&EFA
United States
Environmental Protection
Agency
Office of Water
4503 F
Washington DC 20460
EPA 841-R-00-002
January 2001
Protocol for Developing
Pathogen TMDLs
First Edition
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Acknowledgments
The Protocol for Developing Pathogen TMDLs was developed under the direction of Donald Brady and Myra Price
of EPA's Office of Wetlands, Oceans, and Watersheds, Assessment and Watershed Protection Division, and Mimi
Dannel, Office of Science and Technology, Standards and Applied Science Division. The document was developed
under EPA Contract Number 68-C7-0018. The Protocol for Developing Pathogen TMDLs was written by EPA's
Pathogen Protocol TMDL Team, with assistance from Jonathan Butcher, John Craig, Jessica Koenig, Kevin Kratt,
Esther Peters, and Leslie Shoemaker of Tetra Tech, Inc., in Fairfax, Virginia. The authors gratefully acknowledge
the many comments of reviewers from within EPA and state environmental agencies, as well as the detailed reviews
conducted by Charles Chamberlin of Humboldt State, Arcata, California; Douglas Gunnison of the Corps of
Engineers' Waterways Experiment Station, Vicksburg, Mississippi; and William H. Benson of the University of
Mississippi, Oxford, Mississippi.
This report should be cited as:
U.S. Environmental Protection Agency. 2001. Protocol for Developing Pathogen TMDLs. EPA 841-R-00-002.
Office of Water (4503F), United States Environmental Protection Agency, Washington, DC. 132 pp.
To obtain a copy of the Protocol for Developing Pathogen TMDLs/EPA 841-R-00-002 (2001) free of charge,
contact:
National Service Center for Environmental Publications (NSCEP)
Phone: 1-800-490-9198
Fax: 513-489-8695
This EPA report is available on the Internet at:
http://www.epa.gov/owow/tmdl/techsupp.html
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Protocol for Developing Pathogen TMDLs
First Edition: January 2001
Watershed Branch
Assessment and Watershed Protection Division
Office of Wetlands, Oceans, and Watersheds
Office of Water
United States Environmental Protection Agency
Washington, DC 20460
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Foreword
Although many pollution sources have implemented the required levels of pollution control technology, there are still
waters in the Nation that do not meet the Clean Water Act goal of "fishable, swimmable." Section 303(d) of the act
addresses these waters that are not "fishable, swimmable" by requiring states, territories, and authorized tribes to
identify and list impaired waters every two years and to develop Total Maximum Daily Loads (TMDLs) for
pollutants in these waters, with oversight from the U.S. Environmental Protection Agency. TMDLs establish the
allowable pollutant loadings, thereby providing the basis for states to establish water quality-based controls.
Historically, wasteload allocations have been developed for particular point sources discharging to a particular
waterbody to set effluent limitations in the point source's National Pollutant Discharge Elimination System (NPDES)
discharge permit. This approach has produced significant improvements in water quality by establishing point source
controls for many chemical pollutants. But water quality impairments continue to exist in the Nation's waters. Some
point sources need more controls, and many nonpoint source impacts (from agriculture, forestry, development
activities, urban runoff, and so forth) cause or contribute to impairments in water quality. To address the combined,
cumulative impacts of both point and nonpoint sources, EPA has adopted a watershed approach, of which TMDLs
are a part. This approach provides a means to integrate governmental programs and improve decision making by both
government and private parties. It enables a broad view of water resources that reflects the interrelationship of
surface water, groundwater, chemical pollutants and nonchemical stressors, water quantity, and land management.
The Protocol for Developing Pathogen TMDLs is a TMDL technical guidance document prepared to help state,
interstate, territorial, tribal, local, and federal agency staff involved in TMDL development, as well as watershed
stakeholders and private consultants. Comments and suggestions from readers are encouraged and will be used to
help improve the available guidance as EPA continues to build experience and understanding of TMDLs and
watershed management.
Robert H. Wayland III, Director
Office of Wetlands, Oceans, and Watersheds
Office of Water
U.S. Environmental Protection Agency
Washington, DC 20460
First Edition: January 2001 iii
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Preface
EPA has developed several protocols as programmatic and technical support guidance documents for those involved
in TMDL development. These guidance documents, developed by an interdisciplinary team, provide an overall
framework for completing the technical and programmatic steps in the TMDL development process. The Protocol
for Developing Pathogen TMDLs is one of the three TMDL technical guidance documents prepared to date. The
process presented here will assist with the development of rational, science-based assessments and decisions and
ideally will lead to the assemblage of an understandable and justifiable pathogen TMDL. It is important to note that
this guidance document presents a suggested approach, but not the only approach to TMDL development.
This document provides guidance to states, territories, and authorized tribes exercising responsibility under section
303(d) of the Clean Water Act for the development of pathogen TMDLs. This protocol is designed as programmatic
and technical support guidance to those involved in TMDL development. The protocol does not, however, substitute
for section 303(d) of the Clean Water Act or EPA's regulations; nor is it a regulation itself. Thus, it cannot impose
legally binding requirements on EPA, states, territories, authorized tribes, or the regulated community and may not
apply to a particular situation based upon the circumstances. EPA and state, territorial, and tribal decision makers
retain the discretion to adopt approaches on a case-by-case basis that differ from this protocol where appropriate.
EPA may change this protocol in the future.
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Contents
Foreword iii
Preface iv
Figures vi
Tables vi
Introduction and Purpose of This Protocol 1-1
General Principles of Pathogen Water Quality Analysis 2-1
Problem Identification 3-1
Identification of Water Quality Indicators and Target Values 4-1
Source Assessment 5-1
Linkage Between Water Quality Targets and Sources 6-1
Allocations 7-1
Follow-Up Monitoring and Evaluation 8-1
Assembling the TMDL 9-1
Appendix A: How Pathogen Indicators are Measured A-l
Appendix B: Case Study
Muddy Creek, Virginia B-l
References References-1
Acronyms Acronyms-1
Glossary Glossary-1
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Figures
Figure 1-1. General elements of the water quality-based approach (USEPA, 199 la) 1-3
Figure 1-2. General components of TMDL development 1-4
Figure 2-1. Relationship between indicator organisms 2-5
Figure 2-2. Factors influencing the level of detail for the TMDL analysis 2-12
Figure 2-3. Regression of E. coli to fecal coliform for Lower Geddes Pond samples—dry weather 2-14
Figure 2-4. Regression of E. coli to fecal coliform for Lower Geddes Pond samples—wet weather 2-14
Figure 3-1. Example schematic showing processes important to waterbody impairment 3-7
Figure 4-1. Factors for determining indicators and endpoints 4-2
Figure 6-1. Daily in-stream fecal coliform concentrations resulting from POTW effluent 6-14
Figure 6-2. Daily in-stream fecal coliform concentrations resulting from POTW effluent plus storm
water discharge 6-14
Tables
Table 2-1. Pathogenic bacteria of concern to water quality and their associated diseases 2-3
Table 2-2. Protozoans of concern to water quality and their associated diseases 2-3
Table 2-3. Viruses of concern to water quality and their associated diseases 2-4
Table 2-4. Methods of control for agricultural nonpoint sources and their associated types of controls 2-11
Table 2-5. Load reductions to Chickasawatchee Creek 2-18
Table 3-1. Advantages and disadvantages of different TMDL watershed analysis scales 3-4
Table 3-2. Approaches for incorporating margins of safety into pathogen TMDLs 3-7
Table 4-1. Currently recommended criteria for indicators of elevated levels of pathogens 4-3
Table 4-2. Human pathogens likely to be associated with sewage 4-4
Table 4-3. Some potential indicator organisms for TMDL development 4-5
Table 5-1. Sources and transport pathways for pathogens 5-4
Table 5-2. Summary of source-specific pathogen and fecal indicator concentrations 5-6
Table 6-1. Examples of fecal indicator and pathogen die-off rates 6-7
Table 6-2. Steady-state predictions of fecal coliform count in the estuary (organisms/100 mL) 6-11
Table 7-1. Data for calculating the TMDL 7-5
Table A-l. Potential measurement endpoints for some pathogens and indicator bacteria A-2
Table B-l. Wasteload allocations to point sources in the Muddy Creek watershed B-5
Table B-2. Overall fecal coliform bacteria nonpoint source allocations for the Muddy Creek
watershed for the representative hydrologic period B-6
Table B-3. Overall Phase I fecal coliform bacteria nonpoint source allocations for the Muddy Creek
watershed for the representative hydrologic period B-7
VI
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Protocol for Developing Pathogen TMDLs
Introduction and Purpose of This Protocol
Objective: This Total Maximum Daily Load (TMDL)
protocol was developed at the request of EPA regions,
states1, and tribes and is intended to provide users with
an organizational framework for the TMDL
development process for fecal pathogens. The process
presented here will assist with the development of
rational, science-based assessments and decisions and
ideally will lead to the assembly of an understandable
and justifiable TMDL.
Audience: The protocols are designed as tools for state
TMDL staff, EPA regional TMDL staff, tribal TMDL
staff, watershed stakeholders, and other agencies and
private consultants involved in TMDL development.
OVERVIEW
Section 303(d) of the Clean Water Act provides that
states, territories, and authorized tribes are to list waters
for which technology-based limits alone do not ensure
attainment of water quality standards (WQS).
Beginning in 1992, states, territories, and authorized
tribes were to submit their lists to EPA every two years.
Beginning in 1994, lists were due to EPA on April 1 of
each even-numbered year. States, territories, and
authorized tribes are to set priority rankings for the
listed waters, taking into account the severity of the
pollution and the intended uses of the waters.
EPA's regulations for implementing section 303(d) are
codified in the Water Quality Planning and Management
Regulations at 40 CFR Part 130, specifically at sections
130.2, 130.7, and 130.10. The regulations define terms
used in section 303(d) and otherwise interpret and
expand upon the statutory requirements. The purpose of
the Protocol for Developing Pathogen TMDLs is to
provide more detailed guidance on the TMDL
development process for waterbodies impaired because
of pathogens.
EPA's regional offices are responsible for approving or
disapproving state, territorial, or tribal section 303(d)
lists and TMDLs, and for establishing lists and TMDLs
in cases of disapproval. Public participation is to be
provided for by states and tribes (or EPA regional
offices, in the case of disapproval) when they establish
lists or TMDLs.
In accordance with the priority ranking, states,
territories, and authorized tribes are to establish TMDLs
that will meet water quality standards for each listed
water, considering seasonal variations and a margin of
safety that accounts for uncertainty. States, territories,
and authorized tribes are to submit their lists and
TMDLs to EPA for approval and, once EPA approves
them, are to incorporate these items into their continuing
planning process. If EPA disapproves a state, territorial,
or tribal list and/or TMDL, EPA will (within 30 days of
disapproval and allowing for public comment) establish
the list and/or TMDL. The state, territory, or tribe is
then to incorporate EPA's action into its continuing
planning process.
A TMDL is a tool for implementing state water quality
standards. It is based on the relationship between
sources of pollutants and in-stream water quality
conditions. The TMDL establishes the allowable
loadings for specific pollutants that a waterbody can
receive without exceeding water quality standards,
thereby providing the basis for states to establish water
quality-based pollution controls.
Note: The term "states" will be used to denote states, territories, and
authorized tribes.
A TMDL is the sum of the individual wasteload allocations for point
sources and load allocations for nonpoint sources and natural
background (40 CFR 130.2) with a margin of safety (CWA section
303(d)(1)(c)). The TMDL can be generically described by the
following equation:
TMDL = LC = • WLA + • LA + MOS
where: LC = loading capacity,3 or the greatest loading a waterbody
can receive without exceeding water quality standards;
WLA = wasteload allocation, or the portion of the TMDL
allocated to existing or future point sources;
LA = load allocation, or the portion of the TMDL allocated to
existing or future nonpoint sources and natural
background; and
MOS = margin of safety, or an accounting of uncertainty
about the relationship between pollutant loads and
receiving water quality. The margin of safety can be
provided implicitly through analytical assumptions or
explicitly by reserving a portion of loading capacity.
aTMDLs can be expressed in terms of mass per time, toxicity, or
other appropriate measures.
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Introduction and Purpose of This Protocol
Guidance on developing TMDLs is readily available for
many chemical pollutants. For some pollutants,
however, the development of TMDLs is complicated
because of the lack of adequate or proven tools or
information on the fate, transport, or impact of each
pollutant within the natural system. EPA is developing
TMDL protocols to provide guidance on TMDL
development. The protocols represent a suggested
approach, but not the only approach to TMDL
development. EPA will continue to review all TMDLs
submitted by states pursuant to Section 303(d) of the
Clean Water Act and 40 CFR 130.7.
The TMDL protocols focus on Step 3 (Development of
TMDLs) of the water quality-based approach, depicted
in Figure 1-1 (USEPA, 1991a; 1999). This specific step
is divided into seven components common to all
TMDLs, and each component is designed to yield a
product that is part of a TMDL submittal document.
COMPONENTS OF TMDL DEVELOPMENT
The following components of TMDL development may
be completed concurrently or iteratively depending on
the site-specific situation (Figure 1-2):
•• Problem identification
•• Identification of water quality indicators and targets
•• Source assessment
•• Linkage between water quality targets and sources
•• Allocations
•• Follow-up monitoring and evaluation
•• Assembling the TMDL
Note that these components are not necessarily
sequential steps, but are provided more as a guide and
framework for TMDL development. Although some of
the submittal components (e.g., TMDL calculation and
allocations) are part of the legally required TMDL
submittal and others are part of the administrative record
supporting the TMDL, this protocol considers each
component equally.
Problem Identification
The objective of problem identification is to identify the
key factors and background information for a listed
waterbody that describe the nature of the impairment
and the context for the TMDL. Problem identification is
a guiding factor in development of the remaining
elements of the TMDL process.
Identification of Water Quality Indicators and
Target Values
The purpose of this component is to identify numeric or
measurable indicators and target values that can be used
to evaluate attainment of water quality standards in the
listed waterbody. Often the TMDL target will be the
numeric water quality criteria for the pollutant of
concern. In some cases, however, TMDLs must be
developed for parameters that do not have numeric
water quality standards. When numeric water quality
criteria do not exist, impairment is determined by
narrative water quality standards or identifiable
impairment of designated uses (e.g., degraded fishery).
The narrative standard is then interpreted to develop a
quantifiable target value to measure attainment or
maintenance of the water quality standards.
Source Assessment
During source assessment, the sources of loading for the
pollutant of concern to the waterbody are identified and
characterized by type, magnitude, and location.
Linkage Between Water Quality Targets and
Sources
To develop a TMDL, a linkage between the selected
indicator(s) and target(s) and the identified sources must
be defined. This linkage establishes the cause-and-
effect relationship between the pollutant sources and the
in-stream pollutant response and allows for an
estimation of the loading capacity. Once defined, the
linkage yields the estimate of total loading capacity,
which is the maximum amount of pollutant loading (e.g.,
fecal indicators) a waterbody can assimilate and still
attain without exceeding water quality standards. The
relationship can vary seasonally, particularly for
nonpoint sources, with factors such as precipitation.
Allocations
Based on the established target/source linkage, pollutant
loadings that will not exceed the loading capacity and
will lead to attainment of the water quality standard can
be determined. These loadings are distributed or
1-2
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Protocol for Developing Pathogen TMDLs
1. Identification of Water
Quality-Limited Waters
Review water quality standards
Evaluate monitoring data
Determine if adequate controls
are in place
5. Assessment of Water
Quality-Based Control Actions
• Monitor point/nonpoint sources
. Audit NPS controls for effectiveness
• Evaluate TMDL for attainment of
water quality standards
4. Implementation of
Control Actions
Update water quality management plan
Issue water quality-based permits
Implement nonpoint source controls
(section 319 management plans)
2. Priority Ranking
and Targeting
Integrate priority ranking with
other water quality planning and
management activities
Use priority ranking to target
waterbodies for TMDLs
3. Development of TMDLs
Apply geographic approach
where applicable
Establish schedule for phased
approach, if necessary
Complete TMDL development
Figure 1-1. General elements of the water quality-based approach (adapted from USEPA, 1991 a)
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Introduction and Purpose of This Protocol
Components in TMDL Development
Suggested TMDL Submittal Elements
Identify Problem >- Problem Statement
TI
Develop Numeric
Targets
Select indicator(s) >> Numeric Targets
Identify target values
Compare existing and
target conditions
Source Assessment
• ldentify sources _>, Source Assessment
• Estimate source
loadings
Link Targets and Sources
Assess linkages >» Linkage Analysis
Estimate total loading capacity
i
Load Allocation
Divide loads among sources ^" Allocations
i
Develop Monitoring and ^ Monitoring/Evaluation Plan
Review Plan and Schedule (for phased approach)
T
Develop Implementation Plan - >~ Implementation Measures in
State Water Quality
Management Plan
Figure 1-2. General components of TMDL development
1-4
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"allocated" among the significant sources of the
pollutant of concern. The allocations are a component
of the legally approved TMDL. Wasteload allocations
contain the allowable loadings from existing or future
point sources, while load allocations establish the
allowable loadings from natural background and from
existing and future nonpoint sources. The margin of
safety is usually identified during this step to account for
uncertainty in the analysis, although it may also be
identified in other TMDL components. The margin of
safety may be applied implicitly by using conservative
assumptions in the TMDL development process or
explicitly by setting aside a portion of the allowable
loading.
Follow-up Monitoring and Evaluation
TMDL submittals should include a monitoring plan to
determine whether the TMDL has resulted in attaining
water quality standards and to support any revisions to
the TMDL that might be required. Follow-up
monitoring is recommended for all TMDLs, given the
uncertainties inherent in TMDL development (USEPA,
199 la; 1997a; 1999). The rigor of the monitoring plan
should be based on the confidence in the TMDL
analysis. A more rigorous monitoring plan should be
included for TMDLs with greater uncertainty and where
the environmental and economic consequences of the
decisions are greatest.
Assembling the TMDL
In this component, those elements of a TMDL submittal
required by statute or regulation are clearly identified
and compiled, and supplemental information is provided
to facilitate TMDL review.
For each component addressed in this protocol, the
following format is used:
•• Guidance on key questions or factors to consider.
•• Brief discussions of analytical methods.
•• Discussions of products needed to express the
results of the analysis.
• Examples of approaches used in actual settings to
complete the step.
• References on methods and additional guidance.
By addressing each of the seven TMDL components,
TMDL developers can complete the technical aspects of
TMDL development. Although public participation
requirements are largely outside the scope of this
document, early involvement of stakeholders affected by
the TMDL is strongly encouraged because of the
complex and often controversial nature of TMDLs. The
protocols also do not discuss issues associated with
TMDL implementation (note bottom of Figure 1-2).
Methods of implementation, such as National Pollutant
Discharge Elimination System (NPDES) permits, state
nonpoint source (NPS) management programs, the
Coastal Zone Act Reauthorization Amendments
(CZARA), and public participation are discussed in
Guidance for Water Quality-based Decisions: The
TMDL Process (USEPA, 1991a, 1999) and in the
August 8, 1997, memorandum "New Policies for
Establishing and Implementing Total Maximum Daily
Loads (TMDLs)" (USEPA, 1997a).
PURPOSE
This protocol provides a description of the TMDL
development process for pathogens and includes case
study examples to illustrate the major points in the
process. It emphasizes the use of rational, science-based
methods and tools for each step of TMDL development
to assist readers in applying a TMDL development
process that addresses all regulatory requirements.
Note that this protocol focuses mainly on fecal coliform
bacteria as pathogen indicators since that is the indicator
currently in most state water quality standards.
However, EPA strongly encourages states that have not
already done so, to adopt the recommendations set forth
in Ambient Water Quality Criteria for Bacteria - 1986
or other water quality criteria for bacteria based on
scientifically defensible methods into their water quality
standards to replace water quality criteria for total or
fecal coliforms. EPA's 1986 water quality criteria for
bacteria recommend the use of enterococci for marine
waters and E. coll or enterococci for fresh waters. It is
also important to realize that the presence of indicator
bacteria does not always prove or disprove the presence
of human pathogenic bacteria, viruses, or protozoans.
References and recommended reading lists are provided
for readers interested in obtaining more detailed
background information. This protocol has been
written with the assumption that users have a general
background in the technical aspects of water quality
management and are familiar with the statutory and
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Introduction and Purpose of This Protocol
regulatory basis for the TMDL program. A glossary is
included at the end of the document with definitions of
some commonly used terms.
RECOMMENDED READING
(Note that a full list of references is included at the end
of this document.)
USEPA. 1991a. Guidance for Water Quality-based
Decisions: The TMDL Process. EPA 440/4-91-001.
U.S. Environmental Protection Agency, Washington,
DC. .
USEPA. 1995a. Watershed Protection: A Project
Focus. EPA 841-R-95-003. U.S. Environmental
Protection Agency, Office of Water, Washington, DC.
USEPA. 1995b. Watershed Protection: A Statewide
Approach. EPA 841-R-95-001. U.S. Environmental
Protection Agency, Office of Water, Washington, DC.
USEPA. 1997a. New Policies for Establishing and
Implementing Total Maximum Daily Loads (TMDLs).
U.S. Environmental Protection Agency, Washington,
DC. .
USEPA. 1999. Draft Guidance for Water Quality-
based Decisions: The TMDL Process. 2nd ed. EPA 841-
D-99-001. U.S. Environmental Protection Agency,
Washington, DC.
.
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Protocol for Developing Pathogen TMDLs
General Principles of Pathogen Water Quality Analysis
Objective: To develop a pathogen TMDL, it is important
to have a basic understanding of pathogen processes in a
watershed and how excessive pathogens can affect water
quality and designated uses of water. This section
provides background information on pathogen impacts
on designated uses, types of pathogens, pathogen
sources and transport, indicator organisms, survival
factors, and potential control strategies.
IMPACTS OF PATHOGENS ON DESIGNATED USES
Microorganisms are ever present in terrestrial and
aquatic ecosystems. Most types are beneficial,
functioning as agents for organic and synthetic chemical
decomposition, as food sources for larger animals, and
as essential components of the nitrogen cycle and other
biogeochemical cycles. Some reside within the bodies
of higher-order animals and aid in the digestion of food;
others are used for medical purposes such as providing
antibiotics. A small subset of microorganisms, however,
is harmful. If taken into the body they can cause
sickness or even death. As a group, these disease-
causing microorganisms are known as pathogens.
Pathogens are a serious concern for managers of water
resources. Because of the pathogens' small size, they
are easily carried by storm water runoff or other
discharges into natural waterbodies. Once in a stream,
lake, or estuary, they can infect humans through
contaminated fish and shellfish, skin contact, or
ingestion of water. Of the designated uses listed in
section 303(c) of the Clean Water Act, protection from
pathogenic contamination is most important for waters
designated for recreation (primary and secondary
contact); public water supplies; aquifer protection; and
protection and propagation of fish, shellfish, and
wildlife. Some of the impairments to designated uses
caused by pathogens are discussed here.
Recreational use
Excessive amounts of fecal bacteria in surface water
used for recreation have been known to indicate an
increased risk of pathogen-induced illness to humans.
Infection due to pathogen-contaminated recreational
waters include gastrointestinal, respiratory, eye, ear,
nose, throat, and skin diseases (USEPA, 1986).
Gastrointestinal symptoms include vomiting, diarrhea,
fever, and stomachache or nausea accompanied by fever.
In 1968 criteria were established by the Federal Water
Pollution Control Administration (FWPCA) of the
Department of the Interior for fecal coliforms at a level of
200 fecal coliform organisms (colony-forming units
[CPU1] when cultured) per 100 mL of water (USEPA,
1968). In addition to the presence of fecal coliform
bacteria in the water column, many studies have shown
the presence and survival of fecal coliforms, as well as
pathogens, in marine and freshwater sediments (Nix et
al., 1994). A study done in Oak Creek, Arizona found
that water quality violations only occurred when
sediments were found to have high levels of fecal
coliforms in the sediments (Crabill et al., 1999). These
fecal coliforms may signify the presence of pathogens,
which pose a potential health risk. Activities such as
recreational swimming that resuspend contaminated
sediments and the associated fecal bacteria and pathogens
can increase the health risk posed by waters.
In 1986, EPA published Ambient Water Quality Criteria
for Bacteria-19%6. The data supporting the water quality
criteria were obtained from a series of research studies
conducted by EPA examining the relationship between
swimming-associated illness and the microbiological
quality of the waters used by recreational bathers
(USEPA, 1986).
The results of those studies demonstrated that fecal
coliforms, the indicator originally recommended in 1968
by the FWPCA, showed less correlation to swimming-
associated gastroenteritis than some other indicator
organisms. Two indicator organisms, E. coll and
enterococci, showed a strong correlation, the former in
fresh waters only and the latter in both fresh and marine
waters.
Throughout this document, fecal coliform units are expressed as
CFU, counts, organisms, and most probable number (MPN). "CFU"
and "MPN" represent units specific to analytical techniques used to
quantify fecal coliform concentration, whereas "counts" and
"organisms" are generic terms used to express bacteria concentration.
In this protocol, specific units (e.g., MPN) are used where appropriate,
but all unit expressions are considered equivalent measures of fecal
coliform bacteria concentration.
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General Principles of Pathogen Water Quality Analysis
Consequently, EPA's Ambient Water Quality Criteria
for Bacteria-1986 recommends the use of E. coli and
enterococci rather than fecal coliforms. The
recommended steady-state geometric mean values of
these water quality criteria for bacteria are 33
enterococcci per 100 mL and 126 E. coli per 100 mL for
fresh waters; and a geometric mean of 35 enterococci
per 100 mL for marine waters. These values are based
on specific levels of risk of acute gastrointestinal illness.
The levels of risk used by EPA correlating to these
values are no more than eight illnesses per 1,000
swimmers for fresh waters, and no more than 19
illnesses per 1,000 swimmers for marine waters. The
illness rates are EPA's best estimates of the accepted
illness rates for areas that had previously applied the
fecal coliform criterion. EPA determined that when
implemented in a conservative manner, these water
quality criteria are protective of gastrointestinal illness
resulting from primary contact recreation.
Drinking water supply
The presence of any fecal indicators indicates that
drinking water is potentially unsafe for consumption.
The maximum contaminant level goal (MCLG2) is set at
zero for Cryptosporidium and Giardia lamblia, total
coliforms, and viruses for public drinking water systems.
However, for surface waters used as drinking water,
most viruses and bacteria are inactivated by chlorine or
other disinfectants used during the treatment process,
although some human pathogens are more resistant to
disinfection than others. Further, prospective drinking
water treatment requirements to reduce the formation of
carcinogenic disinfection byproducts during treatment
will complicate the task of pathogen control by shifting
the use of technology to more advanced and expensive
techniques such as ozone, membranes or ultraviolet
radiation. All disinfection and filtration technologies
are designed to remove a proportion, but not all of,
pathogen contamination from the influent e.g., 2 logs or
99 percent removal. Therefore, higher pathogen
loadings in the source water (waterbody) translate into
higher pathogen contamination levels in the treated
water and a greater public risk.
A non-enforceable concentration of a drinking water contaminant
that is protective of human health and allows an adequate margin of
safety.
Of particular concern has been the occurrence of the
encysted protozoans Cryptosporidium parvum and
Giardia lamblia, which, particularly in the case of
Cryptosporidium, are not appreciably killed by
chlorination and may require special filtration procedures
to eliminate risks from exposure to these pathogens.
Protozoans can be responsible for causing giardiasis and
cryptosporidiosis in humans through ingestion of Giardia
and Cryptosporidium cysts (NCSU, 1997). Giardiasis is
a gastrointestinal disease that causes diarrhea and
vomiting. Cryptosporidiosis affects the cells of the
digestive tract, epithelium, liver, kidneys, and blood.
Cryptosporidium is capable of causing life-threatening
infections in people with weakened immune systems
(Graczyk et al., 1998).
Aquatic life and fisheries
Filter-feeding shellfish such as clams, oysters, and
mussels, and other shellfish, such as shrimp and crabs,
concentrate microbial contaminants in their tissues and
may be harmful to humans when consumed raw or
undercooked. Fecal and total coliform indicator levels
are used to protect consumers of raw bivalve mollusks
from viruses causing Norwalk-like viral gastroenteritis,
enteric bacteria and Hepatitis A, and the highly
pathogenic Vibrio bacteria. The Food and Drug
Administration (FDA) has established guidelines to
reduce the risk from microbial contaminants that might
be found in filter-feeding shellfish.
PATHOGEN TYPES
Pathogens most commonly identified and associated with
waterborne diseases can be grouped into the three general
categories: bacteria, protozoans, and viruses. (Appendix
A provides descriptions of the various techniques for
measurement of pathogens.)
Bacteria
Bacteria are unicellular organisms that lack an organized
nucleus and contain no chlorophyll (Chapra, 1997). They
contain a single strand of DNA and typically reproduce
by binary fission, during which a single cell divides to
form two new cells. Wastes from warm-blooded animals
are a source for many types of bacteria found in
waterbodies, including the coliform group and
Streptococcus, Lactobacillus, Staphylococcus, and
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Clostridia. Not all bacteria are pathogenic, however.
Table 2-1 presents information on some of the major
pathogenic waterborne bacteria of concern.
Protozoans
Protozoans are unicellular organisms that reproduce by
fission and occur primarily in the aquatic environment.
Pathogenic protozoans constitute almost 30 percent (or
10,000) of the 35,000 known species of protozoans
(Mitchell et al, 1988, cited in NCSU, 1997).
Pathogenic protozoans exist in the environment as cysts
that hatch, grow, and multiply after ingestion,
manifesting as the associated illness. Encystation of
protozoans facilitates their survival, protecting them
from harsh conditions such as high temperature and
salinity. Two protozoans of major concern as
waterborne pathogens are Giardia lamblia and
Cryptosporidium. Giardia causes giardiasis, one of the
most prevalent waterborne diseases in the United States;
Cryptosporidium causes cryptosporidiosis. Some
waterborne protozoans from fecal sources posing threats
to human health are listed with their associated diseases
in Table 2-2.
Viruses
Viruses are a group of infectious agents that require a
host in which to live. They are composed of highly
organized sequences of nucleic acids, either DNA or
Table 2-1. Pathogenic bacteria of concern to water quality and
their associated diseases
Bacteria
Escherichia coli
0157:H7
(enteropathogenic)
Salmonella typhi
Salmonella
Shigella
Vibrio cholerae
Yersinia
enterolitica
Disease
Gastroenteritis
Typhoid fever
Salmonellosis
Shigellosis
Cholera
Yersinosis
Effects
Vomiting, diarrhea
High fever, diarrhea, ulceration
of the small intestine
Diarrhea, dehydration
Bacillary dysentery
Extremely heavy diarrhea,
dehydration
Diarrhea
Cryptosporidiosis Outbreak in Milwaukee
In 1993, a substantial outbreak of cryptosporidiosis occurred in
Milwaukee, Wisconsin. The outbreak was caused by
Cryptosporidium in the municipally treated drinking water, illustrating
the seriousness of the threat of this protozoan in public water
supplies. The outbreak is the largest documented waterborne
disease outbreak in U.S. history (Craun et al., 1997). At least 26
percent of the population in the five counties constituting the
Milwaukee metropolitan area contracted cryptosporidiosis during the
6-week outbreak (Wisconsin Division of Health, cited in Halpern et
al., 1997). There were 110 deaths from the outbreak; most of the
fatalities were people with weakened immune systems (Milwaukee
Health Department, cited in Halpern et al., 1997). According to one
study, including the 725,000 lost "work/school days," this outbreak
cost an estimated $166 million in medical charges and lost work time
(Levin, 1994, cited in Halpern etal., 1997).
RNA, depending on the virus. All viruses have a protein
covering that encloses the nucleic acid. Some viruses
have a lipoprotein (protein in which at least one of the
components is a lipid) envelope over the protein
covering. The protein or lipoprotein covering determines
to what surface the virus will adhere.
The most significant virus group affecting water quality
and human health originates in the gastrointestinal tract
of infected individuals. These enteric viruses are
excreted in feces and include hepatitis A, rotaviruses,
Norwalk-type viruses, adenoviruses, enteroviruses, and
reoviruses. Table 2-3 presents some important viruses
and their associated diseases.
Table 2-2. Protozoans of concern to water quality and their
associated diseases
Protozoan
Balantidium coli
Cryptosporidium
Entamoeba
histolytica
Giardia lamblia
Disease
Balantidiasis
Cryptosporidiosis
Amebiasis (amoebic
dysentery)
Giardiasis
Effects
Diarrhea, dysentery
Diarrhea, death in
susceptible
populations
Prolonged diarrhea
with bleeding,
abscesses of the liver
and small intestine
Mild to severe
diarrhea, nausea,
indigestion
Adapted from Metcalf and Eddy, 1991
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General Principles of Pathogen Water Quality Analysis
Table 2-3. Viruses of concern to water quality and their
associated diseases1
Virus
Adenovirus (48 serotypes;
types 40 and 41 are of
primary concern)
Enterovirus (68 types, e.g.,
polio, echo, encephalitis,
conjunctivitis, and Coxsackie
viruses)
Hepatitis A
Reovirus
Rotavirus
Calicivirus (e.g.,Norwalk-
like and Sapporo-like viruses)
Astrovirus
Disease
Respiratory disease,
gastroenteritis
Gastroenteritis,
heart anomolies,
meningitis
Infectious hepatitis
Gastroenteritis
Gastroenteritis
Gastroenteritis
Gastroenteritis
Effects
Various effects
Various effects
Jaundice, fever
Vomiting, diarrhea
Vomiting, diarrhea
Vomiting, diarrhea
Vomiting, diarrhea
1 Hepatitis E is an emerging virus that has caused large outbreaks of
infectious hepatitis outside of the U.S.
Adapted from Metcalf and Eddy, 1991 and G. Shay Fout, USEPA,
2000
INDICATOR ORGANISMS
The numbers of pathogenic organisms present in
polluted waters are generally few and difficult to
identify and isolate, as well as highly varied in their
characteristic or type. Therefore, scientists and public
health officials typically choose to monitor
nonpathogenic bacteria that are usually associated with
pathogens transmitted by fecal contamination but are
more easily sampled and measured. These associated
bacteria are called indicator organisms. Indicator
organisms are assumed to indicate the presence of
human pathogenic organisms. When large fecal
coliform populations are present in the water, it is
assumed that there is a greater likelihood that pathogens
are present (McMurray et al., 1998). Fecal indicators
are used to develop water quality criteria to support
designated uses, such as primary contact recreation and
drinking water supply. EPA publishes 304(a) criteria as
guidance to states and tribes. States and tribes may
adopt EPA's 304(a) criteria, 304(a) criteria modified to
reflect site-specific conditions or criteria based on other
scientifically-defensible methods. Fecal indicators may
also be used to assess the degree of pathogen removal by
treatment processes or to detect contamination of
distribution systems.
The selection of fecal indicator organisms is a difficult
and controversial process. To function as an indicator of
fecal contamination in surface water and groundwater,
the organism should (1) be easily detected using simple
laboratory tests, (2) generally not be present in
unpolluted waters, (3) appear in concentrations that can
be correlated with the extent of contamination (Thomann
and Mueller, 1987), and (4) have a die-off rate that is not
faster than the die-off rate for the pathogens of concern.
Some commonly used indicators include coliform
bacteria and fecal streptococci. Coliform bacteria, which
are able to ferment lactose and produce carbon dioxide
gas (CO2), include total coliforms, fecal coliforms, and
Escherichia coli (E. coli). The term "total coliforms"
includes several genera of gram-negative, facultative
anaerobic, non-spore-forming, rod-shaped bacteria, some
of which occur naturally in the intestinal tract of animals
and humans, as well as others that occur naturally in soil
and in fresh or marine waters and could be pathogenic to
a variety of specific hosts. Fecal coliforms (a subset of
total coliforms) include several species of coliform
bacteria and are found in the intestines and feces of
warm-blooded animals. The presence of E. coli (a subset
of fecal coliforms) in a water sample also indicates fecal
contamination since E. coli is one of the ubiquitous
coliform members of the intestinal microflora of warm-
blooded animals (Jawetz et al., 1987). (For more detailed
descriptions of these bacteria, see the glossary.) (See
Figure 2-1 for indicator organism relationships.)
There has been a resurgence of interest in the
enterococcus group as indicators (Davies-Colley et al.,
1994). Enterococci (a subgroup of the fecal streptococci
[FS] group) are round, coccoid bacteria that live in the
intestinal tract. Streptococcus faecalis and Streptococcus
faecium (part of the enterococci family) are thought to be
more human-specific than other streptococci, but they
can be found in the intestinal tracts of other warm-
blooded animals such as cats, dogs, cows, horses, and
sheep. The risk to swimmers of contracting
gastrointestinal illness seems to be predicted better by
enterococci than by fecal coliform bacteria since the die-
off rate of fecal coliform bacteria is much greater than the
enterococci die-off rate.
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Indicator Organisms
Fecal Streptococci
I '
Streptococcus
bow's
Streptococcus
equ/nus
Streptococcus
aw'um
Enferococcus
faeca//'s
Enferococcus
faec/um
Figure 2-1. Relationships among indicator organisms.
Some officials present fecal coliform and fecal
streptococci data as a ratio in an attempt to indicate the
origin of bacterial pollution. A fecal coliform/fecal
streptococci ratio of 4 or greater has been said to
indicate a human source. An FC/FS ratio for domestic
animals is on average 0.1-0.6, and the FC/FS ratio for
wild animals is on average less than 0.1 (Howel et al.,
1995). This generalization, however, does not hold true
in many cases (Novotny and Olem, 1994). As applied to
FC/FS ratios in surface and ground water samples, these
numbers hold true only for recent fecal contamination.
The FC/FS ratio is not recommended as a means of
differentiating human and animal sources of pollution,
mainly because of the variable die-off rates of fecal
streptococci species (APHA, 1995).
The 1986 federal bacteriological water quality criteria
document (USEPA, 1986) critically reviewed a series of
epidemiological and water quality monitoring studies at
marine and freshwater beaches since 1972. A
comparison of various fecal indicators of potential
pathogens with disease incidence revealed that elevated
levels of enterococci bacteria were most strongly
correlated with gastroenteritis in both fresh and marine
recreational waters. The gastroenteritis was assumed to
be related to the elevated levels of enterococci. E. coll
also showed a correlation with gastroenteritis, primarily
in freshwater, but total coliform and fecal coliform
bacteria, which were commonly measured throughout
the United States (established on the basis of
the 1968 recommended criteria), were only
weakly correlated with this disease. The
recommended criteria for enterococci and E.
coll were then developed. These
recommended criteria are discussed in more
detail in Section 4 of this document.
Many issues surround the use of fecal
indicators in determining the quality of
waterbodies relative to pathogens. Major
issues of concern are the correlation between
the measured indicator and the presence of
pathogens and the correlation between those
pathogens and the incidence of disease. A
review by Pruess (1998) of 22 studies of
recreational waters showed that the indicator
organisms that correlate best with illness are
enterococci/fecal streptococci for both marine
water and fresh water and E. coll for
freshwater. The microbiological indicators yield a
general assessment of water quality and safety for the
designated or existing use and do not identify specific
human pathogens; that is, the exceedance of criteria
developed for E. coll and enterococci bacteria indicates
that the water might cause some type of illness following
exposure to that water. For example, recreational use by
swimmers or surfers could be impaired by the presence of
high densities of fecal indicators because there is a
chance that some of those microorganisms could cause
gastrointestinal illnesses if the water is swallowed.
Commercial or recreational harvesting of clams in an
estuary could be impaired because the presence of high
densities of these bacteria suggests that other human
pathogens such as the infectious hepatitis A virus might
be present in the shellfish tissues. A public water supply
may be impaired by high levels of a pathogen indicator
originating from human sources or activities.
PATHOGEN SOURCES AND TRANSPORT
Pathogenic organisms are one of many types of pollutants
generated at a source (point or nonpoint) and then
transported by a pipe, storm water runoff, groundwater,
or other mechanism to a body of water. Identifying these
sources and tracking the movement of pathogens is often
a difficult and resource-intensive task.
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General Principles of Pathogen Water Quality Analysis
Point sources
The transport of pathogens to a waterbody occurs either
directly or indirectly from both point and nonpoint
sources. For point sources, the direct transport pathway
is straightforward—the point source (e.g., wastewater
treatment plant [WWTP]) end-of-pipe pathogen
concentration is directly discharged into a waterbody.
Major point sources of pathogens are discharges from
WWTPs and combined sewer overflows (CSOs). Raw
sewage entering the WWTP typically has a total
coliform count of 107 to 109 most probable number
(MPN) per 100 mL (Novotny et al., 1989). Associated
with raw sewage are proportionally high concentrations
of pathogenic bacteria, viruses, and protozoans. A
typical plant reduces the total coliform count by about 3
orders of magnitude, to the range of 104 to 106 MPN/100
mL. The magnitude of pathogen reduction, however,
varies with the treatment process employed. For
example, the protozoan Giardia is treated effectively
with chlorine, but chlorine does not effectively kill
Cryptosporidium (Chapra, 1997). For Cryptosporidium,
filtration or ozonation must be applied.
In some instances raw sewage can bypass WWTPs and
enter waterbodies directly. This can occur because of
failures or leaks in sanitary sewer systems or, in the case
of CSOs, when wet-weather flows exceed the
conveyance and storage capacity of the combined
system. In CSOs, urban runoff and sanitary sewage are
conveyed in the same system. Typical CSO
concentrations for total coliforms are reported as 105 to
107 MPN/100 mL (Novotny et al., 1989), or about 1
order of magnitude greater than treatment plant effluent.
In contrast to WWTP effluent, CSOs discharge for short
periods of time, discharge at random intervals, and are
associated with storm flows that provide dilution of the
effluent.
Other point sources that can contribute substantial loads
of pathogens and fecal indicators to waterbodies include
concentrated animal feeding operations, slaughterhouses
and meat processing facilities; tanning, textile, and pulp
and paper factories; and fish and shellfish processing
facilities.
Nonpoint sources
Nonpoint sources of pollution differ from point sources
because the former are typically wet-weather-dominated.
In addition, nonpoint source pollutants are diffuse in
nature and do not enter waterbodies from any single
point. Indirect nonpoint sources include any source
located far enough from waterbodies to allow attenuation
of the pathogens in runoff, infiltrated water, or
groundwater. Identification of sources and quantification
of pathogen loads from nonpoint sources can be difficult.
In urban3 and suburban areas, nonpoint sources of
pathogens include urban litter, contaminated refuse,
domestic pet and wildlife excrement, and failing sewer
lines. In a study of bacterial loading in urban streams,
Young and Thackston (1999) found that fecal bacteria
densities were directly related to the density of housing,
population, development, percent impervious area, and
domestic animal density.
Rural nonpoint source loads originate from both land
use-specific and natural sources. The primary rural
nonpoint source for pathogens is confined animal
operations, in which large quantities of fecal matter are
produced. Livestock excrement from barnyards,
pastures, rangelands, feedlots4, and uncontrolled manure
storage areas is a significant nonpoint source of bacteria,
viruses, and protozoal cysts. The occurrence and degree
of fecal indicator and pathogen loads from livestock are
linked to temporally and spatially variable hydrologic
factors such as rainfall and runoff except when manure is
deposited directly into a waterbody (Edwards et al.,
1997). Other significant sources include leaking septic
systems and land application of manure and sewage
sludge5. Septic systems that fail hydraulically (surface
breakouts) or hydrogeologically (inadequate soils to filter
pathogens) can adversely affect downgradient surface
waters (Horsley and Witten, 1996). Because the majority
of pathogens are filtered or attenuated in soil zones over
Some urban stormwater sources are considered as point sources by
the CWA.
If feedlots meet the regulatory definition of a Concentrated Animal
Feeding Operation (CAFO), they are treated as point sources by the
CWA and therefore are not considered in nonpoint source load
contributions.
Much of the application of sewage sludge is regulated by permits
under state and federal laws.
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the water table, groundwater has traditionally been
considered the water source least susceptible to
contamination by pathogens. However, depending on
soils and geology, connections between groundwater
and a contaminated surface or subsurface source can
pose threats to the quality of aquifers in the area.
Seepage from a waste lagoon, a leaking septic tank, or
an improperly designed landfill can result in
contamination of aquifer resources.
Wildlife can also be a significant nonpoint source of
pathogens in many areas. Many wildlife species are
reservoirs of microorganisms that are potentially
pathogenic to themselves and to humans. Beaver and
deer are large contributors of Giardia and
Cryptosporidium, respectively. Waterfowl such as
geese, ducks, and heron also can contaminate surface
water with microbial pathogens (Graczyk et al., 1998).
These pathogens, such as Giardia cysts, are a potentially
dangerous health risk for humans, livestock, and
wildlife.
Although many nonpoint sources of pathogens are
diffuse in nature, some can act as direct sources to a
waterbody. Examples of these direct nonpoint sources
of pathogens are boat discharges, landfills, waterfowl,
and failing septic systems. Boats lacking holding tanks
for pumpout contribute human pathogens to surface
water; groundwater impacts could occur due to seepage
from landfill oxidation ponds that contain fecal bacteria
(Metcalf and Eddy, 1991); waterfowl contributions of
pathogens are often directly deposited to the waterbody
of concern; and failing septic systems may contribute
significant pathogen loads directly to a waterbody
without significant reduction in numbers, especially in
coastal areas or areas of coarse-textured soils or karst
geology.
Another potential nonpoint source of pathogens is the
resuspension of bacteria indicators and pathogens in
sediments. For example, Weiskel et al. (1996) reported
significantly increased values of water column fecal
coliform density after artificial disturbance of the
surface 2 cm of sediments in Buttermilk Bay,
Massachusetts. These increased levels of fecal coliform
bacteria might indicate the presence of pathogens in the
waterbody. The most pronounced increases occurred at
sites underlain by fine-grained, high-organic-carbon
muds. As runoff during a storm event begins, the
discharge and velocity increase, in turn scouring bacteria
from the benthic areas of the stream (Yagow and
Shanholtz, 1998). This scouring causes increased levels
of bacteria concentrations in the water column and
decreased levels in the stream sediments. After peak
discharge, the bacteria concentrations in the water
column decrease at a faster rate than the discharge. This
causes the sediment to be deposited downstream, where
the sediment bacteria concentrations increase and water
column concentrations return to background levels. The
increasing usage of recreational waters can cause
resuspension of the high numbers of bacterial indicators
and pathogens occurring in the sediments (Burton et al.,
1987). This creates a potential health hazard from the
possible ingestion of the resuspended pathogens.
Although the type of source provides information on the
concentrations and possible loads of pathogens to
waterbodies, another important consideration is the
proximity of the source to the waterbody of concern.
Nonpoint sources closer to a waterbody have a greater
likelihood to pollute the water than those located farther
away, where attenuation factors and dilution will reduce
the actual load delivered to the waterbody.
FACTORS INFLUENCING PATHOGEN SURVIVAL
Determining what happens to the microorganisms once
they reach the waterbody is often as challenging as
identifying and tracking their sources. As living
organisms they require certain conditions to survive,
grow, and reproduce. Thus, risks to human health can be
increased or decreased depending on water temperature
and other factors associated with the waterbody. Many
factors influence the die-off rate of viruses, bacteria, and
protozoans in the environment. These factors include
sunlight, temperature, moisture conditions, salinity, soil
conditions, waterbody conditions, settling, association
with particles, and encystation. Many other factors affect
the die-off rate of pathogens, but not all are described in
this protocol. Some of these other factors include the age
of the fecal deposit, pH, starvation, structural damage,
chemical damage, predation (Davies-Colley et al., 1994),
osmotic stress in moving from fresh to marine waters,
nutrient deficiencies, turbidity (water clarity), variation
of spectral quality of sunlight, microbial composition of
effluents, and oxygen concentrations. Some of these
factors have a direct influence on mortality, whereas
others indirectly affect die-off in the environment by
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General Principles of Pathogen Water Quality Analysis
increasing exposure to other factors. For example, a
longer distance traveled from the source to the
waterbody affects die-off by increasing exposure to
attenuation factors, such as temperature, sunlight, and
moisture. The factors are influenced by many variables,
the most notable being the medium in which they occur.
For example, ultraviolet light increases the die-off rate
of fecal indicator bacteria, but the magnitude of the
die-off is different if the bacteria are on the ground
surface, in the upper water column, or in the lower water
column.
Although die-off rates vary by species for each group of
pathogens and bacterial indicators, the following
overview describes the general factors that influence
pathogen extinction; organism- or species-specific
effects are not discussed. Of the many direct factors that
might influence the inactivation of pathogens in the
environment, the most important are sunlight (ultraviolet
and near ultraviolet radiation), temperature, and
moisture conditions. In addition, many other factors
potentially influence pathogen mortality in the
environment. Some factors may affect die-off by
prolonging direct exposure to the attenuation factors
listed above; others, such as predation, affect die-off but
are less important than sunlight, temperature, or
moisture.
Sunlight (ultraviolet radiation)
Bacterial survival after deposition onto the land surface
is greatly dependent on solar radiation, especially in the
ultraviolet range (Auer and Niehaus, 1992). Because of
solar radiation, bacteria have a shorter survival time at
the surface than in soil. Solar radiation is also a major
factor in the survival time of viruses. Increased solar
and ultraviolet radiation greatly decreases the survival
rate of viruses, and like bacteria, viruses have a
decreased survival time on the surface relative to
survival in soil. Limited information is available on the
influence of ultraviolet radiation on protozoans.
Sunlight does, however, play an important role in the
inactivation of Giardia and Cryptosporidium (Johnson
et al., 1997). In a study done by Johnson et al. (1997),
Giardia cyst and Cryptosporidium oocyst die-off rates
were both affected by sunlight. Both protozoans
persisted longer in the dark than in direct sunlight, but
Cryptosporidium oocysts survived longer. Johnson et al.
(1997) found that the order of survival for some
waterborne pathogens in sunlight to be Cryptosporidium
> poliovirus > Giardia > Salmonella.
Temperature
Pathogen and bacterial indicator survival is highly
dependent on temperature. Temperature has an inverse
relationship with the survival of microorganisms
originating in fecal waste, with survival decreasing as
temperature increases. Many laboratory studies have
been conducted to determine conditions that affect the
infectivity of Cryptosporidium spp. oocysts. Researchers
found infectivity was lost when the oocysts were frozen,
boiled, or heated to 60 • C or more for 5 to 10 minutes or
longer (Badenoch et al., 1990); freeze-dried (Tzipori,
1983); or stored for 2 weeks at 15 to 20 • C or stored for 5
days at 37 • C (Sherwood et al., 1982). Oocysts of
Cryptosporidium have been observed to survive for up to
6 months in river water at ambient temperatures
(Medema et al., 1997). Giardia lamblia cysts can survive
more than 2 months at 4 • C (Adam, 1991; Bingham et
al., 1979). Temperature is apparently the major factor for
virus and coliform bacteria survival in soils, with an
estimated doubling of the die-off rate for each 10 • C rise
(Gerba and Bitton, 1984; Reddy et al., 1981).
Temperature is also the dominant factor affecting virus
survival in freshwater, with greater survival occurring at
lower temperatures. Enteric viruses can survive from 2
to more than 188 days in freshwater (Novotny and Olem,
1994).
Moisture
Soil moisture is another important factor in the survival
time of bacteria in soil. Survival time of bacteria
increases with the moisture content and moisture holding
capacity of the soils (Reddy et al., 1981). Typically,
higher clay content in soil results in increased soil
moisture retention and, consequently, increased bacteria
survival. Cryptosporidium oocysts lost their infectivity
when dried for 1 to 4 days at -1 to 29 • C (Anderson,
1986). Dry fecal specimens lost their infectivity more
rapidly than those kept moist.
Salinity
The survival of bacteria in water is largely dependent on
salinity. Chapra (1997) reports a formula for the
calculation of the natural mortality rate of total coliforms
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Protocol for Developing Pathogen TMDLs
that assumes a freshwater loss rate of 0.8 organisms per
day (d"1) regardless of outside factors. The freshwater
loss is supplemented by a saltwater loss that is linearly
dependent on salinity, resulting in a total loss rate range
of 0.8 d"1 for freshwater to 1.4 d"1 for saltwater. The
total loss can be modified to account for other factors,
such as temperature or insolation.
Soil conditions
Pathogen survival in soil is affected by such soil
conditions as pH and predation. Shorter survival times
have been noted in acid soils (pH 3 to 5) than in neutral
calcareous soils (Novotny and Olem, 1994). In general,
bacteria survival decreases in soil with low pH, with
bacteria attenuation occurring in the soils with pH levels
between 3 and 4 (Horsley and Witten, 1996). Viruses
cannot reproduce in soil, but can survive in soil from as
short a time as 7 days to as long as 6 months, depending
on the nature of the soil, temperature, pH, moisture, and
predation by soil microflora (Howell et al., 1996,
Novotny and Olem, 1994). Longer survival of some
bacteria and viruses has also been noted when sufficient
amounts of organic matter are present.
Waterbody conditions
After discharge into a waterbody, pathogenic organisms
are subject to many additional factors during dispersion
and transport. The factors that influence the survival of
the pathogenic organisms within the waterbody are the
physical conditions of the water (Baudisova,1997),
sunlight, temperature, salinity, predation, nutrient
deficiencies, toxic substances, settling, resuspension of
particles with sorbed organisms, and aftergrowth
(growth of the organisms in the waterbody) (Thomann
and Mueller, 1987). Typically, conditions favorable to
the survival of pathogens in water are lower amounts of
light energy, lower salinity, elevated levels of nutrients
and organic matter, and lower temperatures.
Settling
Many studies have shown that there are often much
higher numbers of indicator and pathogenic bacteria in
sediments than in the overlying waters (Burton et al.,
1987). These higher concentrations of bacteria in the
sediments are apparently due to a combination of
sedimentation, sorption, and the phenomenon of
extended survival in sediments. Bacterial cells settle
from the water column as discrete entities and as part of
larger aggregates of fecal material, storm water debris,
and other suspended solids (Schillinger and Gannon,
1982, cited in Auer and Niehaus, 1992). Gannon et al.
(1983) concluded that sedimentation played an important
role in the overall removal of fecal coliform from the
water column after observing that viable fecal coliform
bacteria accumulated at the sediment surface in Ford
Lake, Michigan. Once settled, pathogens and bacterial
indicators can have an increased survival time due to
protection from harmful factors such as sunlight and
temperature. Levels of fecal coliform and specific
pathogenic organisms have been shown to survive for
longer periods of time in the sediments than in the
overlying water column (Sherer et al., 1992; Burton et al.,
1987; Thomann and Mueller, 1987). The sediment
reservoir allows for the enteric and pathogenic bacteria to
survive for up to several months, making resuspension
and ingestion in primary contact waters a real threat to
swimmers (Burton et al., 1987). Increased survival rates
for viruses in estuarine sediments have been reported in
LaBelle and Gerba (1980), Roper and Marshall (1979),
Burton et al., 1987 and Sherer et al., 1992. Due to the
accumulation of pathogens in bottom sediments,
resuspension of the sediment and the subsequent
desorption of the pathogens is a potential source of
contamination to the overlying water. A study by Sherer
et al. (1992) showed the survival of fecal coliform and
fecal streptococci to be significantly longer in sediment-
laden waters than in waters without sediment. Fecal
coliform and fecal streptococci bacteria showed half-lives
from 11 to 30 days and 9 to 17 days, respectively, when
incubated with sediment. These are longer half-lives than
those when sediment was not present. During the study
the stream bottom was disturbed several times. The mean
concentration of fecal coliform in the stream increased by
1.7 times the initial concentration after the stream bottom
was disturbed. The fecal streptococci concentration
increased by 2.7 times. This study showed that enteric
bacteria can survive in sediments for several months as
compared to a only few days in the overlying water.
Encystation
Protozoans occur primarily in aquatic environments,
where they exist in resting stages, called cysts or oocysts.
Giardia cysts can survive in water for 1 to 3 months
(NCSU, 1997). Although protozoans can extend their
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General Principles of Pathogen Water Quality Analysis
survival time by encystation, the cysts and oocysts can
become nonviable in the environment, causing only a
fraction of the total concentration to be capable of
leading to infection. Lower viability tends to occur at
high temperatures (Chapra, 1997).
PATHOGEN SOURCE CONTROLS
A key objective of water quality protection is to protect
human health from the deleterious effects of waterborne
pathogens. Water quality standards define the goals for
a waterbody by designating the use(s), by setting
numeric or narrative criteria necessary to protect the
use(s), and by protecting water quality through
antidegradation provisions. The numeric or narrative
criteria are to be based on sound scientific rationale and
should contain sufficient parameters or constituents to
protect the designated or existing use.
Controlling point sources
NPDES permits are required for the discharge of
pollutants from most point source dischargers into the
waters of the United States. These permits translate
wasteload allocations into enforceable limits and
requirements for point sources by setting restrictions on
the quantities, discharge rates, and/or concentrations of
the specified pollutants. Point sources typically rely on
a range of treatment options before discharging effluent.
Treatment of municipal waste is generally identified as
primary, secondary, or advanced (previously called
tertiary treatment), although the distinctions are
somewhat arbitrary. Primary treatment involves
removing suspended solids with screens and the use of
gravity settling ponds followed by disinfection. Most
protozoan cysts settle out in ponds after 11 days due to
their size (EnvironmentalMicrobiology, 1997).
Secondary treatment uses biological treatment to
decompose organic matter to cell material and by-
products, and the subsequent removal of cell matter,
usually by gravity settling. Secondary treatment can
also be followed by disinfection. Activated sludge
processes involve the production of an activated mass of
microorganisms capable of stabilizing waste aerobically.
Aerobic processes are preferred due to their higher rates
of decomposition and because pathogenic
microorganisms tend to grow poorly or not at all under
aerobic conditions. Secondary treatment by activated
sludge typically reduces coliform bacteria concentrations
by 90 to 99 percent.
Advanced treatment is any practice beyond secondary
treatment and is very effective in destroying most
pathogens. Advanced treatment can include filtration,
coagulants, and disinfection. Conventional filtration
units are helpful prior to disinfection in removing
substances that interfere with effluent disinfection
(Wright, 1997). An emerging practice is the use of
microfiltration after pretreatment (primary), during which
water passes through clusters of 20,000 fibers with a
nominal pore size of 0.2 micron (Wright, 1997). These
microfilters easily capture Cryptosporidium oocysts (3 to
7 microns in diameter). Conventional filtration units are
aimed at removing larger particles of 15 to 30 microns.
Chemical pretreatment involves the addition of alum or
other chemicals to form clumps of impurities, or floe,
which settle out or are easily filtered out of the raw
drinking water.
Disinfection is the most common treatment technique to
combat waterborne diseases, and can be used as part of
primary, secondary, and advanced treatment. The most
frequently used disinfectant is chlorine, which kills many
microbes, including most pathogens, except encysted
protozoans, which are resistant to chlorine (Bryant et al.,
1992). However, chlorine's efficiency is a function of
initial mixing, contact time, temperature, pH, amount of
residual, and characteristics of the microorganisms (such
as their age). Application of chlorine in a highly
turbulent system will result in kills 2 orders of magnitude
greater than those when chlorine is added separately to a
complete-mix reactor with constant and uniform
distribution (Metcalf and Eddy, 1991). Different chlorine
compounds are used for disinfection, with chlorine
dioxide being equal to or greater than chlorine in
disinfecting power. Chlorine dioxide has been proven to
be more effective than chlorine in the inactivation of
viruses, but produces the toxic and problematic
byproduct of chlorite. Chloramine might also be more
effective than chlorine because it breaks down slowly,
resulting in longer-lasting disinfection properties. All
chlorine disinfection is dependent on concentration of the
chlorine residual and temperature of the water, and time
of contact.
Other disinfectants used are ozone, ultraviolet light, and
iodine. Ozone is an extremely reactive oxidant that kills
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Protocol for Developing Pathogen TMDLs
pathogens directly through cell wall disintegration.
Ozone is believed to be more effective in killing viruses
than are chlorine compounds (Metcalf and Eddy, 1991),
and it is thought to be an effective means of eliminating
Cryptosporidium (Oppenheimer etal., 2000; Wright,
1997). Ultraviolet (UV) light penetrates the cell wall of
microorganisms and is absorbed by cellular material,
which either prevents replication or causes death of the
cell. Disinfection by UV light is more effective at
shallower depths and lower turbidity because turbidity
absorbs the UV energy and shields the pathogens. UV
light does not leave a residue in the water to kill
remaining organisms during discharge and is not
effective against Giardia.
The most recommended and effective approach to
removal of pathogens is a multiple-barrier approach
using some combination of sedimentation, chemical
pretreatment and flocculation, filtration, and
disinfection. Complete water treatment with chemical
coagulation, filtration, and disinfection might be
necessary to effectively treat encysted protozoans.
Controlling nonpoint sources
The use of best management practices (BMPs) should
consider the most efficient and cost-effective methods to
achieve load allocations for nonpoint sources. Because
livestock operations contribute high pathogen loads,
agricultural BMPs may provide considerable reduction
of rural nonpoint source pollution by pathogens.
Methods to control agricultural nonpoint sources include
minimizing the source, minimizing the movement (to
increase die-off), and treating the water. BMPs can be
classified into three categories—management, structural,
and vegetative. To select the most effective BMP or
combination of BMPs, a manager must determine the
primary source of the pollutant and its method of
transport to the waterbody. Some controls associated
with BMPs are listed in Table 2-4.
PATHOGEN TMDLS
This protocol provides a step-by-step description of the
TMDL development process for pathogens and includes
case studies and hypothetical examples to illustrate the
major points in the process. The protocol emphasizes the
use of rational, science-based methods and tools for each
step of TMDL development. TMDL development is site-
specific. The availability of data influences the types of
methods that developers can use. Ideally, extensive
monitoring data are available to establish baseline water
quality conditions, pollutant source loading, and
waterbody system dynamics. However, without long-
term monitoring data, the developer will have to use a
combination of monitoring, analytical tools (including
models), and qualitative assessments to collect
information, assess system processes and responses, and
make decisions. Although some aspects of TMDLs must
be quantified (e.g., numeric targets, loading capacity, and
allocations), qualitative assessments are acceptable as
long as they are supported by sound scientific
justification or result from rigorous modeling techniques.
A goal of this document is to assist developers in using a
rational TMDL development process that incorporates
the required elements of a TMDL.
Range of viable TMDL approaches
Analysts should be resourceful and creative in selecting
TMDL approaches and should learn from the results of
similar analytical efforts. The degree of analysis required
for each of the components of TMDL development can
range from simple, screening-level approaches based on
Table 2-4. Methods of control for agricultural nonpoinl sources and their associated types of controls
Methods of Control
Minimize source
Minimize movement
Treat water
Types of Controls
Structural
Fences (livestock exclusion)
Animal waste storage; detention
pond
Waste treatment lagoon; filtration
Vegetative
Filter strips; riparian buffer zones
Artificial wetland; rock reed microbial filter
Management
Animal waste management, especially
proper application rate and timing
Proper site selection for animal feeding
facility; proper waste application rate
Recycle and reuse
Source: Novotny and Olem, 1994.
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General Principles of Pathogen Water Quality Analysis
limited data to detailed investigations that might take
several months or even years to complete. A variety of
interrelated factors affect the degree of analysis
necessary. These factors include the type of impairment
(e.g., violation of a numeric criterion versus designated
or existing use impairment); the physical, chemical, and
biological processes occurring in the waterbody and its
watershed; the size of the watershed; the number of
sources; the data and resources available to develop the
TMDL; and the types and costs of actions needed to
implement the TMDL (Figure 2-2).
Decisions regarding the extent of the analysis must
always be made on a site-specific basis as part of a
comprehensive problem-solving approach. TMDLs are
essentially a problem-solving process to which no
"cookbook" approach can be applied. Not only will
analyses for different TMDL studies vary in complexity,
but the degree of complexity in the methods used within
individual TMDLs might also vary substantially.
Screening-level approaches afford cost and time savings,
can be applied by a wide range of personnel, and are
generally easier to understand than more detailed
analyses.
The trade-offs associated with using simpler approaches
include a potential decrease in predictive accuracy and
often an inability to predict water quality at fine
geographic and time scales (e.g., watershed-scale source
predictions versus parcel-by-parcel predictions, and
annual estimates versus seasonal estimates). When
using simpler approaches, analysts should consider these
two shortcomings in determining an appropriate margin
of safety.
The advantages of more detailed approaches are
presumably an increase in predictive accuracy and
greater spatial and temporal resolution. These
advantages can translate into greater stakeholder
acceptance and a smaller margin of safety, which
usually reduces source management costs. Detailed
approaches might be necessary when the screening-level
approaches have been tried and have proven ineffective
or when it is especially important to "get it right the first
time" (e.g., where protection from waterborne diseases
is a TMDL issue). In addition, more detailed
approaches might be warranted when there is significant
uncertainty regarding whether pathogen discharges are
attributable to human or to natural sources and the
Stanrlarrl
Violation 1
Suuice
Wdleiblied
Plucebbeb
Undeibluud
Few Data 1
Rebuuiceb
I ISP
". .il,';,;!' | Impairment
' "" " Suuiceb
1 """"" Wdleiblied
' ""' Plucebbeb
' """""" Aled
. Mi.Ln, 1 More Data
1 """"" Rebuuiceb
fc-
Increasing Level of Detail
Figure 2-2. Factors influencing the level of detail for the
TMDL analysis
anticipated cost of controls is especially high. However,
more detailed approaches are likely to cost more, require
more data, and take more time to complete.
A variety of approaches to developing a TMDL are
justifiable as long as they adequately identify the load
reductions or other actions needed to restore designated
or existing uses. Because all situations requiring
development of a TMDL are different, one cannot specify
that if X and Y are true a certain approach must be used.
Site-specific factors should always be taken into account
and an appropriate balance struck between cost and time
issues and the benefits of additional analyses.
PATHOGEN TMDL EXAMPLES
The following brief summaries of five pathogen TMDLs
show that a range of methods is appropriate for TMDL
development and that individual TMDLs often combine
relatively detailed analysis for certain elements with
simple analysis supporting other elements. A more
detailed case study is provided in Appendix B.
Republican River, Kansas
Fecal bacteria contamination had been identified in two
segments of the main stem of the Republican River and
two tributary segments (Crosby and Otter creeks) in
Kansas (KDHE, 1999). The main stem segments are
designated for primary and secondary recreation, aquatic
life support, domestic water supply, food procurement,
and irrigation/stockwater. The designated uses for the
two tributary segments are aquatic life support and
secondary contact recreation. Elevated fecal coliform
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Protocol for Developing Pathogen TMDLs
bacteria loadings are mainly from nonpoint sources in
the watershed. These elevated fecal coliform levels are
causing impairment of primary and secondary contact
recreation use on the main stem and secondary contact
recreation on Crosby and Otter creeks. Placement on
Kansas' 303(d) list was supported by in-stream
monitoring that indicated that 10 percent of spring
samples and 41 percent of summer-fall samples
exceeded the primary criterion for fecal coliform
bacteria. Overall, 17 percent of the samples exceeded
the water quality criteria.
Development of a fecal coliform bacteria TMDL for the
Republican River began with an assessment of the
existing fecal coliform loads to the river. Nonpoint
sources of fecal coliform loading to the river include
livestock waste management systems, runoff from
cropland and grassland, and wildlife (although loading
from wildlife is minimal). There are no point sources in
this watershed. To determine the needed load
reductions Kansas Department of Health and
Environment (KDHE) used a TMDL curve
methodology. The TMDL curve is the concentration of
fecal coliform bacteria per day vs. the percent of days
the load is exceeded at a specific monitoring station.
Points falling above the curve represent deviations from
the water quality standard and the permissible loading
function. Points falling below the curve represent
compliance with standards and support for the
designated use. The curve helps to determine the issues
surrounding the problem and differentiate between point
and nonpoint sources; show seasonal water quality
effects; address frequency of deviation, magnitude of
deviation, and duration questions; compare water quality
conditions between multiple watersheds; and establish
the level of implementation needed. Loads that fall
above the curve in the flow regime defined as being
exceeded 85-99 percent of the time are considered point
source influences. Points that fall above the curve over
the range of 10-70 percent exceedance are considered to
be nonpoint sources. Therefore, the percentage of the
area to the right of the 85 percent exceedance mark is
the Wasteload Allocation and percentage of the area to
the left of the 85 percent exceedance mark is the Load
Allocation.
The nature of bacteria loading is too dynamic to assign
fixed allocations for waste loads and nonpoint loads.
Instead, allocation decisions were made that reflect the
expected reduction of bacteria loading under defined
flow conditions. There are no point sources in the
watershed, and therefore wasteload allocations
established under this TMDL are equal to zero. The
proposed allocation plan requires that less than 10
percent of samples taken in spring exceed the primary
criterion at flows under 660 cubic feet per second (ft3/s),
with no samples exceeding the criterion at flows less than
165 ft3/s; less than 10 percent of samples taken in
summer or fall exceed the primary criterion at flows
under 660 ftVs, with no samples exceeding the criterion
at flows less than 140 ftVs; and less than 10 percent of
samples taken in winter exceed the secondary criterion at
flows under 660 ftVs. These endpoints will be reached
through unspecified reductions in loading from the
smaller, unpermitted livestock operations and rural
homesteads and farmsteads in the watershed. Best
management practices will be directed toward those
activities in the upstream watersheds so that there will be
accrued benefits of reduced violations of the applicable
fecal coliform criteria at higher flows on the main stem of
the river.
To determine whether the TMDL will improve conditions
to support designated uses and maintain water quality
standards, KDHE will continue to collect bimonthly
samples during the spring, summer-fall, and winter from
1999 to 2003. The status of the 303(d) listing will be
evaluated in 2004 based on these samples. If the
impaired status remains in 2004, the desired endpoints
will be refined and more intensive sampling will be
conducted under specified seasonal flow conditions from
2004 to 2008.
Lower Geddes Pond, Michigan (Preliminary)
This preliminary example is based on a study conducted
on Lower Geddes Pond, Michigan. The pond is a
segment of the Huron River near Ann Arbor, Michigan.
The results of the study have not yet been used to prepare
a TMDL submittal, but they have been used to discuss
the options for TMDL development. The Lower Geddes
Pond example has been included despite its preliminary
nature because of the use of E. coli as the indicator
bacteria for this waterbody.
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General Principles of Pathogen Water Quality Analysis
Level of Analysis
Simple
Republican River
Detailed
Problem
Definition
Select
Indicator
Source
Analysis
Link Source
to Impact
Allocate
Controls
Monitoring
Water Quality
Indicators
Primary Criterion for
Fecal Coliform
% exceedance
dependent on season
and flow conditions
TMDL
WLA and LA
derived from TMDL
Curve Methodology
Controls
Reduction in loads
from small,
unpermitted livestock
operations and rural
homesteads and
farmsteads
Source Analysis: Qualitative Source Characterization
Link to Indicator: TMDL Curve Methodology
Lower Geddes Pond has been placed on Michigan's
303(d) list because of impairment of total body contact
recreational uses by elevated levels of pathogens, and it
requires the development of a TMDL for the indicator
bacteria E. co//(LTI, 1999). The water quality
standards in the state of Michigan for E. coll require that
all waters of the state protected for total body contact
recreation may not exceed 130 E. coli/100 mL, as a 30-
day geometric mean, and at no time may the waters of
the state protected for total body contact recreation
exceed a maximum of 300 E. coli/100 mL. Current data
for E. coli levels in Lower Geddes Pond are not
available, but data are available for fecal coliform
bacteria. Linear regression was used to estimate the
levels of E. coli in Lower Geddes Pond based on fecal
coliform levels (Figures 2-3 and 2-4.) This relationship
3
i
fc
G.
u
c
o
U
1
W
Regression of £. coli to fecal coliform in
Lower Geddes Pond — Wet weather
40000
35000
30000
25000
20000 -
15000
0 <
0
•
^
, /
-*s
* s*
s*
i-X^ R2 = 0.9307
I***
10000 20000 30000 40000 50000
Fecal Coliform Cone, (per 100 m L)
seems possible for wet weather, but dry weather shows
more variability. The linear regression shows an
estimated E. coli geometric mean for wet and dry weather
well below the state standard. The estimated E. coli
maximum, however, is well above the state standard of
300/100 mL.
The available data are sufficient to verify the existence of
a problem, but are not sufficient to provide detail on the
sources contributing bacteria to the waterbody.
Additional sampling can be conducted to help identify
the existing sources of bacteria to Lower Geddes Pond.
Techniques such as DNA fingerprinting can also be used
to help in identifying whether the bacteria are of human,
wildlife, or domestic pet origin. Detailed sampling can
be conducted throughout the watershed to determine the
E. coli Cone, (per 100 mL)
Regression of £. coli to fecal coliform in
Lower Geddes Pond — Dry weather
1600
1400
1200 -
1000
800
600
400 -
200 -
fl J
I * y = 0 7"*OS
R2 = 0.415
* ^^^
+*^^+
*&*\
0 500 1000 1500
Fecal Coliform Cone, (per 100 mL)
Figure 2-3. Regression off. co//lo fecal coliform for Lower
Geddes Pond samples—wet weather
Figure 2-4. Regression off. co//lo fecal coliform for Lower
Geddes Pond samples—dry weather
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Protocol for Developing Pathogen TMDLs
Problem
Definition
Select
Indicator
Source
Analysis
Link Source
to Impact
Allocate
Controls
Monitoring
Level of Analysis
Simple
Lower Geddes Pond
Detailed
Water Quality
Indicators
130E. Coli/100mL
(30-day geometric
mean)
300 E. Coli/100mL
maximum
TMDL
Not Applicable
Controls
Not Applicable
Source Analysis: Qualitative Source Characterization
Link to Indicator: Not Applicable
contributions and impacts of subwatersheds and
individual land use categories.
Additional monitoring will be required to identify the
sources of bacteria to Lower Geddes Pond and to
confirm that water quality standards are being met.
Without further monitoring, bacteria conditions in
Lower Geddes Pond will remain highly uncertain and
the success of implementation efforts will be unknown.
There are two possible alternatives for the development
of the Lower Geddes Pond TMDL. The first option is to
conduct a phased TMDL using the existing data. The
second option is to conduct extensive additional
sampling before TMDL development. The first option
can generate an approvable TMDL in a shorter amount
of time, but cannot include an implementation plan
because of the lack of current data. Adjustments would
have to be made to the TMDL as new data are collected
and analyzed. The second option would postpone
development of the TMDL until suitable data are
collected. The main difference between these two
approaches is the timing of the different elements.
Rio Chamita, New Mexico
Rio Chamita, New Mexico, flows from its headwaters in
Colorado to its connection with the Rio Chama below
the village of Chama, New Mexico (TMDL for the Rio
Chamita, undated). The Rio Chamita is within the 38-
mi2 Rio Chama Basin. Part of the river is located within
the Edward Sargent Fish and Wildlife Area. The river
has several significant tributaries and groundwater inputs.
Eighty-five percent of the surrounding land is in New
Mexico, while 15 percent of the surrounding land
belongs to the state of Colorado. Land uses in the state
of New Mexico include rangeland (42 percent), forest (43
percent), and water (<1 percent). The designated uses for
the river include high-quality coldwater fishery, domestic
water supply, fish culture, irrigation, livestock watering,
wildlife habitat, and secondary contact recreation. The
Rio Chamita was placed on the New Mexico 303(d) list
with fecal coliform as a pollutant of concern. Elevated
fecal coliform levels have impaired the designated use of
the river as a high-quality coldwater fishery.
The Rio Chamita's standards require that the monthly
geometric mean of fecal coliform bacteria may not
exceed 100 feu (fecal coliform units)/100 mL and no
single sample may exceed 200 feu/100 mL. Two
significant sources of fecal coliform bacteria have been
identified for this segment of the Rio Chamita. One
source is the Village of Chama WWTP, which is a point
source. The Village of Chama WWTP serves a
population of about 400 people and is monitored through
an NPDES permit. The current permit allows a 7-day
geometric mean fecal coliform limit of 500 feu/100 mL
and a 30-day geometric mean of 500 feu/100 mL. These
limits are not consistently met. Uncharacterized nonpoint
sources of fecal coliform also cause fecal coliform levels
upstream of the WWTP discharge to be above current
stream criteria. Current fecal coliform levels in the river
from nonpoint sources average 450 feu/100 mL, which is
well above the allowable amount of 100 feu/100 mL.
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General Principles of Pathogen Water Quality
.
l
Using the 4Q3 low flow and the target concentration of
100 feu/100 mL, loading capacity of the stream has been
calculated to be 1.117 x 1010 feu/day. The NPDES
permit for the WWTP had a limit that was five times the
applicable water qaulity criterion and frequently
discharged in exceedance of their permit limits and the
Rio Chamita water quality standards for fecal coliform
bacteria. The end-of-pipe discharge was lowered to
equal the in-stream water quality standard. Using the
limit of 100 feu/100 mL and the WWTP design flow, a
wasteload allocation for the WWTP has been set at
1.136 x 109/day. The load allocation for nonpoint
sources upstream from the WWTP has been set at
1.0034 x 1010 feu/day, yielding a 30-day geometric mean
of 100 feu/100 mL and a reduction of almost 75 percent
in nonpoint source contributions.
A combination of BMPs will be used to implement the
TMDL. Public outreach and stakeholder involvement
will be ongoing. New Mexico will use a long-term
monitoring system that is already being used by the
Surface Water Quality Bureau (SWQB). It is a rotating
basin system approach to water quality monitoring. A
select number of watersheds are intensively monitored
each year with a return frequency of 5 years. The
rotating basin program will also be supplemented with
other data collection efforts. There are limited available
data on nonpoint sources, so additional sampling needs
to be conducted to characterize upstream sources of
fecal coliform bacteria. In addition to the regularly
scheduled monitoring, NPDES compliance monitoring
will be conducted.
Lost River, West Virginia
The Lost River is part of the Potomac River headwaters
in Hardy County, West Virginia and flows northeast to
the Cacapon River, then to the Potomac River and
eventually to the Chesapeake Bay (USEPA Region 3,
1998). The primary land uses of the approximately
116,600-acre watershed are forest and agriculture. The
designated uses of the Lost River include propagation
and maintenance of fish and other aquatic life, water
contact recreation, and trout water. The applicable water
quality standards for the state of West Virginia are a 30-
day geometric mean of 200 cfu/100 mL and an
instantaneous maximum of 400 cfu/100 mL in no more
than 10 percent of the samples taken in one month. The
instream fecal coliform levels were occasionally above
these standards in the Lost River, therefore the West
Virginia Division of Environmental Protection (WVDEP)
placed a 26.03-mile segment of the Lost River on the
303(d) list due to impairment by fecal coliform
contamination from undetermined. EPA gathered data
from various local sources (e.g., local District
Conservationist, local watershed groups, national
databases) to identify, characterize, and estimate
potential fecal coliform loading from various land use
categories distributed throughout the watershed. EPA
used the Better Assessment Science Integrating Point and
Nonpoint Sources (BASINS) computer model to develop
the TMDL, using a hydrologically representative time
period that captured the varying hydrologic and climatic
conditions in the watershed.
Problem
Definition
Select
Indicator
Source
Analysis
Link Source
to Impact
Allocate
Controls
Monitoring
Level of Analysis
Simple
Rio Chamita
Detailed
Water Quality
Indicators
100 feu/ 100 ml
(monthly geometric
mean)
200 feu/ 100 ml
maximum
TMDL
WLA:
1. 1 4x1 09 feu/ day
LA:
1. 01x1 010fcu /day
Controls
• NPDES permit
revision
• Public outreach
• Combination of
BMPs
Source Analysis: Monitoring
Link to Indicator: Divide load by flow for estimated concentration
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Protocol for Developing Pathogen TMDLs
Both point and nonpoint sources were identified in the
watershed. The three point sources identified for the
watershed are East Hardy High School, East Hardy
Early/Middle School, and the E.A. Hawse Continuous
Care Center (a fifty unit nursing home). Point sources
were evaluated based on available inspection reports and
loads were estimated using observed average effluent
flow and concentrations, where available, or permit
limits for concentration and flow. The TMDL does not
prescribe any load reductions from these sources since
the wasteload allocation is fairly insignificant compared
to the load allocation.
The watershed was broken down into seven land uses to
evaluate nonpoint sources of bacteria. These seven land
uses include barren, cropland, forest, other rural,
pasture, residential-pervious, and residential-impervious.
Failing septic systems were also identified as fecal
coliform nonpoint sources to the river. Information on
watershed activities were collected from published
watershed studies, state and local agencies, and local
watershed groups. The information was evaluated to
characterize potential nonpoint sources within the
watershed, by quantifying all possible sources of
bacteria accumulation on the land use surface or direct
input of bacteria to watershed streams. Activities
contributing bacteria loads include the land application
of poultry litter and cattle feedlot waste to 100 percent
of cropland and 75 percent of pasture land. Failing
septic systems and wildlife contributions were also
identified as bacteria sources of concern. Land use
sources were represented in the model with bacteria
accumulation rates which were calculated based on
accumulation from the various sources. For example,
accumulation on pasture land was the sum of
accumulation rates from the application of poultry litter
and feedlot waste and from wildlife and grazing
livestock.
BASINS provided continuous simulation of bacteria
buildup and washoff, bacteria loading and delivery, point
source discharge and instream water quality response and
output daily loads from each land use and point source.
Existing loads were established through calibration of the
model to existing water quality data. Loads were reduced
until instream concentrations met water quality standards.
The TMDL established necessary load reductions of 38
percent from cropland, 39 percent from pasture land, 13
percent from forest and 50 percent from failing septic
systems.
Many best management practices (BMPs) were to be
implemented to reduce the loading of fecal coliform
bacteria to the river from nonpoint sources. Some of
these BMPs include composting, increased transport of
litter to less vulnerable areas, and vegetated buffer strips
to prevent delivery of fecal coliform to the Lost River.
Periodic monitoring of fecal coliform bacteria in a
number of locations throughout the Lost River watershed
has been conducted for many years and was scheduled to
continue.
Chickasawatchee Creek, Georgia
USEPA Region 4 completed a fecal coliform TMDL for
each of 42 waterbodies in the state using the same
analysis methods. This summary of the Chickasawatchee
TMDL (Fecal Coliform TMDL development
Lost River
Level of Analysis
Simple
Detailed
Problem
Definition
Select
Indicator
Source
Analysis
Link Source
to Impact
Allocate
Controls
Monitoring
Water Quality
Indicators
200 cfu/ 100 ml 30-
day geometric mean
400 cfu/ 100mL
instantaneous
maximum in no more
than 10% of samples
taken in a month
TMDL
• No load reductions from
point sources
• 38% reduction in
cropland
• 39% reduction in
pasutreland
• 13% reduction in forest
• 50% reduction in septics
Controls
• Composting
• Transport of
litter
• Vegetated
buffer strips
Source Analysis: BASINS model
Link to Indicator: BASINS model
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General Principles of Pathogen Water Quality
Chickasawhatchee Creek Watershed, undated) provides
an example of one of the 42 TMDLs. The
Chickasawatchee Creek watershed is located in Terrell,
Calhoun, and Dougherty counties, Georgia in the Flint
River Basin. The creek's designated use is fishing. The
surrounding land uses include urban-pervious, urban-
impervious, agriculture/pastureland, forest, and barren.
Chickasawatchee Creek was placed on Georgia's 303(d)
list due to more than 20% of water samples having a
fecal coliform concentration of greater than 400 cfu/100
mL. The water quality standards in Georgia are
different for summer and winter. From May-October the
standards are a 30-day geometric mean of 200 cfu/100
mL. From November-April the standards are a
geometric mean of 1,000 cfu/100 mL with an
instantaneous maximum of 4,000 cfu/100 mL.
There is one permitted wastewater treatment facility in
the watershed, Dawson WPCP. The standard monthly
average effluent limitation contained in Georgia's
NPDES permits is 200 cfu/ 100 mL. Potential nonpoint
sources of fecal coliform bacteria include baseflow and
the five different land uses in the watershed. Baseflow
includes septic tank seepage, leaking sanitary sewer
pipes, illicit sewer connections, and animal feedlots.
USEPA's BASINS model was used to derive the
linkages between the measured fecal coliform levels in
the stream and the sources of fecal coliform. The
parameters needed to run the model were derived or
estimated from existing land use data, rainfall data,
available stream geometry information, land slope data,
soil characteristics, literature values, and best
professional judgement. There are many activities and
land uses that contribute to the fecal coliform loading to
Table 2-5. Load reductions to Chickasawatchee Creek
Land Use
Baseflow
Agriculture/Pastureland
Urban-impervious
Urban-pervious
Forest
Barren
Percent Load Reduction
94%
25%
50%
50%
0%
50%
the stream system at various rates and time. Therefore,
many allocation scenarios for the TMDL were developed
to reflect different reduction strategies for the various
sources and their respective loadings. One of the
allocation scenarios that achieves the target value of 175
cfu/100 mL is shown in Table 2-5.
The model indicates that the fecal coliform loading from
agricultural runoff, urban runoff, and baseflow are the
primary sources of impairment to the stream. This
TMDL is based on the limited amount of readily
available fecal coliform data that was used to put the
stream segment on the Georgia 303(d) list. No watershed
or stream-specific modeling data were collected,
therefore, this TMDL is primarily useful for making
screening level decisions, useful as one factor to priority
rank the watersheds for additional monitoring or for
planning the implementation of pollution controls.
Additional monitoring of the stream was recommended to
increase the confidence of the model results. If
additional modeling shows continued exceedance of the
water quality standards, more data would be collected to
develop a belter model.
Problem
Definition
Select
Indicator
Source
Analysis
Link Source
to Impact
Allocate
Controls
Monitoring
Chickasawatchee Creek
Level of Analysis
Simple
Detailed
Water Quality Indicators
May to Oct.
200 cfu/ 100 ml (30-day
geometric mean)
Nov. to April
1,000 cfu/ 100 ml (30-day
geometric mean)
Instantaneous Maximum
4,000 cfu/ 100 ml
TMDL
Various allocations
possible
Controls
Source Analysis: BASINS model
Link to Indicator: BASINS model
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Problem Identification
Objective: Identify background information and
establish a strategy for specific 303(d) listed waters that
will guide the overall TMDL development process.
Summarize the pathogen-related impairment(s),
geographic setting and scale, pollutant sources of
concern, and other information needed to guide the
overall TMDL development process and provide a
preliminary assessment of the complexity of the TMDL
(what approaches are justified and where resources
should be focused).
Procedure: Inventory and collect data and information
needed to develop the TMDL. Information collected
should include an identification of degree and type of
water quality standards impairment and preliminary
identification of sources, numeric targets, proposed
analytical methods, data needs, resources required, and
possible management and control techniques. Interview
watershed stakeholders and local, state, tribal, and
federal agency staff to identify all information relevant
to the waterbody and its watershed. Establish plans for
incorporating public involvement in the development of
the TMDL. Revise the problem definition as new
information is obtained during TMDL development.
OVERVIEW
Developing a TMDL requires formulating a strategy that
addresses the potential causes of the water quality
impairment and available management options. The
characterization of the causes and pollutant sources
should be an extension of the process originally used to
place the waterbody on the 303(d) list. Typically, the
impairment that caused the listing will relate to water
quality standards being exceeded—either pollutant
concentrations that exceed numeric criteria or
waterbody conditions that do not achieve a narrative
water quality standard or support the designated use. In
many cases, the problem is self-evident and its
identification will be relatively straightforward. In other
cases, the complexity of the system might make it more
difficult to definitively state the relationship between the
pathogen sources and the impairment.
The following key questions should be addressed during
the initial strategy-forming stage. Answering these
questions results in defining the approach for developing
Key Questions to Consider for Problem Identification
1 .
What are the designated uses and associated
impairments?
What data are readily available?
What is the geographic setting of the TMDL?
What temporal considerations affect the TMDL?
What characteristics of the waterbody and/or its
watershed might be exacerbating or mitigating the
problem?
What are the sources of the pollutant and what are the
pathways it might take to reach the waterbody?
How will margin of safety and uncertainty issues be
addressed in the TMDL?
8. What are some potential control options?
6.
7.
the TMDL. A problem statement based on this problem
identification analysis is an important part of the TMDL
because it relates the TMDL to the 303(d) listing and
describes the context of the TMDL, thereby making the
TMDL more understandable and useful for
implementation planning.
KEY QUESTIONS TO CONSIDER FOR PROBLEM
IDENTIFICATION
1. What are the designated uses and associated
impairments?
The goal of developing and implementing a TMDL is to
attain and maintain water quality standards in an
impaired waterbody to support designated uses. With
that in mind, TMDL developers should stay focused on
addressing the pathogen-related problem interfering
with the designated uses. The problem identification
should answer the following:
How are water quality criteria expressed (narrative
or numeric criteria, average or instantaneous
concentration)?
What nonattainment of standards caused the listing?
What data or qualitative analyses were used to
support this decision?
Where in the waterbody are designated uses
supported and where are they impaired?
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Problem Identification
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• What are the critical conditions, in terms of flow
and season of the year, during which designated
uses are not supported?
• How do pathogens affect the designated uses of
concern (e.g., Do the presence of pathogens in the
water at a bathing beach create a health hazard?)
States, tribes, and other jurisdictions commonly compare
measurements of various physical, chemical, and
biological indicators to established water quality
standards to determine whether waters support
designated or existing uses such as recreation, fish and
shellfish harvesting and consumption, and domestic
drinking water supply. Exceedances of water quality
standards evident through comparison of existing
monitoring data to water quality criteria usually form the
basis for listing the waterbody.
For human pathogens, routine monitoring data for
specific viruses, fecal indicator bacteria, or protozoans
might be collected for the waterbody. Routine
monitoring is usually conducted for sources of drinking
water and shellfish harvesting, and at recreational
beaches. Densities of total coliform bacteria and fecal
coliform bacteria are frequently measured and
evaluated. One of the most important issues for
pathogen loading assessments is that the presence of
bacterial indicators does not always prove or disprove
the presence of human pathogenic bacteria, viruses, or
protozoans.
Documented nonsupport of the designated use may
cause a waterbody not to attain water quality standards,
whether in combination with exceedance of numeric
criteria or without any criteria exceedances. Public
complaints of disease associated with use of the surface
waters could be a factor leading to listing of the
waterbody. Epidemiologic data, including reports of
diseases that might be caused by waterborne pathogens,
are collected by the Centers for Disease Control and
Prevention (and published in Morbidity and Mortality
Weekly Report), and surveys are conducted for diseases
that occur following exposure to contaminated water
(e.g., acute gastroenteritis, hepatitis, cholera, ear
infections). Existing epidemiologic data might be used
to help identify waterbodies that might pose disease
risks to humans, in particular diseases caused by
microorganisms that are not or cannot be identified by
routine monitoring methods. Both the CDC and EPA
Problem Identification in Maquoit Bay
The Maquoit Bay watershed in Maine covers an area of 7,878 acres
and primarily comprises three land uses—forest (60%), agriculture
(13%), and residential (12%). The remaining 15% is divided among
roads, wetlands, and a small amount of commercial land. Fecal
coliform bacteria have been identified as a potential source of
contamination to the bay, affecting both water quality and the
economically important shellfish resource. Shellfish closures due to
high fecal indicator concentrations have been problematic for years.
Water quality monitoring indicates that storm water runoff from land
uses in the watershed is the primary source of fecal indicators. The
preliminary problem statement was:
Maquoit Bay is experiencing bacterial impairment (by fecal coliform
bacteria) to water quality, resulting in the closure of nearly one-third
of its productive shellfish resource. Based on an analysis of water
quality data and land use practices, the primary source of the
impairment was identified as runoff from the agricultural lands and
failing septic systems.
To determine if a proposed zoning ordinance would result in slowing
water quality impairment, additional monitoring data were collected and
used to support the development of the watershed model FecaLOAD.
The results of the modeling indicated that manure applications
accounted for the largest load of fecal coliform, followed by failing
septic systems. Actions have been taken to reduce loadings from
these sources.
Source: Horsely and Witten (1996).
acknowledge that waterborne outbreak and disease data
are vastly under reported.
Recommendation: In the problem identification,
identify and summarize the events leading to the listing
and the data used to support the listing.
2. What data are readily available?
A waterbody is considered impaired when a water
quality standard is violated, whether through exceedance
of a numeric or narrative criterion, impairment of
designated use, or violation of an antidegradation policy.
It is important that the data and rationale used to list the
waterbody as impaired be made available to staff
responsible for developing the TMDL. In addition to
water quality monitoring data, documentation for the
listing of waters based on narrative standards or other
information should also be provided.
As much as possible, managers should identify the
problem based on currently available information,
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Protocol for Developing Pathogen TMDLs
including water quality monitoring data, watershed
analyses, best professional judgement, information from
the public, and any previous studies of the waterbody
(e.g., state and federal agency reports, university
sponsored studies, reports prepared by environmental
organizations). These data ideally will provide insight
into the nature of the impairment, potential pathogen
and indicator bacteria sources, and pathways by which
pathogens and indicator bacteria enter the waterbody.
Managers should also compile data that will be needed
for actual development of the TMDL during the problem
identification stage. These data likely will include the
following:
• Water quality measurements (e.g., enterococci
concentrations).
• Waterbody size and shape information (e.g.,
volume, area, depth, width, length).
• Waterbody flow and runoff information.
• Tributary location and contributions (flow and water
quality).
• Watershed land uses and land use issues.
• Meteorological data (temperature and precipitation).
• Soil surveys and geologic information.
• Topographical information.
• Information on local contacts.
• Past studies/surveys, which may include source
water assessments conducted under the SDWA.
Maps of the watershed also will be invaluable, either
hard copies, such as USGS quad maps, or (if available)
electronic files or GIS systems. Point sources, known
nonpoint sources, and land uses should be identified on
these maps to provide an overview of the watershed and
to identify priority areas for pathogen and/or indicator
bacteria loading caused by human activities.
Information on related assessment and planning efforts
in the study area should also be collected. TMDL
development should be coordinated with similar efforts
to reduce TMDL analysis costs, to increase stakeholder
participation and support, and to improve the outlook for
timely implementation of needed control or restoration
activities. Examples of related efforts that should be
identified include:
• State, local, or landowner-developed watershed
management plans.
• Source water protection activities under the SDWA.
• Nonpoint source control projects.
• Stormwater management plans and permits.
• Natural Resource Conservation Service (NRCS)
conservation plans, Environmental Quality
Incentives Program (EQUIP) projects, and Public
Law 566 (PL-566) small watershed plans.
• Land management agency assessment or land use
plans (e.g., Federal Ecosystem Management Team
[FEMAT] watershed analyses or Bureau of Land
Management [BLM] proper functioning condition
assessments).
• Clean Lakes program projects.
• Comprehensive monitoring efforts (e.g., National
Water Quality Assessment [NAWQA] and
Environmental Monitoring and Assessment Program
[EMAP] projects).
Recommendation: Contact agency staff responsible for
the waterbody listing and collect any information they
have available. Contact other relevant agencies,
including state natural resources, water resources, fish
and wildlife, and public health agencies, and state
drinking water and source water protection
administrators and prepare an inventory of available
information. Universities are often a good source of
data for a waterbody.
3. What is the geographic setting of the TMDL?
TMDLs can be developed to address various geographic
scales. The geographic scale of the TMDL primarily
will be a function of the impairment that prompted the
waterbody listing, the type of waterbody impaired, the
spatial distribution of use impairments, and the scale of
similar assessment and planning efforts already under
way.
The selection of TMDL scale may involve trade-offs
between comprehensiveness in addressing all designated
use and source issues of concern and the precision of the
analysis. Table 3-1 summarizes the advantages and
disadvantages of developing TMDLs for larger (i.e.,
greater than 50 mi2) and smaller (less than 50 mi2)
watersheds.
Recommendation: When the designated use
impairments are at the bottom of a watershed (e.g., in a
lake or estuary), address the entire watershed at once by
using less-intensive, screening-level assessment
methods. Follow-up monitoring can assess the
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Table 3-1. Advantages and disadvantages of different TMDL watershed analysis scales
Large TMDL Study Units
(> 50 square miles)
Small TMDL Study Units
(< 50 square miles)
Advantages
Accounts for watershed processes operating at
larger scales
More likely to account for cumulative effects
Avoids need to complete separate studies for
multiple tributaries
Easier to identify and address fine-scale source-
impact relationships and to identify needed
control actions
Possible to use more accurate, data-intensive
methods
Disadvantages
Confounding variables obscure cause-effect
relationships
Numeric target setting harder for heterogeneous
waterbody features
Source estimation more difficult because land
areas more heterogeneous
Lag time between pollutant discharge and
instream effects potentially longer, effectiveness
of source controls therefore harder to detect
Analysis may not give sufficient detail to provide
allocations at the scale of the waterbody listing
May miss cause-effect relationships detectable
only at broad scale (cumulative impacts)
May necessitate many separate TMDL studies in
a basin
effectiveness of the pathogen or indicator bacteria
reduction and, if necessary, more in-depth analysis can
target specific high-priority areas within the watershed
that have local problems.
When impairments occur throughout a watershed, the
analysis should be conducted for smaller, more
homogenous analytical units (i.e., subwatersheds). For
example, specific river reaches that are impaired might
require detailed TMDLs to address upstream point and
nonpoint sources. If this subwatershed approach is
chosen, care should be taken to apply consistent
methodologies from one subwatershed to the next so
that an additive approach eventually can apply to the
larger watershed.
4. What temporal considerations affect the
TMDL?
TMDLs must consider temporal (e.g. seasonal or
interannual) variations in discharge rates, receiving
water flows, and designated use impacts. These
considerations are especially important for stream
pathogen TMDLs because both point and nonpoint
pathogen sources can discharge at different rates during
different time periods, causing the critical conditions for
a pathogen TMDL to vary.
For example, point sources or continuous loading
sources (e.g., wastewater treatment plants) tend to have
the greatest impact on stream water quality under low-
flow, dry weather conditions, when dilution is minimal.
The lowest in-stream flows normally occur in summer or
early fall when in-stream temperatures are high.
Nonpoint loading sources that may deliver bacteria
loads (e.g., surface runoff from pasture) are typically
precipitation-driven. Storm event producing surface
runoff can wash-off bacteria deposited and accumulated
on the land surfaces, resulting in the delivery of
sometimes significant loads of bacteria to the receiving
waterbody. Maximum impacts from rain-related
nonpoint source loading generally occur at high flows.
The critical conditions of impairment are determined by
the source behavior. Often, sources of bacteria are
diverse and occur in combination. For example, a
stream may receive bacteria loads from such direct
sources as watering livestock and illicit sewer
connections and from runoff from agricultural areas.
Varying sources can result in multiple critical
conditions. In some cases, it may be necessary to
evaluate a TMDL under a variety of conditions to
account for the different times of greatest impact from
sources (e.g., low flow and high flow). Analysts may
want to identify the different critical conditions and
evaluate them separately. Another option is to develop
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Protocol for Developing Pathogen TMDLs
the TMDL for a time period that encompasses all of the
possible critical conditions. For example, develop a
TMDL based on various flow rates or develop separate
TMDL allocations for different seasons. When using
dynamic modeling, a representative time period can be
chosen for the TMDL development to represent
conditions likely to occur (i.e., a year with wet and dry
seasons, a multiple-year period to account for
meteorologic and source variations).
Seasonal variations are also important for pathogen
TMDLs. In-stream concentrations of bacterial
indicators and pathogens vary over the course of the
year in response to many factors, including weather and
source characteristics. For example, the coliform
removal efficiency may be lower during winter months,
resulting in less die-off than in warmer months. Source
behavior may also influence the seasonal variability of
bacterial loading in a watershed. Significant bacterial
loads can originate from agricultural land receiving land
application of manure; however, farmers may only
spread manure during the spring season, resulting in
high spring loads and lower summer, fall and winter
loads.
Several states have bacterial indicator standards that
vary based on season. These standards usually
correspond with the seasonal use designation (e.g.,
primary contact recreation for summer months and
secondary recreation for the non-summer months).
TMDLs are developed for waters exceeding the
applicable water quality standards and are applied
according to the conditions of those standards (e.g.,
criteria set for the season or flow). Therefore, if a
waterbody exceeds a seasonal designated or existing use
or criterion, a TMDL is developed and applied on a
corresponding seasonal basis.
Recommendation: Address temporal considerations
during the problem identification stage of TMDL
development to ensure that a good strategy is in place as
the specific technical components of the TMDL are
completed. Specific guidance on addressing temporal
issues is provided in each section of this protocol.
5. What characteristics of the waterbody and/or
its watershed might be exacerbating or
mitigating the problem?
The problem identification is based on an evaluation of
available data to gauge whether water quality conditions
and loadings are causing impairment of the waterbody.
If information concerning likely future stresses to be
placed on the watershed (e.g., development projects,
industrial use proposals) is available, it should also be
included. Waterbodies currently impaired by pathogens,
as well as good-quality waters, can be significantly
affected by alterations in land use. For example,
pathogen loading might increase if incorrectly designed,
sited, operated, or maintained septic systems are built, if
more cows are grazed in a pasture adjacent to a stream,
if a marina is added to a lake, or if wildlife populations
increase in a protected forest. Pathogen loading can
decrease if sewage treatment plants are upgraded,
manure application to cropland is properly managed, or
discharges from boats are prohibited. Evaluation of
monitoring data over one or more years, as well as
evaluation of all available information on the resources,
trends, and policies potentially affecting pathogen
loading in the watershed, is needed to develop the most
effective and appropriate TMDL for the watershed. The
data should be reviewed to develop an understanding of
the spatial (throughout the watershed) and temporal
(e.g., seasonal, daily) variation in densities of pathogens.
Data from special analyses for specific pathogens in
water and in fish and shellfish and epidemiologic
surveys of diseases in humans and animals that come
into contact with or ingest the surface water could help
identify the major health concerns.
Recommendation: Identify any characteristics of the
watershed and waterbody and predictions of future use
that might affect the TMDL analysis.
6. What are the sources of the pollutant and
what are the pathways it might take to reach
the waterbody?
During the problem identification, the TMDL developer
should first understand the relative magnitude of the
various indicator bacteria and/or pathogen sources,
including identifying when loading occurs and how
pathogens enter the waterbody. Any practice that might
result in human or animal fecal matter entering a
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Problem Identification
waterbody, including runoff from the land surface,
direct discharges, and contaminated groundwater
flowing into surface waters, should be considered as a
potential source of human pathogens. Using readily
available information, it is possible to identify potential
point sources of pathogen loading and provide a
preliminary determination of land uses in the watershed,
as well as potential "hot spots" for nonpoint sources of
pathogens (e.g., runoff from pastures and feedlots,
wildlife). Land uses provide important clues to the
sources of pathogens in the watershed (e.g., forest,
pastureland, concentrated animal operations, impervious
surfaces in urban areas).
In addition, information on ineffective treatment,
failures, or bypasses under high flow conditions from
wastewater treatment plant discharges should be
included. The problem statement should include
relevant information on the characteristics of the
waterbody and its watershed, especially characteristics
or conditions that might exacerbate or mitigate the
problem (e.g., size of the watershed, land uses,
topography, soil data, climatological data, reservoir
depth, residence time). Any other complicating factors
that could potentially contribute to the problem should
also be included.
Regardless of the pollutant or source, the TMDL should
demonstrate an understanding of the entire process of
pollutant delivery and impact, including the role that
bacteria play in affecting the impairment, even when the
stressor is self-evident. In other cases, care must be
taken to ensure that the correct relationship between the
pollutant and the impairment is identified. An
understanding of the physical process of pathogen
loading should include all potential sources of pathogens
(including runoff from nonpoint sources, rainfall-driven
point sources, and WWTP discharges), the transport of
pathogens, and mixing processes in a waterbody that
affect pathogens; the biological relationship between
pathogen survival and light availability, temperature,
salinity, and pH; and the chemical process(es) by which
the pathogen density might increase or decrease (e.g.,
processes that influence the availability of nutrients and
organic compounds in the water column and sediments).
It is often helpful to prepare a schematic that illustrates,
in words, diagrams, or pictures, how different ecosystem
processes interact with the pollutants and their sources
to cause the waterbody impairment. Some of these
processes could substantially alter pathogen loading or
affect human health concerns. For example, the
viability of bacteria, viruses, and protozoans could be
greatly reduced in a shallow stream with low turbidity
that receives high levels of ultraviolet radiation
compared to a very turbid stream. Water currents at the
mouth of an estuary might significantly dilute the load,
or they could concentrate the pathogens in a protected
cove. Consideration of the effects of environmental
factors and processes affects how the TMDL allocations
are determined.
Recommendation: Conduct an inventory of available
information on point sources using information available
from state or local agencies or databases such as the
EPA's Permit Compliance System (PCS). For nonpoint
sources, identify all possible land use-specific sources
through analysis of aerial photos, land cover maps or
databases, and information from federal, state, and local
agencies. When using maps or GIS coverages to
determine land uses, document the scale, resolution, and
date of the information. In large watersheds, the only
available data might be at a small scale and the ability to
conduct field verification will be limited. In smaller
watersheds, the utility of the same data might be limited
because the scale and minimum mapping unit might hide
important details, but field verification of such data is
possible. In all cases, rely on the best and most relevant
data sets, document all issues related to scale and date,
and verify analysis with field visits.
Prepare a flowchart or schematic detailing the processes
that might affect impairment of the waterbody (see
Figure 3-1). In the schematic, identify the critical
pathways and processes of the pollutant and the relative
magnitude of the sources. The schematic will help
provide a visual guide to what information is still
needed to conduct the analysis.
7. How will margin of safety and uncertainty
issues be addressed in the TMDL?
Considerable uncertainty is usually inherent in
estimating pathogen loading from nonpoint sources, as
well as predicting water quality response. The
effectiveness of management measures (e.g., support of
agricultural BMPs) in reducing loading is also subject to
significant uncertainty. These uncertainties, however,
should not delay development of the TMDL and
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Protocol for Developing Pathogen TMDLs
Source 1: WWTP Outfall *W
Source 2: Urban Runoff
Source 3; Failing Septic
Source 4: Wildlife/Background
**********
Mixing
I 1 J
Deposition Die-off
.'•,"'';'; Resuspension
J
Figure 3-1. Example schematic showing processes important to waterbody impairment
implementation of control measures. EPA regulations
(40 CFR 130.2(g)) state that load allocations for
nonpoint sources "are best estimates of the loading
which may range from reasonably accurate estimates to
gross allotments, depending on the availability of data
and appropriate techniques for predicting the loading."
USEPA (199 la; 1999) advocated the use of a phased
approach to TMDL development as a means of
addressing these uncertainties. Under the phased
approach, load allocations and wasteload allocations are
calculated using the best available data and information,
recognizing the need for additional monitoring data to
determine if the load reductions required by the TMDL
lead to attainment of water quality standards. The
approach provides for the implementation of the TMDL
while additional data are collected to reduce uncertainty.
When using models during the development of the
TMDL, either to predict loadings or to simulate water
quality, managers should address the inherent
uncertainty in the predictions. Various techniques for
doing so include sensitivity analysis, first-order analysis,
and Monte Carlo analysis. These techniques are briefly
summarized in Section 6 and are also discussed in
various documents (e.g., IAEA, 1989; Cox and Baybutt,
1981; Chapra, 1997; Reckhow and Chapra, 1983).
TMDLs also address uncertainty issues by incorporating
a margin of safety into the analysis. The margin of
safety is a required component of a TMDL and accounts
for the uncertainty about the relationship between
pollutant loads and the quality of the receiving
waterbody (CWA section 303(d)(l)(c)). The results of
the uncertainty analysis performed for any modeling
predictions can be factored into the decision regarding a
margin of safety. The margin of safety is traditionally
either implicitly accounted for by choosing conservative
assumptions about loading and/or water quality
response, or is explicitly accounted for during the
allocation of loads. For example, a margin of safety is
explicitly set at 5 x 107 cfu/day (or 10 percent of the
loading capacity of 5 x 10s cfu/day) with the remainder
of 4.5 x 10s cfu/day allocated as wasteload and load
allocations. Table 3-2 lists several approaches for
incorporating margins of safety into pathogen TMDLs.
Table 3-2. Approaches for incorporating margins of safety into
pathogen TMDLs
Type of
MOS
Explicit
Implicit
Available Approaches
Do not allocate a portion of available pathogen
loading capacity; reserve for MOS
Conservative assumptions in pathogen loading
and transport rates
Conservative assumptions in the estimate of
pathogen control effectiveness
Conservative assumptions in deriving the
numeric target (e.g., set lower than water
quality criteria)
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Problem Identification
Recommendation: During the problem identification
process, the TMDL developer should decide, to the
extent possible, how to incorporate a margin of safety
into the analysis. The degree of uncertainty associated
with the source estimates and water quality response
should be considered with the value of the resource and
the anticipated cost of controls. In general, greater
margins of safety should be included when there is more
uncertainty in the information used to develop the
TMDL. It may also prove feasible to include margins of
safety in more than one TMDL analytical step. For
example, relatively conservative numeric targets and
source estimates could be developed that, in
combination, create an overall margin of safety adequate
to account for uncertainty in the entire analysis.
8. What are some potential control options?
The problem identification should begin to identify
potential management alternatives, such as BMPs and
load reduction from point sources. A general level of
understanding should be reached concerning the relative
load reductions that must be obtained from point versus
nonpoint sources and whether uncontrollable pathogen
sources are a significant factor. If no obvious level of
pathogen/indicator bacteria control will achieve the
designated use of the waterbody, the appropriateness of
the applicable water quality standard should be
evaluated.
The problem statement should identify and stress the
opportunity to take advantage of any ongoing watershed
protection efforts. It should also address coordination
with other state agencies (e.g., human health and
pollution control agencies) and federal agencies (e.g.,
USEPA, U.S. Department of Agriculture, U.S.
Geological Survey, U.S. Department of Health and
Human Services, U.S. Forest Service, Bureau of Land
Management, Department of the Interior) to avoid
duplication of effort. In some cases, related watershed
studies (e.g., CWA section 319, Clean Lakes, USDA
PL-566) might provide the basis for many elements of
the TMDL.
Local organizations can also be instrumental in
developing grassroots protection programs for
waterbodies, and they should be included in the problem
statement formulation. For example, the town of
Orleans, Massachusetts, developed and installed several
remediation options to reduce fecal indicator loading
and protect shellfish harvesting waters from nonpoint
sources (Bingham et al., 1996).
Recommendation: Identify and document all ongoing
efforts, including watershed characterization efforts,
restoration efforts, and volunteer monitoring activities
by local stakeholders. Include all efforts, regardless of
the scale. Many local watershed groups support
volunteer monitoring programs for specific stream
reaches that may be a very small segment of the
impaired waterbody.
RECOMMENDATIONS FOR PROBLEM
IDENTIFICATION
• Identify events resulting in the 303(d) listing and the
data to support the listing. Include any data or
anecdotal information that supports qualitative
approaches to develop the TMDL.
• Identify the specific role pathogens play in affecting
designated or existing uses, usually through
qualitative judgment and consultation with experts.
• Contact agency staff responsible for the waterbody
listing and collect any available information.
• Prepare a flowchart or schematic detailing the
processes that might affect waterbody impairment.
• Conduct an inventory of available information on
point or nonpoint sources using information
available from state or local agencies or databases.
• Identify temporal (e.g., seasonal) factors affecting
such issues as discharge rates, receiving water
flows, and designated use impacts. Temporal
considerations will affect all subsequent stages of
TMDL development for pathogens.
• Identify and document all current watershed
restoration or volunteer monitoring efforts.
• Identify any characteristics or future uses of the
watershed or waterbody that might affect the TMDL
analysis.
RECOMMENDED READING
(Note that a full list of references is included at the end
of this document.)
USEPA. Undated. TMDL Case Study Series.
. U.S.
Environmental Protection Agency, Washington, DC.
3-8
First Edition: January 2001
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sis
Protocol for Developing Pathogen TMDLs
USEPA. 1991a. Guidance for Water Quality-based
Decisions: The TMDL Process. EPA 440/4-91-001.
U.S. Environmental Protection Agency, Assessment and
Watershed Protection Division, Washington, DC.
USEPA. 1995a. Watershed Protection: A Project
Focus. EPA 841-R-95-003. U.S. Environmental
Protection Agency, Office of Water, Washington, DC.
USEPA, 1995b. Watershed Protection: A Statewide
Approach. EPA 841-R-95-001. U.S. Environmental
Protection Agency, Office of Water, Washington, DC.
USEPA. 1996a. TMDL Development Cost Estimates:
Case Studies of 14 TMDLs. EPAR-96-001. U.S.
Environmental Protection Agency, Office of Water,
Washington, DC.
USEPA 1999. Draft Guidance for Water Quality-based
Decisions: The TMDL Process. 2nd ed. EPA 841-D-99-
001. U.S. Environmental Protection Agency,
Washington, DC.
.
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Problem Identification
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Protocol for Developing Pathogen TMDLs
Identification of Water Quality Indicators and Target Values
Objective: Identify numeric or measurable indicators
and target values that can be used to evaluate the TMDL
and the restoration of water quality in the listed
waterbody.
Procedure: Select one or more indicators that are
appropriate to the waterbody and local conditions. Key
factors to consider include scientific and technical
validity, as well as practical issues such as cost and
available data. Identify target values for the indicator(s)
that represent achievement of water quality standards
and are linked (through acceptable technical analysis) to
the reason for waterbody listing.
OVERVIEW
To develop a TMDL, it is necessary to have a
quantitative measure that can be used to evaluate the
relationship between pollutant sources and their impact
on water quality; such measurable parameters are called
indicators in this document. For pathogen TMDLs,
indicators will often be based on state water quality
standards developed to protect human health from
exposure to pathogens in surface waters. The standards
establish designated uses for a waterbody and the
narrative or numeric water quality criteria necessary to
support those uses, as well as the development of an
antidegradation policy. The standards developed for
pathogen pollutants are usually based on the detection of
generic groups of microorganisms that have been
associated with fecal contamination and that indicate
pathogenic microorganisms are likely to be present in
the water. These standards are the basis on which a
waterbody's impairment by pathogens is determined.
Pathogen impairments may be identified through either
the violation of a numeric water quality standard
Key Questions to Consider for the Identification of Water
Quality Indicators and Target Values
1. What is the water quality standard that applies to the
waterbody?
2. What factors affect indicator selection?
3. What water quality measures could be used as
indicators?
4. What are appropriate target values for the chosen
indicators?
Water quality standards consist of the following elements:
• Designated uses
• Numeric and narrative criteria for supporting each use
• Antidegradation statement (40 CFR Part 131)
(e.g.Criterion for E. coli or enterococcus bacteria) or the
nonattainment of a waterbody's designated or existing
use (e.g., primary contact recreation).
This section of the protocol provides background on
water quality standards and their relationship to TMDL
indicators, lists various factors that should be addressed
in choosing a TMDL indicator, and provides
recommendations for setting target values under different
circumstances.
KEY QUESTIONS TO CONSIDER IN THE
IDENTIFICATION OF WATER QUALITY INDICATORS
AND TARGET VALUES
For many TMDLs, the numeric target will be determined
directly by the numeric criteria associated with the state
water quality standards. However, in those cases where
the numeric criteria are not available or are not protective
of designated or existing uses, the use of an alternative or
supplementary fecal indicator may be required (Figure
4-1). Whether using the water quality standards or
numeric targets for other indicators, a number of factors
should be considered.
1. What is the water quality standard that applies
to the waterbody?
Federal recommended microbiological water quality
criteria have been developed to provide guidance to states
for the establishment of their own standards for
identifying pollution problems. These standards are often
used as the TMDL target value. These criteria, which are
based on indicator organisms, are summarized in Table
4-1. The microbiological indicators yield a general
assessment of water quality and safety for the designated
or existing use and do not identify specific human
pathogens; that is, the exceedance of criteria developed
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Identification of Water Quality Indicators and Target
Identify the Exceedance that P laced the
Waterbody on the 303 (d) List
i
Numeric Water
Quality Standard
Non-Numeric Water
Quality Standard
Develop Supporting Indicators for Follow-up Monitoring
Develop TMDL Using
Numeric Standard
Identify Potential
Indicators
Select Target Value Protective of
Designated Uses
Develop TMDL Using Selected
Target Value
Figure 4-1. Factors for determining indicators and endpoints
It should be noted that several states
have fecal indicator standards that vary
based on season, usually in conjunction
with seasonal use designations (e.g.,
primary contact recreation for summer
months and secondary recreation for the
rest of the year). TMDLs are developed
for waters violating the applicable water
quality standards and are applied
according to the conditions of those
standards (e.g., criteria set for a season
or flow). Therefore, if a waterbody
violates a seasonal designated or existing
use or criterion, a TMDL is developed
and applied on a corresponding seasonal
basis.
Where a numeric water quality standard
exists and is an appropriate indicator for
use attainment, the TMDL analysis
should use the standard. In some cases,
for E. coll and enterococci bacteria indicates that the
water might cause some type of illness following
exposure to that water. For example, recreational use by
swimmers or surfers could be impaired by the presence
of high densities of fecal indicator bacteria because
some of those fecal indicator bacteria might cause
gastrointestinal illness if the water is swallowed;
commercial or recreational harvesting of oysters in an
estuary could be impaired because the presence of high
densities of these fecal indicators suggests that other
human pathogens such as the infectious hepatitis A virus
might have accumulated in the shellfish tissues.
Some jurisdictions have adopted the federal 304(a)
criteria for enterococci or E. coll. (See box defining
304(a) criteria). EPA publishes 304(a) criteria as
guidance to states and tribes. States and tribes may
adopt EPA's 304(a) criteria, 304(a) criteria modified to
reflect site-specific conditions or criteria based on other
scientifically defensible methods. A 1998 summary of
state standards used in the United States for recreational
waters can be found at
http://www.epa.gov/ost/beaches/local/statept.pdf;
however, the most recent standards should be obtained
from the particular jurisdiction because changes might
have occurred since the publication of this document.
the waterbody of concern has a numeric water quality
standard that might not appropriately or sufficiently
reflect the use impairment, and the use of a
supplementary indicator or set of indicators might
provide additional means for measuring attainment of
designated or existing uses. For example, if a waterbody
meets its established numeric criteria for E. coll or
enterococci but does not support its designated use of
primary contact recreation, a TMDL must be developed
for the waterbody.
The term "water quality criteria" is used in two sections of the Clean
Water Act—section 304(a)(1) and section 303(c)(2). Section
304(a)(1) requires the Administrator of EPA to publish criteria for
water quality accurately reflecting the latest scientific knowledge on
the kind and extent of all identifiable effects on health and welfare that
may be expected from the presence of a pollutant in any body of
water. Under section 303, water quality criteria associated with
specific stream uses form the basis for enforceable water-quality
based limits in CWA permits when adopted as state water quality
standards. It is not until their adoption as part of the state water
quality standards that the criteria become regulatory. The water
quality criteria adopted in the state water quality standards could
have the same numerical limits as the criteria developed under
section 304. However, in many situations, states may want to adjust
the water quality criteria developed under section 304 to reflect local
environmental conditions and human exposure patterns before
incorporating those criteria into the state water quality standards.
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Protocol for Developing Pathogen TMDLs
Table 4-1. Currently recommended criteria for indicators of elevated levels of pathogens
Designated Use
Recreation
Primary (e.g.,
swimming,
surfing, diving)
Secondary (e.g.,
wading, boating)
Shellfish harvesting
waters
Public drinking water
sources
Pathogens Evaluated
E.coli*
Enterococci*
Fecal coliformb
Total coliformb
Fecal coliformb
Total coliform0
Fecal coliform0
£co//d'e
Cryptosporidium
oocystsd'f
Criteria
Freshwater: Geometric mean of 126 CFU per 100 ml, based on not less than 5 samples equally
spaced over a 30-day period; no sample should exceed a one-sided confidence limit (CL) calculated
using the following as guidance: designated bathing beach - 75% CL; moderate use for bathing -
82% CL; light use for bathing - 90% CL; infrequent use for bathing - 95% CL; based on a site-specific
log standard deviation, or if site data are insufficient to establish a log standard deviation, then using
0.4 as the log standard deviation
Freshwater: Geometric mean of 33 CFU per 100 ml, based on not less than 5 samples equally
spaced over a 30-day period; no sample should exceed a one-sided confidence limit (CL) calculated
using the following as guidance: designated bathing beach - 75% CL; moderate use for bathing - 82%
CL; light use for bathing - 90% CL; infrequent use for bathing - 95% CL; based on a site-specific log
standard deviation, or if site data are insufficient to establish a log standard deviation, then using 0.4
as the log standard deviation
Marine: Geometric mean of 35 CFU per 100 ml, based on not less than 5 samples equally spaced
over a 30-day period; no sample should exceed a one-sided confidence limit (CL) calculated using
the following as guidance: designated bathing beach - 75% CL; moderate use for bathing - 82% CL;
light use for bathing - 90% CL; infrequent use for bathing - 95% CL; based on a site-specific log
standard deviation, or if site data are insufficient to establish a log standard deviation, then using 0.7
as the log standard deviation
Geometric mean of 200 CFU per 100 ml, based on not less than 5 samples equally spaced over a
30-day period and no more than 1 0 percent of the samples exceeding 400 CFU per 1 00 mL during
any 30-day period. [Note: fecal coliform criteria are used by many states; however, EPA
recommends the use of the E. coli and enterococci criteria.]
Geometric mean of 70 MPN per 1 00 mL, with not more than 10 percent of the samples taken during
any 30-day period exceeding 230 MPN per 100 mL.
Median concentration should not exceed 14 MPN per 1 00 mL with not more than 10 percent of the
samples taken during any 30-day period exceeding 43 MPN per 100 mL.
Ninety percent of daily raw water samples • 1 00 CFU/1 00 mL for surface water systems to remain
unfiltered
Ninety percent of daily raw water samples • 20 CFU/1 00 mL for surface water systems to remain
unfiltered
Lakes and Reservoirs - 10 CFU/1 00 mL as annual average
Flowing Streams and Rivers - 50 CFU/1 00 mL as annual average
0.075 oocysts/L (7.5 oocysts/100 L) to avoid upgrading treatment
a Source: Federal 304(a) Ambient Water Quality Criteria for Bacteria (USEPA, 1986)
b Source: Quality Criteria for Water (USEPA, 1976)
0 See 40 CFR 141.71 (a)(1) and sampling frequency table under §141.74(b)(1)
d These provisions are scheduled to be proposed in the Spring of 2001
e For a small system (<10,000) that tests for £ coli as a surrogate for Cryptosporidium, exceeding an £ coli threshold would require that system to
either test directly for Cryptosporidium or to upgrade its treatment.
' The current treatment requirement for all surface water systems is 2 logs (99%) removal. Sampling results >0.075 oocysts/L would trigger a
requirement to upgrade to 3 logs (99.9%) removal orinactivation; >1 oocyst/L would trigger a requirement to provide 4 logs (99.99%) removal or
inactivation; and >3 oocysts/L would trigger a requirement to provide 4.5 logs (99.995%) removal or inactivation of Cryptosporidium.
First Edition: January 2001
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Identification of Water Quality Indicators and Target
It would be helpful to use a supplementary indicator that
is more clearly linked to the designated use impairment
(e.g., Cryptosporidium when cases of cryptosporidiosis
are associated with use of the waterbody) and establish a
corresponding target value to develop a TMDL.
Recommendation: Determine the water quality standard
for the waterbody. Use the water quality standard when
it is numeric and it represents the best available measure
of designated or existing use impairment. Use
supplementary indicators when the numeric standard is
not an appropriate measure of designated or existing use
support. When a numeric standard is used, note any
important issues, including where the standard is applied
(e.g., end of pipe or open water, segment or entire
length), number of samples required, averaging period,
applicable time period (e.g., summer months) and
number of exceedances allowed.
2. What factors affect indicator selection?
Even when attainment of designated or existing uses can
be measured using numeric water quality standards,
other factors should be considered before developing the
TMDL. The factors include the relative value of the
waterbody, staff expertise available for monitoring and
analyzing data for other indicators, and resources
available. For example, the primary drinking water
source for a large population might rely on a different
fecal indicator than that used for secondary contact
Table 4-2. Human pathogens likely to be associated with sewage
Designated uses are the desirable uses that the water quality
should support. Examples are drinking water supply, primary
contact recreation (e.g., swimming), and aquatic life support.
Each designated use has a unique set of water quality
requirements or criteria that must be met for the use to be
realized. Waterbodies may be designated for multiple uses
(USEPA, 1995c).
Bacteria
Aeromonas hydrophila
Bacillus anthacis
Campy lobacter pylori
Campylobacter spp.
Clostridium botulinum
Clostridium perfringens
Escherichia coli
Helicobacter
Klebsiella penumoniae
Listeria monocytogenes
Mycobacterium spp.
Pseudomonas spp.
Salmonella spp.
Shigella spp.
Staphylococcus aureus
Vibrio spp.
Yersina spp.
Viruses
Adenovirus
Coxsackie A and B
Echovirus
Hepatitis A
Non-A, non-B hepatitis
Norwalk/Snow Mountain/
small round viruses-related
gastroenteric viruses
Parvovirus
Poliovirus
Reovirus
Rotavirus
Protozoa
Entamoeba histolytica
Acanthamoeba spp.
Giardia spp.
Cryptosporidium
Sources: Ahmed, 1991; Kennish, 1992; McNeill, 1992; Koenraad etal., 1997
recreation waters. In other cases, the designated use
might be impaired despite no observed violation of the
numeric criteria. For these situations, alternative or
supplementary fecal indicators might need to be
evaluated. In addition, sampling protocols might need to
be modified to better examine the concentration of the
fecal indicators.
Most pathogen-impaired waterbodies are of concern
because of the human health risks associated with
exposure to the pathogens. Table 4-2 presents some of
the pathogens associated with sewage that can cause
disease following exposure. Where information is
available on the concentrations of these pathogens in the
environment, their infectivity, and sources, they all
qualify for use as possible indicators. However, for most
of these pathogens, this type of information is lacking
and pathogens are often difficult to reliably detect using
simple and inexpensive laboratory methods.
Some criteria that should be considered during the
selection of an indicator are the following: it should be
easily detected using simple laboratory tests, it
should not be present in unpolluted waters, and
it should appear in concentrations that can be
correlated with the extent of contamination
(Thomann and Mueller, 1987). Table 4-3
presents potentially useful indicators, including
coliform and enterococcus bacteria. These
indicators satisfy many of the criteria suggested
by Thomann and Mueller (1987) and are used
in many state water quality standards.
Recommendation: Select an appropriate fecal
indicator based on the information known
about and the impairment to the waterbody.
Consider the established water quality
standard, alternative indicators, designated or
existing use, and resources. Document all steps
in the process, and, if possible, involve
stakeholders in the decisions. If a pathogen is
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Protocol for Developing Pathogen TMDLs
Table 4-3. Some potential indicator organisms for TMDL
development
Group
Viruses
Coliform bacteria
Enterococcal
Bacteria
Protozoa
Indicator Organisms3
F1 coliphage; MS2 bacteriophage; poliovirus
type 1 strain Lsc2ab; enteroviruses
Total coliform; fecal coliform; Escherichia
coll, Klebsiella spp.
Streptococcus faecalis; Streptococcus
faecium
Cryptosporidium spp.
Giardia spp.
a Water quality standards often exist for the indicator organisms in bold type.
considered for use as an appropriate fecal indicator,
consult a trained sanitary/environmental microbiologist.
3. What water quality measures could be used as
indicators?
As discussed earlier in this section, EPA publishes
304(a) criteria as guidance to the states and tribes in
establishing their water quality standards. Current
304(a) criteria recommendations as related to pathogens
are a geometric mean of 126 CPU/100 mL for E. coll, a
freshwater geometric mean of 33 CFU/100 mL for
enterococci, and a marine geometric mean of 35
CFU/100 mL for enterococci (USEPA, 1986). EPA
believes E. coll and enterococci are more accurate
indicators of the presence of pathogens than fecal
coliform bacteria. Therefore, the current 304(a) criteria
suggest the use of E. coll or entorococci bacteria,
replacing the 1968 criteria (USEPA, 1968), which
recommended a geometric mean of 200 CFU/100 mL for
fecal coliform.
Presently, most states are using the 1968 water quality
criteria for fecal coliform bacteria in their water quality
standards. However, since 1986, the EPA has
recommended the use of E. coll and enterococci bacteria
as indicators of pathogenic contamination in
waterbodies.
States and tribes may adopt EPA's 304(a) criteria,
304(a) criteria modified to reflect site specific
conditions, or criteria based on other scientifically
defensible methods. The state must develop the TMDL
using the current, approved, state water quality
standards. If states or tribes do not have their own
bacterial water quality criteria, then the federal criteria
should be used. If the state chooses at any time to revise
its criteria, then the standards need to be revised and
approved according to state procedures, which typically
include public notification and review and approval by
the EPA Standards Branch at the Regional EPA office.
The Alaska decision (Alaska Clean Water Alliance v.
Clark (1997)) has set the precedent in regard to states
revising their water quality standards. The rule states
that standards submitted to EPA after the effective date
of the rule do not become "applicable" water quality
standards for CWA purposes until approved by EPA, and
that "applicable" standards remain the CWA standards
until EPA approves state or tribal revisions or publishes
replacement water quality standards (USEPA, 2000).
States may also use additional indicators (i.e., alternative
bacteria, protozoa, viruses) for tracking and analysis
purposes as long as the TMDL is written to meet the
current applicable water quality standards. For example,
the water quality standards may not always reflect the
actual problem, such as a cryptosporidiosis outbreak. A
protozoan, such as Cryptosporidium, can be used as an
indicator in cases where Cryptosporidium is known to be
the pathogen of concern in the waterbody. If this is the
case, the TMDL needs to show that once the target for
Cryptosporidium is met, the water quality standards will
also attained.
Recommendation: Many states and tribes presently use
the 1968 fecal coliform water quality criteria as indicator
values. The EPA, however, recommends the use of E.
coll and enterococci as bacterial indicators, as stated in
the federal 304(a) criteria. Regardless of what indicator
states use to develop the TMDLs, either from state water
quality standards or alternate indicators, the TMDL must
be written to result in the attainment of water quality
standards. Therefore, the states must establish a
relationship between the indicator used and existing state
water quality standards to prove that meeting the
indicator target value will correlate to attainment of water
quality standards.
4. What are appropriate target values for the
chosen indicators?
For the indicators used in developing pathogen TMDLs, a
desired or target condition must be established to provide
measurable environmental management goals and a clear
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Identification of Water Quality Indicators and Target
linkage to attaining the applicable water quality
standards. In the case of pathogen TMDLs, the target
values for most indicators are already established
directly through the numerical criteria in state water
quality standards. These water quality criteria can be
used or a more stringent or more appropriate value can
be used as the target value.
Often, states have multiple parts to their standards. For
example, standards may express a "not to exceed,"
instantaneous criteria as well as a geometric mean based
on a minimum number of samples collected in a specific
time frame. The availability of data and nature of the
impairment may dictate which part of the standard
should be used as the target. For example, the geometric
mean criteria used for the bacterial indicator target value
may be based on at least 5 samples collected in a 30-day
period. As many monitoring programs are based on
quarterly sampling, there may not be enough historical
data to support the use of the geometric mean criteria as
the target. In this case the "not to exceed" value may be
used. For example, the recommended federal criteria for
enterococci at freshwater bathing beaches is 33
CFU/100 mL, based on not less than 5 samples equally
spaced over a 30-day period. Unfortunately, data
containing 5 samples taken at equal intervals throughout
the month are often not available. In this case, the "not
to exceed" criteria of a one-sided confidence limit of
75% for enterococci bacteria should not be exceeded
according to the federal criteria for freshwater bathing
beaches.
Before developing the TMDL, it is necessary to
determine the appropriate target value. In most cases the
state water quality criteria will be the appropriate target.
If the state standards contain multipart criteria, it should
be decided whether it is necessary to use one or all parts
as the target.
If the state water quality criteria do not reflect the
impairment or problem, alternate indicators should be
used and appropriate target values established. The
target value must be set at a level that represents the
attainment of the current water quality standards.
Recommendation: The target values for most bacteria
indicators are already established directly through the
numeric criteria in state water quality standards. The
TMDL must be written to attain these standards. If an
alternate indicator is used, the TMDL must establish
some relationship between the water quality standards
and the alternate indicator, showing that the target value
represents attainment of water quality standards.
RECOMMENDATIONS FOR IDENTIFICATION OF
WATER QUALITY INDICATORS AND TARGET VALUES
• When appropriate, use the established water quality
standard as the numeric target for TMDL
development.
• Select a fecal indicator based on its scientific and
technical appropriateness and information known
about the waterbody, including the established water
quality standard, the identified impairment,
supplementary indicators, designated or existing use,
and resources, while considering practicality and
cost. Document all steps in the process and involve
stakeholders in the decisions.
RECOMMENDED READING
(Note that a full list of references is included at the end of
this document.)
Francy, D.S., D.N. Myers, and K.D. Metzker. 1993.
Escherichia coli and fecal-coliform bacteria as indicators
of recreational water quality. Water Resources Investig.
Rep. 93-4083, U.S. Geological Survey, Earth Science
Information Center, Denver, CO.
USEPA. 1994a. Guidelines for deriving site-specific
water quality criteria for the protection of aquatic life and
its uses. Chapter 4 in Water Quality Standards
Handbook. U.S. Environmental Protection Agency,
Office of Water Regulations and Standards, Washington,
DC.
USEPA. 1986. Ambient water quality criteria for
bacteria) 1986. EPA-A440/5-84-002. U.S.
Environmental Protection Agency, Washington, DC.
USEPA. 1998. Bacterial water quality standards status
report. EPA 823/R-98/003. U.S. Environmental
Protection Agency, Office of Water, Washington, DC.
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Protocol for Developing Pathogen TMDLs
Source Assessment
Objective: Characterize the type, magnitude, and
location of sources of fecal indicator loading to the
waterbody.
Procedure: Compile an inventory of all possible sources
of pathogens to the waterbody. Sources may be
identified through assessment of maps, data, reports,
and/or field surveys. It is likely that a combination of
techniques will be needed depending on the complexity
of the source loading and watershed delivery processes.
After compiling an inventory, monitoring, statistical
analysis, modeling, or a combination of methods should
be used to determine the relative magnitude of source
loadings.
OVERVIEW
The source assessment is needed to evaluate the type,
magnitude, timing, and location of loading to an
impaired waterbody. It further describes the sources
initially identified during the problem identification.
Several factors should be considered in conducting the
source assessment. These factors include the various
types of sources (e.g., point, nonpoint, background), the
relative location and magnitude of loads from the
sources, the transport mechanisms of concern (e.g.,
runoff, direct deposit), and the time scale of loading to
the waterbody (duration and frequency of fecal indicator
loading to receiving waters).
Once sources have been identified and a relative ranking
of their contribution has been conducted, the loadings
from each source should be estimated using a variety of
techniques, including relying on existing monitoring
data, doing simple calculations, performing spreadsheet
analysis using empirical methods, or using one or more
Key Questions to Consider for the Source Assessment
1. What are the potential sources of pathogens to the
waterbody of concern?
2. How can sources of pathogens to the waterbody of
concern be characterized?
3. How should sources be grouped for assessment and
load allocation?
4. How can pathogen loads be estimated?
of a range of computer modeling systems. The selection
of the appropriate technique is an outgrowth of the
problem identification and watershed characterization
performed during the initial phase of TMDL
development.
A TMDL should include an evaluation of all the
sources contributing to the fecal indicator loading of the
waterbody. The detail of the assessment will vary,
however, depending on the overall approach best suited
to the site-specific conditions. The selection of the
appropriate method for estimating loads should be based
on the complexity of the problem, the time constraints,
the availability of resources and monitoring data, and
the management objectives under consideration. It is
usually advantageous to select the simplest method that
addresses the questions at hand, uses existing
monitoring information, and considers the available
resources and time constraints for completing the
TMDL. This section of the protocol describes various
types of sources, identifies procedures for characterizing
loadings, and introduces a process for selecting a source
assessment technique.
KEY QUESTIONS TO CONSIDER FOR SOURCE
ASSESSMENT
1. What are the potential sources of pathogens
to the waterbody of concern?
Pathogens are delivered to waterbodies by a wide
variety of point and nonpoint sources (see box on page
5-2). Treated municipal sewage is a point source of
bacterial, viral, and protozoal contamination. Not all
human pathogens are removed or rendered harmless by
treatment processes. Periodic effluent overflows and
high-flow bypass from wastewater treatment plants
(WWTPs) can cause occasional high loadings of
pathogens. Other major point sources include combined
sewer overflows (CSOs) and sanitary sewer overflows
(SSOs). CSOs contribute significant pathogen loads
during storm events; SSOs may contribute pathogens
under both wet and dry weather conditions. Illicit
discharges of residential and industrial wastes are
difficult to identify but are often a major source of
pathogens.
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Source Assessment
i»
Potential Pathogen Sources
Point Sources
WWTPs
CSOs
SSOs
Slaughterhouses
Meat processing facilities
Poultry processing facilities
Animal feedlots
Illicit sewage connections
Nonpoint Sources
Domestic pets
Animal feedlots
Septic systems
Livestock
Pastures
Boat pumpout
Landfills
Land application of manure
Land application of sludge
Storm water runoff from urban watersheds might also be
a significant source of pathogens, delivering pathogens
present in the waste of domestic pets and wildlife and in
litter. On-site wastewater systems (septic tanks,
cesspools) that are poorly installed, faulty, improperly
located, or are in close proximity to waterbodies are
potential sources of human pathogens to surface and
ground waters. Boats lacking holding tanks for pumpout
also contribute potential human pathogens; marinas and
waterbodies that are heavily used for recreational
boating have been shown to have elevated levels of fecal
indicator bacteria. Rural storm water runoff can
transport significant loads of bacteria and pathogens
from livestock pastures, livestock and poultry feeding
facilities, and feedlots. Livestock areas with high
concentrations of animal waste contribute pathogens
primarily through surface runoff. Some of these sources
may be subject to the requirements of the NPDES
program. Facilities that process food, meat, or poultry
are potential sources from overflow of holding lagoons.
Manure storage and application practices might lead to
pathogen loads in surface runoff depending on the time
of year, the timing of the manure application with
runoff-producing storm events, or the proximity of the
application to the waterbody of concern. Wildlife can
also contribute pathogen loadings and may be
particularly important in the transmission of the
protozoan pathogens Giardia lamblia and
Cryptosporidium. Wildlife of concern include deer,
beaver, ducks, and geese. In urban or suburban areas,
large populations of deer can provide a significant
source of pathogens. Although remote, pristine forested
lands might appear to be unlikely candidates for
pathogen sources, many wildlife species harbor
microorganisms that can be pathogenic to themselves,
other wildlife, and humans.
Most fecal indicators are indirect and only warn of the
possibility of the presence of fecal pathogens, which are
not necessarily from humans, but potentially from
several sources. Current monitoring and analytical
methods for coliforms and enterococci do not
distinguish between indicator bacteria of human and
nonhuman origin (Turner et al., 1997). Therefore, the
environmental and public health implications of
monitoring data are difficult to interpret in cases where
contamination comes from multiple sources. This
would not be a problem if there was a way to identify
bacterial strains that are specific to a particular host.
Other indicators and methods that permit more rapid
identification of fecal indicators are under development
or have been developed. Some of these alternate
methods include agglutination assays, DNA
hybridization tests, and polymerase chain reactions
(PCR) (Koenraad et al., 1997). The PCR process shows
promise for distinguishing between particular sources of
fecal indicator bacteria contamination and may be used
for other environmental applications. This method of
microbial source tracking is known as DNA
fingerprinting.
Some states have begun using DNA fingerprinting to
identify sources of fecal indicator contamination in
water (Pelley, 1998; Blankenship, 1996). DNA
fingerprints can be used to match the genetic
characteristics of bacteria in animals such as chickens,
cows, and wildlife to identify pollution sources. Each
animal species hosts unique strains of bacteria that are
adapted to the intestinal environment of that particular
host. By comparing the bacteria from the sample to
fingerprints of known strains, the bacteria can be tracked
to the source. DNA fingerprinting identifies the
pollution source and helps managers/planners target the
problem and formulate a mitigation strategy (Pelley,
1998). The current challenge is to develop a complete
library of bacterial strains that is specific to each locale.
Although DNA fingerprinting has only recently been
used to identify water pollution sources and is an
expensive and lengthy process, it offers the promise of
providing a large amount of high-quality information.
5-2
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Protocol for Developing Pathogen TMDLs
DMA Fingerprinting in Virginia
DNA fingerprinting proved helpful when a farmer on Virginia's Eastern Shore was faced with the closure of his shellfish beds due to elevated
levels of £ coli. Failing septic tanks were assumed to be the primary source of the fecal pollution, but a survey of septic systems in the
sparsely populated watershed indicated that they were not the cause and it became necessary to identify another source. The highest levels
of coliform bacteria were measured in the small tidal inlets and rivulets of the wetlands located upstream of local houses, shifting
suspected sources from human to other sources. Researchers collected fecal samples from raccoon, waterfowl, otter, muskrat, deer, and
humans in the area and used DNA fingerprinting to confirm bacteria the suspicion that the source was not anthropogenic in nature. The
DNA of the samples was analyzed and characterized, resulting in a library of more than 200 DNA patterns distributed through more than 700
£ coli strains. Comparing £ coli from the shellfish beds against the fingerprints of the known strains in the DNA library, the researchers
linked the in-stream £ co//to deer and raccoon (mostly raccoon). Several hundred animals, including 180 raccoon, were removed from
areas adjacent to the wetlands. £ coli levels subsequently declined by 1 to 2 orders of magnitude throughout the watershed, and
previously closed or threatened areas of the tidal creeks were reopened to shellfishing.
Sources: Blankenship, 1996, and News-Notes, 1997.
Recommendation: Develop a comprehensive list of the
potential pathogen sources to the waterbody of concern.
Use the list of potential sources of pathogens and the
watershed inventory to identify actual sources and
develop a plan for identifying and accounting for the
load from each.
2. How can sources of pathogens to the
waterbody of concern be characterized?
Sources of pathogens can be characterized using a
variety of approaches. The determination of the most
appropriate techniques will be based on the extent of the
problem, the size of the watershed, the availability of
watershed information, the types of sources (point
and/or nonpoint), and the resources available. All
possible sources of information should be consulted.
For example, the under the SDWA states must develop
source water assessments that inventory all potential
contamination sources of drinking water contaminants
and their locations and state Wellhead Protection
Programs typically have information on ground water
recharge areas and the locations of potential
contaminant sources. Polluted groundwater that is
hydrologically connected to a waterbody is likely to
contribute to its impairment, so potential sources of
groundwater contamination should also be reviewed,
particularly those near the waterbody. This information
is usually available from the state drinking water or
public health agency. In addition, the Safe Drinking
Water Amendments of 1996 require the delineation of
source water protection areas and contamination source
inventories (USEPA, 1997b). Other agencies, such as
USDA and state natural resources, extension service, or
public health agencies, might provide useful information
on the location of possible sources of pathogens.
Although agency staff can often provide significant
information, other approaches can be used to identify
sources, including literature and historical records
searches, surveys (phone, door-to-door, windshield), and
field reconnaissance, including the use of GIS data or
aerial photographs (USEPA, 1991b). Reports and
articles in the literature can provide useful information
on past and present land uses, activities, and
disturbances. Public records from which information
can be obtained include registries of industrial and
commercial activities, property transfer, titles, and
deeds. Anecdotal information about the area should also
be obtained. The local Chamber of Commerce can
normally provide direction for this effort. Various
survey methods can also be used to locate possible
sources of contamination. Phone, mail, or personal
interviews with landowners and stakeholders can often
produce a significant amount of information about local
sources.
Driving through the watershed is another method of
identifying potential sources. This type of survey
(called a windshield survey) is much less detailed than a
field reconnaissance effort and can cover larger areas in
less time. Field reconnaissance activities are resource-
intensive and require additional resources and planning.
These searches involve extensive on-site reconnaissance
and may not be practical for large watersheds. Use
available aerial photos from several years to identify
particular sources, such as failing septic systems or land
uses that generate pathogen loads (e.g., pastures). For
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Source Assessment
watersheds with GIS data, good-quality land use
information is often available to identify the spatial
pattern of land use, as well as the proportion of coverage
for each land use. Using this information, source classes
can be identified to allow lumping of source
information, which is especially important in watersheds
with scattered nonpoint sources that are difficult to
characterize independently.
For each source identified in the waterbody, note
important factors such as the proximity of the source to
the waterbody of concern, the processes that are
important to the delivery of the pathogen to the
waterbody (e.g., runoff or direct deposit), and the
relative importance of each source to the overall
pathogen load. Develop a pathway diagram to describe
how the pollutant can enter the waterbody. Table 5-1
presents information on transport processes for potential
sources of pathogens.
Recommendation: Using all available information,
identify all possible sources of pathogens to the
waterbody of concern. Use GIS or maps to document
the location of sources and the processes important for
delivery to the waterbody. Identify all government
agencies and nongovernment organizations active in the
watershed, and conduct interviews and collect
information.
3. How should sources be grouped for
assessment and load allocation?
To select appropriate analytical tools and management
measures, the sources must be grouped into discrete
units. The definition of each unit should be based not
only on the ability of specific analytical tools to
determine quantifiable loads but also on management
and economic considerations. The sources should be
grouped so that there is a recognizable link between
sources and allocation. Although typically classified as
nonpoint pollution, a groundwater contribution to
pollutant loading does not fall within NPDES
jurisdiction where there is a direct hydrologic
connection from the facility through the groundwater to
the waterbody. Therefore, such groundwater
contributions to pollutant loading should be grouped
with other point sources of pollutants. Grouping of
sources can be accomplished by the use of database
searches or matrices that identify and link these common
processes or political characteristics.
By linking the common mechanisms of pollutant
delivery, the appropriate analytical tools can be
efficiently determined. For example, although there are
different pathogen concentrations in cattle manure than
in chicken manure, the delivery mechanisms are similar
enough that the same analytical tool can be used to
estimate the delivered load from both. An example of
Table 5-1. Sources and transport pathways for pathogens
Source/land use
Agriculture
Urban/
Residential
Forest
Point Sources
Operation/activity
Livestock-feedlot
Livestock-manure storage
Crop-manure/sludge application
Pasture
Domestic pets
Wildlife
Septic systems
Illicit connection
Landfills
Wildlife
WWTP
Slaughterhouse
CSOs;SSOs
Samples of management activity
Manure removal
Storage structures; leachate control
Spreading schedules; storage
Rotation
Waste pickup law
Management; population control
Pumpout; education
Compliance
Disposal
Management; population control
Waste treatment
Waste treatment
Storage/transport redesign
Frequency
weekly
variable
variable
variable
variable
constant
annual
constant
constant
constant
constant
variable
variable
Transport process(es)
runoff; erosion
runoff; erosion; seepage
runoff; erosion
runoff; erosion; direct
runoff
runoff; direct
leaching; interflow
direct
runoff; leaching
runoff; erosion; direct
direct
direct
direct; rainfall-driven
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Protocol for Developing Pathogen TMDLs
Factors to Consider for Grouping Sources
Delivery mechanisms
Location of sources relative to waterbody of concern
Management options under consideration
Social, political, and economic factors
Physical characteristics of the watershed, including
slope, geology, soils, and drainage network.
social and political associations is that within the
watershed there might be several different growers
associations or cooperatives. It might be easier to
propose management initiatives to the cooperative than
to try to implement them on an individual farm basis.
There might be a large source of pollutants from a major
employer in the area, and reducing the loads from that
source might have a significant impact on the local
economy. The spatial organization of the sources is also
important for identifying critical reaches to be studied.
For example, a feedlot located several miles from the
waterbody in the upper reaches of the watershed would
usually have far less impact on loading than an equally
sized parcel located next to the receiving water.
Industry growth is another important factor. Chicken
farm expansion can occur in a much smaller land area
and at a higher density and rate than another agricultural
use, such as feedlots.
Sources can also be grouped by subwatershed. For
example, the watershed that is the focus of the TMDL
can be divided into several smaller subwatersheds and
loading estimates can be made for each of these. This
approach will often be useful during the source
characterization step of TMDL development, allowing
for isolation of specific sources and spatial analysis of
source loading and water quality response.
Subwatershed delineation also makes it easier to
compare the loading estimates for each subwatershed to
the associated water quality observations. However, as
will be discussed later in the allocation section, it will
usually be necessary to group sources within the
subwatersheds by land use or source categories to
facilitate the allocation process.
The end result of this phase should be an efficient
grouping of sources that can be evaluated using
available tools and resources for the development of the
TMDL. The categorization of sources may be an
iterative process, depending on how well the potential
groupings can be analyzed and quantifiable loads
determined using the available analytical tools.
Recommendation: Group sources in a manner that
establishes a link between sources and allocations,
facilitates source assessment, and assists in the
implementation of management actions. When grouping
sources, consider location of source, pollutant transport
and delivery mechanism, spatial distribution,
relationship to potential control actions and necessary
analytical techniques associated with the source.
4. How can pathogen loads be estimated?
The identification of sources within a watershed
provides the answer to "What sources are causing the
impairment?" The next step is to determine "What
effects are these sources having on the waterbody?" For
many sources, it is difficult to predict fecal indicator
loading rates from either physical principles or national
values found in the literature. Table 5-2 summarizes
information from several references to illustrate the
source-specific nature of fecal indicator and pathogen
values. Source concentrations of pathogens and fecal
indicators can also be region-specific, making site-
and/or region-specific monitoring data useful, if
available. Site-specific monitoring is often essential to
establish accurate concentration estimates and can be
combined with modeling of flow and/or sediment
transport to produce load estimates. Monitoring
techniques for bacterial pathogens are addressed in this
protocol where they are applicable.
Estimating pathogen loads for point sources is typically
easier because point sources are relatively constant in
time—the discharge from a municipal wastewater
treatment plant, for instance. Certain other important
load sources mix the characteristics of point and
episodic nonpoint sources. For example, CSOs are point
sources subject to permitting, but, because they are
caused by stormflow into the combined sewer system,
they exhibit the episodic nature of nonpoint sources.
Techniques for estimating pathogen loads to
waterbodies vary according to source type and can range
from qualitative assessments to detailed modeling
efforts. When determining the best approach to estimate
the pathogen load delivered from the source to the
stream, analysts are encouraged to start with the
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Source Assessment
i»
Table 5-2. Summary of source-specific pathogen and fecal indicator concentrations
Indicator
Clostridium perfringens
Clostridium perfringens
Cryptosporidium oocysts
Cryptosporidium oocysts
Cryptosporidium oocysts
Cryptosporidium oocysts
Cryptosporidium oocysts
Escherichia coli
Escherichia coli
Enteric virus
Enterococci
Enterococci
Enterococci
Enterococci
Enterovirus
Enterovirus
Fecal coliforms (FC)
FC
FC
FC
FC
FC
FC
FC
FC
Concentration
4.5 x107 organisms/day3
101-103#/mL
10-1-101#/mL;
0.85 x103- 5.28 x103#/L
13.7#/mL
1.4x104-3.96x104#/L
4.0x10°-1.6x101#/L
370 ± 197 oocysts/gram feces
1 .2x1 05-3.9x1 05 organisms/day"
2.5x108£co///daya
1.7x108£ coli /gram0
101-102#/mL
2.0 x 1 0° - 2.1 x 1 05 enterococci/1 00 ml
102-103enterococci/mL;
5.4 x 1 05 enterococci/1 00 ml
2.2x108enterococci/daya
4.0x105enterococci/gram
6.9x10°-2.8x102PFU/10L
8.7x102PFU/10L
1.5x101 -4.5x105 MPN/100 ml
2 x109 organisms/day
4.9 x1010 organisms/day
0. 24 x109 organisms/day
1.4 x108 organisms/day
0.13 x109 organisms/day
9.5 x107 organisms/day
5.4 x109 organisms/day
1.0 x1011 organisms/day
1.0 x1011 organisms/day
4.2 x108 organisms/day
11 x109 organisms/day
1.2 x108 organisms/day3
2.5 x109 organisms/day
1.6 x108 organisms/gram
Source
Duck
Raw sewage
Raw sewage
Slaughterhouse (cattle) waste effluent
Treated effluent (activated sludge only)
Treated effluent (activated sludge and
sand filtration)
Canada geese
Duck
Pigeon
Raw sewage
Background
Raw sewage
Duck
Pigeon
Background
Raw sewage
Background
Human
Geese
Chicken
Turkey
Cow
Cow (Dairy)
Cow (Beef)
Horse
Duck
Pigeon
Reference
Roll and Fujioka, 1997
Metcalf and Eddy, 1991
Metcalf and Eddy, 1991;
Madoreetal.,1987
Madoreetal.,1987
Madoreetal.,1987
Madoreetal.,1987
Graczyk et al., 1998
Roll and Fujioka, 1997
Oshiro and Fujioka, 1995
Metcalf and Eddy, 1991
Overcash and Davidson, 1980
Metcalf and Eddy, 1991;
Overcash and Davidson, 1980
Roll and Fujioka, 1997
Oshiro and Fujioka, 1995
Overcash and Davidson, 1980
Overcash and Davidson, 1980
Overcash and Davidson, 1980
Metcalf and Eddy, 1991
LIRPB, 1978
Metcalf and Eddy, 1991
ASAE, 1998
Metcalf and Eddy, 1991
ASAE, 1998
Metcalf and Eddy, 1991
ASAE, 1998
ASAE, 1998
ASAE, 1998
Metcalf and Eddy, 1991
Roll and Fujioka, 1997
ASAE, 1998
Oshiro and Fujioka, 1995
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Protocol for Developing Pathogen TMDLs
Table 5-2. Summary of source-specific pathogen and fecal indicator concentrations
Indicator
FC
FC
FC
FC
FC
FC
FC
FC
FC
Fecal streptococci (FS)
FS
FS
FS
FS
FS
FS
FS
FS
FS
FS
FS
FS
FS
FS
Giardia cysts
Concentration
8.9 x109 organisms/day
1.1 x1010 organisms/day
18 x109 organisms/day
1.2 x1010 organisms/day
5 x109 organisms/day
104-105#/mL;
6.3x106MPN/100mL
4.2 x106 organisms/1 00 ml
9.6 x 1 02 - 4.3 x 1 06 organisms/1 00 ml
1 .2 x 1 02 - 1 .3 x 1 06 organisms/1 00 ml
1 .35 x 1 06 - 2.4 x 1 08 organisms/1 00 ml
1 .2 x 1 01 - 1 .43 x 1 0" organisms/1 00 ml
1.0x101-6.6x105#/100mL
0.45 x109 organisms/day
0.62 x109 organisms/day
2.9 x108 organisms/day
1.3 x109 organisms/day
31 x109 organisms/day
5.9 x1011 organisms/day
1.1 x1011 organisms/day
2.6 x1011 organisms/day
8 x109 organisms/day
8.3 x109 organisms/day
230 x109 organisms/day
3.2 x1011 organisms/day
43 x109 organisms/day
1.7 x1010 organisms/day
103-104#/mL;
1.2x106#/100mL
1.7 x106 organisms/100 ml
1 .4 x 1 0" - 1 .7 x 1 06 organisms/1 00 ml
8.0 x 1 03 - 6.1 x106 organisms/1 00 ml
8 x 1 06 - 7.9 x 1 07 organisms/1 00 ml
1 .7 x 1 03 - 3.9 x 1 0" organisms/1 00 ml
10-1-102#/mL
Source
Pig
Sheep
Dogs and cats
Raw sewage
CSO
Urban runoff
Grazed pasture runoff
Feedlot runoff
Cropland runoff
Background
Human
Chicken
Turkey
Cow
Cow (Dairy)
Cow (Beef)
Horse
Duck
Pig
Sheep
Raw sewage
CSO
Urban runoff
Grazed pasture runoff
Feedlot runoff
Cropland runoff
Raw sewage
Reference
Metcalf and Eddy, 1991
ASAE, 1998
Metcalf and Eddy, 1991
ASAE, 1998
Horsley and Witten, 1996
Metcalf and Eddy, 1991;
Overcash and Davidson, 1980
Doranetal., 1981
Doranetal., 1981
Doranetal., 1981
Baxter-Potter and Gilliland, 1988
Doranetal., 1981
Overcash and Davidson, 1980
Metcalf and Eddy, 1991
Metcalf and Eddy, 1991
ASAE, 1998
Metcalf and Eddy, 1991
Metcalf and Eddy, 1991
ASAE, 1998
ASAE, 1998
ASAE, 1998
Metcalf and Eddy, 1991
ASAE, 1998
Metcalf and Eddy, 1991
ASAE, 1998
Metcalf and Eddy, 1991
ASAE, 1998
Metcalf and Eddy, 1991;
Overcash and Davidson, 1980
Doranetal., 1981
Doranetal., 1981
Doranetal., 1981
Baxter-Potter and Gilliland, 1988
Doranetal., 1981
Metcalf and Eddy, 1991
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Source Assessment
i»
Table 5-2. Summary of source-specific pathogen and fecal indicator concentrations
Indicator
Giardia cysts
Protozoan cysts
Psuedomonas aeruginosa
Pseudomonas aeroginosa
Salmonella sp.
Salmonella
Staphylococcus aureus
Staphylococcus aureus
Total coliforms (TC)
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
TC
Concentration
450 cysts/gram of feces
3. 1x105 cysts/day
101-103#/mL
3.1x10°-6.6x103MPN/100mL
101-102#/mL;
2.3x105MPN/100mL
0-1.4x102MPN/10L
10°-102#/mL;
5.0x102MPN/10L
2.5x10°-1.2x102MPN/100mL
2.6x102MPN/100mL
101-106MPN/100mL
7.04 x 1012 organisms/day
2.3 x1011 organisms/day
2.2 x1012 organisms/day
2.7 x1010 organisms/day
5.4 x109 organisms/day
1.98x109 organisms/day
105-109#/mL;
107-109MPN/100mL;
2.3x107MPN/100mL
105-107MPN/100mL;
2.0 x107 organisms/1 00 ml
104-106MPN/100mL
5.8x10"- 2.0 x107 organisms/1 00 ml;
101-108MPN/100mL
7.0 x 1 02 - 4.9 x 1 06 organisms/1 00 ml
1 .25 x 1 07 - 3.5 x 1 08 organisms/1 00 ml
3.2 x 1 03 - 1 .45 x 1 05 organisms/1 00 ml
Source
Canada geese
Raw sewage
Background
Raw sewage
Background
Raw sewage
Background
Raw sewage
Background
Cow (Dairy)
Cow (Beef)
Horse
Pigs
Sheep
Chicken
Raw sewage
CSO
Treated effluent
Urban runoff
Grazed pasture runoff
Feedlot runoff
Cropland runoff
Reference
Graczyk et al., 1998
Metcalf and Eddy, 1991
Overcash and Davidson, 1980
Metcalf and Eddy, 1991;
Overcash and Davidson, 1980
Overcash and Davidson, 1980
Metcalf and Eddy, 1991;
Overcash and Davidson, 1980
Overcash and Davidson, 1980
Overcash and Davidson, 1980
Novotny and Olem, 1994;
Overcash and Davidson, 1980
ASAE, 1998
ASAE, 1998
ASAE, 1998
ASAE, 1998
ASAE, 1998
ASAE, 1998
Metcalf and Eddy, 1991;
Novotny and Olem, 1994;
Overcash and Davidson, 1980
Novotny and Olem, 1994;
Doranetal., 1981
Novotny and Olem, 1994
Doranetal., 1981;
Novotny and Olem, 1994
Doranetal., 1981
Baxter-Potter and Gilliland, 1988
Doranetal., 1981
a Converted from organisms per gram of feces using information in ASAE, 1998.
b Number or Cryptosporidium oocysts per day from geese, assuming that goose total fecal production per day is 4.5 times that of ducks (LIRPB,
1978).
c There is no conversion factor available to convert pigeon numbers to organisms per day.
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Protocol for Developing Pathogen TMDLs
assumption that models are not required. To select
appropriate analytical tools, a number of factors should
be considered, including the following:
• Availability of data and funds to support data
collection
• Familiarity with the analytical tool
• Staff support
• Level of accuracy required
Depending on the complexity of the aggregate sources in
the watershed, load estimation might be as simple as
conducting a literature search or as complex as using a
combination of long-term monitoring and modeling.
The following discussion presents information on the
pathogen concentrations for different sources and
methods of calculating the delivery to the waterbody.
Point source loads
Loads from sewage treatment plants and industrial point
sources
The greatest potential source of human pathogens is raw
sewage. Raw sewage typically has a total coliform
count of 107 to 109 MPN/100 mL (Novotny et al., 1989),
along with significant concentrations of pathogenic
bacteria, viruses, protozoans, and other parasites.
Typical treatment in a municipal plant reduces the total
coliform count in effluent by about 3 orders of
magnitude, to the range of 104 to 106 MPN/100 mL.
Most municipal plants, however, are restricted by their
NPDES permit to discharge at or below the WQS for
fecal coliform. Raw sewage, although usually not
discharged intentionally, may reach waterbodies through
CSOs, SSOs, and leaks in sanitary sewer systems.
Certain industrial processes, such as slaughterhouses
and meat and poultry processing facilities, also have the
potential to contribute substantial point source loads of
pathogens. Analysis of loads from point sources should
generally be based on the effluent monitoring required
for the NPDES permit or on the permit limits, rather
than use of generic assumptions, except when evaluating
potential effects of proposed new sources. In analyzing
such data, it should be noted that variations in
concentration and load can be expected on a daily,
weekly, and seasonal basis depending on the water use
patterns of the community and temperature conditions
within the treatment plant. Ambient upstream and
downstream monitoring can also be valuable,
particularly for assessing the relative contribution of
point and nonpoint sources within urban settings.
In most cases, only indicator bacteria will be measured
in plant effluent, rather than concentrations of specific
human pathogens. Order-of-magnitude estimates of
specific pathogens can, however, be obtained from
information on raw sewage concentrations and effluent
residual chlorine content and kill efficiency.
Loads from CSOs
One way in which raw sewage enters waterbodies
directly is through CSOs. In CSOs, the pathogen load is
dominated by the content of raw sewage, yet the
discharge volume and degree of dilution are determined
by episodic pulses of urban storm water. In many cities,
combined sewer overloading by storm water and
overflow events occur only a few times a year and are
thus unlikely to be monitored. Typical concentrations of
total coliform bacteria in CSOs are reported as 105 to
107 MPN/100 mL (Novotny et al., 1989), or about an
order of magnitude greater than concentrations in
treatment plant effluent. The effect of these
concentrations is reduced, however, because the load is
intermittent. Estimation of loading from CSOs will thus
often require combining information on the pathogen or
fecal indicator load of sanitary sewage with estimates of
the overflow volume associated with large storm events.
Modeling the impacts of CSOs can be a difficult
undertaking. Therefore, USEPA's CSO Control Policy
recommends permittees take one of the following
approaches: (I)presumption approach, consisting of
meeting performance goals in minimizing the number
and volume of CSO events, or (2) demonstration
approach, requiring evidence that a CSO control plan is
adequate to meet water quality standards. In either case,
the CSO Control Policy expects a relatively high degree
of characterization of the hydraulic operation of the
sewer system in order to estimate the number and
volume of CSO events. For instance, many cities
simulate the hydraulic operation of the combined sewer
system and storm drainage sewershed using
mathematical models such as USEPA's Stormwater
Management Model (SWMM). Pathogen loading is
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i»
Example: CSO Fecal Indicator Loading Assessment
A small midwestern city has a combined sewer system, subject to frequent CSO events. The combined impacts of the publicly
owned treatment works (POTW) and CSOs result in exceedances of the fecal coliform criteria in the river flowing through the city.
To characterize the system, the city developed a hydraulic model of its sewer system to predict overflow volumes and undertook
monitoring to characterize coliform concentrations in a variety of CSO events. For initial analysis of the problem, the city calculated
average FC loads during dry weather and "typical" wet weather conditions.
Under dry weather conditions, the POTW discharges on average 5,000 gal/day of effluent at an average fecal coliform concentration
of 3 x 106 organisms/100 ml. During dry weather, the total load is thus
5000 M . 3.785x10* ^ • 3x1(f °rganism = 5.7x10" °rganism
day gal 100 ml day
During an average size storm, the sewer system is able to retain and store about 40% of influent storm water. The average storm
for the area is estimated to be 0.06 in/hr in intensity, with a 6.5-hr duration and about 77 hours of dry weather between storms. A
storm of this size produces a runoff flow of 250 ft3/s for this city, so the CSO discharge is (1 - 0.4) x 250 = 150 ft3/s. Monitoring
by the city indicated that the event mean concentration in CSO discharges was 1 x 107 fecal coliform organisms/100 ml. The
average wet weather load is thus
1x107 organisms
100 mL
150
ft3
28.317X103 — • 3600 - • 6.5— = 9.9x1015 Or9anisms
ft3 hr day day
The long-term average, made up of both wet and dry periods, may be estimated as
(77hr . 57XW11 organismS) . hr . flftf ,0,5 organisms }
_ day _ day
77 hr + 6.5 hr
organisms
day
then typically estimated by measuring the typical
distribution of pathogen concentrations in sanitary
sewage, calculating the concentration resulting from
dilution of sewage by stormflow during a storm event,
and simulating the discharge of overflows at this
concentration in response to the rainfall event. Detailed
information on modeling and monitoring for CSOs is
provided in WPCF (1989) and Nix (1990).
Nonpoint source loads
Nonpoint sources of pathogen loads are typically
separated into urban and rural categories since runoff
and load generation processes differ systematically
between these environments. In urban or suburban
settings with high amounts of paved impervious area,
important sources of loading are the washoff of
contaminated refuse in surface stormflow and leakage of
sanitary sewer systems. In rural settings, the amount of
impervious area is usually much lower and important
sources of pathogen load may include diffuse runoff of
animal wastes associated with the erosion of sediments,
runoff from concentrated animal operations, and failing
or illicitly connected septic tanks.
Most nonpoint loads result from stormwater washoff,
and load estimation requires both flow volume and
pollutant concentration in runoff. Relatively simple
modeling techniques can provide good estimates of
surface stormflow volume, in both urban and rural
settings. Modeling of the pathogen concentration in
stormflow is considerably more difficult, however, and
generally results in a calibration exercise against
measured in-stream data. The data available for use in
calibration often limit the accuracy ultimately
achievable in simulation models of nonpoint pathogen
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Protocol for Developing Pathogen TMDLs
loads. Modeling is typically conducted for single
endpoints such as fecal coliform bacteria. There is
currently little or no experience modeling other
pathogens such as Cryptosporidium, Giardia, and
viruses. In assessing nonpoint loads of fecal indicator
bacteria, both the size of the load and the timing of
loading are of interest. Both urban and rural nonpoint
sources can generate large fecal indicator loads during
individual washoff events. Therefore, analysis of
loading from precipitation-driven nonpoint sources must
consider both "how much" and "how often." The
example in the box below presents a scoping-level
analysis of "how much" and "how often" for runoff from
an animal feedlot.
The fecal indicator load in runoff from animal
operations is highly variable and is affected by the
runoff characteristics of the feeding area, number and
condition of animals, and extent of implementation of
BMPs for runoff control. There is no substitute for site-
specific monitoring and local experience, but in many
cases initial estimates must be made without site-
specific monitoring.
Urban storm water loads1
Estimating urban storm water loads is complicated by a
lack of data and high variability in available monitoring
data. Both viruses and pathogenic bacteria have been
detected in storm runoff from urban areas at densities
high enough to suggest a potential health risk. Indeed,
coliform concentrations in urban stormwater can be of
the same order of magnitude as concentrations in
treatment plant effluent. The origins of urban fecal
indicator loads are diverse and can include leakage from
sanitary sewers and direct loading of human fecal
matter, as well as fecal indicator bacteria derived from
dog and cat feces.
Pathogen loads in urban storm water can be estimated
using techniques at a variety of levels of complexity,
ranging from very simple techniques using loading rate
assumptions and constant concentration estimates, to
statistical estimates, to buildup/washoff simulation.
Some urban stormwater is considered a point source by the CWA,
and is subject to NPDES permitting. However, the techniques used
to assess urban stormwater are more characteristic of nonpoint
sources, and so are described in this section.
Watershed-scale models suitable for TMDL
development are summarized in USEPA (1997b).
The FecaLOAD model is an example of a simple
technique that uses hydrogeological and meteorological
factors such as soil properties related to the suitability
for sewage disposal, distance of source from surface
water, and precipitation and runoff relationships
(Horsley and Witten, 1996) to qualitatively rank
potential bacteria sources and distribute them in the
model. The model was developed for input that is
relatively easy to obtain or estimate (e.g., from the
county soil survey). The model then uses the inputs to
calculate outputs, by land use volume of runoff, loading
of fecal coliform, and average concentration of fecal
coliform in runoff. The FecaLOAD model was
developed and applied for the evaluation of bacterial
loading to Maquoit Bay in Brunswick and Freeport,
Maine (Horsley & Witten, 1996)
Constant concentration estimates assume that all runoff
has the same concentration. Note that this simple
approach is often combined with sophisticated flow
modeling of storm water, in which case the result might
give an accurate picture of load timing even though time
variability in concentration is not simulated. An
obvious question is what constant concentration to use.
One option is to use values reported in the literature.
Literature values can be highly variable, however, and it
is preferable to use site-specific measurements because
of large site-to-site variability. Another option is to
obtain values from the information provided in NPDES
permits. Some urban stormwater dischargers are subject
to a non-continuous discharge NPDES permit. The
permit is based on the frequency of discharge, the total
mass of discharge, the maximum rate of discharge of
pollutants, and the prohibition or limitation of specified
pollutants by mass, concentration, or other measure.
Statistical or regression approaches provide a little more
sophistication by attempting to relate expected
concentration to characteristics of the watershed. For
instance, Glenne (1984) proposed a simple regression
relationship between total coliform concentration in
surface runoff and population density in the watershed.
Regression approaches are developed based on site-
specific relationships and have limited transferability.
Finally, buildup and washoff of pollutants on urban
impervious surfaces can be simulated directly. This
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Source Assessment
method is the ostensibly physically based approach
incorporated into many popular storm water models,
such as SWMM and the Hydrological Simulation
Program-Fortran (HSPF). Buildup refers to all of the
complex spectrum of dry-weather processes that deposit
or remove pollutants between storms, including
deposition and street cleaning. These processes lead to
accumulation of material associated with solids that is
then "washed off" during storm events. Models
incorporating buildup and washoff functions can also
account for pathogen die-off with a component for
pollutant decay transformation.
Failing or illicitly connected septic systems
Septic systems can potentially contribute significant
pathogen loads to receiving waterbodies due to system
failure and surface or subsurface malfunctions. In some
cases, local health departments can provide information
on failing septic systems (e.g., location, frequency,
failure rates). However, in many watersheds, the
specific incidences and locations of malfunctioning
systems is unknown, which makes the task of
characterizing the impact of pathogen loads from failing
septic systems difficult. There are, however, methods of
estimating the distribution of failing septic systems in a
watershed using available information on the occurrence
of failing systems or failure rates in a particular area, or
where no site-specific information is available, county
statistical data and literature values. For example, the
National Small Flows Clearinghouse (NSFC) surveyed
approximately 3,500 local and state public health
agencies about the status of onsite systems across the
country (NSFC, 1993) and provides the number of
reported failing septic systems in the U.S. by county.
Using the county-specific estimates from NSFC (1993),
the number of failing septic systems in a county can be
extrapolated to the watershed level based on county and
watershed land use distribution. The number of failing
systems also can be estimated by applying some
appropriate failure rate, either from literature or
professional judgment, to the total number of septic
systems in a watershed. Local agencies or data from the
U.S. Census Bureau can provide estimates of total septic
systems in a state or county. County-level population,
demographic and housing information, including septic
tank use, can be retrieved from the U.S. Census Bureau
by choosing the appropriated state and county on
or by searching the
Summary Tape File 3A database on the U.S. Census
Bureau website.
In addition to distribution or number, characteristics
about the discharge of failing systems is necessary to
evaluate their contribution of pathogen loads. If site-
specific information on system effluent is not available,
literature values are available on the typical
concentrations of septic system effluent (Horsley &
Witten, 1996) and typical effluent discharge rates
(Metcalf and Eddy, Inc., 1991). Table 5-2 provides
some values that might be useful in characterizing
effluent from a failing septic systems. Because
information on bacteria indicator and pathogen
concentrations in septic effluent is limited, it may be
appropriate to use available literature values for raw
sewage or untreated effluent. Although using site-
specific information is typically preferred, in cases
where data are not available, the use of concentrations
typical of raw sewage results in the incorporation of an
implicit margin of safety into the analysis through the
likely overestimation of the actual indicator bacteria
concentration.
Rural nonpoint loads
The rural nonpoint sources of pathogen load of greatest
concern are typically associated with animal operations,
in which large quantities of fecal matter are generated.
Pathogens from these areas can reach waterbodies
through direct runoff or after the waste has been spread
on fields. For instance, improper application of manure
to frozen land surfaces can result in periodically high
loads of pathogens and nutrients. Land application of
municipal waste biosolids can also be a significant
source of pathogen load. Regardless of the presence of
obvious sources, such as land application of biosolids, a
background loading rate resulting from the net inputs of
domestic animals, wildlife, and leaking septic systems
can always be expected.
As with urban loads, rural nonpoint loads may be
estimated using techniques at a variety of levels of
complexity, ranging from loading function estimates to
use of complex simulation models. The loading
function approach simply assigns an estimated average
rate of pathogen loading to a given land use. Such an
approach is appropriate for scoping long-term average
loads, typically on an annual basis, but it cannot capture
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Protocol for Developing Pathogen TMDLs
Example: Characterizing Fecal Coliform Loads from Failing Septic Systems
The watershed of Buck Creek in Baker County was subdivided into 10 subwatersheds. Literature values, land use information and
Census Bureau data were used to estimate the number of failing septic systems in each of the 1 0 watersheds and their contributing
fecal coliform load. NSFC (1993) reported 305 failing septic systems in Baker County. Without knowing the spatial distribution of
septic systems, functioning or failing, it was assumed that failing systems are distributed evenly throughout the county. Using the
total area of the county (658,093 acres), the density of failing septic systems for the county was calculated as follows
305 failing systems f QQQQAK failin9 systems
658,093 acres ' acre
The county density of failing systems was then multiplied by the area of each subwatershed in the county to estimate the number of
failing systems in each subwatershed. For example, the Buck Creek 1 subwatershed within the Buck Creek watershed is 8,520
acres in size and is contained completely within Baker County. The estimated number of failing septic systems in Buck Creek 1 is
ft00046 failmd systems x
acre
fajjjf]g sepfjc sysfems
Literature values and Census Bureau data were used to estimate the loading from the failing septic systems in Buck Creek 1 using a
representative effluent flow and concentration. Horsley & Witten (1996) estimates septic effluent concentrations as 106 counts/100
mL with an average daily discharge of 70 gallons/person/day. U.S. Census Bureau county data was used to estimate the average
number of people per household that might be served by septic systems. Using this information, the load from failing septic
systems within the Buck Creek 1 subwatershed is estimated as follows:
4 failing systems x
106 counts 70 gal
X
700 mL person *day
x 2.6
person
household
x 3785.2
mL_
gal
2.76 x 10
w
counts
day
This is a simplified example that does not take into account the die-off or attenuation of loadings of fecal coliform from failing septic
systems to the stream. This assumption of the worst case scenario can be used in developing the margin of safety for the TMDL.
the intermittent nature of precipitation-driven loads.
Further, one of the most important determinants of rural
nonpoint load is the extent of adoption and efficiency of
best management practices (BMPs), such as use of
animal waste storage and detention ponds, riparian
buffer zones, and proper timing and methodology for
field application of wastes. Site-specific analysis rather
than use of generic loading functions is usually
appropriate.
More sophisticated approaches are based on the
simulation of surface runoff and movement of sediment
and solids. Indicator bacteria and pathogen loading is
incorporated into such models by assumptions regarding
the concentration present in solids and a pollutant
delivery ratio. At one extreme, estimates of surface
runoff may be combined directly with representative
runoff concentrations to provide a rough estimate of the
time series of loading (McElroy et al., 1976). At the
other extreme are detailed models of rainfall, runoff, and
erosion processes accounting for variability in both
space and time, such as AGNPS (Young et al., 1986).
Like urban storm water models, rural storm water
models are capable of providing a reasonable
representation of flow and, to a lesser degree, sediment
transport. Accurate estimation of indicator bacteria or
pathogen load beyond the scoping level, however, will
be almost entirely dependent on site-specific calibration.
Various agricultural activities can be significant sources
of rural nonpoint source pathogen loads within a
watershed. Livestock produce manure that may contain
pathogens as well as fecal bacteria. The manure and the
associated pathogens may be deposited directly in
watershed streams or on land surfaces where it may be
transported to streams through stormwater runoff.
Information helpful in identifying, characterizing, and
quantifying agricultural sources of pathogens include:
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5-13
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Source Assessment
i»
• Livestock counts or densities (#/acre for pastures and
feedlots).
• Livestock confinement and grazing schedules.
• Access of livestock to watershed streams.
• Application schedules and rates for agricultural
waste (e.g., poultry litter, cow manure).
• Locations of feedlots (if unavailable in land use
coverage).
• Manure production estimates and waste
characteristics.
Local agencies (e.g, extension offices, NRCS, Soil
Water and Conservation Districts) are often an excellent
source of information on agricultural activities and
management practices within the watershed. In addition
to local agencies, good sources of data concerning
agricultural activities are watershed studies, university
studies, and USDA's Census of Agriculture
[http://www.nass.usda.gOv/census/census97/highlights/a
g-state.htm]. Among other data, the Census of
Agriculture provides state and county information on
distribution of agricultural land uses, farm sizes, and
livestock inventories and sales.
The consideration of the distribution of livestock, both
temporally and spatially, is important when evaluating
the contribution of bacteria loads from agricultural
activities. At any time, cattle in a watershed may be
confined, grazing in pastures, or watering in stream
reaches. Where and when cattle or other livestock are
contributing bacteria loads determines the behavior,
transport and impact of the load. Cattle in pastures
deposit pathogen loads on the land surface where they
accumulate and are available for washoff and transport
to receiving waterbodies. The number of livestock in
pastures and the amount of time spent grazing should be
considered in the evaluation of livestock as a source of
bacteria loads.
Cattle within pastures may have access to watershed
streams and spend time watering. Unlike livestock
depositing manure (and fecal bacteria) on pasturelands,
livestock watering in stream reaches can contribute
significant loads of bacteria directly to stream reaches.
Local agencies should be consulted for characterizing
the potential loads from various agricultural activities
within the watershed. Information on fencing and
grazing practices in the watershed, the percentage of
cattle with access to streams or grazing pastures within
proximity to streams, whether site-specific or assumed
on judgment, is useful in estimating the direct
contribution of manure and bacteria from livestock to
streams. Estimated time spent in streams is also key in
the analysis. For example, direct contributions will
likely be higher in summer months when cattle spend
more time cooling in watershed streams.
When cattle are confined, the manure produced might be
collected and spread on pastures and cropland. The
application of cattle manure (and other agricultural
waste such as poultry litter) can provide a significant
source of fecal bacteria to land surfaces. Many states or
localities have guidelines on manure spreading practices
(e.g., timing, amount). Information on application rates
and schedules can assist in appropriately representing
the contribution of bacteria from the land application of
agricultural waste in the TMDL analysis. Also, some of
these operations are regulated by NPDES permits, and
the reporting for these permits may contain information
useful in calculating a load rate.
Groundwater-surface water interactions
Pathogens are of concern in both surface and ground
water, and can move between the two media. Under
USEPA's Enhanced Surface Water Treatment Rule,
groundwater sources of drinking water that are under the
direct influence of surface water are generally treated
the same as surface water sources because the water
table is so close to the surface there is no appreciable
attenuation of pathogen loading from the surface.
Contaminants discharged to ground water can also affect
surface water. For instance, septic fields near streams
can load pathogens to a stream through ground water
transport, particularly very small diameter pathogens
such as viruses. In most geologic settings, however,
such routes are of minor significance compared to the
potential load from malfunctioning (surface-
discharging) septic systems. One major exception is
karst limestone areas, in which surface and ground water
flow may be freely interconnected by solution cavities.
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First Edition: January 2001
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Protocol for Developing Pathogen TMDLs
Example: Estimation of Fecal Coliform Loads from Grazing Beef Cows in a Watershed
Laurel River watershed contains areas in Gunston and Putnam counties. A major potential source of bacteria loading within the watershed is grazing
livestock, primarily cattle. Data from the 1997 USDA Census of Agriculture provided numbers of livestock in each county covering portions of the
watersheds, as well as total pastureland within each county. The livestock counts and pasture areas were used to determine livestock densities (e.g.,
number of cows per acre of pastureland) for each county, assuming livestock are evenly distributed over pasture area in the county.
The area of pastureland in each subwatershed and within each county was determined using GIS data layers. The pasture area of the subwatershed
within each county and the livestock density for the counties were used to calculate the livestock counts within the portion of the subwatershed
intersecting that county. That is to say, each county has a unique livestock density that was applied to the portion of the subwatershed within that
county. The county/subwatershed livestock estimates were then summed to determine livestock counts for the entire subwatershed. The following
example presents the calculation of beef cattle grazing in the West Fork 1 subwatershed of the Laurel River watershed. The county densities for beef
cattle are
Gunston County density • 2,850 beef cows . Qbeef cows
16,485 acres pastureland acre of pastureland
Putnam County density • 6,376 beef cows . Q32 beef cows
19,811 acres pastureland acre of pastureland
The West Laurel 1 subwatershed of the Laurel River watershed has 37 acres of pastureland in Gunston County and 1 72 acres of pastureland in
Putnam County. Therefore, the total number of beef cows in the West Laurel 1 subwatershed is
37 acres x 0.17 - • \ 172 acres x 0.32 —\- 61 beef cows
acre) \ acre
Based on local knowledge, it was assumed that cows spend 25 percent of their time confined and 75 percent of their time in pastures. For
calculation purposes, "percent of time" is equivalent to "percent of cows." Therefore the number of cows in the pasture and in confinement are
61 beef cows x 0.25 = 15.3 beef cows in confinement
61 beef cows x 0.75 = 45.8 beef cows grazing in pastures
Manure produced by cows in confinement is collected and spread on cropland within the watershed. ASAE (1998) provided manure production
estimates and fecal coliform content of manure for various agricultural animals, including beef cows. According to ASAE, Beef cows produce an
estimated 14,400 grams of manure a day with a fecal coliform content of 4.85 x 106 counts per gram. Therefore the amount of manure (and fecal
coliform) available for application to watershed cropland is
15.3 cows x 14,400 9 • 220,320 3^s of manure
day cow day
220,320 J- x 4.85X106 feoal 00liforms • 1.07x10" feca/ ooliforms
day g day
Based on local knowledge, it is assumed that 50 percent of the cows in the pasture have access to streams for watering and that cows with access
to streams spend 25 percent of their time in the water.
45.8 cows x 0.50x0.25 = 5.7 cows in the water at any time
Assuming the fecal coliform production rate for beef cows provided in ASAE (1998), the load contributed directly to watershed streams by watering
beef cattle is
5.7 cows x 14,400 * x 4.85x10
|6 fecal conforms oii fecal col/forms
day cow g day
The number of grazing cattle that are not watering and potentially contributing bacteria loads to the pasture surface is
45.8 cows in pasture - 5.7 cows watering = 40.1 cows grazing
The fecal coliform load contributed to the land surface by grazing cattle is
40.1 cows x 74,400 9 x 4.85x10° feca/ coliforms • 2.8x10" feoal 00liforms
daycow g day
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Source Assessment
i»
Example: Fecal Indicator Loading Assessment for a Feedlot
The example site is a 5-acre unpaved beef cattle feedlot in a plains state, with a normal summer-fall population of 800 cattle. The
site is on silt loam soil and about 1/4 mile from a perennial stream with a baseflow (i.e., return flow from ground water) of 5 ft3/s.
Based on local climate, soil type, and soil condition, local agricultural agencies estimate that 50 percent of summer-fall precipitation
will exit the site as surface runoff. Over the July to November period, the average runoff is 2.5 inches per month, occurring in an
average of 4 runoff events per month, with an average duration of 4 hours. Average runoff per runoff event is thus 2.5 in./4 =
0.625 in. per event.
Local experience also indicates that a good order-of-magnitude estimate of fecal coliform concentration in runoff from a feedlot of
this type with this number of animals is 5 x 107 CFU/100 ml. Not all fecal indicator bacteria washed off the site will reach the
stream. The site has few BMPs in place, however, and the local agricultural agencies estimate that about 80 percent of the coliform
load leaving the site will reach the stream. The concentration delivered to the stream is thus reduced to 80% x 5 x 107 = 4 x 107
cfu/100mL
The first step in the source loading analysis is estimating an approximate loading rate during runoff events. This is accomplished
with a simple mass balance using average flow and concentration. First, calculate the total flow volume from the feedlot during an
average event:
0.625 — — • 5 acres • 43,560 — • 11,344 ft
event 12in. ac event
Next, calculate the flow rate from the feedlot during an event:
ft3 1 event 1 hour 1 min 0.8 —
'
event 4 hours 60 min 60 sec sec
The concentration in the receiving stream is easily calculated by the following equation:
C,
feedlot) & feedlot) ' (® background) (^ background)
'stream ,Q ,. ,Q }
^feedlot' lu background'
where Q is the flow in ft3/s and C is the concentration in cfu/100 ml. Assuming a flow at base levels of 5 ft3/s and a background
concentration in the stream of 15 cfu/100 ml, the resulting instream concentration is:
(0.8 ft3/s)(4 x 107 cfu/100 mL) • (5 ft3/s)(15 cfu/100 mL) m 55x 1Q6 cfu
(0.8 ft3/s)(5 ft3/s) ' 100 mL
Although this concentration is high, it is expected to occur infrequently during wet weather, with an average of 4 runoff events per
month. Although the rain event lasts 4 hours, the resulting concentration is assumed to represent the daily concentration. The
background concentration in the stream is 15 cfu/100 ml and is expected to occur on the other 26 days per month. Given these
assumptions, the monthly geometric mean in the receiving stream is estimated as:
30 '""MX (5.5x106)4 • 3°j3.47x1057 • 82.80 °fu
WOmL
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Protocol for Developing Pathogen TMDLs
Recommendations: Source concentrations of fecal
indicators are often region-specific; therefore, site or
region-specific monitoring data should always be used
when available. Begin with a simple approach to fecal
indicator loading estimation by always starting with the
assumption that a model is not required. Depending on
the complexity of the combined fecal indicator sources
in the watershed, loads can be estimated easily by
conducting a literature search or more complexly when
necessary, by using a combination of long-term
monitoring and modeling. For point sources, estimation
of loads should be obtained from effluent monitoring
reqired for NPDES permits. Nonpoint sources are more
difficult to estimate; therefore, models for flow volume
and pollutant concentration in runoff can be used. The
concentration in runoff can be obtained through a
calibration exercise against measured in-stream data, but
once again, the best estimates come from site-specific
monitoring. Urban storm water loads can be estimated
using a variety of techniques from simple loading rate
assumptions and constant concentration estimates from
literature values (as with the FecaLOAD model), to
more complex models that are capable of accounting for
pathogen die-off Local health departments often have
information on failing septic systems, but if not, a
literature value for failure rate can be multiplied by the
number of septic systems in the area. Rural nonpoint
loads can also be estimated through various levels of
complexity. These various approaches range from a
simple assignment of estimated average rate of pathogen
loading to a given land use from site-specific analysis to
a more detailed model such as AGNPS, which accounts
for temporal and spatial variability. Try to obtain
information on rural nonpoint loading rates from sources
such as local agencies or watershed and university
studies. When literature values or site-specific values
are not available for a particular source, similar source
values can be substituted (i.e., raw sewage for septic
effluent). The use of common raw sewage
concentrations as an alternate value results in the
incorporation of an implicit margin of safety into the
analysis because of the potential for overestimation of
the actual indicator bacteria concentration.
RECOMMENDATIONS FOR SOURCE ASSESSMENT
Using all available information, develop a
comprehensive list of the potential and actual pathogen
sources to the waterbody of concern. Develop a plan for
identifying and accounting for the load originating from
the identified sources in the watershed.
• Use GIS or maps to document the location of sources
and the processes important for delivery to the
waterbody.
• Identify all government agencies and nongovernment
organizations active in the watershed, and conduct
interviews and collect information.
• Group sources into some appropriate and manageable
unit (e.g., by delivery mechanism, location) for
evaluation using the available resources and
analytical tools.
• Ideally, monitoring data should be used to estimate
the magnitude of loads from various sources. In the
absence of such data, some combination of literature
values, best professional judgment, and appropriate
empirical techniques/models is necessary. In
general, the simplest approach that provides
meaningful predictions should be used.
RECOMMENDED READING
(Note that a ful list of references is included at the end
of this document.)
Donigian, A.S. Jr. andW.C. Huber. 1991. Modeling of
Nonpoint Source Water Quality in Urban and Non-
urban Areas. EPA/600/3-91/039. U.S. Environmental
Protection Agency, Athens, GA.
Mills, W.B., G.L. Bowie, T.M. Grieb, K.M. Johnson,
and R.C. Whittemore. 1986. Handbook: Stream
Sampling for Waste Load Allocation Applications. EPA
625/6-86/013. U.S. Environmental Protection Agency,
Office of Research and Development, Washington, DC.
Novotny, V., and H. Olem. 1994. Water Quality:
Prevention, Identification, and Management of Diffuse
Pollution. Van Nostrand Reinhold, New York.
USEPA. 1983. Results of the Nationwide Urban Runoff
Program. NTISPB84-185552. U.S. Environmental
Protection Agency, Water Planning Division, Gulf
Breeze, FL.
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Source Assessment
i»
USEPA. 1991b. Technical Support Document for
Water Quality-Based Toxics Control. EPA/505/2-90-
001. U.S. Environmental Protection Agency, Office of
Water, Washington, DC.
USEPA. 1992a. A Quick Reference Guide: Developing
Nonpoint Source Load Allocations for TMDLs. EPA
841-B-92-001. U.S. Environmental Protection Agency,
Washington, DC.
USEPA. 1997b. Compendium of Tools for Watershed
Assessment and TMDL Development. EPA841-B-97-
006. U.S. Environmental Protection Agency, Office of
Water, Washington, DC.
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First Edition: January 2001
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Protocol for Developing Pathogen TMDLs
Linkage Between Water Quality Targets and Pollutant Sources
Objective: Define a linkage between the selected water
quality targets and the identified source(s) to identify
total assimilative capacity for pathogen or indicator
bacteria loading or total load reduction needed.
Procedure: Determine the cause-and-effect relationship
between the water quality target and the identified
source(s) through data analysis, best professional
judgment, models, and/or previously documented
relationships. Use this linkage to determine what
pathogen loads or conditions are acceptable to achieve
the desired level of water quality. Develop approaches
for determining an appropriate margin of safety.
OVERVIEW
One of the essential components of developing a TMDL
is to establish a relationship (linkage) between source
loadings and the numeric indicators chosen to measure
the attainment of uses. Once this link has been
established, it is possible to determine the capacity of
the waterbody to assimilate fecal indicator loadings
while still supporting its designated uses. Based on this
analysis, allowable loads or needed load reductions can
be allocated among the various pollutant sources. The
link can be established through a range of techniques,
from the use of qualitative assumptions backed up by
sound scientific justification to the use of sophisticated
modeling techniques. Ideally, the link can be based on a
long-term set of monitoring data that allows the TMDL
developer to associate certain waterbody responses to
flow and loading conditions. More often, however, the
link must be established by using a combination of
monitoring data, statistical and analytical tools
(including simulation models), and best professional
judgment.
Key Questions to Consider for Linkage Between Water
Quality Targets and Pollutant Sources
1. What type of analysis is appropriate for linking the water
quality target(s) and identified pollutant sources?
2. What are the basic components of analysis for linking
water quality targets and sources?
3. What complicating factors can influence the linkage
analysis?
This section recommends appropriate techniques for
establishing a source-indicator link. As with the
prediction of pollutant source loadings, the analysis can
be conducted using methods ranging from simple to
complex.
KEY QUESTIONS TO CONSIDER FOR LINKAGE
BETWEEN WATER QUALITY TARGETS AND
POLLUTANT SOURCES
1. What type of analysis is appropriate for
linking the water quality target(s) and
identified pollutant sources?
Before choosing an appropriate method for linking
instream water quality targets and sources, qualitative
assumptions can be used to develop a screening-level
linkage between sources and water quality targets.
Qualitative assumptions must be backed up by sound
scientific justification and thorough literature reviews.
These assumptions can be a starting point in the linkage
process.
Analytical methods appropriate for linking water quality
targets and sources can use empirical approaches based
on observed information, simple approaches, screening-
level model analysis, or detailed modeling. TMDLs can
incorporate one or more of these approaches to
characterize the linkage between a target and source
loading.
Empirical approaches
Empirical approaches use existing data to determine the
linkage between sources and water quality targets.
Bacteria indicators for pathogen TMDLs are relatively
easy and inexpensive to monitor, and many states have
extensive databases from coliform monitoring below
known sources such as WWTPs and CSOs. In some
cases, it might be appropriate to address the linkage
between loading and exposure concentrations
empirically, by comparing historical records of load and
corresponding exposure concentrations. If sufficient
observations are available to characterize the
relationship between loading and exposure
concentration across a range of loads, this information
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could be used to establish the linkage directly, using, for
example, a regression approach.
Simple Approaches
Permitted point sources are required to meet water
quality standards for indicator bacteria at the point of
discharge or edge of the mixing zone, as specified in the
state water quality standard. Simple dilution
calculations and/or compliance monitoring (for existing
discharges) are often adequate for this task. Compliance
with end-of-pipe or mixing zone requirements
establishes a baseline for developing a TMDL.
Wasteload allocations for point sources should first be
evaluated to determine if existing limits are adequate.
If these controls are inadequate to meet standards (due,
for instance, to the combined loading of multiple point
sources), additional reductions in allocations will be
needed to achieve the loading capacity.
Screening-level model analysis and detailed
modeling
In cases where pathogen sources are complex and
subject to influences from physical processes, an
analysis of fate and transport might be needed to
establish the linkage, typically using water quality
models. A modeling approach can incorporate analysis
of fate and transport issues such as mixing zone
considerations, die-off rates, consideration of advection
and dispersion, and influence of external factors, such as
insolation, on die-off rates. Modeling techniques can
vary in complexity, using one of two basic
approaches—steady-state or dynamic modeling. Steady-
state models use constant inputs for effluent flow,
effluent concentration, receiving water flow, and
meteorological conditions. Generally, steady-state
models provide very conservative results when applied
to wet weather sources. Dynamic models consider time-
dependent variation of inputs. A daily averaging time is
suggested for bacteria. The two modeling approaches are
listed in order of increasing complexity as follows:
1. Steady-state analysis, in which a design condition of
maximum impact (maximum load, low dilution
capacity, low die-off rate) is selected and some
interpretation of frequency is added.
2. Dynamic modeling, in which the analysis attempts
to simulate the actual frequency of exposure
concentrations.
Typically, a scoping analysis using empirical analysis
and/or steady-state modeling is used to review and
analyze existing data as a first step prior to any complex
modeling. Scoping helps formalize the objectives of the
process and provides a guide to what type(s) of detailed
modeling, if any, might be appropriate.
2. What are the basic components of analysis for
linking water quality targets and sources?
Identify targets
As described in Section 4 of this protocol, Identification
of Water Quality Indicators and Target Values, the
indicator for pathogen TMDLs may be a numeric water
quality criterion (e.g., fecal coliform count) or some
surrogate measure developed to protect the designated
or existing use (e.g., recreation).
Quantify sources
To what degree are sources known and quantified? Are
all significant sources of a given pollutant contributing
to water quality impairment known? If not, what other
potential sources should be considered? Determining
the relative contributions of different sources to
waterbody impairment is also important to subsequent
analyses. Quantifying sources is addressed in Section 5,
Source Assessment.
Locate critical points
If a watershed has many impaired segments, how is an
analysis constructed to demonstrate that WQSs will be
attained throughout the watershed? That is, where
should the analysis focus? Monitoring or simulating
fecal indicator concentrations at every point throughout
the watershed is often not practical. Instead, the scoping
effort should be targeted to areas where the waterbody is
most sensitive to impacts from loads (critical points).
Where only point sources to a river and a single water
quality standard are concerned, the edges of the mixing
zone below discharges are obvious critical points. More
often, though, there are significant nonpoint sources or
estuarine mixing, making the determination more
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difficult. Finally, more restrictive standards might apply
to areas downstream of discharges, such as recreation
areas, which become additional critical points for
analysis.
Identify critical conditions
Under what conditions is a waterbody most likely to
exceed water quality standards? When will it be most
difficult for it to achieve water quality targets?
Understanding when a waterbody is most vulnerable to
adverse impacts is necessary for deciding on a design
condition or conditions for the linkage analysis. This
means thoroughly understanding the effects of dilution,
temperature, load timing, and sometimes other factors
on pollutant impacts. Ideally, the design condition will
identify the combination of environmental factors that
result in meeting the water quality criterion and will
have an acceptably low frequency of occurrence.
Continuous loading sources (e.g., point sources) often
most greatly impact water quality under low-flow, dry-
weather conditions, when dilution is minimal (USEPA,
199la). Typically, the lowest in-stream flows occur in
summer or early fall when in-stream temperatures are
high. However, high temperatures promote more rapid
pathogen die-off
In contrast, intermittent or episodic loading sources
(e.g., surface runoff) that are rain-related can have
serious water quality impacts under various flow
conditions. Sometimes, maximum impacts from
episodic loading occur at high flows instead of at low
flows. For example, the elevated spring flows
associated with snowmelt can contain high
concentrations of fecal bacteria, especially when
snowmelt originates from agricultural areas where
manure is spread in winter or from urban areas where
residents practice poor pet curbing. Consider also a
more complex case in which a small tributary delivers
fecal indicator bacteria to a river. The river's pathogen
load is positively, although not linearly, correlated with
flow in the higher-order stream. (Both waters respond to
regional precipitation patterns.) The in-stream
concentration from the tributary load will be affected by
the competing influences of increased load and
increased dilution capacity, resulting in a peak impact at
some flow greater than baseflow. If a point source was
also present, a dual design condition might be necessary.
Appropriate critical design conditions for an analysis
should not exceed the frequency of occurrence limit
stated in the water quality standard. For instance, to
approximate the geometric mean coliform count, as
measured over a 30-day period, an appropriate critical
design condition for flow might be the minimum
geometric mean 30-day flow. (As noted above, design
conditions might need to be determined simultaneously
for flow, temperature, and other factors.)
Simple, scoping-level modeling, coupled with empirical
(graphical and statistical) data analysis, can usually
address questions raised in this step. Scoping modeling
typically involves simple, steady-state analytical
solutions (e.g., exponential decay models for bacterial
die-off) for a rough, first-cut analysis of the problem.
These scoping analyses are not expected to provide
highly accurate, quantitative answers, particularly when
episodic wet-weather loads are involved. However, they
can provide a valuable preliminary approximation of
relative impacts, which is essential for focusing the
subsequent analysis.
USEPA (1988) discusses methods for evaluating
multiparameter design conditions from observations.
Procedures for the implementation of state water quality
standards may provide information to guide the
determination of design conditions, as well.
Evaluate need for more complex analyses
Are the simplifying assumptions of the scoping analysis
likely to bias results? For instance, if the effect of an
episodic load is approximated by using a steady-state
model, how is the actual impact likely to differ from the
scoping prediction, which does not take into account the
interaction of pollutant load and runoff flow, presence
of concentration spikes, and other factors? Identifying
sources of bias is crucial to determining the need for
more complex modeling approaches.
3. What complicating factors can influence the
linkage analysis?
Fecal indicator considerations, statistical variability of
fecal indicator standards, mixing zone considerations,
and pathogen die-off rates are important factors that
help to shape the linkage analysis.
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The linkage analysis may address a number of complex
factors, including the following:
• Nonpoint sources, such as runoff from animal
operations, might be a significant source of bacteria
in the watershed.
• Transport via groundwater from septic tanks and
waste lagoons might be important in some settings.
• The typical water quality target of fecal coliform
count might not be a good indicator for certain
disease vectors with significantly longer survival in
the environment than coliforms, such as
Cryptosporidium oocysts.
• Die-off rates can be affected by a wide variety of
environmental factors, including temperature, light,
salinity, and others, as discussed below.
Indicator considerations
The way in which the linkage is evaluated will depend
on how the fecal indicator target value for the analysis
and type of use is specified. The fecal indicator target
value might have been selected based on a direct
evaluation of risk to human health. More typically,
however, fecal indicator TMDLs are based on meeting
state ambient water quality standards for indicator
bacteria. In many cases, fecal indicator TMDLs will
also need to address areas of special concern where the
probability of pollutant exposure is higher and stricter
standards might apply. These include features such as
drinking water intakes, public beaches and other areas of
contact recreation, and shellfishing beds.
Statistical variability of indicator standards
Indicator bacteria standards are written in many different
ways. A typical freshwater standard for contact
recreation is a dual form standard that specifies that the
30-day geometric mean of E. coll counts is not to exceed
126 per 100 mL (on a minimum of five samples) and the
single sample maximum allowable density for a
designated beach area is 235 E. coll counts per 100 mL.
Further, a 30-day average might be difficult to predict in
the presence of variable hydrology or significant loading
from episodic events such as CSOs. Therefore, it is also
important to give some attention to the frequency or
statistical aspects of evaluating the linkage for bacterial
indicator TMDLs.
Mixing zone considerations
Compliance with water quality standards at the edge of a
specified mixing zone depends on how well an effluent
mixes with its receiving water. The degree of mixing
depends on how the discharge is configured, as well as
the character of the receiving water and effluent. For
example, coliform concentrations resulting from a
discharge that mixes rapidly with the entire cross-
sectional area of a river are expected to be much lower
than if the same discharge mixed slowly with only a
portion of that cross-sectional area. Also, an effluent
that is warmer or less saline than the receiving water
will tend to be buoyant and rise or float on, rather than
mix with, the receiving water in the vicinity of the
outfall. It is also important to consider water quality
standards implementation procedures which limit the
size of the mixing zone (e.g., 20 percent of the 7Q10
flow).
These near-field analyses are addressed by mixing zone
models. Mixing analyses are particularly important for
estuaries and stratified lakes, where the advective
energy available for mixing may be less than that in
rivers and buoyancy differences are likely to be
important. To address mixing in estuaries, USEPA
developed the CORMIX expert systems methodology.
This model and other techniques for modeling the
mixing process are discussed in Jirka (1992).
Representation of mixing in waterbodies of all types is
also discussed in detail in Fischer et al. (1979).
Outside the initial mixing zone, transport of bacteria is
usually described as a laterally mixed process in rivers
and narrow reservoirs. More complex two- or three-
dimensional models may be needed for estuaries and
lakes, where vertical mixing is more significant to
pathogen and fecal indicator die-off and transport than
lateral mixing. Whether modeling in one, two, or three
dimensions, a key to predicting far-field bacteria
concentrations is accurately representing natural die-off
or decay of bacteria in the environment.
Pathogen die-off rates
Fecal indicators and pathogenic organisms typically
have a limited ability to survive outside their hosts. A
large number of factors govern the survival of
pathogenic organisms in waterbodies. Indicator bacteria
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die-off is considered to be best represented by a first-
order equation (Tsonis, 1992; Thomann and Mueller,
1987). The overall first-order decay rate, KB (day"1) can
be written in the following form (Thomann and Mueller,
1987):
where KB1 = basic death rate as a function of
temperature, salinity, and predation;
KB1 = death rate due to sunlight;
KBs = net loss (gain) due to settling
(resuspension); and
Ka = aftergrowth rate.
In practice, however, it has often been judged sufficient
to approximate the die-off of organisms with a simple
first-order or exponential assumption, which states that
the rate of loss is proportional to the concentration. This
alternate way of expressing the overall decay rate
describes the decline of bacteria in the time it takes to
obtain 90 percent mortality of the original number of
bacteria assuming a first-order loss. The 90 percent
mortality time, T90, is given by the equation
0.10 = exp(-KBT90)
or
T90 = 2.3/KB
Die-off equations may be applied sequentially to a series
of stream reaches with point source inputs. Analytical
solutions are also available for streams with distributed
nonpoint sources arising from tributary inflow (Mills et
al., 1985) and for distributed input mobilized by
sedimentation and scour (Thomann and Mueller, 1987).
For estuaries and lakes where mixing or dispersion is
important, a dispersion coefficient is included in the
solution, as well as calculations distinguishing between
up- and down-estuary flow. The equations above can be
modified to take into account specific factors
influencing die-off, such as temperature or insolation.
For more detailed and mathematical discussions of die-
off equations and calculations, see Bowie et al. (1985),
Mills et al. (1985), and Thomann and Mueller (1987).
In general, for the exponential decay approach, fecal
indicator bacteria can be modeled like any other
constituent assumed to exhibit exponential decay. Many
different water quality models (both analytical
formulations and computerized models) can be used to
represent fecal indicator bacteria as well as other
bacteria indicators. For instance, QUAL2E allows
direct input of a coliform bacteria concentration, with
temperature-dependent exponential decay. In
WASP/TOXI5, fecal indicator bacteria are not discussed
directly; however, they can be simulated by specifying
coliform bacteria as a "chemical" with an appropriate
exponential biodegradation rate. Other potential models
include CE-QUAL-RIV1, CE-QUAL-W2, and
HSPF/BASINS. A discussion on receiving water
models is included in the Compendium of Tools for
Watershed Assessment and TMDL Development
(USEPA, 1997b).
Factors influencing pathogen die-off rate
Many environmental parameters influence the die-off,
fate, and distribution of fecal indicator bacteria in
waters. The major factors that influence the kinetic
behavior of disease pathogens after discharge to a
waterbody are (Thomann and Mueller, 1987):
• Sunlight
Temperature
Salinity
Predation
• Nutrient deficiencies
Toxic substances
Settling of the organism population after discharge
Resuspension of particulates with associated sorbed
organisms
Aftergrowth, that is, the growth of organisms in the
body of water
A more detailed discussion of the factors influencing
mortality is contained in Bowie et al. (1985). Of these
factors, temperature is the most widely considered. It is
usually represented by an approximate form that relates
mortality at 20 °C (K20) to mortality at any other
temperature (KT). Even when normalized to K20, values
of coliform disappearance rates vary widely. Bowie et
al. (1985) summarize disappearance rates used in a
variety of modeling studies, ranging from 0.01 to 8.0 per
day at 20 °C. Various models have been advanced to
account for some of the other factors that cause the
exponential decay rate to vary. For instance, Mancini
(1978) provides a model for the incorporation of
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Example: Calculation of Exponential Decay of Coliform Concentration
A WWTP discharges to a river with an average coliform concentration of 200 organisms per 100 ml. The decay coefficient for ambient
temperature conditions is 0.5 per day. Stream velocity is 0.2 ft/s, equivalent to 3.3 mi/d. At a point 1 mile downstream, the expected
concentration is given by
C • C0 exp(-KX/U)
where C = concentration of fecal indicator bacteria,
K = decay coefficient,
X = distance along axis of flow, and
U = flow velocity.
The expected concentration (organisms/100 ml) is:
C • 200 • exp (•
0.5 m 1 mi
day 3.3 mi/ day
.
organisms
100 mL
salinity, temperature, and solar radiation into estimation
of the mortality rate. In practice, because the first-order
die-off assumption is itself a gross approximation, there
are limits to the accuracy that can be attained by these
prediction methods. It is clear from many studies that
populations of coliforms and other fecal indicators
typically include susceptible subpopulations, which die
off quickly in the environment, and more resistant
strains, which die off more gradually. The result is that
die-off tends to slow down below the rate predicted by
an exponential fit to the first few days. Because of the
complexity of parameters affecting die-off, it is always
advisable to examine site-specific die-off rates.
Die-off rates are particularly problematic for the
infectious cysts and oocysts of Giardia lamblia and
Cryptosporidiumparvum. Apparently, these can survive
significantly longer in the environment than fecal
coliform bacteria. It does appear, however, that die-off
rates for these organisms exhibit a significant
temperature dependence, so methods similar to those
used for coliform bacteria can be used to adjust for
temperature variability. Information on die-off rates will
likely improve as current monitoring programs progress
and additional data are collected.
Fecal indicator standards are typically written as
geometric means. A full evaluation of the linkage thus
needs to address the statistical distribution of the
resulting concentrations. Prediction that a water quality
standard will be achieved (or not achieved) under a
given set of conditions is not necessarily informative as
to whether the geometric mean count will be achieved.
Even when a point source has constant flow and
constant fecal indicator load, resulting exposure
concentrations in the environment will vary with time
because of continually varying flow and dilution
capacity in the receiving waterbody. Other factors that
influence coliform survival, such as temperature, also
will vary in time. Analysis is made more difficult when
significant nonpoint sources are present. Precipitation-
driven loads are likely to be at their highest when
dilution flows in the receiving water are also elevated
since both respond to precipitation. Examples of some
fecal indicator and pathogen die-off rates are shown in
Table 6-1.
Types of dynamic receiving water models
USEPA (1991b) recommends three dynamic receiving
water modeling techniques to be used when an accurate
estimate of the frequency distribution of projected
receiving water quality is required—continuous
simulation, Monte Carlo simulation, and lognormal
probability modeling.
Continuous simulation models combine daily (or other
time step) measurements or synthesized estimates of
effluent flows, effluent loads, wet-weather source
concentrations/loads, and receiving water flows to
calculate receiving water concentrations. A
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Table 6-1. Examples of fecal indicator and pathogen die-off rales
Organism
KB (day1)3
Remarks
Reference
ColHorm
Total coliform
Fecal coliform
£ co//
1-5.5
0.7-3.0
0.42
37-110
0.51
0.043
0.124
0.146
0.043
0.108
0.156
0.025
0.022
0.083
0.010-0.023
0.08-2.0
0.53
0.54
0.102
0.202
0.049
Freshwater: 20- C
Seawater: 20- C
Nonsterile river water (12 days) (no temperature indicated)
Seawater, sunlighted
Non sterile river water (12 days) (no temperature indicated)
Sand: 4-C
25-C
35-C
Loam: 4-C
25-C
35-C
Clay: 4 • C
25-C
35-C
Sediment at 8 • C
Seawater, 1 0-30 0/00
Nonsterile river water: 37- C (12 days)
Nonsterile river water: 44- C (12 days)
Natural surface water: 5- C
(42 days)
Natural surface water: 15-Cb (0-14 days)
(14-42 days)
Thomann and Mueller, 1987
Thomann and Mueller, 1987
Baudisova, 1997
Thomann and Mueller, 1987
Baudisova, 1997
Howelletal, 1996
Howelletal, 1996
Howelletal., 1996
Shereretal.,1992
Thomann and Mueller, 1987
Baudisova, 1997
Baudisova, 1997
Medemaetal.,1997
Medemaetal.,1997
Fecal streptococci
Streptococcus
faecalis
Streptococcus bovis
0.4-0.9
0.1-0.4
0.3
0.1
1.5
Freshwater: 20- C
Freshwater: 4- C
Storm water: 20- C (0-3 days)
Storm water: 20- C (3-1 4 days)
Storm water: 20- C
Thomann and Mueller, 1987
Thomann and Mueller, 1987
Thomann and Mueller, 1987
Thomann and Mueller, 1987
Thomann and Mueller, 1987
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Table 6-1. Examples of fecal indicator and pathogen die-off rales (continued)
Fecal streptococci
Fecal enterococci
18-55
0.013
0.119
0.103
0.010
0.064
0.130
0.019
0.025
0.095
0.018-0.033
0.077
0.233
0.025
Seawater, sunlighted
Sand: 4-C
25-C
35-C
Loam: 4-C
25-C
35-C
Clay: 4-C
25-C
35-C
Sediment at 8 • C
Natural surface water: 5- C
(42 days)
Natural surface water: 15 • Cb
(0-1 4 days)
(14-42 days)
Thomann and Mueller, 1987
Howelletal, 1996
Howelletal, 1996
Howelletal., 1996
Shereretal.,1992
Medemaetal.,1997
Medemaetal.,1997
Pathogens
Salmonella
typhimurium
Cryptosporidium
Cryptosporidium
1.1
0.1
0.010
0.024
Storm water: 20- C (0-3 days)
Storm water: 20- C (3-1 4 days)
Natural surface water: 5- C
(35 days)
Natural surface water: 15-C
(35 days)
Thomann and Mueller, 1987
Thomann and Mueller, 1987
Medemaetal., 1997
Medemaetal., 1997
Viruses
Coxsackie
Echo 6
Polio type 1
0.12
0.03
0.08
0.03
0.16
0.05
Marine waters: 25- C
Marine waters: 4- C
Marine waters: 25- C
Marine waters: 4- C
Marine waters: 25- C
Marine waters: 4- C
Thomann and Mueller, 1987
Thomann and Mueller, 1987
Thomann and Mueller, 1987
Thomann and Mueller, 1987
Thomann and Mueller, 1987
Thomann and Mueller, 1987
' KB = the overall first-order decay rate.
b Biphasic die-off kinetics: phase 1: days 0-14, phase 2: days 14-42.
deterministic model is applied to a continuous time
series of these variables so that the model predicts the
resulting concentrations in chronological order with the
same time sequence as the input variables. This
approach enables a frequency analysis of concentrations
at any given point of interest. The analysis
automatically takes into account the serial correlation
that may be present in flows and other parameters, as
well as the cross-correlations between measured
variables. Continuous simulation of fecal indicator
bacteria can be undertaken in a variety of modeling
packages, such as HSPF/BASINS.
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Continuous simulation is potentially the most powerful
method available for accurate prediction of the
frequency of receiving water concentrations. However,
it does have limitations. Continuous simulation, if
applied to detailed analysis, can be data-intensive.
Often, limited pollutant monitoring data are available
(especially during storm events) to test the performance
of the model.
Monte Carlo simulation models combine probabilistic
and deterministic analyses. That is, this approach uses a
deterministic water quality model with statistically
described inputs. The model is run repeatedly, with
each iteration randomly selecting input values from the
input statistical distributions. If all the time-varying
inputs (such as flows) and uncertain parameters are
described statistically, the result is a simulated set of
receiving water concentrations that reflects the statistical
distribution of the model inputs; however, these
concentrations will not follow the actual day-to-day
sequence of real data. A particular strength of Monte
Carlo methods is their ability to provide a direct
assessment of model uncertainty by use of statistical
representations of uncertain parameters and to provide
an output distribution that allows specification of a
percent likelihood that the water quality standard will be
maintained. However, it is very difficult to incorporate
realistic patterns of spatial and temporal cross-
correlation between flows, loads, and other factors into
the analysis.
USEPA has developed lognormal probabilistic dilution
models to provide a simpler method of frequency
analysis. For instance, in USEPA's DYNTOX model
(USEPA, 1996b), the lognormal probabilistic approach
takes a simple, deterministic stream dilution model,
assumes that all the input parameters can be represented
by lognormal distributions, and uses numeric integration
to derive the resulting distribution of receiving water
concentrations. Similar to a Monte Carlo analysis, the
objective is to find the distribution of model predictions
based on assumed distributions of loads, flows, and
other factors. By making restrictive lognormal
distribution assumptions, however, the problem can be
solved directly, rather than by using the iterative
procedure of the Monte Carlo method.
EXAMPLE: SCOPING THE LINKAGE FOR A CSO
FECAL INDICATOR TMDL FOR AN ESTUARY
Of particular concern is fecal indicator contamination
from CSOs and other sources that can introduce human
waste and pathogens directly into a waterbody without
treatment. In this example, a CSO discharging to an
estuary causes intermittent impairment of designated or
existing uses, including a shellfishery and contact
recreation.
Identify indicator and water quality target
In this case, the water quality target is taken as the
relevant state WQS. The coliform standard for this state
is a dual form standard that specifies that the 30-day
geometric mean of fecal coliform counts is not to exceed
200 per 100 mL (on a minimum of five samples) and not
more than 20 percent of samples are to exceed 400 per
100 mL. A standard in this form does not specify a not-
to-exceed count, and a 30-day average might be difficult
to predict from episodic, irregularly spaced CSO events.
How can a standard of this type be evaluated for
episodic load? There are two basic approaches:
(1) attempt continuous simulation of a realistic series of
CSO events, driven by historical rainfall records, predict
daily concentrations, and compare the frequency of
excursions to the WQS; and (2) take an approximate
approach, which tries to ensure that the average loading
over time meets the 30-day geometric mean standard
and the maximum concentration meets the 20 percent
criterion. The second approach is more appropriate for
scoping the problem. Indeed, given the difficulties of
obtaining an accurate simulation of CSOs, there is no
guarantee that the more complex approach would yield
more accurate results.
Quantify sources
Information on sources of contamination is a key to this
example. CSOs are identified as the source of
impairment. Their impact has not been rigorously
proven, however, because exact loading is difficult to
quantify and other sources of fecal coliforms may
discharge to the estuary, including upstream storm
water, agricultural runoff, and septic systems. Identified
upstream sources are likely subject to substantial fecal
indicator die-off before reaching the estuary. (With a
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Linkage Between Water Quality Targets and Pollutant Sour
travel time on the order of 2 days, a typical coliform
mortality profile would reduce the original upstream
load to around 10 percent.) Septic systems and
potentially leaking sewer lines near the estuary might be
significant sources. Total loading from sources
upstream of tidal influence is best obtained from
monitoring at the head of the estuary. Additional
monitoring and data interpretation for the estuary are
also needed to assess the relative importance of local
septic systems and other sources in relation to the CSOs.
Locate critical points
Because coliform mortality is fairly rapid,
concentrations are expected to decline away from the
source. This implies that the standard should be
enforced at the edge of the mixing zone, thus involving
only a dilution or near-field analysis. Special-use areas
such as beaches and shellfish beds might require
additional attention and focus as another critical point,
even if outside the mixing zone and subject to the same
WQS. This example concentrates on impacts at a public
beach 1.5 miles upstream from the CSO outfall.
Identify critical conditions
The critical conditions for scoping in this example
reflected the dual nature of the WQS. Interpreting the
400 per 100 mL count as a not-to-be-exceeded target for
the scoping (rather than an 80th percentile) provides a
condition analogous to a design low-flow condition,
which represents the minimum dilution capacity in the
receiving water reasonably expected in conjunction with
the episodic load. Dilution capacity and mixing
processes are not expected to be strongly associated with
the occurrence of CSO events in the estuary because
(1) the tidal mixing component will always be present
and (2) upstream flows are generated by a large
watershed with a reasonable probability of being at
low-flow conditions during a localized CSO loading
event. Recommendations for design (critical dilution)
conditions in estuaries are provided in USEPA (1991b,
p. 74):
In estuaries without stratification, the
critical dilution condition includes a
combination of low-water slack at spring
tide for the estuary and design low flow
for riverine inflow. In estuaries with
stratification, a site-specific analysis of a
period of minimum stratification and a
period of maximum stratification, both at
low-water slack, should be made to
evaluate which one results in the lowest
dilution.
Because this estuary did not exhibit strong stratification
near the CSO outfall, unstratified critical dilution
conditions apply. The low-water slack at spring tide and
design low flow upstream are appropriate only at the
point of the CSO discharge. At special points of
concern farther away, the combination of reasonable
flows and diffusion coefficients, which produces the
maximum impact by combining relatively high rates of
dispersive transport and relatively low dilution, must be
evaluated. Finally, design conditions will also include
temperature and salinity, both of which influence the
coliform die-off rate.
For initial scoping, a steady-state analytical model for
one-dimensional estuarine advection and dispersion was
used. This solution is based on the assumption of an
infinitely long estuary of constant area and is useful for
estuaries that are sufficiently long to approach steady
state near the outfall. The character of the solution is
strongly controlled by the ratio KE/U2, referred to as the
estuarine number, which reflects the relative importance
of diffusive and advective fluxes. As this number
approaches zero, transport in the estuary becomes
increasingly similar to river transport. In this estuary,
the ratio is approximately 1.5, which indicates relatively
strong tidal mixing with significant transport up-estuary.
For scoping, the geometric mean requirement of the
WQS is taken as an average condition over time. That
is, the 30-day time frame for this analysis is assumed to
be long enough to allow the variability in the load, as
well as tidal cycles, to be averaged out. The scoping,
therefore, assumes a steady load in terms of an average
overtime. An advection-dispersion solution can again
be used in this case. Another powerful scoping method
for this type of case is the modified tidal prism method,
which predicts pollutant concentration based on the
observed average salinity profile in the estuary (Mills et
al., 1985).
Results of the scoping analysis based on the one-
dimensional advection-dispersion solution are shown in
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Protocol for Developing Pathogen TMDLs
the box and Table 6-2. A mixing zone of one-half mile
up-estuary and down-estuary of the outfall is allowed.
The beach location, 1.5 miles up-estuary of the outfall,
is of particular concern. The model was applied for a
variety of conditions, including freshwater flows at
7Q10 and 30Q10 flows and loads at the estimated event
maximum daily average load and long-term average
load. Because the answer depends on the value assigned
to the dispersion coefficient, sensitivity of the answer to
dispersion coefficients was examined. Coefficients
ranged from 2 to 3 square miles per day (mi2/d), the
expected range for the part of the estuary near the
outfall.
It is most appropriate to compare the 200 per 100 mL
standard to the 30Q10 upstream flow and average load
(since the standard is written as a 30-day average) and
the 400 per 100 mL standard to the 7Q10 upstream flow
and event maximum load. Scoping indicates that the
CSO can cause the short-term standard to be exceeded at
the mixing zone boundaries and likely causes
impairment of the up-estuary beach. Increasing the
estimate of the dispersion coefficient increases the
estimated concentration at the beach, reflecting
increased up-estuary "smearing" of the contaminant
plume, which illustrates that the minimum mixing power
might not be a reasonable design condition for
evaluating maximum impacts. WQS excursions at the
beach are likely to occur only at low upstream flows,
while the combination of average loads and 30Q10 fresh
water flows is not predicted to cause impairment. In
evaluating impacts at the beach, recall that scoping was
conducted using a one-dimensional model that averages
a cross-section. Even if the cross-sectional average is
correctly estimated, impacts at a specific point (e.g., the
beach) may be higher or lower than the estimated value,
depending on tidal circulation patterns.
Scoping Assumptions for Estuarine CSO Example
Upstream Flows
7Q10
U(7Q10) =
30Q10
U(30Q10) =
Estuary
A =
E =
T =
K =
900 ft3/s
1.5mi/d
1500ft3/s
2.5 mi/d
10,000ft2
2-3 mi2/d
27-C
1.11/d
Unstratified
CSO
C = 1x106coliform/100mL
Qe = 0.1 MGD average, 2 MGD maximum
where:
U = velocity
A = area
E = tidal macrodispersion coefficient
T = temperature
K = first-order decay coefficient
C = concentration
Q = flow rate
Evaluate need for more complex analyses
The scoping analysis suggests a strong probability of
WQS excursions at the mixing zone boundary. The
situation at the beach is less clear, since estimates
depend strongly on the specified values of reasonable
maximum loading and dispersion. The analysis at the
mixing zone boundary alone might be sufficient to
justify control of the source; it depends on the level of
confidence in these estimates. For example, a first-cut
Table 6-2. Steady-stale predictions of fecal coliform count in the estuary (organisms/100 ml)
Flow:
Load:
Dispersion:
Mixing Zone, Upstream
Mixing Zone, Downstream
Beach
Upstream: 900 ft3/s (7Q1 0)
Upstream: 1 500 ftVs (30Q1 0)
Event Maximum Load
E = 2 miVd
838
1212
252
E = 3 mi2/d
821
1050
333
E = 2 mi2/d
596
1102
123
E = 3 miVd
651
981
207
Average Load
E = 2 miVd
30
55
6
E = 3 mi2/d
33
49
10
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Linkage Between Water Quality Targets and Pollutant Sour
estimate of the load required to maintain water quality
standards could be specified for the period of time
required to bring the combined sewer system (CSS) into
accord with the nine minimum controls specified in
USEPA's CSO control policy. When data on the effect
of the minimum controls are collected, the next phase
could involve more complex modeling and a more
sophisticated wasteload allocation (Ambrose et al.,
1992).
Keep in mind, however, that although detailed
simulation of the estuary in two or more dimensions
could provide more accurate results, it would be
warranted only if interest in predicting transport to a
specific point, such as the beach, is strong. An efficient
strategy would be to implement initial CSS controls,
estimate whether a residual problem is still likely at the
beach, and proceed with more complex modeling only if
the answer is unclear. (If it is clear there would still be a
problem, complex modeling would not be needed to
show that additional controls would be required.)
Dynamic modeling approaches seek to develop a
realistic estimate of the time series of WQS excursions
resulting from episodic loads. Consequently, they
attempt to estimate not just whether an excursion will
occur, but at what frequency excursions of a given
duration might be expected. This approach provides for
a more sophisticated analysis of the actual risk posed by
an episodic source. Estimation of the frequency of
excursions of WQSs for waterbodies with wet-weather-
dominated loading typically involves continuous
simulation over a number of years of precipitation
records. It is a logical way to proceed when sufficient
resources are available to undertake such an analysis.
However, continuous simulation is not always feasible
because of a lack of data or constraints on available
resources to perform the modeling analysis.
EXAMPLE: BACTERIAL LINKAGE ANALYSIS FOR
TWO SOURCES TO A RIVER
An important aspect of linkage analysis is estimating the
combined impact of two or more sources of fecal
indicator loading. This is particularly challenging when
episodic nonpoint sources are involved. This example
addresses modeling the linkage between water quality
impacts in a river and two sources of fecal indicator
loading—a steady point source from a POTW and a
dynamic, episodic nonpoint source in an urban separate
storm sewer system.
The POTW has a design flow of 10 ft3/s (6.5 MOD) and
discharges to a small river with a median flow of 35 ftVs
and a 7Q10 low flow of 8 ftVs. The climate is
continental, with a dry summer and early fall.
Maximum flows are associated with spring rain and
snowmelt events. The POTW achieves varying rates of
disinfection over the course of a year. During hot
summer weather, the survival time for fecal coliform
bacteria is shortened. The plant achieves an average
concentration of 400 CPU/100 mL in effluent from July
through September. In spring and fall the average
concentration is 1,000 CFU/100 mL, and in winter the
average concentration is 2,000 CFU/100 mL.
Background fecal coliform concentration in the
receiving stream is typically around 200 CFU/100 mL.
The discharge from an urban separate storm sewer
system is located 3.5 miles (x) upstream. Average flow
velocity in the river is 0.2 ft/s, or 3.28 mi/day (u), so the
average time of travel between the storm water
discharge and the POTW is 1.07 days. Assuming a loss
coefficient (k) of 1 day"1, the average fraction of fecal
indicator loading from the storm sewer still present at
the POTW is:
•kxlu
day
2%milday
0.34
Significant discharge from the storm sewer system
occurs about 20 to 30 times per year. Both flows and
fecal indicator concentrations in the storm water are
highly variable. The median fecal coliform
concentration in storm water is 1,000 CFU/100 mL, but
occasionally it might be an order of magnitude higher,
particularly during first flush after dry periods and
during snowmelt. Flow rates from the storm sewer
system during individual events range up to 50 ft3/s.
Fecal indicator load input from the storm sewer system
may occasionally result in a significant increase in in-
stream concentrations at the point of the POTW
discharge, depending on the dilution capacity available
in the river.
The linkage analysis is conducted using a dynamic
model to account for the dynamic nature of the episodic
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source. The dynamic model is, however, the simplest
type conceivable since it is based on a simple one-
dimensional steady-state mass balance mixing equation:
C
C • Q • C • Q
up ^up w z^w
Q • Q
'-'UP Z-'W
(1)
where
U =
UP
mixed concentration below a discharge;
concentration in the reach immediately
above the discharge;
flow above the discharge;
= concentration in the discharge; and
= the flow in the discharge.
This equation is appropriate for estimating
concentrations in a fully mixed cross section of a river
with steady inputs. Any consistent units may be used in
the equation. Although it is steady-state, it may be
applied in a quasi-dynamic mode by using daily time
series of upstream and discharge flows and
concentrations (observed or predicted) to calculate a
daily time series of mixed concentrations below the
discharge. Mathematical representation of the linkage is
completed through incorporation of a second equation,
representing bacterial die-off via a first-order loss
coefficient:
C.,
• kx/u
(2)
or
C.. - Cn
• kt
(3)
where
x =
k
u
t
mixed concentration at a point a
distance x below a discharge;
distance downstream from the
discharge;
mixed concentration immediately below
a discharge;
first order loss coefficient;
flow velocity; and
travel time, x/u.
Equation (1) may first be used to examine the instream
fecal indicator concentrations resulting from the POTW,
plus natural background, at the point of mixing of the
effluent. Using daily measured upstream flows and
effluent flows and concentrations, expected instream
concentrations over a typical year are shown in
Figure 6-1. The jagged line represents the daily time
series of mixed in-stream concentrations; the smoother,
heavier line shows the moving geometric mean.
In-stream concentrations vary over the course of the
year in response to a number of factors. During cold
weather, the coliform removal efficiency is lower,
resulting in greater loads. Concentration is highest in
mid-winter, when in-stream flows are low.
Concentration declines in February and March because
of increased in-stream dilution capacity. Flows decline
again in the summer, but removal efficiency also
increases, resulting in low in-stream concentrations over
the summer.
For this water, the state has specified a seasonal fecal
coliform standard of 400 CFU/100 mL during the
summer recreation season (May 1-October 15) and
1,000 CFU/100 mL during colder weather as a 30-day
geometric mean. Both standards were generally met by
the POTW effluent during this year, except for a brief
period in October, although individual concentrations
greater than 1,000 CFU/100 mL were observed.
What happens if the storm water effluent is also
considered? Storm water can provide intermittent high
loads; however, the impact is mitigated by the fact that
storm water loading tends to occur when in-stream
flows, and thus dilution capacity, are also higher.
Figure 6-2 shows the sequence of daily concentrations
with upstream stormflow included. As in Figure 6-1,
both daily values and a 30-day moving geometric mean
are shown.
The storm water flow has the potential to cause
temporary high concentrations of fecal coliform bacteria
in the river, primarily associated with the rising limb of
storm water flow that carries the first flush of pollutants,
and often occurs before a significant response in
upstream flows. The geometric mean is, however, less
responsive, unless a number of events occur in quick
succession. This happens primarily during the spring
runoff period. Here, the geometric mean concentrations
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Linkage Between Water Quality Targets
10000
o
o
O
1000
1000
f
rj
U : '•
100
Oct
Dec
Feb Apr
Jun
Aug
Oct
Figure 6-1. Daily in-stream fecal coliform concentrations resulting from POTW effluent. The
jagged line shows the daily time series of mixed in-stream fecal coliform concentrations. The
smooth line shows the moving geometric mean. 400 CFU/mL is the state-specified fecal
coliform standard for the summer season (May 1-October 15). 1000 CFU/100mL is the cold
weather standard.
10000
o
o
O
1000
Sfn
400
Oct Dec
Feb
Apr
Jun
Aug
Oct
Figure 6-2. Daily in-stream fecal coliform concentrations resulting from POTW effluent plus
storm water discharge. (See description under Figure 6-1.)
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Protocol for Developing Pathogen TMDLs
are elevated substantially versus those calculated for the
POTW effluent alone; however, because of the high
dilution capacity in this period, the geometric mean
concentrations generally remain below 1,000 CFU/100
mL.
This preliminary linkage analysis suggests that there are
two periods in which excursions of the geometric mean
standard for fecal coliform bacteria are likely to occur.
These are early spring (March), in which frequent
stormflow events may raise the in-stream geometric
mean above the winter standard of 1000 CFU/100 mL,
and late spring (May), when the warm weather standard
of 400 CFU/100 mL comes into play but storm runoff
events are still frequent. Only a single year is
represented in Figures 6-1 and 6-2, and conditions may
vary substantially from year to year. A more detailed
linkage analysis might focus on the critical May time
period, during which concentrations are likely to exceed
standards and human exposure is likely. Simulation
modeling could be used to examine expected
concentrations across numerous years of May
precipitation and flow.
RECOMMENDATIONS FOR LINKAGE BETWEEN
WATER QUALITY TARGETS AND SOURCES
• Use all available and relevant data; ideally, the
linkage will be supported by monitoring data,
allowing the TMDL developer to associate
waterbody responses with flow and loading
conditions.
• Typically, a scoping analysis using empirical
analysis and/or steady-state modeling can be used to
review and analyze existing data prior to any
complex modeling. A scoping analysis usually
includes identifying targets, quantifying sources,
locating critical points, identifying critical
conditions, and evaluating the need for more
complex analysis.
• When selecting a technique to establish a
relationship between sources and water quality
response, usually the simplest technique that
adequately addresses all relevant factors should be
used.
RECOMMENDED READING
(Note that a full list of references is included at the end
of this document.)
Chapra, S. 1997. Surface Water-Quality Modeling.
McGraw-Hill Publishers, Inc.
Thomann, R.V., and J.A. Mueller. 1987. Principles of
Surface Water Quality Modeling and Control. Harper &
Row, New York.
USEPA. 1997b. Compendium of Tools for Watershed
Assessment and TMDL Development. EPA841 -B-97-
006. U.S. Environmental Protection Agency, Office of
Water, Washington, DC.
.
USEPA. 1988. Technical Guidance on Supplementary
Stream Design Conditions for Steady State Modeling.
Technical Guidance Manual for Performing Waste Load
Allocations, Book VI, Chapter 2. U.S. Environmental
Protections Agency, Office of Water, Washington, DC.
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Protocol for Developing Pathogen TMDLs
Allocations
Objective: Using total assimilative capacity developed
in the linkage component, develop recommendations for
the allocation of loads among the various point and
nonpoint sources, while accounting for uncertainties in
the analyses (i.e., margin of safety) and, in some cases, a
reserve for future loadings.
Procedure: Determine the allocations based on
determination of the acceptable loading (loading
capacity), the margin of safety, and the estimated loads
from all significant sources. The available load is then
allocated among the various sources.
OVERVIEW
TMDLs are composed of the sum of individual
wasteload allocations (WLAs) for point sources and
load allocations (LAs) for both nonpoint sources and
natural background levels for a given waterbody. The
sum of these components must not result in the
exceedance of water quality standards for that
waterbody. In addition, the TMDL must include a
margin of safety (MOS), either implicitly or explicitly,
that accounts for the uncertainty in the relationship
between pollutant loads and the quality of the receiving
waterbody. Conceptually, this definition is denoted by
the equation
TMDL= • WLAs + - LAs + MOS
For most pollutants, TMDLs are expressed on a mass
loading basis (e.g., pounds per day). For fecal
indicators, however, TMDLs can be expressed in terms
of organism counts (or resulting concentration), in
accordance with 40 CFR 130.2(i): "TMDLs can be
expressed in terms of mass per time, toxicity, or other
Key Questions to Consider for Allocations
1. What are the steps involved for completing the
allocations?
2. How should candidate allocations be evaluated?
3. How can TMDLs be translated into controls?
4. How should issues of equitability and fairness be
addressed?
5. How should stakeholders be involved?
appropriate measure," and NPDES regulations at 40
CFR 122.45(f): "All pollutants limited in permits shall
have limitations...expressed in terms of mass except...
pollutants which cannot appropriately be expressed by
mass."
To establish a TMDL, the administering agency must
find an acceptable combination of allocations that
adequately protects water quality standards. However,
deciding how to divide the assimilative capacity of a
given watershed among sources can be a challenging
task. Issues that affect the allocation process include:
• Economics
• Political considerations
• Feasibility
• Equitability
• Types of sources and management options
• Public involvement
• Implementation
• Limits of technology
• Variability in loads, effectiveness of BMPs
Although there is more than one approach to
establishing TMDLs, typical steps in the process are
addressed in following sections.
KEY QUESTIONS TO CONSIDER FOR ALLOCATIONS
1. What are the steps involved for completing
the allocations?
The first step in establishing a TMDL is to specify the
methods that will be used to incorporate an MOS.
Section 303(d) of the CWA requires TMDLs to include
"a margin of safety which takes into account any lack of
knowledge concerning the relationship between effluent
limitations and water quality." Given that TMDLs
address both point source allocations (WLAs) and
nonpoint source allocations (LAs), this concept may be
extended to cover uncertainty in BMP effectiveness in
addition to effluent limitations.
There are two basic methods for incorporating the MOS
(USEPA, 1991a, 1999):
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•• Implicitly incorporate the MOS using conservative
model assumptions to develop allocations, or
•• Explicitly specify a portion of the total TMDL as
the MOS; use the remainder for allocations.
In many cases, the MOS is incorporated implicitly. In
such cases, the conservative assumptions that account
for the MOS should be identified. An explicit
calculation, including evaluation of uncertainty in the
linkage analysis, has the advantage of clarifying the
assumptions that go into the MOS determination.
2. How should candidate allocations be
evaluated?
TMDLs by definition are combinations of WLAs and
LAs that allocate assimilative capacity to achieve water
quality goals, including a margin of safety and a
consideration of seasonal variation. The first step in the
evaluation is to determine which segments and sources
require allocation adjustment to achieve water quality
standards and designated or existing uses. The actual
adjustment to allocations will likely be based on the
administering agencies' policies and procedures. For
instance, should reductions be spread out across all
sources or apply to only a few targeted sources? Each
agency may have its own criteria for making these
decisions (e.g., magnitude of impact, degree of
management controls now in place, feasibility,
probability of success, cost, etc.) The following
subsections provide information on the types of factors
that might need to be considered when making
allocation decisions where technology-based controls on
point sources alone are not sufficient to meet water
quality standards and a TMDL is thus required.
Assessing alternatives
Each allocation strategy under consideration will need to
be tested using the linkage analysis (Section 6) to
evaluate the potential effectiveness of the proposed
alternative. The analysis should include consideration
of the seasonal or annual variability in loadings,
particularly where significant contributions are made by
precipitation-driven nonpoint sources. As alternative
allocation strategies are developed, it might be necessary
to reassess the adequacy of the selection of water quality
targets and linkages.
Achieving a balance between WLAs and LAs
An appropriate balance should be struck between point
source and nonpoint source controls in establishing the
formal TMDL components. Finding a balance between
WLAs and LAs in a TMDL management unit involves
the evaluation of several factors. First, the manager
needs to know how the loads causing impairment are
apportioned between point and nonpoint sources. Is one
source dominating the other? Imposition of controls
should reflect the size of the source where possible. For
instance, if a pollutant load from a nonpoint source was
found to be 80 percent of the total loading to a problem
area and a 40 percent overall reduction in loading was
needed, necessary load reductions could not be achieved
through point source controls alone.
Secondly, the TMDL developer should look at the
potential efficacy of controls. What BMP and point
source controls are feasible, and how effective will they
be? TMDL developers should seek input from the
stakeholders on the control preferences and feasibility.
Discussion and cooperation among and with
stakeholders can result in more successful
implementation of the resulting allocation. Time
constraints might not allow for an in-depth review in
every case, but efforts to gain an understanding of the
efficacy of feasible controls will undoubtedly result in
more successful TMDL strategies.
Finally, cost-effectiveness should be considered. Since
financial resources for controls are limited, emphasis
should be placed where possible on allocations that will
lead to cost-effective controls.
3. How can TMDLs be translated into controls?
Translate WLAs into NPDES permit requirements
The National Pollutant Discharge Elimination System
(NPDES) permit is the mechanism for translating WLAs
into enforceable requirements for point sources. The
NPDES Program is established in section 402 of the
CWA. Under the program, permits are required for the
discharge of pollutants from most point source
discharges into the waters of the United States (see 40
CFR Part 122 for applicability). Although an NPDES
permit authorizes a point source facility to discharge, it
also subjects the permittee to legally enforceable
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requirements set forth in the permit. 40 CFR
122.44(d)(l)(vii)(B) requires effluent limits to be
consistent with WLAs in an approved TMDL.
One way WLAs are translated into permits is through
effluent limitations. Effluent limitations impose
restrictions on the quantities, discharge rates, and/or
concentrations of specified pollutants in the point source
discharge. Effluent limitations reflect either minimum
federal or state technology-based guidelines or levels
needed to protect water quality, whichever is more
stringent. By definition, TMDLs involve WLAs that are
more stringent than technology-based limits to protect
WQSs and are therefore used to establish appropriate
effluent limitations. Effluent limitations may be
expressed either as numerical restrictions on pollutant
discharges or as best management practices when
numerical limitations are infeasible (40 CFR 122.44(k)).
NPDES requirements at 40 CFR 122.45(d) require
numerical effluent limitations for continuous dischargers
to be expressed, unless impracticable, as average weekly
and average monthly discharge limitations for POTWs,
and as daily maximums and as monthly averages for
other dischargers.
Requirements at 40 CFR 122.45(e) provide that non-
continuous discharges, such as combined sewer
overflows or storm water discharges, must be described
and limited in a permit based on consideration of several
factors, as appropriate. These factors include the
frequency of the discharge, the total mass of the
discharge, the maximum rate of discharge of pollutants,
and a prohibition or limitation of specified pollutants by
mass, concentration, or other measure.
WLAs also can be translated into NPDES permit
requirements as part of a general permit, which can be
used to address a similar category of discharges, such as
storm water. In such instances, the WLA may be
allocated to the category of sources subject to the
general permit, a subcategory of those sources, or
individual sources, based on the permitting authority's
assessment of which approach would best control the
target pathogens. Depending on the type of discharges
covered by the general permit, either numeric effluent
limitations or non-numeric controls in lieu of numeric
limitations (e.g., best management practices) can be
required to achieve the WLA. Numeric effluent
limitations are typically applied to relatively continuous
discharges or controlled batch discharges. Non-numeric
controls are typically used to address non-continuous
discharges that tend to be more difficult to model and
predict.
There also may be instances where it is advantageous to
develop a single WLA that addresses all of the point
sources (e.g., POTWs, CSOs, storm sewers) that
discharge pathogens within a municipality and allow the
permit writer, working with the municipality, to
determine how best to allocate the WLA among the
relevant point sources. This "municipal integration"
approach allows the municipality and permit writer to
consider all of the sources of pathogen discharges at the
same time and to optimize the allocation between
sources based on local treatment system capabilities and
control strategies. For example, the EPA 1994 CSO
Control Policy encourages POTWs to capture a greater
portion of combined sewer overflows for treatment at
the municipal wastewater treatment plant (USEPA,
1994d). Other municipalities are considering sewer
separation, which will eliminate the contribution of
pathogens from CSOs, but increase loadings from
municipal storm sewer systems. Municipal integration,
which requires a TMDL that encompasses all of the
major sources of pathogen discharges within a
municipality, provides municipalities with the flexibility
to adjust the proportion of flow and loadings between
storm water, CSO, and POTW discharge locations to
maximize the treatment of sewage and load reductions.
Translate load allocations into implementation
plans
Unlike NPDES permits for point sources, there are no
corresponding permit requirements for nonpoint sources.
Instead, load allocations are addressed, where necessary,
through implementation of best management practices
(BMPs). In some cases, states have certain mandatory
BMP requirements for specific land use activities
associated with fecal indicator loads, such as confined
animal operations. However, implementation of BMPs
usually occurs through voluntary and incentive programs
such as government cost sharing. Therefore, when
establishing nonpoint source load allocations within a
TMDL, the TMDL development documentation should
show (1) that there is reasonable assurance that nonpoint
source controls will be implemented and maintained or
(2) that nonpoint source reductions are demonstrated
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Allocations
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through an effective monitoring program (USEPA,
1991a, 1999).
Although LAs may be used to target BMP
implementation in a watershed, translation of LAs into
specific BMP implementation programs can be
problematic. One reason for this difficulty is that often
many agencies are involved in BMP implementation.
Rather than a single oversight agency, as is the case for
NPDES permits, BMP implementation can typically
include federal, state, and local levels of involvement.
Many times the objectives of the varying agencies are
different, making coordination difficult.
In addition, it is not always easy to predict the
effectiveness of BMPs, particularly in the case of
pathogen management. Therefore, it is also difficult to
determine the level of effort and resources to focus on
BMP implementation to comply with LAs. TMDL
strategies that are heavily dependent on loading
reductions through LAs should include long-term
watershed water quality monitoring programs to
evaluate BMP effectiveness and compliance with LAs.
4. How should issues of equity in allocations be
addressed?
One issue that arises in distributing assimilative capacity
is equity between allocations. Chadderton et al. (1981)
provide an examination of a variety of methods to
establish WLAs among interacting discharges. The
following five methods were reviewed for a situation
involving five interacting discharges of biochemical
oxygen demand (BOD):
• Equal percent removal or equal percent treatment.
• Equal effluent concentration.
• Equal incremental cost above minimum treatment
(normalized on the basis of volumetric flow rate).
• Effluent concentration inversely proportional to
pollutant mass inflow rate.
• Modified optimization (i.e., least cost solution that
includes the minimum treatment requirements of the
technology-based controls).
A comparison of the methods was made based on cost,
equity, efficient use of stream assimilative capacity, and
sensitivity to fundamental stream quality data. The
authors concluded that "equal percent treatment" was
preferable in the example studied because of the
method's insensitivity to data errors and accepted use by
several states. However, although such a method could
be used to balance between various point sources or (in
some cases) between similar nonpoint sources, it likely
would not be feasible for balancing between point and
nonpoint sources. The other methods cited by
Chadderton et al., or combinations of these methods,
might be preferable under different circumstances.
5. How should stakeholders be involved?
In accordance with federal regulations regarding water
quality management planning (40 CFR Part 130),
TMDLs should be made available for public comment.
However, for TMDL strategies to be successful, those
parties likely to be effected by the TMDL (the
stakeholders) should be involved in the TMDL
development process as well. Effective communication
is a key element of the public participation process.
Stakeholders should be made aware of and engaged in
the decisions regarding priority status of a waterbody,
the modeling results or data analyses used to establish
TMDLs for the waterbody, and the pollutant control
strategies resulting from the TMDL (i.e., WLAs and
LAs).
EXAMPLE TMDL ALLOCATION
In this simplified example, a river reach receives a
steady fecal indicator load from a POTW effluent and an
intermittent load from a storm water discharge upstream
of the POTW. The relevant state water quality standard
is a geometric mean of 400 organisms/100 mL. The
storm water discharge has not, however, been
sufficiently characterized to make an accurate analysis
of the exact statistical distribution of in-stream
concentrations. The state is therefore taking the
simplified approach of developing a TMDL that is
predicted to meet the water quality standard under
conditions of mean receiving water flow and event mean
flow and fecal indicator concentration from the storm
water discharge.
The TMDL is calculated at the mouth of the river. For
ease of explication, this example considers only simple
mixing with first order die-off in transport from the
storm water discharge and the POTW outfall to the river
7-4
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Protocol for Developing Pathogen TMDLs
mouth. The receiving water concentration at the river
mouth is given by the mass balance equation
(C e'kT)O
^POTW K /^JpQTW
(C e'k
I SW
C
where
POTW
pOTW
k
T
sw
CR
QR
o
XZ
mixed instream concentration;
concentration in the POTW
effluent;
flow from the POTW;
concentration in the storm water;
decay function;
decay rate;
travel time between source and river
mouth;
flow from the storm water
discharge;
background river concentration; and
river flow (not including flows from
storm water and POTW).
This equation can be rewritten in terms of the load
balance as
• O • O )• C
^SW iW ^
'kTO
^
C e'kTO
^K ^
where the left side is the in-stream load and the right
side is the sum of loads from sources and background.
The loading capacity or TMDL is estimated by replacing
the actual Cm with the WQS:
TMDL • WQS • Q
QR)
4. If the TMDL is exceeded, set allocations by
reducing the existing loads to meet the TMDL.
Step 1 : Calculate the TMDL that will meet the
water quality standard
The data needed to calculate the TMDL (and existing
loads in Step 2) are given in Table 7-1.
The TMDL that will meet the water quality standard is
calculated as
TMDL- 400org(50'25'100ft3/s)' 700
100 mL s'mL
Table 7-1 . Data for calculating
Parameter
Water Quality Standard
River flow (mean)
POTW flow
SW flow (event mean)
Background river
concentration
POTW concentration
SW concentration (event
mean)
Die-off rate
Travel time from POTW to
river mouth
Travel time from SW
discharge to river mouth
the TMDL
Symbol
WQS
QR
QPOTW
Usw
CR
CpOTW
GSW
k
TPOTW
TSW
Value
400 org/100ml_
100 ft3/s
50 ft3/s
25 ft3/s
10 org/100ml_
400 org/100ml_
3000 org/100ml_
0.5 org/day
0.2 days
0.3 days
The steps for calculating the TMDL and allocations are
as follows:
1. Calculate the TMDL that will meet the water quality
standard.
2. Calculate the existing loading and MOS.
3. Compare the TMDL with the existing loading plus
the MOS (i.e., whether TMDL is exceeded by the
loadings plus any reserve and MOS).
Step 2: Calculate the existing loading and MOS
If the existing loading to the river together with the
MOS (plus any reserve for future growth) exceeds the
TMDL calculated in Step 1, a reduction in existing
loading is necessary to ensure that the water quality
standard will be met. The current loading and MOS that
should be compared with the TMDLs are
'kT QPOTW'
MOS'
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A reserve is optional, and no reserve is specified in this
example. Section 303(d) of the Clean Water Act
requires a margin of safety, incorporated either
implicitly or explicitly. For this example, the MOS is
explicitly defined as 10 percent of the TMDL less the
river's background loading.
MOS • 10% (TMDL • CRQR)
Therefore,
MOS • 10% x (700 • 10) • 69
org'ft3
s'mL
The existing loading plus MOS to be compared with the
TMDL is calculated using
Current loading + MOS =
org'ft3
172 • 679 • 10 • 69 • 930
s'mL
where
CD
, • 400
& x 0.861 x 50 J— ' 172
100 mL s s'mL
Cswe'kTQsw ' 3000
K x 0.905 x 25 J— ' 679
100 mL s s'mL
Alloc • TMDL' Background' MOS
and then to reduce the loads to match this allocatable
fraction. The reduced loads are calculated by
multiplying Alloc by the fraction allocated to each
individual source
WLAt (or LA) • Alloc • f.
where
LAt
Alloc
ft
= wasteload allocation for point source /';
= load allocation for nonpoint source /';
= portion of the TMDL allocatable to
sources; and
= fraction of the allocatable load assigned
to source /'.
The allocation fraction assigns reductions proportional
to existing load (other allocation schemes are, of course,
possible). It is used to calculate reduced loading for the
individual sources that taken together will meet the
TMDL, while maintaining the existing percent
contribution of the individual source loads
ft'
where:
Cn - 10
"^u
org
100 mL
- 10
s s'mL
Step 3: Compare the TMDL with the existing
loading plus the MOS
At the specified hydrologic conditions, the existing
loading and MOS exceed the TMDL, so it is necessary
to reduce the existing loadings to set the allocations.
Step 4: Set allocations
Numerous wasteload allocation methods can be used for
calculating necessary reductions in loads to meet the
TMDL. The method in this example is to first
determine the portion of the TMDL available for
allocation to known sources (Alloc),
Lt = existing load from source /' and
• Lj = total existing loading from all significant
identified sources.
Using the existing loads for each source from Step 2, the
allocatable portion of the TMDL is calculated as
Alloc ' TMDL' Background' MOS
Therefore,
Alloc ' 700 ' 10 ' 69 ' 621
org'ft3
s'mL
The allocation fractions,/, are calculated as
JPOTW
172
851
0.202
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Protocol for Developing Pathogen TMDLs
and
J$w
679
LPOTW ' Lsw 879
Allocations are then assigned using
WLA • 621 • 0.202 • 125
WLA,W • 621 • 0.798 • 496
sw
0.798
•ft3
s'mL
'
s-mL
while the LA for background is, as noted above, 10 org-
fWs-mL.
Totaling the reduced loads according to the allocations
and including background and MOS results in the
following:
or
WLAPnTW + WLS,W + MOS + LA
142 ' 480 ' 69 ' 9 ' 700
, = TMDL
org'ft3
s'mL
or
3.54xl06- 1.41xl07- 1.95xl06- 2.83xl05 • 1.99xl07 ^
s
1.72x10
,12 org
day
Thus, the allocations meet the TMDL under the
specified conditions. Because of the simplified nature
of the analysis, however, continued monitoring would be
needed to ensure that the water quality standard is
maintained.
Summary
The allocation step translates the TMDL into allowable
loads, which are distributed among the various sources,
and also accounts for a margin of safety and seasonal
variation. Allocations are required for both point
sources (WLAs) and nonpoint sources (LAs) and must
include either an implicit or explicit margin of safety
(MOS). Point source WLAs can be translated into
NPDES permit requirements; nonpoint source LAs can
be translated into implementation plans. The TMDL
implementation plan for point and nonpoint sources may
be submitted with the TMDL. However, the plan is not
a component of the actual TMDL and is not approved or
disapproved by EPA. Because the allocations will
involve issues such as equity, economics, and politics, it
is important that the administering agency involve
stakeholders throughout development of the TMDL.
RECOMMENDATIONS FOR ALLOCATIONS
Identify the method of incorporating the margin of
safety (i.e., implicitly or explicitly).
Reflect the relative size and magnitude of sources,
where possible, and represent an appropriate and
feasible balance between and among WLAs and
LAs.
Include adequate documentation with allocations to
provide reasonable assurance that water quality
standards will be attained and TMDL will be
implemented.
Involve affected stakeholders in the development of
allocations.
RECOMMENDED READING
(Note that a full list of references is included at the end
of this document.)
Chadderton, R.A., A.C. Miller, and A.J. McDonnell.
1981. Analysis of waste load allocation procedures.
Water Resources Bulletin 17(5):760-766.
Freedman, P.K., and J.K. Marr. 1990. Receiving-water
Impacts. In Control and Treatment of Combined Sewer
Overflows, pp. 79-117. Van Nostrand Reinhold, New
York.
Thomann, R.V., and J.A. Mueller. 1987. Principles of
Surface Water Quality Modeling and Control. Harper &
Row, New York, NY.
USEPA. 1991a. Guidance for Water Quality-based
Decisions: The TMDL Process. EPA 440/4-91-001. U.
S. Environmental Protection Agency, Assessment and
Watershed Protection Division, Washington, DC.
USEPA. 1991b. Technical Support Document for
Water Quality-based Toxics Control. EPA/5 05/2-90-
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Allocations
i»
001. U.S. Environmental Protection Agency, Office of
Water, Washington, DC.
USEPA. 1993a. Guidance Specifying Management
Measures for Sources of Nonpoint Pollution in Coastal
Waters. EPA 840-B-92-002. U.S. Environmental
Protection Agency, Office of Water, Washington, DC.
USEPA. 1995a. Watershed Protection: A Project
Focus. EPA 841-R-95-003. U.S. Environmental
Protection Agency, Office of Water, Washington, DC.
USEPA. 1999. Draft Guidance for Water Quality-
based Decisions: The TMDL Process. 2nd ed. EPA 841-
D-99-001. U.S. Environmental Protection Agency,
Washington, DC.
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Follow-up Monitoring and Evaluation
Objective: Define a monitoring and evaluation plan to
validate TMDL elements, assess the adequacy of control
actions to implement the TMDL, and provide a basis for
reviewing and revising TMDL elements or control
actions in the future.
Procedure: Identify the key questions a monitoring plan
needs to address and evaluate monitoring options and
the feasibility of implementing a monitoring program.
Describe a specific monitoring plan, including the
timing and location of monitoring activities, parties
responsible for conducting monitoring, and quality
assurance/quality control procedures. Provide the
schedule for reviewing monitoring results to consider
the need for TMDL or action plan revisions, and discuss
the adaptive management approach to be taken. The
monitoring component of a TMDL results in a
description of monitoring and adaptive management
plan objectives, methods, schedules, and responsible
parties.
OVERVIEW
TMDL submittals should include a monitoring plan to
determine whether the TMDL has attained water quality
standards and to support any revisions to the TMDL that
might be required. Follow-up monitoring is
recommended for all TMDLs, given the uncertainties
inherent in TMDL development (USEPA 199la;
1997b). The rigor of the monitoring plan should depend
on the confidence in the TMDL analysis: a more
rigorous monitoring plan should be included for TMDLs
with more uncertainty and where the public health,
environmental, or economic consequences of the
decisions are harshest (e.g., in protecting public drinking
water supplies.) This section discusses key factors to
consider in developing the monitoring plan and suggests
additional sources of guidance on monitoring plan
development.
Models often can prove useful in evaluating the results
of monitoring. Because weather and other watershed
process drivers usually are not identical before and after
implementation, it is difficult to compare monitoring
data results. The monitoring must consider that
situation. If models are calibrated to conditions before
and after implementation, they then can be run for the
Key Questions to Consider for Follow-up
Monitoring and Evaluation
1. What factors influence the monitoring plan design?
2. What is an appropriate monitoring plan?
3. What is an appropriate adaptive management plan,
including review and revision schedule?
4. What is an adequate description of the monitoring plan
for the TMDL document?
post-implementation period assuming implementation
practices are not applied. This approach can facilitate
the evaluation of the relative effectiveness of different
implementation approaches and the adequacy of
different TMDL components.
Compliance monitoring by public water systems may
also be useful where public water supply protection is at
issue. As noted earlier, drinking water treatment is
designed to remove a proportion, but not all, of the
pathogen contamination in the influent. Therefore,
higher pathogen loadings in the waterbody translate into
higher pathogen contamination levels in the treated
water and a greater public health risk. If a public water
system experiences repeated exceedances of the
monitoring trigger levels for pathogens (e.g., turbidity or
total coliform), one or more of the TMDL control
measures may need refining.
KEY QUESTIONS TO CONSIDER FOR FOLLOW-UP
MONITORING AND EVALUATION
1. What factors influence the monitoring plan
design?
Key factors to consider in developing the TMDL
monitoring plan include the following:
Need to evaluate specific TMDL elements
TMDL problem identification, indicators, numeric
targets, pollutant load estimates, and allocations might
need to be reevaluated to determine if they are accurate
and effective. The monitoring plan should define
specific questions to be answered about these elements
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Follow-up Monitoring and Evaluation
through the collection of monitoring information.
Potential questions include the following:
• Are the selected indicators capable of detecting
designated or existing use impacts of concern and
responses to control actions?
• Have baseline or background conditions been
adequately characterized?
• Are the numeric targets set at levels that reasonably
represent the appropriate desired conditions for
designated or existing uses of concern?
• Have all important pollutant sources been
identified?
• Have pollutant sources been accurately estimated?
• Has the linkage between pollutant sources and
impacts on the waterbody been accurately
characterized?
• Have other watershed processes (e.g., hydrology)
that affect pathogen loads or affect designated or
existing uses been accurately characterized?
• Where reference sites were used to help determine
TMDL targets and load reduction needs, were
reference site conditions accurately characterized?
• Were models or methods used for the TMDL
accurately calibrated, validated, and verified?
Not all questions are appropriate for all TMDL
monitoring plans because the degree of uncertainty
concerning different TMDL elements will vary from
case to case.
Need to evaluate implementation actions
It is often important to determine whether actions
identified in the implementation plan were actually
carried out (implementation monitoring) and whether
those actions were effective in attaining TMDL
allocations (effectiveness monitoring). Specific
questions to be answered concerning implementation
actions should be articulated as part of the monitoring
plan.
Stakeholders' goals for monitoring efforts
Watershed stakeholders often participate in follow-up
monitoring, and their interests should be considered in
devising monitoring plans.
Existing monitoring activities, resources, and
capabilities
Analysts should identify existing and planned
monitoring activities to coordinate TMDL monitoring
needs with other planned efforts, particularly where a
long-term monitoring program is envisioned, the study
area is large, or water quality agency monitoring
resources are limited. Staff capabilities and training
should also be considered to ensure that monitoring
plans are feasible.
Practical constraints to monitoring
Monitoring options are often limited by practical
constraints such as problems with access to monitoring
sites or concerns about the indirect impacts of
monitoring on habitat. Other factors that influence the
design of monitoring plans and different types of
monitoring of interest for TMDLs are discussed in detail
in MacDonald et al. (1991).
Types of monitoring
Several types of monitoring may be considered in
developing the monitoring plan (modified from
McDonald et al., 1991):
• Baseline monitoring. Baseline monitoring
characterizes existing conditions and provides a
basis for future comparisons. This type of
monitoring is not always necessary for the
monitoring plan. Usually, some baseline data that
were considered during TMDL development already
exist.
• Implementation monitoring. This type of
monitoring would ensure that identified
management actions (such as specific BMPs or
resource restoration or enhancement projects) are
undertaken. This information would also be
analyzed as one of the factors that influences the
conclusions from the trend monitoring.
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Protocol for Developing Pathogen TMDLs
Characteristics of an Effective Monitoring Plan
Quantifiable approach. Results must be discernible over time,
to allow comparison to previous or reference conditions.
Appropriate in scale and application, and relevant to
designated or existing uses and the TMDL methods.
Adequately precise, reproducible by independent
investigators, and consistent with scientific understanding of
the problems and solutions.
Able to distinguish among many different factors/sources
(e.g., pasture/feedlot runoff, urban runoff, septic systems,
wildlife).
Versatile. Generally looks at the problem from a number of
different perspectives.
Understandable to the public and supported by stakeholders.
Feasible and cost-effective.
Anticipates potential future conditions and climatic influences.
Minimizes disruptions to the designated or existing uses while
collecting data.
Facilitates reaching and sustaining conditions that support the
designated or existing use.
Project or effectiveness monitoring. Specific
projects undertaken in the context of the TMDL, or
separate from the TMDL, but potentially affecting
water quality conditions for the watershed area
under consideration, should be monitored to
determine both their immediate effects and the
effects on the water quality downstream of the
project.
Trend monitoring. This type of monitoring assesses
the effectiveness of management actions and the
changes in conditions over time relative to the
baseline and identified target values. Trend
monitoring is the primary type of follow-up
monitoring, assuming the other elements of the
TMDL are appropriately developed. It addresses the
changing conditions in the waterbody that result
from TMDL-specific activities, as well as other land
management activities, over time. Trend monitoring
is the most critical component of the monitoring
program since it also documents progress toward
achieving the desired water quality conditions.
Validation monitoring. This type of monitoring is
used to reevaluate the selection of indicators,
numeric targets, and/or source analysis methods.
2. What is an appropriate monitoring plan?
Identify monitoring goals
Depending on the level of precision in the TMDL
analysis, the first step in developing an appropriate
monitoring and adaptive management plan is to clearly
identify the goals of the monitoring program. It may be
possible to accomplish several of these monitoring goals
simultaneously. For example, the primary need in most
TMDLs is to document progress toward achieving the
numeric targets. During this process, the additional
information collected might lead to a better
understanding of the processes, suggesting a revision to
the source analysis that would better pinpoint the
pathogen problem and lead to faster attainment of water
quality improvements, or it may be that a particular
restoration/enhancement project did not produce the
desired effects and some changes to it should be
undertaken.
Develop and articulate the hypothesis and
experimental design
Address the relationships between the monitoring plan
and the TMDL's numeric targets, source analysis,
linkages, and allocations, as well as the implementation
plan. Articulate specific questions to be answered in the
form of monitoring hypotheses, and explain how the
monitoring program will answer those questions.
Explain any assumptions being made. Explain how the
monitoring plan will address both episodic events and
continuous effects, and discuss the likely effects of
episodic events. The design can be delineated by source
type, by geographical area, or by ownership parcel.
Discuss procedural details
Describe the monitoring methods to be used and provide
rationale for selection of these methods. Define
monitoring locations and frequencies, and specify who
will be responsible for conducting the monitoring (if
known).
Develop an appropriate quality assurance project
plan
Detail sampling methods, selection of sites, and analysis
methods consistent with accepted quality assurance and
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Follow-up Monitoring and Evaluation
quality control practices. Have the monitoring plan peer
reviewed if possible. (For more information see USEPA,
1994b, 1994c.)
3. What is an appropriate adaptive management
plan, including review and revision schedule?
The plan should contain a section addressing the
adaptive management component. This section should
discuss when and how the TMDL will be reviewed. If
possible, the plan should describe criteria that will guide
TMDL review and revision. For example, the plan
could identify expected levels of progress toward
meeting TMDL numeric targets at the time of the initial
review, stated as interim numeric targets or interim load
reduction expectations. In addition, the plan could
identify "red flag" thresholds for key indicators that
would signal fundamental threats to designated or
existing uses and perhaps trigger a more in-depth review
of the components of the TMDL and implementation
plan.
The adaptive management component need not schedule
every TMDL review that will ever be needed; it should
be adequate to indicate the estimated frequency of
review and identify a specific date for the initial review.
It would be difficult to reliably forecast how often
TMDL reviews will be needed, especially where
problems will take several years (or more) to solve.
4. What is an adequate description of the
monitoring plan for the TMDL document?
The monitoring and adaptive management plan is a
required component of TMDLs developed under the
phased approach (USEPA, 199la). The plan should
incorporate each of the components discussed above
along with adequate rationale for the selected
monitoring and adaptive management approach. If it is
infeasible to develop the monitoring plan in detail at the
time of TMDL adoption, it may be adequate to identify
the basic monitoring goals, review the time frame, and
identify responsible parties while committing to develop
the full monitoring plan in the near future. The plan
should clearly indicate the monitoring goals and
hypotheses, the parameters to be monitored, the
locations and frequency of monitoring, the monitoring
methods to be used, the schedule for review and
potential revision, and the parties responsible for
implementing the plan.
RECOMMENDATIONS FOR FOLLOW-UP
MONITORING AND EVALUATION
• Clearly identify the goals of the monitoring
program.
Define specific questions to be answered concerning
the evaluation of individual TMDL elements.
If possible, coordinate with other existing or
planned monitoring activities.
Determine which type(s) of monitoring (e.g.,
implementation, trend, etc.) is appropriate for
accomplishing the desired goals.
Develop an appropriate quality assurance plan;
follow-up monitoring should be designed to yield
defensible data that can support future analysis.
RECOMMENDED READING
(Note that a full list of references is included at the end
of this document.)
MacDonald, L., A.W. Smart, and R.C. Wissmar. 1991.
Monitoring Guidelines to Evaluate Effects of Forestry
Activities on Streams in the Pacific Northwest and
Alaska. EPA 910/9-91-001. U.S. Environmental
Protection Agency, Region 10, Nonpoint Source
Section, Seattle, WA.
USEPA. 1992b. Monitoring Guidance for the National
Estuary Program. EPA 842 B-92-004. U.S.
Environmental Protection Agency, Washington, DC.
USEPA. 1996c. Nonpoint Source Monitoring and
Evaluation Guide. Draft final, November 1996. U.S.
Environmental Protection Agency, Office of Water,
Washington, DC.
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Protocol for Developing Pathogen TMDLs
Assembling the TMDL
Objective: Clearly identify components of a TMDL
submittal in order to support adequate public
participation and to facilitate TMDL review and
approval.
Procedure: Compile all pertinent information used to
develop the TMDL and prepare the final submittal. The
final submittal should document all major assumptions
and analyses.
OVERVIEW
It is important to clearly identify the "pieces" of the
TMDL submittal and show how they fit together to
provide a coherent planning tool that can lead to
attainment of water quality standards for pathogen-
related water quality impairments. Where TMDLs are
derived from other analyses or reports, it is helpful to
develop a separate document or chapter that ties
together the TMDL components and shows where
background information on the derivation of each
component can be found.
RECOMMENDATIONS REGARDING CONTENT OF
SUBMITTALS
Section 303(d) of the CWA and EPA's implementing
regulations provide that a TMDL consists of the sum of
WLAs for future and existing point sources and LAs for
future and existing nonpoint sources and natural
background. These loads are established at levels
necessary to implement applicable water quality
standards with consideration of seasonal variation and a
margin of safety. Experience indicates, however, that
information in addition to the statutory and regulatory
requirements may be needed to support adequate public
participation and to facilitate EPA review and approval.
As partners in the TMDL development process, it is in
the best interest of the state and EPA to work together to
determine how much supporting information is needed
in the TMDL submittal.
Recommended minimum submittal information
The following list of TMDL submittal elements
provides a suggested outline for TMDL submittals:
1. Submittal Letter
• Each TMDL submitted to EPA should be
accompanied by a submittal letter stating that
the submittal is a draft or final TMDL submitted
under § 303(d) of the CWA for EPA review and
approval.
2. Problem Statement
Waterbody name and location.
A map is especially useful if information
displayed indicates the area covered by the
TMDL (e.g., watershed boundary or upper and
lower bounds on the receiving stream segment)
and the location of sources.
Waterbody § 303(d) list status (including
pollutant covered by the TMDL and priority
ranking).
Watershed description (e.g., predominant land
cover or land use, geology, and hydrology).
3. Applicable Water Quality Standards and Water
Quality Numeric Targets
Description of applicable water quality
standards, including designated use(s) affected
by the pollutant of concern, numeric or narrative
criteria, and the antidegradation policy.
If the TMDL is based on a target other than a
numeric water quality standard, describe the
process used to derive the target.
4. Pollutant Assessment
Source inventory, including magnitude and
location of
Background
Point sources
Nonpoint sources
Supporting documentation for the analysis of
pollutant loads from each source.
5. Linkage Analysis
Rationale for the analytical method used to
establish the cause-and-effect relationship
between the numeric target and the identified
pollutant sources.
• Supporting documentation for the analysis (e.g.,
basis for assumptions, strengths and weaknesses
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in the analytical process, results from water
quality modeling).
6. TMDL and Allocations
• Total Maximum Daily Load (TMDL)1
The TMDL is expressed as the sum of the
WLAs, the LAs, and the MOS (if an explicit
MOS is included).
If the TMDL is expressed in terms other
than mass per time, explain the selection of
the other appropriate measure.
Wasteload Allocations (WLAs)2
Loads allocated to existing and future point
sources.
An explanation of any WLAs based on the
assumption that loads from a nonpoint
source will be reduced.
If no point sources are present, list the WLA
as zero.
• Load Allocations (LAs)2
Loads allocated to existing and future
nonpoint sources.
Loads allocated to natural background
(where possible to separate from nonpoint
sources).
If there are no nonpoint sources and/or
natural background, the LA should be listed
as zero.
• Seasonal Variation1
Description of the method chosen to take
into account seasonal and interannual
variation.
• Margin of Safety1
An implicit MOS is accounted for through
conservative assumptions in the analysis.
To justify this type of margin of safety, an
explanation of the conservative assumptions
used is needed.
An explicit MOS is incorporated by setting
aside a portion of the TMDL as the MOS.
• Critical Conditions2
Critical conditions associated with flow,
loading, designated use impacts, and other
water quality factors.
7. Follow-Up Monitoring Plan
• Recommended component for TMDLs.
8. Public Participation2
• Description of public participation process used.
• Summary of the significant comments received
and the responses to those comments.
9. Implementation Plan
• Implementation plans are needed before TMDL
approval if they are necessary to provide
reasonable assurance that the load allocations
contained in the TMDL will be achieved.
Supplementary TMDL submittal information
In addition to the information described above, TMDL
submittals can be improved by preparing supplemental
information, including a TMDL summary memorandum,
a TMDL executive summary, a TMDL technical report,
and an administrative record. The effort required to
develop these documents should be minimal because
they are largely a repackaging of information contained
in the TMDL submittal. For example, the TMDL
executive summary would be prepared for the TMDL
technical report but would also be ideal for press
releases or distribution to the public.
The TMDL summary memorandum provides an
overview of all the essential regulatory elements of a
TMDL submittal. This overview can assist in regulatory
and legal review. The summary memo should include
the following information:
Name, size, and location of waterbody
• Pollutant of concern
Primary pollutant source (s)
Applicable water quality standards
Major data and information sources
Linkage analysis and load capacity (TMDL
establishment)
WLA, LA, MOS, critical condition, seasonality,
background concentrations
• Implementation
Reasonable assurance
• Follow-up monitoring
Public participation
Required by statute.
Required by regulation.
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The TMDL executive summary provides an overview of
the TMDL, the conclusions and implications, the
analyses, and the background. This document is useful
for public information, news releases, and public
hearing announcements.
The TMDL technical report provides a compilation of
the information sources, technical analyses,
assumptions, and conclusions. This document provides
a summary of the technical basis and rationale used in
deriving the TMDL. A sample report outline might
include the following sections:
1. Executive Summary
2. Introduction
3. TMDL Indicators and Numeric Targets
4. Water Quality Assessment
5. Source Assessment
6. Linking the Sources to the Indicators and Targets
7. Allocation
8. Implementation
9. Monitoring
10. References
The administrative record provides the technical
backup, sources of information, calculations, and
analyses used in deriving the TMDL. After-the-fact
explanations or justifications of EPA's decisions are
generally not permitted. A typical administrative record
might include the following:
• Spreadsheets
• Modeling software, input/output files
Description of the methodology/models used,
and a description of the data used for the
models.
• References
List or index of all documents relied upon by
the state or EPA in making a decision.
• Reports
Any EPA documents (i.e., national/regional
guidance, interpretations, protocols, technical
documents relied upon in making a decision).
Comments/correspondence from outside parties
and EPA's or state's responses, including copies
of public notice seeking comment, and final
decision document.
• Communication
Documentation of communication between EPA
and the state or EPA and other federal agencies
regarding the TMDL.
• Paper calculations
• Maps (working copies)
Public participation
Public participation is a requirement of the TMDL
process and is vital to a TMDL's success. EPA believes
that stakeholders can contribute much more than their
comments on a specific TMDL during the public review
process. Given the opportunity, stakeholders can
contribute credible, useful data and information about an
impaired or threatened waterbody. They may also be
able to raise funds for monitoring or to implement a
specific control action and/or management measure.
More importantly, stakeholders can offer insights about
their community that may ensure the success of one
TMDL allocation strategy over an alternative, as well as
the success of follow-up monitoring and evaluation
activities. Stakeholders possess knowledge about a
community's priorities, how decisions are made locally,
and how different residents of a watershed interact with
one another. A thorough understanding of the social,
political, and economic issues of a watershed is as
critical to successful TMDL development as an
understanding of the technical issues. States, territories,
and authorized tribes can create a sense of ownership
among watershed residents and "discover" innovative
TMDL strategies through a properly managed public
participation process.
Each state, territory, and authorized tribe is required to
establish and maintain a continuing planning process
(CPP) as described in § 303(e) of the Clean Water Act.
A CPP contains, among other items, a description of the
process used to identify waters needing water quality-
based controls, a priority ranking of such waters, the
process for developing TMDLs, and a description of the
process used to receive public review of each TMDL.
EPA encourages states, territories, and authorized tribes
to use their CPP as the basis for establishing a process
for public participation, involvement, and in many cases
leadership in TMDL establishment. On a watershed
level, the continuing planning process allows programs
to combine or leverage resources for public outreach and
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involvement, monitoring and assessment, development
of management strategies, and implementation.
RECOMMENDED READING
(Note that a full list of references is included at the end
of this document.)
USEPA. 1991a. Guidance for Water Quality-based
Decisions: The TMDL Process. EPA 440/4-91-001.
U.S. Environmental Protection Agency, Assessment and
Watershed Protection Division, Washington, DC.
USEPA. 1999. Guidance for Water Quality-based
Decisions: The TMDL Process. 2nd ed. EPA 841-D-99-
001. U.S. Environmental Protection Agency,
Assessment and Watershed Protection Division,
Washington, DC.
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APPENDIX A: How Pathogen Indicators Are Measured
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Appendix A: How Pathogen Indicators Are Measured
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The methods for measuring densities of the bacterial
indicators on which water quality standards are based
have evolved over the years; standard methods are now
available for total and fecal coliforms, enterococci, and
E. coli. Specially equipped microbiological laboratories
and highly trained technicians are usually required to
conduct these tests, and appropriate quality assurance
and quality control procedures must be followed to
reduce uncertainties in the estimates of the pathogens.
Basic methods are presented in the 19th edition of
Standard Methods for the Examination of Water and
Wastewater (APHA, 1995). Newer and improved
methods are now being developed and tested for some
groups of pathogens, especially viruses and protozoans.
Few techniques are able to distinguish between human
and animal wastes, although the ability to do this has
been explored in some studies as a means of tracing the
sources of pathogens. Table A-l summarizes some
commonly used measurement methods for pathogens
and indicator bacteria in surface water samples, as well
as some of the newer measurement methods under
consideration, and they are described briefly in this
section.
Different types of gram-negative bacteria can be
distinguished based on their ability to survive, grow, and
reproduce in the presence of a particular organic
compound, known as a substrate. Sometimes they are
distinguished by their ability to produce a particular
metabolic by-product, such as methane gas, their ability
to change the color of a compound, or to produce
fluorescence. Bacteria are also distinguished based on
the color, shape, or other characteristics of the colony
that is formed when they are grown on a particular
organic substrate. In general, two procedures, the
multiple-tube fermentation technique and the membrane
filter technique, are commonly used to identify fecal
bacteria.
The multiple-tube fermentation technique was
developed first. A set of tubes containing enriched
broth are inoculated with different amounts of the water
sample and incubated at a specific temperature for a
predetermined period of time. If gas is produced in the
tube, a sample of the bacteria in the broth is transferred
to one or more additional media to confirm the presence
of fecal coliform bacteria. Additional biochemical tests
can be performed to identify the bacteria to genus and
species or higher, in order to verify that the bacteria
found are coliforms (Pepper et al., 1995). The number
of tubes producing gas are converted to express the
results of the test as the Most-Probable-Number (MPN)
per 100 mL water, a statistical estimation of the number
of coliform bacteria that would give the results shown
by the laboratory examination. This is a statistical
probability number and is not an actual enumeration.
This method may give higher results because of the
built-in 23 percent positive bias.
The membrane filter technique was developed later to
detect and quantify the bacteria found in a water sample
(Pepper et al., 1995; USEPA, 1986). A measured
amount of sample is filtered through a membrane with a
pore size of 0.45 (im. The bacteria are retained on the
membrane and the filter is placed on the surface of a
selective agar medium and incubated at a specific
temperature for a specified period of time. Following
incubation, the colonies formed by the growth of the
bacterial cells are counted under a microscope using low
magnification. The membrane filter technique thus
provides an estimate of the number of coliform bacteria
that form colonies when cultured (colony-forming units
or CPU per 100 mL). Since some of the colonies could
be formed from more than one bacterium, the count is
considered to be an estimate.
USEPA currently recommends the membrane filtration
procedure because it is faster and more precise than the
MPN technique; however, it is more complex and
requires greater interpretive expertise by the analyst
(NRDC, 1996). Parallel tests using both procedures
should be performed to demonstrate applicability and
comparability if they have not been used before (Grandi
et al., 1989). Waters with high turbidity or high
noncoliform (background) bacterial levels can interfere
with the membrane filtration procedure by clogging the
filter or suppressing coliform growth respectively. E
coli and fecal streptococci can also be detected by the
membrane filter procedure. (A new video on the
improved enumeration methods for E. coli and
enterococci is available from USEPA's Office of Water,
Standards and Applied Science Division, Water Quality
Standards Branch).
Another procedure, the Autoanalysis Colilert test, was
developed to detect total coliforms and E. coli in water
samples. It can be performed within 18-24 hours, and a
modification allows this test to be used with highly
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Appendix A: How Pathogen Indicators Are Measured
Table A-1. Potential measurement endpoinls for some pathogens and indicator bacteria
Group
Viruses
Coliform Bacteria
Indicator Organisms
F1 Coliphage
MS2 Bacteriophage
Poliovirus type 1 strain LSc2ab
Enteroviruses
Total Coliform
Fecal Coliform
Escherichia coli
Pseudomonas aeruginosa
Klebsiella spp.
Enterococci
Bacteria
Enterococcus faecalis
Entemcoccus faecium
Staphylococcus aureus
Protozoa
Cryptosporidium spp.
Giardia spp.
Method (Reference)
9211, Coliphage Detection (Proposed) (APHA, 1995)
Adams (1959)
Smith and Gerba (1982)
ICR method (USEPA, 1996d)
9132, Membrane Filtration Technique; 9131, Multiple Tube Fermentation
Technique (Chapter 5 in USEPA, 1984b);
9221 , Total Coliform Fermentation Technique; 9222, Total Coliform Membrane
Filter Procedure; 9223, Chromogenic Substrate Coliform Test (APHA, 1995)
(USEPA, 1978)
9221, Fecal Coliform Procedure and 9222, Fecal Coliform Membrane
Procedure (APHA, 1995)
Filter
1103.1 (USEPA, 1985)
9213, Tests forE coli and 9223, Chromogenic Substrate Coliform Test (APHA,
1995)
9213, Membrane Filter Technique for Psuedomonas aeruginosa (APHA, 1995)
9222, Klebsiella Membrane Filter Procedure (APHA, 1995)
Levin etal. (1975)
(USEPA, 1978)
1106.1 (USEPA, 1985)
9230, Multiple Tube Fermentation or Membrane Filter Techniques (APHA, 1995)
EPA method 1600
921 3, Test for Staphylococcus aureus
(APHA, 1995)
971 1 , Immunofluorescence Method for Giardia and Cryptosporidium
(Proposed) (APHA, 1995)
EPA method 1623
spp.
turbid samples (Bitton et al., 1995). In this test, E. coli
are those coliform bacteria which possess the enzyme • -
glucuronidase and are capable of cleaving the
fluorogenic substrate, 4-methylumbelliferyl-* -D-
glucuronide (MUG), with the corresponding release of
the fluorogen. This same principle is used in the
detection of E. coli in EC-MUG medium, used in the
MPN method and incubated at 44.5* C for 24 hours.
Other indicators and methods are under development.
Most of the indicators are indirect and warn of the
possible presence of fecal pathogens, but not necessarily
from humans and potentially from several sources. Some
direct indicators, such as the bacteria Shigella or
Staphylococcus aureus or poliovirus, are highly specific
for humans but are not usually measured. Other species
of bacteria can also cause disease in humans or are more
likely to be found in human feces; however, their
detection requires special techniques (e.g., gram-positive
spore-forming Clostridium perfringens and
Campylobacter). The usual biochemical tests to
distinguish Campylobacter have been considered
unsatisfactory, but other methods have been developed
that permit more rapid identification, including
agglutination assays, DNA hybridization tests, and
polymerase chain reactions (PCR) (Koenraad et al.,
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1997). These types of methods are also needed to detect
bacteria that transform into a viable, but nonculturable
stage under unfavorable environmental conditions
(Rollins and Colwell, 1986). "Stressed organisms" is
the term used to refer to indicator bacteria that become
injured in waters and wastewaters. These organisms are
unable to grow or reproduce to form colonies under the
usual culture conditions (i.e., they are viable, but
nonculturable) because of structural or metabolic
damage from a variety of factors, including partial or
inadequate disinfection, heavy metals, ultraviolet light,
and extremes of pH and temperature. False negative
microbiological test results, in which some of the
indicator bacteria present are not detected and standards
are not exceeded, could suggest that a waterbody is safe
for its designated use when, in fact, it is not. Injured
organisms may retain the potential for virulence and
may recover after being ingested (reviewed in APHA,
1995). A method to enhance recovery when culturing
viable, but nonculturable organisms is provided in
Standard Methods for the Examination of Water and
Wastewater (APHA, 1995).
Methods are also available or under development to
detect enteric viruses and viruses that infect fecal
bacteria (coliphages). These methods usually require
cell cultures, and specific cells (e.g., bacteria, liver,
kidney, nerve, gastrointestinal epithelium) need to be
cultured to detect specific viruses (APHA, 1995).
Immunofluorescent antibody procedures can also be
used for identifying specific viruses. Alderisio et al.
(1996) described a method for identifying the four
serogroups of male-specific, or F+, RNA coliphages
(viruses that infect fecal coliform bacteria). Two of
these serogroups are known only from humans and the
other two infect fecal coliform from nonhuman sources,
which might help in developing an appropriate TMDL
allocation. However, none of these methods are
associated with water quality standards.
Methods for detecting the encysted parasites
Cryptosporidium spp. and Giardia spp. have been
developed (Pepper et al., 1995; USEPA, 1996d) and are
being used to evaluate densities of these pathogens in
surface waters and drinking water. However, Giardia
spp. cysts and Cryptosporidium spp. oocysts are difficult
to isolate from surface water samples, and detection
requires the use of special microscopy equipment,
complex staining procedures, and a trained analyst. A
fluorescent antibody that specifically binds to the cysts
and oocysts is used to assist in the enumeration of the
parasites. Water samples are obtained by pumping 350
to 1500 L of water through polypropylene yarn-wound
cartridge filters using a gasoline-powered pump (Rose
et al., 1988). After sample collection, the filters are
washed with a solution to rinse the particles off the filter
yarn or are cut into pieces and the fibers teased apart and
homogenized with this solution. The sample is
processed through several steps to separate the cysts and
oocysts, which are collected on a cellulose nitrate or a
cellulose acetate membrane filter, and the antibody is
applied to the samples on the filters. The filters are then
mounted on slides and examined using epifluorescence
microscopy. In addition to specific immunofluoresence,
size, shape, and internal morphology are also examined
using phase contrast or differential interference contrast
microscopy to distinguish these protozoans. The
volume of water sampled, number of cysts and oocysts
present, and water turbidity are the major factors
influencing the identification of these parasites.
Because recoveries from surface water samples have
often been low, resulting in the underestimation of
parasite densities, other procedures are being evaluated
(Newman, 1995). In addition, the antibodies currently
used cannot distinguish species of the parasites and thus
species that are not pathogenic to humans are included
in the counts. Additional work is under way to develop
molecular probes for the differentiation of species of
Cryptosporidium and Giardia; to improve the
immunologic methods used to detect, identify, and
enumerate these organisms; and to determine the
percentage of oocysts and cysts in any sample that are
viable and infectious to humans (reviewed in Adam,
1991; Mahbubani etal., 1991, 1992; USEPA, 1993b;
Webster et al., 1993).
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APPENDIX B: Case Study
Muddy Creek, Virginia, TMDL
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Appendix B: Case Study
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Protocol for Developing Pathogen TMDLs
TMDL Summary: Muddy Creek, Virginia
Waterbody Type:
Pollutant:
Designated Uses:
Size of Waterbody:
Size of Watershed:
Water Quality
Standards:
Indicators:
Analytical Approach:
INTRODUCTION
Stream
Fecal coliform bacteria
Recreational uses; the
propagation and growth of a
balanced, indigenous population
of aquatic life, including game
fish, which might reasonably be
expected to inhabit them;
wildlife; and the production of
edible and marketable natural
resources (e.g., fish and
shellfish) (9VAC 25-260-10).
10.36 miles
20,025 acres
Fecal Coliform: Maximum shall
not exceed 1,000 fecal
coliform/lOOmL at any time or
a geometric mean criterion of
200 fecal coliform/100 mL
based on two or more samples
collected within a 30-day period
Same as water quality standards
USEPA's BASINS modeling
system
The Virginia Department of Environmental Quality has
identified Muddy Creek as being impaired by fecal
coliform bacteria for a length of 10.36 miles, as reported
on the 1998 303(d) list of water quality limited waters.
Muddy Creek is prioritized as "high" on the list.
The Muddy Creek watershed is located in Rockingham
County, Virginia, approximately 10 miles to the west-
northwest of Harrisonburg, Virginia. Muddy Creek
flows south to its connection with the Dry River, which
discharges to the North River approximately 2.25 miles
farther to the south. The North River flows to the South
Fork of the Shenandoah River, a tributary of the
Potomac River, which eventually discharges into the
Chesapeake Bay. The land area of the Muddy Creek
watershed is approximately 20,025 acres, and forest and
agriculture are the primary land uses. Rockingham
County is the largest agricultural county in Virginia for
dairy and poultry production. A majority of the
agricultural land is located in the central and the eastern
portions of the watershed; the forested areas are
generally located in the western portion.
The TMDL developed for Muddy Creek illustrates the
steps that can be taken to address a waterbody impaired
by elevated levels of fecal coliform bacteria. The plan is
consistent with a phased-approach TMDL: estimates are
made of needed reductions of pollutant loads, load-
reduction controls are implemented, and water quality is
monitored to determine plan effectiveness. Flexibility is
built into the plan so that load reduction targets and
control actions can be reviewed if monitoring indicates
continuing water quality problems.
PROBLEM IDENTIFICATION
A cover memo should describe the waterbody as it is
identified on the state's section 303(d) list, the pollutant
of concern, and the priority ranking of the waterbody.
The TMDL submittal must include a description of the
point, nonpoint, and natural background sources of the
TMDL Submittal Elements
Loading Capacity:
Load Allocation:
8.35 x 1012 counts/year (also with
monthly allocations)
8.35 x 1012 counts/year (also with
monthly allocations)
Wasteload Allocation: 8.34 x 108 counts/day (0 percent
reduction)
Seasonal Variation:
Margin of Safety:
Monthly variation in source loading
Implicit
All information contained in this summary was obtained from MCTEW, 1999.
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pollutant of concern, including the magnitude and
location of the sources. The TMDL submittal should
also contain a description of any important assumptions,
such as (1) the assumed distribution of land uses in the
watershed; (2) population characteristics, wildlife
resources, and other relevant characteristics affecting
pollutant characterization and allocation, as applicable;
(3) present and future growth trends, if this factor was
taken into consideration in preparing the TMDL; and
(4) an explanation and analytical basis for expressing
the TMDL through surrogate measures, if applicable.
Muddy Creek has been placed on Virginia's 303(d) list
of water quality impaired waterbodies for fecal coliform
bacteria, which are threatening the creek's designated
uses. The state standard specifies that the maximum
allowable level of fecal coliforms should not exceed
1,000 counts per 100 mL if only one sample is available
for a 30-day period and a geometric mean allowable
level should not exceed 200 counts per 100 mL if more
than one sample is available for a 30-day period. A
review of available monitoring data for the area
indicates that fecal coliform bacteria are consistently
above the 1,000 cfu/100 mL state standard. All waters
of Virginia are designated for recreational uses; the
propagation and growth of a balanced, indigenous
population of aquatic life; wildlife; and the production
of edible and marketable natural resources. The
elevated levels of FC bacteria are threatening the use of
Muddy Creek for recreational purposes.
DESCRIPTION OF THE APPLICABLE WATER
QUALITY STANDARDS AND NUMERIC WATER
QUALITY TARGET
The TMDL submittal must include a description of the
applicable state water quality standard, including the
designated use(s) of the waterbody, the applicable
numeric or narrative water quality criterion, and the
antidegradation policy. This information is necessary
for EPA to review the load and wasteload allocation
required by the regulation. A numeric water quality
target for the TMDL (a quantitative value used to
measure whether the applicable water quality standard is
attained) must be identified. If the TMDL is based on a
target other than a numeric water quality criterion, the
submittal must include a description of the process used
to derive the target.
For the Muddy Creek TMDL, the applicable endpoints
and associated target values can be determined directly
from the Virginia water quality standards. The in-
stream fecal coliform target for this TMDL is an
instantaneous maximum of 1,000 counts per 100 mL.
SOURCE ASSESSMENT
All potential sources of fecal coliform bacteria in the
Muddy Creek watershed were identified based on an
evaluation of current land use/cover, information on
watershed activities (e.g., agricultural management
activities), and discussions with local agency contacts.
The source assessment was used as the basis of
development of the model and ultimate analysis of the
TMDL allocation options. The bacteria sources with the
watershed included both point and nonpoint sources.
Two point sources were identified in EPA's Permit
Compliance System (PCS) as discharging to Muddy
Creek—Wampler Foods, Inc., and the Mount Clinton
Elementary School. Wampler Foods is a poultry
slaughtering and processing facility, and the school is a
fairly small, intermittent, seasonal discharger. The
Mount Clinton Elementary School was not included in
the model analysis because it was scheduled for closure.
Both of these sources discharge under a Virginia
Pollutant Discharge Elimination System (VPDES)
permit. PCS data were used to determine the maximum
observed effluent concentrations and flow rates for
Wampler Foods, which were used to represent the point
source in the model.
To spatially analyze the bacteria loading, the Muddy
Creek watershed was divided into eight subwatersheds.
The land uses in each subwatershed were determined
using National Aerial Photography Program (NAPP) and
Farm Service Agency (FSA) aerial slides. The 24 land
use classes in the watershed were grouped into 9 land
use categories for the TMDL analysis in the Muddy
Creek watershed. Each land use has various nonpoint
sources that contribute fecal coliform bacteria to the
land surface that potentially can be washed off into the
receiving waters of the watershed. These nonpoint
sources include failing septic systems and other
uncontrolled discharges; wildlife; land application of
liquid dairy manure; land application of poultry litter;
cattle contributions directly deposited in-stream; and
grazing animals. Extensive amounts of information on
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agricultural and management activities and watershed
characteristics were obtained through coordination with
state and regional agencies. This information was
evaluated to identify, characterize, and quantify source
contributions of fecal coliform bacteria. Information
used to characterize the type, distribution, and behavior
of sources in the Muddy Creek watershed included the
following:
• Land use distributions (provided by Virginia
Department of Conservation and Recreation
[VADCR]).
• Livestock counts (provided by VADCR and
confirmed by Soil and Water Conservation District
[SWCD] and Natural Resources Conservation
Service [NRCS] representatives).
• Information on cattle access to stream reaches
(provided by VADCR and SWCD).
• Information on grazing and confinement schedules
for cattle (provided by VADCR).
• Application rates and schedules for land application
of liquid dairy manure (provided by VADCR).
• Literature values and site-specific information on
characteristics of dairy manure (provided by
VADCR).
• Application rates and schedules for land application
of poultry litter (provided by VADCR and SWCD).
• Wildlife densities (provided by VADCR).
• Literature values on waste characteristics and fecal
coliform bacteria production rates of various
animals.
• Number of septic systems in the watershed and
population served (provided by VADCR).
• Literature values for septic system failure rates for
the county and discharge concentration and flow
rate.
Based on their characteristics, nonpoint sources were
represented in the analysis as either "direct" or "diffuse"
sources. Runoff of accumulated fecal coliform from
land uses was considered a diffuse source. Failing
septic systems, straight pipes, and cattle contributing
bacteria loads to stream reaches were considered direct
sources discharging loads directly to stream reaches.
LOADING CAPACITY: LINKING WATER QUALITY
AND POLLUTANT SOURCES
As described in EPA guidance, a TMDL describes the
loading capacity of a waterbody for a particular
pollutant. EPA regulations define loading capacity as
the greatest amount of loading a waterbody can receive
without violating water quality standards (40 CFR
130.2(f)). The TMDL submittal must describe the
rationale for the analytical method used to establish the
cause-and-effect relationship between the numeric target
and the identified pollutant sources. In many
circumstances, a critical condition must be described
and related to physical conditions in the waterbody (40
CFR 130.7(c)(l)). Supporting documentation for the
analysis must also be included, including the basis for
assumptions, strengths and weaknesses in the analytical
process, and results from water quality modeling, so that
EPA can properly review the elements of the TMDL
required by the statute and regulations.
The USEPA's Better Assessment Science Integrating
Point and Nonpoint Sources (BASINS) system Version
2.0, with the Nonpoint Source Model (NPSM), was used
to predict the significance of fecal coliform sources and
fecal coliform levels in the Muddy Creek watershed.
BASINS is a multipurpose environmental analysis
system for use in performing watershed and water
quality-based studies. A geographic information system
(GIS) provides the integrating framework for BASINS
and allows for the display and analysis of a wide variety
of landscape information (e.g., land uses, monitoring
stations, point source discharges). The NPSM model
within BASINS simulates nonpoint source runoff from
selected watersheds, as well as the transport and flow of
the pollutants through stream reaches. Through
calibration of model parameters and representation of
watershed sources, the transport and delivery of bacteria
to watershed streams and the resulting in-stream
response and concentrations were simulated.
The hydrologic conditions in the Muddy Creek
watershed are characterized by relatively random
successions of dry, average, and wet rainfall years. A
hydrologically representative time period used in
modeling is necessary to account for the varying
climatic and hydrologic conditions occurring within the
watershed and to represent the potentially varying
critical conditions. During dry weather and low flow,
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Appendix B: Case Study
i»
constant direct discharges dominate the impact on in-
stream concentrations; however, during wet weather and
high flow periods, surface runoff delivers nonpoint
source fecal coliform to the stream, affecting the in-
stream conditions more than constant discharges. To
represent the varying meteorological conditions within
the Muddy Creek watershed, analysts used a 5-year
modeling period that covers a wide range of climatic and
hydrologic conditions, allowing for a more accurate
analysis of source loading and in-stream conditions
within the Muddy Creek watershed.
Point and nonpoint sources were both represented in the
model. Wampler Foods, Inc., was the only point source
included in the model. Using the flow conditions
provided by the Virginia Department of Environmental
Quality (VADEQ), average flow and fecal coliform
concentrations were calculated for Wampler. Fecal
coliform accumulation rates (number/acre/day) were
calculated for each land use based on all sources
contributing fecal coliform to the land use.
The nonpoint sources identified in the watershed were
represented in the model through build-up and wash-off
processes or as "point" sources. For diffuse sources,
fecal coliform accumulation rates (number/acre/day)
were calculated for each land use based on all sources
contributing fecal coliform bacteria to the land surface.
For example, the fecal coliform accumulation rate for
cropland is the sum of accumulation rates due to liquid
dairy manure application, litter application, and deer.
Accumulation rates for the agricultural land uses
(Cropland, Pasture 1, Pasture 2, Pasture 3, and Loafing
Lots) were calculated on a monthly basis to account for
seasonal variations in litter and dairy manure application
and grazing and confinement schedules for livestock.
Literature values for typical fecal coliform production
rates and the fecal coliform content of waste for various
animals were used in the calculation of fecal coliform
contributions from the various sources. Direct sources
were represented in the modeling analysis as discharging
directly to stream reaches with a characteristic flow and
concentration for each month.
ALLOCATIONS
EPA regulations require that a TMDL include wasteload
allocations (WLAs), which identify the portion of the
loading capacity allocated to existing and future point
sources (40 CFR 130.2(g)). If no point sources are
present or the TMDL recommends a zero WLA for point
sources, the WLA must be listed as zero. The TMDL
may recommend a zero WLA if the state determines,
after considering all pollutant sources, that allocating
only to nonpoint sources will still result in attainment of
the applicable water quality standard. In preparing the
WLA, it is not necessary that every individual point
source have a portion of the allocation of pollutant
loading capacity. It is necessary, however, to allocate
the loading capacity among individual point sources as
necessary to meet the water quality standard. The
TMDL submittal should also discuss whether a WLA is
based on an assumption that loads from a nonpoint
source or sources will be reduced. In such cases, the
state needs to demonstrate reasonable assurance that the
nonpoint source reductions will occur within a
reasonable time.
EPA regulations also require that a TMDL include load
allocations (LAs), which identify the portion of the
loading capacity allocated to existing and future
nonpoint sources and to natural background (40 CFR
130.2(h)). Load allocations may range from reasonably
accurate estimates to gross allotments (40 CFR
130.2(g)). Where it is possible to separate natural
background from nonpoint sources, separate LAs should
be made and described. If there are neither nonpoint
sources nor natural background or the TMDL
recommends a zero LA, an explanation must be
provided. The TMDL may recommend a zero LA if the
state determines, after considering all pollutant sources,
that allocating only to point sources will still result in
attainment of the applicable water quality standard.
The statute and regulations require that a TMDL include
a margin of safety to account for any lack of knowledge
concerning the relationship between effluent limitations
and water quality (CWA § 303(d)(l)(C), 40 CFR
130.7(c)(l)). EPA guidance explains that the MOS may
be implicit, i.e., incorporated into the TMDL through
conservative assumptions in the analysis, or explicit, i.e.,
expressed in the TMDL as loadings set aside for the
MOS. If the MOS is implicit, the conservative
assumptions in the analysis that account for the MOS
must be described. If the MOS is explicit, the loading
set aside for the MOS must be identified.
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The statute and regulations require that a TMDL be
established with seasonal variations. The state must
describe the method chosen for including seasonal
variations in the TMDL (CWA § 303(d)(l)(C), 40 CFR
Table B-1. Wasteload allocations to point sources in the
Muddy Creek watershed
After conducting a sensitivity analysis on source
impacts and developing allocation scenarios, a final
TMDL was chosen. Because of the varying source
characteristics and hydrologic conditions in the
watershed, a combination of load reductions was
necessary for both diffuse nonpoint sources (affecting
water quality during high runoff/flow events) and direct
nonpoint sources (affecting water quality during low
flow and dilution events). Point sources in the
watershed were considered negligible in their impact on
in-stream fecal coliform levels, and their allocations
were set equal to their existing load. Table B-l presents
the wasteload allocation for Wampler Foods, Inc.
Table B-2 presents the load allocations for nonpoint
sources in the Muddy Creek watershed. These
allocations represent the overall reductions from the
fecal coliform sources for the year.
Land use activities and animal distribution vary between
subwatersheds and from month to month within the
Muddy Creek watershed. Monthly load allocations by
subwatershed were presented as an appendix in the
Muddy Creek TMDL report. Model simulation and
representation of bacteria accumulation on a monthly
basis and the resulting monthly load allocations account
for seasonal variation in the TMDL analysis.
The margin of safety (MOS) is incorporated implicitly
into the modeling process by setting the TMDL target 5
percent lower than the water quality standard of a
geometric mean of 200 counts/100 mL. TMDL
allocations were developed to meet a target of 190
counts/100 mL.
MONITORING PLAN
EPA's 1991 document Guidance for Water Quality-
Based Decisions: The TMDL Process (EPA 440/4-91-
001) calls for a monitoring plan when a TMDL is
developed under the phased approach. The guidance
provides that a TMDL developed under the phased
approach also needs to provide assurances that nonpoint
Point Source
Wampler
Foods, Inc.
Existing
Load
8.34 x108
counts/day
Allocated
Load
8.34 x108
counts/day
Percent
Reduction
0%
source control measures will achieve expected load
reductions. The phased approach is appropriate when a
TMDL involves both point and nonpoint sources and the
point source WLA is based on an LA for which
nonpoint source controls need to be implemented.
Therefore, EPA's guidance provides that a TMDL
developed under the phased approach should include a
monitoring plan that describes the additional data to be
collected to determine whether the load reductions
required by the TMDL lead to attainment of water
quality standards.
The state of Virginia will continue sampling for fecal
coliform bacteria at two ambient monitoring stations to
evaluate Muddy Creek's future compliance with water
quality standards. Monthly sampling for fecal coliform
bacteria will continue until the violation of the 1,000
counts/100 mL criterion is reduced to 10 percent or less.
After this reduction, the monitoring frequency will
increase to two or more samples within a 30-day period
for evaluation of compliance with the 200 counts/100
mL geometric mean. The reason for this monitoring
approach is that until the effects of the initial load
reductions are reflected in lower fecal coliform counts in
Muddy Creek, additional monthly samples will not
provide additional information and the cost of the
additional sampling is not justified. Two biological
monitoring stations will also be sampled twice a year for
benthic organisms.
IMPLEMENTATION PLANS
On August 8, 1997, EPA's Bob Perciasepe issued a
memorandum, "New Policies for Establishing and
Implementing Total Maximum Daily Loads (TMDLs),"
which directs EPA regions to work in partnership with
states to achieve nonpoint source load allocations
established for 303(d)-listed waters impaired solely or
primarily by nonpoint sources. To this end, the
memorandum asks that the regions assist states in
First Edition: January 2001
B-5
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Appendix B: Case Study
Table B-2. Overall fecal conform bacteria nonpoint source allocations for the Muddy Creek watershed for the representative
hydrologic period
Source
Total Annual Loading for Existing Run
(counts/year)
Total Annual Loading for Allocation
Run (counts/year)
Percent Reduction
Diffuse nonpoint sources
Built-up
Farmstead
Forest
Barren
Cropland
Loafing lots
Pasture 1
Pasture 2
Pasture 3
Subtotal
1.88E+10
1.78E+10
7.33E+10
1.32E+08
2.48E+11
4.11E+12
1.72E+12
2.19E+11
3.34E+12
9.75E+12
1.88E+10
1.78E+10
7.33E+10
1.32E+08
2.16E+11
8.08E+11
1.01E+12
1.28E+11
1.94E+12
4.21E+12
0%
0%
0%
0%
13.1%
80.3%
41 .3%
41 .8%
42.0%
56.8%
Direct nonpoint sources
In-stream cattle
Failing septic systems
Uncontrolled discharges
Subtotal
TOTAL
5.82E+14
7.72E+11
8.12E+13
6.64E+14
6.73E+14
4.14E+12
0
0
4.14E+12
8.35E+12
99.3%
100%
100%
99.4%
98.8%
developing implementation plans that include
reasonable assurances that the nonpoint source load
allocations established in TMDLs for waters impaired
solely or primarily by nonpoint sources will in fact be
achieved; a public participation process; and recognition
of other relevant watershed management processes.
Although implementation plans are not approved by
EPA, they help establish the basis for EPA's approval of
TMDLs.
The state of Virginia will install a phased
implementation process that allows for evaluation of the
effectiveness of management practices and refinement
of the model, as necessary. The target for the first phase
of implementation in the Muddy Creek watershed will
be a 10 percent or less violation of the 1,000 counts/100
mL instantaneous standard, achieved through the load
allocations presented in Table B-3.
The VADEQ plans to incorporate TMDL
implementation plans as part of the 303(e) Water
Quality Management Plans (WQMPs). Virginia also
administers many water quality-related programs, which
will be used to support the Muddy Creek
implementation plan. These programs include the
Shenandoah-Potomac Tributary Strategy, the Watershed
Restoration Action Strategy (WRAS) for the North
River area, Virginia's Water Quality Improvement Fund,
CWA and SDWA funding programs, and Virginia's
agricultural cost share and incentives programs.
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Protocol for Developing Pathogen TMDLs
Table B-3. Overall Phase I fecal conform bacteria nonpoint source allocations for the Muddy Creek watershed for the
representative hydrologic period
Source
Total Annual Loading for Existing
Run (counts/year)
Total Annual Loading for Allocation
Run (counts/year)
Percent Reduction
Diffuse nonpoint sources
Built-up
Farmstead
Forest
Barren
Cropland
Loafing lots
Pasture 1
Pasture 2
Pasture 3
Subtotal
1.88E+10
1.78E+10
7.33E+10
1.32E+08
2.48E+11
4.11E+12
1.72E+12
2.19E+11
3.34E+12
9.75E+12
1.88E+10
1.78E+10
7.33E+10
1.32E+08
2.48E+11
4.11E+12
1.72E+12
2.19E+11
3.34E+12
9.75E+12
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
Direct nonpoint sources
In-stream cattle
Failing septic systems
Uncontrolled discharges
Subtotal
TOTAL
5.8E + 14
7.72E+11
8.12E+13
6.64E+14
6.73E+14
3.2E + 13
0
0
3.2E + 13
4.18E+13
94.4%
100%
100%
94.4%
93.8%
REASONABLE ASSURANCES
EPA guidance calls for reasonable assurances when
TMDLs are developed for waters impaired by both point
and nonpoint sources or for waters impaired solely by
nonpoint sources. In a water impaired by both point and
nonpoint sources, where a point source is given a less
stringent wasteload allocation based on an assumption
that nonpoint source load reductions will occur,
reasonable assurance must be provided for the TMDL to
be approvable. This information is necessary for EPA to
review the load allocations and wasteload allocations
required by the regulation.
In a water impaired solely by nonpoint sources,
reasonable assurances are not required for a TMDL to
be approvable. For such nonpoint source-only waters,
states are encouraged to provide reasonable assurances
regarding achievement of load allocations in the
implementation plans described in Section 7 of the
protocol. As described in the August 8, 1997,
Perciasepe memorandum, such reasonable assurances
should be included in state implementation plans and
"may be non-regulatory, regulatory, or incentive-based,
consistent with applicable laws and programs."
Through the evaluation of a number of allocation
scenarios, the Muddy Creek TMDL represents the most
feasible TMDL for implementation. Load reductions
First Edition: January 2001
B-7
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Appendix B: Case Study
from areas more difficult to control (e.g, cropland and
pastureland) were minimized while reductions from
areas where drainage and runoff control is more feasible
(e.g., feedlots) were emphasized.
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USEPA. 1997a. New Policies for Establishing and
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Protocol for Developing Pathogen TMDLs
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References-8 First Edition: January 2001
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KEY TO ACRONYMS
Protocol for Developing Pathogen TMDLs
AGNPS
BASINS
BMP
BOD
CE-QUAL-RIV1
CE-QUAL-W2
CFR
CPU
CORMIX
CPP
CSO
CSS
CWA
CZARA
DO
DYNTOX
FC
FDA
FS
GIS
HSPF
LA
MCLG
MOS
MPN
NPDES
NPS
NRCS
PCS
POTW
PS
QUAL2E
SDWA
SSO
Agricultural Nonpoint Source
Pollution Model
Better Assessment Science
Integrating Point and Nonpoint
Sources
Best management practice
Biochemical Oxygen Demand
Hydrodynamic and Water Quality
Model for Streams
Two-Dimensional, Laterally
Averaged, Hydrodynamic and
Water Quality Model
Code of Federal Regulations
Colony-forming units
Cornell Mixing Zone Expert System
Continuing planning process
Combined sewer overflow
Combined sewer system
Clean Water Act
Coastal Zone Act Reauthorization
Amendments
Dissolved oxygen
Dynamic Toxics Model
Fecal coliform bacteria
Food and Drug Administration
Fecal streptococci
Geographic Information System
Hydrologic Simulation Program-
Fortran
Load allocation (for nonpoint
sources in TMDLs)
Maximum contaminant level goal
Margin of safety, a required TMDL
element
Most probable number
National Pollutant Discharge
Elimination System
Nonpoint source
Natural Resources Conservation
Service
Permit Compliance System
Publicly-owned treatment works
Point source
The Enhanced Stream Water
Quality Model
Safe Drinking Water Act
Sanitary sewer overflow
SWMM
TC
TMDL
USDA
USEPA
uv
WASP/TOXI5
WLA
WQS
WWTP
Storm Water Management Model
Total coliform bacteria
Total maximum daily load
United States Department of
Agriculture
United States Environmental
Protection Agency
Ultraviolet
Water Quality Analysis Simulation
Program with a Toxic Submodel
Waste load allocation (for point
sources in TMDLs)
Water quality standard
Wastewater treatment plant
First Edition: January 2001
Acronyms-1
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Acronyms
Acronyms-2
First Edition: January 2001
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GLOSSARY
Protocol for Developing Pathogen TMDLs
4Q3. A probability-based statistic representing the 4-
day average low flow occurring once in 3 years.
7Q10. 7Q10 is the 7-day average low flow occurring
once in 10 years; this probability-based statistic is used
in determining stream design flow conditions and for
evaluating the water quality impact of effluent discharge
limits.
Activated Sludge. A biological solid (microorganisms)
capable of stabilizing waste aerobically.
Advection. Bulk transport of the mass of discrete
chemical or biological constituents by fluid flow within
a receiving water. Advection describes the mass
transport due to the velocity, or flow, of the waterbody.
Aerobic. Environmental conditions characterized by the
presence of dissolved oxygen; used to describe
biological or chemical processes that occur in the
presence of oxygen.
Allocations. Allocations are that portion of a receiving
water's loading capacity that is attributed to one of its
existing or future sources (nonpoint or point) of
pollution or to natural background sources. (Wasteload
allocation (WLA) is that portion of the loading capacity
allocated to an existing or future point source and a load
allocation (LA) is that portion allocated to an existing or
future nonpoint source or to natural background source.
Load allocations are best estimates of the loading, which
can range from reasonably accurate estimates to gross
allotments, depending on the availability of data and
appropriate techniques for predicting loading.)
Ambient water quality. Concentration of water quality
constituent as measured within the waterbody.
Anaerobic. Environmental condition characterized by
zero oxygen levels. Describes biological and chemical
processes that occur in the absence of oxygen.
Anthropogenic. Pertains to the [environmental]
influence of human activities.
Aquatic ecosystem. Complex of biotic and abiotic
components of natural waters. The aquatic ecosystem is
an ecological unit that includes the physical
characteristics (such as flow or velocity and depth), the
biological community of the water column and benthos,
and the chemical characteristics such as dissolved solids,
dissolved oxygen, and nutrients. Both living and
nonliving components of the aquatic ecosystem interact
and influence the properties and status of each
component.
Assimilative capacity. The amount of pollutant load
that can be discharged to a specific waterbody without
exceeding water quality standards. Assimilative capacity
is used to define the ability of a waterbody to naturally
absorb and use a discharges substance without impairing
water quality or harming aquatic life.
Bacteria. Single-celled microorganisms that lack a fully-
defined nucleus and contain no chlorophyll. Bacteria of
the coliform group are considered the primary indicators
of fecal contamination and are often used to assess water
quality.
BASINS (Better Assessment Science Integrating Point
and Nonpoint Sources). A computer-run tool that
contains an assessment and planning component that
allows users to organize and display geographic
information for selected watersheds. It also contains a
modeling component to examine impacts of pollutant
loadings from point and nonpoint sources and to
characterize the overall condition of specific watersheds.
Benthic. Refers to material, especially sediment, at the
bottom of an aquatic ecosystem. It can be used to
describe the organisms that live on, or in, the bottom of a
waterbody.
Best management practices (BMPs). Methods,
measures, or practices that are determined to be
reasonable and cost-effective means for a land owner to
meet certain, generally nonpoint source, pollution control
needs. BMPs include structural and nonstructural
controls and operation and maintenance procedures.
Biochemical oxygen demand (BOD). The amount of
oxygen per unit volume of water required to bacterially
or chemically oxidize (stabilize) the oxidizable matter in
water. Biochemical oxygen demand measurements are
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Glossary-1
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usually conducted over specific time intervals
(5,10,20,30 days). The term BOD generally refers to a
standard 5-day BOD test.
Calcareous. Pertaining to or containing calcium
carbonate.
Calibration. The process of adjusting model
parameters within physically defensible ranges until the
resulting predictions give a best possible good fit to
observed data.
Channel. A natural stream that conveys water; a ditch
or channel excavated for the flow of water.
Clean Water Act (CWA). The Clean Water Act
(formerly referred to as the Federal Water Pollution
Control Act or Federal Water Pollution Control Act
Amendments of 1972), Public Law 92-500, as amended
by Public Law 96-483 and Public Law 97-117, 33
U.S.C. 1251 et seq. The Clean Water Act (CWA)
contains a number of provisions to restore and maintain
the quality of the nation's water resources. One of these
provisions is section 303(d), which establishes the
TMDL program.
Coastal Zone. Lands and waters adjacent to the coast
that exert an influence on the uses of the sea and its
ecology, or whose uses and ecology are affected by the
sea.
Coliform bacteria. See Total coliform bacteria.
Combined sewer overflows (CSOs). Discharge of a
mixture of stormwater and domestic waste when the
flow capacity of a sewer system is exceeded during
rainstorms. CSOs discharged to receiving water can
result in contamination problems that may prevent the
attainment of water quality standards.
Combined sewer system (CSS). Sewer system that
receives both domestic wastewater and stormwater and
conducts the mixture to a treatment facility.
Concentration. Amount of a substance or material in a
given unit volume of solution. Usually measured in
milligrams per liter (mg/1) or parts per million (ppm).
Contamination. Act of polluting or making impure; any
indication of chemical, sediment, or biological
impurities.
Cost-share program. Program that allocates project
funds to pay a percentage of the cost of constructing or
implementing a best management practice. The
remainder of the costs are paid by the producer.
Critical condition. The combination of environmental
factors that results in just meeting the water quality
criterion and has an acceptably low frequency of
occurrence.
Cross-sectional area. Wet area of a waterbody normal
to the longitudinal component of the flow.
Cryptosporidium. See protozoa.
Decay. Gradual decrease in the amount of a given
substance in a given system due to various sink processes
including chemical and biological transformation,
dissipation to other environmental media, or deposition
into storage areas.
Decomposition. Metabolic breakdown of organic
materials; the by-products formation releases energy and
simple organics and inorganic compounds. (See also
Respiration.)
Designated uses. Those uses specified in water quality
standards for each waterbody or segment whether or not
they are being attained.
Deterministic model. A model that does not include
built-in variability: same input will always equal the
same output.
Die-off rate. The first-order decay rate for bacteria,
pathogens, and viruses. Die-off depends on the particular
type of water body (i.e. stream, estuary , lake) and
associated factors that influence mortality.
Dilution. Addition of less concentrated liquid (water)
that results in a decrease in the original concentration.
Direct runoff. Water that flows over the ground surface
or through the ground directly into streams, rivers, and
lakes.
Glossary-2
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Discharge. Flow of surface water in a stream or canal
or the outflow of groundwater from a flowing artesian
well, ditch, or spring. Can also apply to discharge of
liquid effluent from a facility or to chemical emissions
into the air through designated venting mechanisms.
Discharge permits (NPDES). A permit issued by the
U.S. EPA or a state regulatory agency that sets specific
limits on the type and amount of pollutants that a
municipality or industry can discharge to a receiving
water; it also includes a compliance schedule for
achieving those limits. It is called the NPDES because
the permit process was established under the National
Pollutant Discharge Elimination System, under
provisions of the Federal Clean Water Act.
Dispersion. The spreading of chemical or biological
constituents, including pollutants, in various directions
from a point source, at varying velocities depending on
the differential instream flow characteristics.
Dissolved oxygen (DO). The amount of oxygen that is
dissolved in water. It also refers to a measure of the
amount of oxygen available for biochemical activity in a
waterbody, and as an indicator of the quality of that
water.
Dynamic model. A mathematical formulation
describing the physical behavior of a system or a process
and its temporal variability.
Ecosystem. An interactive system that includes the
organisms of a natural community association together
with their abiotic physical, chemical, and geochemical
environment.
Effluent. Municipal sewage or industrial liquid waste
(untreated, partially treated, or completely treated) that
flows out of a treatment plant, septic system, pipe, etc.
Effluent limitation. Restrictions established by a state
or EPA on quantities, rates, and concentrations in
pollutant discharges.
Endpoint. An endpoint is a characteristic of an
ecosystem that may be affected by exposure to a
stressor. Assessment endpoints and measurement
endpoints are two distinct types of endpoints that are
commonly used by resource managers. An assessment
endpoint is the formal expression of a valued
environmental characteristic and should have societal
relevance. A measurement endpoint is the expression of
an observed or measured response to a stress or
disturbance. It is a measurable environmental
characteristic that is related to the valued environmental
characteristic chosen as the assessment endpoint. The
numeric criteria that are part of traditional water quality
standards are good examples of measurement endpoints.
Enhancement. In the context of restoration ecology, any
improvement of a structural or functional attribute.
Enteric. Of or within the gastrointestinal tract.
Enter ococci. A subgroup of the fecal streptococci that
includes S. faecalis and S. faecium. The enterococci are
differentiated from other streptococci by their ability to
grow in 6.5% sodium chloride, at pH 9.6, and at 10« C
and 45• C. Enterococci are a valuable bacterial indicator
for determining the extent of fecal contamination of
recreational surface waters.
Epidemiology. All the elements contributing to the
occurrence or non-occurrence of a disease in a
population; ecology of a disease.
Escherichia coli. A subgroup of the fecal coliform
bacteria. E. coli is part of the normal intestinal flora in
humans and animals and is, therefore, a direct indicator
of fecal contamination in a waterbody. The O157 strain,
sometimes transmitted in contaminated waterbodies, can
cause serious infection resulting in gastroenteritis. See
Fecal coliform bacteria.
Estuarine number. Nondimensional parameter
accounting for decay, tidal dispersion, and advection
velocity. Used for classification of tidal rivers and
estuarine systems.
Estuary. Brackish-water areas influenced by the tides
where the mouth of the river meets the sea.
Existing use. Use actually attained in the waterbody on
or after November 28, 1975, whether or not it is included
in the water quality standards (40 CFR 131.3).
Fecal coliform bacteria. A subset of total coliform
bacteria that are present in the intestines or feces of
First Edition: January 2001
Glossary-3
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Glossary
warm-blooded animals. They are often used as
indicators of the sanitary quality of water. They are
measured by running the standard total coliform test at
an elevated temperature (44.5* C). Fecal coliform is
approximately 20% of total coliform. See also Total
coliform bacteria.
Fecal streptococci. These bacteria include several
varieties of streptococci that originate in the
gastrointestinal tract of warm-blooded animals such as
humans (Streptococcus faecalis) and domesticated
animals such as cattle (Streptococcus bovis) and horses
(Streptococcus equinus).
Feedlot. A confined area for the controlled feeding of
animals. Tends to concentrate large amounts of animal
waste that cannot be absorbed by the soil and, hence,
may be carried to nearby streams or lakes by rainfall
runoff.
Flocculation. The process by which suspended
colloidal or very fine particles are assembled into larger
masses or floccules that eventually settle out of
suspension.
Flux. Movement and transport of mass of any water
quality constituent over a given period of time. Units of
mass flux are mass per unit time.
Gastroenteritis.
the intestines.
An inflammation of the stomach and
Geochemical. Refers to chemical reactions related to
earth materials such as soil, rocks, and water.
Giardia lamblia. See protozoa.
Gradient. The rate of decrease (or increase) of one
quantity with respect to another; for example, the rate of
decrease of temperature with depth in a lake.
Groundwater. The supply of fresh water found beneath
the earth's surface, usually in aquifers, which supply
wells and springs. Because groundwater is a major
source of drinking water, there is growing concern over
contamination from leaching agricultural or industrial
pollutants and leaking underground storage tanks.
Hot Spots. Locations in a waterbodies or sediments
where hazardous substances have accumulated to levels
which may pose risks to aquatic life, wildlife, fisheries,
or human health.
Hydrology. The study of the distribution, properties, and
effects of water on the earth's surface, in the soil and
underlying rocks, and in the atmosphere.
Indicator. Measurable quantity that can be used to
evaluate the relationship between pollutant sources and
their impact on water quality.
Indicator organism. Organism used to indicate the
potential presence of other (usually pathogenic)
organisms. Indicator organisms are usually associated
with the other organisms, but are usually more easily
sampled and measured.
Infectivity. Ability to infect a host.
Initial mixing zone. Region immediately downstream of
an outfall where effluent dilution processes occur.
Because of the combined effects of the effluent
buoyancy, ambient stratification, and current, the
prediction of initial dilution can be involved.
Insolation. Exposure to the sun's rays.
Irrigation. Applying water or wastewater to land areas
to supply the water and nutrient needs of plants.
Karst geology. Solution cavities and closely-spaced
sinkholes formed as a result of dissolution of carbonate
bedrock.
Land application. Discharge of wastewater onto the
ground for treatment or reuse. (See: irrigation)
Leachate. Water that collects contaminants as it trickles
through wastes, pesticides, or fertilizers. Leaching can
occur in farming areas, feedlots, and landfills and can
result in hazardous substances entering surface water,
groundwater, or soil.
Load, Loading, Loading rate. The total amount of
material (pollutants) entering the system from one or
multiple sources; measured as a rate in weight per unit
time.
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Protocol for Developing Pathogen TMDLs
Load allocation (LA). The portion of a receiving
water's loading capacity that is attributed either to one
of its existing or future nonpoint sources of pollution or
to natural background sources. Load allocations are best
estimates of the loading, which can range from
reasonably accurate estimates to gross allotments,
depending on the availability of data and appropriate
techniques for predicting the loading. Wherever
possible, natural and nonpoint source loads should be
distinguished. (40 CFR 130.2(g))
Loading capacity (LC). The greatest amount of
loading that a water can receive without violating water
quality standards.
Low-flow. Stream flow during time periods where no
precipitation is contributing to runoff to the stream and
contributions from groundwater recharge are low. Low
flow results in less water available for dilution of
pollutants in the stream. Due to the limited flow, direct
discharges to the stream dominate during low flow
periods. Exceedences of water quality standards during
low flow conditions are likely to be caused by direct
discharges such as point sources, illicit discharges, and
livestock or wildlife in the stream.
Margin of Safety (MOS). A required component of the
TMDL that accounts for the uncertainty about the
relationship between the pollutant loads and the quality
of the receiving waterbody (CWA section
303(d)(l)(C)). The MOS is normally incorporated into
the conservative assumptions used to develop TMDLs
(generally within the calculations or models) and
approved by EPA either individually or in state/EPA
agreements. If the MOS needs to be larger than that
which is allowed through the conservative assumptions,
additional MOS can be added as a separate component
of the TMDL (in this case, quantitatively, a TMDL = LC
= WLA + LA + MOS).
Mass balance. An equation that accounts for the flux of
mass going into a defined area and the flux of mass
leaving the defined area. The flux in must equal the flux
out.
Mass loading. The quantity of a pollutant transported to
a waterbody.
Mathematical model. A system of mathematical
expressions that describe the spatial and temporal
distribution of water quality constituents resulting from
fluid transport and the one, or more, individual processes
and interactions within some prototype aquatic
ecosystem. A mathematical water quality model is used
as the basis for waste load allocation evaluations.
Meningitis. Inflammation of the meninges, especially as
a result of infection by bacteria or viruses.
Mitigation. Actions taken to avoid, reduce, or
compensate for the effects of environmental damage.
Among the broad spectrum of possible actions are those
which restore, enhance, create, or replace damaged
ecosystems.
Monitoring. Periodic or continuous surveillance or
testing to determine the level of compliance with
statutory requirements and/or pollutant levels in various
media or in humans, plants, and animals.
Monte Carlo simulation. A stochastic modeling
technique that involves the random selection of sets of
input data for use in repetitive model runs. Probability
distributions of receiving water quality concentrations are
generated as the output of a Monte Carlo simulation.
National Pollutant Discharge Elimination System
(NPDES). The national program for issuing, modifying,
revoking and reissuing, terminating, monitoring, and
enforcing permits, and imposing and enforcing
pretreatment requirements, under Sections 307, 402, 318,
and 405 of the Clean Water Act.
Natural background levels. Natural background levels
represent the chemical, physical, and biological
conditions that would result from natural
geomorphological processes such as weathering or
dissolution.
Natural waters. Flowing water within a physical system
that has developed without human intervention, in which
natural processes continue to take place.
Nonpoint source. Pollution that is not released through
pipes but rather originates from multiple sources over a
relatively large area. Nonpoint sources can be divided
into source activities related to either land or water use
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Glossary
including failing septic tanks, improper animal-keeping
practices, forest practices, and urban and rural runoff.
Numeric Targets. A measurable value determined for
the pollutant of concern which is expected to result in
the attainment of water quality standards in the listed
waterbody.
Organic matter. The organic fraction that includes
plant and animal residue at various stages of
decomposition, cells and tissues of soil organisms, and
substance synthesized by the soil population. Commonly
determined as the amount of organic material contained
in a soil or water sample.
Outfall. Point where water flows from a conduit,
stream, or drain.
Oxidation. The chemical union of oxygen with metals
or organic compounds accompanied by a removal of
hydrogen or another atom. It is an important factor for
soil formation and permits the release of energy from
cellular fuels.
Oxidation pond. A relatively shallow body of
wastewater contained in an earthen basin; lagoon;
stabilization pond.
Oxygen demand. Measure of the dissolved oxygen
used by a system (microorganisms) in the oxidation of
organic matter. See also biochemical oxygen demand.
Partition coefficients. Chemicals in solution are
partitioned into dissolved and particulate adsorbed phase
based on their corresponding sediment-to-water
partitioning coefficient.
Pathogen. Disease-causing agent, especially
microorganisms such as bacteria, protozoa, and viruses.
Permit. An authorization, license, or equivalent control
document issued by EPA or an approved federal, state,
or local agency to implement the requirements of an
environmental regulation; e.g., a permit to operate a
wastewater treatment plant or to operate a facility that
may generate harmful emissions.
Permit Compliance System (PCS). Computerized
management information system which contains data on
NPDES permit-holding facilities. PCS keeps extensive
records on more than 65,000 active water-discharge
permits on sites located throughout the nation. PCS
tracks permit, compliance, and enforcement status of
NPDES facilities.
Phased approach. Under the phased approach to TMDL
development, LAs and WLAs are calculated using the
best available data and information recognizing the need
for additional monitoring data to accurately characterize
sources and loadings. The phased approach is typically
employed when nonpoint sources dominate. It provides
for the implementation of load reduction strategies while
collecting additional data.
Point source. Pollutant loads discharged at a specific
location from pipes, outfalls, and conveyance channels
from either municipal wastewater treatment plants or
industrial waste treatment facilities. Point sources can
also include pollutant loads contributed by tributaries to
the main receiving water stream or river.
Pollutant. Dredged spoil, solid waste, incinerator
residue, sewage, garbage, sewage sludge, munitions,
chemical wastes, biological materials, radioactive
materials, heat, wrecked or discarded equipment, rock,
sand, cellar dirt and industrial, municipal, and
agricultural waste discharged into water. (CWA Section
502(6)).
Pollution. Generally, the presence of matter or energy
whose nature, location, or quantity produces undesired
environmental effects. Under the Clean Water Act, for
example, the term is defined as the man-made or man-
induced alteration of the physical, biological, chemical,
and radiological integrity of water.
Pretreatment. The treatment of wastewater to remove
or reduce contaminants prior to discharge into another
treatment system or a receiving water.
Primary treatment. A basic wastewater treatment
method that uses settling, skimming, and (usually)
chlorination to remove solids, floating materials, and
pathogens from wastewater. Primary treatment typically
removes about 35 percent of biochemical oxygen demand
(BOD) and less than half of the metals and toxic organic
substances.
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Protozoa. Single-celled organisms that reproduce by
fission and occur primarily in the aquatic environment.
Waterborne pathogenic protozoans of primary concern
include Giardia lamblia and Cryptosporidium, both of
which affect the gastrointestinal tract.
Public comment period. The time allowed for the
public to express its views and concerns regarding
action by EPA or states (e.g., a Federal Register notice
of a proposed rule-making, a public notice of a draft
permit, or a Notice of Intent to Deny).
Publicly Owned Treatment Works (POTW). Any
device or system used in the treatment (including
recycling and reclamation) of municipal sewage or
industrial wastes of a liquid nature that is owned by a
state or municipality. This definition includes sewers,
pipes, or other conveyances only if they convey
wastewater to a POTW providing treatment.
Raw sewage. Untreated municipal sewage.
Receiving waters. Creeks, streams, rivers, lakes,
estuaries, groundwater formations, or other bodies of
water into which surface water and/or treated or
untreated waste are discharged, either naturally or in
man-made systems.
Residence time. Length of time that a pollutant remains
within a section of a waterbody. The residence time is
determined by the streamflow and the volume of the
river reach or the average stream velocity and the length
of the river reach.
Respiration. Biochemical process by means of which
cellular fuels are oxidized with the aid of oxygen to
permit the release of the energy required to sustain life;
during respiration, oxygen is consumed and carbon
dioxide is released.
Restoration. Return of an ecosystem to a close
approximation of its condition prior to disturbance.
Riparian zone. The border or banks of a stream.
Although this term is sometimes used interchangeably
with floodplain, the riparian zone is generally regarded
as relatively narrow compared to a floodplain. The
duration of flooding is generally much shorter, and the
timing less predictable, in a riparian zone than in a river
floodplain.
Runoff. That part of precipitation, snow melt, or
irrigation water that runs off the land into streams or
other surface water. It can carry pollutants from the air
and land into receiving waters.
Safe Drinking Water Act. The Safe Drinking Water
Act authorizes EPA to set national health-based standards
for drinking water to protect against both naturally
occurring and man-made contaminants that may be found
in drinking water. EPA, states, and water systems then
work together to make sure these standards are met.
Sanitary sewer overflow (SSO). When wastewater
treatment systems overflow due to unforseen pipe
blockages or breaks, unforseen structural, mechanical, or
electrical failures, unusually wet weather conditions,
insufficient system capacity, or a deteriorating system.
Scoping modeling. Involves simple, steady-state
analytical solutions for a rough analysis of the problem.
Scour. To abrade and wear away. Used to describe the
weathering away of a terrace or diversion channel or
streambed. The clearing and digging action of flowing
water, especially the downward erosion by stream water
in sweeping away mud and silt on the outside of a
meander or during flood events.
Secondary treatment. The second step in most publicly
owned waste treatment systems, in which bacteria
consume the organic parts of the waste. It is
accomplished by bringing together waste, bacteria, and
oxygen in trickling filters or in the activated sludge
process. This treatment removes floating and settleable
solids and about 90 percent of the oxygen-demanding
substances and suspended solids. Disinfection is the final
stage of secondary treatment. (See primary, tertiary
treatment.)
Sediment. Organic or inorganic material often
suspended in liquid that eventually settles to the bottom.
Sedimentation. Deposition or settlement of suspended
matter in water, wastewater, or other liquids.
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Septic system. An on-site system designed to treat and
dispose of domestic sewage. A typical septic system
consists of a tank that receives waste from a residence or
business and a system of tile lines or a pit for disposal of
the liquid effluent (sludge) that remains after
decomposition of the solids by bacteria in the tank; must
be pumped out periodically.
Sewer. A channel or conduit that carries wastewater
and storm water runoff from the source to a treatment
plant or receiving stream. "Sanitary" sewers carry
household, industrial, and commercial waste. "Storm"
sewers carry runoff from rain or snow. "Combined"
sewers handle both.
Simulation. Refers to the use of mathematical models
to approximate the observed behavior of a natural water
system in response to a specific known set of input and
forcing conditions. Models that have been validated, or
verified, are then used to predict the response of a
natural water system to changes in the input or forcing
conditions.
Slope. The degree of inclination to the horizontal.
Usually expressed as a ratio, such as 1:25 or 1 on 25,
indicating one unit vertical rise in 25 units of horizontal
distance, or in a decimal fraction (0.04); degrees (2
degrees 18 minutes), or percent (4 percent).
Sorption. The adherence of ions or molecules in a gas
or liquid to the surface of a solid particle with which
they are in contact.
Stakeholder. Those parties likely to be affected by the
TMDL.
Steady-state model. Mathematical model of fate and
transport that uses constant values of input variables to
predict constant values of receiving water quality
concentrations.
STORET. U.S. Environmental Protection Agency
(EPA) national water quality database for STORage and
RETrieval (STORET). Mainframe water quality
database that includes physical, chemical, and biological
data measured in waterbodies throughout the United
States.
Storm runoff. Stormwater runoff, snowmelt runoff, and
surface runoff and drainage; rainfall that does not
evaporate or infiltrate the ground because of impervious
land surfaces or a soil infiltration rate lower than rainfall
intensity, but instead flows onto adjacent land or
waterbodies or is routed into a drain or sewer system.
Stormwater. The portion of precipitation that does not
naturally percolate into the ground or evaporate, but
flows via overland flow, interflow, channels or pipes into
a defined surface water channel, or a constructed
infiltration facility.
Stormwater management models (SWMM). USEPA
mathematical model that simulates the hydraulic
operation of the combined sewer system and storm
drainage sewershed.
Stratification (of waterbody). Formation of water
layers each with specific physical, chemical, and
biological characteristics. As the density of water
decreases due to surface heating, a stable situation
develops with lighter water overlaying heavier and denser
water.
Stressor. Any physical, chemical, or biological entity
that can induce an adverse response.
Surface runoff. Precipitation, snowmelt, or irrigation
water in excess of what can infiltrate the soil surface and
be stored in small surface depressions; a major
transporter of nonpoint source pollutants.
Surface water. All water naturally open to the
atmosphere (rivers, lakes, reservoirs, ponds, streams,
impoundments, seas, estuaries, etc.) and all springs,
wells, or other groundwater collectors directly influenced
by surface water.
Suspended solids or load. Organic and inorganic
particles (sediment) suspended in and carried by a fluid
(water). The suspension is governed by the upward
components of turbulence, currents, or colloidal
suspension. Suspended sediment usually consists of
particles <0.1 mm, although size may vary according to
current hydrological conditions. Particles between 0.1
mm and 1 mm may move as suspended or bedload.
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Protocol for Developing Pathogen TMDLs
Technology-based limits. Industry-specified effluent
limitations applied to a discharge when it will not cause
a violation of water quality standards at low stream
flows. Usually applied to discharges into large rivers.
Tertiary treatment. Advanced cleaning of wastewater
that goes beyond the secondary or biological stage,
removing nutrients such as phosphorus, nitrogen, and
most biochemical oxygen demand (BOD) and suspended
solids.
Three-dimensional model (3-D). Mathematical model
defined along three spatial coordinates where the water
quality constituents are considered to vary over all three
spatial coordinates of length, width, and depth.
Topography. The physical features of a surface area
including relative elevations and the position of natural
and man-made features.
Total coliform bacteria. A particular group of bacteria,
found in the feces of warm-blooded animals, that are
used as indicators of possible sewage pollution. They
are characterized as aerobic or facultative anaerobic,
gram-negative, nonspore-forming, rod-shaded bacteria
which ferment lactose with gas formation within 48
hours at 35•. Note that many common soil bacteria are
also total coliforms, but do not indicate fecal
contamination. See also fecal coliform bacteria.
Total Maximum Daily Load (TMDL). The sum of the
individual wasteload allocations (WLAs) for point
sources, load allocations (LAs) for nonpoint sources and
natural background, and a margin of safety (MOS).
TMDLs can be expressed in terms of mass per time,
toxicity, or other appropriate measures that relate to a
state's water quality standard.
Toxic substances. Those chemical substances which
can potentially cause adverse affects on living
organisms. Toxic substances include pesticides,
plastics, heavy metals, detergent, solvent, or any other
materials that are poisonous, carcinogenic, or otherwise
directly harmful to human health and the environment as
a result of dose or exposure concentration and exposure
time. The toxicity of toxic substances is modified by
variables such as temperature, chemical form, and
availability.
Tributary. A lower order stream compared to a
receiving waterbody. "Tributary to" indicates the largest
stream into which the reported stream or tributary flows.
Turbidity. The amount of light that is scattered or
absorbed by a fluid.
Two-dimensional model (2-D). Mathematical model
defined along two spatial coordinates where the water
quality constituents are considered averaged over the
third remaining spatial coordinate. Examples of 2-D
models include descriptions of the variability of water
quality properties along: (a) the length and width of a
river that incorporates vertical averaging or (b) length
and depth of a river that incorporates lateral averaging
across the width of the waterbody.
Unstratified. Indicates a vertically uniform or
well-mixed condition in a waterbody. See also
Stratification.
Urban runoff. Water containing pollutants like oil and
grease from leaking cars and trucks; heavy metals from
vehicle exhaust; soaps and grease removers; pesticides
from gardens; domestic animal waste; and street debris,
which washes into storm drains and enters surface
waters.
Validation (of a model). Process of determining how
well the mathematical representation of the physical
processes of the model code describes the actual system
behavior.
Verification (of a model). Testing the accuracy and
predictive capabilities of the calibrated model on a data
set independent of the data set used for calibration.
Virus. Submicroscopic pathogen consisting of a nucleic
acid core surrounded by a protein coat. Requires a host
in which to replicate (reproduce).
Wasteload allocation (WLA). The portion of a
receiving water's loading capacity that is allocated to one
of its existing or future point sources of pollution. WLAs
constitute a type of water quality-based effluent
limitation (40 CFR 130.2(h)).
Wastewater. Usually refers to effluent from a sewage
treatment plant.
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Glossary
Wastewater treatment. Chemical, biological, and
mechanical procedures applied to an industrial or
municipal discharge or to any other sources of
contaminated water in order to remove, reduce, or
neutralize contaminants.
Water quality. The biological, chemical, and physical
conditions of a waterbody. It is a measure of a
waterbody's ability to support beneficial uses.
Water quality criteria. Elements of state water quality
standards expressed as constituent concentrations,
levels, or narrative statement, representing a quality of
water that supports a particular use. When criteria are
met, water quality will generally protect the designated
use.
Water quality standard. State or federal law or
regulation consisting of a designated use or uses for the
waters of the United States, water quality criteria for
such waters based upon such uses, and an
antidegradation policy and implementation procedures.
Water quality standards protect the public health or
welfare, enhance the quality of water and serve the
purposes of the Clean Water Act.
Watershed. A drainage area or basin in which all land
and water areas drain or flow toward a central collector
such as a stream, river, or lake at a lower elevation.
Wetlands. An area that is constantly or seasonally
saturated by surface water or groundwater with
vegetation adapted for life under those soil conditions,
as in swamps, bogs, fens, marshes, and estuaries.
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