreational water quality. Data were collected during 2004−2006

to develop predictive models for river sites. Of all

the variables tested—E. coli by IMS/ATP, turbidity,

streamflow, and rainfall—turbidity alone was found

to be the best predictor of E. coli concentrations at

one river site. During 2007, the turbidity model produced more correct responses (81%) than the current method (62%), and it had a higher sensitivity

and specificity (A.M.G. Brady, USGS, Columbus,

OH, pers. comm.). Adding the turbidity model for

the CVNP site to the Ohio Nowcast is a possible

next step.

Discussion and conclusions

Recognizing the need for accurate, rapid assessments of recreational water quality, communities

in the USA have applied different solutions. Solutions are beach specific and include the use of

rapid analytical methods and (or) predictive models. Predictive models include the simpler rainfallbased alerts, deterministic models that require data

on sources of contamination, or more complicated

multivariable statistical models. There is no single

rapid-assessment solution that will work for every

community and every beach. The type of rapid assessment chosen for a recreational resource depends

on the technical expertise of available staff and willingness of the community to accept nontraditional

methods.

Although predictive models have been shown to

work well at some beaches and are becoming more

widely accepted, there are several roadblocks to their

widespread implementation. The USEPA BEACH

Grant program (USEPA, 2007) has provided funding to develop and implement predictive models;

but continued funding is needed to maintain and

refine models. Not all agencies have the technical

expertise to develop and maintain models or the

management capability to ensure that models are

used for beach advisory or closure decisions. Given

that, what is needed is coordinated leadership on

the regional or state level to provide oversight and

technical expertise and ensure that high quality, consistent data are collected. Some agencies are hesitant to adopt new methods, especially methods that

are more complicated and different from traditional

methods. Training is needed; work is needed to test

the use of different endpoints for predictive models, such as enterococci for marine and fresh waters.

And last, but most important, there is a lack of supporting epidemiological data. Studies are therefore

needed to link results from predictive models, to

the rate at which recreational users are contracting

gastrointestinal illnesses.

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